The changeable patterns and contractions of land use have become increasingly significant in recent years as the economy and society have rapidly developed. Subsequently, land use change simulation has become a focal point in the study of land use patterns and change processes. Four development scenarios in 2030, including business-as-usual, ecological protection, economic development, and sustainable development scenarios, are proposed to realize the sustainable development of land use in Beijing–Tianjin–Hebei in the context of a low-carbon economy and ecological security. Then, a feasible multi-objective land use optimization scheme suitable for the region’s long-term development was identified through comparative analysis. The GMOP-PLUS model analyzed changes in ecological and economic benefits and carbon emissions by optimizing the quantitative structure and spatial layout of land use in different scenarios. The cultivated land area in the four scenarios decreased, while the construction land area increased for all scenarios other than the ecological protection and sustainable development scenarios. Moreover, the target development of the sustainable development scenario was the most balanced, with carbon emissions and economic benefits reduced by 49.77 million tons and CNY 0.73 billion compared with the business-as-usual scenario, respectively. Meanwhile, the ecological benefits increased by CNY 0.03 billion, and the economic benefits increased by 1.54 times compared with those in 2020. Therefore, the sustainable development scenario was more in line with the needs of Beijing, Tianjin, and Hebei for high-quality economic and ecological development, aiming towards a low-carbon goal. This work provides a theoretical basis for Beijing–Tianjin–Hebei territorial spatial planning and more perspectives for the study of sustainable land use through the obtained results.

Agricultural spatial division and suggestions for the optimization of the partition space were obtained by constructing a recognition system of the dominant agricultural space. The prerequisite was to master natural economic development in agriculture. It was vital to maintain national food security and promote healthy and sustainable agriculture. The suitability evaluation of agricultural production and the dominance evaluation of agricultural development were incorporated to recognize the dominant agricultural space in Cangzhou, Hebei, China in 2020. Besides, priority scenarios were set, e.g., economic development, low-carbon protection, and coordinated development of a low-carbon economy. The NSGA-II genetic algorithm model was used to optimize the quantitative structure of cultivated lands in the agricultural space of Cangzhou in three scenarios in 2030. The research results are as follows: (1) Cangzhou had the largest number of general suitable areas for agricultural production in 2020, accounting for 27.04%; suitable areas were the least, accounting for 10.99%. The proportion of current cultivated lands in unsuitable agricultural production areas still stood at 11.26%; (2) The dominance of agricultural development in 2020 in Cangzhou was mainly at Tier III, accounting for 33.60% with the general dominance of agricultural development; (3) The total area of the dominant agricultural space in Cangzhou was 238208.75 hm², accounting for 16.72% of the national territorial area of Cangzhou. It included 35 villages and towns beyond ecological red lines, mainly distributed in the western part of Cangzhou; (4) The agricultural space of Cangzhou in 2030, optimized by the multi-objective NSGA-II genetic algorithm model, exhibited decreased cultivated lands across three scenarios. The total amount of cultivated lands was the largest under the priority scenario of economic development, and that was the smallest under the priority scenario of coordinated development of a low-carbon economy. Meanwhile, agricultural economic benefits and carbon emission density were reduced under three scenarios. The benefits and density were moderate under the coordinated development of low carbon and economy. The work provides a reference for further formulating and improving the policies of the agricultural space in various regions.

Focusing on Hebei Province in China, the work investigated the impact of implicit land use transition (ILUT) on land use carbon emissions (LUCEs) for dual carbon goals. A county-level evaluation system and a measurement model were constructed to explore ILUT and carbon emissions’ spatiotemporal progression, respectively. The optimal spatial econometric model was selected by employing different testing methods to elucidate how ILUT affected carbon emissions. LUCEs increased from 49.7964 million tons (2000) to 107.401 million tons (2015) and dropped to 92.2173 million tons by 2020. The overall exhibited an inverted V-shape. Values were generally higher in the southeast and lower in the northwest. ILUT index across counties increased from 2000 to 2020, with polarization of implicit indices intensified. Spatial distribution showed that the southeastern area exhibited notably higher values compared to the northwestern parts. Significant positive spatial correlation existed between ILUT and carbon emissions within the county, while a significant negative spatial correlation was observed with carbon emissions in neighboring counties. These findings provide scientific support for formulating differentiated land use policies and optimizing carbon emission control strategies in Hebei Province, holding significant practical value for regional dual carbon targets.

Ecological network optimization can enhance ecological connectivity, regional ecological stability, and carbon sink capacity. Current research on ecological networks employs single-perspective optimization, which overlooks the synergistic requirements between network topological characteristics and the dual carbon goals. It lacks a comprehensive, systemic optimization framework. Focusing on the Beijing–Tianjin–Hebei region, the work constructs an ecological network by integrating ecosystem services, morphological spatial pattern analysis (MSPA), and circuit theory. A framework integrating barrier removal, structural robustness, and carbon sink enhancement is proposed, incorporating ecological barrier identification, complex network theory, and carbon offset patterns for multi-objective structural and functional optimization. The optimized network is evaluated using structural metrics, robustness analysis, and carbon sequestration validation. The network comprises 41 ecological sources and 102 corridors, exhibiting a dense northwest and sparse southeast distribution. Ecological barriers totaling 565.56 km² are removed to improve connectivity in the region. An edge-addition strategy introduces 12 nodes and 49 edges, enhancing connectivity, stability, and carbon sink capacity. Restoration priorities are set with the phased objectives of removing barriers, connecting topological weak points, and optimizing low-value carbon offset areas. Shifting the focus from structural connectivity to integrated function, the work contributes a methodological framework for advancing ecological security and carbon neutrality in urban agglomerations.