{"id":"W4404039318","doi":"10.2139/ssrn.4999656","title":"Greenhouse-Gas-Emission-Aware Portfolio Optimization with Deep Reinforcement Learning &lt;br&gt;","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Reinforcement learning; Portfolio; Greenhouse gas; Reinforcement; Portfolio optimization; Computer science; Artificial intelligence; Economics; Psychology; Geology; Financial economics; Social psychology","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":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0008120133,0.0005993256,0.0004526046,0.000398276,0.0002656084,0.0002727746,0.0004146674,0.0003867801,0.0001715349],"category_scores_gemma":[0.00003334932,0.0005171301,0.0002256226,0.0002933365,0.00002964287,0.0001385577,0.0002795969,0.008109052,0.00002625763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001683789,"about_ca_system_score_gemma":0.001427919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003358015,"about_ca_topic_score_gemma":0.0002002321,"domain_scores_codex":[0.9959786,0.00005262467,0.0006436934,0.0004199759,0.0005627419,0.002342377],"domain_scores_gemma":[0.9990615,0.0000369068,0.0002510869,0.0003074871,0.0001295066,0.0002135434],"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":[0.00002921656,0.0000100277,0.0000611815,0.0001724529,0.0005490861,0.00004735777,0.0002717435,0.985312,0.00005575601,0.001603581,0.000185882,0.01170168],"study_design_scores_gemma":[0.0004543359,0.0003190278,0.000002913027,0.001281242,0.0002424271,0.001007057,0.0003176678,0.9830523,0.0001864294,0.009743576,0.002619519,0.0007734773],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03699962,0.01976116,0.9163862,0.0001833299,0.002279641,0.0004059955,0.000003776075,0.001638536,0.02234178],"genre_scores_gemma":[0.9746331,0.01879835,0.001030327,0.00001802851,0.001088224,0.00002692591,0.0001034853,0.0002994359,0.004002122],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9376335,"threshold_uncertainty_score":0.999728,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005352320843288303,"score_gpt":0.2015035167469184,"score_spread":0.1961511959036301,"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."}}