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• Published: 24 March 2022

Facilitating adoption of AI in natural disaster management through collaboration

• Monique M. Kuglitsch 1 ,
• Ivanka Pelivan   ORCID: orcid.org/0000-0003-0732-8466 1 ,
• Serena Ceola   ORCID: orcid.org/0000-0003-1757-509X 2 ,
• Mythili Menon 3 &
• Elena Xoplaki   ORCID: orcid.org/0000-0002-2745-2467 4  

Nature Communications volume  13 , Article number:  1579 ( 2022 ) Cite this article

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• Climate-change mitigation
• Natural hazards

Artificial intelligence can enhance our ability to manage natural disasters. However, understanding and addressing its limitations is required to realize its benefits. Here, we argue that interdisciplinary, multistakeholder, and international collaboration is needed for developing standards that facilitate its implementation.

Acute events of natural origin (e.g., atmospheric, hydrologic, geophysical, oceanographic, or biologic) can result in disruption and devastation to society, nature, and beyond 1 , 2 . Such events, which disproportionately impact certain regions (e.g., least developed countries 3 ) and populations (e.g., women and children 4 ), are often referred to as natural disasters by experts in the geoscience and disaster risk reduction communities, as reflected in the scientific literature and in Sustainable Development Goals 11.5 and 13.1.

Recently, interest has grown in leveraging innovative technologies such as artificial intelligence (AI) to bolster natural disaster management 5 . In many fields, such as medicine and finance, AI has gained traction due to advances in algorithms, a growth in computational power, and the availability of large data sets. Within natural disaster management, it is hoped that such technologies can also be a boon: capitalizing on a wealth of geospatial data to strengthen our understanding of natural disasters, the timeliness of detections, the accuracy and lead times of forecasts, and the effectiveness of emergency communications.

This Comment looks at successes and limitations of data collection methods and AI development for natural disaster management. It then examines the challenges and solutions surrounding AI implementation. It is shown that, although AI has the promise to enhance our ability to manage natural disasters, its effective adoption depends on collaborative efforts to address these challenges.

Successes and limitations to data

The foundation of any AI-based approach is high-quality data. A recent success is the emergence of new (and novel use of traditional) data collection methods. For example, sensor networks now help us to gather data from topographically complex regions, which are otherwise difficult to monitor, at high spatiotemporal resolutions. Such networks have proven successful for flash flood 6 and avalanche 7 monitoring. Although satellite-derived imagery has long been used for Earth observations, it is now being used in innovative ways. Global luminescence (i.e., nightlights) is being used by scientists to derive quantitative information about flood exposure 8 and, with AI, can improve probabilistic scenarios of flood exposure. Through combining Global Navigation Satellite System data with AI, scientists have been able to predict tsunami amplitudes without characterizing the triggering earthquake 9 ; avoiding issues such as magnitude saturation, which is common in seismic-based detection systems.

However, a number of limitations and/or technical issues must be considered when curating data for AI-based algorithms. Some of these relate to data quantity, such as: Are the data sufficient and representative? How are they stored and shared? Other concerns relate to data quality, such as: Do the data require calibration or correction? Do they have the desired spatiotemporal resolution? Are independent data available for testing the algorithm? When using AI to detect extreme events such as avalanches or earthquakes, the availability of data can be a limiting factor. AI-based methods can be very effective if a training dataset covers very large events. However, the availability of such data is limited because of the rarity of these events. One solution is producing synthetic data, which are based on a physical understanding of these hazards. Alternatively, it is possible to use machine learning algorithms requiring as few as one training event 10 . Another approach is applying transfer learning; a model is trained using data from a certain site and fine-tuned for another site 11 . Sometimes sufficient data are available, but there could be an issue with the spatiotemporal resolution. For instance, flood researchers have detected biases in numerical weather predictions (NWP) of precipitation in Japan, which can be ascribed to the smooth topography that is intrinsic in such algorithms. Rather than producing a higher-resolution NWP (which is computationally costly), these experts have turned to AI to correct these biases and produce a more accurate flood prediction 12 .

Successes and limitations to AI development

If high-quality datasets are available, AI-based algorithms can be used to detect or forecast events by combining multiple data sources or modeling techniques. For instance, seismic source and propagation modeling can be combined in a deep learning algorithm to generate probabilistic forecasts of earthquake shaking levels at a given location 13 . In another example, automatic weather station and snowpack data can be coupled in a random forest algorithm to forecast avalanche danger with human-level accuracy 14 .

However, also at the modeling phase, there are limitations to consider. For instance, is this the best model architecture given the intended use of the algorithm? How should we evaluate the algorithm and what level of explainability do we require? What are our expectations for generalizability (e.g., is our algorithm transferable to other regions where the availability of data might be limited)? In the earthquake example, the AI-based algorithm was evaluated using two earthquake sequences (in Italy and Japan) at different shaking thresholds. It was shown that this algorithm outperformed classical earthquake detection models for most of the shaking thresholds 13 . In the aforementioned avalanche example, the AI-based algorithm agreed with human forecasts in 80% of the cases. Although a false alarm rate would have been desirable, it was not possible to compute as the avalanche danger level is based on a complex combination of many factors—including snowpack and weather—and cannot be directly measured.

Answering such questions is nontrivial because of the diverse ways that AI-based methods are employed to predict natural disasters. These differences can, for example, be ascribed to the hazard type, algorithm type, and overall objective of the algorithm. There do, however, seem to be certain basic requirements that should be met when training and testing an AI-based algorithm. However, no clear guidelines or standards exist to support researchers/developers and those evaluating or implementing the end products (e.g., policy-makers/governments, individuals/consumers, and humanitarian organizations).

Challenges and solutions to AI implementation

Once an AI-based algorithm has been shown to accurately detect (e.g., in the avalanche example) or forecast (e.g., in the flood example) natural disasters, how can we ensure that it will be implemented to support natural disaster management? First, we need to address the disconnect between people developing the AI-based algorithms and people intended to implement them.

Often, these AI-based algorithms are developed by geoscience or machine learning experts in an academic setting (university or research institute) in order to advance the scientific understanding of a natural hazard. Throughout the lifetime of a research project, from funding acquisition to dissemination of outcomes, interaction with stakeholders and end users (including governmental emergency management agencies and humanitarian organizations) is often limited. For instance, once a project is completed, the results are shared at scientific conferences, in specialized committees, and in peer-reviewed publications, rarely reaching the aforementioned stakeholders and end users. This disconnect hinders the adoption of these AI-based algorithms.

Unfortunately, operating in a silo is not limited to geoscience and machine learning experts in an academic setting. Non-academic organizations dealing with DRR will also need an open-mindedness to new technologies and interaction with other experts (including the geoscience and machine learning experts in an academic setting) and stakeholders to reap the benefits of improved detection and forecasting for informed decision-making.

An example of an effective cross-sectoral collaboration is the Operation Risk Insights platform from IBM. This AI-based platform, which has been implemented since 2019, was developed by machine learning experts at IBM in close collaboration with end users from humanitarian organizations. These partnerships, which occurred at all stages of product development, streamlined the adoption of the platform.

Several programs are already championing interdisciplinary, multi-stakeholder, and international approaches. In the Resilient America Program, future projects will explore how new sources of data, for example, social media, can be combined with AI for predictive analysis. The European Union’s CLINT project brings together experts and stakeholders from nine countries and various sectors (national hydrometeorological services, agencies, universities, non-governmental organizations, and industry) to explore how AI can enhance climate services to support policy-makers and the interplay between research and impact. The African Union’s Africa Science and Technology Advisory Group (Af-STAG) on DRR actively liaises with experts on the continent and abroad to explore, for instance, how new data sources like street-level imagery can be combined with AI to improve the transmission of risk information to end users. Af-STAG-DRR has also engaged with the International Telecommunication Union (ITU), World Meteorological Organization (WMO), and UN Environment Programme (UNEP) Focus Group on AI for Natural Disaster Management (FG-AI4NDM), which is laying the groundwork for standards in the use of AI to support natural disaster management. This Focus Group is unique within the standardization landscape because of the diversity of its participants (including geoscientists, AI/ML specialists, DRR experts, governments, industry, and humanitarian organizations from around the globe), which ensures that a multitude of perspectives is considered.

Interdisciplinary collaboration for the future

As we have shown, novel data sources and AI-based methods show great promise in improving the detection, forecasting, and communication of natural disasters. However, their implementation is often hindered by limited interaction between developers and implementers of AI-based solutions, and a lack of clear guidelines for those developing, evaluating (or regulating), and implementing these technologies.

To address the former, we advocate:

expanding the participation in scientific conferences and specialized committees to include experts from relevant disciplines and non-academic stakeholders (including humanitarian organizations and governments),

predicating research funding on partnerships with end users, and

supporting national and international efforts to strengthen these partnerships.

For the latter, we believe that expert-produced, stakeholder-vetted, and internationally recognized standards can provide assurances that innovative technologies are applied in an informed manner with careful consideration of the limitations, and can be invaluable for supporting capacity building.

European Commission. The DIPECHO programme: Reducing the impact of disasters. https://ec.europa.eu/echo/files/evaluation/watsan2005/annex_files/ECHO/ECHO18%20-%20DIPECHO%20Brochure.pdf (2004).

United Nations Office for Disaster Risk Reduction, Centre for Research on Epidemiology of Disasters. https://www.preventionweb.net/files/46796_cop21weatherdisastersreport2015.pdf (2015).

World Meteorological Organization. 2020 State of Climate Services: Risk information and early warning systems. https://library.wmo.int/doc_num.php?explnum_id=10385 (2020).

United Nations Office for Disaster Risk Reduction. Sendai Framework for Disaster Risk Reduction 2015-2030. https://www.undrr.org/publication/sendai-framework-disaster-risk-reduction-2015-2030 (2015).

International Telecommunication Union. Disruptive technologies and their use in disaster risk reduction and management. https://www.itu.int/en/ITU-D/Emergency-Telecommunications/Documents/2019/GET_2019/Disruptive-Technologies.pdf (2019).

Mendoza-Cano, O. et al. Experiments of an IoT-based wireless sensor network for flood monitoring in Colima, Mexico. J. Hydroinformatics 23 , 385–401 (2021).

Article   Google Scholar  

Thüring, T. et al. Robust snow avalanche detection using supervised machine learning with infrasonic sensor arrays. Cold Reg. Sci. Technol. 111 , 60–66 (2015).

Ceola, S. et al. Satellite nighttime lights revealing increased human exposure to floods worldwide. Geophys. Res. Lett. 41 , 7184–7190 (2014).

Article   ADS   Google Scholar  

Lin, J.-T. et al. Early warning for great earthquakes from characterization of crustal deformation patterns with deep learning. J. Geophys. Res. Solid Earth 126 , e2021JB022703 (2021).

ADS   Google Scholar  

Heck, M. et al. Automatic detection of snow avalanches in continuous seismic data using hidden Markov models. Nat. Hazards Earth Syst. Sci. 18 , 383–396 (2018).

Münchmeyer, J. et al. Earthquake magnitude and location estimation from real time seismic waveforms with a transformer network. Geophys. J. Int. 226 , 1086–1104 (2021).

Yoshikane, T. & Yoshimura, K. A machine learning bias correction method for precipitation corresponding to weather conditions using simple input data. Preprint at https://www.essoar.org/doi/abs/10.1002/essoar.10507695.1 (2021).

Münchmeyer, J. et al. The transformer earthquake alerting model: a new versatile approach to earthquake early warning. Geophys. J. Int. 225 , 646–656 (2021).

Pérez-Guillén, C. et al. Data-driven automatic predictions of avalanche danger in Switzerland. EGU Gen. Assem. 2021 , EGU21–EGU6154 (2021).

Google Scholar  

Download references

Acknowledgements

This article was inspired by presentations and discussions that occurred at the first workshop of the ITU/WMO/UNEP FG-AI4NDM. We are greatly appreciative of the keynotes, moderators, technical presenters, and audience for sharing their thoughts on this topic. In particular, we would like to acknowledge Raul Aquino, Rakiya Babamaaji, Brendan Crowell, Jannes Münchmeyer, Steven Stichter, Alec van Herwijnen, and Kei Yoshimura, whose activities feature prominently in this article. We would also like to thank the original proponents of the FG-AI4NDM (including Prof. Juerg Luterbacher, Director of Science & Innovation and Chief Scientist at WMO; Dr. Muralee Thummarukudy, Operations Manager of the Crisis Management Branch at UNEP; and Prof. Thomas Wiegand, Director of Fraunhofer HHI), the FG-AI4NDM management, the experts at ITU (including Dr. Chaesub Lee, Director of the Telecommunications Standardization Bureau; Dr. Bilel Jamoussi, Chief of the Study Groups Department; Dr. Reinhard Scholl, Deputy to the Director of the Telecommunications Standardization Bureau Secretariat; and Study Group 2), the FG-AI4NDM Secretariat, and the ITU events team for making this workshop possible.

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Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute, Einsteinufer 37, 10587, Berlin, Germany

Monique M. Kuglitsch & Ivanka Pelivan

Alma Mater Studiorum Università di Bologna, Department of Civil, Chemical, Environmental, and Materials Engineering, Viale del Risorgimento 2, 40136, Bologna, Italy

Serena Ceola

International Telecommunication Union, Place des Nations 1211, 1202, Genève, Switzerland

Mythili Menon

Department of Geography and Center for International Development and Environmental Research, Justus Liebig University Giessen, Senckenbergstrasse 1, 35390, Giessen, Germany

Elena Xoplaki

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Contributions

Conceptualization: M.M.K., I.P., S.C., M.M. and E.X.; composition: M.M.K., I.P., S.C., M.M. and E.X.; editing: M.M.K., I.P., S.C., M.M. and E.X.

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Correspondence to Monique M. Kuglitsch .

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The authors declare no competing interests.

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Kuglitsch, M.M., Pelivan, I., Ceola, S. et al. Facilitating adoption of AI in natural disaster management through collaboration. Nat Commun 13 , 1579 (2022). https://doi.org/10.1038/s41467-022-29285-6

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• Open access
• Published: 28 March 2023

Reimagining natural hazards and disaster preparedness: charting a new course for the future

• Krzysztof Goniewicz 1 ,
• Md Nazirul Islam Sarker 2 , 3 &
• Monica Schoch-Spana 4  

BMC Public Health volume  23 , Article number:  581 ( 2023 ) Cite this article

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The world is facing unprecedented challenges from disasters and natural hazards, which are increasing in frequency and intensity due to climate change. Moreover, they can overlap, producing compounded and cascading effects [ 1 , 2 ]. While these events alone and together can be devastating, the good news is that we can take steps to mitigate their impact and prepare for their aftermath. This is where the art of disaster preparedness comes into play.

In this collection, we explore the latest research and best practices for preparing for natural hazards and disasters. We welcome submissions that showcase innovative solutions and strategies for dealing with a range of hazards, including hurricanes, earthquakes, floods, and wildfires.

One of the key messages from the collection is the importance of preparedness; anticipation and preemption are powerful interventions. Disaster preparedness is crucial for health security for several reasons. Disasters and emergencies can place an enormous strain on health infrastructure, including hospitals, clinics, and medical supply chains [ 3 ]. By being prepared, communities can take steps to protect this infrastructure and ensure that it is able to function during and after a disaster. Disaster preparedness can help to ensure that emergency responders are able to provide a timely response to emergencies. This can be critical in situations where every minute counts, such as during a disaster or a terrorist attack [ 4 , 5 , 6 ].

Another point is prevention of disease outbreaks. Disasters can increase the risk of disease outbreaks, particularly in areas where sanitation and hygiene are compromised. Preparedness efforts can help to reduce the risk of outbreaks by ensuring that individuals have access to clean water, adequate sanitation facilities, and proper medical care [ 7 ]. At the same time, readiness for infectious disease outbreaks that can complicate established response measures such as sheltering and evacuation is essential [ 8 ].

Disasters can also have a significant impact on mental health, causing stress, anxiety, and depression [ 9 , 10 ]. Preparedness efforts can include providing support for mental health needs, which can help individuals to cope with the effects of a disaster [ 11 , 12 ]. Overall, disaster preparedness is a critical component of health security. By being prepared, communities can help to reduce the impact of disasters on health and wellbeing, and ensure that individuals have access to the medical care and support they need during and after a disaster.

Another theme that we would like to see emerging from the collection of articles is the need for collaboration and cooperation [ 13 , 14 , 15 ]. In order to prepare for and respond to disasters effectively, individuals, organizations, and governments must work together. This can involve sharing resources and expertise, coordinating efforts, and engaging in community outreach to ensure that everyone is on the same page.

Training for mass casualty incidents and disasters should be conducted regularly and refreshed at intervals. In order to improve society preparedness and resiliency, disaster management competencies should be linked to the overall quality improvement process [ 16 , 17 ].

In order to ensure the appropriate level of knowledge and skills of society for mass casualty incidents and disasters, it is necessary (as a minimum) to design training curriculum that is comprehensive and aligns with international standards that include the roles, responsibilities, functions and resources needed for MCI and disaster preparedness and response [ 18 , 19 ]. The provision of training for mass casualty incidents and disasters by the employer should be mandatory, as should the participation of employees [ 20 , 21 , 22 , 23 ].

This collection aims to provide an up-to-date overview of the latest scientific research and innovative solutions related to preparing for and responding to natural hazards. The collection is highly relevant to researchers, policy makers, and other experts in the field of disaster management, as they offer insights into emerging trends, challenges, and best practices for addressing natural hazards. We are interested in articles that provide practical advice and strategies that can be implemented in real-world situations, such as community outreach programs, emergency response plans, and risk reduction measures. By implementing these strategies, communities can build resilience and better navigate the challenges posed by natural hazards.

We believe this collection is an essential resource for anyone involved in disaster research and management. It aims to provide a comprehensive overview of the latest research and best practices for preparing for and responding to natural hazards. We are interested in submissions that contribute to this goal by presenting practical solutions and strategies that can be implemented in a range of settings. By working together and implementing the strategies outlined in this collection, we can build more resilient communities and navigate natural hazards with confidence based on the latest science.

Data availability

Not applicable.

Sohrabizadeh S, Yousefian S, Bahramzadeh A, Vaziri MH. A systematic review of health sector responses to the coincidence of disasters and COVID-19. BMC Public Health. 2021;21(1):709.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Feng K, Ouyang M, Lin N. Tropical cyclone-blackout-heatwave compound hazard resilience in a changing climate. Nat Commun. 2022;13(1):4421.

Nuzzo JB, Meyer D, Snyder M, et al. What makes health systems resilient against infectious disease outbreaks and natural hazards? Results from a scoping review. BMC Public Health. 2019;19(1):1310.

Article   PubMed   PubMed Central   Google Scholar  

Larson RC, Metzger MD, Cahn MF. Responding to emergencies: Lessons learned and the need for analysis. Interfaces. 2006 Dec;36(6):486–501.

Tin D, Barten DG, Goniewicz K, Burkle FM, Ciottone GR. An epidemiological analysis of terrorism-related attacks in eastern Europe from 1970 to 2019. Prehospital and disaster medicine. 2022 Aug;37(4):468–73.

Khorram-Manesh A, Dulebenets MA, Goniewicz K. Implementing public health strategies—the need for educational initiatives: a systematic review. Int J Environ Res Public Health. 2021 May;30(11):5888.

Waring SC, Brown BJ. The threat of communicable diseases following natural disasters: a public health response. Disaster Management & Response. 2005 Apr 1;3(2):41 – 7.

Whytlaw JL, Hutton N, Wie Yusuf JE, et al. Changing vulnerability for hurricane evacuation during a pandemic: issues and anticipated responses in the early days of the COVID-19 pandemic. Int J Disaster Risk Reduct. 2021;61:102386.

Norris FH, Friedman MJ, Watson PJ, Byrne CM, Diaz E, Kaniasty K. 60,000 disaster victims speak: part I. An empirical review of the empirical literature, 1981–2001. Psychiatry. 2002;65(3):207–39.

Article   PubMed   Google Scholar  

Norris FH, Friedman MJ, Watson PJ. 60,000 disaster victims speak: part II. Summary and implications of the disaster mental health research. Psychiatry. 2002;65(3):240–60.

Makwana N. Disaster and its impact on mental health: a narrative review. J family Med Prim care. 2019 Oct;8(10):3090.

Math SB, Nirmala MC, Moirangthem S, Kumar NC. Disaster management: mental health perspective. Indian J Psychol Med. 2015 Jul;37(3):261–71.

Skryabina EA, Betts N, Reedy G, Riley P, Amlôt R. The role of emergency preparedness exercises in the response to a mass casualty terrorist incident: a mixed methods study. International journal of disaster risk reduction. 2020 Jun 1;46:101503.

Khorram-Manesh A, Mortelmans LJ, Robinson Y, Burkle FM, Goniewicz K. Civilian-military collaboration before and during Covid-19 pandemic—A systematic review and a pilot survey among practitioners. Sustainability. 2022 Jan;14(2):624.

Corbin JH, Oyene UE, Manoncourt E, et al. A health promotion approach to emergency management: effective community engagement strategies from five cases. Health Promot Int. 2021;36(Supplement1):i24–i38.

Khorram-Manesh A, Goniewicz K, Phattharapornjaroen P, Gray L, Carlström E, Sundwall A, Hertelendy AJ, Burkle FM. Differences in Ethical Viewpoints among Civilian–Military Populations: A Survey among Practitioners in Two European Countries, Based on a Systematic Literature Review. Sustainability. 2022 Jan 18;14(3):1085.

Goniewicz K, Goniewicz M. Disaster preparedness and professional competence among healthcare providers: Pilot study results.Sustainability. 2020 Jun17;12(12):4931.

Goniewicz K, Goniewicz M, Włoszczak-Szubzda A, Burkle FM, Hertelendy AJ, Al-Wathinani A, Molloy MS, Khorram-Manesh A. The importance of pre-training gap analyses and the identification of competencies and skill requirements of medical personnel for mass casualty incidents and disaster training. BMC Public Health. 2021 Dec;21:1–1.

Bharosa N, Lee J, Janssen M. Challenges and obstacles in sharing and coordinating information during multi-agency disaster response: propositions from field exercises. Inform Syst Front. 2010 Mar;12:49–65.

Burkle FM. Mass casualty management of a large-scale bioterrorist event: an epidemiological approach that shapes triage decisions. Emergency Medicine Clinics. 2002 May 1;20(2):409 – 36.

