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Data governance is a critical section in the construction of smart cities. Current research still has gaps in the overall data governance mechanism, particularly how data openness promotes innovation in detail and potential data security and privacy risks. Taking Shenzhen, China, as a case, this paper aims to explore collecting, sharing, using, innovation, security, and privacy of data governance, focusing on analyzing how data openness promotes social innovation and how the government will deal with potential risks of data security and privacy. At the end of the article, there are some policy implications. As for institutional innovation, the Shenzhen Municipal Government is the leader in the smart city construction, issuing many documents and supporting policies to guide the data collection, sharing, and application. Meanwhile, the government encourages enterprises to co-design the smart city through general contracting, subcontracting, and government purchases. Besides, in terms of data security and privacy protection, when framing the governance system, policymakers should consider the policy feasibility, implementation scope, and the consistency of relevant policies to reduce the confusion of enterprises and the public.

AbstractThis article discusses the technology of city digital twins (CDTs) and its potential applications in the policymaking context. The article analyzes the history of the development of the concept of digital twins and how it is now being adopted on a city-scale. One of the most advanced projects in the field—Virtual Singapore—is discussed in detail to determine the scope of its potential domains of application and highlight challenges associated with it. Concerns related to data privacy, availability, and its applicability for predictive simulations are analyzed, and potential usage of synthetic data is proposed as a way to address these challenges. The authors argue that despite the abundance of urban data, the historical data are not always applicable for predictions about the events for which there does not exist any data, as well as discuss the potential privacy challenges of the usage of micro-level individual mobility data in CDTs. A task-based approach to urban mobility data generation is proposed in the last section of the article. This approach suggests that city authorities can establish services responsible for asking people to conduct certain activities in an urban environment in order to create data for possible policy interventions for which there does not exist useful historical data. This approach can help in addressing the challenges associated with the availability of data without raising privacy concerns, as the data generated through this approach will not represent any real individual in society.

AbstractTo tackle transboundary air pollution in East Asia, international schemes for environmental cooperation have been introduced, including the Acid Deposition Monitoring Network in East Asia (EANET), Long‐Range Transboundary Pollution of China, Japan and Korea (LTP), and the North‐East Asian Subregional Programme for Environmental Cooperation (NEASPEC). These programs, however, have not been successful in establishing robust regimes for effectively reducing transboundary air pollution in the region. This paper aims to examine the process of forming epistemic communities through these programs in East Asia. A bibliometric data on the scientific articles and reports produced were analyzed to examine the network structure of the scientific activities through EANET. The fragmentation of expert groups within EANET and among the major international schemes on air pollution in East Asia contributed to discouraging solid formation of an epistemic community covering air pollution comprehensively in the region. That makes it difficult to reach a consensus based on the current state of scientific knowledge on air pollution for providing effective advice and recommendations for the development of policies and regulations.

Abstract
Contemporary data tools such as online dashboards have been instrumental in monitoring the spread of the COVID-19 pandemic. These real-time interactive platforms allow citizens to understand the local, regional, and global spread of COVID-19 in a consolidated and intuitive manner. Despite this, little research has been conducted on how citizens respond to the data on the dashboards in terms of the pandemic and data governance issues such as privacy. In this paper, we seek to answer the research question: how can governments use data tools, such as dashboards, to balance the trade-offs between safeguarding public health and protecting data privacy during a public health crisis? This study used surveys and semi-structured interviews to understand the perspectives of the developers and users of COVID-19 dashboards in Hong Kong. A typology was also developed to assess how Hong Kong's dashboards navigated trade-offs between data disclosure and privacy at a time of crisis compared to dashboards in other jurisdictions. Results reveal that two key factors were present in the design and improvement of COVID-19 dashboards in Hong Kong: informed actions based on open COVID-19 case data, and significant public trust built on data transparency. Finally, this study argues that norms surrounding reporting on COVID-19 cases, as well as cases for future pandemics, should be co-constructed among citizens and governments so that policies founded on such norms can be acknowledged as salient, credible, and legitimate.

As COVID-19 persists and mutates, governments will need to keep citizens updated with the latest information. During this time of high uncertainty, taking a personalised approach to COVID-19 advice could prove valuable for citizens to protect themselves based on their individual circumstances. Although efforts have been made to develop technologies that could make this approach viable, there is a lack of research focusing on the socio-political barriers that could lead to low public acceptance. Here, we present a survey experiment where we gauged the willingness of Hong Kong citizens to use a mobile application for personalised COVID-19 advice based on different data governance concerns and demographic characteristics, such as the sector of the developer or the method of data storage. We conclude that gender has a statistically significant effect on willingness, possibly due to women having greater concerns over the safety risks of sharing personal data than men. We also note that other concerns surrounding data security and access affect users' willingness to use a personalised advice application where they would need to share health and location data. Finally, we encourage further research on context-specific factors affecting the public acceptability of data tools for crisis management.