Skip to main content

Advertisement

Log in

Energy use efficiency of Indian cement companies: a data envelopment analysis

  • Published:
Energy Efficiency Aims and scope Submit manuscript

Abstract

The present paper aims at measuring energy use efficiency in Indian cement industry and estimating the factors explaining inter-firm variations in energy use efficiency. Within the framework of production theory, data envelopment analysis has been used to measure energy use efficiency. Using firm-level data from electronic PROWESS database for the years 1989–1990 through 2006–2007, the study first estimates energy use efficiency of the firms and then compares the efficiency scores across. Empirical results suggest that there is enough scope for the Indian cement firms to reduce energy uses, though this potential for energy saving varies across firms. A second-stage regression analysis reveals that firms with larger production volume have higher energy efficiency scores and that age of the firms impacts differently on energy use efficiency obtained from two different models. Also, higher quality of labor force associates with higher energy use efficiency.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Notes

  1. Both energy and value of output are measured in monetary units (Rs. Crore INR = Indian rupees)

  2. However, with 663 KCL of thermal energy/KG clinker and 69 KWH/T cement, Indian cement industry is one of the most energy-efficient industries in the world where the world best is 650 KCL/KG clinker and 65 KWH/T cement, respectively (Raina 2006).

  3. Mukherjee (2008a) uses a similar model in the context of Indian manufacturing sector. But in her paper, technology is assumed to exhibit CRS, while we are assuming VRS technology; since we are using firm-level data in our analysis, VRS is the appropriate nature of technology that can be assumed.

  4. All the inputs used in the cost minimization model are in expenditure form. We would have preferred to use physical quantity measures, but they are not available at the firm level. We hypothesize that the use of value measures are unlikely to introduce much bias in our measures because price of these inputs does not vary much across firms in a competitive input market.

  5. See Ray (2004) for a detailed exposition of different DEA models.

  6. The assumption of no technical regress seems to make sense for the sample years under study during which most of the cement companies did experience significant technological improvement.

  7. Slack-adjusted efficient point is Q in Fig. 1. Interested reader may refer to Ray (2004) for a detailed exposition on slack analysis.

  8. We prefer to take the geometric mean instead of the arithmetic mean because our efficiency measures are in ratio form defined as a ratio of optimum to actual use of energy.

  9. We are grateful to one of our referees for providing this information.

  10. The better measure for labor productivity could have been output per unit of labor/man-hour. In the absence of physical data on labor used, we have used this proxy on the assumption that labor market is competitive and they are paid according to their value of marginal product.

  11. The observed efficiency score is right censored at 1 as it is equal to the actual (latent) score whenever the actual score is <1; when the actual score is ≥1, the observed efficiency score is 1.

  12. But we should remember that our measure of efficiency is a relative concept, not absolute. It is measured by the distance from the best practice frontier. That best practice frontier has been constructed from the available information on the existing firms in a particular year. So ACC is 100% energy-efficient compared to other firms in that group, but in absolute sense, its efficiency may be <100%.

  13. See Mukherjee (2008a, b) and Reinhard et al. (2000) for this kind of model specification.

References

  • Ahuja, G., & Majumdar, S. K. (1998). An assessment of the performance of indian state-owned enterprises. Journal of Productivity Analysis, 9, 113–132.

    Article  Google Scholar 

  • Azadeh, A., Amalnick, M. S., Ghaderi, S. F., & Asadzadeh, S. M. (2007). An integrated DEA PCA numerical taxonomy approach for energy efficiency assessment and consumption optimization in energy intensive manufacturing sectors. Energy Policy, 35, 3792–3806.

    Article  Google Scholar 

  • Balakrishnan, P., & Pushpangadan, K. (1994). TFPG in manufacturing industry: A fresh look. Economic and Political Weekly, 30, 2028–2032.

    Google Scholar 

  • Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078–1092.

    Article  MATH  Google Scholar 

  • Berndt, E. R., & Wood, D. (1975). Technology, prices and the derived demand for energy. Review of Economic Studies, 57, 259–268.

    Article  Google Scholar 

  • Bhattacharya, R. N., & Paul, S. (2001). Sectoral changes in consumption and intensity of energy in India. Indian Economic Review, 36(2), 381–392.

