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We discuss food security within the context of welfare economics. We review models of commodity price stabilization brought about by government storage, and/or the private sector. We use data on the stocks of major commodities to discuss the implications of storage models on food security.

The impact of storage on food security is discussed within the context of welfare economics.

Storage is not necessarily a solution to solving long-term world food problems. Also, at times in history, countries such as the United States have accumulated large stocks of commodities such as wheat, which turned out to be a costly policy.

While food insecurity is a major issue worldwide, the solution does not entirely lie in governments being heavily involved in managing and owning food stocks. The private sector manages stocks as a part of commercial transactions. The government’s role is to provide food from stockholdings in times of emergency situations.

Nowadays, there are billions interconnected devices forming Cyber-Physical Systems (CPS), Internet of Things (IoT) and Industrial Internet of Things (IIoT) ecosystems. With an increasing number of devices and systems in use, amount and the value of data, the risks of security breaches increase. One of these risks is posed by open data sources, which are databases that are not properly protected. These poorly protected databases are accessible to external actors, which poses a serious risk to the data holder and the results of data-related activities such as analysis, forecasting, monitoring, decision-making, policy development, and the whole contemporary society. This chapter aims at examining the state of the security of open data databases representing both relational databases and NoSQL, with a particular focus on a later category.

Significant advances in digital technologies impact both organisations and knowledge workers alike. Organisations are now able to effectively analyse significant amounts of data, while accomplishing actionable insight and data-driven decision-making through knowledge workers that understand and manage greater complexity. For decision-makers to be in a position where sufficient information and data-driven insights enable them to make informed decisions, they need to better understand fundamental constructs that lead to the understanding of deep knowledge and wisdom. In an attempt to guide organisations in such a process of understanding, this research study focuses on the design of an organisational transformation framework for data-driven decision-making (OTxDD) based on the collaboration of human and machine for knowledge work. The OTxDD framework was designed through a design science research approach and consists of 4 major enablers (data analytics, data management, data platform, data-driven organisation ethos) and 12 sub-enablers. The OTxDD framework was evaluated in a real-world scenario, where after, based on the evaluation feedback, the OTxDD framework was improved and an organisational measurement tool developed. By considering such an OTxDD framework and measurement tool, organisations will be able to create a clear transformation path to data-driven decision-making, while applying the insight from both knowledge workers and intelligent machines.

Library storage is traditionally viewed as a space management strategy, a way of dealing with overcrowded buildings and growing collections. Storage also is implicitly a preservation strategy: an alternative to weeding, cramming books tightly on shelves, stacking them on the floor, or not purchasing them in the first place. Among its obvious preservation benefits, storage provides security from theft and vandalism, and protection from spills and pests caused by increasingly prevalent food and drink in library buildings. Although transfer to storage may be risky for fragile materials, leaving them in stacks that are constantly being shifted is likely to be more damaging. Many storage facilities provide better environmental conditions for collections than old or poorly maintained modern library buildings.

This chapter provides an integrated overview of digital implementation in smart city ecosystems, focusing on the data ecosystem, the data value chain, and the digital implementation in smart cities. It acknowledges the opportunities and challenges presented by the growth of big data and its impact on delivering public services and coordinating local ecosystems in smart cities. This chapter introduces the concept of a smart city data ecosystem and explores the implementation of digital solutions, highlighting the role of local management and internal and external providers. It examines the digitalization of processes and services, technological infrastructure, and social media in the context of the smart city, emphasizing the interaction between local and national data ecosystems. This chapter incorporates interviews with smart city managers, policymakers, and technicians involved in the digital development process, offering valuable insights into the practical implementation and usage of data management within smart cities and the potential impact on urban development.

Background: Insurance was discovered many centuries before Christ (BC). In the second and third millennia BC, Chinese and Babylonian traders traded risks. Insurance is now the backbone of the economy, but penetration is low in developing countries. Big data, internet of things (IoT), and InsurTech have recently ushered in the fourth industrial revolution in insurance.

Objective: This study examines the Indian challenges and solutions of using Big Data Analytics (BDA).

methodology: A SLR was used to extract themes/variables related to challenges and solutions in adopting BDA in the Indian insurance sector. Google Scholar was searched for relevant literature using keywords. Inclusion and exclusion criteria were used to filter the studies.

Findings: This study identified several barriers to BDA adoption in the Indian insurance industry. Policymakers could use the suggestions to improve insurance service delivery.

Practical implication: Insurers can understand the challenges, and accordingly, they can adopt the proposed solution in this study to enhance the insurance penetration in India.

As the size of the population is growing and the capacity of the planet Earth is limited, human beings are searching for sustainable and technology-enabled solutions to support society, ecology and economy. One of the solutions has been developing smart sustainable cities. Smart sustainable cities are cities as systems, where their infrastructure, different subsystems and different functional domains are virtually connected to the information and communication technologies (ICT) and internet via sensors and devices and the Internet of Things (IoT), to collect and process real-time Big Data and make efficient, effective and sustainable solutions for a democratic and liveable city for its various stakeholders. This chapter explores the concepts and practices of sustainable smart cities across the globe and explores the use of technologies such as IoT, Blockchain technology and Cloud computing, etc. their challenges and then presents a view on business models for sustainable smart cities.

Blockchain is creating many opportunities for business as it is transforming the way sustaining technologies operate in organizations. In blockchain, transactions are recorded as digital blocks and contain immutable properties to preserve data validation, encryption, and algorithms. The goal is to provide uniqueness and resistant to fraud. For example, digital currencies use a shared network to permanently record transactions but operate in decentralized mode to secure independence among participants. The potential applications of blockchain are unlimited and proliferating although several initiatives are still in development. Many industries are already capitalizing on experimental blockchain projects. Leaders in those industries are partnering with R&D and start-up companies to determine opportunities. Also, research universities have dedicated labs to focus on new theoretical concepts or improvements that can be leveraged in healthcare, global rights management, and decentralized publishing to name a few. Blockchain is called as “trustless system” because it can disrupt entire industries. This chapter explores the blockchain origins and its underlying technologies to understand concepts and become familiar with the latest development. A crypto-digital currency like Bitcoin will be introduced in some detail to bring awareness to the benefits, risks, and ethical concerns. A discussion on regulations will be included to investigate how government policy affects cryptocurrencies and related security. In the interest of blockchain inspired projects, the chapter will also introduce a broader discussion on new ventures adopting blockchain attributes and the trends of gradual technology implementation among early adapters.