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The current chapter proposes a theoretical framework to assess the sustainability of economic growth in the Gulf Cooperation Council (GCC) States. The authors integrate insights from endogenous growth models and consider the unique socioeconomic characteristics of the GCC region to provide a comprehensive and tailored approach to understanding the determinants of economic growth and formulating effective policy measures to foster sustainable development and growth. This chapter highlights the environmental challenges faced by GCC; based on this, the authors suggested indicators to construct a theoretical framework (Economic Growth, Climatic Indicators, Energy Indicators, Social Indicators, and Economic Resources Indicators). The authors propose that policymakers and researchers in GCC States should take these factors into account when devising policies or conducting research aimed at fostering sustainable economic growth. Overall, this chapter presents significant insights for policymakers, researchers, and stakeholders involved in promoting the sustainable economic advancement of the GCC States.

The paper aims to investigate the relationship between institutions and economic growth in developing countries, considering the role of financial inclusion, education spending and military spending.

The study employs dynamic panel analysis, specifically two-step system generalized method of moments (GMM), on a sample of 61 developing countries over the period 2009–2020.

The results confirm that weak institutional quality, weak financial inclusion and increased military spending are barriers to economic growth, conversely, increased spending on education and gross capital formation contribute to economic growth in developing countries. Regarding the specific institutional factor, we find that corruption, ineffective government, voice and accountability and weak rule of law contribute negatively to growth.

The study calls for strengthening institutions so that the financial system supports economic growth and suggests increasing spending on education to improve access to and the quality of human capital, which is an important determinant of economic growth.

The study contributes to scarce literature by empirically analyzing the relationship between institutions and economic growth by considering the role of financial inclusion, public spending on education and military spending, factors that have been ignored in previous studies. In addition, the study identifies the institutional dimension that contributes to reduced economic growth in developing countries.

The purpose of this paper is to analyse the influence of economic growth, digitalisation, eco-innovation, energy consumption and patents on environmental technologies on the volume of greenhouse gas emissions (GHG) recorded in European countries for a period of nine years (2010–2018).

Two empirical methods were integrated into the theoretical approach developed based on the analysis of the current scientific framework. Multiple linear regression, an extended version of the OLS model, and a non-causal analysis as a robustness method, Dumitrescu–Hurlin, were used to achieve the proposed research objective.

Digitalisation described by the number of individual Internet users and patents on environmental technologies determines the amount of GHG in Europe, and economic growth continues to have a significant effect on the amount of emissions, as well as the consumption of renewable energy. European countries are not framed in well-established patterns, but the economic growth, digitalisation, eco-innovation and renewable energy have an impact on the amount of GHG in one way or another. In many European countries, the amount of GHGs is decreasing as a result of economic growth, changes in the energy field and digitalisation. The positive influence of economic growth on climate neutrality depends on its degree of sustainability, while patents have the same conditional effect of their translation into environmentally efficient technologies.

This study has a number of limitations which derive, first of all, from the lack of digitalisation indicators. The missing data restricted the inclusion in the analysis of variables relevant to the description of the European digitalisation process, also obtaining conclusive results on the effects of digitalisation on GHG emissions.

A similar analysis of the relationship among the amount of greenhouse gas emissions and economic growth, digitalisation, eco-innovation and renewable energy is less common in the literature. Also, the results can be inspirational in the sphere of macroeconomic policy.

This study aims to examine the links between carbon emissions, electric vehicles, economic growth, energy use, and urbanisation in 15 countries from 2010 to 2020.

This study adopts seminal panel methods of moments quantile regression with fixed effects to trace the distributional aspect of the relationship. The reliability of methods is confirmed via fully modified ordinary least squares coefficients.

This study reveals that fossil fuel use, economic activity, and urbanisation negatively impact environmental quality, whereas renewable energy sources have a significant positive long-term effect on environmental quality in the selected panel of countries.

The main limitation of this study is the generalisability of the findings, as the study is confined to a limited number of countries, and focuses on non-renewable and renewable energy sources.

