The impact of urbanization on regional carbon sequestration in arid areas: A case study of Altay Region, Xinjiang, China

• Innovative coupling of the InVEST model with STIRPAT and its application to the analysis of carbon stock patterns. • A whole-chain carbon stock model identified primary industry, urbanization rate, and CO₂ emissions as key factors in Altai. • Revealed the trend of decreasing carbon stock in Xinjiang Altay region of China in the past 40 years year by year. Scientifically exploring the spatiotemporal variation patterns of carbon storage and accurately identifying its major influencing factors hold practical significance for China to achieve the “dual carbon” goals of peaking carbon emissions by 2030 and achieving carbon neutrality by 2060. In ecologically fragile arid inland areas, multiple factors during the urbanization process drive the evolution of ecosystems, which in turn leads to changes in carbon storage. This study focuses on the arid region of Altay in northern Xinjiang and innovatively proposes a carbon storage analysis method based on multiple influencing factors, aiming to investigate the patterns of carbon storage variation under the combined effects of various elements. The results show that: (1) Over the past four decades, land-use type changes in the study area have exhibited distinct spatiotemporal differentiation. Prior to 2005, grasslands in the southern part of the study area were patchy and scattered; by 2020, the grassland area in the northern region had increased, while that in the southern region had nearly disappeared. Land-use changes mainly involved the conversion of forests and unutilized land into grasslands, cropland, built-up land, and water bodies. (2) The carbon storage change pattern in the study area can be divided into two phases, showing an overall declining trend. The year with the highest carbon storage was 1980, with a total of 689.99 million tons, while the lowest was 2010, with a total of 661.23 million tons. (3) The regression model passed the collinearity test, and the ridge regression results showed that all explanatory variable coefficients were significant at the 5% level, indicating that the fitted equation is reasonable. The influencing factors of carbon sequestration, ranked from greatest to least impact, are: the proportion of the primary industry in GDP, urbanization rate, CO 2 emissions, cropland area, energy intensity, per capita GDP, and temperature. Clarifying the impacts of various influencing factors during the urbanization process on carbon sequestration in dryland cities.