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Role of Economic Instruments in Mitigating Carbon Emissions

This paper first analyses the pattern of energy usage in India and the implications thereof relating to carbon emissions. Second, it examines whether pricing and taxation policies have any role to play in mitigating carbon emissions from industrial usage in important energy products. The paper shows that the pattern of energy usage exhibits a shift towards non-coal based energy products. It also suggests that the extent of carbon emission reduction is not substantial enough to warrant the use of carbon taxation for mitigating emissions.

Role of Economic Instruments in Mitigating Carbon Emissions An Indian Perspective

This paper first analyses the pattern of energy usage in India and the implications thereof relating to carbon emissions. Second, it examines whether pricing and taxation policies have any role to play in mitigating carbon emissions from industrial usage in important energy products. The paper shows that the pattern of energy usage exhibits a shift towards non-coal based energy products. It also suggests that the extent of carbon emission reduction is not substantial enough to warrant the use of carbon taxation for mitigating emissions.


he increased level of greenhouse gas emissions in India and south Asia have serious consequences. A tentative projection from the third assessment report of the Intergovernmental Panel on Climate Change (IPCC) indicates that the region could experience a temperature increase to the order of five degrees celsius by 2080 [IPCC TARWGIL 2001]. The climate change would have a serious impact on agriculture, forest resources, coastal resources as well as on the overall economic growth of the country. Therefore, restricting carbon emissions is a must for India for it to progress towards a sustainable path of development.

Of late, Indian policymakers are considering various policy options that would limit carbon emissions. Stronger environmental measures including use of clean fuel and encouraging energy efficiency are two of them. The academic community as well as policymakers are also showing interests in the clean development mechanism (CDM) that was enshrined in the Kyoto protocol of the United Nations framework convention on climate change. The CDM is perceived as a potential instrument for winwin benefits, aimed at local economic development and environmental improvement concomitant with controlling greenhouse gas emissions.

At the outset, the environmental cost of economic development is a matter of grave concern. At one extreme, environmental deterioration is seen as an unavoidable cost of industrialisation. At the other, it is viewed as a hindrance for developing countries to continue on a development path. However, there is no second opinion about the fact that carbon emission will lead to serious threats to civilisation in the future. In India, carbon emissions will rise from 212 million tonnes in 1995 to 738 million tonnes in 2035, recording a compounded annual rate of growth (CARG) of 3.1 per cent [Shukla Ghosh and Garg 2004].

Carbon emissions could affect India dearly. Results from climate models predict that the average temperature in the country will change between 2.3 and 4.8 degrees celsius following a doubling of the concentration of carbon dioxide from its pre-industrial revolution level [Lonergan 1998]. Higher monsoon activities are also predicted for the subcontinent. The climate sensitive sectors such as agriculture, forestry, coastal and water resources will be adversely affected because of climate change. A devastating impact of climate change in India will be the rise in the sea level, resulting in the inundation of coastal areas. Coupled with these, the increase in cyclones accompanied by enormous volumes of sea-water would bring about mass devastation of human life as well as the economy [Kumar 2004]. An estimate suggests that due to a one metre increase in the sea level, 7 million people would be displaced, about 5,764 sq km land and 4,200 km stretch of roads would be lost [ADB 1994].

India would also face water “stress” implications due to climate change. Apart from receding glaciers in the mountain regions due to global warming, the precipitation pattern will affect the river basin systems as well as the availability of groundwater resources. The change in evapo-transpiration has also been predicted as a result of climate change. The drainage basins of central India as well as the dry sub-basins of the river Ganga would be more affected than the wet sub-basins due to climate change [Mehrotra 1995; Mirza 1997].

This paper is not intended to contribute to this tussle between economic development and environmental deterioration. Rather, the primary objective of this paper is to analyse the rationale behind different economic instruments used for mitigating carbon emissions and the likely impact of carbon taxation on mitigating carbon emission from industrial usage. In the process, the study has also brought out the changes in the energy usage pattern in India over last two decades and the changes in the energy mix observed during this period.

The plan of rest of the paper is as follows. Section I articulates the energy usage and its impacts with special reference to India. This includes a brief discussion on the implications of carbon emissions on the economy of India. The role of different instruments for mitigating carbon emissions is discussed in Section II. The purview includes selected theoretical as well as applied/case studies from the international and domestic arena. In Section III, the linkage between different taxing options on energy products and carbon emissions in the country is analysed using an econometric model. For our analysis, the carbon emission forecast was made for the following important energy products – coal, lignite, coke, high speed diesel oil (HSDO), light diesel oil (LDO), furnace oil and low sulphur heavy stock (LSHS). Finally, Section IV articulates the policy implications.

I Energy Use and Its Implication

International Energy Outlook (2004) has predicted that India would experience one of the fastest growths in energy consumption as a result of the robust economic growth in the country. The total final consumption (TFC) of energy in India has increased by almost four times during the 30-year period from 1971 to 2001 (Table 1). The growth in demand for energy in the country is not commensurate with the growth in total primary energy supply (TPES) within the country. A large portion of the demand is met through imports. The import was at a level of 28 kilo tonnes of oil equivalent (KTOE) in 1971 that accounted for about 22 per cent of the TFC.1 In 2001, it rose to 101 KTOE that constitutes about 58 per cent of the TFC.

