Multi-Objective Optimization of Land Use in the Beijing–Tianjin–Hebei Region of China Based on the GMOP-PLUS Coupling Model

Abstract

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.

Optimization of land use to accommodate nutritional transformation of food systems: a case study from the Beijing–Tianjin–Hebei region

“…Some studies have incorporated residents' happiness, environmental satisfaction, and other factors into the objective functions of regional resource optimization, but these indicators are difficult to quantify. We also compared the results of this study with other studies on land use optimization in the Beijing-Tianjin-Hebei region (Bao et al, 2021;Meng et al, 2023), where the land use change trends showed spatial consistency under the same context, and the scale of the transformation of land classes into each other was roughly the same, but due to the differences in benefit coefficients selection, the economic value of the optimized and the ecosystem service values may be measured differently due to differences in the selection of benefit coefficients. However, compared with the traditional multi-objective optimization, this study based on nutritional goals and taking into account other development scenarios, achieving a combination of micro and macro levels.…”

Sustainable and Resilient Land Use Planning: A Multi-Objective Optimization Approach

“…Table 1 summarizes the applications of multi-objective optimization techniques in sustainable and resilient urban planning, addressing crucial objectives, such as compactness, flood impact mitigation, carbon emission reduction, economic benefits, and sustainable development [22][23][24][25][26]. The utilization of various optimization methods reflects the interdisciplinary nature of the field and the diverse approaches employed to tackle complex urban planning challenges.…”

Simulation and Zoning Research on the Ecosystem Service in the Beijing–Tianjin–Hebei Region Based on SSP–RCP Scenarios

“…The PLUS model consists of the land expansion analysis strategy (LEAS) and the CA model based on multi-type random patch seeds (CARS) [35]. In this study, fourteen natural and social factors (Table 2) were selected as driving factors of land use change according to previous studies and the availability of data [36][37][38]. Combined with the dynamic driving factors (GDP, population, precipitation, and temperature) under the SSP-RCP scenario, the land use under the SSP-RCP scenario was simulated using the PLUS model.…”