Understanding drivers of energy efficiency changes in China

Applied Energy

Volumn 184, Issue , 2016, Pages 1196-1206

  Du, Huibin ^a,b,c   Matisoff, Daniel C ^c   Wang, Yangyang ^a   Liu, Xi ^a  

^a TIANJIN UNIVERSITY   (China)

^b Chinese Academy of Sciences   (China)

^c GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Data envelopment analysis; Energy efficiency diffusion; Spatial panel data

Indexed keywords

COSTS; DATA ENVELOPMENT ANALYSIS; INVESTMENTS;

EFFICIENCY IMPROVEMENT; ENERGY EFFICIENCY IMPROVEMENTS; GOVERNMENT INTERVENTION; PANEL DATA; REGIONAL CLUSTERING; RESEARCH AND DEVELOPMENT; SPATIAL PANEL DATA MODELS; TOTAL FACTOR ENERGY EFFICIENCIES;

ENERGY EFFICIENCY;

DATA ENVELOPMENT ANALYSIS; ENERGY EFFICIENCY; ENERGY MARKET; INDUSTRIAL LOCATION; PANEL DATA; PRICE DETERMINATION; SPATIAL ANALYSIS; STATE ROLE;

CHINA;

EID: 84970003131     PISSN: 03062619     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apenergy.2016.05.002     Document Type: Article

References (102)

