Multi-Scale Ecosystem Service Supply–Demand Dynamics and Driving Mechanisms in Mainland China During the Last Two Decades: Implications for Sustainable Development

Menghao Qi, Mingcan Sun, Qinping Liu (), Hongzhen Tian, Yanchao Sun, Mengmeng Yang and Hui Zhang
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Menghao Qi: College of Economics and Management, Tiangong University, Tianjin 300387, China
Mingcan Sun: College of Economics and Management, Tiangong University, Tianjin 300387, China
Qinping Liu: College of Economics and Management, Tiangong University, Tianjin 300387, China
Hongzhen Tian: College of Economics and Management, Tiangong University, Tianjin 300387, China
Yanchao Sun: College of Economics and Management, Anhui Agricultural University, Hefei 230036, China
Mengmeng Yang: College of Information Science and Technology, Taishan University, Tai’an 271000, China
Hui Zhang: College of Economics and Management, Tiangong University, Tianjin 300387, China

Sustainability, 2025, vol. 17, issue 15, 1-29

Abstract: The growing mismatch between ecosystem service (ES) supply and demand underscores the importance of thoroughly understanding their spatiotemporal patterns and key drivers to promote ecological civilization and sustainable development at the regional level in China. This study investigates six key ES indicators across mainland China—habitat quality (HQ), carbon sequestration (CS), water yield (WY), sediment delivery ratio (SDR), food production (FP), and nutrient delivery ratio (NDR)—by integrating a suite of analytical approaches. These include a spatiotemporal analysis of trade-offs and synergies in supply, demand, and their ratios; self-organizing maps (SOM) for bundle identification; and interpretable machine learning models. While prior research studies have typically examined ES at a single spatial scale, focusing on supply-side bundles or associated drivers, they have often overlooked demand dynamics and cross-scale interactions. In contrast, this study integrates SOM and SHAP-based machine learning into a dual-scale framework (grid and city levels), enabling more precise identification of scale-dependent drivers and a deeper understanding of the complex interrelationships between ES supply, demand, and their spatial mismatches. The results reveal pronounced spatiotemporal heterogeneity in ES supply and demand at both grid and city scales. Overall, the supply services display a spatial pattern of higher values in the east and south, and lower values in the west and north. High-value areas for multiple demand services are concentrated in the densely populated eastern regions. The grid scale better captures spatial clustering, enhancing the detection of trade-offs and synergies. For instance, the correlation between HQ and NDR supply increased from 0.62 (grid scale) to 0.92 (city scale), while the correlation between HQ and SDR demand decreased from −0.03 to −0.58, indicating that upscaling may highlight broader synergistic or conflicting trends missed at finer resolutions. In the spatiotemporal interaction network of supply–demand ratios, CS, WY, FP, and NDR persistently show low values (below −0.5) in western and northern regions, indicating ongoing mismatches and uneven development. Driver analysis demonstrates scale-dependent effects: at the grid scale, HQ and FP are predominantly influenced by socioeconomic factors, SDR and WY by ecological variables, and CS and NDR by climatic conditions. At the city level, socioeconomic drivers dominate most services. Based on these findings, nine distinct supply–demand bundles were identified at both scales. The largest bundle at the grid scale (B3) occupies 29.1% of the study area, while the largest city-scale bundle (B8) covers 26.5%. This study deepens the understanding of trade-offs, synergies, and driving mechanisms of ecosystem services across multiple spatial scales; reveals scale-sensitive patterns of spatial mismatch; and provides scientific support for tiered ecological compensation, integrated regional planning, and sustainable development strategies.

Keywords: ecosystem services; supply–demand relationships; spatiotemporal trade-offs and synergies; machine learning; multi-scale; mainland China (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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