The effect estimation and channel testing of the technological progress on China’s regional environmental performance

Abstract To study the effects of and approaches to technological progress on China’s regional environmental performance, this study first estimates China’s regional environmental performance and its variation indexes by applying a slack-based model (SBM) and an entropy-based model (EBM). The results indicate that the environmental performance in different regions of China has improved, but the rate of improvement differs greatly. This may be attributed to heterogeneous characteristics and changes in the green technology innovation level in different regions. Considering the overflow effect of environmental pollution among different regions, we study the impact of various technological progress patterns on China’s regional environmental performance using spatial econometrics, and we find that there are significant spatial effects of technology innovation, technology transfer, and imitative innovation on China’s regional environmental performance. Also, different technological progress patterns have different effects. Specifically, independent innovation has failed to effectively improve regional environmental performance, whereas the introduction of technology and imitative innovation have significantly improved this performance. Moreover, after the cross-items of independent innovation and human capital are introduced, the effects of technology introduction and imitative innovation on China’s regional environmental performance through the absorptive capacity of human capital remain significant, whereas the effect of independent innovation on regional environmental performance via the absorptive capacity of human capital becomes more obvious. Based on this and from the perspective of environmental enhancement, we believe that China should strengthen human capital accumulation and give consideration to imitative innovation and technology introduction while emphasizing independent innovation.

[1]  Mingxing Sun,et al.  Promoting regional sustainability by eco-province construction in China: A critical assessment , 2015 .

[2]  J. Ang.  CO2 emissions, energy consumption, and output in France , 2007 .

[3]  P. Ehrlich,et al.  IMPACT OF POPULATION GROWTH , 1971, Science.

[4]  H. Patrinos,et al.  Returns to investment in education: a further update , 2002 .

[5]  Soumyananda Dinda.  A theoretical basis for the environmental Kuznets curve , 2005 .

[6]  Amy K. Richmond,et al.  Energy Prices and Turning Points: The Relationship between Income and Energy Use/Carbon Emissions , 2006 .

[7]  William L. Weber,et al.  A directional slacks-based measure of technical inefficiency , 2009 .

[8]  D. Holtz-eakin,et al.  Stoking the Fires? Co2 Emissions and Economic Growth , 1992 .

[9]  W. Nordhaus.  Economic Growth and Climate: The Carbon Dioxide Problem , 1976 .

[10]  A. Jaffe,et al.  The Induced Innovation Hypothesis and Energy-Saving Technological Change , 1998 .

[11]  G. Psacharopoulos.  Returns to investment in education: A global update , 1994 .

[12]  R. Elliott,et al.  Industrial activity and the environment in China: An industry-level analysis , 2008 .

[13]  M. Auffhammer,et al.  Forecasting the Path of China's CO2 Emissions Using Province Level Information , 2007 .

[14]  Kaoru Tone,et al.  An epsilon-based measure of efficiency in DEA - A third pole of technical efficiency , 2009, Eur. J. Oper. Res..

[15]  T. Selden,et al.  Neoclassical Growth, the J Curve for Abatement, and the Inverted U Curve for Pollution , 1995 .

[16]  W. Cooper,et al.  Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software , 1999 .

[17]  David Popp,et al.  Entice: Endogenous Technological Change in the Dice Model of Global Warming , 2003 .

[18]  P. Aghion,et al.  The Environment and Directed Technical Change , 2009, The American economic review.

[19]  Daron Acemoglu,et al.  Introduction to Modern Economic Growth , 2009 .

[20]  Kaoru Tone,et al.  Bridging radial and non-radial measures of efficiency in DEA , 2007, Ann. Oper. Res..