AUTHOR=Yao Yufeng , Zhang Zhejun , Zhang Zucheng , Meng Fan 

TITLE=Are cities ready for climate change? Exploring the spatial discrepancies between urban vulnerability and adaptation readiness

JOURNAL=Frontiers in Climate

VOLUME=Volume 6 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/climate/articles/10.3389/fclim.2024.1293651

DOI=10.3389/fclim.2024.1293651

ISSN=2624-9553

ABSTRACT=Climate change is an increasingly severe global crisis, posing threats to ecosystems, economies, and human lives. To address these threats, different cities around the world have adopted various levels of adaptation strategies to cope with varying degrees of negative impact, such as increasing water efficiency and enhancing health response systems. Despite some progress, there is a spatial discrepancy among cities in terms of their vulnerability to climate change and their adaptation readiness. In cities where the level of adaptation readiness is lower than the degree of vulnerability, there is an adaptation deficit, and the negative impact of climate change will be further exacerbated.Conversely, in cities where the level of adaptation readiness is higher than the degree of vulnerability, there is an adaptation surplus, which could lead to a serious waste of resources.Uncovering the reasons behind this spatial discrepancy could help us formulate better policies to address climate issues. To this end, we conduct an empirical analysis using city response data from 2020, sourced from the Carbon Disclosure Project (CDP), focusing on a dataset that encompasses 421 cities worldwide. Specifically, we first formulate a "vulnerability index" to measure the propensity of cities to suffer negative effects in the event of climate hazards and a "readiness index" to represent their adaptation readiness level. Then we introduce the "discrepancy score" to quantify discrepancies across cities and discover the spatial distribution of the discrepancies through spatial visualization. Further, we employ a clustering analysis method named k-means to group different cities based on vulnerability index and readiness index. Finally, we perform Geographically Weighted Regression (GWR) to quantitatively analyze the spatial correlation between the economy and the discrepancy score of different cities.