Climate neutral in agricultural production system: a regional case from 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.

The symmetric and asymmetric effects of economic policy uncertainty and oil prices on carbon emissions in the USA and China: evidence from the ARDL and non-linear ARDL approaches

The environmental consequences of economic policy uncertainty (EPU) and oil price changes have attracted much attention in recent years, but few studies have estimated the asymmetric impact. This research investigates the symmetric and asymmetric effects of EPU and oil prices on carbon emissions in the USA and China. Based on monthly data from 1995:1 to 2019:12, we address this issue by applying the ARDL and non-linear ARDL cointegration methodology. The symmetric results show that the change in EPU has no evident impact on carbon emissions in the short and long run for the USA and China, and oil prices have a lag effect in this regard. The non-linear ARDL estimation documents significant asymmetric effects of EPU and oil prices. Specifically, the negative change in EPU facilitates emissions in the USA in the long and short run, and a unit increase in EPU increases emissions by 2.24% and 4.95%, respectively, whereas its positive change does not produce any significant effect. Carbon emissions in China are significantly and positively affected by the positive change in EPU only over the long run, and an increase in EPU increases emissions by 0.04%. Furthermore, the positive shock of oil prices significantly and positively influences emissions in the long run, and a unit increase in this factor increases emissions by 15.73% and 0.44% in the USA and China, respectively. Moreover, the short-term effect of the negative shock of oil prices exhibits a lag effect. The empirical results provide a practical reference for the USA and China to craft robust policies on emission cutting.

Data-Driven Evaluation and Optimization of Agricultural Sustainable Development Capability: A Case Study of Northern Anhui

Human survival depends on the sustainable development of agriculture. This study constructs a data-driven evaluation and optimization method of agricultural sustainable development capacity, aiming to better cope with challenges such as environmental pollution and excessive consumption of resources and energy, as well as improve agricultural economic level. Further, an evaluation index system was constructed based on comprehensive consideration of energy and resources utilization, environmental pollution, and agricultural economy. After simplifying and integrating the data, a data envelopment analysis model was constructed to quantitatively evaluate the capability for agricultural sustainable development and its changing trend. Moreover, its influencing factors were analyzed from the perspective of input, which provides accurate countermeasures for improving agricultural sustainable development ability, resource utilization efficiency, and process optimization. This study shows the realization process of the aforementioned method for the agricultural development of six cities in northern Anhui from 2010 to 2019. Our results suggest that the sustainable development ability of northern Anhui is weak, but overall, has a good development trend. Based on our results, some countermeasures were proposed to control production scale reasonably, reduce environmental load, and improve resource efficiency, which provides a reference for policymakers to guide and standardize the development of regional agriculture.