Environmental Science and Pollution Research

, Volume 26, Issue 18, pp 17950–17964 | Cite as

Allocating on coal consumption and CO2 emission from fair and efficient perspective: empirical analysis on provincial panel data of China

  • Bang-jun WangEmail author
  • Jia-lu Zhao
  • Yan-fang Wu
  • Chao-qun Zhu
  • Yin-nan He
  • Yi-xi Wei
Environmental Pollution and Energy Management


This paper considers a problem of how to allocate resource effectively and equitably among provinces. To address the problem, a total factor resource input-oriented data envelopment analysis (DEA) model is used to evaluate the energy and environmental efficiency for 30 provinces in China during 2009–2013 in this paper. Based on the evaluation results, from efficient and fair perspective, a revised DEA-based resource allocation model is established. It is worth pointing out that the model takes the input orientation and output orientation into account at the same time and can be used to allocate coal consumption and carbon emission by 2020 for 30 provinces in China. Results indicate that if the Chinese government wants to fulfill the CO2 emission reduction targets of 40–45% by 2020, and coal consumption intensity reduction target during 13th Five-Year Plan, inefficient provinces will undertake more coal consumption and carbon emission intensity reduction obligation share. And provinces with historical high coal consumption and high CO2 emission intensity will have greater potential of coal consumption and carbon emission intensity reduction. In addition, this paper set several scenarios of gross domestic product (GDP) growth rate, under the scenarios analysis, finds the growth rate of GDP has negative effect on reduction of coal consumption and carbon dioxide emissions intensity. This research provides more realistic practical significance for achieving sustainable economic development.


Resource allocation Input and output orientation Data envelopment analysis (DEA) Coal consumption intensity CO2 emission intensity 


