Impacts of Urbanization on the Spatio-Temporal Patterns of Trade-Offs and Synergies Among Climate-Related Ecosystem Services

Yifeng Qin, Caihua Yang, Rositsa Beluhova-Uzunova, Dobri Dunchev, Boryana Ivanova, Peng Chen and Shengquan Che ()
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Yifeng Qin: School of Design, Shanghai Jiao Tong University, Shanghai 200240, China
Caihua Yang: Department of Economics, Faculty of Economics, Agricultural University Plovdiv, 12 Mendeleev Blvd., 4000 Plovdiv, Bulgaria
Rositsa Beluhova-Uzunova: Department of Economics, Faculty of Economics, Agricultural University Plovdiv, 12 Mendeleev Blvd., 4000 Plovdiv, Bulgaria
Dobri Dunchev: Department of Economics, Faculty of Economics, Agricultural University Plovdiv, 12 Mendeleev Blvd., 4000 Plovdiv, Bulgaria
Boryana Ivanova: Department of Economics, Faculty of Economics, Agricultural University Plovdiv, 12 Mendeleev Blvd., 4000 Plovdiv, Bulgaria
Peng Chen: Lijiang Scenic Area Strategic Development Office, The Lijiang River Tourist Attractions Department, Guilin 541001, China
Shengquan Che: School of Design, Shanghai Jiao Tong University, Shanghai 200240, China

Land, 2025, vol. 14, issue 9, 1-19

Abstract: Under the context of rapid urbanization and climate change, urban ecosystem services (ES) have undergone dramatic transformations. Elucidating the trade-off and synergy relationships among ES and quantifying how urbanization mediates these relationships are critical to achieving urban sustainability. Focusing on Shanghai during 2000–2020, we quantified three climate-related ES—water yield (WY), urban cooling (Heat Mitigation Index, HMI) and carbon storage (CS)—with the InVEST model. We then examined the spatio-temporal evolution of these services, analyzed their trade-offs and synergies, and examined the underlying urbanization drivers. Results show that total WY increased by 76%, with peak volumes concentrated in the central districts; HMI declined, with low-value zones spreading inward; CS rose and became spatially more homogeneous. WY–HMI trade-offs intensified, whereas CS–HMI were synergistic (r = 0.33–0.61) except in core districts where built-up expansion created trade-offs. CS–WY trade-offs weakened, becoming synergistic in most districts by 2020. HMI loss was driven by GDP and industrial output ( p < 0.05). Per-capita green-space area was positively correlated with HMI but exerted no significant influence on CS or WY, highlighting the limitations of ecological interventions focused on single ES enhancement.

Keywords: ecosystem services; spatio-temporal differentiation; trade-offs; synergies (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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