| 摘要: |
| ：“双碳”目标的确立为我国经济
绿色转型指明了新的方向，农业农村是
“双碳”目标实现的重要领域，其中自然
碳汇对农业绿色发展起着重要的作用。因
此，农村人居环境发展水平对于实现“碳
中和”目标至关重要，评估农村人居环境
韧性水平是实现村庄可持续发展的重要技
术手段。文章以碳平衡核算空间单元为基
础，融合韧性理论和人居环境科学，建立
融入碳生态承载系数的“社会系统—居住
系统—人类系统—支撑系统—自然系统”
五大维度的农村人居环境系统综合韧性评
估模型。运用随机森林模型、熵权
TOPSIS 模型、路径分析及障碍度模型评
价咸阳市彬州农村人居环境综合韧性水平
及主要影响要素，并提出政策性建议。不
仅为韧性研究提供新的研究思路和方向，
也为碳中和背景下农村高质量发展提供了
理论和实践指导。 |
| 关键词: 人居环境 韧性 碳生态承载
力 碳排放 |
| DOI：10.13791/j.cnki.hsfwest.20240919003 |
| 分类号: |
| 基金项目:国家自然科学基金面上基金项目（52478070）；新疆自治区区域创新协同创新项目（上海合作组织科技伙伴计划）（2025E01043） |
|
| Resilience assessment of rural human settlement systems under carbon ecological carryingcapacity constraints |
|
ZHAO Qing,GUO Xiaoke,TANG Daqian
|
| Abstract: |
| The establishment of the “Dual Carbon” goals has charted a clear path for China’s green
economic transition, reflecting the country’s strong commitment to addressing global climate change. As
agriculture and rural areas play a vital role in achieving these goals, enhancing carbon sequestration and
reducing emissions have become central tasks, with natural carbon sinks serving as a fundamental pillar
for sustainable agricultural development. Accordingly, the quality of rural human settlements is crucial to
realizing carbon neutrality. Resilience assessment offers an effective means to evaluate the sustainability
and safety of rural areas, providing important insights into their developmental stability.Building on the
structural dimensions of human settlement systems and drawing insights from community resilience
assessment frameworks, this study introduces a carbon ecological carrying capacity coefficient to
develop a comprehensive resilience evaluation model for rural human settlement systems oriented toward
low-carbon emission reduction. Following a technical pathway that proceeds from “county-level carbon
accounting – identification of village-level carbon sources – carbon emission assessment of human
settlements –comprehensive resilience evaluation– identification of resilience determinants – to resilience
enhancement pathways”, the research employs an integrated methodology including the random forest
model, TOPSIS, and path analysis to assess rural human settlement systems. Based on the findings,
differentiated development recommendations are proposed for villages. The research includes the
following steps.Taking Binzhou City in Xianyang, Shaanxi Province as the study area, a carbon emission
accounting model was developed at both county and village scales. At the county level, a model for
estimating carbon emissions from human settlements was established. Following the 2006 IPCC
Inventory Guidelines, seven categories of carbon emission factors were identified, including ecological
carbon sequestration factors, as well as emission factors from agricultural production, industrial
processes a汮祤?楰湲?瑤敵牣浴猠?潳晥?攠摥畮捥慲瑧楹漠湣慯汮?晵慭捰楴汩楯瑮椬攠獡??琠潷畡牳楴獥洠?摡敮癡敧汥潭灥浮整渮琠??慩湬摤?楮湧映牯慮猠瑴牨略挠瑣畯牵敮?y-scale
carbon balance accounting as the macro-level spatial framework, key carbon source factors at the village
scale were selected using a random forest model, enabling the construction of a village-level carbon
emission accounting model. This study defines a comprehensive resilience index as the product of the
resilience value of the human settlement system and the carbon ecological carrying capacity. Integrating
resilience theory with human settlement science, a resilience assessment index system for village-level
human settlements was constructed, consisting of five criterion layers—social system, residential system,
human system, support system, and natural system—encompassing a total of 19 indicators. An integrated
evaluation of the comprehensive resilience of rural human settlement systems, incorporating the carbon
ecological carrying coefficient, was conducted through the combined application of path analysis and the
entropy-weighted TOPSIS model. Results show that the resilience scores of the village human
settlement systems in Shuikou Town range from 0.166 to 0.664, with a standard deviation of 0.157 across
villages and a median value of 0.367, indicating significant variability in resilience levels among the
villages. Among the five dimensional systems, the natural system has the highest mean score of 0.471,
while the residential system has the lowest mean score of 0.312, both falling within the medium-to-low
resilience range. Additionally, the coefficients of variation for all five subsystems exceed 0.4, reflectingsubstantial disparities in resilience levels among villages and revealing an unbalanced development pattern across Shuikou Town. Notably, the residential
system exhibits the highest coefficient of variation at 0.645, indicating the greatest variability in resilience among the villages.The comprehensive resilience
index is primarily influenced by the interaction between the social system and the support system, particular |
| Key words: human settlements resilience carbon ecological carrying capacity carbon emissions |
