Analysis of regional differences and dynamic mechanisms of agricultural carbon emission efficiency in China's seven agricultural regions
Year: 2022
Type: article
Abstract: A profound understanding of the present status and regional characteristics of China’s agricultural carbon emissions (ACE) is the basic prerequisite for exploring a pathway to ACE reduction that is compatible with China’s national conditions. This study uses the inter-provincial agricultural industry panel data from 2001 to 2017, and selects the three-stage slack-based measure data envelope analysis (SBM-DEA) model and Malmquist Luenberger (ML) index model to measure the dynamic efficiency of agricultural carbon emission (ACE). Additionally, this study uses the Dagum Gini coefficient and the panel vector auto-regression (PVAR) model to analyze the sources of regional differences in dynamic efficiency and the internal structure, respectively. The empirical results reveal the following: (i) The dynamic efficiency of China’s ACE is in a state of "efficiency optimization." Although both technological change and technological efficiency change are in an "efficient" state, they also show a decline in technological efficiency change, and a regression in technological change, respectively. (ii) The overall Dagum Gini coefficient of China's ACE dynamic efficiency, technological change, and technological efficiency change all demonstrate upward trends. The gap between regions is the main reason for the long-term gap between the dynamic efficiency of China's ACE, technological change, and technological efficiency change. (iii) Regardless of the time horizon, technological change has always been the main driving force for the continuous growth of dynamic efficiency; the contribution of technological change to dynamic efficiency is far greater than that of technological efficiency change. This conclusion has been verified in samples from different regions of China.
Authors: Xiaodan Zhang, Kaicheng Liao, Xianghong Zhou
Institution: Tongji University
Language: English
Field: Decision Sciences
Domain: Social Sciences
SDGs: Climate action, Zero hunger
Open Access status: green
Funder: Division of Graduate Education
OpenAlex ID: W4207018384