




































Ecology, Economy and Society–the INSEE Journal 4 (2): 45-64, July 2021 

THEMATIC ESSAY 

Using Economic Instruments to Fix the Liability of 
Polluters in India: Assessment of the Information 
Required and Identification of Gaps 

Sukanya Das1, MN Murty2, and Kavita Sardana3 

 

Abstract: The review paper highlights the informational requirements for the 
effective use of environmental policy instruments to achieve ambient standards of 
pollution in India. A section on the Integrated Urban Air Pollution Assessment 
Model is attempted to identify data requirements for, and information gaps 
associated with, using these instruments. We review the available information and 
identify informational gaps that thwart the realization of ambient standards of 
environmental quality. In India, command-and-control instruments are arbitrarily 
used to assign liability without taking cognizance of economic estimates. The 
available cost–benefit estimates of air and water pollution, combined with air 
quality modelling for urban areas and water quality modelling, are essential inputs 
for using environmental policy instruments to ensure compliance with ambient 
standards. We discuss how to use economic estimates while designing and using 
economic instruments such as pollution taxes and pollution permits, in addition to 
command and control. 

 

                                                        
1 Associate Professor, Department of Policy Studies, TERI School of Advanced Studies, 

Plot No. 10, Sankar Rd, Vasant Kunj Institutional Area, Vasant Kunj, Institutional Area, 
New Delhi, Delhi – 110070; sukanya.das@terisas.ac.in.  

2 Retired Professor, Institute of Economic Growth, University Enclave, North Delhi, Delhi 
– 110007; mn.murty71@gmail.com.  

3 Assistant Professor, Department of Policy Studies, TERI School of Advanced Studies, Plot 
No. 10, Sankar Rd, Vasant Kunj Institutional Area, Vasant Kunj, Institutional Area, New 
Delhi, Delhi – 110070; kavita.sardana@terisas.ac.in.  

Copyright © Das, Murty and Sardana 2021. Released under Creative Commons Attribution-

NonCommercial 4.0 International licence (CC BY-NC 4.0) by the author.  

Published by Indian Society for Ecological Economics (INSEE), c/o Institute of Economic 

Growth, University Enclave, North Campus, Delhi 110007.  

ISSN: 2581-6152 (print); 2581-6101 (web). 

DOI: https://doi.org/10.37773/ees.v4i2.363  

 

 

mailto:sukanya.das@terisas.ac.in
mailto:mn.murty71@gmail.com
mailto:kavita.sardana@terisas.ac.in
https://doi.org/10.37773/ees.v4i2.363


Ecology, Economy and Society–the INSEE Journal [46] 

Keywords: Ambient Standards; Taxes; Permits; Carbon Tax; Pollution. 

 

1. INTRODUCTION 

The government could use several policy instruments to hold polluters 
liable for the damage caused to the public through air or water pollution. 
These are economic instruments such as taxes and subsidies, pollution 
permits, regulatory instruments of command and control, and penalties. 
Choosing between these instruments depends both on their efficacy in 
achieving the target level of emissions and on the relative size of welfare 
losses that pollutants produce, such as health damages to households 
(Baumol and Oates 1988). Pollution taxes and pollution permits are price- 
and quantity-based instruments, respectively. In a market economy with full 
information, they are the first best or Pareto optimal instruments. However, 
the available literature shows that if there is incomplete information and 
uncertainty about the measurement of the benefits and costs of pollution 
abatement, there will be efficiency losses while employing such taxes and 
permits (Weitzman 1974). Therefore, there is a clear case for choosing 
between these instruments especially in the context of using them to reduce 
carbon emissions for climate change mitigation, etc. Given the limitations 
concerning the informational requirements for effectively using these 
instruments, many variants are being used.4 A common practice is to use 
one or a combination of these instruments to achieve scientifically 
determined safe standards for air or water quality.5 Apart from the direct 
environmental policy instruments already mentioned, the budgetary policy 
instruments of commodity taxes could be used to achieve environmental 
objectives (Sandmo 1975). There could be additional taxes on polluting or 
carbon-intensive commodities to achieve environmental objectives over 
and above the taxes levied in lieu of general budgetary policy objectives of 
equity and efficiency (Murty 1996). 

To use environmental policy instruments, the regulator or government 
requires information about the estimated welfare losses from pollution. 
Based on this data, regulators can better design instruments that ensure that 
polluters face liabilities for the welfare losses from pollution.  

                                                        
4 See Baumol and Oates (1988) for a theoretical discussion of pollution taxes and permits. 

Murty (2010) provides a less technical discussion of these instruments. 

5 These are known as pollution tax, pollution permit, and command-and-control standards, 
depending upon the prime regulatory instrument used. See Sterner (2003) for a discussion on 
the practical use of these instruments. 



