{"id":"W4404039318","doi":"10.2139/ssrn.4999656","title":"Greenhouse-Gas-Emission-Aware Portfolio Optimization with Deep Reinforcement Learning <br>","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008958396,0.0008818574,0.001110567,0.0003265186,0.000237589,0.0009066542,0.0008769576,0.001717348,0.002614858],"category_scores_gemma":[0.002975345,0.0005093849,0.00053339,0.0003585313,0.0005810845,0.0009791739,0.001003379,0.001369989,0.0003327042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007276633,"about_ca_system_score_gemma":0.0009878433,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005180254,"about_ca_topic_score_gemma":0.004890256,"domain_scores_codex":[0.9997725,0.00007469804,0.000007813313,0.00006057718,0.00003966865,0.00004472636],"domain_scores_gemma":[0.9991428,0.0005538633,0.00007495809,0.00005818262,0.0001078692,0.00006231175],"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":[0.00003181144,0.000024243,0.0003101169,0.00001597422,0.00002510157,0.00002252282,0.000005193044,0.9881957,0.0003206869,0.001918145,0.000465827,0.008664759],"study_design_scores_gemma":[0.000003301836,0.000005251234,0.00003028411,0.000001248403,0.000002325386,0.000002209194,6.259795e-7,0.9987265,0.00006437558,0.001119001,0.00004387807,0.000001060552],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0796172,0.0008703871,0.9076115,0.0009801931,0.0001598931,0.00004274776,0.0002325779,0.0007291923,0.009756266],"genre_scores_gemma":[0.9542487,0.0001419783,0.04071127,0.0001825449,0.00006013551,0.00004699724,0.0001672798,0.00007709691,0.004363928],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005180254,"threshold_uncertainty_score":0.01030016,"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."}}