Loke AY, Guo C, Molassiotis A. Development of disaster nursing education and training programs in the past 20 years (2000–2019): A systematic review. Nurse education today. 2021 Apr 1;99:104809.

Rebmann T, Charney RL, Loux TM, Turner JA, Abbyad YS, Silvestros M. Emergency medical services personnel’s pandemic influenza training received and willingness to work during a future pandemic. Prehospital Emergency Care. 2020 Sep 2;24(5):601-9.

Hughes A, Almeland SK, Leclerc T, Ogura T, Hayashi M, Mills JA, Norton I, Potokar T. Recommendations for burns care in mass casualty incidents: WHO Emergency Medical Teams Technical Working Group on Burns (WHO TWGB) 2017–2020. Burns. 2021 Mar 1;47(2):349 – 70.

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Krzysztof Goniewicz

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Disaster management and emerging technologies: a performance-based perspective

Meditari Accountancy Research

ISSN : 2049-372X

Article publication date: 19 August 2021

Issue publication date: 14 July 2022

This paper aims to analyse how emerging technologies (ETs) impact on improving performance in disaster management (DM) processes and, concretely, their impact on the performance according to the different phases of the DM cycle (preparedness, response, recovery and mitigation).

Design/methodology/approach

The methodology is based on a systematic review of the literature. Scopus, ProQuest, EBSCO and Web of Science were used as data sources, and an initial sample of 373 scientific articles was collected. After abstracts and full texts were read and refinements to the search were made, a final corpus of 69 publications was analysed using VOSviewer software for text mining and cluster visualisation.

The results highlight how ETs foster the preparedness and resilience of specific systems when dealing with different phases of the DM cycle. Simulation and disaster risk reduction are the fields of major relevance in the application of ETs to DM.

Originality/value

This paper contributes to the literature by adding the lenses of performance measurement, management and accountability in analysing the impact of ETs on DM. It thus represents a starting point for scholars to develop future research on a rapidly and continuously developing topic.

• Emerging technologies
• Disaster management
• Performance
• Systematic literature review
• Emergency response

Vermiglio, C. , Noto, G. , Rodríguez Bolívar, M.P. and Zarone, V. (2022), "Disaster management and emerging technologies: a performance-based perspective", Meditari Accountancy Research , Vol. 30 No. 4, pp. 1093-1117. https://doi.org/10.1108/MEDAR-02-2021-1206

Emerald Publishing Limited

Copyright © 2021, Carlo Vermiglio, Guido Noto, Manuel Pedro Rodríguez Bolívar and Vincenzo Zarone.

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

Despite the rising number of catastrophic events occurring in recent years, disaster management (DM) has received little attention from the interdisciplinary accounting community ( Lai et al. , 2014 ; Sargiacomo et al. , 2014 ; Walker, 2014 ; Sciulli, 2018 ; Perkiss and Moerman, 2020 ; Sargiacomo and Walker, 2020 ).

A key aspect of DM theory and practices is related to the information systems used to support decision-making and to measure, manage and report the performance of the whole DM cycle (see, amongst others, Carreño et al. , 2007 ). Information systems have widely supported disaster practitioners in recent decades, providing an increasing volume of data gathered through emerging technologies (ETs), such as big data, Internet of Things (IoT) ( Yang et al. , 2013 ; Shah et al. , 2019 ), machine learning, artificial intelligence (AI), remote sensing, cloud computing, social media communication ( Alexander, 2014 ) and blockchain.

ETs are science-based innovations which provide great transformative potential for an industry, in an “early phase of development” ( Boon and Moors, 2008 ) and can lead to “radical innovations” ( Day and Schoemaker, 2000 ) and/or allow an evolutionary process of technical, institutional and social change; however, they bring risks of uncertainty in terms of network effects, costs and social and ethical concerns ( Halaweh, 2013 ).

All these technologies are spreading their value in a growing variety of domains, effectively contributing to the planning, decision-making, accounting and auditing process of public and private organisations ( Ndou et al. , 2018 ; Bonsón and Bednárová, 2019 ; Lamboglia et al. , 2020 ; Lombardi and Secundo, 2020 ; Rodríguez-Bolívar et al. , 2021 ; Tingey-Holyoak et al. , 2021 ; De Santis and D’Onza, 2021 ; Lombardi et al. , 2021 ).

Indeed, the implementation of digital technologies are becoming increasingly relevant for corporate and performance management ( Oliver, 2018 ; Marrone and Hazelton, 2019 ; Wang et al. , 2020a , 2020b ; Chatterjee et al. , 2021 ; Jun et al. , 2021 ), and especially ETs have demonstrated in the last years to be particularly supportive in fostering these issues in health care ( Spanò and Ginesti, 2021 ), transportation ( Chhabra et al. , 2021 ), manufacturing ( Rezaei et al. , 2017 ) and so on.

With specific regards to DM, extant studies have mainly focused on how technology could support data gathering and visualisation ( Fajardo and Oppus, 2010 ) as well as knowledge management ( Inan et al. , 2018 ; Raman et al. , 2018 ; Oktari et al. , 2020 ). Conversely, literature reviews have focused on how specific technologies influence DM ( Kankanamge et al. , 2019 ), how they support supply chain management ( Ivanov et al ., 2019 ) or how they can be applied to deal with risks in small- and medium-sized enterprises ( Verbano and Venturini, 2013 ).

To date, various streams of research across different disciplines, such as information science, computer science and engineering, have focused on the impact of ETs on disaster and emergency response.

However, to the authors’ knowledge, limited attention has been devoted to understanding how ETs could support performance measurement, management and accountability in the specific setting of DM processes. To fill this gap, this study develops a systematic literature review (SLR) analysing how ETs impact on improving performance in DM, altering and changing DM processes to enhance resilience according to the different phases of the DM cycle (preparedness, response, recovery and mitigation). We used Scopus, ISI Web of Science, ProQuest and EBSCO as the data sources. We selected academic journal articles within the business, management and accounting categories.

The paper is structured as follows. Section 2 presents a theoretical background that links literature on DM, ETs and performance. Section 3 explains the methodology and clarifies the research question, and Section 4 presents the results of the SLR. Finally, the discussion and conclusions are presented.

2. Theoretical background

2.1 an overview of disaster management.

The frequency and magnitude with which natural disasters (earthquakes, floods, landslides, droughts, storms, etc.) have occurred in recent decades are alarming. According to EM-DAT [ 1 ], over the last 20 years, disasters have claimed approximately 1.23 million lives and affected a total of over 4 billion people, leading to US$2.97tn in economic losses worldwide. During the same timeframe, a total of 7,348 disasters related to natural hazards have occurred worldwide.

The concept of disasters is extremely complex and multidimensional in nature; it can be discussed by drawing on several connected fields of research ( Quarantelli, 1998 ).

According to the definition proposed by the United Nations Office for Disaster Risk Reduction, a disaster is:

[…] a serious disruption of the functioning of a community or a society at any scale due to hazardous events interacting with conditions of exposure, vulnerability and capacity, leading to one or more of the following: human, material, economic and environmental losses and impacts [ 2 ].

DM refers to the organisation, planning and application of measures aimed at preparing for, responding to and recovering from disasters. This topic has been widely discussed in the academic literature in recent decades through different perspectives ( Faulkner, 2001 ; Pearce, 2003 ; Lettieri et al. , 2009 ) and, most recently, with a specific focus on how firms ( Kraus et al. , 2020 ; Ferrigno and Cucino, 2021 ) and public institutions ( Steen and Brandsen, 2020 ) have reacted to the COVID-19 pandemic. Social scientists frame disasters from three different perspectives: the hazard , the vulnerability and the holistic view ( Berg and De Majo, 2017 ).

Under the hazard paradigm, disasters are considered extreme physical events with accidental causes and no human or cultural influence on their origin and scope; therefore, DM is mainly focused on post-disaster short-term measures, such as recovery, relief and humanitarian aid for those who need help ( Alexander, 1997 ).

This traditional view has been replaced by the vulnerability paradigm, rooted in development studies, in which disasters are considered the results of natural causes related to the vulnerability of the surrounding social, economic and political environment ( Cutter, 1996 ; McEntire, 2005 ; Buckle, 2005 ; Adger, 2006 ). Natural disasters, rather than being only uncontrollable events, greatly depend on some structural constraints of the population hit by catastrophic events ( Wisner et al. , 2004 ).

Assuming this renewed approach, Gilbert (1998) stated that a “disaster is no longer experienced as a reaction; it can be seen as an action, a result, and more precisely, a social consequence.” This broader perspective sheds light on how human activity, social order and development paths characterise the breadth and severity of natural disasters over time. According to Perry (1998) , “vulnerability is socially produced,” but it “may be also related to the state of technology,” as information systems and ETs play a supportive role and have a key relevance within the various phases of DM ( Von Lubitz et al. , 2008 ).

The increase in the occurrence of natural disasters sheds light on the inadequacy of traditional DM processes and practices around the globe. To tackle the wickedness ( Rittel and Webber, 1973 ; Head and Alford, 2015 ; Pesch and Vermaas, 2020 ) of such problems and reduce their intrinsic complexity, scholars have highlighted the importance of collaborative networks amongst public institutions ( Waugh and Streib, 2006 ; Ansell et al. , 2010 ; Comfort et al. , 2012 ; Kapucu and Garayev, 2016 ), coordination mechanisms to respond and react to the emergence of problems ( Moynihan, 2008 ; Boin et al. , 2013 ; Kuipers et al. , 2015 ), competencies and leadership behaviours ( Rosenthal and Kouzmin, 1997 ; Van Wart and Kapucu, 2011 ) and capacity building and community awareness ( Kitagawa, 2021 ). All these aspects are important in disaster and emergency situations characterised by complexity, urgency and uncertainty ( Kapucu and Van Wart, 2008 ).

The multiple threats posed by disasters suggest the adoption of a holistic view of DM with a more strategic focus on the actions and tools targeted to reduce exposure and vulnerability to disasters ( Berg and De Majo, 2017 ). The holistic view marks a paradigm shift from responsive to proactive management of natural hazards based on the principles of resilience and disaster risk reduction ( Manyena, 2006 ; Demiroz and Haase, 2019 ). The key phases of the DM cycle can be summarised in Figure 1 .

DM requires and generates a huge amount of data coming from different sources, which must be reliable, accurate and real time. Through these data, DM practitioners can gather information on the features, locations and prospective impacts of threats, providing essential inputs for managing all the phases of the disaster cycle in a timely and effective way ( Yu et al. , 2019 ). ETs have, to date, offered opportunities to improve the management of several fields. Table 1 shows the main applications discussed in the academic literature.

These technologies are considered to have a high impact on each of the phases displayed in Table 2 , although all of them are valuable for the whole DM cycle.

2.2 Performance in the disaster management context

Performance is one of the most explored topics by business and public administration scholars in the last half century. It is a broad concept discussed by different streams of literature, which range from the measurement of performance to management accounting and control, behavioural economics and so on ( Moynihan, 2008 ; Ferreira and Otley, 2009 ; Bititci et al. , 2012 ).

The literature usually focuses on performance by adopting three lenses that are strongly connected: performance measurement ( Bititci et al. , 2012 ), performance management ( Ferreira and Otley, 2009 ) and accountability ( Roberts, 1991 ; Gray, 1992 ).

Performance measurement is the activity of collecting data, defining indicators and computing such indicators to evaluate the ability of a certain entity to achieve strategic goals ( Eccles, 1991 ; Hudson et al ., 2001 ).

If performance measurement is concerned with what and how to measure, performance management is instead focused on the utilisation of such information in decision-making processes ( Ferreira and Otley, 2009 ; Bititci et al. , 2012 ). In this sense, performance management could be defined as the process of creating the context for performance ( Lebas, 1995 ). Performance management comprehends the whole process starting from the definition of performance, the identification of related targets and the evaluation ex-post of the results obtained ( Lebas, 1995 ; Ferreira and Otley, 2009 ).

Lastly, performance accountability is a broad concept which covers activities such as reporting performance, communicating the results achieved to stakeholders and the broader community and guaranteeing transparency ( Roberts, 1991 ; Gray, 1992 ). It is a concept which has been widely explored in the literature on both public and private sector organisations ( Kassel, 2008 ; Kaur and Lodhia, 2019 ).

Because of increasingly complex changes in society and the environment, performance studies have rapidly evolved in recent decades. Whilst the first management scholars mainly focused on financial performance, today, the literature agrees that researchers should focus on different performance dimensions, such as social, competitive and environmental ( Kaplan and Norton, 1996 ; Bititci et al. , 2012 ; Khalid et al. , 2019 ). Moreover, the evolution of the discipline has made scholars shift their focus to the inter-organisational level, as in the case of supply chains, strategic alliances or governance networks ( Dekker, 2016 ; Nuti et al. , 2018 ; Dell’ Era et al. , 2020 ; Ferrigno et al. , 2021 ).

DM is amongst the fields of application of management which, more than others, present degrees of social complexity derived from a large set of stakeholders, multiple objectives and goals and the difficulty of measuring many of these because of the high uncertainty given by the unprecedented scenarios characterising every disaster ( Comfort et al. , 2004 ).

The introduction and adoption of ETs are of great support to researchers and practitioners as they cope with the complexities of measuring, managing and reporting performance. According to many authors, information and digital technologies are indeed pivotal to the design and implementation of performance management and accountability systems ( Marr and Neely, 2001 ; Nudurupati and Bititci, 2005 ; Rodríguez-Bolívar et al. , 2006 ; Buys, 2008 ; Marrone and Hazelton, 2019 ; Lombardi and Secundo, 2020 ).

New technologies may support performance in multiple ways. First, they assist in the measurement of performance ( Nudurupati and Bititci, 2005 ; Cockcroft and Russell, 2018 ). Some technologies, such as big data or AI, allow managers to both have access to new sources of information and improve their ability to manage and analyse related data ( Sardi et al. , 2020 ). This may enable the creation of new measures and performance targets. As such, in the case of DM, decision-makers may have access to new forms of information coming from social networks, satellites or sensors.

The second pivotal contribution of ETs is related to the real-time availability of new information, which improves performance management processes ( Marr and Neely, 2001 ; Nudurupati and Bititci, 2005 ). This is of particular interest in the response phase of DM. Having the possibility to promptly react based on real-time reliable information can make a difference in emergency contexts ( Laituri and Kodrich, 2008 ; Ragini et al. , 2018 ; Imran et al. , 2020 ).

Third, ETs as applied to performance have shown great potential for understanding concerns related to reporting and internal and external accountability ( Marrone and Hazelton, 2019 ; Lombardi and Secundo, 2020 ). For example, new forms of data visualisation are being largely used to inform the community about the results achieved by the institutions in charge. What is peculiar in performance accountability in DM is its double directions, i.e. downward in an accountability to the other , in which the focus is on the intrinsic value of the suffering community, and upward in an accounting for itself , in which the focus is on market value ( Sargiacomo et al. , 2014 ).

In light of this theoretical premise, this paper aims at covering a potential gap in understanding how ETs impact on improving performance in DM processes and, concretely, their impact on the performance according to the different phases of the DM cycle (preparedness, response, recovery and mitigation).

3. Data collection and methods

To achieve the research aim, this study conducts an SLR to identify the impact of ETs on performance measurement, management and accountability ( Kraus et al. , 2020 ; Snyder, 2019 ). This methodology has already been applied both in relation to the applications of ETs (i.e. Martinez-Rojas et al. , 2018 ) and to DM (i.e. Lettieri et al. , 2009 ; Akter and Fosso Wamba, 2019 ).

An SLR is a systematic process aimed at defining the research question, identifying relevant studies and evaluating their features, quality and impact on the field. The last phase of an SLR summarises the findings qualitatively and/or quantitatively, reporting evidence to clarify what is and is not known with respect to the object of investigation ( Denyer and Tranfield, 2009 ).

definition of the research questions;

development of the research protocol;

identification of documents for analysis;

development of a coding framework; and

execution of in-depth analyses.

The first phase consisted of defining the research question of the study, which focuses on understanding how ETs contribute to improving DM processes. Consistent with the theme of the special issue, the research question is also explored from the perspective of the emerging issues related to the dimensions of performance and, more specifically, the impact of ETs in terms of management, measurement and accountability within the DM cycle.

In the second phase of the SLR, we define the research protocol to support evidence-based practices and ensure objectivity ( Tranfield et al. , 2003 ). In this phase, the focus of the study, the research strategy, the data sources and the inclusion/exclusion criteria used for the review are specified in accordance with the research question ( Petticrew and Roberts, 2008 ). The background of this study has been created by adopting a wide perspective of analysis, selecting the most relevant articles in the business, management and accounting fields. Later on, we opted for a longitudinal study to collect literature from different scientific databases.

The third phase aims to identify the papers to be added to the literature review, defining the research string to use. We managed to collect research articles via title–abstract–keyword field codes using Boolean operators (AND, OR) as connectors.

Following the parameters, the search strategy was applied in the business, management and accounting areas, referring to the Scopus and JCR lists. A description is reported in Table 2 .

The search query was entered in the ISI Web of Knowledge, Scopus, EBSCO Host and ABI/INFORM (ProQuest) databases, and it allowed us to obtain a total of 101, 172, 184 and 280 articles, respectively, for a total of 737 articles. We first eliminated redundant and non-English articles ( Petticrew and Roberts, 2008 ), which were few and not very significant with respect to the research question. We also restricted the collection to scientific articles only ( Zheng et al. , 2020 ; Lombardi and Secundo, 2020 ) because during the review process, these papers were tested with high-quality standards; the purpose was to ensure the quality of knowledge they provided ( Light and Pillemer, 1984 ).

The timeframe covered the period from 2000 to February 2021. Although few studies have devoted their attention to the potential capabilities and limitations of digital technologies in DM at the end of the last century (amongst others, Wallace and De Balogh, 1985 ; Waugh, 1995 ; Stephenson and Anderson, 1997 ; Barth and Arnold, 1999 ; Chengalur-Smith et al. , 1999 ), the choice of the period was made in light of the growing interest in ETs and their impact in society and the public sector starting from the early 2000s, as confirmed by the academic literature ( Day and Schoemaker, 2000 ; Rotolo et al. , 2015 ).

From a careful reading of the abstracts, we eliminated papers of a specific technical nature, in which the connection between ETs and DM was only mentioned but not developed. Double counting of papers was avoided by including only those that were different across the databases. These processes allowed us to obtain a valid sample of 127 articles. We checked through the full-text articles to further evaluate the quality and eligibility of the studies ( Xiao and Watson, 2019 ). Carrying out a thorough reading of the papers, we selected those relevant to our research question, obtaining a final corpus of 69 papers ( Figure 2 ).

Then, we defined the coding framework, selecting the following parameters: time of publication, distribution of papers amongst journals, author citations and keyword co-occurrence. In this phase, a double analysis was carried out on the final sample: descriptive analysis and clustering. The descriptive analysis aimed to highlight the main characteristics of the articles, indicating their number, evolution over time and distribution amongst journals.

Data analysis was conducted using VOSviewer software ( Van Eck and Waltman, 2017 ). As in other descriptive bibliometric analyses ( Secundo et al. , 2020 ), we analysed keyword co-occurrence and document citations; then, we performed a cluster analysis to capture the focal points and connections between the main topics considered in our study.

We developed the co-occurrence analysis by selecting keywords as a single entity for analysis, as a meaningful description of an article’s content ( Lamboglia et al. , 2020 ) and as an endpoint to add a paper with a minimum number of two occurrences of a keyword. Using this technique, we obtained a twofold visualisation – network and overlay.

The last phase of the SLR aims to carry out a critical and comprehensive analysis of the selected articles. Finally, we clustered the results using VOSviewer. The main findings derived from the SLR are reported in Section 4.

4. Findings

4.1 characteristics of sample selection.

As shown in Figure 3 , the number of articles that investigate the relationship between ETs and DM in accordance with our research question was narrowed until 2016, with an average of three articles per year. The 2018–2020 period seems to be the most prolific, covering almost 65% of the total, with 2019 marking the highest number of publications per year (21).

The descriptive analysis indicates the source titles in which the topic of our research has been mainly discussed. The following table lists the journals with the highest number of published articles concerning the subject of our research question ( Table 3 ).

Source citation indicates that the Journal of Cleaner Production is the source with the highest number of citations for a single article included in the sample ( Papadopoulos et al. , 2017 ), followed by Annals of Operation Research (185), International Journal of Production Economics (138) and Technological Forecasting and Social Change (129).

For article citation counting, we used the Scopus Field-Weighted Citation Impact to compare each paper citation with the average number of citations received by all similar documents over a three-year window. This choice was assumed with the aim of maximising the relevance of our sample, refusing the adoption of an arbitrary cut-off point for citation counting ( Keupp et al. , 2012 ). This way, newer articles were not at a disadvantage compared with older ones. Table 4 lists the top 15 articles with the highest citations within the selected timeframe.

An analysis of documents by country shows that the USA has the highest number of both papers (20) and citations (785), followed by India (14 papers and 379 citations), the UK (13 papers and 632 citations) and France (9 papers and 452 citations). The table also shows the number of citations by source.

4.2 Networking and clustering analysis

Then, we used the VOSviewer algorithm ( Van Eck and Waltman , 2014, 2017 ) to perform the cluster analysis starting from the co-occurrence analysis, which expresses the relatedness of items based on the number of documents in which they occur together. As explained before, our unit of analysis is author keywords, with a threshold of two keywords. We obtained a total of 37 keywords, which fell into four different clusters ( Table 5 and Figure 4 ).

Our analysis includes the overlay visualisation, which is presented in Figure 5 . Keywords in red colour refer to the more recent topics discussed in the academic debate on ETs in DM.

The following paragraph illustrates the findings of each cluster.

4.2.1 Yellow cluster.

Papers included in the yellow cluster are mainly focused on the support that simulation approaches mainly provide to the preparedness phase of DM and to performance measurement.

In the broad management field, the value of simulation is highly recognised when experimentation in the real world is not feasible because of time, cost or ethical constraints ( Davis et al. , 2007 ; Sterman, 2014 ; Noto and Cosenz, 2021 ). These kinds of situations characterise the contexts in which DM operates. In fact, experimenting with a disaster in the real world is never feasible or acceptable. As such, simulated environments are the only way we can discover how DM works and where high leverage points may lie to foster performance.

Simulation in DM studies has been explored in depth by Mishra et al. (2019) , who conducted a literature review of the key approaches adopted by scholars in the field. These authors focused on system dynamics (SD), Monte Carlo simulation (MCS), agent-based modelling (ABM) and discrete event simulation (DES).

