March 2001
ENDOGENOUS SUBSTITUTION OF ENERGY RESOURCES:
THEORY AND APPLICATION TO THE INDIAN ENERGY SECTOR
by
Ujjayant Chakravorty*
*Associate Professor of Economics, Emory University, Atlanta, GA 30322,
unc@emory.edu. I acknowledge generous research support from the Halle Institute and
the Institute for Comparative and International Studies at Emory University. I would like
to thank Darrell Krulce, James Roumasset and Kin-Ping Tse for valuable insights during
earlier research on this topic.
1
ENDOGENOUS SUBSTITUTION OF ENERGY RESOURCES:
THEORY AND APPLICATION TO THE INDIAN CONTEXT
1. Introduction: The Theory of Endogenous Resource Substitution
The analysis of climate change mitigation policies at the global or the national level
ultimately depends on the assumed energy mix. In particular, on the current and projected
mix of carbon-intensive resources such as coal, oil and natural gas as well as cleaner
resources such as nuclear, hydro and renewables. There are several different modeling
approaches currently used by researchers to project carbon emissions.
Most economic analysis of climate change, especially ones that focus on aggregate fossil
fuel use, tend to assume a fixed input-output relationship between aggregate resource use
and Gross Domestic Product (GDP) per capita. For instance, they postulate a certain
extraction rate for fossil fuels such as coal, oil and natural gas, that is assumed to grow
proportionately with increases in GDP per capita, which in turn leads to a proportionate
increase in particulate (e.g., carbon) emissions over time since emissions per unit energy
use are assumed fixed (Nordhaus 1991). These fixed input-output relationships have been
assumed in these models primarily because the focus of the research was on the time
movement of aggregate emissions and not in detailing energy use and substitution
options. However, the above specifications seem inadequate if the objective of the
analysis is to examine energy sector policies and the important question of transition
across fossil fuels in response to fiscal policies or market forces. For example, the
significant shift from coal to natural gas in developing countries such as India within the
last decade suggest that the assumption of fixed or exogenously given resource extraction
rates may need to be relaxed, especially in the study of long run environmental
phenomena.
Even when studies have modeled the energy sector by explicit consideration of individual
energy sources, resource prices have tended to be exogenously given (Manne and Richels
1991; Manne, Mendelsohn and Richels, 1993). However, resource prices change over
time in response to a variety of factors such as discount rates, size of stock, demand
2
growth and more importantly in this case, their relative cost and emission characteristics.
Moreover, substitution between fuels and the economic viability of alternative energy
sources such as solar, biomass and wind directly depend on these relative prices and their
movement over time.
This paper provides an overview of a framework that endogenizes resource use over time
in a Hotelling type exhaustible resource model. The partial equilibrium energy economy
is divided into distinct sectors such as electricity, residential, transportation and industrial
sectors. The supply of energy resources is provided by fossil fuels such as oil, coal and
natural gas and renewable energies such as solar. Demand functions within each sector
are assumed to be known and independent of each other. The cost characteristics of each
resource are known, as well as their estimated reserves. The cost of conversion of each
resource into its end use in a given sector is also assumed to be given as well as the
conversion efficiency. The model delivers resource prices specific to each sector which in
turn determine which resource must be used in a given sector.
In what follows, we provide the basic theory and empirical illustration of the model. We
then use insights from the model to examine the Indian energy mix by individual resource
as well as sector, and discuss their implications for carbon emissions and other
environmental externalities. This analysis complements other approaches based on
macroeconomic modelling and disaggregated sectoral analyses and points the need to
develop projections of energy use and emissions based on aggregate economic
parameters as well as at the level of micro-foundations. The purpose of this theory is to
outline a theory of resource substitution and demonstrate that application of such a
framework could provide valuable insights into India’s energy mix and emissions profile.
A rigorous application of the model to the Indian context is not attempted here, and is left
for future work.
The rest of the paper is organized as follows. Section 2 develops the theoretical
framework. Section 3 provides an empirical illustration of the theory. Section 4 relates
the theoretical and empirical results to the Indian case. Section 5 concludes the paper.
3
2. A Simple Model with Two Sectors and Two Resources
In this section we outline the theory of endogenous resource substitution as developed in
Chakravorty and Krulce (1994) and Chakravorty, Krulce and Roumasset (2000). For
simplicity, let there be two resources, say oil and coal. Let us assume that both resources
are fixed in size, and their cumulative reserves are given by Qo >0 and Qc.>0. Although
the estimated stock of these resources may increase over time because of exploration and
new discoveries, we abstain from considering it in this simple model. Let the unit
extraction cost for each resource be given by Co and Cc, respectively. For simplicity,
these unit costs are assumed constant and do not vary with residual stock. The results go
through if these marginal costs of extraction are increasing and convex, although the
intuition is less obvious in this latter case.
