Structural change in Chinese economy: Impacts onenergy use and CO2 emissions in the period 2013–2030

J. Luukkanena,∗∗, J. Panula-Onttoa,∗, J. Vehmasa, Liu Liyongb, J. Kaivo-ojaa,L. Hayhaa, B. Auffermanna

aFinland Futures Research Centre, University of Turku, Yliopistonkatu 58 D, FI-33100Tampere, Finland

bGraduate School of Chinese Academy of Social Sciences, Fangshan District, 102488,Beijing, China

Abstract

This study investigates the effect of the change of economic structure on the

emissions and energy use in China. The study uses the ChinaLINDA model, a

calculation framework especially well suited for analysing structural change and

sectoral shifts in Chinese economy and industry. We illustrate the implications

of the different economic development paths up to year 2030 regarding emissions,

energy use and employment with a set of three scenarios.

In comparison with the reference scenario, the reduction in CO2 emission

level in policy scenario is less than 13 by 2030 even with huge investment in

renewable energy capacity and a clear turn away from carbon-intensive growth

pattern. Cumulatively the difference of emissions between the reference sce-

nario and policy scenario within 2013–2030 is less than 20%. The Chinese CO2

intensity and energy intensity targets are rather stringent. Reaching the CO2

intensity target is difficult even with the optimistic assumption of fast expan-

sion of renewable energy capacity and fast structural change. However, while

the renewable energy capacity grows fast, targets set for it are not equally ambi-

tious. The set CO2 intensity and energy intensity targets call for concretization

∗Corresponding author∗∗Secondary corresponding author

Email addresses: jyrki.luukkanen@utu.fi (J. Luukkanen ), juha.panula-ontto@utu.fi(J. Panula-Ontto ), jarmo.vehmas@utu.fi (J. Vehmas), liyyongliu@gmail.com (Liu Liyong),jari.kaivo-oja@utu.fi (J. Kaivo-oja), laura.hayha@utu.fi (L. Hayha),burkhard.auffermann@utu.fi (B. Auffermann)

Preprint submitted to Technological Forecasting and Social Change September 28, 2014

of energy policy.

Keywords: China, Structural change, Economic modeling, CO2 emissions,

Energy consumption

Acknowledgements

The authors acknowledge that this article is based on research work sup-

ported by the Academy of Finland under the project ”China and EU in the

context of global climate change–Analysis of changing economic structures and

related policies” (CHEC).

1. Introduction

1.1. Background

The Chinese economy is in a transitional state: Chinese leadership hopes

to steer the economic development away from exports and investment and to-

wards serving the Chinese domestic market, increasing the living standard of the

Chinese citizenry as well as reducing the environmental impact that the long-

enduring high economic growth has caused. In addition to the general vision

laid out in the 12th five year plan and related state planning documents, there

are several factors that also drive the change towards a more service-oriented

economy and higher-value-added industrial profile that will also be discussed in

this paper.

Among the national economies, China is the largest emitter of CO2 emissions

and the development of its economic system in the next 15 years will have great

weight in determining cumulative global emissions. An important question is

what kind of impact these structural changes will have on emissions and energy

use and how do these contrast with the emission-related targets set by the

Chinese leadership for the next decades.

2

1.2. Research task

This study investigates the effect of the change of economic structure on

the emissions and energy use in China. We present three scenarios that illus-

trate the different development paths for China’s economy up to year 2030 and

their implications regarding emissions, energy use and employment. The study

uses the ChinaLINDA model, a calculation framework especially well suited for

analysing structural change and sectoral shifts in Chinese economy and industry.

Scenarios can be useful in handling uncertainty. They also provide a possi-

bility for exploration of the future. In contrast to forecasts, that aim to play out

the consequences of most probable developments, a set of scenarios can illustrate

the possible developments even when they are not the most likely. The scenario

set in this study opens up the alternative developments for Chinese economy

with recent trends and political will directed to a more service-oriented economy

in mind.

We attempt to answer the following two questions:

1. How much can we expect the reorientation of the Chinese economy and

the policy initiatives of the Chinese leadership to lower the energy use and

emissions in the time period 2013–2030?

2. How important is the profile of the industry for the amount of the emis-

sions?

The Chinese targets concerning economic growth, emissions and energy ex-

pressed in planning documents such as the 12th five-year plan [18] and the 2011

white paper on climate change [14, Ch.VII] can already be seen as quite am-

bitious, and reaching these targets is by no means certain. The ChinaLINDA

approach can give us an idea of the potential of the proposed policy actions

and the likely bottlenecks and conflicts in reaching the targets. We present an

answer to the research questions in the form of three scenarios that illustrate

what can be achieved with the successful implementation of the stated policies

concerning structural change and how much the industrial structure influences

the energy use and emissions.

3

This study does not include calculations of the cost implications of different

scenarios, even though extremely relevant for policy making, they are, however,

outside the scope of this paper. Furthermore, the exploration of possible fu-

tures concerning economic structure could be expanded in future research with

more variation to key drivers of economic development, as this study focuses on

exploration of a similar economic activity level with different sectoral emphasis

configurations in the Chinese economy.

1.3. Originality

There are several papers presenting scenarios about emissions and energy

use in China (see for example [36], [38], [30], [10], [12]). However, these studies

differ from our study both in methodology and aims. Some studies ([10],[12]) use

input-output approach for scenario creation. The strength of the ChinaLINDA

approach in comparison to these studies is that it allows for taking technological

change into account clearly better. Input-output approaches usually assume a

constant technology factor, which is an unrealistic assumption when examining

energy systems with a time span of twenty to thirty years. Van Vuuren et al.[36]

present a set of long-range energy and emission scenarios, but the parameters

vary on a general, highly aggregated level and do not focus on the effect of

economic structure on emissions. In comparison, our study takes the different

energy use and emission impacts of economic activity in different sectors clearly

better into account. The scenarios presented by Wang and Watson [38] look

into the impact of economic structure on the economy among other things, but

it is not the primary focus of the scenarios and the impact of economic structure

cannot be isolated and analysed as many other things beside economic structure

and industrial profile vary in the scenarios. Our scenario study focuses specif-

ically on answering the research questions about the impact of the economic

structure and in the scenarios we present only a select number of variables vary

outside economic structure.

