Responding_to_Climate_Change_in_Moz.pdf

October 2012, p 1

Contact details for correspondence:

McKinsey & Co.

Diogo_Almeida_Pinheiro@mckinsey.com

Marcel_Normann@mckinsey.com

Tel: +49 40 3612 1366 Fax: +49 40 3612 1367

Project Director: Joao Tiago MM Ribeiro Project Coordinator: Barbara van Logchem

Science Coordinator: Antonio J Queface

Report to be referenced as:

McKinsey & Co. 2012. Responding to climate change in Mozambique: Theme 3: Preparing cities.

Maputo: INGC.

mailto:Diogo_Almeida_Pinheiro@mckinsey.com mailto:Marcel_Normann@mckinsey.com

October 2012, p 2

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October 2012, p 10

SLIDE 1

INGC Phase II –

“Preparing Cities”

September 2011

Final report

SLIDE 2

Table of contents

Note: D1, D2, D3, and D4 are the 4 deliverables from the Terms of Reference

Executive summary

Economics of climate adaptation methodology

Baseline vulnerability and risk characterization (D1)

Climate change adaptation planning and action best practices (D2)

Key mitigation and adaptation measures (D3)

City disaster risk management system and strategy (D4)

Appendix

October 2012, p 11

SLIDE 3

Table of contents

Executive summary

Economics of climate adaptation methodology

Baseline vulnerability and risk characterization (D1)

Climate change adaptation planning and action best practices (D2)

Key mitigation and adaptation measures (D3)

City disaster risk management system and strategy (D4)

Note: D1, D2, D3, and D4 are the 4 deliverables from the Terms of Reference

SLIDE 4

Context and objectives of preparing cities

SOURCE: Terms of Reference

▪ Phase I of the INGC Climate Adaptation project identified several areas of vulnerability for

Mozambique, namely

– Overexploited natural resources

– Energy projects with significant environmental impact

– Urbanization process leading to half of the population living in areas lacking basic infrastructure

– Increase in severity and frequency of natural disasters

▪ Mozambique’s vulnerability to natural disasters is set to increase given climate change and economic development trends

▪ The country’s preparedness for natural disasters will need to be strengthened in order to ensure it is resilient to these threats

▪ Define the policies, strategies, and programs for Mozambique to decrease its vulnerability to extreme events caused and exacerbated by climate change in the selected key cities

▪ Assess current risk and preparedness levels for each city in scope

▪ Identify international best practices in climate adaptation planning and action

▪ Identify a portfolio of viable adaptation and mitigation options, and assess and prioritize them

▪ Develop comprehensive disaster risk management systems and strategies for the 3 cities

Context Objectives

October 2012, p 12

SLIDE 5

Scope – preparing cities work focuses on Maputo, Beira, and Quelimane

SOURCE: INGC Phase II

Population

Millions

GDP 2010

USD billions Key hazards

▪ Inland floods

▪ Wind damage

▪ Epidemics

Maputo 1.6

Beira 0.4

Quelimane 0.2

National 9.5

1.2

0.4

0.2

23.0

▪ Coastal floods

▪ Inland floods

▪ Wind damage

▪ Epidemics

▪ Coastal floods

▪ Inland floods

▪ Epidemics

SLIDE 6

Theme 3 team applied the tried and tested Economics of Climate

Adaptation methodology to develop city adaptation strategies

SOURCE: Economics of Climate Adaptation

▪ Measure success based upon key performance metrics

▪ Incorporate lessons learned in next iteration of the total climate risk decision cycle

▪ Identify potential adaptation measures

▪ Determine basic feasibility of potential measures

▪ Determine societal costs and benefits (loss averted) of measures

Hazard

▪ Assess frequency and severity per hazard

Value

▪ Quantify population, assets, and income value at risk

Vulnerability

▪ Determine vulnerability of population, assets, and income

▪ Identify most relevant hazard(s)

▪ Identify areas most at risk

– Population

(especially vulnerable population)

– Economic value

(assets, GDP)

▪ Identify key barriers to implementation

▪ Determine actions required to implement measures

Where and from what are we at risk?

What is the magnitude of the expected loss?

How could we respond?

How do we execute?

What are the outcomes and lessons?

October 2012, p 13

SLIDE 7

City adaptation strategies were developed leveraging city visits, best practices, and extensive stakeholder consultations

SOURCE: INGC Phase II Theme 3

March April May June July August

April 18-21

Beira city visit 1

▪ Initial data gathering

▪ Visits to vulnerable areas

▪ Stakeholder interviews

(INGC, Municipality, INAM, MICOA, UCM-

CIG)

May 30-June 22

Quelimane city visit 1

▪ Initial data gathering

▪ Visits to vulnerable areas

▪ Stakeholder interviews

(INGC, Municipality, INAM, Port of

Quelimane, INAHINA, Millennium Challenge

Account)

July 11-16

▪ Validation of Maputo results

▪ Building vulnerability assessment for

Quelimane

August 1-3

Quelimane city visit 2

▪ Presentation of baseline risk analysis and portfolio of measures

▪ Gathering of additional data on adaptation projects and measures

March 14-April 1

First set of stakeholder interviews and data gathering

▪ INGC (J. Ribeiro)

▪ UEM (A. Queface, A. Mavume)

▪ INAM (M. Benissene)

▪ Maputo Municipality

(M. Macaringue, L. Nhaca)

April 11-16

▪ Data gathering and vulnerability assess-ment for Maputo

▪ Continued stakeholder interviews

– Maputo munici-pality Department of Urbanization and

Construction

– APIE

– Port of Maputo

May 2-28

▪ Building vulnerability assessment for Beira

▪ Continued data gathering and stakeholder consultations in

Maputo

June 7-8

Technical review think tank with INGC, INAM, and 11 coastal municipalities

July 18-19

Beira city visit 2

▪ Presentation of baseline risk analysis and portfolio of measures

▪ Gathering of additional data on adaptation projects and measures

August 4-12

Drafting of final city adaptation strategies

SLIDE 8

On September 19 the key recommendations were reviewed by the CCGC

▪ Indentified measures are very important to protect these cities and need to be included into the municipal investment plans, according to the overall city investment strategy

