Photo by Waldemar Brandt on Unsplash
Photo by Waldemar Brandt on Unsplash


Smart city indicators: Towards exploring potential linkages to disaster resilience abilities

Recent advances in Information and Communication Technologies (ICTs) have transformed all aspects of human life. Enabled by these advances, over the past few decades, many smart city initiatives have been developed across the world. Subsequently, various efforts have been made to develop indicators and frameworks for the assessment of smart cities. Generally, smart cities are expected to enhance the quality of life and provide solutions to deal with societal challenges. One major societal challenge is the increase in the frequency and intensity of disasters and adverse events. Therefore, smart cities are expected to contribute to enhancing disaster resilience. Integrating resilience thinking into smart city indicators and assessment frameworks is likely to promote better attention to the resilience contributions of smart cities. Against this background, through reviewing the literature, I first introduce a comprehensive list of indicators for assessing city smartness. Multiple indicators related to economy, people, governance, environment, mobility, living and data dimensions of a smart city are listed. Next, I explore if these indicators are aligned with the four resilience abilities: planning, absorption, recovery, and adaptation. Results show that smart city indicators are particularly linked to planning and absorption abilities. More attention to the recovery and adaptation abilities is, therefore, needed.


Assessment tools · Disaster resilience · Disasters · Index · Indicators · Smart city · Urban



  • Smart cities have increasingly become ubiquitous.
  • Smart cities should contribute to enhancing community resilience.
  • Comprehensive list of indicators for smart city assessment is introduced
  • Resilience thinking is not fully integrated into smart city indicators.
  • Framework to integrate resilience thinking into smart city assessment is proposed

1. Introduction

We now live in the age of digital revolution, and digital technologies have transformed almost every aspect of our lives. As cities have historically been centres of innovation, it is no surprise that they are now at the forefront of developing and implementing digital technologies. In fact, many cities around the globe are increasingly relying on digital technologies, enabled by Information and Communication Technologies (ICTs), to overcome societal challenges, enhance the quality of life, and improve the efficiency and efficacy of urban operations (Ahvenniemi, Huovila, Pinto-Seppä, & Airaksinen, 2017; Clarke, 2013; Kourtit & Nijkamp, 2018; Woods, Labastida, Citron, Chow, & Leuschner, 2017). The ICT-enabled efforts and activities are often referred to as smart city movements.

The smart city concept emerged in the early 2000s and has gradually evolved over the past two decades. During this period, many smart city projects and initiatives have been developed, and this trend is expected to continue further in the coming decades (Angelidou, 2015; Caragliu, Bo, & Nijkamp, 2011; Marsal-Llacuna, Colomer-Llinàs, & Meléndez-Frigola, 2015). This increasing interest in smart cities is not surprising given their multiple utilities. For instance, it is now widely believed that becoming smart is critical to maintaining a competitive advantage in an increasingly connected world (Giffinger et al., 2007; Giffinger, Haindlmaier, & Kramar, 2010). Related to this, smarter cities are likely to be in a better position to attract talented and creative citizens capable of contributing to local economy and growth through promoting innovative and efficient approaches (BSI, 2014; Angelidou, 2015). Furthermore, ICT-enabled smart solutions are expected to contribute to enhancing the urban quality of life, enhance the transparency of urban management, and help overcome some long-standing challenges related to urban inequalities, ageing society, and safety and security ( BSI, 2014; Manville et al., 2014).

Related to the focus of this paper, smart cities are also expected to provide solutions for dealing with a major societal challenge: the increase in the frequency and intensity of disastrous events. These include events related to climate change, as well as natural disasters such as earthquakes and man-made events such as nuclear events (Huovila, Airaksinen, Pinto-Seppä, Piira, & Penttinen, 2016). This is motivated by the fact that an increasing trend in the annual frequency of climate-induced, natural, and human-made disasters can be observed from the analysis of loss events in the past few decades (Hoeppe, 2016; Smith & Katz, 2013). For instance, as a clear sign of global warming, the last six years have been the warmest on record since 1850 and last year was the warmest (WMO, 2020). Extreme heat and multiple other adverse events, cumulatively, result in billions of dollars of economic loss in cities that are often more vulnerable due to their higher concentration of humans and resources.