    Google Scholar 

  • Boyd, G. A., & Pang, J. X. (2000). Estimating the linkage between energy efficiency and productivity. Energy Policy, 28, 289–296.

    Article  Google Scholar 

  • Centre for Monitoring Indian Economy (CMIE) (n.d.) Prowess Database.

  • Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 3, 392–444.

    MathSciNet  Google Scholar 

  • Debreu, G. (1951). The coefficient of resource utilization. Econometrica, 19, 273–290.

    Article  MATH  Google Scholar 

  • Downs, A. (1967). Inside bureaucracy. Boston: Little, Brown & Co.

    Google Scholar 

  • Farrell, M. J. (1957). The measurement of productive efficiency. Journal of Royal Statistical Society, Series A, General, 120(3), 253–281.

    Google Scholar 

  • Fuss, M. A. (1977). The demand for energy in Canadian manufacturing: An example of the estimation of production structures with many inputs. Journal of Econometrics, 5, 89–116

    Article  Google Scholar 

  • Gielen, D., & Tailor, P. (2009). Indicators for industrial energy efficiency in India. Energy, 34, 962–969.

    Article  Google Scholar 

  • Goldar, B. (1986). Productivity Growth in Indian Industry. New Delhi: Allied.

    Google Scholar 

  • Griffin, J. M., & Gregory, P. R. (1976). An intercountry translog model of energy substitution responses. American Economic Review, 66, 845–857.

    Google Scholar 

  • Grösche, P. (2008). Measuring residential energy efficiency improvements with DEA. Ruhr Economic Paper No. 60. Available at SSRN: http://ssrn.com/abstract=1280878.

  • Hannan, M., & Freeman, J. (1989). Organizational ecology. Cambridge: Harvard University Press.

    Google Scholar 

  • Hu, J. L., & Kao, C. H. (2007). Efficient energy-saving targets for APEC economies. Energy Policy, 35, 373–382.

    Article  Google Scholar 

  • Hudson, E. A., & Jorgenson, D. (1974). US energy policy and economic growth, 1975–2000. Bell Journal of Economics and Management Science, 5, 461–514.

    Article  Google Scholar 

  • ICRA Sector Analysis. (2006). The cement industry in India.

  • Koopmans, T. C. (1951). An analysis of production as an efficient combination of activities. In T. C. Koopmans (Ed.), Activity analysis of production and allocation, Cowles Commission for Research in Economics, monograph no. 13. New York: Wiley.

    Google Scholar 

  • Leibenstein, H. (1976). Beyond economic man. Cambridge: Harvard University Press.

    Google Scholar 

  • Lundvall, K., & Battese, G. E. (2000). Firm size, age and efficiency: evidence from Kenyan manufacturing firms. Journal of Development Studies, 36(3), 146–163.

    Article  Google Scholar 

  • Magnus, J. R. (1979). Substitution between energy and non-energy inputs in the Netherlands: 1950–1976. International Economic Review, 20, 465–484.

    Article  Google Scholar 

  • Marshall, A. (1920). Principles of Economics (8th ed.). London: MacMillan.

    Google Scholar 

  • Mongia, P., & Sathaye, J. (1998). Productivity growth and technical change in India’s energy intensive industries: A survey. Lawrence Berkeley National Laboratory.

  • Ministry of Finance, Economic Survey 2006/07. New Delhi:MoF, Government of India.

  • Mukherjee, K. (2008a). Energy use efficiency in the Indian manufacturing sector: An interstate analysis. Energy Policy, 36, 662–672.

    Article  Google Scholar 

  • Mukherjee, K. (2008b). Energy use efficiency in U.S. manufacturing: A nonparametric analysis. Energy Economics, 30, 76–97.

    Article  Google Scholar 

  • Nag, B., & Parikh, J. (2000). Indicators of carbon emission intensity from commercial energy use in India. Energy Economics, 30, 76–96.

    Google Scholar 

  • Nandi, P., & Basu, S. (2008). A review of energy conservation initiatives by the Government of India. Renewable & Sustainable Energy Reviews, 12, 518–530.

    Article  Google Scholar 

  • Onut, S., & Soner, S. (2006). Energy efficiency assessment for the Antalya Region hotels in Turkey. Energy and Buildings, 38, 964–971.