4

2. Drivers of structural change in China

Steenhof and Fulton have presented a high-level framework[31] of the drivers

of the future development of the Chinese electricity sector. Figure 1 presents

this framework. The identification of drivers of structural change in our study

is based mostly on this framework.

Decomposition analysis has also been used for evaluating the contribution

of different driving forces on China’s carbon emissions and energy use. Two

fairly recent examples of this include studies by Kaivo-oja et al.(2014) [17] and

Minx (2011) [20]. These studies underline the contribution of energy efficiency

improvements in lowering the emissions and the opposite contribution of growing

affluence in growth of the emissions.

Development of nuclear power

base

Building of natural gas

infrastructure

Electricity demand

Energy intensity

Economic growth

State development

initiatives

Ren

ewab

le energy

law

Emissio

n lim

its

State environmental

goals

Fuel m

arkets

Techn

olo

gical ch

ange

Changes in • Industrial

structure • Energy

efficiency • Urbanization • Per capita

income

Emissions

Electricity

Fuels

Decision-making in the supply

sector

Domestic to global

environmental and economic

impacts

Figure 1: Conceptual framework of the drivers and processes effecting the future development

of China’s electricity sector [31]

2.1. Goals and policies

Hopes are expressed in the 12th five-year plan that China’s economy should

be transformed from an investment- and export-driven economy to an economy

5

driven mostly by domestic consumption. This is hoped to help in reducing

the unequality of income, decrease imprudent fixed asset investment and lessen

China’s reliance on exports, making it less vulnerable to global trends. Smaller

reliance on exporting should decrease the trade surplus and reduce the need to

artificially devalue Yuan. As the construction sector and China’s current export

industries are very energy- and emission-intensive, the policies aimed at shifting

the focus from investment and exports to consumption are expected to lower

the emissions and energy use.

China has not committed to absolute reductions to its emissions. Its po-

sition, until 2007, has been that developing economies should not be expected

to commit to emission reductions. This position has since been changed. Be-

fore the climate conference of 2009 in Copenhagen China’s premier Wen Jiabao

stated that China would reduce its carbon dioxide emissions per unit of GDP

by 40% – 45% by 2020 compared to the 2005 level. While some commenters [27]

see this target as unambitious to the point of being almost meaningless, even

this target should mean a clear shift away from high-polluting industries such

as steel, cement and aluminum industries.

China has set a target for renewable energy in terms of share of consumption:

by 2020, 15% of all energy should come from renewable sources [30] [32]. Cur-

rently, about 8% of China’s total primary energy come from non-fossil sources.

By 2020, installed capacity should reach 420 GW for hydropower [6], 200 GW

for wind power (with 170 GW onshore and 30 offshore) [6], 100 GW for solar

power [11] and 30 GW for biomass [6]. Vehicle fuels should also have at least

15% renewable energy content by 2020 . [32] The target for nuclear capacity is

86 GW by 2020 [30]. With the currently planned additional reactors, capacity

in 2020 will increase to at least 58 GW.

2.2. Economic growth and economic reform

The last five-year plans have set GDP growth targets which, perhaps sur-

prisingly, are not lower bounds in their nature but rather exact targets that, at

least in theory, should not be exceeded. The 11th five year plan set the growth

6

target at 7.5%, while the actual growth turned out to be about 11% [18]. The

12th five-year plan sets the growth target to 7%, which may be exceeded again.

It looks, however, that the GDP growth might be slowing down slightly. The

component of fixed investment in Chinese GDP remains quite high and the on-

going urbanization process partially explains the abnormally high GDP growth

rates.

The general trend of reforms in the Chinese economy has been an increas-

ing market-economy orientation from 1980 onwards, but some observers argue

that the reforms have stopped and even reversed since 2005 [26]. The policy of

the the state forcing mergers and consolidation in many fields and on the other

hand actively grooming certain big corporations for becoming future ”national

champions” as expressed in the 12th five-year plan [18] can be seen as an indi-

cation of a partial reversal of market-oriented reforms. This is likely to slow the

economic growth and along that, the energy use.

2.3. Demographic change

China is in the final stages of demographic transition, with the most sig-

nificant mortality declines having taken place in the period 1950–1975 and the

fertility level declined from 2.74 in 1979 to well below the replacement level of

2.1 to about 1.6 in 2010[39]. The government-enforced one-child policy has been

central to making the Chinese demographic transition exceptionally fast [13],

but also causing problems such as sex imbalance and a looming dependency

ratio crisis in the future [37], causing experts to recommend hasty modifications

to the policy [23].

The size of Chinese work force is peaking in the next few years and generally

the population aging is fast in comparison to the US and even to Europe[33].

The rapid increase of the number of elderly is a major challenge for the under-

developed pension system [37]. The workforce will still remain big in compar-

ison to EU-27 and USA, but China’s advantage of being easily able to supply

masses of low-paid laborers for simple manufacturing jobs is coming to a close.

China remains a demographic giant well into the future, but its relative share of

7

the global population will shrink considerably over the next decades. In 2030,

China is estimated to be home to less than 17% of world’s population [33]. In

summary, the demographic factors, that have been favourable to growth in the

period 1980–2010, can be argued to be unfavourable to growth for the next

30-year period: the growth of the Chinese economy will slow down, reducing

energy use.

