{"id":"W4399913122","doi":"10.1016/j.jcomm.2024.100419","title":"Nash equilibria in greenhouse gas offset credit markets","year":2024,"lang":"en","type":"article","venue":"Journal of commodity markets","topic":"Climate Change Policy and Economics","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Greenhouse gas; Offset (computer science); Carbon offset; Monte Carlo method; Economics; Nash equilibrium; Homogeneous; Optimal control; Mathematical optimization; Microeconomics; Computer science; Econometrics; Mathematics; Statistics","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":[0.001547521,0.0007234397,0.0008537052,0.0004326645,0.0007274401,0.001684534,0.0009443145,0.001671503,0.006808032],"category_scores_gemma":[0.007394672,0.0003548727,0.0006122873,0.0003111152,0.002090431,0.002207088,0.001114769,0.001119229,0.0003789336],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001288829,"about_ca_system_score_gemma":0.001082889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003916071,"about_ca_topic_score_gemma":0.002715284,"domain_scores_codex":[0.9989976,0.0005606179,0.00002835899,0.0001273606,0.0001389401,0.0001471257],"domain_scores_gemma":[0.9973393,0.001768127,0.0003850455,0.00009020336,0.0002196544,0.0001975166],"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":[0.0001582021,0.00006589622,0.001210391,0.00007645306,0.00004527051,0.0002969356,0.0002175448,0.3703476,0.001962349,0.6197633,0.001156655,0.004699389],"study_design_scores_gemma":[0.00006322421,0.00003868768,0.0002397191,0.0000163789,0.000006594884,0.00003843207,0.00009785048,0.7058468,0.0002725754,0.2924357,0.0009270087,0.00001702488],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3397453,0.0006572019,0.6034228,0.00173507,0.00009522749,0.0001948716,0.0002562948,0.0001995689,0.05369372],"genre_scores_gemma":[0.9709387,0.0001717502,0.02218084,0.0001373346,0.00002275039,0.0001237358,0.00003806622,0.00002592484,0.006360925],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006808032,"threshold_uncertainty_score":0.02277517,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06532144771304554,"score_gpt":0.2672956978993558,"score_spread":0.2019742501863103,"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."}}