Abstract
Green development is the cognition of geography to human-nature nexus under the background of the new era. As China is facing various eco-environment problems, green development has become a key approach towards ecological progress, and it is ultimately an explicit means to respond to support sustainable development in China. Quantifying green development performance is essential to track efforts towards sustainability and guide policymakers. However, applying the balanced property of ‘Economy-Ecology-Society’ of green development to its performance assessment is rarely discussed. Here we elaborated the connotation of green development and developed a quantification model with coupling coordination degree to assess green development performance of the largest old industrial base of China, Northeast China. We found that the green development performance has been improved from a score of 0.443 in 2003 to 0.530 in 2019 but the disparities of green development performance were enlarging over time, especially for the cities in Heilongjiang. A positive spatial autocorrelation phenomenon of green development performance was confirmed, and Low-Low clusters in the northeastern Heilongjiang and High-High clusters in the central-eastern Liaoning were discovered. This study suggests the need to track the spatio-temporal dynamics of green development performance to provide references for achieving sustainable development goals in northeast China and other regions.
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We thank Prof Mark Wang from University of Melbourne for his support and encouragement to write this piece, and also for suggestions on earlier version of this article.
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Foundation item: Under the auspices of the National Natural Science Foundation of China (No. 41771138, 41571152, 41871158, 41771179), National Science and Technology Basic Resources Survey Project (No. 2017FY101303-1)
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Fan, W., Zhang, P., Tong, L. et al. Green Development for Supporting Sustainability of Northeast China: Performance Quantification, Spatio-temporal Dynamics and Implications. Chin. Geogr. Sci. 32, 467–479 (2022). https://doi.org/10.1007/s11769-022-1278-0
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DOI: https://doi.org/10.1007/s11769-022-1278-0