{"id":"W4407251456","doi":"10.1007/s43621-025-00872-z","title":"The growth–environment nexus amid geopolitical risks: cointegration and machine learning algorithm approaches","year":2025,"lang":"en","type":"article","venue":"Discover Sustainability","topic":"Energy, Environment, Economic Growth","field":"Economics, Econometrics and Finance","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Nexus (standard); Cointegration; Geopolitics; Computer science; Economics; Algorithm; Artificial intelligence; Econometrics; Political science; Law","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001176803,0.0002543095,0.0003653902,0.000101938,0.000483987,0.0002111244,0.0002480847,0.0001213763,0.00006401334],"category_scores_gemma":[0.0005026924,0.0002315731,0.0001248722,0.00009843615,0.0005718296,0.0003252442,0.0002978867,0.0003627302,0.00004209692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001046404,"about_ca_system_score_gemma":0.0000474103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001774955,"about_ca_topic_score_gemma":0.00006018493,"domain_scores_codex":[0.9979482,0.0001045527,0.0006505369,0.0007439542,0.00004994221,0.0005028192],"domain_scores_gemma":[0.9988939,0.0002477553,0.0002139408,0.0005307267,0.00001566033,0.00009804736],"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":[0.00001779984,0.0001096122,0.2309814,0.00004778495,0.00005426602,0.000001420924,0.0001840885,0.0006123386,3.109626e-7,0.7638392,0.00005215522,0.004099625],"study_design_scores_gemma":[0.0006082102,0.00007165449,0.2548011,0.000004704915,0.00002053537,0.000002053414,0.002293509,0.06106544,0.00004768536,0.6518468,0.02892307,0.000315261],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6312546,0.008892995,0.2920563,0.01319843,0.0005826948,0.001777722,0.0003182075,0.0001431969,0.05177579],"genre_scores_gemma":[0.9954697,0.0004003089,0.000318524,0.0001373237,0.00004653902,0.0001286462,0.00005040088,0.00002285397,0.003425741],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.364215,"threshold_uncertainty_score":0.9443278,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02509472521911263,"score_gpt":0.2183977407255436,"score_spread":0.1933030155064309,"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."}}