▪ State budget is relatively thin and so measures need to be contingent to existin funding;

positive cost-benefir measures, however, should be pushed trough as they are self-sustainable

▪ There already exists some degree of insurance on buildings, but these need to be widened to cover climate risks, but it makes more sense to consider this at a national level

▪ CCGC

– Prime Minister

– Minister MAE

– Minister MNEC

– Minister MA

– Minister MICOA

– Minister MMAS

– Minister MD

– Minister MIC

– Minister MI

– Minister MOPH

– Minister MPD

– Minister MF

– Minister MS

– Minister MEDU

– Minister MRM

– Minister MCT

– Vice Minister MTC

– General Manger INGC

▪ Presidents of key universities (UEM, UP, ISRI, ISPU, UDM, USTM, ISTEG, ISAP, ISCISA)

▪ Context and methodological overview for the Theme 3

▪ Vulnerability assessment for Maputo, Beira and

Quelimane and implications for prioritization

▪ Definition of potential portfolio of financial and non-financial adaptation measures for each city

▪ Prioritization of measures for each city

▪ Overall cost and impact of proposed adaptation measures

▪ Integration into city strategy

Participants Content Presented Outcome

October 2012, p 14

SLIDE 9

A broad stakeholder group was involved in developing city adaptation strategies, with 3 different degrees of involvement

Note: DUC – Department of Urbanization and Construction

SOURCE: INGC Phase II Theme 3

NON-EXHAUSTIVE

Core working team

▪ Barbara van Logchem ▪ Antonio Queface (UEM)

▪ Fernanda Zermoglio (INGC)

Technical contributors

Maputo

▪ Luis Nhaca (Councilor, Municipality)

▪ Mário Maccaringue

(Councilor, Municipality)

▪ Paulo Júnior

(UNHABITAT/ Municip.)

▪ José Nicols (Municipality

– DUC)

▪ Acélio Rufasse

(Municipality – DUC)

▪ Hipolito Alfino

(Municipality – DUC)

Broader stake-holders

Beira

▪ Luis Pacheco (INGC)

▪ António Charifo

(GIZ/INGC)

▪ Augusto de Jesus

(Municipality)

▪ Samuel Simango

(Municipality)

▪ Augusto Manhoca

(Municipality)

▪ António dos Anjos

(UCM-CIG)

Quelimane

▪ Silvestre Uqueio (INGC)

▪ Milton Barbosa (INGC)

▪ Iria Munguambe

(Municipality – DUC)

▪ Juma Cassimo (MICOA)

Nation-wide

▪ Joāo Ribeiro (INGC)

▪ Roberto White

(former minister)

▪ Alberto Mavume (UEM)

▪ Jose Rafael (UEM)

▪ Gonsalves Júnior

(INAM)

▪ Mark Tadross (UCT)

▪ Elias Massicame (INGC)

▪ Arnaldo Simango (APIE)

▪ Teresa Chissequeme

(Municipality – DUC)

▪ Jorge Morgado (Port of

Maputo)

▪ Silva Magaia

(UNHABITAT)

▪ Manuel Ferrāo

(CENACARTA)

▪ Arnaldo Chimoia (District

Governor)

▪ Ermelinda (MICOA)

▪ Jeremias Isaias (WWF)

▪ Pio Matos

(Municipality President)

▪ Alberto Colario (INAM)

▪ Sousa Alberto (Port of

Quelimane)

▪ Joāo Carlos Lima (UP)

▪ Luiz Paulo (Millenium

Challenge Account)

▪ Moises Benissene

(INAM)

▪ Anastasio Manhique

(INAM)

SLIDE 10

The three cities in scope are highly vulnerable to climate-related hazards, but focused adaptation actions can avert the majority of expected losses

1 Maputo 2030 Projcet GDP = $5.2B 2 Beira 2030 Projected GDP = $2.0B 3 Quelimane 2030 Projected GDP = $0.9B

SOURCE: INGC Phase II Theme 3

Maputo

Beira

Quelimane

▪ Highest expected loss is from inland flooding, followed by coastal flooding (which becomes relevant mainly under high CC scenarios)

▪ Expected loss is 3-5% of GDP by 20301, of which ~37% could be avoided through cost-effective adaptation measures

▪ Priority measures include mangrove planting, inland and coastal drainage improvement, and land bank reinforcement

▪ Highest expected loss is from coastal flooding (which becomes devastating under high CC scenarios) and inland flooding

▪ Expected loss is 5-9% of GDP by 20302, of which ~43% could be avoided through cost-effective adaptation measures

▪ Priority measures include inland drainage improvement, groyne/sea wall rehabilitation and beach nourishment

▪ Highest expected loss is from inland flooding, followed by epidemics (e.g.

malaria)

▪ Expected loss is 4-5% of GDP by 20303, of which ~37% could be avoided through cost-effective adaptation measures

▪ Priority measures include inland drainage improvement and river mangrove replanting

Key findingsCity

October 2012, p 15

SLIDE 11

Current expected losses of 3-4.5% of GDP could increase to 5-9% of GDP by 2030

SOURCE: INGC Phase II Theme 3

D4 – City strategy

D2 – Best practices

D3 – Adaptation measures

D1 – Vulnerability assessment

Expected loss under moderate climate change scenario, USD millions

Range from current climate to high climate change scenario

Expected loss as percentage of city’s GDP

X

276 407158

1,342

Maputo

Beira 515

20 187 32993

1,092

Quelimane

3.0 3.5 4.3

4.5 5.6 7.7

20302010 2060

4.3 4.6 6.9

3.0-5.3

4.6-9.1

4.2-5.0 6.0-7.9

4.9-16.4

3.2-10.5

NOTES FOR SLIDE 11:

Overview of D1 - Vulnerability Assessment

Current GDP losses for each city due to climate change range from 3.0–4.5%, and are projected to expand up to 5-9% of GDP by 2030.

