Rewiring R&D, clean energy, and the road to net zero: A machine learning analysis for France

This research examines how investments in energy efficiency research and development (R&D) and the growth of low-carbon energy use have affected environmental sustainability in France in the period from 1985 to 2022. We measure sustainability using the Load Capacity Factor (LCF), which reflects the balance between ecological pressure and biocapacity. Using machine learning methods, such as Artificial Neural Networks and Long Short-Term Memory models, we aim to analyze nonlinear patterns and the relative impact of economic, energy, and environmental factors. Our results suggest that public spending on energy efficiency R&D is the most important factor, accounting for a substantial share of the variation in the LCF. Further analysis shows that when high levels of such investment coincide with either strong economic growth or significantly higher low-carbon energy use, the predicted LCF is substantially higher than in low-investment situations. This implies that technological innovation and cleaner energy use are likely to work best in combination rather than in isolation. These findings support policies that focus on sustained investment in efficiency and clean technologies. Our results align closely with European climate neutrality goals while offering guidance for linking economic growth with long-term sustainability.

Dergi

RENEWABLE ENERGY

Yıl

2026

Cilt

263

ISSN

0960-1481

eISSN

1879-0682

DOI

10.1016/j.renene.2026.125240

WoS UID

WOS:001697386800001