Search results

On its way to develop a smart grid in Indonesia, one key enabler in the early stage of implementation is advanced metering infrastructure (AMI). Thus, Perusahaan Listrik Negara (PLN), an electrical energy utility company owned by the government of the Republic of Indonesia as the only electricity utility company servicing customers from upstream to downstream in Indonesia, has started AMI program at some main cities. With AMI, real-time energy consumption profile, energy meter status and condition, and customer power quality can be acquired. Subsequently, these data collected by AMI can be used for further smart grid implementation by such IT systems and big data analysis. Instead of its function for smart grid backbone, AMI also significantly support smart energy on the city as a part of smart city initiatives. Nevertheless, its implementation requires more investment than the conventional metering system. This investment needs to be evaluated to define whether AMI is feasible and viable or not. This chapter is intended to observe the feasibility of AMI implementation in Indonesia using cost-benefit analysis (CBA). Two schemes were used as study objects, one scheme in which the communication infrastructure was managed by PLN itself, and the other one in which the communication infrastructure was managed by a third party. From the analysis, it appears that both schemes are proven to be feasible.

The goals of the contemporary environment in this new era of the Internet of Things (IoT), digital technologies (DTs) and smartisation are to enhance economic, social and environmental sustainability while also concentrating on the citizens' quality of life. As these initiatives advance, more determination is required to offer effective approaches to the problem posed by the accomplishment of the Sustainable City Project in Nigeria as a developing nation. To address these problems and facilitate the process for Nigeria's major cities to become ‘smart cities’, universities, research institutions and other stakeholders must collaborate alongside. This chapter aims to establish a model or framework that addresses urban intelligence, social inclusion, resilience and technological innovation, mobility, urbanisation and residents' quality of life. The reviews of the characteristics and management of smart cities in developed countries were documented to serve as a comparison study of the cities in African sub-Saharan regions. This will assist in building models that can produce predictions about possible smart solutions in the areas of mobility, urban infrastructure and ecological problems brought on by climate change in African cities. This chapter brings attention to the body of knowledge by envisioning the benefits to the government and citizens in making appropriate decisions to enhance sustainable development, a better resilience environment, improved infrastructure, smart city environments and residents' quality of life. The study's implications centre on how the government could prioritise urban features and services as indicated in the smart cities framework.

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.

With the plethora of smart mobility innovations, their applications, and their pace of change, it is easy to get distracted by what these innovations can (potentially) do, rather than what we want or need them to do, if we are to meet our societal goals. The focus of this chapter is therefore on the extent to which smart mobility can help create policy change towards the goal of low carbon mobility. The concept of policy is broken down into its component parts, to outline the relationship between policy goals and policy instruments, and identifies the key tools underpinning policy instruments. In turn, the chapter situates policy instruments within an understanding of policy change and triggers for policy change, arguing there are two key ways in which transformative change can occur; exogenously and endogenously. The chapter argues that the onset of smart mobility does not suggest an exogenous shock to the current policy system, in which smart mobility disrupts the authority and beliefs inherent within the current policy approach to mobility. Smart mobility therefore in and of itself is unlikely to lead to a radical policy shift towards low carbon. However, in understanding smart mobility innovations as policy instruments, it is possible to envisage smart mobility incrementally changing policy towards low carbon mobility, if opportunities for reflexivity and learning are embedded within local policy contexts.

As the world continues to urbanise, cities face increasing pressure to become more sustainable, efficient and livable. Sustainable smart cities are emerging as a promising solution to this challenge, leveraging technology and data to improve urban systems and services while reducing environmental impact. This chapter provides an overview of the concept of sustainable smart cities and its implications for urban development. It explores the key features of sustainable smart cities, including their focus on technology, data and citizen engagement and the challenges they are facing in terms of infrastructure, data management, social equity, environmental sustainability, governance and regulations. The chapter also highlights the implications of sustainable smart cities for urban planners, policymakers and other stakeholders, emphasising the need for collaborative approaches that engage citizens and stakeholders in the design and implementation of smart city initiatives.

This chapter examines the six smart city dimensions that serve as pillars in smart city projects. These dimensions are crucial in the development and evaluation of smart city initiatives, representing key areas for consideration. This chapter offers a detailed analysis of the smart city ecosystem, focusing on the governance, environment, people, living, mobility, and economy dimensions. It challenges the prevailing media portrayal of the smart city strategy and engages in the current academic debate surrounding these dimensions. This chapter defines, discusses, and explains each dimension, incorporating case studies from cities such as Copenhagen, San Francisco, Lisbon, and Barcelona. It also includes interviews and factual data to highlight the internal implementation and objectives of the smart city within each dimension. This chapter provides a comprehensive understanding of the smart city ecosystem, its implementation, and the potential benefits and challenges associated with each dimension.