1. [1] Zhou, N., Levine, M.D., Price, L., Overview of current energy-efficiency policies in China. Energy Policy 38:11 (2010), 6439–6452.
2. 2 emissions from fuel combustion. 2009, OECD/IEA, Paris.
3. [3] Ottmar, E., King coal and the queen of subsidies. Science 349:6254 (2015), 1286–1287.
4. [4] Liu, Z., Guan, D.B., Moore, S., Lee, H., Su, J., Zhang, Q., Climate policy: steps to China's carbon peak. Nature 522 (2015), 279–281.
5. [5] Wang, Y., Wang, Y., Zhou, J., Zhu, X.D., Lu, G.F., Energy consumption and economic growth in China: a multivariate causality test. Energy Policy 39:7 (2011), 4399–4406.
6. [6] Chenery, H., Robinson, S., Syrquin, M., Industrialization and growth: a comparative study. 1986, Oxford University Press, New York.
7. [7] de Coninck, H., Fischer, C., Newell, R.G., Ueno, T., International technology-oriented agreements to address climate change. Energy Policy 36:1 (2008), 335–356.
8. [8] Sjöholm, F., Technology gap, competition and spillovers from direct foreign investment: evidence from establishment data. J Develop Stud 36:1 (1999), 53–73.
9. [9] Worrell, E., van Berkel, R., Zhou, F.Q., Menke, C., Schaeffer, R., Williams, R.O., Technology transfer of energy efficient technologies in industry: a review of trends and policy issues. Energy Policy 29:1 (2001), 29–43.
10. [10] Blackman, A., Wu, X., Foreign direct investment in China's power sector: trends, benefits and barriers. Energy Policy 27 (1999), 695–711.
11. [11] Fisher-Vanden, K., Jefferson, G.H., Ma, J.K., Xu, J.Y., Technology development and energy productivity in China. Energy Econom 28 (2006), 690–705.
12. [12] Dechezleprêtre, A., Glachant, M., Ménière, Y., The clean development mechanism and the international diffusion of technologies: an empirical study. Energy Policy 36:4 (2008), 1273–1283.
13. [13] Schneider, M., Holzer, A., Hoffmann, V.H., Understanding the CDM's contribution to technology transfer. Energy Policy 36 (2008), 2930–2938.
14. [14] Adkins, L.C., Moomaw, R.L., Savvides, A., Institutions, freedom, and technical efficiency. South Econ J, 69(1), 2002, 92.
15. [15] Jaffe, A.B., Stavins, R.N., The energy-efficiency gap, what does it mean?. Energy Policy 22:10 (1994), 804–810.
16. [16] Dietz, T., Leshko, C., McCrightc, A.M., Politics shapes individual choices about energy efficiency. PNAS 110:23 (2013), 9191–9192.
17. [17] Dietz, T., Narrowing the US energy efficiency gap. PNAS 107:37 (2010), 16007–16008.
18. [18] Abrahamse, W., Steg, L., Vlek, C., Rothengatter, T., A review of intervention studies aimed at household energy conservation. J Environ Psychol 25:3 (2005), 273–291, 10.1016/j.jenvp.2005.08.002.
19. [19] Griliches, Z.V.I., Hybrid corn: an exploration in the economics of technological change. Econometrica (pre-1986), 25(4), 1957 501-501.
20. [20] Noonan, D.S., Chiang Hsieh, L.H., Matisoff, D.C., Spatial effects in energy-efficient residential HVAC technology adoption. Environ Behav 45:4 (2013), 476–503.
21. [21] Osbaldiston, R., Schott, J.P., Environmental sustainability and behavioral science: meta-analysis of pro-environmental behavior experiments. Environ Behav 44:2 (2012), 257–299.
22. [22] Stern, P.C., Contributions of psychology to limiting climate change. Am Psychol 66:4 (2011), 303–314.
23. [23] Fischer, C., Newell, R., Environmental and technology policies for climate change and renewable energy. 2003, Resources for the Future, Washington, DC.
24. [24] Gillingham, K., Newell, R.G., Palmer, K., Energy efficiency economics and policy. Annu Rev Resour Econ 1:1 (2009), 597–620.
25. [25] Jaffe, A.B., Newell, R.G., Stavins, R.N., Energy efficient technologies and climate change policies: issues and evidence. 1999, Resources for the Future, Washington, DC, 21.
26. [26] Jaffe, A.B., Stavins, R.N., The energy paradox and the diffusion of conservation technology. Resour Energy Econ 16 (1994), 91–122.