According to some estimates, every year, about USD 300 billion is lost to disasters in cities and, unless cities build on their resilience, economic losses to disasters in cities may cross USD 400 billion by 2030 (WB, 2016). Given these threats and challenges, it is clear that one major contribution of smart city solutions, technologies, and projects should be enhancing disaster resilience. Here, resilience refers to the “ability to plan and prepare for, absorb, recover from, and more successfully adapt to adverse events” (Cutter et al., 2013). Resilience is also characterized by multiple attributes such as robustness, stability, diversity, redundancy, resourcefulness, creativity, agility, flexibility, efficiency, self-organization, inclusiveness, and foresight capacity (Sharifi & Yamagata, 2016).

In planning and policymaking circles, assessment is widely recognized as an effective method for improving the performance of projects, and policies and smart city projects and policies are no exception (Sharifi, 2020). Indeed, assessment can provide useful insights to municipal authorities, smart city developers and investors, and the publi (Caird & Hallett, 2018; Mohan, Dubey, Ahmed, & Sidhu, 2017). For instance, it can facilitate regular performance monitoring, highlight strengths and weaknesses, track progress towards targets and goals, identify technical requirements and economic feasibility issues, showcase best practice cases, encourage constructive competition through benchmarking, enhance governance transparency, raise general awareness, and provide engagement motivation (Caird et al., 2018; Mohan et al., 2017). Given these multiple utilities of assessment frameworks, it is essential to ensure that they are well-designed and capable of addressing the capacity to deal with societal challenges.

Against this backdrop, the main objectives of this study are to provide a list of indicators that have been used for smart city assessment and to explore their potential contributions to the four resilience abilities, namely, planning/preparation, absorption, recovery, and adaptation. In other words, it aims to examine if smart city indicators are aligned with resilience abilities. Planning refers to the ability to take preparatory measures before the occurrence of a shock to better deal with possible disasters. Absorption indicates the ability to minimize functionality loss and associated socio-economic damages. Recovery refers to the ability to return to pre-shock conditions in a timely manner. Finally, adaptation indicates the ability to learn from the adverse event to not only bounce back but also bounce forward. The paper is structured as follows. The methods are described in the next section. Section 3 provides the list of indicators and discusses how resilience thinking can be integrated into smart city indicators. Finally, section 4 concludes the study by summarizing the results and providing recommendations.

2. Methodology

Content analysis of smart cities literature is the main method used for developing a comprehensive list of smart city indicators and classifying them into several categories. First, I searched for relevant documents in the Web of Science using combinations of terms related to smart cities and assessment. For this purpose, the following broad-based search string was used:

TS=(((“certificat*” NEAR/1 (“tool*” OR “toolkit*” OR “system*” OR “indicator*” OR “framework*” OR “index” OR “scorecard*” OR “scheme*”)) OR (“evaluat*” NEAR/1 (“tool*” OR “toolkit*” OR “system*” OR “indicator*” OR “framework*” OR “index” OR “scorecard*” OR “scheme*”)) OR (“assess*” NEAR/1 (“tool*” OR “toolkit*” OR “system*” OR “framework*” OR “indicator*” OR “index” OR “scorecard*” OR “scheme*”)) OR (“measur*” NEAR/1 (“tool*” OR “toolkit*” OR “system*” OR “framework*” OR “indicator*” OR “index” OR “scorecard*” OR “scheme*”))) AND (“smart”) AND ((“city” OR “cities” OR “communities” OR “community” OR “neighbo*rhood*” OR “district*”))) (Sharifi, 2020) Documents retrieved using this string were screened, and 58 articles were selected for final analysis (Sharifi, 2019). In addition, I did a Google search to find potentially relevant grey literature that can be used for extracting indicators. After downloading the documents, the inductive content analysis method was used to extract the list of indicators (Mayring, 2014). An inductive content analysis data collection and analysis are conducted simultaneously (Mayring, 2014). In this case, this means that as the first document was reviewed, relevant indicators were added to the list. When reading the next document, it was checked whether the mentioned indicators fall under the previously listed indicators or should be added as new ones. This process was continued for all the documents, and, based on the results, a complete list of indicators was developed that will be presented in the next section. While doing the content analysis, I also noted major smartness dimensions mentioned in the literature. These were economy, people, governance, environment, mobility, living, and data (Sharifi, 2020). In the end, the extracted indicators were assigned to the smartness dimensions. This was done based on the author’s discretion and, therefore, involves some form of subjective judgment. To explore links between the indicators and resilience, each indicator’s relevance to different resilience abilities was examined, and a synthesis table was developed. More specifically, based on the literature and the author’s opinion, it was determined if each indicator contributes to the four resilience abilities (planning, absorption, recovery, and adaptation). This was determined based on yes/no questions. For each theme, depending on the percentage of indicators linked to each resilience ability, its extent of alignment with the resilience abilities was determined.

