Accepted on 15 Jan 2021
Submitted on 08 Dec 2020
Cities are a mark of human civilisation and play a central role in the pursuit of new paradigms of thinking to bring about major transformations to the way people live. Sustainability has, over the last four decades, been one of the most influential paradigms of thinking within urbanism. Modern cities holding unparalleled potential to address and overcome the challenges of sustainable development largely depends on how they can be planned, designed, and managed in response to global trends, scientific discoveries, and technological advances. This is clearly reflected in the Sustainable Development Goal (SGD 11) of the United Nations’ 2030 Agenda—Sustainable Cities and Communities (UN 2015a). Appropriately redesigning and restructuring urban places as sustainable cities and adopting innovative solutions to make urban living more sustainable is a continuous endeavor towards achieving the long-term of goals sustainability.
Compact cities and eco-cities are the central paradigms of sustainable urbanism and the most prevalent and advocated models of sustainable cities. Numerous recent national and international policy reports and papers state that these two models contribute, though to varying degrees, to resource efficiency and reliability, environmental protection, socio-economic development, social cohesion and inclusion, quality of life and well-being, and cultural enhancement (Bibri 2020a). It is argued that the compact city model is able to contribute to and support the balancing of the three goals of sustainability (e.g., Bibri, Krogstie and Kärrholm 2020; Burton 2002; Jenks and Dempsey 2005; Hofstad 2012; Jenks and Jones 2010; OCED 2012), and that the eco–city model is able to achieve the goals of environmental sustainability and to produce some economic and social benefits of sustainability (Bibri and Krogstie 2020a; Joss 2010; Joss, Cowley and Tomozeiu 2013; Kenworthy 2006; Mostafavi and Doherty 2010; Pandis and Brandt 2011; Rapoport and Vernay 2011; Suzuki et al. 2010).
Transformative processes within sustainable cities have been in focus for some time now. The motivation for achieving the United Nations’ SGD 11 has increased the need to understand, plan, and manage sustainable cities in new and innovative ways (UN 2015a). In this respect, the United Nations’s 2030 Agenda regards advanced ICT as a means to promote socio–economic development and protect the environment, increase resource efficiency, achieve human progress and knowledge in societies, upgrade legacy infrastructure, and retrofit industries based on sustainable design principles (UN 2015b). This relates to the multifaceted potential of smart cities, which has been under study with respect to the role of big data technologies and their novel applications in strategic sustainable development within the framework of 2030 Agenda (UN 2015c). The abundance of urban data, coupled with their analytical power, opens up for new opportunities for innovation in sustainable cities. This in turn means tackling the problems and challenges facing sustainable cities in their endeavor to make actual progress towards achieving the vision of sustainability.
Big data technologies are heralding a new era wherein sustainable cities are morphing in response to what has been identified as data-driven urbanism. This transformation—which entails how sustainable cities are being monitored, understood, analyzed, and thus organised, planned, controlled, and regulated—is manifest in the increasingly level of the development and implementation of data-driven technology solutions in their management mechanisms and development planning approaches. In fact, big data technologies have, in the context of sustainability, become as essential to the functioning of smart cities (e.g., Angelidou et al. 2017; Bibri 2019a; Bibri and Krogstie 2020b, c; Bettencourt 2014; Eden Strategy Institute 2018; Hashem et al. 2016; Kumar and Prakash 2016; Nikitin et al. 2016; Perera et al. 2017) as to that of sustainable cities (e.g., Bibri 2018, 2020b, c; Bibri and Krogstie 2017a, b, 2020a, c; Pasichnyi et al. 2019; Shahrokni et al. 2014; Shahrokni, Levihn and Brandt 2014; Shahrokni et al. 2015; Shahrokni, Lazarevic and Brandt 2015; Sun and Du 2017; Thornbush and Golubchikov 2019).
The conscious push for sustainable cities to be smarter and thus more sustainable in the era of big data is due to the problematicity surrounding their development planning approaches and operational management mechanisms, as well as the fragmentation of their designs and technologies. This has a clear bearing on their performance with respect to the contribution to and balancing of the goals of sustainability. Over the last two decades, research within the field of sustainable urban forms, especially compact cities and eco-cities, has produced conflicting, uncertain, and non-conclusive results (e.g., Bibri 2020b, c; Bibri and Krogstie 2017a; Cugurullo 2016; Jenks and Dempsey 2005; Kaido 2005; Kärrholm 2011; Lim and Kain 2016; Neuman 2005; Williams 2010) concerning the actual benefits they claim to deliver. This is compounded by the unprecedented issues engendered by the escalating urbanization and their implications for jeopardizing sustainability. Today, urbanization is one of the greatest environmental, economic, and social challenges that sustainable cities are facing. In recent decades, urban growth has been dramatic, a climate which has made it more challenging for sustainable cities to reconfigure themselves more sustainably without the use of advanced ICT. In a nutshell, new circumstances require new responses. Bibri and Krogstie (2019a) provides a comprehensive review of sustainable urban forms (eco–cities and compact cities), highlighting their inadequacies, shortcomings, struggles, and bottlenecks, as well as the role and potential of advanced ICT in addressing these issues and problems. Most of which tend to relate to how sustainable cities have long been studied, understood, and planned. This pertains to data scarcity, inherent limitations of traditional research methods, inefficient management processes, and long-term planning approaches. This is dramatically changing thanks to the multifaceted potential of smart cities as enabled predominately by the IoT and big data technologies. As a result, many opportunities are yet to explore as to integrating sustainable cities and smart cities in terms of their operational management and development planning on the basis of advanced computational data analytics, thereby mitigating their extreme fragmentation and weak connection (e.g., Ahvenniemi et al. 2017; Angelidou et al. 2017; Bibri 2019b, 2020b, c; Bifulco et al. 2016; Kramers, Wangel and Höjer 2016) under what is labelled “data–driven smart sustainable cities.”
This paper aims to develop a novel model for data-driven smart sustainable cities of the future, and in doing so, it provides a strategic planning process of transformative change towards sustainability. This model combines and integrates the prevailing paradigms of sustainable urbanism and the emerging paradigms of smart urbanism —based on the outcomes of the four case studies conducted on: (1) compact cities (Bibri, Krogstie and Kärrholm 2020), (2) eco-cities (Bibri and Krogstie 2020a), (3) data–driven smart cities (Bibri and Krogstie 2020b), and (4) environmentally data-driven smart sustainable cities (Bibri and Krogstie 2020c). The case study research is associated with the empirical phase of a futures study that consists of 6 steps, each with several guiding questions to answer. This paper reports the outcome of Step 6, which involves answering the following five guiding questions:
Important to note is that the framing of this paper as a set of planning actions and policy measures is justified by the fact that it is concerned with the decisive steps and strategic pathways that should be taken to attain the vision of the desirable future. The primary intent is to provide recommendations for government officials, policymakers, planners, designers, developers, industry experts, and other stakeholders on how to build a data-driven smart sustainable city of the future.
This paper is organised into four sections: Section 2 briefly introduces the methodological framework for the futures study. Section 3 presents the results, detailing the strategic planning process of backcasting in terms of its objectives, targets, vision, and strategies and pathways. Section 4 discusses the results. This paper ends, in Section 5, with some concluding remarks.
The methodological framework applied in the futures study combines and integrates normative backcasting and descriptive case study as qualitative approaches. The backcasting approach was employed to achieve the overall aim of the futures study. The case study approach, which is associated with the empirical phase of the futures study, was adopted to examine and compare two of a total of six cases from the ecologically and technologically leading cities in Europe within each of the frameworks of compact cities, eco-cities, data–driven smart cities, and environmentally data-driven smart sustainable cities. Bibri (2020d) dedicates a whole article to the methodological framework for strategic data-driven smart sustainable city planning whose core objective is clarifying which city model is desired and working towards that specified outcome. Table 1 presents the guiding questions for each of the six steps in the futures study, and highlights the five questions addressed by this paper in bold.
Table 1
The guiding questions for each step in the backcasting-oriented futures study.
| The guiding questions for the backcasting-oriented futures study | Methods and tools |
|---|---|
Step 1: Detail strategic problem orientation (Part 1)
|
Research design and problem formulation |
Step 2: Detail strategic problem orientation (Part 2)
|
Trend analysis and problem analysis |
Step 3: Generate a sustainable future vision
|
Creativity method |
Step 4: Conduct empirical research
|
Case study method |
Step 5: Specify and Integrate the components of the future model of urbanism
|
Creativity method |
Step 6: Perform backwards–looking analysis
|
Backcasting analysis |
Source: Bibri (2020d).
