Development of an assessment-based planting structure optimization model for mitigating agricultural greenhouse gas emissions

Agricultural carbon reduction in China: The synergy effect of trade and technology on sustainable development

The control of carbon emissions from agriculture is imperative in addressing the challenges posed by the greenhouse effect. China must develop a specific pathway for reducing its agricultural carbon emissions, accounting for its unique circumstances and considering the impacts of trade liberalization. (1) The study revealing that agricultural trade liberalization (ATL) has a marked effect on lowering China's agricultural carbon emission intensity (ACEI), with robustness and endogeneity tests supporting these findings. (2) In the pursuit of emissions reduction, the crucial role of technology spillovers (TS) and the optimization of industrial structure (OIS) are essential. (3) The reduction in ACEI is particularly notable in coastal regions, areas with low environmental regulations and during periods characterized by more stable agricultural tariffs. This study shows a synergistic association between ATL and ACEI, indicates the potential for a mutually beneficial situation with advantages in both economic and environmental aspects.

Influencing factors and spatiotemporal heterogeneity of livestock greenhouse gas emission: Evidence from the Yellow River Basin of China

Livestock is one of major sources of greenhouse gas (GHG) emissions in China. Clarifying spatiotemporal characteristics of GHG emissions from livestock and exploring influencing factors can provide reference for grasping regional changes of GHG emission and formulate strategies of carbon reduction for livestock industry. However, existing literatures considered both spatial and temporal impacts and dynamic evolution trend of these factors seldomly. This paper used the life cycle assessment (LCA) method to estimate GHG emissions of livestock in 114 cities of the YRB from 2000 to 2021. On this basis, spatiotemporal heterogeneity of influencing factors was analyzed by using geographically and temporally weighted regression (GTWR) model. Finally, future evolution trend of GHG emissions from livestock was predicted by combining traditional and spatial Markov chain. Four main results were listed as follows. Firstly, GHG emission in the life cycle of livestock industry increased from 57.202 million tons (Mt) carbon dioxide equivalent (CO2e) in 2000 to 77.568 Mt CO2e in 2021. Secondly, structure of livestock industry, labor flow and mechanization were vital factors that led to increase of GHG emissions from livestock. Positive effects of labor flow and mechanization were increasing year by year, while negative effect of urbanization and positive effect of economic development were decreasing year by year. Markov chain analysis shown that probability of keeping high level of GHG emissions of livestock in the YRB unchanged were 96% (T = 1) and 90% (T = 5), and there also existed a Matthew effect. In addition, probability of level transfer of GHG emission in urban livestock was spatially dependent. Government should formulate strategies for livestock development and optimize low-carbon transformation of energy structure for livestock and poultry husbandry based on local conditions and key driving factors in the future. Meanwhile, boundaries of administrative divisions should be broken to promote reduction of GHG emissions in livestock comprehensively.