3. Results and Discussion

In this section, I first present the list of indicators related to economy, people, governance, environment, mobility, living and data. Next, I discuss an approach for integrating resilience thinking into smart city assessment.

3.1 Smart city indicators

In each of the following subsections, indicators related to the seven major smart city dimensions will be presented.

3.1.1 Economy

Many indicators related to the economy were identified, which is not surprising considering that, as mentioned earlier, one of the major objectives of smart city initiatives is to strengthen the position of cities in an increasingly competitive global economy. These indicators are divided into major themes: innovation, knowledge economy, entrepreneurship, finance, tourism, employment, local & global interconnectedness, productivity, the flexibility of the labour market, and impacts (Table 1).

Theme Indicator
Innovation R&D expenditure (% of GDP)
Policies, programs, and plans for promoting creativity/innovation
Patent applications/registration per inhabitant
The competitive position of the city in terms of science and engineering centres
ICT-enabled innovation leading to new businesses and market opportunities
Knowledge economy Green economy
Share of public/private investment in smart industries
Rate of import-export related to smart industry and knowledge-intensive economy
Industry-academia-government cooperation
Contribution of knowledge economy and ICT initiatives to GDP (%)
Space for knowledge exchange and business promotion
Share of e-business and e-commerce transactions
Entrepreneurship Policies, programs, and plans for promoting entrepreneurship
Self-employment rate
Small and Medium Enterprises trends
Number of start-ups
Promotion of start-up companies
Number of businesses and new businesses registered annually
Finance Funding for smart city projects (public/private finance, crowdsourced, etc.)
Consideration of market demands and needs in smart city planning
Total market value of commercial and industrial properties
Financial stability (e.g., city and per capita reserves, city’s debt service ratio)
Global/regional competitiveness in attracting companies with low sales taxes
Tax collected as a percentage of tax billed
Tourism Importance as a tourist hub
Affordability and accessibility as a tourist destination
Tourism impact management
Online and ICT-enabled tourism promotion
Employment City’s employment/unemployment rate, measures to combat unemployment
Availability of labour force, working-age population
Local employment opportunities
Employment rate improved by smart solutions
Rate of employment in tourism industry
Rate of employment in knowledge-intensive sectors/ creative industry
Local & Global Interconnectedness Gross regional product per capita (GRP)
Procurement style
Presence of major international and domestic enterprises and entities in the city
City internationalization activities
Cross-city smart city initiatives and collaboration
Importance on the national and regional scale
Adoption of International Organization for Standardization
Using ICT measures for improving domestic and international communication and cooperation
Productivity GDP per employed person
Primary, secondary, and tertiary industry’s share of GDP
ICT measures to improve industry/economic/employee productivity
Plans and strategies for economic development
Foreign direct investment and inward investment
Cost-benefit analysis
Flexibility of the labour market Measures to improve accessibility to labour market
ICT-enabled flexibility and improvement of traditional industry and job market
Home-based work and workspace flexibilization
Timetable flexibilization
Perception of getting a new job; flexibility of the workforce
Impacts Costs of development, operation, and maintenance of smart city projects
Economic impacts of smart city initiatives
Plans for management of risks

Table 1. Economic indicators. Adapted from (Sharifi, 2019; Sharifi, 2020; Sharifi, Kawakubo, & Milovidova, 2020).

