Data-Driven Evaluation and Optimization of Agricultural Environmental Efficiency with Carbon Emission Constraints

Abstract

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.