{"id":"W4292099683","doi":"10.1111/jiec.13322","title":"Using cumulative carbon budgets and corporate carbon disclosure to inform ambitious corporate emissions targets and long‐term mitigation pathways","year":2022,"lang":"en","type":"article","venue":"Journal of Industrial Ecology","topic":"Climate Change Policy and Economics","field":"Economics, Econometrics and Finance","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Concordia University","keywords":"Overshoot (microwave communication); Greenhouse gas; Business; Global temperature; Environmental science; Carbon tax; Term (time); Carbon fibers; Consistency (knowledge bases); Climate change; Environmental economics; Natural resource economics; Global warming; Economics; Computer science; Ecology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.005281437,0.0003836723,0.0003692026,0.00194178,0.0003694204,0.003557888,0.0005151574,0.001543964,0.002337132],"category_scores_gemma":[0.05376359,0.0003100856,0.0002485794,0.002230716,0.000504347,0.003606722,0.001073259,0.001280302,0.0004255329],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001137635,"about_ca_system_score_gemma":0.001536164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01450616,"about_ca_topic_score_gemma":0.0193235,"domain_scores_codex":[0.9983498,0.0008529309,0.000154559,0.0002280368,0.0002565293,0.0001582493],"domain_scores_gemma":[0.9269433,0.04262213,0.02345639,0.002198918,0.003587357,0.001191787],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002584874,0.0002112434,0.9490638,0.00004306073,0.000267523,0.00005396361,0.0003113781,0.01916436,0.0002006493,0.006566149,0.00196035,0.02189909],"study_design_scores_gemma":[0.00004447222,0.0003136538,0.7998621,0.0001712071,0.0004190449,0.0001369027,0.002738256,0.1413617,0.002105869,0.04408494,0.008662813,0.00009898976],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9796079,0.0007719761,0.004098266,0.003274794,0.00006948321,0.00002064975,0.001888117,0.00004615373,0.01022268],"genre_scores_gemma":[0.9983467,0.0002082206,0.0004819403,0.00009454895,0.00002421858,0.000009018616,0.0004508049,0.000003791246,0.0003808481],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01450616,"threshold_uncertainty_score":0.02884346,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2327721118253616,"score_gpt":0.2850723589478415,"score_spread":0.05230024712247991,"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."}}