2.4. Urbanization

The urbanization level of China is comparatively low, about 50%. It is ris-

ing rapidly and expected to continue rising over the next decades. The urban

population is estimated to number over 1 billion in 2030. [19]. The urbaniza-

tion and the huge migration involved are seen among the most motive forces of

China’s economic growth and development [23]. The fixed investment required

by the urbanization drives the strong growth of the Chinese economy. McKinsey

Global Institute estimates that during the period 1999-2009 as much as 50% of

the GDP has come from urban fixed investment [19], and its share of GDP is

likely to remain high in the future. The construction sector is also very energy-

intensive and will contribute greatly to energy use during the period 2013–2030.

Urbanization of the next 20 years is also estimated to move about 300 million

people from rural to urban areas, with many of these people being employed

in jobs with much higher productivity than before. This process will also have

major implications for the lifestyle of the new Chinese urbanites, changing con-

sumption patterns of much of the Chinese population significantly: urbanization

will also contribute to the environmental impact through this transformation.

2.5. Energy technology

Chinese energy policy has emphasised the use of non-fossil energy sources in

the future energy production. The possibilities for a major increase of non-fossil

energy depend largely technology development. The fast diffusion of Chinese

wind power has relied on competitive domestic production, but in the future the

8

scale-dependent effect in the wind power market can reduce the growth figures

[8].

The main drivers in the increasing installations of photovoltaic power in

China are the reducing costs of the solar panels (< 0.6 USD/Wp), the support

by the Renewable Energy Law of China through the feed-in tariff and the Special

Renewable Energy Fund. The feed-in-tariff was 1 Yuan per kWh in 2012. In

August, 2012, National Energy Administration (NEA) published ”The Twelfth

Five-year Plan of Renewable Energy Development” with the increased target of

PV from 15 GW to 21 GW focusing on distributed PV market and in December

2012 it increased the target from 21 GW to 35 GW and published the target of

100 GW for 2020. [11]

In September 2012 the Chinese government unveiled plans to stimulate local

PV demand with a programme to promote distributed solar generation. The

measures were brought in to address overcapacity in China’s PV manufacturing

industry and trade tensions with Europe and the US. China’s National Energy

Administration (NEA) is formulating plans to set up distributed PV genera-

tion demonstration plants across the country. The plans were announced at a

meeting convened by the NEA in June 2012. The pilot projects will operate in

National Economic Development Zones and industrial parks across China with

large numbers of industrial companies, high energy usage and prices, and strong

solar resources. The NEA requires that the chosen parks should be financed by

a single development team, using a ’self-generate/consume’ model, and that the

demand of the subsidies should be no more than 0.45 CNY / kWh. The ex-

pected fast increase of the natural gas use in China in the future relies on gas

imports and the utilization of shale gas. China has largest shale gas deposits in

the world [35] but the start-up phase has been slowed down due to the under-

developed natural gas infrastructure in terms of pipelines and other facilities

(see e.g.[21]).

Carbon Capture and Storage (CCS) is seen as one possibility of reducing

CO2 emissions in future energy systems. The development of the technology

9

has been slow worldwide due to the high costs. Under the IEA1 BLUE Map

Scenario, which calls for global CO2 emissions from the energy sector to be

reduced by 50% from 2005 levels by 2050, it is anticipated that CCS could

contribute about 19% of total emissions reduction. Under this scenario, China

would need to build 10 to 12 large-scale projects by 2020 and ramp up to 600

projects by 2050 [3]. China is planning several coal and gas power plants that

can bury their carbon deep underground and is looking into offsetting the extra

cost of these power plants by using the captured carbon to recover oil from

nearby wells ([24, see e.g.]).

2.6. Energy markets

During the first decade of the 2000’s, China has turned into a net energy

importer 2. Figure 2 shows the percentage of energy use in China during the

period 1971–2011. The current net energy imports are more than 10% of the

total energy use [39]. China is the worlds largest coal producer, but in the last

years, even coal consumption has exceeded production and China has become

a net coal importer. As United States seems to become more self-sufficient and

demand in EU is not likely to grow significantly, China will probably emerge

as the most important importer of oil. China’s largest oil fields are mature and

their production has already peaked [34].

The possibility of severe problems in supplying oil and gas to China before

2015 has been explored in recent energy scenarios by Shell [28]. The share

of imported energy will increase in the future, because demand is expected to

increase at a much faster rate than domestic energy production. Thus, the global

energy market is an increasingly important driver of China’s energy future. The

rapid change of China’s position from a major energy supplier to one of the

World’s largest energy importers has brought a really big player in the global

oil, gas and coal markets, and made policymakers and businessmen in other

parts of the world anxious [25].

1International Energy Agency

10

-10

-5

0

5

10

15

Energy imports, net (% of energy use) in China 1971-2011

% o

f ene

rgy

use

Figure 2: China’s net energy imports 1971–2011 [39]

China’s increasing oil demand has been driven more by investments in heavy

industry than consumption, which has been a surprise especially in the oil mar-

ket [25]. Imports of natural gas have been at a modest level, but LNG terminals

in the coastal areas of China and a pipeline from gas-rich Siberia in Russia have

been planned. China has a significant share of the World’s coal reserves, but

it is also one of the world’s largest coal importers. Firing coal is still the most

economical way of large-scale electricity production, and increasing coal demand

is due to increasing need of electricity.

3. ChinaLINDA model

4. Model structure

ChinaLINDA is a case or application of a more general LINDA model used in

the analysis of economy from an energy and emissions perspective. The LINDA

(Long-range Integrated Development Analysis) model is based on intensity ap-

proach, building on the Extended Kaya Identity, which is used for calculation of

11

CO2 emissions. Equation 1 presents the Kaya identity used in LINDA model.

