Low carbon development strategy for mexico - an input-output analysis.pdf

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Low Carbon Development Strategy for

Mxico: An Input-Output Analysis

Final report
http://organismos.chubut.gov.ar/cambioclimatico/files/2009/09/pnuma.jpeg


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Low Carbon Development Strategy for Mxico: An Input-

Output Analysis

Final report

Responsable: Dr. Pablo Ruiz Npoles

Este reporte fue elaborado por el Dr. Pablo Ruiz Npoles. El enfoque, la metodologa, las opiniones

y conclusiones son responsabilidad exclusiva del autor.

Este informe ha sido posible gracias al apoyo del Programa de las Naciones Unidas para el Medio

Ambiente (PNUMA) y la Agencia Francesa para el Desarrollo (AFD). Su contenido esresponsabilidad del autor y no refleja necesariamente el punto de vista del PNUMA, de la AFD, de

la Secretara de Medio Ambiente y Recursos Naturales (SEMARNAT) o del Gobierno de los

Estados Unidos Mexicanos.


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LOW CARBON DEVELOPMENT STRATEGY FORMEXICO: AN INPUT-OUTPUT ANALYSIS

Final Report

Pablo Ruiz Npoles

February, 2012


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Low Carbon Strategy for Mexico: I-O Model. Final Report

ii

This report was prepared by Dr Pablo Ruiz Npoles, Faculty of Economics, NationalAutonomous University of Mexico (UNAM) at the request of the Mexican Ministry ofEnvironment and Natural Resources (SEMARNAT). Technical support was provided bythe United Nations Environment Programme (UNEP) and the French Agency forDevelopment (AFD).

The finding, interpretations, and conclusions expressed in this report are entirely those ofthe author and should not be attributed in any manner to UNEP and AFD. UNEP and AFDdo not guarantee the accuracy of the data included in this publication and accepts noresponsibility whatsoever for any consequences of their use.

This publication may be reproduced in whole or in part and in any form for educational ornon-profit purposes without special permission from the copyright holder, providedacknowledgement of the source is made. UNEP would appreciate receiving a copy of anypublication that uses this publication as a source.

No use of this publication may be made for resale or for any other commercial purposewhatsoever without prior permission in writing from the United Nations EnvironmentProgramme.

Financial support was generously provided by the government of Norway and France.


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Acknowledgements

The author of this study wishes to express his gratitude to his colleagues Martn Puchet andValentn Sols, for their modelling advice, to Rosa Gmez, Javier Castaeda, EduardoMoreno and Fernando Pineda, students at UNAM, for their technical assistantship. Special

thanks to Terry Barker, Annela Anger, Douglas Crowford and Serban Scrieciu members ofthe Cambridge Centre for Climate Change Mitigation Research (4CMR) for their valuablecomments, thanks also to Carl Bernadac and an anonymous reviewer, both from the FrenchAgency for Development (AFD) and at last but not least to Noriko Yamada from theUnited Nations Environment Programme (UNEP).


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CONTENTS

I. Introduction ........................................................................................................................ 1

II. Review of selected literature ............................................................................................. 4

1. Climate change, causes and mitigation policies ............................................................. 42. Policy instruments and economic models ...................................................................... 53. General Equilibrium models ........................................................................................... 64. Econometric models ..................................................................................................... 155. Applied Environmental Input-Output models .............................................................. 16

III. A Comparison Mexico-Canada ..................................................................................... 22

1. Method for estimating comparison indicators .............................................................. 222. Estimation results ......................................................................................................... 23

IV. GHG emissions and strategic sectors in the Mexican economy ................................... 27

1. Strategic or Key economic sectors ............................................................................... 272. Main IOM sectors emitting GHG ................................................................................. 343. Key and High Pollutant sectors .................................................................................... 36

V. Environment Input-Output Model for Mexico ............................................................... 38

1. Environment Input-Output models ............................................................................... 382. Pollution abatement and Technological change models............................................... 39

3. Objectives of the Mexican EIO Model ......................................................................... 414. The model and the scenarios ........................................................................................ 42

VI. Technology changes and GHG emissions reduction: the model results ....................... 46

1. Base line or Business as Usual 2008 to 2020 ........................................................... 462. Introduction of technological changes and reductions of GHG emissions ........................ 483. Changes in GHG emissions by sector ................................................................................ 504. Technical change and output growth .................................................................................. 525. The Costs of Pollution model ............................................................................................. 53

VII. Summary, Conclusions and Policy recommendations ................................................. 54

1. Summary and Conclusions ........................................................................................... 542. Lessons from the policy debates ................................................................................... 593. Policy recommendations derived from this study ........................................................ 624. Further research ............................................................................................................ 63

Bibliography ........................................................................................................................ 64


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LIST OF TABLES AND GRAPHS

Tables

Table 1 Canada GGH Emissions and GHG reduction expenses ......................................... 25

Table 2 Mexico GHG Emissions and Estimated Reduction Costs ...................................... 26

Table 3 North American Industrial Classification System, Input-Output IndustryClassification ....................................................................................................................... 28

Table 4 23 Selected Industries of the 2008 I-O Matrix of Mexico, Backward andForward Linkages Rasmussen Method................................................................................ 31

Table 5 23 Selected Industries of the 2008 I-O Matrix of Mexico, Backward andForward Linkages Impacts by Extraction Method .............................................................. 33

Table 6 GHG Emissions by Sector in Mexico 2008, Selected Industries ........................... 35

Table 7 Main GHG Emissions Coefficients by Sector in Mexico 2008 ............................. 36

Table 8 Mexico Estimated GHG Emissions Trajectories .................................................... 37

Table 9 Mexico Estimated GHG Emissions with Technical Change .................................. 49

Table 10 Estimates of GHG emissions variations 2008-2020 by sector inSelected Industries ............................................................................................................... 51

Table 11 Estimated Gross Output 2008-2020 ..................................................................... 52

Table 12 Estimated Gross Output and Pollution Costs for 2015 ......................................... 55

Graphs

Graph 1 Mexico: GHG Emissions BAU 2008-2020 ........................................................... 47

Graph 2 Mexico: GHG Emissions without and with technological change, 2008-2020 .... 50

Graph 3 Mexico: Gross Output without and with technological change, 2008-2020 ......... 53


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I.INTRODUCTION

Man-made or anthropogenic climate change is defined as: a change of climate which is

attributed directly or indirectly to human activity that alters the composition of the global

atmosphere and which is in addition to natural climate variability observed overcomparable time periods (IPCC, 2007, Annex II, p.78). It is in part the result of the

atmospheric concentrations of greenhouse gases (GHG). They are those gaseous

constituents of the atmosphere, both natural and anthropogenic, that absorb and emit

radiation at specific wavelengths within the spectrum of thermal infrared radiation emitted

by the Earths surface, the atmosphere itself, and by clouds. This property causes the

greenhouse effect. Water vapour (H2O), carbon dioxide (CO2), nitrous oxide (N2O),

methane (CH4) and ozone (O3) are the primary greenhouse gases in the Earths atmosphere.

Besides CO2, N2O and CH4, the Kyoto Protocol deals with the GHG sulphur hexafluoride

(SF6), hydro-fluorocarbons (HFCs) and per-fluorocarbons (PFCs), (IPCC, 2007, Annex II,

p.82). GHG are primarily produced by the combustion of fossil fuels, agriculture, land-use

changes and production of materials such as cement, as well as the burning of waste.

Climate Change consists of a gradual increase in the planets temperature , rise in

sea levels and changes in its rainfall patterns, as well as in the frequency, magnitude and

intensity of extreme weather events such as droughts and floods. Although this tendency

has been scientifically verified, there is still some degree of uncertainty about themagnitude and velocity of these changes at a regional scale. However, based on the current

state of knowledge it is possible to identify some of the cause-effect chain relations

between GHG sources, GHG emissions, global warming and its climatic consequences.

