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Spatiotemporal Differentiation and Attribution Analysis of Ecological Vulnerability in Heilongjiang Province, China, 2000–2020

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  • Yang Li

    (School of Geographical Sciences and Tourism, Jilin Normal University, Siping 136000, China)

  • Jiafu Liu

    (School of Geographical Sciences and Tourism, Jilin Normal University, Siping 136000, China)

  • Yue Zhu

    (School of Geographical Sciences and Tourism, Jilin Normal University, Siping 136000, China)

  • Chunyan Wu

    (School of Geographical Sciences and Tourism, Jilin Normal University, Siping 136000, China)

  • Yuqi Zhang

    (School of Geographical Sciences and Tourism, Jilin Normal University, Siping 136000, China)

Abstract

Heilongjiang Province, a major grain-producing region in China, faces ecological vulnerabilities that directly affect its sustainable development. A scientific assessment of the spatiotemporal characteristics of ecological vulnerability and its influencing factors in Heilongjiang is crucial for a deeper understanding of environmental issues and provides theoretical support for enhancing regional ecological governance capabilities. The SRP model, combined with the AHP-CRITIC weighting method, was employed to assess Heilongjiang Province’s ecological vulnerability’s temporal and regional differentiation trends between 2000 and 2020. The aggregation kinds of ecological vulnerability were examined using spatial autocorrelation. GeoDetector was used to determine the main elements affecting ecological vulnerability in the province. Additionally, the ecological vulnerability status in 2030 was predicted using the CA-Markov model. The findings indicate that (1) the average EVI values for Heilongjiang Province during the three periods were 0.323, 0.317, and 0.347, respectively, indicating a medium level of ecological vulnerability across the province; the ecological vulnerability initially decreased and then worsened. Spatially, the distribution followed a pattern of “high in the east and west, and low in the north and south”. (2) Spatial agglomeration is evident, with high-high (H-H) aggregation primarily occurring in heavily and extremely vulnerable areas characterized by high human activity, while low–low (L-L) aggregation is mainly found in mildly and marginally vulnerable areas with a favorable natural background. (3) Biological abundance, net primary productivity, dry degree, and PM 2.5 were the main drivers of ecological vulnerability, with interactions between these factors amplifying their impact on ecological vulnerability. (4) The CA-Markov model prediction results indicated an upward trend in the overall ecological vulnerability of Heilongjiang Province by 2030, reflecting a decline in the ecological environment. The study indicates that the ecological vulnerability of Heilongjiang Province is closely linked to its natural geographic conditions and is influenced through the interplay of several environmental elements. Based on the vulnerability zoning results, this paper proposes governance recommendations for regions with different vulnerability levels, aiming to provide theoretical support for future ecological restoration and sustainable development.

Suggested Citation

  • Yang Li & Jiafu Liu & Yue Zhu & Chunyan Wu & Yuqi Zhang, 2025. "Spatiotemporal Differentiation and Attribution Analysis of Ecological Vulnerability in Heilongjiang Province, China, 2000–2020," Sustainability, MDPI, vol. 17(5), pages 1-21, March.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:5:p:2239-:d:1605445
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    References listed on IDEAS

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