The energy mix over the period 1971 to 2001 points out a few important facts about the country’s energy demand. The trend and share of energy mix suggests that the importance of oil has increased substantially over time and has increased by 5.5 times during 1971-2001 (Table 2). The quantity of coal consumption has also increased but marginally. Electricity and natural gas consumption have increased manifold during this 30-year period. A share analysis across various energy sources indicates that the share of oil demand to TFC has increased from 38 per cent in 1971 to 57 per cent in 2001. Computation of shares of various energy products in total energy use suggests that the share of coal has declined from 52 per cent, the highest in 1971, to only 19 per cent in 2001. The significant decrease in the share of coal consumption is also attributable to sizeable increases in the shares of electricity and natural gas in the TFC.

Table 2 indicates that over time, coal consumption by industry has increased significantly whereas the same for the transport sector has declined drastically. The phasing out of steam engines by the Indian railways is the prime reason for this observed trend. Electricity has gained significant importance in the energy mix in India. The industry sector was the major consumer of electricity in 2001. Over time, along with industry, the agriculture and household sectors have also become important electricity consumers in India.

The sectoral composition of TFC is given in Table 3. The industry sector had been maintaining the largest share in TFC since 1971. The share of industry was more than 50 per cent of TFC in 1980 and 1990, which recorded a decline in 2001. Transport remains the second largest sector in terms of energy consumption. Another important fact to highlight is that the share of the transport sector has decreased substantially over time, except some recovery in 2001. Consumption by the household sector has also recorded a significant increase, particularly during 1990-2001. Perhaps the increase in the band and affordability level of the Indian middle class population as well as the introduction of new appliances in the Indian market due to liberalisation are responsible for this surge in energy consumption by the household sector.

The predictions for energy consumption in India for the future suggest that the energy demand will increase unabatedly, particularly in view of strong economic growth. Expectedly, studies suggest that the energy consumption trend in India shows a strong association with the gross domestic product (GDP). To obtain more clarity, we have computed the correlation coefficients between GDP and energy consumption from different sources. The strong positive relationship between economic growth and energy consumption in India is evident from Table 4. The correlation coefficients also suggest that energy consumption by most of the sources reveal marginally stronger associations with

Table 1: Total Final Consumption of Energy in India

(in KTOE)

Year Total Final Consumption of Energy

1971 48411 1975 56560 1980 67271 1985 87596 1990 124992 1994 139052 2001 175839$

Note: $ Excluding combustibles, renewables and wastes.

Source: OECD, Energy Statistics and Balances of Non-OECD Countries, various years.

Table 2: Energy Consumption by Source and Sectors

(in KTOE)

Energy Consuming Sectors Sources Total Industry Transport Agriculture Household Others

TFC 1971 48411 23599 14939 1229 6081 2563 1980 67271 36242 18436 2204 7096 3293 1990 124992 71935 26124 4186 15755 6992 2001 175839 77207 44555 7646 33035 13396

Oil 1971 18272 4549 6839 782 3941 2161 1980 27888 7269 12336 920 4783 2580 1990 52077 10820 23265 195 12598 5199 2001 99638 26386 43832 0 20939 8481

Coal 1971 25433 15686 7960 0 1786 0 1980 31129 23718 5905 0 1506 0 1990 49607 46470 2468 0 669 0 2001 32642 26325 0 0 4997 1320

Electricity 1971 4416 3089 140 431 353 403 1980 7533 4583 195 1246 794 715 1990 17785 9248 391 3910 2444 1792 2001 32382 13815 723 7524 6698 3622

Natural Gas 1971290 274 0 16 0 0 1980721 671 0 38 12 0 1990 5523 5397 0 82 44 0 2001 11177 10653 0 122 402 0

Source: OECD, Energy Statistics and Balances of Non-OECD Countries, various years.

Table 3: Share of Various Sectors in TFC from 1971 to 2001

Year Industry Transport Agriculture Household Others

1971 48.7 30.9 2.5 12.6 5.3 1980 53.9 27.4 3.3 10.5 4.9 1990 57.6 20.9 3.3 12.6 5.6 2001 43.9 25.3 4.3 18.8 7.6

Source: Calculated from energy balance statistics.

Table 4: Correlation between Energy Consumption andGross Domestic Product

Energy Source GDP Industrial GDP

Oil 0.999 0.992 Electricity 0.985 0.998 Coal 0.378 0.437 Natural Gas 0.980 0.995 Total 0.979 0.989

Source: Computed for the period 1971 to 2001.

industrial GDP compared to overall GDP of the country. However, this corroborates the fact that industry is the major energyconsuming sector of the country and increase in industrial activity will lead to an additional demand for energy.