The term “backcasting,” which was coined by Robinson in 1982, can denote a concept, a study, an approach, a methodology, a framework, or an interactive process among stakeholders. Hence, it has been defined in multiple ways. Robinson (1990, p. 823) defines backcasting as a normative approach which works “backwards from a particular desired end point to the present in order to determine the feasibility of that future and what policy measures would be required to reach that point.” Thus, backcasting is a planning process by which a desired outcome is envisioned and articulated, followed by the question: “what do we need to do today to reach that specified outcome?” (Figure 1) This question is about figuring out the “next steps,” which are quite literally the next concrete actions to undertake.
The backcasting process from the Natural Step.
Source: Holmberg (1998).
In recent years, backcasting has been mostly applied in the futures studies that deal with long-term problems and sustainability solutions (see, e.g., Åkerman 2005; Akerman and Höjer 2006; Höjer, Gullberg and Pettersson 2011; Miola 2011; Quist et al. 2011; Quist 2007; Vergragt and Quist 2011; Wangel 2011). The backcasting process in the futures study represents a strategic planning tool for facilitating the progress towards achieving the goals of sustainability for those cities that are badging or regenerating themselves as sustainable, or manifestly planning to be or become smart sustainable in the era of big data.
The descriptive case study approach was applied in the four case studies to investigate the prevailing models of sustainable urbanism and the emerging models of smart urbanism (Step 4). The intention of this investigation is to identify the underlying components of the new model of urbanism in terms of its core dimensions, strategies, and solutions, and then to integrate these components into an applied framework for strategic sustainable urban development planning (Step 5). This is in turn intended to guide the strategic planning process of transformative change towards sustainability, which represents the novel model for data-driven smart sustainable cities of the future (Step 6). Overall, by carefully studying any unit of a certain universe, we find out about some general aspects of it, at least a perspective that guides subsequent research. Case studies often represent the first scholarly toe in the water in new research areas.
The case study is a descriptive qualitative methodology that is used as a tool to study specific characteristics of a complex phenomenon. The descriptive case study approach, as defined by Yin (2009, 2014, 2017), was identified as the most suitable methodology for the empirical phase of the futures study. This methodology has been chosen considering the nature of the problem being investigated, the research aim, and the present state of knowledge with respect to the topic of data-driven smart sustainable cities. In this context, it involves the description, analysis, and interpretation of the four urban phenomena, with a particular focus on the prevailing conditions pertaining to plans, projects, and achievements. That is, how the selected cities behave as to what has been realized and the ongoing implementation of plans based on the corresponding practices and strategies for sustainable development and technological development. Accordingly, the four case studies examine contemporary real-world phenomena and seek to inform the theory and practice of data-driven smart sustainable urbanism by illustrating what has worked well, what needs to be improved, and how this can be done in the future. They are particularly useful for understanding how different elements fit together and (co-)produce the observed impacts in a particular urban context based on a set of intertwined factors.
As a roadmap to transformational change, the backcasting process articulates strategic thinking—the why—behind both the vision of the future and the plan for getting there. Strategic planning denotes a systematic process of generating a vision of a desirable future and translating it into broadly defined objectives and targets, and then identifying a sequence of actions and measures to achieve that specified future. Accordingly, this section is structured into three main phases, (1) the vision of the future, (2) the objectives and targets of sustainable development, and (3) the strategies and pathways for transformative change.
The vision of the future is where the problems, issues, and challenges related to sustainable cities (Bibri and Krogstie 2019a) have been solved by means of the data-driven technologies and solutions offered by smart cities of the future. However, the overall goal, which builds the vision of what the future should look like once manifested, is the indicator established to determine whether the objectives have successfully been achieved. The data-driven smart sustainable city is envisioned as (Bibri and Krogstie 2020d, p. 89):
“A form of human settlements that secures and upholds environmentally sound, economically viable, and socially beneficial development through the synergistic integration of the more established strategies of sustainable cities and the more innovative solutions of data-driven smart cities towards achieving the long-term goals of sustainability.”
In constructing the future vision, we have attempted to retain the best of what we already have that have been successfully enacted in real-world cities, making use of the things that have been demonstrably better in the past, while being selective in adopting the best of what is emerging and promising, making use of the things that will add a whole new dimension to sustainability in terms of harnessing its synergic effects, balancing its dimensions, and thus boosting its benefits.
The future vision is translated into broadly defined objectives and targets, which are of a long-term nature. The objectives and targets can also be used to develop the future vision. The targets are established first as specific desired outcomes that support the achievement of the objectives. These define an endpoint of concern and the direction of change that is preferred. The targets and objectives are to be—specific, measurable, achievable, relevant, and targeted when adopting the future model of urbanism. They are decided on according to what the data-driven smart sustainable city of the future aspires to achieve, an ambition which can be adapted to existing sustainable cities in their own contexts.
The objectives describe the measurable contribution of the data-driven smart sustainable city of the future as to achieving the overall goal of the future vision. Therefore, they define what is to be achieved and should have a specified timescale and be linked to the performance of the data-driven smart sustainable city of the future to ensure that policy commitments are prioritized and addressed in terms of improving and advancing the environmental, economic, and social goals of sustainability. This improvement should also be continual in line with sustainability policies in relevance to the national and local context of existing sustainable cities so that new objectives can be agreed on when the original objectives have been met. However, the objectives are of a qualitatively descriptive nature because the future vision is not concerned with a given sustainable city, or departs from a basic standard in mind accordingly. With that in regard, the data-driven smart sustainable city of the future aims to achieve the objectives of sustainable development, the most prominent among them are presented in Table 2.
Table 2
The prominent objectives of sustainable development.
|
The targets are the indicators that are established to determine how successfully the objectives have been achieved by providing relevant benchmarks for the compact, ecological, and technological components of the data-driven smart sustainable city of the future. This involves how synergistically these components are integrated, cooperate, and beneficially complement one another. The targets can quantify or qualify the objectives over time. The specific targets set in relation to the future vision are specified in terms of the dimensions, strategies, and solutions of the four investigated models of urbanism. They help to set up a clear course of action and guide the future vision. The target-setting here denotes the strategic process to establish performance goals for the physical, environmental, economic, social, and technological areas of the data-driven smart sustainable city of the future. Each area uses a different tool that starts with establishing a baseline, e.g., how much energy is currently being used, or how dense and diverse is a given urban area or district. In the context of the futures study, the targets are of a qualitatively descriptive nature because the future vision is not concerned with a given sustainable city, or departs from a basic standard in mind accordingly. Nonetheless, the qualitative targets should, when planning the development of the data-driven smart sustainable city of the future, be turned into quantifiable targets that can be achievable within an agreed timescale in accordance with the objectives. This in turn depends on the nature of the areas targeted (e.g., GHG emissions reduction, energy efficiency, well-being, etc.), the level of progress already made in these areas, and so forth. As to the level of progress, for example, the targets should be set in the areas where improvement is most needed or prioritized in order to meet the requirements for regulatory compliance, to improve performance, to reduce risks, and so on.
The future vision as a long-term goal represents the set of targets that should move the city from its current state (sustainable) to its future state (data-driven smart sustainable). Hence, these targets incorporate the objectives of sustainable development as well as the objectives of technology associated with the readiness of the city to introduce data-driven technology in, and the implementation of applied technology solutions for, city operational management and development planning with regard to sustainability. Accordingly, they should be based on the synergistic integration of the strategies and solutions of the four investigated models of urbanism (see Table 3).
Table 3
The core compact, ecological, and technological targets of the future model of urbanism.
|
Furthermore, one objective may involve the different categories of the targets, e.g., reducing energy usage includes targets related to building density, green energy, smart energy, passive and net-zero energy building, sustainable transport, smart transport, and so forth. Each of these targets may in turn include a set of sub-targets. Generally, a strategic vision can have multiple goals and each goal can have many objectives. At the same time, each objective can be linked to multiple targets and each target can be linked to various key performance indicators (KPIs). The targets specified represent the key areas that drive urban sustainability performance and the way it can be improved and advanced with the support of data-driven smart technologies and solutions. Finding a way to measure these areas is normally followed by, as a natural next step, starting setting performance targets.
