Sustainable agricultural development increasingly emphasizes the synergistic management of water-energy-carbon-ecosystem services. However, existing optimization frameworks for the water-energy-food-carbon nexus lack integration of ecosystem services and simultaneous consideration of algorithm performance, strategy sustainability, and robustness. To fill this gap, this study develops a decision-making framework for sustainable grain production based on the water-energy-carbon-ecosystem services nexus, incorporating a nondominated sorting genetic algorithm, hypervolume indicator, hybrid multi‑criteria decision‑making, and Monte Carlo simulation. Application in China’s major grain-producing areas demonstrates that systematic cropping structure optimization serves as an effective lever to break the low-sustainability lock-in and achieve a multi-win outcome. By implementing structural adjustments to moderately reduce total sown area, decreasing maize cultivation and increasing the potato proportion, these strategies maintain system coordination and stable grain output while delivering substantial sustainability gains: cutting grey and blue water footprints by 192–1,444 and 878–8,738 million m3, reducing GHG emissions by 1.42–15.14 Mt CO2-eq, saving 901–4,507 million MJ of energy, increasing green water occupancy by 1.74 %-2.03 % and alleviating water stress by 7.2 %-26.9 %. Moreover, the optimization strategies exhibit high robustness across 10,000 Monte Carlo runs (coefficient of variation < 0.08). The proposed integrated framework circumvents the limitations of traditional multi-objective methods that may exclude potential optimal solutions or yield suboptimal outcomes. It provides a transferable, data-driven decision-support tool for regions worldwide facing complex resource and ecological constraints, contributing to sustainable agricultural transformation and the advancement of multiple SDGs.
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