Losses due to climate change are charted for 2010, 2030, and 2060. The base case shown in grey for each year are the losses projected from the moderate climate change scenario, and the orange bubbles below show the losses as a percentage of each city’s GDP. The ranges to the right of each grey bar show the projected losses from the other two climate scenarios, with the low bound at continuing the current climate and the high bound at impacts from the high climate change scenario.

The high bounds for each city’s damages for 2030 give the 5-9% estimate for the worst case scenario.

October 2012, p 16

SLIDE 12

Many key learnings from best practice cities are applicable to the Mozambican city context

SOURCE: City stakeholder interviews

Applicability to MozambiqueKey learning Rationale

▪ Powerful city mayors in Mozambique not yet fully engaged in adaptation

Get political backing from highest level possible

▪ Recent, very disruptive events in all three cities are in the public memory

Take advantage of climate related events to change planning strategy

▪ Experience from Durban worth evaluating and potentially testing

Conceive of climate action holistically, but organize by sector

▪ No strong climate adaptation unit exists in any of the three cities

Create adaptation champions in other municipal departments

▪ In the face of fast urban and industrial development engagement and cost sharing with private sector is desirable

Engage companies as part of wider climate/regulatory discussions and foster business champions

▪ Insurable losses are significant in all three cities and would benefit greatly from risk transfer mechanisms

Start thinking about financial regulations such as insurance at a municipal level

Low

Medium

High

SLIDE 13

Best practice cities offer key learnings for adaptation planning and implementation for Mozambique as a whole and for the specific cities in scope

Learnings applicable to specific citiesGeneral key learnings

Maputo

Quelimane

▪ Ensure resilience of road bridge over water (Catembe) and regulate development on erosion slopes as in Monterrey

▪ Use recent extreme weather events as catalysts for action

Beira

▪ Use experience of changes in planning in Monterrey in post storm rebuilding to increase resilience of the planned new urban developments

▪ Learn from Durban experience when updating city master plan to include adaptation

▪ Use experience of Monterrey and Amsterdam in protecting against inland flooding

Get political backing from highest level possible

Take advantage of climate related events to change planning strategy

Organise climate action in a sectoral rather than an integrated way

Create adaptation champions in other municipal departments

Engage companies as part of wider climate/regulatory discussions and foster business champions

Start thinking about financial regulations such as climate insurance at a municipal level

October 2012, p 17

SLIDE 14

D1 – Vulnerability assessment

D2 – Best practices

Adaptation and mitigation measures would allow cities to reduce the economic impact of disasters by ~50% to 60%

SOURCE: INGC Phase II Theme 3

D4 – City strategy

D3 – Adaptation measures

100% = total expected loss from selected analyzed climate risks

Remaining loss

Non-cost-effective measures, CB > 1.5

Cost-effective measures, CB < 1.5

For comparison:

international experience

40-68

4-47

13-40

Quelimane

Beira

Maputo

Percent of expected loss, moderate climate change scenario, 2030

NOTES FOR SLIDE 14:

Overview of D3 – Adaptation measures

Undertaking adaptation and mitigation measures would allow cities to reduce the economic impact of disasters by ~50% to 60%

The data presented for each city breaks down the projected GDP losses from climate-related disasters into three buckets.

The first bucket, in light orange, is the percentage for which cost-effective measures exist to prevent the damage. This means less will need to be spent on the prevention measures than will be gained from the reduced losses, so the investments will have a positive net present value.

The second category, in grey, shows the percentage of damages which could be prevented, but only by measures that would cost more than the damages prevented, so should not be undertaken.

The third bucket, in red, shows the climate-related damages that cannot be prevented.

Given that all measures in the first bucket are cost-effective and should be undertaken, cities can capture the benefits in orange, which range from ~50 to ~60% of GDP.

An average of similar analyses for other countries is shown at the right for comparison.

October 2012, p 18

SLIDE 15

D1 – Vulnerability assessment

D2 – Best practices

Adaptation and mitigation measures would allow cities to reduce the economic impact of disasters from 35% to 45%

SOURCE: INGC Phase II Theme 3

D4 – City strategy

D3 – Adaptation measures

USD millions, moderate climate change scenario

Maputo

Beira

Quelimane

93183 -37%

52041

-37%

“Optimized” expected loss

+ cost component

Cost of measures

(annualized)

Loss averted with adaptation measures with CBR<1.5

Expected loss in 2030

64112

-43%

Priority measures

▪ Mangrove revival

▪ Coastal drainage

▪ Local drainage

▪ Building codes

▪ Local drainage

▪ Groyne/sea wall rehabilitation

▪ Beach nourishment

▪ River mangrove replanting

▪ Local drainage

▪ Inland zoning

MODERATE CC SCENARIO

SLIDE 16

D1 – vulnerability assessment

D2 – best practices

D3 – adaptation measures

A comprehensive adaptation strategy and implementation plan allows

Mozambican cities to achieve their climate resilience ambition levels by

SOURCE: INGC Phase II Theme 3

D4 – city strategy

Climate change effects

▪ Sea level rise of 15-45 cm

▪ Increased air temperature by 0.9-1.1°C

▪ Increased precipitation by 1-4 mm during Dec-Jan season

▪ Increased sea surface temperature by 1-2°C

Current state

Expected loss as percentage of GDP

▪ Maputo: 3.0%

▪ Beira: 4.5%

▪ Quelimane: 4.3%

Adaptation strategy

▪ Prepare 5-year investment plans with adaptation measures

▪ Form adaptation management units within municipalities

▪ Create a systematic process for updating and monitoring measures

Ambition level

Reduce expected losses from climate change hazards to 50% of current level

▪ Maputo: 1.5% of GDP

▪ Beira: 2.2% of GDP

▪ Quelimane: 2.1% of GDP

Implementation plan

▪ Maputo: ~USD 400 million Capex over next 5 years can reduce losses by ~USD 80 million

