Top

Mitigation and Adaptation Strategies for Global Change

Published in:

01-01-2022 | Original article

A comparison of flood-protective decision-making between German households and businesses

Authors: Paul Hudson, Philip Bubeck, Annegret H. Thieken

Published in: Mitigation and Adaptation Strategies for Global Change | Issue 1/2022

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Integrated flood risk management requires all stakeholders to limit flood impacts. Adaptation to flooding is a major avenue through which society designs our living spaces to cope with the threat of flooding. Within this context, there are many studies investigating the employment of property-level adaptation for households and the related decision-making process as both climate change adaptation and disaster risk reduction measures. By comparison, businesses are a relatively neglected topic of study. This is a limitation, as businesses are important community members and suffer from a large share of flood losses, and their lack of preparedness undermines social resilience against climate change. Using survey data from the 2013 German flood, we compare the implementation of property-level adaptation measures by households and businesses. We further investigate whether similar factors drive adaptive behaviour using a structural statistical model of a hybrid of two socio-psychological models: the protection motivation theory (PMT) and the protective action decision model (PADM). Based on the empirical analysis of the combined framework, the main conclusion is that there is no great difference between the households and businesses in terms of their pre-disaster adaptation decision processes. However, companies did have a lower level of overall preparedness than households. This implies that results of decision-making from one stakeholder set may be applicable elsewhere, e.g., in developing agent-based models of disaster risk reduction or climate change adaptation. However, most of the businesses studied were SMEs and may not be representative of larger businesses, where decision-making processes are increasingly formalized. This is important, since small and medium enterprises (SMEs) are often not well prepared against flooding or other climate change impacts.

previous article Probability-based accounting for carbon in forests to consider wildfire and other stochastic events: synchronizing science, policy, and carbon offsets

next article EU’s bioethanol potential from wheat straw and maize stover and the environmental footprint of residue-based bioethanol

Available only for authorised users

A 90% confidence interval is selected because the 10% significance level is the smallest level of acceptable statistical significance in the underlying regression models (see Tables A3–A5). The use of a 95% confidence interval, for example, would not alter the overall finding of the paper because the confidence interval then becomes wider. This helps to reinforce the overall result.

A marginal effect (ME) is the first derivative of the logit model with respect to the variable of interest, when the explanatory variables are evaluated at their sample mean values. MEs can be understood as the percentage point change in the likelihood of a respondent employing an adaptive behaviour.

These results hold if ordinal rather than binary versions of the input data is used.

Attems M-S, Thaler T, Genovese E, Fuchs S (2020) Implementation of property-level flood risk adaptation (PLFRA) measures: choices and decisions WIREs. Water 7:e1404. https://doi.org/10.1002/wat2.1404CrossRef

Babcicky P, Seebauer S (2017) The two faces of social captial in private flood mitigation: opposing effects on risk perception, self-efficacy, and coping capacity. J Risk Res 20:1017–1037CrossRef

Baker GP, Jensen MC, Murphy KJ (1988) Compensation and incentives: practice vs. theory. J Finance 43:593–616. https://doi.org/10.2307/2328185CrossRef

Bamberg S, Masson T, Brewitt K, Nemetschek N (2017) Threat, coping and flood prevetion - a meta-analysis. J Environ Psychol. https://doi.org/10.1016/j.jenvp.2017.08.001CrossRef

Becker G, Aerts JCJH, Huitema D (2014) Influence of flood risk perception and other factors on risk-reducing behaviour: a survey of municipalities along the Rhine. J Flood Risk Manag 7:16–30. https://doi.org/10.1111/jfr3.12025CrossRef

Bhattacharya-Mis N, Lamond J, Montz B, Kreibich H, Wilkinson S, Chan F, Proverbs D (2018) Flood risk to commercial property: training and education needs of built environment professionals. Int J Disaster Resilience Built Environ 9:385–401. https://doi.org/10.1108/IJDRBE-03-2017-0024CrossRef

