{"id":"W4410781192","doi":"10.1007/s10668-025-06250-z","title":"From penalties to profits: how government regulation and cost-revenue trade-offs shape green production and marketing","year":2025,"lang":"en","type":"article","venue":"Environment Development and Sustainability","topic":"Environmental Sustainability in Business","field":"Business, Management and Accounting","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"National Social Science Fund of China","keywords":"Production (economics); Revenue; Business; Government (linguistics); Industrial organization; Sustainable development; Government revenue; Economics; Marketing; Microeconomics; Finance","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":[0.001211045,0.0003533891,0.0003075486,0.0001166953,0.0005254475,0.0002812067,0.0001277119,0.0001097916,0.00006215213],"category_scores_gemma":[0.0005466308,0.0003554438,0.00002815778,0.0002065032,0.0002789615,0.0008720806,0.0007508291,0.0001515303,0.000003361354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009996193,"about_ca_system_score_gemma":0.00003907322,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002139152,"about_ca_topic_score_gemma":0.00007269774,"domain_scores_codex":[0.9977813,0.00006894427,0.000380638,0.000909229,0.0004549727,0.0004048852],"domain_scores_gemma":[0.999347,0.00009912222,0.0001549882,0.0003217992,0.00002960462,0.00004752943],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002679176,0.0001488289,0.7690179,0.001182163,0.00004690728,0.000004847158,0.001124485,0.00005369872,0.0002491157,0.0002766262,0.0007184049,0.2269091],"study_design_scores_gemma":[0.0003725798,0.000009463915,0.8960931,0.00007953552,0.00004961001,8.452332e-7,0.003920564,0.0004227621,0.000224555,0.002472087,0.09600257,0.0003523596],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9643111,0.0002556729,0.0003715416,0.03263738,0.0001167076,0.002140133,0.000007501344,0.00004821104,0.0001117086],"genre_scores_gemma":[0.9952289,0.00003974499,0.00100691,0.0005387174,0.0001847229,0.0003246326,0.00003774536,0.00002330236,0.002615253],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2265568,"threshold_uncertainty_score":0.9998897,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007081230790982723,"score_gpt":0.1950459123110745,"score_spread":0.1879646815200918,"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."}}