This allows us to foresee various future scenarios for the economy, based on which

we can assess, from an economic perspective, the possible consequences of climate change

and the alternative options for adaptation and mitigation policies, in order to face the

problem. Mitigation has been defined as: the technological change and substitution that

reduce resource inputs and emissions per unit of output. Although several social, economic

and technological policies would produce an emission reduction, with respect to Climate

Change, mitigation means implementing policies to reduce greenhouse gas emissions and

enhance sinks (IPCC, 2007, Annex II, p.84). However, as some expert has pointed out it is


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not only emissions intensity reduction (i.e., GHG emissions per unit of output) but also

absolute emissions reduction which is important in mitigation.

In general, mitigation policies aim to the reduction of fossils fuels consumption and

substitution, towards low-carbon sources (and/or the capture and storage of carbon from

emissions) therefore the factors that cause it must be dealt with. These factors are mainly:

population dynamics, urbanization, production and consumption increases; energy

efficiency and technology innovation tendencies, as well as the economic structure, in each

country. All these factors are, one way or another, related to economic activity in a broad

sense: production, trading, consumption and investment.

From an economic perspective, in order to design a mitigation scenario, it is

necessary to identify those economic sectors of production, or industries, which directly or

indirectly generate GHG emissions becoming, therefore, the sectors that call for specialattention; these are key sectors for mitigation. This can be seen as a supply-side view,

though, since there is also a demand-side of the problem which is related to consumption,

investment and exporting and could also be subject to mitigation policy actions.

In turn, the costs of mitigation measures depend on various local circumstances, for

example, in the case of production, the specific form of economic growth and the

introduction of technology developments in the production process aimed to reduce GHG

emissions. Besides, climate change mitigation impacts are unevenly distributed among

sectors and depend on the direct or indirect use of fossil fuels combustion of each and every

sector of the economy. In short, the economic costs of climate change mitigation depend

fundamentally on both, the energy-use intensities of economic sectors and industries, and

the absolute value of their corresponding GHG emissions. These two are associated with

the technological characteristics of their respective processes of production.

Economic models of different types deal with various aspects of Climate Change

mitigation policies, or with the same aspects but using different approaches and inbuilt

assumptions (Macroeconomic models, Econometric models, General Equilibrium models,

etc.).

The present study is in principle concerned only with those models within the Input-

Output or Structural Analysis tradition, which can be defined as meso economic models,


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that is to say they are not macro, nor micro economic models. They deal with sectoral

economic magnitudes.

We are building and developing an Environmental Input-Output (EIO) model of the

Mexican Economy for the purpose of analysing the effects of a change in technology in

some of the key sectors of the economy identified as both strategic and highly GHG

emitters. The period in which this impact analysis is studied goes from 2008 to 2020. The

main idea is to find out to what extent the use of more efficient technologies in key

economic sectors makes the reduction of GHG emissions possible under different scenarios

of GDP growth.

It must be said from the beginning that, although most of the information we are

using here may be called hard data since it comes from official surveys and has been

subject to verification, the resulting forecasted data and the simulations only indicatetendencies subject to assumptions and not real values, as in any other model interpreting

the economic reality.

This report is divided into seven sections including this introduction. The second

section is devoted to review the literature concerning some of the main economic aspects of

climate change and mitigation. The third section is a comparison between Mexico and

Canada. In the fourth section we determine the key or strategic sectors, and also the most

polluting sectors of the Mexican economy, using Input-Output analysis. The fifth section

deals with the Environment Input-Output model designed to estimate GHG emissions by

sector for the period 2008-2020 under various scenarios including technology changes

aimed to reduce GHG emissions. In the sixth section the outcomes of the model are

analysed. Finally, in the last section, we present some conclusions and policy

recommendations based on the results of the model.


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II.REVIEW OF SELECTED LITERATURE

1. Climate Change, Causes and Mitigation Policies

All the IPCC reports and technical papers consulted (IPCC, 1996, 2000, 2001, 2007)

indicated, and the Stern Review (2006) confirmed, that economic activities are the sourcesof GHG emissions and, therefore, represent the anthropogenic causes of Climate Change.

Even though the Climate Change problem is a global one, it is originally caused by

the GHG emissions in the production processes of specific sectors of the economy of every

country. Thus, in order to first assess the magnitude of the GHG emissions, the IPCC

defined instruments and methods for that purpose applicable to each country. Among these

methods, there is a common sector classification system. This has been useful to identify

those sectors that contribute most to generate GHG emissions in each country. There was,

however, a second consideration of equal importance and that is the GHG emissions

intensity of each sector, i.e. GHG emissions per unit of output in the respective sector. Here

we find as a challenge how to make compatible IPCC sectors classification with each

countrys National Income Accounts classification of industries and sectors in order to

estimate correctly absolute and relative GHG emissions in each economic sector or

industry. In any case, the important point to stress is that the relevant information, for

purposes of measuring and, therefore, applying policies towards abating GHG emissions is

clearly sector specific.According to all the literature reviewed, in the sphere of production GHG some

mitigation policies are centred on the introduction of abatement technologies. In some cases

these technologies are sector-specific. In other words, in Economics the definition of an

industry (sometimes called a sector) is a group of firms that produce more or less the same

good or service and therefore share, more or less, the same technology. In practice,

however, there may be significant technological differences between firms in the same

sector, especially when the market structure is imperfectly competitive.

The abatement technologies according to the various authors reviewed can be of

two types: one is called end-of-the-pipe technology that reduces GHG emissions without

implying other changes in the production process; another is the technology that implies an

important change of the production process reflected in the input-output technical

coefficientsin order to reduce GHG emissions.


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2. Policy instruments and economic models

In order to induce the use of technology that reduces GHG emissions by the producers

(switching from a conventional technology to an abatement one), every government has a

variety of instruments and measures to apply: market based programs, regulatory measures,

voluntary agreements, scientific research and development (R&D), and infrastructural

measures. IPCC maintains the idea that there is no best single instrument or measure to

apply but rather a combination of measures adapted to national, regional and local

conditions will be required (IPCC, 1996). The same position is favoured by the OECD in

its studies.

For estimating the economic impacts of Climate Change and the future economic

scenarios, either globally or regionally, there have been built economic models of various

types, among these the most relevant are: Econometric models, General Equilibriummodels and Environmental Input-Output models. Some of these models, at country-level,

have also been used to estimate the impact on GHG emissions reduction of different

policies aimed to that purpose. These types of models have been widely recognised as valid

and useful by the IPCC and the OECD in their respective studies.

But only some of these models where favoured in the beginning for analysing

Climate Changes economic impacts. As Barker pointed out by 1998: Most of the research

effort has gone into the development of computable general equilibrium (CGE) models, and

this methodology dominates the field (Barker, 1998). This type of models is based on

Neoclassical thinking, that is, they favour market mechanism solutions over state policies

instruments, in almost any economic issue including of course, ecological ones. Therefore,

in the policy instruments utilized for ecological problems the use of market based policies

was predominant. All this has been due to the prevalence of the so called Mainstream

Economics in most countries and in the most important international financial institutions,

for over twenty five years.

Whatever the extent of market oriented policies carried out between 1988 and 2005

they did very little in solving the GHG emissions problem, called Climate Change.

Nicholas Stern pointed out in his Review, in 2006 after eighteen years of IPCC foundation,

that Climate Change was the greatest and widest-ranging market failure ever seen

(Stern, 2006). Of course he had not witnessed the so called Sub-prime financial crisis of


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2008, initiated in the US but extended to rest of the world, which was also a market failure.

Stern (2006) also called for a major change (as opposed to a marginal one) in GHG

reductions which, as all major changes in the economy must, in our opinion, be led by the

state in each country case.

The need for state intervention arises also from the existence of market

imperfections in each and every economy in the world. It is not surprising that the OECD

emphasises that putting a price on GHG emissions through price mechanisms, has the

limitation that they do not address the full range of market imperfections that prevent

emissions to be cut at least cost, such as information problems, (Duval, 2008).

