Massachusetts Institute of Technology
Empowering Energy Conservation: Low-Cost Interventions for Commercial and Residential Settings
Abstract
dc:description.abstractThis thesis aims to investigate the effectiveness of low-cost interventions in promoting energy conservation in commercial and residential environments. The first chapter employs social norms to design and analyze three behavioral change programs in a large biopharmaceutical company, with a focus on reducing electricity consumption and plastic waste. The second chapter evaluates the effectiveness of a new behavioral initiative that aims to reduce residential electric and gas consumption. We employ econometric and machine learning techniques to measure average and heterogeneous treatment effects, as well as to identify disparities in households with the highest versus lowest reductions. Covering the process from designing to evaluation, these chapters collectively offer a holistic perspective on the application of low-cost behavioral nudges in both workplace and residential energy usage. The implications drawn from our findings hold significant relevance for corporations, utilities, households, policymakers, and researchers alike, offering invaluable insights in promoting sustainable practices in both the workplace and the home.
Degree
thesis:*- Name thesis:degree_name
- Master
- Department dc:contributor.department
- Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
- Grantor dc:publisher
- Massachusetts Institute of Technology
- Year dc:date.issued
- 2024
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Ha, Lan L.
- Advisors dc:contributor.advisor
-
- Knittel, Christopher R.
- Raghavan, Manish
Rights
dc:rights- Statement dc:rights
-
- Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
- Copyright retained by author(s)
- Licence dc:rights.uri
Identifiers
dc:identifier.*- Handle dc:identifier.uri
- https://hdl.handle.net/1721.1/157020
- OAI identifier oai:identifier
- oai:dspace.mit.edu:1721.1/157020