3.1.2 People

People are the main users of smart city solutions and technologies. In addition, as major stakeholders, they can contribute to the enhanced design and development of smart cities. Indicators related to people and their capacities are related to education, ICT skills and open-mindedness (Table 2).

Theme Indicator
Education Importance as a knowledge hub
Percentage of the population working in higher education and R&D sector
Update and adjustment of educational facilities, curricula, and material to improve digital skills
Measures to improve quality of educational infrastructure
Adult literacy trends
Availability and penetration of e-learning and distance education systems
Application of ICT technology, analytics platforms, and e-learning
IT training and raising awareness about smart city benefits
Student/teacher ratio
Level of qualification/ ICT skills Percentage of population with secondary-level education
Percentage of population with tertiary-level education
Foreign language skills of the citizens
Individual-level of computer skills
Internet penetration (netizen ratio)
Social networking penetration
Level of digital and ICT literacy and technical capability
Open-mindedness Inhabitants’ attitude towards international treaties
Share of foreigners and nationals born abroad
Use of ICT measures to create an immigrant-friendly environment

Table 2. People indicators. Adapted from (Sharifi, 2019; Sharifi, 2020; Sharifi et al., 2020).

3.1.3 Governance

Integrated governance mechanisms are critical to ensure the efficacy and efficiency of smart city solutions and technologies. As Table 3 shows, governance indicators are related to themes such as visioning and leadership, legal frameworks, participation, transparency, public services, and integrated management.

Theme Indicator
Visioning and leadership Clear and inclusive digital strategy and smart city vision
Smart city roadmap
Historical experience of technology development
A broad-based leadership team that features appropriate mix of skills
Sustained leadership commitment to long-term smart city programs
Strong Leadership
Plans and strategies for mainstreaming smart city planning
Plans and strategies for performance monitoring and assessment
Availability of risk governance plans and strategies and using smart solutions
Legal frameworks Laws and regulations for smart city planning
Strategies to overcome organizational, legal and regulatory barriers
Legal and regulatory frameworks to protect consumer privacy
Participation Democracy, individual freedom, freedom of media, speech etc.
Extent of involvement of local authority/city administration in smart solution programs
Public participation and stakeholder engagement in decision making
Political activity of inhabitants
ICT-enabled participation in bottom-up voluntary work/service
Online civic engagement and feedback system
Dynamic interconnection with citizens, communities, and businesses
Collaborative service production and delivery
Transparency Governmental transparency
Leadership accountability
Mapping skills and transparent division of responsibilities between different actors
Bureaucracy status
Corruption index and measures to fight corruption
Public services Digitalization of governance and public expenditure on ICT and smart city transition
One-stop platform for data integration and for online accessibility and coordination of city services
Presence of people and public entities in social networks/media
Penetration rate of online government service
Presence of electronic and mobile payment platforms
Integrated management Interoperability between urban systems and subsystems
The state of data/information sharing among various institutions
Shared architecture for multi-level governance and inter-agency collaboration
Cross-agency coordination for integrated infrastructure management
Public-private partnership
Efficiency in the provision of services
Appropriate balance of top-down and bottom-up governance processes
Cross-city engagements and collaborations for knowledge exchange

Table 3. Governance indicators. Adapted from (Sharifi, 2019; Sharifi, 2020; Sharifi et al., 2020).

3.1.4 Environment

Smart cities can provide solutions to promote environmentally friendly cities. However, it is also essential to take measures to minimize their own environmental footprint. This dimension focuses on issues such as environmental monitoring, infrastructure, built environment, materials, energy, water, waste and environmental quality (Table 4).

