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Disaster Risk Reduction Regime in Japan: An Analysis in the Perspective of Open Data, Open Governance

Authors: Sakiko Kanbara; Rajib Shaw;

Disaster Risk Reduction Regime in Japan: An Analysis in the Perspective of Open Data, Open Governance

Abstract

This paper addresses open data, open governance, and disruptive/emerging technologies from the perspectives of disaster risk reduction (DRR). With an in-depth literature review of open governance, the paper identifies five principles for open data adopted in the disaster risk reduction field: (1) open by default, (2) accessible, licensed and documented, (3) co-created, (4) locally owned, and (5) communicated in ways that meet the needs of diverse users. The paper also analyzes the evolution of emerging technologies and their application in Japan. The four-phased evolution in the disaster risk reduction is mentioned as DRR 1.0 (Isewan typhoon, 1959), DRR 2.0 (the Great Hanshin Awaji Earthquake, 1995), DRR 3.0 (the Great East Japan Earthquake and Tsunami: GEJE, 2011) and DRR 4.0 (post GEJE). After the GEJE of 2011, different initiatives have emerged in open data, as well as collaboration/partnership with tech firms for emerging technologies in DRR. This paper analyzes the lessons from the July 2021 landslide in Atami, and draws some lessons based on the above-mentioned five principles. Some of the key lessons for open data movement include characterizing open and usable data, local governance systems, co-creating to co-delivering solutions, data democratization, and interpreting de-segregated data with community engagement. These lessons are useful for outside Japan in terms of data licensing, adaptive governance, stakeholder usage, and community engagement. However, as governance systems are rooted in local decision-making and cultural contexts, some of these lessons need to be customized based on the local conditions. Open governance is still an evolving culture in many countries, and open data is considered as an important tool for that. While there is a trend to develop open data for geo-spatial information, it emerged from the discussion in the paper that it is important to have customized open data for people, wellbeing, health care, and for keeping the balance of data privacy. The evolution of emerging technologies and their usage is proceeding at a higher speed than ever, while the governance system employed to support and use emerging technologies needs time to change and adapt. Therefore, it is very important to properly synchronize and customize open data, open governance and emerging/disruptive technologies for their effective use in disaster risk reduction.

Keywords

Environmental effects of industries and plants, partnership, open data, TJ807-830, community engagement, TD194-195, Renewable energy sources, emerging technologies, Environmental sciences, open data; open governance; emerging technologies; partnership; community engagement, GE1-350, open governance

  • BIP!
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    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    25
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
25
Top 10%
Top 10%
Top 10%
gold