2026-09-18T18:40:14Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1624322025-08-22T03:06:04Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Knittel, Christopher
Harrison, Ethan
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
2025-08-21T17:01:24Z
2025-08-21T17:01:24Z
2025-05
2025-06-16T14:46:36.141Z
https://hdl.handle.net/1721.1/162432
How do popular grievances about (the lack of) access to energy lead to political violence and instability? I use a mixed-methods approach to answer this question, based on a qualitative case study in Sri Lanka and a quantitative framework for tracking energy protests worldwide. Specifically, through an analysis of the 2022 Aragalaya protest movement in Sri Lanka, I elaborate on how breakdowns in state capacity to provide energy to its citizens can trigger civilian unrest. Building on this case study, as well as insights from the empirical literature on the drivers of instability related to energy access, I then pilot a machine learning (ML) framework to identify energy-related protest events in the Armed Conflict Events Database (ACLED) based on context-specific keywords, which results in the creation of the first global dataset on energy protests. This novel source of evidence, in turn, will open new avenues for research on the conflict-energy nexus, particularly on the impact of market shocks on civilian unrest and instability in low- and middle-income countries – a topic for which current empirical work is limited. I show how the ML framework I develop here can be used to enable continuous monitoring of protest activity related to energy access, as well as how the framework can be extended to other forms of political violence, offering a promising tool for peace-building initiatives across contexts. Therefore, such a framework could inform key evidence to support policymakers, practitioners, and researchers in the design of strategic policies that facilitate the provision of energy while mitigating the risk of conflict and instability worldwide, particularly in "energy-poor" countries.
S.M.
S.M.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright retained by author(s)
https://rightsstatements.org/page/InC-EDU/1.0/
Fueling Conflict: A Global Dataset of Energy Protests
Thesis
application/pdf
Master
Master of Science in Technology and Policy
Master of Science in Electrical Engineering and Computer Science
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="df18c2a3-1ea2-4fde-aea8-52ad992c2b39">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Title>Fueling Conflict: A Global Dataset of Energy Protests</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2025-05</PublicationDate>
<Authors>
<Author>
<DisplayName>Harrison, Ethan</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>https://rightsstatements.org/page/InC-EDU/1.0/</License>
<Abstract>How do popular grievances about (the lack of) access to energy lead to political violence and instability? I use a mixed-methods approach to answer this question, based on a qualitative case study in Sri Lanka and a quantitative framework for tracking energy protests worldwide. Specifically, through an analysis of the 2022 Aragalaya protest movement in Sri Lanka, I elaborate on how breakdowns in state capacity to provide energy to its citizens can trigger civilian unrest. Building on this case study, as well as insights from the empirical literature on the drivers of instability related to energy access, I then pilot a machine learning (ML) framework to identify energy-related protest events in the Armed Conflict Events Database (ACLED) based on context-specific keywords, which results in the creation of the first global dataset on energy protests. This novel source of evidence, in turn, will open new avenues for research on the conflict-energy nexus, particularly on the impact of market shocks on civilian unrest and instability in low- and middle-income countries – a topic for which current empirical work is limited. I show how the ML framework I develop here can be used to enable continuous monitoring of protest activity related to energy access, as well as how the framework can be extended to other forms of political violence, offering a promising tool for peace-building initiatives across contexts. Therefore, such a framework could inform key evidence to support policymakers, practitioners, and researchers in the design of strategic policies that facilitate the provision of energy while mitigating the risk of conflict and instability worldwide, particularly in "energy-poor" countries.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>