The OECD finds also that empirical analysis indicates that the most important

determinant of innovation in the area of renewable energy technologies is general

innovative capacity. According to Furman, et al. (2002), National innovative capacity isthe ability of a country to produce and commercialize a flow of innovative technology over

the long term. National innovative capacity depends on the strength of a nations common

innovation infrastructure, the environment for innovation in a nations industrial clusters,

and the strength of linkages between these two. However the OECD study says in the

case of energy public policy makes a difference. Public R&D expenditures on renewable

energies induce innovation, as do targeted measures such as renewable energy certificates

and feed-in tariffs (Hai, et al., 2010).

Finally, another issue that calls for state action is the issue of equity, namely the

extent to which the impacts of climate change or mitigation policies create or exacerbate

inequities both within and across nations and regions. This implies the need for the

application of state policy measures aiming to prevent or to compensate any inequities that

may result from either climate change impacts or mitigation policies, between sectors or

population groups within a country, and internationally agreed regulations in the same

direction for inequities between countries.

3. General Equilibrium models and Climate change.

General Equilibrium in general

General equilibrium models are rooted in Neoclassical thinking since they pay particular

attention to the specification of demand and supply functions, derived from the assumption


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of utility and profit maximizing consumers and firms; to assume perfect competition; and to

impose market clearing (Mercado, 2003).

It is said, from the conventional neoclassical approach, that Economics is about

choice subject to constraints. Under this approach, the main problem for Economics is to

decide upon the allocation of resources in order to maximize Social Welfare which is a

function of the addition of the welfare functions of all individuals in society, that is, their

utility functions. This optimization problem is subject to three sets of constraints related to:

factor endowments, technology and tastes. Optimization takes place in consumption,

production and product-mix. This process looks for the most efficient solution in Paretos

terminology. The solution is a set of prices for goods and factor services satisfying

simultaneously all the equations in the various markets. The only possible way that a free

market economy finds the most efficient solution rests in four basic assumptions: perfectcompetitive markets, constant returns to scale for every firm in the market, that there are no

externalities and that there is no market failure connected to uncertainty (Layard and

Walters, 1978, Chapter I). Another important assumption not always explicit is full

employment of all factors of production. This, in fact, is what General Equilibrium is all

about.

Most of the assumptions, implicit or explicit, in a General Equilibrium model are so

unrealistic that make it difficult to apply to the real world economy. In general there is a

high probability that many real world economic issues cannot be solved by free market

forces working by themselves but require state action, that is, a state or public policy. This

situation is called market failure. Be it due to market imperfections, the presence of

externalities, the existence of monopoly rents from increasing returns to scale, or the

existence of uncertainties.

Computable General Equilibrium models

Computable General Equilibrium (CGE) models are those models almost exactly as the

above mentioned, in this case trying to use information taken from real economic data, but

keeping the usual assumptions, which become actually very strong limitations. They try to

capture a wide range of economy interactions between a variety of economic agents and

institutions. Given some behavioural assumptions with respect to those agents and

institutions and with respect to the functioning of markets, these models are used to


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determine relative prices and quantities produced and consumed. They sometimes provide a

relatively high disaggregated picture of the economy.

A CGE model requires computational techniques to be solved. Many times the

parameters and variable values of these models are estimated with information obtained

from a Social Accounting Matrix (SAM) which contains information on the flow of goods

and payments between institutions in the economy. This is the only real link between these

models and the Input-Output models, since SAM and Input-Output data come from the

same source: the National Income Accounts of a country.

CGE models have traditionally been used to answer comparative static or what if

questions, that is, they are mostly static. However, dynamic specifications are been

increasingly used for forecasting purposes (Dixon and Rimmer, 2009).

CGE models for Climate ChangeThere is a wide variety of CGE models dealing with Climate Change problematic, some of

them using Input-Output Tables, Social Account Matrices, and/or Econometric equations to

forecasting specific variables and some of them are considered hybrid models because they

deal, simultaneously, with economic and physical data of Climate Change. Therefore, there

is sometimes a combination of different techniques in the same model.

There are CGE studies for a region or area within a country (see for instance Rose,

et al., 2000); there are others for only one sector in a given economy (see for instance Zhai,

et al., 2009); others are for a single country (see Fullerton and Heutel, 2010; Dejuan, et al.,

2008), for various world regions, or for the whole world (see for instance, Ross, 2008;

Sassi, et al., 2010). The topics treated vary, there are: sector analyses, energy demand

estimations, Climate Change policy analyses, sustainable economic growth policies. In all

of these cases the idea is to reduce the expected economic costs or impacts of Climate

Change, choosing the right (more efficient) Climate Change mitigation and/or adaptation

policies. These costs are generally measured in terms of welfare gains or losses for present

and/or of future consumers.

Nordhaus GE models

Although there seems to be several important General Equilibrium models dealing with

various aspects of Climate Change, the most cited in the literature are those of William D.

Nordhaus from the University of Yale in New Haven, USA.


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Nordhaus modelling on energy can be traced as far back as 1973 (Nordhaus, 1973).

But it seems that the initial relevant work done in the seventies was in his energy model of

1979 for the US energy sector where he tries to determine the prices of energy resources,

for an efficient use of those resources (called efficient prices). The investigation is

oriented towards establishing the time pattern of the efficient use of the energy resources

assuming that those resources which are scarce have a royalty attached to it that increases

over time with the market interest rate. The difficulties the study finds in trying to adapt

economic theory to real world facts, for instance the assumption of competitive oil markets

that yield competitive oil prices versus actual oil prices determined by some degree of

monopoly in the real oil market, leads the investigation to formulate the actual question of

what is the chance that global environmental effects will appear as a result of unrestricted

market forces?. In answering this question Nordhaus concludes that we are probably

heading for major climatic changes over the next 200 years if market forces are

unchecked, He therefore propose a carbon tax as the most efficient control strategy

(Nordhaus, 1979). The existence of non-competitive markets brings about some degree of

uncertainty which adds to that inherent to the costs of new technologies estimates. It is

therefore recognized that the validity of the results in this type of models is restricted by the

very optimistic assumptions that there are no significant impediments for the action of

market forces (Nordhaus, 1979).

In 1983, Nordhaus and Yohe presented a world probabilistic model for estimating

CO2 emissions as influenced by major uncertain variables or parameters. The technique

utilized is called probabilistic scenario analysis. The model is a highly aggregated model

of the world economy and energy sector. The main equation is a multi-input production that

related Gross National Product to labour, fossil fuels and non-fossil fuels inputs. The so

called key uncertainties included in the model are, the rate of population growth, the

availability and cost of fossil fuels, the productivity growth rate, and some others. The

important findings in this model are odds are even whether the doubling of carbon dioxide

will occur in the period 2050-2100 or outside that period it is a 1-in-4 possibility that

CO2 doubling will occur before 2050 and 1-in-20 possibility that doubling will occur before

2035 (Nordhaus and Yohe, 1983, p. 94).


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Nordhaus DICE model is presented in 1992 (Nordhaus, 1992). It is called DICE

for a Dynamic Integrated Climate Economy model. This model is said to correct for the

shortcomings of previous studies. The basic idea is to use a Ramsey model of optimal

economic growth with certain adjustments and to calculate the optimal path for both capital

accumulation and GHG emissions reductions.

The model is an optimal-growth model for the world economy. It is designed to

maximize the discounted utility or satisfaction from consumption subject to a number of

economic and climatic constraints. The global economy is assumed to produce a composite

commodity. The composite economy is endowed with initial stock of capital and labour and

an initial level of technology and all industries behave competitively. Each country

maximizes an inter-temporal objective function identical in each region which is the sum of

discounted utilities. Population growth and technological change are exogenous. There isno need for international trade since the outputs of the different countries are perfect

substitutes.

