Objective Natural climate solutions (NCS) centered on carbon sequestration and emission reduction can effectively respond to climate change. However, the rapid socio-economic development has presented significant challenges to NCS implementation by constantly threatening ecological lands. This research proposes to optimize territorial space to coordinate diverse development and bolster climate resilience through developing a multi-objective optimization model.

Methods Taking the Meishan area of Sichuan Tianfu New Area as an example, this research sets up two scenarios for the period up to 2030: Climate-resilient development and balanced development. The climate-resilient development scenario prioritizes the development goal of mitigating and adapting to climate change, and actively explores how to maximize the climate regulation benefits of ecosystem. The balanced development scenario aims to actively address the adverse impacts of climate change while promoting urban development and construction. Based on development requirements, both scenarios incorporate six optimization objectives: Climate regulation benefits, carbon emissions, population capacity, and so on. With the evaluation results of resource and environment carrying capacity and territorial spatial development suitability of the research area as guiding constraints, an optimization model based on the principles of the third-generation non-dominated sorting genetic algorithm (NSGA-III) is constructed. This model can help accomplish the optimization of territorial space and analyze the performance and optimization results of the multi-objective optimization model.

Results The evaluation results of resource and environment carrying capacity and territorial spatial development suitability of the research area indicate that ecologically crucial areas and ecologically important areas are primarily distributed in the Pengzu Mountain and Longquan Mountain ecological belts accounting for 3.32% of the total area of the research area. The water area is mainly composed of the Min River and the Fu River, accounting for 0.93% of the total area. The research area lacks carbon sink resources, which is unfavorable for climate-resilient development. The areas suitable for agricultural production and urban construction completely overlap, accounting for over 95% of the total area of the research area, and can be utilized for either food production or urban development. According to the evaluation results of the resource and environment carrying capacity and territorial spatial development suitability of the research area, the land use change proportion is 10.92% in the climate-resilient development scenario and 13.21% in the balanced development scenario. Among the five subdistricts/towns within the research area’s jurisdiction, the forestland area in Qinglong Subdistrict, characterized by limited ecological lands, has expanded by over 100%, while the built-up area in Gaojia Town and Guiping Town with relatively low urban development intensity, has surged by over 600%. Land use changes in Jinjiang Town and Shigao Subdistrict have remained comparatively stable. Upon optimization, the shapes of various land patches have become more intricate compared to 2020, resulting in a more fragmented land layout, particularly evident in the balanced development scenario. The shifts in land use utilization have increased the climate regulation benefits by 23.12% and 9.88% respectively in the climate-resilient development scenario and the balanced development scenario, with forestlands primarily contributing to the former while grasslands to the latter, and both the forestlands and grasslands predominantly converted from arable lands. Population capacity has risen by 64.94% and 69.15% respectively in the two scenarios. Carbon emissions have surged by 27.79% and 34.50%, predominantly driven by construction lands, while emissions from forestlands, grasslands, and water bodies have decreased by 10.64% and 8.63% respectively in the two scenarios. The performance of the three development objectives in terms of spatial layout optimization is relatively favorable. In summary, the climate-resilient development scenario successfully meets climate resilience targets while achieving social development objectives. In this scenario, existing forestlands are effectively preserved, and new ones are significantly generated. Similarly, in the balanced development scenario, there is a notable increase in forestland area, albeit with some degradation of existing forestlands, requiring the conversion of additional arable lands to offset these losses. Moreover, grasslands emerge around and within built-up areas in both scenarios, further augmenting the climate regulation benefits within the research area. Nonetheless, in both scenarios, the interconnection of newly formed small forestland and grassland patches with newly developed construction land patches compromises the integrity of arable land patches in the spatial layout.

Conclusion The multi-objective territorial spatial optimization model, guided by climate resilience while considering social development needs, can effectively address the risk of encroachment on ecological lands such as forestlands and grasslands. It provides a viable technical pathway for the research area to advance the pilot zero-carbon emission program and implement climate-resilient development practices. In the future, through targeted on-site monitoring of land use changes and climate change responses in the research area at small scales, we can systematically improve the model’s performance to facilitate the implementation of territorial spatial planning with a focus on climate resilience.