Chen Bin, Tu Ying, Wu Shengbiao, Song Yimeng, Jin Yufang, Webster Chris, Xu Bing, Gong Peng
Future Urbanity & Sustainable Environment (FUSE) Lab, Division of Landscape Architecture, Faculty of Architecture, The University of Hong Kong, Hong Kong Special Administrative Region; Institute for Climate and Carbon Neutrality, The University of Hong Kong, Hong Kong Special Administrative Region; Institute of Data Science, The University of Hong Kong, Hong Kong Special Administrative Region.
Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing, China.
Environ Int. 2022 Aug;166:107348. doi: 10.1016/j.envint.2022.107348. Epub 2022 Jun 14.
Greenspace exposure metrics can allow for comparisons of green space supply across time, space, and population groups, and for inferring patterns of variation in opportunities for people to enjoy the health and recreational benefits of nearby green environments. A better understanding of greenspace exposure differences across various spatial scales is a critical requirement for lessening environmental health disparities. However, existing studies are typically limited to a single city or across selected cities, which severely limits the use of results in measuring systemic national and regional scale differences that might need policy at above individual city planning level. To close this knowledge gap, our study aims to provide a holistic assessment of multi-scale greenspace exposure across provinces, cities, counties, towns, and land parcels for the whole of China. We mapped the nationwide fractional greenspace coverage at 10 m with Sentinel-2 satellite imagery, and then modeled population-weighted greenspace exposure to examine variation of greenspace exposure across scales. Our results show a prominent scaling effect of greenspace exposure across multi-scale administrative divisions in China, suggesting, as expected, an increase in heterogeneity with finer spatial scales. We also identify an asymmetric pattern of the difference between greenspace exposure and greenspace coverage, across a geo-demographic demarcation boundary (i.e., along the Heihe-Tengchong Line). In general, the greenspace coverage rate will overestimate more realistic human exposure to greenspace in East China while underestimating in West China. We further found that, in China, more recently urbanized areas have much better greenspace exposure than older urban areas. Our study provides a spatially explicit greenspace exposure metric for discovering multi-scale greenspace exposure difference, which will enhance governments' capacity to quantify environmental justice, detect vulnerable greenspace exposure risk hotspots, prioritize greenspace management at the supra-city scale, and monitor the balance between greenspace supply and demand.
绿地暴露指标可以用于比较不同时间、空间和人群组的绿地供应情况，并推断人们享受附近绿色环境的健康和娱乐益处的机会的变化模式。更好地了解不同空间尺度上的绿地暴露差异是减少环境卫生差距的关键要求。然而，现有研究通常局限于单个城市或选定的城市，这严重限制了研究结果在衡量可能需要高于单个城市规划层面政策的系统性国家和区域尺度差异方面的应用。为了填补这一知识空白，我们的研究旨在对中国整个省份、城市、县、镇和地块的多尺度绿地暴露进行全面评估。我们利用哨兵 -2 卫星图像绘制了全国 10 米分辨率的绿地覆盖率图，然后对人口加权的绿地暴露进行建模，以研究不同尺度上绿地暴露的变化。我们的结果表明，中国多尺度行政区划的绿地暴露存在显著的尺度效应，正如预期的那样，随着空间尺度的细化，异质性增加。我们还确定了绿地暴露与绿地覆盖率之间差异的不对称模式，跨越了地理人口分界线（即沿黑河 - 腾冲线）。总体而言，绿地覆盖率在华东地区会高估人们对绿地的实际暴露，而在西部地区则会低估。我们进一步发现，在中国，较新城市化地区的绿地暴露情况比老城区好得多。我们的研究提供了一个空间明确的绿地暴露指标，用于发现多尺度绿地暴露差异，这将增强政府量化环境正义、检测脆弱绿地暴露风险热点、在城市以上尺度优先进行绿地管理以及监测绿地供需平衡的能力。
