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Technological progress is a determining factor in the factors leading to economic and social well-being. Simultaneously, the development of a sustainable economy is based on the conservation of resources. In the energy sector, this fact can be corroborated with the reduction of energy consumption, thus increasing economic efficiency. On the one hand, improving energy efficiency contributes to increasing the quality of life, productivity, and, implicitly, the economy, but on the other hand, it leads to excess energy use – this behavioral change is known as rebound. The research estimates the rebound effect at the macroeconomic level for European countries in the period 2000–2019, referring the analysis to each country's gross domestic product (GDP) and energy consumption, as well as comparing the preaccession and postaccession periods of Romania in the EU space. The rebound effect is determined using multidimensional analysis methods, depending on the GDP of each country as well as the behavior of each in the use of energy resources in industry, agriculture, and services. Although the study results confirm the strong link between energy consumption and GDP at the level of each state, they did not show considerable changes between countries at the level of the two periods.

The development of information technologies has led to a considerable transformation in human resource management from conventional or commonly known as personnel management to modern one. Data mining technology, which has been widely used in several applications, including those that function on the web, includes clustering algorithms as a key component. Web intelligence is a recent academic field that calls for sophisticated analytics and machine learning techniques to facilitate information discovery, particularly on the web. Human resource data gathered from the web are typically enormous, highly complex, dynamic, and unstructured. Traditional clustering methods need to be upgraded because they are ineffective. Standard clustering algorithms are enhanced and expanded with optimization capabilities to address this difficulty by swarm intelligence, a subset of nature-inspired computing. We collect the initial raw human resource data and preprocess the data wherein data cleaning, data normalization, and data integration takes place. The proposed K-C-means-data driven cuckoo bat optimization algorithm (KCM-DCBOA) is used for clustering of the human resource data. The feature extraction is done using principal component analysis (PCA) and the classification of human resource data is done using support vector machine (SVM). Other approaches from the literature were contrasted with the suggested approach. According to the experimental findings, the suggested technique has extremely promising features in terms of the quality of clustering and execution time.

Introduction: In the context of Sri Lanka, this study compares how institutions and financial development (FD) affect economic growth (EG) and inclusive growth (IG).

Purpose: The well-structured administration and judicial system at the provincial level have been established against the socioeconomic vulnerabilities in the country for an extended period. Still, the country as a whole and provincial level is experiencing huge income and social inequality, though there are required provisions for enhancing the well-being of the people.

Methodology: The study consists of data from the nine provinces from 2013 to 2019. The analysis used the Dynamic Spatial Durbin Model (D-SDM) to explore the spatial dependencies between the provinces. Two models were developed: the interaction of the financial service activities (FSA) and insurance, reinsurance, and pension (INPEN), representing the FD with the EG and IG with and without. The IG index was estimated by principal component analysis (PCA) using indicators of the four dimensions. The results indicated spatial dependency among FD’s interaction with EG when provincial tax (PROTAX) and provincial expenses (PROEXP) are the provincial institutions.

Findings: The IG model results showed the IG’s spatial dependency moderated by the FD and only the IG model between the provinces. PROEXP showed a significant positive spillover impact among provinces towards the IG.

Practical Implications: The finding inform economic policy making while identifying weaknesses in existing local governments. Attention must be given to how poverty can be reduced, enhancing the well-being of the people with the proper channelling of finance and government institutional mechanisms.

This chapter provides an empirical assessment of the effects of infrastructure provision on structural change and aggregate productivity using industrylevel data for a set of developed and developing countries over 1995–2010. A distinctive feature of the empirical strategy followed is that it allows the measurement of the resource reallocation directly attributable to infrastructure provision. To achieve this, a two-level top-down decomposition of aggregate productivity that combines and extends several strands of the literature is proposed. The empirical application reveals significant production losses attributable to misallocation of inputs across firms, especially among African countries. Also, the results show that infrastructure provision has stimulated aggregate total factor productivity growth through both within and between industry productivity gains.

Purpose: The purpose of this study is to identify the elements that can enhance financial inclusion (FI) in a nation, which in turn promotes economic development and growth.

