772 PUB Report Wp12

The Trade-Off Among Carbon Emissions, Economic
Growth and Poverty Reduction in

India
VIJAY PRAKASH OJHA
Rajiv Gandhi Institute for Contemporary Studies (RGICS)

New Delhi, India
August 2005
South Asian Network for Development and Environmental Economics (SANDEE)

PO Box 8975, EPC 1056
Kathmandu, Nepal
SANDEE Working Paper No. 12-05

SANDEE Working Paper No. 12-05 I
Published by the South Asian Network for Development and Environmental Economics
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National Library of Nepal Catalogue Service:
Vijay Prakash Ojha

The Trade-off Among Carbon Emission, Economic Growth and Poverty Reduction in India
(SANDEE Working Papers, ISSN 1813-1891; 2005 - WP 12)
ISBN: 99946-810-1-X
Key Words
1. CGE model
2. Carbon Emissions
3. Economic Growth
4. Poverty Reduction
5. India
6. Climate Change
7. Carbon Tax policy
8. Tradable Emission Permits
The views expressed in this publication are those of the author and do not necessarily
represent those of the South Asian Network for Development and Environmental
Economics or its sponsors unless otherwise stated.
II SANDEE Working Paper No. 12-05

The South Asian Network for Development and Environmental
Economics
The South Asian Network for Development and Environmental Economics (SANDEE)
is a regional network that brings together analysts from different countries in South
Asia to address environment-development problems. SANDEEs activities include
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Cooperation (NORAD).
Technical Editor
Priya Shyamsundar
English Editor
Carmen Wickramagae
Comments should be sent to Vijay Prakash Ojha, Rajiv Gandhi Institute for Contem-
porary Studies (RGICS) New Delhi, India. Email: vpojha@gmail.com
SANDEE Working Paper No. 12-05 III
IV SANDEE Working Paper No. 12-05
TABLE OF CONTENTS
1. INTRODUCTION 1
1.1 The energy and emissions scene in India 2
1.2 Policies for carbon emissions reduction 3
1.3 The present study 5
2. MODEL STRUCTURE 6
2.1 Sectoral disaggregation 7
2.2 The production structure 8
2.3 Technological change 9
2.4 Carbon emissions 9
2.5 Carbon Taxes 10
2.6 Investment 10
2.7 Capital stocks 11
2.8 Labour markets and wage rates 11
2.9 Factor incomes and transfers 11
2.10 Income distribution 11
2.11 Savings 13
2.12 Market equilibrium and macroeconomic closure 14
2.13 Dynamics 14
3. THE BUSINESS-AS-USUAL SCENARIO 15
3.1 The macro variables 15
3.2 Poverty ratio 15
3.3 Energy use 16
3.4 Carbon emissions 16
4. POLICY SIMULATIONS 16
4.1 Policy simulations 1 and 1(TT) 19
4.5 Policy simulations: caveats 23
5. CONCLUSIONS AND POLICY IMPLICATIONS 24
6. ACKNOWLEDGEMENTS 26
REFERENCES 27
APPENDIX 1 31
APPENDIX 2 36
APPENDIX 3 51
SANDEE Working Paper No. 12-05 V
LIST OF TABLES
Table 1 : Energy consumption in India (petajoules) 2

Table 2 : Energy consumption and carbon emission trends 3
Table 3 : The policy simulations 17
Table 4 : BAU Scenario and the policy simulations: Selected 30
variables in 1990
Table 5 : BAU Scenario and the policy simulations: Selected 30
variables in 2020
Table 6 : Macro variables and carbon emissions of the BAU scenario 31
Table 7 : Energy Use 31
Table 8 : Carbon tax rates 31
Table 9 : Energy Prices (percentage difference from BAU scenario) 32
Table 10 : Carbon emissions (percentage share of fossil fuels) 32
Table 11 : Carbon emissions 32
Table 12 : Per capita carbon emissions 33
Table 13 : GDP 33
Table 14 : Consumption 33
Table 15 : Poverty ratio (in percent) 34
Table 16 : Number of poor 35
LIST OF FIGURES
Figure 1 : The production structure 8

Figure 2 : BAU Scenario: Growth rates of macro variables 35
Figure 3 : BAU Scenario: GDP/K & GDP/L 35
VI SANDEE Working Paper No. 12-05

Abstract
This study examines the consequences of a) a domestic carbon tax policy, and, b)
participation in a global tradable emission permits regime on carbon emissions, Gross
Domestic Product (GDP), and poverty, in India. The results, based a computable general
equilibrium model of the Indian economy, show that a carbon tax policy that simply
recycles carbon tax revenues to households imposes heavy costs in terms of lower
economic growth and higher poverty. However, the fall in GDP and rise in poverty can
be minimized or even prevented if the emission restriction target is a very mild one and
tax revenues are transferred to the poor. A soft emission reduction target is all that
India needs to set for itself, given that even a ten percent annual reduction in aggregate
emissions will bring down its per capita emissions to a level far below global per capita
emissions. On the other hand, participation in the tradable emission permits regime
opens up an opportunity for India to sell surplus permits. India would then be able to
use the revenues from permits to speed up GDP growth and poverty reduction and
keep its per capita emission below the 1990 per capita global emissions level.
Key words: CGE model, carbon emissions, economic growth, poverty reduction, India,
climate change, carbon tax policy, tradable emission permits.
SANDEE Working Paper No. 12-05 VII

The Trade-Off Among Carbon Emissions, Economic Growth and
Poverty Reduction in India
Vijay Prakash Ojha
1. Introduction
The linkage between carbon emission reduction, economic growth and poverty
alleviation is an issue of immense relevance for India. India is highly vulnerable to
global warming and global climate change caused by emissions of greenhouse gases
such as carbon dioxide. The adverse effects of climate change would in all likelihood
retard the developmental process and aggravate poverty. At the same time, Indias per
capita carbon emission is already very low. It is 0.26 tonne per annum, which is one-
fourth of the world average per capita emission of one tonne per annum (Parikh et al,
1991). In other words, Indias per capita contribution to global warming problem is a
relatively minor one. However, because of its large and growing population, its total
emissions are large. Internationally, India is expected to stabilize its energy related
carbon emissions 1. Moreover, the fact that India has a real stake in a global policy
regime to stabilize global carbon emissions is being realized in Indian policy circles.
More specifically, Indian policy makers are beginning to see the need to understand
the implications for India of a Kyoto-type global emissions trading regime.
At the domestic level, India is concerned with the reduction of carbon emissions whether
a global system of tradable emission permits materializes or not. This concern, however,
is a very long term one. Switching over to non-polluting sources of energy such as,
hydro and nuclear, is often mentioned as a strategy that will sweep away the problem
of carbon emissions. A medium term policy option such as a carbon tax, however, is
viewed with suspicion, largely because of its likely adverse impact on economic growth
and poverty reduction. For a low-income country like India, the more pressing need
obviously is achieving poverty reduction rather than controlling carbon emissions.
Nevertheless, it would be worthwhile exploring how much, if at all, carbon taxes trade-
off growth and poverty reduction, and what compensatory mechanisms can be built
into the system to mitigate the undesirable effects of carbon taxes on GDP growth and
poverty alleviation.
This study seeks to answer three questions related to policy trade-offs between carbon
emission reduction, growth and poverty: a) what are the economic and distributional
impacts of imposing carbon taxes when tax revenues are recycled back into the
economy? b) How do the effects on growth and poverty change if emission targets are
1
India is the fifth-largest emitter of fossil-fuel-derived carbon dioxide, and its total emissions grew at an
annual average rate of almost 6 percent in the 1990s (Marland et al , 2001). Moreover, Sagar (2002, 3925)
argues that : the pressure already on them (developing countries), to show meaningful participation is
likely to intensify in the continuing negotiation, making it quite likely that they will have to take on some
commitments to reduce their greenhouse gas emissions in the post-Kyoto phase. Even though its
(Indias) annual per capita emissions for 1998 of 0.3 tonnes of carbon are well below the global average of
1.1 tonnes per capita, the size of its (Indias) aggregate emissions makes its participation in any future
developing country commitment regime a foregone conclusion.
SANDEE Working Paper No. 12-05 1
lowered and tax revenues are transferred directly to the poor? And c) How are GDP
growth, poverty and carbon emissions affected if India participates in a global tradable
emissions regime? These issues are addressed by using a Computable General
Equilibrium (CGE) Model of the Indian economy.
1.1 The energy and emissions scene in India
In India, about 30% of the total energy requirements are still met by the traditional or
non-commercial sources of energy like fuelwood, crop residue, animal waste and animal
draught power. The share of these non-commercial forms of energy in the total energy
consumption has, however, been on decline. It was as high as 50% in 1970-71, but
came down to only 33% in 1990-91. In other words, the energy consumption pattern
has been increasingly shifting in favor of the commercial forms of energy like coal,
refined oil , natural gas, and electricity. So much so, that in the last four decades,
growth rate of commercial energy consumption has been higher than that of the total
energy consumption. Coal itself accounts for more than 37% of the total energy
consumption in 1990-91, with the share of refined oil and natural gas being about 18%
and 5% respectively. The non-fossil sources of energy, such as, hydro-electricity has a
small share of about 6.5%, with the remaining 0.5% share of the total energy
consumption being accounted for by the non-conventional energy sources, such as,
nuclear, wind and solar power.
In the two decades from 1970 to 1990, energy consumption in India has more than
doubled (table 1). More importantly, during this period biomass, which is a carbon
neutral fuel (Ravindranath and Somsekhar, 1995), has been increasingly substituted by
the fossil fuels, mainly coal. This has resulted in a major increase in the level of carbon
emissions in India (table 2 ).
Table 1 : Energy consumption in India (petajoules)

Year 1970 1975 1980 1985 1990 2000 2005
Lignite 19 29 44 77 130 216 259
(0.39) (0.48) (0.62) (0.85) (1.12) (1.22) (1.21)
Coal 1466 1910 2222 3124 4201 8498 10198
(29.77) (31.81) (31.07) (34.49) (36.10) (48.07) (47.58)
Refined 622 799 1082 1480 2035 2813 3785
Oil & LPG (12.63) (13.31) (15.13) (16.34) (17.49) (15.91) (17.68)
Natural Gas 42 79 86 270 606 815 1156
(0.85) (1.32) (1.20) (2.98) (5.21) (4.61) (5.39)
Biomass 2492 2821 3202 3518 3866 4456 5052
(50.61) (46.98) (44.77) (38.83) (33.22) (25.20) (23.67)
Hydropower 258 334 484 540 723 744 775
(5.24) (5.56) (6.77) (5.96) (6.21) (4.21) (3.62)
Other 25 33 32 49 74 138 211
(0.51) (0.55) (0.45) (0.54) (0.64) (0.78) (0.99)
Total 4924 6005 7152 9059 11636 17680 21437
(100) (100) (100) (100) (100) (100) (100)
Notes: 1. Refined Oil and LPG includes non-energy use of gas and fuel oil for fertiliser and
petrochemical production.
2. For hydro, nuclear and renewables, energy is the coal equivalent for electricity generation
3. Other includes nuclear, wind, solar etc.
4. The italicized figures in the parantheses show the percentages with respect to the total.
Source : Authors estimates based on CMIE Energy and TEDDY (2002/03).
2 SANDEE Working Paper No. 12-05
Table 2 : Energy consumption and carbon emission trends
Year 1970 1975 1980 1985 1990 2000 2005
Energy consumption (PJ) 4923 6005 7152 9059 11636 17680 21437
Net carbon emission (MT) 61.58 79.54 95.78 134.63 183.39 247.69 292.26
Gross carbon emission (MT) 129.64 156.59 183.23 230.72 288.99
Notes : Net carbon emission excludes emissions from biomass combustion.

Gross carbon emission includes emissions from biomass combustion.
PJ : petajoules, MT : metric tons
Source: Fisher-Vanden et al (1997) & Marland, Gregg, Tom Boden, Robert J Andres (2003).
In the 1980s, the Indian economy grew at an average annual rate of 5%, with industrial
output rising at about 6.3% per year. During this time, Indias commercial energy sector
grew at about 6% a year, with electricity use growing faster at 9% annually. In the
post-liberalization (i.e., after 1990-91) phase, the Indian economy averaged a higher
annual growth rate of about 6%. Indias energy demand can only grow even more
rapidly in the future on account of high prospective economic growth, spreading
industrial base, a rapid population growth and increasingly energy-intensive consumption
patterns that results from higher incomes. In fact, projections show that Indias energy
demand could increase four-fold by 2025, while its carbon emissions could increase
six-fold as traditional biomass fuels are replaced by higher fossil fuel use.
1.2 Policies for carbon emissions reduction
The standard policy measures for greenhouse gases abatement are basically four -
energy efficiency improvement measures, command-and-control measures (i.e.,
implementing emission reduction targets by decree), domestic carbon taxes and an
international emissions trading regime of the kind envisaged for the Annex B countries2
in the Kyoto protocol. Of these while the first one is, so to say, desirable per se, the
other three are regarded as policy alternatives.
A lot of avoidable CO 2 emissions is due to the rampant energy inefficiency, which, in

turn is the result of energy subsidies still prevailing in India, as in many other countries.
However, since the early nineties, there is an increasing realization of the link between
energy inefficiency and unnecessary CO2 emissions leading to a worldwide decline in
energy subsidies. In India also the energy subsidies have been reduced since the onset
of economic reforms in 1991. The reduction in the energy subsidies notwithstanding
final-use energy prices in India, again as in many other countries, are still well below
the opportunity cost (Fischer and Toman, 2000). In fact, the energy price reforms in
India are far from complete, and not surprisingly, they have, as yet, had only an
insignificant impact on energy efficiency and, thereby, on carbon emissions (Sengupta
and Gupta, 2004).
2
Annex B countries refer to the OECD countries, the countries in Central and Eastern Europe, and the
Russian Federation, which have agreed to emissions reduction obligations under the Kyoto Protocol.
The specific emissions reduction commitments of these countries are listed in Annex B of the Kyoto
Protocol, hence they are referred to as Annex B countries.
Unlike the energy efficiency improvement measures, the other three measures for
emissions abatement - command-and-control, carbon taxes and international emissions
trading - are in India not yet at the implementation stage. As far as international emissions
trading is concerned, India threw its hat in the Kyoto ring a little too late. By the time
India acceded to the Kyoto protocol in August 2003 as a prelude to the eighth annual
Conference of Parties, which it was hosting, the protocol had already gone into abeyance
because of USAs withdrawal from it. Gupta (2002) has infact argued that had India
been more proactive in its approach and acceded to the Kyoto protocol in its early
phases, the American stand of not joining the protocol without any commitment from
the developing countries would have become difficult to maintain. And the turn of events
could have been completely different.
Now (16 February 2005) that the Kyoto Protocal has come into force, the industrialized
countries are required to cut their combined emissions to 5% below 1990 levels by the
first commitment period, 2008-2012. The developing countries have been absolved of
any responsibility towards reducing emissions in the first commitment period. This,
however, is no reason for developing countries climate change should ultimately aim at
fixing pollution rights or entitlements for each country according to some agreed upon
equity principles, and the Kyoto Protocol can be and may be viewed as a step in this
direction (Chander, 2004: 272). In other words, once competitive emissions trade
among Annex B countries is established, the developing countries will be able to better
assess the potential gains from such trade, and might be tempted to participate in a
global emissions trade in the post-Kyoto phase of climate change negotiations.
The command-and-control measure, i.e., enforcing carbon emission reduction targets

