Inclusive and Sustainable Growth for Madhya Pradesh

Inclusive and Sustainable Growth for Madhya Pradesh: A Low-Carbon Development Paradigm 5

Acknowledgements

Shakti Sustainable Energy Foundation (Shakti) acknowledges Ernst & Young LLP, India (EY) for providing services related to technical assistance in research, analysis and preparation of the report. Shakti expresses its gratitude to the Environmental Planning & Coordination Organisation (EPCO), Government of Madhya Pradesh, for the guidance and support provided during the course of the study. Shakti also gratefully acknowledges the contributions made by several teams, individuals and stakeholders from different organisations that provided useful suggestions and inputs at various meetings and interactions held, including the following departments of the Government of Madhya Pradesh:

• Housing and Environment Department • Forest Department• Urban Administration & Development Department (UADD)• Directorate of Town and Country Planning• Department of Animal Husbandry• Agriculture Department• Department of State Urban Transportation• Transport Department• Department of Agriculture Engineering• Department of Commercial Tax• MP Electricity Regulatory Commission (MPERC)• Energy Department• MP Trade and Investment Facilitation (MP TRIFAC) • Directorate of Economics and Statistics (DES)

Shakti also expresses its gratitude to Dr. Sumana Bhattacharya for her valuable inputs and guidance in the final stages of the study.

Disclaimer

This report uses publicly available information and information gathered through stakeholder consultations. The information gathered or contained in the report is not independently verified by Shakti, and accordingly, Shakti expresses no opinions or makes any representations concerning its accuracy or complete reliability or sufficiency. The recipients should carry their own due diligence in respect of information contained in the report. Shakti and EY disclaim any and all liability for, or based on or relating to any such information and/or contained in, or errors in or omissions from, their inputs or information in this report.

6 Inclusive and Sustainable Growth for Madhya Pradesh: A Low-Carbon Development Paradigm

Table of Contents

Executive summary 11Chapter I: Introduction 19Chapter 2: Approach and methodology 23 Methodology — assessment of GHG inventory and BAU scenario 23 Methodology — assessment of low carbon pathway 25 Structure of the report 25Chapter 3: GHG inventory and BAU forecast 27 Power sector 27 Industry sector 30 Transport sector 36 Buildings sector 39 Agriculture and livestock sector 46 Forestry sector 60 Waste sector 63 Black carbon emissions 68 Uncertainty analysis 69Chapter 4: Low Carbon Pathway 73 Marginal abatement cost curve 73 Abatement lever profiles 80 Power sector 80 Industry sector 85 Transport sector 88 Buildings sector 92 Agriculture and livestock sector 96 Forestry sector 98 Waste sector 100Chapter 5: Co-benefits and barriers to GHG emission abatement 103Chapter 6: Lock-in potential and cost of delay 107Chapter 7: Financing mechanisms for low-carbon growth 111Chapter 8: Conclusion 115Annexure I – GHG inventorisation (equations, data and sources) 119Annexure II – GHG Emissions Forecasting 2030 (equations, data and sources) 139Annexure III – GHG Abatement options, potential and costs(data and sources) 155About the study 164About Shakti Sustainable Energy Foundation 164


List of Tables

Table 1: Summary of GHG inventory and forecasted emissions 12Table 2: Summary of GHG inventory by type of GHG emitted in 2008 12Table 3: List of abatement opportunities identified 14Table 4: MP socioeconomic indicators 19Table 5: Methodological tiers 24Table 6: Methodological tiers (IPCC) followed for GHG emission inventory 25Table 7: Report Structure 26Table 8: Installed thermal power generation capacity in MP 27Table 9: Key emissions sources and methodology for forecasting 34Table 10: Data sources for key variables — industry sector forecasts 34Table 11: Key Assumptions — industry sector forecasts 35Table 12: Emissions by source — industry sector (tCO2e) 35Table 13: Coal consumption in the major industrial sectors of MP (thousand tons) 35Table 14: Emissions from various fossil fuels in Industries in MP (tCO2e) 36Table 15: Data sources for key variables — transport sector projections 38Table 16: Key assumptions — transport sector projections 39Table 17: GHG emissions — transport sector 2008 39Table 18: Data sources for key variables — building sector forecasts 43Table 19: Key assumptions — building sector forecasts 44Table 20: LPG and kerosene consumption — buildings sector 45Table 21: GHG emissions inventory and projections for building sector 45Table 22: Electricity consumption — buildings sector 45Table 23: Forecast electricity consumption — buildings sector 46Table 24: Data sources for key variables — agriculture and livestock sector 50Table 25: Key assumptions — agricultural pump sets forecasts 50Table 26: Projections of key variables — diesel-based agricultural pump sets 51Table 27: Key assumptions — paddy cultivation forecasts 53Table 28: Projections — area under paddy cultivation (thousand hectares) 53Table 29: Key assumptions — livestock forecasts 55Table 30: Livestock in MP, number (thousand) 56Table 31: Key assumptions — fertilizer usage emissions forecast 57Table 32: Projection results of key input variables — nitrous oxide emissions 57Table 33: GHG emissions — burning of crop residues-2008 59Table 34: Summary of GHG emissions — agriculture and livestock sector 2008 59Table 35: Summary of forecast GHG emissions — agriculture and livestock sector 2030 59Table 36: Key assumptions — carbon sinks due to existing forest land 62Table 37: Projected carbon sinks 63Table 38: Emissions from waste sector in MP, 2008 (tCO2e) 66Table 39: Measures for mitigation of black carbon emissions 69Table 40: Uncertainty analysis — CO2 70Table 41: Uncertainty analysis — CH4 70Table 42: Uncertainty analysis — N2O 71Table 43: Summary of abatement opportunities 77


Table 44: Power Sector — Initiatives 82Table 45: Industry Sector — Initiatives 86Table 46: Transport Sector — Initiatives 89Table 47: Transport Sector — Initiatives for electric and hybrid electric vehicles 90Table 48: Demand-side management in buildings — ECBC recommendations 92Table 49: Buildings sector — Initiatives 93Table 50: Agriculture and Livestock Sector — Initiatives 97Table 51: Waste Sector — Initiatives 100Table 52: Sector — wise co-benefits and barriers 104Table 53: Lock - in of carbon-intensive infrastructure 107Table 54: Cost of Delay 108Table 55: Sources of Finance 113Table 56: Indicative time horizon of investments 114Table 57: Inventory of various GHG gases in MP — 2008 and 2030 115


List of Figures

Figure 1: MACC-Madhya Pradesh-2030 13Figure 2: MACC-Summary of abatement opportunity by type 15Figure 3: Role of MACC as a policy making tool 17Figure 4: Vision, mission, strategy and activities 20Figure 5: Key outputs of GHG emissions abatement opportunity assessment 21Figure 6: Methodology followed for GHG emission inventory 23Figure 7: Forecast GHG emissions from power generation 30Figure 8: Forecast thermal power capacity addition (GW) 30Figure 9: GHG emission sources — industry sector 32Figure 10: Emissions forecast — industry sector (million tCO2) 36Figure 11: Projected emissions from the transport sector 39Figure 12: Emissions forecast — building sector (million tCO2e) 45Figure 13: Policies / Initiatives to support agriculture and livestock sector 46Figure 14: Emissions from diesel pumps in agriculture (million tCO2e) 51Figure 15: Emissions from paddy cultivation (million tCO2e) 54Figure 16: Emissions from livestock 56Figure 17: Emissions due to fertilizer usage (million tCO2e) 58Figure 18: Carbon sinks from forest land 63Figure 19: Emissions projections from waste sector (in million tCO2e) 67Figure 20: Reading a marginal abatement cost curve 73Figure 21: 2030 MACC-Madhya Pradesh 76Figure 22: Summary of abatement opportunity by type 79Figure 23: Format for abatement profile diagrams 80Figure 24: Base year GHG inventory, BAU and low-carbon scenario 115Figure 25: Next steps for institutionalizing low-carbon growth in MP 117


List of Abbreviations

Abbreviation Full formBAU Business As UsualBOD Biological Oxygen DemandCEA Central Electricity AuthorityCH4 MethaneCO2 Carbon Dioxide COD Chemical Oxygen DemandEPCO Environmental Planning and Coordination OrganisationGDP Gross Domestic ProductGHG Greenhouse GasGoMP Government of Madhya PradeshHFC Hydro-fluorocarbons HVAC Heating Ventilation and Air ConditioningIGCC Integrated Gasification Combined CycleINCCA Indian Network for Climate Change AssessmentIPCC Intergovernmental Panel on Climate ChangeIPP Independent Power ProducerMoPNG Ministry Of Petroleum & Natural GasMP Madhya PradeshMPPGCL Madhya Pradesh Power Generating Company LimitedMP TRIFAC MP Trade and Investment Facilitation Corporation LimitedN2O Nitrous OxideNAPCC National Action Plan on Climate ChangeNATCOM 2 India’s Second National Communication to the United Nations Framework Convention on

Climate ChangeNSDP Net State Domestic ProductNSSO National Sample Survey OrganisationNTPC National Thermal Power CorporationPFC PerfluorocarbonsPLF Plant Load FactorSAPCC State Action Plan on Climate ChangeSF6 Sulphur HexafluorideSLDC State Load Dispatch CentreUADD Urban Administration & Development DepartmentUNFCCC United Nations Framework Convention on Climate ChangeUSC Ultra supercriticalWBCSD World Business Council for Sustainable DevelopmentWRI World Resources InstitutetCO2e Tonnes of CO2 equivalent


Executive summary

Background and purpose of the reportThe National Action Plan on Climate Change (NAPCC) was formally unveiled in June 2008. The Plan endeavors to outline a strategy for addressing the challenge of sustaining economic growth, while coping with the global threat of climate change. The state governments have been advised by MoEF to formulate a State Action Plan on Climate Change (SAPCC) in line with the NAPCC. One of the main objectives of the SAPCC is to address climate change as part of the state’s development agenda.

The Government of Madhya Pradesh (GoMP) accords high priority to issues relating to climate change. In line with this, it has designated the Environmental Planning and Coordination Organisation (EPCO) as the state nodal agency for addressing climate change and other environmental challenges. EPCO has drafted the SAPCC for Madhya Pradesh (MP)1.

The objective of this study is to assess technology options, best practices and policy measures that are supportive of a low carbon development pathway for MP, and consistent with inclusive and sustainable development of the state. The study entails a detailed assessment of base year (2008) Greenhouse Gas (GHG) emissions of MP, projection of GHG emissions (business as usual-BAU) until 2030 and an assessment of opportunities for emission reduction by 2030. This report describes the outcomes of the study and aims to support policy makers in designing policies and strategies consistent with low carbon development in MP. The study has benefited from support and guidance provided by EPCO.

Coverage of the reportThe purview of the study is limited to GHG emission sources associated with human activities in MP in the following sectors:• Power• Industry• Transport• Buildings• Agriculture and livestock• Forestry• Waste

Emission sources that cannot be influenced by the state or cannot be attributed only to the state (e.g., aviation and railways) have not been included. In other words, emission sources where state policy makers have the potential to influence and incentivize emission reduction have been considered in the study. Furthermore, carbon sinks due to existing forests (land conserved as forest land) and afforestation activities have been included.

Greenhouse gases within the purview of the study include all of the six GHGs covered under the Kyoto Protocol, namely carbon dioxide (CO2), nitrous oxide (N2O), methane (CH4), sulphur hexafluoride (SF6), hydrofluorocarbons (HFC) and perfluorocarbons (PFC). Industrial sectors generating emissions of F-gases (SF6, HFCs, PFCs) are negligible in MP; hence, the major emissions occur from CO2, CH4 and N2O.

1 http://www.epco.in/pdf/Draft_MP_SAPCC.pdf


Key results — assessment of GHG inventory and BAU scenarioThe results of the GHG inventorisation of MP for 2008 and the forecast figures for 2030 are given in Table 1. Further, the GHG inventory of MP for 2008, with the break-up of emissions by type of GHG emitted, is given in Table 2.

Table 1: Summary of GHG inventory and forecasted emissions

Sector 2008 baseline emissions (tCO2e)

2030 BAU forecast emissions (tCO2e)

Power 41,912,905 187,800,050 Industry 24,263,487 50,959,807 Transport 6,853,174 34,882,852 Buildings 3,079,551 4,026,313 Agriculture and Livestock 32,908,004 49,387,439 Waste 4,794,047 8,276,587 Total emissions in MP 113,811,168 335,333,046

Forestry (carbon sink) 26,381,998 27,331,749Total (accounting for carbon sinks) 87,429,170 308,001,297

Table 2: Summary of GHG inventory by type of GHG emitted in 2008

Sector CO2 emissions (tCO2)

CH4 emissions (tCH4)

N2O emissions (tN2O)

Total eq. GHG emissions 2008 (tCO2e)

Power 41,912,905 - - 41,912,905 Industry 20,879,347 161,149 - 24,263,487 Transport 6,739,882 694 318 6,853,174 Buildings 2,943,074 6,499 - 3,079,551 Agriculture and Livestock

1,043,118 1,070,035 30,304 32,908,004

Waste - 228,288 - 4,794,047 Total 73,518,326 1,466,665 30,622 113,811,168

Forestry (carbon sink) 26,381,998 - - 26,381,998Total (accounting for carbon sinks)

47,136,329 1,466,665 30,622 87,429,170

In the BAU scenario developed for MP, the power sector is clearly the largest contributor to the increase in GHG emissions. There is an overall increase in GHG emissions by 221 million tCO2e from 2008 to 2030, and 66% of this increase is attributable to the power sector. This result is because MP would generate surplus power for export to the national grid, considering that it holds 8% of the nation’s coal reserves and that major independent power producers (IPPs) are expected to set up operations in the state.

Rapid infrastructure development is expected in MP. This would likely lead to both, economic growth and a rise in GHG emissions. The industrial and urban landscape is expected to change, with a greater proportion of GDP coming in from manufacturing and services. The rapid pace of infrastructure development will carry the risk of lock-in of carbon-intensive infrastructure in sectors such as power, industry, buildings and transport. At the same time, infrastructure development will provide opportunities for introducing low-


carbon technologies. Therefore, this provides an opportunity for MP to adopt low-carbon policies and strategies to avoid this lock-in. This would be a critical factor shaping the profile of emissions growth in MP over the next few decades.

Key results — assessment of low carbon pathwayThe abatement opportunity for MP (or the low-carbon scenario) described in this report entails the implementation of low-carbon technologies and measures (abatement levers) spanning all sectors covered in the study. Abatement opportunities with the potential to reduce around 46 million tCO2e by 2030 have been identified and are summarised in the marginal abatement cost curve (MACC). It is important to note that the cost curve does not capture the total abatement potential for MP. Some important opportunities have not been quantified in the study but could potentially contribute to low-carbon growth in MP.

Figure 1: MACC-Madhya Pradesh-2030


Some of the key findings with respect to abatement potential and marginal abatement cost derived from the cost curve are:1. The GHG abatement of proposed levers in 2030 is 46 million tCO2e, with an investment cost of INR 1,382

billion.2. Approximately 48% (22 million tCO2e) of the GHG abatement of proposed levers can be met through the

implementation of cost-effective levers (which have a negative marginal abatement cost).3. Five opportunities with the highest potential are energy-efficient pump sets, energy-efficient lighting-

LEDs, CFLs, biomass-based power generation and IGCC. The implementation of these opportunities would result in a combined reduction of around 27.6 million tCO2e by 2030, translating into 60% of the abatement potential captured in the cost curve.

4. Solar energy technologies comprising solar PV power plants, solar thermal power plants, solar PV pumps, solar water heaters and rooftop solar collectively comprise a relatively high abatement potential of 6.5 million tCO2e by 2030.

The costs of abatement have been evaluated from a societal perspective and not from the perspective of any one particular investor (e.g., project developer or customer). Societal costs include capital costs, as well as operating and maintenance costs, and exclude revenues/costs incurred by individual investors such as taxes, tariffs and subsidies.

The abatement levers identified in the cost curve can be broadly classified under six categories:• Energy efficiency/demand-side-management (EE/DSM)• Renewable energy technologies (RET)• Clean coal technologies (CCT)• Alternative fuels/raw materials (AFRM)• Waste management technologies (WMT)• Carbon sinks (CS)

The opportunities identified in each sector (including those described qualitatively but not included in the cost curve) are listed below.

Table 3: List of abatement opportunities identified

Power sector – opportunities in cost curve Power sector – other important opportunities

Integrated Gasification Combined Cycle (IGCC)-CCT

Renovation and modernisation (covering all sub-critical units in the state)

Ultra-super-critical – CCT Natural gas/coal-bed methane based power plants – AFRM

Biomass based power plant – RET Smart grid technologiesSolar PV – RETSolar Thermal – RETIndustry sector – opportunities in cost curve Industry sector – other important opportunities

WHR in cement kilns – EE/DSM Energy-efficiency improvement in various industry types such as the textiles, food processing, paper manufacturing and metal casting industries

Use of alternate fuels in cement kilns – AFRM Efficient group captive power plantsImproving the electrical efficiency of cement production – EE/DSM

Efficient combined effluent treatment plants (with potential for methane recovery)

Use of de-carbonated raw materials in cement production – AFRMTransport Sector – Opportunities in Cost Curve Transport Sector – Other Important Opportunities

Bus rapid transit systems – EE/DSM Electric four wheelers, three wheelers, buses and LCVs


Metro rail projects – EE/DSM Switch to CNG/LNGElectric two wheelers – EE/DSM Sustainable urban planning Building Sector – Opportunities in Cost Curve Building Sector – Other Important Opportunities

Energy efficient Air Conditioning (2 star to 5 star) – EE/DSM

Efficient design of building envelope

Solar water heaters – RET Efficient design of HVAC systems Household Rooftop Solar PV – RET Efficient water pumping, lighting controls and

electrical systems Incandescent lamps to LEDs – EE/DSMIncandescent bulbs to CFLS – EE/DSMAgriculture Sector – Opportunities in Cost Curve

Agriculture Sector – Other Important Opportunities

Energy Efficient Pumps – EE/DSM Reducing nitrous oxides emissions from soils (increasing nitrogen fixation, use of nitrification inhibitors, and other means)

Solar photovoltaic pumps – RET Reducing enteric methane emissions (straw silage and straw ammonization)

Forestry Sector – Opportunities in Cost Curve

Afforestation: Conversion of non-forest area/wasteland into forest area – CSReforestation: Restoring and recreating areas of forests that may have existed long ago but were deforested -CSWaste Sector – Opportunities in Cost Curve

Integrated waste management plant (PPP model) – collection, composting, RDF, bricks, recyclables – WMTLandfill gas recovery with electricity generation – WMTAerobic bioreactor landfill – WMT

The summary of the abatement opportunity, with a break-up by category of abatement lever, is illustrated below.

Figure 2: MACC - Summary of abatement opportunity by type


Co-benefits and barriersCosts and abatement potential are not the only consideration for implementing low-carbon technologies/measures. It is equally important to consider co-benefits and barriers or challenges to implementation. Challenges to low-carbon growth that need to be addressed by the state include access to finance for abatement opportunities with high capital costs, disaggregated energy consumption, subsidised rates of electricity and fossil fuels, and management of large scale projects. In addition, each sector poses a unique set of barriers, based on the stakeholders involved and the types of opportunities available. These factors need to be considered while planning low-carbon policies and programmes. At the same time, it is useful to consider socio-economic benefits of abatement opportunities such as health benefits of displacement of conventional fuels through reduction in air pollution, employment opportunities associated with renewable energy projects and reduced subsidy burden on the state government associated with reduced energy demand. Sector-wise barriers and co-benefits, as well as overarching challenges for low-carbon growth, are discussed in Chapter 5.

Cost of delayDelay in implementing the assessed opportunities would carry costs for MP. These costs would be of two types:• Loss in abatement potential due to lock–in of carbon-intensive infrastructure, expressed in tCO2• Monetary losses due to delay in the implementation of levers that have a negative abatement cost

(economically attractive opportunities), expressed in Indian Rupees.

The total cost of delaying action from 2015 to 2020 has been identified for the opportunities which have lifetime of more than a decade and will have lock in potential. These are: the displacement of sub-critical coal-based power plants (with efficient coal-based power plants), the displacement of captive coal-based power plants in the industry, and the displacement of electric geysers for heating in buildings. These opportunities have been selected as delaying their implementation beyond 2020 would result in carbon-intensive BAU technologies to be locked-in for a period extending beyond the year 2030. This would result in a total cost of 5.16 million tCO2e in abatement potential achievable by 2030, as well as a loss of INR 6,415 million per year. The cost of delay for other opportunities such as the designing of buildings with an efficient building envelope, and the implementation of low-carbon urban infrastructure (spacing and location of residential, commercial areas, roadways, transit routes) has not been quantified in this report; nevertheless, it would be of significance for MP. The details for cost of delay analysis are included in Chapter 6.

Financing mechanisms for low-carbon growthFinancing for climate change mitigation typically flows from four sources: (i) domestically generated sources of funds, including private sector and public sector financing; (ii) foreign direct investment; (iii) the international carbon market; and (iv) bilateral and multilateral development assistance. These opportunities are described in Chapter 7. MP could consider taking specific actions to avail different types of financing for low-carbon growth through initiatives such as: • A green fund through cess on electricity generated from fossil fuels (on grid connected power plants)• A green transportation fund through road tax collection and/or toll tax collection• Encouragement to ESCOs to invest in energy-efficiency projects in large commercial buildings and

government buildings• Funding through REDD+ or the voluntary carbon market for afforestation and reforestation projects• Partnerships with international financing institutions for investing in capacity building/ institutional

strengthening


Next stepsMP has recently begun to significantly invest in infrastructure (in power, industry and the urban environment). The state is now presented with a unique opportunity to make use of the best available technologies and practices to achieve sustainable and low-carbon growth. To realise this opportunity, the GoMP could take up certain follow-up actions to this study to further quantify, prioritise and avail low-carbon opportunities.

Figure 3: Role of MACC as a policy making tool

The marginal abatement cost curve study presented in this report provides analyses that can be used by the state as a reference point to begin to institutionalise and implement low-carbon growth. Institutionalizing low-carbon growth would involve incorporating low-carbon growth targets in five-year plans, finalizing the list of low-carbon projects/programmes to be undertaken, and carrying out micro-level implementation plans of projects/programmes to be carried out by individual departments.

Further, to support this high-level planning, additional analyses will be required such as deciding the most suitable institutional framework for the overall low-carbon growth planning, as well as for individual project/programme planning. It would be useful to carry out more detailed analyses to quantify co-benefits of the low-carbon opportunities, which could be a valuable input for developing the final list of project/programmes to be taken up. Further, some of the more capital-intensive projects/programmes would require detailed techno-feasibility analyses (e.g.: IGCC, ultra-supercritical power plants, and others) before they may be considered in the final list.

Finally, a monitoring framework would be required to continuously track the progress of the low-carbon growth plan for the state, and also to make any periodic revisions to plans based on changes in technology costs and availability. Ultimately, the institutionalisation of low-carbon growth in Madhya Pradesh would only be complete when it becomes part of a continuous process, incorporated into the state’s planning and budgeting activities.


Chapter I: Introduction

1.1 MP — at a glanceThe state of MP, in its present geographical form, came into existence on 1 November 2000, when Chhattisgarh had been bifurcated. MP, with an area of 308 thousand sq. kms, is now the second-largest state in India, constituting 9.4% of the country’s total geographical area.

Table 4: MP Socioeconomic indicators2

Geographical area 307,560 sq. Km. No. of districts 50 DistrictsPopulation 725.98 lakh Rural to urban population 72.37 % Literacy rate 70.6 % GSDP (current prices 2009-10) INR 2,170 billionGSDP annual growth rate 2009-10 (at constant prices 2004-05)

9.55 %

MP is endowed with abundant natural resources, including rich and diverse forests, 11 agro-climatic regions, and land suitable for growing almost all the crops. The state has around 25% forest cover, and nearly half of its total geographical area is used for crop cultivation. A large portion of the state’s population is dependent on agriculture and forest resources for livelihood. MP has socially and culturally diverse people. The state has a substantial tribal population, which is dispersed throughout the state and often has limited access to energy and infrastructure.

MP has the fifth highest rate of urbanisation in India3. Significant urban growth centres in Madhya Pradesh include Indore, Bhopal, Gwalior and Jabalpur. Infrastructure development in Madhya Pradesh is on the rise, with the state government undertaking various urban development projects, and encouraging IPPs to set up operations in the state.

1.2 MP climate change agenda — EPCO and State Knowledge Management Centre on Climate ChangeThe GoMP accords high priority to issues relating to climate change. In line with this, it has designated the EPCO as the state nodal agency for addressing climate change issues. The EPCO, in turn, has set up a State Knowledge Management Centre on Climate Change.

The EPCO was established by the Housing and Environment Department of the Government of Madhya Pradesh in 1981. Over the years, the EPCO has steadily grown to become the state’s premier organisation in environment-related matters. It has worked closely with the state government on various projects and has established its own identity as an autonomous organisation.

2 Twelfth Five Year Plan 2012-2017, Government Of Madhya Pradesh, Planning, Economics And Statistics Department3 MP Planning Commission Annual Report 2011-2012


The State Knowledge Management Centre on Climate Change of the EPCO intends to establish a mechanism that facilitates the management of long-term climate risks and uncertainties as an integral part of state development planning. The main purpose of establishing the Centre is to enable state government officials to coordinate and mainstream climate change issues in development activities and policies. The State Knowledge Management Centre on Climate Change will manage information and knowledge, and provide services to ensure environmental sustainability through the facilitation, identification and implementation of development projects. The State Action Plan on Climate Change (SAPCC) of MP has already been approved in line to address climate change actions for MP.

1.3 Guiding principles of MP SAPCCMP SAPCC aims to address the regional concerns and to outline strategies required to develop a climate resilient state. To achieve this, GoMP has defined following vision, mission, strategy and activities for SAPCC.

Figure 4: Vision, mission, strategy and activities4

1.4 Needs and objectives of the studyTo support inclusive and sustainable growth in MP that considers a low-carbon development paradigm, this study undertakes a thorough review of technology options, best practices and policy measures that the state may choose from over the next couple of decades.

Developing a GHG emissions inventory for MP is an important pre-requisite for assessing low-carbon development opportunities and for evaluating the economic/social/environmental impact of the strategies.

Greenhouse gas inventory is an accounting of greenhouse gases emitted to (or removed from) the atmosphere due to human activities within a specified boundary. Six greenhouse gases are covered under the Kyoto Protocol, namely, carbon dioxide (CO2), nitrous oxide (N2O), methane (CH4), sulphur hexafluoride (SF6), hydrofluorocarbons (HFC) and perfluorocarbons (PFC).

Apart from assessing the GHG inventory for MP, the study aims to forecast GHG emissions in the state in a BAU scenario. The BAU forecast of emissions is important from a policy perspective, particularly where infrastructure development and economic growth are likely to change the emissions profile significantly over time.

4 http://www.epco.in/pdf/Draft_MP_SAPCC.pdf


Introduction

Finally, the study aims to assess the GHG emission abatement opportunity for MP with the following key outputs:

Figure 5: Key outputs of GHG emissions abatement opportunity assessment

To summarise, the current study has the following broad objectives:1. Assessment of the GHG emissions inventory of the state in the base year (2008)2. Forecasting of GHG emissions in MP until 2030 in a BAU scenario 3. Assessment of the GHG emissions abatement opportunity for MP

A brief description of the approach and methodology applied to meet the listed objectives is discussed in the next section.


Chapter 2: Approach and methodology

The methodology and calculations of GHG emissions in MP are based on the Intergovernmental Panel on Climate Change (IPCC) Guidelines for National Greenhouse Gas Inventories and the GHG Protocol of WRI-WBCSD. Sector-wise analysis has been carried out and aggregated to arrive at the GHG inventory, BAU forecast, and abatement potential of the state. The sectors included in the study are power, industry, transport, buildings, agriculture and livestock, forestry and waste.

2.1 Methodology — assessment of GHG inventory and BAU scenarioThe following steps have been undertaken to assess the GHG inventory and BAU forecast for MP.

Figure 6: Methodology followed for GHG emission inventory

Boundary settingThe geographical boundary of MP is considered as the organisational boundary. To determine the operational boundary for MP, a control approach has been employed. Sources that are under the control of MP and where state policy makers can potentially influence/incentivise emission reduction are considered under the operational boundary of MP. The sources of emissions included in various sectors are:• Power o Emissions due to generation of electricity • Industry o Emissions due to the combustion of fossil fuels o Process emissions during production o Fugitive emissions due to mining• Transport o Emissions due to fuel consumption in road transportation• Buildings o Emissions due to fuel consumption


• Agriculture and livestock o Emissions due to fuel consumption in diesel-based agricultural pump sets o Emissions from paddy cultivation o Emissions from livestock o Emissions from managed agricultural soils (fertilizer usage) o Emissions due to burning of crop residues• Forestry o Sinks due to forest land remaining forest land5 .• Waste o Emissions from municipal solid waste o Emissions from domestic and industrial wastewater

Base year: The calendar year 2008 (henceforth referred to as 2008) has been considered as the base year for the study, since this is the latest year for which the baseline data is available across all the sectors. Although, most of the government data is reported on a financial year basis, the GHG inventory is calculated considering one-fourth of the data of 2007-08 and three-fourths of the data for 2008-09. This methodology is in line with NATCOM 2 and the requirements of IPCC, which recommends considering the calendar year for GHG inventories.

Data collection: A top-down approach involves data collection and aggregation at the state level, whereas a bottom-up approach involves collecting and aggregating data from local end users (i.e., plant/factory/building level). A hybrid approach has been employed for the GHG inventory of Madhya Pradesh, where data was collected from statistics published at the state level, as well as at the local level. For example, in the power sector, plant level data was considered to calculate emissions from the sector. In sectors such as transport, agriculture, waste and buildings, state level data was considered; for the industry sector, plant level data was used for certain emission sources and state level data for others.

Choice of emission factors: The emission factors chosen are congruent with national emission factors applied in India’s national communication to UNFCCC (NATCOM), where applicable. Conditions specific to Madhya Pradesh have been considered for the selection of emission factors wherever possible. At sources where country-specific factors were not available, IPCC defaults have been applied.

Table 5: Methodological tiers

Methodological Tiers Description

Tier I Employs activity data that is relatively coarse, such as nationally or globally available estimates of deforestation rates, agricultural production statistics and global land cover maps

Tier 2 Uses the same methodological approach as Tier 1 but applies emission factors and activity data defined by the country

Tier 3 Uses higher order methods, including models and inventory measurement systems tailored to address national circumstances, repeated over time, and driven by disaggregated levels

The methodological tiers followed for the sectors covered in the study are listed in Table 6.

5 Category of land defined by IPCC where the area of land which was forest in previous years remains forest in the current year as well and does not change to agriculture or infrastructure or any other category of land.


Approach and methodology

Table 6: Methodological tiers (IPCC) followed for GHG emission inventory

Sector Methodological tier followed Emission factors

Power Tier III MPS Industry Tier II CS Agriculture Tier II CS, IPCC Transport Tier II CS Waste Tier II CS, IPCC Forest Tier II IPCC Buildings Tier II CS MPS: MP Specific; CS: Country Specific; IPCC – Factors given by Intergovernmental Panel on Climate Change

Forecast of GHG emissions: Forecasts are made for each sector based on end-use models and/or regression analysis. The choice of method has been based on precedence (sector-specific publications on energy demand/GHG emission forecasts), as well as the level of data available for the specific sector. Wherever lack of data has influenced the choice in the forecast, cross-checks have been carried out through stakeholder consultation and/or published forecasts for MP or India to validate/moderate the forecast emissions. The specific methodology applied in each sector is described in respective sections for each sector.

2.2 Methodology — assessment of low carbon pathwayThe identification of GHG abatement opportunities has been carried out based on consultation with key stakeholders in Madhya Pradesh, as well as with sector-specific experts. A larger set of GHG abatement opportunities has been narrowed down through consideration of:• Socio-economic and political compatibility and relevance for Madhya Pradesh• Potential for the state to enable/incentivise the opportunities• Likelihood of significant penetration of the technology/measure in the BAU scenario

For instance, in the agricultural sector, measures related to changes in water management practices to reduce emissions due to paddy cultivation have not been considered, as the state is already pursuing practices such as “system of rice intensification (SRI).” Similarly, improvements in vehicle efficiency norms have not been considered, as these have not been decided at the state level. On the other hand, renewable energy power plants have been considered, as it is within the power of the state to decide renewable energy tariff, as well as introduce additional incentives for renewable energy project developers to set up operations in the state.

Once abatement opportunities were identified, information on abatement potential, ease of implementation and economic attractiveness was obtained through secondary research and inputs from stakeholders. BAU forecasts developed in this study were used as the basis for calculating potential abatement by 2030. The costs of abatement are based on present day costs, and the prediction of future costs of technologies was not attempted. Although an effort has been made to quantify the major GHG emissions opportunities available, there are also several opportunities discussed qualitatively in this report (due to the lack of data) which should be considered in the development of policies/strategies for enabling low-carbon growth in the state.

2.3 Structure of the reportThe following sections of this report, which describe the results of the study, as well as additional methodological details, are structured as follows:


Table 7: Report Structure

Chapter Title Contents

3 GHG inventory and BAU projections

This chapter includes sector-wise sections with the following information: • Current status of the sector and expected changes in coming years• Sources of GHG emissions in the sector• Methodology followed for the assessment of GHG inventory for the sector• Methodology followed for forecasting emissions until 2030 for the sector • Results of the baseline GHG inventory assessment and forecasting for the sector

Furthermore, a discussion on black carbon emissions in Madhya Pradesh and uncertainty analysis has also been covered in this chapter.

4 Low carbon pathway

This chapter describes the marginal abatement cost curve for Madhya Pradesh, and key information that can be read from the cost curve. It also includes sector-wise sections, covering descriptions of GHG abatement opportunities in each sector, as well as key initiatives that can be taken up by the state to incentivise and enable implementation of the identified opportunities.

5 Co-benefits and barriers to low-carbon growth

This chapter discusses the socio-economic benefits (apart from fuel savings and GHG mitigation) of GHG emissions opportunities and barriers to low-carbon growth.

6 Lock-in potential and cost of delay

The importance of acting swiftly to implement low-carbon strategies is discussed in this chapter. The costs of delaying action to mitigate GHG emissions have been described.

7 Financing mechanisms for low-carbon growth

Avenues for funding the implementation of low-carbon growth opportunities have been discussed in this chapter.

8 Conclusion Key results and conclusions of the study have been summarised.


Chapter 3: GHG inventory and BAU Forecast

3.1 Power sector

3.1.1 BackgroundEnergy is of strategic importance for India, particularly because of its fast-growing economy, rising population and its commitments to inclusive socio-economic development. India’s energy usage has grown by more than 25% over the last two decades, and energy has been sourced predominantly through the utilisation of fossil fuels6 .

When the boundaries of Madhya Pradesh were re-defined in 2000, after separation from Chhattisgarh, the state suffered a power deficit. In this scenario, one of the main objectives of the GoMP with respect to the power sector is increasing supply to its urban and rural population and, thereby, facilitating economic growth. Power sector reforms were initiated in 2001 and were augmented following the Electricity Act 2003, which set the legal framework for developing the sector. These reforms led to the establishment of separate companies for the generation, transmission and distribution of power. Madhya Pradesh Power Generating Company Limited (MPPGCL), a wholly owned company of GoMP, is engaged in power generation.

The present installed capacity of thermal power plants comes from three power plants owned by MPPGCL and one power plant owned by NTPC Limited, the largest power generation company in India (with a major share owned by Government of India).

Table 8: Installed thermal power generation capacity in MP

Type Name Ownership Capacity MW as on 31/03/2011

Ownership

Thermal Satpura MPPGCL 1,142.50 StateAmar kantak MPPGCL 450 StateSanjay Gandhi MPPGCL 1,340 State

Vindhyachal STPS Coal Power Station India

NTPC 3,260 Centre

Total (Thermal) 6,192.50 -

Thermal power generation capacity has historically consisted only of state/national government-owned power plants. Furthermore, renewable energy power plants have been set up by various developers, including 169 MW of wind power, 25 MW of biomass-based power and 71 MW of small hydro power 7.

