Climate Change Analytics
Motivated by the need to implement data-driven solutions for better evaluation and policymaking of regional climate change policies, a multidisciplinary evaluation toolkit is proposed. Bridging the gap between policy making and data is expected to contribute to facing one of the biggest challenges of our days: climate change. This study uses machine learning techniques to exploit publicly available datasets for classification and prediction, analyzing climate change policies and programs. This project covers topics at the intersection of machine learning, sustainability, and governance.
The research will generate information supporting targeted interventions and tailoring policy actions to empower policymaking by creating a policy support tool for climate change mitigation and better adaptation (through data-supported policy design and evaluation). With predictive analytics, policy scenarios can be generated, identifying the most significant factors for a policy to achieve its targets and helping policymakers choose suitable policy interventions. A second analysis focuses on energy poverty, aiming to identify useful insights for the current energy burden crisis.