How Does Technological Innovation Drive Urban Sustainability? Insights from a Coupled System Dynamics and Explainable Machine Learning Approach

This dataset forms the core empirical foundation for my research on Huzhou, incorporating a comprehensive set of indicators across four critical subsystems—social, economic, environmental, and innovation—over the extended period from 2000 to 2050, under five Shared Socioeconomic Pathways (SSPs). The dataset is organised into three main components: (1) Origina data – collected from official statistical yearbooks, local government reports, and specialised surveys, providing a factual baseline for Huzhou’s historical development. (2) System dynamics‑generated scenario data– produced through a tailored simulation model that captures the interconnections, feedback loops, and time‑delayed effects among the four subsystems. These simulations are run separately for each of the five SSPs, allowing for the exploration of diverse socioeconomic and environmental trajectories under varying global challenges and policy assumptions. (3) Extracted innovation variables and the computed Sustainability Index (SI) – from both the historical and simulated data, key innovation‑related metrics are isolated using factor‑extraction techniques. These variables, combined with the other subsystem indicators, are then aggregated via a weighted composite methodology to derive the Sustainability Index (SI). The SI offers a measure of Huzhou’s overall sustainability performance