Another important feature of this model is that it is assumed that GHG emissions

can be controlled by increasing the prices of factors or outputs that are GHG- intensive.

The presentation also says that the model can be interpreted either as an optimizing

framework or as an outcome of idealized competitive markets. It is assumed that the public

goods nature of climate change is somehow overcome in an efficient manner. That is, it

assumes that, through some mechanism, countries internalize, in their decision making, the

global costs of their emissions decisions.

Two subjective variables which are not discussed, seem to be important in the

model: in the Objective function, defined as the pure rate of social time preference later

introduced as the market rate of interest or the marginal productivity of capital, and g

in the Utility equation defined as inequality aversion which is assumed equal to zero for

no apparent reason.

We emphasize on the importance of these two parameters, for the following

reasons: plays an important role in determining the discounted value of utility, which

later would become a major issue in Nordhaus criticisms of the Stern Review (Nordhaus,

2006); in turn g is related to the Atkinson Index (Atkinson,1970) which captures subjective

inequality. The Atkinson index depends on the inequality aversion parameter, e, of the


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decision-maker or society, and measures the fraction of total income which could be given

up with no loss of social welfare if the remainder were to be distributed equally (Lambert

et al., 2003). While Nordhaus assumed this parameter as equal to zero in his growth model,

Stern (1977) found e = 1.97 for the UK income tax code in fiscal year 1973/4 Sterns

paper also contains a comprehensive survey of many other approaches to evaluating the

elasticities of both private and social marginal utilities of income, reporting values found by

a range of authors for different countries using various methodologies as high as e = 10 and

as low as e= 0.4 (Lambert et al., 2003).

The important conclusions from this version of Nordhaus model results are that an

efficient strategy for coping with greenhouse warming must weigh the costs and benefits of

different policies at different points of timeEstimates of both costs and damages are

highly uncertain and incompleteIn terms of damages the impact of climate change

coming from a 3C rise in global mean surface temperatureis estimated to be a about 1.3

of output for the global economy (Nordhaus, 1992).

As an improvement of the DICE model, a new model called RICE is presented in

1996, by Nordhaus and Yang. The name stands for Regional Integrated model of Climate

and the Economy. This is described as a regional dynamic general equilibrium model of the

economy which integrates economic activity with the sources emissions and consequences

of greenhouse-gas emissions and climate change. By disaggregating into countries the

model analyses different national strategies in climate change policy. The model asks how

nations would in practice choose climate-change policies in light of economic trade-offs

and national self-interests for reductions of GHGs. In the RICE model the world is divided

into 10 regions, each is endowed with an initial capital stock, population, and technology.

Of these three variables capital accumulation is determined by optimizing the flow of

consumption over time. The major economic choices faced by nations (or the group of

nations) are: (a) to consume goods and services; (b) to invest in productive capital and (c)

to slow climate change through reducing CO2 emissions. In the model there are also three

types of strategies undertaken by nations to deal with GHG emissions: (1) Market policies,

meaning no controls on GHG emissions; (2) Cooperative policies, nations agree to reduce

CO2 emissions in a globally efficient way and (3) Non cooperative policies, individual

nations undertake policies that are in their national self-interests ignoring the spill overs of


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their actions to other nations. From to the results of the model there are seven basic

conclusions. The most important seems that the model estimates the difference between

cooperative efficient policy and the non-cooperative policy. This latter is one in which

countries maximize their economic welfare taking policies of other countries as given.

This implies that small countries whose climate change policies have little effect on their

own economic welfare, will have little incentive to reduce emissions while the largest

countries will have greatly attenuated incentives to engage in costly reductions in CO2

emissions (Nordhaus, 1996). The results of the model indicate that the stakes in

controlling global warming are modest in the context of overall economic activity over the

next century. The estimates indicate that losses from global warming will be in the range of

1 to 2 per cent of global income over the next century. According to the model successful

cooperation would lead to net gains, but the failure to cooperate is unlikely to lead toeconomic disaster over the next century.

In a book published in a digital and paper version as well, called Roll the DICE

again: Economic Models of Global Warming by Nordhaus and Boyer (1999) the authors

made a detailed description of Nordhaus general equilibrium world models built until then

and they run a new version of DICE. In Chapter 4 of this book the impacts of climate

change are analysed. The model called RICE-99 estimates damage functions for both the

world and by region and sector. The results seem to be of the greatest importance. The

chapter says in page 31: The results differ markedly by region. The impacts (of a 2.5C

global warming) range, from a net benefit of 0.7 per cent of output, for Russia, to a net

damage of almost 5 per cent, for India. The global average impact is estimated to be 1.5 per

cent of output, using projected output weights and 1.9 per cent of output using 1995

regional population weights. Current projections of RICE-99 indicate that total warming

in an uncontrolled environment will be slightly below 2.5C around 2100. Our estimate is

that damages are likely to be around 1.9 per cent of global income using 2100 output

weights. The damages for the US, Japan Russia and China are essentially zero over that

time frame, assuming that catastrophic scenarios do not materialize. Europe, India and

many low income regions appear vulnerable to significant damages over the next century.

Right after his debate with Stern, Nordhaus published a new book The Challenge of

Global Warming: Economic Models and Environmental Policy (Nordhaus, 2007) with new


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or revised projections and estimations of his RICE and DICE models. The models are again

General Equilibrium models, with the usual assumptions for general equilibrium and the

emphasis on welfare economics considerations as central for the evaluation of policies. In

this case the central questions to be answered are: How sharply should countries reduce

CO2 and other GHG emissions? Should there be a system of emissions limits imposed on

firms, industries and nations? Or should emissions reductions be primarily imposed through

taxes on GHG?

The author of the study begins right away to answer these questions: In practice an

economic analysis of climate change weights the costs of slowing climate change against

the damages of more rapid climate change. Economic history and analysis indicate that it

will be most effective to use market signals, primarily higher prices of carbon fuels, to give

signals and provide incentives for consumers and firms to change their energy use andreduce their carbon emissions. In the longer run, higher carbon prices will provide

incentives for firms to ease the transition to a low-carbon future.

The major results of the model are: The base line case projects a rapid and

continued increase in CO2 emissions by 2100, which will increase the mean global surface

temperature by 3.1C by 2100 and 5.3C by 2200, relative to 1900. Climate changes are

estimated to increase global damages by 3 per cent of global output in 2100 and close to 8

per cent of global output in 2200.

As in previous studies an important result is the estimation of the optimal carbon

price or optimal carbon tax, since it is the policy most favoured by this type of analysis. So

it is called the efficient policy.

Genera l Equilibrium Environment models in Mexico

Although in Mexico there have been many General Equilibrium models dealing with

various aspects of the economy. The only known GE models that we know deal with

environmental issues are the one by Roy Boyd and Maria E. Ibarrarn (Boyd and Ibarrarn,

2008) dealing with one of the probable effects of climate change drought in Mexico; the

other is called Anlisis econmico robusto para el desarrollo de estrategias de bajas

emisiones para Mxico (A robust economic analysis for the development of low emissions

strategies for Mexico), just recently produced by Hctor M. Bravo Prez, Juan C. Castro


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Ramrez y Miguel A. Gutirrez Andrade, all staff members of the Faculty of Economics at

the University of Mexico (UNAM), still unpublished.

The authors of the first study mentioned (Boyd and Ibarrarn) state in the articles

abstract the following: Climate change is increasing the intensity of extreme weather

events. Mexico is particularly prone to suffer at least two different types of these events:

droughts and hurricanes. This paper focuses on the effects of an extended drought on the

Mexican economy. Through a computable general equilibrium model, we simulate the

impact of a drought that affects primarily agriculture, livestock, forestry, and hydropower

generation. We look at the effects on the overall economy. We then simulate the effects of

several adaptation strategies in (chiefly) the agricultural, forestry, and power sectors, and

we arrive at some tentative yet significant conclusions. We find that the effects of such an

event vary substantially by sector with moderate to severe overall impacts. Furthermore, wefind that adaptation policies can only effect modest changes to the economic losses to be

suffered.

