A City-Level Dataset for Green Technology Innovation Pathways: FinTech, Green Finance, Regulation, Talent, and Urban Structure
Main article
Abstract
This article develops a reusable city-level dataset framework for analysing green technology innovation pathways under the joint influence of FinTech, green finance, environmental regulation, talent, economic development, industrial structure, and urbanization. Different from studies that estimate the net effect of a single factor, the proposed dataset treats green technology innovation as a pathway outcome shaped by complementary technology-finance-government-talent-structure conditions. The article specifies the unit of analysis, variable schema, data sources, harmonization procedures, quality-control rules, and analytical workflows for descriptive benchmarking, necessary-condition diagnostics, fuzzy-set configurational analysis, panel modelling, machine learning classification, and policy scenario scoring. A synthetic demonstration illustrates how the dataset can identify structural-technology, finance-regulation, talent-structure, and full-synergy pathways. The study contributes a transparent data infrastructure for researchers and policy analysts seeking to compare urban green innovation capacity and diagnose city-specific bottlenecks in sustainable transformation.