Let us consider only two energy sectors, and for exposition purposes, assume that they
are electricity and transportation. Non-zero energy demand in these sectors is denoted by
DE and DT respectively. In this simplified partial equilibrium economy, we choose the
amount of oil and coal resources to be extracted over time for each of these sectors,
denoted by qOE, qOT, qCE and qCT respectively, where for instance, the subscript ‘OE’
referes to the amount of oil extracted for electricity. Note that these four variables are
functions of time denoted by the variable t. Let z be the cost of converting each unit of
coal from electricity to transportation. An ideal two resource two end-use model would
have four conversion costs from each resource into each sector. However, it can be
shown that the model presented here is a reduced form of the general model with four
conversion costs achieved by appropriately shifting demand and cost functions. The
optimization program could be written as the sum of the consumer plus producer surplus
as follows:
Maximize
∞
(1) ∫ e
t0
− rt

 q (t ) + q ( t ) − 1
q ( t )+ qCT ( t ) − 1 ( x)dx −
CE
zqCT (t )
∫ OE
( x )dx + ∫ OT
DT
D
E
0
0

− (q (t ) + q (t )) cOo − (q (t ) +

OE
OT
CE

4


dt

qCT (t )) cC 
subject to
(2) q (t ) ≥ 0, q (t ) ≥ 0
OE
q
CE
OT
(t ) ≥ 0, q (t ) ≥ 0, Q ≥ 0, Q ≥ 0,
CE
O
O
⋅
⋅
(3) Q (t ) = −
o
q
OE
(t ) −
qOT (t ), and Q C (t ) = −
q
CE
(t ) −
q
CT
(t ).
where (2) details the standard non-negativity constraints and (3) gives the rate of
depletion of the stock of the two exhaustible resources at each instant and r is the social
discount rate. Given λO(t) and λC(t) as the shadow prices associated with the two
constraints in (3), we obtain the Hamiltonian:
q (t ) + q CE ( t ) − 1
q OT ( t ) + qCT ( t ) DT− 1 ( x )dx −
+
(
)
H = ∫ OE
x
dx
DE
∫0
0
− (q (t ) +
OE
− (q (t ) +
OE
which in turn generates the necessary conditions:
⋅
(4)Q (t) = − q (t) −
OE
o
q
(t)
OT
⋅
(5) Q (t ) = −
C
q
CE
(t ) −
q
(t ).
CT
⋅
(6) λo (t ) = r λO (t )
⋅
(7) λC (t ) = r λC (t )
(8) p (t) ≤p (t), if < then
q
(9) p (t) ≤p (t), if < then
q
(10) p (t) ≤p (t), if < then
q
E
E
T
O
C
O
OE
CE
(t ) = 0.
(t ) = 0.
OT
(t ) = 0.
5
zq (t )
q (t )) c − (q (t ) + q (t )) c
q (t )) λ (t ) − (q (t ) + q (t )) λ (t )
CT
OT
OT
O
O
CE
C
CT
CE
CT
C
(11) p (t) ≤p (t) + z,
T
C
if < then
λ (t ) Q
(13) lim e λ (t ) Q
− rt
(12) lim e
t→ ∞
O
O
C
C
− rt
t→ ∞
q
CT
(t ) = 0.
(t ) = 0, and
(t ) = 0.
where pO(t) = cO + λO(t) and pC(t) = cC + λC(t) can be interpreted as the prices of oil and
coal. That is, they are the sum of the extraction cost and the shadow price of the resource
at any given instant. The above problem is strictly concave and therefore the necessary
conditions are also sufficient for a unique solution.
We now focus on a heuristic discussion of the above necessary conditions. Notice that by
(6) and (7), the shadow prices of the two resources must rise at the rate of discount, a
standard property in Hotelling models with constant extraction costs. This then implies
that the price paths of the two resources pO and pC must increase over time. Because this
is an infinite horizon model with finite resources, both resources must be exhausted at
infinity. However, the order of resource use at any given instant of time in the two sectors
will be determined by conditions (8-11). Comparing (8) and (9), it is clear that the
cheaper of two resources (pO and pC) will be used in the electricity sector. In the
transportation sector, the comparison is between pO and pC + z, since an additional cost of
z is incurred in converting coal for use in the transportation sector. Notice also that in this
exhaustible resource framework, the price of energy in both sectors (given by pO , pC and
pC + z) must increase over time, possibly at different rates.
Although the above model is not formulated with a backstop technology, it can be easily
introduced through an additional condition that specifies a terminal price in the two
sectors that equals the exogenously given backstop price. In that case, the price of the
resource grows until it equals the backstop price. Moreover, different mines with
different extraction costs can be modeled either as distinct resources (as done in the
empirical model below) or as a single resource with a rising extraction cost function. The
latter of course, does not yield monotone increasing shadow price functions.