The second study longer and maybe more ambitious has a summary that goes like

this (translated from Spanish):

The work has the objective of calculating the effect on the distribution of income in

Mexico of a tax applied on fossil fuels demand with the purpose of reducing CO2

emissions. To this end two Computable General Equilibrium models are built, one is static

the other dynamic. The methodology followed is the one proposed by Shoven and Whalley

and the software used is GAMS. There are various taxes according to the different fossils

fuels types and there is also one general tax to all. There is a simulation of the economic

behaviour in two scenarios according to the value of the elasticity of energy substitution: a

rigid elasticity of 0.2 and a flexible of 20. The Mexican economy turned out to be closest to

rigid elasticity values. The results of the model show that the tax strategy has the desired

effect of reducing the fossil fuel demand, but taxing coal affect only its demand not

generating a substitution effect towards any other energy good. On the other hand the

welfare effect is clearly differentiated. It depends on the relative importance coal play in the

household as an input in home meals preparation. As a consequence of this simulated tax,

households are divided into two groups. The distributive effects are negligible given the

small importance coal has, compared to other energy goods in the economy. But for other


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energy goods different from coal, there are substitution and complementary effects when a

tax is applied to these goods. In this case there is a high income concentration effect. For a

flexible economy, welfare losses are lower than for a rigid economy. Therefore, the most

important conclusion is that in a rigid economy substitution between energy goods is more

difficult and produces a welfare loss. Thus, a public policy aimed to reduce the social costs

of reducing CO2 emissions by way of taxes, must take into account the type of fossil energy

good to be taxed and besides it should try to induce technological change and/or change the

type of current regulation rules for energy substitution, that is subsidies or taxes to energy

products (Bravo, H. et al., 2012).

4. Econometric models

With respect to applied models we reviewed only two types: Macro-econometric models

and Environment Input-Output models. Among the first group there is one world-widemodel called the Energy-Environment-Economy Model at the Global level (E3MG)

elaborated by experts from the University of Cambridge, and which in combination with a

chemistry transport model called p-TOMCAT has also been applied to the case of Mexico.

The E3MG model is highly appealing to us, for two main reasons: (1) It is based on

historical data collected by official agencies; (2) It is defined as Post Keynesian, in the

sense that, it is demand driven and it does not assume full employment or perfect

competition in the neoclassical fashion. Also because it takes into account various

economic sectors, that is, it is defined as a multi-sector model (Barker, et al., 2008).

The model applied to Mexico by Terry Barker and his group provided an important

insight on the situation and perspectives of Climate Change impacts and mitigation options

for Mexico. The results show that if Mexicos government applies policy measures oriented

to reduce fossil fuel consumption scenario called low carbon Mexicoit can improve its

rate of growth in the medium and long run from 3.61 per cent a year 2005-2050 in real

terms (baseline trend) to 3.64 per cent, (0.03 percentage points increase), reducing at the

same time GHG emissions by 80 per cent in 2050 with respect to the trend. The strategy is

clear and straightforward: Stronger regulations help to upgrade the vehicle stock towards

low or zeroemission vehicles, switch the power sector substantially to renewables and

improve energy efficiency in industry and buildings. These are complemented by a carbon-

emissions trading scheme, to reduce emissions from the energy sectors, and carbon taxes on


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other sectors (Barker, et. al., 2010). The results could be improved if the rest of the world

follows a similar low-carbon strategy at the same time. In such a case, Mexicos annual

growth rate would be increased even further (to 3.65 per cent a year 2005-2050).

The other model applied to Mexico (SEMARNAT, 2009a) is also a macro-

econometric and multi-sectoral model. Its results, however, are not as optimistic as Barker

models. The argument of SEMARNAT goes like this: if we assume a mean growth rate of

3.5 per cent of GDP in real terms a year for the Mexican economy and do nothing for

mitigation, then the desired 50 per cent reduction of GHG emissions by 2050 (with respect

to 2002) cannot be accomplished. In order to reach this reduction target we have to incur in

various mitigation costs that amount to an average of 2 percentage points in the annual

level of GDP. So the economy cannot grow as fast, due to high mitigation costs.

In that respect Barker argues that: such results are largely based on equilibrium

modelling in which the cost increases are assumed in the theory underlying the models, and

technological change is assumed to be exogenous. He also explains that:the difference in

results for GDP comes from the assumptions that revenues [in his model] are recycled to

fund the low-carbon investment, and that there are underemployed resources in the

economy to allow for faster growth. If we assume that the extra in vestment crowds out

private consumption then the model gives a reduction in GDP of 0.5 per cent by 2050.

5. Applied Environmental Input-Output models

Since Leontiefs important works on environmental issues (Leontief, 1970, 1973), Input-

Output models have incorporated into the analysis, pollution and pollution elimination

activities, for instance in modern Input-Output textbooks (Miller and Blair, 2009; Ten Raa,

2005) and in a wide variety of published books, articles and chapters in books, some of

them cited in the Bibliography.

In some of the various studies and reports dealing with Climate Change and

technology, published by Intergovernmental Panel on Climate Change (IPCC) (1996, 2001)

and the Organization for Economic Cooperation and Development (OECD) (Hai et al.,

2010, Duval, 2008), Input-Output models are considered useful and valid methods for

estimating economic costs and impacts of climate change and its abatement technologies.

The same consideration is given by Terry Barker (see Barker, 1998).


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With respect to I-O models specifically relating to climate change, mitigation

technologies, impacts and costs evaluation at a national level, we have found a very limited

number of references. Of them all, there are four that called our attention for various

reasons: they were applied to a country not a region within a country, or a region consisting

of several countries; they explicitely dealt with the application of abatement technologies

for reducing GHG emissions in the Input-Output matrix of that country; the models were

explicitely formulated; they were published and/or easy accesible; and finally there were

actually applied and produced results in a practical case. These were the models applied to

the Dutch economy: Idenburg (1998); Idenburg and Wilting (2000, 2004); Wilting, Faber

and Idenburg (2004); Brink and Idenburg (2007).

The EIO models applied to the Netherlands are in fact two different ones, with

different characteristics and objectives. The first one in time was DIMITRI. This wastheoretically developed by Idenburg (1998) mostly based on Duchin and Langes model

regarding technological change and Input-Output (1992, 1994) and explicitly aimed to

evaluate the impacts of the implementation by the Dutch government of GHG emissions

abatement technologies on the Environment, and Health and Welfare of Dutch population.

However, the model so designed could not be used with empirical data until few years later

in a second paper (Idenburg and Wilting, 2000, 2004). In this paper the authors claim that

they developed a dynamic input-output model of the Dutch economy that enabled the

investigation of effects of technical changes in individual economic sectors on the whole

economy and the environment.

A crucial aspect for the dynamics and the introduction of new technologies in the

model are the variables and equations regarding: investment by sector, capital goods

capacity (existing, expected and planned) by sector, depreciation rates by sector, and the so

called Matrix of capital coefficients. The installed technology is a mix of technologies

implemented in previous periods. As a result of depreciation and new investments, the

installed technology in all sectors changes every period. After installing new technologies,

the technological matrix depicts the new installed mix of technologies. The model estimates

the technological matrices for each period.

