How can smart cities shape a happier life? The mechanism for developing a “Happiness Driven Smart City”

Smart cities are expected to provide better solutions for the intensified socio-economic and environmental challenges associated with unprecedented urbanisation by embracing advanced information and communication technologies (ICTs), where challenges like climate change, energy crisis, or social inequality could be addressed through the development and application of state-of-the-art technologies (United Nations, 2019). The idealised narrative of the smart city has been widely accepted and turned into global movement at an appreciable speed for economic expansion and societal transformation, with more than 1,000 cities globally having introduced smart city initiatives with Europe, North America, Japan and South Korea in the leading position (Deloitte, 2018).

However, it appears that smart city’s influence on human happiness has been paid only marginal attention amidst the practices of smart city development. It is often assumed that smart cities may carry both opportunities and risks to human happiness due to its technology-oriented essence, but little information is available to demonstrate which specific aspects of human happiness might benefit or get harmed from the introduction of smart city initiatives. In fact, until we understand how human happiness is affected by smart city initiatives, the holistic benefits of smart city development upon urban inhabitants will remain under question, no matter how much prosperity the advocators promise. The cost of rectifying the progress of smart city development would be huge if any strategy or policy leads to unexpected or unwanted directions which might jeopardise human happiness. Therefore, the big question with regard to the influence upon human happiness brought by smart city development is whether and how smart cities act upon human happiness.

This blog introduces the concept of the Happiness Driven Smart City (HDSC), which will be constructed as a three-layer interrelated functioning structure underpinned by a set of Strategic Measures. For this, four procedures will be conducted, 1) to propose the conceptualisation and principle characteristics of the Happiness Driven Smart City; 2) to construct the key factors contributing to the performance of HDSC characteristics; 3) to build-up the HDSC development mechanism; and 4) to develop the underpinning Strategic Measures and specific application toolkit of the HDSC mechanism.

  1. Happiness Driven Smart City: conceptualisation and characteristics

Figure 1 Happiness Driven Smart City: Conceptualisation and characteristics

2. Key factors contributing to characteristic performance in a Happiness Driven Smart City system

Table 1 Key factors contributing to HDSC characteristic performance

HDSC CharacteristicsFactor 1Factor 2Factor 3Factor 4
Efficient and green physical infrastructure (HDSC1)MobilityEnergyPublic Utilities 
Labour-friendly and innovative economy (HDSC2)EmploymentInnovative SpiritEntrepreneurship 
Inclusive and attractive society (HDSC3)EducationHealthSafetyCulture and Leisure
Sustainable and eco-friendly natural environment (HDSC4)Air qualityPollution and Waste Treatment  

3. Mechanism for developing Happiness Driven Smart City

The synthesised characteristics of a Happiness Driven Smart City presented in Section 1 and the corresponding key factors identified in Section 2 are used to build up the mechanism for developing a Happiness Driven Smart City. The mechanism for developing a Happiness Driven Smart City can be portrayed graphically in Figure 2 and is composed of three components, namely, Overarching objective (top-layer), Characteristics (medium-layer) and Factors (bottom-layer). The working mechanism of HDSC shows the structural relationships between these three components. The bottom-layer Factors are the sources of changing the performance of HDSC Characteristics, which in turn contribute to the Overarching objective of developing a Happiness Driven Smart City. The functions of the HDSC system are underpinned by a set of Strategic Measures which act directly upon the Factors in the HDSC system. By applying various Strategic Measures to change Factors, momentum can be gained to improve the performance of medium-layer Characteristics. Consequently, the Overarching objective of developing a Happiness Driven Smart City can be achieved.

Figure 2 Three-layer mechanism system for developing a Happiness Driven Smart City

4. Strategic measures for the HDSC development mechanism

As shown in Figure 2, the key to make a Happiness Driven Smart City happen is the application of a set of Strategic Measures. These Strategic Measures are developed through examining the interrelationship between the three components in the HDSC system, and the examination and establishment of the Strategic Measures is conducted through a comprehensive literature review. Consequently, a set of Strategic Measures have been developed, which are presented by reference to each of the four HDSC Characteristics, as shown in Tables 2, 3, 4 and 5 respectively. In these tables, a shortlist of Strategic Measures is developed in addressing different factors which exert a driving influence upon the concerned HDSC Characteristic.

