Keywords

5.1 Introduction

Forecasts suggest that up to seven out of ten of the world’s population will live in urban areas by 2050 (World Health Organization, 2021), a shift bringing both economic opportunities and substantial challenges for governments and municipal authorities. Urban centres are major consumers of energy, accounting for more than two-thirds of global consumption, and they are responsible for up to 70% of greenhouse gas emissions (GHG) (World Bank, 2023). This intensification of urbanisation not only exacerbates environmental issues but also poses significant health risks, including those related to road traffic injuries, pollution, and limited access to safe physical activities (World Health Organization, 2021). Concurrently, many cities and indeed towns are grappling with the pressures of urbanisation on ageing infrastructures (KPMG, 2012).

In response to these challenges, the concept of the ‘smart city’ has evolved and gained significant popularity over the past thirty years. A number of definitions of smart city have been proposed and, despite some differences, they share a common conceptualisation of leveraging information and communication technology (ICT) to enhance the functionality of urban subsystems, thereby fulfilling the needs of inhabitants and communities (Albino et al., 2015; Batty et al., 2012). Despite the promise of smart city technologies, these projects often face governance, economic, and technological hurdles that have negatively affected their widespread adoption and implementation (Del Real et al., 2023; Rana et al., 2019). Additionally, the concept of ‘smart city’ creates a disproportionate focus on large-scale urban agglomerations (i.e., cities) and neglects the needs of smaller and more rural–urban areas and communities which may be affected by similar challenges but have less resources. In response, concepts relating to digital towns and smart streets have been developed to address the needs of both urban and rural areas (Hosseini et al., 2018; Lynn & Wood, 2023; Lynn et al., 2022).

In this chapter, we explore four key themes in research relating to digital sustainability in smart cities and towns, namely smart transportation systems, building energy optimisation, smart waste management, and environmental monitoring. Each of the following sections provides a high-level overview of these themes including the benefits of and challenges to adoption. Finally, Sect. 5.6 concludes the chapter with some final remarks.

5.2 Smart Transportation

Smart transportation, sometimes referred to as intelligent transportation systems (ITS), refers to the integration of advanced information and communication technologies (ICTs) into the transportation infrastructure and vehicles. In contrast, while often conflated with smart transportation and ITS, smart mobility as a concept encompasses all types of transport users including cyclists and pedestrians (Chen et al., 2017). In this chapter, we focus on smart transportation systems as the targets of these systems (e.g., cars, etc.) are those who contribute most to adverse environmental impacts in cities and towns.

Smart transportation systems aim to improve traffic and transit management, manage road use and behaviour, enhance safety, reduce energy and environmental impact, and increase the efficiency of transportation networks (Lynn & Wood, 2023; McGregor et al., 2003). At an infrastructural level, smart transportation systems are enabled by advances in sensor technologies, mobile communication networks, the Internet of Things (IoT), smart transportation communication protocols, and novel computing architectures that expand from the cloud to the edge (Oladimeji et al., 2023). As such, smart transportation can leverage a wide range of technologies including but not limited to:

  • Smart traffic signalling, traffic demand management, and control systems to support and actuate decision-making (European Commission, 2020b);

  • Automated street bollards, licence plate recognition, and embedded road lighting to prioritise users and manage transportation, change street use, and record infringements (Ghaemi, 2017; Lynn et al., 2020; Dabrowska-Zółtak et al., 2021);

  • On-street parking sensors for identifying vacant spots, charging, recording usage, and signalling pricing (Christensen et al., 2021);

  • Autonomous vehicles to support public transportation, freight, and micro-mobility (Iclodean et al., 2020; Sell et al., 2021);

  • Road anomaly and incident detection (Santosh et al., 2020; Amandio et al., 2021); and,

  • Route optimisation, driver, and vehicle information systems (Rammohan, 2023).

