Eco-efficiency analysis in generalized IO models: Methods and examples

Gurgul, Henryk and Lach, Łukasz (2019): Eco-efficiency analysis in generalized IO models: Methods and examples.

Abstract

Performance assessment in the presence of undesirable outputs, such as pollutant emissions, is usually modelled within the framework of data envelopment analysis (DEA). In this paper we propose a new approach to measuring eco-efficiency in generalized input-output (gIO) models which may be used as a supplementary method to traditional DEA. Unlike DEA this approach takes into account detailed data on intersectoral flows in supply- and demand-driven gIO models. We focus on cases of traditional and sector-size-adjusted measures of interindustry linkages in gIO models and in each case we suggest respective indices of eco-efficiency and prove their usefulness in policymaking. In order to illustrate possible applications of the new approach we conduct an empirical analysis aimed at identifying the eco-efficient sectors based on the 1995 and 2009 national input-output tables and environmental accounts for Poland which are provided by the World Input Output Data (WIOD) database.

• Item Type: MPRA Paper
• Language: English
• Keywords: generalized input-output models, intersectoral linkages, eco-efficiency, nonlinear optimization
• Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling; O - Economic Development, Innovation, Technological Change, and Growth > O1 - Economic Development; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q3 - Nonrenewable Resources and Conservation

References

1. Aklin, M., Bayer, P., Harish, S. P., Urpelainen, J., 2018, Economics of household technology adoption in developing countries: Evidence from solar technology adoption in rural India. Energy Economics 72, 35-46.

2. Ali, A.I., Seiford, L.M., 1990. Translation invariance in data envelopment analysis. Operations Research Letters 9, 403-405.

3. Allevi, E., Basso, A., Bonenti, F., Oggioni, G., Riccardi, R., 2018, Measuring the environmental performance of green SRI funds: A DEA approach. Energy Economics, https://doi.org/10.1016/j.eneco.2018.07.023.

4. Andrić, I., Koc, M., Al-Ghamdi, S.G., 2019, A review of climate change implications for built environment: Impacts, mitigation measures and associated challenges in developed and developing countries. Journal of Cleaner Production 211, 83-102.

5. Avkiran, N.K., Rowlands, T., 2008. How to better identify the true managerial performance: state of the art using DEA. Omega 36(2), 317-324.

6. Banker, R.D., Charnes, A., Cooper, W.W., 1984. Some models for estimating technical scale inefficiencies in data envelopment analysis. Management Science 30, 1078-1092.

7. Boratyński, J., 2015. Ekonomiczne skutki eksploatacji gazu łupkowego (The economic impact of shale gas extraction). Gospodarka Narodowa 1, 55–82.

8. Brunel, C., Johnson, E.P. 2019, Two birds, one stone? Local pollution regulation and greenhouse gas emissions. Energy Economics 78, 1-12.

9. Charnes, A., Cooper, W.W., Golany, B., Seiford, L.M., Stutz, J., 1985. Foundations of data envelopment analysis for Pareto–Koopmans efficient empirical production functions. Journal of Econometrics 30, 91-127.

10. Charnes, W., Cooper, W., Rhodes, E., 1978. Measuring the efficiency of decision making units. European Journal of Operational Research 2(6), 429–444.

11. Chenery, H.B., Watanabe, T., 1958. International comparisons of the structure of production, Econometrica 26(4), 487–521.

12. Cheng, W., Appolloni, A., D'Amato, A., Zhu, Q., 2018, Green Public Procurement, missing concepts and future trends – A critical review. Journal of Cleaner Production 176, 770-784.

13. Cooper, W.W., Seiford, L.M., Tone, K., 2000. Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software. Kluwer Academic Publishers: Boston.

14. Czaplicka-Kolarz, K., Burchart-Korol, D., Turek, M., Borkowski, W., 2015. Model of eco-efficiency assessment of mining production processes. Archives of Mining Sciences 60(2), 477–486.

15. Defourny, J., Thorbecke, E., 1984. Structural path analysis and multiplier decomposition within a social accounting framework. The Economic Journal 94, 111–136.

16. Dobrowolski, J.W., Wagner, A., Śliwka, M., Mazur, R., Jakubiak, M., 2006. Polish long-term interdisciplinary co-operation of scientists, youth and local communities for the promotion of eco-tourism and sustainable development in regions of particular natural and cultural values in Poland, Spain and Italy. Polish Journal of Environmental Studies 15, 71–72.