MCS has been mainly adopted for risk modelling, SD has been proposed as an effective tool for prevention. ABM has shown effectiveness in considering the behaviour of the multiple agents involved in the DM cycle. Less adopted, according to Mishra et al. ’s (2019) study, was the DES, which is mainly used when modelling for large-scale disasters.

Whilst the literature on performance is already combined with simulation ( Bianchi, 2016 ), with a few exceptions ( Wang et al. , 2020a , 2020b ), the resulting frameworks have not been applied to DM studies. However, in the analysed articles, simulation is mainly examined from the performance point of view. For example, Gul et al. (2020) used DES to assess the preparedness of an emergency department during an earthquake by using length of stay and utilisation of medical staff as measures of performance. Sahebjamnia et al. (2017) used coverage, cost and response time as performance measures in a decision support system for managing humanitarian relief chains. Lee and Lee (2021) focused on disaster response performance in a multi-agent environment. Fan et al. (2021) emphasised how ETs, such as AI algorithms and deep learning architectures, significantly contribute to disaster preparedness at the city level where, through the combination of multiple sources of data (geospatial, sensors, social media, crowdsourcing) and the interactions amongst different entities, the inefficiencies induced by their complex relationships can be easily explored. Moreover, the authors pointed out how temporal information recorded in the Disaster City Digital Twin enables monitoring, analysing and predicting the dynamic structures of the networks involved and their potential effects on the efficiency of relief and response actions.

In all the above-mentioned cases, scenario analysis through simulation was used to explore the preparedness and resilience of a specific system when dealing with different phases of the DM cycle by observing how the measures of performance identified may evolve under different environmental conditions.

4.2.2 Red cluster.

Articles which fall into this cluster are mainly focused on the response phase of DM and provide interesting implications for what concern performance management. In light of our findings, the ETs which mostly support these phases are geospatial data (GIS), volunteered geographic information (VGI), IoT and robotics and automation (RA), such as drones and chatbots. Some scholars clearly described the complementary role of GIS and VGI in the provision of information, which can be helpful in coordinating response tasks amongst volunteer groups and official disaster agencies ( Hung et al. , 2016 ; Contreras et al. , 2016 ; Schumann, 2018 ; Akter and Fosso Wamba, 2019 ; Sharma et al. , 2020 ). Other studies have shown the main challenges (digital divide, lack of resources, poor data quality) associated with their use in emergency response contexts ( Haworth, 2016 ).

RA are effective tools for relief and response operations. To date, unmanned aerial vehicles (UAVs), which are a subcategory of RA, have been used in response to a wide range of disasters that have occurred in the last decade ( Chowdhury et al. , 2017 ; Kim et al. , 2018 ), providing valuable support in searching the victims, mapping the affected zones, making structural inspections, estimating debris and assessing damage.

More recently, UAVs have become of key relevance in supplying emergency commodities in disaster-affected regions. In this regard, some scholars ( Bravo et al. , 2019 ; Zwęgliński, 2020 ) stressed the impact of RA technologies in minimising the time and costs of disaster relief operations.

A further ET used in both the response and recovery phases of DM is IoT ( Shahat et al. , 2020 ), which enables accurate and real-time accountability of resources and personnel allocated to emergency response operations.

Sinha et al. (2019) showed the role of IoT-based solutions in catering to the task requirements of the personnel involved in DM, specifically rescue operations. A critical aspect here is improper resource allocation, which slows down recovery efforts.

Performance measurement seems the main concern of the articles which fall into the red cluster. KPIs are mainly used to calculate the extent to which ETs might reduce time, distance covered, number of lives saved and relief provided. To some extent, ETs enhance the level of accountability of response operations, coping with the lack of visibility of resources available on the disaster scene or dispatched to other places prior to the event ( Yang et al. , 2013 ).

4.2.3 Blue cluster.

This cluster introduced an interesting topic concerning the contribution of data mining, machine learning and social media to performance measurement, management and accountability during disaster events. Data mining and machine learning algorithms are widely recognised tools to support decision making in many areas and, more specifically, along the DM cycle ( Zagorecki et al. , 2013 ).

Machine learning is an umbrella term which sometimes overlaps with other concepts and applications, i.e. deep learning and AI. In any case, our findings show the high relatedness of this ET to the whole DM cycle, specifically to the emergency response phase ( Chaudhuri and Bose, 2020 ).

The key role of social media in DM has been widely recognised in the literature ( Xiao et al. , 2015 ). User generated content (UGC) from disaster-affected areas provides valuable information for emergency response when dealing with DM, as stated by Han et al. (2019) . Nevertheless, this study points out the nature of UGC, which is huge, disordered and continuous. As a consequence, its exploitation has a direct impact on the effectiveness of response actions during disaster events.

On the one hand, the huge amount of data generated by social media – Twitter, Facebook, TikTok and other platforms – provides a big picture of the ongoing disaster situation in terms of location, temporal sequence and entity-related information ( Hoang and Mothe, 2018 ; Singh et al. , 2019 ). On the other hand, the effective use of these tools raises critical issues in terms of text classification, data selection and validation, which are relevant when dealing with unpredictable and catastrophic events. More recently, sentiment analysis, topic modelling and other natural language processing tools have become promising techniques for assessing the reliability and accuracy of data gathered from social media during disasters ( Thekdi and Chatterjee, 2019 ; Karami et al. , 2020 ). These ETs enable situational awareness in disaster response ( Li et al. , 2018 ), especially through the analysis of crowdsourced data provided by the eyewitnesses of disaster events ( Zahra et al. , 2020 ). From a performance-based view, it can be argued that the aforementioned ETs mainly support performance measurement through the real-time data gathered from social media. This result is coherent with our theoretical background. Moreover, social media are largely used by local and national authorities, as they show great potential for improving efficiency and widening the audience of information systems during disasters and for enhancing relations (e.g. improved transparency and accountability) between governments and the community affected by the event ( Wehn and Evers, 2015 ).

4.2.4 Green cluster.

The last cluster obtained from our bibliometric analysis consists of papers which focus on the post-disaster phase (i.e. recovery and mitigation), namely, the humanitarian relief and the related humanitarian supply chain (HSC) logistics. In this regard, the ETs linked with this phase mainly impact on performance management and accountability.

As is well known, humanitarian logistics refers to the mobilisation and management of resources (human and material) through which support for post-disaster response and rehabilitation operations is provided.

HSC management is crucial for the efficiency and effectiveness of DM systems. As observed by Rodríguez-Espíndola et al. (2020) , the “duplication of efforts for data input, multiple formats, lack of control of budgets, absence of accountability, lack of integrity in procurement procedures, absence of a central database, and manual reporting and tracking” affect current DM systems.

The adoption of ETs, such as big data and predictive analytics (BDPA), provides valuable support to overcome the limitations in disaster relief operations. Indeed, scholars agree on the contribution that BDPA can offer when dealing with disasters ( Ragini et al. , 2018 ). Akter and Fosso Wamba (2019) highlighted how BDPA can help address various challenges by providing critical recovery services in disasters. Considering the main properties of BD, such as volume (referring to the amount of data), velocity (referring to the frequency or speed by which data are generated and delivered), veracity (referring to data quality) and value (referring to the benefits from the analysis and use of big data), many authors have underlined how these help improve the visibility, coordination and sustainability of the HSC after a disaster ( Papadopoulos et al. , 2017 ; Dubey et al. , 2018 ; Dubey et al. , 2019 ; Jeble et al. , 2019 ).

The subset of articles which fall into the green cluster gives relevance to some aspects related to both performance management and measurement. Abidi et al. (2014) analysed the state of the art of performance measurement, management and accountability in HSC. They pointed out some factors that determine reluctance to implement performance measurement in the humanitarian sector, such as a short-term perspective of disaster response actions, limited IT capacity and infrastructure and a chaotic environment.

Other scholars have underlined how ETs have enabled officials and non-government organisations involved in disaster relief and rehabilitation operations to reduce information asymmetry ( Dubey et al. , 2018 ) and address the lack of trust amongst agents, volunteers and the affected community using blockchain technology ( Dubey et al. , 2020 ); this has a critical role in enhancing collaboration and quickly building trust amongst various actors engaged in disaster relief operations.

5. Discussion and conclusions

This paper has sought to analyse how ETs impact on improving performance in DM processes, using a SLR as methodology of research and visualizing this impact with the VOSviewer software. The selected articles included in this review use different methodologies and focus on different phases of disasters, technologies and performance perspectives.

In many cases, we observed an inconsistent use of terms. This mainly happens in relation to the DM cycle. As mentioned in the theoretical background, DM can be framed into four phases: mitigation, preparedness, response and recovery. Many of the studies analysed, although focusing on specific phases, broadly refer to DM. This lack of specification poses challenges in the analysis and identification of the relationships between ETs and the DM phases. In some cases, DM is even used as a synonym for emergency management, resulting in a lack of clarity and confusion in the discipline. It is evident that ETs largely contribute to the management of disasters in each phase.

The complexity of DM often makes researchers and practitioners combine different technologies to improve the performance measurement, management and accountability of related activities. Although ETs may all be applied and successfully contribute to the different phases of the DM cycle, our analysis highlights some stronger linkages between some technologies, or features, and specific DM phases.

Many of the technologies considered rely on simulation features, which can be considered as a transversal tool supporting decision-makers at different levels in assessing the preparedness and resilience of a certain system prior to the occurrence of a natural disaster. Simulation enables experimentation with the consequences of a potential disaster in a virtual environment. This experimentation allows us to embrace the disaster risk reduction logic required to effectively tackle natural disasters. As such, simulation could be a valuable tool to improve preparedness. A simulated environment may foster the comprehension of the complex relationships characterizing disasters ex ante; thus, it may support the definition of consistent performance measures applicable to the preparedness phase.

Robotics and IoT are often associated with the improvement of operations in the response phase. ETs, such as drones or sensors, allow people to run activities that are not accessible to humans during disasters. These are valuable tools to monitor and manage performance during the response phases of the DM cycle.

Social media and related analytics tools have been widely used in two ways. On the one hand, they allow decision-makers to have access to a wider range of data sources (e.g. citizens, service users and other people involved in disasters) and to analyse this information through algorithms, such as topic modelling or sentiment analysis; this contribution is thus highly related to performance measurement. On the other hand, such tools foster performance accountability and disclosure towards the community.

In the following table, we highlighted the links between the performance perspectives here considered (measurement, management and accountability) and the main ETs identified by our review of the literature on DM ( Table 6 ).

As emerges from the table above, all these ETs are key to the decision support systems in every DM phase as also emerged from the reviewed papers. However, it is evident that the ability to process the data obtained and to verify their reliability and quality requires much effort. This aspect is probably linked to the lack of performance-related aspects in many of the papers analysed here. In fact, although many of the papers in our sample focus on performance, few of them embrace a theoretical framework based on performance measurement, management or accountability.

In this paper, we attempted to frame existing literature on DM and ETs according to a performance-based perspective to orient future studies and to highlight how and which ET contributes to the different phases of DM cycle.

As a result of this literature review, it emerges that prior research has put emphasis on the usefulness of ETs for preventing and managing disasters as well as to provide channels for reducing the harmful consequences of these disasters. Our systematization of previous literature results may have important implications both for theory and practice. At the theoretical level, the paper provides a framework that links the key performance perspectives and DM phases with the implementation of ETs in the DM field; such a framework may represent a useful reference for studies aimed at deepening related aspect. Moreover, the study highlights that simulation and simulation-based tools allow scholars to explore and test the development of new theories and solutions to analyse performance in DM contexts ( Davis et al. , 2007 ; Mishra et al. , 2019 ). At the practical level, the research suggests to the key involved actors (i.e. public administration, emergency managers, civil protection, experts and other stakeholders) to improve DM performance: analysing the importance of simulation tools to assess their preparedness; examining the ETs successfully used in the different DM phases (thus showing them how to invest in technologies); studying the importance to promote and enable citizens involvement as a new powerful source of data; and examining the need to invest in technologies to improve the ability to process, understand and use for decision-making purposes such data.

Despite its contributions, such as shedding light on the current state of the literature and providing future research directions about the theme addressed, this paper also has some limitations. Although frequently used in SLR, the criteria used to select our source of information – i.e. the exclusive focus on business, management and accounting categories; the exclusive focus on scientific articles in English language – may have excluded some valuable contributions. Future research could thus compare our results with other sources of information such as books and grey literature. Moreover, consistently with prior research, we have mainly analysed the implementation of ETs as “isolated islands.” Nonetheless, future research could analyse integration processes of these ETs for managing all disasters in an efficient manner.

Finally, the study did not consider the question of technological acceptance by the users of the technologies. Verifying whether specific technologies or certain phases of the DM cycle are associated with greater reluctance on users’ side could be interesting.

Disaster risk management cycle. Our elaboration

Selection, screening, eligibility and inclusion process of articles

Documents per year

Network visualization

Overlay visualization

Suitable emerging technologies in the DM field

Search criteria

Top ten journals publishing papers regarding DM

Citation counting. Top 15 cited documents

VOSviewer cluster description

Linking PM and ETs in DM cycle

Centre for Research on the Epidemiology of Disasters – CRED. School of Public Health Université Catholique de Louvain.

http://www.undrr.org/terminology/disaster#:∼:text=A%20serious%20disruption%20of%20the,and%20environmental%20losses%20and%20impacts . Accessed on 1 February 2021.

Abidi , H. , De Leeuw , S. and Klumpp , M. ( 2014 ), “ Humanitarian supply chain performance management: a systematic literature review ”, Supply Chain Management: An International Journal , Vol. 19 Nos 5/6 , pp. 592 - 608 .

Adger , W.N. ( 2006 ), “ Vulnerability ”, Global Environmental Change , Vol. 16 No. 3 , pp. 268 - 281 .

Akter , S. and Fosso Wamba , S. ( 2019 ), “ Big data and disaster management: a systematic review and agenda for future research ”, Annals of Operations Research , Vol. 283 Nos 1/2 , pp. 939 - 959 .

Alexander , D. ( 1997 ), “ The study of natural disasters, 1977–97: some reflections on a changing field of knowledge ”, Disasters , Vol. 21 No. 4 , pp. 284 - 304 .

Alexander , D.E. ( 2014 ), “ Social media in disaster risk reduction and crisis management ”, Science and Engineering Ethics , Vol. 20 No. 3 , pp. 717 - 733 .

Ansell , C. , Boin , A. and Keller , A. ( 2010 ), “ Managing transboundary crises: identifying the building blocks of an effective response system ”, Journal of Contingencies and Crisis Management , Vol. 18 No. 4 , pp. 195 - 207 .

Barcevičius , E. , Cibaitė , G. , Codagnone , C. , Gineikytė , V. , Klimavičiūtė , L. , Liva , G. , … and Vanini , I. ( 2019 ), “ Exploring digital government transformation in the EU-analysis of the state of the art and review of literature ,” Publications Office of the European Union, JRC118857 .

Barth , T.J. and Arnold , E. ( 1999 ), “ Artificial intelligence and administrative discretion: implications for public administration ”, The American Review of Public Administration , Vol. 29 No. 4 , pp. 332 - 351 .

Berg , M. and De Majo , V. ( 2017 ), “ Understanding the global strategy for disaster risk reduction ”, Risk, Hazards and Crisis in Public Policy , Vol. 8 No. 2 , pp. 147 - 167 .

Bianchi , C. ( 2016 ), Dynamic Performance Management , Springer , Berlin Vol. 1 .

Bititci , U. , Garengo , P. , Dörfler , V. and Nudurupati , S. ( 2012 ), “ Performance measurement: challenges for tomorrow ”, International Journal of Management Reviews , Vol. 14 No. 3 , pp. 305 - 327 .

Boin , A. , Ekengren , M. and Rhinard , M. ( 2013 ), The European Union as Crisis Manager: Patterns and Prospects , Cambridge University Press , Cambridge .

Bonsón , E. and Bednárová , M. ( 2019 ), “ Blockchain and its implications for accounting and auditing ”, Meditari Accountancy Research , Vol. 27 No. 5 , pp. 725 - 740 .

Boon , W. and Moors , E. ( 2008 ), “ Exploring emerging technologies using metaphors – a study of orphan drugs and pharmacogenomics ”, Social Science and Medicine , Vol. 66 No. 9 , pp. 1915 - 1927 .

Bravo , R.Z.B. , Leiras , A. and Cyrino Oliveira , F.L. ( 2019 ), “ The use of UAV s in humanitarian relief: an application of POMDP-based methodology for finding victims ”, Production and Operations Management , Vol. 28 No. 2 , pp. 421 - 440 .

Buckle , P. ( 2005 ), “ Disaster: mandated definitions, local knowledge and complexity ”, in Perry , R.W. and Quarantelli , E.L. (Eds), What is a Disaster? New Answers to Old Questions , Xlibris . Philadelphia, PA , pp. 173 ‐ 200 .

Buys , P.W. ( 2008 ), “ The impact of XBRL on the financial reporting supply chain: a South African case study ”, Meditari Accountancy Research , Vol. 16 No. 1 , pp. 43 - 58 .

Carreño , M.L. , Cardona , O.D. and Barbat , A.H. ( 2007 ), “ A disaster risk management performance index ”, Natural Hazards , Vol. 41 No. 1 , pp. 1 - 20 .

Chatterjee , S. , Chaudhuri , R. , Vrontis , D. and Basile , G. ( 2021 ), “ Digital transformation and entrepreneurship process in SMEs of India: a moderating role of adoption of AI-CRM capability and strategic planning ”, Journal of Strategy and Management .

Chaudhuri , N. and Bose , I. ( 2020 ), “ Exploring the role of deep neural networks for post-disaster decision support ”, Decision Support Systems , Vol. 130 , p. 113234 .

Chengalur-Smith , I. , Belardo , S. and Pazer , H. ( 1999 ), “ Adopting a disaster-management-based contingency model to the problem of ad hoc forecasting: toward information technology-based strategies ”, IEEE Transactions on Engineering Management , Vol. 46 No. 2 , pp. 210 - 220 .

Chhabra , D. , Singh , R.K. and Kumar , V. ( 2021 ), “ Developing IT-enabled performance monitoring system for green logistics: a case study ”, International Journal of Productivity and Performance Management .

Chowdhury , S. , Emelogu , A. , Marufuzzaman , M. , Nurre , S.G. and Bian , L. ( 2017 ), “ Drones for disaster response and relief operations: a continuous approximation model ”, International Journal of Production Economics , Vol. 188 , pp. 167 - 184 .

Cockcroft , S. and Russell , M. ( 2018 ), “ Big data opportunities for accounting and finance practice and research ”, Australian Accounting Review , Vol. 28 No. 3 , pp. 323 - 333 .

Comfort , L.K. , Dunn , M. , Johnson , D. , Skertich , R. and Zagorecki , A. ( 2004 ), “ Coordination in complex systems: increasing efficiency in disaster mitigation and response ”, International Journal of Emergency Management , Vol. 2 Nos 1/2 , pp. 62 - 80 .

Comfort , L.K. , Waugh , W. and Cigler , B.A. ( 2012 ), “ Emergency management research and practice in public administration: emergency, evolution, expansion, and future directions ”, Public Administration Review , Vol. 72 No. 4 , pp. 539 - 547 .

Contreras , D. , Blaschke , T. , Tiede , D. and Jilge , M. ( 2016 ), “ Monitoring recovery after earthquakes through the integration of remote sensing, GIS, and ground observations: the case of L’Aquila (Italy) ”, Cartography and Geographic Information Science , Vol. 43 No. 2 , pp. 115 - 133 .

Cutter , S.L. ( 1996 ), “ Vulnerability to environmental hazards ”, Progress in Human Geography , Vol. 20 No. 4 , pp. 529 - 539 .

Davenport , T.H. and Ronanki , R. ( 2018 ). “ Artificial intelligence for the real world ”, Harvard Business Review , Vol. 96 No. 1 , pp. 108 - 116 .

Davis , J.P. , Eisenhardt , K.M. and Bingham , C.B. ( 2007 ), “ Developing theory through simulation methods ”, Academy of Management Review , Vol. 32 No. 2 , pp. 480 - 499 .

Day , G.S. and Schoemaker , P.J. ( 2000 ), “ Avoiding the pitfalls of emerging technologies ”, California Management Review , Vol. 42 No. 2 , pp. 8 - 33 .

De Santis , F. and D’Onza , G. ( 2021 ), “ Big data and data analytics in auditing: in search of legitimacy ”, Meditari Accountancy Research .

Dekker , H.C. ( 2016 ), “ On the boundaries between intrafirm and interfirm management accounting research ”, Management Accounting Research , Vol. 31 , pp. 86 - 99 .

Dell'Era , C. , Di Minin , A. , Ferrigno , G. , Frattini , F. , Landoni , P. and Verganti , R. ( 2020 ), “ Value capture in open innovation processes with radical circles: a qualitative analysis of firms’ collaborations with slow food, Memphis, and free software foundation ”, Technological Forecasting and Social Change , Vol. 158 , p. 120128 .

Demiroz , F. and Haase , T.W. ( 2019 ), “ The concept of resilience: a bibliometric analysis of the emergency and disaster management literature ”, Local Government Studies , Vol. 45 No. 3 , pp. 308 - 327 .

Denyer , D. and Tranfield , D. ( 2009 ), “ Producing a systematic review ”, in Buchanan , D.A. and Bryman , A. (Eds), The Sage Handbook of Organizational Research Methods , Sage , Los Angeles, CA , pp. 671 - 689 .

Dubey , R. , Luo , Z. , Gunasekaran , A. , Akter , S. , Hazen , B.T. and Douglas , M.A. ( 2018 ), “ Big data and predictive analytics in humanitarian supply chains ”, The International Journal of Logistics Management , Vol. 29 No. 2 , pp. 485 - 512 .

Dubey , R. , Gunasekaran , A. , Childe , S.J. , Roubaud , D. , Fosso Wamba , S. and Foropon , C. ( 2019 ), “ Big data analytics and organizational culture as complements to swift trust and collaborative performance in the humanitarian supply chain ”, International Journal of Production Economics , Vol. 210 , pp. 120 - 136 .

Dubey , R. , Gunasekaran , A. , Bryde , D.J. , Dwivedi , Y.K. and Papadopoulos , T. ( 2020 ), “ Blockchain technology for enhancing swift-trust, collaboration and resilience within a humanitarian supply chain setting ”, International Journal of Production Research , Vol. 58 No. 11 , pp. 3381 - 3398 .

