Climate Changes through Data Science: Understanding and Mitigating Environmental Crisis

Abstract

Climate change represents an urgent environmental crisis with far-reaching risks to ecosystems and human communities worldwide. Rapid development of mitigation strategies and solutions is imperative but relies profoundly on advancements in detection, attribution, and prediction derived from climate data analytics. This paper examines the growing role of data science in not only quantifying anthropogenic climate change but also informing impact assessment and targeted intervention across climate-sensitive sectors. First, we survey established and emerging techniques for climate characterization, including machine learning applications on Earth systems data. Next, we discuss how sophisticated climate models alongside statistical analysis of multi-domain datasets—from migration patterns to crop yields—deepens scientific comprehension of climate change repercussions. Building on these insights, we spotlight data-enabled solution paradigms enabling smart climate action, ranging from high-resolution climate risk mapping, emissions reductions via optimized renewable energy infrastructure, to global warming suppression via solar radiation management. However, we also carefully examine the practical limitations hindering deployment and the ethical concerns posed by certain climate intervention proposals. Ultimately, while data science delivers powerful tools for climate change detection, attribution, and response, this paper underscores how continued climate data gathering alongside cross-disciplinary collaboration is vital to overcome analytical uncertainties, implementation barriers, and moral objections as we work to avert profound environmental breakdown.