{"id":"W4387455083","doi":"10.1088/1748-9326/acf949","title":"The value of reducing the Green Premium: cost-saving innovation, emissions abatement, and climate goals","year":2023,"lang":"en","type":"article","venue":"Environmental Research Letters","topic":"Climate Change Policy and Economics","field":"Economics, Econometrics and Finance","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Subsidy; Net present value; Greenhouse gas; Environmental economics; Investment (military); Present value; Environmental science; Constraint (computer-aided design); Budget constraint; Carbon sequestration; Carbon price; Climate policy; Value (mathematics); Marginal abatement cost; Climate change; Natural resource economics; Marginal value; Economics; Carbon dioxide; Microeconomics; Production (economics); Computer science; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003412737,0.0001027168,0.0001565468,0.0002325771,0.0008146695,0.00009236123,0.0003235804,0.00004349263,0.0001088795],"category_scores_gemma":[0.0002050487,0.00008143952,0.00004274628,0.000407353,0.0004293228,0.0001565344,0.0004844614,0.0002587794,0.0002950289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001763588,"about_ca_system_score_gemma":0.00000617771,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003802452,"about_ca_topic_score_gemma":0.00001475994,"domain_scores_codex":[0.9984855,0.00006084075,0.0005269186,0.0002870476,0.00009025211,0.0005494297],"domain_scores_gemma":[0.9988372,0.0005302257,0.0001979624,0.0003723679,0.000005181927,0.00005709178],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001463023,0.0001950563,0.631705,0.0002868322,0.0003493513,0.00001491104,0.01615977,0.001839352,0.0931712,0.1891956,0.04614164,0.02079499],"study_design_scores_gemma":[0.001402956,0.0001539753,0.7361178,0.0001539866,0.00001438394,0.00001249514,0.007566179,0.01564336,0.006597137,0.02462164,0.2070172,0.0006988298],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.973479,0.0004340039,0.0000111061,0.0240238,0.0001020912,0.0004079773,0.0002633935,0.00001455194,0.001264112],"genre_scores_gemma":[0.9933916,0.00515164,0.00002895305,0.0005165516,0.0001086071,0.0001014191,0.00004592636,0.0000253469,0.0006300081],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1645739,"threshold_uncertainty_score":0.6265861,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1461805515497645,"score_gpt":0.3369059281653995,"score_spread":0.190725376615635,"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."}}