[47] Sukanya Das, M.N. Murty, and Kavita Sardana 

In environmental economics, estimates of welfare losses from air and water 
pollution can be either benefit-based or cost-based. Benefit-based estimates 
account for health damages and loss of public environmental services as a 
result of pollution. Cost-based estimates account for the costs that the 
polluter avoids or saves by doing nothing to reduce pollution.6 Pollution 
taxes and permits incentivize polluters to use the following cost-minimizing 
production and abatement technologies: end-of-pipe treatment 
technologies, production process changes, input changes, changes in the 
quality of products, etc. However, in India, only command-and-control and 
penalty instruments have been used. These instruments are inefficient in 
that polluters have no incentive to minimize their abatement costs. 
Pollution taxes and permits are ideal, efficient instruments because they 
minimize abatement costs by mandating a switch to cleaner technologies. 
To use these instruments, the regulator requires detailed estimates of 
abatement costs and potential damages and information on air and water 
quality modelling.  

There have been several central and state legislations to ensure 
environmental protection in India. 7  They provide for using the policy 
instruments described earlier to control air and water pollution and to 
maintain safe air and water quality standards. Source-specific and ambient 
pollution standards for particulate matter (PM10), sulphur dioxide (SO2), 
nitrous oxide (NOx), carbon dioxide (CO2), and other emissions for air 
pollution, and suspended solids (SS), biological oxygen demand (BOD), and 
chemical oxygen demand (COD) for water pollution, are fixed by the 
regulator or government. The Central Pollution Control Board (CPCB) and 
State Pollution Control Boards (SPCBs) are empowered to use 
environmental policy instruments to make polluters comply with these 

                                                        
6  Benefit-based estimates of welfare losses are made using specially designed valuation 
methods which are classified as stated and revealed preference methods (Freeman 1993; 
Mitchell and Carson 1989). Several studies have been conducted in India using these 
methods, some of which are discussed in this paper. Cost-based estimates of welfare losses 
are calculated using the methods of theory of production: cost functions, distance functions, 
and by-production models. Some of these estimates for India are discussed in this paper. 

7 The Indian Parliament has enacted various legislations concurrently with, and as follow-ups 
of, constitutional amendments to protect and improve the environment. The most 
important among them are the Wildlife (Protection) Act, 1972; the Water (Prevention and 
Control of Pollution) Act, 1974; the Water (Prevention and Control of Pollution) Cess Act, 
1977; the Forest (Conservation) Act, 1980; the Air (Prevention and Control of Pollution) 
Act, 1981; the Environment (Protection) Act, 1986; the Public Liability Insurance Act, 1991; 
the National Environment Tribunal Act, 1995; and the National Environment Appellate 
Authority Act, 1997.  



Ecology, Economy and Society–the INSEE Journal [48] 

standards. However, thus far, the Indian Government and these agencies 
have been using only command-and-control regulations. 

Command-and-control instruments do not make it clear to what extent 
polluters are complying with source-specific and ambient standards. Indeed, 
ambient standards can more accurately assess damages to the public as a 
result of pollution. Given the higher ambient standards of air pollution in 
urban areas, there is a permissible pollution load that needs to be achieved 
to realize them. The pollution load compatible with ambient standards can 
be obtained by considering source-specific pollution standards in terms of 
volume, level of activity, the scale of production, and the abatement 
technologies the polluter uses. The pollution load in an urban area depends 
on the amount of pollution emitted by each polluter and the number of 
polluters; this requires that source-specific standards be dynamic. 
Information on air quality modelling8 is essential for fixing source-specific 
standards that are compatible with ambient ones and for using economic 
instruments.  

In the following sections, we discuss informational requirements for using 
regulatory instruments to control air and water pollution in India. In 
Section 2, we provide a brief description of the Integrated Urban Air 
Pollution Assessment Model (IUAPAM), which could be used to estimate 
pollution levels in the National Capital Territory (NCT) of Delhi. In Section 
3, we review the information available in a number of research studies done 
in India that estimate welfare losses from air and water pollution. In Section 
4, we describe the command-and-control regulations currently used in India 
and how best the estimates of welfare losses can be used. In Section 5, we 
discuss data requirements and gaps for using taxes and permits to control 
air pollution in India. In Section 6, we consider methods for fixing liability 
for water pollution in India. Finally, in Section 7, we present a way forward, 
highlighting issues of immediate concern to environmental policy in India. 

 

2. INTEGRATED URBAN AIR POLLUTION ASSESSMENT 
MODEL IUAPAM 

The IUAPAM constitutes the following:  

                                                        
8 See Section 2 for a discussion on air quality modelling and Baumol and Oates (1988) for a 

theoretical discussion on air quality modelling in the context of using pollution permits to 
reduce air pollution in urban areas. It explains that ambient pollution in urban areas comes 
from different sources. Therefore, the effect of a certain source on ambient pollution 
depends upon wind direction, meteorological conditions, distance, etc. 



[49] Sukanya Das, M.N. Murty, and Kavita Sardana 

(a) Air quality modelling for urban areas 

(b) Assessment of benefits and costs of air pollution reduction 

(c) Exploration of strategies to reduce air pollution  

The information in the IUAPAM helps with designing emissions reduction 
strategies and judging their efficacy in determining the impacts on cost, air 
quality, and health. Given the achieved air quality with a given abatement 
strategy, the model helps to estimate health damages avoided to populations 
exposed to air pollution.  

