{"id":"W4313830771","doi":"10.1016/j.ijpe.2023.108772","title":"Integrate exergy costs and carbon reduction policy in order to optimize the sustainability development of coal supply chains in uncertain conditions","year":2023,"lang":"en","type":"article","venue":"International Journal of Production Economics","topic":"Process Optimization and Integration","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Exergy; Sustainability; Supply chain; Carbon tax; Environmental economics; Coal; Carbon footprint; Economic order quantity; Exergy efficiency; Economics; Environmental science; Greenhouse gas; Business; Process engineering; Waste management; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0005367161,0.00008965514,0.0001416118,0.0008064589,0.0000241238,0.00003092598,0.0001405913,0.00004337639,0.000008077325],"category_scores_gemma":[0.0005354218,0.00008069463,0.00001861205,0.0005366301,0.00004214573,0.0002773464,0.00002454126,0.0001535526,9.550027e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00108974,"about_ca_system_score_gemma":0.0003553129,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001116966,"about_ca_topic_score_gemma":0.0004199475,"domain_scores_codex":[0.9990564,0.00003118782,0.000597294,0.0001136351,0.00009371139,0.0001077523],"domain_scores_gemma":[0.9990675,0.00002462095,0.0001454019,0.00007140862,0.0006543355,0.00003669733],"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":[0.00005965223,0.00003272781,0.001539531,0.00001232577,0.00003064567,0.000001029542,0.002546955,0.9861228,0.0006097421,0.001212819,0.0001547069,0.007677045],"study_design_scores_gemma":[0.002645837,0.000165973,0.08532643,0.000406137,0.00002059612,0.0002591093,0.02252551,0.8278832,0.04926864,0.006059886,0.004826066,0.0006126352],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9887058,0.00002753392,0.002617698,0.007332962,0.0009343788,0.0001924062,0.000007887864,0.00001860148,0.0001627943],"genre_scores_gemma":[0.9979179,0.0004180744,0.00130697,0.00002754971,0.0001751339,0.0000230174,0.00002331582,0.00001110583,0.00009694255],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1582396,"threshold_uncertainty_score":0.3290632,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01027728804927472,"score_gpt":0.2695921727667361,"score_spread":0.2593148847174614,"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."}}