CO2 =CO2

TPES× TPES

FEC× FEC

GDP× GDP

POP× POP (1)

where

CO2 is carbon dioxide emissions from fuel combustion;

TPES is total primary energy supply (including all fuels and other forms of pri-

mary energy, before the combustion process and transfer and distribution

of electricity or heat);

FEC is final energy consumption, meaning consumption of energy carriers such

as district heat and electricity, and fuels used in residential heating and

transport;

GDP is gross domestic product in real prices; and

POP is the amount of population.

This Kaya identity forms the basic conceptual framework behind the LINDA

model and the choice of modeled factors is somewhat based on the Extended

Kaya Identity. ChinaLINDA is a so-called accounting framework-type of model,

and compared against the Extended Kaya Identity the model is much more

detailed including different fuels and electricity, electricity production as well

as different sectors of economy in the calculation procedures. In addition, the

population, accounted as households, is divided into rural and urban groups.

Figure 3 shows a simplified diagram for the calculation procedure of resi-

dential electricity demand. The change rates and other parameters the model

needs are provided by experts of relevant fields: The dashed lines in the figure

indicate the flow of expert knowledge inputs. Similar calculation procedure is

applied for the calculation of different fuels used at households.

The LINDA model, based on the given inputs, calculates future scenarios for

the economy and the energy use in different sectors. Figure 4 gives a simplified

12

Number of rural households

Persons per rural household

Electricity use per rural household

Annual % change (given by user)

Annual % change (given by user)

History Future

Future number of rural households

Future electricity use per rural household

Annual % change (given by user)

Future persons per rural

household

Future rural population

Future rural electricity use

Future urban electricity use

Rural

Urban

Future residential electricity use

Annual % change

Annual % change

Annual % change

Figure 3: Linda model procedure for calculation of electricity demand and population

illustration of the main calculation blocks of the model for energy demand con-

struction in the different production sectors. This calculation is carried out for

the different fuels and electricity in each modelled economic sector (agriculture,

industry, transport, commercial, construction, others).

Historical economic development - Sector 1 - Sector 2 - . . .

Historical energy use - Sector 1 - Sector 2 - . . .

Historical energy intensity - Sector 1 - Sector 2 - . . .

Future intensity changes (by user) - Sector 1 - Sector 2 - . . .

Future energy demand - Sector 1 - Sector 2 - . . .

Future economic development - Sector 1 - Sector 2 - . . .

Future economic growth rates (by user) - Sector 1 - Sector 2 - . . .

Figure 4: Calculation procedures of the LINDA model

Figure 5 indicates the linkages of different calculation modules in the LINDA

model. The historical data is given as input in the model. This includes time

13

series data of: a) the use of different fuels and electricity in different economic

sectors; b) the electricity production; c) the power plant capacity using different

energy sources; d) technical information of power plant operation (plant capacity

factor and efficiency); e) the value added in different economic sectors; and f) the

population data.

The energy demand for different fuels and electricity in different sectors of

economy is calculated based on the historical data and user given inputs about

the future changes in energy intensities and economic growth in each sector.

The future economic growth rates that are used in the model can be based on

government plans, econometric models or expert information. The change rates

in future energy intensities in different sectors can be based on information of

different technological development trends, plans for new investments in dif-

ferent sectors (e.g. investments in energy intensive metal production) or other

expert information (see e.g. discussion by Steenhof[31]).

In the electricity production sector the investment plans for different types

of power plants are given as input data for future scenarios. The fuel efficiency

and plant load factor in addition to the installed capacity determine the needed

fuel input to produce the electricity needed to cover the demand. The LINDA

model does not utilize hourly load curves to calculate the electricity demand

but uses a more straightforward calculation based on yearly averages.

The LINDA model output is future energy use by fuel in different sectors and

related CO2 emissions. The emissions are calculated using the IPCC guidelines.

[16] The model can be used to create different scenarios by varying for instance

the future economic growth figures in different sectors or the energy intensities.

The shares of different fuels can also be varied easily to illustrate e.g. changes

in government policies.

4.1. Input data

ChinaLINDA model draws input data on multiple sources. The population

data in the model is supplied by Institute of population and labor economics,

Chinese academy of Social Science [15]. The energy data is from Energy Statis-

14

Agriculture

Industry

Transport

Commercial

Construction

Others

Electricity Heat

Residential

Historical data

Energy data Economic data

Input

Scenario

CO2

Energy use

Population data

Fuels

Figure 5: Linkages of LINDA model calculation modules

tics Division, Chinese National Bureau of Statistics [9]. Economic data and data

on other issues such as transportation and construction is from the China Sta-

tistical Yearbook [22]. Some input data concerning the sub-sectoral breakdown

of the economy comes from a 2011 paper by Chen [4].

5. Scenarios

Our scenario study concentrates on the impacts of economic structure on

energy demand and related CO2 emissions. The starting point has been the

construction of a reference scenario projecting the development in China up

to year 2030. The reference scenario is based on the economic growth rates

presented in a CASS2 research paper [29]. These figures are quite close to the

growth rates of NBSC 3 and DRC 4 [7]. The purpose of the reference scenario

is to provide a robust general projection of future developments in China that

2Chinese Academy of Social Science3National Bureau of Statistics of China4Development Research Center of the State Council

15

can be used as a point of reference for the scenario comparisons.

The ChinaLinda model uses the sub-sectoral differentiation of the industrial

sector shown in table 2. A clear strength of the ChinaLINDA model in com-

parison to many other systems of providing projections for energy demand and

emissions in China is that the scenario construction is based on data and projec-

tions of the sub-sectoral developments. An obvious but not always accounted-for

fact is that all sub-sectors of the industry do not grow at the same speed. The

energy intensities of different sub-sectors of industry vary considerably (see fig-

ure 9) as well as the potential of decreasing energy intensity in these sub-sectors.