Consider a more inclusive method of measuring air quality in an urban area 
in terms of pollution concentration at many receptor points (N) (Baumal 
and Oates 1988). Identify the number of sources contributing to pollution 
(M) at each receptor point. The contribution of a source to the pollution at 
a given receptor point depends upon its distance from the receptor point 
and prevailing meteorological conditions in the urban area. Let  

ei: i = 1,2 …M, quantity of pollution at source i 

qj: j 1, 2 …---N, air quality or pollution concentration at receptor point j  

dij: contribution of one unit of pollution from source i to pollution 
concentration at receptor point j 

The air quality or pollution concentration at receptor point j, qj, is given as  

i

M

i

ijj edq 



1  (1) 

 There are NM  air quality diffusion coefficients (dij) in the model forming 

a NM  matrix. 

For practical purposes, the air quality at each receptor point could be an 
annual average, and the relationship between the pollution at a source and 
the air quality at a receptor point may be linear. Developing a full matrix 
with information about diffusion coefficients may not be practical. A 
practical approach could involve dividing a big urban area into a small 
number of manageable zones, with a receptor point in each zone to 
measure air quality. In each zone, it could be assumed that the contribution 
of one unit (tonne) of pollution in the region to the pollution concentration 
at the receptor point is the same irrespective of the source of pollution. If 
the city is divided into S number of zones, the observed air quality in sth 
zone is given as  



Ecology, Economy and Society–the INSEE Journal [50] 

Ssedq sss ,...,2,1, 
 (2) 

where es is the total quantity of pollution from all sources in region s, and ds 
is the contribution of one unit of pollution to the pollution concentration in 
region s, which is assumed to be constant. Pollution concentration qs 
increases with the pollution load given the diffusion coefficient ds in a given 
region. In a scenario without regulations, the pollution concentration for 
the sth region has to be reduced from qs to q*, where q* represents the safe 
urban air quality standard, approved by the World Health Organization 
(WHO). Therefore, the pollution load that has to be reduced in the sth 
region to comply with the WHO air quality standard could be obtained as  

sss dqqe /)( *
 (3) 

Using the information from this model about the pollution load reduction 
required (∆es), and the consequent improvement of air quality from the 
current level qs to WHO standards q*, the loss of well-being from air 
pollution in an urban region can be estimated. The estimates of the cost-
based and benefit-based shadow prices of air pollution discussed in this 
paper could be used along with air quality modelling information for 
estimating the loss of well-being. The cost-based shadow price (pc) is the 
cost to industry for reducing a tonne of PM10 emissions and the benefit-
based shadow price (pb) is described as the reduction in health damages 
avoided by urban households per unit of reduction in pollution 
concentration (signifying a unit increase in ambient air quality). The cost-
based welfare losses (∆C) and benefit-based welfare losses (∆B) from urban 
air pollution could be obtained as  

)( *qqpB

epC

sb

sc





 (5) 

The information from this model could be used to design regulatory 
instruments of command and control; the economic instruments of 
pollution taxes and permits to deal with point source pollution; and 
subsidies and incentives for polluters to use appropriate production 
technologies to reduce pollution from non-point sources to realize the safe 
WHO air quality standard q* in a given region. The required reduction in 
emissions amounting to ∆es could be realized using any of these 
instruments. However, taxes and permits can achieve this required 
reduction at minimal cost, while command-and-control regulations are 
much more expensive. 



[51] Sukanya Das, M.N. Murty, and Kavita Sardana 

To use command-and-control regulations, the regulator can determine the 
percentage by which the observed pollution load (qs/ds) exceeds the 
pollution load permitted by the WHO standard (q*/ds) in the region. Then, 
the regulator could make it mandatory for each polluter to reduce their 
pollution by the required percentage points and, failing to do so, face 
penalties or closure. 

The regulator can use cap-and-trade regulations to reduce the pollution load 
to the permissible level or the WHO standard. Initially, the allowable 
pollution load (permits) could be distributed among polluters based on their 
historically observed pollution levels. Since a tonne of pollution, 
irrespective of its source, contributes the same amount (ds) to the ambient 
pollution concentration in the region, polluters could trade on a one-to-one 
basis. Trade between low- and high-abatement cost polluters could help 
reduce pollution to the permissible level in the region at the least cost. 

The model could be considered for the National Capital Region (NCR), 
which consists of four regions: National Capital Territory (NCT) of Delhi, 
some districts in Haryana, some districts in Uttar Pradesh, and some 
districts in Rajasthan. The IUAPAM could be developed on the lines 
discussed above and used in the NCT, especially to reduce particulate 
matter emissions. The NCT Planning Board constituted under the NCT 
Planning Board Act, 1985, could coordinate between the governments of 
Delhi, Haryana, Uttar Pradesh, and Rajasthan for this purpose. An 
interdisciplinary group of experts drawn from the CPCB and concerned 
SPCBs, research institutes, and universities could be created to develop and 
implement this model in the NCT. 

 

3. SOME AVAILABLE ESTIMATES OF THE SHADOW PRICES 
OF AIR AND WATER POLLUTION FOR INDIA 

Several studies have been done in India that provide estimates of welfare 
losses from air and water pollution. There are also many studies that offer 
estimated costs of air pollution and water pollution abatement. Further, 
there are several scholarly studies that provide economic estimates of 
damages due to air and water pollution.  

