Transcript AFRICAN RISK CAPACITY INSURANCE LIMITED

The Impact of Climate
Change in Africa
David Simmons
26th July 2016
1
CONTENTS
1.
2.
3.
4.
5.
6.
Is climate change real?
What drives climate variability?
How will climate change affect Africa?
Role of insurance
African Risk Capacity – an example of the possible
Conclusion
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2
1. Is climate change
real?
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3
Is climate change real?
International panel on climate change report 2013
Observed change in surface temperature 1901-2012
“Warming of the climate system is
unequivocal, and since the 1950s many of the
observed changes are unprecedented over
decades to millennia. The atmosphere and
ocean have warmed, the amounts of snow
and ice have diminished, sea level has risen,
and the concentrations of greenhouse gases
have increased”
“Each of the last three decades have been
successively warmer at the Earth’s surface
than any preceding decade since 1850. In the
Northern Hemisphere, 1983–2012 was likely
the warmest 30-year period of the last 1400
years”
Observed change in annual precipitation over land 1951 to 2010
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4
Is climate change real?
Results observed are within global climate-model-predicted ranges
Source: IPCC 2013
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5
Is climate change real?
Yes and it is likely to get worse
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6
Is climate change real?
7 of the top 10 most vulnerable countries are in Africa
Climate Change Vulnerability Index 2015
Source: Verisk Maplecroft
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7
Is climate change real?
The impacts are widespread
An increase in frequency and severity of floods and droughts leads to multiple issues:
 food security
 health issues
 eco-system degradation
 population displacement
All compromise the security and safety of Africa’s peoples
Agriculture could be particularly hard hit
 Jones and Thornton’s 2003 study estimated a reduction in smallholder rain-feed maize production
across Africa of 15% by 2055
 But this overall number masks considerable regional variability with larger reductions predicted in
Southern Africa
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Is climate change real?
Agriculture is vulnerable
Simulated maize yields (baseline) and changes 2055
Source: The potential impacts of climate change on maize production in Africa and Latin
America in 2055, Peter Jones, Philip Thornton: 2003
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9
Is climate change real?
Changes are not consistent across Africa
Predicted
Baseline
2055
Production
Production Production
Change
 There is considerable variability of
predicted impact across Africa BUT
Jones and Thornton predict only four
countries will see a rise in maize
production and only two significantly
(Morocco 74% and Lesotho 26%)
 But the majority show modelled yield
reductions; e.g. South Africa 19% down,
typical of Southern Africa
 Similarly, the probability of crop yields
falling below 200 kilo per hectare
increases for the majority of African
countries, for example South Africa
moving from 11% (1 year in 9) to 18%
(almost 1 year in 5)
Angola
Benin
Botswana
Burkina Faso
Burundi
Cameroon
Central African Republic
Chad
DR Congo
Congo
Cote d'Ivoire
Egypt
Equatorial Guinea
Eritrea
Ethiopia
Gabon
Gambia
Ghana
Guinea
Guinea-Bissau
Kenya
Lesotho
Liberia
Madagascar
Malawi
Mali
Mauritania
Morocco
Mozambique
Namibia
Niger
Nigeria
Rwanda
Senegal
Sierra Leone
Somalia
South Africa
Sudan
Swaziland
Tanzania
Togo
Uganda
Zambia
Zimbabwe
Total
Change %
863,383
710,400
82,425
270,480
239,545
408,450
118,710
111,042
2,293,907
4,500
763,700
0
0
36,624
2,670,900
20,800
17,400
685,300
119,488
35,262
1,731,000
104,608
0
238,470
2,080,350
147,420
7,683
75,288
1,330,256
31,305
3,894
4,182,954
164,100
101,300
13,845
141,500
4,585,000
85,500
72,170
4,389,442
503,100
1,166,795
1,151,250
2,009,800
705,915
586,200
66,107
226,520
202,515
326,200
102,150
90,056
1,755,995
3,882
776,300
0
0
29,876
2,579,550
18,352
14,100
584,500
106,713
30,402
1,641,000
132,048
0
200,469
1,844,100
103,600
7,579
130,625
1,080,901
25,244
3,234
