{"id":"W1991088917","doi":"10.1016/j.ecolecon.2010.04.006","title":"Evaluating China's urban environmental sustainability with Data Envelopment Analysis","year":2010,"lang":"en","type":"article","venue":"Ecological Economics","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":75,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"McGill University; World Bank Group","keywords":"Sustainability; Environmental Sustainability Index; Data envelopment analysis; Per capita; Beijing; China; Sustainable development; Urbanization; Gross domestic product; Benchmarking; Index (typography); Natural resource economics; Geography; Business; Economic growth; Economics; Population; Statistics; Political science","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":[0.003912915,0.001068127,0.001156896,0.003103169,0.0009917383,0.002633071,0.0005955444,0.0006738867,0.0007509778],"category_scores_gemma":[0.004464597,0.0003396199,0.00118083,0.00407973,0.0008347669,0.00177349,0.001163585,0.000388806,0.00005785499],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0086508,"about_ca_system_score_gemma":0.008399063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08418669,"about_ca_topic_score_gemma":0.06842995,"domain_scores_codex":[0.9978339,0.0008892887,0.0001116695,0.0002264516,0.0006315573,0.0003072026],"domain_scores_gemma":[0.9982273,0.0009541506,0.0001467952,0.0001822268,0.0003807089,0.0001087905],"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":[0.000129204,0.0001001206,0.03229509,0.0001239128,0.0002005349,0.0001252539,0.0001212014,0.9254807,0.001573069,0.008508187,0.000446724,0.03089611],"study_design_scores_gemma":[0.00001893908,0.00007774618,0.01578686,0.00001260363,0.00009421059,0.00001311177,0.0002054074,0.9762639,0.001884854,0.005053097,0.0005599484,0.00002932517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9578542,0.0003193918,0.03695555,0.0002025423,0.00001264233,0.0000719691,0.0003419588,0.0000626879,0.004179105],"genre_scores_gemma":[0.9952413,0.00008767623,0.004157936,0.000008467887,0.000002445967,0.00003169354,0.0001517729,0.000008334839,0.0003104215],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08418669,"threshold_uncertainty_score":0.1673933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0911556531172484,"score_gpt":0.3859296661094064,"score_spread":0.294774012992158,"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."}}