6
3. An Empirical Application
In this section we present the empirical model, which is described in detail in
Chakravorty, Roumasset, and Tse (1997). New results from that model that deal with
sector-specific energy prices and technological change are discussed below. The model
consists of four demand sectors: electricity, industry, residential/commercial, and
transportation. As in Nordhaus (1979), industrial demand is composed of non-electric
process and space heating, residential includes non-electric space and other heating,
while transportation includes all private and public transportation such as trucks, buses,
autos, and air, water and rail transport. To keep the model tractable, we only consider oil,
coal, and natural gas, which together account for 90 percent of the world’s primary
energy consumption (International Energy Agency (IEA) 1995). Other resources such as
nuclear, hydropower, geothermal, and wind energy have much smaller shares of global
energy consumption and are neglected in this model. Similarly, although solar is chosen
as the single backstop technology, nuclear fusion is another candidate expected to
become economical by the middle of the next century (Furth 1995). However, solar is
chosen over nuclear fusion because major advances have been made in improving the
former technology in the last two decades and it is already commercially viable in many
developed and developing countries with annual sales in billions of dollars (Hoagland
1995).
Global annual demand functions for each sector are assumed to be a Cobb-Douglas
function of price and income as follows:
α
D j = A j Pj j Y
βj
(14)
where α j and β j are respectively the price and income elasticities of demand in end-use
j, A j is the constant coefficient, Pj is the price of delivered energy service in sector j,
and Y is the aggregate income or output level measured by the GDP of the world
economy. The price and income elasticities are obtained from Nordhaus (1979) while the
constant A j is computed using equation (14) from world GDP, energy consumption, and
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price of energy resources in the base year, taken to be 1990.
Efficiencies of conversion from resources to end-uses are obtained from engineering data.
Typically, there are a multitude of devices that convert a certain resource into an end-use;
for example, buses, trains, ships, and autos all convert oil to transportation units. Even
within each of these categories, fuel efficiencies vary dramatically. Instead of choosing
the more appropriate but tedious procedure of computing weighted (by the proportion of
resource consumed by that particular device) efficiencies, some of which are not well
documented, we choose representative activities for each end-use sector such as lightduty passenger cars for transportation, stove heating for the residential/commercial
sector, industrial process heating for the industrial sector, and electricity generation for
the electricity sector. The conversion cost for these devices was computed from
amortizing capital and maintenance costs over the lifetime of the device. In some cases,
multi-step conversion processes were used. For example, since oil, coal, and solar energy
are seldom used for stove heating, oil was first converted to Liquefied Petroleum Gas
(LPG) and the cost of a LPG stove was used. Similarly, coal is gasified and then used for
stove heating. Solar energy is converted to each demand by first converting solar energy
to electricity and then electricity to that particular demand.
Extraction costs are estimated from data on worldwide proven and estimated reserves for
oil, natural gas, and coal. Since extraction cost functions are expected to increase with
cumulative extraction, non-linear exponential and S-specifications are estimated; these
are in turn further approximated by step functions to reduce computational difficulties
with continuous functional forms. This reduces the problem to one grade of natural gas,
two grades of oil, and three grades of coal, each with a constant marginal cost of
extraction. For oil, grade I has a lower cost (high quality) than grade II, and similarly for
coal with three different grades.
Energy demand is expected to grow continually as the large low energy intensity
countries in Asia such as China, India, and Indonesia continue to modernize their
economies. Asian energy demand has been growing historically at rates significantly
8
higher than the world average (International Energy Agency 1995), although analysts
have been downgrading near-term projections of demand growth because of the recent
economic slowdown in several important Asian economies such as Japan, South Korea,
Thailand, and Indonesia. However, various recovery scenarios constructed by the energy
industry suggest that although short-run demand in these countries may decline by 10
percent or so, longer-run growth may not be significantly affected. Most global warming
models assume a GDP growth rate of approximately 3 percent for the period 1990-2000
declining to 1.96 percent during 2050-75 (Manne and Richels 1991; Nordhaus 1992). Our
model assumes an intermediate value of 3.0 percent world GDP growth in 1990 and
decreasing at the rate of 10 percent every decade.
Simulation Procedure and Results
The simulation algorithm is written in the programming language Pascal and guesses the
scarcity rents of the six resources and grades (three grades for coal, two for oil and one
for natural gas) in the initial time period. Since solar energy is available in unlimited
supply, it is not an exhaustible resource, and so its scarcity rent is zero. The scarcity rents
of the other six resources rise at the rate of interest and are completely determined by
their initial guesses. Resources are allocated to each demand by comparing their prices,
i.e., the sum of their extraction cost, conversion cost to the specific demand, and the
scarcity rent. The six-dimensional vector that yields the maximum net benefits using an
annual discount rate of 2 percent yields the optimal solution.
