Environmental Economics and Policy Studies

, Volume 20, Issue 4, pp 879–902 | Cite as

Shadow price of CO2 emissions in Indian thermal power sector

  • Rakesh Kumar Jain
  • Surender Kumar
Research Article


This paper estimates production efficiency and shadow prices of CO2 emissions for coal-fired thermal power plants in India. It employs a unique sample of 56 power plants for 2000–2013 acquired primarily by invoking the Right to Information Act 2005. It estimates parametric quadratic directional output distance function using linear programming approach. We find that CO2 intensity of electricity generation could be reduced by about 16 and 23% if the power plants were made to operate efficiently. The estimated average shadow prices of US$ 14.54 and 18.68 for a ton of CO2 emission, depending upon a plant’s strategies for enhancing electricity and reducing CO2 emissions, reflect that the prevailing Clean Energy Cess of US$ 6.15 on a ton of coal or US$ 3.81 on a ton of CO2 emissions is not enough to induce the required emission mitigation. Significant variation in the estimates of shadow prices across the thermal power plants calls for use of economic instruments to reduce the emissions.


CO2 emissions Shadow price Directional distance function Thermal power plants India 

JEL Classification

D24 Q25 Q52 


  1. Aigner DJ, Chu S-F (1968) On estimating the industry production function. Am Econ Rev 58(4):826–839Google Scholar
  2. Boyd G, Molburg J, Prince R (1996) Alternative methods of marginal abatement cost estimation: nonparametric distance functions. In: Proceed. USAEE/IAEE 17th Conference, pp. 86–95Google Scholar
  3. Central Electricity Authority (CEA) (2003) Report of the Committee to Recommend Next Higher Size of Coal Fired Thermal Power Stations. Central Electricity Authority, Ministry of Power, Government of IndiaGoogle Scholar
  4. Central Electricity Authority (CEA) (2016) Draft National Electricity Plant. Central Electricity Authority, Ministry of Power, Government of IndiaGoogle Scholar
  5. Chambers RG, Chung Y, Färe R (1998) Profit, distance functions and Nerlovian efficiency. J Optim Theory Appl 98(2):351–364CrossRefGoogle Scholar
  6. Chan HS, Cropper ML, Malik K (2014) Why are power plants in India less efficient than in the US? Am Econ Rev 104(5):586–590CrossRefGoogle Scholar
  7. Coggins JS, Swinton JR (1996) The price of pollution: a dual approach to valuing SO2 allowance. J Environ Econ Manag 30:58–72CrossRefGoogle Scholar
  8. Dasgupta S, Huq M, Wheeler D, Zhang C (2001) Water pollution abatement by Chinese industry: cost estimates and policy implications. Appl Econ 33(4):547–557CrossRefGoogle Scholar
  9. Dhryms PJ, Kurz M (1964) Technology and scale in electricity generation. Econometrica 32(3):287–315CrossRefGoogle Scholar
  10. Färe R, Grosskopf S (1990) A distance function approach to price efficiency. J Public Econ 43:123–126CrossRefGoogle Scholar
  11. Färe R, Grosskopf S, Lovell CA, Yaisawarng S (1993) Derivation of shadow prices for undesirable outputs: a distance function approach. Rev Econ Stat 75:374–380CrossRefGoogle Scholar
  12. Färe R, Grosskopf S, Noh D-W, Weber W (2005) Characteristics of a polluting technology: theory and practice. J Econom 126(2):469–492CrossRefGoogle Scholar
  13. Färe R, Grosskopf S, Weber WL (2006) Shadow prices and pollution costs in U.S. agriculture. Ecol Econ 56(1):89–103CrossRefGoogle Scholar
  14. Färe R, Martins-Filho C, Vardanyan M (2010) On functional form representation of multi-output production technologies. J Product Anal 33:81–96CrossRefGoogle Scholar
  15. Fujii H, Managi S (2015) Optimal production resource reallocation for CO2 emissions reduction in manufacturing sectors. Glob Environ Change 35:505–513CrossRefGoogle Scholar
  16. Gollop FM, Roberts MJ (1985) Cost-minimizing regulation of sulfur emissions: regional gains in electric power. Rev Econ Stat 67:81–90CrossRefGoogle Scholar
  17. Gupta M (2006) Costs of reducing greenhouse gas emissions: a case study of India’s power generation sector. FEEM Working Paper No. 147Google Scholar
  18. Halkos G, Managi S (2017) Measuring the effect of economic growth on countries’ environmental efficiency: a conditional directional distance function approach. Environ Resour Econ 68(3):753–775CrossRefGoogle Scholar
  19. Harkness E (2006) CO2 shadow prices in the U.S. electric utility industry: calculating the costs of reducing CO2 emissions. Senior thesis Projects, 2003–2006.
  20. Hettige H, Huq M, Pargal S, Wheeler D (1996) determinants of pollution abatement in developing countries: evidence from South and South-East Asia. World Dev 24(12):1891–1904CrossRefGoogle Scholar
  21. Hudgins LB, Primont D (2007) Derivative properties of directional technology distance functions. In: Färe R, Grosskopf S, Primont D (eds) Aggregation, efficiency and measurement. Springer, New YorkGoogle Scholar
  22. International Energy Agency (IEA) (2015) World Energy Outlook. International Energy Agency, ParisGoogle Scholar
  23. Johnstone N, Managi S, Rodríguez M, Haščič I, Fujii H, Souchier M (2017) Environmental policy design, innovation and efficiency gains in electricity generation. Energy Econ 63:106–115CrossRefGoogle Scholar
