Multi-Temporal Remote Sensing Assessment and Pareto-Based Land-Use Optimization for Balancing Carbon Storage, Ecological Value, and Economic Value in Jiangsu Province, China

Ecosystem carbon storage is highly sensitive to land-use/land-cover change (LUCC). However, retrospective carbon assessment, spatial factor analysis, and future land-use optimization are often conducted separately, limiting their ability to support territorial planning involving competing economic, ecological, and carbon-storage objectives. Taking Jiangsu Province, China, as a rapidly urbanizing region, this study developed an integrated framework combining multi-temporal remote-sensing-derived land-use data with the InVEST carbon storage model, Geodetector, multi-objective programming (MOP), and the intPLUS model. Land-use data from 2000, 2010, and 2020 were used to quantify changes in land use and ecosystem carbon storage and to examine how natural, socioeconomic, and accessibility conditions corresponded spatially to the observed land-use configuration. Pareto optimization was then applied to identify non-dominated land-use allocations, which were spatialized to simulate five land-use scenarios for 2030. From 2000 to 2020, cropland decreased by 7475 km2, while construction land increased by 6974 km2. Correspondingly, ecosystem carbon storage declined from 1431.85 Mt to 1402.55 Mt, representing a cumulative loss of 29.30 Mt. This decline was primarily associated with cropland loss and construction land expansion, particularly the conversion of cropland into construction land. Although the overall spatial pattern of carbon storage remained relatively stable, high-value areas gradually contracted, whereas low-value areas expanded in intensively urbanized regions. Geodetector results showed that pairwise factor combinations generally corresponded more closely to the spatial differentiation of land-use patterns than individual topographic factors, and that associations involving socioeconomic activity, transport accessibility, and vegetation cover became more pronounced over time. The 2030 scenario analysis revealed clear trade-offs: the carbon storage maximization scenario produced the highest carbon storage (1497.47 Mt), the economic development scenario generated the highest economic benefit but the lowest ecosystem service value, and the natural development scenario produced the lowest carbon storage (1384.46 Mt). The Pareto-based comprehensive optimization scenario maintained 1487.12 Mt of carbon while achieving a comparatively balanced combination of economic benefit and ecosystem service value. These findings demonstrate that integrating multi-temporal remote sensing, ecosystem-service modeling, and Pareto optimization can translate historical land-use information into flexible spatial planning alternatives for rapidly urbanizing regions.