Finally, this study proposes several policy recommendations for decision-makers and policymakers in the 15 nations to address climate change, boost sales of electric vehicles, and increase the use of renewable energy sources.

This study calls for a comprehensive transition towards green energy in the transportation sector, enhancing economic growth, fostering employment opportunities, and improving environmental quality.

Economic literature highlights both positive and negative impact of FDI on economic growth. The purpose of this study is to confirm the relationship between various economic factors and FDI equity inflows and find out deviations, if any. This is investigated using standard time-series econometric models. The long and short run relationship is inquired with respect to market size, inflation rate, level of infrastructure, domestic investment and openness to trade. The choice of variables for Indian economy is purely based on empirical observations obtained from scientific literature review.

The study involves application of autoregressive distributive lag (ARDL) model to investigate the relationship. The long run co-integration between FDI and economic growth is tested by Pesaran ARDL model. The stationarity of data is tested by augmented Dickey Fuller test and Phillip–Perron unit root test. Error correction model is applied to study the short run relationship using Johansen’s vector error correction model method besides other tests.

The results show that the domestic investment, inflation rate, level of infrastructure and trade openness influence inward FDI flows. These factors have both long and short-term relationship with FDI inflows. However, market size is insignificant in influencing the foreign investments inflows. There lies an inverse relation between FDI and inflation rate.

To the best of the authors’ knowledge, the study is original. The methodology and interpretation of results are distinct and different from other similar studies.

The purpose of this study is to solve the problem of healthcare waste management in developing countries. The buildup of medical waste has attracted the attention of all spheres of society due to the expanding population and developing economy. Timely collection and processing of medical waste are extremely important due to its potential hazards. Although the problem of planning medical waste management has been addressed in developed countries, it persists in several developing countries. This research is motivated by an example of a city in India characterized by a dense population, abundant health-care facilities and a lack of planning for managing large medical waste generated daily.

The authors address the problem of designing the network of collection and processing facilities for medical waste and optimizing the vehicle route that collects and transfers the waste between facilities. Due to distinct topographic restrictions in the considered city, the collection and transfer process needs to be conducted in two echelons – from hospitals to collection centers using smaller vehicles and then to the processing facilities using trucks. This work addresses these two problems as a two-echelon location-routing problem.

A mixed-integer programming model is developed to minimize the cost of opening the facilities and transporting medical waste. Several managerial insights are drawn up to assist planners and decision-makers.

This study follows a case study approach to provide a descriptive and prescriptive approach to hospital waste management in the ancient city of Varanasi. The city has witnessed unplanned growth over the years and is densely populated. The health-care facilities in the city have a large catchment area and attract patients from neighboring districts. The situation analysis based on secondary data and unstructured interviews of the stakeholders suggests that the ad hoc approach prevails in present hospital waste management in the city.

The pressing issues of climate change and environmental degradation call for a reevaluation of how we approach economic activities. Both leaders and corporations are now shifting their focus, toward adopting practices and embracing the concept of circular economy (CE). Within this context, the Food and Beverage (F&B) sector, which significantly contributes to greenhouse gas (GHG) emissions, holds the potential for undergoing transformations. This study aims to explore the role that Artificial Intelligence (AI) can play in facilitating the adoption of CE principles, within the F&B sector.

This research employs the Best Worst Method, a technique in multi-criteria decision-making. It focuses on identifying and ranking the challenges in implementing AI-driven CE in the F&B sector, with expert insights enhancing the ranking’s credibility and precision.

The study reveals and prioritizes barriers to AI-supported CE in the F&B sector and offers actionable insights. It also outlines strategies to overcome these barriers, providing a targeted roadmap for businesses seeking sustainable practices.

This research is socially significant as it supports the F&B industry’s shift to sustainable practices. It identifies key barriers and solutions, contributing to global climate change mitigation and sustainable development.

