Climate change adaptation advantage for African road infrastructure
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The African continent is facing the potential of a $183.6 billion USD liability to repair and maintain roads damaged from temperature and precipitation changes directly related to predicted climate change through 2100. This cost is strictly to retain the current road inventory. This cost does not include costs associated with impacts to critically needed new roads. In many African countries, limited or non-existent funds for adaptation and mitigation are challenging these countries to identify the threats that are posed by climate change, develop adaptation approaches to the predicted changes, incorporate changes into mid-range and long-term development plans, and secure funding for the proposed and necessary adaptations. Existing studies have attempted to quantify the impact of climate change on infrastructure assets that will be affected by climate change in the coming decades. The current study extends these efforts by specifically addressing the effect of climate change on the African road infrastructure. The study identifies both total costs and opportunity costs of repairing and maintaining infrastructure due to increased stressors from climate change. Proactive and reactive costs are examined for six climate scenarios, with costs ranging, respectively, from an average of $22 million USD to $54 million USD annually per country. A regional analysis shows contrast between impacts in five areas of the continent, with impacts ranging from 22 % opportunity cost to 168 %. These costs have the potential to delay critical infrastructure development on the continent and present a challenge to policy makers balancing short-term needs with long-term planning.
KeywordsOpportunity Cost Road Network Climate Change Impact Climate Change Adaptation Pave Road
This research is partly funded by a grant from the University of Copenhagen. The authors also recognize the World Bank for supporting the initial work in that enabled the final comparison study. The authors acknowledge the modeling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP’s Working Group on Coupled Modelling (WGCM) for their roles in making available the WCRP CMIP3 multi-model dataset. Support of this dataset is provided by the Office of Science, U.S. Department of Energy. Finally, the authors acknowledge Xiang Guo, David Johnson and William Farmer JP, for data analysis.
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