How does climate change shape citizens’ views of political leaders and institutions? At a CDDRL seminar series talk, Amanda Kennard, assistant professor of political science at Stanford University, and Brandon de la Cuesta, a postdoctoral fellow at CDDRL and the Center on Food Security and the Environment (FSE), explored the effects of climate shocks on political trust, employing innovative machine learning methods.

To illustrate the link between the environment and human social systems, Kennard began with an anecdote about street vendors in Liberia. She noted that research has found that the single largest factor influencing vendors’ decision to join the formal economy is precipitation. From crop cultivation to the uptake of government programs, climate sets the stage for all other systems.

The existing literature on the political effects of climate links extreme temperatures to civil conflict and flood events to anti-incumbent voting. However, it has yet to fully explore the effect of climate on state actors or state structures.

Kennard’s and de la Cuesta’s paper focuses on Sub-Saharan Africa, characterized by a diverse set of political, economic, and demographic circumstances.

The authors studied public confidence in heads of state, local leaders, opposition parties, the police, the courts, and the bureaucracy. In the face of the changing climate, the authors are seeking to understand how people perceive state performance, corruption, and democracy.

At the start of this research project, the presenters explained, the available data lacked the spatial and temporal resolution necessary for credible subnational analysis. Accordingly, Kennard, de la Cuesta, and their team worked to create a machine learning algorithm that could harmonize survey questions across survey rounds and countries. These geolocated surveys were then paired with remote sensing data to create a complete picture of the association between climate change and public opinion in a given place and time.

Kennard and de la Cuesta used the data to study the effects of environmental shocks on relevant economic, social, and political outcomes. In terms of economic and social well-being, the team found that temperature shocks led to meaningful increases in dependence on informal networks for borrowing and remittances and to a decrease in interpersonal trust.

On the political side, they found that climate change is associated with statistically significant (albeit small) negative effects on public confidence in local bodies, the president, and the ruling party, along with some state institutions like the police and court system. While the effects of climate change on satisfaction with democracy were directionally consistent, they were, for the most part, not statistically significant.

Several countries in Africa have already seen devastating temperature changes of two degrees Celsius, with another two likely to come before 2050. This change is pushing many people out of the so-called human habitable zone, so the current effects on measures of economic, social, and political distress are only likely to increase going forward.

Kennard and de la Cuesta indicated that they hope to make the machine learning infrastructure they have developed scalable and open source, thereby allowing researchers to access specific geolocated survey responses.