A Comprehensive Framework for Monitoring and Providing Early Warning of Resource and Environmental Carrying Capacity Within the Yangtze River Economic Belt Based on Big Data

Abstract

The Yangtze River Economic Belt (YREB), spanning 11 provinces and municipalities across China, is of paramount importance due to its high economic development and strategic role in national distribution. However, the YREB, which has experienced rapid economic growth, faces challenges resulting from its previously expansive development model, including regional resource and environmental issues. Consequently, a systematic analysis encompassing socio-economic, ecological, and resource-environmental aspects is vital for a comprehensive and quantitative understanding of the YREB’s overall condition. This study explores resource and environmental carrying capacity (RECC) by constructing an integrated framework that includes remote sensing data, geographic information data and social statistical data, which allows for a precise analysis of RECC dynamics from 2010 to 2020. The findings demonstrate an upward trend in the overall quality of RECC from 2010 to 2020, achieving higher grades over time. However, there is significant spatial heterogeneity, with a notable decrease in RECC levels moving from the eastern to the western regions within the YREB. Moreover, low-level RECC areas situated in the northwest of the YREB, show a trend of moving toward regions of higher altitude from 2010 to 2020 based on analysis using the standard deviation ellipse (SDE) method. When considering to the three major urban agglomerations within the YREB, overall RECC in middle and lower agglomerations is generally stable and on an upward trend while cities in upper reaches exhibit significant variation and fluctuations, highlighting them as areas requiring future focus. Therefore, specific indicators were applied to monitor RECC risk for each of these three agglomerations, respectively, after which optimized strategies could be proposed based on different early warning levels. Ultimately this study allows local authorities to implement timely and effective interventions to mitigate risks and promote sustainable development.