II Review of Policy Instruments for Mitigating Emissions

The pollution problems in developing countries are most likely to worsen in the coming years with the success of industrial development policies. In large part, this problem is attributable to the downstream impacts which, in economic terms, is referred to as “externality”. Externality describes the fact that the costs of pollution and other forms of environmental degradation are not taken into consideration by the decision-makers while undertaking emission activities which cause these problems. Thus, a rationale exists for government policies to correct this market failure and achieve a more efficient allocation of resources.

Economics of Environmental Policy Instruments

There are broadly two instruments available to any government for pursuing policies aimed at improving environmental quality. The command and control (CAC) type of instruments directly restrict the quantities of harmful activities. There are other policy instruments that lean more towards economic incentives (EI). The former includes emission and abatement standards while the latter includes emission charges, taxes on production and consumption, and tradable permits.

CAC Instruments: Traditional Approach to Environmental Protection

Until about 15-20 years ago, the environmental policies actually chosen were heavily dominated by CAC approaches (direct regulations). In shaping the early environmental policies of the 1970s, policymakers instituted standard-based systems in keeping with prevailing legal traditions of dealing with activities deemed excessive by society [Spence and Weitzman 1994]. The early CAC regulations were often based on “end of pipe” solutions with little or no thought given to how pollution could be reduced through more systematic changes in the core production process or even in the product design. However, with the passage of time, even this pattern of regulation has started dictating the processes that should be used to meet the set uniform emission targets.

Though traditional in nature, the CAC type of policy instruments have also undergone changes and modifications. Presently, two broad types of CAC regulations are discernible, viz, technology-based and performance-based [Austin 1999]. The former specifies the methods and equipment that firms must use to meet targets. The latter sets an overall target for each firm or plant, and gives firms some discretion in how to meet the standards.

While CAC (or direct) regulations were successful in securing the first tranche of emission reductions from previously unregulated industries, economists have long been advocating the use of EI as an alternative or supplement to direct regulation. Most importantly, economists argue that direct regulations ignore the possibility that some companies may be able to make reductions in emissions more readily than others and these regulations hardly give freedom to firms and plants about how to comply with the emission norms/standards. Moreover, the CAC approach involves more administrative costs of enforcing compliance.

Of late, there has been a surge of interest in EI approaches in environmental policy, which are examined below.

EI: A Better Approach?

The underlying premise for economic instruments is to correct market failure by placing a cost on the release of pollutants. The cost will internalise the “externalities” into the decision-making process. Placing a charge or a fee on every unit of effluent released transforms the manufacturer’s decisions regarding how much he will produce and how he will produce it. Thus, the cost of effluent output would become an important part of total production costs, which the manufacturer tends to minimise. On the other hand, by adjusting the charge level or the cost attached to effluent outputs, the regulator can induce a different degree of response from manufacturers and hence, control the overall level of pollution. By changing the charge level over time, the regulator has a relatively simple way of ratcheting up standards.

Evaluation of Policy Instruments

In a path-breaking work, Harrington et al (2004) examine several real-life environmental case studies involving applications of CAC and EI to understand the merits and demerits of the same. The findings from their case studies have clearly confirmed the argument that EI instruments have lower social costs through their lower unit cost of abatement as well as their continual incentive to reduce emissions. However, the finding of economic efficiency of EI instruments is mitigated by the evidence that polluting firms prefer a CAC instrument because of its perceived lower costs to them. In most of the case studies, it is found that the actual or potential revenue raised by EI instruments had to be reimbursed to the firms in some way. This, of course, means that the revenue cannot be used for other purposes. This again may create a form of distortion in the market. With regard to the common perception that CAC policies achieve their objectives quicker and with greater certainty than EI policies, the case studies revealed mixed evidence. Another important issue, which needs attention is that almost all the policies analysed in the case studies were a blend of EI and CAC. These policies started with a major thrust on CAC elements but EI instruments were added or substituted. It may be argued that in practice, with a wellestablished regulatory system based on traditional measures already in place, the key issue will be to work out how economic instruments can complement and integrate with conventional measures.

One potential application of economic instruments deserves particular mention. Many groups have proposed a “carbon tax” to reduce the carbon dioxide emissions that come from fossil fuels and which threaten to change the climate. Since the present paper attempts to analyse the impact of tax changes on carbon emission in the context of the Indian economy, the issue of carbon taxation deserves focus.

Carbon/Energy Taxes in Practice

A carbon tax would essentially be a product charge placed on fossil fuels in proportion to their carbon content. Coal, which has a higher carbon content than oil and natural gas would thus be taxed relatively more. This would lead to a relatively larger increase in the price of coal. In fact, the principal reason for carbon/energy taxation is to increase prices according to the energy and/or carbon content of different fuel sources. The rising prices of these fossil fuels would induce people (a) to use oil and gas in favour of coal; (b) to use more renewables instead of fossil fuels; and (c) to be more efficient in their use of energy in general. Applying such a tax would ensure that the economy, as a whole, would achieve a given level of carbon dioxide reduction for the lowest overall cost.