Due to these facts, the differences in the growth patterns within the industrial

sub-sectors will lead to considerable differences in energy demand and emissions

even when the total value added in the industrial sector would be equal in the

different set-ups. The same is also true for the general sectoral composition

of the economy: Different growth rates for service sector and industrial sector

will lead to very different levels of energy demand and emissions. The 12th five

year plan has a strong emphasis on developing the service sector, and the future

growth rate of the service sector is very important from this point of view.

We answer the research questions presented in section 1.2 by comparison

of three scenarios constructed with the ChinaLINDA model. The scenarios are

called a) Reference scenario, or REF for short, b) Policy scenario, or POL for

short and c) Heavy industry scenario, or IND for short.

The economic growth rate of different sectors in the Reference scenario

(REF ) is presented in table 1. The industrial growth rate is high in this scenario:

it represents a continuation of historical trends and policies emphasizing indus-

trial growth in China. The total GDP growth rate is equal to the projections

by Liu [29].

The future sectoral growth rates for the second scenario, titled ”Policy”

(POL), are also presented in table 1. The Policy scenario plays out the imple-

mentation of energy and climate policy targets in China. In this scenario, the

industrial growth is much slower than in the Reference scenario, but the growth

in commercial sector is faster. The total growth rate in these two scenarios is

16

identical.

Table 1: Sectoral annual growth rates: historical rates, ”Reference” scenario rates, ”Policy”

scenario rates and ”Heavy industry” scenario growth rates.Historical Reference Policy Heavy industry

1990-95 1995-98 1998-2001 2001-06 2006-10 2011-15 2016-20 2021-30 2011-15 2016-20 2021-30 2011-15 2016-20 2021-30

Agriculture 4.2 % 4.0 % 2.7 % 4.4 % 4.4 % 4.0 % 3.5 % 2.5 % 4.0 % 4.0 % 4.0 % 4.0 % 4.0 % 4.0 %

Industry 17.7 % 10.9 % 9.0 % 11.7 % 11.4 % 10.2 % 8.1 % 6.1 % 7.1 % 4.8 % 3.9 % 7.1 % 4.8 % 3.9 %

Transportation, communication 10.9 % 9.5 % 9.8 % 11.3 % 11.3 % 9.0 % 9.0 % 6.0 % 10.0 % 9.0 % 6.0 % 10.0 % 9.0 % 6.0 %

Commercial 10.9 % 9.5 % 9.8 % 11.3 % 11.3 % 9.0 % 10.0 % 9.0 % 14.0 % 14.0 % 10.0 % 14.0 % 14.0 % 10.0 %

Construction 14.6 % 7.1 % 5.6 % 12.4 % 14.4 % 9.0 % 9.0 % 5.0 % 10.0 % 9.0 % 4.0 % 10.0 % 9.0 % 4.0 %

Others 10.9 % 5.6 % 5.8 % 11.3 % 9.0 % 6.0 % 6 % 0 % 11.0 % 10 % 5 % 11.0 % 10 % 5 %

Total 12.2 % 9.1 % 5.3 % 12.5 % 10.9 % 8.6 % 7.5 % 5.2 % 8.6 % 7.4 % 5.2 % 8.6 % 7.5 % 5.2 %

In order to compare the impacts of industrial structure on the emissions

we have derived the scenario titled ”Heavy industry” (IND) from the policy

scenario. In this scenario, the sectoral growth rates are equal to the Policy

scenario but the growth rates of industrial sub-sectors are different. The energy

intensive sub-sectors are growing faster than in the Policy scenario, but the

total industrial growth rates are equal. The growth rates of value added in

the industrial sub-sectors in the three scenarios is presented in table 2; The

differences in the value added of the industrial sub-sectors in 2030 in the three

scenarios are shown in figure 6.

The fastest growth in all industrial sub-sectors occurs in the Reference sce-

nario. The Heavy industry scenario derived from the Policy scenario differs

from Policy scenario in that the growth is higher in paper, chemical, medicines,

rubber and minerals and basic metals industries, but is correspondingly slower

in electrical and electronics industry and the metal products industry, summing

to an equal total growth for the industrial sector in both scenarios.

Figures 7 and 8 illustrate the differences in development of absolute sectoral

value added between Reference and Policy scenarios as well as the relative share

of the sectors in the economy. The Heavy industry scenario is equivalent in the

development of value added of economic sectors to the policy scenario, from

which it is derived, so it is not presented here.

In the Reference scenario, the share of commercial sector is declining while

the share of industry continues to grow fast. The service sector will not reach

the 2015 target of 47 % of GDP set in the 12th Five Year Plan as can be seen

17

Table 2: Annual industrial sub-sector growth rates: historical rates, ”Reference” scenario

rates, ”Policy” scenario rates and ”Heavy industry” scenario growth rates.Historical Reference Policy Heavy industry

1990-95 1995-98 1998-2001 2001-06 2006-08 2009-15 2016-20 2021-30 2009-15 2016-20 2021-30 2009-15 2016-20 2021-30

Food, beverages and tobacco 18.0 % 13.8 % 4.5 % 6.9 % 10.4 % 10 % 7 % 5 % 4 % 3 % 2 % 4 % 3 % 2 %

Textile, leather 12.8 % 8.7 % 7.0 % 8.3 % 10.4 % 10 % 7 % 5 % 4 % 3 % 2 % 5 % 3 % 2 %

Wood, paper and printing, education, artwork 20.5 % 13.7 % 6.7 % 8.1 % 13.1 % 8 % 6 % 4 % 5 % 4 % 3 % 10 % 8 % 5 %

