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• The island development of functional polycentricity was lagging than that of the morphological dimension. • Spatial regression model performs well in fitting the association between polycentric urban variables and island landscape changes. • The effects of dual polycentric urban indicators on island landscape changes varied spatially and temporally. • Polycentric urban development generally contributed to fragmented and complex island landscape patterns on the periphery areas of subcenters and reclamation. Polycentric urban development has become popular worldwide as expected to generate greater agglomeration externality and facilitate socioeconomic achievements. Despite much insightful work on urban polycentricity in coastal agglomerations or megacities, few studies have focused on how polycentric urban development affects landscape sustainability in vulnerable island cities. This paper characterized the urban polycentricity from the perspective of morphological and functional dimensions during 1990–2020 in Yuhuan island city, China, and quantified the association between polycentric urban variables and island landscape changes by spatial regression. The results showed that there was a mismatch in development between the morphological and functional polycentricity, in which the functional dimension was lagging. In response, island landscape patterns became fragmented and scattered. With the application of GWR models, the effects of two polycentric factors on island landscape changes varied spatially and temporally. The stronger relationships between them were identified in regions where more heterogeneous patterns were presented, such as the subcenters and their surroundings and reclamation areas. These trends were mainly attributed to industrial suburbanization and the development of new economic zone and Damaiyu port. Our findings call for a more balanced functional urban polycentricity; and local-specific landscape effects should be scheduled in island planning practice and sustainable development. [ABSTRACT FROM AUTHOR]

Industrial parks have been adopted as one way to promote industrial development in China since the early 1980s. However, most of current performance evaluation indicators highlight the maximization of industrial yield, leading to both ecological degradation and environmental pollution due to intensive industrial activities. Therefore, it is critical to develop appropriate evaluation indicators so that industrial parks can move toward sustainable development. This paper aims to fill such a research gap by developing innovative emergy indicators. Kaya formula and index decomposition analysis (IDA) were integrated together in order to quantify the key factors influencing the sustainability of an industrial park. Tianjin Economic Development area (TEDA), the largest industrial park in terms of its economic scale in China, was chosen as one case study area for testing the related methods. The results show that during the period of 2006–2010 the sustainability of TEDA experienced a declining trend. From the Kaya formula and IDA analysis, indicators on economic efficiency of process, economic return on investment, renewable empower density and nonrenewable empower density were calculated. Such research findings provide valuable policy insights to the industrial park managers so that they can prepare appropriate development strategies for achieving sustainable development of their industrial parks by considering the local realities. [ABSTRACT FROM AUTHOR]

Sustainable Development Goals (SDGs) proposed by the United Nations (UN) General Assembly in September 2015, provided a global development agenda for all countries to use as a blueprint for progress on economic, social, and environmental sustainability. There are 17 Goals, 169 Targets and 230+ Indicators in SDGs, which specifically depends on multi-source data for monitoring and reporting on progress towards the associated targets and indicators. The existing evaluation methods are mainly based on statistic and survey data with lower spatiotemporal resolution, longer evaluation cycle, and higher cost. To monitor indicators of the SDGs quickly and accurately, it required us to explore new data sources and technology. In this paper, firstly, we reviewed comprehensively all indicators information (concepts and definitions, computation method, and data sources) in three Goals (SDG6, SDG11, and SDG15). Then, we analyzed the feasibility of monitoring accurately and quickly the above indicators on basis of available multi-source data (statistics, remote sensing, ground-based observations and internet data). Finally, a series of integration methods are developed to monitor several specific indicators with multi-source data. The methodology of this study provided experience for monitoring SDGs indicators in developing countries and filled the gap for the assessment of SDGs indicators at fine scale. [ABSTRACT FROM AUTHOR]