17. Dyckhoff, H., Allen, K., 2001. Measuring ecological efficiency with data envelopment analysis (DEA). European Journal of Operational Research 132, 312–325.

18. Färe, R., Grosskopf, S., 2003. Nonparametric productivity analysis with undesirable outputs: Comment. American Journal of Agricultural Economics 85, 1070–1074.

19. Färe, R., Grosskopf, S., 2004. Modelling undesirable factors in efficiency evaluation: Comment. European Journal of Operational Research 157, 242–245.

20. Färe, R., Grosskopf, S., Lovell, C.A.K., Pasurka, C., 1989. Multilateral productivity comparisons when some outputs are undesirable: A nonparametric approach. The Review of Economics and Statistics 71, 90–98.

21. Färe, R., Grosskopf, S., Zaim, O., 2000. An index number approach to measuring environmental performance: An environmental Kuznets curve for OECD countries. New Zealand Econometrics Study Group Meeting, University of Canterbury.

22. Freire-González, J., Puig-Ventosa, I., 2019, Reformulating taxes for an energy transition. Energy Economics 78, 312-323.

23. Galindro, B.M., Zanghelini, G.M., Soares, S.R. 2019 Use of benchmarking techniques to improve communication in life cycle assessment: A general review. Journal of Cleaner Production 213, 143-157.

24. Garrett-Peltier, H., 2017. Green versus brown: Comparing the employment impacts of energy efficiency, renewable energy, and fossil fuels using an input-output model. Economic Modelling 61, 439-447.

25. Genty, A., Arto, I., Neuwahl, F., 2012. Final database of environmental satellite accounts: technical report on their compilation. WIOD Deliverable 4.6, Documentation, downloadable at http://www.wiod.org/publications/source_docs/Environmental_Sources.pdf.

26. Golany, B., Roll, Y., 1989. An application procedure for DEA. Omega 17, 237–250.

27. Gomes, E.G., Lins, M.P.E. 2008. Modelling undesirable outputs with zero sum gains data envelopment analysis models. Journal of the Operational Research Society 59(5), 616–623.

28. Gomes, E.G., Soares de Mello, J.C.C.B., Lins, M.P.E., 2003. Step by step target search in Zero Sum Gains DEA models. Investigacion Operacional 23, 1–16.

29. Gurgul H., Lach Ł., 2011, The interdependence between energy consumption and economic growth in the Polish economy in the last decade. Managerial Economics 9, 25-48.

30. Gurgul, H., Lach, Ł., 2015a. Key sectors in the post-communist CEE economies: What does the transition data say? Communist and Post-Communist Studies 48(1), 15-32.

31. Gurgul H., Lach Ł., 2015b. Key sectors after a decade of transformation: evidence from Poland, Managerial Economics 16(1), 39–76.

32. Gurgul, H., Lach, Ł., 2016a. Simulating evolution of interindustry linkages in endogenous dynamic IO model with layers of techniques. Metroeconomica 67(4), 632-666.

33. Gurgul H., Lach Ł., 2016b. Comparative advantage of the EU in global value chains: How important and efficient are new EU members in transition? Managerial Economics 17(1), 21–58.

34. Gurgul, H., Lach, Ł., 2018a. Sectoral linkages at the beginning of the 21st century: the role of Polish economy in global production structures. Communist and Post-Communist Studies 51(4): 299-314.

35. Gurgul, H., Lach, Ł., 2018b. Some Remarks on a Social Network Approach to Identifying Key Sectors. Economic Systems Research 30: 120-135.

36. Gurgul, H., Lach, Ł., 2018c. Tracing VARDI coefficients: A proposal. Economic Systems Research, DOI: https://doi.org/10.1080/09535314.2018.1516633.

37. Gurgul, H., Lach, Ł., 2018d. On using dynamic IO models with layers of techniques to measure value added in global value chains. Structural Change and Economic Dynamics 47:155-170.

38. Gurgul, H., Lach, Ł., 2019. On approximating the accelerator part in dynamic input-output models. Central European Journal of Operations Research, Volume 27, Issue 1, pp 219–239.

39. Hailu, A., Veeman, T.S., 2001. Non-parametric productivity analysis with undesirable outputs: An application to the Canadian pulp and paper industry. American Journal of Agricultural Economics 83, 605–616.