Murty, Kumar, and Dhavala (2007) use the generalized directional distance 
functions methodology to estimate the shadow prices of SPM, SO2, and 
NOx using data from five coal-fired thermal power–generating plants 
belonging to Andhra Pradesh Power Generation Corporation 



Ecology, Economy and Society–the INSEE Journal [52] 

(APGENCO).9 This study uses panel data containing 480 observations of 
electricity produced, air pollutants generated (SPM, SO2, and NOx), and 
coal and other inputs that the five electricity-generating plants employ. In 
the estimation, the electricity generated is considered a good output while 
the three pollutants SPM, SO2, and NOx are considered bad outputs. On 
reviewing the shadow prices of SPM, SO2, and NOx, it is clear that to 
reduce the emissions of a pollutant by one tonne, a representative firm has 
to spend INR 12,571, INR 4,956, and INR 17,698 (at 2018 prices), 
respectively. 

Recent studies by Jain and Kumar (2018) and Murty and Nagpal (2019) 
derive estimates of the shadow prices of CO2 emissions for the Indian 
thermal power generation industry. Murty and Nagpal (2019) arrive at two 
estimates using a by-production model and distance function model with a 
weak disposability assumption. They use unbalanced panel data from 51 
firms for the nine years between 2004–2015. The estimates at 2018 prices 
using the by-production model and the distance function model are INR 
6,806 and INR 6,106, respectively. An effective carbon rate of EUR 30 
(INR 2,382.1 at 2018 prices) per tonne was reported in a publication by 
OECD (2018); this is considered the lowest damage estimate of carbon 
emissions. Most European countries price carbon at this or a higher rate. It 
is suggested that to achieve the goals of the Paris Agreement, a carbon rate 
of USD 40–80 (INR 2,840–5,680 at 2019 prices) by 2020 and USD 50–100 
(INR 3,550–7,100 at 2019 prices) by 2030 is required due to the 
accumulation of CO2 emissions and the consequent increase in marginal 
damages over time. These estimates of CO2 form a range of INR 2,840–
7,100 at 2019 prices.  

Estimates of the damages resulting from air pollution to households in an 
urban area, based on current pollution levels above safe ambient standards, 
and the pollution load to be reduced for each pollutant, have to be obtained 
to estimate the benefit-based welfare losses. Murty, Gulati, and Banerjee 
(2004) use the revealed preferences method of hedonic property prices to 
estimate the environmental benefits of reducing the SPM concentration to 
the minimal national standards (MINAS) in the megacities of Delhi and 
Kolkata. The study estimates a typical household’s willingness to pay to 
reduce the level of ambient SPM pollution to the MINAS standards level in 
Delhi and Kolkata to be INR 46,498 and INR 23,346, respectively, at 2018 
prices. By extrapolating these estimates for the entire population of each 

                                                        
9 See Murty and Russell (2020) and Chambers and Färe (2020) for recent discussions on the 

methodologies of by-production models and distance functions for estimating shadow prices 
of pollutants or bad outputs. 



[53] Sukanya Das, M.N. Murty, and Kavita Sardana 

city, the annual benefits come to INR 109,173 million for Delhi and INR 
73,719 million for Kolkata, at 2018 prices. It is necessary to conduct similar 
studies to estimate the benefits of reducing PM10, SO2, and NOx to MINAS 
standards. Additionally, there is an urgent need for air quality modelling 
information for megacities in India10 to ensure that polluters are held liable 
using command-and-control regulations to realize ambient standards.  

Several studies have estimated the costs of water pollution abatement—
shadow prices or marginal abatement costs (MAC)—in India using the 
pollution abatement cost function methodology. 11  These studies were 
conducted during the 1992–1999 period. Murty and Kumar (2002, 2004) 
estimate the shadow prices of water pollutants for Indian industries using 
the distance function methodology with the assumption of weak 
disposability. The data used in these studies are from a survey of water-
polluting industries in India done by the Institute of Economic Growth, 
Delhi. These data reveal the characteristics of the main plants and effluent 
treatment plants for the years between 1994 and 1995. Estimates of average 
shadow prices or marginal costs of abatement per tonne of BOD, COD, 
and SS for Indian water-polluting industries are INR 57,427.92, INR 
216,854.94, and INR 71,570.6, respectively, at 2018 prices. 

Many studies estimate welfare losses from ambient river water pollution and 
groundwater pollution in India using environmental valuation methods. For 
example, Markandya and Murty (2000) attempted a comprehensive 
evaluation of the Ganga Action Plan (GAP), a project initiated by the 
Government of India to clean the river and bring the water up to a bathing 
quality standard. This study estimated the user and nonuser benefits of a 
clean Ganga using a contingent valuation method. Nonuser benefits were 
assessed through a survey of urban households in all major cities in India. 
User benefits, including health benefits, were estimated through a survey of 
households along the river, covering major urban and rural areas. A typical 
household’s annual willingness to pay to improve the quality of water up to 
a bathing standard was estimated to be INR 1,501 for non user benefits and 
INR 1,593 for user benefits, at 2018 prices. The total annual nonuser and 
user benefits were estimated at INR 16,081 million and 22,044 million, 
respectively, at 2018 prices.  

