Capturing the economic and environmental potentials of a behavioral disruption in transportation

Limiting global warming to 1.5 °C or 2 °C requires deep decarbonization across energy, economic, and behavioral systems. While hybrid modeling frameworks combining top–down computable general equilibrium (CGE) models with bottom–up energy system models (e.g. TIMES) are well-established, few studies have integrated large-scale behavioral disruptions or quantified their indirect, economy-wide rebound effects. This study addresses this gap by soft-linking a CGE and TIMES model to evaluate the consequences of a 20% reduction in private vehicle demand in Quebec, Canada, a scenario consistent with regional sustainable mobility policies and empirical evidence on car-sharing. The analysis examines key indicators, including greenhouse gas (GHG) emissions, sector-specific energy consumption, and economic metrics like GDP, household incomes, and investments. Results show that behavioral disruption can be a win–win measure, improving economic performance while reducing decarbonization costs. The linked framework reveals sectoral reallocations, with industrial emissions declining and service-sector emissions partially increasing, reflecting rebound effects that evolve over time—from 17% in 2025% to 67% in 2050—consistent with transport rebound effects reported in the literature (16%–92%). Energy savings remain substantial, particularly for fossil fuels, with transportation energy use decreasing by 4%–10% relative to the baseline of 461.6 PJ of which private vehicles accounted for 44.4% in 2021, though increased low-carbon electricity consumption moderates long-term GHG reductions. This study highlights the importance of incorporating behavioral dynamics and rebound effects into prospective decarbonization modeling. It contributes to life cycle systems thinking and provides critical insights for policymakers designing robust, demand-side climate strategies.