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Corporate Social Responsibility (CSR) reporting has been widely accepted as a vital tool for communicating with stakeholders on a range of social, environmental, and governance issues, but how companies define, interpret, apply, integrate, and communicate their CSR efforts and impacts in corporate reporting is anything but a straightforward task. The purpose of this chapter is to explore the concept of materiality in CSR reporting and demonstrate practical examples of good CSR and Sustainable Development Goals (SDGs) reporting practices. We chose the aviation industry because of its economic relevance, constant growth, and future expected changes in the aftermath of COVID-19. In addition, airlines affect many of the SDGs directly and indirectly with contending results. This chapter is timely because of the growing willingness by companies to integrate CSR and environmental, social, and governance (ESG) thinking into the corporate strategy and business operations using materiality assessment and enhancing their competitive advantage and ability to maintain long-term value and because ESG and ethical investing have become part of the mainstream investing. Thus, this chapter contributes to an understanding of the wide range of existing and new reporting frameworks and regulations and reinforces the importance of discussing how this diversity of approaches can affect the work toward worldwide comparability of CSR and sustainability reporting.

The standard method to estimate a stochastic frontier (SF) model is the maximum likelihood (ML) approach with the distribution assumptions of a symmetric two-sided stochastic error v and a one-sided inefficiency random component u. When v or u has a nonstandard distribution, such as v follows a generalized t distribution or u has a χ2 distribution, the likelihood function can be complicated or untractable. This chapter introduces using indirect inference to estimate the SF models, where only least squares estimation is used. There is no need to derive the density or likelihood function, thus it is easier to handle a model with complicated distributions in practice. The author examines the finite sample performance of the proposed estimator and also compare it with the standard ML estimator as well as the maximum simulated likelihood (MSL) estimator using Monte Carlo simulations. The author found that the indirect inference estimator performs quite well in finite samples.

For decades, the fast population growth worldwide was interrelated with the adopted rapid lifestyle behavior that relies on the extensive use of fossil fuels. This primary energy source has caused various urban and environmental impacts, such as global warming, air pollution, and so forth. Consequently, the identified circumstance issues have caused many health, social, and economic hindering effects for global citizens. It poses an existential threat to humanity and the global earth's ecosystem. The alarming levels of urban pollution emissions are putting enormous challenges to the related stakeholders (governments, businesses, investors, automakers, and citizens) to admit the need to decarbonize the global economy and reach sustainable development goals (SDGs) for cleaner and smarter cities across borders. As such, a vital part of a smart city is the transport sector. The road transport sector, precisely, is one of the primary consumers of fossil fuels that contribute to high carbon dioxide emissions. Accordingly, it is essential to adopt novel and sustainable urban transport solutions and promote the achievement of the SDG's eleventh goal for sustainable cities and communities. This chapter provides insight into the present global energy situation with particular attention to the road transport sector. Indeed, it highlights different emerging technologies for a sustainable and smart urban mobility future that will mitigate the environmental situation thanks to the development of drive and internet telecommunication technologies. Furthermore, we aim in this chapter to study the international progress of the transition project using the Political, Economic, Social, Technological, Environmental, and Legal (PESTEL) analysis methodology. This study is to pinpoint opportunities for project development and the challenges that set back its evolution.

The performance of thermal comfort utilising machine learning and its acceptability by students and other users at the Professor Sir Edouard Lim Fat Engineering Tower at the University of Mauritius are evaluated in this study. Students and building occupants were asked to fill out surveys on-site as data was gathered from sensors throughout the structure. The Thermal Sensation Vote (TSV) and other important data were collected through the surveys, including the effect of wind on thermal comfort. An adaptive model incorporating solar and wind effects was evaluated using multiple linear regression techniques and RStudio. Three models were used to evaluate thermal comfort, including the adaptive one. Numerous models were compared and evaluated in order to select the best one. It was found that the adaptive model (Model 1) was deemed to be the best model for its application. It was also found that Fanger's PMV/PPD (Model 2) was a very good approach to determining thermal comfort. Through thorough analysis, it was concluded that the range of air temperature and wind speed for thermal comfort was 25.830°C–28.0°C and 0.26 m/s to 0.42 m/s, respectively. In order for cities to remain secure, resilient and sustainable, it will be important to manage thermal comfort and reduce populations' exposure to heat stress (SDG 11). The achievement of income and productivity goals will be hampered if measures to protect populations from heat stress are not taken (SDG 8). Thermal regulation is also necessary for the provision of numerous health services (SDG 3).

Industry 4.0 and the digital supply chain (DSC) are changing how things are made and moved around the world. This change is all about how smart technologies like the Internet of Things (IoT), artificial intelligence (AI), and blockchain are making supply chains work better. These tools help companies react faster and more clearly to what's needed. By using these new technologies, businesses can get better at guessing what customers want, keeping the right amount of stock, and quickly adjusting to new market trends. With these advanced technologies, companies can see big improvements, like being able to match supply with demand more closely and change their plans fast when things in the market change. It is really important for businesses to get how these tech tools work together as the world of making and selling things keeps changing. This chapter examines the convergence of traditional supply chain systems with Industry 4.0, focusing on the transformative impact of technologies such as the IoT, AI, and blockchain.