Can Industrial Structural Adjustment Improve the Total-Factor Carbon Emission Performance in China?

How to improve the industrial total-factor carbon emission performance (TCPI), or total-factor carbon productivity, through industrial structural adjustment, is crucial to China’s energy conservation and emission reduction and sustainable growth. In this paper, we use a dynamic spatial panel model to empirically analyze the effect of industrial structural adjustment on TCPI of 30 provinces in China from 2000 to 2015. The results show that most of the provinces with high TCPI are located in the eastern coastal areas, while the provinces with relatively low TCPI are to be found in the central and western regions. The spatial auto-correlation tests show that there are significant global spatial auto-correlation and local spatial agglomeration characteristics in TCPI. The regression results of the dynamic spatial panel models show that at the national level, the structure of industrialization, the industrial structure of heavy industrialization, the coal-based energy consumption structure and the endowment structure have significant negative effects on the improvement of TCPI. The expansion of industrial enterprise scale, on the other hand, is conducive to an improvement in TCPI while the effects of foreign direct investment (FDI) structure and ownership structure on TCPI are not significant. At the regional level, there are certain differences in the effects of different types of industrial structural adjustment on TCPI.

[1]  Jidong Kang,et al.  Changes in carbon intensity in China's industrial sector: Decomposition and attribution analysis , 2015 .

[2]  Jeong-Dong Lee,et al.  A metafrontier approach for measuring Malmquist productivity index , 2010 .

[3]  Yan Wang,et al.  Environmental regulation and environmental productivity: The case of China , 2016 .

[4]  Jiangfeng Hu,et al.  Environmental Regulation, Foreign Direct Investment and Green Technological Progress—Evidence from Chinese Manufacturing Industries , 2018, International journal of environmental research and public health.

[5]  Huiming Zhang,et al.  Total-factor carbon emission efficiency of China's provincial industrial sector and its dynamic evolution , 2018, Renewable and Sustainable Energy Reviews.

[6]  Yi-Ming Wei,et al.  Potential impacts of industrial structure on energy consumption and CO2 emission: a case study of Beijing , 2015 .

[7]  Hendrik Vrijburg,et al.  Dynamic Panel Data Models Featuring Endogenous Interaction and Spatially Correlated Errors , 2009 .

[8]  Yung‐ho Chiu,et al.  Non-radial metafrontier approach to identify carbon emission performance and intensity , 2017 .

[9]  Yi-Ming Wei,et al.  Changes in carbon intensity in China: Empirical findings from 1980-2003 , 2007 .

[10]  Wei Zhang,et al.  Decomposition of intensity of energy-related CO2 emission in Chinese provinces using the LMDI method , 2016 .

[11]  Madina Kukenova,et al.  Spatial Dynamic Panel Model and System GMM: A Monte Carlo Investigation , 2008 .

[12]  Lianshui Li,et al.  Industrial structure, technical progress and carbon intensity in China's provinces , 2018 .

[13]  Boqiang Lin,et al.  Regional differences of pollution emissions in China: contributing factors and mitigation strategies , 2016 .

[14]  Abid Rashid Gill,et al.  The Environmental Kuznets Curve (EKC) and the environmental problem of the day , 2018 .

[15]  Z. Mi,et al.  China's “Exported Carbon” Peak: Patterns, Drivers, and Implications , 2018 .

[16]  Ning Zhang,et al.  Total-factor carbon emission performance of fossil fuel power plants in China: A metafrontier non-radial Malmquist index analysis , 2013 .

[17]  Xinye Zheng,et al.  Identifying the determinants and spatial nexus of provincial carbon intensity in China: a dynamic spatial panel approach , 2014, Regional Environmental Change.

[18]  J. Elhorst Unconditional Maximum Likelihood Estimation of Linear and Log‐Linear Dynamic Models for Spatial Panels , 2005 .

[19]  B. W. Ang,et al.  A Multi-region Structural Decomposition Analysis of Global CO2 Emission Intensity , 2017 .

[20]  Feng Dong,et al.  Drivers Analysis of CO2 Emissions from the Perspective of Carbon Density: The Case of Shandong Province, China , 2018, International journal of environmental research and public health.

[21]  Bin Su,et al.  Multiplicative structural decomposition analysis of energy and emission intensities: Some methodological issues , 2017 .

[22]  Jin-Hua Xu,et al.  Determining factors and diverse scenarios of CO2 emissions intensity reduction to achieve the 40–45% target by 2020 in China – a historical and prospective analysis for the period 2005–2020 , 2016 .

[23]  Ruyin Long,et al.  Spatial econometric analysis of China’s province-level industrial carbon productivity and its influencing factors , 2016 .

[24]  Qunwei Wang,et al.  Measuring total-factor CO2 emission performance and technology gaps using a non-radial directional distance function: A modified approach , 2016 .

[25]  Dong-hyun Oh A metafrontier approach for measuring an environmentally sensitive productivity growth index , 2010 .

[26]  Zhaohua Wang,et al.  Empirical analysis on the factors influencing national and regional carbon intensity in China , 2016 .

[27]  Yi-Ming Wei,et al.  Socioeconomic impact assessment of China's CO2 emissions peak prior to 2030 , 2017 .

[28]  Jun Liu,et al.  Identifying the spatial effects and driving factors of urban PM2.5 pollution in China , 2017 .

[29]  G. Grossman,et al.  Economic Growth and the Environment , 1994 .

[30]  Xiao Wei,et al.  Dynamic total factor carbon emissions performance changes in the Chinese transportation industry , 2015 .

[31]  Zhujun Jiang,et al.  Total-factor CO2 emission performance of China’s provincial industrial sector: A meta-frontier non-radial Malmquist index approach , 2016 .

[32]  Can Wang,et al.  Industrial CO2 intensity, indigenous innovation and R&D spillovers in China’s provinces , 2014 .

[33]  Youguo Zhang,et al.  Structural decomposition analysis of sources of decarbonizing economic development in China; 1992-2006 , 2009 .

[34]  Boqiang Lin,et al.  Economic growth model, structural transformation, and green productivity in China , 2017 .

[35]  J. Burnett,et al.  Spatial analysis of China province-level CO2 emission intensity , 2014 .

[36]  Boqiang Lin,et al.  A dynamic analysis of air pollution emissions in China: Evidence from nonparametric additive regression models , 2016 .

[37]  Zhongfu Tan,et al.  Examining the driving forces for improving China’s CO2 emission intensity using the decomposing method , 2011 .

[38]  Alwyn Young,et al.  Gold into Base Metals: Productivity Growth in the People’s Republic of China during the Reform Period , 2000, Journal of Political Economy.

[39]  Davoud Gholamiangonabadi,et al.  Dynamic changes in CO2 emission performance of different types of Iranian fossil-fuel power plants , 2015 .

[40]  Manuel Martinez,et al.  Changes in CO2 emission intensities in the Mexican industry , 2012 .

[41]  Sanfeng Zhang,et al.  Heterogeneous governance capabilities, reference emission levels and emissions from deforestation and degradation: A signaling model approach , 2017 .

[42]  Yi-Ming Wei,et al.  Chinese CO2 emission flows have reversed since the global financial crisis , 2017, Nature Communications.