27. [27] Trianni, A., Cagno, E., Farné, S., Barriers, drivers and decision-making process for industrial energy efficiency: A broad study among manufacturing small and medium-sized enterprises. Appl Energy 162 (2016), 1537–1551.
28. [28] Cagno, E., Trianni, A., Exploring drivers for energy efficiency within small- and medium-sized enterprises: first evidences from Italian manufacturing enterprises. Appl Energy 104 (2013), 276–285.
29. [29] Thollander, P., Backlund, S., Trianni, A., Cagno, E., Beyond barriers – a case study on driving forces for improved energy efficiency in the foundry industries in Finland, France, Germany, Italy, Poland, Spain, and Sweden. Appl Energy 111 (2013), 636–643.
30. [30] May, G., Barletta, I., Stahl, B., Taisch, M., Energy management in production: a novel method to develop key performance indicators for improving energy efficiency. Appl Energy 149 (2015), 46–61.
31. [31] Welch, E., Barnum, D., Joint environmental and cost efficiency analysis of electricity generation. Ecol Econ 68 (2009), 2336–2343.
32. [32] Wang, K., Wei, Y.M., China's regional industrial energy efficiency and carbon emissions abatement costs. Appl Energy 130 (2014), 617–631.
33. [33] Leibenstein, H., Allocative efficiency and X-efficiency. Am Econ Rev 56 (1966), 392–415.
34. [34] Leibenstein, H., Maital, S., Empirical estimation and partitioning of X-inefficiency: a data envelopment approach. Am Econ Rev 82:2 (1992), 428–433.
35. [35] Zhou, P., Ang, B., Poh, K., A survey of data envelopment analysis in energy and environmental studies. Eur J Oper Res 189:1 (2008), 1–18.
36. [36] Hu, J., Wang, S., Total-factor energy efficiency of regions in China. Energy Policy 34:17 (2006), 3206–3217.
37. [37] Renshaw, E.F., Energy efficiency and the slump in labor productivity in the USA. Energy Econ 3:1 (1981), 36–42.
38. [38] Wilson, B., Trieu, L.H., Bowen, B., Energy efficiency trends in Australia. Energy Policy 22:4 (1994), 287–295.
39. [39] Hu, J.L., Lio, M.C., Kao, C.H., Lin, Y.L., Total-factor energy efficiency for regions in Taiwan. Energy Sources Part B 7:3 (2012), 292–300.
40. [40] Honma, S., Hu, J., Total-factor energy efficiency of regions in Japan. Energy Policy 36:2 (2008), 821–833.
41. [41] Zhang, X., Cheng, X., Yuan, J., Gao, X., Total-factor energy efficiency in developing countries. Energy Policy 39:2 (2011), 644–650.
42. [42] Rao, X., Wu, J., Zhang, Z.Y., Liu, B., Energy efficiency and energy saving potential in China: an analysis based on slacks-based measure model. Comput Ind Eng 63:3 (2012), 578–584.
43. [43] Wang, Z.H., Zeng, H.L., Wei, Y.M., Zhang, Y.X., Regional total factor energy efficiency: an empirical analysis of industrial sector in China. Appl Energy 97 (2012), 11–123.
44. [44] Honma, S., Hu, J.L., Industry-level total-factor energy efficiency in developed countries: a Japan-centered analysis. Appl Energy 119 (2014), 67–78.
45. [45] Färe, R., Grosskopf, S., Lindgren, B., Roos, P., Productivity changes in Swedish pharmacies 1980–1989. J Prod Anal 3 (1992), 85–101.
46. [46] Färe, R., Grosskopf, S., Lovell, C., Production frontiers. 1994, Cambridge University Press, Cambridge.
47. [47] Malmquist, S., Index numbers and indifference surfaces. Trabajos Estatistica 4 (1953), 209–242.
48. [48] Honma, S., Hu, J., Total-factor energy productivity growth of regions in Japan. Energy Policy 37:10 (2009), 3941–3950.
49. [49] Chang, T., Hu, J., Total-factor energy productivity growth, technical progress, and efficiency change: an empirical study of China. Appl Energy 87:10 (2010), 3262–3270.
50. [50] Färe, R., Grosskopf, S., Pasurka, C., Environmental production functions and environmental directional distance functions. Energy 32:7 (2007), 11055–11066.
51. [51] Ho, C.T.B., Performance measurement using data envelopment analysis and financial statement analysis. Int J Oper Res 2:1 (2007), 26–38.
52. [52] Burnett, R.D., Hansen, D.R., Ecoefficiency: defining a role for environmental cost management. Acc Organ Soc 33:6 (2008), 551–581.