Table 2 Strategic Measures for improving the performance of HDSC characteristic “Efficient and green physical infrastructure” (HDSC1)

Factors influencing HDSC1Strategic Measures to act on factors for improving the performance of HDSC1
F11:MobilitySM111: To integrate the principles of green transport into the process of developing smart mobility thus the performance of green infrastructure will be improved. By developing a leveraged mobility paradigm between walking, cycling and driving, greenhouse gas emission will be reduced. SM112: To eliminate possible negative effects caused by the misuse and monopoly of smart mobility technologies which would hurt the users’ benefits and affect negatively the development of HDSC in the long run. For example, regulations shall be formulated to ensure smart mobility service providers take responsibilities in delivering a greener and more equal transport system.
F12:EnergySM121: From the industrial aspect, to take consideration of both the optimisation of energy sources and conservation on the consumption end. For example, integrating smart grids with renewable energy resources is a direct and effective solution for improving green grid management; adopting artificial intelligence and big data analysis into building, manufacture and transportation sectors to forecast and minimize energy consumption is proved effective for minimizing energy consumption and consequently mitigating impacts upon environment and climate. SM122: From the individual aspect, to create an advantageous environment with the facility of technology to encourage individual energy saving behaviours. The potential impact of occupants energy saving behaviour on buildings is evidenced and identified as an essential approach to improve energy efficiency in green buildings and communities without jeopardizing the level of comfort.
F13:Public UtilitiesSM131: To explore resource efficiency and utilization opportunities from both service providers and consumers in smart utility network development. For example, artificial intelligence application in leak detection of water distribution pipelines will increase water resource utilization efficiency. The water/gas consumption feedback technology can help to reduce household wasteful behaviour to trigger the decrease of environmental impact in every step along the whole journey of resources processed by public utility facilities.

Table 3 Strategic Measures for improving the performance of HDSC characteristic “Labour-friendly and innovative economy” (HDSC2)

Factors influencing HDSC2Strategic Measures to act on factors for improving the performance of HDSC2
F21:EmploymentSM211: To prepare solutions and resources to minimize the potential disruptive impact on employment during the dramatic change process and to create a labour-friendly employment market environment to help citizens quickly fit into the new economy. Policies to stimulate education evolution for promptly fulfilling high profile new business requirements and training programs to look after disadvantaged labour shall be taken into full implementation. SM212: To enact labour protection and social protection rules and regulations that are suitable and in favour of employees to keep new digital economy development under a labour-friendly premise. It would be ideal if the policymakers are able to take a longer view and make foreseeable actions on the employment influence arising from the digital economy instead of keeping the legal and social regulation system in a passive adaption situation.
F22:Innovative SpiritSM221: To make the smart city as a nexus for open innovation, which should not just refer to industry but also the ways government and other institutions work and collaborate with society, to jointly create an inclusive, labour-friendly and innovative economy. All sources of innovation from different levels and different sectors shall be encouraged to actualize an innovative and diverse economy where technology plays the catalyst role. SM222: To inspire the potential of new bottom-up approaches based on user-generated content through the experiences of the citizens themselves, which enables citizens to build social capital and the capacity required to become co-creators and co-producers of new and innovative services with the means to ensure that they are delivered in more effective and inclusive ways, taking full advantage of new Internet-based technologies and applications.
F23:EntrepreneurshipSM231: To encourage the utilization of new technologies and promote a strong pro-entrepreneurial state ethos where an innovative economy is nurtured and accelerated. SM232: To encourage and facilitate entrepreneurial citizens to trigger, apply and transform emerging technologies and knowledge into new products, new jobs, and new firms, which in turn enables the creation of a labour-friendly innovative economy. SM233: To promote neo-liberal attempts to inclusively and effectively incorporate the local community into the entrepreneurial city, via the approaches of various participatory ICT projects.