Chemical 13.6 % 10.1 % 7.5 % 8.7 % 15.2 % 12 % 9 % 6 % 7 % 5 % 4 % 12 % 7 % 7 %

Coal and gas, mining and processing 7.7 % 3.8 % -2.9 % 2.3 % 1.2 % 0 % -1 % -2 % 0 % -1 % -2 % 0 % -1 % -2 %

Mining and processing ores 16.0 % 13.9 % -6.3 % 2.9 % 10.5 % 9 % 6 % 5 % 7 % 4 % 3 % 10 % 4 % 3 %

Medicines, rubber, plastics, mineral products 18.6 % 12.2 % 8.2 % 8.3 % 11.9 % 11 % 8 % 7 % 8 % 5 % 4 % 12 % 5 % 4 %

Smelting and processing metals 14.4 % 3.9 % 13.0 % 14.1 % 11.2 % 10 % 8 % 6 % 7 % 4 % 3 % 12 % 10 % 8 %

Metal products and machinery, transport equipment 21.0 % 7.7 % 8.8 % 13.0 % 19.3 % 12 % 9 % 6 % 8 % 5 % 4 % 5 % 4 % 3 %

Electrical and electronics 31.0 % 19.6 % 20.4 % 19.6 % 9.7 % 10 % 9 % 7 % 9 % 6 % 5 % 5 % 3 % 2 %

Electricity, gas and water 16.1 % 4.0 % 7.7 % 6.9 % 15.6 % 4 % 3 % 2 % 4 % 3 % 2 % 5 % 3 % 2 %

Total 17.7 % 10.9 % 9.0 % 11.7 % 8.0 % 10.2 % 8.1 % 6.1 % 7.1 % 4.8 % 3.9 % 7.1 % 4.8 % 3.9 %

0 20 000 40 000 60 000 80 000 100 000

Food, beverages and tobacco

Textile, leather

Wood, paper and printing, education,artwork

Chemical

Coal and gas, mining and processing

Mining and processing ores

Medicines, rubber, plastics, mineral products

Smelting and processing metals

Metal products and machinery, transportequipment

Electrical and electronics

Electricity, gas and water

100 mill CNY

Reference

Policy

Heavy Industry

Figure 6: The value added in industrial subsectors in 2030 in Reference, Policy and Heavy

industry scenarios

in table 3. In the Policy scenario the share of service sector grows and in this

scenario the government target is almost reached.

The comparison shows that the the targets of the 12th five year plan are

actually considerably stringent. Reaching the energy intensity and CO2 inten-

sity targets is not easy even when extremely high speed of renewable energy

production capacity construction is assumed. The pressure to increase energy

production, especially electricity production, is high due to the rapidly increas-

ing industrial production as well as the lifestyle change towards more energy-

intensive, consumerist lifestyle associated with the urbanization process.

The development of the value added in industrial sub-sectors in the three

18

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100 billion CNY POL: Value added in China

1990 1995 2000 2005 2010 2015 2020 2025 2030

Construction

Commercial

Transportation, communication

Industry

Agriculture

Figure 7: Comparison of sectoral value added in Reference and Policy scenarios

0 %

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REF: Economic structure in China

0 %

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100 %

1990 1995 2000 2005 2010 2015 2020 2025 2030

POL: Economic structure in China

1990 1995 2000 2005 2010 2015 2020 2025 2030

Construction

Commercial

Transportation, communication

Industry

Agriculture

Figure 8: Comparison of sectoral value added shares in Reference and Policy scenarios

Table 3: 12th five-year plan targets for year 2015 and the outcome in the scenarios

Target Reference Policy Heavy Industry

Energy intensity reduction -16 % -7.8 % -14.6 % -8.2 %

CO2 emission intensity reduction -17 % -9.7 % -16.7 % -9.8 %

Non-fossil fuel share 11.4 % 11.4 % 12.4 % 11.5 %

Service sector share 47 % 36.0 % 43.4 % 43.4 %

19

scenarios is presented in table 4. The fast increase in the share of electrical and

electronics sub-sector is assumed to continue in the Policy scenario. In addition,

the share of metal products and machinery and transport equipment is assumed

to increase. This type of industrial development would lead to a sizeable reduc-

tion of the energy intensity of the industrial sector since the fastest growing sec-

tors are not very energy intensive. Compared to Policy scenario, the growth in

the Heavy industry scenario is higher in the paper, chemical, medicines, rubber

and mineral and basic metal industries, but slower in electrical and electronics

as well as metal products industries. This increases the overall energy intensity

considerably in the industrial sector, increasing the energy demand.

Table 4: Development of value added in different industrial sub-sectors in the scenarios. Values

as billion CNY in 1990 price.Reference Policy Heavy Industry

2000 2015 2020 2030 2015 2020 2030 2015 2020 2030

Food, beverages and tobacco 283 993 1393 2268 750 870 1060 750 870 1060

Textile, leather 226 868 1218 1984 656 760 927 688 798 972

Wood, paper and printing, education, artwork 195 718 961 1423 624 759 1020 787 1157 1884

Chemical 188 842 1295 2320 670 855 1266 842 1181 2323

Coal and gas, mining and processing 98 137 131 107 137 131 107 137 131 107

Mining and processing ores 66 162 217 354 148 180 242 170 207 278

Medicines, rubber, plastics, mineral products 321 1310 1925 3786 1142 1458 2158 1370 1748 2588

Smelting and processing metals 147 785 1153 2065 683 831 1117 859 1383 2850

Metal products and machinery, transport equipment 385 2461 3787 6781 2052 2619 3876 1782 2168 2914

Electrical and electronics 507 3317 5104 10039 3169 4241 6908 2629 3047 3715

Electricity, gas and water 100 304 353 430 304 353 430 319 370 451

Total 2515 11898 17535 31557 10336 13056 19111 10333 13059 19142

It is assumed in the scenario comparison that the changes in electricity in-

tensity of production in the industrial sub-sectors are similar in the different

scenarios. The development of electricity intensity of different industrial sectors

is shown in figure 9.