There is a large body of literature in electrical and power engineering that suggests that
historically the costs of solar energy, more specifically the cost of converting solar energy
to electrical energy through photovoltaic systems, has been going down in real terms by
approximately 50 percent every ten years (Ahmad 1994). Surveys of the future potential
for renewable energy resources have suggested that the costs of electricity generation are
expected to decrease from the current 25¢/kwh (kilowatt-hour) to around 4-6¢/kwh. The
precise estimates of a lower bound of course cannot be predicted accurately and hence
vary across studies, but there is a clear consensus regarding its continuous decline in the
next 3-4 decades, as a result of increased R&D as well as an expansion in the size of the
9
market. For example, the Clinton Energy Plan, announced by the previous US
administration before the 1997 Kyoto Summit on Global Warming, envisages a five-fold
increase in renewable energy research that if implemented may have significant impacts
on solar energy costs in the near term. Similarly, the Kyoto Protocol has called for
“promoting research, development, and increased use of new and renewable forms of
energy...” (Kyoto Protocol, 1997, p.2).
Depending on how these proposals are implemented, the costs of production for
renewable energy, in particular solar energy, are expected to decline at varying speeds. In
this paper, we examine a range of cases, from the baseline case (no solar cost reduction)
to the 50 percent per decade solar cost reduction projected by Ahmed (1994) and others,
and set a lower bound for the cost of solar electricity at 2¢/kwh in all experiments. In the
optimistic scenario of a 50 percent per decade cost reduction, the cost of electricity from
solar falls to 4¢/kwh in about 40 years, a trend supported by several studies (Ahmed
1994; Dracker and De Laquil 1996; National Renewable Energy Laboratory (NREL)
1992). It is important to understand that solar energy prices decline at different (assumed)
rates but asymptotically approach the lower bound.
Table 1 summarizes the main results from the modeling exercise under three different
rates of technological change, given by percentage reductions in the cost of the backstop
technology. Under the marginal 10% reduction in the cost of the backstop every ten
years, notice that fossil fuels continue to be the exclusive supplier of energy for the next
70 years. Coal is used for power generation, natural gas for residential use and oil for
transportation and industrial consumption. Oil is substituted by coal in the industrial
sector, and is completely exhausted by about 2070. Natural gas is exhausted around 2060,
and coal and solar are the only energy resources for several decades. When the rate of
backstop cost reduction increases to 30%, more oil is used in industry, and solar energy
becomes economical much earlier than in the previous case. A 50% rate of solar energy
cost reduction accelerates this process. Table 2 shows the same runs in the baseline (no
technological change) and carbon tax scenarios. It suggests that $100 or even $200/ton of
carbon taxes will not make a significant difference to the long run usage profile.
10
A key insight from this overly simplified model is the natural specialization of resources
in each sector. Given the conversion cost of each resource into the end-use, it is clear that
coal is the fuel of choice in power generation, natural gas for domestic use and oil in the
industrial and transportation sectors.
Fig.1 shows the time path of scarcity rents for selected grades of fossil fuels. As
discussed in the theoretical section above, oil and natural gas are “scarce” resources and
therefore their shadow prices (or scarcity rents) are higher at the beginning of the
extraction period. Their higher usage is also reflected in the faster rate of growth. Coal
shadow prices are the lowest, and reflects the relative abundance of the resource.
Fig.2 shows the total costs of different resources for the transportation sector. That is,
these costs sum the cost of extraction and conversion to end use as well as the shadow
price of the resource. Oil is the cheapest resource, followed by natural gas. However their
exhaustion over time allows coal to kick in as the most economical resource, followed by
the backstop technology in the final phase of extraction. Fig.3 shows the real price of
energy in the elecricity sector under alternative scenarios for technological change. These
prices are relative to the model base year. Thus, under no technological change, sectoral
prices rise monotonically until they hit the backstop price, as theory predicts. However,
with technological change, prices increase marginally as the exhaustible resource is used,
but once the sector makes the transition to the renewable energy, prices remain constant
over time. Different rates of technological change affect the transition path of prices to
the backstop price.
Fig. 3 shows the aggregate use of coal under alternative scenarios of technological
change. It suggests that even under low rates of technological change, coal use will
decline significantly. For countries like India, with a heavy dependence on coal reserves,
this figure suggests that the rate of coal use may be highly sensitive to the assumed rates
of technological change in the energy sector, especially the availability of cleaner
backstop technologies.
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4. Implications of the Proposed Framework for the Indian Energy Sector
In this section, we use the above theoretical and empirical insights and apply them to the
Indian context. While detailed descriptions of the Indian energy sector are provided in
companion chapters in this volume and formal modeling of the Indian energy economy
using the proposed framework is beyond the scope of this paper, we attempt to highlight
important issues relating to the long term trends in fuel substitution and energy use using
the above framework.
In India, coal, oil and natural gas are the primary energy resources. At current rates of
use, coal is the most abundant resource and its life expectancy is expected to be more
than 200 years. The proven reserves of the major fossil fuels and their reserve-production
ratios are shown in Table 3. Notice that while coal resources are substantial, the reserve
to production (R/P) ratio of natural gas is higher than for crude oil. This suggests the
likelihood of increased substitution away from oil to natural gas in the future. Already the
share of natural gas in the fuel mix has increased from 16 million TOE in 1991-92 to 22.6
million TOE in 1997-98 (TERI, 2000). Moreover, the R/P ratio for crude oil has fallen
from 25.6 in 1991 to 15.6 in 1997, indicating that if historical trends continue, domestic
production of crude oil is unlikely to increase drastically in the future while natural gas
use will increase, a trend that is observed in other developing and developed countries as
well.
