Climate change is not only an environmental issue but also an economic and policy challenge that demands data-driven solutions. Climate Change Economics with Data Science: Emissions, Energy, and Policy Analytics offers a structured approach to understanding and addressing climate-related problems by merging the principles of environmental economics with modern data analysis techniques. From greenhouse gas mitigation strategies to renewable energy adoption, this course provides the analytical tools needed to assess climate policies and market transitions effectively.

You’ll begin by exploring low-carbon strategies through the Marginal Abatement Cost (MAC) framework, learning how to draw, interpret, and apply MAC curves to shape cost-effective climate action plans. The course then examines how climate change impacts human migration patterns, showing how data science can uncover trends in climate-driven immigration and their implications for policy-making.

The program also dives deep into tracking and visualizing carbon emissions intensity over time, with a case study analyzing two decades of emissions data across Europe. This is followed by real-world applications of electricity access analytics in South Asia, highlighting the link between sustainable energy development and social equity. Finally, you’ll learn to assess the share of renewables in electricity generation, using data-driven insights to evaluate progress towards a cleaner energy future.

By the end of this course, you will have the skills to combine economic theory, climate science, and data analytics to evaluate policies, forecast trends, and support sustainable development initiatives. The course bridges the gap between academic knowledge and practical applications, preparing you to contribute to climate change mitigation efforts in both public and private sectors.