Abstract
The identification and management of ecological restoration areas play important roles in promoting sustainable urban development. However, current research lacks a scientific basis for the scope and scale of ecological restoration. Further, the absence of a framework to assess policy goals and public preferences that leads to identification of ecological restoration areas across the science-policy interface is difficult, and the existing frameworks’ performance has little applicability. We proposed a transdisciplinary framework to combine ecological quality, ecological health, and ecosystem services as an assessment endpoint to identify priority restoration areas. Further, we classified the ecological restoration areas on a township scale by K-means. Based upon policy goals and public preferences of the Pearl River Delta urban agglomeration, we chose air quality, biodiversity, soil fragility, recreation quality, ecosystem vigor, landscape metrics, and the water supply ecosystem service as elements of the evaluation system. This study showed that priority restoration areas accounted for 10.8% of the urban agglomeration area and classified township, largely in the difference between natural and semi-natural ecosystems and the human environment. Policymakers can use this framework comprehensively and flexibly to identify and classify ecological restoration areas to achieve policy goals and fulfil public preferences.
Highlights
IntroductionHuman activities have significant adverse effects on the regional ecological environment that result in climate change, loss of biodiversity, and ecosystem degradation [1]
By choosing the hotspot areas according to ecological space quality, a land area of 3692.5 km2 was selected as the ecological quality hotspot, which was distributed primarily in the cropland that accounted for 99.5% of the built-up land (Figure 4a)
Based upon policy goals and public preferences, this study proposed a comprehensive method to identify priority ecological restoration areas by integrating the ecological space’s quality and health, and performing a cluster analysis of a township using machine learning
Summary
Human activities have significant adverse effects on the regional ecological environment that result in climate change, loss of biodiversity, and ecosystem degradation [1]. 2 billion hectares of the world’s terrestrial ecosystems are considered degraded and in need of ecological restoration [3,4]. These ecosystems’ decrease in area and widespread degradation has reduced ecosystem services and increased ecological risk [5]. Assessing ecological space is dominated largely by social and economic activities, policy-oriented and decision-making needs [9,10]. Comprehensive research on the application of scientific assessment of ecological space to help make decisions and policies should be strengthened [11,12]
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