Smart city indicators: Towards exploring potential linkages to disaster resilience abilities
Keywords
Assessment tools · Disaster resilience · Disasters · Index · Indicators · Smart city · Urban
Highlights
- 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 | |
Reliability | |
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.
Acknowledgement
I appreciate the financial support from the Asia-Pacific Network for Global Change Research (Project No CRRP2019-03SY-Sharifi