They applied the model so constructed to the Dutch economy for the period 1980-97

trying to interpret the actual technological change occurred with the model and then tested


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its estimated results to actual values, finding no statistically significant discrepancy

between them. The authors compared also the models outcomes to a set of imaginary

projected data assuming that no technological change had occurred, that is, assuming fixed

1980 technology. The results of this comparison are somehow contradictory. While in the

sector of business services production changing technology caused a higher growth rate

than fixed technology, in agriculture it was the opposite. With respect to energy use the

results show the opposite situation in each of these two sectors, namely, in business

services there is an increase in energy use by the changing technology and in agriculture

there is a decrease, both compared to fixed imaginary technology. But the authors claim

that the exercise was just to validate the use of DIMITRI for investigating the effects on

new technologies in future scenarios. And to do that they say a technology database

including at least a new available technology for each sector is required.The third application of DIMITRI to the Dutch economy reviewed is the one

presented in the paper by Wilting, Faber and Idenburg (2004). In this case the idea was to

explore possible scenarios of technological change aimed to reducing GHG emissions.

This paper presents a method that combines an extrapolation method of technical

coefficients with more specific knowledge on technologies. The extrapolation method

generates a reference path, independent of the scenarios. This reference path can be seen as

a more or less autonomous path to the future, based on the past trends. The scenarios are

variations based on this reference path. For the specific technology scenarios, detailed

information on the rise and fall of technologies is implemented. In the reference scenario,

technical coefficients are based on historical trends.

The method was applied in four scenarios, which are based on the framework

developed by the IPCC (2000). A large number of climate change scenarios are clustered

into four which are referred to as A1, B1, A2 and B2. These scenarios are descriptive in

nature, distinguished from each other along two lines: from efficiency to equity; from

globalization to regionalization.

The production calculated for the four IPCC scenarios fluctuates around the values

in the reference scenario. For almost all sectors the production in the A1-scenario is the

highest. This scenario shows a further increase in production in business services, which


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leads to an increase of production in other sectors. In all scenarios electricity production

increases, due to a higher rate of electrification in the future.

The emissions in the A-scenarios are lower due to a relative high share of genetic

modification techniques. Organic agricultural techniques will be partly applied in the B2-

scenario (and to a lesser extent in the B1 scenario) in 2030. In A1, A2 and B1 scenarios, a

further decrease in nitrogen emissions is achieved as a result from some general efficiency

improvements. Finally, the introduction of alternative agricultural production chains results

in fewer emissions especially in B1 and to a lesser extent in A1 and B2.

The second type of model applied to the Dutch economy is the one developed by

Brink and Idenburg (2007). It is different from DIMITRI in two important features: (1) it is

not dynamic, so it does not include any explicit data or estimation of capital stock,

investment, depreciation by sector, nor a capital coefficients matrix; (2) it is built with thepurpose of studying the effects of the application of the best GHG abatement technology

per sector, choosing one among various alternatives. The selection is based on a total cost

analysis implemented in an optimization I-O model. The technologies considered for

election are all add-on technologies, that is, they do not imply a change in the product or in

the production process.

An important feature of this model is that it assumes the working of a permit

scheme under free market rules. That is to say the model privileges an environmental policy

of GHG reduction through a permit market system.

The model is said to be based on a modified Leontief EIO model, which is, in turn,

extended for cost effective analysis. The model is extended by including q abatement

technologies that can be applied to reduce the emissions of k pollutants. Abatement

technologies are included as separate production processes that can be added to the

production processes producing the commodities. The total reduction in emissions is the

sum of the reduction by the separate technologies. The cost of abatement depends on the

technologies available and their cost. The cost of abatement is made up of the amount of

inputs from other sectors and the primary inputs (labour) required for using the abatement

technologies. The price of emission permits depends on the quantity of permits available

and the abatement cost of the various trading partners.


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The model is formulated as an optimization problem minimizing total cost of

production (i.e. the sum of the cost of the primary inputs in the various sectors) given

restrictions on final demand and quantities of emission permits. Decision variables are the

production level in the various sectors, the level of abatement by the various abatement

technologies and the amount of permits obtained by each sector.

Abatement technologies are aimed to the reduction of one pollutant. The price of

emission permits for a specific pollutant paid by a sector cannot be lower than the marginal

cost of reduction of that pollutant in the same sector. The price of commodities is affected

by environmental policy through the price of emissions which depends on the permit price

and the cost of abatement. This implies that with more stringent restrictions on the quantity

of emission permits, resulting in a higher permit prices and marginal abatement costs, the

cost of polluting production increases and hence the price of the associated commodities.The authors used a highly aggregated Input-Output table of the Dutch economy,

with five production sectors causing emissions of two pollutants. Emissions can be abated

by a number of abatement technologies with different abatement potential and different

costs. If a sector faces a price for emission permits that is higher than the price of abatement

by a certain abatement option in that sector, the sector will implement this abatement

option. The price of the permits will at least be as high as the price of abatement by the

most expensive abatement option that is implemented in the economy.

Since there is free trade of permits among sectors, permits will be allocated over

sectors in such a way that the total abatement cost over all sectors is minimized. As a

consequence of the system of tradable emission permits, emissions will have a price that

adds to the total production cost. Results of calculations with the model show that a cost-

efficient overall reduction of CO2 emissions by up to 5 per cent can be realized at relatively

low cost by abatement in the agriculture and industry. A further reduction in emissions (up

to 20 per cent) requires substantial abatement costs, in particular for abatement options in

the energy sector. Total abatement cost in the energy sector increase to more than 7 per cent

of value added.

These two types of EIO models applied to the Dutch economy have both different

purposes and therefore different assumptions: DIMITRI is dynamic and Brink and

Idenburgs (B&I) is static. DIMITRI considers technological change as a change in each


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sectors production function, while B&Is model considers end-of-the-pipe technologies.

The first is looking for effects of technical change in various scenarios (it is a forecast

model). The second is looking for the optimal technology in terms of sectoral costs (it is an

optimization model). Both deal with Input-Output matrices highly aggregated.

The different characteristics of these models and the last similarity just mentioned

may represent a disadvantage for a richer analysis in each of them, as rightfully expressed

by B&I (2007). However, we consider both models results very important for policy-

making and technically worthy. As always the major difficulty is the availability,

opportunity and reliability of the information needed to apply them to another countrys

case.


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III.ACOMPARISON MEXICO-CANADA

In a previous study (SEMARNAT, 2009b) we analysed the 2003, Mexican Input-Output

matrix and identified the most pollutant sectors, both in absolute and relative terms,

according to GHG emissions inventory reported by Mexican official agencies. We alsomade a comparison of our estimates to another countrys forwhich we chose Canada, a

nation which has been close to Mexico especially since the operation of the North

American Free Trade Agreement (NAFTA) in 1994. Canada is among those countries

engaged in implementing GHG emissions reduction policies, as required by the Kyoto

Protocol.

Canada has had a complete follow up of GHG emissions reduction by sector and of

the costs incurred by firms for GHG emissions reduction associated in particular to their

respective production levels. With this information and Canadas GDP by sector we have

estimated current and capital expenses realized per sector for GHG emissions reduction.

We use this information to estimate the corresponding costs for the same sectors of the

Mexican economy, as if these Canadian abatement technologies were applied in Mexico.

1. Method for estimating comparison indicators

We gathered annual data about current and capital expenses incurred to reducing GHG by

sector in Canada this was available for the years 2002 and 2004. We also obtained the

actual levels of GHG emissions for the years 2003 and 2005. This allowed us to assume aperiod of adaptation for new investments to actually reduce GHG emissions. With this

information we calculated the cost of reduction of GHG per ton of CO 2 equivalent. In turn

this allowed the comparison with the same sectors in the Mexican economy and the

estimation of their corresponding GHG emissions reduction in these selected sectors.

There were fifteen industries for which this information was available in Canada all

of which belong to the energy sector in its stationary and industrial subsectors, according to

the IPCC source classification. They emitted in the whole 45 and 38 per cent of total of

GHG emissions for the years 2003 and 2005. These 15 industries represented more than 20

per cent of Canadas GDP, in the economy 79 industries. The only important industries

missing were those related to transportation, that in many countries are high GHG

emissions producers. But, at that time, there was no information about any GHG reduction

activities and their corresponding costs for these industries. It is important to stress the fact


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that Canada reduced in an important way the level of GHG emissions intensity relative to

GDP in the period analysed.

