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Spatiotemporal dynamics and future predictions of ecosystem service synergies and trade-offs in China

  • Xiaohui Bai
  • , Shilong Ren*
  • , Shuping Ji
  • , Yuchao Ren
  • , Lei Fang
  • , Jinyue Chen
  • , Qiao Wang
  • *Corresponding author for this work

Research output: Contribution to journal › Journal article › peer-review

9 Citations (Scopus)

Abstract

Ecosystems provide extensive benefits to human well-being through food production, climate regulation, and cultural services. Climate change and socioeconomic development have substantially altered the capacity of ecosystem services and pose ongoing risks to their long-term sustainability. However, our understanding of the dynamic evolution patterns of ecosystem service interactions and their underlying mechanisms remains limited. This study comprehensively explored the spatiotemporal characteristics of ecosystem services and their synergistic/trade-off relationships over China by combining historical assessments (2001-2022) with future predictions (2030-2100) through machine learning algorithms. Results show that carbon sequestration (CS) and food production (FP) have significantly increased since 2001 in forest, grassland, and cropland. Future projections indicate that CS and soil retention (SR) will continue to rise after 2030 under different scenarios, while FP will keep relatively stable. A dominant synergy pattern was detected for all pairs of ecosystem services, distributed mainly in Northeast China, indicating mutual promotion of ecosystem functions. However, segmented trend analysis reveals that the synergies among ecosystem services have significantly declined since 2004, except for CS & water yield (WY) and FP & WY. Concerningly, these synergies will continue weakening during 2030-2100, while trade-offs will expand significantly for CS & FP and FP & SR. Although geoclimatic factors are vital in controlling service supply, overlapping implementation of engineering projects has also largely contributed to ecosystem service growth. Our findings highlight the necessity for implementing sophisticated intervention policies to navigate increasing trade-off risks and ensure optimal ecosystem service provision.

Original languageEnglish
Article number124194
Number of pages12
JournalEnvironmental Research
Volume297
Early online date5 Mar 2026
DOIs
Publication statusPublished - 15 May 2026

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 2 - Zero Hunger
    SDG 2 Zero Hunger
  2. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being
  3. SDG 13 - Climate Action
    SDG 13 Climate Action

User-Defined Keywords

  • Ecosystem service
  • Future prediction
  • Machine learning
  • Spatiotemporal pattern
  • Synergy/trade-offs

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