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应用生态学报 ›› 2026, Vol. 37 ›› Issue (8): 2771-2781.doi: 10.13287/j.1001-9332.202608.025

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基于建成环境和行为意愿的社区低碳更新潜力类型识别: 以南京中心城区为例

王天豪1, 孔繁花2, 胡宏1,3,4*, 赵慧敏1   

  1. 1南京大学建筑与城市规划学院, 南京 210093;
    2南京大学地理与海洋科学学院, 南京 210023;
    3城市AI与绿色人居环境营造省高校重点实验室, 南京 210093;
    4江苏智慧城市研究基地, 南京 210093
  • 收稿日期:2026-03-13 修回日期:2026-06-24 出版日期:2026-08-18 发布日期:2027-02-18
  • 通讯作者: *E-mail: h.hu@nju.edu.cn
  • 作者简介:王天豪, 男, 2001年生, 硕士研究生。主要从事低碳城市规划支持方法研究。E-mail: 2543255429@qq.com
  • 基金资助:
    国家自然科学基金面上项目(42271202)、国家自然科学基金重点基金项目(42530514)和国家重点研发计划项目(2024YFF1307104)资助。

Spatial correlation network characteristics and influencing factors of urban ecological vulnerability in the Yellow River Basin, China

WANG Tianhao1, KONG Fanhua2, HU Hong1,3,4*, ZHAO Huimin1   

  1. 1School of Architecture and Urban Planning, Nanjing University, Nanjing 210093, China;
    2School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China;
    3Key Laboratory of Urban AI and Green Built Environment of Provincial Higher Education Institutes, Nanjing 210093, China;
    4Jiangsu Smart City Research Base, Nanjing 210093, China
  • Received:2026-03-13 Revised:2026-06-24 Online:2026-08-18 Published:2027-02-18

摘要: 在存量更新与低碳转型背景下,针对社区更新中“空间具备但行动不足”或“意愿较强却难以实施”的困境,本研究构建了“建成环境-行为意愿”双维度评价方法。以南京市中心城区35个社区为样本,采集766份有效问卷,综合运用熵权-TOPSIS法、Fogg行为模型及GIS分析,对低碳更新潜力进行量化评价和划分。结果表明:社区建成环境与居民意愿普遍存在错配现象,依据匹配度将社区低碳更新潜力划分为4类,其空间分布差异显著,其中,协同引领型(数量占比6%)集中于主城核心和沿江;环境领先型(37%)聚集于主城中部;意愿领先型(46%)多见于主城外围及新城沿江;双低约束型(11%)则零散分布于外围沿江。据此提出差异化协同更新策略:协同引领型社区作为先行示范区,应推进深度节能改造和共建共治;环境领先型社区应聚焦场景微更新提升便利性,激发行动动机和触发机制;意愿领先型社区应优先补齐设施短板并推进基础改造,结合参与式议事降低践行门槛;双低约束型社区应采取渐进更新,优先改善基本宜居与节能条件并培育低碳能力。研究成果可为推动我国城市社区实现绿色低碳的高质量更新提供科学决策依据。

关键词: 建成环境, 行为意愿, 低碳导向, 社区更新, Fogg行为模型

Abstract: Against the backdrop of built stock renewal and low-carbon transition, we constructed a evaluation framework from the dimensions of “adequate spatial conditions but insufficient practical actions” and “strong willingness with poor implementation feasibility”, which covered built environment and behavioral intention to address the dilemmas in community renewal. Taking 35 communities in the central urban area of Nanjing as objects, a total of 766 valid questionnaire responses were collected. We used the entropy weight-TOPSIS method, the Fogg behavior model, and GIS spatial analysis to quantitatively assess and classify the low-carbon renewal potential of target communities. The results revealed a widespread mismatch between community built environment and low-carbon intention of residents. The low-carbon renewal potential of the community was categorized into four types based on their matching degrees, with significant spatial distribution differences. The collaborative leading type (accounting for 6%) was concentrated in the central downtown and riverside zones. The environment-leading type (37%) was clustered in the central main urban area. The intention-leading type (46%) was mostly located in the peripheral downtown and riverside new towns. The dual-low constrained type (11%) was scattered across outer riverside areas. Targeted differentiated collaborative renewal strategies were proposed accordingly. As a leading demonstration zone, collaborative leading communities should promote deep energy-saving transformation and joint construction and governance. Environment-leading communities should focus on scene micro-scale updates to enhance convenience, stimulate action motivation, and trigger mechanisms. Intention-leading communities should address the shortcomings of facilities and promoting basic renovation for those who are willing to take the lead, combined with participatory discussions to lower the threshold for implementation. Dual-low constrained communities should adopt gradual updates, prioritize improving basic livability and energy-saving conditions, and cultivate low-carbon capabilities. This study could provide scientific decision support for the high-quality green and low-carbon renewal of urban communities across China.

Key words: built environment, behavioral intention, low-carbon orientation, community renewal, Fogg behavior model