Dumay , J. and Cai , L. ( 2015 ), “ Using content analysis as a research methodology for investigating intellectual capital disclosure ”, Journal of Intellectual Capital , Vol. 16 No. 1 , pp. 121 - 155 .

Eccles , R.G. ( 1991 ), “ The performance measurement manifesto ”, Harvard Business Review , Vol. 69 No. 1 , pp. 131 - 137 .

Fajardo , J.T.B. and Oppus , C.M. ( 2010 ), “ A mobile disaster management system using the android technology ”, WSEAS Transactions on Communications , Vol. 9 No. 6 , pp. 343 - 353 .

Fan , C. , Zhang , C. , Yahja , A. and Mostafavi , A. ( 2021 ), “ Disaster city digital twin: a vision for integrating artificial and human intelligence for disaster management ”, International Journal of Information Management , Vol. 56 , p. 102049 .

Faulkner , B. ( 2001 ), “ Towards a framework for tourism disaster management ”, Tourism Management , Vol. 22 No. 2 , pp. 135 - 147 .

Ferreira , A. and Otley , D. ( 2009 ), “ The design and use of performance management systems: an extended framework for analysis ”, Management Accounting Research , Vol. 20 No. 4 , pp. 263 - 282 .

Ferrigno , G. and Cucino , V. ( 2021 ), “ Innovating and transforming during COVID-19. Insights from Italian firms ”, R&D Management , In Press .

Ferrigno , G. , Dagnino , G.B. and Di Paola , N. ( 2021 ), “ R&D alliance partner attributes and innovation performance: a fuzzy set qualitative comparative analysis ”, Journal of Business and Industrial Marketing , Vol. 36 No. 13 .

Gilbert , C. ( 1998 ), “ Studying disaster: changes in the main conceptual tools ”, Quarantelli , E.L. (Ed.), What is a Disaster? Perspectives on the Question , Routledge , New York, NY , pp. 11 - 18 .

Gray , R. ( 1992 ), “ Accounting and environmentalism: an exploration of the challenge of gently accounting for accountability, transparency and sustainability ”, Accounting, Organizations and Society , Vol. 17 No. 5 , pp. 399 - 425 .

Gul , M. , Fuat Guneri , A. and Gunal , M.M. ( 2020 ), “ Emergency department network under disaster conditions: the case of possible major Istanbul earthquake ”, Journal of the Operational Research Society , Vol. 71 No. 5 , pp. 733 - 747 .

Halaweh , M. ( 2013 ), “ Emerging technology: what is it ”, Journal of Technology Management and Innovation , Vol. 8 No. 3 , pp. 108 - 115 .

Han , S. , Huang , H. , Luo , Z. and Foropon , C. ( 2019 ), “ Harnessing the power of crowdsourcing and internet of things in disaster response ”, Annals of Operations Research , Vol. 283 Nos 1/2 , pp. 1175 - 1190 .

Harrison , S.E. and Johnson , P.A. ( 2016 ), “ Crowdsourcing the disaster management cycle ”, International Journal of Information Systems for Crisis Response and Management (IJISCRAM) , Vol. 8 No. 4 , pp. 17 - 40 .

Haworth , B. ( 2016 ), “ Emergency management perspectives on volunteered geographic information: opportunities, challenges and change ”, Computers, Environment and Urban Systems , Vol. 57 , pp. 189 - 198 .

Head , B.W. and Alford , J. ( 2015 ), “ Wicked problems: implications for public policy and management ”, Administration and Society , Vol. 47 No. 6 , pp. 711 - 739 .

Hoang , T.B.N. and Mothe , J. ( 2018 ), “ Location extraction from tweets ”, Information Processing and Management , Vol. 54 No. 2 , pp. 129 - 144 .

Hudson , M. , Smart , A. and Bourne , M. ( 2001 ), “ Theory and practice in SME performance measurement systems ”, International Journal of Operations & Production Management , Vol. 21 No. 8 , pp. 1096 - 1115 .

Hung , K.C. , Kalantari , M. and Rajabifard , A. ( 2016 ), “ Methods for assessing the credibility of volunteered geographic information in flood response: a case study in Brisbane, Australia ”, Applied Geography , Vol. 68 , pp. 37 - 47 .

Imran , M. , Ofli , F. , Caragea , D. and Torralba , A. ( 2020 ), “ Using AI and social media multimodal content for disaster response and management: opportunities, challenges, and future directions ”, Information Processing and Management , Vol. 57 No. 5 , p. 102261 .

Inan , D.I. , Beydoun , G. and Pradhan , B. ( 2018 ), “ Developing a decision support system for disaster management: case study of an indonesia volcano eruption ”, International Journal of Disaster Risk Reduction , Vol. 31 , pp. 711 - 721 .

Ivanov , D. , Dolgui , A. and Sokolov , B. ( 2019 ), “ The impact of digital technology and industry 4.0 on the ripple effect and supply chain risk analytics ”, International Journal of Production Research , Vol. 57 No. 3 , pp. 829 - 846 .

Jeble , S. , Kumari , S. , Venkatesh , V.G. and Singh , M. ( 2019 ), “ Influence of big data and predictive analytics and social capital on performance of humanitarian supply chain ”, Benchmarking: An International Journal , Vol. 27 No. 2 , pp. 606 - 633 .

Jun , W. , Nasir , M.H. , Yousaf , Z. , Khattak , A. , Yasir , M. , Javed , A. and Shirazi , S.H. ( 2021 ), “ Innovation performance in digital economy: does digital platform capability, improvisation capability and organizational readiness really matter? ”, European Journal of Innovation Management .

Kankanamge , N. , Yigitcanlar , T. , Goonetilleke , A. and Kamruzzaman , M. ( 2019 ), “ Can volunteer crowdsourcing reduce disaster risk? A systematic review of the literature ”, International Journal of Disaster Risk Reduction , Vol. 35 , p. 101097 .

Kaplan , R.S. and Norton , D.P. ( 1996 ), “ Using the balanced scorecard as a strategic management system ”, Harvard Business Review , pp. 75 - 85 .

Kapucu , N. and Garayev , V. ( 2016 ), “ Structure and network performance: horizontal and vertical networks in emergency management ”, Administration and Society , Vol. 48 No. 8 , pp. 931 - 961 .

Kapucu , N. and Van Wart , M. ( 2008 ), “ Making matters worse: an anatomy of leadership failures in managing catastrophic events ”, Administration and Society , Vol. 40 No. 7 , pp. 711 - 740 .

Karami , A. , Shah , V. , Vaezi , R. and Bansal , A. ( 2020 ), “ Twitter speaks: a case of national disaster situational awareness ”, Journal of Information Science , Vol. 46 No. 3 , pp. 313 - 324 .

Kassel , D.S. ( 2008 ), “ Performance, accountability, and the debate over rules ”, Public Administration Review , Vol. 68 No. 2 , pp. 241 - 252 .

Kaur , A. and Lodhia , S.K. ( 2019 ), “ Sustainability accounting, accountability and reporting in the public sector: an overview and suggestions for future research ”, Meditari Accountancy Research , Vol. 27 No. 4 , pp. 498 - 504 .

Keupp , M.M. , Palmié , M. and Gassmann , O. ( 2012 ), “ The strategic management of innovation: a systematic review and paths for future research ”, International Journal of Management Reviews , Vol. 14 No. 4 , pp. 367 - 390 .

Khalid , S. , Beattie , C. , Sands , J. and Hampson , V. ( 2019 ), “ Incorporating the environmental dimension into the balanced scorecard: a case study in health care ”, Meditari Accountancy Research , Vol. 27 No. 4 , pp. 652 - 674 .

Kim , Y.D. , Son , G.J. , Kim , H. , Song , C. and Lee , J.H. ( 2018 ), “ Smart disaster response in vehicular tunnels: technologies for search and rescue applications ”, Sustainability , Vol. 10 No. 7 , p. 2509 .

Kitagawa , K. ( 2021 ), “ Disaster risk reduction activities as learning ”, Natural Hazards , Vol. 105 No. 3 , pp. 3099 - 3118 .

Kraus , S. , Breier , M. and Dasí-Rodríguez , S. ( 2020 ), “ The art of crafting a systematic literature review in entrepreneurship research ”, International Entrepreneurship and Management Journal , Vol. 16 No. 3 , pp. 1 - 20 .

Kuipers , S. , Boin , A. , Bossong , R. and Hegemann , H. ( 2015 ), “ Building joint crisis management capacity? Comparing civil security systems in 22 European countries ”, Risk, Hazards and Crisis in Public Policy , Vol. 6 No. 1 , pp. 1 - 21 .

Lai , A. , Leoni , G. and Stacchezzini , R. ( 2014 ), “ The socializing effects of accounting in flood recovery ”, Critical Perspectives on Accounting , Vol. 25 No. 7 , pp. 579 - 603 .

Laituri , M. and Kodrich , K. ( 2008 ), “ Online disaster response community: people as sensors of high magnitude disasters using internet GIS ”, Sensors , Vol. 8 No. 5 , pp. 3037 - 3055 .

Lamboglia , R. , Lavorato , D. , Scornavacca , E. and Za , S. ( 2020 ), “ Exploring the relationship between audit and technology. A bibliometric analysis ”, Meditari Accountancy Research .

Lebas , M.J. ( 1995 ), “ Performance measurement and performance management ”, International Journal of Production Economics , Vol. 41 Nos 1/3 , pp. 23 - 35 .

Lee , H.R. and Lee , T. ( 2021 ), “ Multi-agent reinforcement learning algorithm to solve a partially-observable multi-agent problem in disaster response ”, European Journal of Operational Research , Vol. 291 No. 1 , pp. 296 - 308 .

Lettieri , E. , Masella , C. and Radaelli , G. ( 2009 ), “ Disaster management: findings from a systematic review ”, Disaster Prevention and Management: An International Journal , Vol. 18 No. 2 , pp. 117 - 136 .

Li , H. , Caragea , D. , Caragea , C. and Herndon , N. ( 2018 ), “ Disaster response aided by tweet classification with a domain adaptation approach ”, Journal of Contingencies and Crisis Management , Vol. 26 No. 1 , pp. 16 - 27 .

Light , R. and Pillemer , D. ( 1984 ), Summing up: The Science of Reviewing Research , Harvard University Press . Cambridge, MA .

Lombardi , R. and Secundo , G. ( 2020 ), “ The digital transformation of corporate reporting-a systematic literature review and avenues for future research ”, Meditari Accountancy Research .

Lombardi , R. , de Villiers , C. , Moscariello , N. and Pizzo , M. ( 2021 ), “ The disruption of blockchain in auditing – a systematic literature review and an agenda for future research ”, Accounting, Auditing and Accountability Journal .

McEntire , D.A. ( 2005 ), “ Why vulnerability matters: exploring the merit of an inclusive disaster reduction concept ”, Disaster Prevention and Management: An International Journal , Vol. 14 No. 2 , pp. 206 - 222 .

Manyena , S.B. ( 2006 ), “ The concept of resilience revisited ”, Disasters , Vol. 30 No. 4 , pp. 434 - 450 .

Marr , B. and Neely , A. ( 2001 ), “ Organisational performance measurement in the emerging digital age ”, International Journal of Business Performance Management , Vol. 3 Nos 2/3/4 , pp. 191 - 215 .

Marrone , M. and Hazelton , J. ( 2019 ), “ The disruptive and transformative potential of new technologies for accounting, accountants and accountability ”, Meditari Accountancy Research , Vol. 27 No. 5 , pp. 677 - 694 .

Martinez-Rojas , M. , del Carmen Pardo-Ferreira , M. and Rubio-Romero , J.C. ( 2018 ), “ Twitter as a tool for the management and analysis of emergency situations: a systematic literature review ”, International Journal of Information Management , Vol. 43 , pp. 196 - 208 .

Mishra , D. , Kumar , S. and Hassini , E. ( 2019 ), “ Current trends in disaster management simulation modelling research ”, Annals of Operations Research , Vol. 283 Nos 1/2 , pp. 1387 - 1411 .

Moynihan , D.P. ( 2008 ), “ Learning under uncertainty: Networks in crisis management ”, Public Administration Review , Vol. 68 No. 2 , pp. 350 - 365 .

Ndou , V. , Secundo , G. , Dumay , J. and Gjevori , E. ( 2018 ), “ Understanding intellectual Capital disclosure in online media big data: an exploratory case study in a university ”, Meditari Accountancy Research , Vol. 26 No. 3 , pp. 499 - 530 .

Noto , G. and Cosenz , F. ( 2021 ), “ Introducing a strategic perspective in lean thinking applications through system dynamics modelling: the dynamic value stream map ”, Business Process Management Journal , Vol. 27 No. 1 , pp. 306 - 327 .

Nudurupati , S.S. and Bititci , U.S. ( 2005 ), “ Implementation and impact of IT-supported performance measurement systems ”, Production Planning and Control , Vol. 16 No. 2 , pp. 152 - 162 .

Nuti , S. , Noto , G. , Vola , F. and Vainieri , M. ( 2018 ), “ Let's play the patients music: a new generation of performance measurement systems in healthcare ”, Management Decision , Vol. 56 No. 10 , pp. 2252 - 2272 .

Oktari , R.S. , Munadi , K. and Idroes , R. ( 2020 ), “ Knowledge management practices in disaster management: systematic review ”, International Journal of Disaster Risk Reduction .

Oliver , J.J. ( 2018 ), “ Strategic transformations in a disruptive digital environment ”, Strategic Direction , Vol. 34 No. 5 , pp. 5 - 8 .

Papadopoulos , T. , Gunasekaran , A. , Dubey , R. , Altay , N. , Childe , S.J. and Fosso-Wamba , S. ( 2017 ), “ The role of big data in explaining disaster resilience in supply chains for sustainability ”, Journal of Cleaner Production , Vol. 142 , pp. 1108 - 1118 .

Pearce , L. ( 2003 ), “ Disaster management and community planning, and public participation: how to achieve sustainable hazard mitigation ”, Natural Hazards , Vol. 28 Nos 2/3 , pp. 211 - 228 .

Perkiss , S. and Moerman , L. ( 2020 ), “ Hurricane Katrina: exploring justice and fairness as a sociology of common good(s) ”, Critical Perspectives on Accounting , Vols 67/68 , p. 102022 .

Perry , R.W. ( 1998 ), “ Definitions and the development of a theoretical superstructure for disaster research ”, in Quarantelli , E.L. (Ed.), What is a Disaster? Perspectives on the Question , Routledge , New York, NY , pp. 197 ‐ 215 .

Pesch , U. and Vermaas , P.E. ( 2020 ), “ The wickedness of Rittel and Webber’s dilemmas ”, Administration and Society , Vol. 52 No. 6 , pp. 960 - 979 .

Petticrew , M. and Roberts , H. ( 2008 ), Systematic Reviews in the Social Sciences: A Practical Guide , Blackwell Publishing , Malden, MA .

Quarantelli , E.L. (Ed.) ( 1998 ), What is a Disaster? Perspectives on the Question , Routledge , New York, NY .

Ragini , J.R. , Anand , P.R. and Bhaskar , V. ( 2018 ), “ Big data analytics for disaster response and recovery through sentiment analysis ”, International Journal of Information Management , Vol. 42 , pp. 13 - 24 .

Raman , M. , Kuppusamy , M.V. , Dorasamy , M. and Nair , S. ( 2014 ), “ Knowledge management systems and disaster management in Malaysia: an action research approach ”, Journal of Information & Knowledge Management , Vol. 13 No. 1 .

Rezaei , M. , Akbarpour Shirazi , M. and Karimi , B. ( 2017 ), “ IoT-based framework for performance measurement: a real-time supply chain decision alignment ”, Industrial Management and Data Systems , Vol. 117 No. 4 , pp. 688 - 712 .

Rittel , H.W.J. and Webber , M.M. ( 1973 ), “ Dilemmas in a general theory of planning ”, Policy Sciences , Vol. 4 No. 2 , pp. 155 - 169 .

Roberts , J. ( 1991 ), “ The possibilities of accountability ”, Accounting, Organizations and Society , Vol. 16 No. 4 , pp. 355 - 368 .

Rodríguez-Bolívar , M.P. , Pérez , C.C. and Hernández , A.M.L. ( 2006 ), “ Cultural contexts and governmental digital reporting ”, International Review of Administrative Sciences , Vol. 72 No. 2 , pp. 269 - 290 .

Rodríguez-Bolívar , M.P. , Scholl , J. and Reddick , C.G. (Eds) ( 2021 ), “ Blockchain and the public sector: theories ”, Reforms, and Case Studies , Springer .

Rodríguez-Espíndola , O. , Chowdhury , S. , Beltagui , A. and Albores , P. ( 2020 ), “ The potential of emergent disruptive technologies for humanitarian supply chains: the integration of blockchain, artificial intelligence and 3D printing ”, International Journal of Production Research , Vol. 58 No. 15 , pp. 4610 - 4630 .

Rosenthal , U. and Kouzmin , A. ( 1997 ), “ Crises and crisis management: toward comprehensive government decision making ”, Journal of Public Administration Research and Theory , Vol. 7 No. 2 , pp. 277 - 305 .

Rotolo , D. , Hicks , D. and Martin , B.R. ( 2015 ), “ What is an emerging technology? ”, Research Policy , Vol. 44 No. 10 , pp. 1827 - 1843 .

Sahebjamnia , N. , Torabi , S.A. and Mansouri , S.A. ( 2017 ), “ A hybrid decision support system for managing humanitarian relief chains ”, Decision Support Systems , Vol. 95 , pp. 12 - 26 .

Sardi , A. , Sorano , E. , Cantino , V. and Garengo , P. ( 2020 ), “ Big data and performance measurement research: trends, evolution and future opportunities ”, Measuring Business Excellence .

Sargiacomo , M. and Walker , S.P. ( 2020 ), “ Disaster governance and hybrid organizations: accounting, performance challenges and evacuee housing ”, Accounting, Auditing and Accountability Journal , doi: 10.1108/AAAJ-12-2019-4323 .

Sargiacomo , M. , Ianni , L. and Everett , J. ( 2014 ), “ Accounting for suffering: calculative practices in the field of disaster relief ”, Critical Perspectives on Accounting , Vol. 25 No. 7 , pp. 652 - 669 .

Schumann , R.L. III ( 2018 ), “ Ground truthing spatial disaster recovery metrics with participatory mapping in post-Katrina Mississippi ”, Applied Geography , Vol. 99 , pp. 63 - 76 .

Sciulli , N. ( 2018 ), “ Weathering the storm: accountability implications for flood relief and recovery from a local government perspective ”, Financial Accountability and Management , Vol. 34 No. 1 , pp. 30 - 44 .

Secundo , G. , Ndou , V. , Del Vecchio , P. and De Pascale , G. ( 2020 ), “ Sustainable development, intellectual capital and technology policies: a structured literature review and future research agenda ”, Technological Forecasting and Social Change , Vol. 153 , p. 119917 .

Shah , S.A. , Seker , D.Z. , Rathore , M.M. , Hameed , S. , Yahia , S.B. and Draheim , D. ( 2019 ), “ Towards disaster resilient smart cities: can internet of things and big data analytics be the game changers? ”, IEEE Access , Vol. 7 , pp. 91885 - 91903 .

Shahat , E. , Hyun , C.T. and Yeom , C. ( 2020 ), “ Conceptualizing smart disaster governance: an integrative conceptual framework ”, Sustainability , Vol. 12 No. 22 , p. 9536 .

Sharma , S.K. , Misra , S.K. and Singh , J.B. ( 2020 ), “ The role of GIS-enabled mobile applications in disaster management: a case analysis of cyclone Gaja in India ”, International Journal of Information Management , Vol. 51 , p. 102030 .

Singh , J.P. , Dwivedi , Y.K. , Rana , N.P. , Kumar , A. and Kapoor , K.K. ( 2019 ), “ Event classification and location prediction from tweets during disasters ”, Annals of Operations Research , Vol. 283 Nos 1/2 , pp. 737 - 757 .

Sinha , A. , Kumar , P. , Rana , N.P. , Islam , R. and Dwivedi , Y.K. ( 2019 ), “ Impact of internet of things (IoT) in disaster management: a task-technology fit perspective ”, Annals of Operations Research , Vol. 283 Nos 1/2 , pp. 759 - 794 .

Snyder , H. ( 2019 ), “ Literature review as a research methodology: an overview and guidelines ”, Journal of Business Research , Vol. 104 , pp. 333 - 339 .

Spanò , R. and Ginesti , G. ( 2021 ), “ Fostering performance management in healthcare: insights into the role of big data ”, Meditari Accountancy Research .

Steen , T. and Brandsen , T. ( 2020 ), “ Coproduction during and after the COVID‐19 pandemic: will it last? ”, Public Administration Review , Vol. 80 No. 5 , pp. 851 - 855 .

Stephenson , R. and Anderson , P.S. ( 1997 ), “ Disasters and the information technology revolution ”, Disasters , Vol. 21 No. 4 , pp. 305 - 334 .

Sterman , J. ( 2014 ), “ Interactive web‐based simulations for strategy and sustainability: the MIT sloan learning edge management flight simulators, part I ”, System Dynamics Review , Vol. 30 Nos 1/2 , pp. 89 - 121 .

Thekdi , S.A. and Chatterjee , S. ( 2019 ), “ Toward adaptive decision support for assessing infrastructure system resilience using hidden performance measures ”, Journal of Risk Research , Vol. 22 No. 8 , pp. 1020 - 1043 .

Thorpe , R. , Holt , R. , Macpherson , A. and Pittaway , L. ( 2005 ), “ Using knowledge within small and medium-sized firms: a systematic review of the evidence ”, International Journal of Management Reviews , Vol. 7 No. 4 , pp. 257 - 281 .

Tingey-Holyoak , J.L. , Pisaniello , J.D. and Buss , P. ( 2021 ), “ Embedding smart technologies in accounting to meet global irrigation challenges ”, Meditari Accountancy Research .

Toth , C. and Jozkow , G. ( 2016 ), “ Remote sensing platforms and sensors: a survey ”, ISPRS Journal of Photogrammetry and Remote Sensing , Vol. 115 , pp. 22 - 36 .

Tranfield , D. , Denyer , D. and Smart , P. ( 2003 ), “ Towards a methodology for developing evidence-informed management knowledge by means of systematic review ”, British Journal of Management , Vol. 14 No. 3 , pp. 207 - 222 .

Van Eck , N.J. and Waltman , L. ( 2014 ), “ Visualizing bibliometric networks ”, in Ding Y , Rousseau R. and Wolfram D. (Eds), Measuring Scholarly Impact: Methods and Practice , Springer , Cham , pp. 285 - 320 .

