Efficiency-driven tax rebates for low-carbon transition: A translog–evolutionary game approach

Achieving an effective energy transition requires carbon policies that adapt to firm behavior and reward performance rather than penalize uniformly. While existing rebate schemes often overlook firm-level heterogeneity, this study hypothesizes that aligning rebates with efficiency, workforce, and R&D performance can deliver stronger environmental and economic outcomes. To test this, we propose the Efficiency-Enhanced Carbon Tax Rebate Allocation (EECRA) framework, a firm-sensitive system that integrates policy design with stakeholder dynamics. In the first stage, EECRA applies a translog production function to estimate firm-level efficiency, deriving workforce- and R&D-oriented efficiency scores that guide conditional rebate allocation. In the second stage, an evolutionary game framework models stakeholder adaptation through interconnected dynamics of replication, workforce expansion, and R&D investment. Evidence from a Canadian case study utilizing five years of firm-level data, alongside a Norwegian case study employing three years of data, indicates that EECRA generates stable evolutionary equilibria, enhances energy output, reduces emission intensity, promotes green employment, and boosts wage-based GDP and social welfare. By aligning fiscal signals with firm-specific performance, EECRA has the potential to transform rising uniform carbon taxes into scalable drivers of cleaner production, innovation, and competitiveness, while strengthening economic resilience and offering policymakers a robust tool for accelerating low-carbon transitions across diverse economies.