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One major impact of demonetisation is rise of cashless or digital transactions. The extension of the transition from a cash-based to a cashless economy has expanded even more now, based on the lessons learnt from the COVID-19 pandemic. This chapter discusses the various electronic payment methods used by the people, the frequency of using these methods and also to examine the reasons of changing habits in using electronic modes of payments. This study is both theoretical and empirical in nature, based on both primary and secondary data. Digitalising rural and poor population is much in talks but earlier literature did not acknowledge the status of slums and its inhabitants. Slum dwellers are the ones to be in the margin, and therefore are subject to more societal sufferings. In the context of female population particularly, the mechanism of urbanisation and increment in urban slums are subject to unique causes and unique consequences, and still these challenges are greatly underexplored by earlier literature in this field. Thus, the aim of this chapter is to find out the schemes, achievements and challenges for the cashless transaction practised by the female slum dwellers. To be extremely explicit, the sample area consists of two biggest slums of Kolkata and 100 female respondents are inquired for this study, taking 50 from each slum. For analysing the collected data, descriptive statistics tools and percentage analysis have been used. This chapter will also analyse India's gradual transition towards a cashless economy. Through the examination of digitalisation of slum dwellers, this study also attempts to identify whether digital modes result in empowerment of these women, of any kind. By paying a visit in the discussion of women empowerment, this chapter wants to revisit the 2030 Agenda for Sustainable Development and its 17 Sustainable Development Goals (SDGs) adopted by world leaders in 2015, that embodies a roadmap for progress which leaves no one behind. The questions impacting achievement of SDG through women empowerment may not seem of utmost concern during the current situation but is equally important and needs to be discussed on a platform of its own.

Climate change and digitisation are unquestionably the two defining features of this era. Both present immense challenges with unimaginable consequences for humankind while promising enormous rewards for those who can adequately address their adverse effects. These two critical factors must be considered while establishing strategies for the businesses' future operations. Hence, post-pandemic, especially with the rise of online commerce, packages and documents are delivered around the globe nearly every day, propelling the logistics industry's growth. This is not the critical challenge in logistics, the issue of sustainability, particularly as the returns are increasing exponentially, leading to a significant impact on transportation is and its reliance on fossil fuel has made it a prime target for society's growing environmental concerns. Thus, real-time visibility, collaboration and integration in reverse logistics (RL) are imperative for business sustainability. The most applicable Industry 4.0 technologies in RL are the Internet of Things (IoT), cloud computing, blockchain and digital twin that enable the defragmentation of the RL market.

This chapter analyses the technological impact of Industry 4.0 on RL. This research investigates the challenges faced by the logistics industry in the context of sustainability and how digital transformation can bring many potential benefits across the entire value chain. This chapter also presents a guidance for a framework based on the literature review that tends to favour the development of elastic logistics, implying improved company responsiveness to market conditions. The study contributes to the body of literature and the establishment of the framework for planning on the application of various Industry 4.0 technologies in developing eco-friendly and sustainable reverse logistics framework.

This study analyzes the trends and patterns of strategic and innovative macroeconomic variables during recession or slowdown periods of 10 countries – Brazil, the United States, UK, Germany, France, China, Japan, India, Saudi Arabia, and South Korea for the period 1980–2018, which includes major recessions like the 1982 debt crisis, 1991 economic crisis, 1997 Asian Financial crisis, and 2008 Sub-prime crisis. This study devised two models – Logistic Regression and a Range-based Custom-Made Recession-cum-Economy State indicator, based on Dynamic Ordinary Least Squares (DOLS) model parameters. Results for the logistic regression model show that most of the marginal effect values are positive for variables linked with globalization indicating that increase in adverse impact on such variables increases the probability of recession. The custom-made statistical index provides an individual country-wise range, along with a global range for the weighted total of the variables, the weights for which are derived from the DOLS model, which has a 59.66% accuracy in estimating the condition of an economy. The recent worldwide experience indicates that probability of recessions has decreased, and slowdowns have increased over a period. This is evident from the Cumulative Trend of Economic State analysis that indicates that the recession probabilities of countries have decreased 1990 and that of slowdown have become highly volatile.

One of the most hyped concepts in the manufacturing industry is ‘Industry 4.0’. The ‘Industry 4.0’ concept is grabbing the attention of every manufacturing industry across the globe because of its immense applications. This phenomenon is an advanced version of Industry 3.0, combining manufacturing processes and the latest Internet of Things (IoT) technologies. The main advantage of this paradigm shift is efficiency and efficacy in the manufacturing process with the help of advanced automated technologies. The concept of ‘Industry 4.0’ is contemporary, so it falls under exploratory study. Therefore, the research methodology is thematic narration grounded on secondary data (online) analysis. In this light, this chapter aims to explain ‘Industry 4.0’ in terms of concepts, theories and models based on the Web of Science (WoS) database. The data include research manuscripts, book chapters, blogs, white papers, news items and proceedings. The study details the latest technologies behind the ‘Industry 4.0’ phenomenon, different business intelligence technologies and their practical implications in some manufacturing industries. This chapter mainly elaborates on Industry 4.0 frameworks designed by (1) PwC (2) IBM (3) Frost & Sullivan.

The United Nations (UN) adopted Sustainable Development Goals (SDGs); agenda 2030 focuses on Climate Action (goal 13), targeting climate adaptability, as well as resilience, awareness and improving policy mechanisms on climate change. In order to enhance climate adaptability, climate-smart agricultural practices (CSAP) is a necessary step. CSAP is a sustainable agriculture approach with a strong focus on climate dimensions. The three pillars of climate-smart agriculture (CSA) are ‘Adaptation’: adapting to climate change; ‘Resilience’: building resilience against it and ‘Remove’: reducing carbon emissions. The new world economy uses Industry 4.0 technologies for sustainable advancement, including blockchain technology, big data analytics, artificial intelligence (AI), augmented and virtual reality, industrial Internet of Things (IoT) and services. Hence, technology plays a significant role in climate sustainable agriculture practices. This chapter shall consider three technologies consisting of IoT, AI and blockchain technology which contribute to CSAP in pre-harvesting (monitoring climate as well as fertility status, soil testing, etc.), harvesting (tilling, fertilisation, seed operations, etc.) and post-harvesting (predicting weather factors, seed varieties, etc.) periods of agriculture. All these three technologies work like the human nervous system; IoT helps in converting various information regarding demography, climate change, local agricultural needs, etc. into world data; AI works like a brain in combination with IoT, helps predict the use of climate-smart technology and blockchain, the memory part of the nervous system which deals with supply-side and ensures traceability as well as transparency for consumers as well as farmers. Hence, this chapter shall contribute to the importance of these three technologies in adopting CSAP in three stages of agriculture.

Among the most fragile, complex, and productive ecosystems to benefit humans are coastal ecosystems, and they offer many services. These ecosystem services range from provisioning services to protection services, cultural services, and supporting services. The coastal zone is defined as a transition between the land and the sea, making it clear that there would be dynamic activities both complementing and sometimes conflicting with each other. Coastal resources constitute an important component of natural resources, particularly in a country such as India.