The electricity consumption increases fast in the Reference scenario due to

the fast growth of energy intensive industrial production. This naturally in-

creases also the fuel use and the related CO2 emissions from fuel combustion.

The electricity demand in the Policy scenario is much lower because the in-

dustrial growth is smaller than in the Reference scenario. The higher growth

of service sector in the Policy scenario does not increase electricity demand as

20

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1990 1995 2000 2005 2010 2015 2020 2025 2030

ktoe/100mill CNY REF: Electricity intensity in industry Food, beverages and tobacco

Textile, leather

Wood, paper and printing,education, artwork

Chemical

Coal and gas, mining andprocessing

Mining and processing ores

Medicines, rubber, plastics,mineral products

Smelting and processing metals

Metal products and machinery,transport equipment

Electrical and electronics

Electricity, gas and water

Total

Figure 9: Electricity intensity in different industrial sectors in China and its development in

the scenarios. Identical development concerning electricity intensity is assumed in all three

scenarios.

much due to the lower electricity intensity of the service sector. In the Heavy

Industry scenario the electricity consumption increases fast even though the

total industrial value added is similar to the Policy scenario. As the energy

intensive sectors grow faster in the Heavy Industry scenario, faster energy use

growth follows. Figure 10 illustrates the differences in electricity consumption

in the different scenarios.

The differences concerning the electricity production in the scenarios are

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in China

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in China

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in China

0 2000000 4000000 6000000 8000000

10000000 12000000

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2020

2025

2030

GWh IND: Electricity consumption in China

Others

Construction

Transportation

Residential（Rural）

Residential（Urban）

Commercial

Industry

Agriculture

Figure 10: Comparison of electricity consumption in the scenarios

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China

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10000000 12000000

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Thermal

Wind

Nuclear

Hydro

Figure 11: Comparison of electricity production from different energy sources in the scenarios

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12222222

ktoe

Others

Construction

Residential(Rural)

Residential(Urban)

Transport

Commercial

Industry

Agriculture

0

Figure 12: Comparison of sectoral final energy use in the scenarios

mainly in the construction of thermal power plants and nuclear power. In all the

scenarios the construction of hydro, solar and wind power capacity is assumed

to continue fast and at the same speed. In the Reference scenario the higher

growth in electricity demand is mainly covered by construction of additional

thermal and nuclear power plant capacity. The electricity production from

different energy sources in the scenarios is shown in figure 11.

The sectoral final energy use in the different scenario is shown in figure 12.

The industrial energy use is dominant in the reference scenario due to the fast

economic growth in the industrial sector and the energy intensity of the sector.

In the Policy scenario the industrial energy use saturates by 2020 and the growth

comes mainly from residential sector (which includes the use of private cars).

The fuel use in the scenarios is shown in 13 In the Reference and Heavy

industry scenarios coal remains the main source of energy even though the use

of oil products in the transport sector is increasing fast. The use of natural

22

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ktoe

Solid biomass

Natural gas

Oil

Coal

Figure 13: Comparison of fuel use in the scenarios

gas is also increasing fast both in industry and in electricity production where

the shift from coal to gas is remarkable. The fuel shares in the different sectors

are assumed to be identical in the different scenarios and the differences in fuel

consumption result from the different growth rates in different sectors.

The 12th Five Year Plan has set a target of non-fossil fuels to account for

11.4% of primary energy consumption. This target is reached in all the scenarios.

In the Reference scenario the share of non-fossil fuels would be 11.4%, in the

Policy scenario 12.4% and in the Heavy Industry scenario 11.4% (see table 3.

The target can be reached with speedy construction of wind and solar energy

capacity. In all the scenarios the capacity of wind power would be 414 GW,

solar PV power capacity 341 GW and hydro power capacity which would be

about 585 GW by the year 2030. In the Reference scenario the nuclear power

capacity would be about 194 GW in 2030 and in the Policy and Heavy Industry

scenarios 171 GW in 2030. In 2030 the thermal power capacity, using mainly

coal (80%) and gas as energy sources, would be in 2030 about 1 672 GW in

the Reference scenario, 1 104 GW in Policy scenario and 1 597 GW in Heavy

Industry scenario. The energy sector investments would need to be huge in all

the scenarios.

The development of the CO2 emissions in the different scenarios is shown

in figure 14. The CO2 emission level in 2030 compared across scenarios is also

presented in table 5. In the Policy scenario the emissions are 27% lower than

in the Reference scenario and the CO2 emissions from fuel combustion reach a

23

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REF: CO2 emissions from fuels

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1990 1995 2000 2005 2010 2015 2020 2025 2030

ktons of CO2

Natural gas

Oil

Coal

Figure 14: CO2 emissions from fuel combustion in the scenarios

level of about 6000 million tons of CO2 in 2020 and stabilize after that.

The 12th five-year plan has also set a target for energy intensity reduction

and CO2 intensity reduction for 2020. The target for energy intensity reduction

is 16%, but in the Reference scenario a reduction of only 8% is reached. Policy

scenario reaches 14.6% reduction while the Heavy industry scenario also ends

up to about 8% reduction (see table 3). The target for CO2 intensity reduction

is 17%. The Reference and Heavy industry scenarios reach only 10% reduction

while the Policy scenario comes very close to reaching the target with 16.7%

reduction (see table 3). The policy scenario demonstrates that the targets of

the 12th five-year plan are actually quite ambitious and require a fast shift to

more efficient production systems and non-fossil fuel use in energy production.

