This study examines the price dynamics of the UK Emission Trading Scheme (UK ETS) by integrating advanced computational methods, including deep learning and statistical modelling, to analyze and simulate carbon market behaviour. By analyzing long-memory effects and price volatility, it assesses whether UK carbon prices align with theoretical expectations from carbon pricing mechanisms and market efficiency theories. Findings indicate that UK carbon prices exhibit persistent long-memory effects, contradicting the Efficient Market Hypothesis, which assumes price movements are random and fully reflect available information. Furthermore, regulatory interventions exert significant downward pressure on prices, suggesting that policy uncertainty disrupts price equilibrium in cap-and-trade markets. Deep learning models, such as Time-series Generative Adversarial Networks (TGANs) and adjusted fractional Brownian motion, outperform traditional approaches in capturing price dependencies but are prone to overfitting, highlighting trade-offs in AI-based forecasting for carbon markets. These results underscore the need for predictable regulatory frameworks, hybrid pricing mechanisms, and data-driven approaches to enhance market efficiency. By integrating empirical findings with economic theory, this study contributes to the carbon finance literature and provides insights for policymakers on improving the stability and effectiveness of emissions trading systems.

Even though the literature implies that customers and banking organizations can profit from digital banking in various ways, client adoption of this service is still low, especially in emerging and developing nations. Consumers’ openness to digital services limits their willingness to adopt digital banking, especially mobile banking services. We used a quantitative research method based on a questionnaire sent during August–December 2022 to Romanian individuals and received 118 answers, which we analyzed using the logistic regression model; throughout, we determined the extent of mobile banking use, payments, and banking products needed within the population with tertiary education, as well as new developments that the shift to digitalization brings to users, with new features for existing products, cryptocurrency accounts, and fintech companies now being complementary to traditional banks. Our study presents current customer perceptions of implementing bank digitalization through mobile applications in a developing nation like Romania; here, advantages are counterbalanced by limitations and there are, undoubtedly, difficulties to be overcome in the quest for a more effective e-business framework. We determined the factors that are relevant in making people use fintech accounts using logit analysis.

The rise of smart manufacturing, driven by digital transformation and Industry 4.0, has introduced new opportunities for investors seeking to diversify their portfolios. Smart manufacturing ETFs offer a unique risk-return profile tailored to the evolving landscape of industrial automation and data-driven processes. This paper explores the comparative risk-adjusted performance of a smart manufacturing ETF, a conventional industrial portfolio, and a broad-market SP500 tracking portfolio, utilizing daily data from October 2019 to October 2022. By deconstructing the excess returns of these portfolios through one-factor, three-factor, and five-factor asset pricing models, we provide insights into the risk exposure and performance drivers of smart manufacturing investments. Results indicate that investing in smart manufacturing, while less performant relative to the other investments, carries less exposure to market risk and can provide important diversification benefits to equity portfolios. Moreover, there is a positive loading for the size factor and a negative loading for the value and profitability factors for the smart manufacturing portfolio during a period of positive premium for all factors except for size. This implies that the consequent payoff in terms of profitability will eventually turn the loading of the profitability factor into positive territory, increasing returns on smart manufacturing investing.

This study examines the price dynamics of the UK Emission Trading Scheme (UK ETS) by integrating advanced computational methods, including deep learning and statistical modelling, to analyze and simulate carbon market behaviour. By analyzing long-memory effects and price volatility, it assesses whether UK carbon prices align with theoretical expectations from carbon pricing mechanisms and market efficiency theories. Findings indicate that UK carbon prices exhibit persistent long-memory effects, contradicting the Efficient Market Hypothesis, which assumes price movements are random and fully reflect available information. Furthermore, regulatory interventions exert significant downward pressure on prices, suggesting that policy uncertainty disrupts price equilibrium in cap-and-trade markets. Deep learning models, such as Time-series Generative Adversarial Networks (TGANs) and adjusted fractional Brownian motion, outperform traditional approaches in capturing price dependencies but are prone to overfitting, highlighting trade-offs in AI-based forecasting for carbon markets. These results underscore the need for predictable regulatory frameworks, hybrid pricing mechanisms, and data-driven approaches to enhance market efficiency. By integrating empirical findings with economic theory, this study contributes to the carbon finance literature and provides insights for policymakers on improving the stability and effectiveness of emissions trading systems.