Research on the spatial zoning and driving factors of ecosystem service bundles and human well-being in China

Incorporating ecosystem service bundles (ESBs) into socioecological management strategies can contribute to the achievement of sustainable development goals (SDGs) and increase human well-being (HWB). Exploring the spatial zoning of ESBs and HWB and their driving factors is important for supporting the sustainable development planning of prefecture-level cities in China. The ecosystem services (ESs) and HWB at the prefecture level in China were evaluated using the value method and the SDGs, and the ESBs were subsequently identified through the Gaussian mixture model (GMM). On this basis, the spatial partitioning of ESB-HWB was carried out using the self-organizing map (SOM) method, and the main driving factors affecting the spatial partitioning were revealed using the XGBoost-SHAP model. The results were as follows: (1) During the study period, the value of the ES equivalent factor in China increased from 692 CNY/hm2 to 724 CNY/hm2. The high-value areas of ESs are distributed mainly in the southern hills, the northeastern forest areas, and the southwestern Qinghai‒Tibet Plateau region, while the low-value areas are concentrated in the eastern plains and the northwestern arid regions. Four ESBs were identified through the GMM, and they have significant spatial differentiation, including the multifunctional comprehensive cluster, the agriculture-dominated cluster, the water source prominent cluster, and the regulation core cluster. The human well-being index increased from 0.206 to 0.472, showing a distribution pattern of higher values in the east and lower values in the west, but the regional differences significantly improved during the study period. (2) Six regions were identified through the SOM based on the relationship between the ESBs and HWB, including the regulating core-medium well-being-volatility zone, the agricultural provision-high wellbeing stability zone, the multifunctional-high wellbeing ultrastability zone, the multifunctional-super wellbeing optimization zone, the hydrological scarcity-low wellbeing sensitivity zone, and the multifunctional-hyper resilience wellbeing zone. (3) The results of XGBoost-SHAP revealed the differential impacts of different factors on the results of the six types of spatial partitions, but it was generally found that human activity index, per capita GDP, and average annual precipitation were the main factors affecting the spatial partition. Therefore, when considering both the ecosystem and the sustainable development goals, targeted spatial management strategies need to be formulated based on the differentiated partitioning of ESB-HWB to promote the development of China’s social ecosystem and the achievement of the SDGs.