Fuel substitution has taken place in all the major sectors of the Indian economy. Kerosene
and Liquefied Petroleum Gas (LPG) have substituted soft coke in residential use. In rail
transportation, which is a major provider of public transportation, diesel oil has replaced
traditional steam locomotives. Natural gas is increasingly the fuel of choice as fuel and
feedstock in the fertilizer, petrochemicals, power and sponge iron industries.
Applying the results of the theoretical framework presented earlier, it is easy to see that
the shadow prices of coal are likely to remain low relative to those of oil and natural gas.
This is mainly because available stocks of coal are abundant relative to the stocks of oil
12
and natural gas. It is possible that with significant policy reforms, such as those currently
on the table regarding deregulation and liberalization of oil and gas exploration,
significant increases in the stocks of oil and natural gas relative to coal could happen. For
example, with increased investment in exploration and use of new technology in drilling,
crude oil reserves could be increased. However, this is unlikely to happen in a businessas-usual scenario. On the other hand, the increase in the extraction of coal in recent years,
by which the share of coal in total energy supply has increased from 60 to 61.3% between
1991-92 and 1997-98 (see Table 4) has also benefited from an increase in open cast
mining and the use of new mining technology. In the same period, as shown in Table 4,
the share of crude oil has declined from 28.2 to 26.5% while the share of natural gas has
increased from 8.3 to 9.2%.
Table 5 shows the sectoral consumption of energy in India. The big-ticket items are
industry and transport with 48% and 24% of the total share respectively. Between 199198, the agriculture sector although small, grew at a high 7.7% a year while industrial
consumption increased at about 4% a year.
In terms of the contribution of the major fuels in individual sectors, coal is the premier
energy source in the industry sector supplying about 72% of the total demand in 1997-98.
Oil and petroleum products supply more than 98% of transportation demand.
Surprisingly, oil is the major fuel used in the residential sector. This is mainly because of
the widespread use of kerosene for cooking purposes, while Liquefied Petroleum Gas
(LPG) has a small but growing share in the urban areas. Biomass fuels supply most of the
cooking energy in rural areas, where consumers are unable to afford commercial cooking
energy in the form of kerosene or LPG. Even government subsidies for kerosene have not
brought it within the economic reach of most rural households. In the electricity sector,
coal is the fuel of choice with more than a 70% share. Hydroelectricity supplies another
25%, and other fuels such as natural gas, nuclear power, oil and renewables all account
for the residual 5%.
The substitution of fuels in the energy supply mix is also driven by the heavy reliance on
13
imports, mainly of coking coal, crude oil and petroleum products. Between 1985-86 and
1997-98, imports of crude oil and petroleum products has increased from 2 million tonnes
to 17 million tonnes. In 1997-98, these imports alone accounted for 24% of all export
earnings. This trend may continue without serious harm to the Indian balance of
payments if increasing energy imports are compensated by rising exports, such as from
the booming software sector. However, if this does not happen, then the steady increase
in energy imports will soon prove to be a severe drain on the Indian economy. Although
not considered explicitly in the theoretical and empirical framework proposed above, it is
easy to see that high import costs will lead to a higher shadow price of foreign exchange
which in turn will result in an increased shadow price for imported resources such as oil,
natural gas and higher quality coal. This in turn will exacerbate the substitution towards
cheaper resources, which esssentially means increased reliance on domestic coal. If coal
use tends to gain market share as recent trends predict, then the availability of clean coal
technology will make a huge difference in the magnitude of environmental externalities
of energy use in India. However, clean coal technology is currently expensive and will
further increase the cost of energy to consumers or stress the government’s tight
budgetary resources. Technology transfer projects such as under the Clean Development
Mechanism (CDM) could be used to reduce the environmental effects of a large-scale
substitution to coal use.
The other major sources of energy are hydroelectricity and other renewable energy
sources such as biomass and agricultural crop residues. India has a large hydroelectric
potential estimated at around 84,000 MW. However, the problems in building new
generation facilities are primarily social and political. Increased environmental
consciousness and intense lobbying by activist environmental groups as in the recent
Narmada dam case, will prevent any large-scale development of water resources to
produce energy. The same can be said with regard to a proposed significant increase in
nuclear power generation which is compounded by issues relating to the availability of
modern technology and the country not signing the Nuclear Non-Proliferation Treaty.
At the same time, increasing population pressure and degradation of the rural
14
environment has meant a serious scarcity of traditional fuels such as fuelwood, animal
and crop residues. In rural India, these fuels supply the bulk of the cooking energy, and
government efforts at providing commercial substitutes such as kerosene have not been
very successful because it is still out of reach of the low-income rural households.
