In recent years, global warming, characterised as the regular rise in Earth's temperature, has drawn significant attention due to the ongoing increase in sea levels caused by the melting of ice caps and glaciers. The expansion of water coverage areas has resulted in flash floods in numerous states, leading to the loss of life, property, and food supplies. This increase in floods has emerged as an ESG risk for investors, as it can lead to financial and non-financial damage. It is therefore crucial to develop technology that can comprehend the climatic conditions that lead to floods and accurately predict regions that could be affected, enabling authorities to extend timely assistance and prepare for the disaster. This chapter aims to educate readers on ESG risks and proposes using ensemble machine learning models for flood risk assessment in a region, which would analyse various features to comprehend the climatic conditions responsible for flooding.
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ESG principles, defined as a strategy adopted by multiple investment firms to imbue responsibility in organisations by evaluating the Environmental (E), Social (S), and Governance (G) factors (Li et al., 2021), have become widely accepted choice for facilitating sustainable development in modern markets. Emerging marketplaces have been seen to benefit from adopting the ESG framework; however, to outperform using a previous methodology, potential investors need to have a detailed understanding of the framework and would find ways to adopt the same to better their results (Friede et al., 2015). Carbon footprints (Garvey et al., 2018) and biodiversity conservation practices (Aich et al., 2021) have a significant impact on the overall efficiency of an organisation, directly influencing its solvency. Hence, complex but robust risk assessment strategies are incorporated into the ESG investment system when discussing environmental hazards that can both positively and negatively affect its financial condition (Bruno & Lagasio, 2021).
Global warming and climate change events are such factors that are considered in the ESG investment framework, with projected increase both the frequency as well as magnitude of natural disasters, hitting people and organisations in developing countries the hardest (Benevolenza & DeRigne, 2019). Relevant research in the topic has shown that climate change negatively affects sustainable development as a whole, especially in regions like Southeast Asia and Central Asia (Kimuli et al., 2021), with the notion being supported by various other studies as well. Floods, which according to the World Health Organization (WHO), is described as a phenomenon that causes water bodies like oceans and rivers to overflow. Unnatural flooding is a direct consequence of global warming and climate change, since water bodies overflow due to increased precipitation and abnormal rate of snow caps melting (Choubin et al., 2019; Nema et al., 2012). The loss of farms, homes, recreational structures, educational institutions, and public service facilities results in considerable losses for the region and worsens the lives of those affected (De Silva & Kawasaki, 2020), making investments in the businesses in that region null and void, becoming detrimental to sustainable development since the consequences of these directly affects the economy of the affected area (Khan et al., 2022). Impacts of floods have been seen on health of people, with the development of disorders between victims, highlighting a need for sustainable development in the management of epidemics in these areas (Mohamed et al., 2017). However, these environmental factors are seldom addressed in strategy meetings (Reichstein et al., 2021). Case studies show that while various Southeast Asian regions have developed proper management planning strategies countries like Pakistan still face consequences due to lack of proper policy planning (Abbas et al., 2015), needing modern practices in order to move towards a sustainable future. Thus, it becomes necessary for readers, investors, and industry professionals to be made aware of various ESG risks to sustainable development of their businesses posed due to extreme climate change and floods through relevant research and book chapters.
The visible impacts of climate change are becoming increasingly severe, posing a threat to human health and safety, quality of life, and economic growth. Effects of climate change cannot be fully understood by solely studying the relationship between natural events and their impacts (Ren et al., 2020). For instance, flash floods has become a global issue with a significant impact on multiple regions as demonstrated by their emergence as a persistent problem over the last ten years, accounting for more than 73% of natural disaster losses and costing almost $30 billion overall (Dhunny et al., 2020). Understanding flood risk assessments is important to address this issue, especially for sustainable development and ESG principles. This study aims to provide valuable information on floods in vulnerable areas using machine learning and deep learning to create flood risk assessments and maps based on satellite images. These findings emphasize the importance of technology in assessing climate features and flood mapping, which can help assess natural disaster risk and remote sensing in general.