摘要: |
加剧的高温天气是全球气候变化的主要气候风险之一,绿色空间营造是适应高温天气的有效举措。诸多城市在绿地建设方面已取得显著成效,但高温脆
弱群体的降温需求与城市绿地的有效供给之间仍存在一定的空间错配。以北京市中心城区为例,在以风险评估为核心的传统研究框架基础上创新性地引入了可行
性和有效性2个维度,提出了综合“风险-可行性-有效性”的绿色空间营造优先级评估框架,结合高时空分辨率的遥感数据开展高精度实证分析,旨在识别需要优
先营造绿色空间的区域,以有效降低高温脆弱群体的风险。研究表明:北京中心城区绿色空间营造优先级呈现出“核心区低、四环内高”的空间格局,高优先级
区域主要集中于南四环周围,并对不同优先级提出针对性规划响应策略。与传统方法相比,通过引入可行性和有效性2个维度,不仅关注高温健康风险,还综合考
虑了绿色空间扩增的可实施性与非线性降温效能差异。这一框架不仅适用于北京市的案例研究,同时也为其他城市在资源有限的条件下优化绿色空间配置提供了
有价值的模型方法和策略支持 |
关键词: 风景园林 韧性城市 高温健康风险 绿色基础设施 生态系统服务 空间优化 |
DOI:10.19775/j.cla.2025.07.0038 |
投稿时间:2024-12-28修订日期:2025-04-22 |
基金项目:国家自然科学基金重点项目(52130804) |
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Prioritization Study on Green Space Planning for High-temperature Adaptation: A Case Study ofBeijing's Central Urban Districts |
CHEN Maini,,DANG Anrong*,,LI Xiangyu,,WENG Yang,,HUANG Jingxiong |
Abstract: |
Intensifying high-temperature weather has become a key climate
risk globally, exacerbated by urbanization and anthropogenic climate change.
Green space development is widely recognized as an effective adaptation
strategy due to its proven capacity to mitigate urban heat islands and reduce
health risks associated with extreme heat. While significant progress has been
made in urban greening initiatives globally, a persistent spatial mismatch exists
between the cooling needs of heat-vulnerable populations and the equitable
provision of urban green spaces. Addressing this mismatch requires a nuanced
understanding of localized priorities for green infrastructure investment. This
study focuses on Beijing's central urban area, a region characterized by rapid
urbanization, high population density, and escalating heat-related vulnerabilities.
Building upon the traditional risk assessment framework, we innovatively
integrate feasibility and effectiveness dimensions to propose a comprehensive
"risk-feasibility-effectiveness" evaluation framework for prioritizing green space
development. Methodologically, this research leverages multi-source, highresolution
spatiotemporal data to enhance analytical precision. High-temperature
hazard parameters are derived from ECOSTRESS satellite thermal imagery (70 m
resolution) and Landsat data, capturing diurnal variations in surface temperature
to quantify heatwave intensity and frequency. Population exposure is mapped
using WorldPop 2020 demographic data (100 m resolution), while vulnerability is
assessed through indicators such as elderly population density, socioeconomic
status (derived from NPP-VIIRS nighttime light data), and healthcare accessibility
(based on OpenStreetMap POI analysis). Feasibility is evaluated using GF-2
satellite imagery (2 m resolution) to identify available land resources (e.g.,
grasslands, bare land, and vacant plots), constrained by permanent basic
farmland boundaries. Effectiveness is quantified through Meta's Global Canopy
Height dataset (1 m resolution), enabling precise modeling of nonlinear cooling
efficiencies linked to tree canopy coverage. The findings reveal a distinct spatial
pattern in green space prioritization across Beijing's central urban area: low-priority
zones dominate the historic core due to land scarcity, while high-priority areas
cluster within the Fourth Ring Road, particularly southward. These high-priority
regions exhibit synergistic advantages – elevated heat-health risks, sufficient land
availability, and high marginal cooling efficiency. For instance, neighborhoods near
the southern Fourth Ring Road demonstrate acute vulnerabilities (e.g., dense
elderly populations and limited healthcare access) alongside underutilized land
parcels suitable for afforestation. In contrast, the urban core, despite its high
risk and cooling potential, ranks lower due to infrastructural constraints. This
study advances traditional frameworks by embedding practical considerations
of implementation feasibility and ecological effectiveness into risk-based
assessments. Unlike conventional approaches that prioritize risk hotspots alone,
our tripartite framework ensures that green space interventions are both actionable
and optimized for maximum cooling returns. For example, marginal cooling
efficiency analysis reveals that incremental tree canopy increases in low-coverage
areas (e.g., 10% to 20%) yield disproportionately higher temperature reductions
compared to saturated zones. Such insights are critical for cities like Beijing, where
land scarcity demands strategic resource allocation. The implications extend
beyond local contexts. By providing a scalable model for integrating high-resolution
data, multi-criteria decision-making, and spatial optimization, this framework offers
a blueprint for cities worldwide grappling with climate adaptation under resource
constraints. Future applications could incorporate dynamic population mobility
data and seasonal thermal variations to refine prioritization further. Policymakers
and planners are urged to adopt tiered strategies: directing immediate investments
to high-priority zones, integrating green mandates into urban renewal plans for
medium-priority areas, and deploying complementary measures (e.g., building
retrofits) in low-priority yet high-risk locales. |
Key words: landscape architecture resilient city heat-health risk green
infrastructure ecosystem services spatial optimization |