{"id":"W2791496770","doi":"10.1142/s2010007818400092","title":"REVENUE RECYCLING AND COST EFFECTIVE GHG ABATEMENT: AN EXPLORATORY ANALYSIS USING A GLOBAL MULTI-SECTOR MULTI-REGION CGE MODEL","year":2018,"lang":"en","type":"article","venue":"Climate Change Economics","topic":"Fiscal Policy and Economic Growth","field":"Economics, Econometrics and Finance","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"Government of Canada","keywords":"Computable general equilibrium; Carbon tax; Economics; Subsidy; Revenue; Investment (military); Tax revenue; Greenhouse gas; Welfare; Lump sum; Natural resource economics; Public economics; Macroeconomics; Finance; Market economy; Payment","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009576981,0.0008308974,0.001021704,0.0008300502,0.0005642892,0.001602094,0.001595622,0.002173699,0.004870629],"category_scores_gemma":[0.002106475,0.0004835247,0.001736893,0.001166437,0.0009868856,0.001301777,0.001128131,0.001313242,0.0002496847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002592589,"about_ca_system_score_gemma":0.001922033,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07079956,"about_ca_topic_score_gemma":0.0355975,"domain_scores_codex":[0.9997177,0.0001331932,0.000006438047,0.00004686627,0.00002725688,0.00006854576],"domain_scores_gemma":[0.9990239,0.0006651063,0.00009555023,0.00004515906,0.0001061091,0.00006420061],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003203388,0.00001882129,0.0006523553,0.00001258154,0.00001143262,0.00004312274,0.00001027327,0.996341,0.0001156526,0.002235054,0.0001232762,0.0004043025],"study_design_scores_gemma":[0.00003647116,0.00004363077,0.0005869337,0.000007971713,0.00002483845,0.00001570348,0.00006921867,0.9971331,0.0001252682,0.001532394,0.000414377,0.00001002856],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9136885,0.0006231743,0.03881729,0.001648694,0.00004829119,0.0001560462,0.002494017,0.0001840246,0.04233999],"genre_scores_gemma":[0.987865,0.0002328384,0.007049695,0.0001131472,0.000009009854,0.00008455799,0.0003450915,0.00003914729,0.004261448],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07079956,"threshold_uncertainty_score":0.1407749,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2452035700432265,"score_gpt":0.319260645581469,"score_spread":0.07405707553824248,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}