Transforming energy taxation policy: A dual cooperative game and stochastic frontier approach for sustainable transitions in Canada
Bibliographic record
Abstract
A successful energy transition demands more than simply raising carbon taxes—it requires smart incentives that align environmental goals with economic resilience. This study introduces a novel Conditional Government Carbon Tax Rebate (CGCTR), which offers carbon tax reductions conditional on firms increasing salaries and expanding their workforce, fostering a dual benefit across environmental and labor dimensions within a government–industry energy network. We develop an integrated modeling framework that combines stochastic frontier analysis (SFA) with a dual-layer cooperative game theory approach to assess and optimize CGCTR adoption under multiple tax relief scenarios. The SFA component uses a policy-sensitive, time-varying Cobb–Douglas production function with workforce and salary as inputs and a policy-induced productivity shift from carbon tax to estimate firm-level energy output and inefficiency integrated with environmental performance, classifying firms as fully efficient, efficient, and less efficient. Strategic interactions among these tiers are modeled through intra-group and inter-group cooperation, enabling the identification of cooperative equilibria that support the acceptance of CGCTR. A Canadian case study using historical financial and operational data illustrates the practical utility of the framework. Results reveal that CGCTR can induce cooperative behavior even among heterogeneous firms, leading to (1) stable policy equilibria, (2) increased energy production and decreased emission intensity, (3) improved workforce sustainability through hiring and wage dynamics, and (4) broader social welfare gains reflected in rising wage-based GDP, increased employment, and enhanced productivity. This framework offers a novel decision-support tool for governments seeking to design adaptive, efficiency-driven carbon tax policies that align environmental goals with economic viability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".