by fiat is, not surprisingly, not regarded in India as feasible or desirable. Firstly, there
are the usual arguments of command-and-control measures being statically and
dynamically inefficient as compared to say market-based instruments, such as, carbon
taxes (Pearson, 2000). Secondly, under the command-and-control measure, the
economic cost of emission abatement (arising mainly due to curtailment of output, given
limited input substitution possibilities) represents a deadweight loss in welfare. On the
other hand, in case of a market-based instrument like carbon taxes, the government
can use the tax revenue in a variety of ways to generate benefits for the economy in
addition to those resulting from reduced emissions, thereby, reducing the net loss in
welfare. It can use the carbon tax to replace some other more distorting tax and thus
garner efficiency gains for the economy, i.e., reap double-dividend (Pearson, 2000).
Or what is more pertinent, in case of a developing economy like India, it can use the
tax revenue for targeted transfers to reduce poverty, or more specifically, recycle the
carbon tax revenue to the low-income groups to compensate the latter for the burden
imposed on them by the carbon emission reduction strategy.
It follows that, although policy action in India for carbon emissions abatement, apart
from the ongoing energy price reform, has not yet materialized, the status-quo cannot
be maintained for long. Fortunately, the prelude to policy action, i.e., informed policy
discussion has been initiated in the literature on carbon emission reduction strategies in
India. Two policy instruments domestic carbon taxes and internationally tradable
emission permits have been discussed in the literature on India. For the latter, Murthy,
Panda and Parikh (2000) have shown, using an activity analysis framework, that India
stands to gain both in terms of GDP and poverty reduction, if the emission permits are
allocated on the basis of equal per capita emission. Fischer-Vanden et al (1997) have
used a CGE model to compare the impacts of the two policy instruments on GDP, and
found that tradable permits are preferable to carbon taxes. In a comparison of the two
types of schemes for emission permits the grandfathered emission allocation scheme
in which permits are allocated on the basis of 1990 emissions, and the equal per capita
emission allocation scheme they found the latter to be more beneficial for India.
Incidentally, the CGE model of Fischer-Vanden et al (1997) is based on the assumption
of a single representative household. Hence, it does not reflect the impact of carbon
taxes on income distribution or on the poverty ratio.
1.3 The present study
In the present study we have used a top-down, quasi-dynamic CGE model, with an
endogenous income distribution mechanism, for the Indian economy. Our model has
been formulated with a view to capture the adverse effects of carbon taxes on GDP
losses and the poverty ratio through increased prices of fossil fuels (coal, refined oil
and natural gas). The non-uniform increases in the prices of fossil fuels will lead to
some fuel switching as well as an overall fuel reducing effect. Our model will effectively
capture the net impact of these effects on GDP as well as income distribution. Compared
to the model of Murthy, Panda and Parikh (2000), ours is a neoclassical price driven
CGE model, ideally suited for simulating the impact of a carbon tax and of a system of
global trade in carbon emission quotas. And compared to the CGE model of Fischer-
Vanden et al (1997) 3 which is based on the assumption of a single representative
household, our model has an elaborate income and consumption distribution mechanism,
in which factoral incomes are first mapped onto 15 income percentiles and then onto 5
consumption expenditure classes. The bottom consumption expenditure class corresp-
onds to those below the poverty line so that we get a measure of the poverty ratio as
well.
As is usually done in a CGE modeling analysis, we first generate a business-as-usual

scenario, and then simulate alternative policy scenarios for assessing the consequences
for growth and poverty in India of different carbon emission reduction strategies. The
specific policy questions to which the policy scenarios are addressed are the following:
(i) What is the impact of imposing carbon taxes to ensure that aggregate carbon emissions
do not exceed the 1990 levels in each period during the time span 1990-2020 given
that the carbon tax revenues for each period are recycled to the households by way
of additions to personal disposable income ?
(ii) What is the impact of imposing carbon taxes to bring about a 10% annual reduction
in aggregate carbon emission levels during the time span 1990-2020 given that the
carbon tax revenues for each period are recycled to the households ?
(iii) What is the impact of participating in an internationally tradable permits scheme in
which the carbon emission allowances are allocated on the basis of equal per capita
emissions allocation which are kept fixed to the participating countrys 1990
population, when the revenues earned, if any, from the permits are recycled to the
households ?
3
The Fischer-Vanden et al (1997) study uses a nine-sector CGE model of the Indian economy based on the
Indian module of the Second Generation Model (SGM) version 0.0 detailed in Edmonds, Pitcher, Barns,
Baron and Wise (1993).
There are two variants considered for each policy question mentioned above, one in
which the revenues earned from carbon taxes or sale of emission permits are distributed
across household groups in proportions same as those for the routine government
transfers - i.e., the case of across-the-board transfers, and the other in which these
revenues are transferred exclusively to a target group, which consists of the four lowest
income classes (deciles) or the poorest 40% households in the economy- i.e., the case
of targeted transfers4.
The rest of the paper is organized as follows. Section 2 presents the overall structure
of the model, with special emphasis on the production structure, the production-CO 2
emission linkages and the income distribution mechanism. Section 3 presents the main
features, such as GDP growth and emissions growth, of the business-as-usual (BAU)
scenario. In section 4, we report the simulation results of eight alternative policy
scenarios in comparison with the BAU scenario. Section 5 concludes and suggests
policy implications of our results. Appendix 1 gives the tables and figures related to
the BAU scenario and the policy simulations. In Appendix 2 we present the equations
of the model. Appendix 3 describes the database of the model.
2. Model Structure
Our model is based on a neoclassical CGE framework that includes institutional features
peculiar to the Indian economy. It is multi-sectoral and quasi-dynamic. The overall
structure of our model is similar to the one presented in Mitra (1994). However, in
formulating the details of the model - the production structure, the CO 2 emission
generation and the income distribution mechanism - we follow an eclectic approach,
keeping in mind the focus on the linkages between inter-fossil-fuel substitutions, CO2
emissions, GDP growth and poverty reduction.
The model includes the interactions of producers, households, the government and the
rest of the world in response to relative prices given certain initial conditions and
exogenously given set of parameters. Producers act as profit maximizers in perfectly
competitive markets, i.e., they take factor and output prices (inclusive of any taxes) as
given and generate demands for factors so as to minimize unit costs of output. The
factors of production include intermediates, energy inputs and the primary inputs -
capital, land and different types of labour. For households, the initial factor endowments
are fixed. They, therefore, supply factors inelastically. Their commodity-wise demands
are expressed, for given income and market prices, through the Stone-Geary linear
expenditure system (LES). Also households save and pay taxes to the government.
Furthermore, households are classified into five rural and five urban consumer
expenditure groups. The government is not asssumed to be an optimizing agent. Instead,
goverment consumption, transfers and tax rates are exogenous policy instruments. The
total CO2 emissions in the economy are determined on the basis of the inputs of fossil
fuels in the production process, the gross outputs produced and the consumption
demands of the households and the government, using fixed emission coefficients.
4
For a detailed description of the two types of transfer of revenues earned through carbon taxes or sale of
permits the across-the-board transfers and the targeted transfers see section 2.10.
The rest of the world supplies goods which are imperfect substitutes for domestic output to
the Indian economy, makes transfer payments and demands exports. The standard small-
country assumption is made implying that India is a price-taker in import markets and can
import as much as it wants. However, because the imported goods are differentiated from
the domestically produced goods, the two varieties are aggregated using a constant elasticity
of substitution (CES) function, based on the Armington assumption5. As a result, the imports
of a given good depend on the relation between the prices of the imported and the
domestically produced varieties of that good. For exports, a downward sloping world
demand curve is assumed. On the supply side, a constant elasticity of transformation (CET)
function is used to define the output of a given sector as a revenue-maximizing aggregate of
goods for the domestic market and goods for the foreign markets. This implies that the
response of the domestic supply of goods in favor or against exports depends upon the
price of those goods in the foreign markets vis--vis their prices in the domestic markets,
given the elasticity of transformation between goods for the two types of markets.
The model is Walrasian in character. Markets for all commodities and non-fixed factors -
capital stocks are fixed and intersectorally immobile - clear through adjustment in prices.
However, by virtue of Walras law, the model determines only relative prices. The overall
price index is chosen to be the numeraire and is, therefore, normalized to unity. With the
(domestic) price level fixed exogenously, the model determines endogenously both the
nominal exchange rate and the foreign savings in the external closure (Robinson, 1999).
Finally, because the aggregate investment is exogenously fixed, the model follows an
investment-driven macro closure, in which the aggregate savings - i.e., the sum of household,
government and foreign savings - adjusts, to satisfy the saving-investment balance.
2.1 Sectoral disaggregation
Our model is based on an eleven sector disaggregation of the Indian economy :

(i) Agriculture (agricult),
(ii) Electricity (elec),
(iii) Coal (coal),
(iv) Refined Oil (refoil),
(v) Natural Gas (nat-gas),
(vi) Crude Petroleum (crude-pet),
(vii) Transport (trans),
(viii) Energy Intensive Industries (enerint),
(ix) Other Intermediates including capital goods (otherint),
(x) Consumer goods (cons-good),
(xi) Services (services) .
There are 5 energy sectors elec, coal, refoil, nat-gas, crude-pet and 6 non-energy
sectors - agricult, trans, enerint, otherint, cons-good and services. The sectoral division
of the economy was decided after a perusal of the sectoral disaggregation in various
other models - such as EPPA, SGM and Murthy, Panda and Parikh (2000) - and
bearing in mind the focus of our model on the possibilities of fuel switching in the
provision of energy inputs in the production process.
5
The Armington assumption states that commodities imported and exported are imperfect susbtitutes of
domestically produced and used commodities. This assumption is necessary to take into account two-
way trade and, at the same time, avoid an unrealistically high degree of specialisation (Armington, 1969).
2.2 The production structure
Production technologies for all sectors are defined using nested CES functions, with
the nesting structure of inputs differing across the sectors, or groups of sectors as in
the EPPA model (Babiker et al, 2001 and Yang et al, 1996).
For the transport, energy intensive industries, other intermediates, consumer goods
and services sectors, the following tree describes the production structure (fig. 1).
Fig. 1 : The production structure
Domestic Sectoral Gross Output (X)
Non-Energy Intermediate (N) Energy-Labour-Capital Aggregate ( Z )

Inputs Aggregate
Domestic Imported
Intermediate Intermediate
Inputs Aggregate (Nd ) Inputs Aggregate (Nm)
Energy-Aggregate (EA) Value-Added (VA)
Electricity (E) Non-Electricity (NE) K Ls Lw
Coal (CL) Gas (GS) Refined Oil (RO)
Note : K Capital ; Ls Self-employed labour ; Lw Wage-labour.
In case of the remaining sectors, there are minor variations in the nesting structure. For
coal, natural-gas, crude petroleum and refined oil, there is an extra layer at the top
combining non-fixed factor inputs aggregate (NF) and fixed factor input (f) to produce
domestic gross output. In the electricity sector, the non-electricity inputs bundle is
formed in two stages instead of one i.e., first coal and refined oil are combined to
form coal-oil aggregate (COIL) and the latter subsequently combines with natural gas
(GS) to form non-electricity inputs aggregate (NE). In agriculture, at the top level of
the nesting structure, the domestic gross output is produced as a combination of resource
intensive bundle (RS) and value added (VA), where the former is made up of land and
energy-materials (EM) aggregate. The latter in turn is an Armington combination of non-energy
intermediate inputs bundle (N) and energy aggregate (EA).
In other words, for each sector there is a nested tree-type production function. At
each level of the nested production function, the assumption of constant elasticity of
substitution (CES) and constant returns to scale (CRS) is made 6. For every level, the
producers problem is to minimize cost (or maximize profit) given the factor and output
prices and express demands for inputs. It follows that for every level, the following
three relationships hold : the CES function relating output to inputs, the first order
conditions, and the product exhausation theorem. For all the levels taken together, the
production system thus determines, for each sector, the gross domestic output, the
input demands, value-added as well as the demands for wage-labour and self-employed
labour7.
2.3 Technological change
Energy-saving technological progress is incorporated in our model by making the

autonomous energy efficiency improvement (AEEI) assumption used in other carbon
emission reduction models such as, GREEN (Burniaux et al, 1992) and EPPA (Babiker
et al, 2001). As in the EPPA and GREEN models, we also assume that AEEI occurs in
all sectors except the primary energy sectors (coal, crude petroleum and natural gas)
and the refined oil sector. The GREEN model assumes a one percent annual increase
in energy efficiency, while in the EPPA model there is an even higher annual growth
rate of energy efficiency 1.4 percent initially, though it slows down over time according
to a logistic function. However, we are of the opinion that the exogenous annual growth
rates of energy efficiency assumed for India in these models are overly optimistic.
India has embarked on the path towards energy efficiency after 1991, but its record in
energy efficiency improvement in the last one decade is far from encouraging (Sengupta
and Gupta, 2004). We have thus assumed a much more modest annual growth rate of
energy efficiency for the Indian economy i.e., 0.5 percent.
2.4 Carbon emissions
CO 2 is emitted owing to burning of fossil fuel inputs. The major fossil fuels used in
India are coal, natural gas, refined oil and crude petroleum8. In addition to CO2 emitted
by fuel combustion, there may be CO 2 emanating from the very process of output
generation. For example, the cement sector (a part of the enerint sector in our sectoral
classification) releases CO2 in the limestone calcination process. Finally, CO2 emissions
also result from the final consumption of households and the government.
6
Although, the domestic and intermediate inputs aggregates themselves are fixed-coefficients aggregates
of domestic and imported inputs respectively from the non-energy sectors.
7
The capital stock in a particular period is given, so that the first-order condition effectively determines
the sectoral return on capital.
8
Note that crude petroleum is used exclusively as an input in the refined oil sector (see Appendix 2).
We use fixed CO2 emission coefficients to calculate the sector-specific CO2 emissions
from each of the three sources of carbon emissions. For the total CO 2 emissions
generated in the economy, we first aggregate the emissions from each of the sources
over the eleven sectors and subsequently sum up the aggregate emissions across the
three sources.
2.5 Carbon Taxes
Carbon taxes are applicable only on the CO 2 emitted in the production process (i.e.,
on the first two sources of carbon emissions), not on the final consumption of households
and the government (the third source of carbon emissions). Carbon taxes are based on
the proportion of each fuels carbon content, i.e., Rs per ton of carbon emitted. The
carbon tax rate multiplied by a sectors carbon emission gives the carbon emission tax
payments by that sector. Summing across sectors we get the total carbon tax payments,
which is then recycled to the household sector as additional transfer payments by the
government. (In the BAU scenario, the carbon tax rate is fixed at zero and there are,
therefore, no carbon tax payments). It may be noted that, the producers cost function
is modified to include the carbon emission taxes so that these taxes induce a substitution
in favor of lower carbon-emitting fossil fuels (see equations 35-38 in Appendix 2). A
carbon tax is translated into price increases for each of the fossil fuels coal, refined
oil and natural gas. The price increase is maximum for coal which has the highest carbon
content, followed by refined oil and natural gas. In response, a cost minimizing (or a
profit maximizing) producer changes the input mix away from coal and towards refined
oil and natural gas.
2.6 Investment
Public and private investment is fed into the model as two distinct constituents of the
total investment. There are fixed share parameters for distributing the aggregate
investment across sectors of origin. However, the allocation mechanisms for sectors of
destination are different in the two cases of public and private investment. For public
investment there is discretionary allocation, and the allocation ratios are set exogenously.
On the other hand, for private investment the allocation ratios are given in a particular
period, but are revised from period to period on the basis of sectoral relative returns
on capital. The relative return on capital in any sector is given by the normalization of
the implicit price of capital in that sector to the economy-wide returns. This rule does
not imply full factor price equalization, but only a sluggish reallocation of investment
from sectors where rate of return is low to ones having higher rates of return.
Needless to say, this bifurcation of total investment into its public and private components
with their differing allocation mechanisms is an attempt to approximate the way
investments are actually made in the Indian economy. Incidentally, it also allows for
public investments to be directed towards strategic sectors disregarding short-run
considerations of profit maximization.