 

                                                        
10 See Section 2 for a discussion on air quality modelling. 

11 The studies include those by Gupta, Murty, and Pandey (1989), James and Murty (1996), 
Mehta, Mundle, and Sankar (1997), Ganguli and Roy (1999), Goldar and Pandey (2001), and 
Appasamy (2002), among others. 



Ecology, Economy and Society–the INSEE Journal [54] 

4. USING COMMAND-AND-CONTROL REGULATIONS 

There are stack emission standards and ambient standards for air pollution 
and approaches to ensure the liability of firms depending on whether one 
considers the former or the latter. The stack emission standards for PM, 
SO2, and NOx in India are 115, 80, and 80 milligrams per cubic metre 
(Nm3), respectively. The command-and-control regulations used to estimate 
the liability of each firm that exceeds the permissible pollution load are 
based on the pollution load that each firm needs to reduce to achieve 
industry-wise source-specific standards. This value is the product of the 
total pollution load in tonnes that the firm has to reduce and the shadow 
price of the pollutant. The pollution load to be reduced is the difference 
between the observed concentration of the pollutant and the recommended 
concentration as per industry-wise source-specific standards. The shadow 
price of the pollutant is the change in the abatement cost to the firm when 
one unit of the pollution load is reduced at margin. A review of studies 
done in India provides estimates of the shadow prices of PM, SO2, and 
NOx at INR 11,651.98, INR 4,592.98, and INR 16,403.52 per tonne, 
respectively, at 2018 prices. 

Polluting firms’ immediate compliance with fixed stack emission standards 
does not necessarily ensure a change in the pollution load in an urban area 
to ambient standards. Indeed, stack emission standards have to be changed 
dynamically, with the rising number of polluters, to maintain ambient 
standards. As discussed in Section 2, information from air quality modelling 
for urban areas in India could be used to determine permissible pollution 
loads of PM, SO2, and NOx for maintaining ambient air quality standards. 
Regular monitoring of polluting firms by pollution control boards would 
reveal the total loads of these three pollutants. The actual pollution load in 
the city may exceed the estimated pollution load for ambient standards, as 
explained by the IUAPAM model discussed in Section 2. As per this model, 
if the load exceeds permissible standards by x%, the regulator using the 
command-and-control method could mandate an x% reduction of the 
pollution load of each firm.  

Different approaches are needed to determine liability for water pollution 
depending on the polluter: big factories, small factories in industrial estates, 
and households in urban areas. Big factories employ in-house treatment 
technologies and effluent treatment plants, small factories in industrial 
estates have common effluent treatment plants (CETP), and household-
based effluents in urban areas are treated in municipal sewage treatment 
plants. In the case of a big factory, the quantity of each pollutant (BOD, 
COD, and SS) to be reduced to comply with source-specific standards 
could be estimated, given the observed volume of residual water, influent 



[55] Sukanya Das, M.N. Murty, and Kavita Sardana 

and effluent quality, and standards. Given an estimate of the unit cost of 
abatement or shadow price for each pollutant, the penalty for the factory 
could be fixed by pollution control boards using the command-and-control 
method. Again, factories complying with fixed source-specific water 
pollution standards for polluters in a river basin does not guarantee ambient 
river water quality. As the number of polluters around the river basin 
increase, the pollution load to be reduced to maintain the ambient standards 
of the river increases. This necessitates dynamic source-specific water 
pollution standards. Information on water quality modelling similar to the 
IUAPAM model discussed in Section 2 for the river basin and dynamic 
source-specific standards for polluters is needed to fix the liability of 
polluters to uphold ambient river water quality standards. 

 

5. USING MARKET-BASED INSTRUMENTS 

The command-and-control regulations described in Section 4 are 
ineffectual, resulting in firms using cost-inefficient abatement technologies 
to comply with the standards. It is, therefore, vital to use pollution taxes, 
permits, and standards. These instruments provide incentives to polluting 
firms to choose cost-minimizing abatement technologies.  

5.1 Pollution Taxes and Standards  

This method requires the regulator or government to estimate the pollution 
abatement cost function that explains the polluters’ choices of cost-efficient 
abatement technologies. Given an estimate of the marginal cost of 
abatement function and scientifically fixed safe environmental standards, 
the pollution tax could be determined and levied uniformly on all polluting 
firms. The pollution tax on a particular pollutant is fixed based on the 
marginal cost of abatement at the level of pollution corresponding to the 
standard. The tax imposed makes the liability of a firm higher than the cost 
of complying with the standards. Therefore, the firm has an incentive to 
reduce pollution rather than pay a tax. 

Some studies 12  offer a method of determining pollution taxes for the 
thermal power–generating industry to reduce air pollution using the 
estimated marginal cost of pollution abatement functions for SPM, SO2, 
and NOx. Pollution tax rates are derived for air pollutants for a 
representative thermal power plant in Andhra Pradesh, using the estimated 
MAC functions and MINAS stack emission standards. Given the emission 
standards of 115, 80, and 80 milligrams per Nm3for SPM, SO2, and NOx, 

                                                        
12 See Murty and Gulati (2006) and Murty, Kumar, and Dhavala (2007). 



Ecology, Economy and Society–the INSEE Journal [56] 

respectively, and the MAC functions for each of these pollutants, the tax 
rates are estimated at INR 5,524.03, INR 54,000.71, and INR 14,616.69, 
respectively, at 2018 prices.  