▪ Beira: ~USD 270 million Capex over next 5 years can reduce losses by ~USD 55 million

▪ Quelimane: ~USD 40 million Capex over next 5 years can reduce losses by ~USD 15 million

Level of climate resilience and develop-ment

2010 2030 Time

October 2012, p 19

NOTES FOR SLIDE 16:

Overview of D4 – City strategy

A comprehensive adaptation strategy and implementation plan allows Mozambican cities to achieve their climate resilience ambition levels by 2030

Charts the path to a Mozambican climate adaptation implementation plan through 2030 by following five steps:

First, Mozambican cities are already losing GDP through climate impacts.

Second, projections on future climate trends show these impacts will become more severe.

Third, Mozambique has set an ambition to curb losses from climate change to 50% of Current level, even given the increasing severity of climate change impacts.

Fourth, to meet these ambitions, Mozambican cities must begin a 5-year investment plan in adaptation measures, and form the necessary organizational bodies and processes to manage and adjust those plans.

Fifth and finally, this adaptation strategy must be translated into a tactical implementation plan, with specific investments tied to projected adaptation benefits through avoided losses.

SLIDE 17

16 16

Despite a strong economic rationale, adaptation measures represent a very significant investment

SOURCE: INGC Phase II Theme 3

Total investment to implement measures. Million USD

Inland

Flooding

Coastal

Flooding

Epidemics

Maputo

Beira Quelimane

• Significant investment for the three cities given current local and national budget constraints

▪ Significant international amounts are, however, available for adaptation for which Mozambique should compete

▪ Measure rollout should be spread across 5 years and naturally contingent to funding each project/measure in each area

Investments should be part of a 5-year plan, dependent on availability of funding and projects of each type

October 2012, p 20

SLIDE 18

Next steps to transform these analyses and insights into actions

SOURCE: INGC Phase II Theme 3

Key next steps City-specific next steps

▪ Immediately begin implementation of highly attractive “no regret” measures

(with low cost-benefit ratio, low capex required)

▪ Prepare a 5-year adaptation investment plan identifying timeline for measure implementation, actors responsible, and sources of funding

▪ Form an adaptation planning and management unit within the Municipality to lead implementation of measures

▪ Establish a systematic process for updating the prioritization of adaptation measures and for monitoring progress of measure implementation

Maputo

Beira

Quelimane

▪ Push implementation of mangrove planting project in northern Costa do Sol

▪ Secure funding for inland/ coastal drainage and land bank reinforcement projects

▪ Accelerate implementation of World Bank and BADEA inland drainage and coast-al protection projects

▪ Seek funding for beach nourishment

▪ Update city master plan to incorporate adaptation strategy

▪ Accelerate implementation of MCA drainage project

SLIDE 19

Table of contents

Executive summary

Economics of climate adaptation methodology

Baseline vulnerability and risk characterization (D1)

Climate change adaptation planning and action best practices (D2)

Key mitigation and adaptation measures (D3)

City disaster risk management system and strategy (D4)

October 2012, p 21

SLIDE 20

Current climate already presents significant risks

SOURCE: SwissRe sigma catastrophe database

1 Dead and missing

130130

494345

2008200420001996199219881984198019761972

Number of victims

(Thousands) 1

Insured loss

(USD millions)

Major natural catastrophes, 1972-2008

Number of natural catastrophes

52,50454,944

15,88618,678

44,151

12,9087,1077,2596,6055,185

17 308 12 5 41 13 14 8 235 235

GLOBAL PERSPECTIVE

NOTES FOR SLIDE 20:

You can see here that the number of catastrophes has risen sharply. Not all of these are climate-related, but you can see that more people are being hurt.

The financial costs of disasters are also rising.

In a few minutes, we’ll talk about why. But looking ahead to even warmer conditions, scientists are contemplating much greater impacts in terms of human health, social costs and economic trouble.

October 2012, p 22

SLIDE 21

Climate change has the potential to significantly increase these threats Temperature change (relative to preindustrial)

Nature

GDP

Weather

Water

Food

Social

Changes in water availability, threaten up to 1 billion people

Local water supply threatened by glacier loss

Major world cities threatened by sea-level rise (e.g., London)

Considerable number of species face extinction

Coral-reef natures extensively and eventually irreversibly damaged

More than 1 billion people at risk of having to migrate – increased risk of conflicts

Potential loss of up to 20% of global GDP Loss of GDP in developing countries

Yields in many developed regions declineFalling crop yields in many developing regions

Rising intensity of storms, forest fires, droughts, flooding, and heat waves

Cancun pledges

Business as usual

2°C scenario

SOURCE: Stern Review; IPCC AR4; Climate Action Tracker

1ºC 2ºC 3ºC 4ºC 5ºC

NOTES FOR SLIDE 21:

This chart shows the estimated impact of each degree of increase in the average global temperature. These findings are from the Stern report and have been confirmed by the

IPCC.

Because we experience +/-3°C variation in temperatures on a daily basis, it is often difficult for people to imagine the impact of a net 3°C increase in the average global temperature. Do remember though that this is an average global increase – that means that in certain places temperatures will rise more than the average 3 degrees. Even more problematic than the absolute temperature rise is the rate of change. While natures may be able to make long-term adjustments driven by climate shifts, the human species has never before needed to adjust to such rapid changes.