2.7 Capital stocks
Sectoral capital stocks are exogenously given at the beginning of a particular period.
However, our model is recursively dynamic, which means that it is run for many periods
as a sequence of equilibria. Between two periods there will be additions to capital
stocks in each sector because of the investment undertaken in that sector in the previous
period. More precisely, sectoral capital stocks for any year are arrived at by adding
the investments by sectors of destination, net of depreciation, in year t-1 to the sectoral
capital stocks at the beginning of the year t-1.
2.8 Labour markets and wage rates
For the non-agricultural sectors (i.e. sectors 2-11), the total labour supply available
for employment is exogenously given. From this stock of labour those who are unable
to find wage-employment resort to self-employment. In the agricultural sector, on the
other hand, there is a fixed supply of self-employed labour (those owning land of
whatever size) and, over and above, there is a pool of labour (landless) waiting to to
find employment. Those who are unable to find wage employment become openly
unemployed, rather than resort to self-employment.
The real wage rates, for wage labour, in the current period are indexed to the previous
periods wage rates. This rule is applied to both the agricultural and non-agricultural
wage rates. In the non- agricultural sectors, those unable to find wage employment (at
the adjusted wage rate) spill over into the pool of self-employed labour to clear the
labour market. In other words, there is inflexible wage (keynesian) in the organized
sector and a market-clearing remuneration rate for the self-employed in the
unorganized sector (neo-classical).
2.9 Factor incomes and transfers
Factor incomes - i.e, self-employment incomes, wage incomes, incomes from rent
accruing to fixed factors including land, and capital (profit) incomes are generated by
summing the product of factor remunerations and their employment levels over all the
sectors. From these, taxes are netted out to arrive at disposable incomes. To these
five types of income is added a sixth type transfer payments by government and rest
of the world. Through these transfer payments the government can recycle the total
carbon tax revenues to the households. Factor incomes by region rural and urban
are worked out for each of the six types of income using fixed shares to split these
factor incomes into two parts, one for the rural and the other for the urban area 9.
2.10 Income distribution
The treatment of income and consumption distribution in our model is quite elaborate,
as it should be. However, it needs to be stressed that there is hardly any degree of
freedom in modeling the distribution of income in India. The mechanics of the income
distribution is strictly guided by the type of data available. A detailed account of the
9
The parametric values of the rural-urban split ratios are obtained from Pradhan et al (2000), and add up
to one for each of the six sources of income.
income distribution module is provided in Narayana, Parikh and Srinivasan (1991)
and Mitra (1994). Here we outline the main steps. (In what follows the account is the
same for the rural and urban areas, and so we shall not make a distinction between the
two).
Step 1 - We start with the factoral incomes and map them onto incomes accruing to 15
income classes 10 using a constant share income allocation scheme (obtained from
secondary data sources of the Indian economy see Appendix 3) for all the 6 types of
income self-employment income, wage income, capital income, incomes from land
and fixed factors and transfer payments by government and rest of the world11. Given
Y h , the income accruing to class h, and q h , the share of households in class h in the
total population (also known from data sources), we compute the mean and variance
of income .
It may be noted here that, in case of across-the-board transfers of revenues earned

from carbon taxes or sale of emission permits, these revenues are distributed across
the 15 income classes according to the same constant share income allocation scheme
applicable to the transfer payments above. To put it another way, in the across-the-
board transfers case, the carbon tax or permit revenues are simply treated like additonal
government transfers, and, hence, distributed across the 15 income classes in
proportions same as those for routine government transfers.
On the other hand, in case of the targeted transfers, the carbon tax or the permit
revenues are distributed exclusively and equally to the lowest four income deciles.
That is to say, each of the lowest four income classes (deciles) receive 25% of the
revenues earned from carbon taxes or sale of permits, while the remaining 11 income
groups or classes get nothing.
Finally, it must be stressed that, the lowest four income deciles or the poorest 40% of
the population are conceptually and quantitatively different from what we call the poverty
ratio (defined below in Step3). While the former specifies the relative income position
of a section of the population, the latter is the share of population at or below a pre-
defined minimum level of consumption necessary for sustenance. The relative income
inequality in most economies change slowly, but that does not mean that poverty cannot
be eradicated fast. The relative income position of the poor might remain unchanged,
but their consumption reach can be extended beyond the minimum sustenance level.
Hence, poverty ratio can decline rapidly even when relative income inequality is stable.
That said, it must be recognized that, in another sense which is important in this modeling
exercise, there is an overlap between the two concepts. That is, if there is poverty in
an economy, in the sense of absolute deprivation of basic minimum consumption, it
obviously exists in the lower rungs of the income ladder. From the poverty removal
10
The 15 income classes are percentiles taken in tens, fives and ones. The first nine income classes are,
from bottom to top, nine deciles, followed by the 10 th class which is more than
90 th percentile and upto 95 th percentile, and, finally, we have the top five income classes
i.e., the 96th, 97th, 98th, 99th and 100th percentile.
11
The constant shares i.e., the exogenously given split ratios - for each income-type add up across the 15
income classes to one.
policy point of view, therefore, it is the lowest four or three or two income deciles that
have to be targeted.
Step 2 - We first make the assumption that the distribution of population according to
per capita income and per capita consumption expenditure is bivariate log-normal.
(a) Since the distribution of income and consumption expenditure is assumed to be
bivariate log-normal, the mean and variance of the logarithm of per capita income
is computed from the mean and variance of income of Step 1.
(b) The bivariate lognormality assumption implies that log income and log consumption
expenditure are linearly related, so the mean and variance of log per capita
consumption expenditure can be easily calculated.
Step 3 Given the mean and standard deviation of log income and log consumption
expenditure, we derive the distributions of population, consumption and total income
by 5 consumption classes. (The upper boundaries of the 5 consumption classes cel1,
cel 2, cel3, cel 4, cel5 are taken from the consumption expenditure data published by the
NSSO (National Sample Survey Organization)-45th Round). More specifically, we
find the shares of (i) population (ii) consumption and (iii) total income accruing to the
households that fall under expenditure level celk , for k = 1,2,,5, using the standardized
cumulative normal distribution. The poverty ratio is the share of population with per
capita consumption expenditure less than or equal to cel5 .
Step 4 - From the cumulative shares of the five consumption expenditure classes we
arrive at the per capita expenditure and income for each of these classes by simply
taking the difference between the cumulative shares of the class in question and the
preceding class.
Step 5 Once we have the per capita consumption expenditure for each of the 5
consumption classes, we use the Stone-Geary linear expenditure system to determine
separately the sectoral per capita consumption demands for each of these classes.
Step 6 The sectoral per capita consumption demands for each class are then
multiplied by the class-specific population, and the resulting product aggregated, first,
over the five classes and, then over, the two regions to arrive at the commoditywise
consumption demands.
2.11 Savings
Total household savings in the economy is an aggregate of the savings of the 10 urban
and rural consumption expenditure classes. For each of the five rural and five urban
classes, household savings is determined residually from their respective budget
constraints, which state that household income is either allocated to household
consumption or to household savings. Government savings is obtained as sum of the
tax and tariff revenues, less the value of its consumption and transfers. Government
revenue originates from the following five sources: taxes on domestic intermediates,
tariffs on imported intermediates, taxes on consumption and investment, taxes on final
imports and income taxes - i.e., taxes on wage, self-employed and capital (profit)
incomes. All taxes (excluding carbon tax) are of the proportional and ad valorem type,
and all the tax rates are exogenously given. Government expenditure takes place on
account of government consumption and transfers to households, both of which are
exogenously fixed. The CO 2 emission taxes are recycled to the households via the
government, which means that they be included in (or excluded from) both the revenue
and the expenditure of the government budget. Foreign savings in the model is expressed
as the excess of payments for intermediate and final imports over the sum of exports
earnings, net current transfers and net factor income from abroad The latter two, it
may be noted, are exogenously given values in the model.
2.12 Market equilibrium and macroeconomic closure
Market clearing equilibrium in the commodity markets is ensured by the condition that
sectoral supply of composite commodity must equal demand faced by that sector. In
the production structure of the model the domestic gross output of a sector is defined
to be a combination of domestic sales and exports, based on a CET transformation
function. In turn, the domestic sales part of the sectoral gross output and the final
imports of that sector are aggregated through an Armington-type CES function to arrive
at the sectoral composite commodity supply 12. On the other hand, the demand for the
composite commodity consists of intermediate demand, final demand - which in turn is
an aggregation of consumption, investment and government demands - and change in
stocks.
The model is Walrasian in spirit with the sectoral prices being the equilibrating variables
for the market-clearing equations. The Walras law holds and the model is, therefore,
homogeneous of degree zero in prices determining only relative prices. The price index
defined to be a weighted average of the sectoral prices serves as the numeraire,
and is, therefore, fixed at one.
Finally, note that although the model is neoclassical in nature, it follows investment-
driven macro closure in which aggregate investment is fixed and the components of
savings - household savings, government savings and foreign savings - are endogenous
variables and adjust to equalize saving and investment.
2.13 Dynamics
The model is multiperiod in nature, where the unit of period is one year. However, it is
not an an inter-temporal dynamic optimization model; it is only recursively dynamic.
That is, it is solved as a sequence of static single-year CGE models, where investment
in the current year enhances the available capital stock and depreciation depletes that
stock, resulting in net additions (reductions) to sectoral capital stocks between two
periods. Likewise, the sectoral allocation ratios for private investment are revised from
period to period on the basis of sectoral relative rates of return on capital. Hence,
prior to solving the CGE model for any given year other than the base-year an
interim-period-sub-model (eqs. 101 to 103) is worked out to update the sectoral capital
stocks and the sectoral allocation shares of private investment.
12
Note that in the nesting structure diagram given above (fig. 1), these 2 functions are not shown. The
nesting diagram starts with the sectoral gross output at the top, and goes down the vertical linkages of
inputs.
3. The Business-as-Usual Scenario
Our CGE model has been calibrated to the benchmark equilibrium data set of the
Indian economy for the year 1989-90. The basic data set of the Indian economy for
the year 1989-90 has been obtained from the Central Statistical Organization - National
Accounts Statistics of India (various issues) and the CSO (1997) - Input-Output
Transactions Table - 1989-90. Other parameters and initial values of different variables
have been estimated from the data available in various other published sources.
Given the benchmark data set for all the variables and the elasticity parameters, the
shift and share parameters are calibrated in such a manner that if we solve the model
using the base-year data inputs, the result will be the input data itself (Shoven and
Whalley, 1992).
Finally, using a time series of the exogenous variables of the model, we generate a
sequence of equilibria for the period 1990-2020. From the sequence of equilibria,
with 5-year time intervals 13 , the growth paths of selected (macro) variables of the
economy are outlined to describe the BAU scenario.
3.1 The macro variables
In the BAU scenario, real GDP growth throughout the period 1990-2020 varies in the
range 4%-6%. The GDP growth rate, which is 5.7% per year during 1990-95, slows
down to less than 5% in the period 1995-2005 (table 6). After that the growth rate
picks up again to more than 5% per year till 2020 (figure 2). The driving force of GDP
growth in our model comes from growth in the two main exogenous variables - investment
and labour supply. In fact, the directional changes and the turning points in the
quinquennial GDP growth rates seems to be governed by the exogenously given
investment growth rates over the thirty year period. Investment adds to the capital
stock, inducing a substitution away from labour into capital. This results in an increase
in labour productivity, measured as GDP per unit of labour (figure 3). Growth in labour
productivity coupled with the simultaneous growth in labour supply is what provides
the main impetus to GDP growth.
3.2 Poverty ratio
The poverty ratio in the BAU scenario declines from 37.5% in 1990 to 2% in 2020
(table 15). However, the noteworthy fact is that the decline in poverty ratio is very
much linked to the growth in GDP. That is to say, with the GDP growing faster after
2005, the decline in poverty also speeds up. In the first 15-year period, 1990-2005,
the poverty ratio declines quinquennially by about 4-5 percentage points; in the later 15-
year period 2005-2020 it declines quinquennially by about 7-8 percentage points.
13
Since Indian database is on an annual basis, we solved the model annually for thirty years. However, the
results are reported for five-year intervals. This is because, results presented on a year-to-year basis for
thirty years, would not be amenable to any meaningful analysis.
3.3 Energy use
Total energy use increases by about 320% over the 30-year period 1990-2020.
However, the annual growth rate of energy use along with the annual growth rate of
GDP declines each quinquennium until 2005, with the decline being sharper in case of
the former after 2005 (table 7). Increased employment of capital in the production
process as well as modest autonomous energy efficiency improvement results in an
economy of the energy inputs in the production process as reflected in the declining
energy use per unit of GDP.
3.4 Carbon emissions
Total carbon emissions in the period 1990-2020 rise from 168 million tonnes to 559
million tonnes at an average rate of 4.1% per year (table 6). However, the growth rate
is not uniform. It drops from more than 4% in the pre-2005 period to less than 4% in
the post 2005 period. This is largely explained by the decline in the energy-GDP ratio
after 2005 (table 7). In the Indian economy carbon is emitted predominantly - as much
as 72% of the total emissions - from the combustion of coal. The share of coal in the
total emissions remains unchanged throughout the period (table 10).
In assessing Indias contribution to global carbon emissions, it is important to look at

the per capita carbon emissions 14. Indias per capita emissions in 1990 turn out to be
0.21 tonnes. It increases quite rapidly over the 30-year period and goes up to 0.69
tonnes by the year 2020 (table 12). Even this level of per capita emissions is considerably
less than the global per capita emissions which are approximately 1 tonne per year.
4. Policy Simulations
We develop eight alternative policy scenarios for two basic policy instruments for carbon
emission reduction - domestic carbon tax and internationally tradable permits based
on equal per capita emissions allocation.
For the carbon tax policy we have four policy scenarios - simulations 1, 1(TT), 2 and 2(TT). Policy
simulations 1 and 2 deal respectively with the two cases of fixing the carbon emission at the 1990 level
all through the 30-year period, and of 10% annual reduction in emissions, with 2 variants in each - one
in which the carbon tax revenues are recycled to the households like additional government transfers,
i.e., the across-the-board transfers case, and the other in which the tax revenues are exclusively transferred
to a target group comprising of the four lowest income deciles - i.e., the targeted transfers case.
For internationally tradable permits, we have again four policy scenarios - simulations 3, 3(TT), 4
and 4(TT) - representing the same 2 variants, with the difference that instead of carbon tax revenues,
we have, in this case, revenues earned from the sale of permits. For the policy scenarios 3 and
3(TT), the emissions quota is fixed at 1 tonne per capita14 based on 1990 population as suggested
by Parikh and Parikh (1998), who have argued that this would ensure equity between developed
and developing countries and simultaneously discourage the latter from increasing their population.
14
Note that the per capita emissions have been calculated on the basis of the 1990 population for all the
years, so that a higher population in the years subsequent to 1990 is not allowed to undermine the total
emissions in the economy.
The permit price for the simulations 3 and 3 (TT) is exogenously given to be US$ 6 per tonne of
carbon emission, which is Rs 100 per tonne at the 1989-90 exchange rate of Rs 16.60 per
dollar. In reality, the permit price will emerge from a global trading system of permits,
which, for example, has been modeled by Edmonds et al (1993) in the SGM. However,
ours is a country-specific exercise focusing on how it stands to gain or lose from an
internationally tradable regime of permits. We, therefore, take the world market price
of permits as given, but do consider alternative permit prices in different policy
simulations. Hence, the policy simulations 4 and 4(TT) are simply repeat exercises of
simulations 3 and 3(TT) respectively, with the permit price exogenously fixed at Rs
200 per tonne.
The eight policy simulations are summarized in table 3 given below.
Table 3 : The policy simulations

