Abstract
This paper develops a new measurement for regional/national sustainable social economic development based on data envelopment analysis. This new measurement can reveal the impact of energy over-consumption and pollutant over-emission on economic development, giving regional/national sustainable development a more proper measure. This new measurement is applied into an empirical study for 10 year (2004–2013) sustainable development analysis of 30 regions in mainland China. The empirical results show that: (1) China has a quite unsustainable development in 2004–2013, and the level of unsustainability increased over time. The primary driver of these two phenomenon is pollutant over-emission and resource over-consumption respectively. (2) Area-wide sustainable development in China is quite unbalanced. Eastern China has a much better sustainable development as compared to other areas, and the variation of Eastern China’s sustainable level is very little in 2004–2013. (3) Resource over-consumption and pollutant over-emission in western China are serious, even the absolute values are quite low. This makes western China develop unsustainably in 2004–2013.
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Andersen, P., & Petersen, N. C. (1993). A procedure for ranking efficient units in data envelopment analysis. Management Science, 39(10), 1261–1264.
Azadi, M., Jafarian, M., Saen, R. F., & Mirhedayatian, S. M. (2015). A new fuzzy DEA model for evaluation of efficiency and effectiveness of suppliers in sustainable supply chain management context. Computers and Operations Research, 54, 274–285.
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.
Baucells, M., & Sarin, R. K. (2003). Group decisions with multiple criteria. Management Science, 49(8), 1105–1118.
Bettencourt, L. M., Lobo, J., Helbing, D., Kühnert, C., & West, G. B. (2007). Growth, innovation, scaling, and the pace of life in cities. Proceedings of the National Academy of Sciences, 104(17), 7301–7306.
Charnes, A., & Cooper, W. W. (1962). Programming with linear fractional functionals. Naval Research Logistics Quarterly, 9(3–4), 181–186.
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444.
Chen, C. M. (2014). Evaluating eco-efficiency with data envelopment analysis: An analytical reexamination. Annals of Operations Research, 214(1), 49–71.
Chen, C. M., & Delmas, M. (2011). Measuring corporate social performance: An efficiency perspective. Production and Operations Management, 20(6), 789–804.
Chen, C. M., & Delmas, M. A. (2012). Measuring eco-inefficiency: A new frontier approach. Operations Research, 60(5), 1064–1079.
Chung, Y. H., Färe, R., & Grosskopf, S. (1997). Productivity and undesirable outputs: A directional distance function approach. Journal of Environmental Management, 51(3), 229–240.
Cooper, W. W., Hemphill, H., Huang, Z., Li, S., Lelas, V., & Sullivan, D. W. (1997). Survey of mathematical programming models in air pollution management. European Journal of Operational Research, 96(1), 1–35.
Cooper, W. W., Seiford, L. M., & Tone, K. (2007). Data envelopment analysis: A comprehensive text with models, applications, references and DEA-solver software. Berlin: Springer Science & Business Media.
Dasgupta, P., & Mäler, K. G. (2000). Net national product, wealth, and social well-being. Environment and Development Economics, 5(01), 69–93.
Doyle, J., & Green, R. (1994). Efficiency and cross-efficiency in DEA: Derivations, meanings and uses. Journal of the Operational Research Society, 45, 567–578.
Emrouznejad, A., Parker, B. R., & Tavares, G. (2008). Evaluation of research in efficiency and productivity: A survey and analysis of the first 30 years of scholarly literature in DEA. Socio-Economic Planning Sciences, 42(3), 151–157.
England, R. W. (1998). Measurement of social well-being: Alternatives to gross domestic product. Ecological Economics, 25(1), 89–103.
Färe, R., Grosskopf, S., Lovell, C. K., & Pasurka, C. (1989). Multilateral productivity comparisons when some outputs are undesirable: A nonparametric approach. The Review of Economics and Statistics, 71, 90–98.
Fukuyama, H., & Weber, W. L. (2010). A slacks-based inefficiency measure for a two-stage system with bad outputs. Omega, 38(5), 398–409.
Griffin, J. J. (2000). Corporate social performance: Research directions for the 21st century. Business and Society, 39(4), 479–491.
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(3), 605–616.
Huang, J., & Rozelle, S. (1995). Environmental stress and grain yields in China. American Journal of Agricultural Economics, 77(4), 853–864.
Ji, X., Sun, J., Wang, Y., & Yuan, Q. (2017). Allocation of emission permits in large data sets: A robust multi-criteria approach. Journal of Cleaner Production, 142, 894–906.
Ji, X., Wu, J., & Zhu, Q. (2016). Eco-design of transportation in sustainable supply chain management: A DEA-like method. Transportation Research Part D: Transport and Environment, 48, 451–459.
Lawn, P. A. (2003). A theoretical foundation to support the Index of Sustainable Economic Welfare (ISEW), Genuine Progress Indicator (GPI), and other related indexes. Ecological Economics, 44(1), 105–118.
Lawn, P. A. (2005). An assessment of the valuation methods used to calculate the index of sustainable economic welfare (ISEW), genuine progress indicator (GPI), and sustainable net benefit index (SNBI). Environment, Development and Sustainability, 7(2), 185–208.
Leichenko, R., & O’Brien, K. (2008). Environmental change and globalization: Double exposures. Oxford: Oxford University Press.
Li, G., Liu, W., Wang, Z., & Liu, M. (2016). An empirical examination of energy consumption, behavioral intention, and situational factors: Evidence from Beijing. Annals of Operations Research, doi:10.1007/s10479-016-2202-8.
