Factorial enviro-economic equilibrium analysis for the effects of hierarchical carbon policy on China's socio-economic and environmental systems
Bibliographic record
Abstract
Under the coordinated development demands of climate change, carbon emission reduction, energy demand and economic progress, it is desired to methodically study the compound effects of the joint implementation of different policies, especially the detailed impact of the hierarchical carbon-mitigation (HCM) policy. A multi-level factorial enviro-economic equilibrium (MFEEE) model is developed to i) explore the multi-level interactive effects among carbon policy, income tax and production tax on social-economic and environmental (SEE) systems; ii) access the effectiveness of hierarchical carbon-mitigation (HCM) policy among socio-economic sectors. MFEEE is based on a static computable general equilibrium (CGE) model using 2017 as the base year. The results indicated that HCM policy could be regarded as an effective measure to make a greater contribution to related SEE issues compared with the non-hierarchical policy. The interactive effects of carbon policy, income tax and production tax require full attention. As the intensity of the HCM policy increases, the contribution of the interaction between carbon policy and production tax becomes more significant. The impact of HCM on system's GDP may be greater when the sectoral differences are given greater attention in tiered tax rate. These findings suggest that a sectoral differentiated hierarchical carbon-mitigation policy can achieve significant emissions reductions with minimal economic disruption. Policymakers should consider balancing carbon tax structures to mitigate adverse effects on economic growth while maximizing environmental benefits. • A Factorial enviro-economic equilibrium (FEEE) model is developed. • Interaction effects among carbon policies and income/production taxes are analyzed. • Effectiveness of hierarchical carbon-mitigation policy is examined. • Multi-level enviro-economic factorial analysis is initiated. • Raised second-order rate has the least effect on reducing carbon emissions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 source (direct Gemma or distilled Codex), 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".