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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