3,431,142
152,925
84,900
11,918
144,250
3,713,500
70,832
48,569
3,751,196
400,050
1,001,368
977,250
1,675,700
-157,468
-124,200
-16,319
-43,960
-37,030
-82,250
-16,560
-20,987
-537,912
-618
12,600
0
0
-6,748
-91,350
-2,448
-3,300
-100,800
-12,774
-4,860
-90,000
27,440
0
-38,001
-236,250
-43,820
-104
55,338
-249,355
-6,060
-660
-751,812
-11,175
-16,400
-1,927
2,750
-871,500
-14,668
-23,600
-638,245
-103,050
-165,427
-174,000
-334,100
-18%
-17%
-20%
-16%
-15%
-20%
-14%
-19%
-23%
-14%
2%
33,769,345
28,837,733
-4,931,611
-15%
-18%
-3%
-12%
-19%
-15%
-11%
-14%
-5%
26%
-16%
-11%
-30%
-1%
74%
-19%
-19%
-17%
-18%
-7%
-16%
-14%
2%
-19%
-17%
-33%
-15%
-20%
-14%
-15%
-17%
Probability Probability
yield <200 yield <200
kg/ha,
kg/ha,
Baseline
2055
15%
19%
9%
11%
12%
16%
4%
6%
7%
1%
8%
14%
13%
14%
6%
7%
4%
6%
11%
28%
4%
6%
67%
8%
7%
12%
1%
7%
5%
7%
21%
28%
2%
6%
2%
6%
7%
63%
14%
53%
8%
6%
22%
6%
1%
32%
11%
6%
4%
6%
5%
4%
2%
4%
40%
9%
3%
22%
3%
13%
21%
7%
18%
19%
5%
9%
2%
8%
0%
39%
11%
56%
12%
11%
10%
8%
5%
22%
18%
8%
19%
4%
10%
4%
5%
7%
Prodected
Probability
Change
4%
2%
4%
2%
-6%
6%
1%
1%
2%
17%
2%
0%
-27%
1%
-4%
10%
2%
6%
16%
0%
-3%
-9%
3%
3%
0%
2%
-7%
-24%
-3%
3%
4%
5%
-12%
2%
4%
-10%
7%
2%
15%
-2%
5%
0%
3%
3%
Source: Jones and Thornton 2003
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10
2. What drives
climate variability
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What drives climate variability?
2015 Report on African climate by Tadross & Hauser
The African Risk Capacity commissioned
report aims to understand:
 What drives the correlation of losses
across African countries
 How climatic phenomena like El Nino
change the risk and where
 How this may change in a warmer world
The report considers three hazards:
 Drought
 Tropical Cyclone
 Rainfall
The results showed that the position is
Source: Tadross and Hauser: From Hoell et al., 2014
complex!
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What drives climate variability?
It’s not just El Nino,,,,
There are many climatological measures linked to
African drought/rain, including:
 El Nino Southern Oscillation (ENSO)
 North Atlantic Oscillation (NAO)
 Antarctic Oscillation (AAO)
 Southern Annular Mode (SAM)
 Pacific Decadal Oscillation (PDO)
 Indian Ocean Dipole (IOD)
Regional Impacts are Complex
 Tadross and Hauser analyse impacts over 5 regions of
Africa (then later 11 sub-regions themselves often broken
down into areas)
 Local climatic factors also come into play, including
topography and land use
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13
What drives climate variability?
El Nino Southern Oscillation (ENSO)
 One of many climatological measures linked to African drought/rain
 ENSO is not simple – many varieties and different impacts
El Nino: warm water in Eastern Pacific
La Nina: cold water in Eastern Pacific
Source: Tadross and Hauser: From Hoell et al., 2014
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What drives climate variability?
ENSO – El Nino
 Composite SST anomalies for
different realisations of ElNino
 Observed between
September and February
1950-2010
Source: Tadross and Hauser: From Hoell et al., 2014
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What drives climate variability?
ENSO – La Nina
 Composite SST anomalies
for different realisations of
La-Nina
 Observed between
September and February
1950-2010
Source: Tadross and Hauser: From Hoell et al., 2014
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16
3. How will climate
change affect Africa?
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How Will Climate Change Affect Africa?
It hard to predict
Climate studies focus on changes in averages, not changes in extremes
 This reflects limitations of current climate modes
Most studies assume that climate change will be superimposed on current
climate variability rather than change it
 But some assume that responses will intensify or increase in frequency
If so, in areas where global drivers predominate,
 Large scale cross region events may increase
 e.g. the Sahel
BUT where regional/local factors dominate
 The picture is complex
 e.g. Southern Africa
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How Will Climate Change Affect Africa?
What do we know?