Theme Indicator
Environmental monitoring Sustainable natural resource management
(ICT-enabled) environmental monitoring infrastructure
Environmental/ecosystem protection activities
(ICT-enabled) activities to disseminate environmental quality information
Life cycle impacts of ICT infrastructure and smart cities
Citizen involvement in resource management
Availability and implementation of climate resilience plans/strategies
General infrastructure Availability of basic critical infrastructure
Decentralized and modular (autonomous) infrastructure systems
Green infrastructure and green city initiatives
Penetration level of energy-saving technologies
Use of integrated smart management, operation, and monitoring systems
Local food production
Built environment Urban sprawl containment
Mixed-use development
Area of green/blue space
Preservation of historic buildings
Ambitiousness of building energy efficiency standards
Building Information System
ICT-enabled urban planning
Materials Efficiency of material consumption
Share of recycled and renewable materials used in projects
Energy resources Energy management plans and policies
Total energy consumption
Penetration of clean and renewable energy sources
Efficient management and use of energy
Greenhouse gas emission intensity of energy consumption
Smart grids
Using ICT measures for management, monitoring and saving of energy
Reliability and quality of electricity supply
Water resources Water management plans and policies
Quality of water resources and water bodies, quality monitoring
Efficient generation, distribution, and use of water
Total annual water consumption
Water loss monitoring and reduction
Water energy consumption
Use of smart water meters
Using ICT measures for management, monitoring, and saving of water
Waste Waste management plans and policies
Efficient and smart solid waste collection
Total per capita municipal waste
Proportion of recycled waste
Energy production from waste and wastewater
Sewage and wastewater management and treatment/recycling
Drainage system management, stormwater management
Using ICT measures such as smart sensors for management of solid waste
Environmental quality Air quality index/ pollution concentration levels
Per capita GHG emissions
Water pollution index; reduce water contamination
Soil pollution
Noise pollution

Table 4. Environmental indicators. Adapted from (Sharifi, 2019; Sharifi, 2020; Sharifi et al., 2020).

3.1.5 Living

One of the major goals of smart cities is to enhance the quality of life of citizens. This dimension focusses on issues such as social cohesion, justice, culture, housing quality, healthcare, safety and security and subjective well-being (Table 5).

Theme Indicator
Social cohesion Community cohesion
Demographic structure
Trust and norms of reciprocity
Diversity and measures for promoting diversity
Volunteer activities and civic engagement in social networks
Universal design of the physical environment and ICT services
Using ICT for promoting community connectivity and mutual support
Justice Income level
Ethnic, cultural, and gender equality
Protection of human rights
Physical access to amenities
Affordable, authorized and sustainable access to services and utilities
Enhancement in affordability and accessibility to services
Culture Percentage of municipal/individual budget allocated to culture
Cultural infrastructure
Size and quality of community centres
Use of ICT for promotion of culture
Protection and management of cultural heritage
Housing quality Cost of living
Housing quality
Housing expenditure
Healthcare Healthcare expenditure
Health insurance coverage
Healthcare services and infrastructure per capita
General well-being
Childcare system, daycare services for children
Healthcare for elderly; well-being of seniors
Use of ICT and smart technologies for promoting well-being
Use of ICT for trace-back monitoring of food and drugs
Percentage of citizens archiving electronic health records
Sharing rate of records, information, and resources among clinics
Adoption of telemedicine
Safety and security Disaster risk planning, monitoring, and management
Response time for police and emergency departments
Use of ICT for disaster prevention and prediction
Disaster-related economic losses
Individual safety and security
Community safety and crime rate
Using technology and ICT for crime prediction, prevention and control
Crime reduction rate attributable to ICT usage
Subjective well-being Satisfaction (perception of) with quality of life
ICT-enabled increase in employee satisfaction

Table 5. Living indicators. Adapted from (Sharifi, 2019; Sharifi, 2020; Sharifi et al., 2020).

3.1.6 Mobility and communication

Mobility and communication are major sectors that have adopted smart technologies. Indicators used to assess the smartness of mobility are related to transport infrastructure and management, ICT infrastructure and management, and ICT accessibility (Table 6).

