The catastrophe of economic loss is brought on by climate and weather change, which is highly complex and tied to numerous causes. And data is the most effective tool for analyzing every aspect, helping to make decisions for better plans and assisting in taking the essential actions. The reason for collecting this data is now a question that may arise. This is why data is required to increase resilience, assist in the development of better adaptation strategy policies, understand the potential consequences of climate change, track the causes of climate change and the factors that cause the most economic loss, increase insurance coverage, assist in the existing decision-making process for insurance companies, and inform policymakers about threatening processes. In addition, it is important to collect the data to monitor the impact to inform policy to ensure that appropriate actions are taken to minimize damage by the management. Furthermore, it helps to plan for getting better infrastructure in the future and forecast the long-term solution.

1. ECONOMIC LOSS
CAUSED BY THE CLIMATE AND WEATHER IN EEA MEMBERS
COUNTRIES (1980-2020)
D A T A P R O C U R E M E N T A N D D A T A P R O C E S S I N G I N T E C H N I C A L A N D E C O L O G I C A L E C O S Y S T E M M A N A G E M E N T
SUMMER SEMESTER 2022
BY:
Most Sarmin Akter
Prof. Dr. Ute Hansen

2. OUTLINE
1. Introduction
2. Aim of the study and Importance
3. Who work with loss events?
4. Data collection
5. Methodology
6. Results
7. What policy & prevention can take into consideration?
8. Conclusion

3. Introduction
● What is economy loss?
● To know the causes of it.
● And the factor – weather & climate-related
hazards, such as temperature extremes, heavy
precipitation and droughts.
● To understand the factor which lead to substantial
economic losses
● Used various method to prevent the losses
According to European Environment Agency (EEA), “Total economic losses from weather-
and climate-related events amounted to between EUR 450 and EUR 520 billion (in 2020
euros), for the 1980-2020 period in EEA member states”.

4. Continue...
The Hazards considered include those categorized by Munich Re as meteorological, hydrological, and
climatological hazards, based on the International Council for Science classification (ICSU).
•
Classification of natural hazards. Source: Author Licia Felicioni.

5. Aim of the study and Importance
Despite the difficulty of studying patterns in economic losses and the likelihood that
future losses will increase as a result of climate-related extremes if no action is taken to
mitigate them. Thus, the aim of this study is followed by the below questions:
▪How did they do the monitoring or with the help of what?
▪Who is in charge of the monitoring activities?
▪what are the results of the monitoring and it caused?
▪Are the losses continuously increasing compared to previous decades?
▪Are they taken necessary action or progresses after the analysis?
And, it’s essential and important to monitoring to prevent future losses due to weather
and climate in the economy, society, and environment.
•

6. Which organization work with loss events dataset?
Munich Re GmbH NatCatSERVICE
Swiss Re/Sigma and
CRED/EmDat
The trend curve indicates, within the previous 35 years, the number of loss-relevant natural disasters has increased
by a factor of three.
Source: Author Angelika wirtz

7. Continue...
Risklayer GmbH CATDAT
Entry criteria for earthquake events-
▪• Collapse of structural components.
▪• Number of death, injury or homelessness people.
▪• Damage exceeding $100,000 international dollars.
▪• A legitimate economic or social impact, as determined.
▪• External validation if the Corruption Index is less than 2.7, subject to Polity ranking.
Source: Author James Daniell

8. DATA COLLECTION
Source: Author Angelika wirtz

9. CONTINUE...
Source: Author Angelika wirtz

10. METHODS
Source: The process used to create the CATDAT Database (Daniell, 2010a)
Risk Layer CATDAT - one of the world’s
largest and most detailed historical
natural catastrophe loss databases.
Developed - founding partner James
Daniell since 2003.
Studies: Over 60,000 natural disaster
events globally with 42,000 events
since 1900.
Source: 30,000 original sources in over
90 languages.

11. CONTINUE...
Source: Author Angelika wirtz
Munich Re NatCatSERVICE - the most
comprehensive global natural
catastrophe loss databases.
Developed - Founded in 1880.
Studies: More than 36,000 global level
major events starting in 1950 and all
loss relevant events from 1980
onwards.
Source: Around 200 sources including
evaluations by aid the EU, the UN, the
World Bank, scientific analyses and
studies, and news agencies.

12. CONTINUE...
1.Event data – record property
damage / bodily injury.
2. Economic and insured losses –
direct losses, indirect losses, material
losses.
3. Statistical significance of trends - T-
Tests of linear Regression calculations.
4. Catastrophe classes
Source: Author Peter Hoeppe

13. CONTINUE...
Source: Author Peter Hoeppe
5. Categorization of perils

14. RESULTS
Annual economic damage caused by weather and climate-related extreme events in the EU Member States- per year based on CATDAT. The
units used in this indicator are the number of events, and damages in euros (2020 euro value). (Source: EEA)

15. CONTINUE...
Annual economic damage caused by weather and climate-related extreme events in the EU Member States- per year based on NatCatSERVICE
(Source: EEA)

16. CONTINUE...
Based on the
damage records
from CATDAT of
RiskLayer,
NatCatSERVICE
of Munich Re
and the Eurostat
sturctural
indicators
(Source: EEA)
Notes:
-Economic damages, insured
economic damages and fatalities per
country for all weather- and climate-
related hazard types.