The GoMP is making continuous efforts to promote the addition of generation capacity of various types including through state-owned power plants, Central Government-owned power plants and Independent

6 “Low Carbon Strategies for Inclusive Growth,” Planning Commission of India, Govt. of India, 20117 Sector Profile: Power including Renewable Energy, Destination Madhya Pradesh: Global Investors Summit-II, 2010, MP TRIFAC


Power Producers (IPPs). In line with this, the Madhya Pradesh (Investment in Power Generation Projects) Policy was established by the GoMP to set clear guidelines for Independent Power Producers (IPPs) to set up thermal power projects in the state. This policy was formulated in 2012 (which superseded the provisions of the policy initially notified in 2010) to define clear technical and financial criteria for IPPs to set up their projects. The GoMP has signed 42 MoUs with various developers for capacity creation of around 51,405 MW8. The construction of 4 power projects with a total capacity of about 3,000 MW is already underway9. These planned capacity additions primarily consist of thermal power plants. MP can potentially become a power surplus state within a few years with ongoing efforts to increase power generation capacity.

In order to ensure low-carbon growth of the power sector, the GoMP is exploring various opportunities including (but not limited to):• Utilisation of untapped potential for solar, biomass, small hydro and wind power generation• Increasing off-grid power supply from solar PV• Utilisation of coal bed methane for power generation as a substitute for coal• Renovation and modernisation of older thermal power plants with declining efficiency• Applying clean coal technologies such as IGCC

The GoMP is already making progress in this direction, particularly in renewable energy generation. Ongoing initiatives of the GoMP include the development of a solar energy park in the Rajgarh District, various off-grid power applications in the rural and residential sectors, and incentive policies for renewable power generation (for example, exemption from open-access charges, exemption from the payment of cess on small hydro power, access to government land for RE projects and designated command area for biomass-based power plants).

3.1.2 Sources of GHG emissions in the power sectorAlthough various initiatives are underway for low-carbon power generation, approximately 72% of the currently installed power capacity in the state comes from fossil fuel-fired thermal power plants. The primary fuel in thermal power plants in Madhya Pradesh is coal, which is supplemented with small quantities of other fuels such as furnace oil. All of the thermal power plants in the state, including the NTPC power plant (which is not state owned), as listed by the Central Electricity Authority, emit GHG.

3.1.3 GHG accounting methodology for the power sectorGHG emissions in the power sector have been sourced from plant-wise data published by the Central Electricity Authority and the Ministry of Power in the CO2 Baseline Database for the Indian power sector. Calculations of GHG emissions for specific power plants, as well as the calculation of grid emission factors, are in accordance with the UNFCCC’s Tool to Calculate the Emission Factor for an Electricity System, Version 2.2.1. The emissions have been calculated based on the fuels consumed, station heat rate, efficiency of the plant and emission factors for the fuels used. The IPCC Tier III approach has been employed to calculate emissions in the power sector.

Assumptions and data sources• The direct emissions from electricity generation in the state are considered in the GHG inventory, and

the indirect emissions from the import of electricity are not considered. Based on the geographical boundary being considered for this study, the electricity exported (not used in MP) is included in the GHG emissions of the state, since it is generated inside MP. This is also in line with the 2006 IPCC guidelines.

• Plant-wise data for MP and emission factors are taken from the CEA database for the base year.

8 Sector Profile: Power including Renewable Energy, Destination Madhya Pradesh: Global Investors Summit-II, 2010, MP TRIFAC9 IDS Scoping study for Madhya Pradesh State Climate Change Action Plan


GHG inventory and BAU Forecast

3.1.4 GHG forecasting methodology for the power sectorGHG emissions from the power sector have been forecasted based on the following approach:• Existing power capacity addition plans of the GoMP until 2020 were identified.• A comparison of actual historical capacity addition with planned capacity addition was carried out.• The capacity addition plans were moderated based on actual implementation in the past. The moderated

capacity addition plans were then extrapolated until 2030 using the linear growth model.• Heat rate/efficiency of power generation was projected based on the technology mix included in the

projection (existing/less-efficient sub-critical power plants, new sub-critical coal power plants, and super-critical power plants).

• GHG emissions from coal consumption were determined, based on the identified heat rate/efficiency for power generation.

The GoMP has defined power capacity addition plans based on power demand envisioned in the state up to 2020. A cumulative capacity addition of 22,655 MW is planned by 2020, consisting of thermal power capacity addition of 20,708 MW (around 20 GW).10 The capacity addition plans of the GoMP have been taken into account in the forecast of GHG emissions for the state. In the Eleventh Five-year Plan (FYP), around 42% of the planned capacity addition was actually implemented. Therefore, it has been projected that the capacity addition of 8.6 GW (42% of planned addition) will be achieved by 2020, and the rate of capacity addition is extrapolated to 2030. Capacity addition of approximately 20 GW is projected until 2030, amounting to a cumulative thermal power generation capacity of 26.6 GW in 2030. Given that MP has adequate coal resources and existing baseline power sector projections for India project ongoing growth in electricity demand up to 2030 11, thermal power capacity addition in MP has been projected to continue at the same rate beyond 2020 up to 2030.

Existing policies in MP do not impose any restrictions or incentives with respect to any one type of technology. However, they do specify a preference for supercritical, ultra-super-critical, CFBC technology, integrated coal and IGCC coal technology. Therefore, the new capacity additions in the baseline scenario are expected to consist of more efficient power plants compared to the existing installations (including supercritical power plants), but these would not consist of high-end ultra-super-critical or IGCC technologies.

Other key assumptions in drawing up power sector forecasts include:• Emission factor of coal is assumed to remain constant up to 2030. The emission factor considered

includes the variation of NCVs in various grades of coal used by different power plants. • Plant Load Factor (PLF) is applied, corresponding to the highest PLF among the existing power plants

(89%) 12. • Efficiencies are applied at 31.80% for sub-critical power plants on the basis of historical data and are

assumed to increase up to 38% based on technology improvement. Efficiency for super-critical power plants is applied at 42%.13

• Power generation in the state is expected to be driven by coal-based power plants. Gas-based power generation has not been included in the forecasts due to the lack of availability of natural gas in the region, as well as the shortage of natural gas in the country.

3.1.5 ResultsGHG emissions from the power sector of Madhya Pradesh due to the generation of electricity in 2008 amounted to 41.91 million tCO2e (as per the data from CEA). These are projected to increase to 187.80 million tCO2e in 2030. Power generation capacity in MP has been projected to increase up to 26.6 GW by 2030.

10 Source: Energy Department, GoMP11 Source: Technology Development Prospects for the Indian Power Sector, International Energy Agency12 This is assumed since the PLF would increase over time and it is assumed that the average would reach the current best PLF.13 Reference: Exploring the use of Carbon Financing in supercritical technology for power generation, Isabel Boira-Segarra, Mott MacDonald


Figure 7: Forecast GHG emissions from power generation

Figure 8: Forecast thermal power capacity addition (GW)

The considerable increase in GHG emissions in the power sector is due to power capacity addition in the state (driven by a policy for incentivizing independent power producers to set up operations) and the presences of coal mines in the region. Clean coal technologies, renovation and modernisation of power plants, and large-scale adoption of renewable energy are some of the factors driving down the increase in GHG emissions without compromising on power generation.

3.2 Industry sector

3.2.1 BackgroundIndustries are significant sources of GHG emissions in India. As per the national greenhouse inventory, direct emissions from industrial sources accounted for nearly 21% of the total CO2 emissions from the country 14. Energy-intensive sectors in India include fertilizer, iron and steel, cement, textile, aluminium, pulp and paper, and chlor alkali.

14 INCCA 2007.



Madhya Pradesh is a major mineral-producing state with adequate resources of coal, limestone, manganese ore, bauxite, copper, dolomite, fire clay, among others15. The industry sector in MP has high growth potential, considering the abundance of mineral resources, as well as other factors such as ongoing infrastructure development, a central location with respect to markets, and industrial policy for promoting growth. Industry is an important source of employment for the state, with 733 large and medium industrial units (as of January 2011) providing livelihood to about 1.75 lakh people.16 The GoMP is taking initiatives, including expanding growth centres/industrial parks/SEZs, to attract investment in various industry sectors. The planned dedicated freight corridor between Delhi and Mumbai would give further impetus to the industry sector in Madhya Pradesh, reducing logistics cost and increasing accessibility to markets.

The following are some of the significant industry sectors in Madhya Pradesh in terms of size and energy consumption:

CementConsidering the abundance of limestone in Madhya Pradesh, and the presence of major cement manufacturers, the cement industry is the most significant industry sector in terms of energy consumption. Madhya Pradesh is the third-largest producer of cement in the country. The cement industry in MP produced 20.02 MT of cement in 2008–09. Furthermore, cement manufacturing units have been operating at more than 100% capacity utilisation in the last four years. Total installed capacity of production of cement in MP is 19.37 million tons (as of March 2009)17, and the state accounts for around 12% of India’s total cement production capacity. Approximately 40% of the total cement production in MP caters to demand within the state18. High growth in the cement production capacity of MP is expected due to its large limestone reserves. MoUs for capacity addition of 110 million tons per annum have already been signed by various parties with the MP Government.

MiningThe mining and quarrying sector in Madhya Pradesh contributed to 4.5% of the Net State Domestic Product (NSDP) in 2007-0819. Madhya Pradesh ranks third in terms of mineral production in India20. Madhya Pradesh has substantial mineral resources such as diamond, gold, copper, lead, zinc, bauxite, manganese, limestone, PGE groups of minerals, pyrophyllite, diaspore and rock phosphate. It is the only diamond-producing state of India. It leads the production of pyrophyllite and copper ore in the country. Madhya Pradesh contributed to 15.39% of the total coal production in the country in 2009-201021. Coal is significant as a fuel for both, the power sector and for the industry sector. Most energy-intensive industry sectors depend on coal for captive power generation and thermal energy.

Other sectorsApart from cement and mining, other industry sectors such as textile, food processing, chemicals are significant in terms of size/number of establishments (though not as significant in terms of cumulative energy consumption/GHG emissions).

Apart from the above, a limited numbers of pulp and paper manufacturers, as well as chemical and fertilizer manufacturing units operate in the country. Although these sectors are significant contributors to India’s GHG emissions, their presence in MP is currently limited. They do not make a significant contribution to energy consumption or GHG emissions in the state.

15 IDS Scoping Study for Madhya Pradesh State Climate Change Action Plan16 Madhya Pradesh State Action Plan on Climate change.17 Cement Manufacturers Association of India18 Cement Manufacturers Association of India19 Ministry of Mines-State wise mineral scenario 2008-09, Madhya Pradesh calling brochure, Investors guide book for mines & minerals in MP-201020 Investors guide book for mines & minerals in MP-201021 MP Annual Report 2011-2012


3.2.2 Source of GHG emissions in the industry sectorThe typical sources of GHG emissions for a manufacturing process are as illustrated below.

Figure 9: GHG emission sources — industry sector

The following major sources of GHG emissions are considered for the industry sector in the state:• Combustion of fossil fuels in the industry sector of MP (primarily coal, supplemented with petroleum

products)• Process emissions from industry (primarily CO2 emissions from cement manufacturing)• Fugitive emissions from coal mining

To prevent double accounting, emissions from the consumption of grid electricity are not included and are part of electricity generation in the power sector.

3.2.3 GHG Accounting methodology for the industry sector

Combustion of fossil fuels in industryThe combustion of coal and oil for energy and heat requirements is a source of direct emissions in the industry. As per the Annual survey of Industries (MOSPI), around 96% of the coal consumption in MP is accounted for under non-metallic mineral products (includes cement and ceramics), spinning weaving and finishing of textiles, vegetable and animal fats (food processing), manufacture of pulp and paper, and casting of metals. Of these categories, 68% of the coal consumption is accounted for under non-metallic mineral products (of which around 90% is due to cement manufacturing). Coal consumption is used for both captive power generation and process heat requirements.

Alternative fuels consumed for various other energy requirements include furnace oil (FO), light diesel oil (LDO) and naphtha.

Emissions = Fuelconsumed *Emission Factor

The IPCC methodological Tier II approach is followed for the industry sector, since the data used is aggregated at the state level and emission factors applied are country specific.

Assumptions and data sourcesThe following data is used for the calculation of GHG emissions:• Data on the consumption of coal in various industries of MP is sourced from the Annual Survey of

Industries conducted by MOSPI. • The emission factors and calorific values of fuels are taken as country specific from NATCOM 2.



• Data on the consumption of furnace oil (FO), light diesel oil (LDO) and naphtha consumption in MP is taken from Ministry of Petroleum and Natural Gas (MoPNG). The entire consumption of FO, LDO and naphtha in MP is attributed to the industry sector22.

• Diesel consumption in industries in MP is calculated from data on diesel generators (DG) set based generation for captive power in industries in MP (sourced from the Ministry of Power, India)23.

Process emissions from industry The state does not have any large semi-conductor manufacturing units that serve as the major sources of process emissions of SF6 gases. Madhya Pradesh also does not have any refrigerant manufacturing units that are major sources of HFC process emissions. Similarly, there are no aluminium manufacturing units serving as typical sources of PFC emissions.

Nevertheless, Madhya Pradesh accounts for 22 million tons of cement manufacturing capacity, which is third highest in the country. The emissions from the production of cement due to the calcinations process are calculated based on the production of cement and country-specific emission factor sourced from the INCCA. Process emissions from other industrial sectors are negligible in Madhya Pradesh.

Assumptions and data sourcesThe following assumptions and data sources are used for accounting process emissions from cement manufacturing:• Cement production data in MP is taken from plant-wise production data in MP and from the Cement

Manufacturers Association (CMA).• Emissions have been determined for the mix of Plain Pozzolana Cement (PPC) and Ordinary Portland

Cement (OPC) production volume in the state. The clinker percentage is 68% for PPC and 95% for OPC, based on data from the CII. For companies with clinker-to-cement ratios published for different manufacturing units, company-specific data is considered. Accordingly, the weighted average clinker content is determined as 74%.

• The process emissions factor is taken as 0.537 tCO2/cement production, as adopted by the National Greenhouse Gas Inventory (INCAA report).

Fugitive emissions from coal miningFugitive emissions from the mining of coal are determined on the basis of coal production in MP and country-specific emissions factors from NATCOM 2. The production percentage of coal from underground mines and open-cast mines (surface mines) is considered, and corresponding emission factors are used from NATCOM 2 to calculate total emissions from both the types of mining.

Assumptions and data sources• Data for coal production in MP is sourced from provisional coal statistics 2007-08 and 2008-09, Ministry

Of Coal, Coal Controller’s Organisation Kolkata. • Corresponding country-specific emission factors for underground mines and open-cast mines are

sourced from NATCOM 2.

3.2.4 GHG forecasting methodology for the industry sectorOver 90% of the GHG emissions from fossil fuel combustion in industries are attributed to coal consumption in 200824. This trend is expected to continue in the BAU scenario. Coal is an economical fossil fuel used by

22 More than 90% of FO, LDO, and naphtha consumption in India is accounted under the industry sector (as per the data from MoPNG). The other application of FO and LDO is in power sector, however as seen in MP power sector these fuels are not used for power generation from grid. Similarly, naphtha is used for power generation (which is not the case in MP grid) or as raw material in fertilizer sector. In MP there is only one fertilizer company and the naphtha consumption as raw material is expected to be insignificant. It can be safely assumed that the entire consumption of these fuels is done in Industry sector in MP23 Diesel is consumed for various sectors including power, industry, agriculture, transportation and buildings. The break-up of this consumption is not available for the state. Also, to calculate the emissions from diesel consumption, it is assumed that DG sets in the range of 0.1 to 1 MW capacity are pri-marily used. The specific emissions for diesel-based power generation are accordingly selected from the CEA CO

2 database.

24 Reference: Table 14 Emissions from various fossil fuels in Industries in MP (tCO2e)


most energy-intensive industries for captive power generation, as well as for thermal energy requirements. Furthermore, based on the cement production in MP and the specific energy consumption, around half of the coal consumption is attributed to the cement industry in 2008. A marginal increase in the share of coal consumption by the cement industry is expected by 2030. The textile and food processing industries are the second and third largest sources of energy consumption, respectively, in the state, after the cement industry25. Balanced growth across all industry sub-sectors is expected with the opening of SEZs and fiscal incentives provided for small and medium enterprises.

The methodology for forecasting GHG emissions in the industry sector consists of the following components:

Table 9: Key emissions sources and methodology for forecasting

Emission Source Methodology

Emissions due to fossil fuel consumption

Linear growth projections are applied for each type of fossil fuel used in the industry sector. Cross-checks are carried out with projected energy demand for the cement sector, the most energy-intensive industry sub-sector in the state (accounting for around 50% of the coal consumption). Energy demand projections are carried out based on expected trends in specific energy consumption, alternative fuel utilisation and production. It is expected that the cement sector would continue to account for 50%–60% of energy consumption in the industry sector until 2030.

Process emissions due to cement production

Estimates about potential capacity addition in the cement sector until 2030 have been applied on the basis of limestone reserves available and MoUs signed between the state government and cement companies. Cement capacity addition has been estimated based on a Gompertz function26, with an upper limit on cement capacity of 65 million tons per annum. Process emission factors have been applied on clinker production, determined from projected cement production and fly ash percentage.

Fugitive emissions due to coal mining

Fugitive emissions from coal mining have been projected on the basis of projections of coal production based on a linear growth model.

Emissions from fuel consumption and fugitive emissions are estimated applying national default emission factors. Data sources for key parameters are given in Table 10, and the key assumptions are given in Table 11.

Table 10: Data sources for key variables — industry sector forecasts

Parameters Data Source

Historical data on naphtha, light diesel oil, fuel oil consumption in Madhya Pradesh

Ministry of Petroleum & Natural Gas (MoPNG)

Historical data on coal mining Statistics handbook for MP by Directorate of Economics and Statistics

Limestone reserves in Madhya Pradesh Report of the Working Group on Cement Industry for XII Five Year Plan, Planning Commission

Specific energy consumption and limestone requirements for cement production

CII Low Carbon Roadmap for Cement Industry

Historical cement capacity in Madhya Pradesh Cement Manufacturer’s Association

25 Reference: NSSO Survey of Industries26 It is a type of mathematical model for a time series, where growth is slowest at the start and end of a time period.



Table 11: Key Assumptions — industry sector forecasts

Variable Assumptions

Naphtha, Light Diesel Oil (LDO), Fuel Oil (FO) consumption in Madhya Pradesh

Naphtha, Light Diesel Oil (LDO), Fuel Oil (FO) consumed in Madhya Pradesh : 100% by industries

Diesel consumption in the industries of Madhya Pradesh

Diesel consumed for power generation is used in DG sets in the range of 0.1 to 1 MW capacity

3.2.5 ResultsTotal GHG emissions from the industry sector are projected to increase from 24.26 million tons of CO2 in 2008 to 50.96 million tons of CO2 in 2030, indicating a CAGR of approximately 3.8%. The break-up up of GHG emissions from various sources is as given in Table 12 and Figure 10.

Table 12: Emissions by source — industry sector (tCO2e)

Year Emissions due to fuel consumption in the industry (million tCO2e)

Process emissions due to cement production (million tCO2e)

Fugitive emissions due to coal mining (million tCO2e)

Total emissions (million tCO2e)

2008 12.95 7.93 3.38 24.262030 24.82 20.86 5.28 50.96

Table 13: Coal consumption in the major industrial sectors of MP - 2008 (thousand tons)

Industrial sector Qty of coal (thousand tons)

Manufacture of non-metallic mineral products (majorly cement and others like ceramics, bricks etc.)

4,162

Spinning, weaving and finishing of textiles 691.75Manufacture of vegetable and animal oils and fats 471.75Manufacture of paper and paper products 239Casting of metals 48.75


Figure 10: Emissions forecast — industry sector (million tCO2)

Estimated emissions from fossil fuel consumption in 2008 from the industry sector in MP are summarized below.

Table 14: Emissions from various fossil fuels in Industries in MP - 2008 (tCO2e)

Emission source Emissions (tCO2e)

Coal consumption 11,758,096Diesel consumption 331,935Naphtha consumption 234,853FO consumption 571,941LDO consumption 50,031Total from Fuel Consumption 12,946,856

3.3 Transport sector

3.3.1 BackgroundThe transport sector plays a crucial role in shaping the nation’s economic development. GDP from the transport sector is the aggregate of GDP from various means such as railways, road, water and air transport. In the national context, the transport sector accounts for 8% of GHG emissions, and the contribution of road transport to total transport emissions is around 94.5%.27

Substantial focus on the development of transport infrastructure, both for long-distance freight and the movement of people within cities, is expected in the next few decades, as India’s urban population is expected to double by 2031.28 This need for sustainable transport infrastructure can be met through the development of extensive mass transit systems in cities, investment in the shift of freight transport from road to rail, and improvement in facilities for non-motorized travel. However, sustainable transport

27 INCCA Report, India28 ESMAP, World Bank



infrastructure development is not without its institutional and technological challenges. Apart from these measures, the reduction of fossil fuel consumption in vehicles may be pursued through standards for high fuel economy for new vehicles entering service, and the utilisation of low-carbon/sustainable bio-fuels and electric vehicles.

Madhya Pradesh is at the centre of India and provides vital connectivity for inter-state networked roads in the country. The Government of MP has focused on road construction and has created a robust road infrastructure to ensure all-round development of the state’s economy.29 MP has a road network of about 74,000 kilometres comprising national highways, state highways, district roads and village roads. To improve road transport functioning and carry out work in a project mode, a new company/corporation, namely, MP Road Development Corporation, was created in July 2004. The MP Government is implementing BRTS and is planning to introduce metro railway in its major cities.

3.3.2 Sources of GHG emissions in the transport sectorEmissions from the burning of fuels in road transportation are considered in the GHG inventory. Petrol and diesel are primarily used in Madhya Pradesh in road vehicles.

3.3.3 GHG accounting methodology for the transport sectorThe quantity of diesel and petrol consumed in the transport sector are considered, and their respective default emission factors for India are used to calculate emissions from the transport sector. GHG emissions have been calculated as per the following equation:

Emissions = Fuelconsumed * Emission Factor

The IPCC methodological Tier II approach is employed for the transport sector, since aggregated data at the state level is used, and emission factors applied are country specific.

The approach for determining GHG emissions based on vehicle mileage, distance travelled and the number of vehicles has also been applied. However, as average distance travelled by each vehicle type is available only at a national level, this approach led to an overestimation of emissions. This is because the national average is more than the state average due to the variation in regional patterns of vehicles and corresponding distances travelled. As state-specific data on average distance travelled by each vehicle type is not currently available, the alternative approach of fuel consumption is utilized for estimating the GHG inventory and making forecasts for the transport sector.

Assumptions and data sources• Diesel and petroleum consumption in MP is sourced from the Ministry of Petroleum and Natural gas

(MoPNG). • It is assumed that all of the petrol is consumed for road transportation, as its usage is comparatively

negligible in other applications.• Diesel consumption in the transport sector is calculated by considering the total consumption in the

state and subtracting the consumption in industry and agricultural pumps, which are calculated for the respective sectors in GHG inventorisation.

• LPG and CNG consumption is negligible in the transport sector in MP in the base year chosen.• GHG emissions from rail transportation are not accounted in the GHG inventory for the state, as the

fuel consumption is primarily under the control of the Central government (Indian Railways) and is associated with inter-state transportation of passenger and cargo.

• Similarly, emissions from aviation are not included in the GHG emissions inventory of the state, as fuel consumption is associated with inter-state transportation.

29 Madhya Pradesh State action plan on Climate Change Madhya Pradesh State action plan on Climate Change


3.3.4 GHG forecasting methodology for the transport sectorThe methodology for forecasting GHG emissions in the transport sector consists of the following components:

• The forecast is based on the methodology applied in the ADB study on Madhya Pradesh State Roads Project III. The number of vehicles has been projected based on transport sector GSDP, population, per capita income and type of transport mode. The different types of vehicles covered include:o freight vehicles (medium and heavy duty trucks)o LCV freight vehicles (four-wheelers and three-wheelers)o buses (public and private)o passenger four-wheelers (cars/jeeps/taxis)o passenger three-wheelers o two-wheelers o tractors and trailers, and o other vehicles.

• Projections of vehicle numbers were moderated to account for the transport policy of age limit on permits for buses of 10–15 years.30

• Fuel consumption is projected by carrying out a regression analysis based on the number of vehicles using each type of fuel. Diesel consumption has been regressed with the forecast numbers of diesel vehicles, and petrol consumption has been regressed with the forecast number of petrol vehicles.

Growth in the number of vehicles during 2005–08 was 8% p.a. for freight vehicles and 10% for cars, against GSDP growth of 6%. Other key input parameters and their respective data sources are presented in Table 15. Key assumptions applied for forecasting GHG emissions are presented in Table 16.

Table 15: Data sources for key variables — transport sector projections

Variable Data Source

Population Population projections for India and states from 2001–2026, Report of the technical group on population projections constituted by the National Commission on Population May 2006.

Registered vehicles Statistics book for Madhya Pradesh for 2004, 2006 and 2010.GSDP Ministry of Statistical and Programme ImplementationPer capita Income Annual Plan 2011–12, Madhya PradeshYear wise Motor vehicles on road in MP

Madhya Pradesh Transport Statistics and Statistics book for Madhya Pradesh for 2004, 2006 and 2010

30 http://www.mpinfo.org/mpinfonew/english/cd/2009/211209.asp



Table 16: Key assumptions — transport sector projections

Variable Assumptions31

Population Population projections are available until 2026 (Census Report )32. In this study, the existing projections have been extrapolated until 2030 based on past trends.

GSDP The BAU GSDP growth is assumed to be 5.7% per annum until 2030 based on past trends (2007–11).

Per capita income (PCY) An average annual growth of 2% in the PCY has been assumed until 2030. This assumption is in line with the current PCY growth of 2% in Madhya Pradesh.

3.3.5 ResultsEmissions from the road transport sector in MP in 2008 are presented in Table 17. The projections for the transport sector (presented in Figure 11) indicate a steep increase in GHG emissions from approximately 6.85 million tCO2eq in 2008 to around 34.88 million tCO2eq by 2030. This can be attributed to rapid infrastructure development (urbanisation; road expansion) and an increase in per capita income, which is set to lead to growth in the uptake of non-commercial vehicles in Madhya Pradesh.

Table 17: GHG emissions — transport sector 2008

2008 CO2 emissions (tCO2)

2008 CH4 emissions (tCH4)

2008 N2O emissions (tN2O)

Total GHG emissions – transport 2008 (tCO2e)

6,739,882 694 318 6,853,174

Figure 11: Projected emissions from the transport sector

3.4 Buildings sector

3.4.1 BackgroundIndia is urbanizing rapidly, and its cities have become engines of growth for the economy. This holds true for Madhya Pradesh as well, which is undergoing a substantial change in terms of urban and economic growth. Madhya Pradesh has a population of 72.6 million (as per the census 2011), and it is expected to have a population of 88 million by 202033. The urban population of Madhya Pradesh accounts for 28% of the

31 The assumptions are based on several studies on energy demand for Indian economy by various organisations such as TERI, World Bank, Lawrence Berkeley National Laboratory. 32 Population projections for India and states 2001-2026, Report of the technical group on population projections constituted by the National Commission on Population May 2006


state’s total population, while the remaining resides in rural areas. Although the majority of the population presently resides in rural areas, the urbanisation growth rate in Madhya Pradesh is the fifth highest in India34.Significant urban growth centres in Madhya Pradesh include Indore, Bhopal, Gwalior and Jabalpur. Growth in the number of residential and commercial buildings in Madhya Pradesh is expected to continue over the next decades, driven by increasing population, high urbanisation rate and service sector growth.

According to ECBC Climatic Zone classifications, Madhya Pradesh falls under the composite zone35, which is characterized by seasonal variation in temperature (high temperatures in summer and low in winter), as well as humidity (low humidity in summer and high humidity in monsoon periods)36. Climatic conditions that prevail in the composite zone create electricity demand for both cooling (during summer and monsoon) and heating (during winter) in buildings.

3.4.2 Sources of GHG emissions in the buildings sectorTypical sources of emissions in the buildings sector include electrical and thermal energy consumption in commercial and residential buildings for various applications, including heating/cooling, lighting, appliances, and cooking.

Electrical energy consumptionIn commercial buildings, Heating, Ventilation and Air conditioning Systems (HVAC) and lighting consumes more than 80% of the electricity. In residential buildings without air conditioning, lighting and fans/coolers account for the majority of the electricity consumption.

Emissions from electricity generation are covered under the power sector, and these are classified as indirect emissions from buildings. To avoid double counting, emissions from electricity consumption are not added in the GHG inventory of buildings. However, as appliances used in buildings are the major consumers of electricity, emissions from electricity consumption are presented in the results section.

Electricity is produced using diesel gen sets in commercial buildings in the case of power outages. As per NATCOM, this source is scattered, and lack of data for this consumption is a gap area. To estimate the scale of this diesel consumption, it is assumed that DGs are employed in commercial buildings only, as the majority of MP’s population lives in rural areas. The percentage power deficit in MP is applied to the commercial building sector, and it is assumed that the entire deficit of commercial buildings (leading to overestimation) is fulfilled by diesel generators. Diesel consumption in this sector is only about 1% of the total diesel consumption in the state. As this is a negligible amount, emissions from this source are not considered in the GHG inventory.

Thermal energy consumptionThermal energy consumption in buildings is primarily due to cooking requirements. The primary fuels consumed for cooking in rural areas of Madhya Pradesh are firewood/chips and dung cakes, whereas the primary fuel consumed for cooking in urban areas of Madhya Pradesh is LPG37. The usage of LPG is increasing in rural areas on the back of initiatives such as Rajiv Gandhi Gramin LPG Vitaran. In addition to cooking requirements, fuel (primarily kerosene) is also consumed for lighting purposes in rural areas, as these have limited access to electricity.

33 Population projection for India (census 2011)34 MP Planning Commission Annual Report 2011-201235 National Building Code,200536 ECBC User Guide 37 NSSO Statistics-Consumer Expenditure in India



3.4.3 GHG accounting methodology for the buildings sectorFor estimating GHG emissions in the building sector, total LPG consumption, kerosene consumption and biomass (firewood) consumption in Madhya Pradesh has been estimated and has been attributed to the building sector. GHG emissions are calculated using the following approach:

Emissions = Fuelconsumed * Emission Factor

The combustion of LPG and kerosene primarily generates CO2 emissions, and the combustion of biomass generates CO2, CH4 and black carbon emissions. For the purpose of simplification, it is assumed that biomass is utilized in a sustainable manner and is considered as a renewable fuel. Therefore, while CH4 emissions and black carbon emissions emitted from biomass consumption (primarily in traditional cook-stoves) are accounted, CO2 emissions from biomass consumption are not accounted (as CO2 is sequestered in biomass, and biomass is collected in a sustainable manner).

The IPCC methodological Tier II approach is followed for the building sector, since the aggregated data at the state level is used and emission factors applied are country specific.

Assumptions and data sources• LPG and kerosene are primarily used in households and commercial establishments. The amount of

LPG/kerosene used by industry, agriculture or transport is negligible.• LPG and kerosene consumption in MP is sourced from the MoPNG. • India-specific emission factors are used from India NATCOM 2 for LPG and kerosene.• Monthly per capita biomass consumption for the rural and urban population of MP is assumed to be the

same as that for India and is sourced from NSSO, Key Indicators of Household Consumer Expenditure. • The emission factor and net calorific values for biomass are used from the IPCC. • Diesel consumption in buildings was estimated based on the unmet electricity requirement of

commercial buildings due to the power deficit in MP. Data about electricity consumption in commercial buildings is sourced from the MP statistics handbook 2010 and the deficit from MP’s Twelfth FYP. Data on power deficit in MP was sourced from the Energy Department. In the BAU scenario, the entire deficit is met by diesel generators in commercial buildings. Diesel consumption based on this estimate works out to approximately 1% of the total diesel consumption in the state. Considering that this is a negligible amount and that actual diesel consumption in commercial buildings is likely to be much lower than this estimate, diesel consumption in buildings has not been considered in the inventory.

3.4.4 GHG forecasting methodology for the buildings sectorIn order to forecast GHG emissions from the building sector, energy demand from cooking (in case of residential households in rural and urban areas), lighting (for both urban residential and commercial buildings and rural residential buildings) and appliances has been estimated by applying an end-use model. Only thermal energy consumption (LPG and kerosene) have been accounted for under the buildings sector, as electrical energy consumption is already covered under the power sector. However, as buildings contribute significantly to electricity consumption, forecasts of electricity consumption due to lighting and appliance usage in buildings have been carried out for completeness.

The following key steps have been applied for forecasting energy consumption in the building sector for Madhya Pradesh: • Projecting numbers of households and monthly per capita expenditure38 • Projecting the number of households that are electrified 39

• Projecting the total energy demand (electricity, kerosene and LPG) from cooking and lighting activities

38 Indian Census reports and World Bank reports give projections of number of households till 2026. The forecasts have been extrapolated till 2030.39 Electrification rate for urban sector is assumed to be 100% by 2020.


• Forecasting electricity consumption though use of electrical appliances40 (e.g., fans, televisions, air conditioner, air cooler, washing machine, water heater)

• Computing fuel and electricity consumption from the use of appliances, lighting, and cooking 41

Electricity consumption in buildings due to the use of appliances and lighting applications has been projected, but has not been accounted for in the GHG emissions inventory under the buildings sector (as emission due to electricity generation is covered under the power sector).

Energy demand and emissions from lighting in the building sectorThe following equation (source: Stephane et al. in “Residential and Transport Energy Use in India: Past Trend and Future Outlook”, 2009) gives the aggregate energy consumption from lighting:

Where,El,t = energy demand from lighting Pr,t = Rural population in the year tFr,t = Number of persons per rural household in the year t.Er,t = Rural household electrification rate in the year t.Pu,t = Urban population in the year tFu,t = Number of persons per urban household in the year t.Eu,t = Urban household electrification rate in the year t.i = Type of lighting bulb (incandescent bulbs and tube lights, fluorescent),Li,t = Number of lighting bulb of type i per household in the year t.Pi,t = Power of lights/bulbs of type i in the year t.Hi,t = Hours of use of bulb of type i in the year t.k = Fuel typeLk,t = Lighting energy use of fuel k per capita in the year t.

Energy demand and emissions from cooking in the residential sector The equation below gives the linear regression model42 used for projecting cooking energy demand, based on the population and change in the income level.

Ec,t = (Pr,t×PEr)+(Pu,t×PEu)

Ec,t = Energy consumption from cooking;Pr,t = Rural population in the year t Pu,t = Urban population in the year t PEr = Per capita energy requirement for rural householdsPEu = Per capita energy requirement for urban householdsEmissions from cooking have been projected for the building sector, given the projected mix LPG, kerosene, and biomass and corresponding emission factors.

Energy demand and emissions from appliances in residential and commercial buildingsThe diffusion of appliances in electrified households has been estimated using the following equation:

40 Rate of appliance diffusion is assumed to follow Gompertz function41 Stephane de la Rue du Can, Michael McNeil, and Jayant Sathaye, “India Energy Outlook: End Use Demand in India to 2020,”, 200942 A simple linear regression model is a regression model where the dependent variable is continuous, explained by a single exogenous variable, and linear in the parameters. : Y = a + b.X



Diffi,j,t=Elecj,t*αj*exp (γj*exp(βj*MPCEj,t))

Where,Diffi,j,t = Diffusion of appliance i in the year t for jth local.Elecj,t = Electrification of households in the year t for jth local.MPCEj,t = Average monthly per capita household expenditure in the year t. for jth localαj,βj,γj = Parameters estimated for jth local.i = type of appliancej = rural (r), urban (u)

The equation below gives the end-use model used for the projecting energy demand from appliances.

Where,Ea,t = Energy consumption from appliancesPj,t = Population for the jth local in the year tFj,t = Number of persons per household for the jth local in the year t.i = Type of appliance (fan, television, water heating, washing machine, air conditioner, air cooler, etc.)Diffi,j,t = Diffusion rate of appliances for the jth local in the year t.UECi,j,t = Unit energy consumption for ith appliance for the jth local in the year t.

