Carbon emission efficiency of forest processing industries in China

This paper uses an input-output table of China's provinces(2007-2016) to measure carbon emissions of these industries.It employs a Malmquist-Luenberger(ML) index with expected and undesired outputs,and an absolute β convergence and a conditional β convergence model,to conduct an in-depth analysis of dynamic changes and spatial convergence.Carbon emission efficiency of forest processing industries in 25 regions,including Shanghai,Chongqing,Zhejiang,and Jiangsu are increasing,whereas those of Tianjin,Liaoning,Heilongjiang,and Tibet are decreasing.The main contributing factors of carbon emission efficiency in three major regions vary over time.Further,carbon emission efficiency in the eastern,central,and western regions all have absolute β convergence and conditional β convergence,indicating that different regions are developing toward their own goals and industry,yet regions with lower efficiency are catching up with those where with more efficient strategies in place.Finally,this paper proposes according recommendations.

Study on Carbon Emission Efficiency Evaluation and Influencing Factors of Chinese Pharmaceutical Manufacturing Industry

To measure the carbon emission efficiency of China’s pharmaceutical manufacturing industry, explore the factors affecting the carbon emission efficiency of China’s pharmaceutical manufacturing industry, and provide reference for improving the carbon emission efficiency of China’s pharmaceutical manufacturing industry and promoting the government to formulate macro policies. Based on the data of the pharmaceutical manufacturing industry in 30 provinces of China from 2010 to 2019, and based on the SBM model and ML (Malmquist-Luenberger) index model, the carbon emission efficiency of the pharmaceutical manufacturing industry was calculated and its dynamic change was investigated, and the Tobit model was further used to explore the influencing factors of the carbon emission efficiency of the pharmaceutical manufacturing industry. The carbon emission efficiency of China’s inter-provincial pharmaceutical manufacturing industry has steadily improved. The carbon emission efficiency of the eastern region is higher than that of the western region, and that of the western region is higher than that of the central region. The eastern region is dominated by technological progress, and there is room for improvement in technological efficiency. The central and western regions are dominated by technological efficiency. Compared with technological efficiency, technological progress needs to be further improved. Environmental regulation, industrial agglomeration and technological innovation level positively affect carbon emission efficiency, while foreign investment level has no significant impact on carbon emission efficiency.

Spatial network structure of transportation carbon emission efficiency in China and its influencing factors

Grasping the spatial correlation structure of transportation carbon emission efficiency(TCEE)and its influencing factors is significant for promoting high-quality and coordinated development of the transportation industry and the relevant region.Based on the ideal point cross-efficiency(IPCE)model,the social network analysis method was employed herein to explore the spatial correlation network structure of China’s provincial TCEE and its influencing factors.The results obtained showed the following outcomes.(1)During the study period,China’s provincial TCEE formed a complex and multithreaded network association relationship,but its network association structure was still relatively loose and presented the hierarchical gradient characteristics of dense in the east and sparse in the west.(2)The correlation of China’s TCEE formed a block segmentation based on the regional boundaries,and its factional structure was relatively obvious.The eastern region was closely connected with the central region,and generally connected with the western and northeastern regions.The central region was mainly connected with the eastern and western regions,and relatively less connected with the northeastern region.Besides,the northeastern region was weakly connected with the western region.(3)Shanghai,Beijing,Zhejiang,Guangdong,Jiangsu,Tianjin,and other developed provinces were in the core leading position in the TCEE network,which significantly impacted the spatial correlation of TCEE.However,Heilongjiang,Jilin,Xinjiang,Qinghai,and other remote provinces in the northeast and northwest were at the absolute edge of the network,which weakly impacted the spatial correlation of TCEE.(4)Provincial distance,economic development-level difference,transportation intensity difference,and transportation structure difference had significant negative impacts on the spatial correlation network of China’s provincial TCEE.In contrast,the energy-saving technology level difference had a significant positive impact on it.The regression coefficients of transportation energy structure and environmental regulation differences were positive but insignificant;their response mechanism and effects need to be improved and enhanced.