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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.

In this chapter we investigate the role of family-specific factors in facilitating or constraining business exit in family firms. Family business literature seems to have an implicit bias toward continuity and persistence in the founder's business. This is explained by heavy emotional involvement and development of path-dependent core competences over generations. However, several long-lived family firms were able to successfully exit the founder's business. Exit allowed them to free significant strategic resources, which were later reinvested in exploiting novel entrepreneurial opportunities. Our aim is to investigate the process of exit from the founder's business in family firms, to explain both triggers and obstacles to decommitment and de-escalation. We address this issue through the study of the Italian Falck Group's exit from the steel industry in the 1990s, followed by successful startup of a renewable energy business. By carefully triangulating different data sources and different voices within and outside the controlling family, we develop a framework describing family-specific facilitators and inhibitors of business exit, and subsequent startup of a new business. Three types of family-specific factors emerge as relevant in shaping a family firm's likelihood and speed of exit from a failing business: family-related psychological triggers and obstacles to business exit; family-specific components of the structural de-escalation context; family responses to ensuing de-escalation and exit needs. The emerging framework offers a more nuanced interpretation of decommitment activities in family firms, pointing to the differential role family-specific factors may play as facilitators or inhibitors of business exit. We also suggest how these family-specific results may contribute to a deeper understanding of exit in nonfamily firms. Our results also have practical implications for family business entrepreneurial management. Actively managing the different determinants of exit choices that emerged from our study will set the stage for de-escalation from a failing course of action – a dynamic capability all family firms should learn and practice if they intend to transfer their entrepreneurial orientation to next generations.

Before the September–October 2008 Financial Crisis, investment banks were hooked on debt. In 2007, a year before its failure, Lehman Brothers held equity just 3.3% of its balance sheet (that is, its debt/equity ratio well exceeded 29); virtually all the rest was financed by borrowing. Leverage is an elixir that makes profits soar when times are good but magnifies losses when the economy sours. Currently in India, several companies have seen their balance sheet out of shape because of overleverage, but banks continue to be benevolent, often forced by political interventions (see Cases 6.1 and 6.2). Most of these business groups are nearly dead, with their equity almost wiped out. There is little chance they will survive but for their banker’s largesse. Ever-greening of loans is keeping them alive, but what could be the end game? For instance, just a year before economic liberalization in India, few enterprising men invested in the steel business. They borrowed monies from the banks and banks continued to finance their operations, and now they are realizing that the promoters cannot meet with their debt obligations. The banks, however, did not want to accept financial loss and hence commonly agreed to ease the payment obligations so that the loans remained good and not degenerate to NPAs. This is tantamount to refinancing to service your loans. But now the banks overwhelmed with accumulated NPAs are trying to sell debt. How do you legally, ethically, morally, and spiritually (LEMS) justify share-market concentration in the hands of very few promoter investors? What are their long-run unintended economic, legal, ethical, and moral consequences, and why? This chapter studies this market turbulence and the role of bankruptcy laws and court systems in bringing about some change in the debt-overleveraged corporations.

With the increasing importance of the service-providing sectors, information from these sectors has become essential to the understanding of contemporary business cycles. Contribution of services to GDP during postwar recessions is clearly recorded in Table 4.1. On average, decline in real GDP during recessions would have been at least 70% more severe without the stabilization effect from services. Moore (1987) noted that the ability of the service sectors to create jobs has differentiated business cycles since the 1980s, and has led economy-wide recessions to be shorter and less severe. This is reflected as mild declines in employment of service sectors and its dominance in the total nonfarm employment, as plotted in Figure 4.1a. The growth in real GDP by major type of products obtained from National NIPA is depicted in Figure 4.1b. Since 1985, services never had a negative growth, which has muted the volatility in goods and structures, and resulted in more stable economy measured by total GDP (see also McConnell and Perez-Quiros, 2000).