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The global economy is facing a steep challenge from volatility, risk and uncertainty associated with climate change, pandemics, regional conflicts and trade wars which are unprecedented and overlapping. These crises are leading to macro- and microeconomic imbalances. The immediate effects like rising inflation, shortage of energy and fertiliser, food insecurity, loss of jobs and poverty are looming large, leading to existential threat. It is evident that decades of progress are at risk and pursuing sustainable development goals (SDGs) requires dedicated and customised efforts by the governments and other relevant actors, especially in the low- and middle-income countries (LMICS). The concept of circular economy is considered to bring a paradigm shift by reducing the dependence on natural resource extraction and decoupling economic growth from use of natural resources. Bioeconomy is another emerging field which deals with the use of renewable biological resources such as biomass to produce renewable biofuels, bioproducts, and biopower for economic, environmental and social benefits. Circular bioeconomy (CBE) lies at the intersection and is defined as the production of recoverable biological (waste) resources and the conversion of these resources into high-value-added products, such as food, feed, bio-based products and bioenergy. It has been estimated that the economic opportunity for the sector to complement or even substitute conventional ones is estimated to be USD 7.7 trillion by 2030 for food and feed waste products, and energy. CBE is perceived as a pathway for development and has the potential to target different SDGs directly like 6, 7 and 12 and SDGs 2, 3, 11, 12, 13 and 15 indirectly. This study explores the linkages of CBE with the SDG goals and provides recommendations to stimulate the sector.

Knowledge management (KM) is an important topic in the age of big data, and this study adds to the existing body of literature by providing a novel KM perspective on the technological phenomenon of artificial intelligence (AI). This study aims to discover how AI might facilitate knowledge-based business-to-business (B2B) marketing. In this chapter, the authors take a close look at the building blocks of AI and the relationships between them. Future research directions and also the effects of the various market information building components on B2B marketing are discussed. The study’s approach is theoretical; it tries to provide a framework for characterising the phenomenon of AI and its constituent parts. Additionally, this chapter provides a methodical analysis of the three categories of market information crucial to B2B marketing: knowledge of customers, knowledge of users, and knowledge of external markets. This research looks at AI through the lens of the conventional data processing framework, analysing the six pillars upon which AI systems are founded. It also explained how the framework’s components work together to transform data into actionable information. In this chapter, the authors will look at how AI works and how it can benefit B2B knowledge-based marketing. It’s not aimed at AI experts but rather at general marketing managers. In this chapter, the possible effects of AI on B2B marketing are discussed using examples from the real world.