Simulation of Land Use and Carbon Storage Evolution in Multi-Scenario: A Case Study in Beijing-Tianjin-Hebei Urban Agglomeration, China

Abstract

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.

The Response of Carbon Stocks to Land Use/Cover Change and a Vulnerability Multi-Scenario Analysis of the Karst Region in Southern China Based on PLUS-InVEST

“…Carbon density represents the basic data for calculating regional carbon stocks using the InVEST model. The equations are as follows: Domain factor weights reflect the interaction between land use types, which can be filled in empirically or calculated using the percentage expansion of the land type, and this study used the percentage of expansion of land types as the domain factor weights for LUCC prediction [59], and the neighborhood weights were 0.6519, 0.3259, 0.0129, 0.0041, 0.0001, and 0.0052.…”

Scenario Simulation of the Relationship between Land-Use Changes and Ecosystem Carbon Storage: A Case Study in Dongting Lake Basin, China

“…Currently, the CS of terrestrial ecosystems in China is declining at a scale of 9.3 Tg per year due to land-use changes [ 3 ]. However, China is in a critical period of modernization development, and the demand for production and living land is expanding, and the blind development and disorderly expansion of cities will further accelerate carbon loss [ 4 ]. With the goal of “carbon peaking and carbon neutrality” in China, new requirements have been put forward for territorial planning programs, which also aim to stimulate the carbon sequestration and sink potential of terrestrial ecosystems.…”

“…The integrated valuation of ecosystem services and trade-offs (InVEST) model has been widely used in the evaluation of CS due to its high operating efficiency, ease of access to data, and high accuracy [ 10 , 11 ]. On this basis, researchers have studied land-use changes and CS at different regional scales, including basins [ 5 , 11 ], administrative districts [ 12 , 13 ], and urban agglomerations [ 4 , 14 ], by combining scenario simulation and InVEST. Numerous studies have shown that higher and more stable CS can be realized by inhibiting blind urban expansion and relying on low land-use transition [ 13 , 15 , 16 ].…”

Spatiotemporal Variation and Quantitative Attribution of Carbon Storage Based on Multiple Satellite Data and a Coupled Model for Jinan City, China

“…While the carbon density of biomass and soil carbon has a significant relationship with the precipitation factor [32], many studies have been conducted based on factors such as the precipitation density, which is corrected to improve accuracy [32,33]. Thirdly, although the current research methods on carbon storage drivers and the selection of driver indicators can provide scientific guidance for subsequent related studies, the research objects tend to focus on regions such as urban clusters, provinces, and ecologically fragile areas [34][35][36]. However, less attention has been paid to the dynamics of carbon storage in megacity ecosystems and its driving mechanisms, and the relevant research base is weak.…”