Search results

This research navigates the confluence of data analytics, machine learning, and artificial intelligence to revolutionize the management of urban services in smart cities. The study thoroughly investigated with advanced tools to scrutinize key performance indicators integral to the functioning of smart cities, thereby enhancing leadership and decision-making strategies. Our work involves the implementation of various machine learning models such as Logistic Regression, Support Vector Machine, Decision Tree, Naive Bayes, and Artificial Neural Networks (ANN), to the data. Notably, the Support Vector Machine and Bernoulli Naive Bayes models exhibit robust performance with an accuracy rate of 70% precision score. In particular, the study underscores the employment of an ANN model on our existing dataset, optimized using the Adam optimizer. Although the model yields an overall accuracy of 61% and a precision score of 58%, implying correct predictions for the positive class 58% of the time, a comprehensive performance assessment using the Area Under the Receiver Operating Characteristic Curve (AUC-ROC) metrics was necessary. This evaluation results in a score of 0.475 at a threshold of 0.5, indicating that there's room for model enhancement. These models and their performance metrics serve as a key cog in our data analytics pipeline, providing decision-makers and city leaders with actionable insights that can steer urban service management decisions. Through real-time data availability and intuitive visualization dashboards, these leaders can promptly comprehend the current state of their services, pinpoint areas requiring improvement, and make informed decisions to bolster these services. This research illuminates the potential for data analytics, machine learning, and AI to significantly upgrade urban service management in smart cities, fostering sustainable and livable communities. Moreover, our findings contribute valuable knowledge to other cities aiming to adopt similar strategies, thus aiding the continued development of smart cities globally.

The digital euro is not a monolithic project. On the contrary, the application fields are broad, and possible solutions are diverse. This chapter provides an overview of use cases, application domains, and infrastructures for the digital euro that differ significantly. A comparison with solutions for the digital dollar and the digital yuan leads to the conclusion that, in the most extreme case, the euro could become a regional currency for Europe. The main reason for this argument is the design and prioritization of current approaches within Europe as well as the European Central Bank’s digital euro project, and that stablecoin approaches seem to be neglected as solutions for the digital euro.

This chapter presents an overview of the Bitcoin and its impacts on the environment and economics from the viewpoint of carrying out a systematic analysis of the literature related to the environmental and economic effect of digital currency. It is aimed to summarize and critically examine the points of view regarding Bitcoin mining, considering its effects on global warming and the social environment, employing peer-reviewed data associated through literatures. As a result, this study provides the chance to analyze the set of knowledge regarding the effects of the Bitcoin mining procedure on the ecosystem in regard to energy use and CO₂ emissions regarding unit root tests and causality test based on nonlinear models. The results show that there exists a nonlinear causal relationship between statistics on Bitcoin mining and the CO₂ emissions. The results also imply that Bitcoin remains to be a tool utilized in the economic environment for a range of objectives despite high energy consumption and some negative environmental impact within the scope of renewable energy; hence, authorities would take Bitcoin mining impacts into account to reduce CO₂ emissions.

The Internet, the Web and social media have radically transformed a number of core pillars of our social fabric. The way billions of citizens work, interact and socialise is underpinned by our global network infrastructure. Unfortunately, we have also seen a number of negative effects from this transformation. As has been widely publicised, undesirable impacts include the spread of disinformation and fake news; attacks on democratic elections and the ‘weaponisation’ of personal data. This article describes some of the technological approaches that are being taken to address some of the above issues. At the core of these technologies are notions around decentralisation. With blockchains it is possible that citizens can create their own ‘self-sovereign’ identity – the digital equivalent of writing one's name onto a piece of paper – and acquiring verification through blockchain-based techniques. An approach to alleviating the ‘weaponisation’ of personal and sensitive data is to give citizens their own data store. Initiatives such as Sir Tim Berners-Lee's Solid allow users to store, manage and control their own data according to any personal preferences or constraints. We believe that a combination of personal data stores and blockchains will lead to a new type of resilient communication and collaboration mechanism, whereby personal rights and empowerment are enhanced and transparency at the community level is integral.

This chapter reviews some of the technical background relating to the designs of digital infrastructures for financial services. Such technical background could be useful to the understanding of the design choices that the central banks and pertinent regulators face when developing or promoting open digital infrastructures for financial services. First, the chapter reviews modularity and interoperability in the design of digital infrastructure. The chapter then examines three broad architectural models of open digital infrastructures: decentralized, centralized and distributed. The chapter ends with some lessons learnt from the different architectural models that have been implemented in practice.

“Web3” is a practice in participatory digital infrastructures through the ability to read, write, and control own digital assets. Web3 is hailed as the alternative to the failings of big tech, offering a participatory mode of digital organization and shared ownership of digital infrastructure through algorithmic governance. This paper offers an introductory playbook to researchers entering the field of Web3 by providing an analytical lens to approach the emergent field of Web3 as “infrastructuring.” It argues that Web3 can be understood as a collective, community exploration of “how to infrastructure.” Drawing on qualitative examples derived from digital ethnographic methods, the study reveals that play, politics, and prefiguration are fundamental qualities underpinning Web3’s vision of offering an “exit” from established institutional infrastructures. Therefore, a primary challenge Web3 faces in its governance experiments centers around the question of how to effectively build and manage infrastructure.