Theoretically speaking, because of the large scale of fossil fuel use in developing economies, any carbon tax could raise significant amounts of revenue. This revenue generation could be termed efficient in the fiscal sense if it has little impact on production and consumption patterns, i e, minimum market distortion or dead-weight losses. The same can be termed efficient in the environmental terms if the policy induces agents to reduce emissions at the socially optimal level, which can be set through social consensus or scientific measurements. Empirical evidence shows that there is a contradiction, at least once rigidities and reaction times have been allowed for, between the environmental effectiveness of the tax and its fiscal effectiveness [Barde 2000]. It is true that the tax rate must be sufficiently high to have an incentive effect but the more the incentive works, the more pollution will diminish and therefore less tax revenue will be collected. For instance, taxes on polluting fuel oils in Sweden have led to their virtual disappearance from the market. Again in Sweden, the revenue obtained from sulphur tax has fallen rapidly owing to the environmental success of the tax and for the same reason leaded petrol has disappeared altogether in many Organisation for Economic Cooperation and Development (OECD) countries.

Macroeconomic issues also have an important bearing on carbon/energy taxation policies. In developing countries, inflationary tendencies are sometimes chronic in nature. With energy being an intermediate input for all productive activities, a rise in its price increases the cost of production which leads to an inflationary spurt. Another worry is the impact of environmental tax on employment. In the short run, a decrease in demand as a result of a price rise will reduce employment, assuming technology to be fixed.

The key issue countries face when considering green tax reform is the possible loss of international competitiveness of some sectors. Since the bulk of environment related taxes concern energy and transport taxes, there is an obvious risk that some industries may become non-competitive. This is why these sectors are strongly opposed to environmental taxes and there is an explicit threat of relocation of activities to countries that do not apply such taxes. To date, environmentally related taxes imposed by OECD countries have not been identified as causing significant reductions in the competitiveness of any sector [Flip de Kam 2002]. However, this may, in part, be due to the fact that countries applying carbon/energy taxes provide total or partial exemptions for energy-intensive industries.

Departing from theoretical discussions, let us now focus on country experiences with carbon/energy taxation. As far as the carbon tax is concerned, Finland was the first country in the world to introduce a carbon tax as early as 1990 ($ 6.10 per tonne of carbon on all fossil fuels), followed by a progressive greening of the tax system [Gupta 2004]. It was estimated for Finland that in the absence of carbon dioxide tax, carbon emissions would have been higher by 7 per cent in 1998 if taxes had remained at the 1990 level [Barde and Braathen 2002]. Norway implemented a carbon dioxide tax on mineral oils in 1991, which was then extended to coal and coke. In the aftermath of the introduction of carbon taxes in Norway in 1991, the carbon dioxide emissions of some stationary combustion plants lowered by 21 per cent, whereas in other sectors the fall was much less. It was estimated that carbon dioxide emissions produced by mobile household combustion devices fell by 2 to 3 per cent. It was also estimated that carbon dioxide emissions per unit of oil produced by the Norwegian oil sector fell by 1.5 per cent due to measures taken by the industry in response to the carbon dioxide tax [Larsen and Nesbakken 1997]. In Sweden, a major tax reform was introduced in 1991 in a strict revenue neutral context. It was based on a significant reduction in income tax, which was offset by a series of environmental taxes, especially on carbon and sulphur. Denmark introduced a carbon dioxide tax on fuels in 1992 with a continuing evolution of taxes until 2002. The national target is to reduce carbon dioxide emissions by 20 per cent between 1988 and 2005. The tax reform aims at a reduction of marginal tax rates in all income brackets, elimination of a series of loopholes in the tax law and a gradual transfer of tax revenue from income and labour to pollution and scarce environmental resources. Between 1992 and 1996, industry was exempted from the energy tax. After the first wave of green tax reforms in the early 1990s in the above countries, France, Germany, Italy, Switzerland and the UK initiated a similar process in 1999.

Indian Scenario

The future economic development and energy policies of large developing countries like India and China will have a significant impact on the output of greenhouse gases [Oliveria and Skea 1989]. Even though the literature acknowledges this, there are very few studies, which take into account the plausible impacts of carbon/energy tax in India. In the absence of carbon/energy tax in India, the studies could at best try to analyse the impacts based on some models.

An early study by Shah and Larsen (1992) examined the efficiency costs of carbon taxes for five countries including India.2 These costs are defined as the net marginal welfare cost of replacing other taxes (in this case personal income tax and corporate income tax) by a carbon tax. For India, Shah and Larsen had found that if welfare gains from the removal of other price distortions were taken into account, the efficiency cost of a revenue neutral switch to a carbon tax would be zero. The paper has also mentioned that a carbon tax can result in reduced emissions of local and regional pollutants such as oxides of sulphur, nitrogen, carbon monoxide and particulates and for India the impact would be the highest since coal is the pre-dominant fossil fuel consumed here.

