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Purpose: This study aims to investigate the complex and multifaceted issue of changes in the quality of life of residents of the European Union (EU) member states from a dynamic perspective. This issue encompasses various social, economic, and political dimensions.

Methodology: This study uses the vector measure construction method (VMCM) to compare the quality of life in 27 EU countries since 2004. The VMCM approach, based on vector calculus properties, uses a scalar product to analyse the actual objects of analysis. Indicators such as per capita income, housing conditions, healthcare, education, and social and environmental inequality will be identified. The aggregate measure and available data will be used to create a ranking of the quality of life in each EU country, with the top-ranked country serving as a benchmark for comparison in the second phase.

Results: This study reveals that Ireland, Greece, Cyprus, and Luxembourg are the top performers in quality of life, while Hungary and Bulgaria consistently rank lower. Malta and Estonia show improvements in education, gross domestic product (GDP) per capita, income, and employment rates, while Poland and Spain experience declines. Slovenia is the top performer, followed by Malta and Lithuania, which have improved their ranking over time.

Practical implications: This study underscores the dynamic nature of quality of life and provides valuable insights for policymakers and researchers alike.

Purpose: The concept of a blue economy is gaining importance. 40% of the world’s population lives near coastal areas, and 80% of world trade is achieved using the seas. Sustainable financing for the blue economy is an emerging scope in climate finance. To date, little research has been published on this topic. Shiiba et al. (2022) proposed a conceptual framework for a blue finance mechanism; however, this approach was incomplete as it referred only to ocean sustainability and overlooked financial instruments and various financial models and schemes determined by a financial system, such as those in the United States, Germany, and Japan.

Methodology: The chapter aims to show the state of the art in sustainable financing in a blue economy and provide recommendations to improve the existing financial model.

A critical literature review, network approach, and case study.

Findings: The diversity and often uniqueness of blue economy projects underline the necessity for their financing system to be based on established regulatory frameworks in this area. However, specific solutions (e.g. the structure of acquired capital) should be considered on a case-by-case basis.

Limitations: Challenges include competition with government-owned sectors, infrastructure limitations, and limited public awareness of sustainable blue economy opportunities. Additionally, constraints like risk-averse local financial institutions and a lack of innovative business models hinder financing and entrepreneurship.

Purpose/Objective: This study aims to investigate the inferences of technological innovations introduced by companies, emphasised by firm investments in innovative products, the use of information technologies (ITs), and the number of innovative products in achieving economic and environmental sustainability.

Design/Methodology/Approach: Simple regression models with Driscoll–Kraay standard errors processed through the Pooled OLS method, and cluster analysis performed through the Ward method inset on hierarchical clustering. A newly compiled dataset with information extracted from the European Innovation Scoreboard (EIS) 2023 was employed in the analysis, covering the time lapse from 2016 to 2023 and integrating 28 European countries.

Findings: A statistically significant relationship between firm investments, technological innovations, and companies’ environmental and sustainability credentials. Clusters associated with the impact of innovative technologies on environmental and economic sustainability were identified. The results showed four different clusters, including countries that present similarities among the variables or distinctive tendencies from the countries belonging to other clusters.

Significance/Implications/Conclusions: New insights for firms, managers, entrepreneurs, and local or foreign investors and emphasise the need for innovation and technological investments within companies to improve their business activities and support more effective and sustainable development.

Limitations: The reduced availability of data and regarding the sample of representative indicators.

Future Research: Future research avenues might explore the importance of collaboration among technologists, financiers, policymakers, and environmentalists to harness technology’s full potential and navigate the complexities of integrating it with sustainable practices.

Purpose/Objective: In the context of the service economy, the chapter analyses a dynamic model created for a competition market, aiming to reveal changes that take place when the stake of economic activity goes beyond the strictly competitive meanings, a coproduction with the customer being involved.

Design/Methodology/Approach: A mathematical model is analyzed, describing the interactions between potential consumers, suppliers acting in traditional production conditions, and those applying coproduction with customs. The analysis of the model is done in the frame of nonlinear dynamical systems with time delay.

Findings: The positive equilibrium point is determined, and the conditions for stability and the existence of the oscillatory solutions are given. Numerical simulations verify the theoretical findings, revealing when the system is stable or when it becomes unstable.

Significance/Implications/Conclusions: Consistent with the service approach, our chapter considers not just the common private angle of the economic action but also the environment of the economic actor. The mathematical model brings a special conceptual development by involving coproduction in the competition between the providers. It also reveals the systemic effects of such new interaction involvement.

Limitations: The present chapter develops a mathematical model for the single case of coproduction with the customer. Two kinds of suppliers are concerned, the third one representing the demand side. Numerical simulation is developed using theoretical scenarios.

Future Research: Subsequent developments will make the role of economic entities more complex, with competition and cooperation being assumed among them all. Diverse other types of joint production will also be approached.

Purpose: The main purpose of the undertaken research is to analyse the effects induced by the specific coordinates of circular economy (CE), environment, and sustainability on the ASEAN-5 countries by assessing the economic dynamics in the context of this transition.