53. [53] Yang, H., Pollitt, M., Incorporating both undesirable outputs and uncontrollable variables into DEA: the performance of Chinese coal-fired power plants. Eur J Oper Res 197:3 (2009), 1095–1105.
54. [54] Welch, E., Barnum, D., Joint environmental and cost efficiency analysis of electricity generation. Ecol Econ 68:8–9 (2009), 2336–2343.
55. [55] Wang, K., Lu, B., Wei, Y.M., China's regional energy and environmental efficiency: a range-adjusted measure based analysis. Appl Energy 112 (2013), 1403–1415.
56. [56] Wang, K., Wei, Y.M., Zhang, X., Energy and emissions efficiency patterns of Chinese regions: a multi-directional efficiency analysis. Appl Energy 104 (2013), 105–116.
57. [57] Tone, K., Dealing with undesirable outputs in DEA: a slacks-based measure (SBM) approach. Oper Res Soc Jpn, 2004, 44–45.
58. [58] Charnes, A., Cooper, W.W., Rhodes, E., Measuring efficiency of decision making units. Eur J Oper Res 2 (1978), 429–444.
59. [59] Banker, R.D., Charnes, A., Cooper, W.W., Models for estimation of technical and scale inefficiencies in data envelopment analysis. Manage Sci 30 (1984), 1078–1092.
60. [60] Cooper, W.W., Seiford, L.M., Tone, T., Introduction to data envelopment analysis and its uses: with DEA-solvers software and references. 2006, Springer, New York.
61. [61] Guo, X., Zhu, L., Fan, Y., Xie, B., Evaluation of potential reductions in carbon emissions in Chinese provinces based on environmental DEA. Energy Policy 39:5 (2011), 2352–2360.
62. [62] Tone, K., A slacks-based measure of efficiency in data envelopment analysis. Eur J Oper Res 130:3 (2001), 498–509.
63. [63] Charnes, A., Cooper, W.W., Preface to topics in data envelopment analysis. Ann Oper Res 2 (1985), 59–94.
64. [64] Charnes, A., Cooper, W.W., Seiford, L.M., Extension to DEA models. Charnes, A., Cooper, W.W., Lewin, A.Y., Seiford, L.M., (eds.) Data envelopment analysis: theory, methodology and applications, 1994, Kluwer Academic Publishers, Norwell.
65. [65] Yue, P., Data envelopment analysis and commercial bank performance: a primer with applications to Missouri banks. Federal Reserve Bank St. Louis Rev 74 (1992), 31–45.
66. [66] Halkos, G.E., Tzeremes, N.G., Exploring the existence of Kuznets curve in countries’ environmental efficiency using DEA window analysis. Ecol Econ 68 (2009), 2168–2176.
67. [67] Webb, R.M., Levels of efficiency in UK retail banks: a DEA window analysis. Int J Econ Business 10:3 (2003), 305–322.
68. [68] Charnes, A., Cooper, W.W., Lewin, A.Y., Seiford, L.M., Data envelopment analysis: theory, methodology, and application. 1994, Kluwer Academic Publishers, Norwell.
69. [69] Asmild, M., Paradi, C.V., Aggarwall, V., Schaffnit, C., Combining DEA window analysis with the Malmquist index approach in a study of the Canadian banking industry. J Prod Anal 21 (2004), 67–89.
70. [70] Shan, H.J., Re-estimating the capital stock of China: 1952–2006. J Quanti Tech Econ 10 (2008), 17–31 [in Chinese].
71. [71] IPCC, IPCC guidelines for national greenhouse gas inventories – a primer. Prepared by the National Greenhouse Gas Inventories Programme. 2006, Institute for Global Environmental Strategies, Japan, 2006.
72. [72] Zha, Y., Zhao, L., Bian, Y., Measuring regional efficiency of energy and carbon dioxide emissions in China: a chance constrained DEA approach. Comput Oper Res 66 (2016), 351–361.
73. [73] Huang, Y., Todd, D., The energy implications of Chinese regional disparities. Energy Policy 38:11 (2010), 7531–7538.
74. [74] Keller, W., Geographic localization of international technology diffusion. Am Econ Rev 92:1 (2002), 120–142.
75. [75] Le Gallo, J., Ertur, C., Exploratory spatial data analysis of the distribution of regional per capita in Europe, 1980–1995. Papers Regional Sci 82 (2003), 175–201.
76. [76] Patacchini, E., Rice, P., Geography and economic performance: exploratory spatial data analysis for Great Britain. Regional Stud 41:4 (2007), 489–508.