5.2 Pollution Permits and Standards  

Pollution permit trading is a quantity-based regulation for reducing 
pollution. Implementing pollution permit trading requires the following: a 
cap on emissions, distributing emission permits among polluters, and 
ensuring monitoring and compliance.13 Examples of successful emissions 
trading systems are SO2 trading in the US and CO2 trading in the European 
Union. In case of local pollutants like particulate matter, given the ambient 
pollution standards for a city or a specified region (mg per cubic metre), 
permissible pollution loads from sources have to be identified and comply 
with ambient standards. For example, the total pollution load from sources 
in a region may far exceed the permitted pollution load for ambient 
standards. The permitted pollution load has to be fixed as a cap. 
Information about air quality modelling for the region as per the IUAPAM 
model described in Section 2 is needed to decide the cap on emissions.14 
After deciding the target reduction or cap on emissions, emission permits 
are created, accounting for tonnes of emissions. These permits are 
distributed among firms based on their current emissions or through 
auctions. This, in turn, creates incentives for firms to engage in permit 
trading, with low-abatement cost firms supplying permits and high-
abatement cost firms demanding them. Trading takes place until the 
marginal cost of abatement is equal among all firms, resulting in cost 
minimization to achieve target reductions. The success of the permit trading 
system depends on the regulator monitoring the actions of firms and, if 
necessary, resetting the cap or target reductions over time. Given that 
particulate matter emissions are the main cause of concern in urban regions 
of India, pollution control boards and the government could design and 
implement permit trading systems to deal with this problem. At present, no 
country in the world uses cap-and-trade regulation to deal with particulate 
matter emissions. Historically, this regulation is used in developed countries 
to deal with SO2 and CO2 emissions.15 In India, there are currently two cap-
and-trade schemes: the renewable energy certificates scheme, and the 
Bureau of Energy Efficiency’s (BEE) Perform, Achieve, Trade (PAT) 
scheme. In addition, there was a study conducted in India to estimate the 

                                                        
13See Duflo, Pande, Greenstone, and Ryan (2010). 

14See Section 2 for details. 

15Sulphur dioxide (SO2) emissions in the US and carbon dioxide (CO2) emissions in the 
European Union. 



[57] Sukanya Das, M.N. Murty, and Kavita Sardana 

cost-effectiveness of a cap-and-trade programme for intra-firm trade to 
control particulate matter emissions.16 

Recently, the Gujarat Pollution Control Board (GPCB) launched a novel 
cap-and-trade regulation for particulate pollution in Surat.17 Those who are 
planning and executing this scheme observed that the most challenging 
problem was to get all the necessary information about particulate matter 
emissions from different sources in the region. First, this involved the 
development of protocols for the continuous monitoring of particulate 
matter coming from stacks and, second, the installation of a continuous 
emissions monitors system (CEMS). The CEMS constitutes a network of 
sensors installed at industries that sends live readings of stack pollution. 
The Indian Government has made it mandatory for 17 highly polluting 
sectors (such as pulp and paper, distilleries, sugar, tanneries, power plants, 
and iron and steel) to install CEMS. However, the study points out that the 
challenge is in getting the CEMS to produce reliable information, for which 
the GPCB has been making all possible monitoring efforts. Continuous 
monitoring by GPCB is necessary so that the CEMS is calibrated to 
accurately detect pollution in stack emissions.  

5.3 Economic Instruments to Control CO2 Emissions  

Greenhouse gas emissions have both domestic externality effects (health 
damage) and global externality effects (climate change). Some specific 
institutional arrangements are required to use taxes and permit instruments 
to deal with these externalities. The literature shows that the optimal tax for 
a carbon-intensive commodity in a country can be broken down into 
revenue tax, local air pollution tax, and international carbon tax (Murty 
1996). There are many possible institutional alternatives for international 
agreements to tax carbon-intensive commodities to control local and global 
environmental pollution. Harmonization of domestic taxes on carbon-
intensive commodities is one such option. However, such an arrangement 
could not result in optimal carbon taxes to reduce carbon emissions to the 
level of the global optimum. Additionally, this arrangement could provide 
incentives to countries that are party to the agreement to freeride (Hoel 
1991a, 1991b). Research shows that a uniform international tax on carbon 

                                                        
16See Rita Pandey (2004). 

17The Gujarat Pollution Control Board (GPCB), in collaboration with the Energy Policy 
Institute of the University of Chicago (EPIC-India), the Abdul Latif Jameel Poverty Action 
Lab (J-PAL South Asia), and the Evidence for Policy Design at Harvard University (EPoD 
India), has been experimenting since 2011 on using emissions trading regulations for 
particulate matter emissions in Surat, Gujarat. 



Ecology, Economy and Society–the INSEE Journal [58] 

emissions, agreed to by all member countries, may help reduce emissions to 
a global optimum. 