October 2012, p 23

SLIDE 22

There are two main levers to combat climate change

SOURCE: Economics of Climate Adaptation

1ºC 2ºC 3ºC 4ºC 5ºC Temperature change

Mitigation – avoid climate change by reducing GHG1 emissions

Eco-system

GDP

Weather

Water

Food

Social

Main levers

▪ Energy efficiency

▪ Renewables

▪ Clean tech/low-carbon growth

▪ Risk prevention

– Physical infrastructure

– Process/technology optimization

▪ Risk transfer and financingAdaptation – reducing vulnerability to climate change effects

1 Greenhouse gas

SLIDE 23

Climate change and development are inseparable

Mitigation Adaptation

Development

Climate- Compatible

Develop-ment

Mitigation Adaptation

Development

Poverty and climate change are the two great challenges of the 21st century. Our responses to them will define our generation, and because they are linked to each other, if we fail on one, we will fail on the other.

– Nicholas Stern

… to this?How do we move from this …

SOURCE: McKinsey Quarterly, La Stampa

October 2012, p 24

SLIDE 24

Climate-compatible development requires both mitigation and adaptation

Focus of the ECA methodology

Climate-compatible growth requires new knowledge building, planning and preparation, disaster management and risk transfer, and investments in climate-resilient infrastructure and technology

Mitigation is aligning with the broader development agenda as people accept that high-carbon growth is unsustainable

Adaptation will require incremental funds beyond business as usual;

adaptation and development are often indiscernible

“Business as usual” high-carbon growth is no longer an option

Mitigation

Climate-resilient development

Low-carbon development

Adaptation Climate-proofed abatement

Development

Achieving the Millennium Development Goals

Climate-compatible development

SOURCE: McKinsey

NOTES FOR SLIDE 24:

These overlapping circles represent three different human needs: development, mitigation and adaptation. We can and must meet all of those needs.

Development has to be low-carbon, as we explain here, and be adapted to resist the impacts of climate change.

Of course, this won’t be easy–even if everyone gets with the program. We can’t do everything because financial and human resources, and time, are limited.

To make wise choices about how to invest scarce resources – to achieve climate-compatible development -- we need more knowledge, planning and preparation. We need better disaster management and insurance. We need new climate-resilient infrastructure and technology.

And that brings us to our second topic…

October 2012, p 25

SLIDE 25

Climate change

Aim of ECA methodology – help decision makers assess and address total climate risk in a fact-based manner

▪ Provide decision makers with facts and a common approach to assess and address any location’s “total climate risk” (TCR)

Questions

▪ How can we measure and predict the impact of climate change on our economies?

▪ How can we prepare to adapt to this impact?

ECA methodology’s objective

Total climate risk

Current climate risk

Economic growth

SOURCE: Economics of Climate Adaptation Working Group

NOTES FOR SLIDE 25:

We need ways to measure and predict the impacts of climate change. And we need to prepare for those changes.

Our work is meant to help you and other leaders use a fact-based approach to answering those questions.

To see if our approach worked, we applied it in eight distinctly different real-world environments…

October 2012, p 26

SLIDE 26

Economics of Climate Adaptation (ECA) – a collaborative effort of major global organizations

Climate Works is a newly formed global philanthropic network organized to win the battle against climate change

The Global Environment Facility

(GEF) is a trust fund partnership among

178 countries, international institutions, nongovernmental organizations

(NGOs), and the private sector

The United Nations Environment

Programme (UNEP) is an international intergovernmental organization established by the General Assembly of the United Nations

Standard Chartered operates in many of the world's fastest-growing markets, and derives over 90% of its profits from the emerging trade corridors of Asia, Africa, and the Middle East

The Rockefeller Foundation is a global philanthropic corporation

The European Commission is the executive branch of the EU responsible for proposing legislation, implementing decisions, and upholding the Union's treaties

Swiss Re is a highly diversified and global reinsurer

McKinsey & Company drove the analytical execution and contributed to the fact base

SLIDE 27

The ECA total climate risk management approach has been tried and tested in over 15 regions and countries

Florida

Mali

India Maharashtra

UK / Hull China North, Northeast

Guyana

Tanzania

Samoa

Approach has been applied in >10 additional countries so far

October 2012, p 27

NOTES FOR SLIDE 27:

Even with real estate crashing, Florida is still rich. It's also a huge target for hurricanes.

Guyana is relatively poor and relies heavily on commodities like sugar and bauxite, whose prices are beyond the country's control.

Despite their many differences, we found that we could use our approach to think about the risks in all of these areas -- and to build a cost-effective portfolio to manage those risks overall, over many years.

SLIDE 28

The ECA approach for total climate risk management Details follow

SOURCE: Economics of Climate Adaptation Working Group

Where and from what are we at risk?

What is the magnitude of the expected loss?

How do we execute?

What are the outcomes and lessons?

Total climate risk management

How could we respond?

▪ Identify potential adaptation measures

▪ Determine basic feasibility of potential measures

▪ Determine societal costs and benefits (loss averted) of measures

▪ Identify most relevant hazard(s)

▪ Identify areas most at risk

– Population (espe-cially vulnerable population)

– Economic value

(assets, GDP)

Hazard

▪ Assess frequency and severity per scenario

Value

▪ Quantify population, assets, and income value at risk

Vulnerability

▪ Determine vulnerability of population, assets, and incomes

▪ Measure success based upon key performance metrics

▪ Incorporate lessons learned in next iteration of the total climate risk decision cycle

▪ Identify key barriers to implementation

▪ Determine actions required to implement measures

October 2012, p 28

NOTES FOR SLIDE 28:

We begin the process in Step 1 by assessing risks given relevant hazards and what’s at stake.

