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Mechanisms and Resilience Governance of Built Heritage Spatial Differentiation in China: A Sustainability Perspective

Author

Listed:
  • Yangyang Lu

    (College of Architecture and Urban Planning, Beijing University of Technology, Beijing 100124, China
    College of Architecture, Nanyang Institute of Technology, Nanyang 473006, China
    These authors contributed to the work equally and should be regarded as co-first author.)

  • Longyin Teng

    (College of Architecture and Urban Planning, Beijing University of Technology, Beijing 100124, China
    These authors contributed to the work equally and should be regarded as co-first author.)

  • Jian Dai

    (College of Architecture and Urban Planning, Beijing University of Technology, Beijing 100124, China)

  • Qingwen Han

    (College of Architecture, Nanyang Institute of Technology, Nanyang 473006, China)

  • Zhong Sun

    (College of Architecture, Nanyang Institute of Technology, Nanyang 473006, China)

  • Lin Li

    (College of Architecture and Urban Planning, Beijing University of Technology, Beijing 100124, China)

Abstract

Built heritage serves as a vital repository of human history and culture, and an examination of its spatial distribution and influencing factors holds significant value for the preservation and advancement of our historical and cultural narratives. This thesis brings together various forms of built heritage, employing methodologies such as kernel density estimation, average nearest neighbor analysis, and standard deviation ellipses to elucidate the characteristics of spatial distribution. Additionally, it investigates the influencing factors through geographical detectors and Multiscale Geographically Weighted Regression (MGWR). The findings reveal several key insights: (1) In terms of geographical patterns, built heritage is predominantly located southeast of the “Hu-Huanyong” line, with notable concentrations at the confluence of Shanxi and Henan provinces, the southeastern region of Guizhou, as well as in southern Anhui, Fujian, and Zhejiang. Moreover, distinct types of built heritage exhibit marked spatial variations. (2) The reliability and significance of the analytical results derived from prefecture and city-level units surpass those obtained from grid and provincial-level analyses. Among the influencing factors, the explanatory power associated with the number of counties emerges as the strongest, while that relating to population density was the weakest; furthermore, interactions among factors that meet significance thresholds reveal enhanced explanatory capabilities. (3) Both road density and population density demonstrate positive correlations; conversely, the positive influence of topographic relief and river density accounts for 90% of their variance. GDP exhibits a negative correlation, with the number of counties contributing to 70% of this negative impact; thus, the distribution of positive and negative influences from various factors varies significantly. Drawing upon these spatial distribution characteristics and the disparities observed in regression coefficients, this thesis delves into potential influence factors and proposes recommendations for the development and safeguarding of built heritage.

Suggested Citation

  • Yangyang Lu & Longyin Teng & Jian Dai & Qingwen Han & Zhong Sun & Lin Li, 2025. "Mechanisms and Resilience Governance of Built Heritage Spatial Differentiation in China: A Sustainability Perspective," Sustainability, MDPI, vol. 17(13), pages 1-24, July.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:13:p:6065-:d:1693220
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    References listed on IDEAS

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    1. Meiyan Li & Wen Ouyang & Dayu Zhang, 2022. "Spatial Distribution Characteristics and Influencing Factors of Traditional Villages in Guangxi Zhuang Autonomous Region," Sustainability, MDPI, vol. 15(1), pages 1-11, December.
    2. Liu Jin & Zongqi Wang & Xiaohong Chen, 2022. "Spatial Distribution Characteristics and Influencing Factors of Traditional Villages on the Tibetan Plateau in China," IJERPH, MDPI, vol. 19(20), pages 1-19, October.
    3. Mateusz Tomal, 2022. "Exploring the meso-determinants of apartment prices in Polish counties using spatial autoregressive multiscale geographically weighted regression," Applied Economics Letters, Taylor & Francis Journals, vol. 29(9), pages 822-830, May.
    4. A. Stewart Fotheringham & Wenbai Yang & Wei Kang, 2017. "Multiscale Geographically Weighted Regression (MGWR)," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 107(6), pages 1247-1265, November.
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