{"id":"W4386687822","doi":"10.54254/2754-1169/11/20230551","title":"Analysis of the Evolutionary Game Between Enterprises and Local Governments under Pollution Control","year":2023,"lang":"en","type":"article","venue":"Advances in Economics Management and Political Sciences","topic":"Energy, Environment, Economic Growth","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Corporate governance; Government (linguistics); Business; Control (management); Game theory; Evolutionary game theory; Sequential game; Industrial organization; Environmental pollution; Evolutionarily stable strategy; Pollution; Environmental economics; Nash equilibrium; Pollutant; Economics; Microeconomics; Ecology; Environmental protection; Finance; Management","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":[0.001665956,0.0004216503,0.0006697825,0.0004996417,0.0009067713,0.00173646,0.001111384,0.001530741,0.006218345],"category_scores_gemma":[0.005004148,0.0002500899,0.0008159307,0.0004023872,0.001378102,0.002094163,0.00125341,0.001184622,0.0002052502],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002877967,"about_ca_system_score_gemma":0.00207367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01267727,"about_ca_topic_score_gemma":0.007108225,"domain_scores_codex":[0.998713,0.0007145989,0.0000298437,0.0001408043,0.0001267476,0.0002749127],"domain_scores_gemma":[0.9979489,0.001269891,0.0002919786,0.00006623799,0.0001828877,0.0002401455],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001387885,0.0001552787,0.005824734,0.00008398847,0.00009446248,0.0008507062,0.0005974239,0.4168708,0.00156693,0.5651943,0.001322256,0.007300261],"study_design_scores_gemma":[0.00004209805,0.00007866453,0.001583961,0.00001478941,0.00004218522,0.00009595093,0.0005127033,0.8862349,0.0002336414,0.1099474,0.001190684,0.00002288724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6823967,0.0003635281,0.2503439,0.003332975,0.00006049568,0.000220671,0.0001872162,0.00005382486,0.06304075],"genre_scores_gemma":[0.9891489,0.0001506787,0.004428434,0.00007203509,0.00001054812,0.00006639298,0.00002917183,0.0000050497,0.006088801],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01267727,"threshold_uncertainty_score":0.02520698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01366824600858382,"score_gpt":0.2279441401384349,"score_spread":0.2142758941298511,"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."}}