In Mexico all the industries considered for comparison were for the same period

high GHG emissions producers; they represented 57 per cent of total GHG emissions and

their share of the GDP was also of more than 20 per cent. The only industry which is not

even close as important as that of Canada is Pipeline Transportation.

2. Estimation results

The results of the calculations and comparison are shown in Tables 1 and 2. The first is

related to Canada and indicates that with a total expense equivalent to 0.5 per cent of GDP

of these industries, GHG emissions were reduced in 6 per cent per year. It must be said,

however, that we did not apply an I-O analysis or a macro dynamic analysis. So, there are

no feedback and multiplier effects of these investments on the Canadian economy acrosstime being considered here.

The results varied among industries, in some cases GHG emissions variations were

not negative as expected but positive. This might be attributed to the different periods the

various investments take to start producing positive results in different industries, also to

the fact that different industries have different rates of growth.

There is an important case that must be mentioned, that of industry (3) Forestry, that

switched from high CO2 emitter to important CO2 capturer. The outstanding performance

of this industry is due to the very successful reforestation policy followed by Canada, at

very low cost per GHG ton relative to the rest of the industries.

For Mexico the estimation of GHG emission intensities with respect to GDP, as

compared to Canadas indicates that in that period and in the same group of industries,

Mexico was in general more inefficient than Canada, that is to say, each unit of product

generated as a by-product a higher GHG emission quantity in Mexico than in Canada.

But probably the most important result is that by applying the same GHG abatement

technical methods used in Canada in each of the selected industries and assuming the same

Canadian costs converted into Mexican pesos Mexico would have to expend 8.5 per

cent of these industries GDP in order to eliminate, or reduce to zero, the GHG emissions of

these industries, which represented 57 per cent of the total GHG emissions.


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Someone may say that these comparisons Mexico-Canada may not be totally valid

due to the important differences existing between these two countries regarding the specific

characteristics of each countrys climate, economic structure and institutional setup. So, the

Canadian abatement methods could not simply be transposed into the Mexican context.

This is true to some extent. However, Mexico and Canada have the same technical

coefficient matrix structure with, of course, different numbers in each. If we understand a

sectors technology as a function of production represented by the sectors column of the

technical coefficient matrix and we are referring to the same sectors in Mexico and Canada

we can compare technologies and we can even substitute them one for the other. Assuming

of course, everything else remains the same.


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Table 1

Expenses on GHG Cost per

No. Industry emissions reductio Ton of GHG Var.

2003 2005 Variation Mill Dlls % GDP reduced 2003 2005

3 Forestry and logging 11,000 -27,000 -19,000 45.5 0.76 2.39 2.03 -4.43 -3.23

6 Oil and gas extraction 69,164 68,244 -460 181.6 0.38 394.64 1.22 0.78 -0.22

7 Metal ores and mineral mining 15,700 15,600 -50 38.0 0.35 760.00 1.69 0.98 -0.36

9 Electric power generation, transmission, and dist. 135,000 129,000 -3,000 172.1 0.71 57.35 5.20 4.66 -0.27

14 Food manufacturing 8,729 9,517 394 34.3 0.19 34.30 0.48 0.49 0.00

15 Beverage and tobacco manufacturing 2,988 3,399 205 6.8 0.11 6.80 0.48 0.49 0.00

20 Wood product manufacturing 5,538 6,048 255 146.5 1.12 146.50 0.48 0.49 0.00

21 Pulp, paper, and paperboard mills and paper products 8,990 7,340 -825 200.2 1.74 242.67 0.84 0.68 -0.08

23 Petroleum and coal products manufacturing 4,836 4,756 -40 33.3 0.82 833.33 1.22 1.02 -0.10

24 Chemical product and preparation manufacturing 13,210 14,250 520 91.6 0.61 91.60 0.87 0.97 0.05

26 Nonmetallic mineral product manufacturing 13,180 14,080 450 23.7 0.44 23.65 2.41 2.35 -0.03

27 Iron and steel mills and manufacturing 6,370 6,520 75 50.0 0.43 50.00 0.60 0.51 -0.05

28 Fabricated metal product manufacturing 17,200 16,200 -500 32.5 0.23 65.00 1.23 1.10 -0.07

32 Transportation equipment manufacturing 14,546 12,936 -805 29.6 0.10 36.78 0.48 0.49 0.00

41 Pipeline transportation 9,110 10,100 495 23.8 0.44 23.80 1.64 1.82 0.09

Selected Industries Total 335,560 290,990 -22,285 1,109.3 0.49 49.78 1.46 1.07 -0.20

CANADA GHG EMISSIONS AND GHG REDUCTION EXPENSESGHG Emissions GHG Emission Intensity

Gg CO2 eq. GgCO2/GDP


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Table 2

GHG Emis sion Cos t per

No. Selected Industries Emissions intensity GHG Ton % of

Pesos Can Dls. GgCO2eq GHG/GVA reduced Can Dls. Pes os GVA

3 Forestry and logging 15,513.2 2,005.5 51,500 25.68 2.39 123.2 952.9 6.146 Oil and gas extraction 369,934.5 47,824.2 37,253 0.78 394.64 14,701.7 113,721.8 30.74

7 Metal ores and nonmetallic mineral mining 35,923.4 4,644.1 2,637 0.57 760.00 2,004.2 15,503.4 43.16

9 Electric power generation, transmission, and dist. 77,012.1 9,955.9 120,845 12.14 57.35 6,930.4 53,609.1 69.61

14 Food manufacturing 301,297.5 38,951.0 7,052 0.18 34.30 241.9 1,871.0 0.62

15 Beverage and tobacco manufacturing 69,672.6 9,007.1 1,374 0.15 6.80 9.3 72.3 0.10

20 Wood product manufacturing 17,392.5 2,248.5 551 0.25 146.50 80.7 624.4 3.59

21 Pulp, paper, and paperboard mills and paper products 27,740.5 3,586.2 1,644 0.46 242.67 398.9 3,085.4 11.12

23 Petroleum and coal products manufacturing 33,838.5 4,374.6 36,941 8.44 833.33 30,783.8 238,121.6 703.70

24 Chemical product and preparation manufacturing 116,740.0 15,091.9 8,476 0.56 91.60 776.4 6,005.6 5.14

26 Nonmetallic mineral product manufacturing 84,604.7 10,937.5 40,157 3.67 23.65 949.7 7,346.3 8.68

27 Iron and steel mills and manufacturing 76,476.1 9,886.6 11,080 1.12 50.00 554.0 4,285.2 5.60

28 Fabricated metal product manufacturing 40,461.0 5,230.7 1,282 0.25 65.00 83.3 644.5 1.59

32 Transportation equipment manufacturing 191,874.8 24,805.1 209 0.01 36.78 7.7 59.5 0.03

41 Pipeline transportation 4,999.6 646.3 0 0.00 23.80 0.0 0.0 0.00

Selected Industries Total 1,463,481.1 189,195.1 321,000 1.70 49.78 15,978.7 123,600.0 8.45

MEXICO GHG EMISSIONS AND ESTIMATED REDUCTION COSTSGross Value Added Total Costs of Reduction

Millions Millons


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IV.STRATEGIC AND HIGH GHG SECTORS IN THE MEXICAN ECONOMY

In the first part of this section we use various Input-Output techniques to identify those

industries of the Mexican economy that may be called strategic from a structural point of

view. In the second, we measure GHG emissions by industry and identify those consideredas highly emitters both in relative and absolute terms. In this section of the study we are

using the National Symmetric Input-Output Matrix (IOM) of Mexico for the year 2003 and

an estimate for 2008. This matrix is presented in two formats: the domestic requirements

matrix and the total requirement matrix (including imported requirements). For this analysis

we utilize the total requirement matrix. For GHG emissions we are using the Inventario

Nacional de Gases de Efecto Invernadero (National Inventory Report of GHG), produced

by the Mexican Instituto Nacional de Ecologa (INE). In Table 1, bellow there is a

description of each one of the 79 industries of the IOM of Mexico and its classification

number in the North American Industry Classification System (NAICS). This is a similar

analysis, of the previous one we did for SEMARNAT, but this time with the most recent

information, and with different results.

