{"id":"W4225150352","doi":"10.1007/s10668-022-02372-w","title":"The optimal environmental regulation policy combination for high-quality economic development based on spatial Durbin and threshold regression models","year":2022,"lang":"en","type":"article","venue":"Environment Development and Sustainability","topic":"Energy, Environment, Economic Growth","field":"Economics, Econometrics and Finance","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Spillover effect; Environmental quality; Threshold model; Economics; Environmental regulation; Quality (philosophy); Panel data; China; Natural resource economics; Econometrics; Macroeconomics; Political science; Ecology; Biology","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":[0.003239592,0.0007990451,0.002599653,0.00181602,0.0005794717,0.003036904,0.001301123,0.001976473,0.004613218],"category_scores_gemma":[0.01008281,0.000796777,0.001037813,0.001346984,0.001033512,0.003512872,0.001431303,0.001735206,0.0003142863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002686525,"about_ca_system_score_gemma":0.004000544,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01168674,"about_ca_topic_score_gemma":0.007866075,"domain_scores_codex":[0.9986304,0.0005540128,0.00006224109,0.0002679762,0.0001275674,0.0003577071],"domain_scores_gemma":[0.9959496,0.002854793,0.0004573069,0.0001349214,0.0003775926,0.0002257228],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002383293,0.0001483128,0.002442904,0.0001989476,0.0001808665,0.00009880617,0.00005673777,0.8877987,0.0009733163,0.08684983,0.002564905,0.01844832],"study_design_scores_gemma":[0.0000379618,0.00002858255,0.0008174424,0.00002244455,0.00007015021,0.00001492449,0.00006764712,0.9669115,0.0003271562,0.03130082,0.0003857491,0.00001562945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3445697,0.001733959,0.6318253,0.004163738,0.0002121771,0.0001695464,0.0006476711,0.0007900109,0.01588785],"genre_scores_gemma":[0.9818785,0.0004223306,0.01506293,0.0001363368,0.00004142542,0.00006384964,0.0001450499,0.00005878347,0.002190814],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01168674,"threshold_uncertainty_score":0.02323741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01525184204384243,"score_gpt":0.2118173647807867,"score_spread":0.1965655227369442,"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."}}