2026-10-13T06:25:28Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34040238 2026-10-01T06:01:32Z category_7 category_272 category_45 category_734 category_61 category_106 category_133 portal_87 item_type_6 month_year_10_2026

Urban sustainable development evenness and nonlinear drivers: evidence from three urban agglomerations in China Houbo Zhou (3967586) Qiuli Lv (25153185) Yangmingxin Tan (25153188) Longyu Shi (15419390) Yang Zhao (39052) Medicine Environmental Sciences not elsewhere classified Sociology Biological Sciences not elsewhere classified Developmental Biology Science Policy Plant Biology Sustainable development goals urban sustainable development development evenness explainable machine learning nonlinear relationships <p>Urban sustainable development relies on the even advancement of multiple Sustainable Development Goals (SDGs), yet systematic quantification of development evenness and its nonlinear drivers remains limited. This study develops a localized SDG assessment framework using multi-source data integration and introduces an evenness index to characterize SDG progress among cities in three major urban agglomerations in China from 2015 to 2022. Furthermore, explainable machine learning integrating Tabular Prior-data Fitted Network (TabPFN) and Shapley Additive Explanations (SHAP) is employed to identify key drivers and their nonlinear relationships. The results indicate that: (1) although urban sustainable development has improved, SDG progress exhibits persistent structural imbalance, with the average comprehensive score of the 30 cities increasing by 7.19 points from 2015 to 2022; (2) development evenness shows notable temporal fluctuations and stage-wise reversals, with the evenness index (EI) primarily ranging from 0.40 to 0.65, alongside dynamic spatial reconfiguration and heterogeneous evolution; (3) the explanatory contributions of driving factors are highly heterogeneous across cities, with magnitude and direction varying significantly; and (4) complex nonlinear relationships are observed between drivers and evenness, primarily characterized by threshold effects and non-monotonic responses. In particular, per capita greenhouse gas emissions display an inverted U-shaped response pattern broadly resembling that of the Environmental Kuznets Curve (EKC), whereas ecological and infrastructure-related variables exhibit heterogeneous threshold and nonlinear response patterns. This study provides insights into the spatiotemporal dynamics and nonlinear relationships underlying SDG evenness, and offers empirical support for localized SDG assessment and differentiated policy design.</p> 2026-10-01T06:01:32Z Text Journal contribution 10.6084/m9.figshare.34040238.v1 https://figshare.com/articles/journal_contribution/Urban_sustainable_development_evenness_and_nonlinear_drivers_evidence_from_three_urban_agglomerations_in_China/34040238 CC BY 4.0