The data-driven smart sustainable city of the future should establish CityScore as an online dashboard to show how the city government is performing against its targets in the areas identified on a wide range of metrics. The metrics measured can be used as a gauge of how well the city government is serving its citizens and responding to their concerns in regard to the three dimensions of sustainability. The daily activity updates make performance and progress transparent to the public and city administrators. A single, combined number can summarize how the administration is performing overall. Tracking performance against targets enables problem areas to be quickly identified and remedied, and offers citizens the opportunity to hold administrators to account. Aggregating and dividing the data collected automatically by sensors as well as by city workers using their mobile devices by the target figure can generate a daily, weekly, or quarterly score: above 1 means the city is exceeding its targets, below 1 means it is falling short.
The targets should be clear and there should be no ambiguity about the objectives that should be prioritized. This ensures that stakeholders understand how the different objectives are being attained and balanced. This in turn can help secure stakeholders’ buy-in and support. In particular, the ICT infrastructure should be planned, implemented, and managed while being dependent on the initiative by and interest of the other stakeholders involved in the sustainability efforts, including planners, developers, architects, building owners, utilities, energy cooperatives, and citizens. The initiatives of these stakeholders should be coordinated in order for them to be able to work together more effectively and support each other. Among the benefits of setting the targets in this context are:
Worth noting is that the above stated targets embody the targets of the SDG 11 (Table 4). These are slightly adapted from the United Nations (2015a) as the focus of the futures study is on the cities that are already badging or regenerating themselves as sustainable, or manifestly planning to be or become smart sustainable.
Table 4
The SDG 11 targets embodied in the future vision.
|
Source: Adapted from United Nations (2015a).
This phase entails building a feasible and logical path between the state of the future and the present based on the criteria defined for the future vision. Such path represents a set of strategies and pathways to be pursued in order to bring about the needed transformative change towards sustainability. Determining the strategies and the specific pathways to execute them is the main part of the effort to achieve the overall goal of the future vision by meeting the specified objectives and targets. At the heart of these strategies and pathways is the practice-oriented process of designing and developing the data-driven smart sustainable city of the future. Different multiple strategies are needed in order to attain the future vision. To employ each of these strategies in turn requires a set of specific actions, sequences of actions, and agendas within each strategy, ways of achieving specified results. However, as cities generally need to be able to respond to new global trends and shifts, it is important for the future vision, strategies, and pathways to have flexibility as well as some firmness. The future vision needs to remain firm and should be the anchor that holds all the rest together. The strategies as a form of long-term plan may need to pivot in response to external factors such as global trends and technological shifts. The pathways are the most flexible in terms of adjustment and modification, especially the future vision does not pertain to a given sustainable city. In a nutshell, the goal needs to be developed and planned strategically, held to be achieved, and the pathways that can help move towards fulfilling the strategies need to be flexible. The key strategies and pathways are presented next in accordance with the five dimensions of the landscape of the data-driven smart sustainable city of the future, namely:
The built infrastructure involves the patterns of the physical objects in the city pertaining to the built-up areas as well as those areas planned for new development and redevelopment together with transport, communication, anergy, and waste systems. The compact and ecological dimensions of urban design characterize most of the built infrastructure as regards its buildings, blocks, streets, open space, public space, green space, and essential urban infrastructure.
Urban form denotes the physical aspects that characterize the built-up areas. The form of the city is seen as a salient factor for enacting more sustainable, efficient, equitable, and livable urban environments though design. It is associated with the development strategies related to urbanization dimensions, namely physical (land use change), geographical (population), economic (agglomeration), and societal (social and cultural change). These largely pertain to the design strategies of the compact city, namely compactness, density, multidimensional mixed-land use, sustainable transformation, and green open space.
The strategies for development planning are based on a number of important challenges, notably increasing population, plans to create more jobs, new demands from the business sector, the possibility of a simpler daily life for more people, measures to decrease social inequality and socio-spatial segregation, the development of urban areas for a more closely connected city, plans to enhance transport infrastructure, and the even distribution of green areas and parks. These are at the core of the compactness of the built form.
Main Directions for Compactness
To attain the compactness of the built form of the data-driven smart sustainable city of the future with the expected benefits of sustainability, there are four main directions to take:
Staged Expansion and Intensification of Urban Fabrics
To pursue these directions, it is important to stage an expansion of the city based on the current level of its compactness with respect to the extent to which land areas can be used close to existing development, or urban development can take place adjacent to existing urban fabrics and structures. This relates to the intensification strategy of compaction, which encompasses a range of substrategies for urban renewal, infill, development, and redevelopment. These substrategies in the context of the data-driven smart sustainable city of the future are to be applied on the six specific urban fabrics that were identified based on the case studies conducted on compact cities and eco-cities (Bibri, Krogstie and Kärrholm 2020; Bibri and Krogstie 2020a). An urban fabric denotes the physical characteristics of urban areas in terms of components, materials, buildings, spatial patterns, scales, streetscapes, infrastructure, networks, and functions, as well as socio-cultural, ecological, economic and organisational structures. The identified urban fabrics together with the applied substrategies of the intensification strategy are presented below:
Build and Develop Centrally
The Central Renewal Area
The Inner City
Concentrate on Strategic Nodes
Complement and Mix
Reserve Outer Areas for Future Consideration
The outer area, which is not yet developed or about to be developed, should be strategically planned based on the core strategies of the compact city in terms of density, diversity, mixed land use, sustainable transportation, green space, and other design features pertaining to the eco-city with respect to various types of sustainable buildings.
Build New Districts
Building new districts should be based on the integration of the core strategies of the compact city and the eco-city as planning systems as regards designs and buildings, with support of new technologies.
Ecological design is a design form which integrates itself with living processes to minimize environmentally negative or destructive impacts. It is associated with the green structure in the city and how it should be developed, distributed, and managed (e.g., Austin 2013; Bibri and Krgostie 2020a; Beatley 2010; Beatley 2000; Farr 2008; Mostafavi and Doherty 2010). The green and blue structure strategy can be broken into eleven substrategies, namely:
The green structure strategy relates to the idea of letting nature do the work by designing multifunctional green structure to provide important ecosystem services of various categories, including provisioning, regulating, cultural, and supporting services. To let nature do the work entails ensuring that greenery and water are used as active components in the design and operation of the city. The green structure replaces and complements technical systems, creates a richer plant and animal life, and contribute to human health and well-being. Important to note is that the green structure strategy as an integrated approach is best to be implemented in new urban areas or outer areas with development potential. Also, a number of the aforementioned substrategies can be implemented as part of the individual urban development projects related to the other urban fabrics mentioned earlier, when it is feasible from a design perspective. However, below are the key pathways needed for executing the green structure strategy:
As a wide-ranging term, infrastructure is the basic structure which supports the operation of a city. This makes economic and social development possible. The focus here is on the essential sustainable and smart infrastructures that make up the city, including transportation systems, communication systems, energy systems, waste systems, lighting systems, sewage systems, and waste disposal systems. These are associated with the basic facilities, services, and installations needed for the functioning of the city in terms of engineered systems. Worth pointing out is that the essential urban infrastructure embodies economic infrastructure, the internal facilities of the city that make business activity possible or promote economic activity, such as communication, transportation, distribution networks, and energy supply systems.
The essential urban infrastructure involves six key strategies:
To be able to effectively improve and strategically advance the contribution of the city to the goals of sustainability, it is necessary to fully integrate sustainable transportation system with smart transportation system. Accordingly, the smart sustainable transportation strategy encompasses seven substrategies, namely:
Sustainable transportation: Sustainable transportation is a major strategy for achieving sustainability. It denotes any means of transportation that is green and has low impacts on the environment. Below are the key pathways for executing the sustainable transportation strategy.
Smart Transportation: Smart transportation is one of the main ways modern cities can improve the daily lives of citizens and sustainability. It involves information systems that collect data about traffic, vehicles, and the use of different modes of transport for further processing and analysis in city operations center. Transport and traffic management is one of the most common areas that use data-driven technology solutions. The key pathways for executing the smart transportation strategy are:
The smart sustainable energy strategy aims to reduce energy consumption, increase renewable energy adoption, and decrease carbon footprint. Here technological innovations can play a prominent role in the light of the high predicted rate of urbanization. Integrating sustainable energy with smart energy will drive data-driven smart sustainable cities of the future to become fossil fuel–free and climate positive. Therefore, the energy system should combine green energy technologies and energy efficiency technologies. Accordingly, the smart sustainable energy strategy involves five key substrategies, with some overlaps among them, namely:
The key pathways for executing the four last substrategies have already been addressed in (Bibri 2020e). This paper specifically provides the key strategic pathways for achieving the goals of energy efficiency and pollution reduction. The identified data-driven smart solutions are found to have significant potential to improve and advance environmental sustainability in the context of emerging smart sustainable cities.
