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Energy Efficiency and CO2 Emission Abatement Technology

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Low-Carbon Consumption in China: Residential Behavior, Corporate Practices and Policy Implication

Abstract

With the rapid pace of domestic industrialization and urbanization, the energy consumption of China has grown so fast that it has become the second-largest energy consuming and CO2 emitting country in the world. According to the prediction of International Energy Agency (IEA), China and India will be the main drivers of non-OECD primary energy demand growth until 2030, and China will account for 39% of global increase in primary energy use, whose share in total demand will jump from 16% in 2007 to 23% in 2030. Furthermore, there is no reason to feel optimistic about the energy intensity of China. According to the latest data from World Bank, GDP per unit of energy use of China in 2008 was only $3.6 per kg, while the United States had $5.8 per kg and Japan reached even as high as $8.1 per kg, which means that China consumes too much energy to support its economic growth, and economic efficiency of energy use is rather low.

This chapter takes the following literature for reference:

Wang Z, Zeng H, Wei Y, et al. 2012. Regional total-factor energy efficiency: an empirical analysis of industrial sector in China. Applied Energy, 97(9): 115–123.

Wang Z, Chao F, Zhang B. 2014. An empirical analysis of China’s energy efficiency from both static and dynamic perspectives. Energy, 74(C): 322–330.

Wang Z, Yang Z M, Zhang Y X, et al. 2012. Energy technology patents—CO2, emissions nexus: an empirical analysis from China. Energy Policy, 42(2): 248–260.

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References

  • Andrews-Speed, P. 2009. China’s ongoing energy efficiency drive: origins progress and prospects. Energy Policy 37: 1331–1344.

    Article  Google Scholar 

  • Ang, J.B. 2009. CO2 emissions, research and technology transfer in China. Ecological Economics 68: 2658–2665.

    Google Scholar 

  • Arellano, M., and S. Bond. 1991. Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Econometric Studies 58: 277–297.

    Google Scholar 

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

    Google Scholar 

  • Bernstein, P.M., W.D. Montgomery, and S.D. Tuladhar. 2006. Potential for reducing carbon emissions from non-Annex B countries through changes in technology. Energy Economics 28: 742–762.

    Google Scholar 

  • Blundell, R., and S. Bond. 1998. Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics 87: 115–143.

    Google Scholar 

  • Chang, T.P, and J.L. Hu. 2010. Total-factor energy productivity growth, technical progress, and efficiency change: an empirical study of China. Applied Energy 87 (10): 3262–3270.

    Google Scholar 

  • Charnes, A., W.W. Cooper, and E. Rhodes. 1978. Measuring the efficiency of decision making units. European Journal of Operational Research 2: 429–444.

    Article  MathSciNet  Google Scholar 

  • Charnes, A., W.W. Cooper, and B. Golany, et al. 1985. Foundations of data envelopment analysis for Pareto-Koopmans efficient empirical production functions. Journal of Econometrics 30 (1–2): 91–107.

    Google Scholar 

  • Chen, Q., C. Kang, and Q. Xia, et al. 2011. Preliminary exploration on low-carbon technology roadmap of China’s power sector. Energy 36: 1500–1512.

    Google Scholar 

  • Färe, R., S. Grosskopf, and M. Norris, et al. 1994. Productivity growth, technical progress, and efficiency change in industrialized countries. American Economic Review 84 (1): 66–83.

    Google Scholar 

  • Fischer, C., and G. Newell. 2008. Environmental and technology policies for climate mitigation. Journal of Environmental Economics and Management 55: 142–162.

    Google Scholar 

  • Fisher-Vanden, K., and I.S. Wing. 2008. Accounting for quality: issues with modeling the impact of R&D on economic growth and carbon emissions in developing economies. Energy Economics 30 (6): 2771–2784.

    Google Scholar 

  • Garrone, P., and L. Grilli. 2010. Is there a relationship between public expenditures in energy R&D and carbon emissions per GDP? An empirical investigation. Energy Policy 38: 5600–5613.

    Google Scholar 

  • Gnansounou, E., J. Dong, and D. Bedniaguine. 2004. The strategic technology options for mitigating CO2 emissions in power sector: assessment of Shanghai electricity-generating system. Ecological Economics 50 (1–2): 117–133.

    Google Scholar 

  • Gonsense. 2011. The energy industry research report. http://www.gosense.cn/baogao_neirong_14.html [2011-10-21].

  • Hao, H., and J.F. Zong. 2007. The Analysis of System and Methods of Evaluation. Beijing: Economic Science Press.

    Google Scholar 

  • Herring, H. 1999. Does energy efficiency save energy? The debate and its consequences. Applied Energy 63 (3): 209–226.

    Google Scholar 

  • Hu, J.L., and S.C. Wang. 2006. Total-factor energy efficiency of regions in China. Energy Policy 34: 3206–3217.