Boateng GO, Neilands TB, Frongillo EA, Melgar-Quiñonez HR, Young SL (2018) Best practices for developing and validating scales for health, social, and behavioral research: a primer. Front Public Health 6:149–149. https://doi.org/10.3389/fpubh.2018.00149CrossRef

Bubeck P, Botzen WJW, Aerts JCJH (2012) A review of risk perceptions and other factors that influence flood mitigation behavior. Risk Anal 32:1481–1495CrossRef

Bubeck P, Botzen WJW, Kreibich H, Aerts JCJH (2013) Detailed insights into the influence of flood-coping appraisals on mitigation behaviour. Glob Environ Chang 23:1327–1338. https://doi.org/10.1016/j.gloenvcha.2013.05.009CrossRef

Bubeck P, Aerts JCJH, de Moel H, Kreibich H (2016) Preface: Flood-risk analysis and integrated management. Nat Hazards Earth Syst Sci 16:1005–1010CrossRef

Bubeck P, Wouter Botzen WJ, Laudan J, Aerts JCJH, Thieken AH (2018) Insights into flood-coping appraisals of protection motivation theory: empirical Evidence from Germany and France. Risk Anal 38:1239–1257. https://doi.org/10.1111/risa.12938CrossRef

Bubeck P, Berghäuser L, Hudson P, Thieken AH (2020) Using panel data to understand the dynamics of human behavior in response to flooding. Risk Anal 40:2340–2359. https://doi.org/10.1111/risa.13548CrossRef

Chinh DT, Bubeck P, Dung NV, Kreibich H (2016) The 2011 flood event in the Mekong Delta: preparedness, response, damage and recovery of private households and small businesses. Disasters 40:753–778. https://doi.org/10.1111/disa.12171CrossRef

CRED, UNDRR (2018) Economic losses, poverty, & diasters: 1998–2017

CRED-UNISDR (2015) The human cost of weather related disasters, 1995–2015. Centre for research on the epidemiology of disasters and United Nations office for disaster risk reduction,

Diamantopoulos A, Sarstedt M, Fuchs C, Wilczynski P, Kaiser S (2012) Guidelines for choosing between multi-item and single-item scales for construct measurement: a predictive validity perspective. J Acad Mark Sci 40:434–449. https://doi.org/10.1007/s11747-011-0300-3CrossRef

Geaves LH, Penning-Rowsell EC (2016) Flood risk management as a public or a private good, and the implications for stakeholder engagement. Environ Sci Polic 55:281–291. https://doi.org/10.1016/j.envsci.2015.06.004CrossRef

Grothmann T, Patt A (2005) Adaptive capacity and human cognition: The process of individual adaptation to climate change. Glob Environ Chang 15:199–213CrossRef

Grothmann T, Reusswig F (2006) People at risk of flooding: why some residents take precautionary action while others do not. Natural Hazards 38:101–120. https://doi.org/10.1007/s11069-005-8604-6CrossRef

Haer T, Husby TG, Botzen WJW, Aerts JCJH (2020) The safe development paradox: an agent-based model for flood risk under climate change in the European Union. Glob Environ Chang 60:102009. https://doi.org/10.1016/j.gloenvcha.2019.102009CrossRef

Hartmann T, Driessen P (2017) The flood risk management plan: towards spatial water governance. Journal of Flood Risk Management 10:145–154. https://doi.org/10.1111/jfr3.12077CrossRef

Herbane B (2015) Threat orientation in small and medium-sized enterprises. Disaster Prev Manag 24:583–595. https://doi.org/10.1108/DPM-12-2014-0272CrossRef

Hudson P, Botzen WJW, Aerts JCJH (2019) Flood insurance arrangements in the European Union for future flood risk under climate and socioeconomic change. Glob Environ Chang 58:101966. https://doi.org/10.1016/j.gloenvcha.2019.101966CrossRef

