Abstract for: The Role of Social Learning in Climate Change Inaction: Modeling Opinion Polarization and Issue Prioritization

Despite scientific consensus on anthropogenic climate change, public support for mitigation remains limited in the U.S. While a majority acknowledges climate change as a concern, this does not always translate into policy prioritization. This research examines how partisan social learning and the perception of economic trade-offs contribute to polarization. By modeling these dynamics, we aim to identify mechanisms that drive opinion divergence and propose strategies to align public opinion with scientific consensus. Using differential equation modeling, we develop a nonlinear system that captures opinion formation through social and independent information channels. The model integrates normative conformity, cross-party connectivity, and partisan differences in economic and climate concerns to analyze how small initial biases escalate into persistent polarization. By explicitly modeling the climate-economy trade-off, we assess the conditions under which climate mitigation remains a low priority despite widespread concern. Simulations show that strong reliance on social learning amplifies small differences in concern, resulting in climate opinion polarization. Even when a majority expresses concern, climate action prioritization remains lower than expected when weighed against immediate economic considerations. Cross-party interactions may eliminate the possibility of extreme polarization. Higher cross-party connectivity fosters greater consensus, while issue complexity and partisan dynamics contribute to sustained misalignment between concern levels and climate policy prioritization. Reducing partisan-driven social learning and fostering cross-party connections can mitigate climate opinion polarization. While climate concerns may be widespread, prioritization remains suppressed due to issue complexity and economic trade-offs. Aligning climate and economic interests—such as promoting green industries—helps dismantle this trade-off perception. Additionally, interventions like structured bipartisan dialogues, science-based simulations, and depoliticized climate messaging can enhance cross-party learning, ultimately fostering more consistent public support for climate action. Manuscript and simulation code editing