{"id":"W4408435155","doi":"10.5194/egusphere-egu25-10488","title":"A solution to energy transition paradox: optimal subsidy policy for minimizing the carbon emissions from future hybrid electricity system with hydropower and variable renewables.","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Integrated Energy Systems Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Hydropower; Renewable energy; Electricity system; Subsidy; Energy transition; Variable renewable energy; Electricity; Variable (mathematics); Energy system; Greenhouse gas; Economics; Natural resource economics; Environmental economics; Energy policy; Electricity generation; Environmental science; Electric power system; Engineering; Market economy; Ecology; Mathematics; Power (physics)","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"],"consensus_categories":[],"category_scores_codex":[0.0002216583,0.0005515228,0.0005816673,0.0003903586,0.0002265108,0.0001649757,0.0002726221,0.0004981112,0.000005191734],"category_scores_gemma":[0.00002433639,0.000409689,0.00009547555,0.000546832,0.00002256609,0.00008532531,0.00007333888,0.0003312115,2.436352e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008191133,"about_ca_system_score_gemma":0.0003679049,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03254959,"about_ca_topic_score_gemma":0.001144416,"domain_scores_codex":[0.9979833,0.0001157456,0.0005187059,0.0006426374,0.0002411673,0.0004983904],"domain_scores_gemma":[0.9988212,0.0001160482,0.0001133325,0.0005656253,0.0002355181,0.0001482758],"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":[0.0001224071,0.00001478849,9.192426e-7,0.0003133068,0.0002629912,0.000002659743,0.000389584,0.9916157,0.001914221,0.003208336,0.001911778,0.0002433256],"study_design_scores_gemma":[0.0004241267,0.00005279807,0.000001894724,0.0009499309,0.0002044221,0.00001900387,0.0003863066,0.9820859,0.01290075,0.00009640084,0.002408597,0.0004697997],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005163481,0.0007367764,0.982887,0.000606541,0.0006344836,0.0008997578,0.0003268976,0.0006932962,0.008051747],"genre_scores_gemma":[0.9584954,0.0001981702,0.03673149,0.0001886921,0.001218509,0.0009668058,0.0008936728,0.0001277202,0.001179583],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9533319,"threshold_uncertainty_score":0.9998355,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004066925802660386,"score_gpt":0.1913839535927069,"score_spread":0.1873170277900465,"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."}}