Safeguarding food security through optimized environmental governance: a dual-objective model for China's agricultural pollution control
Background Feeding a growing global population while protecting environmental quality stands as one of the defining governance challenges of our time. As the largest food producer in the world, China faces this challenge acutely, as it must simultaneously safeguard food security and curb agricultural non-point source pollution (ANPSP). ANPSP contributes more than half of the national water pollution load and undermines agricultural production capacity through soil degradation and deteriorating water quality. Effective ANPSP control is therefore foundational to sustainable food production. The Action Plan for Agricultural and Rural Pollution Control of China sets phased targets to be met by 2025. These include raising the rural wastewater treatment rate to 40 percent, the fertilizer and pesticide utilization rate to 43 percent, and the livestock manure utilization rate to 80 percent or above. Building on these targets, this study sets a national emission reduction rate of 38 percent as the policy goal. This rate is calculated as a weighted average across provinces, with each province's pollution load used as the weight. Yet how to allocate these reduction duties among 28 provinces in a way that is both fair and cost-effective remains an unresolved policy challenge. Method Current methods use either uniform quotas or pollution-proportional rules. Neither can balance efficiency and equity. Both methods also ignore large differences between provinces in governance capacity, economic development, and industrial structure. This study builds a two-objective optimization model using NSGA-II to balance governance efficiency and fair allocation. The model accounts for provincial differences in four areas: fiscal capacity, economic level, industrial structure, and past emission reduction performance. The study uses panel data from 28 provinces covering 1980 to 2023. Missing values are filled using a similarity-based method. Emissions from seven source categories are estimated using a standard inventory approach. Result The optimized scheme meets the 38% reduction target and cuts governance costs by 7.4% compared to equal allocation. The Gini coefficient falls from 0.433 to 0.303. Provinces with higher governance capacity receive reduction targets of 55%−90%. Provinces with lower capacity receive targets of 12%−26%. The high-reduction group has an average capacity index of 0.58 and handles 39.9% of total cuts. The low-reduction group has an average capacity index of 0.24 and handles 15.4% of total cuts. The standard deviation of relative burden falls by 40.2%, showing greater fairness in burden distribution. Conclusion This study provides a practical framework for allocating ANPSP reduction duties among Chinese provinces. The framework supports ANPSP control and water quality improvement across China. It also contributes to the United Nations Sustainable Development Goals (SDGs) 2, 3, 6, and 14.