Van Eck , N.J. and Waltman , L. ( 2017 ), “ Citation-based clustering of publications using CitNetExplorer and VOSviewer ”, Scientometrics , Vol. 111 No. 2 , pp. 1053 - 1070 .

Van Wart , M. and Kapucu , N. ( 2011 ), “ Crisis management competencies: the case of emergency managers in the USA ”, Public Management Review , Vol. 13 No. 4 , pp. 489 - 511 .

Verbano , C. and Venturini , K. ( 2013 ), “ Managing risks in SMEs: a literature review and research agenda ”, Journal of Technology Management and Innovation , Vol. 8 No. 3 , pp. 186 - 197 .

Von Lubitz , D.K. , Beakley , J.E. and Patricelli , F. ( 2008 ), “ All hazards approach to disaster management: the role of information and knowledge management, Boyd's OODA loop, and network‐centricity ”, Disasters , Vol. 32 No. 4 , pp. 561 - 585 .

Walker , S.P. ( 2014 ), “ Drought, resettlement and accounting ”, Critical Perspectives on Accounting , Vol. 25 No. 7 , pp. 604 - 619 .

Wallace , W.A. and De Balogh , F. ( 1985 ), “ Decision support systems for disaster management ”, Public Administration Review , Vol. 45 , pp. 134 - 146 .

Wang , H. , Feng , J. , Zhang , H. and Li , X. ( 2020a ), “ The effect of digital transformation strategy on performance: the moderating role of cognitive conflict ”, International Journal of Conflict Management , Vol. 31 No. 3 , pp. 441 - 462 .

Wang , L. , Bivona , E. , Yan , H. and Qi , J. ( 2020b ), “ Applying dynamic performance management to public emergency management: an analysis of the Wenchuan earthquake ”, in Bianchi , C. E. , Luis , F. , Luna-Reyes , L.F. and Rich , E. (Eds), Enabling Collaborative Governance through Systems Modeling Methods , Springer International Publishing , Berlin , pp. 263 - 276 .

Waugh , W.L. ( 1995 ), “ Geographic information systems: the case of disaster management ”, Social Science Computer Review , Vol. 13 No. 4 , pp. 422 - 431 .

Waugh , W.L. and Streib , G. ( 2006 ), “ Collaboration and leadership for effective emergency management ”, Public Administration Review , Vol. 66 No. s1 , pp. 131 - 140 .

Wehn , U. and Evers , J. ( 2015 ), “ The social innovation potential of ICT-enabled citizen observatories to increase eParticipation in local flood risk management ”, Technology in Society , Vol. 42 , pp. 187 - 198 .

Wisner , B. , Blaikie , P. , Cannon , T. and Davis , I. ( 2004 ), At Risk: Natural Hazards People's Vulnerability and Disaster , Routledge , London .

Xiao , Y. and Watson , M. ( 2019 ), “ Guidance on conducting a systematic literature review ”, Journal of Planning Education and Research , Vol. 39 No. 1 , pp. 93 - 112 .

Xiao , Y. , Huang , Q. and Wu , K. ( 2015 ), “ Understanding social media data for disaster management ”, Natural Hazards , Vol. 79 No. 3 , pp. 1663 - 1679 .

Yang , L. , Yang , S.H. and Plotnick , L. ( 2013 ), “ How the internet of things technology enhances emergency response operations ”, Technological Forecasting and Social Change , Vol. 80 No. 9 , pp. 1854 - 1867 .

Yu , M. , Huang , Q. , Qin , H. , Scheele , C. and Yang , C. ( 2019 ), “ Deep learning for real-time social media text classification for situation awareness – using Hurricanes Sandy, Harvey, and Irma as case studies ”, International Journal of Digital Earth , Vol. 12 No. 11 , pp. 1230 - 1247 .

Zagorecki , A.T. , Johnson , D.E. and Ristvej , J. ( 2013 ), “ Data mining and machine learning in the context of disaster and crisis management ”, International Journal of Emergency Management , Vol. 9 No. 4 , pp. 351 - 365 .

Zahra , K. , Imran , M. and Ostermann , F.O. ( 2020 ), “ Automatic identification of eyewitness messages on twitter during disasters ”, Information Processing and Management , Vol. 57 No. 1 , p. 102107 .

Zheng , T. , Ardolino , M. , Bacchetti , A. and Perona , M. ( 2020 ), “ The applications of industry 4.0 technologies in manufacturing context: a systematic literature review ”, International Journal of Production Research , Vol. 59 No. 6 .

Zwęgliński , T. ( 2020 ), “ The use of drones in disaster aerial needs reconnaissance and damage assessment–three-dimensional modeling and orthophoto map study ”, Sustainability , Vol. 12 No. 15 , p. 6080 .

Further reading

Choi , T.M. , Wallace , S.W. and Wang , Y. ( 2018 ), “ Big data analytics in operations management ”, Production and Operations Management , Vol. 27 No. 10 , pp. 1868 - 1883 .

Davis , F.D. ( 1989 ), “ Perceived usefulness, perceived ease of use, and user acceptance of information technology ”, MIS Quarterly , Vol. 13 No. 3 , pp. 319 - 340 .

Kanbara , S. , Ozawa , W. , Ishimine , Y. , Ngatu , N.R. , Nakayama , Y. and Nojima , S. ( 2016 ), “ Operational definition of disaster risk-reduction literacy ”, Health Emergency and Disaster Nursing , Vol. 3 No. 1 , pp. 1 - 8 .

Suárez , E. , Calvo-Mora , A. , Roldán , J.L. and Periáñez-Cristóbal , R. ( 2017 ), “ Quantitative research on the EFQM excellence model: a systematic literature review (1991-2015) ”, European Research on Management and Business Economics , Vol. 23 No. 3 , pp. 147 - 156 .

Venkatesh , V. and Bala , H. ( 2008 ), “ Technology acceptance model 3 and a research agenda on interventions ”, Decision Sciences , Vol. 39 No. 2 , pp. 273 - 315 .

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Journal No 3 | 2021 - Disasters and crisis management

• European Court of Auditors

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Natural disasters know no borders – neither do solidarity and mutual support in times of need

Editorial By Gaston Moonen Solidarity in the face of a clear and present danger

A picture can say more than a thousand words. This is true of many situations and particularly of emergency situations caused by disasters. A natural disaster enters your mind with the image of a child rescued from the rubble, with houses and nature devastated by wildfires or tornadoes, with people swimming away from their house since they don’t have any other option. But also, in the case of man-made disasters, the image of women and children escaping from the violence of war, or refugees clinging on to a life jacket. These images subdue us, stay with us and create a connection to fellow humans at risk.

When people are facing a clear and present danger, political differences and animosities fade away and are replaced by an urge to help and offer solidarity. In my first job, working on human rights issues in the UN, I soon heard the saying ‘human rights start after breakfast’, although some would argue that human rights begin with breakfast. The idea behind this is that some of their basic needs must be fulfilled before people can start worrying, on an equal footing and in dignity, about other issues, such as human rights. The COVID-19 pandemic reminded me of this expression, since health concerns are primary concerns compared with many other human needs. With health taken for granted by many of us, the pandemic has shown that when a disaster strikes, and on such a global scale, priorities change quickly towards maintaining the physical well-being of your loved ones and yourself. At almost no matter what cost... even at the cost of certain rights considered sacred before.

While COVID-19 might spring to mind as the most obvious disaster spilling over from last year, 2021 is by no means an easy year when it comes to natural disasters. According to the International Disaster Database, the year 2020 had a higher number of disasters than the average of the last 20 years – apparently, with Atlantic hurricanes so numerous that there were not enough letters in the alphabet to name them all. But what’s new? Reports from the early 1990s identified a fivefold and record increase in disasters between 1960 and 1980 and in 1987 the UN designated the 1990s as the ‘International Decade for Natural Disaster Reduction,’ calling for concerted international action. And not for the last time! The World Wide Fund for Nature (WWF) has labelled 2021 as a record year when it comes to natural disasters. Increasingly, politicians are catching up scientists when it comes to recognising the link between these disasters and climate change, as also seen during the recent COP26 in Glasgow. The good news is that at least in 2020 these disasters led to substantially fewer human casualties than in many previous years. A matter of better disaster preparedness...?

When a disaster strikes, the first concern is to react quickly and properly. Proper crisis management can prove to be crucial in this first emergency phase, requiring pre-set structures for help, coordination and decisive action. In particular, it requires leadership to trigger that action: digesting various data, handling procedures and being inventive about possible solutions, the latter being particularly challenging since every disaster is unique, with its own characteristics. But leadership requires more than only decisiveness, particularly in transboundary crises. It requires empathy and a capacity to adapt, as research on crisis processes by Marij Swinkels shows (see page 7).

The bigger the disaster, the greater the coordination needs seem to be. The COVID-19 pandemic has shown that creating such awareness takes time and some gap plugging (see page 35) and it can actually cost human lives when coordination is slow or only allowed reluctantly. Hence the importance of proper ex ante coordination mechanisms in humanitarian aid in disasters, as both the UN Acting Assistant Secretary-General in this area, Ramesh Rajasingham, and the EU Commissioner for Crisis Management, Janez Lenarčič, emphasise regarding their roles in global and EU crisis management (see pages 11 and 18). Noteworthy here is also that their humanitarian aid efforts are based on values showing the unconditional solidarity that sets disaster aid provision apart: both the UN and the EU are principled donors, meaning working exclusively on the basis of needs, without any regard to political or other situations. Also in the COVID-19 pandemic, particularly at the start, we saw that disagreements were set aside when facing a clear and present danger to health.

Not surprisingly, these values are also essential to the actions of major non-governmental aid organisations. Most visible perhaps are the Red Cross and Red Crescent Societies, whose quick and impartial presence when disaster strikes is impressive. But also to a ‘single country NGO’ such as Friendship, whose founder Runa Khan identifies adherence to values such as integrity, dignity, justice, quality and hope as preconditions for starting any of the multiple actions her NGO carries out in Bangladesh (see page 39). Such values also include transparency and accountability, not only because of donor requirements, but also since accountability mechanisms are also very important to the people affected by disaster (see page 48).

Not only are the organisations and people involved in disaster action impressive, but also the amounts of funding. This depends of course on what you define as emergency and disaster relief. Does it include disaster prevention and preparedness efforts? How do you label EU expenditure related to the COVID-19 pandemic and where does disaster aid stop and reconstruction aid commence? This last question is also relevant in view of the enormous EU efforts undertaken to mitigate the economic and fiscal consequences of the ongoing pandemic, with long- term impact for Europe (see page 140). But whatever definition you apply, the EU-funded amounts involved are substantial and are being used by the Member States and regions affected, be it by the wildfires in Greece or by an unprecedented flood in the Liège region (see pages 26 and 30).

However good the intentions for accountability in disaster aid may be, they do not form a natural symbiosis for several reasons. The very nature of emergency action - where speed is essential - creates additional risks of cutting corners when it comes to financial management. Furthermore, disasters may involve many actors, both from the aid-providing and the aid-receiving side, which often makes tracking aid flows difficult. While the urgent needs are clearly visible, the risks of fraud and corruption are just around the corner, particularly in disaster-affected areas with weak governmental structures. On top of this, it is also an area where reporting on results is essential to preserve trust: the trust of those providing the aid - be it by people directly or their governments on behalf of them - and those receiving the aid, since clear results are essential for hope, trust in future progress and ownership of the solutions the results are meant to be part of.

Enough reasons for Professor Arjen Boin to learn lessons from crises and undertake crisis audits (see page 53). Enough reasons, as ECA Member Leo Brincat and several other contributors argue (see pages 58 and 82), for public auditors to proceed with care, yet with stamina to assess compliance and performance aspects (see page 70). For the ECA, the COVID-19 pandemic led to a substantial shift in its audit planning soon after the pandemic started, with audits and reviews published or planned relating to the health and economic measures taken and envisaged or the institutional resilience displayed (see pages 65, 75 and 79). Other audit institutions in the EU have done the same, in reaction to the current pandemic, or in reaction to or anticipation of earlier disaster situations (see pages 88 and 136), sometimes leading to new solutions for assessing and reporting to add value in an expedient way (see page 107). Enough reason also for the European Parliament to insist on proper and timely reporting on the various EU funding instruments created, as MEP Corina Crețu does for example regarding the EU Solidarity Fund (see page 126).

Public auditors themselves identified quite some time ago – following the tsunami in 2004 – that it would be useful if peers provided guidance on how to audit different elements of the disaster management cycle. This translated into international guidance adopted by the global platform of public external auditors, INTOSAI. This guidance has been used, for example by the SAI of Indonesia (see page 117), and updated (see page 111). While prevention and preparedness had already been identified as important elements in this cycle, the pandemic and even more the effects of climate change - sometimes labelled climate crisis – have more than ever underlined their importance. For several public audit institutions this shows the need for more and deeper assessments of publicly funded actions for disaster prevention and preparedness. Arno Visser, President of the Netherlands Court of Audit, pleads for increased attention by auditors to‘accidents waiting to happen’(see page 91). Michel Huissoud, who heads the Swiss Federal Audit Office, even goes a step further in relation to measures taken regarding the pandemic, addressing a data gap which, if left untouched by his institution, would create serious compliance problems at a later stage (page 99).

Disaster prevention and preparedness are also key elements in many other contributions to this Journal. EU Commissioner Lenarčič observes limits to how prepared one can be if preventive measures, particularly regarding climate change, are not taken. He identifies the paradox that the urgency and visibility of disaster aid measures come at the cost of long-term measures meant to decrease the cost of disaster aid. Kevin Cardiff, former ECA Member and crisis manager, gives a practitioner’s view on how audit can do more to contribute to crisis readiness, how auditors are in a unique position to assess interactions between crisis management systems - or the lack of them – and the need for real coordination (see page 100). His call regarding risk assessments is echoed in other articles, including by IDI experts pleading for enhanced risk assessment work by SAIs (see page 122).

We have produced this Journal to share information on solidarity in times of crisis and on how public auditors are contributing to alleviating future crises. We also produced this Journal to bow to all those giving aid without any interest but the benefit of the receivers: human kindness in its pure form, aid that provides hope of a change for the better, hope in the face of clear and present danger, as for the child portrayed on our cover picture (a 2021 World Press prize winning picture), waiting to be saved before the wildfires come too close. These are pictures connecting the world to stories that matter. I hope this edition of the Journal will connect you to a theme that can hit anyone of us. Let’s hope the disaster aid provisions then work as intended.

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ICT in disaster management context: a descriptive and critical review

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• Volume 29 , pages 86796–86814, ( 2022 )

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• Mandeep Kaur   ORCID: orcid.org/0000-0002-7632-5966 1 ,
• Pankaj Deep Kaur 1 &
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Disasters cause catastrophic events that lead to fatalities, damage, and social disturbance. Hydrological and meteorological disasters have an enormous impact worldwide. The impact of IT (Information Technology) in managing these disasters has been neglected. This study is intended to reveal the worldwide research status of hydro-meteorological disasters and various ITs in hazard management through a descriptive and critical review of existing literature. The bibliographic data is collected from Scopus and PATSTAT from 2010 to 2019. This study provides a basic framework for data acquisition, literature selection, and analysis of published documents. A descriptive review of selected literature is conducted to reveal the growth of publications w.r.t. year-wise reported hazards, citation analysis of published documents, patent analysis, geographical status of different hazards research, most influential journals, institutions, and documents. Further, critical review is conducted to analyze the environmental issues, recent developments in ICT-based disaster management, resilience concerns, key research areas, and challenges to implement ICT in disaster management. The present analysis depicts the importance of information technology in disaster management and offers guidance for future disaster management work supported by IT.

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Agrawal N (2018) Disaster resilience. Nat Disasters Risk Manag Canada Springer 147–191

Aldunce P, Beilin R, Howden SM, Handmer J (2015) Resilience for disaster risk management in a changing climate: Practitioners’ frames and practices. Glob Environ Chang 30:1–11

Article   Google Scholar  

Amit SNKB, Aoki Y (2017) Disaster detection from aerial imagery with convolutional neural network. International electronics symposium on knowledge creation and intelligent computing (IES-KCIC). IEEE, p 239–245

Anbarasan M, Muthu BA, Sivaparthipan CB, Sundarasekar R, Kadry S, Krishnamoorthy S, Samuel DJ, Dasel A (2019) Detection of flood disaster system based on IoT, big data and convolutional deep neural network. Computer Communications 150. https://doi.org/10.1016/j.comcom.2019.11.022

Burton CG (2015) A validation of metrics for community resilience to natural hazards and disasters using the recovery from Hurricane Katrina as a case study. Ann Assoc Am Geogr 105(1):67–86

Canon MJ, Satuito A, Sy C (2018) Determining disaster risk management priorities through a neural network-based text classifier. In: Proceedings of the 2018 international symposium on computer, consumer and control (IS3C). IEEE, pp 237–241

Cauffman SA, Dillard MK, Helgeson JF (2018) Implementation of the NIST community resilience planning guide for buildings and infrastructure systems. Technical Report NISTIR 8231, National Institute of Standards and Technology. https://nvlpubs.nist.gov/nistpubs/ir/2018/NIST.IR.8231.pdf . Accessed  April 2022

Chen F, Deng P, Wan J, Zhang D, Vasilakos AV, Rong X (2015) Data mining for the internet of things: literature review and challenges. Int J Distrib Sensor Netw 1–14

Chanza N, Siyongwana P, Williams-Bruinders L, Jakarasi V, Mudavanhu C, Sithole VB, Manyani A (2020) Closing the gaps in disaster management and response: Drawing on local experiences with cyclone Idai in Chimanimani, Zimbabwe. Int J Disaster Risk Sci 11. https://doi.org/10.1007/s13753-020-00290-x

Cheng MY, Chiu KC, Hsieh YM, Yang IT, Chou JS, Wu YW (2017) BIM integrated smart monitoring technique for building fire prevention and disaster relief. Autom Constr 84:14–30

Cimellaro GP, Scura G, Renschler CS, Reinhorn AM, Kim HU (2014) Rapid building damage assessment system using mobile phone technology. Earthq Eng Eng Vib 13:519–533

Cimellaro GP, Kammouh O (2019) SHM role in the Framework of infrastructure resilience. 9th Int Conf Struct Heal Monit Intell Infrastructure St Louis Missouri 1–8

Cobo MJ (2011) Science mapping software tools: review, analysis and cooperative study among tools. J Am Soc Inf Sci Technol 62(7):1382–1402

Cox RS, Hamlen M (2015) Community disaster resilience and the rural resilience index. Am Behav Sci 59(2):220–237

Cutter SL, Ash KD, Emrich CT (2014) The geographies of community disaster resilience. Global Environ Change 29:65–77

Dakshin D, Rupesh VR, Kumar SP (2019) Water hazard prediction using machine learning. Int J Innov Technol Exploring Eng (IJITEE) 9(1):2278–3075

Google Scholar  

Dhyani S, Thummarukuddy M (2016) Ecological engineering for disaster risk reduction and climate change adaptation. Environ Sci Pollut Res 23:20049–20052

Digital India Action Group (2016) Internet of things (iot) for effective disaster management. Whitepaper. DIAG Report dated 29/06/2016

Dorasamy M, Raman M, Kaliannan M (2013) Knowledge management systems in support of disasters management: a two decade review. Technol Forecast Soc Chang 80(9):1834–53

Downe-Wamboldt B (1992) Content analysis: Method, applications, and issues. Health Care Women Int 13:313–21

Article   CAS   Google Scholar  

Erdelj M, Natalizio E, Chowdhury KR, Akyildiz IF (2017) Help from the sky: leveraging UAVs for disaster management. IEEE Pervasive Comput 16(1):24–32

Erdelj M, Król M, Natalizio E (2017) Wireless sensor networks and multi-UAV systems for natural disaster management. Comput Netw 124:72–86

Eriksen S, Aldunce P, Bahinipati CS, Martins RD, Molefe JI, Nhemachena C, O’Brien K, Olorunfemi F, Park J, Sygna L, Ulstrud K (2011) When not every response to climate change is a good one: Identifying principles for sustainable adaptation. Clim Dev 3(1):7–20

Fang S, Xu L, Zhu Y, Liu Y, Liu Z, Pei H, Yan J, Zhang H (2013) An integrated information system for snowmelt flood early-warning based on internet of things. Inf Syst Front 17. https://doi.org/10.1007/s10796-013-9466-1

Federal Geographic Data Committee (2016) Emerging Technologies and the Geospatial Landscape. A Report of the National Geospatial Advisory Committee. Available online: https://www.fgdc.gov/ngac/meetings/dec-2016/ngac-paper-emerging-technologies-and-the.pdf . Accessed in June 2021

Fengchun Y, Wei M (2012) The role of ICT in public safety and emergency management in China. J E-Gov 35:81–90

Fonte CC, Fontes D (2018) A web GIS-based platform to harvest georeferenced data from social networks: examples of data collection regarding disaster events. Int J Online Eng (iJOE) 14(02):165–172

George SM, Zhou W, Chenji H et al (2010) DistressNet: a wireless ad hoc and sensor network architecture for situation management in disaster response. IEEE Commun Mag 48(3):128–136

Habiba M, Akhter S (2013) A cloud based natural disaster management system. Int Conf Grid Pervasive Comput 152–161

Han SY, Tsou MH, Knaap E, Rey S, Cao G (2019) How do cities flow in an emergency? Tracing human mobility patterns during a natural disaster with big data and geospatial data science. Urban Sci 3(2):51

Hanna NK (2010) Enabling enterprise transformation Business and grassroots innovation for the knowledge economy. Springer, New York

Book   Google Scholar  

Hao Z, Hao F, Singh V, Ouyang W, Hong C (2017) An integrated package for drought monitoring, prediction and analysis to aid drought modeling and assessment. Environ Modell Softw 91:199–209

Harnesk D (2013) Collective IT artifacts: Toward inclusive crisis infrastructures. J Inf Technol Theory and Appl 14:3

Hussain A (2011) A citation analysis of top research papers of computer science. Int Res J Libr Inf Sci 1(2):121–132

IRNSS (2017) Indian Regional Navigation Satellite System (IRNSS) Available online: https://www.isro.gov.in/irnss-programme . Accessed in July 2021

ISDR (2008) Disaster risk reduction strategies and risk management practices: Critical elements for adaptation to climate change. Report dated 11/11/2008

Ivić M (2019) Artificial Intelligence and geospatial analysis in disaster management. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 161–166