Hudson P et al (2020) Best practices of extreme weather insurance in Europe and directions for a more resilient society. Environ Hazards 19:301–321. https://doi.org/10.1080/17477891.2019.1608148CrossRef

Hudson P, Hagedoorn L, Bubeck P (2020) Potential linkages between social capital, flood risk perceptions, and self-efficacy. Int J Disast Risk Sci 11:251–262. https://doi.org/10.1007/s13753-020-00259-wCrossRef

Ingirige B (2014) Putting policy initiatives into practice. Structural Survey 32:123–139. https://doi.org/10.1108/SS-01-2013-0011CrossRef

Jehmlich C, Hudson P, Thieken AH (2020) Short contribution on adaptive behaviour of flood-prone companies: a pilot study of Dresden-Laubegast, Germany. J Flood Risk Manag 13:e12653. https://doi.org/10.1111/jfr3.12653CrossRef

Kato M, Charoenrat T (2018) Business continuity management of small and medium sized enterprises: evidence from Thailand. Int J Disast Risk Reduc 27:577–587. https://doi.org/10.1016/j.ijdrr.2017.10.002CrossRef

Kousky C, Kunreuther H, Lingle B, Shabman L (2018) The emerging private residential flood insurance market in the United States. Wharton Risk Management and Decision Procecsses Center, University of Pennsylvania, Philadelphia

Kreibich H, Müller M, Thieken AH, Merz B (2007) Flood precaution of companies and their ability to cope with the flood in August 2002 in Saxony, Germany. Water Resour Res 43:4691. https://doi.org/10.1029/2005WR004691CrossRef

Kreibich H, Seifert I, Thieken AH, Lindquist E, Wagner K, Merz B (2011) Recent changes in flood preparedness of private households and businesses in Germany. Reg Environ Change 11:59–71. https://doi.org/10.1007/s10113-010-0119-3CrossRef

Kuhlicke C, Masson T, Kienzler S, Sieg T, Thieken AH, Kreibich H (2019) Multiple flood experiences and social resilience: findings from three surveys on households and companies exposed to the 2013 flood in Germany Weather. Climate, and Society. https://doi.org/10.1175/WCAS-D-18-0069.1CrossRef

Kuhlicke C et al (2020) The behavioural turn in flood disaster risk management and its implication for future research and policy, WIREs. Water 7:e1418. https://doi.org/10.1002/wat2.1418CrossRef

Kunreuther H (2015) The role of insurance in reducing losses from extreme events: the need for public-private partnerships. Geneva Papers Risk Insur Issues Pract 40:471

Lindell MK, Perry RW (2012) The protective action decision model: theoretical modifications and additional evidence. Risk Anal 32:616–632. https://doi.org/10.1111/j.1539-6924.2011.01647.xCrossRef

Lo AY, Xu B, Chan FKS, Su R (2015) Social capital and community preparation for urban flooding in China. Appl Geogr 64:1–11. https://doi.org/10.1016/j.apgeog.2015.08.003CrossRef

Lucas CH, Booth KI (2020) Privatizing climate adaptation: how insurance weakens solidaristic and collective disaster recovery. WIREs Climate Change 11:e676. https://doi.org/10.1002/wcc.676CrossRef

Maddux JE, Rogers RW (1983) Protection motivation and self-efficacy: a revised theory of fear appeals and attitude change. J Exp Soc Psychol 19:469–479. https://doi.org/10.1016/0022-1031(83)90023-9CrossRef

Mas-Colell A, Whinston MD, Green JR (1995) Microeconomic Theory. Oxford University Press, Oxford

McKnight B, Linnenluecke MK (2019) Patterns of firm responses to different types of natural disasters. Bus Soc 58:813–840. https://doi.org/10.1177/0007650317698946CrossRef

Merz B, Elmer F, Kunz M, Muhr B, Schroter K, Uhlemann-Elmer S (2014) The extreme flood in June 2013 in Germany. Houille Blanche 1:5–10. https://doi.org/10.1051/lhb/2014001CrossRef

