Identification and analysis of driving factors for ecosystem service bundles in Shanxi Province under multiple...

In the context of carbon peak and energy structure transformation, ecosystem service bundle(ESB) have obvious changes. As a typical ecologically fragile area in China, the study of ESB in Shanxi Province plays a significant effect in regional sustainable development and ecological governance. This paper employs the PLUS model to simulate land use patterns, which utilizes land use data about Shanxi Province for the years 1980, 2000, and 2020. By integrating this with the dynamic ecosystem service value (ESV) model to assess the ESV, the evolutionary trajectory of the ESB is systematically revealed. Additionally, the driving factors behind the changes in ESB are analyzed using geographic detectors. The results indicated that: (1)From 1980 to 2020, the area of cultivated land consistently decreased, while the area of construction land expanded rapidly. By 2040, the area of cultivated land under the NDS is projected to decrease by 4.21%, whereas under the FPS, it is expected to increase by 4.35% due to policy intervention. (2)The total value of ESV exhibited fluctuations in an 'N-type' pattern. From 1980 to 2020, there was an overall decline of 2.05%, but the ESV is projected to rebound by 0.84% in 2040(FPS). (3)The synergistic relationship among ecosystem services was dominant, accounting for 88.79%, yet the trade-off coefficient between FP-CR increased by 23.5% over the past decade, which is underscoring the significant conflict between food production and ecological protection.(4)Three types of ESBs were identified: the agricultural production-leading bundle (ESB1), the ecological regulation-strengthening bundle (ESB2), and the water conservation-sensitive bundle (ESB3).The proportion of stable types reached its peak at 82.91% under the AEDS, highlighting the reinforcing effect of market mechanisms on ecological function lock-in. The research findings can provide valuable decision support for land space optimization in ecologically fragile areas and the value transformation of ecological product.