Jacso P (2006) Deflated, inflated and phantom citation counts. Online Inf Rev 30(3):297–309

Jayawardena AW (2015) Hydro-meteorological disasters: Causes, effects and mitigation measures with special reference to early warning with data driven approaches of forecasting. Procedia IUTAM 17:3–12

Jennex M, White C, Plotnick L (2012) A framework to identify best practices. https://doi.org/10.4018/978-1-4666-0167-3.ch004

Jiang H (2019) Mobile fire evacuation system for large public buildings based on artificial intelligence and IoT. IEEE Access 7:64101–64109

Jing Y, Cheng C, Song C, Shen S, Ning L (2019) Visual analysis of the evolution and focus in landslide research field. J Mt Sci 16(5):991–1004

Juan BD (2005) New proposals for the analysis of language in the media. Questiones Publicitarias 10:13–33

Kaku K (2018) Satellite remote sensing for disaster management support: A holistic and staged approach based on case studies in Sentinel Asia. Int J Disaster Risk Reduction 33:417–432

Kaamin M, Hilaham M, Rahman M, Ngadiman N, Sahat S, Ahmad NFA, Razali S (2017) Flood prediction using geographical information system (GIS) application at Sungai Sembrong. Journal of Telecommunication. Electron Comput Eng 9:113–117

Kang H, Sridhar V (2019) Drought assessment with a surface-groundwater coupled model in the Chesapeake Bay watershed. Environmental Modelling & Software. 119. https://doi.org/10.1016/j.envsoft.2019.07.002

Kapucu N, Haupt B (2016) Information communication technology use for public safety in the United States. Front Commun 1:8

Kato A, Wakabayashi H, Hayakawa Y, Bradford M, Watanabe M, Yamaguchi Y (2017) Tropical forest disaster monitoring with multi-scale sensors from terrestrial laser, UAV, to satellite radar. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2883–2886

Kaur A, Sood SK (2020) Cloud-centric IoT-based green framework for smart drought prediction. IEEE Internet of Things J 7(2):1111–1121

Kaur H, Sood SK (2020) Energy-efficient IoT-fog-cloud architectural paradigm for real-time wildfire prediction and forecasting. IEEE Syst J 14(2):2003–2011

Kaur M, Sood SK (2020) Hydro-meteorological hazards and role of ICT during 2010-2019: A scientometric analysis. Earth Sci Inform 13:1201–1223

Kaur M, Kaur PD, Sood SK (2021) Energy efficient IoT-based cloud framework for early flood prediction. Nat Hazards 109:2053–2076

Krippendorff K (1984) Content analysis: An introduction to its methodology. https://doi.org/10.2307/2288384

Kron W, Steuer M, Low P, Wirtz A (2012) How to deal properly with a natural catastrophe database—Analysis of flood losses. Nat Hazards Earth Syst Sci 12:535–550

Kumar RS, Kaliyaperuma K (2015) A scientometric analysis of mobile technology publications. Scientometrics 105(2):921–939

Lai V, Ahmed AN, Abdul Malek M, El-Shafie A (2018) Evolutionary algorithm for forecastng mean sea level based on meta-heuristic approach. Int J Civil Eng Technol 9:1404–1413

Leung Y, Wong M, Wong K, Zhang W, Leung K (2012) A novel web-based system for tropical cyclone analysis and prediction. Int J Geogr Inf Sci 26:75–97

Lettieri E, Masella C, Radaelli G (2009) Disaster management: Findings from a systematic review. Disaster Prev Manag 18:117–136

Li C, Liang W, Quigley C, Zhao Y, Yu LF (2017) Earthquake safety training through virtual drills. IEEE Trans Vis Comput Graph 23(4):1275–84

Luo T, Maddocks A, Iceland C, Ward P, Winsemius H (2015) World’s 15 countries with the most people exposed to river floods. World Resources Institute. On 5/3/2015

Matjaž M (2018) The bibliometric impact of books published by the International Consortium on Landslides. Landslides 15(8):1459–1482

Maynard-Ford MC, Phillips EC, Chirico PG (2008) Mapping vulnerability to disasters in Latin America and the Caribbean. US Geological Survey Open-File Report 1900–2007

Mehta P, Muller SA, Voisard A (2013) MoveSafe: a framework for transportation mode-based targeted alerting in disaster response. 15–22. https://doi.org/10.1145/2534732.2534735

MunichRE (2020) NatCatSERVICE. Available online: https://natcatservice.munichre.com/ . Accessed in May 2021

National Institute of Disaster Management (2014) East Asia Summit Earthquake Risk Reduction Centre New Delhi . Available online: https://nidm.gov.in/easindia2014/err/pdf/themesissue/env/disasterenvironment.pdf . Accessed on July 2021

Nemoto Y, Hamaguchi K (2014) Resilient ICT research based on lessons learned from the great east Japan Earth Quake. IEEE Commun Mag 38–43

NIST (2018) Community resilience planning guide. Available online: https://www.nist.gov/topics/community-resilience/community-resilience-planning-guide . Accessed  April 2022

Nguyen L, Yang Z, Li J, Pan Z, Cao G, Jin F (2019) Forecasting people’s needs in hurricane events from social network. IEEE Trans Big Data 8:229–240

One Concern (2020) Online: https://www.oneconcern.com/ . Accessed July 2021

Pradhan B (2012) A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Comput Geosci 51:350–365

Radianti J, Granmo O (2015) CIEM emergency management living-lab: concepts and design for research, innovation and knowledge exchange. Conference: TAMSEC, Kista, Sweden

Rahman MS, Di L, Esraz-Ul-Zannat M (2017) The role of big data in disaster management. Proceedings International Conference on Disaster Risk Mitigation 23–24

Rahman MU, Rahman S, Mansoor S, Deep V, Aashkaar M (2016) Implementation of ICT and wireless sensor networks for earthquake alert and disaster management in earthquake prone areas. Procedia Comput Sci 85:92–99

Ray PP, Mukherjee M, Shu L (2017) Internet of things for disaster management: State-of-the-art and prospects. IEEE Access 5:18818–18835

Pagano RR (2012) Understanding statistics in the behavioral sciences. Cengage Learning

Ruggiero L, Charitha D, Xiang S, Lucia B (2018) Investigating pedestrian navigation in indoor open space environments using big data. Appl Math Model 62:499–509

Sadiq FI, Selamat A, Ibrahim R (2018) Performance valuation of classifiers on activity recognition for disasters mitigation using smartphone sensing. Jurnal Teknologi 77:11–19

Saja A, Teo M, Goonetilleke A, Ziyath AM (2018) An inclusive and adaptive framework for measuring social resilience to disasters. Int J Disaster Risk Reduction 28:862–873

Sakhardande P, Hanagal S, Kulkarni S (2016) Design of disaster management system using IoT based interconnected network with smart city monitoring. International Conference on Internet of Things and Applications (IOTA) Pune 185–190

Sakurai M, Jiro K (2014) Design of a resilient information system for disaster response. Proceedings of the 35th International Conference on Information Systems. International Conference on Information Systems (ICIS). https://aisel.aisnet.org/icis2014/proceedings/ConferenceTheme/5/

Sandelowski M (1995) Qualitative analysis: what it is and how to begin? Res Nurs Health 18:371–375

Sarker MNI, Wu M, Alam GM, Shouse RC (2020) Livelihood diversification in rural Bangladesh: patterns and determinants in disaster prone riverine islands. Land Use Pol 96:104720

Saxena NK, Murty TS (1988) Tsunami research—a review and new concepts. Natural and Man-Made Hazards. https://doi.org/10.1007/978-94-009-1433-9_12

Schwartz J (2018) How can AI help to prepare for floods in a climate-changed world? https://www.scientificamerican.com/article/former-fema-chief-uses-ai-to-prepare-for-hurricanes-and-rising-seas/ . Accessed  March 2022

Shen S, Cheng C, Jing Y, Yang S (2018) Visualized analysis of developing trends and hot topics in natural disaster research. PLoS ONE 13(1):e0191250

Shibata Y, Uchida N, Shiratori N (2014) Analysis of and proposal for a disaster information network from experience of the great east Japan Earthquake. IEEE Commun Mag 44–50

Sinha A, Kumar P, Rana N, Islam R, Dwivedi YK (2019) Impact of internet of things (IoT) in disaster management: a task-technology fit perspective. Ann Oper Res 283:759–794

Sood R (2019) The interplay between China, the United States, and India. Observer research foundation. https://www.orfonline.org/research/the-interplay-between-china-the-united-states-and-india-59385/ . Accessed  May 2021

Sood SK, Sandhu R, Singla K, Chang V (2018) IoT, big data and HPC based smart flood management framework. Sustai Comput Inform Syst 20:102–117

Strauss A, Corbin J (1990) Basics of qualitative research: Grounded theory procedures and techniques. Sage, Newbury Park

Sun W, Bocchini P, Davison BD (2020) Applications of artificial intelligence for disaster management. Nat Hazards 103:2631–2689

Sun W, Bocchini P, Davison BD (2020a) Model for estimating the impact of interdependencies on system recovery. J Infrast Syst 26(3):04020031

Taohidul Islam S, Chik Z (2011) Disaster in Bangladesh and management with advanced information system. Disaster Prev Manag 20(5):521–30

Tonmoy FN, Hasan S, Tomlinson R (2020) Increasing coastal disaster resilience using smart city frameworks: Current state, challenges, and opportunities. Front Water 2:3

Truong XL, Mitamura M, Kon Y, Raghavan V, Yonezaw G, Truong X, Do H, Bui D, Lee S (2018) Enhancing prediction performance of landslide susceptibility model using hybrid machine learning approach of bagging ensemble and logistic model tree. Appl Sci 8(7):1046

UNISDR (2015) Sendai framework for disaster risk reduction 2015–2030. UNDRR. United Nations International Strategy for Disaster Reduction. https://www.undrr.org/publication/sendai-framework-disaster-risk-reduction-2015-2030 . Accessed June 2021

United Nation (2020) Big data for climate change and disaster resilience: Realizing the benefits for developing countries. Data-pop alliance synthesis report. https://datapopalliance.org/item/dfid-big-data-for-resilience-synthesis-report/ . Accessed on June 2021

Ushahidi (2021) Available online: https://www.ushahidi.com/

Van Eck NJ, Waltman L (2010) Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics. 84:523–538

Wang D (2012) Research on urban earthquake damage prediction virtual simulation system. In: 2nd International conference on civil engineering and transportation Guilin PEOPLES R China, pp 2034–2039

Wang TY, Huang R, Li L, Xu WG, Nie JG (2011) The application of the shortest path algorithm in the evacuation system. In: 2011 International conference of information technology, computer engineering and management sciences, vol 2, pp 250–253

Westen CJ (2013) Remote sensing and GIS for natural hazards assessment and disaster risk management. Treatise Geomorphol 3:259–298

Williams F, Oke A, Zachary I (2019) Public health delivery in the information age: the role of informatics and technology. Perspect Public Health 139(5):236–254

Wirtz A, Kron W, Low P, Steuer M (2014) The need for data: Natural disasters and the challenges of database management. Nat Hazards 70:135–157

Wu H, Huang M, Tang Q, Kirschbaum DB, Ward P (2016) Hydrometeorological hazards: monitoring, forecasting, risk assessment, and socioeconomic responses. Advances in Meteorology 1–3. https://doi.org/10.1155/2016/2367939

Xu Y, Wang Z, Zheng Q, Han Z (2012) The application of dijkstra’s algorithm in the intelligent fire evacuation system. In: 2012 4th international conference on intelligent human–machine systems and cybernetics, vol 1, pp 3–6

Yang C, Su G, Chen J (2017) Using big data to enhance crisis response and disaster resilience for a smart city. Int Conf Big Data Anal ICBDA Institute of Electrical and Electronics Engineers Inc 504–507

Yang F, Yuan Q, Du S, Liang L (2014) Reserving relief supplies for earthquake: a multi-attribute decision making of China Red Cross. Ann Oper Res 247(2):759–785

Yang J, Shi M, Han Z (2014) Research intelligent fire evacuation system based on ant colony algorithm and MapX. In: 2014 Seventh international symposium on computational intelligence and design, vol 2, pp 100–103

Yang L, Su G, Yuan H (2012) Design principles of integrated information platform for emergency responses: the case of 2008 Beijing Olympic Games. Inf Syst Res 23(3-part-1):761–86

Zainal H, Zainab AN (2011) Biomedical and health sciences publication productivity from Malaysia. Health Inform Library J 28(3):216–25

Zhou L, Zhang P, Zhang Z, Fan L, Tang S, Hu K, Xiao N, Li S (2018) A bibliometric profile of disaster medicine research from 2008 to 2017: A scientometric analysis. Disaster Med and Public Health Preparedness 13(2):165–172

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Kaur, M., Kaur, P.D. & Sood, S.K. ICT in disaster management context: a descriptive and critical review. Environ Sci Pollut Res 29 , 86796–86814 (2022). https://doi.org/10.1007/s11356-022-21475-5

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Disaster Risk Reduction, Climate Change Adaptation and Their Linkages with Sustainable Development over the Past 30 Years: A Review

Jiahong wen.

1 School of Environment and Geographical Sciences, Shanghai Normal University, Shanghai, 200234 China

Chengcheng Wan

2 Integrated Risk Governance Project, Beijing, 100875 China

Jianping Yan

3 Rodel Risk Solutions Inc., Toronto, ON M1W1J3 Canada

Weijiang Li

The severe damage and impacts caused by extreme events in a changing climate will not only make the sustainable development goals difficult to achieve, but also erode the hard-won development gains of the past. This article reviews the major impacts and challenges of disaster and climate change risks on sustainable development, and summarizes the courses and linkages of disaster risk reduction (DRR), climate change adaptation (CCA), and sustainable development over the past 30 years. Our findings show that the conceptual development of DRR actions has gone through three general phases: disaster management in the 1990s, risk management in the 2000s, and resilient management and development in the 2010s. Gradually, CCA has been widely implemented to overcome the adverse effects of climate change. A framework is proposed for tackling climate change and disaster risks in the context of resilient, sustainable development, indicating that CCA is not a subset of DRR while they have similarities and differences in their scope and emphasis. It is crucial to transform governance mechanisms at different levels, so as to integrate CCA and DRR to reduce disaster and climate change risks, and achieve safe growth and a resilient future in the era of the Anthropocene.

Introduction

Frequent disasters triggered by natural hazards around the world have caused huge losses of life and property to human society (CRED and UNDRR 2020 ). Climate change is further exacerbating disaster risks, increasing the frequency and severity of disaster damage and losses, and seriously hindering our efforts to achieve the sustainable development goals (SDGs) (IPCC 2022 ). Disaster risk reduction (DRR) and climate change adaptation (CCA) have become significant common challenges facing the international community in the era of the Anthropocene.

In December 1989, the United Nations adopted a historical resolution, declaring that the International Decade for Natural Disaster Reduction (IDNDR) would be launched on 1 January 1990 (United Nations 1989 ). Since then, international disaster reduction efforts have been developing vigorously for more than 30 years. Global actions on climate change mitigation and adaptation also go back more than 30 years. In November 1988, the World Meteorological Organization and the United Nations Environment Programme jointly established the Intergovernmental Panel on Climate Change (IPCC). 1 In December 1990, the 45th session of the United Nations General Assembly endorsed resolution 45/212, deciding to establish the Intergovernmental Negotiating Committee for the United Nations Framework Convention on Climate Change (UNFCCC) (United Nations 1992a ) with the participation of all member states of the United Nations, to negotiate international conventions on climate change, which was finally adopted in May 1992 (United Nations 1992a ). Since then DRR and CCA have become the core themes for international sustainable development.

Some previous studies have considered that CCA is a subset of disaster risk reduction and one of many processes within disaster risk reduction (Kelman 2015 ; Kelman et al. 2015 ). This may not be the case, however, in many ways, disaster risk reduction and CCA have overlapping aims and involve similar kinds of intervention (Twigg 2015 ; Islam et al. 2020 ). Therefore, many studies have suggested that addressing CCA and DRR together could be more beneficial (Clegg et al. 2019 ), and various studies have also explored ways and barriers of integrating DRR with CCA, as well as mainstreaming both into development (Mitchell et al. 2010 ; Florano 2015 ; Twigg 2015 ; Hore et al. 2018 ; Mal et al. 2018 ; Gabriel et al. 2021 ).

In the context that more than three years of the COVID-19 pandemic have affected all dimensions of social-ecological systems, and the proposed 2015−2030 sustainable development agenda has already been implemented halfway, the three main objectives of this study are to: (1) review the challenges, impacts, and risks of climate change and extreme events; (2) summarize the agenda and concept evolution of international DRR, CCA, and sustainable development since 1990; and (3) discuss the governance mechanisms and practices of integration of DRR and CCA—and their linkages with sustainable and resilient development—employed by the members of the international community over the past 30 years. Such work could help us find ways to achieve the goals set by the United Nations’ Sendai Framework for Disaster Risk Reduction 2015−2030 (United Nations 2015a ), the Paris Agreement (United Nations 2015b ), and the 2030 Agenda for Sustainable Development (United Nations 2015c ).

Disaster Risk Reduction and Sustainable Development

From 2000 to 2019, 7,348 disaster events were recorded worldwide by EM-DAT (The International Disaster Database at the Centre for Research on the Epidemiology of Disasters) (CRED and UNDRR 2020 ). These disasters claimed approximately 1.23 million lives, an annual average of 60,000 lost lives, and affected a total of over 4 billion people (many on more than one occasion) (CRED and UNDRR 2020 ). These disasters also led to approximately USD 2.97 trillion in direct economic losses worldwide. If the expected annual losses induced by natural hazards were shared equally among the world’s population, it would be equivalent to an annual loss of almost USD 70 for each individual of working age, or two months’ income for people living below the poverty line (UNISDR 2015 ). Clearly, sustainable development cannot be achieved without taking account of disaster risk reduction (UNDP 2004 ; UNDRR 2022 ). To do so, however, there are three major obstacles that need to be addressed.

First, there is still a lack of scientific and technological capabilities (including risk monitoring, risk assessment, early warning, and so on) and risk governance mechanisms to reduce the loss of life and property caused by very large-scale disasters globally. The 2008 Wenchuan Earthquake in China caused a total of 87,150 deaths and missing persons; in 2010, the Haiti Earthquake killed 222,500 people; the 2015/2016 droughts in India affected 330 million people; the direct economic losses caused by the 2011 East Japan Earthquake and Tsunami were as high as USD 210 billion (CRED and UNDRR 2020 ).

Second, EM-DAT does not record many small-scale but recurring disasters caused by extensive risks (minor but recurrent disaster risks) (UNISDR 2015 ), as well as indirect losses. From 2005 to 2014, direct economic losses due to extensive risks in 85 countries and territories were equivalent to a total of USD 94 billion (UNISDR 2015 ). Extensive risks are responsible for most disaster morbidity and displacement, and represent an ongoing erosion of development assets, such as houses, schools, health facilities, and local infrastructures. However, the cost of extensive risk is not visible and tends to be underestimated, as it is usually absorbed by low-income households and communities and small businesses. In addition, better recording and sharing of disaster information is needed for disaster loss accounting, forensics, and risk modeling (De Groeve et al. 2013 ; De Groeve et al. 2015 ; Hallegatte 2015 ; Khadka 2022 ; UNDRR 2022 ).

Third, in today’s crowded and interconnected world, indirect, cascading impacts can also be significant, and disaster impacts increasingly cascade across geographies and sectors (UNDRR 2022 ). Indirect losses, including output losses (such as business interruptions, supply-chain disruptions, and lost production due to capital damages), and macroeconomic feedbacks, may extend over a longer period of time than the event, and affect a larger spatial scale or different economic sectors (Hallegatte 2015 ). Therefore, indirect, cascading impacts may cause more serious harm to socioeconomic development in a region or society (Khadka 2022 ; UNDRR 2022 ).

Climate Change Risks and Sustainable Development

The best estimate of total human-caused global surface temperature increase from 1850–1900 to 2010–2019 is around 1.1 °C, and each of the last four decades has been successively warmer than any decade that preceded it since 1850 (IPCC 2021 ; WMO 2021 ). If the temperature continues to rise at the current rate, global warming could reach 1.5 °C between 2030 and 2052 (IPCC 2018 ). Increasing risks associated with health, livelihoods, food security, water supply, human security, and economic growth are all expected in a rapidly changing climate (Carleton and Hsiang 2016 ; IPCC 2018 ). The Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6) has identified over 130 key risks (KRs) that may become severe under particular conditions of climate hazards, exposure, and vulnerability. These key risks are represented in eight so-called Representative Key Risk (RKR) clusters of key risks relating to low-lying coastal systems; terrestrial and ocean ecosystems; critical physical infrastructure, networks, and services; living standards; human health; food security; water security; and peace and mobility (IPCC 2022 ). The international scientific community has warned that without quick actions on the following three urgent issues, the severe damage and impacts of climate change and extreme events will not only put the achievement of the SDGs out of reach but also erode the hard-won development gains of the past.

The first issue is that as human-induced climate change, including more frequent and intense extreme events, has affected and will continue to threaten the lives and livelihoods of millions to billions of people, the challenges of how to significantly reduce the emerging risks of climate change are enormous ((IPCC 2018 , 2022 ; Rising et al. 2022 ). Currently, climate-related disasters account for more than 80% of disasters caused by natural hazards (UNDRR 2021 ). Around the world 3.3−3.6 billion people live in areas of high vulnerability to climate change (IPCC 2022 ).

The second issue is that under higher warming scenarios (for example, 3−4 °C) it is almost certain that Planet Earth will cross tipping points, leading to irreversible changes in ecosystems or climate patterns, which will significantly limit our ability to adapt (Steffen et al. 2018 ; Lenton et al. 2019 ; Ritchie et al. 2021 ). The challenges of how to address the adaptation limits that are already being confronted across the world will only increase (Future Earth et al. 2022 ). For example, in high-emission scenarios, week-long heat extremes that break records by three or more standard deviations are two to seven times more probable in 2021–2050 and three to 21 times more probable in 2051–2080, compared to the last three decades (Fischer et al. 2021 ). Building codes in many areas have to be modified and even redesigned.

