Climate Decisions Lab
We meet regularly and conduct studies pertaining to climate change, sustainability and related matters. We produce reports from our work products. These reports are posted on the Web and/or submitted for refereed publication. The lab has been operating since 2000. Many students have found it useful for getting started post-Penn, either on the job or for graduate school. All levels of "technical" expertise welcome. Example of work product: Bina, Rachel and Luong, Kha and Mehta, Shrey and Pang, Daphne and Xie, Kristen and Chou, Christine and Kimbrough, Steven O., On Large Language Models as Data Sources for Policy Deliberation on Climate Change and Sustainability (February 02, 2025). The Wharton School Research Paper , Available at SSRN: https://ssrn.com/abstract=5123359 or http://dx.doi.org/10.2139/ssrn.5123359
Ai Chatbot Site Developer
A project for Wharton's Professor Schweitzer is in need of software developers.
The project is a web app for non-technical users to create custom chatbots for negotiations and other types of text based interactions. The chatbots will be used for research and education purposes, and are powered by LLMs like OpenAl's GPT models.
Using AI to improve the environmental and nutritional efficacy of food subsidies
Food consumption is a key lever for enhancing human health and environmental sustainability on earth. Our goal in this research is to better understand and find effective approaches to jointly alleviate malnutrition and climate-change outcomes in developing countries. In-kind food subsidies are a primary policy intervention in economically underprivileged communities. However, despite the proven efficacy of machine learning and data analytics in various industries, these technologies have not been widely adopted to enhance the design and impact of such subsidies. We aim to bridge this gap: this study develops a method to design nutrition- and climate-targeted food programs i.e., we algorithmically decide which foods to subsidize to maximize the nutrition value and minimize the carbon footprint of a subsidy program. This project builds on extensive field data collection in an underserved community in India, which comprises a randomized controlled trial, collection of large-scale point-of-sale transaction data, and survey data. Our methodology will combine a predictive choice model based on machine learning techniques and an optimization algorithm. Through this research, we aim to offer policymakers a tangible, data-driven pathway to refine food subsidy schemes, achieving healthier populations and a reduced carbon footprint.
The undergraduate RA is expected to have strong proficiency in coding, and basic understanding of statistics and/or econometrics.