Analyzing the spatial network structure of agricultural greenhouse gases in China

Spatial autocorrelation and driving factors of carbon emission density of crop production in China

Mitigating carbon emissions from crop production is essential for addressing global warming. At a macro-level, existing studies have often relied on the calculation of carbon emission intensity of crop production to understand comparable carbon effects between regions. However, this approach obscures the differences in crop planting scale and natural attributes across regions, leaving room for improvement in the methods and scope of analysis. To extend the existing research, we proposed an idea for calculating the carbon emission density of crop production based on planting area. Additionally, we developed an analytical framework for driving factors of carbon emission density of crop production from a spatial interaction perspective. The provincial carbon emission density of crop production in mainland China between 2000 and 2020 was calculated, and spatial econometric models were utilized to investigate the spatial autocorrelation and driving factors. The results indicate that the national average carbon emission density of crop production was 1.462 t/hm2 annually. Over 21 years, the carbon emission density of agricultural materials, rice cultivation, soil management, and straw burning evolved from 0.384 to 0.470 t/hm2, 0.409 to 0.367 t/hm2, 0.171 to 0.169 t/hm2, and 0.317 to 0.448 t/hm2, respectively. The global Moran's index indicated a positive spatial autocorrelation of carbon emission density of crop production and the subdivided carbon sources among provinces. Regarding direct effects, an increase in the proportion of paddy fields in cropland composition and irrigation efficiency would significantly promote the carbon emission density, while factors such as cropland area, multiple cropping, agricultural personnel numbers, departmental proportion, and disaster degree would decrease the local carbon emission density. Certain factors, such as cropland area and agricultural disasters, had a spatial spillover effect on carbon emission density between provinces. The study suggests harnessing key drivers and spatial spillover effects to achieve regional low-carbon crop production.

Performance of cropland low-carbon use in China: Measurement, spatiotemporal characteristics, and driving factors

Low-carbon cropland use is of significant importance in addressing global warming. Most research has paid attention to measuring cropland use efficiency under carbon emission constraints to reconcile carbon mitigation and food security. However, there are limitations with carbon accounting, approach selection, and subsequent regression. To extend existing research, this study aimed to evaluate the performance of cropland low-carbon use (PCLU) and determine the driving factors. In this study, carbon emissions and sequestration of cropland use in Chinese provinces from 2000 to 2019 were calculated. Based on the carbon accounting, an extended slack-based efficiency measurement was applied to evaluate the provincial PCLU. Besides, spatiotemporal characteristics of the PCLU were portrayed. Owing to spatial correlation, the spatial Durbin model was employed to elucidate the driving factors. The results indicate that: (1) cropland use systems acted as net carbon sinks in China with sequestration and emissions of 5.624 t/hm² and 1.980 t/hm², respectively. (2) In China, the average PCLU was 0.727, whereas the provinces had values ranging from 0.2-1.2, with significant gaps. (3) PCLU evolved stably in northeastern China and fluctuated in the other three regions. Meanwhile, provinces with high PCLU shifted from the south to the north of the country during the study period. The global Moran's index demonstrated that a positive spatial correlation existed. (4) Agricultural structure adjustment and urbanization can promote PCLU, whereas investment and disaster can undermine it. PCLU of a province would be affected by the agricultural structure and disaster in its nearby provinces owing to spillover effects. Consequently, it is suggested that emissions in provinces should be mitigated according to the local carbon structure. Harnessing the key factors and spatial interactions can potentially help achieve regional low-carbon cropland use.

Spatial Correlation of Air Pollution and Its Causes in Northeast China

To understand the status of air pollution in northeastern China, we explore the structure of air pollution transmission networks and propose targeted policy recommendations. Using air pollution data from 35 cities in northeastern China for a total of 879 periods from 6 January 2015 to 3 June 2017, this paper used social network analysis (SNA) to construct a spatial association network of air pollution in the region, and analyzed the spatial association of air pollution among cities and its causes in an attempt to reveal the transmission path of air pollution in the region. The results show that inter-city air pollution in northeast China forms a complex and stable correlation network with obvious seasonal differences of "high in winter and low in summer". Different cities in the region play the roles of "spillover", "intermediary" and "receiver" of air pollution in the network. Small respirable particulate (PM2.5) pollution constitutes a significant component of air pollution in northeast China, which spreads from Liaoning province to Heilongjiang province via Jilin province. Therefore, regional joint pollution prevention and control measures should be adopted to combat the air pollution problem, and different treatment measures should be developed for different city "roles" in the pollution network, in order to fundamentally solve the air pollution problem in the region.