40. Hewings, G.J.D., 1982. The empirical identification of key-sectors in an economy: a regional perspective. Journal of Development Economics 20, 173–195.

41. Hewings, G.J.D., Romanos, M.C., 1981. Simulating less developed regional economies under conditions of limited information. Geographical Analysis 13(4), 373–390.

42. Hirschman, A.O., 1958. Interdependence and Industrialization. In: Hirschman AO (ed.) The strategy of economic development. Yale University Press, New Haven, pp 98–119.

43. Jordá, P., Cascajo, R., Monzón, A., 2012. Analysis of the Technical Efficiency of Urban Bus Services in Spain Based on SBM Models. ISRN Civil Engineering, Article ID 984758.

44. Kim, J., Park, K., 2018. Effect of the Clean Development Mechanism on the deployment of renewable energy: Less developed vs. well-developed financial markets. Energy Economics 75, 1-13.

45. Köppl, A., Schleicher, S.P., 2018. What Will Make Energy Systems Sustainable? Sustainability 10(7), 2537.

46. Korhonen, P., Luptáčik, M., 2003. Eco-efficiency analysis of power plants: An extension of data envelopment analysis. European Journal of Operational Research 154, 437-446.

47. Kounetas, K.E. 2018 Energy consumption and CO2 emissions convergence in European Union member countries. A tonneau des Danaides? Energy Economics 69, 111-127.

48. Kuosmanen, T., 2005 Weak Disposability in Nonparametric Production Analysis with Undesirable Outputs. American Journal of Agricultural Economics 87(4), 1077-1082.

49. Lach Ł., 2011. Impact of hard coal usage for metal production on economic growth of Poland. Managerial Economics 9, 103–120.

50. Lahr, M.L. (2001) A strategy for producing hybrid regional input-output tables, in: Lahr, M.L., Dietzenbacher, E. (eds.) Input–Output Analysis: Frontiers and Extensions, Basingstoke: Palgrave Macmillan.

51. Lenzen, M., 2003. Environmentally important paths, linkages and key sectors in the Australian economy. Structural Change and Economic Dynamics 14, 1–34.

52. Lovell, C.A.K., Pastor, J.T., Turner, J.A., 1995. Measuring macroeconomic performance in the OECD: A comparison of European and non-European countries. European Journal of Operational Research 87, 507–518.

53. Luptáčik, M., Böhm, B., 2010. Efficiency analysis of a multisectoral economic system. Central European Journal of Operations Research 18(4), 609–619.

54. Ma, L., Hosseini, R.M., Jiang, W., Martek, I., Mills, A., 2018 ,Energy productivity convergence within the Australian construction industry: A panel data study. Energy Economics 72, 313-320.

55. Mahlberg, B., Luptáčik, M., 2014. Eco-efficiency and eco-productivity change over time in a multisectoral economic system. European Journal of Operational Research 234(3), 885-897.

56. Masternak‐Janus, A., Rybaczewska‐Błażejowska, M., 2017. Comprehensive Regional Eco‐Efficiency Analysis Based on Data Envelopment Analysis: The Case of Polish Regions. Journal of Industrial Ecology 21, 180-190.

57. Merli, R., Preziosi, M., Acampora, A., 2018, How do scholars approach the circular economy? A systematic literature review. Journal of Cleaner Production 178, 703-722.

58. Miller, R.E., Blair, P.D., 2009. Input-Output Analysis, Cambridge University Press.

59. Muller, C., Yan, H., 2018. Household fuel use in developing countries: Review of theory and evidence. Energy Economics 70, 429-439.

60. Nicoletti, G., Scarpetta, S., Boylaud, O., 2000. Summary indicators of product market regulation with in extension to employment protection legislation, OECD, Economics department working papers No. 226, ECO/WKP(99)18.

61. Odeck, J., Alkadi, A., 2001. Evaluating efficiency in the Norwegian bus industry using data envelopment analysis. Transportation 28(3), 211–232.

62. Oosterhaven, J., 1981. Interregional Input-Output Analysis and Dutch Regional Policy Problems. Aldershot: Gower.

63. Palan, N. 2010. Measurement of Specialization – The Choice of Indices. FIW Working Paper series 062, FIW.

64. Pestana, C. Peypoch, N., 2010. Productivity changes in Portuguese bus companies. Transport Policy 17(5), 295–302.

65. Pham D.D.T., Paillé, P., Halilem, N., 2019, Systematic review on environmental innovativeness: A knowledge-based resource view. Journal of Cleaner Production 211, 1088-1099.