Climate Change is not just something for the future
 Man-made global warming has been happening for over a century (albeit getting faster)
 It has already changed the climate; we are feeling the impact
We do know that climate uncertainty will increase
 Warmer average temperatures but uncertainty about impact on frequency and severity of
climatic drivers
This leads to greater uncertainty about amount, timing and severity of
drought, flood and severe wind events, impacts may be:
 Increased risk of crop failure
 More flood events (flash floods and river basin)
 Uncertainty about tropical cyclone frequency, path and severity
But there are things we can do
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19
4. The role of
insurance
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The role of insurance
Uncertainty influences behaviour
Uncertainty inhibits growth
 Unknown risk  excessive caution
 Sub-optimal decision making
 Delay of investment decisions
 Productivity becomes limited
 Economic growth harmed
Insurance can bring that certainty
 Increasing confidence
 Increasing efficiency
 Allows Africa to grow to its true potential
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The role of insurance
The value of insurance is not just financial
Encourages proper understanding of risk
 The hazards faced
 Their likely frequency and severity and so their likely impacts
Through the pricing mechanism, insurance can encourage appropriate risk
behaviour
 E.g. for property, better building standards, improved planning procedures
 E.g. for agriculture, better farming methods, more appropriate crops/varieties
Encourages better post-loss behaviour
 Contingency planning
 Post-loss co-ordination
Leaves people and society better equipped to recover
 Leading to greater societal resilience protecting the inheritance of current and future
African citizens
 Allowing for strong economic growth
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22
5. African Risk
Capacity – an example
of the possible
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African Risk Capacity – an example of the possible
Introduction to African Risk Capacity (ARC)
 ARC Ltd is a sovereign risk pool
 designed to provide immediate post catastrophe
immediate financial
 32 African countries are members of ARC Agency
 But not all currently buy insurance
 For 2014/15, the pool covered 5 countries
 Aim is to increase this to 20+ countries within 4
years
 Drought cover is triggered by a parametric index
developed with the World Food Programme
 Based on staple crop rainfall requirement
 Rainfall measured by a network of satellites
matched to ground recordings
 Tropical Cyclone cover was added in 2016/7 with
flood cover planned to follow
African Risk Capacity Agency Members
Page 24
African Risk Capacity – an example of the possible
Introduction to ARC Limited
A hybrid-mutual insurance company, owned by its members




Operates independently of ARC Agency
A separate legal and financial entity
Organised as a “Class 2” insurer, based in Bermuda
Issues index-based weather insurance policies, based on Africa RiskView, to eligible member
countries
Membership
 Class A: Agency Member States with current Certificates of Good Standing and active
Insurance policies
 Class B: Entities that give funds to ARC Ltd without the expectation that they will be returned
 Class C: Entities that give funds to ARC Ltd for a fixed term with the expectation that the
funds will be returned without interest
United Kingdom (DFID) and Germany (KfW) provided capital
 Initial funding circa $100m with more promised if needed as ARC expands
Page 25
African Risk Capacity – an example of the possible
ARC is a demonstration of the fiscal and non-fiscal value of reinsurance
ARC Value Multiplier for Member States
ARC is an efficient tool to manage droughts and other risks:
 Improved risk management through risk transfer and risk pooling
 Early response actions and improved targeting
Financial benefit of insurance through ARC:
•
•
•
•
Low operating costs for the ARC, thus lower
premiums for countries
Capitalises on natural diversification in Africa
Better conditions on insurance markets
Focusing on more extreme coverage
> 1-in-5 year events better value
Enables
Development benefit of planning and early
response:
•
•
•
•
Protect lives and livelihoods
Protect development gains
Maintain economic growth
Scaling up social safety nets and contingent
transfers most effective
Estimated cost benefit for every US$ 1 spent on ARC versus
traditional emergency response:
US$ 4.41 + possible direct cost savings
1 ARC
Cost Benefit Analysis, IFPRI, Oxford University and Boston Consulting Group
African Risk Capacity – an example of the possible
Customisation of ARC coverage for each member country
Determine Africa RiskView (ARV) parameters
 The basis of their parametric index, set with ARC staff
Calculate Water Requirement Satisfaction Index
 Using rainfall measurements from satellites using NOAA
(the US Weather Agency) algorithms
 The staple crop currently grown, recent climatic condition
and farming practice
Determine a WRSI benchmark
 Reflect recent and expected conditions
 Based upon historical as-if losses and local experience
Set drought thresholds as % of WRSI benchmark
 Thresholds represent severe, medium and mild drought
Estimate the population affected
 Based upon population surveys
10-day rainfall imagery at 10x10 km
resolution across Africa.