The third issue is the lack of scientific research to better understand the mechanisms of systemic risks caused by climate change in the context of deep uncertainty. For example, record-shattering extremes—nearly impossible in the absence of warming—are likely to occur in the coming decades (Fischer et al. 2021 ), which may lead to the emergence of systemic risks with large-scale, non-linear, and cascading consequences in socioeconomic systems (Helbing 2012 ; Renn et al. 2019 ). Deep uncertainty is mainly reflected in three aspects, including uncertain scenarios of climate change, uncertain consequences of decision making, and uncertain schemes of decision making. Due to the deep uncertainty of the changes, over- or under-adaptation can occur, leading policymakers and planners to make suboptimal decisions (Linstone 2004 ; Kwakkel et al. 2016 ; Marchau et al. 2019 ; Webber and Samaras 2022 ).

Agenda and Evolution of International Disaster Risk Reduction, Climate Change Adaptation, and Sustainable Development

A landmark year for DRR, CCA, and sustainable development was 2015 because three important events occurred in that year—the Sendai Framework for Disaster Risk Reduction 2015−2030, the Sustainable Development Goals (SDGs), and the Paris Agreement under the UNFCCC (United Nations 2015a ; United Nations 2015b ; United Nations 2015c ) were adopted by the international community. Looking back in history can help us understand the governance of international DRR and CCA, and their important processes and context (Fig. ​ (Fig.1 1 ).

Important events of disaster risk reduction (DRR), climate change adaptation (CCA), and sustainable development since 1990. IPCC: Intergovernmental Panel on Climate Change.

Source Modified from Mal et al. ( 2018 )

International Disaster Risk Reduction Action Framework and Concept Evolution

In 1987, the 42nd session of the United Nations General Assembly passed a resolution and decided to designate the 1990s as the International Decade for Natural Disaster Reduction (IDNDR) (United Nations 1987 ), calling on governments from all over the world to actively participate in and support this action. The main goal of the IDNDR was to minimize the losses of life and property, as well as the impacts and damage to the economy and society caused by disasters. In 1999, the United Nations International Strategy for Disaster Reduction (UNISDR) and the UNISDR Secretariat were established as the successor arrangements for the IDNDR to be responsible for the implementation of DRR plans and strategies among UN member states, with a view to further strengthening international disaster reduction efforts. In 2019, the Secretariat changed its name to the UN Office for Disaster Risk Reduction (UNDRR). 2

The First World Conference on Natural Disaster Reduction held at Yokohama, Japan in 1994 adopted the Yokohama Strategy and Plan of Action for a Safer World (IDNDR 1994 ), reiterating the focus of the IDNDR. The Yokohama Plan of Action urged the incorporation of disaster prevention, preparedness, early warning, recovery, local capacity building, and improvement of disaster response mechanisms into national policies in order to reduce the impacts of disasters.

In 2005, the Second World Conference on Natural Disaster Reduction held at Kobe, Hyogo, Japan, adopted the Hyogo Declaration and the Hyogo Framework for Action 2005−2015: Building the Resilience of Nations and Communities to Disasters (United Nations 2005 ). The goals of the Hyogo Framework were to substantially reduce the loss of human, socioeconomic, and environmental assets of communities and countries from disasters by 2015 by integrating DRR into strategies and planning processes, and by promoting the effective role of local knowledge, resilience building, and climate adaptation. The action framework includes an expected outcome, three strategic goals, and five priorities for actions (Fig. ​ (Fig.2 2 ).

The Hyogo Framework for Action 2005−2015: Expected outcome, strategic goals, and priorities for action (United Nations 2005 )

In March 2015, the Third World Conference on Natural Disaster Reduction held in Sendai, Japan, adopted the Sendai Framework for Disaster Risk Reduction 2015−2030 (United Nations 2015a ). The Sendai Framework set out an expected outcome and seven quantitative goals to be achieved in the following 15 years, together with four priorities for actions—understanding disaster risk, strengthening disaster risk governance to manage disaster risk, investing in DRR for resilience, and enhancing disaster preparedness for effective response and to “Build Back Better” in recovery, rehabilitation, and reconstruction (Fig. ​ (Fig.3). 3 ). The endorsement of the Sendai Framework opened a new chapter for DRR and sustainable development.

The Sendai Framework for Disaster Risk Reduction 2015−2030: Expected outcome, strategic goals, and priorities for action (United Nations 2015a )

Over the past 30 years, in general, the development of DRR and related goals and priorities for action can be divided into three stages of disaster management in the 1990s, disaster risk management in the 2000s, and resilience management and development in the 2010s. The three stages reflect the key characteristics and important conceptual development of DRR actions at different stages rather than being separated from each other. Disaster management focuses on disaster-centered approaches (Fig. ​ (Fig.4), 4 ), and countermeasures are focused on disaster preparedness and response. Disaster risk management is to prevent new disaster risk, reduce existing disaster risk, and manage residual risk on the basis of risk-based decisions. It emphasizes risk-centered approaches (Fig. ​ (Fig.4), 4 ), and prevention and reduction are superior to response and relief. Resilience management (Fig. ​ (Fig.4) 4 ) is a new paradigm, which puts the emphasis on enhancing the ability of a system, community, or society to resist, absorb, accommodate, adapt to, transform, and recover from the effects of a hazard (predictable or unpredictable) in a timely and efficient manner, including through the preservation and restoration of its essential basic structures and functions through risk management. 3 These ideas are embodied in the three World Conferences on Natural Disaster Reduction held by the United Nations and the adopted disaster risk reduction strategies and action frameworks.

A comparison between disaster management, risk management, and resilience management

The 1990s coincided with the IDNDR, which emphasized the enhancement of national disaster management capabilities in disaster prevention, mitigation, preparedness, and relief. The Yokohama Strategy urged the enhancement of disaster management for achieving sustainable development, and clarified that to achieve the goals of the IDNDR, disaster prevention, mitigation, and preparedness were more effective than disaster relief (IDNDR 1994 ). The 2000s witnessed the transition from disaster management to risk management. The Hyogo Framework emphasized that the focus of DRR should shift to disaster risk management and that DRR should be a national and a local priority and incorporated into national development policies (United Nations 2005 ). In the 2010s, the concept of the DRR field further shifted to resilience building. Researchers and practitioners at different levels worked a lot on the theory and practice of resilience, and gradually resilient management and development became an international consensus (Cutter et al. 2013 ; Florin and Linkov 2016 ; Gencer 2017 ).

Climate Change Risk Assessment and Adaptation

Over the past 30 years, the IPCC has issued a series of comprehensive assessment reports about the state of scientific, technical, and socioeconomic knowledge on climate change impacts, risks, and adaptation. The adaptation negotiations under the UNFCCC have also made significant progress, and gradually, CCA has been widely implemented to overcome the adverse effects of climate change at all levels.

The Intergovernmental Panel on Climate Change Reports

Since 1988, every 6−7 years, nearly a thousand scientists around the world have engaged in various fields of climate change and socioeconomic and sustainable development to provide policymakers with a comprehensive explanation of the current international scientific community’s latest understanding of climate system changes in so far six assessment reports (see Fig. ​ Fig.1). 1 ). Since 1990, IPCC’s six climate change assessment reports have made fruitful evaluations of the scientific progress of climate system changes, the impacts and risks of climate change on natural and socioeconomic systems, and the options for limiting greenhouse gas emissions and mitigating climate change. The reports have become authoritative documents for the international community’s combat of climate change, providing a scientific basis for the negotiations of the UNFCCC, and an important scientific basis for governments to formulate policies and take actions on climate change mitigation and adaptation (Qin 2018 ). In order to assess the relationship between climate change and extreme weather events, and their impacts on the sustainable development of society, the IPCC issued a special report on “Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation” in February 2012 (IPCC 2012 ). The report pointed out that the extent of damage caused by extreme weather to elements at risk depends not only on the extreme events, but also on the exposure and vulnerability of the social-ecological systems. The report also systematically explains the paths and methods of disaster risk management to adapt to climate change.

Adaptation is an important part of the IPCC reports. The IPCC Fifth Assessment Report (AR5) summarizes the adaptation needs, options, plans, and measures of climate change, and assesses the role of adaptation, the limitations of adaptation, and the transformation of adaptation in four chapters. The report gives a variety of adaptation measures, which can be grouped into three categories—measures to reduce exposure, incremental adaptation measures, and transformational adaptation measures (IPCC 2014 ). The IPCC Sixth Assessment Report (AR6) Working Group II (WGII) report describes the current status of adaptation and its benefit, future adaptation options and their feasibility, adaptation limitations, and maladaptation and how to avoid it. The feasibility of 23 adaptation measures is evaluated, which shows adaptation is subject to hard and soft limits (IPCC 2022 ).

Adaptation Negotiations Under the United Nations Framework Convention on Climate Change

Damage and loss associated with climate change impacts have emerged as key issues underpinning climate change adaptation at the global level during recent climate change negotiations under the UNFCCC (Prabhakar et al. 2015 ). Along with the rise in climate-related hazards, and the impacts and risks of fast-onset extremes and slow-onset changes (such as sea level rise) in the climate system, adaptation started attracting more attention at COP 10 (Conference of the Parties in 2004), then received successive boosts from the adoption of the Bali Action Plan in 2007 and the following COPs in Cancun (Mexico) in 2010 and others leading up to the 2015 Paris Agreement (Shaw et al. 2016 ) (see Fig. ​ Fig.1 1 ).

In December 2015, the Paris Climate Change Conference reached a series of results centered on the Paris Agreement, which became an important historical and binding international framework aiming to strengthen the global response to the threat of climate change (United Nations 2015b ).The Paris Agreement puts forward three goals:

• Holding the increase in the global average temperature to well below 2 °C above pre-industrial levels and striving to limit the temperature increase to 1.5 °C above the pre-industrial levels;
• Increasing the ability to adapt to the adverse impacts of climate change and foster climate resilience and low greenhouse gas emissions development, in a manner that does not threaten food production; and
• Making finance flows consistent with a pathway towards low greenhouse gas emissions and climate-resilient development.

In terms of adaptation and reduction of the damage and loss caused by climate change, global adaptation goals have been proposed to enhance adaptability, strengthen resilience, and reduce vulnerability to climate change.

Over the past 30 years, the adaptation negotiations under the UNFCCC can be roughly divided into three stages of early slow progress, equal emphasis on adaptation and mitigation, and enhanced adaptation action. The climate negotiations were characterized by “emphasis on mitigation, neglect of adaptation” in the early stage. After the 2007 Bali Roadmap adopted by the 13th Conference of the Parties (COP 13) that put equal emphasis on mitigation and adaptation, the adaptation-related agenda and its importance were increased under the UNFCCC negotiation regime. The 2010 Cancun Adaptation Framework and the 2015 Paris Agreement put forward specific action frameworks to enhance global adaptation actions, and to establish an international governance and mechanism for global adaptation to climate change, which laid a good foundation for enhancing climate resilience, reducing vulnerability, and achieving the goals of the UNFCCC (Tao 2014 ; Chen et al. 2016 ; Chen 2020 ).

Linkages of Disaster Risk Reduction, Climate Change Adaptation, and the Sustainable Development Goals

In 1987, the Report of the World Commission on Environment and Development “Our Common Future” put forward the strategy of sustainable development, marking the birth of a new concept of development (WCED 1987 ). In June 1992, the United Nations Conference on Environment and Development (also known as the Earth Summit) adopted a series of important documents—the Rio Declaration on Environment and Development (also known as the Earth Charter); Agenda 21; the Framework Convention on Climate Change; and the Convention on Biological Diversity. The United Nations Convention to Combat Desertification was adopted on 17 June 1994. The Earth Summit established a road map of sustainable development with harmonious coexistence between humans and nature (United Nations 1992b ; Cicin-Sain 1996 ). A considerable incentive for rethinking disaster risk as an integral part of the development process comes from the aim of achieving the goals laid out in the Millennium Declaration. The Declaration sets forth a road map for human development supported by 191 nations in 2000 (UNDP 2004 ). Following the end of the 2000−2015 Millennium Development Goals (United Nations 2000 ), the United Nations Development Summit in September 2015 unanimously adopted the draft resolution “Transforming our world: The 2030 Agenda for Sustainable Development,” submitted by the 69th session of the United Nations General Assembly (United Nations 2015c ). The SDGs in the United Nations 2030 Agenda replaced the Millennium Development Goals launched by the United Nations at the beginning of the 21st century.

The agenda includes 17 SDGs and 169 associated targets. These development goals all closely interact and influence climate change and disaster risks. For example, Goal 9 building resilient infrastructure, Goal 11 building inclusive, safe, resilient, and sustainable cities and human settlements, and Goal 13 taking urgent action to combat climate change and its impacts, all are directly related to DRR and CCA. Many of these 169 associated targets also involve reducing disaster risks and disaster impacts. For example, one of the specific targets of Goal 1 is to build the resilience of the poor and those in vulnerable situations and reduce their exposure and vulnerability to climate-related extreme events and other economic, social, and environmental shocks and disasters by 2030. Disasters put development at risk, and losses caused by climate change and extreme events may severely hinder many countries from achieving SDGs. At the same time, the realization of the SDGs will also help reduce human vulnerability to climate change and disasters, thereby greatly reducing disaster risks.

Climate change adaptation and DRR have similarities and differences in their scope and emphasis (Twigg 2015 ; Clegg et al. 2019 ). The common aim of CCA and DRR is to manage the risk induced by weather/climate-related hazards, including extreme events and climate-related creeping environmental changes, which is part of climate risk management (see Fig. ​ Fig.4). 4 ). Their difference is that DRR not only deals with hydrometeorological disaster risk closely related to climate change, but also manages risks caused by other natural hazards, such as earthquakes and volcanic eruptions (Twigg 2015 ). In addition, DRR focuses more on reducing the potential losses of people and assets. Climate change adaptation also has its focus areas, such as the impact of climate change on ecosystems and biodiversity, and infectious diseases and health (IPCC 2022 ). According to the Adaptation Gap Report 2022 (UNEP 2022 ), CCA actions are currently mainly focused on agriculture, water, ecosystems, and cross-cutting sectors. Disaster risk reduction and CCA are two major areas of integrated risk management (Fig. ​ (Fig.5), 5 ), thus both should be joined within the integrated risk management that is an important pillar and field of resilient, sustainable development. Under the framework of resilient development, there are two areas that are closely related to climate change and DRR, that is, emergency management and climate change mitigation (Fig. ​ (Fig.5). 5 ). The synergistic effects of integrated risk management, emergency management, and climate change mitigation will effectively ensure safe growth and resilient development.

A framework for addressing disaster and climate change risks in the context of resilient, sustainable development

In many ways, DRR and CCA have overlapping aims and involve similar kinds of intervention (Begum et al. 2014 ; Forino et al. 2015 ; Twigg 2015 ; Amaratunga et al. 2017 ).

People and ecosystems across the world are already confronted with limits to adaptation, and if the planet warms beyond 1.5 °C or even 2 °C, more widespread breaching of adaptation limits is expected (Forino et al. 2015 ; Twigg 2015 ).

Addressing climate change may have the potential to create or exacerbate other development concerns (Kelman et al. 2015 ). Large dams might contribute to climate change mitigation and adaptation through reduced dependence on fossil fuels and regulating floods. But large dams tend to increase flood risk over the long term in a process termed ‘‘risk transference’’ (Etkin 1999 ). Seawalls and infrastructural development along coastlines may also induce changes in water currents, destruction of natural ecosystems, and increased or shifted erosion from protected to unprotected areas (Dahl et al. 2017 ; Rahman and Hickey 2019 ; Piggott-Mckellar et al. 2020 ; Simon et al. 2020 ). Seawalls may effectively reduce impacts to people and assets in the short term but may also result in lock-ins and increase exposure to coastal hazards in the long term unless they are integrated into a long-term climate risk management plan. Although fire suppression in naturally fire-adapted ecosystems prevents fire damage, such action reduces the space for natural processes, thus reducing the ecosystem’s resistance to climate change and its ecosystem service value (Ruffault and Mouillot 2015 ; Hope et al. 2016 ).

Therefore, DRR and CCA should be addressed together under integrated risk management to overcome limits and maladaptation, and optimize the use of limited resources (Mitchell et al. 2010 ; Twigg 2015 ; Flood et al. 2022 ). Thus, the integration of CCA and DRR can contribute to achieving the goals of international frameworks such as the SDGs (Kelman and Gaillard 2010 ; UN DESA 2014 ; Kelman 2017 ; Clegg et al. 2019 ), the Sendai Framework, and the Paris Agreement (Amaratunga et al. 2017 ).

However, there are many factors that hinder successful integration of CCA and DRR (Amaratunga et al. 2017 ; Seidler et al. 2018 ; Dias et al. 2020 ; Islam et al. 2020 ). Barriers include poor communication between organizations, coordination challenges, lack of political willingness, lack of capacity among actors and institutions, policy gaps, mismatches, different funding systems, fund shortages, and so on. Disaster risk reduction and CCA are frequently addressed, studied, and analyzed independently (O’Brien and Li 2006 ; Ireland 2010 ; Kelman et al. 2015 ; Chmutina et al. 2016 ; Clegg et al. 2019 ), separated by institutional and administrative boundaries (Schipper and Pelling 2006 ; Kelman 2017 ; Pilli-Sihvola 2020 ). For historical and political reasons, internationally, the way we are currently working addresses climate change, DRR, development-related projects, and humanitarian relief separately (Fig. ​ (Fig.6). 6 ). International funding mechanisms establish and implement independent projects of CCA, DRR, and so on in target countries through international organizations (such as different agencies of the United Nations), resulting in segmented practices.

A scheme showing international funding mechanisms for target countries

At the national level, CCA and DRR are also frequently handled independently, separated by institutional and administrative boundaries (Schipper and Pelling 2006 ; Kelman 2017 ; Dias et al. 2018 ; Clegg et al. 2019 ). In China, for example, the Fourteenth Five Year Plan for National Comprehensive Disaster Prevention and Reduction (2021−2025) was formulated by the National Disaster Reduction Commission, which is only a deliberative body and thus it is difficult to promote the implementation of the plan. In 2022, 17 national departments jointly issued the National Climate Change Adaptation Strategy 2035, with the Ministry of Ecology and Environment as the leading department. Climate change adaptation and DRR efforts are still addressed by two sets of organizations in China. In the Philippines, DRR and CCA are operationalized independently of one another (Florano 2015 ; De Leon and Pittock 2017 ). There are two separate laws on climate change and disaster risk reduction and management—the Climate Change Act of 2009 and the Philippine National Disaster Risk Reduction and Management Act of 2010, respectively. This is also the case in national level arrangements in the UK, where DRR and CCA are managed by separate government departments (Dias et al. 2018 ; Clegg et al. 2019 ).

To change this situation, effective governance mechanisms, such as policy, agreement, culture, leadership, and coordination need to be established among international organizations, as well as between international organizations and target countries, while countries also need to establish overarching national risk governance systems (Fig. ​ (Fig.7). 7 ). Thus, tailored country programs can be established through international risk governance solutions, and implemented in target countries by a unified mechanism under the national risk governance system.

Integrated risk governance solution among international organizations and countries

Moreover, a wide range of climate change impacts and disaster risks (especially the cascading and systemic risks) are understudied or challenging to quantify, and are missing from current evaluations of climate change and other disaster risks to lives and assets (Mamuji and Etkin 2019 ; Mcglade et al. 2019 ; Rising et al. 2022 ). Importantly, integrated risk and resilience management is about managing known risks but also about preparing for the unpredictable (Pirani and Tolkoff 2015 ). Thus, better data, actionable information, and relevant knowledge on climate change and disaster risk are needed to promote the integration of CCA and DRR (Mysiak et al. 2018 ; Zuccaro et al. 2020 ).

This study reviews the major impacts and challenges of disaster and climate change risks on sustainable development, summarizes the important events and evolution of international disaster risk reduction and climate change adaptation over the past 30 years, and reviews the linkages of DRR and CCA to sustainable development. The three main conclusions are:

• Disasters caused by both intensive and extensive disaster risks have a huge impact on lives and livelihoods. Indirect losses and cascading effects may cause even more serious damage to the socioeconomic development of a region or a society. Most disasters triggered by natural hazards are related to weather/climate events. Especially under a changing climate, compound events and systemic risks are increasing, and record-shattering extremes are likely to occur in the coming decades, which will significantly limit our ability to adapt.
• Over the past 30 years, the evolution of paradigms in DRR actions can be roughly divided into three stages—disaster management in the 1990s, disaster risk management in the 2000s, and resilient management and development in the 2010s. These ideas are embodied in the three World Conferences on Natural Disaster Reduction held by the United Nations and the adopted disaster reduction strategies and action frameworks. The adaptation negotiations under the UNFCCC over the past 30 years also can be roughly divided into three stages of early slow progress, equal emphasis on adaptation and mitigation, and enhanced adaptation action. Climate change adaptation has been widely carried out to overcome the adverse effects of climate change. The integrated risk management community has also learned the current status of adaptation and its benefit, future adaptation options and their feasibility, adaptation limitations, and maladaptation and how to avoid it.
• This article proposes a framework for addressing climate change and disaster risks in the context of resilient, sustainable development. Climate change adaptation is not a subset of DRR, and they have both similarities and differences in their scope and emphasis. Disaster risk reduction and CCA should be joined under the integrated risk management that is an important pillar of resilient and sustainable development. Under the umbrella of resilient development, there are two areas that are closely related to climate change and DRR—disaster management and climate change mitigation. The synergistic effects of integrated risk management, emergency management, and climate change mitigation will effectively support safe growth and resilient development.

To successfully integrate CCA and DRR, it is urgently needed to transform governance mechanisms, and to strengthen cooperation among international organizations, as well as between international organizations and countries, while countries also need to establish overarching national risk governance systems. Moreover, better data, actionable information, and relevant knowledge are needed for understanding climate change and disaster risks in a context of deep uncertainty.

The severe effects of the COVID-19 pandemic on our health and socioeconomic well-being are a stark warning of the dangers of insufficient actions, prevention, and preparedness—but people and societies can adopt new behaviors when the problems and situations are changing. In the context of climate emergency, the feasibility and effectiveness of adaptation measures will decrease with increasing warming. It is urgently needed to leverage the synergies of CCA and DRR, together with climate change mitigation and disaster management, in order to prevent new risks, reduce and mitigate existing vulnerabilities and risks, and to realize the goals of the Sendai Framework, the Paris Agreement, and the Sustainable Development Goals.

Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant Nos. 42171080, 41771540), the National Social Science Foundation of China (Grant No. 18ZDA105), and the Humanities and Social Sciences Program of the Ministry of Education (Grant No. 21YJC630146).

1 https://www.ipcc.ch/about/history/ .

2 https://www.undrr.org/about-undrr/history .