A paper by Jayadevappa and Chhatre (1996) examines the impact of implementing a global carbon emission tax on India’s economy using an input-output model. The findings of the study show that a lack of global perspective when designing such a policy would not only put its overall efficiency into question but would also sharpen the existing global economic and social disparities between the industrialised and developing world. The authors argue that removing the existing distortions and imposing an energy-efficient pricing policy would lead to a substantial reduction in the demand for energy and thereby reduce carbon dioxide emissions.

Fisher et al (1997) use the Indian module of the second generation model and explore a reference case and scenarios in which greenhouse gas emissions were controlled. Two alternative policy instruments, carbon taxes and tradable permits, were analysed to determine comparative costs of stabilising emissions. The study reveals that tradable permits represent a lower-cost method to stabilise Indian emissions than carbon taxes.

In a recent study by Bussolo and O’Connor (2001) computable general equilibrium (CGE) model has been used to examine the ancillary benefits of limiting carbon dioxide emissions for India. Ancillary benefits are defined in terms of reduced mortality and morbidity due to reduced particulate concentrations and the financial implication is estimated to be US $ 58 per tonne of carbon emissions reduced. These ancillary benefits of limiting carbon dioxide are put next to the welfare costs of carbon dioxide abatement through a tax to arrive at the level of abatement where ancillary benefits are at least as much as the cost of abatement. The paper estimates that this level ranges from 13 to 23 per cent of baseline carbon dioxide emissions in the year 2010.

Mckibbin (2004) has captured the impact of a carbon tax in India using a new model developed within the G-cubed multicountry model. The G-cubed model is designed to provide a bridge between CGE and macroeconomic models by integrating the more desirable features of both approaches (full details can be found at The paper suggests that imposing a shock of a carbon tax in India of $US 10 (real in 2002 prices) per tonne of carbon reduces GDP by up to 0.75 per cent by 2012. The carbon tax is effective in reducing carbon emissions by 16 per cent in the first year and then by up to 10 per cent after 30 years.

It must be mentioned that economic incentive based mechanisms to limit pollution work effectively under certain assumptions, viz, low replacement cost of old technology and no supply constraints on “green technology”. In India, the replacement cost of old technology is high and there are constraints on the supply of green technology. The adoption of CNG fuel for the public transport system of Delhi is a good example in this regard. Lack of adequate capital to replace old technology with respect to a large number of small players and irregular supply of CNG drew serious criticism from various quarters. In the context of supply constraints on green technology and high replacement cost of old technology, it may be argued that Indian firms facing an increase in energy prices due to carbon taxation will treat it as just another increase in input cost and pass the burden onto the consumers without any significant changes in technology. The firms may lose the incentive to switch to greener technology. The rise in energy prices will eventually lead to inflation in the economy.

It must be noted that India’s tax policy on energy products in the past or even now has not been governed by emission consideration but by economic consideration. Energy products, particularly petroleum products, are one of the main sources of revenue for the government of India and so for this reason, tax rates on these products are by and large on the higher side.

III Energy Consumption and Carbon Emission Forecasts

The previous sections have discussed the energy scenario, its implications along with various available options towards mitigating carbon emissions from energy usage. This section presents demand forecasts for various energy products used by the industry sector for the next five years. Furthermore, an attempt has also been made here to estimate the likely carbon emissions from the industrial consumption of these energy products.

Actual consumption data for various energy products has been used for the modelling purpose without converting it into oilequivalent or coal-equivalent. The data sources consulted for the Indian consumption data for various energy products are Indian petroleum and natural gas statistics, energy statistics, TERI energy data directory and yearbook, annual reports of ministry of coal, etc. The macroeconomic indicators have been collated from Economic Survey of India, Handbook of Indian Statistics, census of India as well as National Council of Applied Economic Research’s (NCAER) own database.


In this study, we have used the econometric multiple correlation forecasting approach for product-wise analysis. Industrial consumption data for various energy products suggests that some of the products are used significantly by the industry sector compared to the consumption of other products. These products have been identified as coal, lignite, coke, HSDO, LDO, furnace oil and LSHS.3 Our analysis has remained restricted to the forecasts of these products only.

The analysis has started with various macroeconomic and other relevant economic measures as explanatory variables for the

Table 5: Regression Result for Coal

Equation for Coal: lncoal = α + β1 lprcn + β2 liip + εt

Variable Regression t-statistic R2 D-W Number of Period Coefficient Observations

Constant 12.184 21.06* 0.997 2.09 20 1982-83 to

2001-02 Lprcn -0.089 -2.54* Liip 0.188 2.35* AR (1) 0.944 54.37*

Notes: lncoal represents log of coal consumption, lprcn stands for log of the price ratio between coal and naphtha and liip is log of index of industrial production.

* Significant at 5 per cent level of significance.

Table 6: Regression Result for Lignite

Equation for Lignite: lnlignite = α + β1 lplignite + β2 lgdpind + εt

Variable Regression t-statistic R2 D-W Number of Period Coefficient Observations

Constant 9.097 1.87** 0.985 2.28 21 1982-83 to

2002-03 Lplignite -0.182 -1.73** Lgdpind 0.195 0.535 AR (1) 0.934 28.00*

Notes: lnlignite represents log of lignite consumption, lplignite stands for log of the price of lignite and lgdpind is log of industrial GDP.

