{"id":"W4206304122","doi":"10.3390/su14010501","title":"Research and Optimization of the Coupling and Coordination of Environmental Regulation, Technological Innovation, and Green Development","year":2022,"lang":"en","type":"article","venue":"Sustainability","topic":"Energy, Environment, Economic Growth","field":"Economics, Econometrics and Finance","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Social Science Fund of China","keywords":"Lagging; Environmental regulation; Economic geography; Industrial organization; Business; Green development; Environmental governance; Environmental pollution; Government (linguistics); Technological change; Data envelopment analysis; Environmental economics; Economic system; Natural resource economics; Regional science; Economics; Sustainable development; Corporate governance; Geography; Environmental protection; Political science; Macroeconomics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001901624,0.00005876348,0.0001400685,0.000194332,0.0002553962,0.000008451713,0.00008909069,0.00004698572,0.00006314152],"category_scores_gemma":[0.0002322217,0.00006215996,0.000008721412,0.0003009606,0.0005310736,0.00009231987,0.0004267707,0.0001155641,1.208066e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004828398,"about_ca_system_score_gemma":0.00002841065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000444043,"about_ca_topic_score_gemma":0.000002766275,"domain_scores_codex":[0.9990966,0.00002978821,0.0004460436,0.0002656709,0.00005449356,0.0001074098],"domain_scores_gemma":[0.9994323,0.00006281708,0.0002612725,0.0001867028,0.00004260613,0.00001432936],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001383339,0.00008999841,0.7270026,0.0000613917,0.00001073026,7.78932e-8,0.0003607006,0.01434656,0.00009810247,0.2566065,0.000005832733,0.001403599],"study_design_scores_gemma":[0.0002821715,0.00005581638,0.8391061,0.000001969022,0.000001484752,0.000001372044,0.001297167,0.01224892,0.0003861058,0.1457567,0.0007888789,0.00007324904],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966701,0.0002321572,0.001634292,0.001016185,0.00001273864,0.0003051823,0.00002091955,0.00000523054,0.0001031905],"genre_scores_gemma":[0.9989479,0.00001902854,0.0007516376,0.000008204705,0.00000314988,0.00004031394,0.000009405994,0.000006083154,0.0002143191],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1121035,"threshold_uncertainty_score":0.253481,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02253297420841999,"score_gpt":0.2292877233697284,"score_spread":0.2067547491613085,"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."}}