Theme Indicator
Transport infrastructure Green transportation modes
Number of EV charging stations in the city
Autonomous Vehicle (AV) testing and deployment
Public transport system and its quality, diversity, and multi-modality
Private car ownership rate
Car and bike-sharing services
Cycling infrastructure options and facilities
Pedestrian environment and walking options
Street/pedestrian area smart/automatic lighting management system
Transportation management Strategic transportation network management
Travel distance
Share of total trips made by active /public transport modes
Performance, safety, and efficiency of public transportation
Real-time information about transit services and parking
Road traffic efficiency
Road safety, rate of traffic accidents
ICT-enabled transportation damage and fatalities reduction
Private car traffic restriction
Sensing and monitoring for real-time, smart and automated traffic management
Trackability and traceability of goods and vehicles
Smart pricing, smart price policies, demand-based pricing
ICT infrastructure Availability of IT and digital infrastructure
Broadband internet
Maintenance and regular revision of the ICT infrastructure
Integrated platform for real-time smart city operation and management
Fixed phone (landline) and mobile phone network coverage
Rate of coverage by mobile broadband (3G, 4G, 5G)
Availability of apps
Availability of smart computing technologies and platforms
ICT management Quality of internet service
Information privacy and security management
Existence of systems and regulations to ensure child online protection
Application of cloud computing services
Diversity of booking/payment options
Integrated fare/payment system for inter-service digital fare collection capability
ICT accessibility Physical accessibility of IT infrastructure
Socio-economic accessibility to digital technologies
Per-capita public/private ICT expenditure
Fixed and wireless broadband subscriptions
Personal computer/laptop/tablet ownership rate
Smartphone penetration
Free Wi-Fi coverage in public spaces

Table 6. Mobility indicators. Adapted from (Sharifi, 2019; Sharifi, 2020; Sharifi et al., 2020).

3.1.7 Data

Data is the cornerstone of smart city projects. Indicators belonging to this dimension cover issues such as data openness, data collection, data analytics, data use, and learning (Table 7).

Theme Indicator
Data openness Availability and publication of data in an open format
Open data platforms for making information open to the public
The user-friendliness of the open data platform/portal
Data platforms that are linked to each other
Sensing and collecting Infrastructure, systems, and strategies for data collection
Strategies and infrastructure for autonomous real-time sensing of data
Citizen participation in collecting real-time data and using them
Infrastructure for storing and structuring data
Systems, strategies, protocols, and infrastructure for timely data communication
Judging (analytics) Data quality management
Strategies, tools, and infrastructure for data filtering and classification
Systems, strategies, protocols, tools and infrastructure for data analytics
Strategies, tools, and infrastructure to evaluate data and use it for making predictions
Reacting Government decision-making based on data and evidence
Enterprise decision making
Citizen decision making
Learning Mode upgrading
Process upgrading
Experience upgrading

Table 7. Data indicators. Adapted from (Sharifi, 2019; Sharifi, 2020; Sharifi et al., 2020).

3.2 Links between the indicators and disaster resilience

As mentioned earlier, in this study, resilience is defined as the ability to plan and prepare for, absorb, recover from, and adapt to adverse events (four abilities). To determine if the smart city indicators can contribute to resilience, their potential to contribute to each of the four abilities was examined. The synthesis results are shown in Table 8. More elaboration on how each theme is linked to resilience abilities is beyond the scope of this study. Interested readers are referred to (Sharifi & Allam, 2021) for more information. This table shows the extent of relevance of indicators related to each theme to resilience abilities (in %). As can be seen, the highest linkages to resilience abilities are to planning and absorption with 63% and 58%, respectively.

In contrast, only 34% and 25% are related to adaptation and recovery, respectively. Overall, these results show that smartness assessment indicators can, to some extent, also be used to evaluate the resilience abilities of cities and projects. There is clearly limited attention to recovery and adaptation abilities. Further research is needed to understand better why those abilities have not been well accounted for. One possible reason could be that the concepts of smart city and resilience city are relatively new and have often been undertaken in isolation from one another. More integrated approaches towards them are likely to help solve this issue.