UECs are a function of the efficiency and the capacity of the appliance used as well as the level of use.

The tables below give an overview of the key variables used and the key assumptions made for estimating emissions from this sector.

Table 18: Data sources for key variables — building sector forecasts

Variable Data Source

Number of households Selected Socio Economic Statistics, India 2011, Ministry of Statistical and Programme Implementation

Household size Residential Consumption Of Electricity In India, India: Strategies For Low Carbon Growth, World Bank, July 2008

Monthly per capita expenditure (MPCE) in the rural and urban areas of Madhya Pradesh

National Sample Survey Office reports (Round 58, 61, 66)

Distribution of households according to MPCE class

National Energy Map of India, Technology Vision 2030, TERI

Household electrification rate Ministry of Statistical and Programme Implementation reports

Electricity consumption data for residential and commercial buildings

Madhya Pradesh Statistics Handbook – 2010

LPG and kerosene consumption data Ministry of Petroleum & Natural GasPer capita biomass consumption NSSO, Level and Pattern of Consumer Expenditure, NSS 66th

round


Table 19: Key assumptions — building sector forecasts

Variable Assumptions43

Population Population projections are available until 2026 (Census Report44 ). In this study, the existing projections have been extrapolated until 2030, based on past trends.

Household size The World Bank methodology45 has been adopted in this study for projecting household size until 2030.

Electrification rate

Household electrification would increase at a CAGR growth rate of 1.5% per annum for rural household and 0.3% for urban household (reaching 100% electrification in 2020) until 2030, based on the decadal growth rate of household electrification (Ministry of Statistical and Programme Implementation reports).

Diffusion rate of appliances

Diffusion rate of appliances follows a logistic function46 depending on electrification rate and marginal per capita expenditure47. The national rate of diffusion of appliances is taken as a proxy for both the residential and commercial sectors due to lack of data availability for Madhya Pradesh.

Unit Energy Consumption for appliances

Unit Energy Consumption (UEC) is assumed to stay constant 48 over time for most of the appliances. However, it is assumed to change for refrigerators, air conditioners and water heaters. The national level data is taken as a proxy for Madhya Pradesh.

Cooking Based on literature review, it is assumed that the average per capita daily energy requirement for cooking to be 620 kcal in rural areas and 520 kcal for urban areas49.

Lighting Energy is mainly used for electric lighting (incandescent bulbs and fluorescent lights) and kerosene lighting. Although household electrification will reach 100% for the urban sector by 2020 and 95% for the rural sector by 2030 (as against the current rate50 of 95.3% in urban area and 75.3% in rural area), the use of kerosene for lighting will not be completely eliminated. Even with 100% household electrification, kerosene would be consumed for lighting as a backup, mainly in the rural areas of MP51.

3.4.5 ResultsEmission from the building sector is projected to increase from around 3.08 million tons of CO2 in 2008 to 4.03 million tons of CO2 in 2030. The change in energy use pattern in the building sector will be characterized by increased purchasing power, increased access to electricity and rising urbanisation. Although the increase in direct GHG emissions due to fuel consumption is not significant, a substantial increase is expected in electricity consumption in the buildings sector. The shift from kerosene-based lighting to electricity-based lighting leads to a decline in kerosene consumption over time, which has a dampening effect on the increase in overall direct emissions.

43 The assumptions are based on several studies on energy demand for Indian economy by various organisations such as TERI, World Bank, Lawrence Berkeley National Laboratory. Details of the references are given in the footnotes and bibliography.44 Population projections for India and states 2001-2026, Report of the technical group on population projections constituted by the National Commis-sion on Population May 200645 This methodology is also followed by World Bank in its report “Residential Consumption of Electricity in India: Strategies for Low Carbon Growth”. It is based on the United Nations methodology for estimation of household size.46 The initial stage of growth is approximately exponential; then, as saturation begins, the growth slows, and at maturity, growth stops.47 The methodology followed here is similar to Stephane et al.’s “Residential and Transport Energy Use in India: Past Trend and Future Outlook”. The parameters estimated by them are used in this study. 48 UEC is a function of technology co-efficient of the appliance. UEC for most of the appliance are taken to be constant. The changes in UEC of refrigera-tors, air conditioners and water heaters are based on the assumptions in Stephane et al. (2009).49 TERI, in their energy study “National Energy Map for India, Technology Vision 2030” assumed that per capita per day energy requirement for cooking is 620 kcal rural areas and 520 kcal in urban areas. This energy requirement is a function of cooking habits in rural area, primary sources of energy used for cooking purposes in India. Since the food habits and the cooking habits will not change drastically over the next 20 years, the energy requirement for cooking is kept constant. 50 NSSO Round 6451 Stephen et al. (2009). It reports that according to NCAER survey, rural electrified households still use kerosene for lighting, with only 27% less quantity than non-electrified households.



Table 20: LPG and kerosene consumption — buildings sector

Parameter Unit 2008 2030

LPG thousand tons 468 790Kerosene (used in cooking as well as lighting) thousand tons 491 479Biomass thousand tons 13,887 16,305

The trend in emissions resulting from fuel consumption (LPG, kerosene and biomass) used in cooking and lighting in the building sector is given in Figure 12.

Table 21: GHG emissions inventory and projections for building sector

Parameter Unit 2008 2030

GHG emissions — LPG million tCO2e 1.40 2.36GHG emissions — kerosene (cooking and lighting) million tCO2e 1.55 1.51GHG emissions — biomass tCH4 6,499 7,631GHG emissions — biomass (CO2 equivalent) million tCO2e 0.14 0.16

Figure 12: Emissions forecast — building sector (million tCO2e)

Table 22: Electricity consumption — buildings sector52

Parameter Unit Value (2008)

Residential and commercial electricity consumption MWh 4,038,300GHG emissions (emissions due to purchased electricity) Million tCO2 3.39

52 The emissions from electricity consumption are not included in the total GHG emissions to avoid double counting as they are already included in the power sector. The electricity consumption in buildings is presented here since appliances and lighting usage in buildings contribute significantly to elec-tricity demand, and indirectly to GHG emissions (in power sector).


Table 23: Forecast electricity consumption — buildings sector

Parameter Unit 2015 2020 2025 2030

Electricity usage in appliances MWh 4,950,683 7,694,913 12,723,889 23,568,868Electricity usage lighting MWh 2,487,675 3,716,225 4,559,470 5,775,689Emissions from electricity usage in appliances

million tCO2e 4.16 6.46 10.69 19.80

Emissions from electricity usage in lighting

million tCO2e 2.09 3.12 3.83 4.85

3.5 Agriculture and livestock sector

3.5.1 BackgroundThe contribution of the agriculture sector to the overall GDP of India has reduced from about 30% in 1990–91 to less than 15% in 2011–12 as a result of development in other sectors over the decades.53 Agriculture is a critical sector of the Indian economy and remains vital for the country’s development. An average Indian spends approximately half of his/her expenditure on food, and approximately half of India’s workforce is engaged in agriculture. Given the significance of agriculture as a source of livelihood and food security for a vast majority of the rural population, the Government of India has undertaken several policies/initiatives to promote development of the sector. These include:

Figure 13: Policies / Initiatives to support agriculture and livestock sector

In Madhya Pradesh, 70% of the rural population is engaged in agriculture and allied activities including horticulture, animal husbandry, fisheries and dairy development.54 This sector contributes about 30% to the state’s net domestic product.

The major kharif crops in the state are paddy, jowar, maize, bajra, tur, urad, moong, soybean, groundnut and cotton. The major rabi crops are wheat, gram, lentil, peas, mustard and linseed. Around 41% of the agricultural area in MP is used for non-food crops, which include cotton and oilseeds. Madhya Pradesh is

53 State of Indian Agriculture 2011-201254 Madhya Pradesh State action Plan on climate change



one of the largest cotton-producing states in India — 14.50 lakhs bales of production in 2009-1055. Around 6% of the India’s cotton is produced in MP56. Around 70% of the gross cropped area in MP is rain-fed farming area. Erratic and uneven distribution of rainfall is the major constraint to achieving agriculture production targets.57 In the recent past, there has been an impetus on increasing facilities for irrigation. The gross irrigated area in MP has increased from 4.73 million hectares in 2001-02 to 6.99 million hectares in 2009-10.

The state comprises around 8% of the livestock population of India. The majority of the livestock population in MP (around 99%) consists of cattle and buffalo. Cattle are an important contributor to the agrarian economy and are used both for dairy production and as labour.

Historically, Madhya Pradesh is among the low fertilizer consumption states in the country. This trend, however has been changing over the recent past, as the use of fertilizer has steadily increased from 40.35 kg/ha in 2001-02 to 77 kg/ha in 2008-09.

The economy of Madhya Pradesh is undergoing a shift toward emphasis on growth in manufacturing and services. However, agricultural growth remains an area of high priority for the state from a socio-economic perspective. Some of the long-term goals with respect to agricultural growth include: 58

• Increasing crop intensity from 131% to 145%• Increasing crop productivity in rain fed areas by 20%-40%• Increasing agricultural productivity from INR47,225/ha to INR57,225/ha of net sown area.

Furthermore, short-term goals59 with respect to agricultural growth include inter alia increased irrigation, usage of farm machinery, and increased infrastructure in rural areas (rural roads, access to energy, markets, storage, credit and others).

3.5.2 Sources of GHG emissions in the agriculture and livestock sectorThe following are sources of emissions from the agriculture sector:• Fuel consumption in diesel-based agricultural pump sets• Methane emissions from paddy cultivation• Methane emissions from livestock• Nitrous oxide emissions from managed agricultural soils (fertilizer usage)• Emissions due to burning of crop residues.

Emissions from tractors are incorporated in the transport sector, and emissions from electricity consumption for agricultural purposes are accounted for in the power sector.

The IPCC methodological Tier II approach is followed for the agriculture sector (except for burning of crop residues), since the aggregated data at the state level is used and emission factors applied are country specific. For estimating emissions from burning crop residues, state crop production data is used, along with default factors provided by the IPCC (since India-specific factors are not available).

3.5.3 GHG accounting and forecasting methodology for the agriculture and livestock sector

3.5.3.1 Emissions from agricultural pump setsGHG accounting methodology — emissions from agricultural pump setsDiesel consumption in pump sets has been calculated, and a corresponding emission factor has been used to calculate emissions. The numbers of diesel-based agricultural pump sets in the state and usage level of

55 Economic Survey of MP 2009-10, TRIFAC56 Economic Survey of MP 2009-10, TRIFAC57 MP Annual Plan 2011-201258 Madhya Pradesh Agricultural Economics survey report 201259 Madhya Pradesh Agricultural Economics survey report 2012


agricultural pumps have been used as the basis for the estimation of diesel consumption in pump sets. Emissions = Fuelconsumed * Emission Factor

Assumptions and data sources• Data for the number of diesel-based pumps in MP has been sourced from the Statistics Handbook for

MP by the Directorate of Economics and Statistics. Data on running hours and diesel consumption rate in pumps is taken from the International Water Management Institute’s study of ground water use.

• Emissions from diesel consumption in tractors are covered under the transport sector and not under agriculture.

GHG forecasting methodology — emissions from agricultural pump setsFor estimating emissions from irrigation pump sets, final energy consumption (both electric and diesel consumption) in the agricultural sector is calculated following the approach suggested in Stephane et. al., 2009. Emissions from electric pumps have been forecast for developing abatement levers and calculating abatement potential. These are not included in emission estimations for 2030 to avoid double counting, since emissions from electricity are already included in the power sector. CO2 is the predominant GHG emitted during diesel consumption in pump sets, as well as electricity generation. Diesel and electricity consumption have been projected as a function of the following variables60: • Agricultural area under irrigation• Number of electrical and diesel pumps (electric, diesel pumps)• Average energy used of each type of pumps

The projection is based on an end-use demand estimation using statistical/econometric modelling. End-use demand estimation or end-use modelling focuses on estimation of final needs (eg: energy demand) at a disaggregated level. End-use modelling attempts to account for energy demand using engineering representation of an energy system (eg: factors such as technology choice, energy source, and efficiency of end-use appliances may be used). Econometric modeeling establishes a relationship between a dependent variable and certain chosen independent variables by statistical analysis of historical data. Hybrid approaches make use of both end-use models, and econometric modelling, and have been applied in this analysis.

Energy consumption in agriculture depends on the use of electric pumps, diesel pumps and tractors. However, in our projections, emissions from tractors have been incorporated in the transport sector, and emissions from electricity generation have been covered under the power sector.

Agricultural pump setsEnergy consumption in agricultural pump sets has been projected using a combination of end-use modelling and econometric modelling. Final energy consumption is calculated using the following equations61:

Eagri_diesel,t=[ILt*Pdiesel,t*IPdiesel,t]Eagri_elec,t=[ILt*Pelec,t*IPelec,t]

Where,Eagri_diesel,t= energy consumption in diesel based pump-sets in the year t ILt = irrigated land area in the year tPdiesel,t = number of diesel based pumps per area of irrigated arable land in year tIPdiesel,t = average energy use (diesel) per pump-set in the year tEagri_elec,t = energy consumption in electricity based pump-sets in the year tIPelec,t = average energy use (electricity) per pump-set in the year t

60 (Stephane de la Rue du Can M. M., 2009)61 The methodology applied is from “India Energy Outlook: End Use Demand in India to 2020,” by Stephane de la Rue du Can, Michael McNeil, and Jay-ant Sathaye



Projection of Irrigated land areaIrrigated land area (ILt), or Gross irrigated area (GIA), is determined on the basis of incremental gross cropped area (GCA), which depends on cropping intensities and net cropped area. The projection of gross irrigated area is carried out by applying the following steps:62

1. It is assumed that the cropping intensities will increase until a saturation level is reached. Cropping intensities are expected to follow a logistic curve, computed as63:

Where, CIt = Cropping intensity in the year t,CI0 = Limiting cropping intensity. a,b = Parameters estimated

2. Gross cropped area (GCA) is obtained as follows64:

GCAt = Gross Cropped Area in year tNCAt = Net Cropped Area in the year t; NCA is assumed to be constant at 141 Mha

3. The increase in gross cropped area is only possible by providing additional irrigation facilities and that the increase in gross irrigated area is directly proportional to an increase in gross irrigated area.

65 Therefore, gross irrigated area is given by :

Projection of number of pump sets and energy use per pump setThe share of electric pumps is set to increase over time on the back of more electrification. However, the required number of pumps used per hectare will reach a saturation level, based on the area under irrigation.66 Average energy used by electric pumps is assumed as 15.98 GJ/unit.67 As the area under irrigation increases, average energy used per pump is projected to increase68.

Table 24 below gives an overview of the key variables and their sources for all the emission sources in the agriculture and livestock sector. These variables are used to forecast the rest of the emission sources as well.

62 The methodology followed to estimate the irrigated land area till 2030 is from National Energy Map for India, Technology Vision 2030, TERI.63 The limiting cropping intensity is taken to be 3 per year as per the assumptions given in the National Energy Map for India, Technology Vision 2030.64 Since the net cropped area is kept constant, the changes in gross cropped area are due to the changes in cropping intensities. Source: National Energy Map for India, Technology Vision 2030, TERI. 65 Source: National Energy Map for India, Technology Vision 2030, TERI. 66 The growth rates of pumps (both electric and diesel) follow a logistic pattern, since no. of pumps per hectare will reach a saturation level after a point of time and then remain constant (assuming increase in cropping intensities continues).67 Bureau of Energy Efficiency 68 Average energy used per pump is positively correlated with increase in area under irrigation.


Table 24: Data sources for key variables — agriculture and livestock sector

Variable considered for projection of GHG emissions

Data source

Population Population projections for India and states 2001-2026, Report of the technical group on population projections constituted by the National Commission on Population May 2006

Agricultural GSDP Agricultural Statistics, Madhya Pradesh, Various yearsNumber of diesel and electric pump-sets

Madhya Pradesh Agricultural Economic Survey 2012

Agricultural land (irrigated, area sown, cropping intensity)


Production of food grains, yield of food grains


Average energy use of electric pumps Bureau of Energy Efficiency (BEE)69

Average energy use of diesel pumps International Water Management Institute Reports70

Livestock Agricultural Statistics, Madhya Pradesh Various yearsFertilizer Agricultural Statistics, Madhya Pradesh Various years

Table 25: Key assumptions — agricultural pump sets forecasts

General Assumptions

Agricultural GSDP BAU Agricultural GSDP growth is assumed to be 2% per annum till 203071 .Agricultural pump sets — key assumptionsVariables Assumptions Remarks

Agricultural area under irrigation

Area under irrigation is estimated to increase at a CAGR of 2% between 2001 and 2030.

Consistent with the historical growth of irrigated land area under cultivation (1.5% CAGR between 2001 and 2008).

Pump sets The share of electric pumps will increase over time amid more rural electrification. However, the required number of pumps used per hectare will reach a saturation level based on the area under irrigation. Diesel pumps will still be used as a backup during power cuts, though their numbers will decrease over time.

Rural electrification and energy efficiency measures willensure less usage of diesel pumps.

Average energy consumption by different pump sets

As area under irrigation increases, average energy used per pump will also increase. 72

As irrigated area under cultivation increases, the use of pumps will increase. Moreover, reduction in groundwater level also increases the energy consumption of pumps.

69 Based on Stakeholder Consultation with BEE70 Irrigation Management in Pakistan and India: Comparing Notes on Institutions and Policies71 In the Planning Commissions’ Data Book, projection for Agricultural GDP at the national level is estimated in 3 different scenarios, namely 2% growth, 4% growth and 8% growth. These scenarios are based on the assumptions of GDP growth of the country. The current downturn of the Indian economy is also affecting the overall growth of Madhya Pradesh, which is assumed to grow at 5.7% p.a. based on the past trends. Hence the agricultural GDP growth is assumed to be 2% in the BAU scenario.72 In the BAU scenario, technology improvements due to new innovations are not included. Such improvements will be assessed in the low carbon growth scenario.



Results — emissions from agricultural pump sets • Emissions in 2008 due to diesel consumption in agricultural pump sets — 1.04 million tCO2e• Forecast emissions in 2030 due to diesel consumption in agricultural pump sets — 0.56 million tCO2eEmissions from diesel-based pump sets have declined in the forecast due to increased trend of electrification and access to energy.

Key trends related to projections for diesel-based agricultural pump sets are given below.

Table 26: Projections of key variables — diesel-based agricultural pump sets

Year Gross irrigated area (000 ha)

Number of diesel pumps (Lakh)

Number of diesel pumps per irrigated land (unit/ha)

Average energy use per diesel pump (GJ/unit)

Energy demand from diesel pumps (PJ)

2008 6,567 3.3 0.05 41.5 13.72015 7,175 2.7 0.03 42.0 11.32020 7,188 2.3 0.03 42.5 9.92025 7,201 2.0 0.02 43.0 8.72030 7,214 1.7 0.02 43.5 7.6

Figure 14: Emissions from diesel pumps in agriculture (million tCO2e)


3.5.3.2 Methane emissions from paddy cultivationMethane emissions from paddy (rice) cultivation are dependent on the characteristics of the cultivated area (irrigated/rain fed), as well as on water management techniques. In Madhya Pradesh, approximately 36% of the land area used for growing rice is irrigated. Due to droughts and erratic rainfall, paddy cultivation in rain-fed (non-irrigated) areas emits comparatively lower methane volumes. Water management practices have a significant influence on the amount of methane emitted in a given area of rice cultivation. Reduced water usage can lead to less flooding and mitigate anaerobic processes that lead to methane emissions in water-submerged soils. In Madhya Pradesh, water-efficient rice cultivation practices such as System of Rice Intensification contribute to lower methane emissions from rice cultivation.

GHG accounting methodology — methane emissions from paddy cultivationEmission factors for estimating GHG emission from this category in this report are sourced from the latest national communication submitted to UNFCCC (NATCOM 2). The emission factor corresponding to multiple aerations has been applied for irrigated land (since flooding conditions do not prevail in MP), and the emission factor corresponding to drought-prone areas has been applied for rain-fed land.

GHG forecasting methodology — emissions from paddy cultivationEmissions from paddy cultivation are forecast using the following steps:• The gross area under paddy cultivation is estimated until 2030 as a function of cropping intensity,

production, yield and agricultural GSDP. • The cultivated area is classified into irrigated area and rain-fed area. • Appropriate emission factors are applied under each category.

Paddy cultivation:A regression analysis is carried out for determining area under paddy cultivation based on agricultural GDP, yield and production, as follows:

Area under paddy cultivation = f(Agricultural GDP,Yield,Production)

For estimating emissions from paddy cultivation, gross irrigated area under paddy cultivation has been estimated based on historical data on irrigated area under paddy cultivation, as follows:

GIAt= f(GIAt-1,…,GIAt-k)

Emissions due to paddy cultivation are computed separated for area under paddy cultivation that falls under irrigated areas and area under paddy cultivation in rain-fed areas.



Table 27: Key assumptions — paddy cultivation forecasts

Paddy cultivation — key assumptions

Variables Assumptions Remarks

Area under paddy cultivation

Projected to increase as a function of agricultural GDP, rice yield and rice production

The net area sown remains constant over time. However, gross area under paddy cultivation is increasing due to the increasing cropping intensity.

Production Increasing at 5% per annum The initiatives taken by the state governments to increase production by 5% have been taken into account.

Yield Projected based on time series analysis that follows historical changes in rice yield

The use of high-yielding varieties and fertilizers, and an increase in irrigation area have a positive impact on yield.

Agricultural GSDP

Increasing at 2% per annum The current downturn in the Indian economy is affecting the overall growth of MadhyaPradesh, which is assumed to grow at 5.7% p.a. based on past trends. Hence, agricultural GDP growth is assumed to be 2% in the BAU scenario.

Irrigated area

Percentage of gross irrigated area under paddy would increase at the same rate projected by Stephane et al for agriculture sector, India73 (1%)

The projection has been cross-checked with the irrigation potential of the state.

Rain-fed area

This is calculated as gross area under paddy cultivation less irrigated area.

Emission factor

Irrigated land: 18 kg CH4 ha-1

Rain-fed land: 66 kg CH4 ha-1Emission factors given in India’s National Communication to UNFCCC (2012) have been applied. The emission factor corresponding to multiple aerations has been applied for irrigated land (lowest emission factor) and the emission factor corresponding to drought-prone areas has been applied for rain-fed land.

Results — methane emissions from paddy cultivation• Emissions in 2008 due to paddy cultivation — 2.062 million tCO2e• Forecast emissions in 2030 due to paddy cultivation — 2.32 Million tCO2e

Key results for forecasts related to paddy cultivation are given in the table below.

Table 28: Projections — area under paddy cultivation (thousand hectares)

Year Area under cultivation (000 ha)

Percentage of gross irrigated area

Gross irrigated area (000 ha)

Gross rain-fed area (000 ha)

2008 1,645 15% 252 1,3932015 1,743 19% 327 1,4172020 1,796 20% 354 1,4422025 1,874 21% 388 1,4872030 1,990 22% 433 1,557

73 Stephane de la Rue du Can, Michael McNeil, and Jayant Sathaye, “India Energy Outlook: End Use Demand in India to 2020,”, 2009


Total emissions from paddy cultivation are shown in the figure below.

Figure 15: Emissions from paddy cultivation (million tCO2e)

3.5.3.3 Methane emissions from livestockAnimal husbandry is one of the major sources of livelihood for the rural population of Madhya Pradesh. It complements crop production by providing employment to small and marginal farmers and landless labourers. Methane emissions result from animal husbandry activities, primarily due to enteric fermentation associated with livestock. Cattle are an important source of methane emissions, because of its large population and its ruminant digestive system, which is conducive to a higher rate of methane emissions.

GHG accounting methodology — methane emissions from livestockMethane emissions from livestock have been computed by applying the methodology provided by 2006 IPCC Guidelines for National Greenhouse Gas Inventories for enteric fermentation. The population of various types of livestock (cows, buffalos, sheep, goats, horses, mules and others) in MP has been multiplied with corresponding India-specific emission factors to calculate methane emissions. In the case of cattle, a weighted average emission factor has been applied corresponding to different types of cattle (crossbred/indigenous) and different age groups of cattle.

Assumptions and data sources • Livestock population statistics are taken from the Department of Animal husbandry census for 2003 and

2007 and have been extrapolated based on a CAGR to arrive at estimated population for 2008.• Emissions factors applied are India specific and are taken from NATCOM 2.• Emissions from manure management in MP are considered as negligible. The manure management

practice followed in MP involves drying dung cakes under the sun or indoor-drying against walls for 3-10 days. Under the prevailing practices, methane emissions are not expected from dung cakes.

GHG forecasting methodology — methane emissions from livestockTo estimate emissions from livestock, the following approach has been adopted:• The population of livestock has been estimated using livestock population census for 2003 and 2007. • Appropriate emission factors have been applied.• Since the contribution to emissions is mainly from cows and buffalos, which are found in large numbers

in MP, only these two categories of livestock were considered in the projections. A simple linear growth model has been applied and has been cross-checked against national-level forecasts for livestock population.



LivestockEmissions due to livestock are projected using time series analysis, applying a linear model. Livestock population has been projected to grow at an overall CAGR of approximately 2% (around 1% for cattle, 3% for buffalo, and declining trends for other types of livestock). These growth rates have been cross-checked with projections of national populations of livestock published by the National Centre for Agricultural Economics and Policy Research (CAGR of approximately 2.5% from 2010 to 2020 for dairy producing livestock). Emissions due to livestock are estimated as follows:

Emission from Livestockk,t (tCO2)=(Lk,t×EFk )×21

Where, Lk,t = Population of kth category livestock (k=cattle, buffalo) in year tEFk = Emission factor of kth livestock in tCH4/head/year

Table 29: Key assumptions-livestock forecasts

Livestock — key assumptions


Livestock population

Consideration of emissions from cows and buffalos

Apart from cows and buffalos, other types of livestock have relatively low populations (and low emission factors), which have further been declining over the last few years. Cows and buffaloes account for over 95% of the emissions from livestock in MP.

Growth in livestock

2% per annum until 2030

To estimate growth in livestock population, statistical analysis has been undertaken, where the growth rate of livestock is a linear function of the past trends of livestock population. Mechanisation is expected to grow over time, thereby decreasing dependency on the livestock population. At the same time, demand for dairy products is expected to increase. This would lead to an increase in demand for livestock.

Emission factors

Cattle: 26.68 kg CH4/head/yearBuffalo: 33.76 kg CH4/head/year

Weighted average emission factor for the cow and buffalo populations in India has been applied, based on emission factors given in India’s National Communication to UNFCCC (2012) for different types of livestock. The weighted average emission factor is derived based on the following categories of cattle (age groups and breeds):• Dairy indigenous • Dairy cross-bred• Non-diary indigenous (0-1 year)• Non-diary indigenous (1-3 year)• Non-dairy adult indigenous• Non-diary cross-bred (0-1 year)• Non-diary cross-bred (1-3 year)• Non-dairy adult cross-bredEmission factors have been kept constant until 2030.


Results — methane emission from livestock• Emissions in 2008 due to enteric fermentation of livestock — 20.35 million tCO2e• Forecast emissions in 2030 due to enteric fermentation of livestock — 31.72 Million tCO2e

A moderate increase is expected in emission from livestock (CAGR of 2.67%). Growth in the population of various livestock and the increasing trend in GHG emissions are depicted below.

Table 30: Livestock in MP, number (thousand)

Year Cattle Buffaloes Horses & Ponies

Sheep Goats Camels Total

2008 22,737 9,565 26 359 9,247 3 41,9362015 24,676 11,943 13 219 11,660 1 48,5122020 26,162 13,995 8 154 13,760 1 54,0792025 27,737 16,399 5 108 16,239 0 60,4892030 29,406 19,217 3 76 19,165 0 67,868

Total emission from livestock is shown in the figure below.

Figure 16: Emissions from livestock

3.5.3.4 Nitrous oxide emissions from managed agricultural soils (fertilizer usage)GHG accounting methodology — emissions due to fertilizer usageEmissions from managed agricultural soils are calculated using the approach given in IPCC Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories. The consumption of N-type fertilizers in Madhya Pradesh is taken as the basis for the calculation of N2O emissions.

Assumptions and data sources• Fertilizer consumption data has been sourced from agricultural statistics published in the MP Statistics

Handbook.• Emission factors have been sourced from Good Practice Guidance and Uncertainty Management in

National Greenhouse Gas Inventories, IPCC.

GHG forecasting methodology — emissions due to fertilizer usageFertilizer demand for Type-N fertilizers has been estimated using annual time series data from 1993-94 to 2009-10 using a simple linear regression model74. Demand for fertilizer is a function of prices (specifically

74 Performance of Agriculture Sector and Policy Initiatives, Planning Commission Report, 2011



price of fertilizers and food grains), as well as non-price factors such as irrigated area, coverage of high-yielding varieties, area under food grains and non-food grains, and cropping intensity.

The empirical model for fertilizer use is specified as follows:

Fn,t= α0+α1 HYVt+α2 CIt+α3 GIAper,t+α4 Pfert,t+α5 Pc,t+α6 creditt+errort

Where, Fn,t = Fertilizer consumption ( n denotes Type-N fertilizer consumption in thousand tones and t denotes the year)HYVt = Percentage of area under HYV to gross cropped area GIAper,t = Percentage of gross irrigated area to gross cropped area CIt = Cropping intensity (%)Pfert,t = Prices of Type N fertilizer is represented by price of N through UreaPc,t = Output price is represented by procurement price of rice and wheat (main users of fertilizers) and weighted by the share of their production. creditt = Short-term production credit per hectare of gross cropped area (Rs.)

Table 31: Key assumptions — fertilizer usage emissions forecast

Fertilizers — key assumptions


Type N fertilizer demand Nitrous oxide is the only significant GHG among all of the emissions from fertilizers.

Other types of GHG emissions are not considered significant.

Results — emissions due to fertilizer usage• Emissions in 2008 due to nitrous oxide emissions from agricultural soils — 9.38 million tCO2e• Forecast emissions in 2030 due to nitrous oxide emissions from agricultural soils — 14.79 million tCO2e

There is a moderate increase in emissions due to nitrous oxide emissions from agricultural soils from 2008 to 2030 as per the forecast (CAGR of 2.21%).

Key trends related to projections for emissions from fertilizer usage are given below.

Table 32: Projection results of key input variables — nitrous oxide emissions

Year Fertilizer Type-N (thousand tons)

Gross cropped area (thousand hectares)

Fertilizer consumption(kg/ha)

Emissions rate (kg N2O-N ha-1 y-1)

2008 801.48 20,226.50 39.62 1.492015 1,156.68 22,339.38 51.78 1.652020 1,350.00 23,638.00 57.00 1.702025 1,536.88 24,929.27 61.65 1.772030 1,719.60 26,204.45 65.62 1.82


Figure 17: Emissions due to fertilizer usage (million tCO2e)

3.5.3.5 Emissions from the burning of crop residuesThe burning of agricultural wastes in fields is a common practice in the developing world. It is primarily done to clear the remaining straw and stubble after harvest and to prepare the field for the next cropping cycle. The practise is not thought to be a net source of carbon dioxide (CO2), because the carbon released into the atmosphere during the burning is reabsorbed during the next growing season. However, crop residue burning is a significant net source of CH4 and N2O.

GHG account methodology — emissions from burning of crop residuesThe estimation of methane and nitrous oxide emissions due to the burning of crop residues was arrived at using IPCC’s revised inventory preparation guideline (IPCC, 1996). The residues of rice, wheat, maize, cotton, groundnut, mustard and sugarcane are typically burnt. Emissions due to the burning of residues from rice, wheat and maize have been considered for MP, since these crops have much higher production compared to other crops, and since the default factors for other crops are not available in IPCC. The following calculation steps are applied:

Total carbon released (tons of carbon) = ∑ annual production (tons of crop per year), x the ratio of residue to crop product (fraction), x the average dry matter fraction of residue (tons of dry matter / tons of biomass), x the fraction actually burned in the field, x the fraction oxidised, x the carbon fraction (tons of carbon / tons of dry matter)

CH4Emissions = Carbon Released x (emission ratio) x 16/12N2O Emissions = Carbon Released x (N/C ratio) x (emission ratio) x 44/28

Assumptions and data sources• Crop production for MP is taken from the Compendium of Agricultural Statistics, 2009-10, Mpkrishi.• The ratio of residue to product, dry matter fraction, fraction oxidised, carbon fraction, emission ratio

and N/C ratio are sourced from revised IPCC 1996 guidelines. • The fraction of crops actually burnt is taken as 10% and is sourced from IPCC good practice guidance.

Results — emission from burning of crop residuesEmissions from the burning of crop residues in 2008 in Madhya Pradesh are tabulated below.



Table 33: GHG emissions – burning of crop residues-2008

CH4 emissions (tons) N2O emissions (tons) Tons of CO2 eq. (tCO2e)

2,810.99 58.80 77,257.79

Since the numbers are insignificant in the context of total emissions in the agriculture and livestock sector, emissions from burning crop residues have not been forecast.

3.5.4 Aggregated results for the agriculture and livestock sectorThe summary of GHG emissions from the agriculture and livestock sector is presented in Table 34 and Table 35.

Table 34: Summary of GHG emissions – agriculture and livestock sector 2008

Emission sources CO2 emissions (tCO2)



Total GHG emissions (million tCO2e)

Diesel consumption in pumps 1,043,118 1.04Livestock — enteric fermentation

969,022 20.35

Paddy cultivation 98,202 2.06N2O from agricultural soils 30,245 9.38

Burning of crop residues 2,811 59 0.08Total direct emissions 1,043,118 1,070,035 30,304 32.91

Table 35: Summary of forecast GHG emissions– agriculture and livestock sector 2030

Emission sources CO2 emissions (tCO2)



Total GHG emissions (million tCO2e)

Diesel consumption in pumps 562,457 - - 0.56Livestock — enteric fermentation - 1,510,320 - 31.72Paddy cultivation - 110,544 - 2.32N2O from agricultural soils - - 47,699 14.79Burning of crop residues Not forecasted, since it is insignificantTotal direct emissions 562,457 1,620,864 47,699 49.39

Emissions due to diesel-based pump sets have not been included in the forecast due to increased trend of electrification and access to energy. There is a marginal increase in emissions due to paddy cultivation (around CAGR of 0.6% per annum) and a moderate increase in emission due to livestock (CAGR of 2.67%) and nitrous oxide emissions (CAGR of 2.21%).


3.6 Forestry sector

3.6.1 BackgroundForests naturally store biomass and carbon and are a vital source of sequestration of CO2 from the atmosphere. Therefore, maintaining/enhancing forest cover is recognized as an important focus area for mitigating the impact of climate change. Madhya Pradesh has the largest forest cover of all states in India. Forests of Madhya Pradesh cover approximately 30.72% of the total geographical area of the state75. The forest cover in the state is 77,700 Km2 based on the interpretation of satellite data of Oct–Dec 200876. Forest cover reported includes land where canopy density is at least 10% and area covered is at least 1 hectare.

Forests in Madhya Pradesh are an important source of livelihood for the rural and tribal population and for harbour resources such as fuel, fodder, fibre and timber used by the local communities. MP is a key source of minor forest products such as tendu, harra, sal seed and gum. These products not only generate revenue, but also generate employment through trade and business.

However, rising population and developmental needs are exerting a steadily increasing pressure on forests. The Government of MP has taken steps to counteract the pressure on forests through the banning of “felling” of trees and initiatives such as the Joint Forest Management programme, where local communities are engaged to preserve forests. The state government has also increased the use of Information and Communication Technology (ICT) solutions for managing forest areas. These efforts are vital to preserving bio-diversity in the region, as well as mitigating climate change.

3.6.2 GHG emissions sink in the forestry sectorForests act as a carbon sink and sequester carbon dioxide from the atmosphere. As per India state of forest Report 2011, there has been no change in the forest cover of MP in 2008 when compared to the previous assessment of satellite data of Oct 2006–Dec 2006. Here, the sink is estimated based on the annual increase in biomass carbon stock for the “forest land remaining forest land”.

