Detection of Multi-dimensional Driving Forces of Public Environmental Concern in China: Based on Spatial Heterogeneity Perspectives
Chinese Geographical Science, ISSN: 1993-064X, Vol: 33, Issue: 6, Page: 1109-1126
2023
- 2Citations
- 13Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations2
- Citation Indexes2
- CrossRef1
- Captures13
- Readers13
- 13
Article Description
Public environmental concern (PEC) is an important bottom-up force in building an environmentally sustainable society. Guided by attitude theory, this paper innovatively constructed a PEC evaluation index system, while introducing entropy weighted-TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) to realize the assessment of PEC. Exploratory spatial data analysis was used to portray the spatio-temporal evolution patterns of PEC in 362 Chinese cities at prefecture-level and above from 2011 to 2018. Furthermore, the Geodetector model was performed to identify the multi-dimensional determinants of PEC from the perspective of spatial heterogeneity. The results indicated that: 1) PEC in China exhibited a fluctuating upward trend, consistent with the spatial distribution law of ‘Heihe-Tengchong Line’ and ‘Bole-Taipei Line’; 2) the driving effect of each factor varied dynamically, but in general, economic development level, population size, industrial wastewater, and education level were the dominant driving factors explaining the spatial variation of PEC; 3) risk detection revealed that four factors, government environmental regulations, PM, vegetation coverage, and natural resource endowment, had nonlinear effects on PEC; 4) the interactions between factors all demonstrated an enhancement in explaining the spatial differentiation of PEC. PEC was driven by the comprehensive interaction of four-dimensional factors of economy, society, pollutant emissions, and ecology. Among them, population agglomeration accompanied by a high level of regional economy and information technology can explain the increase in PEC to the greatest extent.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85164974250&origin=inward; http://dx.doi.org/10.1007/s11769-023-1378-5; https://link.springer.com/10.1007/s11769-023-1378-5; http://sciencechina.cn/gw.jsp?action=cited_outline.jsp&type=1&id=7576967&internal_id=7576967&from=elsevier; https://dx.doi.org/10.1007/s11769-023-1378-5; https://link.springer.com/article/10.1007/s11769-023-1378-5
Springer Science and Business Media LLC
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