Need for the Study: FI is crucial in providing people with the skills and resources to manage their money effectively and make informed financial decisions. Accessible, reliable and secure financial services play a significant role in achieving sustainable development goals (SDGs) and fostering economic progress.

Methodology: Data from 571 respondents were collected for analysis. The study utilises Statistical Package for Social Sciences SPSS and Analysis of Moment Structures AMOS software to analyse data and achieve the study’s objectives. The researchers employ these tools to obtain substantial results.

Findings: The findings indicate that FI contributes to economic growth (84%) and helps in accomplishing SDGs. Access, usage, affordability, technology, availability and technology adoption all play a vital role in increasing FI in the nation.

Practical Implications: The study’s outcomes have practical implications for policymakers and stakeholders, emphasising the importance of promoting FI through various measures such as enhancing access, affordability and technological advancements in financial services.

Estimation of (in)efficiency became a popular practice that witnessed applications in virtually any sector of the economy over the last few decades. Many different models were deployed for such endeavors, with Stochastic Frontier Analysis (SFA) models dominating the econometric literature. Among the most popular variants of SFA are Aigner, Lovell, and Schmidt (1977), which launched the literature, and Kumbhakar, Ghosh, and McGuckin (1991), which pioneered the branch taking account of the (in)efficiency term via the so-called environmental variables or determinants of inefficiency. Focusing on these two prominent approaches in SFA, the goal of this chapter is to try to understand the production inefficiency of public hospitals in Queensland. While doing so, a recognized yet often overlooked phenomenon emerges where possible dramatic differences (and consequently very different policy implications) can be derived from different models, even within one paradigm of SFA models. This emphasizes the importance of exploring many alternative models, and scrutinizing their assumptions, before drawing policy implications, especially when such implications may substantially affect people’s lives, as is the case in the hospital sector.

Purpose: The purpose of this chapter is to identify various factors that may influence the adoption of online education by students in higher education institutions (HEIs) and contribute to the achievement of sustainable development goals (SDGs) post-COVID-19.

Need for the Study: The study addresses the need to ensure inclusive and equitable quality education, as emphasised by SDG4. It focuses on lifelong learning opportunities and aims to understand the impact of online education on students in HEIs. By identifying the factors that influence adoption, the study aims to contribute to the development of effective strategies for promoting online education.

Methodology: The study utilises a framework incorporating the use of Statistical Package for the Social Sciences (SPSS) and Analysis of Moment Structures (AMOS) software. The framework allows for the analysis of the collected data’s reliability, validity and adequacy.

Findings: The study’s findings indicate that providing students with affordable online higher education can facilitate skill enhancement and create job opportunities. These findings highlight the potential of online education in contributing to the achievement of SDGs, particularly SDG4.

Practical Implications: The practical implications of the study suggest that promoting affordable online higher Education can have a positive impact on students, enabling them to acquire new skills and access job opportunities. By embracing online education, institutions can contribute to the advancement of SDGs and ensure inclusive and equitable quality education for all.

Globally, environmental concerns affect all aspects of human activity, and the economy for environmentally and socially aware goods and services is expanding. Entrepreneurs today are adapting their business practices to address new environmental problems or other environmental risks impacting their business. To bring about the transformation towards green economic systems, all green entrepreneurs are encouraged. Evidence from around the world shows that people's concerns for the environment are growing, and they are constantly adapting their behaviour to reflect these concerns. The objectives of the study were to assess the prevalence of green business practices among SMEs and also identify the elements that support these practices in Karachi, Pakistan. The study used a descriptive questionnaire as its research methodology. Self-completed questionnaires were used to collect primary data. The conclusions of the article stated that SMEs were using green business practices in their business areas. This can be explained by the great appreciation for green entrepreneurship in Pakistan. The variables that influence green entrepreneurship have been found to have different effects in practice. Stakeholders were advised to develop initiatives to promote adoption and use by most entities, including SMEs, as green business practices by SMEs in Karachi were still in their infancy. Through relevant authorities and green entrepreneurship, shareholders should lobby to provide them with a stronger negotiating strategy with other stakeholders. This study has some limitations. They study law in Karachi. Results are based on scenario-based surveys and methods and their applicability in a more complex relationship between green entrepreneurship practices and the performance of small- and medium-sized businesses.