In Step 2, we model scenarios to calculate the total risk. In simple terms, that means considering what’s at stake now, what will be at stake as the economy and population grow, and the range of possible climate changes.

In Step 3, we use those scenarios to build a portfolio of possible responses. Drought- or flood-resistant crops might be one worthy investment. Abandoning some land and allowing it to return to wetlands might be another.

Step 4 in this schematic is putting those actions into place, in the right order, after identifying and overcoming barriers to implementation.

The last step in the cycle is assessing the effects of our approaches, adjusting them based on new information, new technology and fresh predictions.

The climate will keep changing, and this framework accounts for that by looking at total climate risk management as a continuous process.

It is complex, given the many uncertainties about the global climate and the number of moving parts in these equations. But scenario planning allows us to set priorities even without complete or perfect knowledge…

SLIDE 29

4 4

BermudaJamaica St. LuciaDominica Antigua & Barbuda Barbados

Cayman Islands Anguilla

Current

High change, Expected loss from hurricanes, inland and coastal flooding

Percent of GDP, p.a.

Example Caribbean – climate impact on economies already today equivalent to severe recession

1 2+ CARIBBEAN EXAMPLE

SOURCE: CCRIF: “Enhancing the climate risk and adaptation fact base for the Caribbean”

October 2012, p 29

SLIDE 30

Three modules are used to quantify current expected losses and for each climate scenario

SOURCE: CCRIF: “Enhancing the climate risk and adaptation fact base for the Caribbean”

CARIBBEAN

EXAMPLE

Current climate and 3 climate change scenarios

Scenario 1 Scenario 3Scenario 2

Modules

H x VA x V

Expected loss per climate change scenario

Description Calculation Output

Vulnera-bility module

V

0 10 20 30 40 50 60 70 80

Damage ratio of production at risk

Wind speed m/s

Vulnerability curves for different assets based on hazard severity

Hazard module H

Severity and frequency of hazard for different climate change scenarios

Value module

VA

Geographically distributed value of assets, incomes and human elements

Current

EL

S

NOTES FOR SLIDE 30:

Considering both economic and human losses, we can use this equation to come up with an expected loss for each scenario.

The "hazard module" incorporates the severity and frequency of each risk depending on scenarios.

The "value module" takes into account what is at stake today, and the value of each element as economies and populations grow.

"Vulnerability" refers to how each element at risk may be affected by climate changes.

Together, they give us what we call a "loss exceedence curve"…

October 2012, p 30

SLIDE 31

The future risk landscape is subject to significant uncertainties – scenario planning is required

SOURCE: IPCC (2007); Rahmstorf (2009); CSIRO

Current climate

Moderate based on IPCC

A2 scenario without ice flow

High based on IPCC

A2 scenario with ice flow

Estimated sea-level rise (SLR) until 2030 vs. 2008

Centimeters

0.9

3.5

11.9

26.224.2

Sea-level rise in

Samoa

Geological subsidence in Samoa

2.0

Correction for local effect in

Samoa

Global sea-level rise

Annual

SLR

MillimetersScenario

7.58.0 2.02.5

2.02.000

2 S SAMOA EXAMPLE

SLIDE 32

0 50 100 150

Storm tide level above 2008 mean sea level Meters

0.9

0.8

0.7

0.6

2.1

2.0

1.9

1.8

1.7

1.6

1.5

1.4

1.3

1.2

1.1

1.0

Return period (2030) Years

The return period function for each hazard is computed for each climate change scenario

Reference event1

High change

Moderate change

Today’s climate 2030

Today’s climate 2008

Example

▪ Current climate, 2008: ~ 50 years

▪ Current climate, 2030: ~ 47 years

▪ Moderate change: ~ 38 years

▪ High change: ~ 18 years

1 One or more reference events are to ensure consistency between model predictions and historical data

SAMOA EXAMPLE2 H

October 2012, p 31

SLIDE 33

We use GIS data to map countries’ economic value against hazard exposure

SOURCE: CCRIF: “Enhancing the climate risk and adaptation fact base for the Caribbean”

IncomePhysical assets

Additionally we are using following data for analysis in St. Lucia

▪ Residential buildings

▪ Roads/bridges

▪ Power substations

▪ Health centres

▪ Police/fire depts.

▪ Airports

▪ Gas stations

▪ Banks

▪ Shopping malls

2 VA ST LUCIA EXAMPLE

SLIDE 34

Linking GIS data with value estimates we determine the geographic distribution of the economic value in the country

IncomePhysical assets

Choiseul

Vieux Fort

Soufrière

Canaries

Anse-la-Raye

Dennery

Laborie

Micoud

Castries

Gros Islet

Split of economic value by flood risk area

Percent

Mid-high riskMid-low riskLow riskLowest risk High risk Highest risk

Split of economic value by district

Percent

Split of economic value by asset type

Percent

Other assets include:

Commercial assets: banks, shopping malls, gas stations

Governmental buildings: schools, fire/police depts.

Churches

Power distribution

Hospitals/ health centresOther assets

Roads

Hotels

Airports

Ports

Bridges

Power substations

Power plants

Residential

2 VA ST. LUCIA EXAMPLE

October 2012, p 32

SLIDE 35

Banana Sugar cane YamOranges Papaya

We generate a vulnerability curve for different assets based on hazard severity, calibrated with historic loss data

SOURCE: CCRIF: “Enhancing the climate risk and adaptation fact base for the Caribbean”

0 10 20 30 40 50 60 70 80

Damage ratio of production at risk Percent

Wind speed m/s

Agricultural damage caused by hurricanes

Percent

▪ Production at risk potentially lower than yearly production

▪ Determined by number of harvesting cycles

Approach description

▪ Collection of damage report data from past hurricanes and link to hurricane wind speeds

▪ For bananas, evaluation of damage records from

WINCROP insurance company

– Amount paid/ amount insured for all major hurricanes

– Evaluation on single-country level

2 V CARIBBEAN EXAMPLE

NOTES FOR SLIDE 35:

Economics of Climate Change Approach for Total Climate Risk Management – Magnitude of Expected Loss – Vulnerability Module

This example of a Caribbean country shows a vulnerability curve generated for an asset, bananas, based on hazard (wind) severity, calibrated with historic loss data.