66. Picazo-Tadeoa, A.J., Gómez-Limón, J.A. Reig-Martínez, E. 2011. Assessing farming eco-efficiency: A Data Envelopment Analysis approach. Journal of Environmental Management 92(4), 1154-1164.

67. Prieto, A.M., Zofío, J.L., 2007. Network DEA efficiency in input–output models: With an application to OECD countries. European Journal of Operational Research 178(1), 292-304.

68. Rączka, J., 2001. Explaining the performance of heat plants in Poland. Energy Economics 23(4), 355-370.

69. Rheinhard, S., Lovell, C.A.K., Thijssen, G., 1999. Econometric estimation of technical and environmental efficiency: An application to Dutch dairy farms. American Journal of Agricultural Economics 81, 44–60.

70. Rheinhard, 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.

71. Rybaczewska-Błażejowska, M., Masternak-Janus, A., 2018. Eco-efficiency assessment of Polish regions: Joint application of life cycle assessment and data envelopment analysis. Journal of Cleaner Production 172, 1180-1192.

72. Scheel, H., 2001. Undesirable outputs in efficiency evaluations. European Journal of Operational Research 132, 400–410.

73. Seiford, L.M., Zhu, J., 2002. Modelling undesirable outputs in efficiency evaluation. European Journal of Operational Research 142, 16–20.

74. Sueyoshi, T., Wang, D., 2018, DEA environmental assessment on US petroleum industry: Non-radial approach with translation invariance in time horizon. Energy Economics 72, 276-289.

75. Sun, S., 2002. Measuring the relative efficiency of police precincts using data envelopment analysis. Socio-Economic Planning Sciences 36, 51–71.

76. Tajbakhsh, A., Hassini, E., 2018, Evaluating sustainability performance in fossil-fuel power plants using a two-stage data envelopment analysis. Energy Economics 74, 154-178.

77. Tarancón, M.A., Callejas, F. Dietzenbacher, E., Lahr, M.L., 2008. A Revision of the Tolerable Limits Approach: Searching for the Important Coefficients. Economic Systems Research 20, 75-95.

78. Temurshoev, U., 2010. Identifying optimal sector groupings with the hypothetical extraction method. Journal of Regional Science 50(4), 872-890.

79. Temurshoev, U., 2016. Backward and forward linkages and key sectors in the Kazakhstan economy. Asian Development Bank Policy Report TA 8414-KAZ.

80. Temurshoev, U., Oosterhaven, J., 2014. Analytical and Empirical Comparison of Policy-Relevant Key Sector Measures. Spatial Economic Analysis 9, 284–308.

81. Thanassoulis, E., 1995. Assessing police forces in England and Wales using data envelopment analysis. European Journal of Operational Research 87, 641–657.

82. Wei, Y., Li, Y., Wu, M., Li, Y., 2019, The decomposition of total-factor CO2 emission efficiency of 97 contracting countries in Paris Agreement. Energy Economics 78, 365-378.

83. Wu, J., Li, M., Zhu, Q., Zhou, Z., Liang, L., 2018, Energy and Environmental Efficiency Measurement of China’s Industrial Sectors: a DEA Model with Non-homogeneous Inputs and Outputs. Energy Economics, https://doi.org/10.1016/j.eneco.2018.11.036.

84. Xing, Z., Wang, J., Zhang, J., 2018. Expansion of environmental impact assessment for eco-efficiency evaluation of China's economic sectors: An economic input-output based frontier approach. Science of the Total Environment 635, 284-293.

85. Yaisawarng, S., Klein, J.D., 1994. The effects of sulfur dioxide controls on productivity change in the US electric power industry. The Review of Economics and Statistics 76, 447–460.

86. Zheng, H., Fang, Q., Wang, C., Jiang, Y., Ren, R., 2018. Updating China's input-output tables series using MTT method and its comparison. Economic Modelling 74, 186-193.

87. Zhu, J., 2015. Data Envelopment Analysis A Handbook of Models and Methods. Springer: New York

88. Zhu, J., 2016. Data Envelopment Analysis Handbook of Empirical Studies and Applications. Springer: New York.

89. Zofío, J.L., Prieto, A.M., 2001. Environmental efficiency and regulatory standards: The case of CO2 emissions from OECD industries. Resource and Energy Economics 23: 63–83.