Using the InVEST-PLUS Model to Predict and Analyze the Pattern of Ecosystem Carbon storage in Liaoning Province, China

Abstract

Studying the spatiotemporal distribution pattern of carbon storage, balancing land development and utilization with ecological protection, and promoting urban low-carbon sustainable development are important topics under China’s “dual carbon strategy” (Carbon emissions stabilize and harmonize with natural carbon absorption). However, existing research has paid little attention to the impact of land use changes under different spatial policies on the provincial-scale ecosystem carbon storage. In this study, we established a carbon density database for Liaoning Province and obtained the spatial and temporal distribution of carbon storage over the past 20 years. Then, based on 16 driving factors and multiple spatial policies in Liaoning Province, we predicted land use and land cover changes (LUCC) under three scenarios for 2050 and analyzed the spatiotemporal distribution characteristics and response mechanisms of carbon storage under different scenarios. The results showed that (1) LUCC directly affected carbon storage, with a 35.61% increase in construction land and a decrease in carbon storage of 0.51 Tg over the 20-year period. (2) From 2020 to 2050, the carbon storage varied significantly among the natural trend scenario (NTS), ecological restoration scenario (ERS), and economic priority scenario (EPS), with values of 2112.05 Tg, 2164.40 Tg, and 2105.90 Tg, respectively. Carbon storage in the ecological restoration scenario exhibited positive growth, mainly due to a substantial increase in forest area. (3) The spatial pattern of carbon storage in Liaoning Province was characterized by “low in the center, high in the east, and balanced in the west”. Therefore, Liaoning Province can consider rationally formulating and strictly implementing the spatial policy of ecological protection in the future land planning so as to control the disorderly growth of construction land, realize the growth of ecological land area, effectively enhance carbon storage, and ensure the realization of the goal of “dual carbon strategy”.

Spatial-temporal evolution and prediction of carbon storage in Mohe city by linking the logistic-CA-Markov and InVEST models

“…Moreover, limiting the exploitation of forest and wetland resources, reduces the loss of carbon storage. This suggests that the ecological conservation policy is beneficial to increasing carbon storage, which is consistent with the findings of and Li (2019). The implementation of ecological protection policies is observed to be conducive to limiting the transfer of high carbon density land types to low carbon density land types, promoting the stable development of land types Local spatial autocorrelation analysis of carbon storage under two scenarios for Mohe City in 2030、2040.…”

Simulation and Attribution Analysis of Spatial–Temporal Variation in Carbon Storage in the Northern Slope Economic Belt of Tianshan Mountains, China

“…The LEAS module incorporated various driving factors to extract the expansion of initial LUCC to final land use. To assess the development potential of the different LUCC types and investigate the relationship between these driving factors and LUCC expansion, we employed the random forest classification algorithm, which determined the contribution of each driving factor to LUCC expansion [40]; among them, the sampling rate of random forest was set to 0.01, and the running parameter was set to 5. The Markov chain method was used to predict future LUCC demand, and simulated patches were generated in the CARS module to obtain a simulated future LUCC map, among them, the default value within the domain range was set to 3, with 5 parallel threads, a decay coefficient of 0.9 for the decrement threshold, and a diffusion coefficient of 0.1.…”

Spatiotemporal Evolution and Future of Carbon Storage in Resource-Based Chinese Province: A Case Study from Shanxi Using PLUS–InVEST Model Prediction

“…In fact, Jiang et al conducted a 10-year study of three urban growth scenarios in the Chang-ZhuTan urban agglomeration to examine how urban expansion affects carbon sinks [27]. Similarly, Li et al performed a study in Liaoning Province that used 16 elements and geographic techniques to predict LULC patterns and future carbon stocks [28]. The study also examined the regional and temporal distribution of carbon stocks and the effects of land-use changes.…”