* Significant at 5 per cent level of significance. ** Significant at 10 per cent level of significance.

Table 7: Regression Result for Coke

Equation for Coke: lncoke = α + β1 lgdp + εt Variable Regression t-statistic R 2 Coefficient D-W Number of Period Observations
Constant Lpcoke Lgdp 3.282 -0.074 0.554 3.282* -0.729 5.365* 0.937 1.56 22 1981-82 to 2002-03

Notes: lncoke represents log of coke consumption, lgdp is log of GDP and lpcoke stands for the log of the price of coke.

* Significant at 5 per cent level of significance.

industrial use of these products. After the initial examination we have identified the most relevant and contributing variables for the modelling purpose and dropped the other relatively irrelevant variables. The important variables considered for this analysis are price variables including own price, substitute’s price as well as price ratios, GDP, industrial GDP and index of industrial production (IIP) for the manufacturing sector. Since the major objective of the study is to examine the impact of carbon taxation on these products, the model estimation deliberately included own price or price of the substitute as one of the explanatory variables. The wholesale price index (WPI) has been used as the price for all products. The GDP, both overall and industrial, are taken at constant prices for the base year 1993-94. The doublelog model that represents a system where logarithmic values of both dependent and explanatory variables are used for estimation. The advantage of this model is that it directly furnishes the elasticity in the form of a regression coefficient.

Models Used for Forecasts

After several regressions, the best models for our analysis are identified. From the point of view of convenience of the readers as well as the scope of the study, we have considered only one model for each product. The regression results for each of the energy products considered for forecasts are presented in Tables 5 to 10.

Forecast: Growth Scenarios and Assumptions

The forecasts of consumption of coal, lignite, coke, HSDO, LDO and furnace oil and LSHS under alternative tax scenarios and thereby the reduction in carbon emissions has been carried out using the models specified in the previous section. At the outset it should be mentioned that the alternative tax scenarios are only indicative ones. They do not in any way portray any likely tax scenario. Our forecasts have been made for the period 2004-05 to 2008-09 under three hypothetical tax scenarios, viz,

(a) 10 per cent increase in tax, (b) 25 per cent increase in tax, and (c) 50 per cent increase in tax.

To recapitulate, our forecast requires the following projections for the same period: (a) price WPI of the above mentioned products, (b) GDP and industrial GDP, and (c) IIP. The key assumptions for projecting the above three variables are mentioned below. We only report the most likely scenario for all the three variables as conceived by us.4

Projection of WPI

The projection of WPI has two components, viz, the elasticity of taxes on price and inflation. The impact of an increase in tax on the prices of each of the above mentioned products has been computed on the basis of actual wholesale price and taxes (in the form of royalty, excise, etc). The average actual wholesale prices of the products for 1994-95 were secured from ‘Index Numbers of Wholesale Prices in India: Base 1981-82’, Monthly Bulletin for September 1994, Special Issue Containing Price Quotations’. Along with it, the wholesale price indices of coal, lignite, coke, HSDO, LDO and furnace oil and LSHS are used to compute actual prices of these products for the period 1995-96 to 2002-03. The tax elements included in the selling prices of the products were collated from various government publications. Thus, for each year we have two components of the wholesale price, viz, the tax element and wholesale price less the tax element. Next, we have computed the tax elasticity of prices for each product for the period 1994-95 to 2002-03. Average tax elasticity has been arrived at for each product under each of the tax scenarios. In the forecast exercise, we have assumed that the price of a product would increase by a normal inflation rate of 4.5 per cent and the average tax elasticity.

Projection of GDP/Industrial GDP

GDP/industrial GDP has been projected using the growth rates estimated by NCAER’s medium term macro model. The NCAER forecasts of GDP/industrial GDP growth rates are given in Table 11.

Projection of Index of Industrial Production

It is seen that the past growth rates of IIP, GDP and industrial GDP are similar and highly correlated. We projected the

Table 8: Regression Result for HSDO

Equation for HSDO: lnHSDO = α + β1 lphsdo + β2 lgdpind + εt

Variable Regression t-statistic R2 D-W Number of Period Coefficient Observations

Constant -7.005 -1.17 0.935 1.92 21 1982-83 to

2002-03 Lphsdo -0.457 -1.71** Lgdpind 1.419 2.50* AR (1) 0.821 5.09*

Notes: lnhsdo represents log of HSDO consumption, lphsdo stands for log of the price of HSDO and lgdpind is log of industrial GDP.

* Significant at 5 per cent level of significance. ** Significant at 10 per cent level of significance.

Table 9: Regression Result for LDO

Equation for LDO: lnLDO = α + β1 lpldo + εt Variable Regression t-statistic R 2 Coefficient D-W Number of Period Observations
Constant Lpldo AR (1) 9.4223 -0.465 0.956 3.63* -2.07* 22.67* 0.919 2.30 2 1 1982-83 to 2002-03

Notes: lnldo represents log of LDO consumption, lpldo stands for log of the price of LDO.

