{"id":"W3013687980","doi":"10.24545/00001727","title":"Environmental Taxes and Productivity: Lessons from Canadian Manufacturing","year":2020,"lang":"en","type":"preprint","venue":"Institutional Repository at Grips (National Graduate Institute for Policy Studies)","topic":"Fiscal Policy and Economic Growth","field":"Economics, Econometrics and Finance","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Productivity; Revenue; Dividend; Natural resource economics; Economics; Tax revenue; Production (economics); Investment (military); Business; Monetary economics; Public economics; Labour economics; Agricultural economics; Microeconomics; Macroeconomics; Finance","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005773577,0.0003908382,0.0005241503,0.001600419,0.002263587,0.001937924,0.0007794901,0.0006044029,0.007046302],"category_scores_gemma":[0.005068261,0.0001593595,0.0005102577,0.00426014,0.0009896735,0.0006671796,0.000662352,0.001170766,0.0003478621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0515917,"about_ca_system_score_gemma":0.02737169,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9947582,"about_ca_topic_score_gemma":0.9952309,"domain_scores_codex":[0.999443,0.0000492834,0.00001387077,0.00005621378,0.0001948063,0.0002428182],"domain_scores_gemma":[0.9978349,0.000696883,0.0001695491,0.0001021112,0.0009862829,0.000210408],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007604409,0.0004257279,0.4584923,0.0004765964,0.0003663478,0.00152314,0.004330524,0.09645105,0.001276426,0.1862242,0.05293977,0.1967334],"study_design_scores_gemma":[0.0001906667,0.0001416726,0.7663792,0.0003672286,0.0004387637,0.0002110383,0.01189454,0.04612204,0.002177404,0.05168465,0.1201959,0.0001968901],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8418093,0.006701251,0.002087565,0.01658614,0.00008750467,0.00007119448,0.006266531,0.0001282759,0.1262623],"genre_scores_gemma":[0.9794531,0.004229237,0.0005182509,0.0003734144,0.00002174893,0.00001026424,0.001213506,0.00001704955,0.01416341],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0515917,"threshold_uncertainty_score":0.3743257,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1417087540982493,"score_gpt":0.2896677928475725,"score_spread":0.1479590387493232,"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."}}