Subsidies on kerosene have the unintended effect of encouraging illegal adulteration of
higher priced diesel with kerosene (TERI 2000).
These trends in the Indian economy suggest that without major technological change,
increased reliance on coal is the most obvious outcome. However, technological change
both on the supply and demand side are directly dependent on government policies. The
current administratively fixed price of energy, and the complex web of taxes and
subsidies distorts private investment and encourages waste in the energy sector. While
controlled energy prices provide quasi-rents to a selected few, it encourages inefficiency
and a higher degree of negative environmental externalities. For example, if energy
pricing reforms are undertaken so that consumers pay the marginal cost of energy,
significant improvements in energy efficiency and conservation could result, as was seen
in the US economy during the post oil price shock era. A rising price of energy that
reflects opportunity costs and liberalization of the energy sector will mean higher profits
in the energy sector, and more foreign firms will enter with scarce investment resources
and advanced technologies. Given the current state of reforms, major investment in
generating new energy supplies and demand side conservation is not deemed economical
by the private sector. Privatization in the energy sector can also spur investment in
decentralized renewable technologies such as wind, solar and small-scale hydro, a
considerable potential for which exists in the country. A detailed analysis of these
alternatives is provided in the companion paper by Shukla, Ghosh and Garg (2001). Past
efforts at popularizing these technologies have all been initiated by the public sector, and
have failed because of the usual incentive problems associated with top-down technology
transfer.
Yet another scenario would suggest that India continue to rely on imported oil and gas,
and the share of oil and gas in the overall energy mix rises over time. In that case, the
15
bulk of these resources will need to be imported from abroad, mainly from the Middle
East countries. It is then likely that any international obligations the country makes
towards reducing its aggregate carbon emissions will exacerbate this shift away from coal
to cleaner natural gas in power generation. As the paper by Sengupta and Gupta (2001)
suggests, prospects for substantive additions to domestic reserves are dim, hence one may
see a continued reliance on imported fossil fuels. If the domestic coal industry does not
become more productive even increased imports of coal are likely, causing strains on the
national economy and on the world price of coal.
5. Concluding Remarks: Energy Substitution and Implications for Climate Change
This paper develops a theory of endogenous resource substitution that is driven by rising
resource shadow prices in a Hotelling-type framework. A simple model demonstrates the
theoretical formulation and an empirical model suggests how this framework could be
used to obtain dynamic resource use profiles in a partial equilibrium model. The insights
from the model are used to examine sectoral energy use profiles for the Indian economy.
This framework complements more detailed models of the Indian economy, such as
MARKAL (Shukla 1996). The unique feature of the proposed model is the determination
of resource prices that are a function of resource scarcity, discount rate, and the cost of
extraction and conversion to alternative end uses. At the country level, the model could
be expanded to include exports and imports of energy and the added cost of imports
could be incorporated by means of the shadow price of foreign exchange. Alternative
price regimes in world resource markets could be easily incorporated as policy shocks.
One important insight from the endogenous substitution model is that assumptions
regarding technological change could make a significant difference to projections of the
resource use profile. While most forecasts of energy use and the emissions trajectory for
India tend to be alarmist, it is clear that much will depend on government policies. As
India becomes a more open and deregulated economy, large-scale investments in energy
efficiency and conservation could make a significant reduction in emissions per capita
GDP, and possibly result in a major restructuring of existing smoke stack industries as is
16
happening in the economies of the Former Soviet Union. If anything, the pessimistic
projections point to the urgent need to develop and transfer cleaner energy technologies
under the Kyoto Protocol.
17
REFERENCES
BPSR 1998. BP Statistical Review of World Energy. London: British Petroleum Co.
Ahmad, K.,1994. Renewable Energy Technologies – A Review of the Status and Costs of
Selected Technologies. Technical Paper No. 240, Washington D.C.: The World Bank.
Chakravorty, U., and D. Krulce, 1994. “Heterogenous Demand and Order of Resource
Extraction,” Econometrica 62: 1445-52.
Chakravorty, U., D. Krulce and J. Roumasset, 2000. “Optimal Use of Multiple,
Nonrenewable Resources.” Unpublished Manuscript.
Chakravorty, U., J. Roumasset and K-P. Tse, 1997. “Endogenous Substitution among
Energy Resources and Global Warming.” Journal of Political Economy 105: 1201-34.
Dracker, R., and P. De Laquil, 1996. Progress Commercializing Solar Electric Power
Systems.” Annual Review of Energy and the Environment 21:371-402.
Furth, H.P., 1995. “Fusion.” Scientific American 273: 174-77.
Hoagland, W., 1995. “Solar Energy.” Scientific American 273: 170-73.
International Energy Agency (IEA) 1995. World Energy Outlook. Paris: OECD/IEA.
Kyoto Protocol, 1997. United Nations Framework Convention on Climate Change. Draft
by the Chairman of the Committee of the Whole, Kyoto.
Manne, A.S., and R.G. Richels, 1991. “Global CO2 Emission Reductions: The Impacts of
Rising Energy Costs.” The Energy Journal 12:87-107.
