Post on 13-Aug-2020
THE DISASTER RISKSCAPE ACROSS ASIA-PACIFIC
Key Takeaways For Policymakers In North-East Asia
Mr SungEun Kim Programme Officer, Subregional Office for East and North-East Asia, ESCAP
NORTH-EAST ASIA SDG MULTI-STAKEHOLDER FORUM Vladivostok, 15-16 October 2019
| KEY FINDINGS | Message 1
North-East Asia faces a new climate reality
• In North-East Asia, 81% of annualized economic losses due to disaster are
caused by hydro-meteorological hazards
• With the inclusion of slow onset disasters, annualized economic losses more
than double from 184 to 409 USD billion
• The North-East Asian subregion has the highest economic losses due to
disaster for the entire Asia-Pacific region
| KEY FINDINGS | Message 1
In North-East Asia, 81% of annualized economic losses due to disaster are caused by hydro-meteorological hazards
Source: ESCAP, based on probabilistic risk assessment.
Note:
1. Volumetric analysis is a measurement by volume (impacted population, geographical area and economic losses).
2. Drought average annual losses data of Democratic People’s Republic of Korea is not available.
Earthquake
17%
Floods
10%
Drought
55%Tsunami
1%
US$ 409 billion (100%)
Tropical
cyclone
16%
4
Mongolia
DPR of Korea
Russian Federati
on
Republic of Korea
China
Japan
050100150
MULTI-HAZARD AAL WITHOUT SLOW-ONSET DISASTERS, US DOLLARS, BILLIONS
DPR of Korea
Mongolia
Russian Federation
Republic of Korea
Japan
China
0 50 100 150 200 250
TOTAL MULTI-HAZARD AAL, US DOLLARS, BILLIONS
Source: ESCAP, based on probabilistic risk assessment.
Riskscape in numbers (AAL, billions of US dollars)
With the inclusion of slow onset disasters, annualized economic losses more than double from 184 to 409 USD billion
| KEY FINDINGS | Message 1
0% 20% 40% 60% 80% 100%
China
Democratic People's Republic of Korea
Japan
Mongolia
Republic of Korea
Russian Federation
Tsunami AAL Flood AAL Tropical cyclone AAL Earthquake AAL Drought AAL
$245 billion
$251 million
$118.2 billion
$412.2 million
$24.3 billion
$20.4 billion
Source: ESCAP, based on probabilistic risk assessment.
Note: Drought average annual losses data of Democratic People’s Republic of Korea is not available.
• AAL in North-East
Asia accounts for
61% of the total AAL
for the entire Asia-
Pacific region
• China and Japan
account for 36 and
18 per cent of Asia-
Pacific AAL,
respectively
The North-East Asian subregion has the highest economic
losses due to disaster for the entire Asia-Pacific region
| KEY FINDINGS | Message 1
| KEY FINDINGS | Message 2
• There is an increasing exposure of populations, economic stock and infrastructure to seismic and hydro-meteorological hazards
• Critical infrastructure exposure is concentrated in a transboundary hotspot surrounding the Pacific Ring of Fire
The North-East Asian riskscape is becoming more complex and harder to predict
| KEY FINDINGS | Message 2
• Most of the economic losses due to disaster in the Asia-Pacific region over the past 5 decades have been sustained within North-East Asia
• Between 1970 and 2018, North-East Asia lost 2.2 USD trillion due to disasters
Average number of economic losses as percentage of GDP
0.0%
0.1%
0.2%
0.3%
0.4%
0.5%
1970-1980 1980-1990 1990-2000 2000-2010 2010-2019
PER
CEN
TAG
E O
F G
DP
North and East Asia Asia-Pacific Linear (North and East Asia) Linear (Asia-Pacific )
There is an increasing exposure of economic stock and infrastructure to hydro-meteorological and seismic hazards
Source: Based on data from EM-DAT (Accessed on 30 May 2019).
.
APDR 2019 identified four transboundary disaster risk hotspots, in which the Ring of Fire hotspots have critical infrastructure exposure.
