Submitted by Jonas Meckling on
This project explores the politics of renewable energy investment among middle income countries, using a novel dataset of geolocated firm-project level observations. Middle income countries face rapidly rising energy demand and are the most vulnerable to climate change. It is essential to lay their foundations for economic growth through sustainable energy generation. Since the turn of the millennium, most have adopted subsidies for renewable energy popularized by early industry leaders like Germany and Denmark. Yet adopters vary widely in policy outcomes; some completely failed to catalyze investment while others developed thriving domestic solar industries. Why did some states develop locally powered solar industries, while others remain reliant on foreign investment? When does investment lead to local job growth and long-term sustainable energy transition? This project maps how investor characteristics determine long term renewable energy successes among emerging economies.
We are seeking to hire several students. The students will assist with compiling and coding data on solar energy projects, the firms involved, and the political conditions under which investment occurs. Students will identify the name and nationality of solar investment firms using the World Electric Power Plant Database and the ORBIS database of firm information. Students will compile public, verifiable sources for each solar project through Internet searches. Sample code and a codebook will be provided for firm metadata collection and name matching.
This database will be used to analyze the characteristics of renewable energy investors, and how different types of firms contribute to long term solar market growth. Students also have the option to (1) work on webscraping firm and lender information from relevant companies and financial institutions’ websites and (2) assist with data labeling for a component of the project which analyzes firms’ public participation in renewable energy policymaking using a machine-learning text analysis approach.
Interest in renewable energy deployment and climate policy is necessary. An understanding of a basic programming language like R or Python is preferred, but not necessary. Experience with advanced programming, including webscraping, SQL, and machine learning is excellent, but not required. Students will have the opportunity to develop any/all of these programming skills as desired. 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 collection analysis in R and/or Python in your application. Please also indicate how many hours you can work on the project.