Combining global Malmquist–Luenberger index and generalized method of moments to investigate industrial total factor CO2 emission performance: A case of Shanghai (China)

Since the industry is economic backbone and the largest sector in energy-related CO2 emissions, whether it can coordinate industrial growth and CO2 emissions plays a vital role in achieving economic sustainable development. Using the global Malmquist–Luenberger (GML) index method, this paper estimates and decomposes the total factor CO2 emission performance (TFCEP, i.e., the environmentally sensitive productivity growth considering CO2 emissions as an undesirable output) of 32 industrial sub-sectors in Shanghai (China) over 1994–2011 for the first time. Furthermore, it adopts the system generalized method of moments (SGMM) to investigate the determinants of the TFCEP. We find that the environmentally sensitive productivity of overall industry in Shanghai keeps improved in recent years. Technical progress rather than efficiency promotion is the main contributor to ameliorate the TFCEP. Enhancing R&D intensity, optimizing energy consumption structure, and improving energy efficiency and labor productivity are beneficial to enhance the TFCEP, while capital deepening is the detriment of the TFCEP. Encouraging green technological innovation and adoption by combining the government intervention with market mechanism is significant to promote the TFCEP.

[1]  Shouyang Wang,et al.  A comprehensive eco-efficiency model and dynamics of regional eco-efficiency in China , 2014 .

[2]  Pravin K. Trivedi,et al.  Microeconometrics Using Stata , 2009 .

[3]  Jing Li,et al.  Regional innovation efficiency in China: The role of local government , 2011 .

[4]  Min Zhao,et al.  Decomposing the influencing factors of industrial carbon emissions in Shanghai using the LMDI method , 2010 .

[5]  Shiyi Chen.  The Abatement of Carbon Dioxide Intensity in China: Factors Decomposition and Policy Implications , 2011 .

[6]  Surender Kumar,et al.  Environmentally Sensitive Productivity Growth: A Global Analysis Using Malmquist-Luenberger Index , 2006 .

[7]  Rolf Färe,et al.  Productivity and Undesirable Outputs: A Directional Distance Function Approach , 1995 .

[8]  Almas Heshmati,et al.  A sequential Malmquist-Luenberger productivity index : Environmentally sensitive productivity growth considering the progressive nature of technology , 2010 .

[9]  Rolf Färe,et al.  Environmental production functions and environmental directional distance functions , 2007 .

[10]  F. Windmeijer.  A Finite Sample Correction for the Variance of Linear Two-Step GMM Estimators , 2000 .

[11]  James D. Adams,et al.  Research Productivity in a System of Universities , 1996 .

[12]  Yong-Tae Park,et al.  An international comparison of R&D efficiency: DEA approach , 2005 .

[13]  Guo Ru,et al.  The strategy of energy-related carbon emission reduction in Shanghai , 2010 .

[14]  Shiyi Chen.  Environmental pollution emissions, regional productivity growth and ecological economic development in China , 2015 .

[15]  Xu Xinhua,et al.  Estimates of CO2 emissions in Shanghai (China) in 1990 and 2010 , 1997 .

[16]  B. W. Ang,et al.  Total factor carbon emission performance: A Malmquist index analysis , 2010 .

[17]  Gilbert E. Metcalf,et al.  An Empirical Analysis of Energy Intensity and Its Determinants at the State Level , 2008 .

[18]  Dominique Guellec,et al.  From R&D to Productivity Growth: Do the Institutional Settings and the Source of Funds of R&D Matter? , 2004 .

[19]  Feng He,et al.  Energy efficiency and productivity change of China’s iron and steel industry: Accounting for undesirable outputs , 2013 .

[20]  Behrouz Arabi,et al.  Power Industry Restructuring and Eco-Efficiency Changes: A New Slacks-Based Model in Malmquist- Luenberger Index Measurement , 2014 .

[21]  Shiyi Chen,et al.  'Green' productivity growth in China's industrial economy , 2014 .

[22]  Hans Bressers,et al.  Productivity growth and environmental regulations - accounting for undesirable outputs: Analysis of China's thirty provincial regions using the Malmquist–Luenberger index , 2011 .

[23]  David G. Streets,et al.  Reductions in emissions of local air pollutants and co-benefits of Chinese energy policy: a Shanghai case study , 2006 .

[24]  M. Arellano,et al.  Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations , 1991 .

[25]  Dong-hyun Oh,et al.  A global Malmquist-Luenberger productivity index , 2010 .

[26]  Theodore Sougiannis.  The Accounting Based Valuation of Corporate R&D , 1994 .

[27]  Lili Yang,et al.  Estimation, characteristics, and determinants of energy-related industrial CO2 emissions in Shanghai (China), 1994-2009 , 2011 .

[28]  N. H. Ravindranath,et al.  2006 IPCC Guidelines for National Greenhouse Gas Inventories , 2006 .

[29]  Zhen Cheng,et al.  Energy demand and carbon emissions under different development scenarios for Shanghai, China , 2010 .

[30]  Katsuya Tanaka,et al.  Efficiency analysis of Chinese industry : A directional distance function approach , 2007 .

[31]  J. Pastor,et al.  A global Malmquist productivity index , 2005 .

[32]  Li Zong-zhi,et al.  Regional Difference and Influence Factors of China's Carbon Dioxide Emissions , 2010 .

[33]  Shunsuke Managi,et al.  Economic growth and the environment in China: An empirical analysis of productivity , 2006 .

[34]  E. Wang,et al.  Relative efficiency of R&D activities: A cross-country study accounting for environmental factors in the DEA approach , 2007 .

[35]  Dequn Zhou,et al.  Scenario-based energy efficiency and productivity in China: A non-radial directional distance function analysis , 2013 .

[36]  Xiaolong Xue,et al.  Measuring energy consumption efficiency of the construction industry: the case of China , 2015 .

[37]  M. Arellano,et al.  Another look at the instrumental variable estimation of error-components models , 1995 .

[38]  Jun Zhang,et al.  Structural change, productivity growth and industrial transformation in China , 2011 .

[39]  R. Blundell,et al.  Initial Conditions and Moment Restrictions in Dynamic Panel Data Models , 1998 .

[40]  David Roodman.  How to do Xtabond2: An Introduction to Difference and System GMM in Stata , 2006 .

[41]  Chen Changhong,et al.  The CO2 emission reduction benefits of Chinese energy policies and environmental policies:: A case study for Shanghai, period 1995-2020 , 2001 .

[42]  Lili Yang,et al.  Using latent variable approach to estimate China's economy-wide energy rebound effect over 1954-2010 , 2014 .

[43]  Chih-Hai Yang,et al.  R&D Efficiency and the National Innovation System: An International Comparison Using the Distance Function Approach , 2014 .

[44]  Weichun Ma,et al.  A study on carbon emissions in Shanghai 2000–2008, China , 2013 .

[45]  Bai-Chen Xie,et al.  Measures to improve the performance of China's thermal power industry in view of cost efficiency , 2013 .

[46]  R. Färe,et al.  Accounting for Air Pollution Emissions in Measures of State Manufacturing Productivity Growth , 2001 .