{"id":"W3001525554","doi":"10.5267/j.dsl.2019.10.001","title":"Impact of globalization on CO2 emissions in Vietnam: An autoregressive distributed lag approach","year":2020,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Energy, Environment, Economic Growth","field":"Economics, Econometrics and Finance","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Distributed lag; Per capita; Cointegration; Globalization; Economics; Foreign direct investment; Lag; Fossil fuel; Time series; Short run; Investment (military); Natural resource economics; Coal; Consumption (sociology); Electricity; Environmental science; Econometrics; Macroeconomics; Engineering; Waste management; Market economy; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000636326,0.0001605462,0.0003531176,0.0003139716,0.0001008243,0.00008153782,0.0006988245,0.00006841614,0.000125355],"category_scores_gemma":[0.0005551389,0.0001564271,0.0001040008,0.0009304464,0.0002696577,0.0005813611,0.0001170956,0.0001243653,0.00009330185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004305187,"about_ca_system_score_gemma":0.00003476837,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005868789,"about_ca_topic_score_gemma":0.000001880885,"domain_scores_codex":[0.9981264,0.00002182388,0.0006545313,0.0007417829,0.0001344306,0.0003210623],"domain_scores_gemma":[0.9987629,0.00006014982,0.0004489636,0.000460089,0.00001414128,0.0002537537],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001039673,0.0003462338,0.6790506,0.000008059404,0.00001715343,0.000009018742,0.001010598,0.2857198,0.007690219,0.021237,0.003138063,0.001669247],"study_design_scores_gemma":[0.0008263075,0.0001825477,0.8635257,0.00002290454,0.000002136975,0.000001903634,0.00008247594,0.1323226,0.0004023535,0.001702664,0.000646065,0.0002824032],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9509916,0.0000276586,0.046216,0.001085614,0.000101826,0.0001500968,0.0002118219,0.00001870216,0.00119671],"genre_scores_gemma":[0.9966831,0.00001508371,0.002051258,0.001148537,0.00003107592,0.000008285946,0.00004592565,0.00001295288,0.000003769782],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.184475,"threshold_uncertainty_score":0.6378914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03920324934986179,"score_gpt":0.2730959873211071,"score_spread":0.2338927379712453,"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."}}