The Rebound and Fixed Effect of Technological Progress on Energy CO2 Emissions in China

Chunyan Du, Qiang Zhang, Dekai Huang

Abstract


The goal of this study is to develop a model based on panel data from 30 Chinese provinces to assess rebound and fixed effects, as well as policy implications, in order to provide a theoretical foundation for the rapid advancement of low-carbon transformation, which is a necessary result of a new stage in environmental growth. The results of primary analysis are the CO2 emission intensity reached 15.72 kt/million yuan in 2019, which was an great decline of 21.86 kt/million yuan as compared with the intensity in 2001; the average value of technological progress reached 1.017, waved like a U shape. The findings of empirical study show that the amount of energy CO2 emission rebound ranged widely; the average value of the rebound impact was -0.449; a 1% rise of technological progress led a 0.442% CO2 emission decrease, notably in Central and Western China. The results lower the cost of drafting energy policies as well as the decision-making involved and increase the economic advantages and transformation rate of technical breakthroughs in practice.


Keywords


China; CO2 emissions; Energy consumption; Rebound effect; Technological progress

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References


Li B., Gasser T., Ciais P., Piao S., and Feng Z., 2016. The contribution of China’s emissions to global climate forcing. Nature 531(7594): 357-361.

Minx J.C., Baiocchi G., Peters G.P., Weber C.L., Guan D., and Hubacek K.,2011. A "carbonizing dragon": China’s fast growing CO2 emissions revisited. Environmental Science & Technology 45(21): 9144-9153.

Lu L., Wang W.D., Wang M., Zhang C.J., and Lu H.L., 2019. Breakthrough low-carbon technology innovation and carbon emissions: direct and spatial spillover effect. China Population, Resources and Environment 29(5):30-39.

Ehrlich P.R. and J.P. Holdren. 1971. Impact of population growth. Science 171(3977): 1212-1217.

Dietz T. and E.A. Rosa. 1994. Rethinking the environmental impacts of population, affluence, and technology. Human Ecology Review 1: 277-300.

Goulder L.H. and S.H. Schneider. 1999. Induced technological change and the attractiveness of CO2 abatement policies. Resource & Energy Economics 21(3-4): 211-253.

Cole M.A., Elliott R., and Shanshan W.U., 2008. Industrial activity and the environment in China: an industry-level analysis. China Economic Review 19(3): 393-408.

Kumar S. and S. Managi. 2008. Energy price-induced and exogenous technological change: Assessing the economic and environmental outcomes. Resource and Energy Economics 31(4): 0-353.

Wang D.P., Du K.R., and Yan Z.M., 2018. Does low-carbon technology innovation effectively curb carbon emission? Empirical analysis based on PSTR Model. Journal of Nanjing University of Finance and Economics 6: 1-14.

Wei W.X. and F. Yang. 2010.Impact of technology advance on carbon dioxide emission in China. Statistical Research 27(7): 36-44.

Li K.J. and R.X. Qu. 2012. The effect of technological change on China's carbon dioxide emission: an empirical analysis based on the vector error correction model. China Soft Science 6: 51-58.

Li S.S. and L. Niu. 2014. Analysis of the impact of technological progress on carbon dioxide emissions-based on static and dynamic panel data models. Research on Economics and Management 36(19): 115-117.

Kim K.H. , Sul K.H. , Szulejko J.E. , Chambers S. D. , Feng X. , and Lee M.H., 2015. Progress in the reduction of carbon monoxide levels in major urban areas in Korea. Environmental Pollution 207: 420-428.

Wang Z.L. and J. Wei. 2015. An analysis on the effect of the carbon emission reduction of industrial technological progress. Ecological Economy 31(4): 64-67+85.

Yang L.S., Zhu J.P., and Jia Z.J., 2019. Influencing factors and current challenges of CO2 emission reduction in China: a perspective based on technological progress. Economic Research Journal 54(11): 118-132.

Zhong C., Liu Y., Wang M.Y. and Shi Q.L., 2018. Feasibility study on China's potential paths to intensity-based carbon reduction targets. China Population, Resources and Environment 28(10): 18-26.

Liu W.D., Tang Z.P., Xia Y., Han M.Y., and Jiang W.B., 2019.Identifying the key factors influencing Chinese carbon intensity using machine learning, the random forest algorithm, and evolutionary analysis. Acta Geographica Sinica 74(12): 2592-2603.

Wang P., Wu W.S., Zhu B.Z. and Wei Y., 2013. Examining the impact factors of energy-related CO2 emissions using the STIRPAT model in Guangdong Province. Applied Energy 106:65-71.

Yang L.S. and Z. Li. 2017.Technology advance and the carbon dioxide emission in China-Empirical research based on the rebound effect. Energy Policy 101: 150-161.

Shao S., Zhang K., and Dou J.M., 2019.Effects of economic agglomeration on energy saving and emission reduction: theory and empirical evidence from China. Management World 35(1): 36-60+226.

