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.

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