3.6.3 GHG accounting methodology for the forestry sectorThe IPCC Tier 1 approach is used for calculating the sink created for “forest land remaining forest land”77. The annual increase in biomass carbon stock is estimated using the IPCC Equation, where area under each forest sub-category is multiplied by mean annual increment in tons of dry matter per hectare per year.

ΔCG = A * GTOTAL * CF∆CG = Annual increase in biomass carbon stocks due to biomass growth in land remaining in the same land-use category by vegetation type and climatic zone, tons C yr-1

A = Area of land remaining in the same land-use category, ha GTOTAL = Mean annual biomass growth, tons d. m. ha-1 yr-1

i = Ecological zone (i = 1 to n) j = Climate domain (j = 1 to m)CF = Carbon fraction of dry matter, ton C (ton d.m.)-1

Assumptions and data sources• Existing forests have been assumed to be at least 20 years old.• The total forest cover and percentages of forests under Tropical Dry, Tropical Moist, Plantations and

Tropical Thorn Forests are sourced from the India State of Forest Report, 2011. Plantations / Trees

75 Annual Report Government of Madhya Pradesh-Planning, Economics and Statistics Department, 2011-1276 India State of Forest Report, 2011, Forest Survey of India77 Category of land defined by IPCC where the area of land which was forest in previous years remains forest in the current year as well and does not change to agriculture or infrastructure or any other category of land.



Outside Forests (TOF) and Tropical Thorn Forests are taken as part of Tropical Dry Forests, since the default factors are not available for these two categories in IPCC. The area under these two categories is much smaller than that reported under other categories, so their impact on the estimation of sinks is minimal.

• The default values of annual above-ground biomass growth, ratio of below-ground biomass to above ground biomass and carbon fraction are sourced from IPCC 2006.

3.6.4 GHG forecasting methodology for the forestry sectorThere has been no change in the forest cover (as per forest survey of India) in MP in the past few years. Due to various initiatives by the MP Government, the existing forest land is expected to be sustained in future. Madhya Pradesh is the first state to implement an inclusive forest management approach through the formulation of village-level Joint Forest Management Committees (JFMCs) in 1991. The new State Forest Policy, 2005 of Madhya Pradesh gives impetus to efforts toward monitoring and preventing forest encroachments, as well as efforts to curb forest-related crimes.

As stated in the Twelfth FYP of MP, forest cover can be increased at a rate of 1% (for 2012-17), which corresponds to an increase of 15,540 ha every year. It is not envisaged that the target would be increased in subsequent FYPs. Since MP has the highest forest cover of all states, increasing forest cover significantly would be a daunting task.

Therefore, in the BAU scenario, 15,540 hectares of non-forest land will be converted to forest land every year until 2030. The forest department in MP has been undertaking various schemes and initiatives to plant new saplings inside as well as outside the designated forest area to increase forest cover.

Carbon sinks due to the conversion of non-forest land to forest land (afforestation) and due to the existing forest land remaining forest land have been projected.

The sinks due to the conversion of non-forest land to forest land are estimated as follows:

ΔCG = A * GTOTAL * CF∆CG = Annual increase in biomass carbon stocks due to biomass growth, tons C yr-1

A = Area of land converted to forest land, ha GTOTAL = Mean annual biomass growth, tons d. m. ha-1 yr-1

i = Ecological zone (i = 1 to n) j = Climate domain (j = 1 to m)CF = Carbon fraction of dry matter, ton C (ton d.m.)-1

GTOTAL = GW * (1 + R)GW = Average annual above-ground biomass growth (tons dm/ha/yr)\R = Ratio of below ground biomass to above-ground biomass (tons bg dm / ton ag dm)

Carbon sinks due to existing forest land are calculated as explained above, in the GHG accounting methodology for forestry sector.


Table 36: Key assumptions – carbon sinks due to existing forest land

Forestry — key assumptions


Types of forest lands Type of forest PercentageTropical dry 88.65%Tropical moist deciduous

8.97%

Plantation /TOF (taken as tropical dry)

2.12%

Tropical thorn (taken as tropical dry)

0.26%

Source: India state of forest report, 2011

Plantation/TOF and tropical thorn forests are taken as part of tropical dry forests, since the default factors are not available for these two categories in IPCC.

Average annual above-ground biomass growth (tons dm/ha/yr)

For tropical dry forest: 1.5 d. m. ha-1 yr-1

For tropical moist deciduous forest: 2 d. m. ha-1 yr-1

Default IPCC 2006 values

Ratio of below ground biomass to above-ground biomass (tons bg dm / ton ag dm)

For tropical dry forest: 0.28 tons bg dm / ton ag dmFor tropical moist deciduous forest: 0.24 tons bg dm / ton ag dm


Carbon fraction of dry matter, ton C (ton d.m.)-1

0.47 (for all forest types) Default IPCC 2006 values

3.6.5 ResultsCarbon sinks due to forest land remaining forest land is estimated at 26.38 million tCO2e in 2008. Since the existing forest land is assumed to be sustained as per historical data, this carbon sink would remain intact every year. Carbon sinks resulting from the addition of new saplings/plantations will contribute to increasing carbon sinks every year, as illustrated in Figure 18.



Figure 18: Carbon sinks from forest land

Forest cover in MP accounts for more than 12% of the forest cover of the entire nation and is an important carbon sink for India as a whole. It is important for the Government of India to support the state in maintaining its huge natural resource for environmental and economic stability in the country.

Projected area under forest and projected carbon sinks are given in Table 37.

Table 37: Projected carbon sinks

Year Total forest cover (ha)

Carbon sinks due to forest land remaining forest land (tCO2)

Carbon sinks due to conversion of non-forest land to forest land (tCO2)

Total carbon sinks (tCO2)

2008 7,770,000 26,381,998 - 26,381,9982015 7,816,620 26,381,998 158,292 26,540,2902020 7,894,320 26,381,998 422,112 26,804,1102025 7,972,020 26,381,998 685,932 27,067,9292030 8,049,720 26,381,998 949,752 27,331,749

3.7 Waste sector

3.7.1 BackgroundWaste generation is closely linked to population, urbanisation and affluence. In most of the developed and developing countries with increasing population, prosperity and urbanisation, it remains a major challenge for municipalities to collect, recycle, treat and dispose solid waste and wastewater. Sustainable waste management practices are essential for all-round development of the country. It must be further emphasized that multiple public health, safety and environmental co-benefits accrue from effective waste management practices including reduction in GHG emissions and improvement in the quality of life.


In India, the systematic collection and dumping of waste is only carried out in urban areas, leading to potential recovery of methane emissions. Effective waste management is one of the key issues in India. A number of laws have been framed for mandating waste management, including laws on biomedical waste, batteries, municipal waste management, hazardous waste management and plastics waste management.

The Madhya Pradesh Government has taken various initiatives in line with the national goal of sustainable waste management. The GoMP has launched The Integrated Urban Sanitation Programme (IUSP), in line with the National Urban Sanitation Policy 2008 of the Ministry of Urban Development, Government of India. With this, it aims to develop cities and towns in Madhya Pradesh as sanitized, healthy and liveable habitats.

The Urban Administration and Development Department provides grants-in-aid to urban local bodies for water supply, construction and maintenance of roads, sewerage and sanitation, solid waste management, street lighting, and slum development and infrastructure in urban areas. Presently, 360 urban local bodies operate in the state that run 14 municipal corporation, 96 municipal councils and 250 nagar panchayats. For urban administration (local bodies), an outlay of INR110,104.12 lakh has been proposed for the annual plan 2011-12.

The MP Government has undertaken Project Uday, which is financially assisted by the Asian Development Bank through the Government of India. It aims to address water supply issues and reduce environmental impact in the 4 mega cities of Madhya Pradesh, viz., Bhopal, Indore, Gwalior and Jabalpur. The primary objective of the project is to promote sustainable growth and reduce poverty in the four project cities in Madhya Pradesh and, thereafter, in other cities in the state. Specifically, the project will:• provide sustainable basic urban infrastructure and services to all citizens of the four project cities; and• enable the cities to plan and manage urban water supply and sanitation systems in a more effective,

transparent, and sustainable manner.

3.7.2 Sources of GHG emissions in the waste sectorThe main greenhouse gases emitted from waste management is methane (CH4). It is produced and released into the atmosphere as a by-product of the anaerobic decomposition of solid waste, where methanogenic bacteria break down organic matter in the waste. CH4 generation potential of the waste that is disposed in a certain year decreases gradually throughout the following decades. In this process, the release of CH4 from this specific amount of waste also decreases. GHG emissions from industrial solid waste are expected to be insignificant and, thus, are not considered in the GHG inventory of the state. This is in line with NATCOM 2. Emissions from energy requirement for the treatment of industrial waste are included in the emissions of the industry sector.

Similarly, industrial and domestic wastewater becomes a source of CH4 when treated or disposed anaerobically. It can also be a source of nitrous oxide (N2O) emissions due to the protein content of domestically generated wastewater. However, N2O emissions are much lower and insignificant compared to CH4 emissions, these are not considered in our analysis.The sources of emissions considered in the study are:• Methane emissions from municipal solid waste• Methane emissions from domestic wastewater• Methane emissions from industrial wastewater

The IPCC methodological Tier II approach is followed for the waste sector, since state-specific data is used, along with country-specific assumptions and IPCC-based emission factors.



3.7.3 GHG accounting methodology for the waste sector

3.7.3.1 Methane emissions from Municipal Solid Waste (MSW)MSW in MP is disposed in landfills by means of open dumping, resulting in CH4 emission from anaerobic conditions. The first order decay methodology provided by IPCC has been used for estimating CH4 from landfills. The waste generated in a particular year would decay over a decade and would cause GHG emissions over its decay life. Hence, total emissions due to the waste generated in 2008, caused to date and to be caused over its decay life, are taken into account in 2008. Tool to determine methane emissions avoided from the disposal of waste at a solid waste disposal site provided by UNFCCC has been used. It employs the IPCC first-order decay methodology to calculate methane emissions from solid waste.

Assumptions and data sources• The total waste generated in small and big cities of MP is calculated based on per capita waste

generation, sourced from P.U. Asnani, India infrastructure report, IDFC 2006, which further references NEERI data.

• The physical characteristics (i.e., composition of food waste, garden waste, paper, inert rubber and synthetics) of waste are decided based on size of various cities of MP (as quoted in the India infrastructure report, IDFC 2006).

• Population data is taken from Census 2011, and the population for 2008 is calculated based on the projected CAGR growth in population from 2001 to 2011.

3.7.3.2 Methane emissions from domestic waste water The methodology provided by 2006 IPCC Guidelines for National Greenhouse Gas Inventories to calculate emissions from the treatment and disposal of waste water has been applied to calculate emissions from domestic wastewater in MP. Emissions from domestic wastewater handling are estimated for both urban and rural centres in MP. Domestic wastewater has been categorized into urban high, urban low and rural, since the characteristics of the municipal wastewater vary across regions and depends on factors such as economic status, food habits of the community, water supply status, and climatic conditions of the area. The degree of utilisation of various treatment methods, discharge methods, along with the BOD in wastewater for MP and the population data, are used to calculate emissions, in line with the IPCC methodology.

Assumptions and data sourcesData on BOD, urbanisation fraction, degree of utilisation of treatment and discharge pathways is specific to MP and is sourced from Kartik. 2010. Inventorisation of Methane Emissions from Domestic & Key Industries Wastewater. Indian Network for Climate Change Assessment, Report number 1, MoEF, Government of India.

3.7.3.3. Methane emissions from industrial wastewater CH4 emission from industrial wastewater has been estimated based on the wastewater produced in industries. The methodology provided by 2006 IPCC Guidelines for National Greenhouse Gas Inventories to calculate emissions from industrial wastewater has been applied to calculate emissions from industrial wastewater in MP. As per IPCC, pulp and paper, meat and poultry processing (slaughterhouses), alcohol, beer, starch production, organic chemicals production, dairy industry and food processing are major industrial wastewater sources with high CH4 gas production potential. Out of these industries, the pulp and paper, and dairy industries have significant presence in MP. Methane emissions due to wastewater from other industries in MP are expected to be insignificant.

Emissions from industrial wastewater in MP are calculated based on the wastewater generated in the paper and dairy industries in MP and multiplying it with methane-generating potential (based on the degree and method of treatment and discharge).


Assumptions and data sources• Data on paper production in the paper industry in MP is calculated on the basis of paper production of

each paper mill sourced from IPPTA directory 2009 (Indian Pulp and Paper Technical Association).• The quantity of dairy production (milk) in MP for the base year is considered from Madhya Pradesh’s

Twelfth FYP.• Chemical oxygen demand and wastewater generation for each industry considered is country specific

and is sourced from NATCOM 2.

3.7.4 GHG forecasting methodology for the waste sectorGHG emissions from the waste sector are forecast for the following components: • Emissions from municipal solid waste• Emissions from domestic wastewater

Emissions from industrial wastewater have not been forecast, since these account for a mere 2% of the emissions from the waste sector and are negligible.

The forecasting model for the waste sector has been developed considering historical waste generation in MP and corresponding emissions.

Key input variables used and data sources:Forecasting for emissions from municipal solid waste:• The population projection for MP has been developed based on the Report of the technical group on

population projections constituted by the National Commission on Population May 2006.• Solid waste generation per capita for MP has been calculated taking into account the composition of

small and big cities. It has been sourced from the India infrastructure report 2006, IDFC. • The increase in per capita solid waste generation due to urbanisation and change in lifestyle year-on-

year has been considered as 1.3% for MP. It is assumed to be the same as the country’s and is sourced from the Position Paper on The Solid Waste Management Sector in India, Department of Economic Affairs, India.

• Physical composition of solid waste (mix of various types of waste) for MP is assumed to be the same as that for India and has been sourced from the India infrastructure report 2006, IDFC.

Forecasting for emissions from domestic wastewater:• The urban-rural fraction of the population has been projected until 2030, based on which the change in

domestic water generation and discharge pathways is projected. • Data on BOD, urbanisation fraction, degree of utilisation of treatment and discharge pathways is specific

to MP and is sourced from Inventorisation of Methane Emissions from Domestic & Key Industries Wastewater – Indian Network for Climate Change Assessment, Kartik.

3.7.5 ResultsThe summary of GHG emissions in the base year 2008 from various sources is as illustrated in Table 38

Table 38: Emissions from waste sector in MP, 2008 (tCO2e)

Type Emissions (tCO2e) – 2008

Emissions from solid waste 3,192,546Emissions from Domestic waste water 1,511,509Emissions from Industrial waste water 89,992Total Emissions from Waste sector 4,794,047



Figure 19: Emissions projections from waste sector (in million tCO2e)


3.8 Black carbon emissions

Black carbon is not a greenhouse gas, but black carbon emissions warm the atmosphere by intercepting sunlight and absorbing it. Black carbon results from the incomplete combustion of fossil fuels, wood and other fuels. Complete combustion of fuels would turn all carbon in the fuel into carbon dioxide (CO2). However, combustion is never complete, and the products of fuel combustion usually include CO2, carbon monoxide (CO), volatile organic compounds (VOCs), organic carbon (OC) particles and BC particles. There is a close relationship between the emissions of BC (a warming agent) and OC (a cooling agent), which are usually emitted in different proportions for varying sources of fuel.

Over the past few years, the significance of black carbon’s contribution to global warming has been increasingly discussed in the scientific community. However, BC acts more locally than CO2 and has an atmospheric residence time of weeks, as opposed to years. Furthermore, BC emissions affect the local climate due to their influence on cloud formation and rainfall. BC is usually emitted along with compounds that also cause cooling. Nonetheless, climate scientists agree that reducing BC emissions could substantially slow warming. BC emissions are not usually monitored. Instead, they are generally estimated as a product of two factors:• PM10 or PM2.5 emissions from a given fuel and/or a given application• BC content of that PM emission

BC acts more locally than CO2, because it is airborne for a relatively shorter period. The relative warming potential of BC is estimated to be in the range of 680 to 900, based on assessments carried out in different studies (Bond, T. C. and H. Sun. 2005. “Can reducing black carbon emissions counteract global warming?” Environmental Science and Technology 39:5921–5926 and Bond et. al., 2013 “ Bounding the role of black carbon in the climate system: A scientific assessment”).

Black Carbon in MP Black carbon emitted in MP is estimated due to fossil fuel combustion in power generation, industries, transport and due to cooking fuel in the residential sector:• The fuels considered in the estimation of black carbon emissions are Coal, diesel, fuel oil, light diesel

oil, petrol, LPG and kerosene. • The quantities considered for these fuels are taken from the sectoral analysis of this study itself. • The black carbon emission factors of fuels are sourced from Reddy, M. and C. Venkataraman. 2002a.

Inventory of aerosol and sulphur dioxide emissions from India I: Fossil fuel combustion. Atmospheric Environment. Same source is referred by US EPA as well in their studies.

The amount of black carbon emitted in MP from the sources stated above in 2008 is approximately 5,458 tons. This translates to a range of 3.71 million tCO2e (considering GWP 680) to 4.91 million tCO2e (considering GWP of 900).



Table 39: Measures for mitigation of black carbon emissions

Industries Transportation Residential• Use more efficient pellet stoves

and boilers• Improve the efficiency of brick

kilns and coke ovens• Switch to cleaner fuels • Ensure optimum operating

conditions in coal firing• Carry out training on best

practices for kiln operation and maintenance staff

• Retrofit diesel engines with diesel particulate filters

• Switch to cleaner transport fuels (CNG or LPG)

• Come up with more stringent emission standards combined with in-use vehicle emission inspection and maintenance programmes

• Improve public and private fleet efficiency and

• Increase the use of better and efficient cookstoves

• Switch to cleaner fuels

3.9 Uncertainty analysisUncertainty estimates are an important aspect of a complete emissions inventory. The purpose of uncertainty estimates is not to dispute the validity of the inventory estimates, but to help improve the accuracy of inventories in and guide decisions on methodological choice.

The assessment of GHG inventory is associated with a certain amount of uncertainty. Uncertainties are generally associated with activity data, emission factors and assumptions applied based on expert judgment. In the assessment carried out for Madhya Pradesh, emission factors applied are in line with emission factors used in India’s second National Communication to UNFCCC (NATCOM 2). Furthermore, activity data has been taken primarily from national databases such as CEA CO2 Baseline Database, Ministry of Petroleum and Natural Gas (MoPNG) statistics, Census 2011 and Annual Survey of Industries. Activity data has also been sourced from state-level sources/databases such as MP Statistics Handbook and MP Agricultural Economic Survey.

However, for the purpose of assessment, it is assumed that the uncertainty of activity level data and emission factors is the same as that for India-level data. In the uncertainty analysis carried out for NATCOM 2, the Tier 1 approach of the IPCC Good Practice Guidance 2000 has been applied to estimate uncertainties. The percentage of uncertainties associated with activity data has been discussed with researchers and is based on their expert judgment. Furthermore, emission factor uncertainties have been derived based on standard deviation in the case of measured emission factors and from relevant sources where emission factors have been sourced from literature.

For the estimation of uncertainty levels for the GHG inventory for MP, activity data and emission factor uncertainty levels are applied as per NATCOM 2, as follows:


Table 40: Uncertainty analysis — CO2

Emissions source

Sector Year 2008 emissions (Gg CO2)

Activitydatauncertainty

Emissionfactoruncertainty

Combineduncertainty

Combined uncertainty as % of totalemissions in year 2008

Electricity production

Power 41,912.9 10 5 11.2 4.1

Road transport — fuel consumption

Transport 6,739.9 5 0 5.0 0.3

Industry (non-specific) — fuel consumption

Industry 1,2946.9 10 5 11.2 1.3

Industry (cement) — process emissions

Industry 7,932.5 40 10 41.2 2.9

Agriculture — fuel consumption

Agriculture and livestock

1,043.1 25 5 25.5 0.2

Buildings — fuel consumption

Buildings — fuel consumption Buildings

2,943.1 25 5 25.5 0.7

Table 41: Uncertainty analysis — CH4

Emissions source

Sector Year 2008 emissions (Gg CH4)



Combineduncertainty


Enteric fermentation


969.0 5 50 50.2 9.0

Paddy cultivation


98.2 0.2 8 8.0 0.1

Residential — biomass consumption

Buildings 6.5 10 150 150.3 0.2

Industrial wastewater

Waste 4.3 10 125 125.4 0.1



Table 42: Uncertainty analysis — N2O

Emissions source

Sector Year 2008 emissions (Gg N2O)



Combineduncertainty


Managed agricultural soils (fertilizer use)


30.2 5 25.5 2.10 0.2

Uncertainty in baseline projections and abatement potential assessmentIn addition to the uncertainty of GHG inventory activity data and emission factors, there are uncertainties associated with the baseline-as-usual projections, and the abatement potential of various opportunities described in the next chapter. BAU projections may differ significantly with small changes in some of the key variables, as follows:• Population growth• GDP growth• Monthly per capita expenditure• Power plant capacity installation (vis-a-vis planned capacity addition)• Fuel consumption (based on market conditions)

As abatement potential factors in BAU growth in respective sectors by 2030, any uncertainty in BAU forecasts would also factor in abatement potential. Therefore, the projections and the abatement potential described in this report may need to be updated on a periodic basis, based on any changes to key assumptions for the growth of Madhya Pradesh.


Chapter 4: Low-Carbon Pathway

4.1 Marginal abatement cost curveThe marginal abatement cost curve is a useful tool for policy makers, investors and other decision makers to visualize and compare a set of abatement levers or GHG emission abatement opportunities available for a particular region or organisation. The cost curve provides a quick overview of the identified abatement levers and can be a useful starting point for prioritisation of the available options and the development of a low-carbon roadmap. Key information that can be read from an abatement cost curve is described in Figure 20.

Figure 20: Reading a marginal abatement cost curve


Abatement levers falling below the x-axis have negative abatement costs. These levers will result in financial benefits that would be greater than the costs incurred due to capital investment and operating expenses, in comparison to a baseline technology/measure. Abatement levers falling above the x-axis have positive abatement costs and will require fiscal incentives or other policy reforms to make them financially viable. Levers are arranged from left to right in the order of most financially attractive (lowest abatement cost) to least financially attractive (highest abatement cost). However, it is pertinent to note that some of the levers that are financially attractive may have regulatory, institutional or technical barriers. Alleviating these barriers may be a necessary component of a low-carbon roadmap. Additionally, though levers with negative abatement costs are financially viable, some of them may involve large investments. This would need to be taken into consideration while formulating a roadmap.

The cost curve for Madhya Pradesh would represent GHG abatement potential for 2030. As illustrated in Figure 20, the width of each bar would represent the annual GHG emissions reduction potential. In this case, it would represent the GHG abatement potential in 2030. Therefore, the sum of the widths of each bar would be equivalent to the total GHG emissions abatement potential from identified levers for the state in 2030.

4.1.1 Determining marginal abatement costMarginal abatement cost calculations may vary depending on perspective, namely, state, country, company or organisation. In the cost curve developed for Madhya Pradesh, marginal abatement costs have been taken from a societal perspective.

Societal costsSocietal costs of an abatement opportunity include only capital, operating and maintenance costs. Costs and/or revenues specific to a project developer such as taxes, tariffs and subsidies are excluded. This creates a significant difference in the marginal abatement cost. For instance, the cost of BRTS is relatively high from a societal perspective, which considers capital investment, operating costs and fuel savings with reference to BAU transport modes. However, from a project developer’s point of view, the high costs can be passed on to customers through ticket sales and, therefore, the cost may be negative (payback may be as short as three years, as in the case of the Delhi BRTS). Since project developer perspectives vary significantly, all abatement opportunities or levers are addressed from a societal perspective in this study.

Costs for a particular abatement lever are incremental to the corresponding baseline technology or measure for Madhya Pradesh. For instance, abatement cost for renewable energy-based power generation has been determined as the difference between the cost of generation of renewable energy-based power generation and the cost of generation of coal-based power generation (baseline technology). The feed-in-tariff that would be availed by the owner of the renewable energy power plant is not considered. Therefore, societal costs are different from the costs incurred by individual businesses or customers, and represent consolidated financial costs (whether positive or negative) incurred by the society at large.

The calculation of marginal abatement cost has been carried out as follows:

Marginal abatement cost = Present Value (PV)of abatement costs GHG abatement potential

The present value of abatement costs has been calculated with the following inputs: capital cost, annual operational benefit/cost, lifetime of the technology/measure and discount value. As explained above, capital costs and annual operational benefit/cost have been applied with reference to a baseline technology/measure. For instance, if the investment cost of an energy efficient appliance is y, the investment cost of a BAU appliance is x and n numbers of appliances are envisaged in 2030, the capital cost for implementing


the abatement lever is applied as: (y – x)*n. In cases where baseline does not involve any investment (e.g., continuation of the existing practice), as in the case of reforestation initiatives, the capital cost is simply the cost of implementation of the lever.

Annual operational benefits/costs are determined as the difference in variable costs (e.g., energy, operational and maintenance costs) between the project technology/measure and the baseline technology/measure. In determining annual operational benefits/costs, the following factors have not been considered: taxes, depreciation, financing costs (e.g., interest payments), and transaction costs (e.g., the cost of implementing programmes/policy initiatives, carrying out feasibility studies). Furthermore, salaries or wages are not considered as costs, as they benefit one stakeholder at the expense of another, and do not necessarily represent a cost to society.

The discount value for the present study is applied as 8.5%. While defining the concept of time value of money for the calculation of present value, the Reserve Bank of India (RBI) explains, “in an inflationary environment, a Rupee today will have greater purchasing power than after a year” and illustrates that interest rates can be applied for discounting future cash flows on investments78.” As per RBI statistics, weighted average interest rates on state government securities have ranged from 8.11% to 8.79% between 2009-10 and 2011-1279. An average rounded-off figure of 8.5% is, therefore, considered for discounting future cash flows, while determining marginal abatement costs in the present study.

The GHG abatement potential used for the calculation of marginal abatement cost has been determined as the reduction in GHG emissions compared to the baseline technology/measure over the lifetime of the proposed lever. The GHG emission reduction has been converted to CO2 equivalent units to allow comparison of abatement levers. For instance, if the implementation of engineered landfill projects would reduce Q tons of methane/year, the abatement potential would be determined as: Q*21 (where the global warming potential of methane is applied as 21).

4.1.2 Limitations of cost curvesIt is important to note that there are some limitations of marginal abatement cost curves. The prioritizing of levers and planning of low-carbon roadmaps requires the consideration of other factors that are not captured in cost curves. For instance, a cost curve does not identify institutional, regulatory or technical barriers associated with some of the levers. Similarly, co-benefits such as reduced health hazards or employment generation would not be captured in the cost curve. Therefore, the marginal abatement cost curve should not be used in isolation, but rather in conjunction with other analyses that aid in the prioritisation of available options.

4.1.3 MACC for Madhya PradeshAs explained above, for the present study, the marginal abatement cost curve has been compiled considering the technologies/measures that can potentially be implemented in Madhya Pradesh up to 2030. Baseline/Reference technologies have been considered based on prevailing technology trends in India and the state. The identification of GHG abatement opportunities has been carried out based on consultation with key stakeholders in Madhya Pradesh, as well as with sector-specific experts. A larger set of GHG abatement opportunities has been narrowed down through the consideration of:• socio-economic and political compatibility and relevance for Madhya Pradesh,• potential for the state to enable/incentivize the opportunities, and• likelihood of significant penetration of the technology/measure in the BAU scenario.

78 Source: http://www.rbi.org.in/commonman/English/scripts/faqs.aspx?id=711, accessed June 201379 Source: http://rbidocs.rbi.org.in/rdocs/Publications/PDFs/119_EHS110912F.pdf, accessed June 2013

Low Carbon Pathway


For instance, in the agricultural sector, measures related to changes in water management practices to reduce emissions due to paddy cultivation have not been considered, as the state is already pursuing practices such as “system of rice cultivation.” Similarly, improvements in vehicle efficiency norms have not been considered, as these are not decided at the state level. Furthermore, certain types of renewable energy power plants have not been considered (namely wind and small hydro), as their penetration in the state is relatively high, considering the available potential. However, other types of renewable energy power plants (solar and biomass) have been considered, as there is a large untapped opportunity. It is within the power of the state to introduce additional incentives for renewable energy project developers to set up operations in the state.

Finally, certain opportunities that were selected and recognized as relevant for Madhya Pradesh have not been quantified due to limitations in the availability of data and have not been included in the cost curve. However, these opportunities are qualitatively discussed in this study. The cost curve for the state is presented in Figure 21

Figure 21: 2030 MACC-Madhya Pradesh


Low Carbon Pathway

4.1.4 Key conclusions derived from the MAC curve1. The GHG abatement of proposed levers in 2030 is 46 million tCO2e at an investment cost of INR 1,382

billion.2. Approximately 48% (22 million tCO2e) of the total annual GHG abatement of proposed levers can be

met through the implementation of cost-effective levers (which have a negative marginal abatement cost).

3. Five opportunities with the largest potential are energy-efficient pump sets, energy-efficient lighting-LEDs, CFLs, biomass-based power generation and IGCC. The implementation of these would result in a combined reduction of around 27.6 million tCO2e by 2030, which would account for 60% of the abatement potential captured in the cost curve.

4. Solar energy technologies comprising solar PV power plants, solar thermal power plants, solar PV pumps, solar water heaters and rooftop solar collectively comprise a large abatement potential of 6.5 million tCO2e by 2030.

As explained above, the MAC curve includes abatement levers for which abatement potential and marginal abatement cost were quantified in the present study. There are other additional levers, described qualitatively, which can be taken up by the state in respective sectors. These opportunities are described in subsequent sections of this chapter for corresponding sectors. A summary of all opportunities available for the state is given in the table below.

Table 43: Summary of abatement opportunities

Lever Annual GHG Abatement (Million tCO2)

Marginal Abatement Cost (INR / tCO2)

Power sector

Integrated Gasification Combined Cycle (IGCC)

3.54 1,321

Ultra-super-critical 1.50 -1,238Biomass-based power plant 5.85 103Solar PV 1.10 1,679Solar thermal 1.38 2,178Renovation and modernisation (covering all sub-critical units in the state)Natural gas/coal bed methane-based power plantsSmart grid technologiesIndustry sector

WHR in cement kilns 0.81 -933Use of alternate fuels in cement kilns

1.92 195

Improving the electrical efficiency of cement production

0.41 -1,869

Use of de-carbonated raw materials in cement production

0.61 5,083


Energy efficiency improvement in various industry types such as textiles, food processing, paper manufacturing, metal casting industriesTransport sector

Bus rapid transit systems 0.30 1,591Metro rail projects 1.34 11,329Electric two wheelers 0.15 -31,802Electric four wheelers, three wheelers, buses and LCVsSwitch to CNG/LNGSustainable urban planningBuilding sector

Energy efficient Air Conditioning (2 star to 5 star)

0.34 730

Solar water heaters 1.09 -1,682Household rooftop solar PV 1.55 2,703Incandescent lamps to LEDs 6.22 -1,805Incandescent bulbs to CFLS 4.60 -2,925Efficient design of building envelopeEfficient design of HVAC systemsEfficient water pumping, lighting controls and electrical systemsAgriculture sector

Energy-efficient pumps 7.40 -882Solar photovoltaic pumps 1.36 8,410Reducing nitrous oxides emissions from soils (increasing nitrogen fixation, use of nitrification inhibitors, and other means)Reducing enteric methane emissions (straw silage and straw ammonisation)Forestry sector

Afforestation: conversion of non-forest area/wasteland into forest area

1.77 196

Reforestation: restoring and recreating areas of forests that may have existed long ago but were deforested

0.49 523


Low Carbon Pathway

Waste sector

Integrated waste management plant (PPP model): collection, composting, RDF, bricks, recyclables

0.66 1,368

Landfill gas recovery with electricity generation

0.95 291

Aerobic bioreactor landfill 0.78 3,104

The abatement levers identified in the cost curve can be broadly classified under six categories, as follows:• Energy efficiency/demand-side-management (EE/DSM)• Renewable energy technologies (RET)• Clean coal technologies (CCT)• Alternative fuels/raw materials (AFRM)• Waste management technologies (WMT)• Carbon sinks (CS)

The summary of the abatement opportunity, with a break-up by category of abatement lever, is illustrated in Figure 22.

Figure 22: Summary of abatement opportunity by type


4.2 Abatement lever profilesIn order to capture essential information regarding the identified abatement levers included in the cost curve, abatement lever profiles have been presented in the following diagrammatic form:

Figure 23: Format for abatement profile diagrams

A description of key abatement levers in the power, industry, transport, buildings, agriculture, waste and forestry sectors is given in the following sections, along with initiatives that can be taken up by the state to promote the identified technologies/measures.

4.3 Power sectorCoal-based power generation is presently the least cost option for India and is likely to remain the prominent source of power for Madhya Pradesh. The Planning Commission, Government of India, has estimated that India would require an addition of at least 230 GW of coal-based power generation by 202080. Madhya Pradesh holds approximately 8% of the coal reserves in India, and new coal fired power plants will definitely come up in the state in years to come. Considering this, clean coal technologies (including ultra-super-critical power and integrated gasification combined cycle) are important opportunities for achieving low-carbon development in the state. As power plants have a lifetime of 25 years, it is imperative that new coal-based power plants installed in the state are as efficient as possible to avoid the lock-in of carbon-intensive technology, which would then result in loss of GHG abatement opportunity for the state.

Existing coal-based power plants in MP operate on sub-critical technologies. The oldest power units of the Satpura and Amar Kantak thermal power stations were commissioned as far back as the late sixties and seventies81. The Ministry of Power has an ongoing programme for Renovation and Modernisation (R&M) and Life Extension (LE) of older thermal power plants. The R&M programme is aimed at sustaining generation, overcoming operational difficulties and ultimately enhancing efficiency by 8% to 10%, as well as rated capacity by 4% to 8%. The LE programme is focused on extending the lifetime of power plant units beyond their original lifetime. The Ministry of Power is focusing on covering all 200/210 MW LMZ design

80 Reference: Low Carbon Strategies for Inclusive Growth: An Interim Report, May 201181 Reference: Central Electricity Authority: CO

2 Baseline Database


units in the country.82 Although many of the units in MP power plants would get covered under these programmes in the Twelfth and Thirteenth FYP, it would benefit the state to ensure that R&M activities are carried out for all sub-critical units in the state.

A balanced approach to low-carbon growth of the power sector would involve a combination of clean coal technologies and renewable energy technologies to reduce dependence on fossil fuels. Among the various renewable energy technologies, there is significant untapped potential in solar and biomass-based power. On the other hand, hydro power potential is almost completely harnessed, and wind power potential is also likely to follow suit, considering that it has almost reached grid parity.

Natural gas-based power plants represent a low-carbon, economically attractive alternative to coal-based power generation. However, considering the shortage of natural gas in the country, uncertainty regarding its supply, and the priority given to natural gas for fertilizer manufacturing (among other sectors), it is not considered a reliable alternative for power generation in the state. Nevertheless, Madhya Pradesh has reserves of approximately 144 billion cubic meters of coal bed methane (CBM). Harnessing CBM as a source of natural gas could be an important long-term mitigation strategy. However, the lack of clarity in gas pricing, as well as delayed clearances under current national policies has stalled the development of CBM-based power plants in India83.

Demand-side management is arguably the most economically attractive option for addressing energy security and mitigating GHG emissions from power generation. Energy-efficient technologies and the required measures in the industry, buildings and agriculture sectors have been discussed in sections on the respective sectors.

Another important avenue for demand-side management is smart grids, which involve cross-sectoral interventions and a combination of technologies such as smart metering, smart appliances and integrated grid management software. A smart grid in Madhya Pradesh can be a long-term strategy, as the technology is yet to be commercialized in India. Smart grids have the potential to increase the economic viability of distributed renewable energy systems and create opportunities for selling surplus renewable power (e.g., from rooftop solar installations, biomass gasifiers, and other applications) back to the grid. Distributed renewable energy systems can be an effective means for Madhya Pradesh to meet energy demand in rural areas and of the geographically dispersed population.

