{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002296665,0.0006119994,0.0005766192,0.001329572,0.0005109819,0.001709674,0.0004882404,0.000603002,0.001138905],"category_scores_gemma":[0.003859424,0.0003983458,0.0009678441,0.001572806,0.00098783,0.001982095,0.001417115,0.0006300549,0.00005652914],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0044846,"about_ca_system_score_gemma":0.006002566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03498185,"about_ca_topic_score_gemma":0.02518878,"domain_scores_codex":[0.9984577,0.0006734256,0.00006417181,0.0002895145,0.000205594,0.000309662],"domain_scores_gemma":[0.9986026,0.000687636,0.0002756477,0.00006457393,0.0001981671,0.0001713352],"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":[0.00004776715,0.0001153301,0.04174139,0.000126518,0.0002229032,0.0001818312,0.0001911242,0.8874042,0.001323947,0.04878609,0.0005829039,0.01927599],"study_design_scores_gemma":[0.00001792529,0.00006244161,0.02025352,0.00002087499,0.00009223406,0.00001948963,0.0002673553,0.958737,0.0005733694,0.01898577,0.0009507158,0.00001940612],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8068133,0.0008632586,0.1794459,0.0009897776,0.00003090877,0.0001010493,0.0001634687,0.0000589738,0.01153341],"genre_scores_gemma":[0.9925842,0.0002552671,0.006350327,0.00001798383,0.000005377748,0.00002706095,0.00005737693,0.000007136014,0.0006954282],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03498185,"threshold_uncertainty_score":0.06955647,"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."}}