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.
To cope with global carbon reduction pressure, improved agricultural production efficiency, and optimize regional sustainability, we constructed a data-driven evaluation and optimization method for agricultural environmental efficiency (AEE) under carbon constraints. This study constructs a comprehensive input-output AEE evaluation index system, incorporates carbon emissions from agricultural production processes as undesired outputs, and optimizes their calculation. The Minimum Distance to Strong Efficient Frontier evaluation model considering undesired output, and the kernel density estimation, are used to quantitatively evaluate AEE from static and dynamic perspectives. Tobit regression models are further used to analyze the driving influences of AEE and propose countermeasures to optimize AEE. The feasibility of the above methodological process was tested using 2015–2020 data from the Anhui Province, China. Although there is still scope for optimizing the AEE in Anhui, the overall trend is positive and shows a development trend of “double peaks”. The levels of education, economic development, agricultural water supply capacity, and rural management are important factors contributing to AEE differences in Anhui. Data and regression analysis results contribute to the optimization of AEE and proposes optimization strategies. This study provides extensions and refinements of the AEE evaluation and optimization, and contributes to sustainable development of regions.
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