Policy Instrument Carbon Reveues from Carbon
Emission Tax/ Internationally
Restriction Tradable Permits
Policy Simulation 1 Domestic Carbon Fixed at 1990 Recycled to the households

Taxes level like additional government
transfers
Policy Simulation 1 (TT) Domestic Carbon Fixed at 1990 Recycled exclusively to a
[TT : Targeted Transfers] Taxes level target group of households
comprising of the four
lowest income deciles
Policy Simulation 2 Domestic Carbon 10 % annual Recycled to the households
Taxes reduction like additional government
transfers
Policy Simulation 2 (TT) Domestic Carbon 10 % annual Recycled exclusively to a
[TT : Targeted Transfers] Taxes reduction target group of households
comprising of the four
lowest income deciles
Policy Simulation 3 Internationally 1 tonne of Recycled to the households
Tradable Permits carbon per capita like additional government
[Permit Price= based on the transfers
$6 / tonne, i.e., Rs 1990 population
100 /tonne]
Policy Simulation 3 (TT) Internationally 1 tonne of Recycled exclusively to a
[TT : Targeted Transfers] Tradable Permits carbon per capita target group of households
[Permit Price= based on the comprising of the four
$6 / tonne, i.e., Rs 1990 population lowest income deciles
100 / tonne]
Policy Simulation 4 Internationally 1 tonne of Recycled to the households
Tradable Permits carbon per capita like additional government
[Permit Price = based on the transfers
$12 /tonne, i.e., Rs 1990 population
200/tonne]
Policy Simulation 4 (TT) Internationally 1 tonne of Recycled exclusively to a
[TT : Targeted Transfers] Tradable Permits carbon per capita target group of households
[Permit Price = based on the comprising of the four
$12/tonne, i.e., Rs 1990 population lowest income deciles
200/tonne]

It would be useful to bear in mind how the economy would adjust to the introduction
of domestic carbon taxes (policy simulations 1, 1(TT), 2 and 2(TT)) and internationally
tradable permits (policy simulations 3, 3(TT), 4 and 4(TT)) before going into a detailed
discussion of the eight policy scenarios.
A carbon tax results in price increases for each of the fossil fuels coal, refined oil
and natural gas. The extent of price increase in case of each of these fuels is determined
by the carbon content of the respective fuels. The price increase is largest for coal
because coal has the highest carbon content, and smallest for natural gas which has
the lowest carbon content. Producers respond by switching from coal towards refined
oil and natural gas as a source of energy. At the same time, higher energy prices force
a reduction in overall energy use. Carbon emissions are reduced on account of both
fuel switching and overall reduction in fuel use. Usually (inter-fossil-fuel substitutions
elasticities being low), the fuel reducing effect dominates over the fuel switching effect,
resulting in a retardation of GDP growth. Typically, the adverse effect of reduced energy
use on GDP growth diminishes over time as energy efficiency improvement coupled
with a higher capital intensity in the production process results in a declining energy
use per unit of GDP. Typically also, the slowdown in consumption growth is more
severe than that in case of GDP growth. When production activity goes down, labour
demand and wages decline leading to a fall in personal incomes (unless the addition to
personal income from the recycled carbon tax revenue is large enough to offset this
fall). Moreover, higher energy prices end up as higher prices for consumer goods, thus
lowering real consumption.
With the introduction of internationally tradable permits with equal per capita emissions,
India will most likely turn out to be a net seller of permits. A carbon emission quota of
1 tonne per capita based on the 1990 population of 810 million effectively means an
upper limit of 810 million tonnes of total carbon emissions for the Indian economy.
Looking at the carbon emissions in the BAU scenario (table 9), it is easy to see that
India will be a net seller of tradable permits for the next two or three decades. That is,
countries with high per capita emissions would purchase permits from countries with
low per capita emissions, such as India. That would in effect imply a transfer of wealth
into India. 16 The total revenue from the sale of permits in the international market for
permits is recycled to the households as transfer payments from rest of the world.
These transfer payments are akin to an autonomous increase in consumption demand
(like an increase in government expenditure), and, therefore, result in a higher demand-
driven GDP growth. Higher incomes boost consumption further, so that consumption
rises faster than GDP. However, over time as the economy gets close to the upper limit
of 810 million tonnes of total carbon emissions, the revenue earned from the sale of
permits will shrink, and the GDP gains will become progressively smaller. In fact, in
not so distant a future, the economy will turn around from being a net seller of permits
to a net buyer of permits.
It may be mentioned that, for our policy scenarios concerned with Indias participation
in a regime of internationally tradable permits with equal per capita emissions, we are
16
A net buyer of permits would amount to a transfer of wealth out of India, but that eventuality does not
arise till 2020 in our scenarios 3, 3 (TT), 4 and 4(TT).
assuming that the emission permit payments take place through the government, and
the latter decides to recycle these to the consumers, rather than producers. Till India is
a low per capita emissions country (i.e., till its per capita emissions remain below 1
tonne, the world average) it need not give priority to curbing emissions, but to income
distribution and poverty etc. Subsequently, it can switch priorities. That is our view, and our
policy scenarios 3, 3(TT), 4, 4(TT) emanate from this view.17
We now turn to an appraisal the policy scenarios. A summary of the key results of the policy
simulations are presented in the tables 4 and 5 (Appendix 1). In these tables, selected
variables GDP, consumption, aggregate carbon emissions, per capita carbon emissions,
poverty ratio and the absolute number of poor of the various policy scenarios are compared
with those of the BAU scenario. Needless to say, henceforth, all comparisons for all the policy
simulations have been made with respect to the BAU scenario.
4.1 Policy simulations 1 and 1(TT)
In this simulation the procedure followed is to fix the carbon emission level at the 1990 level and
to endogenize the carbon tax rate (which was fixed at zero in the BAU scenario). The sequential
equilibrium solution of the model then generates, among other values, the appropriate carbon tax
rates for each of the years subsequent to 1990. The tax rates rise from Rs 417 per tonne in 1995
to Rs 2765 per tonne in 2020. The growth rate of the carbon tax rate is lower 2005 onwards,
because of the lower energy consumption growth rates in this period (table 8). Carbon taxes
raise the price of the fossil fuels differentially the increase in price is maximum for coal which
has the highest carbon content, followed by that of refined oil and natural gas (table 9) and thus
induce fuel switching. The share of coal in total emissions, which was almost 73% throughout the
period in the BAU scenario, declines considerably, particularly after 2005. There are corresponding
increases in the share of refined oil. The share of natural gas increases only marginally (table 10).
The aggregate emission levels fall relative to the BAU scenario by 19% in 1995 and by 70% in
2020. Cumulative emissions in the 30-year period fall by 50% (table 11). Per capita carbon
emissions, based on the 1990 population, also fall drastically. In 2020, it is down to 0.21 tonne
per capita while it was 0.69 tonnes per capita in the BAU scenario (table 12).
The energy use and GDP trends of simulation 1 suggest that upto 2000, the fuel-reducing effect
dominates, and subsequently fuel-saving becomes more important in determining the impact on
GDP18. Upto 2000, the decline in GDP is more than that in the use of energy inputs. However,
from 2005 to 2020, energy use declines much faster than GDP. After 2005, the energy-GDP
ratio in simulation 1 is significantly lower than that in the BAU scenario (table 7).
17
Some analysts would want the emission permit revenues to be recycled to producers, who would then
invest them in new technology with lower carbon emissions. That would be another policy scenario
which we have not done in this study. However, it can be done in this model with some changes.
18
When carbon taxes are imposed , fuel inputs become costly. So, the immediate impact is a reduction in the
use of fuels leading to a large decline in output. As a consequence, energy-output ratio goes up. This is
known as fuel-reducing effect. However, over time the economy adjusts by indulging in more efficient
use of fuels. This results in a decline in the energy-output ratio. This is known as the fuel-saving effect.
Losses in consumption are higher than losses in GDP even though the carbon tax
revenues are recycled to the consumers (table 14). This is because the reduced economic
activity (reflected in a lower GDP) results in a decrease in the demand for labour and
wages causing disposable personal incomes to fall. Moreover, higher energy prices
are passed on to consumers through higher consumer goods prices which in turn lower
real consumption. The addition to household incomes from the recycled carbon tax
revenues are not sufficient to compensate for the fall in their incomes.
The poverty ratio, i.e., the percentage of poor below the poverty line, in simulation 1
increases drastically and progressively from 1995 to 2020. In the BAU scenario, the
poverty ratio is 32% in 1995, but declines to 2% in 2020. In simulation 1, the poverty
ratio is 34% in 1995 and declines to only 8% in 2020 (table 15). In other words, the
number of poor in 2020 in scenario 1 is 4 times the number of poor found in the BAU
scenario during the same year (table 16).
In the targeted transfers case of scenario 1 (TT)19, the poverty ratio improves a little vis-
-vis the across-the-board transfers case of scenario 1. However, in relation to the
BAU scenario, it is progressively higher from 1995 to 2020 (table 15). Moreover, the
number of poor in the year 2020 under scenario 1(TT) is almost 3.4 times that in the
BAU scenario in the same year (table 16).
Policy simulation 2, on the whole, is a milder version of policy simulation 1. In simulation

1, the average annual reduction in carbon emission works out to be 50%, while, in
simulation 2, the annual reduction in carbon emissions is fixed to be only 10% (table
11). Per capita emissions, fall progressively from 1990 to 2020. As compared to the
BAU scenario, they are 0.02 tonnes less in 1990 and 0.07 tonnes less in 2020 (table
12).
Expectedly, the carbon tax rates in simulation 2 are of much lower orders of magnitude.
The carbon tax rate is Rs.218 per tonne in 1990, rises a little in 1995, and, thereafter,
declines gradually to Rs.174 per tonne, because of lower energy consumption growth
rates in the latter period (table 8). Energy prices also increase moderately (table 9).
GDP and consumption losses in scenario 2, as compared to the BAU scenario, are of
much lower orders of magnitude than those in scenario 1 (tables 13 and 14). However,
consumption losses are more than GDP losses as in scenario 1. In scenario 2, GDP
losses vary from 0.75% to 1.20%, while consumption losses vary from 1.20% to
1.55%.
19
Note that for simulation 1(TT), and likewise for all other TT versions of the remaining 3 simulations, the
results are discussed for poverty ratio and the number of poor only. This is because the figures for the
macro variables in case of the targeted transfers versions of the simulations do not differ much from
those in their respective across-the-board transfers versions.
The poverty ratio in scenario 2 increases only marginally with respect to the BAU
scenario. It increases by 1.34 percentage points in 1990, and by only 0.1 percentage
point in 2020 (tables 4 and 5). However, the real adverse impact of simulation 2 on
poverty comes out in terms of the number of poor. The number of poor in simulation 2,
relative to the BAU scenario, increases by 3.58% in 1990 and 4.89% in 2020 (tables
4 and 5).
Under targeted transfers of simulation 2(TT), the poverty scenario is much less adverse
than under simulation 2. Poverty ratio, as compared to that of the BAU scenario,
increases by 0.56 percentage point in 1990, and by only 0.02 percentage point in year
2020 (tables 4 an 5). The number of poor in simulation 2(TT), compared to that in the
BAU scenario, increases by 1.49% in 1990, and by only 0.92% in 2020 (tables 4 and
5). The results of this simulation clearly show that the costs to GDP and poverty
reduction imposed by a carbon tax can be reduced to a great extent by moderating the
carbon emission reduction target and at the same time recycling the carbon tax revenues
to those living below the poverty line.
In policy simulation 3, the carbon emission quota is fixed at 1 tonne per capita based
on the 1990 population of 810 million. In other words, the maximum permitted total
emission of carbon is fixed at 810 milllion tonnes annually for the Indian economy. For
every tonne of carbon emitted less than the permitted 810 million tonnes, the Indian
economy earns $6, which is Rs100 at the base-year exchange rate, through the sale of
a permit in a global market of permits, and the total revenue form the sale of permits is
recycled to the households as transfers from the rest of the world.
The exact procedure followed in this simulation is to fix an upper bound for total
emissions - i.e., 810 million tonnes for each year. The actual total emission of carbon
turns out to be much less than the upper bound for each period (The upper bound is
not binding in any of the years). The difference between the permitted emissions and
the actual emissions is then multiplied by the permit price to arrive at the total revenue
from the sale of permits, which is then recycled to the households like additional transfer
payments from the rest of the world. In the process, the model generates a set of
equilibrium values for GDP, consumption, poverty ratio etc.
In simulation 3 the carbon emissions increase relative to the BAU scenario. The increase
in emissions is almost 14% in the year 1990, but, in the later years, declines to be in
the range of 5.50-9.00% (table 11). Per capita emissions also increase throughout the
period, with the increases being in the range of 0.02-0.04 tonnes (table 12). However,
what needs to be noted is that, even in the last year, 2020, per capita emissions are
only 0.73 tonnes, which is less than the world average of 1 tonne per capita.
The infusion of additional transfer payments from the rest of the world, in the form of
permit revenue, leads to substantial increases in GDP and consumption in this simulation.
GDP increases by 6.7% in the year 1990. However, in the later years, GDP increases
are progressively smaller. In the final year, 2020, GDP increases by only 1.8%. The
consumption gains are higher than the GDP gains in each of the periods (tables 13 and
14). Apart from the increases in consumption resulting from the increased transfers to
households, there are second-round increases in consumption when there is additional
income generated from the demand-induced increase in production activities.
The poverty ratio declines significantly in scenario 3. It declines by 2.43 percentage

points in the year 1990, and by 0.38 percentage points in the year 2020 (tables 4 an
5). The number of poor, relative to the BAU scenario, decreases by 6.5% in 1990,
and by 18.8% in the year 2020. That is, in the final year, 2020, the number of poor is
only 21.24 million in this simulation, as compared to 26.15 million in the BAU scenario
(table 5).
Poverty declines even faster under the targeted transfers version of simulation 3. The
number of poor in this scenario, compared to the BAU scenario, declines by 11% in
1990 and by 50% in 2020. By the year 2020, the number of poor in this simulation is
only 13.18 million, i.e., half of the number of poor in the BAU scenario (table 5).
Simulation 4 is worked out exactly like the simulation 3, with the difference that, in the
former, the permit price is given to be $12 per tonne of carbon emitted.
The increase in carbon emissions in this simulation, relative to the BAU scenario, is as
high as 19% in 1990. However, emissions decline progressively over the 30-year period.
By the end of the period, in the year 2020, the increase in emissions, compared to the
BAU scenario, is around 6% (table 11). The increases in the per capita emissions in
the various years are in the range of 0.03-0.04 tonnes. In the last year, 2020, per
capita emissions in this scenario are 0.73 tonnes, as against 0.69 tonnes of the BAU
scenario (table 12).
GDP gains in this simulation are expectedly larger than that in simulation 3. GDP, as
compared to the BAU scenario, increases by about 12% in 1990, and by almost 2% in
2020. Consumption gains are even bigger. Consumption increases by more than 12%
in 1990, and by more than 3% in 2020 (tables 4 and 5) .
There is a very substantial decline in the poverty ratio in simulation 4. Poverty ratio is
only 30.02% in 1990, as compared to 37.45% in the BAU scenario in that year. In
2020, poverty ratio is 0.87%, as compared to 2.01% of the BAU scenario. The number
of poor in 2020 declines by 57% and is only 11.28 million, as against 26.15 million of
the BAU scenario (tables 4 and 5).
In simulation 4(TT), there is an even speedier decline of poverty. Poverty ratio is

25.45% in 1990, and only 0.08% in 2020. The number of people in poverty, relative
to the BAU scenario, decreases by 32% in 1990 and by 96% in 2020. In that year, the
number of poor in scenario 4(TT) is only 1.02 million as against 26.15 million of the
BAU scenario (tables 4 and 5).

