{"id":"W3092766282","doi":"10.1287/mnsc.2020.3724","title":"Incentives and Emission Responsibility Allocation in Supply Chains","year":2020,"lang":"en","type":"article","venue":"Management Science","topic":"Sustainable Supply Chain Management","field":"Business, Management and Accounting","cited_by":158,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; University of British Columbia","funders":"","keywords":"Greenhouse gas; Shapley value; Supply chain; Incentive; Carbon footprint; Environmental economics; Microeconomics; Emissions trading; Economics; Value (mathematics); Business; Production (economics); Game theory; Natural resource economics; Industrial organization; Computer science; Marketing","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003774309,0.0005839607,0.0005362025,0.0006779794,0.001433136,0.002422498,0.0009145213,0.002343068,0.004138871],"category_scores_gemma":[0.009973385,0.0005361762,0.000571179,0.0008342985,0.00331143,0.003624212,0.002279919,0.001374417,0.0002901338],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00324753,"about_ca_system_score_gemma":0.003217333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003360612,"about_ca_topic_score_gemma":0.002835428,"domain_scores_codex":[0.996561,0.001975042,0.0001363639,0.0003879561,0.000525425,0.0004141394],"domain_scores_gemma":[0.9946591,0.003080857,0.0009515985,0.0003706967,0.0005562304,0.000381546],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00007199244,0.00007541104,0.0009249453,0.00005429944,0.00002333726,0.0001246737,0.0003824644,0.1433157,0.001360562,0.8405765,0.0005647018,0.01252544],"study_design_scores_gemma":[0.00007402446,0.00006314859,0.0003879072,0.00003181992,0.0000140091,0.00003746472,0.0001837309,0.2098024,0.0006683368,0.7847288,0.003983233,0.00002513459],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3049853,0.0005193613,0.6363279,0.004318786,0.0001332288,0.0002978668,0.0001117694,0.0001299282,0.05317596],"genre_scores_gemma":[0.9755248,0.0002627409,0.01866676,0.0001215986,0.00003950944,0.0001065747,0.00001754737,0.00001226729,0.005248081],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004138871,"threshold_uncertainty_score":0.02356261,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01431521025893132,"score_gpt":0.2362326163833497,"score_spread":0.2219174061244183,"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."}}