The Madhya Pradesh Government can adopt several initiatives for capitalizing on low-carbon growth opportunities in the context of the power sector, as follows:

82 Reference: Report of the Working Group on Power for Twelfth Plan (2012-2017), Ministry of Power83 Reference: Business Today, <http://businesstoday.intoday.in/story/coal-bed-methane-industry-india-policy/1/24632.html>, accessed June 2013

Low Carbon Pathway


Table 44: Power Sector — Initiatives

Initiative Rationale

Engage with technology suppliers and IPPs to facilitate R&D and feasibility studies for setting up ultra-super-critical and IGCC technologies in MP

These technologies have high investment costs and have not yet been commercialized in India, though there are examples of successfully running plants in other parts of the world. Technical challenges associated with high ash content in Indian coal need to be addressed. Although the Low Carbon Expert Group, Planning Commission, has estimated that these technologies will not arrive in India until 2020, Madhya Pradesh needs to prepare in advance to capitalize on these opportunities at the earliest.

Secure additional funds for R&M (renovation and modernization) of existing power plants

Though the Central Government has a programme on R&M of thermal power plants and is securing funds through indigenous financial institutions, as well as multi-lateral organisations (the World Bank and KfW), the state government can look to additional funding through private sector participation. Some of the options through which private companies can get involved in the R&M, as well as operation & maintenance (O&M) of state-owned power plants, include:• Lease, rehabilitate, operate and transfer (LROT): involves a private

promoter taking over the power plant on a long-term lease• Formation of joint ventures between power utilities and private

companies• Sale of the power plant to a private company

Set up a regularly updated and publicly available database on RE technology costs, resource availability and policy incentives that can be used by both potential investors and financing institutions to assess the viability of a project in a specific location

Investment decisions on RE projects are often delayed and project implementation timeframes are extended due to the basic lack of immediate information required for assessing the financial viability of RE projects. These challenges can be countered by disseminating the necessary information more transparently.

Enforcement of Renewable Purchase Obligation (RPO)

Clear guidelines are required for ensuring compliance with RPO targets. Clarity on the enforcement of RPO targets would bring in some level of certainty in the REC market, which has a large potential for accelerating investment in renewable energy. Apart from the state utilities, other obligated entities, namely captive power producers and open access consumers, should be accountable for meeting their RPO targets.

Description of key technologies identified as abatement levers for the power sector and included in the cost curve for the state are given below.

4.3.1 Integrated Gasification Combined Cycle (IGCC)IGCC is a clean coal technology that involves the pyrolysis and partial oxidation of coal to produce syngas, which is then cooled and used to generate power in a gas turbine. Furthermore, steam is produced from the cooling of high-temperature syngas, and additional power is generated using the steam in a steam turbine. IGCC results in increased efficiency of power generation, reduced environmental impacts compared to conventional coal based power, and greater compatibility with carbon capture technologies. It is assumed


that in a low-carbon scenario, IGCC technology can potentially displace around 10% of the coal-based power generation capacity to be added by 2030.

4.3.2 Ultra-super-critical powerThermal power generation using ultra-super-critical (or advanced supercritical) power plants leads to greater efficiency of up to 43% net (based on lower heat value). Although the capital cost for boiler and turbines in ultra-super-critical power plants on a per-MW basis is higher than that of a sub-critical plant, this is partially offset by the balance of plant equipment (e.g., coal and ash plant, cooling tower) due to higher efficiency. Additionally there are significant reductions in specific fuel consumption and specific emissions (tCO2/MWh) compared to a sub-critical system.84

Out of the various renewable energy options available for power generation, biomass and solar power have the greatest un-utilized potential in the context of Madhya Pradesh.

4.3.3 Solar PV and solar thermal powerMadhya Pradesh is endowed with high solar radiation, with several locations offering a potential of 5.5 to 5.9 kWh/squared meter. Solar photo-voltaic-based power generation (suitable for modular or small-scale deployment) and solar thermal power plants (suitable for large scale capacities) can be deployed. In a low-carbon scenario, it is assumed that planned solar capacity addition can be further increased by 16% (based on difference between CEA electricity plans – baseline scenario and high-renewables scenario). An additional 1462 MW may be deployed in Madhya Pradesh by 2030. The first phase of the National Solar Mission of India has targeted a 50:50 split between solar PV and solar thermal technologies, and the same split is considered for MP. Therefore, an additional 731 MW of each technology has been considered in the low-carbon scenario for the state.

84 Source: “Exploring the use of Carbon Financing in Supercritical Technology for Power Generation,” Isabel Boira-Segarra, Mott MacDonald

Low Carbon Pathway


4.3.4 Biomass-based powerMadhya Pradesh has a large agricultural base, and biomass resources in the form of agricultural residues can be harnessed by the state for sustainable power generation. Biomass is an emission-free source for power generation, provided that it is sourced in a sustainable manner. Considering the low capital investment on a per-MW basis and significantly higher plant load factor compared to other forms of renewable energy technologies, biomass-based power generation is a relatively economical option compared to other forms of renewable power.

Biomass-based power is also well-suited for off-grid and distributed energy generation and can contribute to improving access to energy in areas with limited or no grid connectivity. Apart from direct combustion of biomass for power generation, biomass can also be gasified and utilized for a variety of purposes including power generation, cooking fuel, heating/cooling applications, and as a direct fuel for internal combustion engines.


Low Carbon Pathway

4.4 Industry sectorThe industry sector is a major source of GHG emissions for the state and offers opportunities for mitigation through energy efficiency, fuel switch and reduction in process emissions. As discussed in the previous sections, the cement industry is the largest contributor to GHG emissions in the industry sector, and MP has an abundance of limestone reserves, which would enable high growth in the cement industry for the next couple decades.

The cement industry in India is already one of the most efficient in the world due to high energy costs and the competitiveness of the industry. The level of technological advancement of the cement industry in Madhya Pradesh is in line with that in the rest of the country. Most cement companies operating in MP (e.g., Birla Corporation, Ultratech Cement, Jaypee Group) have operations across the nation. Despite the relatively advanced state of the industry, there is scope to increase the penetration of waste heat recovery, drive down specific electricity consumption, increase alternative fuel usage in kilns and substitute limestone with de-carbonated raw materials.

The most intensive process in cement manufacturing is clinker production (calcination of limestone raw material) and, therefore, the blending of clinker with substitutes such as fly ash and slag is recognized as having immense potential for mitigating CO2 emission from cement manufacturing. In Madhya Pradesh, the blending practise with fly ash is already widely adopted and would continue to be an important avenue to de-carbonize cement production. However, the blending of clinker with slag (e.g., blast furnace slag from the iron and steel industry, lead zinc slag, or copper slag) is an untapped opportunity. In order to realize this opportunity, large iron and steel industries or other metals industries need to be present in the vicinity of cement clusters in MP, so that a supply chain for slag can be established. At the moment, this opportunity does not seem to be available. Nevertheless, it can be realized, considering that Madhya Pradesh is a mineral-rich state with reserves of copper, zinc and other metals.

Apart from the cement industry, the textiles, food processing, paper manufacturing, and metals casting industries also contribute to GHG emissions through the consumption of coal or through the purchase of electricity from the grid. Efficiency levels would depend on plant vintages, product types, manufacturing process and raw materials, which may vary considerably across industrial units (particularly in the case of small-and mid-sized industries).

The state government is promoting industrial development under the Industrial Promotion Policy, 2010 and Action Plan, with measures such as single-window clearances, the establishment of a “land bank” for future industrial development, the creation of an infrastructure development fund, concessions on land for industrial clusters and fiscal incentives for micro, small and medium enterprises (MSME). There are opportunities for the state to ensure that resource efficiency is a key aspect of industrial planning and development of new industrial capacity. The following measures can be considered by the state:


Table 45: Industry Sector — Initiatives


Energy management policies for large industries can be integrated with the existing Industrial Promotion Policy, 2010 and Action Plan of Madhya Pradesh. Investment support or allotment of land provided to large industrial units can be contingent to best available technologies for energy efficiency being considered for new plants. A regularly updated repository of the best available technologies can be maintained for this purpose.

Technology introduced in new industrial installations is likely to remain locked-in for a considerable amount of time (lock-in period would depend on the type of equipment – pumps, motors, boilers, compressors, various process equipment or others). Therefore, ensuring that low-carbon technologies are selected for new installations is a key opportunity.

Support to industrial clusters and MSMEs can be extended for the following specific measures:• Efficient group captive power plants using

renewable sources, where feasible (either co-firing of biomass with coal, or pure RE power plants)

• Efficient combined effluent treat plants with recovery of methane for power generation, where feasible

With economies of scale, opportunities for energy efficiency and GHG mitigation are higher. These opportunities can be harnessed for combined facilities for power and water management in industrial clusters and MSMEs.

Establish collaborative projects between the states departments responsible for urban development and promotion of industry to incentivize waste-to-energy projects to meet industry energy requirements

Industrial units can utilize refuse-derived fuel as a substitute for coal and power generated from methane from MSW management facilities or sewage treatment plants.

The description of key technologies identified as abatement levers for the industry sector and included in the cost curve for the state are given below.

4.4.1 Waste Heat Recovery (WHR) in cement kilnsThe manufacturing of cement involves a high-temperature sintering process in cement kilns, where fossil fuels are fired along with raw meal (limestone and additives). In the dry cement manufacturing process (predominant in India), approximately 40% of the heat input to cement kilns is available as waste heat in the exit gasses of pre-heaters and clinker coolers. This waste heat can be used for the generation of electricity using a steam rankine cycle, along with the installation of waste heat recovery boilers and steam turbines. In the absence of this technology, the waste heat remains mostly unutilized.

Overall, in India, only 12 kilns have associated waste heat recovery power plants installed.85 In Madhya Pradesh, Birla Corporation has implemented waste heat recovery systems for two kilns at its cement manufacturing units in Satna. It is assumed that the state would be capturing 20% of its waste heat recovery potential in the BAU scenario and 100% in the low-carbon scenario.

85 Source: Existing and Ptential Technologies for Carbon Emissions Reductions in the Indian Cement Industry, CSI & WBSCD


Low Carbon Pathway

4.4.2 Improving electrical efficiency in cement productionPrecise design specifications (avoiding excess capacity of more than 10%) and appropriate control mechanisms have the ability to generate significant energy savings in the cement industry. Some of the measures for improving electrical energy efficiency include:• speed control or variable frequency drives for process fans including pre-heater fans, raw mill fans,

cement mill separator fans, cooler fans, auxiliary and bag filter fans, reverse air bag house fans and cooler vent fans

• energy efficiency improvement in auxiliary equipment such as conveyors, elevators, blowers, compressors, and pumps (which consume around 10% of the total electrical energy of cement manufacturing process)

• energy efficiency improvement in electrical systems through implementation of intelligent motor control systems (MCC) and energy management systems (EMS)

Power is predominantly sourced from fossil fuel-based captive power plants in the cement industry. As such, electrical energy efficiency systems ultimately lead to savings in fossil fuels consumption. WBSCD has set a target for specific electricity consumption (SEC) of 69.5 kWh/MT cement for the Indian cement industry, which is considered in the low-carbon scenario for Madhya Pradesh. The BAU specific electricity consumption has been considered at 76 kWh/MT (forecast figure).

4.4.3 Alternative fuels in kilnsThe use of alternative fuels in kilns remains a largely untapped opportunity in India and in MP. Cement kilns can theoretically utilize up to 100% of alternative fuels, displacing emissions-intensive fossil fuels. However, there are several technical limitations with alternative fuels such as low calorific values, trace elements in alternative fuels such as chlorine, and the availability of alternative fuels. An average calorific value of at least 20-22 GJ/ton is recommended for cement kilns that operate under high-temperature conditions. However, in the pre-calciner sections of cement kilns, it is possible to fire lower calorific value fuels.86

86 Source: “Development of State of the Art – Technologies in Cement Manufacturing: Trying to Look Ahead”, European Cement Research Academy and Cement Sustainability Initiative


WBSCD has set a target of 30% utilisation of alternative fuels for the Indian cement industry. Certain plants in India have achieved a maximum of around 12% TSR, and 12.5% is the average global TSR, which has been considered as the baseline TSR by 2030 for the MP cement industry as a whole. In a low-carbon scenario, it is assumed that the cement industry in MP would meet the target of 30% usage of alternative fuels.

Establishing a sustainable supply chain of alternative fuels is a challenge. However, as discussed for the waste sector, one of the key by-products of effective waste management strategies is the production of refuse derived fuel (RDF). The production of RDF from processed waste can potentially establish a reliable supply chain of alternative fuel for the cement industry. Therefore, the abatement potential and cost of GHG emission abatement have been estimated assuming that most of the fuel requirements would be through refuse-derived fuel.

4.4.4 Utilisation of de-carbonated raw materialThe substitution of limestone with raw materials that are de-carbonated can lead to a significant reduction in process CO2 emissions from cement manufacturing. For every 1% of substituted limestone, it is estimated that 5.25 kg CO2/ton of clinker (or up to 8 kg/ton of cement) can be reduced. Some of the de-carbonated raw material options are fly ash, blast furnace slag, steel slag, carbide sludge, phospho-gypsum, Pb-Zn slag, flue dust from blast furnaces and dolochar waste. Although there are significant technical challenges in the use of these de-carbonated raw materials, it is assumed that in the low-carbon scenario, up to 3% of limestone used in the Madhya Pradesh cement industry can be substituted by de-carbonated raw materials87.

4.5 Transport sectorMadhya Pradesh has taken several initiatives to improve transport services in the state. As mentioned in previous sections of this report, the Government of MP has focused on road construction and has set up a road network of about 74,000 kilometres across Madhya Pradesh in the form of national highways, state highways, district roads and village roads. Additionally, the state is implementing bus rapid transit systems (BRTS) in Indore and Bhopal and is planning metro rail projects in these two cities as well.

87 Some cement plants in India are presently using fly ash as a raw mix component in the range of 1 – 3 % (Source: Existing and Potential Technologies for Carbon Emissions Reductions in the Indian Cement Industry, CSI & WBSCD)


Low Carbon Pathway

There is potential to implement BRTs and metro rail projects in additional cities in the state. As urbanisation in Madhya Pradesh is on the rise, the implementation of these projects would be easier before population densities and road congestion increase significantly, and impede or slow down construction-intensive activities on road.

Another important opportunity for Madhya Pradesh is electric vehicles and hybrid electric vehicles. Electric two wheelers represent the greatest opportunity for fuel saving among various vehicle types. In addition, increasing the market share of electric two wheelers is relatively more viable in India compared to other vehicle types88. Electric four wheelers, three wheelers, buses and LCVs also present an important opportunity, but their market would need to be augmented through increased localized manufacturing to reduce costs.

The use of CNG/LNG in vehicles in major cities in India including Delhi and Bangalore has been instrumental in reducing city smog, black carbon, as well as GHG emissions. The feasibility of increasing CNG and LNG for transport use is dependent on national policies, as well as the availability of gas.

The most important avenue for Madhya Pradesh to manage GHG emissions from transport is to focus on sustainable urban planning as a means to mitigate future demand for transport through initiatives described below.

Table 46: Transport Sector — Initiatives


Reduce travel time and/or demand for motorized transport through sustainable urban planning, as follows:• Planning residential areas closer to workplaces

(whether commercial or industrial)• Promoting mixed-land use to ensure that

workplaces, shops and schools are located in the same area

• Promoting higher urban densities, so that trips are shorter

• Increasing urban densities around mass transit routes to increase the usage of public transport

Cities can be planned in ways that will encourage people to travel less and reduce the necessity of travel. However, once city plans are implemented, urban infrastructure gets locked-in for several decades. If the plans do not consider aspects of sustainable transport, opportunities for greenhouse gas emission reductions are lost for decades. Therefore, it is vital to grab these opportunities at the early stages of urban development, which makes this an important opportunity for Madhya Pradesh.

Make cities more walkable by incorporating the following aspects into urban plans, as follows:• Provision of un-blocked sidewalks, walkways and

walk-over bridges to enable pedestrians to get around the city in a safe unrestricted manner

• Town planning measures to increase the proportion of journeys that can be walked

• Integration of walkway networks with mass transit networks

• Separation of walkways from motorized traffic to ensure the safety and convenience of pedestrians

Although people in Madhya Pradesh are not necessarily averse to walking, with increasing disposable income and road congestion in years to come, it is essential to plan urban growth in a manner that ensures walking remains a safe and convenient substitute to motorized transport. Apart from cutting down on fuel consumption from motorized transport journeys, walking leads to several social benefits including improved physical and mental health.

88 Reference: National Electric Mobility Plan 2020, Department of Heavy Industry, Govt. of India


Make cities more convenient for non-motorized transport modes such as bicycles and rickshaws through the following measures:• Provision of safe cycling routes or lanes, separated

from motorized traffic• Support service centres for bicycles/rickshaws and

ensure the availability of replacement parts• Support the design improvement of rickshaws• Raise public awareness about the benefits of non-

motorized transport

Enhancing infrastructure to support non-motorized transport would be key to ensuring that it is safe and remains a preferred choice of transport for shorter journeys across all income groups.

The following initiatives can be introduced to help develop the market for electric vehicles and hybrid electric vehicles:

Table 47: Transport Sector — Initiatives for electric and hybrid electric vehicles


Provide subsidies on electric and hybrid vehicles to compensate for higher cost

Although running costs of electric vehicles may be lower than those for petrol-or diesel-based vehicles, higher costs can be a deterrent for consumers.

Develop adequate recharging infrastructure, and encourage real estate developers to incorporate charging stations in parking facilities

Consumers may hesitate to invest in an electric vehicle for the lack of re-charging facilities. This barrier would need to be alleviated to harness the full potential of electric and hybrid vehicles.

Introduce temporary advantages for electric vehicles, including priority access to parking spaces, or lower parking charges for electric vehicles

Small temporary incentives could also go a long way in changing consumers’ mind-set toward electric vehicles.

The description of key technologies/measures identified as abatement levers for the transport sector and included in the cost curve for the state are given below.

4.5.1 Bus rapid transit systems (BRTS) with CNG busesBRT can be defined as “a bus-based mass transit system that delivers fast, comfortable, and cost-effective urban mobility”. BRT systems typically include dedicated bus corridors, fare collection prior to boarding, high quality stations, intelligent transportation technologies, and other features designed to maximize convenience and reduce travel times.

BRT can help reduce GHG emissions through shift from private transport to public transport, better fuel efficiency due to less traffic congestion and reduced vehicle kilometres due to rationalized routes.

In Madhya Pradesh, BRTS is already being implemented in Indore and Bhopal. Cities with the highest population density in Madhya Pradesh can potentially implement BRT systems. Abatement potential in the low-carbon scenario has been assessed considering that the top eight cities, in terms of population density (namely, Bhopal, Indore, Jabalpur, Gwalior, Morena, Rewa, Bhind and Ujjain), have already implemented/are implementing BRTS. Bhopal and Indore are already in the process of implementing BRTS, and it is assumed to be fully operational in these two cities in the BAU scenario.


Low Carbon Pathway

4.5.2 Metro rail systemsMetro rail systems provide an efficient, safe, rapid and convenient mode of transport for commuters. Any city with a population of more than 2 million can put up a proposal for implementing a metro rail system in India. Bhopal and Indore have already planned to implement metro rail projects. Six cities with high population, as well as high population density, are being considered for the potential implementation of metro rail systems in the low-carbon scenario. Metro rail systems are assumed to be partially elevated, partially underground and partially at-grade heavy duty metro.

4.5.3 Electric two wheelersThe Government of India has recently unveiled the National Electric Mobility Mission Plan 2020, which targets sales of 6-7 million electric vehicles (including 4.8 million two wheelers) by 2020. Considering the relatively low penetration rate of private vehicles in India (11 cars and 32 two wheelers per thousand people) and social aspirations for vehicle ownership, the number of privately owned vehicles is bound to increase. Therefore, ensuring low-carbon alternatives to conventional vehicles is imperative. As per the National Electric Mobility Mission Plan 2020, the greatest amount of fuel savings is projected from electric two wheelers (other segments being electric four wheelers, buses, three wheelers, and LCVs). The potential GHG mitigation for MP in 2030 from electric two wheelers is tabulated below, considering the market penetration rates targeted by India are achieved in the state.


4.6 Buildings sectorThe concept of “green buildings” has been advocated in major cities across the world as a means to make buildings more sustainable and climate friendly. Green buildings are designed to consume less energy, water and other natural resources, and to be healthier for their occupants. The concept is gaining popularity in major real estate markets in India such as Gurgaon, Mumbai, Bangalore and Hyderabad, with a total of 253 buildings certified by the Indian Green Building Council (IGBC) as on April 2013. However, no such building has been certified in Madhya Pradesh.89 Another form of green building certification emerging in India is MNRE – an endorsed programme for Green Rating for Integrated Habitat Assessment (GRIHA). While IGBC is more focused on larger scale residential and commercial buildings with a HVAC system, GRIHA extends the concept to small buildings without central HVAC systems. Specific measures to encourage real estate developers to adopt green building certification (whether IGBC or GRIHA) would be an important avenue for Madhya Pradesh to reduce energy demand in its growing cities.

While green building certification is a voluntary mechanism, the Energy Building Conservation Code (ECBC) is a compliance-based mechanism for achieving energy-efficient design and construction of buildings and associated systems. The code remains voluntary at the national level, but once it is adopted by state governments, it will enforce mandatory energy-efficiency requirements for all new buildings, as well as major retrofits to existing buildings. The code is applicable for buildings that have a connected load of 100 kW or greater, or a contract demand of 120 kVA or greater.90 Madhya Pradesh is currently finalizing its draft by-law modification for the adaptation of ECBC (customizing the code for local conditions in the state).91

The process of making ECBC mandatory for commercial buildings in MP is being led by the Public Works Department, Town and Country Planning Department, and Housing and Urban Development Department.Some of the methods for demand-side management in buildings recommended in the ECBC are:

Table 48: Demand-side management in buildings — ECBC recommendations

Building envelope Heating, ventilation and air conditioning (HVAC)

Water heating and pumping

Lighting Electrical power

Insulation materialsFenestration (the arrangement of windows in a building)Envelope sealingCool roofs

Efficient HVAC systemsDuct sealingNatural VentilationSystem Balancing

Solar water heatingEquipment efficiencyPiping insulationHeat traps (pipe valves to prevent hot water loss

Efficient lightingAutomatic lighting shut-offOccupancy sensorsMaster lighting controls

Motor efficiencyTransformer efficiencyElectric metering and monitoringPower distribution systems

A combination of energy-efficiency options promoted through programmes such as ECBC and Green Building certification can contribute not only to reduced energy demand in the building sector, but also to increasing the economic viability of renewable energy systems. Solar PV systems and solar water heaters installed on the rooftops of households can meet energy demand for energy-efficient and sustainable buildings with low capacities and costs.

89 Reference: Times of India, <http://articles.timesofindia.indiatimes.com/2013-04-08/indore/38372608_1_real-estate-developers-green-certification-jitendra-mehta>, accessed June 201390 Reference: Energy Conservation Building Code User Guide, Bureau of Energy Efficiency, April 201191 Reference: Status of Energy Conservation Building Code (ECBC) Implementation in Various States and Union Territories of India, < http://ibecc.in/node/148>, accessed June 2013


Low Carbon Pathway

Apart from codes and certifications for buildings, demand-side management in the buildings sector can also be addressed through standards and labelling for appliances and lighting devices. India’s Standards and Labelling programme, spearheaded by the Bureau of Energy Efficiency, presently applies to 14 equipment/appliances, of which 4 (air conditioners, tubular fluorescent lights, frost free refrigerators, distribution transformers) have been notified under mandatory labelling effective 7 January 2010. The other 10 types of appliances are presently under the voluntary labelling phase (including colour televisions, electric water heaters, ceiling fans, domestic LPG stoves, washing machines and others). The S&L programme of the Bureau of Energy Efficiency under the Ministry of Power has been designed to bring about market transformation and to stimulate the development of energy-efficient technologies. The programme intends to reduce the energy consumption of appliances without compromising on quality and services provided to consumers.

There are a number of existing mandatory and voluntary programmes running in India. Madhya Pradesh can utilize these to make buildings less energy intensive and more sustainable. Some of the measures that the state can take are:

Table 49: Buildings sector — Initiatives


Programmes for the distribution of EE devices to rural households

To increase the penetration of high-volume energy-efficient appliances, including efficient lighting (e.g., CFLs/LEDs) and efficient appliances, programmes such as the Bachat Lamp Yojna need to be replicated in Madhya Pradesh. In the absence of a robust carbon market, other potential sources of funding such as NCEF need to be explored for subsidizing the cost of efficient lighting and appliances. Subsidized distribution would help influence consumer preferences and mitigate market barriers.

Certification of government buildings under IGBC/GRIHA

The state government can lead by example and get government buildings certified across the state. Savings from energy costs and resource efficiency realized could then be publicly disclosed to help create awareness and demand for green buildings.

Fiscal incentives to real estate developers adopting certifications

Since the benefits of reduced electricity bills are often not realized by developers but are passed on to tenants, there is a need to incentivize certification. Fiscal incentives may be in the form of concessions on taxes and/or soft loans.

Description of key technologies/measures identified as abatement levers for the building sector and included in the cost curve for the state are given below.


4.6.1 Rooftop solar PV/solar home lighting systems Rooftop solar PV installations, or solar home lighting solar systems (SHLS), are fixed decentralized installations designed to generate electricity at the point of demand, thereby displacing not only conventional grid based power generation, but also transmission and distribution losses associated with the grid. In areas where grid electricity is limited/unavailable, these systems can lead to increased access to energy and the displacement of kerosene used for lighting purposes. The components of these systems include solar PV modules (solar cells), charge controller, batteries and lighting systems or other appliances. In the case of Madhya Pradesh, it is assumed that in a low-carbon scenario, 12% of the households in MP have solar home lighting systems as compared to 6% in BAU (based on MNRE strategic plan for new and renewable energy).

4.6.2 Solar water heatersSolar water heaters can be effective substitutes for electric geysers in households. A solar water heater consists of a collector and an insulated storage tank to store hot water. Water circulated through riser pipes in the collector absorbs heat and can be raised up to a temperature of 60 to 80 degrees Celsius, depending on solar radiation and weather conditions. A solar water heater of 100 litres capacity can potentially save 1500 kilo-watt hours of electricity per year by replacing an electric geyser.

4.6.3 Energy-efficient air conditioning — shift from three star to five starAir conditioners (ACs) are the most energy-intensive household appliances and are covered under mandatory labelling under India’s S&L programme. The energy consumption of ACs depends on both the sizing/capacity of an AC and the energy-efficiency rating. The capacity of an AC (typically ranging from 0.75 to 2 tons refrigerant) needs to be selected as per the size of the particular room or space that needs to be cooled. As ACs are used for a limited number of months, this serves as a market barrier for investing in energy-efficient air conditioners. Abatement potential for MP has been evaluated, considering energy savings of shifting from two-star ACs to five-star ones (considering present ratings).


Low Carbon Pathway

4.6.4 Energy-efficient lighting (CFLs and LEDs)Lighting is one of the basic needs of households and is important for the quality of life of residents, as well as for ensuring productivity in workplaces. The most inexpensive electric lighting devices are incandescent bulbs. Since an incandescent bulb converts about 95% of electricity into heat and 5% to light, incandescent bulbs produce lower output for a given wattage compared to more efficient alternatives.

Energy demand for lighting can be reduced by phasing out incandescent lighting with compact fluorescent lamps (CFLs) and light emitting diodes (LEDs). CFLs work on the same principle as standard fluorescent lamps (tube lights) but have the compactness of incandescent lamps. LEDs are lighting devices that become illuminated by the movement of electrons through a semiconductor material (diodes). LEDs waste even less energy than CFLs as electricity is directly converted into light.

However, there are market barriers to the entry of CFLs and LEDs. Particularly in rural areas, incandescent lamps continue to be the preferred option for lighting devices, both because of their low cost and poor grid conditions (fluctuating voltage conditions) under which CFLs do not work well.

At the same time, the quality of light delivered by CFLs and LEDs is superior to that of incandescent lights. Furthermore, if lighting loads are powered by rooftop solar PV or other distributed renewable energy systems, the cost of the RE system is lower (due to reduced capacity) when combined with energy-efficient lighting.

In a low-carbon scenario for Madhya Pradesh, it is assumed that 50% of incandescent lights are replaced by CFLs and 50% by LEDs by 2030.


4.7 Agriculture and livestock sectorIt is widely recognized that the agriculture sector is vulnerable to the effects of climate change. Technological advancement in the sector can contribute to both making agricultural production more resilient to climate change and mitigating GHG emissions. As Madhya Pradesh works toward amelioration of subsistence farmers, increased irrigation and farm mechanisation can be expected. On one hand, increased agricultural productivity leads to increased sequestration of CO2, and socio-economic benefits for the millions who depend on agriculture as a livelihood. On the other hand, for long-term sustainability of agricultural growth, the use of resources such as water, energy and fertilizers has to be optimized.

Water is a vital resource for the agriculture and livestock sector. Agricultural productivity in Madhya Pradesh has been adversely affected by droughts in the past. Making agricultural practices more water efficient would enhance long-term sustainability and help reduce energy consumed to ground pump water for irrigation. Water management techniques such as drip irrigation, water harvesting and surface canal infrastructure development are some of the solutions for decreasing dependence on ground water and reducing the energy consumption of irrigation pumps.

Reducing CO2 emissions due to energy consumptionEnergy-efficient pump sets represent a major opportunity for reducing excessive electricity consumption in irrigation pumps. Approximately half of the electricity consumed in Madhya Pradesh is attributed to the agriculture sector92. A common problem in the agriculture sector is that efficiency is not considered by farmers as a key criterion while selecting pumps since electricity tariff is highly subsidized. Retailers/Dealers of pump sets often sell oversized pumps with the maximum head surface and, as a result, often an inappropriate size and type of pump gets selected. Therefore, influencing the pump selection behaviour of farmers and promoting the sale of efficient and correctly sized pump sets are imperative, considering the large agricultural base of MP.

Solar photovoltaic pumps are another important opportunity, which can help mitigate grid electricity consumption and enhance access to energy in areas where grid electricity is limited/unavailable. The technology is presently prohibited by high costs, and appropriate financing is critical for its proliferation.

Reducing nitrous oxide emissions from soilsPractices for managing nitrogen oxide emissions from soil, due to the use of nitrogenous fertilizers, can play an important role in reducing overall fertilizer consumption, mitigating emissions, and also increasing yield through optimized nutrient uptake. Farmers in Madhya Pradesh are already practicing nitrogen management techniques such as the timing of fertilizer application (synchronizing application of fertilizers with periods of high N demand from crops) and direct injection of fertilizer into soils (to ensure maximum uptake of N by roots). Additionally, bacterial cultures such as Rhizobium and Azotobacter may be promoted

92 Reference: MP Statistics Handbook 2010


Low Carbon Pathway

and applied, along with fertilizers, to increase nitrogen fixation. Similarly, the use of nitrification inhibitors and slow release fertilizers can be considered for managing nitrogenous emissions.

Reducing enteric methane emissionsEmissions from livestock can be curtailed through straw silage or straw ammonization. Preparation of straw silage involves the fermentation of fresh green fodder, forage grass and other fodder by lactobacillus in an airproof container. This improves the digestibility of fodder and reduces enteric methane emissions. Straw ammonization involves treating low-value forage such as rice straw, wheat straw, and straw of other crops with ammonia in the form of liquid ammonia, urea or ammonium bicarbonate. The process breaks down bonds between lignin (un-digestible component of plant material) and cellulose, thereby increasing digestibility and reducing emissions.

Some of the initiatives that can be taken up by the state in the agriculture and livestock sector include:

Table 50: Agriculture and Livestock Sector — Initiatives


Develop a policy for addressing the energy-water nexus

The issue of excessive use of groundwater for irrigation and declining water tables is a problem that can be addressed through a policy and specific programmes for promoting water management practices and water-efficient irrigation technologies. Cooperation with the Agriculture Department, Water Resources Department and Energy Department may be required to address the cross-sectoral issue of the energy-water nexus. The policy may also be aligned with the existing Agricultural DSM programme of BEE and incorporate measures to overcome barriers faced by the programme.

Carry out capacity building programmes and pilot projects for nitrogen management and reduction of enteric methane emissions

Pilot projects can be carried out for various types of crops to quantify the benefits of managing nitrogen in soil (e.g., usage of nitrification inhibitors and bacterial cultures) and improve the digestibility of fodder for livestock. These projects can then be used to carry out capacity building on the demonstrated benefits in terms of increased crop yield and/or health of livestock.

Phasing out/reducing subsidies for grid power Phasing out or reducing subsidies that de-incentivize investment in energy-efficient products, including irrigation pumps, is required in the long run. However, the objective of ensuring universal access to energy should not be comprised and, therefore, this needs to be carried out in a controlled manner and as a long-term strategy.


The description of key technologies/measures identified as abatement levers for agriculture and included in the cost curve for the state are given below.

4.7.1 Energy-efficient pumpsEnergy-efficient agricultural pump sets can lead to both reduction in GHG emissions and relaxed subsidy burden on governments. With the implementation of the BEE Agriculture DSM programme, energy-efficiency improvements would be introduced to some extent by 2030 in the BAU scenario. In a low-carbon scenario, it is expected that the average energy efficiency would be equivalent to that of five-star rated pump sets, whereas in the BAU scenario, it would be equivalent to that of three-star rated pump sets.

4.7.2 Solar photovoltaic pumpsA solar PV pumps consists of an electric motor, a control panel/controller, a battery, an inverter93 and a pump. Solar PV pumps traditionally draw power from conventional sources such as diesel/grid electricity. In the case of efficient pump sets, it is possible to operate the irrigation pump without relying on diesel and electricity, as these pumps are only required to be used for a few hours a day. As electricity is expected to be the main source of energy for pump sets in Madhya Pradesh in 2030, the abatement potential and costs of solar PV pump sets are determined assuming grid connected pump sets as the baseline. It is assumed that up to 10% of pump sets may be powered by solar PV by 2030, considering that a sufficient shadow-free area and adequate solar radiation are pre-requisites for the solar PV array to be installed.

4.8 Forestry sectorForests act as sinks for GHG emissions and sequester carbon dioxide from atmosphere. As mentioned in previous sections of this report, Madhya Pradesh has the largest forest cover of all states in India. Maintaining the existing forest cover is a challenge. The state forest department is already undertaking several initiatives such as the Joint Forest Programme and the utilisation of ICT technologies to manage and protect forest areas. However, there is potential for the state to carry out large scale afforestation and reforestation activities to sequester carbon, support bio-diversity and provide employment opportunities to

93 D.C electricity is generated using solar PV. Operation of an A.C. motor requires an inverter. Otherwise, for D.C. motors, the power generated can be used directly.


Low Carbon Pathway

populations residing in these areas. Additional funding may be sought by the state through various sources such as carbon markets (e.g., REDD+, CDM or voluntary markets), multi-lateral agencies and other forms of climate financing.

4.8.1 AfforestationAfforestation activities can be carried out by increasing plantation or converting non-forest or wastelands into forest areas. This would increase the carbon sink of Madhya Pradesh. In a low-carbon scenario for Madhya Pradesh, it is expected that its forest cover would increase to 33% of its total geographical area in 2030. This is in line with India’s aspiration, as set out by the National Forest Policy, 1989. In the BAU scenario, the forest cover for MP is forecast to reach 26.5% of its geographical area by 2030 due to afforestation activities.

4.8.2 Reforestation of degraded landDegradation of tropical land is a physical, chemical and biological process set in motion by activities that reduce the land’s inherent productivity. This process includes accelerated erosion, leaching, soil compaction, decreased soil fertility, diminished natural plant regeneration, disrupted hydrological cycle, and possible salinization, waterlogging, flooding, or increased drought risk, as well as the establishment of undesirable weedy plants. Reforestation of degraded lands would create additional sinks, along with several co-benefits of ecological balance restoration. About 6% of MP’s geographical area falls under the category of degraded land94. It is expected that in the low-carbon scenario, 50% of the degraded land would be reforested by 2030.

