| 摘要: |
| 协调配置绿灰基础设施对于节约国家资源、应对气候变化、实现碳中和目标至关重要。以浙江省6个村镇功能组片为例，围绕组片间及内部的绿灰设施
空间配置与碳源汇空间格局的交互效应展开探讨。构建生物量-碳汇、消费端-碳源模型，提出绿灰空间配置量化方法，设立绿灰交互指标，基于地理探测器模型
进行交互机制研究。结果显示：1)灰色度、绿灰归一化差异指数及生态核心区百分比是影响净碳排放强度的关键因素；2)组片地理特征和主导功能对绿灰设施空
间配置产生潜在影响，进而影响净碳排放强度，优化绿灰基础设施空间配置应分片分组、因地制宜；3)绿灰空间配置优化需考虑组片所在区域的社会背景，结合
多方面因素以综合调控碳排放。研究结论对绿灰设施配置优化和碳减排政策制定具有一定参考价值 |
| 关键词: 风景园林 绿灰基础设施 空间配置 净碳排放强度 村镇功能组片 空间交互 |
| DOI：10.19775/j.cla.2025.04.0055 |
| 投稿时间：2023-11-13修订日期：2024-10-16 |
| 基金项目:国家社会科学基金项目(24FJYB035)；浙江省住房和城乡建设厅标准科技专项(ZJZX-202108116)；浙江省属高校基本科研业务费专项资金
(GB202301005) |
|
| Interaction Mechanisms Between Green-Grey Infrastructure Spatial Configuration and Net CarbonEmission Intensity: Evidence from Village and Town Groups in Zhejiang |
| ZHU Xiaoqing,,SUN Zhuoyang,,XU Jian*,,HUANG Ge,,ZHONG Yuebin |
| Abstract: |
| The spatial coordination of green and gray infrastructure is essential
for conserving natural resources, mitigating climate change, and achieving carbon
neutrality. This study investigates the interaction mechanisms between green-gray
infrastructure spatial configurations and net carbon emission intensity, using six
functional village and town groups in Zhejiang Province, China, as case studies.
By constructing biomass-carbon sink and production-carbon source models, the
study quantifies carbon sources and sinks and introduces a novel evaluation metric,
the Normalized Difference Green-Gray Index (NDGG). Utilizing the Geodetector
model, the research examines the interplay between infrastructure configurations
and carbon source-sink dynamics across diverse geographical and socio-economic
contexts. Key findings from this research include: 1) Critical Determinants:
Grayness, NDGG, and the percentage of ecological core areas are identified as
pivotal factors influencing net carbon emission intensity. Higher NDGG values,
representing a dominance of gray infrastructure, correlate strongly with increased
carbon emissions, while green infrastructure significantly contributes to carbon
sequestration. These findings emphasize the need for balanced infrastructure
planning to mitigate environmental impacts while supporting sustainable growth.
2) Spatial Variations: The spatial configuration of green and gray infrastructure
is influenced by geographical features and functional characteristics of regions.
Villages and towns in mountainous and hilly terrains show lower grayness and
higher green coverage, resulting in reduced net carbon emissions. Conversely, flat
plains, which favor dense gray infrastructure development, exhibit elevated carbon
emissions due to urbanization and industrial activities. This spatial heterogeneity
underscores the importance of adopting tailored strategies for regional carbon
reduction and infrastructure optimization. 3)Interactive Mechanisms: The
interaction analysis highlights that optimizing green-gray infrastructure requires a
nuanced understanding of regional socio-economic dynamics. In economically
active regions, increasing the proportion of green infrastructure - such as parks,
green belts, and ecological corridors - can effectively offset carbon emissions.
Moreover, enhancing the spatial utilization efficiency of gray infrastructure, through
measures like vertical development and multi-functional land use, further mitigates
carbon footprints. These strategies allow for the coexistence of development
and environmental sustainability. Building on these insights, this study offers
practical recommendations for policymakers and planners seeking sustainable
infrastructure development. In urban areas with high population density or
intensive economic activity, integrating green elements such as rooftop gardens,
urban greenways, and ecological buffers is critical to balancing carbon sources
and sinks. For rural and mountainous regions, the conservation of ecological
core zones and the promotion of low-impact land-use practices are emphasized
to maintain ecological integrity and reduce carbon emissions. Strengthening
the resilience of rural communities by aligning their development plans with
ecological goals is equally vital. The study also emphasizes the significance of
spatial heterogeneity in designing infrastructure layouts. Advanced analytical tools,
including the Geodetector model, provide a robust framework for understanding
the spatial dynamics of infrastructure configurations and their carbon implications.
By integrating ecological, geographical, and socio-economic data, this approach
supports more precise and effective policy interventions aligned with carbon
neutrality goals. Additionally, incorporating these insights into long-term urban and
rural planning can facilitate the achievement of broader sustainability objectives.
Furthermore, the findings contribute to the broader discourse on sustainable
development by bridging theoretical insights and practical applications. They
advocate for synergies between green and gray infrastructure to achieve multiple
objectives, including carbon mitigation, ecological resilience, and socio-economic
sustainability. Policymakers are encouraged to adopt a holistic perspective,
leveraging local contexts to design region-specific strategies that address both
short-term environmental challenges and long-term climate goals. Such an
approach ensures that development initiatives are not only ecologically viable but
also economically feasible and socially inclusive. This study's novel approach to
evaluating green-gray interactions and its focus on spatial heterogeneity provides
a foundation for future research and policy-making. By integrating quantitative
methods such as NDGG and spatial modeling, the research enhances our
understanding of how infrastructure configurations influence carbon dynamics.
These insights are instrumental in achieving a balanced and sustainable
development pathway that aligns with global climate targets. Future work should
explore multi-scale applications of this framework and examine how policy
changes at national and regional levels can further accelerate the transition toward
carbon neutrality. |
| Key words: landscape architecture green-grey infrastructure spatial
configuration net carbon emission intensity village and town group spatial
interaction |
