An Integrated Data Science Framework for Climate Risk Assessment and Sustainable Intervention

Abstract

Environmental calamity of great proportion, climate change represents a critical threat to ecosystems and societies around the globe. However, it is imperative to formulate mitigation strategies and solutions promptly, and this is heavily dependent on the improvement of detection, attribution, and prediction techniques derived from the analysis of climate change. Apart from assessing the quantification of anthropogenic climate change, this paper aims to examine the rising role of data science in informing impact assessment and specific interventions in climate-sensitive industries. First, this paper examines the conventional and novel techniques for understanding the climate, particularly through the application of machine learning techniques using data obtained from Earth systems. Next, this paper overviews how statistical analysis of multi-domain data sets, such as crop yields and migration, and sophisticated climate modelling helps scientists understand the effects of climate change. On this basis, we focus on the role of data-driven solution paradigms in enabling smart actions for addressing the issue of climate change. This includes the suppression of global warming through control of solar radiation, reduction of emissions through optimized renewable infrastructure, and mapping of risk. However, we examine the ethical issues surrounding some of the solutions to this problem and the barriers to implementation. In this paper, we focus on the role of ongoing data collection in addressing the issue of climate change through interdisciplinarity, in spite of the fact that there are good tools for detection, attribution, and response to this issue through data science.