ArticleEnvironmental science and pollution research international2021
The climate change vulnerability of China: spatial evolution and driving factors.
Article in Environmental science and pollution research international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Modeling climate change adaptation for sustainable coastal zones using GIS and AHP.Environmental monitoring and assessment · 2024Article
- Study on the Co-Benefits of Air Pollution Control and Carbon Reduction in the Yellow River Basin: An Assessment Based on a Spatial Econometric Model.International journal of environmental research and public health · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To cope with climate change, it is of great importance to describe the temporal and spatial evolution of climate change vulnerability and its driving factors. Therefore, this paper establishes a comprehensive index of vulnerability to climate change based on the vulnerability scoring diagram (VSD) framework. Moran's I index is used to study the spatial evolution characteristics of vulnerability, and spatial regression analysis is used to explore the factors influencing the spatial distribution of vulnerability. The results show that (1) the climate change vulnerability of China has decreased over time, and the sensitivity state is relatively stable; however, the annual change in exposure and adaptive capacity is significant. (2) The western region of China is more vulnerable than the eastern region, and the most vulnerable provinces are Guizhou and Gansu. (3) The regional vulnerability is generally in a significant spatial agglomeration state. (4) Finally, the driving factors of the spatial distribution of climate change vulnerability include forest coverage, the urban-rural income gap and information technology. These recommendations provide detailed discussions and scientific information for mitigating global warming and formulating long-term emission reduction targets, thereby optimizing resource allocation and providing spatial governance directions for the formulation of adaptation policies.
Indexed as
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33763834What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.