* Significant at 5 per cent level of significance.

Table 10: Regression Result for Furnace Oil and LSHS

Equation for FO and LSHS: lnfolshs = α + β1 lpfolshs + β2 lgdp + εt Variable Regression t-statistic R2 D-W Number of Period Coefficient Observations

Constant -1.448 -1.00 0.951 1.96 21 1982-83 to

2002-03 Lpfolshs -0.163 -1.94** Lgdp 0.805 5.99* AR (1) -0.092 -1.32

Notes: lnfolshs represents log of FO and LSHS consumption, lpfolshs stands for log of the price of FO and lGDP is log of GDP.

* Significant at 5 per cent level of significance. ** Significant at 10 per cent level of significance.

Table 11: Forecast of GDP Growth Rates – Most LikelyScenario (Percentage Change Over Previous Year)

2005-06 2006-07 2007-08 2008-09
Industrial GDP 7.11 7.85 8.25 8.69
Total GDP 6.85 7.24 7.51 7.82

Source: NCAER model forecast.

combined GDP of electricity, mining and manufacturing based on their respective weights as indicated in the IIP and using the NCAER medium term sectoral growth forecast. The growth rate of this combined GDP is assumed to be the growth rate of IIP. The estimated growth rates of IIP are given in Table 12.

Analyses of Results: Consumption and Emission Forecasts

We discuss below our consumption/emission forecasts for the “most likely scenario” based on the above assumptions. The forecasts for product-wise consumption of energy for the most likely scenario by the industry sector are given in Table 13.

We have used standard norms of carbon emissions for the estimation of carbon emissions by the industry sector for 2008-09. To compare the carbon emission for the three scenarios with the assumed changes in the carbon tax and present tax regime, we have computed the reduction of emissions for the different scenarios based on the projected consumption pattern. The proportion of reduction from the current tax scenario has been computed to provide an impression of the relative change with the change in taxation. Both the actual reduction and percentage decrease in carbon emissions are presented in Table 14. The table suggests that maximum emission will be reduced for coal followed by coke and HSDO. Carbon taxation will affect the emission of lignite the least. This is evident from change in proportion of emission compared to the current taxation scenario. Relative reduction in emission due to increase in tax will be at a higher level for HSDO and LDO.

It is evident from Table 14 that the carbon tax would be able to reduce carbon emissions from the industrial usage of energy. However, the emission scenarios presented above reveal that the imposition of carbon tax, even at a high level of 50 per cent would not really reduce the emission to a substantial extent. It has also been noted in the earlier section on the issues related to options for mitigating carbon emissions that the imposition of carbon tax would definitely lead to an adverse impact on economic growth. This is obvious due to the fact that an increase in the price of energy products due to carbon tax would lead to lower industrial production because of technological constraints, particularly in the initial periods. Perhaps exploring the potential for production and industrial usage of non-conventional energies would be a better option to serve the purpose of mitigating carbon emissions rather than the imposition of carbon taxes.

IV Concluding Remarks

The broad objectives of this study are to understand the energy situation in India, its implication thereof, the role of pricing and taxation policy for mitigating the polluting effect of energy consumption in general and their application in the Indian context through model simulations.

The total final consumption of energy in India has gone up significantly over last three decades. Evidence clearly points out a change in the energy mix as well usage by various energyconsuming sectors in the country. Significant changes have been noticed in the usage pattern of the industry and transport sectors. Coal consumption in both these sectors has declined substantially over time. Consumption of oil products has increased in both,

Table 12: Forecast of IIP Growth Rates– Most Likely Scenario(Percentage Change Over Previous Year)

2005-06 2006-07 2007-08 2008-09

Index of industrial production 6.75 7.00 7.25 7.75

Source: Estimated.

Table 13: Forecasts for Consumption of Energy Products

(000 tonnes)

Year No Increase 10 Per Cent 25 Per Cent 50 Per Cent in Tax Increase Increase Increase in Tax in Tax in Tax

Coal consumption 2005-06 563159 561464 558974 554934 2006-07 570680 568391 565033 559594 2007-08 578696 575797 571548 564678 2008-09 587249 583721 578555 570221

Lignite consumption2005-06 39583 39577 39567 39548 2006-07 39852 39843 39829 39804 2007-08 40151 40140 40123 40091 2008-09 40484 40471 40450 40412

Coke consumption 2005-06 45964 45899 45802 45644 2006-07 47623 47534 47401 47182 2007-08 49412 49297 49123 48840 2008-09 51350 51206 50990 50638

HSDO consumption2005-06 3452 3418 3368 3288 2006-07 3766 3717 3645 3530 2007-08 4131 4064 3965 3809 2008-09 4556 4468 4338 4135

LDO consumption 2005-06 920 911 898 877 2006-07 902 890 873 845 2007-08 883 869 848 815 2008-09 866 849 824 785