94 State development report 2009, Madhya Pradesh


4.9 Waste sectorThe ever-increasing urban population has put tremendous pressure on the budgetary resources of States/ULBs, underscoring the necessity of private sector participation in urban development. The responsibility for SWM management lies with the respective Urban Local Bodies (ULBs), consisting of municipal corporations, municipalities, nagar panchayats, etc., (collectively referred to as the Authorities). The Municipal Solid Waste (Management and Handling) Rules, 2000 (the MSW Rules), issued by the Ministry of Environment and Forests, Government of India, under the Environment (Protection) Act, 1986, prescribe the manner in which the Authorities have to undertake the collection, segregation, storage, transportation, processing and disposal of the municipal solid waste (MSW) generated within their jurisdiction under their respective governing legislations. However, hardly a few municipalities have taken initiatives for implementing Municipal Solid Waste (Management & Handling) Rules, 2000 to set up waste processing and waste disposal facilities.

Sustainable waste management needs to be based on the waste management hierarchy of avoiding the generation of waste and reducing, reusing, recycling, recovering, treating and disposing whatever waste is produced.

Sustainable waste management envisages an integrated approach encompassing technological, policy, administrative and legal actions to address the challenge of waste management in the country. Strategic planning based on local needs and long-term goals should inform of any policy addressing community involvement and public health issues. Hence, there is a need for action to effectively translate these approaches into a unified goal, incorporating local, regional and national priorities.

The Government of India, through its various wings, has implemented or sponsored numerous workshops for municipal officials and conferences for businesses and academia on SWM. Apart from such encouragement, it introduced schemes such as Jawaharlal Nehru National Urban Renewal Mission (JnNURM) to develop urban areas. It listed SWM as one of its main objectives. Under JnNURM, the GoI sponsored 42 SWM projects worth USD 500 million (INR 22.5 billion) between 2006 and 2009 (the GOI’s average share is around 20%). It has successfully joined hands with the private sector to form a Public Private Partnerships (PPP). The National Mission on Sustainable Habitat has been launched with three main components, one of which is managing municipal solid waste.

The Madhya Pradesh Government can adopt several initiatives for capitalizing on low-carbon growth opportunities in the context of the waste sector, as follows:

Table 51: Waste Sector - Initiatives


Formulation of a state policy for the implementation of MSW rules, 2000

A state policy is required for the effective implementation of central policy on MSW rules. The policy will ensure that government parks, gardens and farmlands give preference to the use of compost produced by ULBs within the state. It may also ensure availability of land for landfills. The state government may take responsibility for all permissions/clearances for the private partnership in SWM.

The state government, in line with the GoI, can enact Model Municipal Laws to enable PPP, set up regulatory authorities and create a cadre of professionals at ULBs and the state level

The PPP model has been implemented only in one city in MP (Indore), whereas it has shown success in various other states. The integrated solid waste management plants are running successfully in the state of UP. Encouragement from the MP Government to replicate the model in other cities of MP would be required through such policies.


Low Carbon Pathway

Establish partnership between industry and states departments responsible for urban development to incentivize waste utilisation

Industrial units can utilize waste-to-energy initiatives and refuse-derived fuel as a substitute for coal. They can also make use of power generated from methane in MSW management facilities.

4.9.1 Integrated waste processing plant - collection and transport, composting, conversion to RDF, conversion to bricks and recyclables segregationMadhya Pradesh presently has one integrated waste management facility under the PPP model in Indore with 600 TPD capacity. This type of waste management project can be replicated in other cities of MP. The operation of integrated waste processing plants may involve the following activities:• Collection of waste from door to door• Segregation of waste at the project site• Conversion of organic waste into compost aerobically to avoid methane generation• Conversion of fuel components to refuse-derived fuel (RDF)• Conversion of construction debris to bricks• Segregation of other recyclable materials for sale to other parties

4.9.2 Landfill Gas (LFG) recovery with electricity generationLFG is a mixture of various gases generated due to waste decomposition in a landfill. It contains approximately 50% methane, which, when released into the atmosphere, can contribute 2%-4% of the total global release of greenhouse gases95. Other gases in LFG are nitrogen, oxygen, hydrogen sulphide, carbon dioxide, etc.

LFG, if not captured or flared, would be released in the atmosphere causing GHG emissions. Landfill gas recovery, on the other hand, would help cut on emissions, as well as can be utilized to generate energy.

95 Intergovernmental Panel on Climate Change (IPCC), “Climate Change: The IPCC Scientific Assessment.” Report prepared for the Intergovernmental Panel on Climate Change by Working Group 1, 1990


4.9.3 Engineered landfills - aerobic landfillThe aerobic landfill system follows the principle of aerobic digestion and prevents methane from getting generated in the landfills. It also has a leachate management system. The waste is received, placed into the cell and heavily compacted.

After the cell is filled, a series of vertical wells are drilled into the surface with dispersion points (for the distribution of both air and moisture). Low-pressure compressors are used to pump air into the waste mass, and Leachate is injected into the waste. The vertical air and water shafts consist of perforated plastic pipes that extend to the bottom of the waste mass. These wells are interconnected on the surface by flexible plastic pipe through which compressed air is supplied. Leachate and rainwater runoff can be added to the air when it is forced into the shafts. Carbon dioxide and water vapour are released into the atmosphere through vent pipes set at strategic locations between vertical airshafts.

Dramatic changes occur almost immediately. Within a few weeks, the methane-producing anaerobic bacteria quickly perish and are replaced by aerobic bacteria, which break down the organic matter 30 to 60 times faster. Methane gas production ceases, with the only remaining emissions being non-toxic carbon dioxide and water vapour.


Chapter 5: Co-benefits and barriers to GHG emission abatement

Apart from abatement cost and potential, it is useful to consider the co-benefits (socio-economic or environmental benefits, other than GHG emission abatement) of the various opportunities identified. This is useful both from the point of view of aligning low-carbon growth with other development objectives and for prioritizing abatement opportunities. It is also imperative to identify and map barriers to low-carbon growth and ensure that apart from financing of levers, efforts are made to mitigate barriers, particularly for opportunities where abatement potential is high.

The following are overarching barriers to low-carbon growth for Madhya Pradesh that need to be addressed by the state:

• Investments required: The abatement potential identified in the cost curve (46 million tCO2e) would come at an investment cost of around INR 1,382 billion (or INR 138,276 crores). Assuming the investment is carried out over 10 years, this translates into approximately 5% of the state GDP. It is important to note that upfront investments are required even for opportunities that carry a “negative abatement cost”, albeit these investments will lead to economic gains to the state economy over time. Mobilizing capital for investment in low-carbon technologies/measures is a key challenge that needs to be addressed.

• Disaggregated energy consumption: Around 72% of the population of MP resides in rural areas, and around 50% of electricity consumption reported in MP has been attributed to the agriculture sector. Rural electricity demand, consisting both of agricultural demand (irrigation pumps-sets and other agricultural machinery) and rural household demand, presents unique challenges for low-carbon growth. For instance, a programme to replace pump sets, appliances or lighting with more energy-efficient alternatives would require significant resources to ensure maximum coverage, both in terms of manpower and financial resources. At the same time, considering that rural electrification will unlock latent demand for energy in coming years, demand-side management in rural areas would be an essential component of low-carbon growth for the state.

• Subsidized rates of electricity and fossil fuels: Subsidized electricity rates create an environment that de-incentivizes investments in energy efficiency. Convincing farmers or rural households to spend additional money on energy-efficient products/appliances is, therefore, challenging. Financial support in the form of subsidies, rebates or other incentives would be required. Similarly, subsidies on diesel and kerosene may present challenges for low-carbon growth.

• Management of large scale projects: Lack of coordination among various parties can lead to challenges in the planning and implementation of large-scale projects. For instance, urban development projects may involve coordination among various state departments (UADD, Public Works, Transport Department), private organisations involved through PPPs, contractors, owners of buildings (if impacted by the projects) and others. The same may apply to large-scale power or industrial cluster-based projects. The multitude of stakeholders can impede the planning and decision-making process for such initiatives. Delays can lead to increased costs over time.


In addition, co-benefits and barriers relevant to specific sectors are given in Table 52.

Table 52: Sector-wise co - benefits and barriers

Co-benefits Barriers

Power sector• Integrated

Gasification Combined Cycle (IGCC)

• Ultra-super-critical power

• Solar PV• Solar

thermal power

• Biomass-based power

• Abatement levers for the power sector lead to the generation of equivalent power with reduced coal consumption, either through clean coal technologies, or through renewable energy. Reduced coal consumption leads to reduction in SOx and NOx emissions as well as particulate matter, which are known to have adverse environmental and health impact.

• The development of renewable energy power plants can lead to employment opportunities and infrastructure development in remote or rural areas.

• Although IGCC has been commercialized in other parts of the world, it is a relatively new technology and has not been tested in India as a result of the quality of coal available in the country.

• High investment cost is a characteristic of all clean coal technologies, as well as solar technologies.

• The availability of quality biomass and price fluctuations in biomass over time lead to uncertain financial returns from biomass-based power projects.

Industry sector• Waste Heat

Recovery (WHR) in cement kilns

• Improving electrical efficiency in cement production

• Alternative fuels in kilns

• Utilisation of de-carbonated raw material

• The implementation of GHG abatement levers in the industry leads to business opportunities for various equipment suppliers, civil works contractors, technical consultants and alternative fuel/raw material transporters.

• The augmentation of waste heat recovery and energy efficiency can lead to reduced captive power requirements and revenues from the sale of surplus power to the grid.

• Output from waste heat recovery-based power plants is dependent on the ongoing operation and loading of kilns. This makes the output somewhat uncertain.

• Establishing a supply chain for alternative fuels or de-carbonated raw materials is challenging. Without certainty about the supply of alternative fuels/de-carbonated raw materials, it is difficult to make a business case for investing in alternative fuel feeding systems or making system modifications for the use of de-carbonated raw materials.


Co-benefits and barriers to GHG emission abatement

Transport sector• Bus rapid

transit systems (BRTS)

• Metro rail systems

• Electric two wheelers

• Reduced diesel and petrol consumption resulting from low-carbon transport would lead to reduced import of oil and reduced subsidy burden on the state.

• Apart from GHG emissions reduction, low-carbon transportation would lead to reduced SOx, NOx, particulate matter and other noxious emissions.

• The quality of life can be improved through mass transit systems, which can reduce travel time during peak traffic hours and also provide an affordable and convenient alternative to usage of private vehicles.

• Electric vehicles typically consume less energy compared to petrol vehicles of the same cost.

• Transport infrastructure projects are capital intensive and have a long gestation period for planning, engineering and construction.

• Setting up transport infrastructure can lead to a temporary increase in road congestion and inconvenience to commuters during construction.

• Since there is a lack of charging infrastructure in the country, it has a negative impact on the potential buyers of electric vehicles.

Building sector• Solar home

lighting systems

• Energy-efficient refrigerators in households – shift from three star to five star

• Improved access to energy is a major advantage of distributed renewable energy applications in homes (e.g., solar home lighting), which would particularly benefit residents in smaller town and rural areas.

• Even with 100% electrification of households and buildings, there would be power outages resulting from local grid failures or faults in power lines. Distributed renewable energy systems would substitute diesel as a power back-up during these power outages.

• The combination of reduced electricity demand through energy-efficient homes/ buildings and rooftop solar power would result in potential for selling back surplus power from homes/buildings to the grid. This would generate additional revenue for households, businesses and other consumers.

• Benefits of energy efficient building design are usually passed on to the tenants and they are the ones to benefit from reduced electricity bills. Therefore, there is limited incentive for builders to focus on energy–efficient design, particularly if this does not increase the value of the real estate.

• CFLs and LEDs are more expensive than incandescent bulbs. Similarly, energy-efficient star-rated appliances cost more than non-rated appliance. For households with low purchasing power, higher costs are a prohibitive barrier even with a payback from savings in operational costs.

• Consumers are often not aware of the potential reduction in energy bills and pay back periods for investment in star rated appliances compared with non-rated appliances.

• Retailers do not have any incentives for promoting or keeping stock of energy efficient appliances. Retailers may also not be aware of the S&L programme and its benefits to consumers.


Agriculture sector

• Energy-efficient Pumps

• Solar photovoltaic Pumps

• Efficient electricity consumption in the agriculture sector would lead to reduced subsidy burden on DISCOMs for the supply of electricity to rural areas in MP. This would improve the overall financially sustainability of utilities and their capacity to get involved in other

• Solar PV applications for pumping would lead to improved access to energy for irrigation purposes.

• There are market barriers to the uptake of DSM in agriculture. Farmers do not have adequate incentive to invest or participate in these measures, as electricity is supplied at highly subsidised rates in the agriculture sector.

• The geographical spread of pump sets poses challenges for implementing a comprehensive state-wide programme for pump replacement.

• In pilot projects implemented under the BEE Agricultural DSM project, there have been issues related to shared ownership of pump sets between two or more families and reluctance to sign agreements related to ESCOMs.

Waste sector• Integrated

waste processing plants

• Landfill Gas (LFG) recovery with electricity generation

• Engineered landfills — aerobic landfill

• A reduction in methane emissions from waste through various initiatives would lead to value addition and revenue generation from the sale of products such as electricity, compost, refuse derived fuel and plastic granules.

• The risk of contamination of ground water is reduced with waste management systems.

• Waste management systems address odour-related concerns associated with open dumping of waste in landfills.

• Integrated waste management systems help free up land for other socio-economic purposes (as a large area for land filling is not required).

• A number of projects in the waste sector have failed in the past due to the inferior quality of waste and associated difficulties in the operation of waste management facilities. Therefore, the willingness to invest in these projects is impeded by negative perceptions regarding their viability.

• Systems for the efficient collection of waste are essential for waste management initiatives. However, there is a general lack of efficient waste collection systems in India.

Forestry sector• Afforestation• Reforestation

of degraded land

• Apart from carbon sequestration, maintaining or increasing forest cover helps mitigate other forms of environmental degradation such as erosion and desertification.

• With close to 40% of villages in MP located either in forest areas or close to forests, a substantial portion of the population depends on forests for livelihood. Afforestation and re-forestation projects would directly benefit the livelihood of people and also potentially provide employment opportunities through forest management programs.

• Financing projects in the forestry sector is a challenge due to the lack of direct financial returns from afforestation or reforestation activities. Although the carbon market is one potential source of revenue generation from these projects in the long term, neither the CDM nor REDD + seem to be viable options, considering the current downturn in the carbon market.


Chapter 6: Lock-in potential and cost of delay

Delay in the implementation of assessed low-carbon development opportunities would cause two types of costs for Madhya Pradesh, as follows:• Loss in abatement potential due to lock-in of carbon-intensive infrastructure, expressed in tCO2• Monetary losses due to delay in the implementation of levers that have a negative abatement cost

(economically attractive opportunities), expressed in INR

For instance, any power capacity addition in the form of sub-critical coal-based power would be a lost opportunity for the installation of clean coal technologies (such as ultra-super-critical power IGCC). Similarly, the construction of buildings without incorporating energy-efficient building design would represent another lost opportunity and would carry a cost of delay.

Abatement potential can be reduced with the “lock-in” of GHG emissions due to carbon-intensive technologies or infrastructure. Different types of carbon-intensive infrastructure that can get locked in for more than a decade are listed in Table 53.

Table 53: Lock-in of carbon-intensive infrastructure

Carbon Intensive Infrastructure

Lock-in period Low-carbon alternatives

Sub-critical coal-fired power plants in the grid

25 + years • Efficient coal-fired power plants (super-critical and ultra-super-critical)

• Integrated gasification combined cycle power plants• Large-scale renewable energy installations

Captive coal-based power plants in energy-intensive industries

20 to 25 years • Waste heat recovery plants• Captive renewable energy-based power plants

Large commercial buildings without energy-efficient design of building envelope

30 + years • Energy efficient building envelopes incorporated in building design (material of construction, insulation material, arrangement of windows, sealing of the envelope, cool roofs)

Air conditioners and HVAC systems

10 years • Energy-efficient air conditioning systems and HVAC components

Electric geysers 15 years • Solar water heaters• Energy-efficient electric geysers


Urban infrastructure (spacing and location of residential, commercial areas, roadways, transit routes)

May vary from lifetime of buildings (30 + years) up to the lifetime of the city itself

• Urban infrastructure incorporating sustainable planning (leading to lower demand for transport, increased access to mass transit routes, and convenience/safety of non-motorised transport)

In the case of Madhya Pradesh, the lock-in potential of carbon infrastructure is very real risk, considering the large investments expected in power and urban infrastructure in coming decades. If decisions to implement low-carbon alternative to carbon-intensive infrastructure are delayed, this will reduce the potential for GHG emissions abatement and result in economic losses for the state.

Assuming that a low-carbon growth plan for the development of the state can be in effect by 2015, the costs of delaying implementation by 5 years to 2020 are illustrated in Table 54.

Table 54: Cost of Delay

Carbon-intensive infrastructure

Expected installation in MP in the period 2015-2020

Cost of delay

Sub-critical coal-fired power plants

4.7 GW Considering that 2 GW of this capacity could have been ultra-super-critical coal-based power and another 2 GW IGCC (as envisaged in low-carbon scenario), there is a loss of 4.92 million tCO2e in abatement potential per year as a result of the lock-in of sub-critical power.

There is an economic loss of INR 5,639 million per year due to higher cost of generation from sub-critical coal.

Electric geysers 0.44 million Considering that 20% of electric geysers could have been replaced with solar water heaters, there is a loss of 0.11 million tCO2e in abatement potential per year as a result of the lock-in of electric geysers.

There is an economic loss of INR 440 million/year due to the cost of supplying electricity to buildings for water heating.

Captive coal-based power plants

6.37 million tons per annum of clinker production – corresponds to additional captive power generation of 461 GWh

Considering that the new capacity of clinker produced would be installed along with captive power plants (primarily coal based), this translates into a loss of 0.13 million tCO2e in abatement potential per year if BAU addition in WHR takes place (20% of potential) rather than aggressive addition of WHR (100% of potential).

Economic losses of INR 336 million/year due to higher cost of captive power generation would be incurred per year.


Lock-in potential and cost of delay

These examples indicate that the total cost of delaying action from 2015 to 2020 would be 5.16 million tCO2e in abatement potential and INR 6,415 million per year from 2020 onward. Other opportunity costs include the cost of setting up energy-intensive buildings and designing urban infrastructures without considering the impact on transport demand. It is clear that considering the high infrastructure growth rate expected in the state, delaying action toward low-carbon development would be costly for MP.

To avoid the lock-in of carbon-intensive BAU technologies, it is essential for MP to act quickly to secure financing for GHG abatement opportunities. In the next section, financing options available for funding of low-carbon initiatives are reviewed.


Chapter 7: Financing mechanisms for low- carbon growth

Global and Indian scenario for financing low-carbon initiativesClimate change mitigation has attracted large-scale investment in developed countries. Europe alone has seen over €100 billion in investment in renewable energy technologies over the last 10 years. Despite the credit crisis in Europe, investors (such as pension funds) are still active in the space of renewable energy and energy efficiency. New investment funds continue be developed by banks, private equity houses, specialist project developers and others. These are being offered to institutional investors such as pension funds and sovereign wealth funds.

Financing for climate change has generally flowed from four sources: (i) domestically generated sources of funds, including private sector and public sector financing; (ii) foreign direct investment; (iii) the international carbon market; and (iv) bilateral and multilateral development assistance. Each of these sources is expected to continue to play a critical role in the future climate regime. Overall, investment in renewable energy and energy efficiency has increased dramatically in recent years, up more than five times globally and fourteen times in developing countries between 2004 and 2007 as per UNEP New Energy Finance 2008. About 94% of this investment in 2007 came from the private sector. Private sector investment and the carbon market, however, are unlikely to meet the needs of many countries and sectors, particularly those in early stages of development. In the case of a number of developing countries, and particularly the least developed ones, increased Official Development Assistance (ODA) and concessional financing or an expanded CDM will be required.

New ways to generate these sources of funding will be needed, and funds will have to be allocated effectively and efficiently. Developed and developing countries and the environmental community have put forth a number of proposals to expand funding sources. To attract investment for low-carbon growth, MP should have an integrated climate change and a clean energy policy framework.

Public sector incentivesMultilateral and bilateral development institutions and specialist agencies from a number of developed countries have vast experience of using public funds to catalyse private sector investment in climate change-related activities. As per UNEP, For every $1 of public money deployed, between $3 and $15 of private investment can be generated.

The mechanisms used include credit lines to local commercial financial institutions (CFI) for providing both senior and mezzanine debt to projects; guarantees to share with local CFIs the commercial credit risks of lending to projects and companies.

• Debt financing of projects by entities other than CFIs,• Private equity (PE) funds investing risk capital in companies and projects,• Venture capital (VC) funds investing risk capital in technology innovations, • Carbon finance facilities that monetize the advanced sale of emissions reductions to finance project

investment costs,• Grants and contingent grants to share project development costs, and • Technical assistance to build the capacity of all actors along the financing chain.


Multilateral and bilateral development finance institutions could establish mechanisms whereby private sector institutions from both developed and developing countries could access support to establish large-scale infrastructure or private equity funds investing in climate change mitigation. The support mechanisms used could draw on existing and emerging experience of using various debt and equity mechanisms, backed by targeted export credit guarantees and political risk insurance (e.g., against policy change). Crucially, support will need to be made available to funds and not just individual projects. Most institutional investors such as pension fund invest in funds rather than individual projects. They need certainty that potential losses will be mitigated at the fund level, and that sufficient numbers of attractive deals will be available to the fund.

These packages should be available to groups of pension funds or other end-investors, or fund managers who structure funds and raise finance from end-investors. Many of these mechanisms are not new. They need to be coordinated and targeted to climate change.The finance provided and leveraged through these mechanisms will need to be coordinated and recorded in order for it to be monitored, reported and verified.

The Indian business environment is rapidly changingPrimary energy demand in India is expected to more than double by 2030, and coal and oil currently account for over 90% of the country’s energy consumption. At a national level, the Government of India is implementing increasingly wide-reaching programmes and incentives to manage energy use, resultant emissions and prepare adaptation plans. Such measures, including the far-reaching National Action Plan on Climate Change (NAPCC), are a part of India’s growing voluntary efforts in response to climate change. Programmes and incentives to promote renewable energy and energy efficiency are well underway in tandem with the Jawaharlal Nehru National Solar Mission (JNNSM) and National Mission for Enhanced Energy Efficiency (NMEEE).

The resulting business opportunities are considerable. India aims to increase the share of grid connected renewable energy. 20GW of solar power generation is planned by 2022 as part of the JNNSM. There is also a substantial effort to introduce off-grid energy solutions in India’s rural and remote areas. The NMEEE aims to improve energy efficiency in priority industries such as power, cement, fertilizer, aluminium, iron and steel, railways, pulp and paper, and textiles. The energy conservation building code (ECBC) has been developed for commercial buildings. New measures in the Union Budget 2010-11 include the constitution of a National Clean Energy Fund to support clean energy. This will be funded by a coal levy, raising 2,500 crores/annum (USD 500m/annum). The government has planned Renewable Energy Certificates (RECs) and Energy Saving Certificates (ESCerts) to sustain market-based mechanisms.

Such measures can be expected to be maintained or strengthened in coming years and will present growth potential for many sectors, particularly energy, transport, industry, agriculture and forestry.

Sources of finance Madhya Pradesh can tap a number of mechanisms and financing sources to support investment in low-carbon development activity and projects. These mechanisms and sources can be broadly categorized as follows:• Conventional finance mechanisms.• Carbon finance – mainly Clean Development Mechanism (CDM) funding.• Emerging low carbon mechanisms – Renewable Energy Certificates (RECs), Energy Saving Certificates

(ESCerts), Energy Services Companies (ESCos).• UN, multi-lateral and bi-lateral sources.• Other relevant mechanisms – government policies and incentives, guarantees, insurance.


Financing mechanisms for low-carbon growth

The following provides an overview of the sources and mechanisms available to Indian business for low-carbon development. However, individual project developers need to interact with each agency closely to understand if the funding mechanism is appropriate for them and is available to them.

Table 55: Sources of finance

Financing sources Actions that result in direct reduction of GHG emissions

Research and technology Activity that is focused on improving and scaling up low-carbon growth

Market (international and national) Conventional finance (debt/equity), carbon finance for Clean Development Mechanism (CDM), voluntary offsets, Renewable Energy Certificates (RECs), Energy Saving Certificates (ESCerts), Energy Services Companies (ESCos), trade and customer base

Conventional finance (debt/ equity), carbon finance for Clean Development Mechanism (CDM) Trade and customer base

International Institutions (United Nations agencies, World Bank, International Finance Corporation (IFC), Asian Development Bank (ADB), multi-lateral and bi-lateral partnerships).

Conventional finance (debt/equity/concessional debt), Global Environment Facility (GEF), Clean Technology Fund (CTF), Energy Efficiency Initiative (EEI), Renewable Energy & Energy Efficiency Partnership (REEEP), bi-lateral grants and funds, Clinton Climate Initiative (CCI)

Global Environment Facility (GEF), and other multilateral and bilateral funds

Government of India and state government and government institutions (nodal agencies for National Action Plan for Climate Change – NAPCC, Indian Renewable Energy Development Agency Ltd.-IREDA, Bureau of Energy Efficiency-BEE, Rural Electrification Corporation Ltd.-REC, Khadi & Village Industry Commission-KVIC)

Regulations, grants, subsidies, debt, taxes, incentives, tariffs

Government of India (GoI) Clean Energy Fund, GoI National Research Institutes

Scale and time profile of investment required in MPThe section below provides the indicative time scale of investments that would be needed to implement the low-carbon levers suggested in the cost curve. The time horizon of the investments is determined on the basis of the ease of implementation of the levers, as well as commercial maturity level of the suggested technology in context of MP. The scale of investments is the sum total of the capital expenditure required in implementing those levers in short term, medium term and long term (used in the cost curve as well).


Table 56: Indicative time horizon of investments

Time Profile Short term (2013-2018) Medium term (2018-2025) Long term (2025-2030)

Abatement technologies to be implemented

Replacement of incandescent lamps with LEDs Replacement of incandescent lamps to CFLS WHR in cement kilns Use of alternative fuels in cement kilns Improving the electrical efficiency of cement production Afforestation: conversion of non-forest area/wasteland into forest area Biomass based power plants Solar PV power plants

Solar thermal power plants Energy efficient air conditioning (two star to five star) Solar water heaters Household rooftop solar PV (1 KW) Integrated waste management plant (PPP model) — collection, composting, RDF Use of de-carbonated raw materials in cement production Implementing bus rapid transit systems with CNG based buses Metro rail projects

IGCC Ultra-super-critical LFG recovery with electricity generation Aerobic bioreactor landfill Reforestation: restoring and recreating areas of forests that may have existed long ago but were deforested Electric two wheelers

Investment required

INR 121 billion INR902 billion INR157 billion

Some of the funding mechanisms that can be availed by MPSome of the avenues for financing for the state government include:• Create a green fund through cess on electricity generated from fossil fuels (on grid connected power

plants)• Create a green transportation fund through road tax collection and/or toll tax collection• Develop an environment conducive for ESCOs to invest in energy efficiency in large commercial buildings

and government buildings• Secure funding through REDD+ or voluntary carbon market for afforestation and reforestation projects• Develop partnerships with International Financing Institutions for investing in capacity building/

institutional strengthening


Chapter 8: Conclusion

In order to pursue an inclusive and sustainable development path, MP needs to create a policy environment that fosters and accelerates the adoption of low-carbon technologies and practices beyond those in the BAU demand scenario. This can be done through various policy or institutional measures described in this report and by securing additional financing to support these efforts. Once the required environment is created, the people of Madhya Pradesh and the state government would reap significant benefits. The consolidated results of the study have been summarised below.

Table 57: Inventory of various GHG gases in MP— 2008 and 2030

Sector tCO2 2008 tCO2 2030 tCH4 2008 tCH4 2030 tN2O 2008 tN2O 2030

Power 41,912,905 187,800,050 - - - -

Industry 20,879,347 45,679,679 161,149 251,435 - -Transport 6,739,882 34,305,896 694 3,508 318 1,623Buildings 2,943,074 3,866,070 6,499 7,631 - -Agriculture andlivestock

1,043,118 562,457 1,070,035 1,620,864 30,304 47,699

Waste - - 228,288 394,123 - -Total 73,518,326 272,214,152 1,466,665 2,277,560 30,622 49,323Forestry -26,381,998 -27,331,749 - - - -Total with sinks 47,136,329 244,882,402 1,466,665 2,277,560 30,622 49,323

The per capita emissions of MP are projected to increase from 1.66 tCO2e in 2008 to 3.69 tCO2e in 2030

(excluding sinks due to afforestation), considering the rapid growth in infrastructure and economy in coming years, as well as the availability of key resources such as coal and limestone.

With the implementation of low-carbon growth policies and programmes, a reduction of at least 46 million tCO2e is possible by 2030, as illustrated in Figure 24. However, this does not represent the total abatement potential as some opportunities have not been quantified in the present study. These opportunities could potentially contribute to significant additional reductions in GHG emissions in Madhya Pradesh.

Figure 24: Base year GHG inventory, BAU and low-carbon scenario


In order to realise the abatement opportunity available for the state, it would be crucial to take early action and avoid the cost of delay. For instance, the continued implementation of sub-critical coal-based power projects would lead to a lock-in of inefficient coal technology for 25 years. A delay of 5 years in switching to more efficient coal technologies could cost the state up to INR 5,639 million per year due to higher cost of generation per kWH. The total cost of delay for the opportunities discussed in Chapter 6 would amount to INR 6,415 million per year.

To avoid the lock-in of GHG emissions, MP can act swiftly to avail financing mechanisms and create innovative funds to augment low-carbon technologies/measures. Finally, adopting a low-carbon growth path would not only reduce GHG emissions for the state, but also result in various socio-economic and environmental benefits for the people of Madhya Pradesh.

As Madhya Pradesh has recently begun to undertake significant infrastructure growth (in terms of power and industry, and the urban environment), the state is now presented with a unique opportunity to make use of the best available technologies and practices to achieve sustainable and low-carbon growth in coming decades.

To realise this opportunity, the Government of Madhya Pradesh would be required to take certain follow-up actions to the present study, with the objective of further quantifying, prioritizing and availing low-carbon opportunities. These could be as follows:

• Conceptualisation of programmes and schemes for those GHG emission abatement levers that are most promising for the state: For the most promising low carbon opportunities, the GoMP can conceptualise programmes and schemes to make them financially attractive as well as easily implementable.

• Assessment of institutional framework and resource requirements for low-carbon policy/ programme initiatives: Policy and programme initiatives that can be taken up by the state government, to alleviate barriers and/or create incentives for low-carbon measures / technologies, have been identified in this report. Policy and programme initiatives that the state government can take up to avail financing for low-carbon growth have also been identified. A further assessment of institutional frameworks and resource requirements for mobilizing these policy / programme initiatives needs to be carried out. This would include drafting the policy or programme documents and identifying the various stakeholders for implementing these initiatives.

• Detailed techno-commercial feasibility analyses: Technical and commercial analysis of opportunities involving high capital costs and high abatement potential would be useful in building a case for investment. For instance, the feasibility of installation of ultra-supercritical power plants needs to be assessed considering local technical and commercial conditions (e.g.: availability of supporting infrastructure, fuel characteristics, supply chain, cost of technology transfer, etc.). This would also be useful for opportunities described qualitatively in this report, for instance: sustainable urban planning for reducing emissions in the transport sector. Detailed techno-commercial analyses would produce more quantifiable data, and help refine the low-carbon growth roadmap for the state.

• Quantification of co-benefits of abatement opportunities: A further assessment may be carried out of the co-benefits of low-carbon growth identified in the present study. Quantification of the various social, environmental, health and other benefits of abatement levers would be useful in prioritisation of opportunities and justification of investment where marginal abatement cost is high.

• Monitoring of GHG emissions: Although an assessment of GHG inventory has been carried out, it would be useful to regularly update the inventory in order to track the state’s emissions profile and progress towards climate change mitigation, and modify the low-carbon growth roadmap based on any major changes to the baseline emissions.


Conclusion

The marginal abatement cost curve study presented in this report provides analyses that can be used by the state as a reference point to begin to institutionalise and implement low-carbon growth. Institutionalizing low-carbon growth would involve incorporating low-carbon growth targets in five-year plans, finalizing the list of low-carbon projects/programmes to be undertaken, and carrying out micro-level implementation plans of projects/programmes to be carried out by individual departments.

Further, to support this high-level planning, additional analyses may be required such as deciding the most suitable institutional framework for the overall low-carbon growth planning, as well as for individual project/programme planning. It would be useful to carry out more detailed analyses to quantify co-benefits of the low-carbon opportunities, which could be a valuable input for developing the final list of project/programmes to be taken up. Further, some of the more capital-intensive projects/programmes would require detailed techno-feasibility analyses (e.g.: IGCC, ultra-supercritical power plants, and others) before they may be considered in the final list.

Last but not least, a monitoring framework would be required to continuously track the progress of the low-carbon growth plan for the state, and also to make any periodic revisions to plans based on changes in technology costs and availability. Ultimately, the institutionalisation of low-carbon growth in Madhya Pradesh would only be complete when it becomes part of a continuous process, incorporated into the state’s planning and budgeting activities.

Figure 25: Next steps for institutionalizing low-carbon growth in MP

ANNEXURES


Annexure I – GHG inventorisation (equations, data and sources)

The following annexure would present detailed methodology i.e. the equations used along with the data, their sources; emission factors and their sources.