Table 4 Strategic Measures for improving the performance of HDSC characteristic “Inclusive and attractive society” (HDSC3)

Factors influencing HDSC3Strategic Measures to act on factors for improving the performance of HDSC3
F31:EducationSM311: To create an easy environment for innovation during the digital transformation process of education through a participatory approach to enhance social inclusiveness and talent attractiveness in the education sector. All stakeholders in this venturous journey, such as teachers, students, administrators, online platform companies, software and hardware providers etc., shall be encouraged to be more active to innovate and design better solutions to meet the new need. SM312: To address the equity of education resources and opportunities to avoid extreme deprivations caused by the digital transformation to ensure a more inclusive development in cities. For example, online education shall take in consideration of the balance between commercial online education programs and public-free courses so as to benefit the widest audience from the well-educated to the low-skilled learners.
F32:HealthSM321: To improve accessibility to health facilities and services to the widest public for disease prevention and health promotion where technological innovation can be applied as a tool for empowering social inclusiveness. For example, more attention and funding shall be paid to mental health care services where big data and artificial intelligence technology can play an innovative role in prediction and diagnosis. SM322: To encourage the application of new technologies aiming to produce new opportunities to improve the health treatment to a more accurate and effective level where patients can receive higher quality and safer medical service with reduced cost and wastage. The improvement of affordability and effectiveness in medical services and health care driven by smart technology will positively affect the inclusiveness of the society.
F33:SafetySM331: To create a safer social environment by taking actions to prevent the occurrence of crime before it happens via the application of new technology-based crime risk prediction analysis approaches. Safety clearly exerts higher impacts on urban attractiveness.
F34:Culture and LeisureSM341: To create an environment that encourages wide public participation, cultural diversity, digital equity in the context of digital technology to contribute to a city’s attractiveness and social inclusion. The development of a cultural policy that aims for cultural participation may also involve other policy areas such as economic and education sectors. SM342: To provide a pleasant environment and necessary support for both offline and online leisure activities to provide greater opportunities for urban residents to enjoy a higher level of life satisfaction and social inclusion.

Table 5 Strategic Measures for improving the performance of HDSC characteristic “Sustainable and eco-friendly natural environment” (HDSC4)

Factors influencing HDSC4Strategic Measures to act on factors for improving the performance of HDSC4
F41:Air QualitySM411: To monitor and forecast air quality more precisely in both vicinity (building and neighbourhood) and city level through collecting and analysing data with the ubiquitous upgraded devices and advanced algorithms, and to satisfy the evolved need for environment protection and human life by taking more targeted and effective solutions. SM412: To enable citizens to have tailored air quality notification to help prevent any exposure risks amongst vulnerable groups to reduce the ecological threat on public health (ibid).
F42:Pollution and Waste TreatmentSM421: To control emissions and effluents through IoT-enabled smart system in various sectors including building, manufacturing industry and logistics etc. SM422: To modernize traditional waste management systems to prevent the negative effects of incorrect operation on both people and the environment during the whole process including waste collection, disposal, recycling, and recovery (ibid). SM423: To enable citizens to track daily personal pollution footprints via smart approaches to better understand and behave upon reducing waste emission and production towards a sustainable and eco-friendly environment.

The proposed theoretical mechanism can be applied with specific assessment criteria to examine to what extent a smart city initiative implemented in a given city has enhanced urban residents’ happiness and has achieved the goal of a Happiness Driven Smart City. The application of the HDSC mechanism can thus help urban governors to understand the status quo of smart city development and to better guide the design of smart cities towards a happiness-driven and human-centred direction.

Note: This blog is based upon a recent publication on the journal of Sustainable Cities and Society. https://doi.org/10.1016/j.scs.2022.103791

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How Does Technology Affect Smart City Governance?

What is a Smart City?

A Smart City (SC) capitalises on technology, proper governance and collaborations between the various stakeholders to comprehensively promote city prosperity and eventually improve the quality of citizens’ lives.

