{"id":"W4410478308","doi":"10.1016/j.ejor.2025.05.027","title":"Government’s optimal inter-temporal subsidy and manufacturer’s dynamic pricing in the presence of strategic consumers","year":2025,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Energy, Environment, and Transportation Policies","field":"Energy","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Social Science Fund of China; Social Sciences and Humanities Research Council of Canada; Major Program of National Fund of Philosophy and Social Science of China; Hainan Provincial Department of Science and Technology; Hainan University; Natural Science Foundation of Hainan Province","keywords":"Subsidy; Dynamic pricing; Business; Government (linguistics); Industrial organization; Dynamic capabilities; Economics; Microeconomics; Operations research; Marketing; Market economy; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003698101,0.0007141345,0.002974108,0.0009049252,0.001106,0.005055319,0.001951662,0.006146298,0.01252667],"category_scores_gemma":[0.01527175,0.001560852,0.001349301,0.001077146,0.0025024,0.00549483,0.001616393,0.004505032,0.0005796835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005669685,"about_ca_system_score_gemma":0.006173999,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02101241,"about_ca_topic_score_gemma":0.01649306,"domain_scores_codex":[0.9983459,0.0005473627,0.00006517246,0.0002880778,0.000119479,0.0006340176],"domain_scores_gemma":[0.9900449,0.007064385,0.001194685,0.0003105799,0.0006414685,0.0007439796],"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":[0.001575278,0.0008314142,0.007360901,0.000309895,0.0002779309,0.001634044,0.0005336713,0.5586898,0.004083055,0.4033171,0.008937709,0.01244922],"study_design_scores_gemma":[0.000245472,0.0001914518,0.004783967,0.00005914092,0.0001364339,0.000245556,0.0008390595,0.827723,0.0006906181,0.1630255,0.001964089,0.00009570141],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8372864,0.001009992,0.08216044,0.01589884,0.0003194478,0.0001367426,0.0006437497,0.0002860924,0.06225838],"genre_scores_gemma":[0.993881,0.0001614687,0.001431498,0.0001571782,0.00002832017,0.00001325719,0.00003447112,0.00001953897,0.004273273],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02101241,"threshold_uncertainty_score":0.04190588,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04941170529521289,"score_gpt":0.3283235275277842,"score_spread":0.2789118222325713,"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."}}