Spatio-temporal evolution and driving factors of carbon storage in the Western Sichuan Plateau

Abstract

The carbon sequestration function of the ecosystem is one of the most important functions of ecosystem service, and it of great significance to study the spatio-temporal differentiation of carbon storage for promoting regional sustainable development. Ecosystems on the Western Sichuan Plateau are highly variable, but its spatio-temporal differentiation and driving factors are not yet clear. In this study, on the basis of land use monitoring data, meteorological and demographic data interpreted from Landsat remote sensing image, and through GIS analysis tools, the carbon storage module of InVEST (Integrated Valuation of Ecosystem Services and Trade-offs) model was used to estimate carbon storage and geodetector was used to detect the driving factors of carbon storage spatial differentiation. The results show that: (1) The carbon storage increased to 1.2455 × 1010 t from 1.2438 × 1010 t in the past 20 years, the ecosystem developed in a healthy way overall. (2) Carbon storage show High-High and Low-Low aggregation characteristics, but the area decreased by 1481.81 km2 and 311.11 km2 respectively, and the spatial cluster effect gradually weakened. (3) HAI is the leading factor causing the spatio-temporal differentiation of regional carbon storage, followed by temperature and NDVI; the interaction between factors significantly enhances the spatial differentiation of carbon storage, indicating that the change of carbon storage is the result of the joint action of natural and socioeconomic factors. The results of the study provide some theoretical basis for the development of differentiated ecological regulation models and strategies, and help to promote high-quality regional development.

Impact of agricultural irrigation and resettlement practices on carbon storage in arid inland river basins: A case study of the Shule river basin

“…Yumen City and Guazhou County's oasis area in the central area have been detected by single-factor detection, which shows that vegetation, potential evapotranspiration, and precipitation are the main driving factors. Unexpectedly, this paper finds that the single-factor detection results of GDP and population are relatively small, which is different from the results of Li et al [ 45 ] and Xiang et al [ 46 ]. Because the population in the study area is concentrated in the population gathering area such as the county seat and the land type of this part of the region is mainly construction land, and the rest of the agricultural population is scattered in various regions by township.…”

Carbon sinks and carbon emissions balance of land use transition in Xinjiang, China: differences and compensation

“…Considering only net carbon emissions as a baseline value for carbon offsets may result in excessive payments in some areas. If researchers have high precision data, they can use GDP, carbon intensity, carbon efficiency and other indicators to form a revised model, setting a carbon emission threshold for each region 61 . At present, ecological payment is mainly dominated by the government, and the factors such as the ability to pay, willingness to pay, conflicts between different stakeholders and regional consumption coefficient are less considered 26 .…”

“…Zero represents the spatially dependent and random distribution of spatial units. The local indicators of spatial association (LISA) explains the spatial heterogeneity or attribute values of geographical phenomena and estimates the spatial extent and location of grouped areas 61 . Through calculation, the clustering results can be divided into low value cluster (Low-Low), high value cluster (High-High), low-value cluster mainly surrounded by high value (Low–High) and high-value cluster mainly surrounded by low value (High-Low).…”

Spatiotemporal Evolution and Prediction of Land Use and Carbon Stock in Shanghai

“…Even though biogeochemical modeling and biomass approaches are highly accurate in estimating carbon stocks, it is challenging to depict changes in carbon stocks over extended time periods, and the sheer amount of factors needed restricts the application scenarios [18,19]. The InVEST (Integrated Valuation of Environmental Services and Tradeoffs) model can simulate the changes in ecosystem services under different LUCC scenarios [20].…”