This project explores the distributive political economy of renewable energy deployment in middle income countries. Many middle-income countries have adopted climate policy, but renewable energy deployment varies significantly among adopters, conditional on explanatory factors like sunlight or national income. Among policy adopters, the distribution of renewable energy within states remains an outstanding question. Is access to renewable energy concentrated around urban, wealthy households, or are these technologies applied to alleviate challenges of energy access and security? Which actors determine these renewable energy policy outcomes? How can renewable policy design facilitate the equitable and efficient deployment of renewables in emerging economies? This project examines the ways in which the features of domestic energy market structure effect the politics of renewable energy deployment in emerging economies.
We are seeking to hire two students. The students will be responsible for web scraping and geo coding data on renewable energy firms, renewable energy project site locations, and transmission grid infrastructure. Web scraping will involve building scripts to collect information on company address, products, and other firm level characteristics from an online public solar industry online. This task typically involves Python and geopandas, and examples of web scraping script based on previous work will be provided. Geospatial data visualization will entail a combination of geo coding addresses from existing data on solar installations in a subset of country cases and synthesizing existing geospatial data on renewable energy infrastructure from gridfinder.org and the Global Power Plant database. This will involve mapping programs including OpenStreetMap and Google API. These datasets will be used to analyze the relationship between energy market structure and the distribution of renewable energy and identify select cases for in-depth background research.
Overall, interest in renewable energy deployment and understanding of a basic programming language like R or Python is necessary. Experience with web scraping through Python and geopandas or other similar methods is preferred. Experience working with OpenStreetMap, ArcGIS and/or other vectorized geospatial data is also preferred. Coursework or research experience related to energy infrastructure, renewable energy technology, and sustainable development will also be helpful. Please highlight your interest in energy politics and relevant coursework related to data analysis, web scraping, and geospatial data analysis in your application. Please also indicate how many hours you can work on the project.