Renewable Energy Sources and Technologies: It is important to strongly advocate renewable energy generation and usage in order to enable the city to become fossil fuel–free by 2050. Renewable energy is derived from naturally replenished and zero-emission sources such as solar, wind. biomass, hydropower, and geothermal), using a number of industrial and technological systems. Below are the key pathways needed for implementing the substrategy of renewable energy sources and technologies:
It is worth noting that some of the above installations depend on the geographical location and climate of the city as well as its energy needs. Figure 2 illustrates an example of a renewable energy system integrating wind turbine, solar collectors, and photovoltaics cells for heating and cooling. Today, solar panels often cost less than on-grid electricity. Moreover, if all buildings’ electricity is produced with solar energy, carbon emissions can be reduced by more than 50% compared to baseline in sustainable cities by 2030. Therefore, it is important to aim for shifting electric supply from 100% large-scale to 100% local solar power.
Heating and cooling based on renewables in Western Harbor District, Malmö, Sweden.
The main goal of the renewable energy sources and technologies strategy is to phase in renewables and phase out fossil fuels by 2050, resulting in 100% locally produced electricity and heat from clean sources in most districts and ultimately supporting the entire geographical area of the data-driven smart sustainable city of the future. This kind of transformational change requires a strategic roadmap, i.e., a time-based plan that defines a future outcome and determines and assesses the decisive steps needed to reach it.
To achieve far more resource-efficient use of waste that has minimal impacts on the environment requires developing and implementing a number of measures and solutions as part of smart sustainable waste management. This strategy encompasses seven substrategies, namely:
Sustainable waste management: The key pathways needed for executing the sustainable waste management strategy are:
Smart Management of Waste Collection: Smart waste collection systems are becoming more and more wide-spread, and many cities across the globe are already implementing this solution in the city management programs. Typically, smart management of waste collection involves adopting data-driven resolutions intended to improve the efficiency of the city management, especially in relation to the city districts with no vacuum waste chutes systems. The key pathways for executing the strategy of smart management of waste collection are:
As a model used for describing and analysing energy and materials flows in the city and their relationship with its infrastructure and activities, urban metabolism serves to maintain the functional and evolutionary states of the city as a socio-technical organism. Looking at the data-driven smart sustainable city of the future through a metabolic lens, a framework through which to successfully model the flows of its systems becomes of high importance and interest. This aids in understanding the relationship between human activities and the natural environment by studying the interactions of human systems and natural systems in the sphere of the city. Indeed, urban metabolism provides a platform through which the implications of the different dimensions of sustainability can be considered.
The city-wide street lighting system provides tremendous opportunities for modern cities to collect huge amounts of data from urban environments and to transfer them to special centers for their subsequent processing and analysis for enhancing decision making associated with numerous uses and applications. This can be used to make urban living more environmentally sustainable and to enhance the quality of life for citizens. Street lighting is one of the most interesting pathway to using and exploiting the IoT and big data analytics in future cities. Thus, it can be expanded beyond what is originally used for. The key pathways needed for executing the strategy of smart street lighting:
Advanced ICT will be focussed on defining critical problems and events that might emerge rapidly and unexpectedly across the city. Analysing and identifying such problems and events is of great importance to urban sustainability and resilience. The smart management of the essential urban infrastructure involves monitoring and controlling its structural conditions in terms of potential changes that can increase risks and hazards as well as compromise safety and quality. In this context, data-driven smart technologies and solutions tend to be mostly justified by the high significance of the natural resources such infrastructure utilizes or involves in its operation. The key pathways needed for employing the strategy of the smart management of urban infrastructure:
Social infrastructure is the development and maintenance of the basic facilities combined that are necessary for human development. It is a subset of the infrastructure domain and typically includes assets that accommodate social services. These are provided by a city government, either through the public sector (or related entities) or the financing of private provision of services. A huge part of new digital technologies, innovative solutions, interactive platforms, and diverse forms of public-private cooperation have become of critical importance to overcome the social challenges and to bring about the needed transformations in a number of social domains that sustainable cities and smart cities are facing. This is at the core of the assets of the social infrastructure of the data-driven smart sustainable city of the future, particularly in relation to citizen participation, public safety, healthcare, and education and training. Other assets are part of the built infrastructure, the essential urban infrastructure, and the technological infrastructure, such as facilities, community support, housing, sewerage, water and wastewater treatment, transport, public space, recreation, and so forth. In a wider sense, the data-driven smart sustainable city of the future also has a variety of sustainable development institutions and competence centers whose mandate is improving social, economic, and environmental aspects. The role of political and civic institutions and urban centers lies in maintaining the planning, development, governance, and functioning of the city as a data-driven smart sustainable entity in the future. However, the social infrastructure focuses on three strategies:
The social infrastructure is about people. Therefore, the involvement of citizens in the management and planning of the data-driven smart sustainable city of the future using information systems is crucial to the progress towards its ultimate goal. Such involvement is associated with the adoption of the most important resolutions related to living, which intend to improve the level of satisfaction and increase the level of confidence and trust among citizens in the city administration. The strategy “participation and consultation” aims to stimulate citizens’ interest in taking part in the planning and development of the city. Research, knowledge development, and experience feedback are important preconditions for solving complex challenges. The key pathways for executing the participation and consultation strategy are:
It is highly important to develop a much deeper and more informed understanding of the risks, threats, and hazards surrounding the city. This requires a new set of data-driven technologies and collective decision-making processes. Data-driven approaches to urbanism enables understanding the city as strongly interlinked and coupled systems that generates unexpected and surprising dynamics. Emerging technologies are increasingly changing the nature of such dynamics by predicting them on multiple scales in terms of the properties and processes which stimulate change within the city system, thereby outsmarting it. The key pathways needed to execute the smart public safety strategy are:
One of the key areas targeted by technological advancements and innovations is human health. Medical systems and healthcare services are at the core of the IoT and big data applications. Healthcare management is one of the areas where the highest level of technology development and adoption is observed. The use of data analytics and personal wearable devices in medicine for the diagnosis and treatment of patients is one of the most promising areas of applied data-driven solutions in modern cities. Therefore, the focus should be on the electronization of medical services to enhance the quality of healthcare provided to all citizens and thus their well-being, as well as to upraise the effectiveness and efficiency of health system management. This entails using advanced tools, powerful computational processes, and innovative systems, such as embedded sensors and actuators, database system integration, management and monitoring software, simulation models, and decision support systems. The key pathways for executing the smart healthcare strategy are:
Generally, an ICT infrastructure includes hardware, software, networking, data storage, as well as an operating system. These are used to deliver applied solutions to the different stakeholders of the city. The ICT infrastructure of the data-driven smart sustainable city of the future must be able to integrate numerous application domains for sustainability across various spheres of its administration. Vital elements in this regard are the IoT, big data analytics, and artificial intelligence. These are to be used and integrated in more innovative ways to solve the problems related to the city management.
The ICT infrastructure can be deployed within the city’s own facilities or within cloud computing. The ICT infrastructure strategy includes the following substrategies:
The competencies associated with the ICT infrastructure pertain to the process of big data analytics in terms of generating, processing, analyzing, and visualizing data for enhancing decision making across the various domains of the city (transport, traffic, energy, environment, healthcare, public safety, etc.). They depend on the scale and quality of the instrumentation, datafication, and computation dimensions of the city. This in turn determines the nature and range of the solutions provided to optimize, enhance, and maintain the performance of the city with regard to sustainability. Digital instrumentation produces huge amount of data, which are transformed into datasets and thus become easily conjoined and shared and highly appropriate for handling. These datasets allow real-time analysis of the different aspects of urbanity to generate deep insights that can be used in decision-making processes and in developing simulation models for managing, planning, and designing more sustainable cities. The essence of digital instrumentation lies in coordinating and integrating technologies (and hence the strategies of sustainable cities and the solutions of smart cities) that have clear synergies in their implementation within development planning and operational management. This opens up and enables realizing many new opportunities in the context of sustainability. The key pathways needed for executing the ICT infrastructure strategy are:
The ICT infrastructure for the data-driven smart sustainable city of the future comprises a collection of smart solutions for various spheres of its administration. It includes novel applications and services for city agencies and departments to serve different stakeholders, and demonstrates the innovative use and integration of the IoT, big data analytics, and artificial intelligence to solve problems within the aforementioned domains of urban life.