Power SectorThe, GHG emissions calculations of CEA, published in the CO2 baseline database for India have been adopted for determination of GHG emissions from power generation in MP. The database includes an estimate of CO2 emissions from individual power plants in India, calculated using the following equation:

AbsCO2(station)y = ∑FuelConi ,y x GCVi,y x EFi x Oxidi

WhereAbsCO2,y = Absolute CO2 emission of the power plant in the given fiscal year ‘y’ FuelConi,y = Amount of fuel of type i consumed in the fiscal year ‘y’ GCVi,y = Gross calorific value of the fuel i in the fiscal year ‘y’ EFi = CO2 emission factor of the fuel i based on GCV Oxidi = Oxidation factor of the fuel i

The sum of the GHG emissions from each state owned/centre owned power plant in MP, calculated as per the above equation, is considered in the GHG inventory for the power sector of MP

Name Capacity mw as on 31/03/2008

State/ centre

Owned by Fuel 1 Fuel 2 2008-09Net Generation GWh

2008-09AbsoluteEmissionstCO

2

Satpura 1,142.5 State MPGPCL Coal Oil 6,578 9,190,139Amar Kantak

300 State MPGPCL Coal Oil 961 1,589,373

Sanjay Gandhi

1,340 State MPGPCL Coal Oil 6,129 7,926,131

Vindh_chal Stps

3,260 Center NTPC Coal Oil 24,964 23,964,903

Total 6,042.5 38,362 42,670,546 The 2008 calendar year GHG CO2 emissions are determined on the basis of 2007-2008 and 2008-2009 as follows:

Emissions2008 = ¾(Emissions2008-2009) + ¼(Emissions2007-2008)

Period GHG Emissions (tCO2)-due to fossil fuel combustion

2007-2008 39,639,9832008-2009 42,670,5462008 Calendar Year 41,912,905


Industry Sector

Combustion of fossil fuels in industry:The GHG emissions from fuel consumption are calculated as follows:

GHG emissions from coal consumption

Parameter Unit Value Source

A Coal consumption (2008-2009)

‘000 tonnes of coal 5,908 NSSO Annual Survey of Industries

B Coal consumption (2007-2008)

‘000 tonnes of coal 5,730 NSSO Annual Survey of Industries

C Coal consumption (2008 calendar year)

‘000 tonnes of coal 5,864 Calculated (A*3/4+B*1/4)

D Calorific value for Coal kcal/kg 5,000 Ministry of Petroleum and Natural Gas, India

E Calorific value for Coal TJ/MT 0.02 Calculated from BF Emission factor for coal tCO2/TJ 95.81 India’s Second National

Communication to UNFCCC (Natcom 2)

G Emissions from Coal Consumption

tCO2 11,758,096 Calculated (C*E*F)*1000

GHG emissions from diesel consumption


A Power generation from diesel in Industries In Madhya Pradesh (2008-09)

MWh 509,350 Ministry of Power, India

B Power generation from diesel in Industries In Madhya Pradesh (2007-08)

MWh 453,650 Ministry of Power, India

C Power generation from diesel in Industries In Madhya Pradesh (2008 calendar year)

MWh 495,425 Calculated (A*3/4+B*1/4)

D Emission factor for Diesel based power generation

tCO2/MWh 0.67 CEA CO2 Database Version 7

E Emissions from diesel consumption for captive power generation in industries

tCO2 331,935 Calculated (C*D)



GHG emissions from naphtha consumption


A Naphtha consumption in MP (2008-2009)

Thousand tonnes 77 MoPNG

B Naphtha consumption in MP (2007-2008)

Thousand tonnes 57 MoPNG

C Naphtha consumption in MP (2008 Calendar Year)

Thousand tonnes 72 Calculated (A*3/4+B*1/4)

D NCV for Naphtha TJ/Gg 44.5 IPCCE Energy content of naphtha consumed TJ 3,204 Calculated

(C*D)F Emission factor for Naphtha tCO2/TJ 73.30 IPCC, Natcom 2G Emissions from naphtha consumption

in industriestCO2 234,853 Calculated

(E*F)

GHG emissions from FO consumption


A FO consumption in MP (2008-2009) Thousand tonnes 164 MoPNGB FO consumption in MP (2007-2008) Thousand tonnes 226 MoPNGC FO consumption in MP (2008 calendar

year)Thousand tonnes 179.5 Calculated

(A*3/4+B*1/4)D NCV for FO TJ/Gg 43 IPCCE Energy content of FO consumed TJ 7,718.5 Calculated

(C*D)F Emission factor for FO tCO2/TJ 74.10 IPCC, Natcom 2G Emissions from naphtha consumption

in industriestCO2 571,941 Calculated

(E*F)

GHG emissions from LDO consumption


A LDO consumption in MP (2008-2009) Thousand tonnes 15 MoPNGB LDO consumption in MP (2007-2008) Thousand tonnes 19 MoPNGC LDO consumption in MP (2008

calendar year)Thousand tonnes 16 Calculated

(A*3/4+B*1/4)D NCV TJ/Gg 40.4 IPCCE Energy content of LDO consumed TJ 646.4 Calculated

(C*D)F Emission factor for LDO tCO2/TJ 77.40 IPCC, Natcom 2G Emissions from LDO consumption in

industriestCO2 50,031 Calculated

(E*F)

Total CO2 Emissions due to Fuel Consumption (Eindustry, fuel)

12,946,856 tCO2


Fugitive Emissions from coal miningFugitive emissions from mining of coal are determined on the basis of coal production in MP. Data on coal produced through open cast mining and underground mining is obtained from the Ministry of Coal and India specific emission factors have been taken from Natcom 2. Fugitive emissions due to coal mining are calculated as follows:

Efugitive,coal = ( Pcoal, OC mining * EFOC mining + Pcoal,UG mining * EFUG mining ) *DCH4

WhereNotation Parameter Unit Value Source

A Pcoal, OC mining (2007-2008)

Coal production from open cast mining in 2007-2008

Million Tons

55.24 Ministry of Coal

B Pcoal, UG mining(2007-2008)

Coal production from underground mining in 2007-2008

Million Tons

12.59 Ministry of Coal

C Pcoal, OC mining (2008-2009)

Coal production from open cast mining in 2008-2009

Million Tons

58.09 Estimated based on total coal production reported for 2008-2009 by Ministry of Coal and percentage of open cast mining in previous year96

D Pcoal, UG mining(2008-2009)

Coal production from underground mining in 2008-2009

Million Tons

13.24 Estimated based on total coal production reported 2008-2009 by Ministry of Coal and percentage of underground mining in previous year

E Pcoal, OC mining (2008 calendar year)

Coal production from open cast mining in 2008 calendar year

Million Tons

57.38 Calculated (A*1/4+ C*3/4)

F Pcoal, UG mining(2008 calendar year)

Coal production from underground mining in2008 calendar year

Million Tons

13.08 Calculated (B*1/4+D*3/4)

G EFOC mining Methane emission factor for open cast mining

m3 CH4/ ton coal

1.18 Natcom 2

H EFUG mining Methane emission factor for underground mining

m3 CH4/ ton coal

13.21 Average of emission factors for underground mining, as given in Natcom 2 have been considered

I DCH4 Density of methane tCh4 / m3 0.00 IPCCC Guidelines 2006J Efugitive,coal Fugitive emissions

due to coal miningtCH4 161,149 Calculated (E*G+F*H)*I*10^6

96 State-wise breakup of open cast mining and underground mining was not reported for the financial year 2008-2009.



Process emissions from industry Process emissions from cement manufacturing are calculated as follows:

Eprocess = Pcement * CC * EFcement,process

WhereNotation Parameter Unit Value Source

A Pcement (2007-2008)

Cement production

Tons 19,720,000 Cement Manufacturers Association (CMA)

B Pcement(2008-2009)

Cement production


C Pcement(2008 calendar year)

Cement production


D CC Clinker content % 74% Calculated based on product profile of different companies in the state

E EFcement,process Process Emission Factor

tCO2/ ton cement

0.537 India: Greenhouse Gas Emissions 2007

F Eprocess,direct Process emissions due to cement manufacturing

tCO2 7,932,491 Calculated


Transport Sector

Emissions from the transport sector are determined on the basis of diesel and petrol consumed in the transport sector of M.P.

Etransport = EFpetrol* Petrolconsumed + EFdiesel * Dieselconsumed

Where:Notation Parameter Unit Value Source

A Dieselconsumed Total diesel consumption in M.P. (2007-08)

tons 1,774,000 MoPNG

B Dieselconsumed Total diesel consumption in M.P. (2008-09)

tons 2,072,000 MoPNG

C Dieselconsumed Total diesel consumption in M.P. (CY 2008)

tons 1,998,000 Calculated (B*1/4+ A*3/4)

D Dieselconsumed Diesel Consumption in transport sector in M.P.

tons 1,578,800 Calculated by subtracting diesel consumed in industries and agriculture pumps from C

E NCVdiesel Net calorific valued of diesel

Mcal/ton 10,790 MoPNG

F EFdiesel Emission factor of diesel tCO2/TJ 74.1 Natcom, IPCCG EFdieselCH4 Methane Emission

factor of dieselKgCH4/TJ 3.9 NATCOM2

H EFdieselN2O N2O Emission factor of diesel

KgN2O/TJ 3.9 NATCOM2

I Petrolconsumed Petrol consumed in transport sector in M.P. (2008-09)

Tons 470,000 MoPNG (total petrol consumed in the state is assumed to be consumed in transport)

J Petrolconsumed Petrol consumed in transport sector in M.P. (2007-08)

Tons 393,000 MoPNG (total petrol consumed in the state is assumed to be consumed in transport)

K Petrolconsumed Petrol consumed in transport sector in M.P.(CY 2008)

Tons 451,000 Calculated (G*1/4+ H*3/4)

L NCVpetrol Net Calorific Value of petrol

Mcal/ton 11,135 MoPNG

M EFpetrol Emission Factor of petrol

tCO2/TJ 69.3 Natcom2, IPCC

N EFpetrolCH4 Methane Emission factor of petrol

KgCH4/TJ 19.8 NATCOM2

O EFpetrolN2O N2O Emission factor of petrol

KgN2O/TJ 1.92 NATCOM2

P Etransport GHG emissions from road transportation in MP

tCO2e / year

6,853,174 Calculated



Buildings Sector

GHG emissions due to thermal energy consumptionThe GHG emissions due to consumption of fuels (LPG and kerosene) are determined as follows:

Ebuildings, thermal = FCLPG x NCVLPG x EFLPG + FCkerosene x NCVkerosene x EFkerosene

Notation Parameter Unit Value Source

A FCLPG LPG consumption (2008-09)

thousand tonnes (Gg)

473 Ministry of Petroleum & Natural Gas

B FCLPG thousand tonnes (Gg) thousand tonnes (Gg)


C FCLPG thousand tonnes (Gg) thousand tonnes (Gg)

468 A*1/4+B*3/4

D NCVLPG Net calorific value of LPG

TJ/Gg 47.3 IPCC, Default Value

E EFLPG Emission factor of LPG tCO2 1,411,730 CalculatedF FCkerosene Kerosene

consumption(2008-09)thousand tonnes (Gg)


G FCkerosene Kerosene consumption(2007-08)



H FCkerosene Kerosene consumption (CY 2008)


491 F*1/4+G*3/4

I NCVkerosene Net calorific value of kerosene

TJ/Gg 43.8 IPCC, Default Value

J EFkerosene Emission factor of kerosene

tCO2/TJ 71.9 IPCC, Default Value

K FCbiomassRural/cap Monthly per capita biomass consumption-rural

kg / person / month

21.20 NSSO, Key Indicators of Household Consumer Expenditure

L PopulationRural Rural population thousands 49,965 Census 2011 (data interpolated for 2009)

M FCbiomassrural Total biomass consumption – rural(2008)

thousand tonnnes (Gg)

12,715 Calculated


N FCbiomassUrban/cap Monthly per capita biomass consumption-urban

kg / person / month

5.20 NSSO, Key Indicators of Household Consumer Expenditure

O Populationurban Urban population thousands 18,772 Census 2011 (data interpolated for 2009)

P FCbiomassurban Total biomass consumption-urban


1,172 Calculated

Q EFbiomass Emission factor for biomass

kgCH4/TJ 30 IPCC default value

R NCV biomass Net calorific value-biomass

TJ / Gg 15.6 IPCC default value

S FC biomass Total biomass consumption


13,887 Calculated

T Heat Value Total biomass consumption-heat value

TJ 216,630 Calculated

U Methane Emissions

Methane emissions due to combustion of biomass

tCH4 6,499 Calculated



Agriculture Sector

GHG Emissions from Fuel Consumption in Agricultural Pump-setsThe GHG emissions due to diesel based pump-sets are calculated as follows:

Eagriculture,diesel,y = FCdiesel,y x NCVdiesel,y x EF

Where:

Eagriculture,diesel,y = GHG Emissions from diesel consumption in year yFCdiesel,y = Diesel consumption in agricultural pumpsets in year yNCVdiesel,y = Net calorific value of dieselEFdiesel,y = Emission Factor for diesel

The assumptions and sources used in the calculation are as given below:


Number of Diesel Pumps (2008-09)

- 343,700 Statistics Handbook for Madhya Pradesh

Number of Diesel Pumps (2007-08)

325,200 Statistics Handbook for Madhya Pradesh

Number of Diesel Pumps (2008)

339,075 Calculated from above: ¼*(Pumps in 2008-09 )+3/4*( Pumps in 2007-08)

Operating hours of average pump

Hours/year 1,200 IWMI study of groundwater

Assumption on proportion of operational pumps

0.9 Assumption

Diesel Consumption rate Liters/hour 1.03 IWMI study of groundwaterDiesel Consumption liters /year 377,187,030 CalculatedDiesel Consumption Million liters /

year377 Calculated

Diesel density Kg/liter 0.82 MoPNGDiesel Consumption Million kg/

year311.7 Calculated

Calorific Value Kcal/Kg 10,790 MoPNGDiesel Consumption-Energy

TJ 14,077 Calculated

Emission factor tCO2/TJ 74.1 IPCCEmissions (Diesel) tCO2/year 1,043,118 Calculated


GHG Emissions from Paddy CultivationMethane emissions from paddy (rice) cultivation are dependent on the characteristics of the cultivated area (irrigated / rain-fed). Different emissions factors are applied for irrigated areas and rain-fed areas. The following assumptions have been made for calculating the methane emissions.


A Paddy Cultivation Area (2008-09)

Thousand hectares 1,717 MP Agri-economics Survey 2012

B Paddy Cultivation Area (2007-08)

Thousand hectares 1,645 MP Agri-economics Survey 2012

C Paddy Cultivation Area (CY 2008)

Thousand hectares 1,699 Calculated – A*1/4+B*3/4

D Irrigated Area Thousand hectares 290.25 MP Agri-economics Survey 2012 (Calculated as1/4*2008-09 value+3/4*2007-08 value)

E Rainfed area Thousand hectares 1,408.75 Calculated value (B-C)F Emission factor of

Rice Cultivation (irrigated area)97

kg CH4 ha-1 18 Natcom 2 (minimum emission

factor chosen from the types of irrigation systems, as M.P. has advanced techniques as suggested by experts in stakeholder consultations)

G Emission factor of Rice Cultivation (rainfed – draught prone area)98

kg CH4 ha-1 66 Natcom 2, considered for drought

prone area

H Methane emissions from paddy cultivation (irrigated)

tCH4 5,225 Calculated (D*F)

I Methane emissions from paddy cultivation (rainfed)

tCH4 92,978 Calculated (E*G)

J Methane emissions from paddy cultivation (Total)

tCH4 98,202 Calculated (H+I)

K Equivalent CO2 emissions

tCO2e 2,062,242 Calculated (J*21)

97 Based on default factor of 1.30 kg CH4 ha-1 d-1 and scaling factor of 0.78 for irrigated land98 Based on default factor of 1.30 kg CH4 ha-1 d-1 and scaling factor of 0.25 for rain-fed, drought-prone land



Methane Emissions from LivestockMethane emissions from livestock are computed using country specific emission factors given in NATCOM 2 and methodology provided by 2006 IPPC Guidelines for National Greenhouse Gas Inventories for different types of livestock. Livestock population for the year 2009 has been extrapolated from the 2003 and 2007 livestock census.

Livestock Population(2008)

Emission Factorkg CH4 / head / year (Source: NATCOM 2)

Methane EmissionstCH

4

Equivalent Carbon Dioxide EmissionstCO2e

Cows 22,737,872 26.68 (weighted average as per age groups)

606,682 12,740,325

Buffalo 9,565,069 33.76(weighted average as per age groups)

322,897 6,780,827

Total – Cows & Buffaloes

32,302,941 - 929,579 19,521,152

Sheep 358,318 4 1,433 30,099Goats 9,245,822 4 36,983 776,649Horses 26,186 18 471 9,898Mules 2,330 18 42 881Assess 17,167 10 172 3,605Camels 3,830 46 176 3,700

Swine 165,304 1 165 3,471Total 43,613,034 - 969,022 20,349,455


Nitrous oxide emissions from managed agricultural soils Emissions from managed agricultural soils are calculated using the approach given in IPPC Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories. The consumption of N-type fertilizers is Madhya Pradesh is taken as the basis for the calculation of N2O emissions.

Efertilizer,y = GCAfertilizer,y x ERfertilizer,y

WhereEfertilizer,y = N2O emissions from fertilizers consumption in year y, kg N2O GCAfertilizer,y = Gross cropped area of fertilizer use (Type N) in year y, hectares

ERfertilizer,y = Emission rate in year y, kg N2O-N ha-1 y-1

ERfertilizer,y = 1 + 0.0125 * F (IPCC)

Where:F = Fertilizer Application rate (kg N ha-1 y-1)

The assumptions and sources used in the calculation are tabulated below:


Fertilizer Consumption (Type N) CY 2008

Thousand Tonnes 801.48 MP Agri-economics Survey 2012 (calculated as ¼ of 2008-09 value + ¾ of 2007-08)

Gross Cropped Area

Thousand hectares 20,129 MP Agri-economics Survey 2012(calculated as ¼ of 2008-09 value + ¾ of 2007-08)

Emission rate kg N2O-N ha-1 y-1 1.45 CalculatedN2O Emissions tN2O 30,244.94 CalculatedEquivalent CO2 Emissions

tCO2e 9,375,930.63 Calculated



Forestry SectorThe estimation of carbon sinks from forest land remaining forest land is calculated:

ΔCG = A * GTOTAL * CF∆CG = annual increase in biomass carbon stocks due to biomass growth in land remaining in the same land-use category by vegetation type and climatic zone, tonnes C yr-1

A = area of land remaining in the same land-use category, ha GTOTAL = mean annual biomass growth, tonnes d. m. ha-1 yr-1

i = ecological zone (i = 1 to n) j = climate domain (j = 1 to m)CF = carbon fraction of dry matter, tonne C (tonne d.m.)-1


Area of land under forests 7,770,000 ha State of Forest report, 2011, FSITypes of forest lands Type of forest Percentage

Tropical dry 88.65%Tropical moist deciduous

8.97%


2.12%


0.26%


Plantation / TOF and tropical thorn forests are taken as part of tropical dry forests since the default factors are not available for these two categories in IPCC.

Average annual above-ground biomass growth (tonnes dm/ha/yr)




Ratio of below ground biomass to above-ground biomass (tonnes bg dm / tonne ag dm)

For tropical dry forest: 0.28 tonnes bg dm / tonne ag dmFor tropical moist deciduous forest: 0.24 tonnes bg dm / tonne ag dm


Carbon fraction of dry matter, tonne C (tonne d.m.)-1


Waste Sector

Calculation of methane emissions from solid waste Methane emissions from solid waste have been calculated applying the UNFCCC guideline: “Tool to determine methane emissions avoided from disposal of waste at a solid waste disposal site.” The following calculations have been applied.


BECH4,SWDS,y = Baseline methane emissions occurring in year y generated from waste disposal at a SWDS during a time period ending in year y (tCO2e / yr)


A Φ Model correction factor to account for model uncertainties

- 0.9 IPCC

B F Fraction of methane captured at the SWDS and flared, combusted or used in another manner that prevents the emissions of methane to the atmosphere in year

- 0 Methane capturing is practically zero in India (INCCA, 2007)

C OX Oxidation factor (reflecting the amount of methane from SWDS that is oxidised in the soil or other material covering the waste)

- 0 UNFCCC

D GWPCH4 GWPCH4 = Global Warming Potential of methane

- 21 IPCC

E F F = Fraction of methane in the SWDS gas (volume fraction)

- 0.5 IPCC

F DOCf Fraction of degradable organic carbon that can decompose

- 0.5 IPCC

G MCF Methane correction factor for year y

- 0.8 IPCC

H Wj,x Amount of solid waste type j disposed or prevented from disposal in the SWDS in the year x

Tonnes 0.35kg/capita/day for population > 2 Million0.27 Kg/Capita/day for population between 1 million and 2 million 0.25 Kg/capita/day for population less than 1 million (Waste generation /capita for different cities is taken according to the population level and then different types of waste is calculated based on waste characterisation of different cities)

India Infrastructure report, IDFC, 2006 cross checked with CPCB report

=



I DOCj Fraction of degradable organic carbon in the waste type j (weight fraction)

- Waste Type DOCj (%)

Wood and wood products

43

Pulp, paper and cardboard (other than sludge)

40

Food, food waste beverages and tobacco (other

15

IPCC

J Kj Decay rate for the waste type j (1 / yr)

Waste Type kj (%)MAT> 20oCMAP < 1000 mm

SlowlyDegrading

Pulp, paper and cardboard (otherthan sludge), textilesWood and wood products

0.03

RapidlyDegrading

Food, food waste, beverages andtobacco (other than sludge)

0.40

IPCC

K j Type of residual waste or types of waste in the MSW

India Infrastructure report, IDFC, 2006


Population (millions)

Paper % Rubber, leather %

Glass % Metal % Total compostbale matter %

Inert material %

0.1 to 0.5 2.91 0.78 0.56 0.33 44.57 43.590.5 to 1 2.95 0.73 0.56 0.32 40.04 48.38

1 to 2 4.71 0.71 0.46 0.49 38.95 44.732 to 5 3.18 0.48 0.48 0.59 56.67 40.07

5 and above 6.43 0.28 0.94 0.8 30.84 53.9

Calculation of methane emissions from domestic waste water The IPCC, 2006 guidelines to calculate emissions from waste water have been applied. Methane emissions from waste water are calculated as follows:

CH4 Emissions = CH4 emissions in inventory year, kg CH4/yrEFj = Bo * MCFj

TOW = P * BOD * 0.001 * I * 365


A TOW Total organics in wastewater in inventory year

kg BOD/yr 853,026,170 Calculated as Population*BOD (Biological oxygen demand)

P Population of Madhya pradesh

68,737,000 Calculated for 2008 based on CAGR from Census of India

BOD Biological oxygen deman g/person/day

34 Specific to M.P. sourced from “Inventorisation of Methane Emissions from Domestic & Key Industries Wastewater – Indian Network for Climate Change Assessment, Kartik”

B S organic component removed as sludge in inventory year

kg BOD/yr IPCC

C Ui Fraction of population in income group i in inventory year,

0.73:0.13:0.14 (Rural: Urban high : urban low)

M.P. Urban – rural fraction Census data



D Ti,j Ti,j degree of utilisation of treatment/discharge pathway or system, j, for each income groupfraction i in inventory year

Provided in the next table

Specific to M.P. sourced from “Inventorisation of Methane Emissions from Domestic & Key Industries Wastewater – Indian Network for Climate Change Assessment, Kartik”

E i income group: rural, urban high income and urban low income

Rural, Urban high, Urban low

IPCC

F J each treatment/discharge pathway or system

IPCC

G EFj emission factor, kg CH4 / kg BOD

Calculated as B0*MCF

B0 emission factor, kg CH4 / kg BOD

0.6 IPCC

MCF methane correction factor (fraction)

MCFSept ic Tank

0.5

Latrine 0.1Sewer 0.5Other 0.1None 0

IPCC

H R amount of CH4 recovered in inventory year, kg CH4/yr

- INCCA

Rural

Septic Tank Latrine Sewer Other None

Degree of utilisation of treatment or discharge pathway or method for each income group. (Tij)

0.02 0.01 0.02 0.09 0.86

Urban high



0.05 0.06 0.86 0.03

Urban low



0.09 0.03 0.71 0.17


Calculation of methane emissions from industrial waste water IPCC 2006 methodology is used to calculate the methane emissions from industrial waste water. The data used is specific to M.P. and emission factors used are country specific and sourced from NATCOM 2 and Inventorisation of Methane Emissions from Domestic & Key Industries Wastewater – Indian Network for Climate Change Assessment, Kartik.

Ti =Ti = CH4 emissions in inventory year, kg CH4/yr

TOWi = total organically degradable material in wastewater from industry I in inventory year, kg COD/yr. calculated as Production*Volume of waste water generated*COD(Chemical Oxygen demand)i = industrial sector. Dairy and Pulp and Paper sector is considered for M.P., since they are the only major industries with high potential of waste water emissionsSi = organic component removed as sludge in inventory year, kg COD/yrEFi = emission factor for industry i, kg CH4/kg COD for treatment/discharge pathway or system(s) used in inventory year Ri = amount of CH4 recovered in inventory year, kg CH4/yr

Emissions from Industrial waste water from dairy industryParameter Value Unit Source

Dairy production (Milk in 2008)

6,642.75 Thousand tones M.P. 12th five year plan 2012-2017 ((calculated as ¼ of 2008-09 value + ¾ of 2007-08))

Waste water generated (W)

3 m3/t Inventorisation of Methane Emissions from Domestic & Key Industries Wastewater – Indian Network for Climate Change Assessment, Kartik

COD 2.24 Kg COD/m3 Inventorisation of Methane Emissions from Domestic & Key Industries Wastewater – Indian Network for Climate Change Assessment, Kartik

TOW 44,639,280 Kg COD/yr Calculated as per above formulaSi 15,623,748 Kg COD/yr 35% (0.35)-default factor – NATCOM 2, INCCAEF 0.2 NATCOM 2Ri (Methane recovery)

75% NATCOM 2, INCCA

Total CO2 eq. Emissions

30,466.30 tCO2e Calculated as Ti*21 (GWP of methane) Ti is calculated as per formula above



Emissions from Industrial waste water from Pulp and Paper industryParameter Value Unit Source

Paper production (P) 160,679 Tonnes paper production of each paper mill sourced from IPPTA (Indian Pulp and Paper Technical Association)

Waste water generated (W) (m3/t)

230 m3/t Inventorisation of Methane Emissions from Domestic & Key Industries Wastewater – Indian Network for Climate Change Assessment, Kartik

COD KG COD/M3 5.9 Kg COD/m3 Inventorisation of Methane Emissions from Domestic & Key Industries Wastewater – Indian Network for Climate Change Assessment, Kartik

TOW (Kg COD/yr) 218,041,403 Kg COD/yr Calculated as per above formulaSi 76,314,491.05 Kg COD/yr 35% (0.35) – default factor –

NATCOM 2, INCCAEF 0.2 NATCOM 2Ri (Methane recovery) 90% Stakeholder consultationTotal CO2 eq. Emissions 59,525.30 tCO2e Calculated as Ti*21 (GWP of

methane) Ti is calculated as per formula above

Total Emissions from Industrial Waste water (tCO2e)

89,991.61 tCO2e

Black Carbon CalculationsThe Emission factors for black carbon (BC) are sourced from Reddy, M. and C. Venkataraman. 2002a. Inventory of aerosol and sulphur dioxide emissions from India I: Fossil fuel combustion. Atmospheric Environment. Same source is referred by US EPA as well in their studies.

Black Carbon emission factors for Coal used in Power generationBC (g/Kg)

Coal used in power generating utilities 0.07Coal in Industrial boilers, captive power generation 0.00

Black Carbon emission factors for petroleum products used in IndustryBC (g/Kg)

Fuel oil 0.01Diesel oil 0.08Diesel oil (IC engines) 0.17


Black Carbon emission factors in Residential/commercial sectorBC (g/Kg)

LPG 0.01Kerosene 0.16

Black Carbon emission factors for Road TransportAvg. PM(g /kg) Avg. BC (% of PM) Avg. BC (g/kg) (calculated)

Diesel/heavy-duty vehicles 4.62 42 2.19Diesel/light-duty vehicles 3.63 67Unleaded petrol (withoutcatalyticconverters)

0.36 23 0.08

Calculation of Black Carbon in MP from Power, Industry, Transport and Residential sectorThe quantities of various fuels consume din power, industry transport and residential sectors have been taken from various government publications (the same sources have been applied as that in the GHG inventory for respective sectors).

Quantity (Million tons) Black Carbon emission factor(Kg/ton)

Black Carbon emitted (tons) (Calculated as product of 2nd and 3rd

column)

Coal in power sector 23.46 0.07 1,806.42Coal in Industries 5.86 0.00 55.69Diesel used in DG (IC engines) in industry

0.10 0.17 18.19

Diesel oil (LDO) 0.01 0.08 1.28FO 0.17 0.01 1.79LPG in cooking 0.46 0.01 4.68Kerosene in cooking 0.49 0.16 78.56Diesel in Transport 1.57 2.19 3,455.82Petrol in transport 0.45 0.08 36.00Total tons of black carbon emitted 5,458.44


Annexure II – GHG Emissions Forecasting 2030 (equations, data and sources)

The following annexure would present detailed methodology used for forecasting the GHG emissions for MP i.e. the equations used along with the data, their sources; emission factors and their sources.

Power Sector ForecastsThe following capacity addition plan99 (MW) for power plants in MP has been utilised in the forecast:

2012 2013 2014 2015 2016 2017 2018 2019 2020

MP Genco and JV 1,100 600 520 1,708NTPC 251 172 78 256 257 1,104 1,520 1,374 27Nuclear: NPC 125 125GoMP JV (NHDC) 330 330DVC 200 100CSS Hydel 137 50Maheshwar Hydel 320 80Ultra Mega power plants (UMPP) 990 495 100 400 770 425Case 1 and Case II100 150 1,460 1,469

Total Independent Power Producers(IPPs)

161 226 578 1,797 1,255 514 -

Concessional Energy 150 150 150 150 150Renewable Energy 60 60 60 60 60 60Total Capacity Addition 1,082 1,678 2,246 3,265 3,530 4,193 4,424 2,009 237Thermal Capacity Addition 762 1,598 2,246 3,068 3,320 3,808 4,089 1,799 27

The planned capacity addition has been moderated based on historical data on actual installation vis-à-vis planned capacity addition in Madhya Pradesh:

MP-Planned Capacity Addition-11th five year plan MW 1710

Actual Capacity Addition-11th five year plan MW 710Percentage Realisation % 42%

Based on the above percentage realisation rate, the capacity addition plan has been moderated and the capacity addition has been projected till 2030.

The final forecasts of power generation capacity (MW) are as follows:Year Ending 2015 2020 2025 2030

Cumulative Thermal Power Capacity – MW 9,378 14,794 20,985 26,553UMPP-Thermal Power Capacity-MW 616 1,320 2,233 3,274Conventional Thermal Power Capacity – MW 8,762 13,474 18,752 23,279

99 Source: Energy Department, GoMP100 Case 1 is an open bid where the developer / entrepreneur has to decide for fuel and location and compete against any other developers in general. The project developer can bid on the basis of: Any fuel, Any Location, Any technology. In Case 2 bids, the bidder bids for specific fuel, specific location; where the specifics are provided by the Central/State government which is calling for bids.


The net generation is calculated from the forecasted capacity addition as follows:

NGt = Capacityt*365*24*PLF*90%

Where,NGt = Net generation in year t;Capacity = Capacity in year t; 90% = percentage of operation hours in a yearPLF = average expected plant load factor;

Expected plant load factor is assumed to be in line with the highest PLF of the power plants currently installed in MP.

Industry Sector Forecasts

Emissions due to fossil fuel consumptionLinear growth projections are applied for each type of fossil fuel used in the industry sector. Further cross-checks are carried out with projected energy demand for the cement sector, the most energy –intensive industry sub-sector in the state (accounting for around 50% of the coal consumption). The calculation of coal consumption for the cement sector is carried out separately as well, since it is used to calculate the abatement options and potential as well.

The following historical data is applied in the analysis:Fuel consumption in Industry sector in MP (thousand tons)

Year Coal Diesel Naphtha LDO FO

2005-06 5,007 200 41 20 3782006-07 5,752 136 65 17 2972007-08 5,730 98 57 19 2262008-09 5,908 110 77 15 164

Whereas coal and naphtha consumption have been following a trend of increasing consumption, the reverse is true for diesel and furnace oil. FO, LDO and diesel consumption is reduced to zero in later years as per the projections because of this decreasing trend. Projections for coal and naphtha consumption follow a linear trend and are forecasted as follows:

Year Coal Consumption (thousand tons) Naphtha (thousand tons)

2015 7,878 1452020 9,219 1952025 10,559 2452030 11,900 295



Cement sector forecastsCement production and energy consumption in the cement sector is projected using the following assumptions:

Parameter Value Units Source

Cement grade limestone resources in MP

3,934 Million Tons Report of the Working Group on Cement Industry for XII Five Year Plan, Section 4.4.2.5

Report of the Working Group on Cement Industry for XII Five Year Plan, Section 4.4.2.5

60 Million Tons Limestone / Million Ton of Cement Capacity

CII Low Carbon Roadmap for Cement Industry

Total Capacity expected in MP with existing reserves

65.6 Million Tons / Annum

Calculated from limestone resources and limestone requirements per ton of cement capacity

Expected capacity utilisation by 2030

80% - Stakeholder consultation

Average Specific Electric Energy Consumption

82.7 kWh/Ton cement CII Low Carbon Roadmap for Cement Industry

Average Specific Thermal Energy Consumption

742.9 kCal/kg clinker CII Low Carbon Roadmap for Cement Industry

Calorific value of coal 5,000.0 kCal/kg Ministy of Petroleum and Natural GasNet heat rate for coal based power generation (up to 67.5 MW)

3,125.0 kCal/kWh CEA CO2 Baseline Database Version 7

Thermal Subsitution Rate with Alternative Fuels-Indian Cement Industry

0.75% % Report of the Working Group on Cement Industry for XII Five Year Plan, Section 4.7.2.2

Thermal Substituion Rate with Alternative Fuels-Average for World Cement Industry

12.5% % Report of the Working Group on Cement Industry for XII Five Year Plan, Section 4.7.2.2

Emission Factor-Coal 95.81 tCO2/TJ India Second National Communicaiton to UNFCCC

The use of logistic and Gompertz models has been documented for industry sectors with a limited availability of resource / raw material (eg: oil production101. For the cement industry in Madhya Pradesh there is a constraint in the availability of limestone of 3934 million tons102. Cement production in Madhya Pradesh has been estimated using a Gompertz model:

Qc,t= aebe ct

Qc,t = Quantity of cement produced at time t;a = constant which sets the upper limit (asymptote)b = constant which sets the y displacementc = sets the growth rate (y scaling)

101 Reference: http://www.tandfonline.com/doi/abs/10.1080/15567249.2011.603021102 Reference: Report of the Working Group on Cement Industry for XII Five Year Plan, Section 4.4.2.5


The upper limit for cement production has been applied as 65.6 million tons / annum based on the limestone reserves in Madhya Pradesh and typical limestone requirements for cement production. It is estimated that around 80% of the cement production capacity would be achieved by 2030.

The percentage of clinker production in cement (74%) is taken to be constant based on the current mix of OPC and PPC in the state103. Process emission from cement production is estimated as follows:

Ec,t= Qc,t×74%×EFc

Where,Ec,t = Process emissions from cement productionEFc = Emission factor (0.537 tCO2/ton clinker)104

Forecasted cement production and process emissions due to cement production are:

Cement Production Clinker Production Process Emissions

Unit Million Tons Million Tons Million tCO2

2009 20.02 14.82 7.962015 31.51 23.33 12.532020 40.11 29.70 15.952025 47.12 34.89 18.732030 52.45 38.84 20.86

Projections of coal consumption are carried out using, specific fuel consumption and specific electricity consumption. It is assumed that 68% of the electricity requirements of cement industries would continue to be met using captive power plants, as is the present case, and the remainder using electricity from the grid. Further, it is assumed that the present utilisation of alternative fuels (0.75%) in kilns would gradually be increased to that of current global levels (12.5%) by 2030.

Specific Fuel Consumption

Coal Consumption in Kilns

Coal consumption for captive power

Percentage utilisation of alternative fuels

Reduction in Coal Consumption due to utilisation of AF

Net Coal Consumption

Unit kCal/kg clinker

Million kg (thousand tons)


% Million kg (thousand tons)


2009 742.9 2,202.50 701 0.75% 17 2,8872015 725.2 3,384.52 1,078 4.11% 139 4,3232020 710.9 4,222.78 1,344 6.90% 292 5,2762025 696.8 4,861.46 1,548 9.70% 472 5,9382030 682.9 5,304.51 1,689 12.50% 663 6,330

As explained above, the coal consumption forecasts are carried out separately only for cement sector since it is the most energy intensive sector in MP and the calculations are used to calculate the abatement potential as well. The total coal consumption forecasts for the whole industry sector are carried out which includes all the industrial sectors separately above. 103 This percentage is calculated from the actual production figures of each cement manufacturing company in India, and the data is sourced from the individual websites of the companies.104 Reference: India: Second National Communication to UNFCCC, 2012



Transport Sector ForecastsHistorical growth in the various categories of road transport is given below.Trends of the different modes of the road transport

Total Registered Motor Vehicles (Category-wise) Transport

31st March, 2005

31st March, 2006

31st March, 2007

31st March, 2008

31st March, 2009

% CAGR (2005-2009)

Multi-axled/Articulated vehicles/Trucks and lorries

81,436 83,293 88,755 94,661 99,242 4

Light motor vehicles (goods)

34,377 39,943 46,754 55,057 62,984 13

Buses 25,990 27,997 29,177 30,516 31,520 4Taxis 67,570 72,760 77,723 85,295 94,199 7Light motor vehicles (Passengers)

47,976 51,049 54,561 57,395 60,751 5

Total Transport 257,349 275,042 296,970 322,924 348,696 6

Two Wheelers 3,176,549 3,526,416 3,895,557 4,292,649 4,691,218 8Cars 166,393 185,700 208,052 237,022 272,009 10Jeeps 37,704 38,291 37,449 38,181 39,652 1Tractors 355,625 376,771 394,356 411,424 432,618 4Trailers 180,981 192,742 200,719 206,640 210,903 3Others 13,372 13,665 13,990 14,618 15,595 3Total Non transport 3,930,624 4,333,585 4,750,123 5,200,534 5,661,995 8Grand total (Transport + Non Transport)

4,187,973 4,608,627 5,047,093 5,523,458 6,010,691 7

Vehicle growth on road for 2005–2009 was 8% p.a. for goods vehicles and 10% for cars against GSDP growth of 6%. For projecting the on-road vehicles, a trend analysis of the vehicle growth is performed following the ADB study on the Madhya Pradesh Transport sector105. The growth rate of different types of vehicles based on the gross state domestic product (GSDP) growth, population growth and transport GDP growth. The final growth projection is given in the table below.

