Approaching social acceptance of energy technologies: ten European papers showcasing statistical analyses–a targeted review

Abstract

Background

Addressing global climate challenges necessitates a shift toward sustainable energy systems, with public acceptance of energy technologies playing a vital role in their successful adoption. While extensive research has been conducted on this topic, the lack of a unified framework for integrating various data and approaches from existing studies remains a challenge. This inconsistency makes it difficult to compare findings across different contexts and impedes the development of a comprehensive understanding of the factors influencing acceptance. This review aims to address this challenge by systematically evaluating the statistical methods used in ten large-scale studies on public acceptance of energy technologies in Western Europe published between 2012 and 2023. This Work allows researchers to more effectively compare methodologies and results, offering a transparent and structured approach for analysis, thereby enhancing the overall methodological assessment.

Main text

The review of ten large-scale studies identified valuable insights and opportunities for improving the analysis of public acceptance of energy technologies. Traditional methods like regression analysis have provided a solid foundation, highlighting key factors such as perceived benefits, trust, and attitudes. However, the review also revealed potential for growth by integrating more advanced techniques like AI-supported analysis, sentiment analysis, and agent-based modelling. These newer approaches offer the ability to capture complex, non-linear relationships and provide predictive insights. The introduction of statistical pattern graphics significantly enhances the clarity and comparability of methodologies, helping researchers to better understand and improve their approaches, ultimately supporting more accurate and impactful studies.

Conclusions

The review emphasizes the need for a unified analytical framework that integrates diverse methods, including both traditional statistical techniques and emerging approaches such as machine learning and sentiment analysis, to enhance the comparability of studies on public acceptance of energy technologies. By consolidating these varied methodologies into a cohesive framework, researchers can generate more consistent, robust insights that account for the complexities of public attitudes across different contexts. This unified approach not only improves the generalizability of findings but also provides stronger empirical evidence to guide policymakers in crafting more informed, effective strategies for promoting sustainable energy transitions at both local and global levels.