{"id":"W2342893841","doi":"10.5539/jms.v6n2p1","title":"The Effects of US State-Level Energy and Environmental Policies on Clean Tech Innovation and Employment","year":2016,"lang":"en","type":"article","venue":"Journal of Management and Sustainability","topic":"Climate Change Policy and Economics","field":"Economics, Econometrics and Finance","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mandate; Clean technology; Clean energy; Climate change; Renewable energy; Business; Scope (computer science); Portfolio; Renewable portfolio standard; Greenhouse gas; Public economics; Energy policy; Economics; Natural resource economics; Economic policy; Political science; Feed-in tariff","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.0008848555,0.0001014931,0.0001785637,0.0004914746,0.0003130477,0.001187131,0.0001461989,0.0004173972,0.003917688],"category_scores_gemma":[0.00349974,0.00007661211,0.0004746,0.0008125145,0.000509073,0.0004643332,0.0005809178,0.0005129072,0.0003048696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001389605,"about_ca_system_score_gemma":0.001527395,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02495202,"about_ca_topic_score_gemma":0.05634515,"domain_scores_codex":[0.9994709,0.0002189767,0.00002554662,0.00004808129,0.00009340164,0.0001431429],"domain_scores_gemma":[0.9960013,0.002102254,0.001010788,0.000114473,0.0004493535,0.0003217388],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008911434,0.000607594,0.8894798,0.0001715482,0.0004920784,0.0002635232,0.0003086844,0.02783813,0.001756529,0.01809105,0.00651452,0.05358538],"study_design_scores_gemma":[0.00002378154,0.0002422857,0.9787634,0.00005765035,0.0001626291,0.00004031181,0.0008803418,0.006279567,0.001548758,0.004662985,0.007316326,0.00002210878],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9755865,0.00125967,0.0003845999,0.002908038,0.00005399468,0.000008848845,0.0009477017,0.00001896949,0.01883176],"genre_scores_gemma":[0.9963206,0.0008509234,0.0000780976,0.0001832127,0.00002343808,0.000006284693,0.0002974145,0.000003028946,0.002237018],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02495202,"threshold_uncertainty_score":0.0496136,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02332415242456666,"score_gpt":0.2255044143110082,"score_spread":0.2021802618864416,"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."}}