1 May 2014 – 20 March 2015 cumulative
Set a response cost per affected person
 Corresponds to their budgeted contingency plans
Page 27
African Risk Capacity – an example of the possible
Diversification over African improves resilience (and lowers reinsurance cost)
Drought Gambia
Drought Senegal
Drought
Mauritania
Drought Mali
Drought Burkina
Faso
Drought Niger
Drought Kenya
EAR1
Drought Kenya
EAR2
Drought Malawi
Drought
Zimbabwe
Drought
Madagascar
TC Comoros
TC Madagascar
TC Mauritius
TC Mozambique
Drought
Drought Drought
Drought
Mauritan
Gambia Senegal
Mali
ia
1.0
0.8
0.4
0.2
0.8
1.0
0.6
0.4
Drought
Burkina
Faso
0.2
0.2
0.0
0.2
Drought
Kenya
EAR1
0.2
0.0
Drought
Kenya
EAR2
0.0
0.0
Drought
Niger
Drought
Malawi
0.2
0.2
TC
Drought
Drought
TC
TC
TC
Madagas
Zimbabwe Madagascar Comoros
Mauritius Mozambique
car
0.2
-0.2
-0.2
0.0
0.2
0.2
0.2
0.0
-0.2
0.2
0.2
-0.2
0.4
0.6
1.0
0.6
0.6
0.4
0.0
-0.2
0.2
0.0
-0.2
-0.2
0.2
-0.2
-0.2
0.2
0.4
0.6
1.0
0.8
0.4
0.2
0.0
0.0
0.2
-0.2
0.4
-0.2
0.0
-0.2
0.2
0.2
0.6
0.8
1.0
0.6
0.2
-0.2
0.0
0.2
-0.2
0.2
-0.2
-0.2
0.2
0.0
0.2
0.4
0.4
0.6
1.0
-0.2
-0.2
0.0
0.0
0.0
0.2
0.0
0.0
-0.2
0.2
0.0
0.0
0.2
0.2
-0.2
1.0
0.2
0.0
0.0
0.0
-0.2
-0.2
0.0
0.2
0.0
0.0
-0.2
0.0
-0.2
-0.2
0.2
1.0
0.0
-0.2
0.2
-0.2
-0.2
0.0
0.0
0.2
0.2
0.2
0.0
0.0
0.0
0.0
0.0
1.0
0.4
0.2
-0.2
0.0
0.0
0.0
0.2
0.2
0.0
0.2
0.2
0.0
0.0
-0.2
0.4
1.0
0.4
-0.2
-0.2
0.4
-0.2
-0.2
0.0
-0.2
-0.2
-0.2
0.0
0.0
0.2
0.2
0.4
1.0
-0.2
-0.2
0.0
0.0
-0.2
0.0
0.2
0.2
-0.2
0.2
0.2
-0.2
-0.2
0.2
-0.2
-0.2
0.4
-0.2
0.0
-0.2
0.2
-0.2
-0.2
0.2
0.2
0.0
0.0
-0.2
-0.2
-0.2
0.0
0.2
-0.2
-0.2
0.0
0.0
-0.2
0.0
0.0
0.0
-0.2
-0.2
0.4
-0.2
-0.2
-0.2
0.0
0.0
1.0
0.0
0.0
0.0
0.0
1.0
0.0
0.0
0.0
0.0
1.0
0.0
0.0
0.0
0.0
1.0
Drought
Tropical Cyclone
 Countries are ordered roughly in an
 No significant correlation
arc West to East then South
 Close correlation of the West
African and East African countries c
 Some negative correlation between
West, Central, East and South
 Some unexpected positive
correlations occur between Cyclone
and Drought
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African Risk Capacity – an example of the possible
Coverage periods also provide diversification over time
Coverage Periods
Niger
Senegal
Mali
Burkina Faso
Gambia
Mauritania
Mauritania
Kenya EAR2
Kenya EAR2
Zimbabwe
Comoros Islands
Madagascar
Mauritius
Mozambique
Malawi
Madagascar
Kenya EAR1
Kenya EAR1
Millet
Groundnut
Maize
Sorghum
Peanuts
Rangeland
Sorghum
Rangeland
Maize
Maize
Tropical Cyclone
Tropical Cyclone
Tropical Cyclone
1
Tropical Cyclone
Maize
Maize
Rangeland
Maize
*
2016
May
1 1 1
0 1 1
0 0 1
0 0 0
0 0 0
0 0 0
0 0 0
0 0 0
0 0 0
0 0 0
1 1 1
1 1 1
1 1 1
1 1 1
0 0 0
0 0 0
0 0 0
0 0 0
Jun
1 1
1 1
1 1
1 1
0 1
0 0
0 0
0 0
0 0
0 0
1 1
1 1
1 1
1 1
0 0
0 0
0 0
0 0
1
1
1
1
1
1
1
0
0
0
1
1
1
1
0
0
0
0
Jul
1 1
1 1
1 1
1 1
1 1
1 1
1 1
0 0
0 0
0 0
1 1
1 1
1 1
1 1
0 0
0 0
0 0
0 0
1
1
1
1
1
1
1
0
0
0
1
1
1
1
0
0
0
0
Aug
1 1
1 1
1 1
1 1
1 1
1 1
1 1
0 0
0 0
0 0
1 1
1 1
1 1
1 1
0 0
0 0
0 0
0 0
1
1
1
1
1
1
1
0
0
0
1
1
1
1
0
0
0
0
Sep
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
0 0
0 0
1 1
1 1
1 1
1 1
0 0
0 0
0 0
0 0
1
1
1
1
1
1
1
1
0
0
1
1
1
1
0
0
0
0
Oct
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
0 1
1 1
1 1
1 1
1 1
0 0
0 0
0 0
0 0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
0
0
Nov
0 0
1 0
0 0
1 1
1 1
0 0
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
0 1
0 0
0 0
0 0
0
0
0
1
0
0
0
1
1
1
1
1
1
1
1
1
0
0
Dec
0 0
0 0
0 0
1 0
0 0
0 0
0 0