To give an example of the kinds of vulnerability analyses underaken through the ECA tool, the percentage of the banana crop, a valuable asset, that will be damaged is charted at different wind speeds.

The x axis is a number of different wind speeds in m/s. The y axis is the proportion of the production of bananas at risk of wind damage at any one time that will be damaged by a particular wind speed.

This kind of analysis, when plotted with the projected frequency of high wind speed events under different climate scenarios, is used to build a bottom up picture of asset vulnerability to climate change.

October 2012, p 33

SLIDE 36

Cost-effective measure

Non-cost-effective measure

Loss averted

Dollars

Reduction of the expected loss by implementing the measure

Measures below 0 line provide net savings

Measures below are cost effective in a risk-neutral decision frame

Risk-averse decision makers might accept a higher CBR

Cost-benefit curve of adaptation measures

Cost per loss-averted ratio

Adaptation measures are prioritized depending on their cost-benefit ratio

SOURCE: Economics of Climate Adaptation Working Group

3 ILLUSTRATIVE

▪ Benefit refers to the full economic upside from the measure

▪ Costs include cost savings generated by measures and therefore can be negative

SLIDE 37

100% = total expected loss from selected analyzed climate risks

Remaining loss

Non-cost-effective measures, CB > 1

Cost-effective measures, CB < 1

68 65 53 48 47 43 40

50 48

14 47

34 44

Tanzania

India

China2

SamoaUK

Guyana Florida

Mali Quelimane

Beira

Maputo

40 to 70% of the expected damage from climate hazards can be averted cost effectively

1 Based upon selected regions analyzed within the countries (e.g., Mopti, Mali; Georgetown, Guyana; Hull, UK; North and Northeast China; Maharashtra, India; central regions of Tanzania; Southeast Florida, USA)

2 Based upon moderate scenario data and analysis

3 No national estimate is presented for Mozambique for comparison given that data is only available for three cities

Percent of expected loss (high-climate-change scenario), 20301

Mozambique cities3

October 2012, p 34

NOTES FOR SLIDE 37:

On the left of this slide, you see that multiple predictions exist for rainfall in Tanzania 20 years from now. Each prediction has been made by groups of credible scientists, and we can see that they vary widely.

But we can group the predictions into the three scenarios on the right.

Then we use them to calculate expected losses…

SLIDE 38

Risk averse decision makers might accept a higher CBR, e.g., 1.52

500400300200100

1.5

1.00

1.50

Moveable Buildings

9.1

Breakwaters

4.1

Cost benefit ratio

9.5

9.0

0.5

4.0

3.5

3.0

2.5

2.0

1.0

Sea walls

1.4

Dikes

1.2

Stilts (old)

1.2

Flood-proof building structures

1.0

Stilts (new)

0.5

Relocation1

0.5

Flood-adapt contents

0.3

Sandbagging

0.2

Beach nourishment

0.2

Mobile Barriers

0.2

Revive reefs

0.1

PV of averted losses USD millions

800700600

Mangrove

Back Away

SOURCE: Economics of Climate Adaptation Working Group

1 Relocation only includes residential and commercial buildings outside of Apia

2 For example, a cost benefit ratio of ~1.5 is implicitly accepted by customers purchasing an insurance contract with a loss ratio between 60 and 70%

Risk neutral decision makers will base decision on CBR = 1.0

Cost-benefit analysis of short-listed measures – ca. 55% of expected loss can be avoided cost effectively

~ 55% of expected loss can be averted with cost-effective measure in high climate change scenario

SAMOA EXAMPLE3

October 2012, p 35

SLIDE 39

Fact-based adaptation strategy can yield significant returns – disguised client example

Reduction of expected loss due to adaptation measures

-50%

Total cost of adaptation measures

Residual loss plus cost of adaptation measures in

Total expected loss in 2030

Projected increase in expected loss due to climate change and economic growth

Expected loss today

Expected loss (average)

Costs/benefits of adaptation measures Expected losses from climate-related hazards and adaptation measures

USD millions at constant prices

SOURCE: McKinsey

SLIDE 40

Potential next steps to build these analyses and insights into actions

Expected loss per hazard by scenario

Drivers of expected loss for each scenario

Cost-benefit curve of adaptation measures

Measures to cover residual risk

Potential next steps

▪ Understand your risk profile today and in the future

▪ Specify your ‘risk appetite’ in line with your development priorities

▪ (Re-)prioritise risk mitigation and risk transfer measures based on your priorities

▪ Calculate an adaptation business case incl. investment plan

▪ Develop a roadmap incl. priority initiatives

▪ Use roadmap and business case for funding discussions

▪ Speed up implementation with additional funding and further increase resilience

Output from ECA analysis

October 2012, p 36

SLIDE 41

Table of contents

Executive summary

Economics of climate adaptation methodology

Baseline vulnerability and risk characterization (D1)

Climate change adaptation planning and action best practices (D2)

Key mitigation and adaptation measures (D3)

City disaster risk management system and strategy (D4)

SLIDE 42

Preliminary note

SOURCE: INGC Phase II Theme 3

▪ This report was written based on data gathered from city visits, stakeholder consultations and key learnings from international adaptation best practices. Preliminary results were discussed with national and municipal officials and in technical review workshops.

▪ This chapter addresses the losses expected from climate-related natural hazards based on the abovementioned inputs. We expect, however, that the results will still be further refined during the decision and approval process, and by the future Climate Change Knowledge Center.

