IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i16p7409-d1725716.html
   My bibliography  Save this article

Higher Emissions Scenarios Increase Wildland–Urban Interface Fire Hazard in China

Author

Listed:
  • Dapeng Gong

    (Key Laboratory of Forest and Grassland Fire Risk Prevention, Ministry of Emergency Management, China Fire and Rescue Institute, Beijing 102202, China
    College of Forestry, Northeast Forestry University, Harbin 150040, China)

Abstract

Climate change has intensified the occurrence of wildfires, increasing their frequency and intensity worldwide, and threatening sustainable development through ecological and socioeconomic impacts. Understanding the driving factors behind wildland–urban interface (WUI) fire events and predicting future wildfire hazards in WUI areas are essential for effective wildfire management and sustainable land-use planning. In this study, we developed a WUI fire hazard prediction model for China using machine learning techniques. Diagnostic attribution analysis was employed to identify key drivers of WUI fire hazards. The results revealed that the random forest-based WUI fire hazard model outperformed other models, demonstrating strong generalization capability. SHapley Additive exPlanations analysis revealed that meteorological factors are the primary drivers of WUI fire hazards. These factors include temperature, precipitation, and relative humidity. We further examined the evolution of WUI fire hazards under historical and future climate change scenarios. During the historical baseline period (1985–2014), regions classified as moderate and high hazards were concentrated in southern China. These regions include East China, South Central China, and Southwest China. Climate change exacerbates future WUI fire hazards in China. Projections under the high emission scenario (SSP5–8.5) indicate a rapid increase in WUI fire hazard indices in northern China by the end of the 21st century. Concurrently, the gravity center of high hazard areas is predicted to shift northward, accompanied by a substantial expansion in their area coverage. These findings highlight an urgent need to reorient fire management resources toward northern China under high-emission scenarios. Our findings establish a predictive framework for WUI fire hazards and emphasize the urgency of implementing climate-adaptive management strategies aligned with future hazard patterns. These strategies are critical for enhancing sustainability by reducing fire-related ecological and socioeconomic losses in WUI areas.

