Carbon emissions in the logistics industry: driving factors and decoupling effects

The explosive growth of the logistics industry has led to an increase in energy consumption and carbon emissions. To reduce emissions and increase the efficiency of the logistics industry, we studied the driving factors and decoupling effects of carbon emissions of logistics industry (LICE). First, an energy coefficient method is used to calculate the LICE. Second, the Logarithmic Mean Divisia Index (LMDI) decomposition method is used to decompose the driving factors of LICE into five types. Third, the decoupling model is used to explore the decoupling relationship between economic growth and LICE. Considering Anhui Province as an example, this study describes the method's implementation process based on a comparison of the four Yangtze River Delta provinces and cities. The results indicate that the growth rate of LICE in Anhui Province has decreased during the research period, from 9.7% in 2013 to 2.1% in 2021; however, the Tapio decoupling elasticity has been approximately 0.4 for the last 2 years, remaining in a weak decoupling stage from economic development. The LMDI decomposition results indicate that the average contribution of economic level to carbon emissions is 1.763. This study proposes some solutions and recommendations for the logistics industry's low-carbon development to offer methodological and theoretical support for LICE research.

Driving factors and decoupling effects of agricultural carbon emissions in Jiangxi Province based on time-varying parameter C-D production function

The reduction of agricultural emission plays an important role in realizing the dual-carbon goals. It is thus of great significance to examine the characteristics and drivers of regional agricultural carbon emission. We measured agricultural carbon emission in Jiangxi Province from the perspective of input-output and production processes, and explored the drivers and decoupling dynamics of agricultural carbon emission by using the LMDI decomposition method together with the Tapio decoupling model modified by time-varying parameter C-D production function. The results showed that agricultural carbon emission in Jiangxi increased by 26.4% from 2010 to 2021, and the carbon emission intensity decreased year by year with an average annual rate of 4.9%. Factors such as agricultural carbon intensity, labor input, and capital stock collectively reduced carbon emission by a total of 61.05 Mt, with a contribution of 27.0%, 44.5% and 28.5%, respectively. Level of agricultural economic development, agricultural structure, and technological progress had strong driving effects, which accounted for 75.7%, 5.6% and 18.8%, respectively. Agricultural carbon emission in Jiangxi was weakly decoupled from economic development, capital stock, and technological progress factors, but was negatively decoupled from labor input. Moreover, the decoupling state was more desirable in the later period than in the earlier period. Our results suggested that the application of the time-varying parameter C-D production function is innovative and applicable by incorporating technology, labor, and capital factors in the examination of carbon emission drivers and decoupling effects.

Factor decomposition for ecological pressure of the whole industrial energy carbon footprint: a case study of China

The purpose of this paper is to study the influencing factors of the ecological pressure of the energy carbon footprint (EPECF) of China's whole industry from 2000 to 2018. First, the EPECF of 48 sub industries is calculated, then divides 48 sub-industries into high-, medium-, and low-pressure industries, and uses the logarithmic mean Divisia index (LMDI) method to analyze and summarize the main driving forces of China's industrial EPECF changes. Finally, policy suggestions for the future industrial decompression are put forward. The main results are as follows: (1) Economic development is the most important factor to promote the growth of EPECF of the three major industries. (2) At present, the population pressure factors of forest and grassland have little effect, and the effect of returning farmland to forest and grassland has not been truly played. (3) The adjustment of industrial structure has gradually become a key factor in reducing EPECF of the three industries. (4) The gradual stability of energy intensity has a certain inhibitory effect on the increase of EPECF in high-pressure industry. (5) The adjustment of energy structure in low-pressure industry has gradually worked. Therefore, the government should establish an economic sustainable development system, vigorously develop clean energy, and realize the green transformation of various industries. This provides an empirical example for other countries in the world to reduce the EPECF.

Determinants of Decoupling Economic Output from Carbon Emission in the Transport Sector: A Comparison Study of Four Municipalities in China

Quantitative analysis on decoupling between economic output, carbon emission, and the driving factors behind decoupling states can serve to make the economy grow without increasing carbon emission in China’s transport sector. In this work, we investigate the decoupling states and driving factors of decoupling states in the transport sector of China’s four municipalities (Beijing, Shanghai, Tianjin, and Chongqing) through combining the Tapio decoupling approach with the decomposition technique. The results show that (i) the decoupling state of Beijing, Shanghai, and Tianjin improved; Beijing stabilized in weak decoupling; Shanghai and Tianjin appeared to have strong decoupling, but the decoupling state of Chongqing deteriorated from decoupling to negative decoupling. (ii) The energy-saving effect was the primary contributor to decoupling in these four municipalities, promoting transport’s economic growth strongly decouple from carbon emission. The economic scale effect was not optimized enough in Chongqing, facilitating expansive coupling, and expansive negative decoupling emerged. But it had a rather positive impact on decoupling process in Beijing, Shanghai and Tianjin, promoting economic growth to weakly decouple from carbon emission. (iii) The carbon-reduction effect promoted strong decoupling, which emerged in Shanghai’s transport sector, more so than in the other three municipalities, in which weak decoupling emerged. Finally, several relevant policy recommendations were offered to promote the decoupling of carbon emission from economic growth and low-carbon transport.