In considering regional sustainable development, optimizing the distribution of land use and land cover (LULC) and improving terrestrial ecosystem carbon storage (CS) have emerged as major concerns. In this study, considering the synergistic effect between LULC and CS, a coupling model (named MPI) that integrates Multi-objective Optimization (MOP) model, the Patch-generating Land Use Simulation (PLUS) model, and the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, was proposed to simulate the 2030 CS and explore its spatial-temporal characteristics in a Beijing-Tianjin-Hebei urban agglomeration (BTH). The MPI model, which combines the advantages of the above three models, can optimize the LULC structure, simulate the LULC distribution, and efficiently extract CS variation. The results indicated that: (1) LULC changes in BTH were mostly represented in transfers between cropland, forest, and grassland; (2) three different scenarios were simulated using the MPI model, named BAU (Business as usual), EDP (Ecological development priority), and EEB (Ecological and economic balanced). The simulation results of the three scenarios are in line with their respective goals, and the results are quite different; (3) cropland, water, and bare land, will be reduced, and the constant shrinking of water is a pressing issue that must be addressed; and (4) the EEB scenario balanced ecological services and economic rewards, increased the ecosystem carbon sink function, and is an efficient way to investigate “carbon neutrality”. The application of the MPI model is of reference value for exploring the optimal configuration of land resources.

Air pollution endangers human health and sustainable socio-economic development, especially in urban agglomeration (UA). The Chinese government has implemented a series of policies and standards to improve air quality. However, few studies have compared variations in PM_2.5 concentrations across multiple UAs, and current research often lacks analysis relative to the clean air policies implemented by the government. In this study, we used econometric and geostatistical methods to assess the distribution and spatial evolution of PM_2.5 concentrations in five UAs (the Beijing–Tianjin–Hebei UA (BTHUA), middle reaches of the Yangtze River UA (MYRUA), Chengdu–Chongqing UA (CCUA), Harbin Changchun UA (HCUA), and Beibu Gulf UA (BGUA)) in China from 2000 to 2021 to explore the effectiveness of the clean air policies implemented by the government on air pollution control, to analyze the ambient air quality of UAs, and to make recommendations for public outdoor activities. The results indicated that the clean air policy implemented by the Chinese government in 2013 achieved significant treatment results. PM_2.5 concentrations were plotted as an inverted U-shaped curve based on time, which showed an upward trend before 2013 and a downward trend after 2013. PM_2.5 concentrations showed a similar seasonal pattern, with a single-valley “V” shape. PM_2.5 concentration was the highest in winter and the lowest in summer. The PM_2.5 concentration of HCUA and BGUA was lower than that of CCUA, MYRUA, and BTHUA. The increase in PM_2.5 concentration mainly occurred in autumn and winter, while the decrease mainly occurred in spring. In 2021, the PM_2.5 air quality compliance rates (<35 µg/m³) in BTHUA, MYRUA, CCUA, HCUA, and BGUA were 44.57%, 80.00%, 82.04%, 99.74%, and 100%, respectively. However, in 2021, 19.19% of the five UAs still had an ambient air quality of Grade II (i.e., 50 < AQI_PM2.5 < 100). People with abnormally sensitive breathing in these areas should reduce their outdoor activities. These results contribute to epidemiological studies on human health and disease prevention and suggest reasonable pathways by which governments can improve air quality through sustainable urban planning.