Over recent decades, rural areas have been facing significant challenges that exacerbate the existing discontent in their communities. These challenges are mostly reflected in depopulation trends, increased vulnerability to external shocks, and reduced quality of basic services. Local Action Groups (LAGs) all over Europe have been working on these challenges since the early 1990s. More recently, the smart villages concept is starting to generate enthusiasm among rural development stakeholders to try to revert these trends by supporting communities to move toward a more sustainable future while taking advantage of new emerging opportunities. This chapter demonstrates that the LEADER approach and its principles are also part of the smart villages concept. However, practical differences between the two emerge as a result of limitations imposed by restrictive LEADER regulatory frameworks in many member states. Our main argument is that LEADER has what is needed to be the main tool for driving smart villages in Europe as long as there is a policy framework in place that enables LEADER to exploit its full potential. This conclusion is grounded on the analysis of the role that LEADER played in a number of smart village initiatives across the EU.

The majority of the population in the Gulf Cooperation Council (GCC) lives in urban areas. In the year 2030, the percentage of the rural population is expected to be 14% in the Kingdom of Saudi Arabia, 11% in the United Arab Emirates, 10% in Oman, 9% in Bahrain, 1% in Qatar, and will remain 0% in Kuwait. Like many other countries, however, GCC countries continue to invest efforts and resources toward their national agendas aimed at sustainable development. In this context, smart villages are of special interest as, increasingly, they serve as the crossroads between urban living and rural life embracing history, culture, tradition, spiritual values, and human capital. The objective of this chapter is to explore actions taken toward the development of smart villages in the GCC countries, with a comparative overview on pertaining approaches and strategies; challenges related to the implementation of these actions are identified. It is demonstrated that despite GCCs’ tremendous efforts toward developing infrastructure in urban centers, more infrastructure investment is needed with regard to key issues related to developing remote areas – including their smart networks, digital facilities, and e-governance. It is also highlighted that more research is needed, especially on issues related to the transformation of villages into smart villages, including the need for holistic approaches, policies, and strategies toward smart villages.

Stakeholders all over the European Union (EU), including academics, local and national authorities, business representatives, civil society, and the EU institutions are interested in creating a better life for citizens inhabiting rural areas. Building up on previous successful policies, including for instance the Common Agricultural Policy (CAP) and political actions, such as those outlined in the Cork 2.0 Declaration, these stakeholders assessed the current and future challenges of EU rural areas. The EU agri-food sector is not only the backbone of the EU rural areas but it is also a driver of the EU economy, that is, delivering 44 million jobs in the EU and representing 3.7% of EU GDP. The EU is thus the world’s number one exporter of agricultural and food products amounting to 138 billion, in 2017.

Today, the technological progress and the digital economy are transforming the EU economy in a way and speed never seen before; agriculture and food production are no exception. Obviously, the agriculture and the food production activities may have less obvious significance to the smart city but are key in the smart village concept. The ongoing technological transformation of agriculture will certainly enable and influence the design and implementation of any concept of smart villages. The question is therefore complex: How can agriculture and food production contribute and influence the smart villages concept and related policy strategies? This chapter dwells on this issue.

This chapter concludes the book by addressing the complexities and challenges associated with smart cities. Despite the widespread enthusiasm and progressive policies surrounding smart cities, navigating this world remains a desirable yet complex challenge. Drawing on existing literature, this final chapter serves as a synthesis of the key points covered throughout the book and offers practical guidelines for navigating the landscape of smart cities. This chapter provides valuable tips and insights for both citizens and policymakers, recognizing their pivotal roles in shaping the future of smart cities. Additionally, it delves into the current challenges faced by smart city initiatives and discusses potential future directions. Emphasizing the need for ongoing adaptation and innovation, this chapter highlights the importance of overcoming obstacles and capitalizing on opportunities in the rapidly evolving digital age. By offering a comprehensive overview and practical recommendations, this chapter aims to equip readers with the knowledge and tools to navigate the complexities of smart cities effectively. It concludes the book on a forward-looking note, emphasizing the dynamic nature of smart city development and the continuous need for adaptation and innovation.