There is also environmental fragility due to the risk of sand and dust storms.
| KEY FINDINGS | Message 2
Critical infrastructure exposure is concentrated in a transboundary hotspot surrounding the Pacific Ring of Fire
The characteristics of the Pacific
Ring of Fire hotspots
Percentage of infrastructure at risk
to multiple hazards
39.6%
40.0%
14.5%
45.6%
43.3%
45.0%
0% 20% 40% 60% 80% 100%
Energy exposure (numbers)
Energy capacity exposure
ICT
Transport -road
Transport - airports
Transport - ports
Infrastructure at risk Without risk
Source: ESCAP, based on Global Assessment Report on Disaser Risk Reduction (GAR) Risk Atlas, 2015; ESCAP Asia-Pacific Energy Portal, 2018; ESCAP Asia-Pacific Information Superhighway, 2018; ESCAP Transportation Data, 2018. Note: The risk comprises of multi-hazard1. Geological hazard (earthquake)2. hydro-meteorological hazards (flood and cyclone).
Energy (coal, oil, hydropower) exposed to earthquakes Solar and wind energy exposed to cyclones
Coal, oil, hydropowerTotal number: 667
Total capacity: 1,437 MWe
46.4%
34.7%
0% 20% 40% 60% 80% 100%
Energy - numbers(coal, oil, hydropower)
Energy - capacity(coal, oil, hydropower)
% Infrastructure at risk % Without risk
52.5%
51.5%
0% 20% 40% 60% 80% 100%
Energy - numbers(Solar and Wind)
Energy - capacity(Solar and Wind)
% Infrastructure at risk % Without risk
Solar & wind power plantsTotal number: 1,398
Total capacity:977,901 MWe
Asian highway networks exposed to seismic hazards Airports and ports exposed in tropical cyclone
Total number of airports: 239
Total number of ports: 248
26.7%
48.5%
50.8%
0% 20% 40% 60% 80% 100%
Asian highway
Airport
Port
% Infrastructure at risk % Without risk
70.3%
74.6%
0% 20% 40% 60% 80% 100%
Airport
Port
% infrastructure at risk % Without risk
ICT exposed to earthquake
16.7%
0% 20% 40% 60% 80% 100%
ICT
% infrastructure at risk % Without risk
ICT infrastructure is also exposed to seismic hazards.
17% of North-East Asia’s fiber optic infrastructure are exposed to earthquakes.
|POLICY ACTIONS| Message 3
Invest to outpace disaster risk
• Additional investments are required to prevent disasters and build resilient societies
• The additional finance is significant, but less than the damage and losses from disasters
Message: Additional investments are required to prevent disasters from pushing people into poverty
0 2 4 6 8 10
G. Growth + disaster risk +investment in social protection
I. Growth + disaster risk +investment in health
H. Growth + disaster risk +investment in education
J. Growth + disaster risk +investment in infrastructure
F. Growth + disaster risk
Millions
China
0 10 20 30
Thousands
Mongolia
Projected number of people in moderate poverty with disasters
Source: ESCAP based on CGE modelling
| POLICY ACTIONS | Message 3
Within China and Mongolia:
• Projected economic growth will eradicate moderate poverty by 2030.
• However unmitigated disasters will again raise the number of people living in moderate poverty, to 10 million in China, and 31,000 in Mongolia.
• Investing in infrastructure, education, health and social protection can bring the number down to approximately 8 million in China, and approximately 25,000 in Mongolia.
People living in moderate poverty
0 50 100 150 200 250
China
Billions
• Additional investments required per year are lower than the AAL.
• In Mongolia, the additional investments are $80 million, AAL $413 million.
• In china, additional investments are $194 billion, AAL $245 billion.