1. Strategic or Key economic sectors

In Input-Output analysis, sectors or industries are labelled as strategic or key, due to their

effects on others, either through demand or through supply. The relation between any two

industries is called linkage, so we have forward linkages, those related to supply, andbackward linkages, those related to demand. We first need to find out the existence of

linkages between industries and, in each case, its relative importance. So, those industries

that have many linkages with others and these linkages are very strong, will transmit

backwardly or forwardly economic effects to others. These industries are then called

strategic or key. The reason they are called this way is that the increase or decrease in their

production, may cause a demand pull and/or a supply push variations to other industries

with effects on overall gross production, input consumption, and/or labour employment. So

these industries are essential for any growth promoting policy.

We make use of two basic indicators that allow us to evaluate the relative

importance of all industries and classify them according to their capacity to transmit

economic impulses through the system of quantities and prices that represents the IOM.


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This capacity of disseminating impulses among industries, show their potential to generate

external diseconomies, as for instance, ecological ones.

Table 3

No. NAICS Indus try

1 111 Crop production

2 112 Animal production

3 113 Forestry and logging

4 114 Fishing, hunting and trapping

5 115 Support activities for agriculture and forestry

6 211 Oil and gas extraction

7 212 Metal ores and nonmetallic mineral mining

8 213 Support activities for mining

9 221 Electric power generation, transmission, and distribution

10 222 Water, sewage and natural gas distribution

11 236 New nonresidential construction

12 237 New residential construction

13 238 Maintenance and repair construction

14 311 Food manufacturing

15 312 Beverage and tobacco manufacturing

16 313 Textile mills

17 314 Textile product mills

18 315 Apparel manufacturing

19 316 Leather and allied product manufacturing

20 321 Wood product manufacturing

21 322 Pulp, paper, and paperboard mills and paper product manufacturing

22 323 Printing and related support activities

23 324 Petroleum and coal products manufacturing24 325 Chemical product and preparation manufacturing

25 326 Plastics and rubber products manufacturing

26 327 Nonmetallic mineral product manufacturing

27 331 Iron and steel mills and manufacturing

28 332 Fabricated metal product manufacturing

29 333 Machinery and equipment manufacturing

30 334 Computer, communications and electronic equipment and components manufacturing

31 335 Electric lighting equipment, household appliance and other electric components manufacturing

32 336 Transportation equipment manufacturing

33 337 Furniture and related product manufacturing

34 339 Other miscellaneous manufacturing

35 43-46 Wholesale and retail trade36 481 Air transportation

37 482 Rail transportation

38 483 Water transportation

39 484 Truck transportation

40 485 Transit and ground passenger transportation

41 486 Pipeline transportation

42 487 Scenic and sightseeing transportation and support activities

43 488 Transportation support activities

NORTH AMERICAN INDUSTRIAL CLASSIFICATION SYSTEM

INPUT-OUTPUT INDUSTRY CLASSIFICATION


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a) Key Sectors according to Rasmussen coefficients

Matrix (IA)-1, known as Leontiefs inverse, allows one to solve the system in order to

find out the level of production of the ith sector required to satisfy the increment in the final

demand of the jth sector, in one unit. This matrix refers, in consequence, to demand.

Meanwhile the inverse matrix (ID)-1 that has been derived from the distribution matrix is

Table 3 continued

No. NAICS Indus try

44 491 Mail services

45 492 Couriers and messengers

46 493 Warehousing and storage

47 511 Newspaper, periodical, book, and software publishers

48 512 Motion picture and sound recording industries

49 515 Radio and television broadcasting, cable networks and program distribution

50 516 Internet publishing and broadcasting

51 517 Telecommunications

52 518 Internet service providers, web search portals, and data processing

53 519 Other information services

54 521 Monetary authorities

55 522 Credit intermediation and related activities

56 523 Securities, commodity contracts, investments, and related activities

57 524 Insurance carriers, pension funds, and related activities

58 531 Real estate

59 532 Non real state goods rental and leasing

60 533 Lessors of nonfinancial intangible assets

61 541 Professional, scientific, and technical services

62 551 Management of companies and enterprises

63 561 All other administrative and support services

64 562 Waste management and remediation services

65 611 Educational services

66 621 Ambulatory health care services

67 622 Hospitals

68 623 Nursing and residential care facilities

69 624 Social assistance

70 711 Performing arts, spectator sports, and related services

71 712 Museums, zoos and parks

72 713 Amusements, gambling, and recreation

73 721 Accommodation

74 722 Food services and drinking places

75 811 Goods repair and maintenance services

76 812 Personal and laundry services

77 813 Civic, religious, social, professional and similar organizations

78 814 Private households

79 931 Federal State and local Government activities

NORTH AMERICAN INDUSTRIAL CLASSIFICATION SYSTEM

INPUT-OUTPUT INDUSTRY CLASSIFICATION


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referred to supply. From the elements of these two matrices we can get coefficients that

measure the capacity to generate or to absorb, increments in the various industries. For this

we need to consider first the sum of the elements of each raw, zi, and of each column zj,

which are called absorption effect and dispersion effect, respectively (United Nations,

2000). The coefficients created by Rasmussen (1956) are developed on the base of each one

of these effects and are obtained by calculating the average of each effect in each industry,

and express these averages as ratios with respect to the global effects.

The estimated quotient based on the absorption effect is known as the Absorption

Power Index and it is defined by the formula:

n

i

n

j

ij

n

i

ij

jz

n

zn

U

1 12

1

. 1

1

(1)

where: Uj = absorption power index of industry j, n = number of rows or columns in the

matrix, zij = element ij of matrix (IA)-1. This index measures in relative terms, the power

of any given industry to dragging along, or pulling, the whole economy, also called

Backward Linkage.

Similarly, with the dispersion effect, the Dispersion Power Index is calculated by an

equation, based on the distribution matrix:

n

i

n

j

ij

n

j

ij

i

zn

zn

U

1 12

1

.1

1

(2)

where: Uj = dispersion power index, n = number of rows or columns in the matrix, zij =

element ij in matrix (ID)-1. This index measures in relative terms the impact produced by

one industry over the rest, also called Forward Linkage. The results are shown in Table 4.


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Table 4

No. Industry B.L. No. Industry F.L.

1 30 Computer, communications and electronic equipment manuf. 2.1345 24 Chemical product and preparation manufacturing 3.7554

2 31 Electric lighting equipment and household appliance manuf. 1.5614 6 Oil and gas extraction 3.6203

3 32 Transportation equipment manufacturing 1.4964 35 Wholesale and retail trade 3.4202

4 17 Textile product mills 1.4418 30 Computer, communications and electronic equipment manuf. 2.73525 34 Other miscellaneous manufacturing 1.3904 23 Petroleum and coal products manufacturing 2.6556

6 16 Textile mills 1.3437 27 Iron and steel mills and manufacturing 2.6410

7 25 Plastics and rubber products manufacturing 1.3430 61 Professional, scientific, and technical services 2.2770

8 28 Fabricated metal product manufacturing 1.3071 63 All other administrative and support services 2.1490

9 18 Apparel manufacturing 1.3051 25 Plastics and rubber products manufacturing 1.6122

10 21 Pulp, paper, and paperboard mills and manufacturing 1.3009 31 Electric lighting equipment and household appliance manuf. 1.5794

11 9 Electric power generation, transmission, and distribution 1.3005 32 Transportation equipment manufacturing 1.5783

12 19 Leather and allied product manufacturing 1.2862 21 Pulp, paper, and paperboard mills and manufactu

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