Using an extended logarithmic mean Divisia index approach to assess the roles of economic factors on industrial CO2 emissions of China

This paper used an extended logarithmic mean Divisia index (LMDI) approach to decompose the changes of China's industrial CO2 emissions into four traditional factors and three investment and R&D expenditure related factors, including: carbon dioxide emissions coefficient effect, energy structure effect, energy intensity effect, R&D efficiency effect, R&D intensity effect, investment intensity effect, and industrial activity effect. The results show that: (1) industrial activity was the largest stimulating factor in industrial CO2 emissions. Investment intensity and R&D intensity changes displayed overall positive effects in emissions growth but with some fluctuations in different periods and provinces. (2) Energy intensity was the prominent factor to facilitate emissions reduction, followed by the R&D efficiency. The two factors contributed to a considerable decrease in industrial CO2 emissions. (3) Among all factors, energy structure effect was the weakest, and showed alternative influencing directions in different periods and provinces. (4) The effects exerted from various factors were distinctly varied in different economic stages and provinces. And generally, the curbing effects cannot counteract the promoting effects. Finally, the empirical results show that continue to decrease energy intensity, facilitate the investment and R&D efforts aiming at energy-saving and emission-reduction, and reduce the reliance on coal while further raise the renewable energy utilization are beneficial to alleviate industrial CO2 emissions. (C) 2018 Elsevier B.V. All rights reserved.