Growth Rate of Road Vehicles (%)

Car, Jeeps, and Vans

Two-wheelers

Three Wheelers

Buses Goods Vehicles

Tractors and trailers

2009–2015 11 11 6 8 9.3 52015–2020 10 8 4.5 6.5 8.8 42020–2025 8 5 3 5 7 32025–2030 5 4 2 3 5.4 2

105 In this study, the methodology adopted by the ADB report on “Madhya Pradesh State Roads Project III, Economic analysis study” is followed.


Further, projected vehicle numbers have been moderated to account of the policy for phasing out of passenger buses. These projection on vehicles is used in the regression model for projecting the fuel consumption in transport sector and is also used in calculating the abatement potential for the low carbon growth of MP.

Diesel and petrol consumption have been projected by carrying out a regression analysis based on historical data on diesel, petrol, and number of vehicles. Following historical data of fuel consumption in transport sector is calculated based on the following formula for the previous five years:

Fuel in Transport = Total fuel consumption in the state(all sectors) – Diesel consumed in industries – Diesel in agriculture

As explained in the report, fuel consumed in DGs in buildings and power sector is insignificant in MP.

The historical data for fuel consumption in MP transport sector used in the projections is:Year Diesel (TJ) Petrol (TJ)

2004-05 44,199 14,7762005-06 39,929 14,8222006-07 48,172 15,7552007-08 62,197 18,3182008-09 74,345 21,907

The projections of fuel consumption and number of vehicles as per the regression model are as follows:2015 2020 2025 2030

Petrol (TJ) 44,619 66,539 86,114 105,408Diesel (TJ) 152,177 225,276 300,764 364,388Number of vehicles 11,921,701 225,276 22,262,981 27,037,003

Building Sector ForecastsFor the building sector, projections of energy demand due lighting, cooking, and appliances have been carried out using end-use models as explained below. Although electrical energy consumption has been projected (usage of appliances and electrical lighting), only emissions due to direct fuel consumption is accounted under the building sector. Emissions due to electricity generation are accounted under the power sector. However, electrical energy consumption in buildings has been projected for determination of abatement potential through demand side management in buildings.

Energy demand and emissions from lighting in the building sectorThe following equation106 gives the aggregate energy consumption from lighting:

Where,El,t = energy demand from lighting Pr,t = Rural population in the year tFr,t = Number of persons per rural household in the year t.

106 We followed the methodology given by Stephane et al. in “Residential and Transport Energy Use in India: Past Trend and Future Outlook”, 2009



Er,t = Rural household electrification rate in the year t.Pu,t = Urban population in the year tFu,t = Number of persons per urban household in the year t.Eu,t = Urban household electrification rate in the year t.i = Type of lighting bulb (incandescent bulbs and tube lights, fluorescent),Li,t = Number of lighting bulb of type i per household in the year t.Pi,t = Power of lights/bulbs of type i in the year t.Hi,t = Hours of use of bulb of type i in the year t.k = Fuel typeLk,t = Lighting energy use of fuel k per capita in the year t.Emission from lighting is calculated as follows:

Emelec,t=Eelec,t×EFelecEmkerosene,t=Ekerosene,t×EFkerosene

Where,Emelec,t = Emission from electricity;Emkerosene,t=Emission from kerosene;Eelec,t = Final energy demand from electricity used for lighting;Ekerosene,t = Final energy demand from kerosene used for lighting;EFelec = Emission factor for electricity;EFkerosene = Emission factor for kerosene.

Energy Demand and emissions from cooking in the residential sectorThe equation below gives the linear regression model107 used for projection purpose of the cooking energy on the population growth rate and change in the income level.

Ec,t = (Pr,t×PEr )+(Pu,t×PEu )

Ec,t = Energy consumption from cooking;Pi,t = Population for the ith locale in the year t (i= rural, urban);PEi = Per capita energy requirement for ith locale (i= rural, urban).

The emissions due to cooking have been projected for the building sector given the projected mix of LPG and kerosene. The figures below shows historical changes in the percentage of households (both rural and urban) with primary source of energy used for cooking for 2004-05108. As illustrated below, firewood and chips are the major fuel used for cooking purposes across the income class for rural as well as the urban sectors. However, as the Monthly Per Capita Expenditure (MPCE) improves, there is a shift towards usage of LPG for cooking purposes.

107 We followed the methodology of “National Energy Map of India, Technology Vision 2030” TERI.108 Energy Sources of Indian Households for Cooking and Lighting , 2004-05, NSS 61st Round


Therefore, the usage of kerosene and LPG is projected to increase based on MPCE.

Emi,t=Ei,t×EFi

Where,

Emi,t = Emission from ith fuel (i=LPG, kerosene);Ekerosene,t = Final energy demand from ith fuel (i=LPG, kerosene);EFkerosene = Emission factor ith fuel (i=LPG, kerosene).

Energy demand and emissions from appliances in residential and commercial buildingsThe diffusion of appliances in electrified households has been estimated using the following equation109 .

Diffi,j,t=Elecj,t*αj*exp(γj*exp(βj*MPCEj,t ))Where,

Diffi,j,t = Diffusion of appliance i in the year t for jth local.Elecj,t = Electrification of households110 in the year t for jth local.MPCE(j,t) = Average monthly per capita household expenditure in the year t. for jth localαj,βj,γj = Parameters estimated111 for jth local.i = type of appliancej = rural (r), urban (u) The equation below gives the end-use model used for the projection of energy demand from appliances.

Where,Ea,t = Energy consumption from appliances

Per 1000 break-up of households in each MPCE class by primary source of energy for

cooking-Rural

Per 1000 break-up of households in each MPCE class by primary source of energy for

cooking-Urban

109 Stephane et al. (2009)110 Based on the past trend on electrified households in rural India (Census Reports), we estimated the growth in rural household electrification.111 In this study we used the estimated parameters given in Stephane et al. (2009). α is normalized at 1 and β, γ are estimated for each of the appliances which are used in rural India.



Pj,t = Population for the jth local in the year tFj,t = Number of persons per household for the jth local in the year t.i = Type of appliance (fan, television, water heating, washing machine, air conditioner, air cooler, etc.)Diffi,j,t = Diffusion rate of appliances for the jth local in the year t.UECi,j,t = Unit energy consumption for ith appliance for the jth local in the year t.

UECs are a function of the efficiency and the capacity of the appliance used as well as the level of use112 . In the projection exercise, the UEC for air cooler, washing machine, fan, televisions are kept constant at the 2011 level. For air conditioners, we assumed the same pattern in the change of energy as in Hong Kong 113. UEC from Refrigerator is expected to grow at an AAGR of 0.7% till 2030114 . Water heaters are expected to grow at 2.8% CAGR115 .

Various values of UEC of appliances used:Refrigerator 494Air Conditioner 2,160Air Cooler 298Washing Machine 190Fan 145Television 150Water Heater 607

Agriculture Sector Forecasts

Agricultural Pump-setsEnergy consumption in agricultural pump-sets has been projected using a combination of end use modeling and econometric modeling. The final energy consumption is given by the following equations116:

Eagri_diesel,t=[ILt*Pdiesel,t*IPdiesel,t ]Eagri_elec,t=[ILt*Pelec,t*IPelec,t ]

Where,Eagri_diesel,t= energy consumption in diesel based pump-sets in the year t ILt = irrigated land area in the year tPdiesel,t = number of diesel based pumps per area of irrigated arable land in year t IPdiesel,t = average energy use (diesel) per pump-set in the year t Eagri_elec,t = energy consumption in electricity based pump-sets in the year tIPelec,t = average energy use (electricity) per pump-set in the year t

Projection of Irrigated Land AreaIrrigated land area (ILt) or Gross irrigated area (GIA) is determined on the basis of incremental gross cropped area (GCA), which depends in cropping intensities and net cropped area. The projection of gross irrigated area is carried out by applying the following steps117:

112 Stephane et al.’s “Residential and Transport Energy Use in India: Past Trend and Future Outlook”, 2009.113 J.C. Lam (2000). This assumption is taken from Stephane et al.’s “Residential and Transport Energy Use in India: Past Trend and Future Outlook”, 2009.114 This assumption is taken from Stephane et al.’s “Residential and Transport Energy Use in India: Past Trend and Future Outlook”, 2009.115 Stephane et al.’s “Residential and Transport Energy Use in India: Past Trend and Future Outlook”, 2009.116 The methodology applied is from “India Energy Outlook: End Use Demand in India to 2020,” by Stephane de la Rue du Can, Michael McNeil, and Jayant Sathaye117 The methodology followed to estimate the irrigated land area till 2030 is from National Energy Map for India, Technology Vision 2030, TERI.


1. It is assumed that the cropping intensities will increase initially until a saturation level is reached. Cropping Intensities are expected to follow a logistic curve118 :

CIt=CI0 exp (a+bt) 1+exp(a+bt)

Where,

CIt = Cropping intensity in the year t,CI0 = Limiting cropping intensity. a,b = Parameters estimated

2. Gross cropped area (GCA) is obtained as follows119 :

GCAt=NCAt*CItGCAt = Gross Cropped Area in year tNCAt = Net Cropped Area in the year t; NCA is assumed to be constant at 141 Mha

3. It is assumed that increase in gross cropped area is only possible by providing additional irrigation facilities and that the increase in gross irrigated area is directly to an increase in the gross irrigated area120. Therefore gross irrigated area is given by :

GIA_t=GCA_(t-1)+∆GIA_(t,t-1)∆GIA_(t,t-1)=GCA_t-GCA_(t-1)

Projection of Number of Pumps-Sets and Energy Use per Pump-SetIt is expected that the share of electric pumps will increase over time due to increased electrification. However, the required number of pumps used per hectare will reach a saturation level based on the area under irrigation121. Average energy used by the electric pumps is calculated to be 15.98 GJ/unit122. It has been projected that as the area under irrigation increases, average energy used per pump will also increase123.

For diesel based pump-sets, energy consumption per pump is estimated at 41.5 GJ/unit124. The average energy used has been projected to increase decrease over time with increasing rural electrification. Even with increased electrification, it is assumed that diesel based pump-sets will still be used as a backup during power cuts. However, the average energy use per pump will fall drastically as operating hours of diesel-based pump-sets will diminish.

GHG emissions from diesel based pump-sets are calculated as follows:

Emt=Ediesel,t×NCVdiesel×EFdieselWhere,Emt = Emissions due to consumption of diesel in agricultural pump-sets in year tEdiesel,t = Energy demand from diesel based pump-sets in year tNCVdiesel = Net calorific value of dieselEFdiesel = Emission factor for diesel

118 The limiting cropping intensity is taken to be 3 per year as per the assumptions given in the National Energy Map for India, Technology Vision 2030.119 Since the net cropped area is kept constant, the changes in gross cropped area are due to the changes in cropping intensities. Source: National Energy Map for India, Technology Vision 2030, TERI. 120 Source: National Energy Map for India, Technology Vision 2030, TERI. 121 The growth rates of pumps (both electric and diesel) follow a logistic pattern, since no. of pumps per hectare will reach a saturation level after a point of time and then remain constant (assuming increase in cropping intensities continues).122 Bureau of Energy Efficiency 123 Average energy used per pump is positively correlated with increase in area under irrigation. 124 Source: Calculated based on data on diesel based pumps from IWMI study on groundwater use



Emissions due to electricity consumption in agricultural pump-sets are not accounted for, as these emissions are already accounted under the power sector (emissions due to power generation).

Paddy cultivationA regression analysis is carried out for determination of area under paddy cultivation based on Agricultural GDP, yield and production.

Area under paddy cultivation=f(Agricultural GDP,Yield,Production) It has been assumed that Agricultural GSDP would increase at a rate of 2% per annum till 2030125 . Production has been projected to increase at a rate of 5% till 2030. Yield has been projected to increase based on time series analysis, as a function of historical changes in rice yield.

Further, for estimation of emission from paddy cultivation, gross irrigated area under paddy cultivation has been estimated based on historical data on irrigated area under paddy cultivation.

GIAt=f(GIAt-1,…,GIAt-k)

Emissions due to paddy cultivation are computed separated for area under paddy cultivation which falls under irrigated areas, and area under paddy cultivation in rain-fed areas.

LivestockEmissions due to livestock are projected using time series analysis, applying a linear model. Livestock population has been projected to grow at an overall CAGR of approximately 2% (around 1% for cattle,3 % for buffalo, and declining trends for other types of livestock). These growth rates have been cross-checked with projections of national populations of livestock published by the National Centre for Agricultural Economics and Policy Research (CAGR of approximately 2.5% from 2010 to 2020 for dairy producing livestock). Emissions due to livestock are estimated as follows:

Emission from Livestockk,t (tCO2)=(Lk,t×EFk )×21Where,Lk,t = Population of kth category livestock (k=cattle, buffalo) in year tEFk = Emission factor of kth livestock in tCH4/head/year

FertilizersThe empirical model for the fertilizer use is specified as follows:

Fn,t=α0+α1 HYVt+α2 CIt+α3 GIAper,t+α4 Pfert,t+α5 Pc,t+α6 creditt+errortWhere, Fn,t = fertilizer consumption ( n denotes Type-N fertilizer consumption in thousand tones and t denotes the year)HYVt = Percentage of area under HYV to gross cropped area GIAper,t = Percentage of gross irrigated area to gross cropped area CIt = Cropping intensity (%)Pfert,t = Prices of Type N fertilizer is represented by price of N through UreaPc,t = Output price is represented by procurement price of rice and wheat (main users of fertilizers) and weighted by the share of their production. creditt = Short term production credit per hectare of gross cropped area (Rs.)

125 In the Planning Commissions’ Data Book, projection for Agricultural GDP at the national level is estimated in 3 different scenarios, namely 2% growth, 4% growth and 8% growth. These scenarios are based on the assumptions of GDP growth of the country. The current downturn of the Indian economy is also affecting the overall growth of Madhya Pradesh, which is assumed to grow at 5.7% p.a. based on the past trends. Hence the agricultural GDP growth is assumed to be 2% in the BAU scenario.


The estimated regression equation fertilizer consumption for type N is given below.

Coefficient Standard error t-statistics

Constant 301.49 1262.06 0.24HYV -55.15 29.27 -1.88GIA 27.53 13.63 2.02CI 8.05 8.17 0.99P(c,t) 0.88 0.86 1.03P(fert,t) 0.12 0.12 0.95creditt 0.00 0.00 2.93Adjusted R2 0.96

The independent variables are estimated as follows:

Emission from Type N fertilizer is given by:

Eft=GCAt×ERtWhere,GCAt = Gross cropped area at time tERt = Emission rate at time t

ERt=1+ 0.0125 * Ft Where,Ft = Fertilizer Application rate, kg / hectare in year t

Forestry Sector ForecastsCarbon sinks due to the conservation of existing forest land and due to conversion of non-forest land to forest land (afforestation) have been projected.

As per the historical trend and MP forest department policy, the current forest cover of MP is projected to remain intact, and sinks would be created due to increase in the annual biomass growth. 1. Sinks due to forest land remaining forest land:As per the historical trend and MP forest department policy, the current forest cover of MP is projected to remain intact, and sinks would be created due to increase in the annual biomass growth.

ΔCG = A * GTOTAL * CF∆CG = annual increase in biomass carbon stocks due to biomass growth in land remaining in the same land-use category by vegetation type and climatic zone, tonnes C yr-1

A = area of land remaining in the same land-use category, ha



GTOTAL = mean annual biomass growth, tonnes d. m. ha-1 yr-1


2. Sinks due to conversion of non-forest land to forest land:As stated in MP 12th 5 year plan, forest cover can be increased at a rate of 1% in the 12th five year plan (2012-2017), which corresponds to an increase of 15540 ha every year. It is is assumed that the same addition would continue in every five year plan.ΔCG = A * GTOTAL * CF∆CG = annual increase in biomass carbon stocks due to biomass growth, tonnes C yr-1

A = area of land converted to forest land, ha GTOTAL = mean annual biomass growth, tonnes d. m. ha-1 yr-1


GTOTAL = GW * (1 + R)GW = Average annual above-ground biomass growth (tonnes dm/ha/yr)R = Ratio of below ground biomass to above-ground biomass (tonnes bg dm / tonne ag dm)


Types of forest lands Type of forest PercentageTropical dry 88.65%Tropical moist deciduous

8.97%


2.12%


0.26%


Plantation / TOF and tropical thorn forests are taken as part of tropical dry forests since the default factors are not available for these two categories in IPCC.

Average annual above-ground biomass growth (tonnes dm/ha/yr)




Ratio of below ground biomass to above-ground biomass (tonnes bg dm / tonne ag dm)

For tropical dry forest: 0.28 tonnes bg dm / tonne ag dmFor tropical moist deciduous forest: 0.24 tonnes bg dm / tonne ag dm


Carbon fraction of dry matter, tonne C (tonne d.m.)-1



Results of the projections are as below

Year Total forest cover (ha)

Sink due to existing forest land

Sinks due to conversion of non-forest land to forest land

Total Sink (tCO

2)Total Sink (million tCO2)

2008 7,770,000 26,381,998 - 26,381,998 26.382015 7,816,620 26,381,998 158,292 26,540,290 26.542020 7,894,320 26,381,998 422,112 26,804,110 26.802025 7,972,020 26,381,998 685,932 27,067,929 27.072030 8,049,720 26,381,998 949,752 27,331,749 27.33

Waste Sector Forecasts

Emissions Projections from Municipal Solid WasteEmission from solid waste are calculated using the equation as explained in the baseline GHG emissions annexure:

Ew,t = φ× (1-f) × GWPCH4 × (1 - OX)× 16/12 × F × DOCf × MCF × (∑yX =1 ∑j Wj,x × DOCj × (1-ej

-kj) ×e-kj (y-x) × 21

For details about the parameters in the emission calculation, please refer to baseline GHG emission annexure.

Total waste generation is calculated as follows:

SWt=PWt*Popt

Where,SWt = Total solid waste generated at the time t. PWt = Per capita waste generated at the time tPopt = Total population at the time t • Population projection for the Madhya Pradesh is available from the Census report till 2026. This data is

extrapolated till 2030 using the past trend.• Waste generation per capita for the major cities of Madhya Pradesh is available from India infrastructure

report. A weighted average of the waste generation per capita for the state as a whole is estimated based on the number of cities within a particular population range as presented below.

• 0.35kg/capita/day for population > 2 Million• 0.27 Kg/Capita/day for population between 1 million and 2 million • 0.25 Kg/capita/day for population less than 1 million • A weighted average of waste generation per capita for Madhya Pradesh for 2009 is calculated as 0.29155

Kg. • The increase in per capita solid waste generation due to urbanisation and change in lifestyle year on

year has been considered as 1.3% for MP which is assumed to be same as country and is sourced from Position Paper on The Solid Waste Management Sector in India, Department of Economic affairs, India.



Year Total Population (‘000) Waste Generation 1 Kg/Capita/day

2009 68,623 0.292015 76,745 0.312020 82,134 0.332025 86,879 0.352030 90,932 0.38

• Similarly, waste characteristics for Madhya Pradesh are estimated using a weighted average method. The waste characteristics are sourced from India Infrastructure Report, 2006, IDFC and the characteristics is not expected to change much in the future years looking at the developing status of the country.

Paper (%) Rubber, leather, synthetics (%)

Glass (%) Metal (%) Total compostable matter (%)

Inert material (%)

4.01 0.63 0.47 0.51 45.30 43.39

Based on the various decomposition rates of various types of waste generated in the equation of emission calculations, the emissions from solid waste have been projected till 2030.

Emissions Projections from Domestic Waste Water:

The methodology and parameters for emissions calculation from domestic waste water are explained in the annexure for baseline GHG emissions.

Population projection for the Madhya Pradesh is available from the Census report till 2026. We have extrapolated the data till 2030 using the past trend.• Similarly, the rural urban fraction is forecasted for MP till 2030. Based on the urban rural fraction change

the switching over of utilisation of different discharge pathways of sewage is also accounted for.• The BOD value of 34 g/person/day for MP is assumed to remain the same and is sourced from Kartik.

Inventorisation of Methane Emissions from Domestic & Key Industries Wastewater. Indian Network for Climate Change Assessment, Government of India.

• The methane correction factor and maximum methane producing potential have been considered from IPCC.

Year Total Population Rural fraction

Urban-fraction Urban-High fraction

Urban-Low fraction

2015 76,745 0.71 0.28 0.14 0.132020 82,134 0.71 0.28 0.14 0.132025 86,879 0.67 0.32 0.17 0.152030 90,932 0.60 0.39 0.20 0.18


Annexure III – GHG Abatement options, potential and costs(data and sources)

Power Sector

Lever Name Assumptions Sources

IGCC • Costs for 500-560 MW power plants are approximately 1910 to 1950 USD / kW

• Operational efficiency 42%.• Specific Emissions: 0.75 tCO2/MWh PLF:

85% (Similar to subcritical coal power plant)• In low carbon scenario, IGCC technology

can potentially displace around 10% of the coal based power generation capacity to be added in 2030 (20 GW-based on power sector projections for MP).

• Expected baseline specific emissions of 1.01 tCO2 / MWh – calculated

• UNFCCC Technology Transfer Database

• Exploring the use of Carbon Financing in Supercritical Technology for Power generation

Ultra-supercritical • Efficiency: 43%• Specific emissions: 10% lower compared to

sub-critical• PLF: 85% (Similar to subcritical coal power

plant)• In low carbon scenario, this technology

can potentially displace around 10% of the coal based power generation capacity to be added in 2030 (20 GW-based on power sector projections for MP).

• Expected baseline specific emissions of 1.01 tCO2 / MWh. – calculated

• “Energy Intensive Sectors of the Indian Economy” ESMAP, The World Bank

• “Exploring the use of carbon financing in supercritical technology for power generation” Mott MacDonald

Biomass based power plant

• Potential for biomass in MP: 1040 MW-as per MP TRIFAC

• 90% of the potential would be realised in low-carbon scenario

• BAU installation of biomass based power in 2030 is based on projections of share of various types of Renewable Energy projected by CEA

• Project cost: 4.5 crores INR / MW• Auxiliary consumption of 10%• Lifetime 20 years• Cost of fuel: 2100 INR / tone• PLF: 80% • Station heat rate: 3800 kcal/kWH• GCV: 3612 kcal / kg• O&M: 4% of captial cost for 1st year, and

annual escalation 5.72%• Tariff: average of 3.68 INR / KWh over 20

years

• MPERC Tariff Order dated March 2012 http://mperc.nic.in/020312-SMP-77-2011-BiomassMarch-12.pdf

• National Electricity Plan – CEA, January 2012


Solar PV • Percentage share of solar will increase by 34% compared to BAU (based on extrapolations on CEA National Electricity Plan).

• It is assumed that 50% of this share will be fulfilled by Solar PV and rest 50% by solar thermal

• In BAU solar power capacity would be approximately 16% of coal based power generation capacity.

• Therefore in BAU installed capacity in MP would be 4309 MW and an additional 1462 MW would be added in a low carbon scenario.

• As per the forecast coal based power generation capacity is approximately 26.5 GW in 2030 and specific emissions for coal are approximately 1.01 tCO2/MWh.

• Project cost: 10.25 crores/ MW• Project lifetime: 25 years• O&M costs: 0.5% for first year, annual

escalation of 5.72%• PLF: 19%, derated at 1% from third year

onwards• Auxiliary consumption: 0.25%• Tariff: 10.44 INR / kWh

• MPERC http://mperc.nic.in/010812-Tariff-Order-Solar-Energy.pdf

• National Electricity Plan – CEA, January 2012

Solar Thermal • The level of implementation in the low carbon scenario is considered the same as for solar PV.

• Project cost: 13.25 crores/ MW• Project life: 25 years O&M cost: 1% of

capital cost for first year of operation, 5.72% annual escalation

• Auxiliary: 10% of gross generation• PLF: 23%, derated at 0.5% from third year

onwards• Tariff: INR 12.65 INR / kWh

MPERC Tariff Order dated Aug 2012http://mperc.nic.in/010812-Tariff-Order-Solar-Energy.pdf



Industry Sector


WHR in cement kilns

• Up to 22 kWh / t clinker or up to 25% of the power consumption of a cement plant can be produced by using waste heat in exit gases from preheaters and clinker coolers.

• As per BAU forecast for 2030, it is estimated that around 38.8 million tons of clinker would be produced. Accordingly, at around 80% PLF, a WHR power generation capacity of 127 MW may be expected.

• An emission factor for coal based captive power generation of 1.19 tCO2 / MWh is applied (source: CEA CO2 baseline database version 7).

• It is expected that around 20% of the potential would be realised in BAU scenario and 100% in the low-carbon scenario.

Low-Carbon Technology for the Indian Cement Industry, WBSCD, IEA

Use of alternate fuels in cement kilns

• WBCSD has set a target of 30% utilisation of alternative fuels or thermal substitution rate (TSR). Certain plants in India have achieved a maximum of around 12% TSR, and 12.5% is the average global TSR, which has been considered as the baseline TSR by 2030 for the MP cement industry as a whole.

• In the low carbon scenario the target of 30% TSR would be met.

• The Industrial Energy Efficiency Database has specified an abatement potential of 1.48-1.88 tCO2 / ton of RDF for Indian plants, and a displacement of fossil fuels corresponding to 15-19 GJ per ton of RDF.

• Based on the CDM project of Vikram Cement for utilisation of RDF in cement kilns at Neemuch, MP: Cost of heat supply from Indian Coal is approximately INR 103 / GJ and for petcoke approximately INR 106 / GJ. Cost of heat supply from MSW based RDF is approximately INR 83/ GJ.

• Low-Carbon Technology for the Indian Cement Industry, WBSCD, IEA Industrial Energy Efficiency Technology Database (IETD): http://ietd.iipnetwork.org/content/refuse-derived-fuel-rdf-co-processing


Improving the electrical efficiency of cement production

• WBCSD has set a target for specific electricity consumption of 69.5 kWh / MT cement, for the Indian cement industry, based on the best available technologies today. This includes improvement of electrical efficiency in various processes, including raw mill, coal mill, pyro processing, and cement grinding.

• The BAU specific electricity consumption has been considered at 76 kWh / MT.

• The total cement production capacity expected in 2030 is 52.45 million tons per annum (forecasted figure).

• The specific emissions factor for coal based power generation (primarily source of power in industry) is 1.19 tCO2 / MWh (source: CEA CO2 baseline database version 07).

• An average lifetime of 10 years is considered for respective investments.

Low-Carbon Technology for the Indian Cement Industry, WBCSD, IEA

Use of de-carbonated raw materials in cement production

• For every 1% of substituted limestone, the CO2 reduction is 5.25 kg / tonne of clinker (or up to 8 kg / tonne of cement)

• Investment costs include the cost of storage and handling of additional raw materials. Based on ECRA, the costs are estimated at Euro 0-6 / tonne clinker.

• Operational costs including costs of raw material, fuel savings, savings of replaced raw material, and additional power is estimated at 0-4.2 Euros / tonne clinker.

Low-Carbon Technology for the Indian Cement Industry, WBSCD, IEAWBSCD / ECRA: http://www.wbcsdcement.org/pdf/technology/Technology%20papers.pdf



Transport Sector


Implementing Bus rapid transit systems with CNG based buses

• BRTS in Bhopal and Indore are considered to be in the baseline since they are already under implementation.

• Six additional cities are identified based on the population density and area of the city to have BRTS in the low carbon scenario and the lengths of BRT are calculated based on the Bhopal and Indore BRT. Additional length of 722 KM of BRT is calculated to be constructed in 2030.

• A capital cost of INR 12 crores/km is considered based on the costs estimated in the detailed project reports for Indore and Rajkot BRTS.

• The emission reductions and opex is calculated based on the data presented in the Indore CDM PDD submitted to UNFCCC.

UNFCCC CDM databaseDPRs of Indore, Rajkot

Metro rail projects

• Metro rail projects in Bhopal and Indore are considered to be in the baseline since they are already under implementation / planning.

• Six additional cities are identified who have highest population densities and criteria of more than 2 million population.

• The lengths of metro are calculated based on the Bhopal and Indore metro and corresponding population densities. Additional length of 267 KM of Metro is calculated to be constructed in 2030.

• Capital cost of 200 Crores/Km is considered for Metro based on Bhopal and Indore costs, Delhi metro costs, ADB evaluation study, UNEP and other literature review

• Opex is calculated based on the vehicular data provided in Indore BRT PDD and Mumbai Metro CDM PDD and Gurgaon Metro CDM PDD

UNFCCC CDM database

Electric 2 Wheelers

• The share of electric two wheelers in MP is taken as 15% in 2030. This is the target set for India for 2020 as per National Electric Mobility Plan (a delayed implementation is expected)

• Cost of electric vehicle: INR 43,000 (Hero electric, e-sprint model)

• Cost of petrol 2 wheeler: INR 33,000 (Mahindra Kine)• Requirement of Power for India if there are 4.8 Million

electric two wheelers-600 MW (NEMMP)• Fuel savings for India (Projected)due to electric 2

wheelers-1.4 Million Tonnes (NEMMP)• Fuel savings figures are apportioned for MP by considering

ratio of two wheelers in India and in MP which is about 5%.

National electric mobility mission plan 2020 (NEMMP)UNFCCC database


Buildings Sector


Energy efficient Air Conditioning (2 star to 5 star)

• All the AC’s forecasted would be replaced from 2 star to 5 star in 2030 low carbon scenario

• Savings in electricity Consumption/day (Kwh) assuming 8 hours of operation-1.61

• Months of operation in an year-5• Cost of samsung 2 star Purista split ac 1.5

Ton-INR 30400• Cost of samsung 5 star Purista split ac 1.5

Ton-INR 37300

• India Development Gateway website

• Bureau of energy efficiency labeling data

• Samsung product catalogue

Solar water heaters • 20% of electric geysers would be replaced by solar water heaters (100 Litres per day) in low carbon scenario in 2030

• Annual Savings (KWh)-1500.0• Project life (years)-15.0• Cost of solar water heater-17500.0• Cost of electric geyser (Most commonly used

25 Liter geyser)-9790.0

http://mnre.gov.in/file-manager/UserFiles/brief_swhs.pdf

Household Rooftop Solar PV (1 KW)

• Abatement potential is carried out considering that solar home lighting systems can be installed in an additional 6% of households in 2030 compared to BAU scenario (double of BAU scenario).

• In BAU about 6% of the homes will have solar home lighting as per MNRE strategic plan for New and renewable energy sector

• Cost of 1 KW solar PV (INR)-125000.0• Electricity Generation from Solar

PV(KWh)-1314.0 (considering 15% PLF)• Project Life time-25.0

http://mnre.gov.in/file-manager/UserFiles/rtpsvs_features.pdf

http://www.rooftopsolargujarat.com/gpcl_rsg/gandhinagar_solar_rooftop.html#solar_r

Incandescent lamps to LEDs

• Cost differential in LED and incandescent bulb(INR)-550.0

• Savings in electricity consumption (KWh/year)-107.0

• Lifetime of CFL (years)-12.0

• India Development gateway website

• CDM projects

Incandescent bulbs to CFLS

• 60 W incandescent bulbs are replaced with 15 W CFL

• Cost differential in CFL and incandescent bulb-INR 36.0

• Savings in electricity consumption-79.0 KWh/year

• Lifetime of CFL – 4 years

http://www.indg.in/rural-energy/technologies-under-rural-energy/energy-efficient-technologies/re-tech-energy



Agriculture Sector


Energy Efficient Pumps • In the low carbon scenario, it is expected that average energy efficiency would be equivalent to that of 5 –star rated pump-sets, whereas in the business as usual scenario, it would be equivalent to that of 3 star rated pump-sets.

• Around 4.8 million electric pumps are expected to be used for irrigation in MP by 2030(as per forecast in this study), which can be energy efficient.

• Grid emission factor is assumed to remain around 0.9 tCO2 / MWh.

• Cost and energy saving data is sourced from BEE, and pump lifetime is sourced from NABARD.

BEEhttp://www.iitk.ac.in/ime/anoops/for12/7%20-%20Mr.%20Sarabjot%20Singh%20Saini%20-%20BEE%20-%20%20AGDSM%20-%20IIT%20K.pdfNABARDhttp://www.nabard.org/modelbankprojects/mi_pumpsets.asp

Solar photovoltaic pumps

• It is expected that 10% of electric pumps in 2030 may be powered by solar PV instead of the grid. A sufficient shadow free area and adequate solar radiation are pre-requisites for the solar PV array to be installed, and a limited number of electric pumps may be suitably placed for this.

• Grid emission factor is expected to remain approximately 0.9 tCO2 / MWh.

• Solar PV pumps are expected to have an average of 1.8 kW solar PV capacity, and a plant load factor of 20%.

MNRE:http://www.mnre.gov.in/file-manager/offgrid-solar-schemes/aa-jnnsm-2012-13.pdfHWWI CDM Potential of Solar pumps in India:http://www.hwwi.org/uploads/tx_wilpubdb/HWWI_Research_Paper_4.pdf


Forestry Sector


Afforestation: Conversion of non-forest area / wasteland into forest area

• Afforestation in the BAU scenario is estimated based on historical data from 1997 to 2005 as per State Development report. Cross checks are also made by considering data on saplings planted, sourced from MP forest department. In the BAU scenario, the forest cover for MP is forecasted to reach to 26% of its geographical area.

• In the low carbon scenario, the forest cover in MP is assumed to be 33% of the total geographical area in 2030, in line with the national goals as per Planning Commission.

• Costs for afforestation are considered from Arunachal Pradesh State pollution control board report and MoEF data on grants for afforestation

• MP annual plan 2012• State development report

2009• State of forest report 2011

Reforestation: Restoring and recreating areas of forests that may have existed long ago but were deforested

• As per state development report, MP has 6% degraded land. In the low carbon scenario, it is expected that up to 50% of degraded land would be reforested.

• Afforestation costs are based on costs of various afforestation projects published on UNFFCC website.

• MP annual plan 2012• State development report

2009• State of forest report 2011• UNFCCC



Waste SectorLever Assumptions Source

Integrated Waste management plant (PPP model)-Collection, composting, RDF, bricks, recyclables

• Percentage of waste from MP treated in waste management facilities in 2030 in low carbon scenario-30%

• Waste collection efficiency-90%• The data for Opex, capex,

efficiency and reductions are sourced from DPR and PDD of waste management facility at Varanasi.

UNFCCC CDM Database

Landfill Gas recovery with electricity generation

• Percentage of waste sent to landfills with gas recovery in 2030 in low carbon scenario-30%

• Landfill gas collection efficiency – 60%

• Parameters for emission reduction and various costing are sourced from CDM projects in Mumbai and China

UNFCCC CDM Database

Aerobic bioreactor landfill • Percentage of waste sent to aerobic engineered landfills in 2030 in low carbon scenario-30%

• Parameters for emission reduction and various costing are sourced from CDM projects

UNFCCC CDM databasehttp://www.bioreactor.org/BioreactorFinalReport/FinalReportVOLUME1_10/AttachmentforVOLUME9/AerobicLandfillfinal.pdf

About the study

This study is supported by Shakti Sustainable Energy Foundation (Shakti). Ernst & Young LLP, India (EY) has provided services related to technical assistance in research, analysis and preparation of the report.

About Shakti Sustainable Energy Foundation

Shakti Sustainable Energy Foundation works to strengthen the energy security of the country by aiding the design and implementation of policies that encourage energy efficiency as well as renewable energy. Based on both energy savings and carbon mitigation potential, we focus on four broad sectors: Power, Transport, Energy Efficiency and Climate Policy. We act as a systems integrator, bringing together key stakeholders including

government, civil society and business in strategic ways, to enable clean energy policies in these sectors. Shakti is part of an association of technical and policy experts called the ClimateWorks Network. Being a member of this group helps us connect the policy space in India to the rich knowledge pool that resides within this network. For more information, please visit http://www.shaktifoundation.in/

Inclusive and Sustainable Growth for Madhya Pradesh

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