  1. Adewuyi AO, Awodumi OB (2017) Biomass energy consumption, economic growth and carbon emissions: fresh evidence from West Africa using a simultaneous equation model. Energy 119:453–471Google Scholar
  2. Amirteimoori A, Emrouznejad A (2012) Optimal input/output reduction in production processes. Decis Support Syst 52(3):742–747. Google Scholar
  3. Amirteimoori A, Shafiei M (2006) Characterizing an equitable omission of shared resources: a DEA-based approach. Appl Math Comput 177(1):18–23. Google Scholar
  4. An Q, Wen Y, Xiong B, Yang M, Chen X (2017) Allocation of carbon dioxide emission permits with the minimum cost for Chinese provinces in big data environment. J Clean Prod 142(Part 2):886–893. Google Scholar
  5. Asmild M, Paradi JC, Pastor JT (2009) Centralized resource allocation BCC models ☆. Omega 37(1):40–49Google Scholar
  6. Athanassopoulos AD (1998) Decision support for target-based resource allocation of public Services in multiunit and multilevel systems. INFORMS 44(2):173–187Google Scholar
  7. Banker RD (1984) Estimating most productive scale size using data envelopment analysis. Eur J Oper Res 17(1):35–44Google Scholar
  8. Beasley JE (2003) Allocating fixed costs and resources via data envelopment analysis. Eur J Oper Res 147(1):198–216. Google Scholar
  9. Belton V (1992) An integrating data envelopment analysis with multiple criteria decision analysis. Springer, Berlin, pp 71–79Google Scholar
  10. Bennett AR, Macphee S, Betts RP (2004) Resource allocation based on efficiency analysis. Manag Sci 50(8):1134–1144Google Scholar
  11. Bhattacharya M, Paramati SR, Ozturk I, Bhattacharya S (2016) The effect of renewable energy consumption on economic growth: evidence from top 38 countries. Appl Energy 162:733–741Google Scholar
  12. Bian Y, Yang F (2010) Resource and environment efficiency analysis of provinces in China: a DEA approach based on Shannon’s entropy. Energ Policy 38(4):1909–1917Google Scholar
  13. Bouyssou D (2002) Using DEA as a tool for MCDMi some remarks. J Oper Res Soc 50:1–14Google Scholar
  14. Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units ☆. Eur J Oper Res 2(6):429–444Google Scholar
  15. Chen Y, Lin S (2014) Decomposition and allocation of energy-related carbon dioxide emission allowance over provinces of China. Nat Hazards 76(3):1–17Google Scholar
  16. Chen XJ, Jin L, Lei Y, Xue WB, Su M, Yang JT, Wang JN (2015) Study on China coal consumption control under air quality constraints. China Environ Manag 7(05):42–49 (in chinese)Google Scholar
  17. Destek MA (2016) Natural gas consumption and economic growth: panel evidence from OECD countries. Energy 114:1007–1015Google Scholar
  18. Du J, Cook WD, Liang L, Zhu J (2014) Fixed cost and resource allocation based on DEA cross-efficiency. Eur J Oper Res 235(1):206–214Google Scholar
  19. Fang L (2013) A generalized DEA model for centralized resource allocation. Eur J Oper Res 228(2):405–412Google Scholar
  20. Fang L (2015) Centralized resource allocation based on efficiency analysis for step-by-step improvement paths. Omega 51:24–28Google Scholar
  21. Fujii H, Managi S (2015) Optimal production resource reallocation for CO 2 emissions reduction in manufacturing sectors. Glob Environ Chang 35:505–513Google Scholar
  22. Gao G, Chen S, Yang J (2015) Carbon emission allocation standards in China: a case study of shanghai city. Energ Strat Rev 7:55–62Google Scholar
  23. Golany B (1988) An interactive MOLP procedure for the extension of DEA to effectiveness analysis. J Oper Res Soc 39(8):725–734Google Scholar
  24. Golany B, Roll Y (1989) An application procedure for DEA. Omega 17(3):237–250Google Scholar
  25. Golany B,  Tamir E (1995) Evaluating efficiency-effectiveness-equality trade-offs: a data envelopment analysis approach. Manag Sci 41(7):1172–1184Google Scholar
  26. Golany B, Phillips FY, Rousseau JJ (1993) Models for improved effectiveness based on dea efficiency results. IIE Trans 25(6):2–10Google Scholar
  27. Gomes EG, Lins MPE (2008) Modelling undesirable outputs with zero sum gains data envelopment analysis models. J Oper Res Soc 59(5):616–623Google Scholar
  28. Guo XD, Zhu L, Fan Y, Xie BC (2011) Evaluation of potential reductions in carbon emissions in Chinese provinces based on environmental DEA. Energ Policy 39(5):2352–2360Google Scholar
  29. Guo J, Zheng X, Wei C (2016) Disaggregating energy use cap among China's provinces. J Clean Prod 127:374–386Google Scholar