Suggested Citation

  • Dapeng Gong, 2025. "Higher Emissions Scenarios Increase Wildland–Urban Interface Fire Hazard in China," Sustainability, MDPI, vol. 17(16), pages 1-16, August.
  • Handle: RePEc:gam:jsusta:v:17:y:2025:i:16:p:7409-:d:1725716
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/16/7409/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/17/16/7409/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Markus Reichstein & Gustau Camps-Valls & Bjorn Stevens & Martin Jung & Joachim Denzler & Nuno Carvalhais & Prabhat, 2019. "Deep learning and process understanding for data-driven Earth system science," Nature, Nature, vol. 566(7743), pages 195-204, February.
    2. Nikos Koutsias & Panagiotis Balatsos & Kostas Kalabokidis, 2014. "Fire occurrence zones: kernel density estimation of historical wildfire ignitions at the national level, Greece," Journal of Maps, Taylor & Francis Journals, vol. 10(4), pages 630-639, October.
    3. Irene Lorenzoni & Nick F. Pidgeon & Robert E. O'Connor, 2005. "Dangerous Climate Change: The Role for Risk Research," Risk Analysis, John Wiley & Sons, vol. 25(6), pages 1387-1398, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Licheng Liu & Wang Zhou & Kaiyu Guan & Bin Peng & Shaoming Xu & Jinyun Tang & Qing Zhu & Jessica Till & Xiaowei Jia & Chongya Jiang & Sheng Wang & Ziqi Qin & Hui Kong & Robert Grant & Symon Mezbahuddi, 2024. "Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    2. Rozenstein, Offer & Fine, Lior & Malachy, Nitzan & Richard, Antoine & Pradalier, Cedric & Tanny, Josef, 2023. "Data-driven estimation of actual evapotranspiration to support irrigation management: Testing two novel methods based on an unoccupied aerial vehicle and an artificial neural network," Agricultural Water Management, Elsevier, vol. 283(C).
    3. Jiang, Hou & Lu, Ning & Huang, Guanghui & Yao, Ling & Qin, Jun & Liu, Hengzi, 2020. "Spatial scale effects on retrieval accuracy of surface solar radiation using satellite data," Applied Energy, Elsevier, vol. 270(C).
    4. Wen Zhang & Jing Li & Yunhao Chen & Yang Li, 2019. "A Surrogate-Based Optimization Design and Uncertainty Analysis for Urban Flood Mitigation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(12), pages 4201-4214, September.
    5. Feng, Jiaojiao & Wang, Weizhen & Xu, Feinan & Wang, Shengtang, 2024. "Evaluating the ability of deep learning on actual daily evapotranspiration estimation over the heterogeneous surfaces," Agricultural Water Management, Elsevier, vol. 291(C).
    6. Mohanad A. Deif & Ahmed A. A. Solyman & Mohammed H. Alsharif & Seungwon Jung & Eenjun Hwang, 2021. "A Hybrid Multi-Objective Optimizer-Based SVM Model for Enhancing Numerical Weather Prediction: A Study for the Seoul Metropolitan Area," Sustainability, MDPI, vol. 14(1), pages 1-17, December.
    7. Zhang, Shuangyi & Li, Xichen, 2021. "Future projections of offshore wind energy resources in China using CMIP6 simulations and a deep learning-based downscaling method," Energy, Elsevier, vol. 217(C).
    8. Gianluca Biggi & Martina Iori & Julia Mazzei & Andrea Mina, 2025. "Green intelligence: the AI content of green technologies," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 15(3), pages 803-840, September.
    9. Florian Reiner & Martin Brandt & Xiaoye Tong & David Skole & Ankit Kariryaa & Philippe Ciais & Andrew Davies & Pierre Hiernaux & Jérôme Chave & Maurice Mugabowindekwe & Christian Igel & Stefan Oehmcke, 2023. "More than one quarter of Africa’s tree cover is found outside areas previously classified as forest," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    10. Kyle Lesinger & Di Tian, 2025. "Skillful subseasonal soil moisture drought forecasts with deep learning-dynamic models," Nature Communications, Nature, vol. 16(1), pages 1-17, December.
    11. Wang, Yukuan & Liu, Jingxian & Liu, Ryan Wen & Wu, Weihuang & Liu, Yang, 2023. "Interval prediction of vessel trajectory based on lower and upper bound estimation and attention-modified LSTM with bayesian optimization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    12. Yolande Strengers & Cecily Maller, 2017. "Adapting to ‘extreme’ weather: mobile practice memories of keeping warm and cool as a climate change adaptation strategy," Environment and Planning A, , vol. 49(6), pages 1432-1450, June.
    13. He, Xinlei & Liu, Shaomin & Xu, Tongren & Yu, Kailiang & Gentine, Pierre & Zhang, Zhe & Xu, Ziwei & Jiao, Dandan & Wu, Dongxing, 2022. "Improving predictions of evapotranspiration by integrating multi-source observations and land surface model," Agricultural Water Management, Elsevier, vol. 272(C).
    14. P. Ding & M. D. Gerst & A. Bernstein & R. B. Howarth & M. E. Borsuk, 2012. "Rare Disasters and Risk Attitudes: International Differences and Implications for Integrated Assessment Modeling," Risk Analysis, John Wiley & Sons, vol. 32(11), pages 1846-1855, November.
    15. Wang, Yangjun & Liu, Kefeng & Zhang, Ren & Qian, Longxia & Shan, Yulong, 2021. "Feasibility of the Northeast Passage: The role of vessel speed, route planning, and icebreaking assistance determined by sea-ice conditions for the container shipping market during 2020–2030," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    16. Richards, Daniel Rex & Lavorel, Sandra, 2022. "Integrating social media data and machine learning to analyse scenarios of landscape appreciation," Ecosystem Services, Elsevier, vol. 55(C).
    17. Alcasena, Fermín J. & Salis, Michele & Nauslar, Nicholas J. & Aguinaga, A. Eduardo & Vega-García, Cristina, 2016. "Quantifying economic losses from wildfires in black pine afforestations of northern Spain," Forest Policy and Economics, Elsevier, vol. 73(C), pages 153-167.
    18. Wang, Zhanwei & Qin, Yijie & Kong, Yifan & Wang, Lin & Leng, Qiang & Zhang, Chunxiao, 2025. "Advanced fault detection, diagnosis and prognosis in HVAC systems: Lifecycle insight, key challenges, and promising approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 219(C).
    19. Lucia Bosone & Raquel Bertoldo, 2022. "The Greater the Contact, the Closer the Threat: The Influence of Contact with Nature on the Social Perception of Biodiversity Loss and the Effectiveness of Conservation Behaviours," Sustainability, MDPI, vol. 14(24), pages 1-15, December.
    20. Fuzhi Lu & Huayu Lu & Yao Gu & Pengyu Lin & Zhengyao Lu & Qiong Zhang & Hongyan Zhang & Fan Yang & Xiaoyi Dong & Shuangwen Yi & Deliang Chen & Francesco S. R. Pausata & Maya Ben-Yami & Jennifer V. Mec, 2025. "Tipping point-induced abrupt shifts in East Asian hydroclimate since the Last Glacial Maximum," Nature Communications, Nature, vol. 16(1), pages 1-21, December.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:17:y:2025:i:16:p:7409-:d:1725716. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.