Data sources characterize the availability of the actually used and potentially to be used sources of data. Based on the analysis of these data, the data-driven smart sustainable city of the future will be able to make countless and support complex decisions pertaining to planning, design, and operational functioning. However, some data are open and thus accessible to the public for use, while other data are confidential and thus pose privacy issues. Also, some data are available virtually for free, while other data require effort to obtain. Still not all the data needed for the development and implementation of applied data-driven solutions for sustainability exist. However, the key pathways needed for executing the strategy of data sources and open data are:
Up till now, the four models of sustainable urbanism and smart urbanism investigated are weakly connected as approaches and extremely fragmented as landscapes at the technical and policy levels. The compact city and eco-city models of sustainable urbanism, which have been around for over four decades or so, have many overlaps among them in their ideas, concepts, and visions, as well as distinctive concepts and key differences in terms of planning practices and design strategies. The overlap is justified by the fact that they both represent the central models of sustainable urban development Therefore, they are, to some extent, compatible and not mutually exclusive. As to the data-driven smart city, which is an emerging paradigm of smart urbanism, it shares the challenges of sustainable development with the two models of sustainable urbanism, with the main difference being that it focuses more on the use and adoption of data-driven technologies and solutions and related technical and institutional competences to overcome these challenges—than on the planning practices and design strategies of urban sustainability. Concerning the environmentally data-driven smart sustainable city model, it emphasizes the dimension of environmental sustainability and employs data-driven technology solutions to reach environmental targets. In this sense, this model combines concepts and ideas from both the eco-city and the data-driven smart city. The two models are increasingly being merged on the basis of the IoT and big data analytics technologies in a bid to overcome the significant challenges posed by climate change in the face of the escalating trend of urbanization. However, while both implement data-driven technology solutions to improve and advance environmental sustainability, they remain significantly divergent with respect to their priorities, values, visions, policies, strategies, and goals, thereby the meaningfulness of their integration in the fourth case study.
The main outcome of this study is the strategic pathways developed to bring about the preferred future. This involves identifying a set of planning actions and policy measures that enable to build a data-driven smart sustainable city in terms of the built infrastructure, sustainable urban infrastructure, smart urban infrastructure, social infrastructure, and technological infrastructure of its landscape. Developing strategic pathways is at the core of most of the backcasting approaches applied in the futures studies that address the various topics of sustainability transitions, or deal with long-term problems and sustainability solutions. This study is concerned with a pathway-oriented category of backcasting (e.g., Bengston Westphal and Dockry 2020; Wangel 2011), which is about identifying the planning actions and policy measures that connect a desirable state of the future to the present. At the core of this category, in the context of this paper, is how to bring about changes to the landscapes of the data-driven smart sustainable city of the future through different, yet interrelated, transformations related to the compact, ecological, and infrastructural designs of the city. Such transformations are of a complementary nature in line with the requirements of the future vision. However, setting strict goals in perspective of this category is of less importance (Vergragt and Quist 2011; Wangel 2011) compared to the other categories of backcasting, such as action-oriented backcasting, target-oriented backcasting, and participation-oriented backcasting (e.g., Akerman, Höjer 2006; Höjer, Gullberg and Pettersson 2011; Quist et al. 2001; Wangel 2011).
We identified the key strategies and pathways needed to move from the current state of sustainable cities to the future state of data-driven smart sustainable cities. This new integrated model of urbanism provides an important planning tool for facilitating the endeavor to build various models of sustainable cities that respond to the ongoing shifts brought by big data science and analytics and the underlying enabling and driving technologies. This means allowing sustainable cities to enhance, optimize, and potentially maintain their performance in regard to supporting, balancing, and integrating the three dimensions of sustainability thanks to emerging and future data-driven technologies and their uses and applications in many urban domains and across several spatial scales. The construction of the vision of the future based on broadly defined objectives and targets is about setting priorities, incorporating values, and adopting principles pertaining to sustainability while taking advantage of what the multifaceted potential of advanced ICT through its innovative solutions for a large number of sustainability areas. This has indeed been demonstrated by several studies addressing the environmental, economic, and social aspects of sustainability (e.g., Angelidou et al. 2017; Bettencourt 2014; Bibri 2020b; Bibri and Krogstie 2017b; Kramers, Wangel and Höjer 2016; Nikitin et al. 2016; Pasichnyi et al. 2019; Thornbush and Golubchikov. 2019; Trencher, G. 2019; Shahrokni et al. 2014; Shahrokni, Levihn and Brandt 2014) through innovative operational management mechanisms and enhanced development planning practices.
The technological facets of the futures study have been addressed in more detail in the two case studies conducted on the emerging paradigms of smart urbanism. One of the key aspects to highlight in this regard is the role of smart cities in connecting the aforementioned infrastructures associated with the transformations needed to attain the future vision through the identified strategies and pathways. This connection, as enabled by advanced ICT as essentially network-based, is a way to leverage the collective intelligence of the data-driven smart sustainable city of the future through the synergic nature of ICT in regard to producing the tripartite benefits of sustainability. In other words, in making sustainable cities cleaner, safe, and more efficient through the new urban intelligence functions enabled by joined-up and short term planning approaches. This can be accomplished by harnessing the vast troves of data that can be generated from across many urban domains thanks to advanced computational analytics techniques as well as urban operation systems and analytical centers (e.g., Batty et al. 2012; Bibri 2019b; Kitchin 2014, 2016; Kitchin, Lauriault and McArdle 2015; Nikitin et al. 2016). Sustainable cities involve the kind of challenges that are enormous enough to call for a data-driven approach to planning as a function of many diverse city stakeholders. Joined-up planning is a form of integration and coordination that enables the city–wide effects associated with environmental, economic, and social sustainability to be monitored, understood, and embedded into the designs and responses of sustainable cities in terms of their operational functioning, I.e., forms, structures, spacial organisations, activities, and services as embedded in space and time.
The compact and ecological facets of the futures study characterize sustainable cities in terms of urban design. While the environmental goals of sustainability tend to dominate in the discourse of the eco-city (e.g., Mostafavi and Doherty 2010; Holmstedt et al. 2017), the discourse of the compact city emphasizes the economic goals of sustainability (e.g., Hofstad 2012; Jenks and Jones 2010), with the social goals of sustainability being of less focus in the eco-city than in the compact city (see, e.g., Bibri 2020b, c; Lim and Kain 2016; Heinonen and Junnila 2011; Bramley and Power 2009; Raporport and Verney 2011). In view of that, it is of significant importance to integrate both models of sustainable urbanism so as to strengthen their design strategies and green technology solutions to deliver the best outcomes of sustainability. Several studies have supported the idea that the compact city has the ability to support and balance the three dimensions of sustainability (e.g., Bibri, Krogstie and Kärrholm 2020; Burton 2002; Dempsey 2010), but it needs to strengthen the influence of the environmental and social goals of sustainability over urban planning and development practices (e.g., Bibri 2020c; Hofstad 2012). The ultimate goal is to create sustainable cities that can contribute to resource efficiency and reliability, environmental protection, socio-economic development, social cohesion and inclusion, the quality of life and well-being, and cultural enhancement. In fact, the two models tend to overlap in their principles, priorities, objectives, policies, and visions. In short, they are not mutually exclusive. Other attempts undertaken in this direction represent ideal approaches with inherent limitations of practical implementation (e.g., Roseland 1997; Harvey 2011). Farr (2008) discusses an integrated approach combining some elements of eco-cities, some elements of compact cities, and sustainable urban infrastructure. However, this approach lacks empirical basis and is not grounded in specific planning actions and policy measures for implementing it in a real-world setting. Kenworthy (2019) focuses on strengthening the eco-city through a number of urban sustainability principles related to deign, strategic planning, and transport.
The idea of integrating the two models of sustainable urbanism in question is based on fully merging their dimensions and strategies, with some flexibility concerning what to emphasize in urban planning and design and how this can be done depending on the characteristics of the urban areas that demonstrate the potential for future development. The novelty of this integrated approach to sustainable urbanism lies in incorporating sustainable energy systems, waste management systems, passive and low-energy buildings, and green structure in the built environment of the compact city to enhance its contribution to the environmental goals of sustainability. This is predicated on the assumption that these structures create the infrastructure needed to provide the ecosystem services and natural environmental processes, which can bolster compaction strategies to achieve better outcomes.