17. CONTINUE...
Economic damage caused by weather and climate-related extreme events in EEA member countries (1980-2020) - per hazard
type based on CATDAT
(Source: EEA)

18. CONTINUE...
Economic damage caused by weather and climate-related extreme events in EEA member countries (1980-2020) - per hazard
type based on NatCatSERVICE
(Source: EEA)

19. CONTINUE...
(Source: EEA)
Countries of highest
values - Denmark,
Netherlands and
Norway
Lowest -Croatia,
Lithuania and Romania

20. CONTINUE...
(Source: EEA)
Countries of highest
losses - Switzerland,
Slovenia and France
Lowest - Liechtenstein,
Turkey and Iceland

21. CONTINUE...
(Source: EEA)
Countries of highest
losses - Switzerland,
Germany and Italy
Lowest – Liechtenstein,
Iceland, and Norway

22. CONTINUE...
(Source: EEA)
Countries of highest
shares- Slovenia, Bulgaria
and Romania
Lowest –Netherlands,
Malta, and Luxembourg

23. WHAT PREVENTION CAN TAKE INTO CONSIDERATION TO REDUCE IT
IN FUTURE?
• The impact of such dangers must be monitored in order to inform policy and ensure that necessary actions
are made to limit damage.
• The German Federal Environment Agency is currently working on a research and development initiative
called "Adaptation Frameworks," which aims to document climate change-related damage and expenses
in a systematic and timely manner.
• Climate-related hazards will cost more in the future, depending on the growth of the population and the
value of the assets at risk. As a result, to adapt to and manage the risks, a comprehensive, integrated
approach is needed.
• The EU is a signatory to the UN Sendai Framework for Disaster Risk Reduction (SFDRR), which calls for an
assessment of disaster-related losses and economic implications, as well as a goal of reducing such losses
by 2030.
• The European Commission's Joint Research Centre, as part of the PESETA IV project, and the G20 Risk Atlas
are working in diverse ways to reduce damage and losses.
• The new adaption policy is called EU adaptation strategy. The goal of this policy will be to increase
society's resilience to climate change by focusing on prevention, preparedness, reaction, and recovery.

24. CONCLUSION
• Around 80% of total economic losses caused by natural hazards in the EEA Member States, totaling to EUR
487 billion, occurred between 1980 and 2020. This equates to 11.9 billion euros per year.
• Fatalities during the same period ranged between 85,000 to 145,000.
• Germany had the highest economic losses in absolute terms from 1980 -2020, followed by France and Italy.
• Switzerland, Slovenia, and France had the highest losses per capita, whereas Switzerland, Germany, and
Italy had the highest losses per area (based on CATDAT data).
• The study also discovered that heatwaves were responsible for nearly 85% of fatalities over a 40-year span.
• To offer a coherent and authoritative picture of losses and fatalities caused by weather and climate-
related extremes, comparable data from European nations is required. This was identified as critical for
reducing the climate protection gap in the European Green Deal and the EU adaption plan.

25. REFERENCE
• Felicioni, L. Lupíšek, A. & Hájek, P., 2020. Major European Stressors and Potential of Available Tools for
Assessment of Urban and Buildings Resilience. Sustainability 12, no. 18: 7554.
https://doi.org/10.3390/su12187554
• Wirtz, A., 2013. Munich Re Nat. Cat. SERVICE Disaster loss data handling & the data landscape. Munich Re
Geo Risks Research. https://slidetodoc.com/munich-re-nat-cat-service-disaster-loss-data/
• Daniell, J., 2011. Damaging Earthquakes Database 2010 – The Year in Review.
https://www.cedim.kit.edu/download/CATDAT_EQ_Data_-_1st_Annual_Review_-_2010_-_James_Daniell_-
_04.03.2011.pdf
• https://www.eea.europa.eu/ims/economic-losses-from-climate-related
• https://www.eea.europa.eu/publications/economic-losses-and-fatalities-from
• https://www.eea.europa.eu/highlights/economic-losses-from-weather-and
• Hoeppe, P., 2016.Trends in weather related disasters – Consequences for insurers and society,Weather and
Climate Extremes, Volume 11,Pages 70-79,ISSN 2212-0947, https://doi.org/10.1016/j.wace.2015.10.002.
(https://www.sciencedirect.com/science/article/pii/S2212094715300347)

26. THANK YOU