  24. Joskow Paul L, Schmalensee Richard (1987) The performance of coal-burning electric generating units in the United States: 1960–1980. J Appl Econom 2(2):85–109CrossRefGoogle Scholar
  25. Kumar S, Managi S (2009) Economics of sustainable development: the case of India. Springer, New yorkGoogle Scholar
  26. Kumar S, Managi S (2010) Sulfur dioxide allowances: trading and technological progress. Ecol Econ 69(3):623–631CrossRefGoogle Scholar
  27. Kumar S, Rao DN (2002) Estimating the marginal abatement cost of SPM: an application to thermal power sector in India. Energy Stud Rev 11:76–92CrossRefGoogle Scholar
  28. Kumar S, Fujii H, Managi S (2015) Substitute or complement? Assessing renewable and nonrenewable energy in OECD countries. Appl Econ 47(14):1438–1459CrossRefGoogle Scholar
  29. Lange I, Bellas A (2005) Technological change for sulfur dioxide scrubbers under market-based regulation. Land Econ 81(4):546–556CrossRefGoogle Scholar
  30. Lee J-D, Park J-B, Kim T-Y (2002) Estimation of the shadow prices of pollutants with production/environment inefficiency taken into account: a nonparametric directional distance function approach. J Environ Manag 64:365–375CrossRefGoogle Scholar
  31. Lee S-C, Oh D-H, Lee J-D (2014) A new approach to measuring shadow price: reconciling engineering and economic perspectives. Energy Econ 46:66–77CrossRefGoogle Scholar
  32. Marklund P-O, Samakovlis E (2007) What is driving the EU burden-sharing agreement: efficiency or equity? J Environ Manang 85(2):317–329CrossRefGoogle Scholar
  33. Matsushita K, Asano K (2014) Reducing CO2 emissions of Japanese thermal power companies: a directional output distance function approach. Environ Econ Policy Stud 16:1–19CrossRefGoogle Scholar
  34. Mittal ML, Sharma C, Singh R (2014) Decadal emission estimates of carbon dioxide, sulfur dioxide, and nitric oxide emissions from coal burning in electric power generation plants in India. Environ Monit Assess 186:6857–6866CrossRefGoogle Scholar
  35. Murty MN, Kumar S (2002) Measuring the cost of environmentally sustainable industrial development in India: a distance function approach. Environ Dev Econ 7:467–486CrossRefGoogle Scholar
  36. Murty MN, Kumar S (2004) Environmental and economic accounting for industry. Oxford University Press, New DelhiGoogle Scholar
  37. Murty MN, Kumar S, Dhavala KK (2007) Measuring environmental efficiency of industry: a case study of thermal power generation in India. Environ Resour Econ 38:31–50CrossRefGoogle Scholar
  38. Parikh J, Panda M, Ganesh-Kumar A, Singh V (2009) CO2 emission structure of Indian economy. Energy 34(8):1024–1031CrossRefGoogle Scholar
  39. Park H, Lim J (2009) Valuation of marginal CO2 abatement options for electric power plants in Korea. Energy Policy 37:1834–1841CrossRefGoogle Scholar
  40. Pittman RW (1981) Issues in pollution control: interplant cost differences and economies of scale. Land Econ 57:1–17CrossRefGoogle Scholar
  41. Shephard RW (1970) Theory of cost and production functions, 1st edn. Princeton University Press, PrincetonGoogle Scholar
  42. Swinton JR (2002) The potential for cost saving in the sulfur dioxide allowance market: empirical evidence from Florida. Land Econ 78:390–404CrossRefGoogle Scholar
  43. Turner J (1994) Measuring the cost of pollution abatement in the electric utility industry: a production function approach. Ph.D. Dissertation. University of North Carolina, Chapel HillGoogle Scholar
  44. Vardanyan M, Noh D-W (2006) Approximating pollution abatement costs via alternative specifications of a multi-output production technology: a case of the US electric utility industry. J Environ Manag 80:177–190CrossRefGoogle Scholar
  45. Vogel W, Kalb H (2010) Large-scale solar thermal power: technologies, costs and development. Wiley, New YorkCrossRefGoogle Scholar
  46. Wang K, Che L, Ma C, Wei YM (2017) The shadow price of CO2 emissions in China’s iron and steel industry. Sci Total Environ 15(598):272–281CrossRefGoogle Scholar
  47. Wei C, Löschel A, Liu B (2013) An empirical analysis of the CO2 shadow price in Chinese thermal power enterprises. Energy Econ 40:22–31CrossRefGoogle Scholar
  48. Xiao B, Niu D, Wu H, Wang H (2017) Marginal abatement cost of CO2 in China based on directional distance function: an industry perspective. Sustainability 9(138):1–19Google Scholar
  49. Yagi M, Fujii H, Hoang V, Managi S (2015) Environmental efficiency of energy, materials, and emissions. J Environ Manag 161:206–218CrossRefGoogle Scholar
  50. Zhou P, Zhou X, Fan LW (2014) On estimating shadow prices of undesirable outputs with efficiency models: a literature review. Appl Energy 130:799–806CrossRefGoogle Scholar
  51. Zhou X, Fan LW, Zhou P (2015) Marginal CO2 abatement costs: findings from alternative shadow price estimates for Shanghai industrial sectors. Energy Policy 77:109–117CrossRefGoogle Scholar

Copyright information

© Society for Environmental Economics and Policy Studies and Springer Japan KK, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Indian RailwaysNew DelhiIndia
  2. 2.Department of Business EconomicsUniversity of Delhi, South CampusNew DelhiIndia
  3. 3.Department of Economics, Delhi School of EconomicsUniversity of DelhiNew DelhiIndia

Personalised recommendations