There is a clear choice between a carbon tax and carbon permits 
instruments in the context of using them to reduce carbon emissions or 
climate change mitigation by member countries. Weitzman (1974) shows 
that if there is uncertainty about the benefits and costs of pollution 
abatement, there will be efficiency losses in the execution of tax and permit 
instruments. This is a likely hypothetical scenario that a regulator, say the 
World Government in this case, has to encounter in making a choice 
between a carbon tax and carbon permits for climate change mitigation 
(Stern 2007). To reduce carbon emissions to the global optimum level, 
member countries need to agree to use either an international carbon tax or 
a cap-and-trade regime. However, there is no agreement of this type so far 
among countries in spite of international discussions on climate change 
mitigation. Instead, through the latest Paris Agreement on climate change, 
many member countries have agreed to nationally determined emissions 
reductions; India is party to this agreement.18 

India has to consider using various domestic policies: taxes on carbon-
intensive commodities, subsidies for non-conventional energy sources 
(solar, wind, and hydropower), a uniform national carbon tax, and carbon 
permit–trading among polluters (trading between industries using fossil 
fuels, trading between Indian states, etc.). Given that India does not yet use 
the economic instruments of taxes and permits to deal with local pollution 
problems, it could use the budgetary policy instruments of commodity taxes 
and subsidies and even the inefficient regulatory instrument of command 
and control to reduce carbon emissions. The liability of polluters for CO2 
emissions could be fixed using command-and-control regulations, given the 
estimate of the shadow price of CO2 emissions (INR 6,000 per tonne) 
reported earlier.  

5.4 Fixing the Liability for Non-point Sources of Air Pollution 

                                                        
18 At the UN Framework Convention on Climate Change in 2015, the parties communicated 
their intended nationally determined contributions for greenhouse gas reductions. India 
committed by 2030 (a) to reduce greenhouse gas emissions intensity by 33–35% below the 
2005 level, (b) to realize 40% of India’s power capacity from non-fossil fuel sources, and c) 
to create an additional carbon sink of 2.5–3 billion tonnes of CO2 equivalent through 
additional forest and tree cover. However, it has to be noted that the estimated aggregate 
greenhouse gas emissions levels in 2025 and 2030, based on the intended nationally 

determined contributions by all parties, do not fall within the least-cost 2˚C scenarios. A 

much greater emissions reduction effort will be required to hold the increase in the global 
average temperature to below 2˚C above pre-industrial levels. 



[59] Sukanya Das, M.N. Murty, and Kavita Sardana 

Air pollution generated by road transport is a non-point source of pollution; 
hence, it becomes difficult to assign liability to a single major polluter and 
to charge a pollution tax. A study by Pandey and Bharadwaj (2004) 
describes various policy options that address taxes, fuel choices, and 
vehicular technologies to control vehicular pollution in India. Murty and 
Gulati (2006) and Murty, Dhaval, Ghosh, and Singh (2006) discuss a 
method for assigning pollution abatement liability to road vehicles. In these 
studies, the pollution abatement cost for various categories of vehicles is 
estimated as the sum of the cost of switching to prescribed vehicular 
technologies and using better fuels to comply with the stipulated emission 
norms. 

The high degree of correlation among the emission variables recorded in 
this study indicates that the transition from one emission control vehicular 
technology to another simultaneously ensures the reduction of emissions 
from all pollutants. The annual cost of abatement for a passenger car is 
estimated by taking the maximum of the abatement cost values for all 
emissions, i.e., for CO2, NOx, and HC (Hydro Carbon). The pollution 
emissions standards for the road sector adopted in India correspond to 
Euro norms, which vary with the type of vehicle. Subsequently, there exists 
a variation in vehicular technologies and fuel quality according to the 
different stages of the Euro norms, i.e., Euro I, II, III, IV, V, and VI. 

The study estimates the vehicle-wise pollution abatement cost by adding the 
cost of upgrading vehicular technologies and of using better quality fuel to 
conform to the prescribed emissions norms, as shown in Table 1. For 
instance, the annual cost of abating pollution for a passenger car following 
Euro III norms can be approximated to INR 15,315 (INR 11,317 for the 
technology + INR 3,998 for fuel), at 2018 prices. Therefore, regulators or 
the government could impose liability on vehicle owners for not following 
the prescribed norms (Euro V or VI at present) using this method. 

Table 1: Estimates of the cost per vehicle for changing from Pre-Euro to Euro 3 

emissions norms (INR at 2018 prices) 

Vehicle Change in Technology Fuel Cost Total Cost 

Car 11,317 3,998 15,315 

Bus 36,664 20,478 57,142 

Truck 36,664 21,349 58,013 

Two-wheeler 9,847 1,736 11,583 

Three-wheeler 12,448 2,194 14,642 

Source: Murty and Gulati (2006) 



Ecology, Economy and Society–the INSEE Journal [60] 

