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

作者：Ali Hamidoğlu, Hao Wang · 发表于：Applied Energy · 年份：2026 · DOI：10.1016/j.apenergy.2026.127436 · 被引用次数：5 · 研究领域：Climate Change Policy and Economics、Energy, Environment, Economic Growth、Integrated Energy Systems Optimization

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 offerin...