The research addresses a gap in literature at the intersection of AI and CE in the F&B sector. It introduces a system to rank challenges and strategies, offering distinct insights for academia and industry stakeholders.

The purpose is to conduct a systematic review of circular water management and its role in improving water availability amid increased demand and decreased supply.

A systematic literature review was implemented, which helped in the identification, selection and critical appraisal of the various research to answer the research question. It was guided by the Preferred Reporting Items for Systematic Reviews (PRISMA) statement. The review was conducted mainly on Web of Science and Scopus databases between November 20 and December 8, 2022, with search strategies involving free-text searching, phrase searching, truncation and Boolean operators.

The search process yielded 46 articles exploring circular water management. The findings reveal that circular water management offers more promise than linear or business-as-usual approaches. There are various circular water management models, although most of them emphasize a shift from the “take, make, consume and waste” principles. Contrarily, the success of the circular water management framework hinges on its ability to embrace resilience based on changing environmental conditions. Furthermore, the model focuses on improving inclusiveness with various stakeholders working together to improve water management.

The research is the first of its kind as it identifies a critical gap, the imperative need to develop a universal framework that can significantly advance the comprehension of circular water management.

This study aims to evaluate the quality of the energy efficiency disclosures made by Australian cities. As cities are significant energy users, and energy use is a crucial source of greenhouse gas emissions, energy efficiency initiatives can play an essential role in addressing climate change. Yet, little is understood about the energy efficiency disclosures being made.

The authors developed an original energy efficiency disclosure index to assess the reporting quality of the eight largest Australian cities. The websites of these cities were analysed for information on energy efficiency measures from December 2018 to June 2019. Annual reports, environmental reports, climate action plans and any other material related to energy plans were downloaded and then coded using the index.

While all cities provided energy efficiency information, little financial information was provided, limited forward-looking information was disclosed, key challenges were not disclosed, and each city provided energy efficiency disclosures differently. Collectively, these findings demonstrate that public accountability is limited.

An important implication is the need to standardise and improve cities’ energy efficiency reporting, especially concerning financial information. Cities, governments and the Carbon Disclosure Project (formerly the CDP) could achieve this, perhaps as part of the broader update of the CDP city-focused guidelines for greenhouse gas (GHG) reporting.

Although some studies on GHG reporting by cities have already been undertaken, including energy efficiency as part of their disclosure index, no study has focused on energy efficiency disclosures. The authors provide original insights concerning these practices. The study also provides an energy efficiency disclosure index that can be used in further research.

This study focused on exploring the performance factors (PFs) that impact Infrastructure Sanitation Projects (ISSPs) in the construction sector. The aim was twofold: firstly, to identify these crucial PFs and secondly, to develop a robust performance model capable of effectively measuring and assessing the intricate interdependencies and correlations within ISSPs. By achieving these objectives, the study aimed to provide valuable insights into and tools for enhancing the efficiency and effectiveness of sanitation projects in the construction industry.

To achieve the study's aim, the methodology for identifying the PFs for ISSPs involved several steps: extensive literature review, interviews with Egyptian industry experts, a questionnaire survey targeting industry practitioners and an analysis using the Relative Importance Index (RII), Pareto principle and analytic network process (ANP). The RII ranked factor importance,  and Pareto identified the top 20% for ANP, which determined connections and interdependencies among these factors.

The literature review identified 36 PFs, and an additional 13 were uncovered during interviews. The highest-ranked PF is PF5, while PF19 is the lowest-ranked. Pareto principle selected 11 PFs, representing the top 20% of factors. The ANP model produced an application for measuring ISSP effectiveness, validated through two case studies. Application results were 92.25% and 91.48%, compared to actual results of 95.77% and 97.37%, indicating its effectiveness and accuracy, respectively.

This study addresses a significant knowledge gap by identifying the critical PFs that influence ISSPs within the construction industry. Subsequently, it constructs a novel performance model, resulting in the development of a practical computer application aimed at measuring and evaluating the performance of these projects.