Figure 1. Envisaging the smart city[1]

Cities are agglomerations of economic, social, and cultural benefits[2]. On the other hand, cities are increasingly confronted with issues such as diminishing public management efficiency, backward infrastructure, traffic congestion, environmental pollution, and general security concerns, among others.

The Smart City is a concept that has evolved around the world to solve urban problems and enhance urban development. Several municipalities, such as Cape Town, Ottawa, San Diego, Southampton, Barcelona, Seoul, and Shanghai, have developed SCs to serve citizens better and improve the quality of citizens’ lives.

What is Smart City Governance?

New governance patterns are required to manage SCs. The governance models for SCs could be divided into two categories:

  • Some of the governance models are technology-driven, focusing on the role of big data and technology.
  • Other governance models emphasise the human and institutional factors,  such as the role of governance structures, citizen-centricity, social capital, human resources and stakeholders.

At the intersection of these two, Smart City Governance (SCG) emerges mainly due to the growing roles of technology and human capabilities in the functioning of cities, which gives the government the opportunity to optimise the governance process and outcomes. A typical description of SCG is “crafting new forms of human collaboration through the use of ICTs to obtain better outcomes and more open governance processes” [3].

How does technology affect SCG?

The technology revolution has altered the city governance model. The impact of technology on governance models is roughly in two directions. One is to use technology to strengthen the government-centric bureaucratic model, and the other is to use technology to distribute decision-making power to more stakeholders.

  • Technology contributing to the concentration of power

The case in Shenzhen, China shows how technology can strengthen a top-down governance model. The Shenzhen government propagated a programmatic document for SCG, the Shenzhen Municipal New-Type Smart City Construction Master Plan, in 2018[4]. In this plan, the SC structure of Shenzhen includes three layers and two supports, as outlined in the figure below.

The primary layer is the SC Sensory Network System, which mainly includes sensor networks, communication networks, and computing storage centres; the middle layer provides support for government decision-making, which is composed of the Urban Big Data Centre and SC Operation and Management Centre; the top application layer includes four parts public services, public safety, urban governance and smart industries.

In this scenario, technology is the core element of governance and is used to strengthen the government’s decision-making and implementation capabilities. In this kind of governance model, technology is used to collect public management-related data and information, help make governmental decisions and finally reinforce the rationality and efficiency of government.

Figure 2. Shenzhen’s smart city structure [5]

  • Technology contributing to the decentralisation of power

On the other hand, technology may give impetus to the bottom-up governance model. For example, in the case of Amsterdam Smart City (ASC)[6], the Amsterdam Economic Board governs and funds it using an open web-based platform. This platform allows stakeholders to communicate and disseminate information in a fair and transparent manner. Furthermore, open-house programmes and open gatherings help citizens communicate and empower themselves. This case demonstrates how technological innovation has aided in the distribution of information and power to more stakeholders in ASC.

Figure 3. Amsterdam Smart City

In conclusion, data and information bestow stakeholders’ power and legitimacy in urban governance to a certain extent. From the standpoint of technology, the power distribution of data and information may affect the governance model towards decentralisation or concentration.

References

[1] https://www.arcweb.com/industries/smart-cities

[2] https://ec.europa.eu/regional_policy/sources/docgener/studies/pdf/citiesoftomorrow/citiesoftomorrow_final.pdf

[3] Bolívar, M. P. R., & Meijer, A. J. (2016). Smart governance: Using a literature review and empirical analysis to build a research model. Social Science Computer Review, 34(6), 673–692. https://doi.org/10.1177/0894439315611088

[4] http://www.sz.gov.cn/zfgb/2018/gb1062/content/post_4977617.html

[5] Hu, R., (2019). The state of smart cities in China: The case of Shenzhen. Energies, 12(22), p.4375

[6] https://amsterdamsmartcity.com/

Delivering Urban Data Justice for “Smart Cities 2.0”

What new institutions are needed to ensure smart cities are also data-just cities?

