The Circular Economy (CE) and Energy Transition (ET) are crucial solutions for addressing global environmental challenges caused by linear economic models and fossil-based energy systems. Both approaches focus on enhancing energy and resource efficiency while minimizing environmental impacts. This Topic Modelling-based review identifies latent topics within the CE and ET academic literature, tackling the challenge of subjectivity found in traditional reviews. Using Latent Dirichlet Allocation (LDA), six topics are identified. Each topic sheds light on how crucial elements – such as economic and social sustainability, technological innovation, material management and electrification, waste management, local development, and regulatory policies – are interconnected with CE and ET. Together, these elements contribute to a more cohesive and effective transition. The study emphasizes the need for coordinated strategies and targeted policies to ensure that CE and ET not only coexist but also complement and strengthen each other. This holistic approach is vital for fostering a sustainable future that balances economic growth with environmental conservation.

This paper investigates the role of institutional quality in enhancing energy efficiency within Italy’s “hard-to-abate” industrial sectors, which include cement and lime, glass and ceramics, metal, pulp and paper, and refineries. These sectors account for a significant share of global energy consumption and CO2 emissions and face unique challenges in decarbonization. Using a novel dataset from the EU Emissions Trading System (EU ETS), covering 329 Italian plants from 2013 to 2019, the study applies the Directional Distance Function (DDF) to measure energy efficiency and explores the impact of institutional quality on energy performance through Ordinary Least Squares (OLS) and Two-Stage Least Squares (2SLS) regression. The analysis highlights greater energy efficiency among multi-plant firms, firms in the pulp and paper sector, and firms located in Central Italy. Regression results further show that institutional quality has a significant positive effect on energy efficiency, especially in competitive markets, among multi-plant and non-innovative firms, in Northern regions, and in the glass and ceramics and metal sectors. This paper contributes to the literature by underscoring the importance of institutional quality in driving energy efficiency in energy-intensive sectors and offers policy implications for promoting industrial decarbonization.

Climate policies are a key focus of the European Union's political agenda. Lobbyists aim to exert influence on these policies to advance their interests. This paper uses a panel dataset from 2011 to 2022 of European organizations to investigate the relationship between Climate Policy Uncertainty (CPU) and Lobbying Expenditure (LE). The dataset includes annual observations from organizations across eight European countries, incorporating both microeconomic and macroeconomic factors. The results indicate a positive association between CPU and LE, suggesting that higher levels of CPU are systematically linked to increased lobbying efforts within our sample. This relationship remains robust after addressing potential endogeneity concerns using the Two-Stage Least Squares (2SLS) approach. The effect is particularly pronounced in countries with high GDP or high CO₂ emissions, as well as for organizations with lower participation in European Commission meetings. The study also examined the presence of an exogenous shock, specifically the COVID-19 pandemic. While COVID-19 did not alter the existing relationship between CPU and LE, an analysis focused on the pandemic period revealed a reversal in the relationship. These findings carry important policy implications. Governments should prioritize transparency in lobbying activities and address the regulatory challenges posed by CPU to uphold accountability, balance diverse organizational interests, and safeguard the integrity of climate policymaking.

This study investigates public attitudes toward green hydrogen (GH) by analyzing YouTube comments through sentiment analysis and topic modelling. Unlike previous research that situates hydrogen within broader climate or energy debates and focuses on platforms such as Twitter or Bilibili, this work examines GH as a standalone topic and leverages YouTube's longer, context-rich comments to capture richer public discourse. Comments were collected via the YouTube API (Application Programming Interface) from a curated set of videos and analyzed for sentiment using both the rule-based VADER (Valence Aware Dictionary and sEntiment Reasoner) and the generative model ChatGPT-3.5, enabling a qualitative comparison of their performance. Latent Dirichlet Allocation (LDA) was then applied to identify major discussion themes, which were subsequently linked to sentiment trends. The results indicate that ChatGPT-3.5 outperforms VADER in interpreting sarcasm, slang, emoticons, and mixed sentiments. Topic modelling revealed eight key themes, including skepticism about institutional barriers and costs, optimism regarding GH's role in hard-to-decarbonize sectors, comparisons with nuclear energy and electric vehicles, and concerns about environmental and technical challenges. Overall, the study enhances understanding of online public discourse on GH by demonstrating how advanced sentiment analysis tools, combined with topic modelling, can generate deeper insights to inform strategies that better integrate public perceptions with the economic and policy conditions of GH deployment.