4.5 Policy simulations : caveats
In the interpretation of the simulation results, the limitations of our model must be
borne in mind. One limitation of our model is that in the production of electricity, the
input substitution possibilities are confined to be only within the fossil fuels coal,
refined oil and natural gas. Carbon free options such as hydro, wind, solar and nuclear
electricity are not considered in the model. The contributions of these energy sources
in the total energy consumption in India are not likely to increase significantly within
the time frame of our model, 1990-2020. As can be seen from table 1, the contribution
of other energy sources which include wind, solar and nuclear energy, to total energy
consumption in India in 1990 is only 0.6 %. Hydropower provides 6.21% of the total
energy consumed in 1990. But its percentage share does not seem to grow over time.
It was 5.24% in 1970, increased to 6.77% in 1980, but starts declining after that till it
reaches 6.21% in 1990. Even, the post-economic reforms period of 1991-92 to 1997-
98, Sengupta and Gupta (2003) find a declining share of hydro power and an increasing
share of thermal power in the total gross generation of electricity. They conclude that
there has been no success in raising the share of carbon free options of hydro and
nuclear in gross power generation by the introduction of reforms. Bearing in mind the
limited relevance of the carbon free options in the next two or three decades in India,
we have kept our model structure simplified and avoided the unnecessary complication
of introducing the options of hydro, wind and nuclear in the generation of power. That
said, we do recognize that the model, in its present form is incomplete if it has to be
implemented over a longer time horizon of fifty years or more, and should be extended
for further study. The absence of clean energy options such as hydro electricity, means
that the the adverse effects of emission restriction on economic growth and poverty
reduction shown in simulations 1 and 1(TT) are somewhat exaggerated. However,
even with hydro electricity they would remain large, given the high orders of magnitude
of losses in GDP and poverty alleviation in this simulation. In case of policy simulations
2 and 2(TT), with a softer carbon emission reduction target, the relatively small losses
in GDP and poverty alleviation could not possibly be compensated by introducing the
hydropower option, except, perhaps in the last few years of the thirty year period.
Another more serious limitation of the model in its present form is the fact that it is
recursively dynamic and, not fully dynamic. We regard this as a more serious limitation
because it restricts the scope of policy analysis that can be carried out within the
framework of the model. A recursively dynamic model basically generates a sequence
of static equilibria and is, therefore, suitable for analyzing the consequences for GDP
and poverty of annual emission reduction targets. However, an equally viable policy
option is a dynamically optimum strategy with cumulative emission reduction targets.
This, in fact, can be less costly in terms of GDP loss and poverty reduction foregone
because it allows the economy to define an inter-temporal adjustment path. But such a
strategy cannot be examined through a recursively dynamic model. It needs an inter-
temporal optimizing framework like the one used in Murthy, Panda and Parikh (2000).
Our only justification for using a quasi-dynamic instead of a fully dynamic model is the
the economy of effort necessitated by the time constraint specified for this study. We
hope to overcome this limitation in a later version of the model.

Finally, it would be useful to see how the main results of our study based on a recursively
dynamic CGE model compare with the results obtained in other studies using a different
model structure i.e., an inter-temporal model in an activity analysis framework. Two
recent studies in India which have used this modelling framework are Murthy, Panda
and Parikh (2000) and Gupta (2004). Murthy, Panda and Parikh (2000) do not consider
a carbon tax scenario, but their tradable permits with equal per capita emissions
allocation scenario is comparable to our policy simulations 3 and 4. In Murthy, Panda
and Parikh (2000), GDP in the the 30 th year (i.e., year 2020) increases by 6.7% and
13.5% for given permit prices of $6/tonne and $12/tonne respectively. In our model,
GDP in the 30th year increases by only 1.8% and 2% respectively in scenarios 3 (permit
price equals to $6/tonne) and 4 (permit price equals to $12/tonne). In our view, a
model based solely on material balances with prices not playing any role, overestimates
the GDP gains from additonal international transfers through the sale of emission permits.
In Gupta (2004), under what he calls the Trade and Environmental Policy Scenario-1,
total emission is reduced to 94.8% of the 1990 level at the end of the year 2019-20,
using a command-and-control measure. In order to meet this emissions target, CO 2
emissions in 2019-20 have to be reduced by 89.3% as compared to the emissions
under the BAU scenario20. Such a reduction in total emissions entails a loss in GDP of
87% in the year 2019-20 relative to the BAU scenario. On the other hand, in our
study when total emissions are reduced by 70% to bring it down to the 1990 level in
the year 2020 under a carbon tax scenario (policy simulation 1), GDP falls by only
4.61% compared to the BAU scenario. Once again the loss in GDP seems to be over-
estimated in case of the activity analysis based model. This prompts us to reiterate our
view that a price driven CGE model is more likely to generate realistic estimates of
GDP gains or losses arising from the various emissions policies.
5. Conclusions and Policy Implications
We conclude by highlighting the main policy lessons from our simulation exercises. The
policy lessons that emanate from our policy scenarios are fairly clear. They are, however,
in two parts.
In the first part, i.e., in policy scenarios 1 and 2, the lessons learnt are about the
efficacy of a domestic carbon tax policy to reduce carbon emissions without seriously
compromising the growth and poverty reduction goals of the Indian economy. In this
regard, the results of the policy scenario 1 are very discouraging. That is to say, the
employment of a carbon tax to restrict the carbon emissions in the Indian economy to
the 1990 level, imposes heavy costs in terms of lower GDP and higher poverty. With
targeted transfers to the poor, the costs in terms of higher poverty are somewhat
mitigated, but they remain quite high - i.e., the number of poor in 2020 increases by
3.4 times. It needs to be stressed that, these high costs in terms of GDP losses and
poverty reduction foregone in this policy scenario cannot be significantly reduced by
including the contribution of clean energy options, such as hydro electricity. Hydropower
constitutes a very small and stagnant share (5%-6%) of the total energy consumed in
20
The base-year of the model in Gupta (2004) is 1993-94.
India. The share of other clean energy sources (nuclear, wind and solar) is even smaller
less than 1 percent. More importantly, the costs to GDP and poverty alleviation in
this policy scenario are not unexpectedly high. In fact, such high costs are a natural
consequence of an unduly restrictive carbon emissions policy. The latter is obvious
from the fact that, the per capita emissions (based on the 1990 population) in this
simulation in 2020 are 0.21 tonnes as compared to 0.69 tonnes in the BAU scenario in
the same year.
In policy scenario 2, a milder restriction of 10% annual reduction in carbon emission is

achieved through the imposition of a carbon tax. The GDP losses are still significant,
though not very large. But, poverty, relative to the BAU scenario, is higher throughout
the 30-year period. However, this can be changed with targeted recycling of revenues
to the poorest households in the economy. With targeted transfers the number of people
in poverty increases by about 4-5 million in the first half of the period, 1990-2005,
and, subsequently, by less than 2 million in the second half of the 30-year period,
2005-2020. In fact, at the end of the period in year 2020, the number of people in
poverty is only 0.24 million more than that in the business-as-usual scenario. This
result suggests that targeted transfers is a contrivance that can be effectively used to
dodge the trade-off between poverty reduction and carbon emissions, provided the
emission reduction target is a modest one, such as a 10% annual reduction in total
emissions. A 10% annual reduction in total emissions implies that per capita emissions
(based on 1990 population) in 2020 will be 0.62 tonnes. This is no mean target for per
capita emissions given that the average world per capita emissions in 1990 is 1 tonne.
In the second part, i.e., in policy scenarios 3 and 4, the implications of Indias
participation in a global trading system of emission permits are analyzed. In these
scenarios, India is allowed a maximum emission of 180 million tonnes of carbon annually.
The actual annual emissions in these scenarios, however, are much less than the
maximum limit. In an internationally tradable permits regime, India stands to gain by
keeping its emissions as much less than the stipulated maximum as possible. In other
words, India does not have a perverse incentive to emit more in a tradable emission
permits regime, as is sometimes feared. Nor is it true that, India can perpetually induce
a resource flow from the developed countries through the sale of emission permits, by
virtue of having its per capita emissions at a level which are lower than the world
average per capita emissions of 1 tonne of carbon. On the contrary, with actual emissions
increasing faster in the policy scenarios 3 and 4 than in the business-as-usual scenario,
it is safe to expect that the turnaround for India- from being a net seller of permits to a
net buyer of permits - will come before 2050.
Be that as it may, India gains immensely in terms of higher GDP growth and lower
poverty in the tradable emission permits scenarios In case of scenario 3, in which the
permit price is $6 per tonne, in the 30-year period, GDP increases on an average by
3.7% per year and the number of people in poverty in 2020 goes down by about 19%.
In the targeted transfers variant of this scenario, the number of people in poverty in
2020 is in fact halved. In case of scenario 4, in which the permit price is $12 per
tonne, GDP increases in the 30-year period, on an average by 5.7% per year, and the

number of people in poverty in 2020 is reduced by 57%. Moreover, in case of the
targeted transfers version of this scenario, poverty in 2020 virtually vanishes.
It is obvious, that global emissions trade with equal per capita emission entitlements
opens up a unique opportunity for India and other developing countries, to sidestep
the trade-off between carbon emissions, economic growth and poverty reduction. On
its own, India is unlikely to take the hard decision of imposing a domestic carbon tax
to reduce carbon emissions, even though a carbon tax with targeted transfers for a
very modest reduction in carbon emissions is not necessarily detrimental to economic
growth and poverty alleviation.
6. Acknowledgements
I gratefully acknowledge the financial support provided for this study by the South
Asian Network for Development and Environmental Economics (SANDEE). I am also
grateful to Karl-Gran Mler, Lars Bergman, Gopal Kadekodi and other participants
at the SANDEE workshop for their very helpful comments. Last, but not the least, I
am thankful to Bibek Debroy, Director, Rajiv Gandhi Institute for Contemporary Studies,
New Delhi for providing an extremely conducive environment for the execution of this
project during my tenure there as a visiting fellow. The usual disclaimers apply.

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Appendix 1
Table 4 : BAU Scenario and the Policy Simulations : Selected Variables in 1990
GDP Cons. Carbon Per Capita Poverty No. of
(in billion (in billion Emissions Emissions (in Ratio Poor
Rupees) Rupees) (in million tonnes per (in million)
tonnes) capita) (in percent)
BAU 4380.11 3211.25 168.00 0.21 37.45 303.35
Scenario
GDP Cons. (%age Carbon Per Capita Poverty No. of No. of
(%age diff. diff. from Emissions Emissions Ratio Poor Poor
from BAU) BAU) (%age diff. (in tonnes (in percent) (in million) (%age diff.
from BAU) per capita) from BAU)
Sim 1 0.00 0.00 0.00 0.21 37.45 303.35 0.00

Sim 1(TT) 0.00 0.00 0.00 0.21 37.45 303.35 0.00
Sim 2 -0.76 -1.24 -10.00 0.19 38.79 314.20 3.58
Sim 2(TT) -0.63 -1.04 -10.00 0.19 38.01 307.88 1.49
Sim 3 6.69 6.81 13.70 0.24 35.02 283.66 -6.49
Sim 3(TT) 6.79 7.27 14.64 0.24 33.30 269.76 -11.07
Sim 4 11.83 12.01 18.95 0.25 30.02 243.14 -19.85
Sim 4(TT) 11.91 12.57 19.66 0.25 25.45 206.13 -32.05
Table 5 : BAU Scenario and the Policy Simulations : Selected Variables in 2020
GDP Cons. Carbon Per Capita Poverty No. of Poor
(in billion (in billion Emissions Emissions (in Ratio (in million)
Rupees) Rupees) (in million tonnes per (in percent)
tonnes) capita)
BAU 20130.18 14730.50 559.46 0.69 2.01 26.15
Scenario
GDP Cons. Carbon Per Capita Poverty No. of Poor No. of Poor
(%age diff. (%age diff. Emissions Emissions (in Ratio (in (in million) (%age diff.
from BAU) from BAU) (%age diff. tonnes per percent) from BAU)
from BAU) capita)
Sim 1 -4.61 -8.28 -69.97 0.21 8.05 104.65 300.19

Sim 1(TT) -4.52 -8.04 -69.97 0.21 6.87 89.31 241.53
Sim 2 -0.76 -1.20 -10.00 0.62 2.11 27.43 4.89
Sim 2(TT) -0.88 -1.08 -10.00 0.62 2.03 26.39 0.92
Sim 3 1.83 2.53 5.83 0.73 1.63 21.24 -18.78
Sim 3(TT) 1.86 2.70 6.38 0.73 1.01 13.18 -49.60
Sim 4 1.98 3.12 6.24 0.73 0.87 11.28 -56.86
Sim 4(TT) 2.05 3.21 6.94 0.74 0.08 1.02 -96.10

Table 6 : Macro variables and carbon emissions of the BAU scenario
In billion Rupees In million GDP Cons. Inv. Carbon Emissions
tonnes (Growth (Growth (Growth (Growth Rate)
Rate) Rate) Rate)
GDP Cons. Inv. (exo.) Carbon
Emissions
1990 4380.11 3211.25 1539.41 168.00
1995 5835.89 3927.65 2182.17 208.09 5.74 4.03 6.98 4.28
2000 7489.40 4856.58 2944.81 257.74 4.99 4.25 5.99 4.28
2005 9160.77 6201.46 3704.05 315.75 4.03 4.89 4.59 4.06
2010 11865.33 8312.96 4947.21 383.74 5.17 5.87 5.79 3.90
2015 15290.51 10939.08 6580.08 464.50 5.07 5.49 5.70 3.82
2020 20130.18 14730.50 8729.03 559.46 5.50 5.95 5.65 3.72
Note : The growth rates for each of the quinquenniums are the annual growth rates
Table 7 : Energy use

E E E/GDP E/GDP E GDP
(Growth
Rate)
BAU BAU BAU Sim. 1 Sim. 1 (%age Sim. 1 (%age
Scenario diff. from BAU) diff. from BAU)
1990 565.46 5.72 0.1291 0.1291 0.00 0.00
1995 752.84 5.17 0.1290 0.1293 -1.39 -1.64
2000 975.07 4.16 0.1302 0.1304 -3.83 -3.95
2005 1200.72 3.04 0.1311 0.1304 -5.77 -5.25
2010 1397.54 2.64 0.1178 0.1149 -6.16 -3.82
2015 1594.84 2.40 0.1043 0.0980 -10.27 -4.46
2020 1798.64 5.72 0.0894 0.0830 -11.35 -4.61
Note : E : Total energy use in 103 terajoules
E/GDP : Energy input per unit of GDP in 103 terajoules per billion rupees
The growth rates for each of the quinquenniums are the annual growth rates.
Table 8 : Carbon tax rates

Simulation 1 Simulation 2
Rs. per tonne Tax. Rate. Rs. per tonne Tax. Rate.
(Growth Rate) (Growth Rate)
1990 0.00 217.65

1995 417.36 234.36 1.48
2000 828.64 13.72 221.44 -1.13
2005 1261.96 8.41 211.66 -0.90
2010 1724.20 6.24 202.31 -0.90
2015 2203.25 4.90 191.15 -1.13
2020 2765.57 4.55 173.87 -1.90

Table 9 : Energy Prices (percentage difference from BAU scenario)
Simulation 1 Simulation 2
Coal Ref. Oil Nat Gas Coal Ref. Oil Nat Gas
1990 112.38 38.71 33.62