Chen et al. (2017) outline the potential ways in which the adoption of smart transportation systems can contribute to energy efficiency. Firstly, the adoption of smart transportation systems can have a number of short-term benefits. These include energy savings related to changes in transport mode (e.g., to public transport), reductions in travel times (e.g., route optimisation and traffic management), and associated reductions in energy consumption per vehicle (Chen et al., 2017). Secondly, smart transportation systems may enable or catalyse other initiatives or interventions that may result in energy efficiencies and ultimately behavioural change (e.g., change in vehicular ownership, residential location, or activity pattern) (Chen et al., 2017). Jianwei et al. (2010) similarly note that ITS and other smart transportation systems may result in significant reduction in traffic-related costs and socio-economic benefits. Specifically, they note that such systems can result in reduced economic losses due to road construction costs, traffic congestion, environmental pollution, road injuries, and fatalities (Jianwei et al., 2010).

Despite the opportunities presented by smart transportation systems, there are significant challenges. Waqar et al. (2023) identify six distinct categories of barriers to the adoption of smart transportation systems—technical, resource, interoperability, management, economic, and personal challenges. In their analysis, interoperability challenges received the highest mean score, followed by economic and technical challenges. Waqar et al. (2023) identify a wide range of barriers within these categories. Significant barriers included the need for efficient traffic management procedures (technical), inadequate infrastructure for smart transportation systems (resource), guaranteeing compatibility across a range of intricate transport systems and technologies (interoperability), managing, and administering a complex smart transportation system (management), cost of implementation and maintenance (economic), and privacy and security concerns (personal). Both Golub et al. (2019) and Waqar et al. (2023) identify the need for smart transportation systems to be accessible to all users, regardless of ability or money. Golub et al. (2019) caution that while smart transportation systems may have environmental benefits, they may exclude disadvantaged members of the community who do not have access to private vehicles, banking, credit, internet, and mobile phones. Chen et al (2017) highlight three categories of challenges which could equally be viewed as critical success factors in the successful adoption and implementation of smart transportation systems—institutional conditions (including organisational, legal, and policy aspects), technical conditions (concerning technology and analytics), and physical conditions (infrastructure, equipment, and devices). It is important to note that these conditions will be contingent on the development stage of the city or town and the country in which it is located. Consequentyly, the conditions, approach, and prioritisation for smart transportation systems adoption and implementation should reflect local needs and constraints (Chen et al., 2017). In all instances, smart transportation initiatives should involve a wide range of stakeholders and be as transparent as possible (Chen et al., 2017).

5.3 Building Energy Efficiency

85% of buildings in the European Union (EU) were built before 2000; 75% of which have a poor energy performance; over a third of the EU’s GHG emissions come from buildings (European Commission, 2024). As over 80% of the energy used in households is consumed for heating, cooling, and hot water, it is unsurprising that a significant element of EU policy focuses on solutions for these areas by 2050. As the overwhelming majority of the European building stock will continue to be in use by 2050, the goal is to increase the energy efficiency in new and existing building stock dramatically by 2050 (European Commission, 2020a). To achieve this, EU policy seeks to ensure new builds are designed to higher standards of energy efficiency and that the existing building stock is refurbished to reduce building energy consumption by significant levels, so-called deep renovation (European Commission, ).

Vale et al., (2023, p. 431) define a smart building as “cyber-physical solutions able to support and aid the daily routines of users and/or to optimize the management of the building”. They are cyber-physical as they combine ICTs such as building energy management systems (BEMS) and advances in materials and engineering such as pre-fabricated envelope components, biomass insulation, and energy harvesting and renewable energy source (RES) technologies (Lynn et al., 2021). In the context of digital sustainability, the twin goals of smart buildings are efficient energy management combined with a comfortable environment (Zhou et al., 2018). In their review, Al Dakheel et al. (2020) identify four main functions of smart buildings:

  1. 1.

    Climate response: the buildings’ capability to respond to actual and expected external climate conditions to minimise energy consumption and maximise renewable energy generation;

  2. 2.

    Grid response: the building’s capability to respond to actual and expected data from the energy grid(s) to which it is connected to maximise energy and/or economic efficiencies.

  3. 3.

    User response: the capability of a building to respond to user behaviour and priorities.

  4. 4.

    Monitoring and supervision: the capability to monitor the operational aspects of the building including technical systems and user behaviour and take corrective action to support efficient operation and minimise energy consumption.