Liang, L., Wu, J., Cook, W. D., & Zhu, J. (2008). The DEA game cross-efficiency model and its Nash equilibrium. Operations Research, 56(5), 1278–1288.
Liang, L., Yang, F., Cook, W. D., & Zhu, J. (2006). DEA models for supply chain efficiency evaluation. Annals of Operations Research, 145(1), 35–49.
Liu, J., & Diamond, J. (2005). China’s environment in a globalizing world. Nature, 435(7046), 1179–1186.
Lovell, C. 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(3), 507–518.
Mielnik, O., & Goldemberg, J. (2002). Foreign direct investment and decoupling between energy and gross domestic product in developing countries. Energy Policy, 30(2), 87–89.
Morita, H., Hirokawa, K., & Zhu, J. (2005). A slack-based measure of efficiency in context-dependent data envelopment analysis. Omega, 33(4), 357–362.
Pao, H. T., & Tsai, C. M. (2011). Multivariate Granger causality between CO\(_2\) emissions, energy consumption, FDI (foreign direct investment) and GDP (gross domestic product): Evidence from a panel of BRIC (Brazil, Russian Federation, India, and China) countries. Energy, 36(1), 685–693.
Reid, D. J. (1968). Combining three estimates of gross domestic product. Economica, 35, 431–444.
Seiford, L. M., & Zhu, J. (2002). Modeling undesirable factors in efficiency evaluation. European Journal of Operational Research, 142(1), 16–20.
Shafik, N. (1994). Economic development and environmental quality: An econometric analysis. Oxford Economic Papers, 56, 757–773.
Scheel, H. (2001). Undesirable outputs in efficiency valuations. European Journal of Operational Research, 132(2), 400–410.
Song, M., An, Q., Zhang, W., Wang, Z., & Wu, J. (2012). Environmental efficiency evaluation based on data envelopment analysis: A review. Renewable and Sustainable Energy Reviews, 16(7), 4465–4469.
Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis. European journal of operational research, 130(3), 498–509.
Tone, K., & Tsutsui, M. (2010). Dynamic DEA: A slacks-based measure approach. Omega, 38(3), 145–156.
Tone, K., & Tsutsui, M. (2014). Dynamic DEA with network structure: A slacks-based measure approach. Omega, 42(1), 124–131.
Wang, K., Lu, B., & Wei, Y. M. (2013). China’s regional energy and environmental efficiency: A range-adjusted measure based analysis. Applied Energy, 112, 1403–1415.
Wang, Q., Su, B., Sun, J., Zhou, P., & Zhou, D. (2015a). Measurement and decomposition of energy-saving and emissions reduction performance in Chinese cities. Applied Energy, 151, 85–92.
Wang, Q., Zhao, Z., Shen, N., & Liu, T. (2015b). Have Chinese cities achieved the win-win between environmental protection and economic development? From the perspective of environmental efficiency. Ecological Indicators, 51, 151–158.
Wang, Z., Zhang, B., & Li, G. (2015c). Determinants of an energy-saving behavioral intention among residents in Beijing: Extending the theory of planned behavior. Journal of Renewable and Sustainable Energy, 4(5), 45–54.
Wu, J., Lv, L., Sun, J., & Ji, X. (2015). A comprehensive analysis of China’s regional energy saving and emission reduction efficiency: From production and treatment perspectives. Energy Policy, 84, 166–176.
Wu, J., & Zhou, Z. (2014). Environmental efficiency of Chinese paper mills along Huai River: A data envelopment analysis (DEA) based study. Environmental Engineering and Management Journal, 13(5), 1101–1109.
Wu, J., & Zhou, Z. (2015). A mixed-objective integer DEA model. Annals of Operations Research, 228(1), 81–95.
Yin, K., Wang, R., An, Q., Yao, L., & Liang, J. (2014). Using eco-efficiency as an indicator for sustainable urban development: A case study of Chinese provincial capital cities. Ecological Indicators, 36, 665–671.
Zhao, N., Currit, N., & Samson, E. (2011). Net primary production and gross domestic product in China derived from satellite imagery. Ecological Economics, 70(5), 921–928.
Zhou, P., Ang, B. W., & Poh, K. L. (2006). Slacks-based efficiency measures for modeling environmental performance. Ecological Economics, 60(1), 111–118.
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), 1–18.
Zhou, P., Zhou, X., & Fan, L. W. (2014). On estimating shadow prices of undesirable outputs with efficiency models: A literature review. Applied Energy, 130, 799–806.
Zhu, W., & Zhou, Z. (2013). Interval efficiency of two-stage network DEA model with imprecise data. INFOR, 51(3), 142–150.
Acknowledgements
This research has financial supports from the China Scholarship Council (No. 201506340126), Support Funds for Excellent Doctoral Dissertations of USTC (2016-2017), National Natural Science Funds of China (Nos. 71501139, 71571173, 71573186, 71620182), Natural Science Funds of Anhui Province (No. 1708085QG169), Natural Science Funds of Jiangsu Province (No. BK20150307), and Research project of philosophy and Social Sciences in Universities of Jiangsu (2015SJB525).
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Ji, X., Sun, J., Wang, Q. et al. Revealing Energy Over-Consumption and Pollutant Over-Emission Behind GDP: A New Multi-criteria Sustainable Measure. Comput Econ 54, 1391–1421 (2019). https://doi.org/10.1007/s10614-017-9663-y
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DOI: https://doi.org/10.1007/s10614-017-9663-y