Shen M., Li K.J. and Qu R.X., 2012. Technological progress, economic growth and carbon dioxide emissions: theoretical and empirical studies. The Journal of World Economy 35(7): 83-100.

Li K.Q. and D.D. Ma, 2018.Research on the Relationship between Economic Development, Technological Progress and Agricultural Carbon Emissions Growth in Jiangsu Province. Science and Technology Management Research 38(6): 77-83.

Gong L., Tu H.Z., and Gong C., 2018. Study on the influencing factors of carbon emissions from energy consumption based on STIRPAT model-the case of Yangtze River Delta region. Journal of Industrial Technological Economics 37(8): 95-102.

Luo L.W. and S.S. Li. 2014. Technological progress, industrial structure and China's industrial carbon emissions. Science Research Management 35(6): 8-13.

Zhang W.B. and G.P. Li. 2015. Analysis on carbon emission reduction effect of heterogeneous technological progress. Science of Science and Management of S&T 36(9): 54-61.

Yan Z.M., Deng X.L., and Yang Z.M., 2017. The impact of heterogeneous technological innovation on carbon intensity-a global evidence based on patent statistics. Journal of Beijing Institute of Technology (Social Sciences Edition) 19(1): 20-27.

Brunnermeier S.B. and M.A. Cohenc, 2003. Determinants of environmental innovation in US manufacturing industries. Journal of Environmental Economics and Management 45(2): 278-293.

Jaffe A., Newell R., and Stavins R., 2002. Environmental policy and technological change. Environmental and Resource Economics 22(1): 41-70.

Zhu Q., Peng X.Z., Lu Z.M., and Yu J., 2010. Analysis model and empirical study of impacts from population and consumption on carbon emissions. China Population, Resources and Environment 20(2): 98-102.

Wang Z.H., Yin, F.C., Zhang Y.X., 2012. An empirical research on the influencing factors of regional CO2 emissions: evidence from Beijing. Applied Energy 100: 277-284.

Chen L. and W.T. Hu. 2020. Research on the synergistic effect of financial development, technological progress and carbon emissions-based on VAR analysis of carbon emissions in China's 30 provinces from 2005 to 2017. Study & Exploration 6: 117-124.

Huang J., Liu Q., Cai X.C., Hao Y., Lei H.Y., 2018.The effect of technological factors on China's carbon intensity: new evidence from a panel threshold model. Energy Policy 115: 32-42.

Xie Y.Y., Su Y., Li F. and Su Q., 2022. Threshold effect test of technological improvement on agricultural carbon emissions in Xinjiang. Journal of Zhejiang Agricultural Sciences 63(01): 158-165.

Yang L.S. and Z. Li. 2017. Technology advance and the carbon dioxide emission in China-Empirical research based on the rebound effect. Energy Policy 101: 150-161.

Guo Q.B., Luo K., and Yang W.R., 2020. Measurement of carbon emission rebound effect in the Yangtze River Economic Belt based on technological progress. Statistics & Decision 36(19): 115-117.

Lei Z.D., Chen Z.Z., and Li W.M., 2020. Nonlinear demonstration of agricultural technology progress on agricultural carbon emission efficiency. Statistics & Decision 36(5): 67-71.

Long R.Y., Zhou Y. and Wang Y.H., 2016. Regional comparative studies on effect of technological progress on carbon productivity. Journal of Nanjing University of Aeronautics and Astronautics(Social Sciences) 18(02): 10-14+20.

Tone K., 2001. A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research 130(3): 498-509.

Farrell M.J., 1957. The measurement of productive efficiency. Journal of the Royal Statistical Society 120(3): 253-290.

Zhang G.S. and S.S. Wang. 2014. China's agricultural carbon emission: structure, efficiency, and its determinants. Issues in Agricultural Economy 35(7): 18-26.

Fare R., Grifell-Tatje E., Grosskopf S., and Lovell C.A.K., 1995. Biased technical change and the malmquist productivity index. Microeconomics 99(1): 119-127.

Caves D.W., Christensen L.R., and Diewert W.E., 1982. Multilateral comparisons of output, input, and productivity using superlative index numbers. Economic Journal 92(365): 73-86.

Xu D.Y., Ma R.Y., and Zhu Y.G., 2020. Can technological progress suppress carbon dioxide emissions in China? An empirical study based on panel quantile model. Science and Technology Management Research 16:251-259.

Li X.Z. and X.H. Zhang. 2021. Internet, technical progress and China' s carbon intensity: an empirical analysis based on STIRPAT model. Journal of Hangzhou Dianzi University (Social Sciences) 17(06): 1-8+16.

Lv K.J. and Y.X. He. 2021. Economic agglomeration, technological progress, and carbon emission intensity in Yangtze River Delta urban agglomeration: an empirical study-based on spatial econometric and intermediary effect. Ecological Economy 37(01): 13-20.

Tian Y. and W.H. Yin 2021. Does technological progress promote carbon emission reduction of agricultural energy? Test based on rebound effect and spatial spillover effect. Reform 12: 45-58.