As mentioned earlier, these functions are delivered through smart energy management systems including meter data management systems, BEMS, and building automation and control systems (BACS), their connection to IoT-enabled hardware and devices (e.g., sensors and actuators) throughout the building and integrated into key systems (e.g., lighting, heating, etc.). Advanced smart energy management systems can monitor energy supply from the grid and building consumption and through analysis (increasingly enabled by machine learning and deep learning) identify actual or potential inefficiencies, and automatically adjust settings to reduce energy waste. For instance, smart lighting and HVAC (heating, ventilation, and air conditioning) systems can be dynamically adjusted based on the energy grid supply, user behaviour, occupancy, and anticipated weather patterns to ensure comfort is maintained in an energy-efficient or cost-efficient way (Bhutta, 2017). Similarly, RES and other energy storage systems can be programmatically controlled to manage storage, use, or sell excess electricity back to the grid (Al Dakheel et al., 2020).

While the integration of smart technologies into buildings offers significant potential for energy savings, their implementation is not without challenges. Al Dakheel et al. (2020) note that these challenges differ depending on whether the smart building project is a new build or a retrofit. Research suggests that in new builds significant challenges include the high cost of initial construction, lack of guidelines to manage smart building construction, lack of government incentives and policy, planning issues, lack of properly trained energy efficiency professionals and construction workers, and associated resistance to change from using traditional technologies, techniques and designs, external (grid) and internal system interoperability, amongst others (Al Dakheel et al., 2020; Ejidike & Mewomo, 2022; Lynn et al., 2022). For retrofits, the barriers are more complex. Lynn et al. (2022) identify four categories of barriers to smart building technologies including human, organisational, technological, and external environmental barriers. Buildings involve a wide range of stakeholders including owners, managers, residents, and other users. Research suggests that human barriers including social norms and habits, lack of instruction on how to use new technologies, a lack of information on energy consumption and energy saving opportunities, short-termism, and disturbance of daily routines (Lynn et al., 2022). Technological barriers include those mentioned earlier with new builds with the added complexity that existing buildings often have legacy mechanical systems that have not been designed for digital connectivity and therefore these systems need to be optimised and integrated for modern smart energy management and control systems (Al Dakheel et al., 2020). In deep renovation, again many of the challenges listed for new builds apply. Financial barriers, including high upfront investment costs, funding, the duration, and payback period of deep renovation financial investments, are widely cited in the literature (Lynn et al., 2022). While all smart building projects experience some degree of planning and regulatory challenges, retrofitting existing building stock faces additional challenges, not least where buildings may be protected on historical or cultural grounds. External environment barriers, particularly funding, can be compounded for social housing where local authorities have significant financing and account controls (EMBuild, 2017). Furthermore, while there are significant deep renovation incentives, these may be poorly designed (e.g., split incentives) or complex to draw down (EMBuild, 2017; Lynn et al., 2022).

5.4 Smart Waste Management

Increased urbanisation has a direct and significant impact on waste generation and management challenges. Unsurprisingly, the greater population densities in cities and towns result in a higher waste generation than rural and sparsely populated areas, but also different types of waste including increased volumes of electronic, chemical, and plastics waste which are more difficult to dispose of and recycle. This issue is exacerbated by legacy waste management systems leading to even greater environmental impact.

Smart waste management (SWM) refers to the use of enabling ICTs for more efficient, effective, and sustainable waste management operations (Zhang et al., 2019). Extant research and applications range across the entire waste management lifecycle leveraging technology across various stages of waste management, including collection, sorting, recycling, and energy recovery. Digital technologies, including IoT-enabled bins, geographic information systems (GIS), Radio Frequency Identification (RFID), and advanced analytics, are transforming how waste is collected, transported, and tracked through the waste management lifecycle (Hannan et al., 2015; Rada et al., 2013; Shyam et al., 2017; Sosunova & Porras, 2022). For example, waste collection is increasingly digital and sophisticated. IoT-enabled solar-powered waste receptacles with built-in compactors, such those provided by Bigbelly,Footnote 1 cannot only perform multiple functions but notify waste management services of the need to be collected as well as collecting data on volume, fill rate, and collection activity for analysis and chargeback. Similarly, automated vacuum-based systems, such as those offered by ENVAC,Footnote 2 are being developed and used to capture different types of waste through standardised inlets connected to an underground pipe network in buildings or the public realm. Waste receptacles are emptied at pre-programed times or when sensors indicate that the units are full. There is also increasing research and application for autonomous robots for sweeping and steaming pavements or emptying and transporting waste receptacles from smart bins amongst other applications (Roche Cerasi et al., 2020). Once waste is collected new decision support systems are being developed using digital twinning, machine learning and deep learning that optimise waste collection routes dynamically, saving time and fuel but also reducing inconvenience (Yang et al., 2022; Barth et al., 2023; Cardenas et al., 2023).

