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In 2009, Newsweek published a report in which they ranked the 500 largest US companies and the 100 largest global companies based on its environmental performance measures (http://greenrankings2009.newsweek.com/). This ranking is referred to as Newsweek’s Green Ranking. Included in this ranking is information about water and air pollution, solid waste disposal, toxic wastes, carbon emissions, and enforcement actions. The question we are addressing in this study is how well it measures pollution performance? The question is relevant to environmental accounting/reporting since it is part of a dilemma yet to be answered: Aggregated environmental indices/scores are easy for average information users to percept, while specific information may not be preserved when it is aggregated into the overall score(s).

Specifically, we examine whether Newsweek’s Green Ranking is correlated with pollution measures based on Toxics Release Inventory (TRI) in order to determine how valid or reliable Newsweek’s Green Ranking is – in other words, how much Newsweek’s Green Ranking can explain the pollution by the toxic releases. We find that there is no significant correlation between Newsweek’s Green Ranking and the TRI measures except for the firms in the utilities industry. Concluding that on one measure, which we consider a very important one, there is no justification for the overall Green Ranking Score presented by Newsweek. However, in Newsweek’s three-part score the element that is termed the Environmental Impact Score captures pollution performance measured based on TRI. The contrast between the overall ranking and performance ranking indicates that a composite index that incorporates hard performance and soft measures can dilute the information carried by performance data.

Ọsẹ dúdú production and sale constitute a major indigenous business among the Yoruba people. Scholars have noted that the business is capable of boosting the socio-economic status of black soap entrepreneurs and of countries. However, ọsẹ dúdú enterprise has some significant threats and problems that are yet to be researched. This chapter examined the challenges of osẹ dúdú entrepreneurs in Southwest Nigeria. Twenty-six interviews were conducted among indigenous black soap producers and sellers in Ogun, Oyo and Lagos States. Data were analysed in themes. Weather, financial, spiritual, copyright and succession challenges, as well as issues such as a large number of sellers, debt, lack of support, pricing and brand competition, were found to be problems faced by black soap entrepreneurs. This chapter concluded that certain controllable and uncontrollable factors were not only capable of limiting the development of osẹ dúdú business but also have adverse implications for the achievement of the sustainable development goals through the indigenous resource. This chapter suggests that osẹ dúdú business actors such as mechanical engineers, local fabricators, financial institutions, and governmental and non-governmental agencies collaborate with black soap entrepreneurs to ameliorate the challenges of the latter. It is only through this alliance that black soap entrepreneurs can contribute to indigenous business development and the achievement of sustainable development goals in Africa.

For organizational neuroscience to progress, it requires an overarching theoretical framework that locates neural processes appropriately within the wider context of organizational cognitive activities. In this chapter, we argue the case for building such a framework on two foundations: (1) critical realism, and (2) socially situated cognition. Critical realism holds to the importance of identifying biophysical roots for organizational activity (including neurophysiological processes) while acknowledging the top-down influence of higher-level, emergent organizational phenomena such as routines and structures, thereby avoiding the trap of reductionism. Socially situated cognition connects the brain, body, and mind to social, cultural, and environmental forces, as significant components of complex organizational systems. By focusing on adaptive action as the primary explanandum, socially situated cognition posits that, although the brain plays a driving role in adaptive organizational activity, this activity also relies on the body, situational context, and cognitive processes that are distributed across organizational agents and artifacts. The value of the framework that we sketch out is twofold. First, it promises to help organizational neuroscience become more than an arena for validating basic neuroscience concepts, enabling organizational researchers to backfill into social neuroscience, by identifying unique relations between the brain and social organization. Second, it promises to build deeper connections between neuroscience and mainstream theories of organizational behavior, by advancing models of managerial and organizational cognition that are biologically informed and socially situated.

The contamination of groundwater by natural arsenic is currently a worldwide epidemic. Arsenic-contaminated groundwater has been reported in Argentina, Chile, Mexico, China, Hungary, West Bengal in India, Bangladesh, and Vietnam. Of these regions, Bangladesh and West Bengal are the most seriously affected in terms of the size of the population at risk and magnitude of health problems. Hence, chronic exposure to arsenic >50′′μg/L in drinking water can result in serious health problems. Common symptoms of arsenic-related ailments are skin, cardiovascular, renal, hematological, and respiratory disorders. Therefore, this chapter focuses on nature, origin, and extent of groundwater arsenic contamination, probable causes, and its impacts on food, drinking water, and social coverage. It further discloses mitigation approaches proposed and practiced by the different research groups to combat this problem and finally concludes.

The complexity of atmospheric corrosion, further compounded by the effects of climate change, makes existing models inappropriate for corrosion prediction. The commonly used kinetic model and dose-response functions are restricted in their capacity to represent the non-linear behaviour of corrosion phenomena. The application of artificial intelligence (AI)-driven machine learning algorithms to corrosion data can better represent the corrosion mechanism by considering the dynamic behaviour due to changing climatic conditions. Effective use of materials, coating systems and maintenance strategies can then be made with such a corrosivity model. Accurate corrosion prediction will help to improve climate change resilience of the social, economic and energy infrastructure in line with the UN Sustainable Development Goals (SDGs) 7 (Affordable and Clean Energy), 9 (Industry, Innovation and Infrastructure) and 13 (Climate Action). This chapter discusses atmospheric corrosion prediction in relation to the SDGs and the influence of AI in overcoming the challenges.