Decoding the DNA of Green Finance: A Kernel‐Based Learning Odyssey Into the US Economy

ABSTRACT The transition to a sustainable economy requires a profound understanding of the underlying drivers of green finance. This paper decodes the ‘DNA’ of the United States (US) green finance landscape by investigating its multifaceted determinants from 1961 to 2024. Beyond traditional linear assessments, we introduce a sophisticated machine learning framework—comprising Quantile‐on‐Quantile Kernel‐based Regularized Least Squares (QQKRLS), Wavelet KRLS (WKRLS), and Rolling‐Window KRLS (RWKRLS)—to capture the intricate, non‐linear interactions between a novel Green Finance Index (GFI) and its catalysts: Foreign Direct Investment (FDI), Economic growth (GDP), and Environmental, Social and Governance (ESG) performance, as well as its inhibitors: geopolitical risk (GPR) and ecological footprint (ECF). Our findings reveal that while economic expansion and ESG quality act as primary drivers, geopolitical volatility and environmental degradation pose systemic threats to green financial stability. Crucially, the analysis uncovers that these dynamics are time‐varying and predominantly manifest in medium‐to‐long‐term horizons. This paper offers a blueprint for policymakers, emphasizing that a resilient green finance ecosystem in the US necessitates a holistic integration of macroeconomic stability, geopolitical risk mitigation, and stringent ESG standards.