Smart cities play a crucial role in tackling many issues concerning urban sustainability. This ranges from reducing air pollution and increasing energy efficiency to mitigating traffic congestion and maintaining resilience to natural disasters. Data-driven innovation, including the Internet of Things, Blockchain and Artificial Intelligence, has significant potential to address these multifaceted and interdependent issues. Vast amounts of data are increasingly available from a diversity of sources through sensors and devices installed in buildings, automobiles and infrastructure across cities. Effective collection, sharing and usage of data through cooperation and collaboration among stakeholders would be critical for facilitating innovation for smart cities. While open data access and management can contribute to innovation creation, there are many challenges that we need to address while promoting societal benefits. Serious concerns are particularly raised about collecting, sharing and using sensitive data. This especially concerns personal data in terms of safety, security and privacy. Furthermore, the balance between open and proprietary data should also be considered.
As the speed of technological change is rapid and its evolution not entirely predictable or explainable, a widening gap is produced between technological change and institutional readiness. Additionally, sectors such as energy, housing and transportation, which until now were not always interconnected, are increasingly integrated through data in smart cities as cyber-physical systems. Institutional arrangements for data governance, therefore, need to incorporate the ability to learn from real-world use and experience and to improve performance through adaptation. A key question to consider is the kinds of data governance systems appropriate in maximizing innovation potential while minimizing risks to individuals and communities. In-depth research is required to investigate the ways in which institutional arrangements for data governance influence the collection, management and usage of data in smart cities, and what impacts would be made on facilitating data-driven innovation while addressing societal challenges. Case studies in different countries and regions are particularly welcome to understand the mechanisms and processes that promote collaboration on data, which reflect the structure of motivations and incentives that would be specific to local contexts and conditions. Various types of data governance would be addressed, including government-led, industry-led, and public-private partnership approaches.
This Research Topic aims to share theoretical as well as empirical research findings that examine the current situation concerning the collection, sharing and usage of data in the context of smart cities. The effects of organizational and institutional arrangements for data governance on innovative efforts in public and private sectors should additionally be addressed. Possible questions that incoming papers can discuss include:
• What kinds of data are collected in smart cities?
• Who owns and has access to the data?
• For what purposes are the collected data used?
• How and by whom are the data managed?
• What incentives are provided to encourage data sharing?
• What kinds of platforms are established to manage the data collected?
• What impacts are made on stimulating innovation?
• What kinds of policies and institutional arrangements are implemented to address concerns about safety, security, and privacy?
Based on theoretical and empirical research on data governance, implications for public policy and institutional design will be discussed to facilitate data-driven innovation through open data while addressing societal concerns about safety, security, and privacy in sustainable smart cities.
Smart cities play a crucial role in tackling many issues concerning urban sustainability. This ranges from reducing air pollution and increasing energy efficiency to mitigating traffic congestion and maintaining resilience to natural disasters. Data-driven innovation, including the Internet of Things, Blockchain and Artificial Intelligence, has significant potential to address these multifaceted and interdependent issues. Vast amounts of data are increasingly available from a diversity of sources through sensors and devices installed in buildings, automobiles and infrastructure across cities. Effective collection, sharing and usage of data through cooperation and collaboration among stakeholders would be critical for facilitating innovation for smart cities. While open data access and management can contribute to innovation creation, there are many challenges that we need to address while promoting societal benefits. Serious concerns are particularly raised about collecting, sharing and using sensitive data. This especially concerns personal data in terms of safety, security and privacy. Furthermore, the balance between open and proprietary data should also be considered.
As the speed of technological change is rapid and its evolution not entirely predictable or explainable, a widening gap is produced between technological change and institutional readiness. Additionally, sectors such as energy, housing and transportation, which until now were not always interconnected, are increasingly integrated through data in smart cities as cyber-physical systems. Institutional arrangements for data governance, therefore, need to incorporate the ability to learn from real-world use and experience and to improve performance through adaptation. A key question to consider is the kinds of data governance systems appropriate in maximizing innovation potential while minimizing risks to individuals and communities. In-depth research is required to investigate the ways in which institutional arrangements for data governance influence the collection, management and usage of data in smart cities, and what impacts would be made on facilitating data-driven innovation while addressing societal challenges. Case studies in different countries and regions are particularly welcome to understand the mechanisms and processes that promote collaboration on data, which reflect the structure of motivations and incentives that would be specific to local contexts and conditions. Various types of data governance would be addressed, including government-led, industry-led, and public-private partnership approaches.
This Research Topic aims to share theoretical as well as empirical research findings that examine the current situation concerning the collection, sharing and usage of data in the context of smart cities. The effects of organizational and institutional arrangements for data governance on innovative efforts in public and private sectors should additionally be addressed. Possible questions that incoming papers can discuss include:
• What kinds of data are collected in smart cities?
• Who owns and has access to the data?
• For what purposes are the collected data used?
• How and by whom are the data managed?
• What incentives are provided to encourage data sharing?
• What kinds of platforms are established to manage the data collected?
• What impacts are made on stimulating innovation?
• What kinds of policies and institutional arrangements are implemented to address concerns about safety, security, and privacy?
Based on theoretical and empirical research on data governance, implications for public policy and institutional design will be discussed to facilitate data-driven innovation through open data while addressing societal concerns about safety, security, and privacy in sustainable smart cities.