1995 218.48 69.34 61.91 122.68 39.56 34.42
2000 511.00 147.12 138.21 132.62 35.38 35.18
2005 914.37 277.03 228.57 132.38 38.64 36.48
2010 869.74 324.74 279.57 104.33 35.84 32.61
2015 1026.08 370.91 320.66 93.43 30.84 27.76
2020 1137.08 441.88 415.08 79.37 25.27 25.17
Table 10 : Carbon emissions (percentage share of fossil fuels)

BAU Scenario Simulation 1
Coal Ref. Oil Nat Gas Coal Ref. Oil Nat Gas
1990 72.23 22.66 5.11 72.23 22.66 5.11

1995 72.46 22.54 5.00 70.36 24.37 5.27
2000 73.11 22.23 4.66 71.06 23.72 5.21
2005 73.25 22.52 4.23 71.39 24.33 4.28
2010 73.35 22.50 4.15 71.18 24.50 4.32
2015 73.14 23.80 3.06 70.37 26.01 3.62
2020 72.98 23.98 3.03 70.42 26.18 3.40
Table 11 : Carbon Emissions

In million Percentage difference from BAU Scenario
tonnes
BAU Sim. 1 Sim. 1 Sim. 2 Sim. 2 Sim. 3 Sim. 3 Sim. 4 Sim. 4
Scenario (TT) (TT) (TT) (TT)
1990 168.00 -0.00 -0.00 -10.00 -10.00 13.70 14.64 18.95 19.66
1995 208.09 -19.27 -19.27 -10.00 -10.00 8.90 8.26 13.34 14.10
2000 257.74 -34.82 -34.82 -10.00 -10.00 8.27 8.97 8.66 9.36
2005 315.75 -46.79 -46.79 -10.00 -10.00 5.51 5.12 6.15 5.97
2010 383.74 -56.22 -56.22 -10.00 -10.00 6.64 7.59 7.55 6.89
2015 464.50 -63.83 -63.83 -10.00 -10.00 7.29 8.28 7.75 8.73
2020 559.46 -69.97 -69.97 -10.00 -10.00 5.83 6.38 6.24 6.94
-50.11 -50.11 -10.00 -10.00 7.30 7.82 8.54 8.96

Table 12 : Per capita carbon emissions
In tonnes per capita

1990 0.21 0.21 0.21 0.19 0.19 0.24 0.24 0.25 0.25
1995 0.26 0.21 0.21 0.23 0.23 0.28 0.28 0.29 0.29
2000 0.32 0.21 0.21 0.29 0.29 0.34 0.35 0.35 0.35
2005 0.39 0.21 0.21 0.35 0.35 0.41 0.41 0.41 0.41
2010 0.47 0.21 0.21 0.43 0.43 0.51 0.51 0.51 0.51
2015 0.57 0.21 0.21 0.52 0.52 0.62 0.62 0.62 0.62
2020 0.69 0.21 0.21 0.62 0.62 0.73 0.73 0.73 0.74
Table 13 : GDP
In billion Percentage difference from BAU Scenario
Rupees
1990 4380.11 0.00 0.00 -0.76 -0.63 6.69 6.79 11.83 11.91
1995 5835.89 -1.64 -1.52 -0.82 -0.67 4.90 4.99 9.00 9.14
2000 7489.40 -3.95 -4.10 -1.20 -1.24 4.04 4.14 6.47 6.62
2005 9160.77 -5.25 -5.21 -1.17 -1.19 3.38 3.52 4.31 4.40
2010 11865.33 -3.82 -3.70 -1.13 -1.02 2.61 2.62 3.43 3.53
2015 15290.51 -4.46 -4.51 -1.06 -1.03 2.24 2.30 2.75 2.85
2020 20130.18 -4.61 -4.52 -0.76 -0.88 1.83 1.86 1.98 2.05
Table 14 : Consumption
In billion Percentage difference from BAU Scenario
Rupees
1990 3211.25 0.00 0.00 -1.24 -1.04 6.81 7.27 12.01 12.57
1995 3927.65 -2.25 -1.99 -1.36 -1.15 6.19 6.74 10.47 10.92
2000 4856.58 -4.42 -4.05 -1.55 -1.25 5.64 6.00 8.31 8.69
2005 6201.46 -6.40 -6.01 -1.46 -1.13 3.66 4.11 4.96 5.16
2010 8312.96 -6.80 -6.31 -1.38 -1.09 3.55 3.78 3.69 3.97
2015 10939.08 -7.68 -7.20 -1.28 -1.05 2.90 3.09 3.61 3.96
2020 14730.50 -8.28 -8.04 -1.20 -1.08 2.53 2.70 3.12 3.21

Table 15 : Poverty ratio (in percent)
1990 37.45 37.45 37.45 38.79 38.01 35.02 33.30 30.02 25.45
1995 32.48 34.01 33.37 33.73 33.08 30.37 28.83 25.74 19.54
2000 28.41 31.10 30.17 29.55 28.96 26.75 24.63 22.69 16.51
2005 24.86 28.18 27.12 25.75 25.25 22.87 20.88 18.84 13.82
2010 16.26 21.69 19.87 16.81 16.43 15.37 14.02 13.29 10.64
2015 09.04 15.22 13.66 09.39 09.14 08.53 07.11 06.86 05.30
2020 02.01 08.05 06.87 02.11 02.03 01.63 01.01 00.87 00.08
Table 16 : Number of poor (in million)

1990 303.35 303.35 303.35 314.20 307.88 283.66 269.76 243.14 206.13
1995 292.35 306.09 300.33 303.57 297.72 273.29 259.44 231.66 175.88
2000 278.43 304.78 295.67 289.59 283.81 262.14 241.34 222.37 161.78
2005 263.54 298.71 287.47 275.07 267.65 242.39 221.29 199.75 146.54
2010 185.39 247.27 226.52 193.69 187.30 175.17 159.88 151.47 121.34
2015 110.31 185.68 166.65 115.53 111.51 104.02 86.77 83.67 64.64
2020 26.15 104.65 89.31 27.43 26.39 21.24 13.18 11.28 1.02
8.0000
7.0000
6.0000
5.0000
4.0000 GDP
3.0000 Cons.
2.0000 Inv. (exo)
1.0000
0.0000
1995 2000 2005 2010 2015 2020
Fig. 2 : BAU : Growth rates of macrovariables

GDP/K
GDP/L
3.5000
3.0000
2.5000
2.0000
1.5000
1.0000
0.5000
0.0000
1990 1995 2000 2005 2010 2015 2020
Fig. 3 : BAU : GDP/K & GDP/L

Appendix 2
Model Equations, Variables and Parameters
Sectors : 1. Agriculture 2. Electricity 3. Coal 4. Refined Oil

5. Natural Gas 6. Crude-Petroleum 7. Transport 8. Energy-
Intensive Industries 9. Other Industries 10. Consumer Goods
11. Services
Sets :
Sectors :
S = ( Agricult, Elec, Coal, Refoil, Nat-gas, Crude-Pet, Trans, Enerint, Otherind, Cons-good,
Services )
Non-Agricultural Sectors :
NAS = ( Agricult, Elec, Coal, Refoil, Nat-gas, Crude-Pet, Trans, Enerint, Otherind, Cons-
good )
Non-Fixed Factor Sectors :

NFS = ( Elec, Trans, Enerint, Otherind, Cons-good, Services )
Non-Energy Sectors :
NES = ( Agricult, Trans, Enerint, Otherind, Cons-good, Services )
Energy Sectors :
SES = ( Elec, Coal, Refoil, Nat-gas, Crude-Pet )
Primary Energy Sectors :

PES = ( Coal, Nat-gas, Crude-Pet )
Non-electric Energy Sectors :

NEE = ( Coal, Refoil, Nat-gas, Crude-Pet )
Non-electric Fuels Sectors :

NEF = ( Coal, Refoil, Nat-gas )
Exporting Sectors :
EXS = ( Agricult, Coal, Refoil, Trans, Enerint, Otherind, Cons-good, Services)
Non-exporting Sectors :
NXS = (Elec, Nat-gas, Crude-Pet)
Importing Sectors :
IMS = ( Agricult, Coal, Refoil, Nat-gas, Crude-Pet, Enerint, Otherind, Cons-good )
Non-importing Sectors :
NMS = (Elec, Trans, Services )
Regions :
RGN = ( rural, urban )
Sources of Income ( land, fixed-factor, wage-labour, self-employed-labour, capital , transfer

payments) :
TYP = ( l, f, w, s, k, tp )
Consumption Expenditure Classes

CEC = (1, 2, 3, 4, 5)
Income Classes (Percentiles)

H = ( h1 (10%), h2(10%),,h9(10%), h10(5%), h11(1%), h12(1%),,h15(1%) ]
Production Structure
(1)
(1)
Xi = axi * [ xi

* Ni xi
+ (1 xi )* Zi xi ]
1/ xi
i NFS
(2)
xi
Pz i * x i
Ni = Zi * , where xi = 1/(1 + xi )
(2) Pn i * (1 x i ) i NFS
(3) P x i * X i = P n i* Ni + P z i* Z i + t e * i * Xi
(3) i NFS
(Note : i = 0, i 8 )
(4)
(4)
Xi = axi * [ xi

* NFi xi
+ (1 xi )* fi xi ] 1/ xi
i PES
xi
(5)
Pfi * x i
NFi = f i * , where xi = 1/(1 + xi )
(5) Pnfi * (1 x i ) i PES
(6) P x i * X i = P nf i * NFi + Pf i * f i
(6) i PES
1/
(7)
(7)
X4 = ax4 * [ x * NF
4 4
x4
+ (1 x4 )* CP
x4
] x4

x
( Pq6 +te* cp6 ) *
(8)
4
x
4
NF4 = CP * , where x = 1/(1 + x )
Pnf4*(1 x4 )
4 4
(8)
(9) P x 4 * X 4 = Pnf4 * NF4 + ( P q6 + te * cp ) * CP

(9) 6
(10) ]-1/
- -x
(10) X1 = ax1 * [ x
1
* RS1
x
1 + ( 1- x
1
) * VA1
1
x1
(11)
PVA1* x1 1
RS1 = VA1 * ,
(11) Prs1*(1 x1) where x1 = 1/ (1+ x1 )
(12)
(12) PX1 * X1 = Prs1 * RS1 + Pva1 * VA1
-1/
(13)
- -
(13) [
RS1 = ars1 * rs1 * EM1
rs1
+ ( 1- rs
1
) *ld rs1
] rs1
rs1
(14)
Pld * rs1
EM1 = ld * ,
(14) Pem1* (1 rs1) where rs1 = 1/ (1+ rs1 )
(15)
(15) Prs1 * RS1 = Pem1 * EM1 + Pld * ld
(16)
- em - -1/
(16) [
EM1 = aem1 * em1 * N1 1 + (1- em
em1) * EA1 1 ] em1
em1
(17)
Pea1 * em1
N1 = EA * ,
(17) 1
Pn1* (1 em1) where em1 = 1/ (1+ em1 )
(18)
(18) Prs1 * RS1 = Pem1 * EM1 + Pld * ld
1/
(19)
(19) NFi = anfi * [ nfi * Ni
nfi + (1 nfi )* Zi nfi ]
nfi
i NEE
nfi
(20)
Pz i * nfi
Ni = Zi * , where nfi = 1/(1+ )
Pn i * (1 nfi ) i NEE
(20) nfi

(21) Pnf i * NF i = P n i * Ni + P z i * Z i
(21) i NEE
1/
(22)

(22) Zi = a zi * [ zi * EAi
zi
+ (1 zi )* VAi
zi
] zi
i NAS
(23)
z
P vai * zi i
EAi = VAi * , where zi = 1 /(1 + )
Pea i * (1 zi )
zi
(23) i NAS
(24) P Zi * Z i = Pea i * EAi + Pva i * VAi

(24) i NAS
1/ n
(25) [ ]

+ (1 )* Nmi
ni ni
(25) Ni = ani * ni * Ndi ni
i
i S
(26)
ni
PNmi * ni
N di = N mi * , where n i = 1/(1 + ni )
PNd i * (1 n i )
(26) i S
(27)
(27) Pni * Ni = PNdi * Ndi + PNmi * Nmi i S
(28) [ ]1/

(28) EAi = aEAi * EAi * Ei EAi + (1 EAi )* NEi EA i EAi
i S
(29)
P NEi * EAi EA i
Ei = NEi * , where = 1/(1 + )
P Ei * (1 EAi )
EA EA
i i
(29) i S
(30) P EA i * EAi = Pq 2 * Ei + P NE i * NEi

(30) i S
(31)
1 / VA
VA VA VA
(31) VAi = aVAi * Ki * Ki i + lw i * Lwi i + ls i * Lsi i i
i S

[ Note : Ki + lwi + lsi = 1 ]

(32)
Wrg * ki VAi
Ki = Lwi * , where VAi = 1/(1 + VAi )
Pki * lwi
(32) i S
(33)
Wrg * lsi VAi
Lsi = Lwi * , where VAi = 1/(1 + VAi )

Pls rg * lw i

(33) i S
(34) PVAi * VA i = Pki * K i + Wrg * L wi + P LSrg * L s i

(34) i S
.
(35) NE NE
1/ NEi
(35) NEi = aNEi * CL NEi +
cl i gs i * GSi i + ro i * ROi i i S
i
[ Note : cli + gsi + roi = 1 ]
(
Pq gs + t e * )*
(36)
gs i
cli NE i
CLi = GSi * , where = 1/(1 + )

( )*
NE i NE i
P + t *
gsi
q e cl
cl i
(36) i S
Pq + t * ( )* NE i
(37)

gs e gs i ro
i
ROi = GSi * , where NE i = 1/(1 + NE i )
Pq + t * * gs
ro e ro
i i
(37) i S
(38)
(38) PNEi * NEi = ( Pqcl + te * cli ) * CLi + ( Pqro + te * roi ) * ROi
+ ( Pqgs + te * gsi ) * GSi i S
(39) ]-1/
- - ex
(39) Xi = aexi * [ exi *EX
i
exi
+ ( 1- exi) * DD
i
i
ex i
i EXS
(40)
exi
Pdd i* exi
EXi = DDi * ,
(40) Pexi *(1 e x i) where exi = 1/ (1+ exi ) i EXS
(41)
(41) PXi * Xi = Pexi * EXi + Pddi * DDi i EXS

exd
(42) EX i = aexdi * [ PWexi / pwesi] i i EXS
(43) Xi = DDi i NXS
(44) PXi * Xi = Pddi * DDi i NXS

1/
(45) Qi = aqi * [ qi

* Mi
qi
+ (1 qi ) * DDi
qi
] qi
i IMS
q
Pddi * qi i
Mi = DDi * ,
(46) Pmi * (1 qi ) where qi = 1/ (1+ qi ) i IMS
Q i * Pqi = Pmi * Mi + Pdd i * DDi

(47) i IMS
(48) Qi = DDi i NMS
(49) Pqi * Qi = Pddi * DDi i NMS
CO 2Emissions:
CO2 Emissions:
ECO ng =
11 11 11
(50) 2
ECO2 ng cli * CLi + gsi * GSi + roi * ROi + cp * CP
i =1 i =1 i =1
11
+ 8 * X8 + i * Ci
i =1
ECO
11
2gg i
(51) ECO2 = * cgi
i= 1
TECO ECOng + +ECO2

TECO22 = ECO2 ECOg
2ng g
(52)
(53) PAYEM = te * ECO
ECO22ngng
Prices(Exports
Prices (Exports, Imports
, Imports andand Intermediates)
Intermediates)
(54) PWexi = ( Pexi * (1-exsubi ) ) / ER i EXS
(55) Pmi = pwmi * ( 1+ tfmi ) * ER i IMS i IMS