6. FIXING THE LIABILITY OF POLLUTERS FOR WATER 
POLLUTION  

Water-polluting firms in India are required to meet these water quality 
standards set by the CPCB: 35mg/L for BOD, 250mg/L for COD, and 
100mg/L for SSP. Using the tax standard method, a pollution tax could be 
levied given estimates of the marginal cost of abatement for water-polluting 
industries in India and source-specific standards for each pollutant. As 
pointed out in Section 3, numerous studies in India have estimated the 
marginal cost of water pollution abatement for different industries. For 
example, a study by Mehta, Mundle, and Sankar (1994) recommends that 
the MAC for relatively high-cost producers should serve as the basis for 
setting charges/taxes to ensure that producers find it cheaper to abate than 
to pollute. In this study, the authors recommend four options for 
experimentation by policymakers: (i) abatement charges with the 
government undertaking clean up; (ii) abatement charges with clean up 
contracted to organizations through competitive bidding; (iii) a tax 
proportional to the excess pollution for firms violating standards and 
subsidies for those exceeding the prescribed abatement standards; and (iv) a 
private permit trading system.  

This study estimates the MAC function for water pollutants in the pulp and 
paper industry in India. It uses the abatement cost as a function of the 
quantity of treated water and pollution concentrations in the influent and 
effluent. This function estimates the marginal cost in INR per 100 gm 
reduction in the effluent BOD. The average marginal cost per 100 gm of 
BOD to achieve the MINAS level of BOD (50 mg/L) is estimated to be 
INR 10.69, at 2018 prices. MAC functions can be used to estimate the cost 
of a reduction in the effluent for a given level of influent and wastewater. 
Given the MINAS standards for BOD and the estimated MAC function, 
the pollution control authority could set its charges at INR 6.33 per 100 gm 
of extra BOD, at 2018 prices. 

A municipal sewage treatment plant in an urban area receives effluents from 
households and untreated effluents from industries. The municipality 
charges households in the form of a water pollution tax or cess as part of 
the price of the municipal water supply. Given that pollution control boards 
collect penalties or fees from industries that do not comply with standards, 
they have to compensate the municipality for treating industrial and 
household effluents. 

In the case of river pollution, studies by Markandya and Murty (2000, 2004) 
consider several different mechanisms for financing the Ganga Action Plan 
(GAP) of the Government of India for cleaning the Ganga in a sustainable 



[61] Sukanya Das, M.N. Murty, and Kavita Sardana 

manner. They are (i) a polluter pays principle, (ii) a user pays principle (with 
government involvement), (iii) a user pays principle (without government 
involvement), and (iv) funding from the general tax system. Under the 
polluter pays principle, a water charge per KL is collected as a tax or as a 
part of a water tariff on all industrial effluents. The user pays principle with 
government involvement would mean a tax on all beneficiaries. This study 
estimates the annual willingness of a typical urban household to pay for 
ensuring bathing quality of the river water at INR 1,506 for non user 
benefits and INR 1,599 for user benefits, at 2018 prices. The total annual 
nonuser and user benefits are estimated at INR 16,130 million and INR 
22,111 million, respectively, at 2018 prices. Given that a typical household 
in India is willing to pay INR 3,104 annually at 2018 prices for bathing 
quality river water, a river cleaning tax of this amount for each household 
could be a feasible way to raise money to keep the river clean.  

7. A WAY FORWARD  

In India, the failure to control air and water pollution is attributable to the 
arbitrary use of command-and-control regulations for controlling industrial 
pollution and the use of incorrect approaches to deal with non-point 
sources of pollution like in the transport sector in urban areas. Policy 
initiatives by the Indian Government have not taken into account economic 
instruments such as pollution taxes and pollution permits.  

As discussed in this paper, apart from the information in the environmental 
valuation studies, air quality and water quality modelling is essential to 
effectively use pollution taxes, pollution permits, and command-and-control 
instruments. The IUAPAM could potentially be used to control air 
pollution in the National Capital Territory (NCT) of Delhi. There is an 
urgent need for government and non-governmental initiatives to develop 
information systems for all major urban areas and river systems in India. 
Without such information, policy instruments, including command and 
control, become ineffective, as in the current reality in India. 

More studies have to be done in major urban areas in India to estimate 
health and other welfare losses for households and industry-specific 
pollution abatement costs due to air and water pollution. These estimates, 
along with an estimate of the pollution dispersion coefficient matrix, 
become important inputs for using IUAPAM to determine the liability of 
polluters in an urban region. 

Controlling pollution from non-point sources of pollution like road 
transport vehicles and water pollution from agriculture requires designing 
specific approaches. One approach to fixing the liability of vehicular traffic 



Ecology, Economy and Society–the INSEE Journal [62] 

is discussed in this paper. Also, specially designed methods are required to 
deal with household water pollution in urban areas, taking into account that 
the influents to sewage treatment plants come from both households and 
industries. 

ACKNOWLEDGEMENTS 

This paper is drawn to a large extent from the study by MN Murty, 
Haripriya Gundimeda, Sukanya Das, and Kavita Sardana (2019), funded by 
the Central Pollution Control Board, India, and carried out at the TERI 
School of Advanced Studies, Delhi. We gratefully acknowledge the 
comments of Professors Surender Kumar and Rita Pandey on an early draft 
of this paper. We are thankful to two referees from this journal for their 
useful comments and suggestions. 

 

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