Furnace oil and LSHS consumption2005-06 9797 9779 9752 9707 2006-07 10290 10261 10218 10149 2007-08 10829 10789 10729 10631 2008-09 11423 11370 11292 11163

Table 14: Actual and Relative Reduction in Carbon Emission due to Tax Changes

Reduction in Actual Carbon Percentage Decrease Emission (000 Tonnes) in Carbon Emission

1025 50 10 25 50 Per Cent Per Cent Per Cent Per Cent Per Cent Per Cent Change Change Change Change Change Change in Tax in Tax in Tax in Tax in Tax in Tax

Coal 2005-06 925 2285 4491 0.30 0.74 1.46 2006-07 1249 3083 6053 0.40 0.99 1.94 2007-08 1583 3903 7654 0.50 1.24 2.42 2008-09 1927 4747 9298 0.60 1.48 2.90

Lignite 2005-06 3 9 19 0.02 0.04 0.09 2006-07 5 12 26 0.02 0.06 0.12 2007-08 6 15 32 0.03 0.07 0.15 2008-09 7 18 39 0.03 0.08 0.18

Coke 2005-06 35 88 175 0.14 0.35 0.70 2006-07 49 121 241 0.19 0.47 0.93 2007-08 63 158 312 0.23 0.58 1.16 2008-09 79 197 389 0.28 0.70 1.39

HSDO 2005-06 30 74 144 0.98 2.43 4.74 2006-07 43 107 208 1.30 3.22 6.27 2007-08 59 146 283 1.62 4.01 7.77 2008-09 78 192 371 1.94 4.79 9.26

LDO 2005-06 8 20 38 0.98 2.42 4.74 2006-07 10 25 50 1.30 3.21 6.27 2007-08 13 31 60 1.63 4.00 7.78 2008-09 15 36 70 1.95 4.78 9.26

FO and LSHS 2005-06 14 36 71 0.19 0.46 0.92 2006-07 23 56 112 0.28 0.69 1.37 2007-08 32 79 156 0.37 0.92 1.83 2008-09 42 104 206 0.47 1.15 2.28

the industry and transport sectors. Consumption of electricity has also gone up significantly during this period.

India’s current energy per capita consumption and carbon emissions are still far below the world average for the same. But the projected trajectory of growth in energy consumption and carbon emissions for the country is alarming enough for a sustainable path of development.

The review of literature on policy instruments for mitigating carbon emissions indicates that there are broadly two instruments

– CAC and EI – available to any government for pursuing policies aimed at improving environmental quality. The two policy instruments differ in administrative expenses, flexibility in abating emission levels, the need for monitoring and enforcing compliance, incentives for research and development of new pollution abatement technologies and the ability to meet fiscal policy objectives. The existing literature is of the opinion that one has to choose the right instrument depending on the situation. However, the survey of case studies of application of these instruments in other countries does confirm that EI instruments achieve lower overall social costs through their lower unit cost of abatement as well as through continual incentive to reduce carbon emissions.

The role of pricing/taxation policy for mitigating carbon emissions for India is analysed through econometric model simulation. The industry sector being the focal point of the study, the carbon emissions forecast was made for the products relevant to the industry sector, namely coal, lignite, coke, HSDO, LDO, furnace oil and LSHS.

The model estimation included GDP, industrial GDP, IIP, and price indices of the respective products as explanatory variables. The model suggests that a reduction in emissions would be the highest in the case of coal followed by coke and HSDO. However, the amount of reduction of carbon emissions is not substantial enough to suggest that economic instruments (carbon taxation) would be the right ones for mitigating emissions.

It may be noted that the present study is an indicative one and the inferences should be treated with caution. The major problem that arises during the econometric modelling exercise is that it takes into account the past energy prices which were administratively controlled for most of the time period under consideration. As a result, our estimated price elasticity does not fully portray reality. Moreover, throughout the world, energy prices are found to be inelastic. The present study has used the estimation models that are relatively simpler and indicative in nature. The estimation could not consider the impact of taxation on the exante (assumed) growth rate of explanatory variables like GDP, IIP etc in its projection of carbon emissions. However, as this paper presents different scenarios based on different future growths of these explanatory variables, this shortcoming is, in a way, dealt by considering lower growth trajectories of the explanatory variables. In some of the more developed countries, more exhaustive models such as the CGE model are available for estimation purposes that capture the changes in the entire economy in a dynamic framework. Similar kind of models are yet to be developed in the Indian context. This would be one of our future areas of work. Therefore, much detailed, extensive and meticulous analysis is required to reach at any conclusion regarding the role of carbon tax as an instrument for reducing carbon emissions in the country.




[The views expressed in the paper are those of the authors and not of the institute to which they belong. Our sincere thanks to the anonymous referee, whose comments have helped to enrich the paper. The usual disclaimer applies.]

1 Computed from energy balance statistics.

2 The other countries were the US, Japan, Indonesia and Pakistan.

3 Though natural gas usage has been experiencing an increasing trend for industrial purposes, the absence of price data forced us to exclude natural gas from this exercise.

4 The interested readers may obtain the results for other scenarios from the authors.


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