Manne, A.S., R. Mendelsohn and R.G. Richels, 1993. “MERGE: A Model for Evaluating
Regional and Global Effects of GHG Reduction Policies.” Unpublished Manuscript, Palo
Alto: Electric Power Research Institute.
National Renewable Energy Laboratory (NREL) 1992. “Photovoltaics Program Plan FY
1991- FY 1995.” Paper No. DOE/CH10093-92. Colorado: NREL.
Nordhaus, W.D. 1979. The Efficient Use of Energy Resources. New Haven: Yale
University Press.
Nordhaus, W.D. 1991. “The Cost of Slowing Climate Change: A Survey.” Energy
Journal 12:37-65.
Nordhaus, W.D. 1992. “The “DICE” Model: Background and Structure of a Dynamic
Integrated Climate-Economy.” Cowles Foundation Discussion Paper No. 1009. New
18
Haven: Yale University.
Sengupta, R. and M. Gupta, 2001. “Developmental Sustainability Implications of the
Economic Reforms in the Energy Sector of India.” This Volume.
Shukla, P.R., 1996. “The Modelling of Policy Options for Greenhouse Gas Mitigation in
India.” AMBIO XXV(4):240-48.
Shukla, P.R., D. Ghosh and A. Garg, 2001. “Future Trends and GHG Emissions for
India.” This Volume.
TERI 2000. TERI Energy Data Directory and Yearbook, 1999-2000. New Delhi: Tata
Energy Research Institute.
19
TABLE 1
SECTORAL RESOURCE USE PROFILE UNDER ALTERNATIVE RATES OF SOLAR ELECTRICITY COST REDUCTION
10% Rate of Solar Electricity
Cost Reduction
Period
(Year)
Elect
Coal
Coal
2010-19 Coal
Coal
2030-39 Coal
Coal
2050-59 Coal
Coal
2070-79 Coal
Coal
2090-99 Coal
Coal
2110-19 Coal
Coal
2130-39 Coal
Coal
2150-59 Coal
Coal
2170-79 Coal
Solar
2190-99 Solar
:
2210-19
:
1990-99
2230-39
2250-59
2270-79
2290-99
Tran
Resid
Oil
Gas
Oil
Gas
Oil
Gas
Oil
Gas
Oil
Gas
Oil
Gas
Oil Gas/Coal
Coal
Oil
Coal
Solar
Solar
Coal
Solar
Coal
Solar
Coal
Solar
Coal
Solar
Coal
:
Coal
:
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Solar
Solar
:
:
30% Rate of Solar Electricity
Cost Reduction
Indust
Elect
Tran
Indust
Elect
Tran
Resid
Indust
Oil
Oil
Oil
Oil
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Solar
Solar
:
:
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Solar
Solar
Solar
Solar
Solar
Solar
Solar
:
:
Oil
Gas
Oil
Oil
Gas
Oil
Oil
Gas
Oil
Oil
Gas
Oil
Gas
Oil
Oil
Gas
Solar
Oil
Solar Gas/Oil/Coal Coal
Solar
Coal
Coal
Solar
Coal
Coal
Solar
Coal
Solar
Solar
Solar
Coal
Solar
Solar
Coal
Solar
Solar
Solar
Solar
Solar
Solar
:
:
:
:
:
:
Coal
Coal
Coal
Coal
Solar
Solar
Solar
Solar
Solar
Solar
Solar
Solar
Solar
Solar
:
:
Oil
Oil
Oil
Oil
Solar
Solar
Solar
Solar
Solar
Solar
Solar
Solar
Solar
Solar
:
:
Gas
Gas
Gas
Gas
Gas
Gas
Solar
Solar
Solar
Solar
Solar
Solar
Solar
Solar
:
:
Oil
Oil
Oil
Oil
Oil
Oil
Oil
Solar
Solar
Solar
Solar
Solar
Solar
Solar
:
:
20
Resid
50% Rate of Solar Electricity
Cost Reduction
TABLE 2
SECTORAL RESOURCE USE PROFILE FOR BASELINE MODEL WITH CARBON TAX
Baseline
Period
(Year)
1990-99
2010-19
2030-39
2050-59
2070-79
2090-99
2110-19
2120-29
:
:
2260-69
2270-79
2290-99
2310-19
2330-39
2350-59
2370-79
2390-99
:
:
Elect
Tran
Resid
Coal
Oil
Gas
Coal
Oil
Gas
Coal
Oil
Gas
Coal
Oil
Gas
Coal
Oil
Gas
Coal
Oil
Gas
Coal
Oil Gas/Coal
Coal
Oil
Coal
Coal
Oil
Coal
Coal Oil/Coal Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
:
:
:
:
:
:
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Solar
Solar
Coal
Coal
Solar
Coal
Solar
Solar
Solar
Coal
Solar
Solar
Coal
Solar
Solar
Coal
Solar
Solar
Solar
Solar
Solar
Solar
Solar
Solar
Solar
Solar
Solar
Solar
Solar
Solar
Solar
Solar
Solar
Solar
:
:
:
:
:
:
Baseline with $100 Carbon Tax
Baseline with $200 Carbon Tax
Indust
Elect
Tran
Resid
Indust
Elect
Tran
Oil
