Determinants of industrial carbon dioxide emissions growth in Shanghai: A quantile analysis

Abstract The emergence of Shanghai as an international city necessitate that the industrial sector should be less polluting as much as possible because pollution will alienate businesses, professionals and the relocation or setting up of corporate offices that enhances green economic growth. To ensure that policy targeting industrial emissions are achieved, standard mean based models underperforms, so exploring a quantile framework, varying effects of economic development, energy structure, energy efficiency, industrial structure and urbanization across the levels of carbon dioxide emissions were analyzed for the industrial sector of Shanghai where estimates both described the distribution of carbon dioxide emissions and their marginal effects on different quantiles. Explanatory variables were emission friendly and did not exert same effects on the acceleration of carbon emissions. Averagely, the coefficient of GDP, ENS, ENE, IND, and URB at Q(0.50) = 0.06306, 0.39285, 0.07375, 0.52193 and 0.48357 respectively, implying that a unit increase in these economic indicators will increase carbon dioxide emissions by 6.3, 39.3, 7.3, 52.2 and 48.4 percent approximately and the model goodness of fit was 87.8 percent. Urbanization had the greatest impact on carbon dioxide emissions across all quantiles, showing that it is a major driving force that increase carbon emissions, followed by energy structure, industrial structure, economic growth and energy efficiency. An optimized energy efficiency will be the best mitigating variable that could curb carbon dioxide emissions, then followed by energy structure. Favorable economic policies from the government have contributed enormously to the development of the industrial sector, however, additional policies are needed to steer the city from an investment and export growth model to consumption (services).

[1]  Chunping Xie,et al.  Estimation on oil demand and oil saving potential of China's road transport sector , 2013 .

[2]  Ernst Worrell,et al.  Potentials for energy efficiency improvement in the US cement industry , 2000 .

[3]  Victor Chernozhukov,et al.  Quantile regression , 2019, Journal of Econometrics.

[4]  Boqiang Lin,et al.  Impact of energy technology patents in China: Evidence from a panel cointegration and error correction model , 2016 .

[5]  Boqiang Lin,et al.  Green development determinants in China: A non-radial quantile outlook , 2017 .

[6]  Farihana Shahari,et al.  The interdependent relationship between sectoral productivity and disaggregated energy consumption in Malaysia: Markov Switching approach , 2017 .

[7]  Shuai Shao,et al.  How to achieve the 2030 CO2 emission-reduction targets for China's industrial sector: Retrospective decomposition and prospective trajectories , 2017 .

[8]  Boqiang Lin,et al.  Influencing factors on carbon emissions in China transport industry. A new evidence from quantile regression analysis , 2017 .

[9]  Andrew Schiller,et al.  The vulnerability of global cities to climate hazards , 2007 .

[10]  Lei Chen,et al.  Optimization of urban industrial structure under the low-carbon goal and the water constraints: a case in Dalian, China , 2016 .

[11]  Wei Sun,et al.  Regional characteristics of CO2 emissions from China's power generation: affinity propagation and refined Laspeyres decomposition , 2017 .

[12]  N. Kaza Understanding the spectrum of residential energy consumption: A quantile regression approach , 2010 .

[13]  R. Koenker,et al.  Computing regression quantiles , 1987 .

[14]  Michael L. Polemis,et al.  Empirical assessment of the determinants of road energy demand in Greece , 2006 .

[15]  Duc Khuong Nguyen,et al.  Energy prices and CO2 emission allowance prices: A quantile regression approach , 2014 .

[16]  Ali Hasanbeigi,et al.  Moving beyond equipment and to systems optimization: techno-economic analysis of energy efficiency potentials in industrial steam systems in China , 2016 .

[17]  Linli Cui,et al.  Urbanization and its environmental effects in Shanghai, China , 2012 .

[18]  Steven Stoft,et al.  Technical efficiency, production functions and conservation supply curves , 1995 .

[19]  Ali Hasanbeigi,et al.  Analysis of energy-efficiency opportunities for the cement industry in Shandong Province, China: A case study of 16 cement plants , 2010 .

[20]  Linyan Sun,et al.  The relationship between energy consumption structure, economic structure and energy intensity in China , 2009 .

[21]  Chaoqing Yuan,et al.  Research on the energy-saving effect of energy policies in China: 1982–2006 , 2009 .

[22]  Boqiang Lin,et al.  CO2 emissions of China's food industry: an input–output approach , 2016 .