Once waste is collected, it must be sorted and segregated to support both energy recovery and recycling. Robotic sorting systems (see, e.g., Wilts et al., 2021) and automated segregation techniques based on machine vision can significantly improve the efficiency and accuracy of waste separation, essential for effective recycling (Flores & Tan, 2019; Mohammed et al., 2023; Sanathkumar et al., 2021). Santti et al. (2020) sought to use digital technologies to incentivise and change consumer behaviour with respect to waste sorting. By gamifying waste sorting and segregation, they were able to dramatically increase recycling activities within student residencies. In their experiment, the recycling rate of biowaste increased from 76 to 97% and the recycling rate of plastic from 25 to 85% (Santti et al., 2020).

At the later stages of the waste management lifecycle, intelligent systems are being integrated for real-time monitoring and waste-to-energy frameworks, highlighted in studies by Vlachokostas (2020), Curtis et al. (2021), Kaya et al. (2021), and Shu et al. (2022). These advancements not only improve the operational efficiency of waste processing facilities but also bolster the sustainability of energy recovery methods.

In their survey of public and private waste management services, Borchard et al. (2022) find a wide range of motivations for digitalisation of the waste management value chain including efficiency and quality gains, faster payment transactions, cost optimisation, increased process quality, and increased competitiveness. Interestingly, environmental objectives are reported as the least important objective for the services surveyed (Borchard et al., 2022). They may reflect the digital maturity of the sector and associated solutions. For example, it is far easier to adopt digital technologies in the administrative aspects of waste management than in parts of the value chain which require capital investments and significant changes to infrastructure.

Zhang et al. (2019) identify 12 main barriers in their study of the barriers to SWM adoption and implementation, namely lack of SWM knowledge, lack of regulatory pressures, lack of innovative capacity, difficulties in technologies and applications, lack of market pressures and demands, cost and other financial challenges, lack of environmental education and culture of environmental protection, lack of stakeholder cooperation, including service provider co-operation, short termism, lack of cluster effect, lack of leadership commitment, and finally, lack of proper standards of waste management. They note that the relative importance of these barriers may vary across different stakeholders (e.g., government, technology provider, or technology user). In all cases, there was agreement that lack of knowledge of smart waste management, lack of regulatory pressures, and lack of environmental education and culture of environmental protection were important causal barriers (Zhang et al., 2019). However, Zhang et al. (2019) identify other stakeholder-specific barriers. For example, technology users rated lack of innovation capacity, difficulties in technologies, and their applications higher than the technology providers (Zhang et al., 2019). This study provides insights into the need for cities and towns to consider a wide range of stakeholder needs in the design of any SWM initiative.

5.5 Environmental Monitoring

Environmental monitoring in smart cities and towns refers to the systematic collection, analysis, and interpretation of data concerning various environmental parameters, such as air and water quality, noise pollution, temperature, and humidity, using advanced technologies and IoT devices (Catlett et al., 2017; Kennedy, 2023). As we discussed earlier in Sect. 5.3, data on the external environment can determine sustainability decisions within buildings (e.g., external weather changes impact heating and cooling requirements in buildings). However, environmental monitoring can also play a significant role in enhancing public health, and residents' quality of life by identifying pollution sources, monitoring urban environmental trends (e.g., traffic, regulatory compliance), and facilitating data-driven decision-making for urban planning and management. By leveraging real-time environmental data, smart cities and digital towns can proactively manage environmental risks, reduce pollution, and ensure a healthier, more liveable urban environment for their inhabitants. This approach not only addresses current environmental challenges but also contributes to the resilience and adaptability of urban areas in the face of climate change and rapid urbanisation.