  30. Hao Y, Zhang Z-Y, Liao H, Wei Y-M (2015) China’s farewell to coal: a forecast of coal consumption through 2020. Energ Policy 86(Supplement C):444–455. Google Scholar
  31. Honma S, Hu JL (2008) Total-factor energy efficiency of regions in Japan. Energ Policy 36(2):821–833Google Scholar
  32. Huang H, Wang T (2017) The total-factor energy efficiency of regions in China: based on three-stage SBM model. Sustainability 9(9):1664Google Scholar
  33. Inglesi-Lotz R (2016) The impact of renewable energy consumption to economic growth: a panel data application. Energy Econ 53:58–63Google Scholar
  34. Kahia M, Aïssa MSB, Charfeddine L (2016) Impact of renewable and non-renewable energy consumption on economic growth: new evidence from the MENA net oil exporting countries (NOECs). Energy 116(Part 1):102–115Google Scholar
  35. Korhonen P, Syrjänen M (2004) Resource allocation based on efficiency analysis. Manag Sci 50(8):1134–1144Google Scholar
  36. Li Y, Zhu L (2014) Cost of energy saving and CO 2 emissions reduction in China’s iron and steel sector. Appl Energy 130(130):603–616Google Scholar
  37. Li H, Yang W, Zhou Z, Huang C (2013) Resource allocation models’ construction for the reduction of undesirable outputs based on DEA methods. Math Comput Model 58(5–6):913–926Google Scholar
  38. Li B-B, Liang Q-M, Wang J-C (2015) A comparative study on prediction methods for China’s medium- and long-term coal demand. Energy 93(Part 2):1671–1683. Google Scholar
  39. Lin B, Atsagli P (2017) Energy consumption, inter-fuel substitution and economic growth in Nigeria. Energy 120:675–685Google Scholar
  40. Lin W, Yang J, Chen B (2011) Temporal and spatial analysis of integrated energy and environment efficiency in China based on a green GDP index. Energies 4(9):1376–1390Google Scholar
  41. Lins MPE, Gomes EG, Mello JCCBS d, Mello AJRSD (2003) Olympic ranking based on a zero sum gains DEA model. Eur J Oper Res 148(2):312–322Google Scholar
  42. Lv LH, Luo H, Wang X (2015) Research on air pollution situation and coal consumption control in China. China Coal 4:9–15 in chineseGoogle Scholar
  43. Macpherson AJ, Principe PP, Mehaffey M (2013) Using Malmquist Indices to evaluate environmental impacts of alternative land development scenarios. Ecol Indic 34(Supplement C):296–303. Google Scholar
  44. Mahalingam B, Orman WH (2018) GDP and energy consumption: a panel analysis of the US. Appl Energy 213:208–218Google Scholar
  45. Morton, G (2007) Activity analysis of production and allocation. By T. C. Koopmans 16(2):243–243Google Scholar
  46. Narayan S, Doytch N, Narayan S, Doytch N, Narayan S, Doytch N (2017) An investigation of renewable and non-renewable energy consumption and economic growth Nexus using industrial and residential energy consumption. Energy Econ 68:160–176Google Scholar
  47. Niu XM (2016) Regional disparities and convergence of China’s carbon emissions efficiency from theperspective of total factor. Doctoral dissertation, Southwestern University of Finance and EconomicsGoogle Scholar
  48. Ohshita S, Price L, Tian ZY (2011) Target allocation methodology for China’s provinces: energy intensity in the 12th five-­year plan - eceee presentation. Paper presented at the Eceee Summer StudyGoogle Scholar
  49. Ouyang Y, Li P (2018) On the Nexus of financial development, economic growth, and energy consumption in China: new perspective from a GMM panel VAR approach. Energy Econ 71:238–252. Google Scholar
  50. Shahbaz M, Hoang THV, Mahalik MK, Roubaud D (2017) Energy consumption, financial development and economic growth in India: new evidence from a nonlinear and asymmetric analysis. Energy Econ 66:199–212Google Scholar
  51. Shahbaz M, Zakaria M, Shahzad SJH, Mahalik MK (2018) The energy consumption and economic growth Nexus in top ten energy-consuming countries: fresh evidence from using the quantile-on-quantile approach. Energy Econ 71:282–301. Google Scholar
  52. Shi G-M, Bi J, Wang J-N (2010) Chinese regional industrial energy efficiency evaluation based on a DEA model of fixing non-energy inputs. Energ Policy 38(10):6172–6179. Google Scholar
  53. Thanassoulis E, Dyson RG (1992) Estimating preferred target input-output levels using data envelopment analysis. Eur J Oper Res 56(1):80–97Google Scholar
  54. Tyteca D (1997) Linear programming models for the measurement of environmental performance of firms—concepts and empirical results. J Prod Anal 8(2):183–197Google Scholar