11
(56) P nmi = a m ji * pwm j * (1 + tn m j ) * ER i S
j=1
11
(57) P nd i =
j=1 & 7
a ji * P q j * (1 + t nd j ) i S
Factor_Prices,_Consumer_Prices_and_Price_Indices
11
(58) totlab =
i= 2
L wi + L s i
(59) Pci = Pqi * ( 1 + tfdi + te * i ) i S

11 11
(60) CPIrg = (
i =1
Pci * Ci,rg ) /(
i =1
Ci,rg ) rg RGN
(61) Wrg = ( rg * CPIrg * dwrg ) / dcpirg rg RGN

11
(62) PINDEX =
i=1
pwts(i) * Pci
Factor Incomes
Sectoral Factor Incomes :
(63) Yl,1 = Pl * l
(64) Yf, i = Pfi * fi i PES
(65) Yw,i = Wrg * Lwi * (1 twi ) i S

[ Note : Wrg = Wurban for i NAS & Wrg = Wrural for i = agricult ]
(66) Ys, i = Plsrg * Lsi * (1 twi ) i S
(67) Yk,i = Pki *ki *(1tki) i S

(68) GTR = ( gtra + gtrb ) * PINDEX + PAYEM
(69) WTR = ( nct + nfi ) * ER
(70) Ytp = ( GTR + WTR )

Rural and Urban Factor Incomes :
11
(71) YH ty, rg = al ty, i , rg * Yty, i + arrg * Ytp ty TYP, rg RGN
1 ty tp
[Note: rg alty,i,rg = 1 for i & ty (ty tp) ; rg arrg = 1]

Income Distribution
Step 1 : Mapping of Factor Incomes onto incomes of the 15 income classes (percentiles).
(72) Yh, rg = ty h, ty, rg * YH ty, rg h H, rg RGN

[Note: h h, ty, rg = 1]
(73) Yrg = h, rg * Yh,rg rg RGN

h
(74) Vy rg = h,rg * (Yh,rg Yrg ) 2 rg RGN

h
Step 2 : Computing the mean and variance of log income, under the assumption that
the distribution of population according to per capita income and per capita
consumption expenditure is bi-variate log normal.
(75) Y
rg = log Yrg
1
2
( )Y
rg
2
rg RGN
1
Vy rg 2
(76) Y
= rg log 1 + rg RGN
rg
( Y )2
rg
(77) c
rg = rg + rg
Y
rg rg RGN
(78) c
rg = rg * Y
rg rg RGN
Step 3 : Determining the shares of (i) population, (ii) consumption and (iii) total income
accruing to the households that fall under consumption expenditure level k for
k = 1,2, ,5.
log cel , - c
rg
k rg
(79) =N 0,1 k CEC, rg RGN
k , rg c
rg

log celk, rg - c
rg
(80) k ,rg = N c c
rg 0,1 k CEC, rg RGN
rg
log cel k,rg c

k,rg = N
rg
(81) c
Yc Y
rg rg 0,1 k CEC, rg RGN
rg

Step 4 : Computing the per capita expenditure and income for the five expenditure classes.
(82)
C rg = exp c
rg +
1
( )
c
rg
2
rg RGN
2
(83) C k,rg = C rg ( k, rg k -1,rg ) / ( k,rg k -1,rg )
k CEC, rg RGN
(84) Yk,rg = Yrg ( k,rg k 1,rg ) / ( k, rg k -1,rg ) k CEC, rg RGN
Step 5 : Determining the sectoral consumption demands for each of the five expenditure
classes using the Stone-Geary linear expenditure system.

(85) Pci * Ci,k,rg = Pc i i,k,rg + i,k ,rg Ck, rg Pcj i S, k CEC, rg RGN
J j ,k , rg
Step 6 : Determining the sectoral consumption demands.

5
(86) C i,rg = ( ) * pop rg * C i , k , rg i S, rg RGN
k =1 k, rg k - 1, rg
(87) Ci =
rg
C i,rg i S
Savings
5
(88) HSAV =
rg
poprg *
k =1
( k,rg k-1,rg ) * ( Yk,rg - C k,rg )
(89) GSAV =
11
Ndi *
i=1
( a * t
11
j=1
ji ndj * Pqj )+ N * ( a
11
i=1
mi
11
j=1
m ji * tnm j * (wpm j *ER) )
+ ( 11
i =1
tfdi * Pqi (IDi + Ci) )+ ( t 11
i =1
fm i * (wpm i*ER) * M i )
+ ( 11
i =1
twi * Wrg * Lwi ) + ( t 11
i =1
wi * Plsrg * Lsi )
+ ( 11
i =1
tki * Pki * Lki ) + PAYEM
- ( 11
i =1
Pqi * cgi ) - ( GTR )

(90) FSAV =
11

i =1
( wpmi * Mi ) +
11
i =1
Nm i * ( 11
j=1
amji * wpmj )
11
- ( PWexi * EXi ) ( nct + nfi )
i=1
Saving - Investment Balance

11
(91) HSAV + GSAV + ( FSAV * ER ) = IDi * Pqi* ( 1 + tfdi)
i =1
(92) IDi = adi * (pubinv + prinv) i S
(93) GRINVDi = pukvi * pubinv + prkvi * prinv i S

Commodity Market Clearing
(94) FDi = IDi + Ci + cgi i S

11
(95) Qi = aij * Ndj
j= 1 &7
+ FDi i NES
11
(96) Q2 =
i=1
Ei + FD2
11
(97) Q3 =
i=1
CLi + FD3
11
(98) Q4 =
i=1
ROi + FD4
11
(99) Q5 =
i= 1
GS + i FD5
(100) Q6 = CP + FD6
Dynamics :
(101) ki,t = (1-depi ) * ki,t-1 + GRINVDi i S
(102) prkvi,t = 1- exp( 1- (RORi,t-1) ) i S
6
(103) RORi,t-1 = Pki,t-1 / Pkj,t-1 i S
j =1

Endogenous Variables :
Xi Gross domestic output i S

NFi Non-fixed factor inputs aggregate i NEE
RS1 Land-energy-materials aggregate in agriculture
EM1 Energy-materials aggregate in agriculture
Zi Energy-labor-capital aggregate i NAS
Ni Non-energy intermediate inputs aggregate i S
Nmi Imported intermediates aggregate i S
N di Domestic intermediates aggregate i S
Vi Value-added i S
Lwi Input of wage-labour i S
Lsi Input of self-employed labour i NAS
EAi Energy Aggregate i S
Ei Input of Electricity i S
NEi Non-electric fuels aggregate i S
CLi Input of Coal i S
GSi Input of Natural-gas i S
ROi Input of Refined Oil i S
CP Input of Crude-Pet in the Refined Oil Sector
DDi Domestic demand i S
EXi Export demand i EXS
Mi Final Imports i IMS
Qi Composite output i S
PNEi Price of non-electric fuels aggregate i S
Pki Price of capital i S
Pvai Price of value-added i S
Peai Price of energy aggregate i S
Pni Price of non-energy intermediate inputs aggregate i S
Pzi Price of energy-labor-capital aggregate i NAS
Pnfi Price of non-fixed factor inputs aggregate i NEE
Prs1 Price of land-energy-materials aggregate in agriculture
Pem1 Price of energy-materials aggregate in agriculture
Pl Price of land in agriculture
Pfi Price of fixed factor i PES
Pddi Price of domestic demand i S
Pexi Price of export demand i EXS
Pxi Price of domestic output i S
Pqi Price of composite output i S
PWexi Price (in foreign currency) of exports in the international i EXS
market
Pndi Price of domestic intermediates aggregate i S
Pnmi Price of imported intermediates aggregate i S
Pmi Price of Final imports i IMS
ECO2ng CO2 emissions in the non-government sector
ECO2g CO2 emissions in the government sector
TECO2 Total CO2 emissions in the economy
( TECO2 is variable in the base-run, but fixed in the simulations)
te Carbon tax (Rs. / ton of carbon emissions)
( te is fixed at zero in the base-run, but a variable in the
simulations. )
PAYEM Total Emission Payments
Plsurban Remuneration to self-employed labour in the non-agricultural
sectors
Wrg Wage rate for wage-labour by region rg RGN
Pci Consumption Prices i S
CPIrg Consumer price index by region rg RGN
PINDEX Overall price index
Yty, i Factor incomes by sector ty TYP, i S
GTR Government transfers
WTR World Transfers
YHty, rg Factor Incomes by region ty TYP,
rg RGN
Yh, rg Incomes by income classes h H, rg RGN
Yrg Mean Income rg RGN
Vyrg Variance of income rg RGN
Y
rg
Mean of log income rg RGN
Y
rg
Standard Deviation of log income rg RGN
c
rg
Standard Deviation of log consumption rg RGN
c
rg
Mean of log consumption rg RGN
k , rg
Share of population that falls under per capita rg RGN
expenditure level celk,rg
k , rg
Share of consumption accruing to the population k CEC, rg RGN
under per capita expenditure level celk,rg
k,rg Share of income accruing to the population k CEC, rg RGN
under per capita expenditure level celk,rg
C k,rg Per capita consumption by k CEC, rg RGN
consumption expenditure class and region
Yk,rg Per capita income by consumption k CEC, rg RGN
expenditure class and region
Ci,k,rg Consumption of commodity i by consumption i S, k CEC,
expenditure class and region rg RGN
Ci,rg Consumption of commodity i by region i S, rg RGN
Ci Consumption of commodity i i S
HSAV Household Savings
GSAV Government Savings

HSAV Household Savings
GSAV Government Savings
FSAV Foreign Savings (in dollars)

ER Exchange Rate
IDi Investment demand by sector of origin i S
GRINVDi Gross real investment by sector of destination i S
FDi Final demand by sector i S
RORi Relative rate of return on capital in sector i i S
Exogenous Variables and Parameters :
ki Capital stock in sector i i S

l Supply of land in agriculture
totlab Total labour supply in the non-agricultural sectors
cgi Government consumption of commodity i i S
Ls1 Fixed supply of self-employed labour in agriculture
fi Supply of fixed factors in the primary energy sectors i PES
pubinv Aggregate public sector real investment
prinv Aggregate private sector real investment
dwrg Initial wage rate by region rg RGN
depi Depreciation rate of capital in sector i i S
dcpirg Initial consumer price index by region rg RGN
pwesi International price of export substitutes i S
pwmi International price of imports i S
Eco2q Annual allotment of CO2 emission quota
peco2 Price of tradable emission permit ($ per ton)
pwtsi Weights in the price index i S
gtra Governments interest payments
gtrb Governments current transfers
nct Net current transfers from rest of the world
nfi Net factor income from rest of the world
tndi Rate of tax on domestic intermediates i S
tnmi Rate of tax on imported intermediates i S
tfmi Rate of tax on final imports i S
exsubi Rate of subsidy on exports i S
tfdi Rate of tax on final demand i S
t ki Rate of tax on capital income i S
twi Rate of tax on wage and self-employed i S
labour income
al ty, i, rg Shares for allocation of sectoral factor incomes ty TYP, i S,
to regions rg RGN
ar rg Shares for allocation of transfer payments to rg RGN
regions

h,ty,rg Factor income share by income class and region h H , ty TYP,
rg RGN
h,rg Population shares by income class and region h H , rg RGN
rg -value transforming the S.D. of log income to rg RGN
S.D. of log consumption
rg Variance constant in the S.D. of log income rg RGN
equation by region
rg Intercept term in the consumption fuction by region rg RGN
rg Slope term in the consumption fuction by region rg RGN
celk,rg Upper limit of consumption expenditure of class k CEC, rg RGN
k in region rg
i,k ,rg
Consumption expenditure shares by sector, class i S, k CEC,
and region rg RGN
i,k,rg
Commited consumption expenditures by i S, k CEC,
sector, class and region rg RGN
poprg Population by region rg RGN
aij amount of commodity i required to produce i S, j S
1 unit of domestic intermediate input aggregate
for sector j
amij amount of commodity i imports required to i S, j S
fulfill 1 unit of imported intermediate input
aggrgate for sector j
adi Share of aggregate investment by sector of origin i S
prkvi Share of private investment by sector of destination i S
pukvi Share of public investment by sector of destination i S
Factor that ensures that the allocation ratios
(prkvi s ) add to one
cli CO2 emission from one unit of coal used i S
roi CO2 emission from one unit of refined oil used i S
gsi CO2 emission from one unit of natural gas used i S
cp CO2 emission from one unit of crude-petroleum
used
i CO2 emission per unit of production of i S
commodity i
i CO2 emission per unit of household consumption i S
of commodity i
i CO2 emission per unit of government consumption i S
of commodity i
xi Elasticity of substitution at the X-level i S
production function
rs1 Elasticity of substitution at the RS-level
p.f. in agriculture
em1 Elasticity of substitution at the EM-level p.f.
in agriculture

nfi Elasticity of substitution at the NF-level i NEE
production function
ni Elasticity of substitution at the N-level i S
production function
zi Elasticity of substitution at the Z-level i NAS
production function
vai Elasticity of substitution at the VA-level i S
production function
eai Elasticity of substitution at the EA-level i S
production function
nei Elasticity of substitution at the NE-level i S
production function
exi Elasticity of substitution at the EX-level i EXS
production function
qi Elasticity of substitution at the Q-level i IMS
production function
xi Share parameter of the X-level production i S
function
rs1 Share parameter of the RS-level p.f. in agriculture
em1 Share parameter of the EM-level p.f. in agriculture
nfi Share parameter of the NF-level production function i NEE
ni Share parameter of the N-level production function i S
zi Share parameter of the Z-level production function i NAS
vai Share parameter of the VA-level production function i S
eai Share parameter of the EA-level production function i S
nei Share parameter of the NE-level production function i S
exi Share parameter of the EX-level production function i EXS
qi Share parameter of the Q-level production function i IMS
axi Scale parameter of the X-level production function i S
ars1 Scale parameter of the RS-level p.f. in agriculture
aem1 Scale parameter of the EM-level p.f. in agriculture
anfi Scale parameter of the NF-level production function i NEE
ani Scale parameter of the N-level production function i S
azi Scale parameter of the Z-level production function i NAS
avai Scale parameter of the VA-level production function i S
aeai Scale parameter of the EA-level production function i S
anei Scale parameter of the NE-level production function i S
aexi Scale parameter of the EX-level production function i EXS
aqi Scale parameter of the Q-level production function i IMS
Numeraire :
PINDEX Overall Price Index

Appendix 3
It is obvious that data requirements for the CGE model developed for this study are huge and
diverse. In fact, published data rarely fit the requirement of the model. The data collected from
various publications had to go through several stages of processing before it became applicable
to the CGE model. Particularly difficult was the task of creating compatibility between different
sets of data coming from varied sources, using different base-years, classifications, and degrees
and types of disaggregation across sectors. The compatibility problem in pooling of data from
various sources was encountered at almost every step. We have given below a brief description
of the adjustments made in publised data at the various steps.
Our CGE model has been calibrated to the benchmark equilibrium data set, represented in a
Social Accounting Matrix for the Indian economy for the year 1989-90. The basic data set for
the SAM has been obtained from the Central Statistical Organization - National Accounts
Statitstics of India (various issues) and the CSO (1997) - Input-Output Transactions Table -
1989-90. A host of other exogenous variables and parameters have been estimated from the
data available in various other published sources.
The 11x11 input-output transactions table
Our model is based on an eleven sector disaggregation of the Indian economy. The CSO-
IOTT provides a highly disaggregated 115 x 115 input-output matrix for the Indian economy
for the year 1989-90, the base-year of our model. Unfortunately, even in this 115 sectoral
divison Crude Petroleum and Natural Gas are clubbed together in sector no. 24. By using
guessestimates on the split ratios for the inputs and outputs of the Crude Petroleum and
Natural Gas sectors, obtained from the concerned statisticians at the CSO, New Delhi, we
first split the sector 24 of CSO-IOTT into two sectors, and thus generated a 116 x 116 I-O
matrix. We then worked out a mapping scheme (shown below) from the 116 sectors to our 11
sectors and thereafter produced an aggregated 11 x 11 I-O matrix. That gives us the inter-
industry flows as well as the final demand components for the 11 sectors.
Table A3-1 : Sector mapping scheme