Oil
Oil
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
:
:
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Solar
Solar
Solar
Solar
Solar
:
:
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
:
:
Coal
Coal
Coal
Solar
Solar
Solar
Solar
Solar
Solar
Solar
Solar
Solar
Solar
Solar
:
:
Oil
Oil
Oil
Oil
Oil
Oil
Oil
Oil
Oil
Oil
Coal
Coal
Coal
Coal
:
:
Coal
Coal
Coal
Coal
Coal
Coal
Solar
Solar
Solar
Solar
Solar
Solar
Solar
Solar
:
:
Gas
Gas
Gas
Gas
Gas
Gas
Gas/Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
:
:
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Solar
Solar
Solar
:
:
Gas
Oil
Oil
Oil
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
:
:
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Solar
Solar
Solar
:
:
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
:
:
Coal
Coal
Coal
Coal
Coal
Solar
Solar
Solar
Solar
Solar
Solar
Solar
Solar
Solar
:
:
Oil
Oil
Oil
Oil
Oil
Oil
Oil
Oil
Oil
Oil
Coal
Coal
Coal
Coal
:
:
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Solar
Solar
Solar
Solar
Solar
Solar
:
:
21
Resid
Indust
Gas
Gas
Gas
Oil
Gas
Oil
Gas
Oil
Gas
Oil
Gas
Coal
Coal
Gas/Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
:
:
:
:
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Coal
Solar
Coal/Solar
:
:
:
:
Table 3. Proven Reserves of Fossil Fuels and Reserve/Production Ratio in India and the
World
Fuel
End 1987
Reserves
India
World
Coal
62.54
Crude Oil
Natural
End 1997
R/P Ratio
India
Reserves
R/P Ratio
World
India
World
India
World
1040.5 195
239.00
69.9
1031.6
212
219.00
0.8
135.4
25.6
43.4
0.6
140.9
15.6
40.9
0.7
124
48.8
58.7
0.49
144.8
22.9
64.01
Gas
Source: BPSR (1998). The units are coal and crude oil in billion tonnes, and natural gas
in trillion cubic meters.
22
Table 4. Supply of Commercial Energy in India
Energy
1991-92
Energy Supply
1997-98
Share (%)
Energy Supply
Share (%)
Coal
115.3
60.0
150.5
61.3
Oil
54.3
28.25
65.1
26.5
Natural Gas
16.0
8.32
22.6
9.2
Electricity
6.6
3.43
7.1
2.89
Total
192.2
100.00
245.3
100.00
Source: TERI (2000). Energy supply is in million tonnes of oil equivalent (MTOE).
23
Table 5. Sectoral Consumption Patterns in 1997-98 (in MTOE).
Sector
Coal
Petroleum
Natural gas
Electricity
Total
Products
Industry
60.93
11.74
2.91
8.49
84.08
Agriculture
-
0.94
0.11
7.69
8.75
Residential
-
12.80
0.27
4.14
17.21
Transport
0.04
40.94
-
0.56
41.55
Others
-
13.98
6.76
3.72
24.47
Total
60.97
80.41
10.06
24.62
176.08
Source: TERI (2000)
24
140
Base - Grade I Oil
120
Base - Grade I Coal
Scarcity Rent ($/Mmbtu)
Base - Natural Gas
100
50% Rate - Grade I Oil
50% Rate - Grade I Coal
80
50% Rate - Natural Gas
60
40
20
0
1995
2015
2035
2055
2075
2095
2115
2135
2155
Year
Figure 1. Time Paths of Scarcity Rents for Oil, Coal and Natural Gas
25
2175
500
Oil
Total Cost ($/Delivered Mmbtu)
400
Coal
Natural Gas
Solar
300
200
100
0
1995 2015 2035 2055 2075 2095 2115 2135 2155 2175 2195 2215 2235 2255 2275 2295 2315
Year
Figure 2. Total Costs of Using Oil, Coal and Natural Gas in Transportation: Baseline Model
26
7
6
Base
Normalized Price Level
30% Rate
5
50% Rate
4
3
2
1
0
1995
2035
2075
2115
2155
2195
2235
2275
2315
2355
Year
Figure 3. Electricity Sector Prices under Alternative Rates of Solar Electricity Cost Reduction
(Normalized to the Base Year Price of the Baseline Model)
27
100
100
90
Coal Stock Consumed (%)
80
70
66.92
60
50
40
30
25.57
20
10
8.98
3.03
1.54
0
0
10
20
30
40
Rate of Solar Electricity Cost Reduction (%)
Figure 4. Effect of Solar Electricity Cost Reduction on Aggregate Coal Use
28
50