Mapping the Methodological Landscape of Green Finance and Digital Technology Integration: A Scoping Review

The integration of green finance and digital technology exhibits considerable methodological diversity; however, existing scholarship lacks a systematic account of how empirical evidence is generated in this field. This scoping (PRISMA-ScR) review develops a literature classification framework centered on methodological approaches and applies it to 423 English-language articles on the green finance–digital technology nexus from the Web of Science Core Collection (2018 to April 2026). Research practices are organized along a two-dimensional framework spanning the epistemological objectives (explanatory vs. predictive) and the technical toolkit (traditional econometrics vs. machine learning), with causal machine learning integrating both paradigms. Each study is assigned a primary methodological label via a lexicon-based multi-label classification engine employing a 2 × 2 weight matrix. Weight sensitivity analysis confirms that classification outcomes are robust to parameter perturbation. Inter-coder reliability was near-perfect (Cohen’s κ = 0.93); algorithm–gold standard agreement was substantial (κ = 0.76, F1 = 0.79). The classification reveals that traditional econometrics remains the most prevalent method (26.95%), followed by predictive machine learning (17.26%) and technology application (16.31%), while causal machine learning (9.69%) remains underutilized and behavioral research is virtually absent (0.95%). Over a quarter of studies (26.24%) employ multiple methodologies. These findings provide a methodological map for researchers and policymakers navigating the green finance–digital technology landscape.