A Machine Learning and Econometric Framework for Credibility-Aware AI Adoption Measurement and Macroeconomic Impact Assessment in the Energy Sector

Adriana AnaMaria Davidescu, Marina-Diana Agafiței (), Mihai Gheorghe and Vasile Alecsandru Strat
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Adriana AnaMaria Davidescu: Department of Statistics and Econometrics, Bucharest University of Economic Studies, Romana Square 6, 010374 Bucharest, Romania
Marina-Diana Agafiței: Department of Statistics and Econometrics, Bucharest University of Economic Studies, Romana Square 6, 010374 Bucharest, Romania
Mihai Gheorghe: Department of Statistics and Econometrics, Bucharest University of Economic Studies, Romana Square 6, 010374 Bucharest, Romania
Vasile Alecsandru Strat: Department of Statistics and Econometrics, Bucharest University of Economic Studies, Romana Square 6, 010374 Bucharest, Romania

Mathematics, 2025, vol. 13, issue 19, 1-31

Abstract: Artificial intelligence (AI) adoption in strategic sectors such as energy is often framed in optimistic narratives, yet its actual economic contribution remains under-quantified. This study proposes a novel, multi-stage methodology at the intersection of machine learning, statistics, and big data analytics to bridge this gap. First, we construct a media-derived AI Adoption Score using natural language processing (NLP) techniques, including dictionary-based keyword extraction, sentiment analysis, and zero-shot classification, applied to a large corpus of firm-related news and scientific publications. To enhance reliability, we introduce a Misinformation Bias Score (MBS)—developed via zero-shot classification and named entity recognition—to penalise speculative or biased reporting, yielding a credibility-adjusted adoption metric. Using these scores, we classify firms and apply a Fixed Effects Difference-in-Differences (FE DiD) econometric model to estimate the causal effect of AI adoption on turnover. Finally, we scale firm-level results to the macroeconomic level via a Leontief Input–Output model, quantifying direct, indirect, and induced contributions to GDP and employment. Results show that AI adoption in Romania’s energy sector accounts for up to 42.8% of adopter turnover, contributing 3.54% to national GDP in 2023 and yielding a net employment gain of over 65,000 jobs, despite direct labour displacement. By integrating machine learning-based text analytics, statistical causal inference, and big data-driven macroeconomic modelling, this study delivers a replicable framework for measuring credible AI adoption and its economy-wide impacts, offering valuable insights for policymakers and researchers in digital transformation, energy economics, and sustainable development.

Keywords: Artificial Intelligence Adoption; energy sector; natural language processing; misinformation bias score; Difference-in-Differences; Input–Output model; economic impact (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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