Moffitt R et al (2015) Assessing the need for a new nationally representative household panel survey in the United States. J Econ Soc Measur 40:1–26. https://doi.org/10.3233/JEM-150411CrossRef

Mondino E, Scolobig A, Borga M, Albrecht F, Mard J, Weyrich P, Di Baldassarre G (2020) Exploring changes in hydrogeological risk awareness and preparedness over time: a case study in northeastern Italy. Hydrol Sci J 65(7):1049–1059. https://doi.org/10.1080/02626667.2020.1729361CrossRef

Neise T, Revilla-Diez J (2018) Firms’ contribution to flood risk reduction – scenario-based experiments from Jakarta and Semarang, Indonesian. Proc Eng 212:567–574. https://doi.org/10.1016/j.proeng.2018.01.073CrossRef

Neise T, Sambodo MT, Revilla-Diez J (2019) Are micro-, small- and medium-sized enterprises willing to contribute to collective flood risk reduction? Scenario-based field experiments from Jakarta and Semarang, Indonesia. Organiz Environ 34:219–242. https://doi.org/10.1177/1086026619875435CrossRef

O’Keefe P, Westgate K, Wisner B (1976) Taking the naturalness out of natural disasters. Nature 260:566–567CrossRef

Osberghaus D (2017) The effect of flood experiences on household mitigation – evidence form longitudinal and insurance data. Global Environ Change 43:126–136CrossRef

Paleari S (2019) Disaster risk insurance: a comparison of national schemes in the EU-28. Int J Disaster Risk Reduct 35:101059. https://doi.org/10.1016/j.ijdrr.2018.12.021CrossRef

Petrolia DR, Hwang J, Laundry CE, Coble KH (2015) Wind insurance and mitigation in the coastal zone. Land Econ 91:272–295CrossRef

Reiss PC, Wolak FA (2007) Chapter 64 structural econometric modeling: rationales and examples from industrial organization. In: Heckman JJ, Leamer EE (eds) Handbook of Econometrics, vol 6. Elsevier, Amsterdam, pp 4277–4415. https://doi.org/10.1016/S1573-4412(07)06064-3CrossRef

Rogers RW (1975) A protection motivation theory of fear appeals and attitude change. J Psychol 91:93–114. https://doi.org/10.1080/00223980.1975.9915803CrossRef

Rogers RW, Prentice-Dunn S (1997) Protection motivation theory. Handbook of health behavior research 1: Personal and social determinants. Plenum Press, New York, pp 113–132

Savage LJ (1954) The foundations of statistics. Wiley, New York

Schröter K, Kunz M, Elmer F, Mühr B, Merz B (2015) What made the June 2013 flood in Germany an exceptional event? A hydro-meteorological evaluation. Hydrol Earth Syst Sci 19:309–327CrossRef

Seebauer S, Babcicky P (2020) The sources of belief in personal capability: antecedents of self-efficacy in private adaptation to flood risk. Risk Anal 40:1967–1982. https://doi.org/10.1111/risa.13531

Snel KAW, Witte PA, Hartmann T, Geertman SCM (2020) The shifting position of homeowners in flood resilience: from recipients to key-stakeholders WIREs. Water 7:e1451. https://doi.org/10.1002/wat2.1451CrossRef

Surminski S, Thieken A (2017) Promoting flood risk reduction: the role of insurance in Germany and England Earth’s. Future 5:979–1001

Thieken A (2018) Contributions of flood insurance to enhance resilience–findings from Germany. In: Fekete A, Fiedrich F (eds) Urban Disaster Resilience and Security. Springer, Berlin. https://doi.org/10.1007/978-3-319-68606-6CrossRef

Thieken AH, Petrow T, Kreibich H, Merz B (2006) Insurability and mitigation of flood losses in private households in Germany. Risk Anal 26:383–395. https://doi.org/10.1111/j.1539-6924.2006.00741.xCrossRef