ChinaLINDA model can also be used for projecting the change in the labour

force demand and employment by estimating the changes in labour intensity in

the different sectors. The employment in the Reference scenario is decreasing

after 2020. This is due to the lower economic growth after 2020 and the decreas-

ing labour intensity in all the sectors. In the policy scenario the labour demand

also decreases after 2020, but the general employment rate is higher than in

the Reference scenario. This is due to the higher labour intensity in the service

sector and the faster growth in non-industrial sectors. In the Policy scenario

the demand for workforce is about 8 million persons higher in 2030 than in

the Reference scenario due to Policy scenario’s more labour-intensive economic

structure. Figure 15 illustrates the development of employment in different sec-

24

Table 5: Summary of the scenario results. The second column for scenarios ”Policy” (POL)

and ”Heavy industry” (IND) indicates the percentual difference of value in comparison to the

”Reference” (REF) scenario.

Year 2030 REF POL IND

GDP (100 billion CNY) 494 491 −1% 491 −1%

Agriculture 21 24 +19% 24 +19%

Industry 316 191 −39% 191 −39%

Commercial 110 228 +108% 228 +108%

Transport and communication 19 20 +5% 20 +5%

Construction 29 28 −5% 28 −5%

Electricity consumption (Mtoe) 885 683 −23% 853 −4%

Final Energy Consumption (Mtoe) 2 774 2 276 −18% 2 754 −1%

Fuel consumption (Mtoe) 3 584 2 655 −26% 3 464 −3%

CO2 emissions in 2030 (Mtons) 12 446 9 054 −27% 11 973 −4%

Employment (millions) 586 667 +14% 667 +14%

tors in China in the scenarios. The employment in Heavy industry scenario is

similar to the Policy scenario because no data on industrial sub-sector labour

force was available and changes in these could not be included in the model.

6. Comparison to previous scenario studies

As in most other scenario studies on Chinesev economy and emissions, all

scenarios presented feature a sharp increase in electricity use and fuel use. This

is due to high estimated economic growth, comparatively slow decrease in elec-

tricity intensity in economic production sectors and urbanization process, which

greatly increases household electricity consumption. The electricity production

figures in the presented scenarios for 2030 do not differ greatly from the sce-

narios by the International Energy Agency. The reference scenario figures are

only a little higher than IEA Current Policy scenarios, while the policy scenario

figures are little bit lower that the IEA New Policies scenario.

Comparison of the REF, POL and IND scenarios to several scenario studies

25

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1990 1995 2000 2005 2010 2015 2020 2025 2030

10 000 persons

Other

Transport

Construction

Commercial

Industry

Agricullture

Figure 15: Employment in different economic sectors in the Reference and Policy scenarios

on Chinese economy are presented in figure 16.

0

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CO2 emissions in China 2030 in different scenarios

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Energy use in China in 2030 in different scenarios

Figure 16: Comparison of energy production and emissions between REF, POL and IND

scenarios and other scenario studies

The compared scenarios are constructed by International energy agency (IEA

”New Policies” scenario, ”450” Scenario, ”Current policies” scenario) [2], United

States Energy Information Administration (Scenarios IEO ”Ref”, ”High” and

”Low”) [1], Chinese Energy Research Institute (Scenarios ERI ”Baseline”, ”Low

carbon”, ”Accelerated low carbon”) [40], Lawrence Berkley National Laboratory

(Scenarios LBNL ”CIS”, ”CIS with CCS”) [40], Dai et al. (Scenario Dai et al.)

[5],

IEA New Policies IEA 450 IEA Current Policies LBNL CIS LBNL CIS with

CCS ERI Baseline ERI Low carbon ERI Accel low carbon Jiang Baseline Jiang

26

Policy Jiang Low Carbon Jiang ELC Dai et al. IEO Ref IEO High IEO Low

7. Conclusions and discussion

A comparison of the most relevant outcomes of the three scenarios is pre-

sented in table 5. The ”Policy” scenario represents a rather optimistic future

where the current policies, some actually very ambitious, are succcessfully im-

plemented: from that scenario, we can learn how great an impact could those

policies have on emissions and energy use. The ”Heavy industry” scenario,

derived from the ”Policy” scenario, then tells us how the abatement of CO2

reached in the ”Policy” scenario can largely be annulled if the industrial profile

leans towards energy-intensive activities.

The scenario analysis illustrates the stringency of the longer-term CO2 in-

tensity and energy intensity targets. Even with the optimistically fast expansion

of renewable energy capacity simulated in the policy scenario reaching these tar-

gets is difficult. On the other hand, the goals set for renewable energy capacity

[32] seem unambitious in contrast to the capacity growth levels required to reach

the CO2 intensity and energy intensity targets. Many targets previously set for

2020 have already been reached.

The CO2 emissions could be as much as 27% lower assuming a strong devel-

opment of the service sector and a turn away from the energy-intensive industries

and the carbon-intensive pattern of growth, but this will also require huge in-

vestments in renewable energy capacity. The Policy scenario assumes 156 GW

of new hydropower capacity, 124 GW of new photovoltaic capacity and 140 GW

of new wind power capacity (totalling 478 GW) to be constructed during the

period 2014–2020 and another 150 GW of hydropower, 200 GW of photovoltaic

and 200 GW of wind power capacity (totalling 550 GW) to be constructed in

the later period 2021–2030. Even with the heavy investing in renewables, coal

use would need to be roughly on the same level as in 2013 for the entire period

2014–2030 to supply the needed electricity. The cumulative CO2 emissions over

the period 2011–2030 would be 202 GT in the Reference scenario. In the policy

27

scenario, the cumulative emissions would add up to 167 GT, 35 GT less. The

Heavy industry scenario would result in 195 GT of cumulative CO2 emissions

for the period.

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