The nature of the integration of sustainable cities and smart cities is to be determined by the way in and the extent to which the dimensions and strategies of the former are pertinently extended and strengthened by those of the latter in the sense of creating a consolidated approach to data–driven smart sustainable urbanism that harnesses the synergistic effects and boosts the benefits of sustainability while supporting the balancing of its dimensions. This depends on the level of progress and how this can be assessed in regard to the different areas of sustainability within a given city that is badging or regenerating itself as sustainable or sustainable smart. Regardless, underlying the data-driven smart sustainable city of the future is the idea of its conception as processual outcomes of urbanization (building, living, consuming, and producing), rather than as stable unchanging structures. Indeed, the model of the city is no longer predicated on the basis of this conception—rather, it is as much dominated by information flows as material flows. Besides, sustainable cities as complex systems evolve and change dynamically as urban environments in response to emergent proprieties and factors. Accordingly, cities need to be processual in conception, dynamic in planning, scalable in design, and optimisable in operational functioning in order to be responsive to population growth, environmental pressures, changes in socio–economic needs, discontinuities, and societal transitions. And the best way forward is to adopt advanced technologies to deal with the complexities inherent in their development planning and operational management.
Sustainable cities growing ever bigger in terms of their populations and knowledge base lie at the core of the future model of urbanism. ICT will be most clearly demonstrated in large sustainable cities. Building sustainable cities enabled by big data technologies is increasingly seen as a strategic move for containing and tackling the rather mounting challenges of sustainability in the face of the expanding urbanization. This is justified by the influence these advanced technologies can have on the way we control, manage, regulate, govern, and plan sustainable cities. In particular, while planning cannot reproduce the compact and ecological characteristics of sustainable cities that have been developed based on incremental and interactive processes involving many stakeholders over time, the primary role of big data lies in enabling information flows and channels, coordination mechanisms, powerful analytics, well-informed and evidence-based decisions, and learning and sharing processes involving divergent constituents and heterogenous collective and individual actors as data agents. These are indeed the most significant challenges that are currently facing sustainable cities, coupled with the dispersion of power. These complex conditions are continuously exacerbated by the unpredictability of environmental, socio-economic, and demographic changes.
However, sustainable cities are so characterized by their specificities as regards their compact and ecological dimensions and how and the extent to which these are integrated in a given city area or district. This in turn shapes the way in which the IoT and big data technologies can be embedded in the fabrics of sustainable cities, as well as how they can be applied in their operational management processes and development planning practices. In more detail, sustainable cities essentially exhibit key differences in the way they prioritize and implement their strategies and solutions depending on many intertwined factors, notably physical, geographical, socio–political, economic, environmental, and historical. In particular, the IoT and big data technologies might work in one sustainable city in a way that is different in another. Hence, they should sometimes be dramatically reworked to be applicable in the context where they are embedded. Besides, sustainable cities do not have a unified agenda as a form of strategic planning, and data-driven decisions are unique to each sustainable city, so are environmental, economic, and social challenges. Big data are the answer, but each sustainable city sets its own questions based on what characterize it in regard to visions, policies, strategies, pathways, goals, and priorities. Regardless, it is important for sustainable cities to make the best use of their local opportunities and capabilities as well as to assess their potentials and constraints from a more integrated perspective when it comes to the operational management and development planning related to their compact, ecological, and technological landscapes and approaches.
In view of the above, the new model of urbanism is not meant to be universal in its nature, and it follows that the proposed strategic planning process of transformative change towards sustainability should look at the wider picture and be flexible in its means. In this respect, it is important to acknowledge the fact that universal urban models, whether compact, ecological, smart, or a combination of these, are problematic and cannot be applicable in the same manner all over the world (see, e.g., Bibri and Krogstie 2020c; Hofstad 2012; Karvonen, Cugurullo and Caprotti 2019; Rapoport and Vernay 2011; van Bueren et al. 2011). Therefore, urban models should be adapted to the multidimensional context specific to each city. In addition, while automation, big-data analytics, and artificial intelligence can bring numerous advantages to sustainable cities, it is equally important to acknowledge the fact that these advanced technologies can be problematic, and therefore, policy-makers and planners should be careful when employing them. Many recent studies have discussed the potential urban problems and issues triggered by automation, big-data analytics, and artificial intelligence in the context of smart sustainable urbanism (e.g., Bibri 2019a, c; Cugurullo 2020; Yigitcanlar and Cugurullo 2020).
Working with long–term images of the future is meant to increase the possibilities and stimulate the opportunities to attain the kind of sustainable cities that last thanks to emerging and future technologies. Sustainable cities are always about citizens. Being data-driven smart about sustainable cities requires to connect directly to the concerns and feelings of people with respect to environmental protection, economic regeneration, and social justice. Historically, people have always moved to and preferred to live in sustainable cities to improve their lives, and smart urbanism is being embraced anew as a strategic move to create sustainable cities that make urban living more sustainable over the long run. Towards this end, sustainable cities have to learn faster and identify strategies that work. Therefore, it is scholarly worthy to venture some thoughts about where it might be useful to channel the efforts now and in the future in what has been termed “data-driven smart sustainable urbanism.”
This paper developed a novel model for data-driven smart sustainable cities of the future, and in doing so, it offered a strategic planning process of transformative change towards sustainability in the era of big data. It identified a series of actions and measures pertaining to the built infrastructure, sustainable urban infrastructure, smart urban infrastructure, social infrastructure, and technological infrastructure of the landscape of the data-driven smart sustainable city of the future. This empirically grounded model of urbanism is meant to be clearly specific in terms of the underlying components based on the prevailing paradigms of sustainable urbanism and the emerging paradigms of smart urbanism. The essence of this new integrated model lies in providing the needed tools, techniques, methods, systems, platforms, and infrastructures enabled by the core enabling and driving technologies of the IoT and big data analytics for the current model of urbanism to optimize, enhance, and maintain its performance with respect to the contribution to and balancing of the goals of sustainability.
In terms of the practicality of the new model of urbanism, the feasibility of the future vision is underpinned and increased by the outcomes of the four case studies The case study approach as a research strategy facilitates the investigation and understanding of the underlying principles in the real-world phenomena involved in the construction of the future vision in the futures study (Bibri 2020d). This pertains to:
The suitability of backcasting for the kind of problems that are associated with sustainable cities in terms of their contribution to and balancing of the goals of sustainability stems from the problem-solving and goal-oriented character of backcasting that is embedded in its process of strategic planning. Backcasting is useful in studying problems that are complex and associated with persisting trends that contribute to the problems’ complexity. Moreover, it allows to imagine the impacts of the future vision, which should be highly significant and entail extensive and ambitious improvements and advancements compared to the current trend. The advantage of using this framework lies in its foundation and efficacy with regard to providing insights into and developing pathways for sustainability transitions, as well as in its ability to produce desired outcomes. This is of high relevance and importance to policymakers as to informing strategic plans for achieving the objectives of sustainable development and thus making actual progress towards sustainability. Here, backcasting can be viewed as a process of transformative change in the sense of how sustainable cities can be designed and developed so that they become able to monitor, understand, analyze, and plan their infrastructures more effectively so as to enhance and maintain their performance with respect to their contribution to sustainability in terms of its tripartite value. All in all, the new model of urbanism can be seen as an important arena for sustainability transitions in the era of big data. It offers a clear prospect of instigating a major transformation by synergistically connecting the agendas of urban development, sustainable development, and technological development for a better future.
The authors have no competing interests to declare.
S.E.B. designed the research, conducted the literature review, collected and analyzed the data, and wrote the manuscript. J.K. reviewed the manuscript. The authors read and approved the published version of the manuscript.
Ahvenniemi, H, Huovila, A, Pinto–Seppä, I and Airaksinen, M. 2017. What are the differences between sustainable and smart cities? Cities, 60: 234–245. DOI: https://doi.org/10.1016/j.cities.2016.09.009
Akerman, J. 2005. Sustainable air transport – on Track in 2050. Transportation Research–D10, 2: 111–126. DOI: https://doi.org/10.1016/j.trd.2004.11.001
Akerman, J and Höjer, M. 2006. How much transport can the climate stand?—Sweden on a sustainable path in 2050. Energy Policy, 34(14): 1944–1957. DOI: https://doi.org/10.1016/j.enpol.2005.02.009
Angelidou, M, Psaltoglou, A, Komninos, N, Kakderi, C, Tsarchopoulos, P and Panori, A. 2017. Enhancing sustainable urban development through smart city applications. Journal of Science and Technology Policy Management, 9(2): 146–169. DOI: https://doi.org/10.1108/JSTPM-05-2017-0016
Austin, G. 2013. Case study and sustainability assessment of Bo01, Malmo, Sweden. Journal of Green Building: Summer, 8(3): 34–50. DOI: https://doi.org/10.3992/jgb.8.3.34
Batty, M, Axhausen, KW, Giannotti, F, Pozdnoukhov, A, Bazzani, A, Wachowicz, M, et al. 2012. Smart cities of the future. The European Physical Journal, 214: 481–518. DOI: https://doi.org/10.1140/epjst/e2012-01703-3
Beatley, T. 2000. Green urbanism: Learning from European cities. Washington, DC: Island Press.