Thieken AH, Bessel T, Kienzler S, Kreibich H, Müller M, Pisi S, Schröter K (2016a) The flood of June 2013 in Germany: how much do we know about its impacts? Nat Hazard 16:1519–1540. https://doi.org/10.5194/nhess-16-1519-2016CrossRef

Thieken AH et al (2016b) Review of the flood risk management system in Germany after the major flood in 2013. Ecol Soc 21:51. https://doi.org/10.5751/ES-08547-210251CrossRef

Thieken AH, Kreibich H, Mükker M, Lamond JE (2017) Data collection for a better understanding of what causes flood damage– experiences with telephone surveys. In: Molinari D, Menoni S, Ballio F (eds) Flood damage survey and assessment: new insights from research and practice. Wiley, Hoboken, pp 95–106. https://doi.org/10.1002/9781119217930.ch7CrossRef

Tyler J, Sadiq A-A (2019) Business continuity and disaster recovery in the aftermath of Hurricane Irma: exploring whether community-level mitigation activities make a difference. Natural Hazards Review 20:04018026. https://doi.org/10.1061/(ASCE)NH.1527-6996.0000323CrossRef

van Valkengoed AM, Steg L (2019) Meta-analyses of factors motivating climate change adaptation behaviour Nature. Clim Change 9:158–163. https://doi.org/10.1038/s41558-018-0371-yCrossRef

Wedawatta G, Ingirige B (2012) Resilience and adaptation of small and medium-sized enterprises to flood risk. Disast Prev Manag Int J 21:474–488. https://doi.org/10.1108/09653561211256170CrossRef

Wilson RS, Herziger A, Hamilton M, Brooks JS (2020) From incremental to transformative adaptation in individual responses to climate-exacerbated hazards Nature. Clim Change 10:200–208. https://doi.org/10.1038/s41558-020-0691-6CrossRef

Winsemius HC et al (2016a) Global drivers of future river flood risk Nature. Clim Change 6:381–385

Winsemius HC, Aerts JCJH, van Beek LPH, Bierkens MFP, Bouwman A, Jongman B, Kwadijk JCJK (2016b) Global drivers of future river flood risk Nature. Clim Change 6:381–385. https://doi.org/10.1038/nclimate2893CrossRef

Wolf S, Pham M, Matthews N, Bubeck P (2021) Understanding the implementation gap: policy-makers’ perceptions of ecosystem-based adaptation in Central Vietnam. Clim Dev 13:81–94. https://doi.org/10.1080/17565529.2020.1724068CrossRef

Zaalberg R, Midden C, Meijnders A, McCalley T (2009) Prevention, adaptation, and threat denial: flooding experiences in the Netherlands. Risk Anal 29:1759–1778. https://doi.org/10.1111/j.1539-6924.2009.01316.xCrossRef

Title: A comparison of flood-protective decision-making between German households and businesses
Authors: Paul Hudson
Philip Bubeck
Annegret H. Thieken
Publication date: 01-01-2022
Publisher: Springer Netherlands
Published in: Mitigation and Adaptation Strategies for Global Change / Issue 1/2022
Print ISSN: 1381-2386
Electronic ISSN: 1573-1596
DOI: https://doi.org/10.1007/s11027-021-09982-1

Springer Professional

Abstract

Please log in to get access to your license.

Other articles of this Issue 1/2022

Probability-based accounting for carbon in forests to consider wildfire and other stochastic events: synchronizing science, policy, and carbon offsets

EU’s bioethanol potential from wheat straw and maize stover and the environmental footprint of residue-based bioethanol

Implications of spatial priority areas for hydrological ecosystem services through coupling hydrology model and Zonation model under different economic costs of water utilizations in northernmost of Japan

The impact of climate change on global energy use

Do rice varieties matter? Climate change adaptation and livelihood diversification among rural smallholder households in the Mekong Delta region of Vietnam

The economic impact of a deep decarbonisation pathway for China: a hybrid model analysis through bottom-up and top-down linking