3 https://www.undrr.org/terminology/resilience .

• Amaratunga, D., R. Haigh, C. Malalgoda, and M. Dias. 2017. Synthesis report of legal, policy and science approaches within the frame of CCA and DRR: Global perspectives. Project report. University of Huddersfield, Huddersfield, UK. http://eprints.hud.ac.uk/id/eprint/33429/ . Accessed 26 Jan 2023.
• Begum, R.A., M.S.K. Sarkar, A.H. Jaafar, and J.J. Pereira. 2014. Toward conceptual frameworks for linking disaster risk reduction and climate change adaptation. International Journal of Disaster Risk Reduction 10(Part A): 362–373.
• Carleton, T.A., and S.M. Hsiang. 2016. Social and economic impacts of climate. Science 353(6304): Article aad9837. [ PubMed ]
• Chen M. Progress and outlook of adaptation negotiation under the United Nations Framework Convention on Climate Change. Climate Change Research. 2020; 16 (1):105–116. [ Google Scholar ]
• Chen MP, Zhang YC, Li B, Li YE. Interpretation of adaptation and loss and damage elements in Paris Agreement and possible solution for China. Climate Change Research. 2016; 12 (1):251–257. [ Google Scholar ]
• Chmutina, K., R. Jigyasu, and L. Bosher. 2016. Integrating disaster risk reduction and climate change adaptation into the built environment. In Proceedings of the 6th International Conference on Building Resilience 2016 , 7–9 September 2016, The University of Auckland, Auckland, New Zealand.
• Cicin-Sain B. Earth summit implementation: Progress since Rio. Marine Policy. 1996; 20 (2):123–143. doi: 10.1016/S0308-597X(96)00002-4. [ CrossRef ] [ Google Scholar ]
• Clegg, G., N. Dias, D. Amaratunga, R. Haigh, and A. Panda. 2019. Integration of CCA and DRR for flood resilience: A review of good practices in the United Kingdom. Contributing paper to Global Assessment Report on Disaster Risk Reduction (GAR 2019). https://www.unisdr.org/files/66296_f337finalcleggintegrationofccaanddr.pdf . Accessed 30 Jan 2023
• CRED (Centre for Research on the Epidemiology of Disasters), and UNDRR (United Nations Office for Disaster Risk Reduction). 2020. Human cost of disasters: An overview of the last 20 years (2000–2019). Brussels: CRED and Geneva: UNDRR. https://www.undrr.org/sites/default/files/inlineiles/Human%20Cost%20of%20Disasters%202000-2019%20FINAL.pdf . Accessed 30 Jan 2023.
• Cutter, S.L., J.A. Ahearn, B. Amadei, P. Crawford, E.A. Eide, G.E. Galloway, M.F. Goodchild, H.C. Kunreuther, et al. 2013. Disaster resilience: A national imperative. Environment: Science and Policy for Sustainable Development 55(2): 25–29.
• Dahl, K.A., E. Spanger-Siegfried, A. Caldas, and S. Udvardy. 2017. Effective inundation of continental united states communities with 21st century sea level rise. Elementa: Science of the Anthropocene 5: Article 37.
• De Groeve, T., K. Poljansek, and D. Ehrlich. 2013. Recording disaster losses. Recommendations for a European Research. European Commission, Joint Research Centre scientific and policy reports. Luxembourg: Publications Office of the European Union.
• De Groeve, T., C. Corbane, and D. Ehrlich. 2015. Guidance for recording and sharing disaster damage and loss data: Towards the development of operational indicators to translate the Sendai Framework into action. Report by the European Commission, Joint Research Centre. EUR 27192. Luxembourg: Publications Office of the European Union.
• De Leon EG, Pittock J. Integrating climate change adaptation and climate-related disaster risk-reduction policy in developing countries: A case study in the Philippines. Climate and Development. 2017; 9 (5):471–478. doi: 10.1080/17565529.2016.1174659. [ CrossRef ] [ Google Scholar ]
• Dias N, Amaratunga D, Haigh R. Challenges associated with integrating CCA and DRR in the UK – A review on the existing legal and policy background. Procedia Engineering. 2018; 212 :978–985. doi: 10.1016/j.proeng.2018.01.126. [ CrossRef ] [ Google Scholar ]
• Dias N, Amaratunga D, Haigh R, Clegg G, Malalgoda C. Critical factors that hinder integration of CCA and DRR: Global perspective. In: Filho WL, Luetz J, Ayal D, editors. Handbook of climate change management: Research, leadership, transformation. Cham, Switzerland: Springer; 2020. pp. 1–22. [ Google Scholar ]
• Etkin D. Risk transference and related trends: Driving forces towards more mega-disasters. Global Environmental Change Part B: Environmental Hazards. 1999; 1 (2):69–75. doi: 10.1016/S1464-2867(00)00002-4. [ CrossRef ] [ Google Scholar ]
• Fischer EM, Sippel S, Knutti R. Increasing probability of record-shattering climate extremes. Nature Climate Change. 2021; 11 (8):689–695. doi: 10.1038/s41558-021-01092-9. [ CrossRef ] [ Google Scholar ]
• Flood, S., Y. Jerez Columbié, M. Le Tissier, and B. O’Dwyer. 2022. Creating resilient futures: Integrating disaster risk reduction, sustainable development goals and climate change adaptation agendas . Cham, Switzerland: Springer Nature.
• Florano ER. Mainstreaming integrated climate change adaptation and disaster risk reduction in local development plans in the Philippines. In: Filho WL, editor. Handbook of climate change adaptation. Berlin: Springer; 2015. pp. 433–456. [ Google Scholar ]
• Florin, M., and I. Linkov. 2016. IRGC resource guide on resilience. Lausanne, Switzerland: EPFL International Risk Governance Center (IRGC).
• Forino G, von Meding J, Brewer GJ. A conceptual governance framework for climate change adaptation and disaster risk reduction integration. International Journal of Disaster Risk Science. 2015; 6 (4):372–384. doi: 10.1007/s13753-015-0076-z. [ CrossRef ] [ Google Scholar ]
• Future Earth, The Earth League, and WCRP (World Climate Research Programme). 2022. 10 New Insights in Climate Science 2022.10.5281/zenodo.7228926. Accessed 30 Jan 2023.
• Gabriel AG, Santiago PNM, Casimiro RR. Mainstreaming disaster risk reduction and climate change adaptation in comprehensive development planning of the cities in Nueva Ecija in the Philippines. International Journal of Disaster Risk Science. 2021; 12 (3):367–380. doi: 10.1007/s13753-021-00351-9. [ CrossRef ] [ Google Scholar ]
• Gencer, E. 2017. How to make cities more resilient: A handbook for local government leaders. Geneva: United Nations Office for Disaster Risk Reduction (UNISDR).
• Hallegatte, S. 2015. The indirect cost of natural disasters and an economic definition of macroeconomic resilience. World Bank policy research working paper No.7357. Washington, DC: World Bank.
• Helbing, D. 2012. Systemic risks in society and economics. In Social self-organization: Agent-based simulation and experiments to study emergent social behavior , ed. D. Helbing, 261–284: New York: Springer.
• Hope, E.S., D.W. Mckenney, J.H. Pedlar, B.J. Stocks, and S. Gauthier. 2016. Wildfire suppression costs for Canada under a changing climate. PloS One 11(8): Article e157425. [ PMC free article ] [ PubMed ]
• Hore K, Kelman I, Mercer J, Gaillard JC. Climate change and disasters. In: Rodríguez H, Donner W, Trainor JE, editors. Handbook of disaster research. Cham, Switzerland: Springer; 2018. pp. 145–159. [ Google Scholar ]
• IDNDR (International Decade for Natural Disaster Reduction). 1994. Yokohama Strategy and Plan of Action for a safer world: Guidelines for natural disaster prevention, preparedness and mitigation. In Proceedings of World Conference on Natural Disaster Reduction , 23–27 May, Yokohama, Japan. https://www.preventionweb.net/files/8241_doc6841contenido1.pdf . Accessed 26 Jan 2023.
• IPCC (Intergovernmental Panel on Climate Change). 2012. Summary for policymakers. In Managing the risks of extreme events and disasters to advance climate change adaptation. A special report of Working Groups I and II of the Intergovernmental Panel on Climate Change , ed. C.B. Field, V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, et al., 1–19. Cambridge, UK and New York, USA: Cambridge University Press.
• IPCC (Intergovernmental Panel on Climate Change). 2014. Climate change 2014: Impacts, adaptation, and vulnerability: Part A: Global and sectoral aspects . Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK and New York, USA: Cambridge University Press.
• IPCC (Intergovernmental Panel on Climate Change). 2018. Global warming of 1.5°C . An IPCC special report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty. Cambridge, UK and New York, USA: Cambridge University Press.
• IPCC (Intergovernmental Panel on Climate Change). 2021. Summary for policymakers. In Climate change 2021: The physical science basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change , ed. V. Masson-Delmotte, P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, et al. 3−32. Cambridge, UK and New York, USA: Cambridge University Press.
• IPCC (Intergovernmental Panel on Climate Change). 2022. Climate change 2022: Impacts, adaptation, and vulnerability. https://report.ipcc.ch/ar6wg2/pdf/IPCC_AR6_WGII_SummaryForPolicymakers.pdf . Accessed 30 Jan 2023.
• Ireland P. Climate change adaptation and disaster risk reduction: Contested spaces and emerging opportunities in development theory and practice. Climate and Development. 2010; 2 (4):332–345. doi: 10.3763/cdev.2010.0053. [ CrossRef ] [ Google Scholar ]
• Islam S, Chu C, Smart JC, Liew L. Integrating disaster risk reduction and climate change adaptation: A systematic literature review. Climate and Development. 2020; 12 (3):255–267. doi: 10.1080/17565529.2019.1613217. [ CrossRef ] [ Google Scholar ]
• Kelman I. Climate change and the Sendai framework for disaster risk reduction. International Journal of Disaster Risk Science. 2015; 6 (2):117–127. doi: 10.1007/s13753-015-0046-5. [ CrossRef ] [ Google Scholar ]
• Kelman I. Linking disaster risk reduction, climate change, and the sustainable development goals. Disaster Prevention and Management: An International Journal. 2017; 26 (3):254–258. doi: 10.1108/DPM-02-2017-0043. [ CrossRef ] [ Google Scholar ]
• Kelman I, Gaillard JC. Embedding climate change adaptation within disaster risk reduction. In: Shaw R, Pulhin JM, Pereira JJ, editors. Climate change adaptation and disaster risk reduction: Issues and challenges. Bingley, UK: Emerald Group Publishing; 2010. pp. 23–46. [ Google Scholar ]
• Kelman I, Gaillard J, Mercer J. Climate change’s role in disaster risk reduction’s future: Beyond vulnerability and resilience. International Journal of Disaster Risk Science. 2015; 6 (1):21–27. doi: 10.1007/s13753-015-0038-5. [ CrossRef ] [ Google Scholar ]
• Khadka, K. 2022. United Nations Office for Disaster Risk Reduction and stakeholder engagement mechanism value proposition. Geneva: United Nations Office for Disaster Risk Reduction. https://policycommons.net/artifacts/2391961/united-nations-office-for-disaster-risk-reduction-and-stakeholder-engagement-mechanism-value-proposition/3413390/ . Accessed 17 Dec 2022.
• Kwakkel JH, Haasnoot M, Walker WE. Comparing robust decision-making and dynamic adaptive policy pathways for model-based decision support under deep uncertainty. Environmental Modelling & Software. 2016; 86 :168–183. doi: 10.1016/j.envsoft.2016.09.017. [ CrossRef ] [ Google Scholar ]
• Lenton TM, Rockström J, Gaffney O, Rahmstorf S, Richardson K, Steffen W, Schellnhuber HJ. Climate tipping points – Too risky to bet against. Nature. 2019; 575 (7784):592–595. doi: 10.1038/d41586-019-03595-0. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Linstone, H.A. 2004. Review of: Shaping the Next One Hundred Years: New Methods for Quantitative, Long-Term Policy Analysis . R.J. Lempert, S.W. Popper, and S.C. Bankes, Santa Monica, CA: The RAND Corporation, 2003. Technological Forecasting & Social Change 71(3): 305–307.
• Mal, S., R.B. Singh, C. Huggel, and A. Grover. 2018. Introducing linkages between climate change, extreme events, and disaster risk reduction. In Climate change, extreme events and disaster risk reduction , ed. S. Mal, R.B. Singh, and C. Huggel, 1–14. Cham, Switzerland: Springer.
• Mamuji AA, Etkin D. Disaster risk analysis part 2: The systemic underestimation of risk. Journal of Homeland Security and Emergency Management. 2019; 16 (1):1–18. doi: 10.1515/jhsem-2017-0006. [ CrossRef ] [ Google Scholar ]
• Marchau VA, Walker WE, Bloemen PJ, Popper SW. Decision making under deep uncertainty: From theory to practice. Cham, Switzerland: Springer; 2019. [ Google Scholar ]
• Mcglade J, Bankoff G, Abrahams J, Cooper-Knock S-J, Cotecchia F, Desanker P, Erian W, Gencer E, et al. Global assessment report on disaster risk reduction 2019. Geneva: United Nations Office for Disaster Risk Reduction; 2019. [ Google Scholar ]
• Mitchell, T., M. van Aalst, and P. Silva Villanueva. 2010. Assessing progress on integrating disaster risk reduction and climate change adaptation in development processes. Strengthening Climate Resilience Discussion Paper No. 2. Brighton, UK: Institute of Development Studies.
• Mysiak J, Castellari S, Kurnik B, Swart R, Pringle P, Schwarze R, Wolters H, Jeuken A, van der Linden P. Brief communication: Strengthening coherence between climate change adaptation and disaster risk reduction. Natural Hazards and Earth System Sciences. 2018; 18 (11):3137–3143. doi: 10.5194/nhess-18-3137-2018. [ CrossRef ] [ Google Scholar ]
• O’Brien KJ, Li L. Rightful resistance in rural China. Cambridge: Cambridge University Press; 2006. [ Google Scholar ]
• Piggott-Mckellar AE, Nunn PD, Mcnamara KE, Sekinini ST. Dam(n) seawalls: A case of climate change maladaptation in Fiji. In: Filho WL, editor. Managing climate change adaptation in the Pacific region. Cham, Switzerland: Springer; 2020. pp. 69–84. [ Google Scholar ]
• Pilli-Sihvola, K. 2020. Building a bridge between inundated shores: Analyses on integrated disaster risk management and climate change adaptation policies and measures. Finnish Meteorological Institute Contributions No. 168. Helsinki: Finnish Meteorological Institute.
• Pirani R, Tolkoff L. Lessons from Sandy: Federal policies to build climate-resilient coastal regions. Cambridge, MA: Lincoln Institute of Land Policy; 2015. [ Google Scholar ]
• Prabhakar, S.V.R.K, K. Kamat, A. Hakimov, Y. Chiba, and M. Nakata. 2015. Loss and damage associated with climate change: What and why, stakeholder perspectives, and a way forward. The Paris Climate Agreement and beyond: Linking short-term climate actions to long-term goals, ed. K. Tamura, and S. Kojima, 101–125. Kanagawa, Japan: Institute for Global Environmental Strategies (IGES).
• Qin DH. Introduction to climate change science. Beijing: Science Press; 2018. [ Google Scholar ]
• Rahman, H.M.T., and G.M. Hickey. 2019. What does autonomous adaptation to climate change have to teach public policy and planning about avoiding the risks of maladaptation in Bangladesh? Frontiers in Environmental Science 7: Article 2.
• Renn O, Lucas K, Haas A, Jaeger C. Things are different today: The challenge of global systemic risks. Journal of Risk Research. 2019; 22 (4):401–415. doi: 10.1080/13669877.2017.1409252. [ CrossRef ] [ Google Scholar ]
• Rising J, Tedesco M, Piontek F, Stainforth DA. The missing risks of climate change. Nature. 2022; 610 (7933):643–651. doi: 10.1038/s41586-022-05243-6. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ritchie PDL, Clarke JJ, Cox PM, Huntingford C. Overshooting tipping point thresholds in a changing climate. Nature. 2021; 592 (7855):517–523. doi: 10.1038/s41586-021-03263-2. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ruffault J, Mouillot F. How a new fire-suppression policy can abruptly reshape the fire-weather relationship. Ecosphere. 2015; 6 (10):1–19. doi: 10.1890/ES15-00182.1. [ CrossRef ] [ Google Scholar ]
• Schipper L, Pelling M. Disaster risk, climate change and international development: Scope for, and challenges to, integration. Disasters. 2006; 30 (1):19–38. doi: 10.1111/j.1467-9523.2006.00304.x. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Seidler R, Dietrich K, Schweizer S, Bawa KS, Chopde S, Zaman F, Sharma A, Bhattacharya S, et al. Progress on integrating climate change adaptation and disaster risk reduction for sustainable development pathways in South Asia: Evidence from six research projects. International Journal of Disaster Risk Reduction. 2018; 31 :92–101. doi: 10.1016/j.ijdrr.2018.04.023. [ CrossRef ] [ Google Scholar ]
• Shaw, R., S. Prabhakar, and Y. Chiba. 2016. SDGs, DRR and CCA: Potential for strengthening inter-linkages. Policy Brief Number 34. Kanagawa, Japan: Institute for Global Environmental Strategies (IGES).
• Simon, K., G. Diprose, and A.C. Thomas. 2020. Community-led initiatives for climate adaptation and mitigation. Kōtuitui: New Zealand Journal of Social Sciences Online 15(1): 93–105.
• Steffen W, Rockström J, Richardson K, Lenton TM, Folke C, Liverman D, Summerhayes CP, Barnosky AD, et al. Trajectories of the earth system in the anthropocene. Proceedings of the National Academy of Sciences. 2018; 115 (33):8252–8259. doi: 10.1073/pnas.1810141115. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Tao L. The international climate adaptation regime process and its outlook. Journal of Nanjing University. 2014; 51 :52–60. [ Google Scholar ]
• Twigg, J. 2015 Disaster risk reduction . New edition. Good Practice Review 9. London: Humanitarian Practice Network, Overseas Development Institute.
• UN Desa (United Nations Department of Economic and Social Affairs) TST Issues Briefs: A compendium of issues briefs prepared by the United Nations inter-agency technical support team for the United Nations General Assembly Open Working Group on Sustainable Development Goals. New York: UN DESA; 2014. [ Google Scholar ]
• UNDP (United Nations Development Programme) A global report: Reducing disaster risk a challenge for development. New York: United Nations Development Programme, Bureau for Crisis Prevention and Recovery; 2004. [ Google Scholar ]
• UNEP (United Nations Environment Programme). 2022. Adaptation Gap Report 2022: Too little, too slow – Climate adaptation failure puts world at risk. Nairobi: UNEP. https://www.unep.org/adaptation-gap-report-2022 . Accessed 26 Jan 2023.
• UNDRR (United Nations Office for Disaster Risk Reduction). 2021. UNDRR Strategic Framework 2022–2025. https://www.undrr.org/publication/undrr-strategic-framework-2022-2025 . Accessed 26 Jan 2023.
• UNDRR (United Nations Office for Disaster Risk Reduction). 2022. Global assessment report on disaster risk reduction 2022: Our world at risk: Transforming governance for a resilient future. Geneva, Switzerland: UNDRR.
• UNISDR (United Nations Office for Disaster Risk Reduction). 2015. Making development sustainable: The future of disaster risk management. Global Assessment Report on Disaster Risk Reduction. Geneva, Switzerland: United Nations Office for Disaster Risk Reduction (UNISDR).
• United Nations. 1987. International Decade for Natural Disaster Reduction. United Nations General Assembly Resolution A/RES/42/169. https://www.preventionweb.net/files/resolutions/42_169.pdf . Accessed 26 Jan 2023.
• United Nations. 1989. International Decade for Natural Disaster Reduction. United Nations General Assembly Resolution A/RES/44/236. New York: United Nations.
• United Nations. 1992a. United Nations Framework Convention on Climate Change. https://unfccc.int/files/essential_background/background_publications_htmlpdf/application/pdf/conveng.pdf . Accessed 26 Jan 2023.
• United Nations. 1992b. Agenda 21. United Nations Conference on Environment & Development, 3–14 June 1992b, Rio de Jeneiro, Brazil. https://www.un.org/en/conferences/environment/rio1992b . Accessed 29 Jan 2023.
• United Nations. 2000. United Nations Millennium Declaration. United Nations General Assembly Resolution A/RES/55/2. https://www.un.org/en/development/desa/population/migration/generalassembly/docs/globalcompact/A_RES_55_2.pdf . Accessed 26 Jan 2023.
• United Nations. 2005. Hyogo Framework for Action 2005–2015: Building the resilience of nations and communities to disasters. Extract from the final report of the World Conference on Disaster Reduction. https://www.preventionweb.net/files/1037_hyogoframeworkforactionenglish.pdf . Accessed 26 Jan 2023.
• United Nations. 2015a. Sendai Framework for Disaster Risk Reduction 2015a–2030. Geneva, Switzerland: United Nations Office for Disaster Risk Reduction https://www.undrr.org/implementing-sendai-framework/what-sendai-framework . Accessed 26 Jan 2023.
• United Nations. 2015b. Adoption of the Paris Agreement. UNFCCC COP 21st Session, 30 November–11 December 2015b, Paris, France.
• United Nations. 2015c. Transforming our world: The 2030 Agenda for Sustainable Development. United Nations General Assembly Resolution A/RES/70/1. New York: United Nations.
• WCED (World Commission on Environment and Development). 1987. Our common future . New York: United Nations. https://idl-bncidrc.dspacedirect.org/bitstream/handle/10625/152/WCED_v17_doc149.pdf . Accessed 30 Jan 2023.
• Webber MK, Samaras C. A review of decision making under deep uncertainty applications using green infrastructure for flood management. Earth’s Future. 2022; 10 (7):e2021E–e2322E. doi: 10.1029/2021EF002322. [ CrossRef ] [ Google Scholar ]
• WMO (World Meteorological Organization). 2021. State of the global climate 2020. Geneva, Switzerland: World Meteorological Organization. https://public.wmo.int/en/our-mandate/climate/wmo-statement-state-of-global-climate . Accessed 26 Jan 2023.
• Zuccaro, G., M.F. Leone, and C. Martucci. 2020. Future research and innovation priorities in the field of natural hazards, disaster risk reduction, disaster risk management and climate change adaptation: A shared vision from the ESPREssO project. International Journal of Disaster Risk Reduction 51: Article 101783.