The University of Chicago’s Array of Things (AOT) was an experimental urban measurement system based on Waggle, an open platform for edge computing and intelligent, wireless sensors developed at Argonne National Laboratory (Catlett et al., 2017). AOT provided programmable, modular ‘nodes’ with sensors and computing capability so that one can analyse data at the edge and then periodically send this data to fog nodes or the cloud for analysis (Catlett et al., 2017). For example, it included functionality for measuring climate, air quality, noise levels, flood and water levels, as well as counting the number of vehicles at an intersection (and then deleting the image data rather than sending it to a data centre). Use cases identified by the project included consumer recommender systems for healthiest and unhealthiest walking times and routes, real-time detection of urban flooding, and micro-climate measurement and analysis (University of Chicago, 2021). AOT was designed to be attached to existing street infrastructure (e.g., lampposts), and provide insights at a city or municipal level. A follow-on project, Eclipse, sought to provide increasingly granular insights at a neighbourhood level (Esie et al., 2022; Daepp et al., 2022). For example, results from Eclipse were able to identify environmental-related social inequities across neighbourhoods, e.g. particulate matter levels were notably higher in neighbourhoods with larger compositions Hispanic/Latinx and Black populations at different times. In Gorey, a small town in rural Ireland, a similar ‘box of things’ has been put in place, through a collaboration between Dublin City University and Wexford County Council, to collect data on air quality, noise pollution, temperature, humidity, and traffic flow (Kennedy, 2023). One of the benefits of the ‘box of things’ project is to create a critical mass of open data for use by the public, researchers, or industry. However, as Janssen et al. (2012) note open data on its own has little intrinsic value; its value is created by its use.

When combined with other smart city systems and sources of data, the value of environmental monitoring data is significantly enhanced. These systems include traffic control and demand management systems, energy demand response systems, neighbourhood, and district energy management systems, as well as mobile applications for citizens (Lynn & Wood., 2023). In all these instances, environmental data can be used to enhance predictive capabilities and provide insights to actuate change. Furthermore, environmental data can augment and be augmented by data from government socio-economic data on focal populations, public service and utility usage, climate, etc., but also new street-based technologies. For example, there are numerous examples of smart lampposts, street furniture, and smart kiosks that include environmental sensing for data collection (Gomez-Carmona et al., 2018; Baumgartner et al., 2019; Nassar et al., 2019).

Environmental monitoring is not without challenges. From a technological perspective, the availability and scale of enabling infrastructure and technologies, and the associated funding to finance such infrastructure is a significant constraint (Biber, 2013; Lynn & Wood, 2023). Additionally, any public ambient monitoring, on the environment or otherwise, raises concerns regarding trust, data protection, and data security (Lopresti & Shekhar, 2021; Lynn & Wood, 2023). Biber (2013) also notes a number of institutional, political, and legal constraints including the need for institutional continuity, inter-agency conflict, lack of transparency on how data is being used or whether it is effective or not, and lack of skills to analyse and use the data effectively.

5.6 Conclusion

We are witnessing an unprecedented level of urbanisation combined with accelerated climate change. Urban areas, whether cities or towns, have a disproportionate impact on the environment. This chapter discusses the potential impact of digital technologies to proactively manage environmental risks, reduce pollution, and ensure a healthier, more liveable environment. Through smart transportation systems, cities and towns can alleviate congestion, improve and promote eco-friendly modes of mobility, and thereby significantly reduce carbon footprints while increasing safety. Smart buildings, on the other hand, offer a pathway to sustainable urban living by ensuring energy efficiency and fostering healthier indoor environments through intelligent design and operational practices. Furthermore, smart waste management practices enabled by digital technologies not only aim to reduce waste generation but also support and maximise recycling, reuse, and energy recovery, all of which contribute to a circular economy. Lastly, we discussed the critical role of environmental monitoring in identifying, analysing, and mitigating environmental risks through data-driven insights. Realising smart cities and towns is not without challenges however an inclusive, long-term, and multi-stakeholder collaborative approach can help pave the way for a more digital, sustainable, and liveable future for generations to come.