    Article  Google Scholar 

  • Hu, C.Z., and X.J. Huang. 2008. Characteristics of carbon emission in China and analysis on its cause. China Population, Resources and Environment 18 (3): 38–42.

    Google Scholar 

  • Hsiao, C. 2003. Analysis of panel data. Cambridge: Cambridge University Press.

    Google Scholar 

  • IPCC. 2006a. IPCC guidelines for national greenhouse gas inventories. Hayama, Japan: The Institute for Global Environmental Strategies.

    Google Scholar 

  • IPCC. 2006. IPCC guidelines for national greenhouse gas inventories. http://www.ipcc.ch/ipccreports/methodology-reports.htm.

  • IPCC. 2006. IPCC guidelines for national greenhouse gas inventories. http://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/2_Volume2/V2_2_Ch2_ Stationary Combustion.pdf.

  • IPCC. 2006. IPCC guidelines for national greenhouse gas inventories. Hayama: The Institute for Global Environmental Strategies.

    Google Scholar 

  • Jenne, C.A., and R.K. Cattell. 1983. Structural change and energy efficiency in industry. Energy Economics 5 (2): 114–123.

    Google Scholar 

  • Johnstone, N., I. Haščič, and D. Popp. 2010. Renewable energy policies and technological innovation: evidence based on patent counts. Environmental and Resource Economics 45: 133–155.

    Google Scholar 

  • Kaya, Y. 1990. Impact of carbon dioxide emission control on GNP growth: interpretation of proposed scenarios. Paris: IPCC Energy and Industry Subgroup, Response Strategies Working Group.

    Google Scholar 

  • Li, L.S., and Y. Zhou. 2006. Can the advance of technology improve energy efficiency? An empirical study based on Chinese industry department. Management World 10 (10): 82–91.

    Google Scholar 

  • Li, S.X., and J.H. Cheng. 2008. Study on the energy efficiency of China and its determinants. Statistical Research 25 (8): 18–27.

    Google Scholar 

  • Liu, F., and Y. Sun. 2008. Effect of innovation, industrial change on energy consumption. China Population, Resources and Environment 18: 108–113.

    Google Scholar 

  • Liu, J., and X.T. Zheng. 2008. Patent system: the incentive role of technological innovation and optimization technique. Journal of Hubei Administration Institute 1: 58–60.

    Google Scholar 

  • Lüthi, S., and T. Prässler. 2011. Analyzing policy support instruments and regulatory risk factors for wind energy deployment—a developers’ perspective. Energy Policy 39 (9): 4876–4892.

    Google Scholar 

  • Ma, C., C. Gao, and H. Shi. 2003. Energy R&D investment in China. Energy Research and Utilization 3: 3–6.

    Google Scholar 

  • Margolis, R.M., and D.M. Kammen. 1999. Evidence of under-investment in energy R&D in the United States and the impact of federal policy. Energy Policy 27: 575–584.

    Google Scholar 

  • Miketa, A., and P. Mulder. 2005. Energy productivity across developed and developing countries in 10 manufacturing sectors: patterns of growth and convergence. Energy Economics 27 (3): 429–453.

    Google Scholar 

  • Oh, D.H., and A. Heshmati. 2010. A sequential Malmquist-Luenberger productivity index: environmentally sensitive productivity growth considering the progressive nature of technology. Energy Economics 32 (6): 1345–1355.

    Google Scholar 

  • Pastor, J.T., and C.A.K. Lovell. 2005. A global Malmquist productivity index. Economics Letters 80 (3): 266–271.

    Google Scholar 

  • Patterson, M.G. 1996. What is energy efficiency? Concepts, indicators and methodology issues. Energy Policy 24 (5): 377–390.

    Article  Google Scholar 

  • Patterson, M.G., M.A. West, and V.J. Shackleton, et al. 2005. Validating the organizational climate measure: links to managerial practices, productivity and innovation. Journal of Organizational Behavior 26 (4): 379–408.

    Google Scholar 

  • Popp, D. 2006. International innovation and diffusion of air pollution control technologies: the effects of NOx and SO2 regulation in the US, Japan, and Germany. Journal of Environmental Economics and Management 51 (1): 46–71.

    Google Scholar 

  • Popp, D., I. Hascic, and N. Medhi. 2009a. Technology and the diffusion of renewable energy. Draft, the Maxwell School, Syracuse University, Syracuse, NY.

    Google Scholar 

  • Popp, D., R.G. Newell, and A.B. Jaffe. 2009b. Energy, the environment, and technological change. Cambridge: National Bureau of Economic Research.

    Google Scholar 

  • Popp, D., I. Hascic, and N. Medhi. 2011. Technology and the diffusion of renewable energy. Energy Economics 33: 648–662.

