Combining the biennial Malmquist–Luenberger index and panel quantile regression to analyze the green total factor productivity of the industrial sector in China

• Published in: The Science of the total environment Vol. 739; p. 140280
• Main Authors: Wang, Ke-Liang, Pang, Su-Qin, Ding, Li-Li, Miao, Zhuang
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15.10.2020
• Subjects: Biennial Malmquist–Luenberger (BML) productivity index; China; energy; environment; environmental law; environmental protection; Fixed-effect panel quantile regression; foreign direct investment; Green total factor productivity (GTFP); Industrial sector; industrialization; regression analysis; technology; China; Green total factor productivity (GTFP); Industrial sector; Biennial Malmquist–Luenberger (BML) productivity index; Fixed-effect panel quantile regression
• ISSN: 0048-9697; 1879-1026; 1879-1026
• DOI: 10.1016/j.scitotenv.2020.140280

Improving the green total-factor productivity (GTFP) is a key measure to coordinate industrial development and environmental protection in China. This study adopts the biennial Malmquist–Luenberger (BML) productivity index to estimate the GTFP change of China's 34 industrial subsectors covering the period 2005–2015. Subsequently, fixed-effect panel quantile regression is applied to analyze the heterogeneous effects of eight selected influencing factors on China's industrial GTFP change. The results show that China's overall industrial GTFP exhibited an increasing trend during the study period and varied greatly in different sub-sectors. Moreover, technological innovation rather than efficiency promotion was the main contributor to the improvement of industrial GTFP in China. The impact of the scale structure (SS) was significantly positive across the quantiles and maintained a slightly downward trend. The impact of the property rights structure (PTS) was significantly negative and showed an increasing trend across the quantiles. The impact of the energy intensity (EI) slightly increased and was significantly negative at most quantiles. The energy consumption structure (ECS) exhibited an increasing trend and had a significantly negative effect at the middle quantiles. Technological innovation (TI) exerted a significantly positive effect and displayed a downward trend across the quantiles, and it was the most important factor to drive industrial GTFP growth. The “pollution halo” hypothesis and the Porter hypothesis were both verified with a certain range from the analysis of foreign direct investment (FDI) and environmental regulation (ER), as well as the interaction between ER and TI. Our results stress the importance of the heterogeneous effects of these influencing factors on different quantile subsectors when formulating the related measures and policies. The above figure intuitively illustrates the relationship between the eight influencing factors and industrial green total factor productivity change in China according to the estimation results of the fixed-effect panel quantile regression approach, which visually shows the heterogeneous effects of all influencing factors on China's industrial green total factor productivity. [Display omitted] •The biennial Malmquist-Luenberger (BML) index is used to estimate China’s industrial GTFP growth.•The panel quantile regression is employed to investigate the influencing factors of China’s industrial GTFP growth.•Technological progress rather than efficiency promotion is the main contributor to China’s industrial GTFP growth.•The effects of all influencing factors on China’s industrial GTP growth are heterogeneous at different quantiles.