Sector Sector Name CSO - IOTT Sectors
No
1 AGRICULTURE 01-22
2 ELECTRICITY 101
3 COAL 23
4 OIL 59
5 NATURAL GAS 25
6 CRUDE PETROLEUM 24
7 TRANSPORT 104,105
8 ENERGY INTENSIVE INDUSTRIES 26-30, 53,61-64, 69, 71, 73-76,
9 OTHER INTERMEDIATES 31-33, 47, 52, 54, 57, 58, 60, 65-
incl. Capital Goods 68, 70, 72, 77-100, 102, 103,107
10 CONSUMER GOODS 034-046, 048-051, 055, 056
11 SERVICES 106, 108-116

Capital and labour stocks
Data on capital stocks are available in the CSO-NAS(BS), but again not as per our sectoral
classification. We split the aggregated capital stocks with respect to our 11 sectors using the
value added proportions. The resulting capital stocks figures were not all compatible with the
capital incomes figures generated above using CSO-NAS (BS) and CSO-NAS-FI. Assuming
greater reliability of the capital incomes figures, we adjusted the capital stocks figures so that
the sectoral capital rental rates were realistic, as judged from other published data sources.
The labour stock data is available in NSSO-45th Round. The labour stock data posed less of
a problem because, in their case, the sectoral distribution is not required. In the model, sectoral
capital stocks are fixed at exogenously given levels, but labour supply is fixed only in aggregate
terms. The only sector for which labour supply is fixed exogenously is agriculture, and the data
for this is available in NSSO-45th Round.
Table A3-2 : Population, labour supply and labour participation rate

Year Pop. (in billion) Pop. Growth Lab. Sup. Lab. Sup.- LPR
rate (in billion) Growth rate
1990 0.81 0.35 0.4321
1995 0.90 2.11 0.41 3.16 0.4556
2000 0.98 1.70 0.46 2.30 0.4694
2005 1.06 1.57 0.50 1.67 0.4717
2010 1.14 1.46 0.54 1.54 0.4737
2015 1.22 1.36 0.58 1.43 0.4754
2020 1.30 1.27 0.62 1.33 0.4769
Note : LPR : Labour Participation Rate
The growth rates for each of the quinquenniums are the annual growth rates.
Income distribution
Factor income shares by income percentiles for each the two regions rural and urban are
deducible from the income distribution data provided for 1975-76 and 1994-95 in Pradhan et al
(2000). We have used the 1994-95 income distribution data for deriving the factor income
shares for 1989-90, the base year of our model. It is generally agreed that income distribution
pattern changes very slowly in India. Hence, it is fair to assume that the income distribution
pattern of 1994-95 will approximate that of 1989-90.

Table A3-4 : Factor income share by income percentiles
Urban
yself ywage ycap yland yff ynonp
h1 0.0020 0.0559 0.0001 0.0001 0.0001 0.1638

h2 0.0030 0.0585 0.0024 0.0024 0.0024 0.1120
h3 0.0080 0.0619 0.0052 0.0052 0.0052 0.0798
h4 0.0930 0.0643 0.0083 0.0083 0.0083 0.0939
h5 0.1020 0.0727 0.0165 0.0165 0.0165 0.0711
h6 0.0380 0.1283 0.0313 0.0313 0.0313 0.1443
h7 0.0470 0.1335 0.0529 0.0529 0.0529 0.1601
h8 0.0850 0.1640 0.0937 0.0937 0.0937 0.0980
h9 0.1560 0.1661 0.1659 0.1659 0.1659 0.0645
h10 0.0950 0.0229 0.1608 0.1608 0.1608 0.0080
h11 0.0470 0.0206 0.0440 0.0440 0.0440 0.0016
h12 0.0570 0.0153 0.0544 0.0544 0.0544 0.0010
h13 0.0540 0.0150 0.0688 0.0688 0.0688 0.0009
h14 0.1040 0.0110 0.0950 0.0950 0.0950 0.0007
h15 0.1090 0.0100 0.2007 0.2007 0.2007 0.0003
Rural
yself ywage ycap yland yff ynonp
h1 0.0000 0.1195 0.0000 0.0000 0.0016 0.0099

h2 0.0000 0.1496 0.0000 0.0000 0.0061 0.0089
h3 0.0000 0.1827 0.0000 0.0000 0.0134 0.0096
h4 0.0000 0.1925 0.0000 0.0000 0.0238 0.0091
h5 0.0000 0.2757 0.0000 0.0000 0.0375 0.0094
h6 0.0583 0.0800 0.0881 0.0566 0.0566 0.1995
h7 0.0924 0.0000 0.1263 0.1002 0.0802 0.2223
h8 0.1465 0.0000 0.1263 0.1984 0.1160 0.1709
h9 0.1930 0.0000 0.2816 0.1825 0.1825 0.1798
h10 0.1525 0.0000 0.1393 0.1398 0.1498 0.0942
h11 0.0461 0.0000 0.0357 0.0413 0.0413 0.0389
h12 0.0490 0.0000 0.0375 0.0452 0.0452 0.0176
h13 0.0575 0.0000 0.0422 0.0515 0.0515 0.0115
h14 0.0842 0.0000 0.0505 0.0633 0.0633 0.0115
h15 0.1205 0.0000 0.0725 0.1212 0.1312 0.0069
Note : h1 to h9 : 10% each ; h10 : 5% ; h11 to h15 : 1% each .
yself : income of self-employed ; ywage : wage income ; ycap : income from capital ;
yland : income from land ; yff : income from fixed factors ;
ynonp : non-production related incomes (i.e., transfer payments) .

LES parameters for the demand functions
In our model there are 5 rural and 5 urban consumption expenditure classes. To
econometrically estimate the LES parameters for each of these 10 classes from time
series data would have been a daunting task. So we decided to make use of an existing
set of parameters, from another study, Dahl (1989). The latter gives the committed
expenditures and the expenditure shares for the ten rural and urban consumption
expenditure classes, as per a six-sectors classification agriculture, capital goods,
intermediate goods, public infrastrucure, consumer goods and services. Moreover, the
committed expenditures are at the 1973-74 prices. These are first inflated to the 1989-
90 prices using the wholesale price indices obtained from the ES (Economic Survey
(various issues), Government of India). To obtain the demand function parameters for
our nine sectors we first construct a 9x6 transformation matrix which maps the 6x1
vector of the demand parameters (for each expenditure group) in the six-commodities
classification, onto a 9x1 vector of demand parameters for our nine commodity groups.
The transformation matrix is prepared by using the final consumption demand vector
of the input-output transactions table of the CSO-IOTT. From the latter we could
determine the elements of the transformation matrix i.e., proportions of each of the 6
sectors of Dahl (1989) going into the various sectors of our nine-sectors scheme.

Table A3-5 : Expenditure shares by consumption expenditure classes
Urban
c1 c2 c3 c4 c5
Agricult 0.6599 0.5039 0.3979 0.3025 0.2000

Elec 0.0014 0.0020 0.0021 0.0033 0.0128
Coal 0.0001 0.0001 0.0001 0.0001 0.0001
Refoil 0.0071 0.0076 0.0176 0.0375 0.0550
Nat-gas 0.0000 0.0000 0.0000 0.0000 0.0000
Crude-Pet 0.0000 0.0000 0.0000 0.0000 0.0000
Trans 0.0810 0.1204 0.1181 0.1067 0.1092
Enerint 0.0024 0.0025 0.0025 0.0025 0.0033
Otherint 0.0316 0.0375 0.0425 0.0463 0.0739
Cons-good 0.1049 0.1837 0.2420 0.2485 0.2540
Services 0.1116 0.1423 0.1772 0.2526 0.2917
Rural
c1 c2 c3 c4 c5
Agricult 0.6594 0.5472 0.4013 0.3316 0.1928

Elec 0.0011 0.0014 0.0015 0.0017 0.0128
Coal 0.0001 0.0001 0.0001 0.0001 0.0001
Refoil 0.0027 0.0033 0.0031 0.0124 0.0358
Nat-gas 0.0000 0.0000 0.0000 0.0000 0.0000
Crude-Pet 0.0000 0.0000 0.0000 0.0000 0.0000
Trans 0.0130 0.0153 0.0265 0.0372 0.1035
Enerint 0.0008 0.0009 0.0010 0.0014 0.0019
Otherint 0.1113 0.1274 0.1333 0.1318 0.1231
Cons-good 0.1048 0.1830 0.2536 0.2814 0.2824
Services 0.1068 0.1214 0.1796 0.2024 0.2476
Substitution elasticities for the production functions
The substitution elasticities of the production functions in the nested production

structure have taken from Babiker et al (2001), wherever possible . (We have followed
closely, but not entirely, the nesting of the production structure in the EPPA model
presented in Babiker et al, 2001). The substitution elasticities, between the domestic
and imported intermediates aggregates at the N level, and between capital, wage-
labour and self-employed labour at the VA level, have been taken from Ojha (1997).
Finally, the source for the CES (q ) and CET (ex) elasticities in the trade aggregation
functions is Pradhan and Sahoo (2002).

Table A3-6 : Substitution elasticities
coil ne ea va n z x nf em rs q ex
Agricult 0.95 0.50 1.10 1.50 0.60 0.30 0.75 1.39 0.92
Elec 0.30 0.90 0.50 1.20 1.50 0.40 0.10
Coal 0.95 0.50 0.97 1.50 0.40 0.60 0.10 1.62 0.92
Refoil 0.95 0.50 0.96 1.50 0.40 0.10 0.10 2.00 0.92
Nat-gas 0.95 0.50 0.98 1.50 0.40 0.60 0.10 1.62
Crude-pet 0.95 0.50 0.98 1.50 0.40 0.60 0.10 1.62 0.92
Trans 0.95 0.50 1.60 1.50 0.40 0.10 0.50
Enerint 0.95 0.50 1.10 1.50 0.50 0.10 3.36 0.92
Otherint 0.95 0.50 1.60 1.50 0.50 0.10 1.72 0.92
Cons-good 0.95 0.50 1.10 1.50 0.40 0.10 2.24 0.92
Services 0.95 0.50 1.50 1.50 0.40 0.10 0.55
Carbon emission coefficients
For carbon emission coefficients, the source we have used is Yang et al (1996). Yang et al
(1996) provide figures for coefficients of energy contents in India for coal, crude petroleum,
natural gas, refined oil in exajoule per million US$ at 1985 prices. We convert these energy
content coefficients to exajoule per million rupees at 1990 prices using the appropriate exchange
rate and price indices from the ES. These are then multiplied by the coefficients of carbon contents
in million tonnes per exajoule, also given in Yang et al (1996) to arrive at the coefficients of
carbon contents in million tonnes per million rupees. Carbon is emitted in the process of output
generation as well, in the cement industry, which is a part of the energy intensive sector, in our
classification. Carbon emission coefficient per unit of output produced in this sector is obtained
from Murthy, Panda and Parikh (1997). Carbon emission coefficients for private and government
consumption are also taken from Murthy, Panda and Parikh (1997).
Table A3-7 : Emission Coefficients (tonne per rupee)

cl ro gs cp i
Agricult 0.17663159 0.04433592 0.02081120 0.00000008 0.00000267

Elec 0.17663159 0.04433592 0.02081120 0.00029913 0.00074355
Coal 0.17663159 0.04433592 0.02081120 0.00018919 0.00416302
Refoil 0.17663159 0.04433592 0.02081120 0.04385124 0.00000274 0.00000409
Nat-gas 0.17663159 0.04433592 0.02081120 0.00000000 0.00095317
Crude-pet 0.17663159 0.04433592 0.02081120 0.00000000 0.00000000
Trans 0.17663159 0.04433592 0.02081120 0.00001167 0.00001282
Enerint 0.17663159 0.04433592 0.02081120 0.00000372 0.00024586 0.00013460
Otherint 0.17663159 0.04433592 0.02081120 0.00000462 0.00000182
Cons-good 0.17663159 0.04433592 0.02081120 0.00000153 0.00016765
Services 0.17663159 0.04433592 0.02081120 0.00000022 0.00000010

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The Trade-Off Among Carbon Emissions, Economic

Growth and Poverty Reduction in

VIJAY PRAKASH OJHA

Rajiv Gandhi Institute for Contemporary Studies (RGICS)

South Asian Network for Development and Environmental Economics (SANDEE)

SANDEE Working Paper No. 12-05

National Library of Nepal Catalogue Service:

Vijay Prakash Ojha

(SANDEE Working Papers, ISSN 1813-1891; 2005 - WP 12)

7. Carbon Tax policy

8. Tradable Emission Permits

II SANDEE Working Paper No. 12-05

SANDEE is financially supported by International Development Research Centre

Table 1 : Energy consumption in India (petajoules) 2

Figure 1 : The production structure 8

VI SANDEE Working Paper No. 12-05

SANDEE Working Paper No. 12-05 VII

Vijay Prakash Ojha

1.1 The energy and emissions scene in India

Table 1 : Energy consumption in India (petajoules)

Year 1970 1975 1980 1985 1990 2000 2005

Notes : Net carbon emission excludes emissions from biomass combustion.

1.2 Policies for carbon emissions reduction

A lot of avoidable CO 2 emissions is due to the rampant energy inefficiency, which, in

The command-and-control measure, i.e., enforcing carbon emission reduction targets

1.3 The present study

As is usually done in a CGE modeling analysis, we first generate a business-as-usual

2.1 Sectoral disaggregation

Our model is based on an eleven sector disaggregation of the Indian economy :

Fig. 1 : The production structure

Domestic Sectoral Gross Output (X)

Non-Energy Intermediate (N) Energy-Labour-Capital Aggregate ( Z )

Energy-Aggregate (EA) Value-Added (VA)

Electricity (E) Non-Electricity (NE) K Ls Lw

Coal (CL) Gas (GS) Refined Oil (RO)

Note : K Capital ; Ls Self-employed labour ; Lw Wage-labour.

2.3 Technological change

Energy-saving technological progress is incorporated in our model by making the

2.4 Carbon emissions

2.5 Carbon Taxes

10 SANDEE Working Paper No. 12-05

2.8 Labour markets and wage rates

2.9 Factor incomes and transfers

2.10 Income distribution

It may be noted here that, in case of across-the-board transfers of revenues earned

2.12 Market equilibrium and macroeconomic closure

3.1 The macro variables

3.2 Poverty ratio

3.4 Carbon emissions

In assessing Indias contribution to global carbon emissions, it is important to look at

The eight policy simulations are summarized in table 3 given below.

Table 3 : The policy simulations

Policy Simulation 1 Domestic Carbon Fixed at 1990 Recycled to the households

SANDEE Working Paper No. 12-05 17

4.1 Policy simulations 1 and 1(TT)

4.2 Policy simulations 2 and 2(TT)

Policy simulation 2, on the whole, is a milder version of policy simulation 1. In simulation

4.3 Policy simulations 3 and 3(TT)

The poverty ratio declines significantly in scenario 3. It declines by 2.43 percentage