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Impacts of green infrastructure on flood risk perceptions in Hong Kong

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Abstract

To better address climate unpredictability, green infrastructure is increasingly deployed alongside gray infrastructure as an alternative strategy for flood risk mitigation. Previous research has not clearly distinguished the flood-mitigation effects of green infrastructure at the local scale due to its complex range of functions including socioeconomic benefits, ecosystem services, and amenity value. Using data on 3768 housing sales from 2009 to 2019 in Hong Kong, we employ a difference-in-differences framework to examine the effect of green infrastructure on perceptions of flood risk mitigation, with housing prices as a proxy for risk perception. We find a positive effect of green infrastructure on the value of nearby housing. The effect does not exist in apartment units on higher floors, however. This vertical discrepancy further suggests that the observed pricing effects are due to green infrastructure’s capacity to reduce perceptions of flood risk. By contrast, properties near conventional gray infrastructure show no evidence of such effects. The results thus provide quantitative evidence that supports the ongoing shift toward green infrastructure as a form of climate change adaptation.

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Notes

  1. The Hong Kong tropical cyclone warning system consists of Signals 1 (standby), 3 (strong wind), 8 (gale or storm), 9 (increasing gale or storm), and 10 (hurricane). Signal 8 is issued when a sustained wind speed ranges from 63 to 87 km/h. Signal 9 is issued when wind speed ranges from 88 to 117 km/h. Signal 10, the highest level in the warning system, indicates persistent hurricane-forced winds exceeding 117 km/h.

  2. For instance, if a property is at risk of flooding, market participants may expect substantial repair costs from flood damages, leading demand for lower-risk properties to rise. However, this risk does not affect the actual supply of properties at risk of flooding. The subsequent price differential between such properties thus reveals a market participant’s willingness to pay for flood risk reduction.

  3. Overland flood insurance, which is a consideration in many contexts, is another variable that is often included in similar studies. Yet, for Hong Kong and the wider Asia Pacific, such policies are unavailable and/or unpopular (Lamond et al. 2017; Chan et al. 2018). The lack of such policies means that our study does not suffer from a systematic bias. Although some local property insurance covers water damage, this is mostly limited to pipe leakages, seepages, and drainage problems (Chan et al. 2018). Government policy does not typically support private insurers who might wish to offer this service, as there is often a lack of accurate flood risk information. Meanwhile, other private insurers estimate that risks are simply too high to offer flood premium packages.

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Acknowledgements

We are thankful to Nam Young Kwon for assistance in collecting data on housing sales transactions. We acknowledge support from the University of Hong Kong Faculty of Social Sciences Cities 2050 Research Cluster.

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Authors and Affiliations

  1. Faculty of Social Science, University of Hong Kong, 7. 02C, Jockey Club Tower, Centennial Campus, Pok Fu Lam, Hong Kong SAR

    Seung Kyum Kim

  2. Department of Sociology, University of Hong Kong, Pok Fu Lam, Hong Kong SAR

    Paul Joosse

  3. Department of Geography and School of Modern Languages and Cultures (China Studies Programme), University of Hong Kong, Pok Fu Lam, Hong Kong SAR

    Mia M. Bennett

  4. Department of Politics and Public Administration, University of Hong Kong, Pok Fu Lam, Hong Kong SAR

    Terry van Gevelt

  5. Department of Civil Engineering, University of Hong Kong, Pok Fu Lam, Hong Kong SAR

    Terry van Gevelt

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  1. Seung Kyum Kim
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Correspondence to Seung Kyum Kim.

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Appendix

Appendix

Table 7 Details of supplementary GIS data (all for within Hong Kong)
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Table 8 Mature tropical cyclones with Signal 8 or higher between 1954 and 2018 in Hong Kong
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Table 9 Full set of DiD estimation (combined model)
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Table 10 Full set of DiD estimation (separate models)
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Table 11 Balance test
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Table 12 Spatial regression models
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Table 13 Robustness check: 2-year window examination of construction start dates for placebo tests
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Table 14 Robustness check: 4-year window examination of construction start dates for placebo tests
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Kim, S.K., Joosse, P., Bennett, M.M. et al. Impacts of green infrastructure on flood risk perceptions in Hong Kong. Climatic Change 162, 2277–2299 (2020). https://doi.org/10.1007/s10584-020-02803-5

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