Theme Planning (%) Absorption (%) Recovery (%) Adaptation (%)
Innovation 100 0 20 80
Knowledge economy 71 29 14 57
Entrepreneurship 50 67 33 67
Finance 50 50 33 33
Tourism 50 100 25 25
Employment 67 100 50 0
Local & Global Interconnectedness 14 86 86 0
Productivity and efficiency 43 71 57 14
Flexibility of the labor market 20 100 0 80
Impacts 100 33 0 0
Education/ lifelong learning 100 22 0 44
Level of qualification 100 43 29 0
Cosmopolitanism 33 33 0 100
Visioning and leadership 100 0 22 44
Legal and regulatory frameworks 100 0 0 100
Participation 100 0 86 29
Transparency 100 0 100 0
Public and social services 60 80 0 60
Efficient and integrated management 75 50 63 13
Environmental monitoring 71 71 14 43
General infrastructure 50 83 17 33
Built environment/ 29 57 57 57
Materials 0 100 0 100
Energy 50 88 13 50
Water 33 78 11 67
Waste 25 100 0 75
Environmental quality 100 20 0 0
Social cohesion 43 71 86 0
Justice 33 67 83 0
Culture 60 80 40 0
Housing quality 33 100 67 0
Healthcare 18 91 55 36
Safety and security 38 100 0 0
Convenience and satisfaction 100 0 0 0
Transport infrastructure 33 78 0 89
Transportation management 58 92 0 100
ICT infrastructure 75 75 25 0
ICT management 83 67 0 17
ICT accessibility 100 100 0 0
Data openness 100 50 0 0
Sensing and collecting 100 80 20 0
Analytics 100 25 0 0
Reacting 100 0 0 0
Learning 0 0 0 100
Average 63 58 25 34

Table 8. Links between the themes and resilience abilities.

4. Conclusion

Smart city initiatives, enabled by ICTs, have become ubiquitous in the past few years. By developing smart cities, planners and policymakers hope to, among other things, enhance the quality of life, improve the efficacy and efficiency of urban management, and provide solutions for complex societal challenges, such as the increase in the frequency and intensity of disasters. Assessment is argued to be an effective method to mainstream smart city principles into decision- and policymaking processes and to ensure achievement of the smart city objectives.

The main objectives of this study were to provide a comprehensive list of indicators that can be used for smart city assessment and to examine their potential linkages to four resilience abilities: planning/preparation, absorption, recovery and adaptation. The results show that smartness is a multi-dimensional concept and is beyond just technological development. Multiple indicators were introduced that are divided into seven major dimensions: economy, people, governance, environment, living, mobility and data. Obviously, achieving smartness is a challenging ambition and requires concerted efforts across multiple sectors and dimensions. As for operationalizing the introduced assessment framework, it should be noted that using a large list of indicators would not be realistic in most cases due to resource limitations. Therefore, it is suggested that interested stakeholders would consider the suggested list as a pool of indicators and select those that are relevant and context-specific. Some statistical methods, such as principal component analysis, can also be used to establish a more concise and manageable list of indicators.

As for connections to resilience, it was found that smart city indicators are linked to resilience abilities, particularly abilities to plan/prepare for and absorb shocks. This is not surprising as, for instance, early warning capacities facilitated by real-time monitoring and big data analytics can allow cities to better respond to shocks. However, results show that recovery and adaptation abilities are not well accounted for. It was suggested that this could be due to, often, isolated approaches to smartness and resilience. Adopting more integrated approaches is needed to achieve better alignment between smartness indicators and resilience abilities. Further dimension-specific research is needed to better understand through what specific mechanisms smart city indicators can inform resilience-oriented urban planning and management. As resilience is also characterized by multiple attributes such as robustness, stability, diversity, redundancy, resourcefulness, creativity, agility, flexibility, efficiency, self-organization, inclusiveness and foresight capacity, future research should also explore potential connections of smart city indicators to these attributes.


I appreciate the financial support from the Asia-Pacific Network for Global Change Research (Project No CRRP2019-03SY-Sharifi