    Google Scholar 

  • Renshaw, E.F. 1981. Energy efficiency and the slump in labour productivity in the US. Energy Economics 3 (1): 36–42.

    Google Scholar 

  • Sari, R., and U. Soytas. 2009. Are global warming and economic growth compatible? Evidence from five OPEC countries? Applied Energy 86 (10): 1887–1893.

    Google Scholar 

  • Sorrell, S., J. Dimitropoulos, and M. Sommerville. 2009. Empirical estimates of the direct rebound effect: a review. Energy Policy 37: 1356–1371.

    Article  Google Scholar 

  • Teng Y. 2009. Indigenous R&D, technology import and energy intensity: an empirical analysis in panel data of the industrial sectors in China. Industrial Economics Research 5 (42): 1–6.

    Google Scholar 

  • Tulkens, H., and P.V. Eeckaut. 1995. Non-parametric efficiency, progress and regress measures for panel data: methodological aspects. European Journal of Operational Research 80 (3): 474–499.

    Google Scholar 

  • van Vuuren, D., F. Zhou, and B. de Vries, et al. 2003. Energy and emission scenarios for China in the 21st century—exploration of baseline development and mitigation options. Energy Policy 31 (4): 369–387.

    Google Scholar 

  • Wang, Q., and Y. Chen. 2010. Status and outlook of China’s free-carbon electricity. Renewable and Sustainable Energy Reviews 14 (3): 1014–1025.

    Google Scholar 

  • Wang, W., and X. Huang. 2008. Decompositions model of factors and substantiation analysis of area carbon dioxide emissions intensity change—take Jiangsu Province as an example. Ecological Economy 12: 32–35.

    Google Scholar 

  • Wang, K., B. Lu, and Y.M. Wei. 2013. China’s regional energy and environment efficiency: a range-adjusted mearsure based analysis. Applied Energy 112: 1403–1415.

    Article  Google Scholar 

  • Wang, K., Y.M. Wei, and X. Zhang. 2012. A comparative analysis of china’s regional energy and emission performance: which is the better way to deal with undesirable outputs? Energy Policy 46: 574–584.

    Google Scholar 

  • Wei, W., and F. Yang. 2010. Impact of technology advance on carbon dioxide emission in China. Statistical Research 27: 36–44.

    Google Scholar 

  • Wilson, J.K. 1944. Over five hundred reasons for abandoning the cross-inoculation groups of the legumes. Soil Science 58 (1): 61–70.

    Google Scholar 

  • Wu, Q., and C. Wu. 2009. Research on evaluation model of energy efficiency based on DEA. Journal of Management Sciences 22 (1): 103–112.

    Google Scholar 

  • Xu, G. 2010. The research on the relationship for energy consumption, carbon emissions and economic growth in China. Wuhan: Hua Zhong University of Science and Technology.

    Google Scholar 

  • Xu, G., Z. Liu, and Z. Jiang. 2006. Decomposition model and empirical study of carbon emissions for China, 1995–2004. China Population, Resources and Environment 6: 158–161.

    Google Scholar 

  • Yi, W., L. Zou, and J. Guo, et al. 2011. How can China reach its CO2 intensity reduction targets by 2020? A regional allocation based on equity and development. Energy Policy 39: 2407–2415.

    Google Scholar 

  • Yu, F., and J. Qi. 2007. Decomposing and analyzing the environmental pollution under the opening economic circumstances: based on the panel data of provinces—autonomous regions and municipals of China from 1990 to 2003. Statistical Research 24: 47–53.

    Google Scholar 

  • Zhang, J., G.Y. Wu, and J.P. Zhang. 2004. The estimation of China’s provincial capital stock: 1952–2000. Economic Research Journal 10: 35–44. (In Chinese).

    Google Scholar 

  • Zhao, M., W. Zhang, and L. Yu. 2009. Carbon emission from energy consumption in Shanghai. Research of Environmental Sciences 22 (8): 984–989.

    Google Scholar 

  • Zhou, P., B.W. Ang, and J.Y. Han. 2010. Total factor carbon emission performance: a Malmquist index analysis. Energy Economics 32: 194–201.

    Google Scholar 

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Authors and Affiliations

  1. School of Management and Economics, Beijing Institute of Technology, Beijing, China

    Zhaohua Wang

  2. School of Management and Economics, Beijing Institute of Technology, Beijing, China

    Bin Zhang

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  1. Zhaohua Wang
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  2. Bin Zhang
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Wang, Z., Zhang, B. (2020). Energy Efficiency and CO2 Emission Abatement Technology. In: Low-Carbon Consumption in China: Residential Behavior, Corporate Practices and Policy Implication. Springer, Singapore. https://doi.org/10.1007/978-981-15-2792-0_7

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  • DOI: https://doi.org/10.1007/978-981-15-2792-0_7

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