Beatley, T. 2010. Biophilic cities: Integrating nature into urban design and planning. Washington, DC: Island Press. DOI: https://doi.org/10.5822/978-1-59726-986-5
Bengston, DN, Westphal, LM and Dockry, MJ. 2020. Back from the Future: The Backcasting Wheel for Mapping a Pathway to a Preferred Future. World Futures Review, 12(3): 270–278. DOI: https://doi.org/10.1177/1946756720929724
Bettencourt, LMA. 2014. The uses of big data in cities. Santa Fe, New Mexico: Santa Fe Institute.
Bibri, SE. 2018. The IoT for smart sustainable cities of the future: an analytical framework for sensor–based big data applications for environmental sustainability. Sustain Cities and Soc, 38: 230–253. DOI: https://doi.org/10.1016/j.scs.2017.12.034
Bibri, SE. 2019a. On the sustainability of smart and smarter cities in the era of big data: An interdisciplinary and transdisciplinary literature review. J Big Data, 6(25): 2–64. DOI: https://doi.org/10.1186/s40537-019-0182-7
Bibri, SE. 2019b. The anatomy of the data–driven smart sustainable city: Instrumentation, datafication, computerization and related applications. Journal of Big Data, 6: 59. DOI: https://doi.org/10.1186/s40537-019-0221-4
Bibri, SE. 2019c. Data–Driven Smart Sustainable Urbanism: the Intertwined Societal Factors Underlying Its Materialization, Success, Expansion, and Evolution Geojournal. DOI: https://doi.org/10.1007/s10708-019-10061-x
Bibri, SE. 2020a. Advances in the leading paradigms of urbanism and their amalgamation: compact cities, eco–cities, and data–driven smart cities, vol 2020. Cham: Springer Nature Switzerland AG. DOI: https://doi.org/10.1007/978-3-030-41746-8
Bibri, SE. 2020b. The eco-city and its core environmental dimension of sustainability: green energy technologies and their integration with data-driven smart solutions. Energy Inform, 3(4). DOI: https://doi.org/10.1186/s42162-020-00107-7
Bibri, SE. 2020c. Compact urbanism and the synergic potential of its integration with data-driven smart urbanism: An extensive interdisciplinary literature review. Journal of Land Use Policy, 97: 1–20. DOI: https://doi.org/10.1016/j.landusepol.2020.104703
Bibri, SE. 2020d. A methodological framework for futures studies: integrating normative backcasting approaches and descriptive case study design for strategic data-driven smart sustainable city planning. Energy Inform, 3: 31. DOI: https://doi.org/10.1186/s42162-020-00133-5
Bibri, SE. 2020e. Data-driven environmental solutions for smart sustainable cities: strategies and pathways for energy efficiency and pollution reduction. Euro-Mediterr J Environ Integr, 5: 66. DOI: https://doi.org/10.1007/s41207-020-00211-w
Bibri, SE and Krogstie, J. 2017a. Smart sustainable cities of the future: An extensive interdisciplinary literature review. Sustainable Cities and Society, 31: 183–212. DOI: https://doi.org/10.1016/j.scs.2017.02.016
Bibri, SE and Krogstie, J. 2017b. ICT of the new wave of computing for sustainable urban forms: Their big data and context-aware augmented typologies and design concepts. Sustainable Cities Society, 32: 449–474. DOI: https://doi.org/10.1016/j.scs.2017.04.012
Bibri, SE and Krogstie, J. 2019a. A scholarly backcasting approach to a novel model for smart sustainable cities of the future: Strategic problem orientation. City Territory and Architecture, 6(3): 1–27. DOI: https://doi.org/10.1186/s40410-019-0102-3
Bibri, SE and Krogstie, J. 2020a. Smart eco-city strategies and solutions for sustainability: The cases of royal seaport. Stockholm, and Western Harbor, Malmö, Sweden. Urban Science, 4(1): 1–42. DOI: https://doi.org/10.3390/urbansci4010011
Bibri, SE and Krogstie, J. 2020b. The emerging data–driven Smart City and its innovative applied solutions for. sustainability: the cases of London and Barcelona. Energy Inform, 3: 5. DOI: https://doi.org/10.1186/s42162-020-00108-6
Bibri, SE and Krogstie, J. 2020c. Environmentally Data-driven smart sustainable cities: Applied innovative solutions for energy efficiency, pollution reduction, and urban metabolism. Energy Informatics, In Press. DOI: https://doi.org/10.1186/s42162-020-00130-8
Bibri, SE and Krogstie, J. 2020d. Data-Driven Smart Sustainable Cities of the future. A Novel Model of Urbanism and its Core Dimensions, Strategies, and Solutions. Journal of Futures Studies In Press.
Bibri, SE, Krogstie, J and Kärrholm, M. 2020. Compact city planning and development: Emerging practices and strategies for achieving the goals of sustainability. Developments in the built environment, 4: 1–2. DOI: https://doi.org/10.1016/j.dibe.2020.100021
Bifulco, F, Tregua, M, Amitrano, CC and D’Auria, A. 2016. ICT and sustainability in smart cities management. International Journal of Public Sector Management, 29(2): 132–147. DOI: https://doi.org/10.1108/IJPSM-07-2015-0132
Bramley, G and Power, S. 2009. Urban form and social sustainability: The role of density and housing type. Environment and Planning B: Planning and Design, 36(1): 30–48. DOI: https://doi.org/10.1068/b33129
Burton, E. 2002. Measuring urban compactness in UK towns and cities. Environment and Planning B: Planning and Design, 29: 219–250. DOI: https://doi.org/10.1068/b2713
Cugurullo, F. 2016. Exposing smart cities and eco-cities: Frankenstein urbanism and the sustainability challenges of the experimental city. Environ. Plan. A Econ. Space, 50: 73–92. DOI: https://doi.org/10.1177/0308518X17738535
Cugurullo, F. 2020. Urban Artificial Intelligence: From Automation to Autonomy in the Smart City. Front. Sustain. Cities, 2: 38. DOI: https://doi.org/10.3389/frsc.2020.00038
Dempsey, N. 2010. Revisiting the Compact City? Built Environment, 36(1): 5–8. DOI: https://doi.org/10.2148/benv.36.1.5
Eden Strategy Institute. 2018. Report, https://www.smartcitygovt.com.
Hashem, IAT, Chang, V, Anuar, NB, Adewole, K, Yaqoob, I, Gani, A, et al. 2016. The role of big data in smart city, International Journal of Information Management, 36: 748–758. DOI: https://doi.org/10.1016/j.ijinfomgt.2016.05.002
Heinonen, J and Junnila, S. 2011. Implications of urban structure on carbon consumption in metropolitan areas. Environmental Research Letters, 6(1): 014018. DOI: https://doi.org/10.1088/1748-9326/6/1/014018
Hofstad, H. 2012. Compact city development: High ideals and emerging practices. European Journal of Spatial Planning, 49: 1–23.
Höjer, M, Gullberg, A and Pettersson, R. 2011. Backcasting images of the future city—Time and space for sustainable development in Stockholm. Technological Forecasting and Social Change, 78(5): 819–834. DOI: https://doi.org/10.1016/j.techfore.2011.01.009
Holmberg, J. 1998. Backcasting: A natural step in operationalizing sustainable development. Greener Management International (GMI), 23: 30–51.
Jenks, M and Jones, C. (eds.). 2010. Dimensions of the sustainable city. London: SpringerLink.
Joss, S, Cowley, R and Tomozeiu, D. 2013. Towards the ubiquitous eco-city: An analysis of the internationalisation of eco-city policy and practice. Journal of Urban Research and Practice, 76: 16–22. DOI: https://doi.org/10.1080/17535069.2012.762216
Kaido, K. 2005. Urban densities, quality of life and local facility accessibility in principal Japanese cities. In: Jencks, M and Dempsey, N (eds.), Future Forms and Design for Sustainable Cities, pp. 311–338. Oxford: Architectural Press.
