{"id":"W2145924303","doi":"10.1016/j.envsci.2011.10.010","title":"The effect of proactive adaptation on green investment","year":2012,"lang":"en","type":"article","venue":"Environmental Science & Policy","topic":"Climate Change Policy and Economics","field":"Economics, Econometrics and Finance","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"HEC Montréal; Group for Research in Decision Analysis","funders":"Natural Sciences and Engineering Research Council of Canada; Universität Zürich; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Adaptation (eye); Damages; Context (archaeology); Business; Climate change; Environmental resource management; Natural resource economics; Greenhouse gas; Investment (military); Climate change mitigation; Environmental economics; Climate sensitivity; Environmental planning; Environmental science; Economics; Politics; Climate model; Political science; Ecology; Geography","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0008325498,0.0001039777,0.0001392753,0.0001369901,0.000275894,0.00002591931,0.0002437224,0.00003022822,0.00005891443],"category_scores_gemma":[0.00005711989,0.00008324841,0.00005536654,0.0001627963,0.0005261371,0.0003584679,0.00009461597,0.00006457112,0.0006008823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005131819,"about_ca_system_score_gemma":0.000009683641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001035763,"about_ca_topic_score_gemma":0.00001076982,"domain_scores_codex":[0.9991015,0.00001451263,0.000240158,0.000188266,0.0000546914,0.000400941],"domain_scores_gemma":[0.9993619,0.00007534611,0.0002072792,0.0002337193,9.057303e-7,0.000120797],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0001466322,0.0003235857,0.1955066,0.00002052906,0.0000746658,3.370035e-7,0.01744802,0.000236395,0.002716294,0.7499563,0.0002248796,0.03334574],"study_design_scores_gemma":[0.001303982,0.001554954,0.8538339,0.00001402021,0.00001584352,0.000008223817,0.001195314,0.003721302,0.06434216,0.02008192,0.0532825,0.000645845],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9716804,0.0002182399,0.00001238786,0.0007023024,0.0001462418,0.0002400631,0.00007646358,0.000006581054,0.02691734],"genre_scores_gemma":[0.9988909,0.0001255569,0.0000298883,0.0004285325,0.000186923,0.00003851403,0.000004106016,0.000008765899,0.000286833],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7298744,"threshold_uncertainty_score":0.7723326,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04348897291022109,"score_gpt":0.2474170795235423,"score_spread":0.2039281066133212,"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."}}