77. [77] Ramajo, J., Márquez, M.A., Hewings, G.J.D., Salinas, M.M., Spatial heterogeneity and interregional spillovers in the European Union: do cohesion policies encourage convergence across regions?. Eur Econ Rev 52:3 (2008), 551–567.
78. [78] Rey, S.J., Montouri, B.D., US regional income convergence: a spatial econometric perspective. Regional Stud 33:2 (1999), 143–156.
79. [79] Zheng, Y., Qi, J.H., Chen, X.L., The effect of increasing exports on industrial energy intensity in China. Energy Policy 39:5 (2011), 2688–2698.
80. [80] Anselin, L., Gallo, J., Jayet, H., Spatial panel econometrics. Econometr Panel Data 46 (2008), 625–660.
81. [81] Gibbons, S., Overman, H.G., Mostly pointless spatial econometrics?. J Regional Sci 52 (2012), 172–191.
82. [82] Anselin, L., Bera, A.K., Spatial dependence in linear regression models with an introduction to spatial econometrics. Hullah, A., Gelis, D.E.A., (eds.) Handbook of applied economic statistics, 1998, Marcel Deker, New York, 237–290.
83. [83] Elhorst, J., Matlab software for spatial panels. The 4th world conference of the spatial econometrics association (SEA), Chicago, US, 2010.
84. [84] Elhorst, J.P., Spatial panel data models, spatial econometrics. 2014, Springer, Berlin, Heidelberg.
85. [85] Lee, L.F., Yu, J., Estimation of spatial autoregressive panel data models with fixed effects. J Econometr 154 (2010), 165–185.
86. [86] Roberts, J.T., Grimes, P.E., Carbon intensity and economic development 1962–1991: a brief exploration of the environmental Kuznets curve. World Dev 25 (1997), 191–198.
87. [87] Zhang, Z.X., Asian energy and environmental policy: promoting growth while preserving the environment. Energy Policy 36 (2008), 3905–3924.
88. [88] Zhao, X.L., Ma, C.B., Hong, D.Y., Why did China's energy intensity increase during 1998–2006: decomposition and policy analysis. Energy Policy 38:3 (2010), 1379–1388.
89. [89] Zhao, X., Yang, R., Ma, Q., China's total factor energy efficiency of provincial industrial sectors. Energy 65 (2014), 52–61.
90. [90] Yang, M., China's energy efficiency target 2010. Energy Policy 36 (2008), 561–570.
91. [91] Linares, P., Labandeira, X., Energy efficiency: economics and policy. J Econ Surv 24 (2010), 573–592.
92. [92] Li, H., Shi, J., Energy efficiency analysis on Chinese industrial sectors: an improved Super-SBM model with undesirable outputs. J Cleaner Prod 65 (2014), 97–107.
93. [93] Bi, G., Song, W., Zhou, P., Liang, L., Does environmental regulation affect energy efficiency in China's thermal power generation? Empirical evidence from a slacks-based DEA model. Energy Policy 66 (2014), 537–546.
94. [94] Krammer, S., Do good institutions enhance the effect of technological spillovers on productivity? Comparative evidence from developed and transition economies. Technol Forecast Soc Chang 94 (2015), 133–154.
95. [95] Kemfert, C., Induced technological change in a multi-regional, multi-sectoral, integrated assessment model (WIAGEM). Ecol Econ 54:2–3 (2005), 293–305.
96. [96] Sandén, B.A., Azar, C., Near-term technology policies for long-term climate targets – economy wide versus technology specific approaches. Energy Policy 33:12 (2005), 1557–1576.
97. [97] Cui, Q., Kuang, H., Wu, C., Li, Y., The changing trend and influencing factors of energy efficiency: the case of nine countries. Energy 64 (2014), 1026–1034.
98. [98] Wang, Z., Feng, C., Zhang, B., An empirical analysis of China's energy efficiency from both static and dynamic perspectives. Energy 74 (2014), 322–330.
99. [99] Allcott, H., Mullainathan, S., Behavior and energy policy. Science 327 (2010), 1204–1205.
100. [100] Bollinger, B., Gillingham, K., Peer effects in the diffusion of solar photovoltaic panels. Market Sci 31 (2012), 900–912.
101. [101] Sexton, S.E., Sexton, A.L., Conspicuous conservation: the prius effect and willingness to pay for environmental bona fides. 2011, University of California, Berkeley.
102. [102] McMillen, D.P., Perspectives on spatial econometrics: linear smoothing with structured models. J Regional Sci 52 (2012), 192–209.