Literature DB >> 27604755

Assessing urban adaptive capacity to climate change.

Dahyann Araya-Muñoz1, Marc J Metzger2, Neil Stuart2, A Meriwether W Wilson2, Luis Alvarez3.   

Abstract

Despite the growing number of studies focusing on urban vulnerability to climate change, adaptive capacity, which is a key component of the IPCC definition of vulnerability, is rarely assessed quantitatively. We examine the capacity of adaptation in the Concepción Metropolitan Area, Chile. A flexible methodology based on spatial fuzzy modelling was developed to standardise and aggregate, through a stepwise approach, seventeen indicators derived from widely available census statistical data into an adaptive capacity index. The results indicate that all the municipalities in the CMA increased their level of adaptive capacity between 1992 and 2002. However, the relative differences between municipalities did not change significantly over the studied timeframe. Fuzzy overlay allowed us to standardise and to effectively aggregate indicators with differing ranges and granularities of attribute values into an overall index. It also provided a conceptually sound and reproducible means of exploring the interplay of many indicators that individually influence adaptive capacity. Furthermore, it captured the complex, aggregated and continued nature of the adaptive capacity, favouring to deal with gaps of data and knowledge associated with the concept of adaptive capacity. The resulting maps can help identify municipalities where adaptive capacity is weak and identify which components of adaptive capacity need strengthening. Identification of these capacity conditions can stimulate dialogue amongst policymakers and stakeholders regarding how to manage urban areas and how to prioritise resources for urban development in ways that can also improve adaptive capacity and thus reduce vulnerability to climate change.
Copyright © 2016 Elsevier Ltd. All rights reserved.

Keywords:  Bottom-up; Developing countries; Fuzzy modelling; Geographical information system (GIS); Vulnerability

Mesh:

Year:  2016        PMID: 27604755     DOI: 10.1016/j.jenvman.2016.08.060

Source DB:  PubMed          Journal:  J Environ Manage        ISSN: 0301-4797            Impact factor:   6.789


  3 in total

1.  Heat Adaptive Capacity: What Causes the Differences Between Residents of Xiamen Island and Other Areas?

Authors:  Chaowei Wu; Wei Shui; Haifeng Yang; Meiqi Ma; Sufeng Zhu; Yuanmeng Liu; Hui Li; Furong Wu; Kexin Wu; Xiang Sun
Journal:  Front Public Health       Date:  2022-02-21

2.  Constructing a comprehensive disaster resilience index: The case of Italy.

Authors:  Sepehr Marzi; Jaroslav Mysiak; Arthur H Essenfelder; Mattia Amadio; Silvio Giove; Alexander Fekete
Journal:  PLoS One       Date:  2019-09-16       Impact factor: 3.240

3.  Social inequalities in heat-attributable mortality in the city of Turin, northwest of Italy: a time series analysis from 1982 to 2018.

Authors:  Marta Ellena; Joan Ballester; Paola Mercogliano; Elisa Ferracin; Giuliana Barbato; Giuseppe Costa; Vijendra Ingole
Journal:  Environ Health       Date:  2020-11-16       Impact factor: 5.984

  3 in total

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