Environmental policies and innovation potential: Evidence from a deep learning neural network model

This study examines predictive associations between environmental policy mixes, policy stringency, environmental taxation, and R&D budget allocations, and environmental innovation across seven European countries. To this end, this study adopts Partially Linear Neural Network that separates interpretable linear components from nonlinear patterns while explicitly acknowledging the model's predictive, non-causal nature, we find positive associations between all three policy instruments and innovation. In addition, the country-level forecasts project positive innovation trajectories through 2026 for all countries except Portugal, though these projections are conditional on historical patterns. Systematic underprediction for Germany, France, and the United Kingdom suggests contextual factors, institutional capacity, industrial structure, and innovation system characteristics, play important mediating roles. However, Portugal's negative forecast points to potential implementation gaps weakening policy-innovation associations. The study demonstrates combining interpretable machine learning with explicit methodological limitations. Based on the analysis, the findings offer associational evidence informing policy design while pointing out the need for contextual adaptation and rigorous evaluation.