Smart City 1.0 “is primarily focused on diffusing smart technologies for corporate and economic interests”.  Smart City 2.0 is “a decentralised, people-centric approach where smart technologies are employed as tools to tackle social problems, address resident needs and foster collaborative participation”.[1]

Given their people-centrism, a foundation for Smart Cities 2.0 must therefore be delivery of urban data justice: fairness in the way people are made visible, represented and treated as a result of the production of urban digital data.[2]

We already know the constituent parts of urban data justice, as shown in the figure below.[3]

But a key argument of this model is that data justice is significantly shaped by urban social structures.  If those structures are unjust then data practices and outcomes will likely be unjust.  How, then, do we create urban social structures more likely to deliver the data justice that is part of Smart City 2.0?

Setting aside more radical restructuring of the urban polity, three more incremental forms can play a role:

1. Living Labs

“Living labs employ a user-focused design environment, a strategy of co-creation, and, increasingly, an institutionalized space wherein citizens, administrators, entrepreneurs and academics come together to develop smartness into concrete applications. They help identify and join localized expertise, real-life testing and prototyping with strategic networking of resources to address challenges that cannot be solved by single cities or departments.”[4]  Located at the upstream end of the innovation cycle, living labs are well-placed to come up with new, just ways of applying urban data.[5]

2. Urban Data Trusts

Data trusts are “a legal structure that provides independent stewardship of data … an approach to looking after and making decisions about data in a similar way that trusts have been used to look after and make decisions about other forms of asset in the past, such as land trusts that steward land on behalf of local communities.”[6]  These can form an institutional superstructure to ensure justice in the ownership, sharing and use of data; particularly data gathered about urban citizens.[7]

3. Community Data Intermediaries

Community data intermediaries are “organizations that gather data relevant for neighborhood-level analysis and make the information available to community groups and local institutions”.  Alongside their key role in gathering data – for example via community mapping – CDIs may also have features of both living labs (innovating application of that data) and data trusts (acting as stewards of the data for communities).[8]

The devil here will be in the detail: how exactly are these entities structured and run?  Simply attaching a label to an organisation does not make it just, with critiques in circulation of living labs[9], urban data trusts[10], and community data intermediaries[11].  Nonetheless, it is these types of urban institutional innovation that will underlie delivery of data justice in Smart Cities 2.0.  I look forward to further examples of these and similar innovations.

 

[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

[2] Adapted slightly from Taylor, L. (2017) What is data justice? The case for connecting digital rights and freedoms globally, Big Data & Society, 4(2), 2053951717736335

[3] Heeks, R. & Shekhar, S. (2019) Datafication, development and marginalised urban communities: An applied data justice framework, Information, Communication & Society, 22(7), 992-1011

[4] Baykurt, B. (2020) Are “smart” cities living up to the hype?, University of Massachusetts Amherst News, 1 May

[5] For a data justice perspective on the activities of one Living Lab in Kathmandu plus related organisations, see: Mulder, F. (2020) Humanitarian data justice: A structural data justice lens on civic technologies in post‐earthquake Nepal, Journal of Contingencies and Crisis Management, 28(4), 432-445

[6] Hardinges, J. (2020) Data trusts in 2020, Open Data Institute, 17 Mar

[7] For more on urban civic data trusts, see: Kariotis, T. (2020) Civic Data Trusts, Melbourne School of Government, University of Melbourne, Australia

[8] For a guide on creating community data intermedaries and examples, see: Hendey, L., Cowan, J., Kingsley, G.T. & Pettit, K.L. (2016) NNIP’s Guide to Starting a Local Data Intermediary, NNIP, Washington, DC

[9] Taylor, L. (2020) Exploitation as innovation: research ethics and the governance of experimentation in the urban living lab. Regional Studies, advance online publication.

[10] Artyushina, A. (2020) Is civic data governance the key to democratic smart cities? The role of the urban data trust in Sidewalk Toronto, Telematics and Informatics, 55, 101456

[11] Heeks, R. & Shekhar, S. (2019) Datafication, development and marginalised urban communities: An applied data justice framework, Information, Communication & Society, 22(7), 992-1011