October 2012, p 37

SLIDE 43

Risk baseline established for 3 cities using multiple criteria

Municipality Coastal vulnerability

▪ 7% of asset value at risk

▪ 20% of asset value at risk

▪ No coastal vulnerability, but significant risk from inland flooding to > 20% of surface area

Hazard profile

▪ Recent coastal

& inland floods, storms, and epidemics

▪ Rapid development in hazard-prone areas

▪ Recent coastal

& inland floods, storms, and epidemics

▪ Coastal and low-lying area construction

▪ Recent inland floods, tropical storms & epidemics

▪ Rapid peri-urban pop.

growth & flood-prone area development

Sectoral constraints

▪ All three cities face greatest risk to three key sectors:

– Transport

– Housing

– Medical services

▪ These sectors are also amongst most important for functioning emergency response, and could hinder such a response if not properly prepared a major weather event

SOURCE: INGC Phase II Theme 3

Maputo

Quelimane

Beira

Early warning capacity

▪ Little early warning infra-structure currently in place, with the exception of the GIZ-funded project already underway combining sand dune strengthen-ing and neighbor-hood early warning in

South

Africa

Response plans

All three cities well integrated into INGC disaster response system, with provision for multiple levels of response coord.

▪ Naitonal-level emergency response coord.

through

CTGC

▪ Provincial

Emergency

Operations

Centers

▪ Local

Disaster

Mgmt.

committees

Institutional capacity

All three cities need

▪ Institutional capacity through training govt.

officials and INGC staff

▪ Resource including responder training, emergency kits and equipment, and communica tions equip.

Some offered presently through foreign aid

SLIDE 44

Quelimane

Deliverable 1 focuses on identifying and quantifying climate-related risks that affect the cities

BeiraMaputo

Where and from what are we at risk?

What is the magnitude of the expected loss?

How could we respond?

How do we execute?

What are the outcomes and lessons?

City context

Natural hazard profile

Current level of preparedness

Climate change scenarios

Hazard module

Value of assets module

Vulnerability module

Expected loss assessment and sectoral impacts

Focus of this section

SOURCE: INGC Phase II Theme 3

October 2012, p 38

SLIDE 45

Maputo is Mozambique’s capital with 1.2 million inhabitants

SOURCE: EIU; McKinsey Global Institute City Scope; INE

Location and geography

▪ Capital and largest city in Mozambique

▪ Port city located on the Indian ocean

▪ Located on the West side of Maputo

Bay, near where the Matola and

Infulene rivers drain

▪ Total area of ~308km2

Climate

▪ Tropical savanna climate

▪ Average temperature of 22°C

▪ Average of 761 mm of precipitation per year

▪ Rainy season runs from November to

March

Economy

▪ GDP of USD 1.6 billion in 2010 (USD

1,300 per capita)

▪ Main industries – port and maritime activities, commerce, manufacturing

Population

▪ Population of 1.2 million in 2010

▪ 70% lives in peri-urban areas

▪ Divided into 57 neighborhoods

MAPUTO M B Q

SLIDE 46

Natural hazards have caused significant damage to Maputo in recent years

SOURCE: INGC, World Bank, President’s Malaria Initiative

Coastal flooding

MAPUTO

Inland flooding

Tropical storms Epidemics

▪ Storm surges from

Cyclone Eline caused significant damage to the

Avenida Marginal and flooded parts of Costa do Sol in

▪ Flooding in 2000 caused nearly USD

100 million in damages in Maputo forced the evacuation of 8,400 people from their homes

▪ Strong winds in

2005 destroyed 912 homes in the

Maputo province and caused significant damage to schools and health posts

▪ 24% of peri-urban

Maputo and 11% of urban Maputo are infected with Malaria, resulting in an average of 238,000 cases per year during the 1999-2010 period

M B Q

October 2012, p 39

SLIDE 47

Current economic growth trends are likely to increase exposure to natural hazards in the future

SOURCE: INGC; interviews with municipal officials

MAPUTO

Population growth in peri-urban areas

Potential construction of bridge to

Catembe

▪ 75% of Maputo residents live in informal settlements on the periphery of the city

▪ Population growth is more rapid in these areas

▪ Active proposal to build a bridge connecting Maputo city with Catembe, boosting land speculation and development in low-lying Catembe

Increasing development in Costa do Sol

Increasing development on erosion-prone slopes

▪ USD 92 million in new development approved for Costa do Sol in 2010 alone

(33% of new development)

▪ Increasing quantity of assets in low-lying areas prone to coastal flooding

▪ USD 73 million in new development approved in

Sommerschield and

Polano Cimento in

2010, much of it on fragile erosion-prone slopes

MAPUTO

SLIDE 48

Climate change effects are expected to worsen coastal and inland floods, storms, and epidemics

SOURCE: INGC Phase II Theme 3

Hazard

Climate change effect

Rising sea temperatures

Tropical cyclones

(wind damage)

Increased tropical cyclone intensity

Local effect

Inland flooding Increased heavy rainfall events

Coastal flooding, storm surges

Rising sea levels

Rising sea levels

Epidemics Rising air temperatures

Rising air temperatures

MAPUTO

1 In scope for Maputo

October 2012, p 40

SLIDE 49

Maputo is well integrated into the INGC’s disaster response system

MAPUTO

Organ Roles and responsibilities CompositionScale

Size

Number of people

Technical

Council for

Disaster

Management

(CTGC)

▪ Coordinates national emergency response

▪ Can be rapidly convened in emergencies

▪ Once convened for an emergency, meets twice a day

▪ Led by INGC Director, then

Prime Minister, then

President depending on threat level

▪ INGC, INAM, ING, DNA

National ~15-20

Municipal

Emergency

Operations

Center (COE)

▪ Coordinates…