Kärrholm, M. 2011. The scaling of sustainable urban form: some scale—related problems in the context of a Swedish urban landscape. Eur Plan Stud, 19(1): 97–11. DOI: https://doi.org/10.1080/09654313.2011.530394
Karvonen, A, Cugurullo, F and Caprotti, F. 2019. Inside Smart Cities – Place, Politics and Urban Innovation. London: Routledge, 304 pages, ISBN 978-0815348689. DOI: https://doi.org/10.4324/9781351166201
Kenworthy, JR. 2019. Urban transport and eco–urbanism: A global comparative study of cities with a special focus on five larger Swedish urban regions. MDPI: Urbn Science. DOI: https://doi.org/10.3390/urbansci3010025
Kitchin, R. 2014. The real-time city? Big data and smart urbanism. Geo J, 79: 1–14. DOI: https://doi.org/10.1007/s10708-013-9516-8
Kitchin, R. 2016. The ethics of smart cities and urban science. Philosophical Transactions of the Royal Society A, 374: 20160115. DOI: https://doi.org/10.1098/rsta.2016.0115
Kitchin, R, Lauriault, TP and McArdle, G. 2015. Knowing and governing cities through urban indicators, city benchmarking & real–time dashboards. Regional Studies and Science, 2: 1–28. DOI: https://doi.org/10.1080/21681376.2014.983149
Kramers, A, Wangel, J and Höjer, M. 2016. Governing the smart sustainable city: The case of the Stockholm Royal Seaport. In Proceedings of ICT for Sustainability 2016, 46: 99–108., Amsterdam: Atlantis Press. DOI: https://doi.org/10.2991/ict4s-16.2016.12
Kumar, A and Prakash, A. 2016. The role of big data and analytics in smart cities. International Journal of Science and Research (IJSR), 6(14): 12–23. DOI: https://doi.org/10.21275/v5i2.NOV161007
Lim, HK and Kain, J-H. 2016. Compact cities are complex, intense and diverse but: Can we design such emergent urban properties? Urban Planning, 1(1): 95. DOI: https://doi.org/10.17645/up.v1i1.535
Mostafavi, M and Doherty, G. 2010. Ecological Urbanism. Switzerland: Lars Muller.
Nikitin, K, Lantsev, N, Nugaev, A and Yakovleva, A. 2016. Data-driven cities: From concept to applied solutions. Pricewater- houseCoopers (PwC). http://docplayer.net/50140321-From- concept-to-applied-solutions-data-driven-cities.html.
Pandis Iverot, S and Brandt, N. 2011. The development of a sustainable urban district in Hammarby Sjöstad, Stockholm, Sweden? Environment, Development and Sustainability, 13(6): 1043–1064. DOI: https://doi.org/10.1007/s10668-011-9304-x
Pasichnyi, O, Levihn, F, Shahrokni, H, Wallin, J and Kordas, O 2019. Data- driven strategic planning of building energy retrofitting: The case of Stockholm. J Clean Prod, 233: 546–560. DOI: https://doi.org/10.1016/j.jclepro.2019.05.373
Perera, C, Qin, Y, Estrella, JC, Reiff-Marganiec, S and Vasilakos, AV. 2017. Fog Computing for Sustainable Smart Cities: A Survey. ACM Computing Surveys, 50(3). DOI: https://doi.org/10.1145/3057266
Quist, J. 2007. Backcasting for a sustainable future: the impact after 10 years, Ph.D. thesis, Faculty of Technology, policy and management, Delft University of Technology, Delft.
Quist, J, Knot, M, Young, W, Green, K and Vergragt, P. 2001. Strategies towards sustainable households using stakeholder workshops and scenarios. Int J Sustain Dev, 4: 75–89. DOI: https://doi.org/10.1504/IJSD.2001.001547
Rapoport, E and Vernay, AL. 2011. Defining the eco-city: A discursive approach. In Paper presented at the management and innovation for a sustainable built environment conference, international eco-cities initiative, Amsterdam, The Netherlands, pp. 1–15.
Robinson, J. 1990. Futures under glass: a recipe for people who hate to predict. Futures, 22(8): 820–842. DOI: https://doi.org/10.1016/0016-3287(90)90018-D
Roseland, M. 1997. Dimensions of the eco–city. Cities, 14(4): 197–202. DOI: https://doi.org/10.1016/S0264-2751(97)00003-6
Shahrokni, H, Årman, L, Lazarevic, D, Nilsson, A and Brandt, N. 2015. Implementing smart urban metabolism in the Stockholm Royal Seaport: smart city SRS. Journal of Industrial Ecology, 19(5): 917–929. DOI: https://doi.org/10.1111/jiec.12308
Shahrokni, H, Lazarevic, D and Brandt, N. 2015. Smart Urban Metabolism: Towards a real-time understanding of the energy and material flows of a city and its citizens. Journal of Urban Technology, 22(1): 65–86. DOI: https://doi.org/10.1080/10630732.2014.954899
Shahrokni, H, Levihn, F and Brandt, N. 2014. Big meter data analysis of the energy efficiency potential in Stockholm’s building stock, Energy and Buildings. 78: 153–164. DOI: https://doi.org/10.1016/j.enbuild.2014.04.017
Shahrokni, H, van der Heijde, B, Lazarevic, D and Brandt, N. 2014. Big data GIS analytics towards efficient waste management in Stockholm. In ICT4S–ICT for Sustainability. Stockholm: Alantis Press. DOI: https://doi.org/10.2991/ict4s-14.2014.17
Sun, Y and Du, Y. 2017. Big data and sustainable cities: applications of new and emerging forms of geospatial data in urban studies. Open geospatial data, softw. stand, 2: 24 DOI: https://doi.org/10.1186/s40965-017-0037-0
Thornbush, M and Golubchikov, O. 2019. Sustainable Urbanism in Digital Transitions: From Low Carbon to Smart Sustainable Cities. Berlin: Springer. DOI: https://doi.org/10.1007/978-3-030-25947-1_1
Trencher, G. 2019. Towards the Smart City 2.0: Empirical Evidence of Using Smartness as a Tool for Tackling Social Challenges. Technological Forecasting and Social Change, 142: 117–128. DOI: https://doi.org/10.1016/j.techfore.2018.07.033
United Nations. 2015a. Transforming Our World: the 2030 Agenda for Sustainable Development. New York, NY. Available at: https://sustainabledevelopment.un.org/post2015/transformingourworld.
United Nations. 2015b. Habitat III Issue Papers, 21—Smart cities (V2.0), New York, NY. Available at: https://collaboration.worldbank.org/docs/DOC–20778. Accessed 2 May 2017.
United Nations. 2015c. Big Data and the 2030 agenda for sustainable development. Prepared by A. Maaroof. Available at: www.unescap.org/events/call–participants–big–data–and–2030–agendasustainable–development–achieving–development.
Van Bueren, E, van Bohemen, H, Itard, L and Visscher, H. 2011. Sustainable urban environments: An ecosystem approach. The Netherland: Springer. DOI: https://doi.org/10.1007/978-94-007-1294-2
Vergragt, PJ and Quist, J. 2011. Backcasting for sustainability: Introduction to the special issue 1, Technological Forecasting and Social Change, 78(5): 747–755. DOI: https://doi.org/10.1016/j.techfore.2011.03.010
Wangel, J. 2011. Exploring social structures and agency in backcasting studies for sustainable development. Technological Forecasting & Social Change, 78(5): 872–882. DOI: https://doi.org/10.1016/j.techfore.2011.03.007
Williams, K. 2010. Sustainable cities: Research and practice challenges. International Journal of Urban Sustainable Developement, 1(1): 128–132. DOI: https://doi.org/10.1080/19463131003654863
Yigitcanlar, T and Cugurullo, F. 2020. The Sustainability of Artificial Intelligence: An Urbanistic Viewpoint from the Lens of Smart and Sustainable Cities. Sustainability, 12: 8548. DOI: https://doi.org/10.3390/su12208548
Yin, RK. 2009. Case study research: design and methods (4th ed.). London: Sage.
Yin, RK. 2014. Case study research: Design and methods. Los Angeles, CA: Sage.
Yin, RK. 2017. Case study research and applications: design and methods (6th ed.). SAGE Publications, Inc.