    Article  Google Scholar 

  • Paul, S., & Bhattacharya, R. N. (2004). CO2 emission from energy use in India: A decomposition analysis. Energy Policy, 32, 585–593.

    Article  Google Scholar 

  • Penrose, E. T. (1959). The theory of the growth of the firm. Oxford: Basil Blackwell.

    Google Scholar 

  • Prescott, E. C., & Vischer, R. (1980). Organisation capital. Journal of Political Economy, 88, 446–461.

    Article  Google Scholar 

  • Raina, S. J. (2006). Energy efficiency improvement opportunity in the cement sector. Presentation in the Cement Sector Task Force of Bureau of Energy Efficiency (BEE).

  • Ramanathan, R. (2000). A holistic approach to compare energy efficiencies of different transport modes. Energy Policy, 28, 743–747.

    Article  Google Scholar 

  • Ramanathan, R. (2005). An analysis of energy consumption and carbon dioxide emissions in countries of the Middle East and North Africa. Energy, 30, 2831–2842.

    Google Scholar 

  • Ray, S. C. (2004). Data envelopment analysis: Theory and techniques for economics and operations research. Cambridge: Cambridge University Press.

    Book  MATH  Google Scholar 

  • Reinhard, S., Lovell, C. A. K., & Thijssen, G. (2000). Environmental efficiency with multiple environmentally detrimental variables; estimated with SFA and DEA. European Journal of Operational Research, 121, 287–303.

    Google Scholar 

  • Roy, J., Sathaye, J., Standard, A., Mongia, P., & Schumacher, K. (1999). Productivity trends in India’s energy intensive industries. The Energy Journal, 20(3), 33–61.

    Google Scholar 

  • Shepherd, W. G. (1986). On the core concepts of industrial economics. In H. W. De Jong & W. G. Shepherd (Eds.), Mainstreams in industrial organization. Dordrecht: Martinus Nijhoff.

    Google Scholar 

  • Shestalova, V. (2003). Sequential Malmquist indices of productivity growth: an application to OECD industrial activities. Journal of Productivity Analysis, 19, 211–226.

    Google Scholar 

  • Sticchcombe, A. L. (1965). Social structure and organizations. In J. G. March (Ed.), Handbook of organization. Dordrecht: Martinus Nijhoff.

    Google Scholar 

  • Tulkens, H., & Eeckaut, P. V. (1995). Non-parametric efficiency, progress and regress measure for panel data: methodological aspects. European Journal of Operational Research, 80, 474–499.

    Article  MATH  Google Scholar 

  • Walton, A. L. (1981). Variations in the substitutability of energy and nonenergy inputs: the case of the Middle Atlantic region. Journal of Regional Science, 21, 441–420.

    Article  Google Scholar 

  • Wei, Y. M., Liao, H., & Fan, Y. (2007). An empirical analysis of energy efficiency in China’s iron and steel sector. Energy, 32, 2262–2270.

    Google Scholar 

  • Yang, M. (2006). Energy efficiency policy impact in India: Case study of investment in industrial energy efficiency. Energy Policy, 34, 3104–3114.

    Article  Google Scholar 

  • Zhou, P., & Ang, P. W. (2008). Linear programming models for measuring economy-wide energy efficiency performance. Energy Policy, 36, 2911–2916.

    Article  Google Scholar 

  • Zhou, P., Ang, B. W., & Poh, K. L. (2008). A survey of data envelopment analysis in energy and environmental studies. European Journal of Operational Research, 189, 1–18.

    Article  MATH  MathSciNet  Google Scholar 

Download references

Acknowledgement

We gratefully acknowledge the valuable comments and suggestions from Prof. Rabindranath Bhattacharya, Mr. Anup Kumar Bhandari, and Prof. Meenakshi Rajeev at the various stages of preparing the paper. We are also grateful to two anonymous referees of this journal for their helpful and constructive comments on an earlier draft of this paper. However, the usual disclaimer applies.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sabuj Kumar Mandal.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Mandal, S.K., Madheswaran, S. Energy use efficiency of Indian cement companies: a data envelopment analysis. Energy Efficiency 4, 57–73 (2011). https://doi.org/10.1007/s12053-010-9081-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12053-010-9081-7

Keywords

Navigation