Investigating Multi-Objective Optimal Allocation of Coastal Cropland Driven by Industrial Clusters: A Case Study of Nantong, Jiangsu Province (China)

The coastal zone exhibits complex resource constraints and environmental pressures, with marked industrial structure differentiation and considerable spatial stress on agriculture. This study enhances industrial cluster resilience by employing shift-share analysis to delineate industrial structure and constructing a multi-objective optimization model using the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The model encompasses industrial cluster-driven development, economic benefits, social food security, ecological advantages, and land use efficiency, integrating coastal-specific constraints including soil salinity, tidal influence, and aquaculture competition. An empirical study in Nantong City, Jiangsu Province, China, demonstrates that optimized land allocation achieves a 3.13% reduction in cropland area while maintaining 42.56% coverage, increases forest land by 0.28% to 75.3847 km2, and enhances other land uses by 2.21% to 2169.6563 km2. The multi-objective optimization successfully balances five competing objectives with an overall improvement index of 0.847, validating both scientific robustness and practical feasibility. This research provides a scientific basis for agricultural space reconstruction and rural revitalization in coastal regions.