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Abstract Climate change and urbanization are increasing the risk of flood disasters in vulnerable areas. Urban road infrastructure can be affected by floods, and subsequent restoration creates an additional carbon emission burden. These emissions are likely to compromise local decarbonization efforts, but there remains a lack of tools for quantifying the environmental impact of reconstruction projects after disasters. This study aims to develop an assessment framework to reveal the carbon footprint of post-flood road network restoration projects. An interdisciplinary model was developed, integrating knowledge in hydrology, civil engineering, and environmental science. This paper presents scenario simulations with the maximum flood depth (MFD) ranging from 0.1 m to 5.0 m and a case study in Carlisle, UK. Results show that floods with MFD over 0.5 m had significant impacts on pavements. The resilience to floods on low-volume roads was weaker, but the restoration emission intensity of main roads was significantly higher. From the perspective of an urban case, after a 70-hour flood event with an average MFD of 0.9 m, the total carbon footprint of road network restoration was 73.89 tCO2e, offsetting about 0.49% of local carbon reduction efforts for the month. Specifically, emissions from restoring low-volume roads accounted for 58.64%. Indirect carbon emissions from material production (approx. 66%) and delivery (approx. 30%) were much higher than direct emissions from onsite tasks (approx. 4%). Measures such as material improvement, delivery optimization, and construction material reuse can help mitigate the emission burden of restoration projects. A major uncertainty in the computation was ignoring the differences induced by the climatic context, but addressing this limitation would require support from pavement damage experts. This study provides quantitative tools for understanding infrastructure-related environmental impacts caused by flood disasters. The assessment framework proposed in this work is also expected to be applied to wider spatial-temporal scenarios to enrich decision-making references.
Zhong et al. (Tue,) studied this question.