Methodology: The research methodology focuses on robust regression (RREG) models and Gaussian graphical models (GGMs) applied to panel data, including a complex set of fundamental macroeconomic aggregates as variables. Indicators were compiled for the ASEAN-5 member states: the Philippines, Indonesia, Malaysia, Singapore, and Thailand, over the period 2000–2023, including International Monetary Fund projections for the period 2024–2028.

Findings: The results highlight the importance of environmental coordinates in modelling the sustainable economic development of the ASEAN countries. Significant positive developments in the transition to a CE have been recorded with the end of the pandemic period, but fluctuations in carbon dioxide emissions (CO₂_EMIS) are still present. Although values have fallen by almost 30% compared to the last decade, countries have failed so far to ensure a downward trend.

Significance/Implications/Conclusions: To bring about change, ASEAN-5 countries should focus on implementing a CE framework, enforcing stricter measures, and raising awareness among the population.

Limitations: The current research encountered certain limitations primarily due to the lack of data availability and the restricted sample of indicators.

Future Research: Innovation, digitalisation, and adopting emerging technologies are indispensable in expediting diverse processes. These facets are fundamental in the assimilation and institutionalisation of CE principles and signify the future trajectory of research.

Purpose: This study explores potential effects and challenges of integrating Principal Adverse Impact (PAI) indicators in a sovereign bond portfolio through select exclusion policies.

Design/Methodology/Approach: The authors created two model portfolios based on BlackRock’s IGOV and EMB ETFs. After recreating the data supplied to asset managers by MSCI, the authors apply two sample policies excluding the worst scoring sovereign bonds. This approach mimics the actual integration of PAI indicators into sustainable investments and, thus, lends itself to discussing the challenges of implementing PAI indicators in practice.

Findings: Sovereign PAI indicators disadvantage emerging markets (EMs) and exhibit a regional bias towards European and North American countries. Excluding sovereign bonds belonging to the 10% of the worst-scoring countries does not significantly impact the developed market (DM) portfolio composition, while the same threshold heavily affects the portfolio composition for developing markets. The current indicators have an inherent income bias, with four out of seven numeric indicators substantially correlated with GNI per capita, systematically disadvantaging EMs.

Implications: Applying PAI indicators for exclusion raises concerns about sustainable investment in sovereigns where a large transition is still ahead.

Limitations: Limitations are associated with the approach adopted by the authors, primarily due to the novelty of the regulation, as well as the absence of relevant data.

Future Research: Research is needed to explore the influence of PAI indicators on the composition of financial products, particularly their effects on sovereign bonds, as they remain underexplored in terms of environmental, social, and governance metrics.

Purpose/Objective: This research aims to ensure that the technologies that promote the digital tourism economy play an important role in its development in the near future, and students will have an open attitude towards digitisation, artificial intelligence (AI), robotisation, and gamification in tourism.

Design/Methodology/Approach: A survey questionnaire collected primary data. Simple random sampling was used, and the sample size was 40 students. The questionnaire was distributed via email in March 2024. Data obtained from the questionnaire were analysed based on correlations.

Findings: AI usage in the education system for tourism students should be to personalise the learning process as much as possible, provide personalised learning paths according to student’s strengths and weaknesses, and adapt learning materials to the characteristics of students while maintaining the quality of education and the principles of integration into the educational system.

Significance/Implications/Conclusions: Intelligent IT systems fully support people’s needs and intuitively respond to their problems. However, creating such support requires long hours of work and programming. Indeed, the challenge of providing AI that can intelligently interact with humans remains significant. Rephrasing requests to obtain specific and accurate answers is unnecessary.

Limitations: Students’ attitudes towards AI are not always positive, as attested by their attitudes towards AI in tourism in the research analysis carried out in the chapter. This could lead to limited perspectives on AI in education.

Future Research: Specific strategies implemented by companies to offer customers virtual guided tours, allowing them to visualise a place before they get there.

Purpose: In Greece, large companies have started to focus more and more on corporate social responsibility (CSR) and ESG (environmental, social, and governance) activities, realising the importance of sustainability and social responsibility beyond traditional profits. Using machine-learning (ML) methods and artificial neural networks (ANNs) can enhance the process of measuring performance in these areas in several ways, including data analytics. This paper investigates and explores the correlation between CSR and ESG actions with financial and non-financial factors for the 100 largest companies operating in Greece.

Methodology: The study runs from January 2019 until December 2021, and ANNs and ML techniques are employed. The comparison concerns both the control variables and the predictability of the methods.

Findings: The main findings that emerged are the confirmation of the correlation between CSR and ESG actions and the financial performance and determinants of corporate responsibility of the companies in the sample. Moreover, good results were obtained for almost all of the techniques examined, but the superiority of deep learning models and gradient-boosted trees (GBTs) was found for the selected variables.

Significance/Implications/Conclusions: The findings suggest that using ML techniques and neural networks to measure CSR actions can help companies evaluate their performance and make effective decisions to improve their sustainability. It can also be a valuable tool for institutional investors, banks, and regulators.

Future Research: We believe that future research should focus on improving these models, exploring hybrid approaches that combine the strengths of different techniques, and expanding the range of variables considered.