  55. Wang J, Dong Y, Wu J, Mu R, Jiang H (2011) Coal production forecast and low carbon policies in China. Energ Policy 39(10):5970–5979Google Scholar
  56. Wang ZH, Zeng HL, Wei YM, Zhang YX (2012) Regional total factor energy efficiency: an empirical analysis of industrial sector in China. Appl Energy 97(9):115–123Google Scholar
  57. Wang K, Lu B, Wei YM (2013a) China’s regional energy and environmental efficiency: a range-adjusted measure based analysis. Appl Energy 112(C:1403–1415Google Scholar
  58. Wang K, Yu S, Zhang W (2013b) China’s regional energy and environmental efficiency: a DEA window analysis based dynamic evaluation. Math Comput Model 58(5–6):1117–1127Google Scholar
  59. Wang K, Zhang X, Wei YM, Yu S (2013c) Regional allocation of CO 2 emissions allowance over provinces in China by 2020. Energ Policy 54(3):214–229Google Scholar
  60. Wang D, Nie R, Long R, Shi R, Zhao Y (2018) Scenario prediction of China’s coal production capacity based on system dynamics model. Resour Conserv Recycl 129(Supplement C):432–442. Google Scholar
  61. Wu C, Li Y, Liu Q, Wang K (2013a) A stochastic DEA model considering undesirable outputs with weak disposability. Math Comput Model 58(5–6):980–989Google Scholar
  62. Wu J, An Q, Ali S, Liang L (2013b) DEA based resource allocation considering environmental factors. Math Comput Model 58(5):1128–1137. Google Scholar
  63. Wu J, An Q, Ali S, Liang L (2013c) DEA based resource allocation considering environmental factors. Math Comput Model 58(5–6):1128–1137Google Scholar
  64. Wu J, Zhu Q, An Q, Chu J, Ji X (2016a) Resource allocation based on context-dependent data envelopment analysis and a multi-objective linear programming approach. Comput Ind Eng 101:81–90Google Scholar
  65. Wu J, Zhu Q, Chu J, An Q, Liang L (2016b) A DEA-based approach for allocation of emission reduction tasks. Int J Prod Res 54(18):1–16Google Scholar
  66. Wu J, Zhu Q, Liang L (2016c) CO2 emissions and energy intensity reduction allocation over provincial industrial sectors in China. Appl Energy 166:282–291. Google Scholar
  67. Wu J, Zhu Q, An Q, Chu J, Ji X (2016d) Resource allocation based on context-dependent data envelopment analysis and a multi-objective linear programming approach. Comput Ind Eng 101(Supplement C):81–90. Google Scholar
  68. Yang X, Teng F (2018) The air quality co-benefit of coal control strategy in China. Resour Conserv Recycl 129:373–382.
  69. Yang X, Li H, Wallin F, Wang Z (2017) Impacts of emission reduction target and external costs on provincial natural gas distribution in China. Energy Procedia 105:3326–3331Google Scholar
  70. Ye B, Jiang J, Miao L, Xie D (2017) Interprovincial allocation of China’s national carbon emission allowance: an uncertainty analysis based on Monte-Carlo simulations. Clim Pol 17(4):401–422Google Scholar
  71. Yi WJ, Zou LL, Guo J, Wang K, Wei YM (2011) How can China reach its CO intensity reduction targets by 2020? A regional allocation based on equity and development. Energ Policy 39(5):2407–2415Google Scholar
  72. Zhang J, Wang S (2017) Analysis on coal consumption total control policy stage in China. Eviron Prot 45(7):44–46 (in chinese)Google Scholar
  73. Zhang YJ, Wang AD, Da YB (2014) Regional allocation of carbon emission quotas in China: evidence from the Shapley value method. Energ Policy 74(C:454–464Google Scholar
  74. Zhang LX, Feng YY, Zhao BH (2015a) Disaggregation of energy-saving targets for China's provinces: modeling results and real choices. J Clean Prod 103:837–846Google Scholar
  75. Zhang BL, Luo H, Zhang BL, Luo H, Zhang BL, Luo H (2015b) China’s coal consumption demand under air pollution constraints. Paper presented at the International Conference on Energy, Environmental and Sustainable Ecosystem DevelopmentGoogle Scholar
  76. Zhang Y, Liu C, Li K, Zhou Y (2018) Strategy on China’s regional coal consumption control: a case study of Shandong province. Energ Policy 112(Supplement C):316–327. Google Scholar
  77. Zheng Y J, Qi ZY (2011) Total factor energy efficiency of regions in China: a nonparametric analysis. Paper presented at the International Conference on Management Science and EngineeringGoogle Scholar
  78. Zhou P, Sun ZR, Zhou DQ (2014) Optimal path for controlling CO 2 emissions in China: a perspective of efficiency analysis. Energy Econ 45(C):99–110Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of ManagementChina University of Mining and TechnologyXuzhouChina

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