Source: ESCAP based on CGE modelling
| POLICY ACTIONS | Message 3
• The additional finance is significant, but less than the damage and losses from disasters
0 100 200 300 400 500
Mongolia
Millions
Average additional investment requiredper year, 2016-2030 (US dollars)
AAL (multi hazard, including extensiverisk, indirect loss and agriculture) (USdollars)
Empower the communities through big data and new technologies
• Big data offers unprecedented opportunities to support all phases of disaster management
• Technological applications are already being used within North and East Asian countries to increase delivery time of information for disaster preparedness, response and recovery
• Machine learning can be used for smart resilience
• Technologies must be applied to ensure that everyone is included within social policy and disaster risk reduction interventions
| POLICY ACTIONS | Message 4
Big data can help in all phases of disaster management
Big data can help in all phases of disaster management (pre-response and post-disaster
situations) by filling in gaps in information flows in pre-, response and post-disaster
situations, using four types of analytics: descriptive, predictive, prescriptive and discursive.
DESCRIPTIVE
ANALYTICS
PREDICTIVE
ANALYTICS
PRESCRIPTIVE
ANALYTICS
Focuses on predicting future
probabilities and trends by
linking static and dynamic
data.
Policy making for
disaster risk reduction.
Using historical
data to describe
what occurred.
DISCURSIVE
ANALYTICS
Empowering community
as end-users.
Big data offers unprecedented opportunities to support all phases of disaster management
| POLICY ACTIONS | Message 4
| POLICY ACTIONS | Message 4Technological applications are already being used within North-East Asian countries to increase delivery time of information for disaster preparedness, response and recovery
2018 Typhoon Mangkhut, track and diameter
Improvements of satellite observations:
- Increasing numbers and specification of satellites in orbit
- Higher time resolution and horizontal resolution of satellite images
- Higher number of channels
Only 6 people died, 1.5 million people were evacuated.
24h48h72h
2006 Typhoon Saomeo, track
Source: China Meteorological Association (2018)
Disclaimer: The boundaries and names shown and the
designations used on this map do not imply official
endorsement or acceptance by the United Nations.
483 people died, 1.8 million people were evacuated.
Due to the improvement of typhoon forecasts and warnings, and more effective
emergency responses for typhoon events, the casualties and the ratio of typhoon-induced losses to GDP reduce remarkably
Source: WANG Jianjie, CMA 2019
Source: China Meteorological Administration (2018)
Typhoon casualties and losses in China, 1987-2018
Big data
application
Big data
application
A significant decrease in casualties, even for super-typhoons, and a reduction in disaster losses as a proportion of GDP as a result of improvements of satellite observations.
5D-World Map System: Resilience Monitoring from Global to LocalEnvironmental Computing System with Big-Data Mining
“5-Dimensional World Map System”
Machine learning can be used for smart resilience.
© Keio University SFC, MDBL
Local/city level – Parameters: Earthquake Hazard Zone + Highest Flood Risk Zone + Residential roads + Educational facilities in Kathmandu
Multi-hazard-risk Visualization Experiment
Trunk Road
Primary Road
Secondary Road
Residential
School
Highest Flood
Risk Zone
Earthquake Hazard
Zone
The entire region of Kathmandu is covered by earthquake hazard
zone, and the southern part of Kathmandu is included in both
earthquake and highest flood risk zone.
© Keio University SFC, MDBL
| POLICY ACTIONS| Message 5
Scale up regional action to tackle the challenges posed by the new riskscape
• Multi-stakeholder engagement for resilience across the riskscape
| POLICY ACTIONS | Message 5
• Leverage new technologies to seamlessly integrate disaster risk and early warning information across all timescales into decision-making
• Adapt fiscal frameworks to tackle the new climate reality, and diversify financing sources, through public-private partnerships, working through existing initiatives
Multi-stakeholder engagement for resilience across the riskscape
KEY MESSAGES
North-East Asia faces a new climate realityMessage 1
The North-East Asian riskscape is becoming more complex and harder to predict
Message 2
Invest to outpace disaster riskMessage 3
Empower the communities through big data and new technologiesMessage 4
KEY FINDINGS
POLICY ACTIONS
Message 5 Scale up regional action to tackle the challenges posed by the new riskscape
Thank you for kind attention !
Sung Eun KIM (Mr) Programme Officer
Subregional Office for East and North-East Asia
United Nations Economic and Social Commission for
Asia and the Pacific
Email: kim54@un.org
THANK YOU!
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