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
0 0
0 0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
0
0
Jan
0 0
0 0
0 0
0 0
0 0
0 0
0 0
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
0 0
0 0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
0
0
Feb
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
1 0
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
0 0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
0
Mar
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
Apr
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
2017
May
0 0 0
0 0 0
0 0 0
0 0 0
0 0 0
0 0 0
0 0 0
0 0 0
0 0 0
1 1 0
0 0 0
0 0 0
0 0 0
0 0 0
1 1 1
1 1 1
1 1 1
1 1 1
Jun
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
1 1
1 1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
Jul
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
1 1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Key:
Drought: Growing Season
2
Tropical Cyclone: Core Season
Tropical Cyclone: Pre-Season
*
Drought: Growing Season are spread throughout the year driving another element
of diversification even for adjacent countries (e.g. Senegal and The Gambia)
Tropical Cyclone: Peak season for South Western Indian Ocean tropical cyclones
is November to April, but storms are possible thoughout the policy year
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29
African Risk Capacity – an example of the possible
Proven in action, 2014 West/Central Africa Drought
Portfolio
Pool 1 Countries
Expected
Losses ($’m)
Observed
Rank
Observed
Return Period
Observed
Probability
Modelled
Return Period
Modelled
Probability
26.3
6/32
1 in 5.2
19.4%
1 in 5.7
17.4%
ARC Ltd covers are written to pay claims frequently
 Typically covers attachment at a 1 in 4 year probability
ARC Ltd’s reinsurance is written to respond frequently
 Pays when ARC Ltd suffers a modelled 1 in 3 aggregate annual loss
So loss event not unusual
 2014/15 was the 6th highest loss (as if) out of 31 years
 The probability of such a loss based on the model was 1 in 5.7 years
The insurance process recognised the impending event first
 Allowed pre-arranged contingency plans to kick in early, saving lives and livelihoods
 UN confirms pre-launch estimates that early payment of $1 worth perhaps $5 3 months later
Page 30
African Risk Capacity – an example of the possible
Still innovating – Replica contracts
ARC Board has agreed to allow International Organisations and NGOs to
purchase Replica insurance contracts
 Aimed at aligning part of the international assistance that would be required for
a drought with the infrastructure that ARC helps to build
 Maximum of 100% of the amount purchased by the country may be replicated
 Replica Partner will pay the premium and receive the pay-out
 Prime likely purchasers the World Food Programme and the START network of
NGOs
 Danish Government and the EU have agreed to fund Replica covers
 The NGO community increasingly sees value of insurance in transforming,
modernising and increasing the efficiency of disaster relief
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31
African Risk Capacity – an example of the possible
Still innovating – Licence for Development
The ARC Ltd Board has agreed to allow Willis Towers Watson to offer
covers based on ARC’s technology and data to commercial buyers
 The Licence for Development (L4D) allows Willis Towers Watson to customise
Africa RiskView for cash crops
 Commercial buyers can get the benefit of the WRSI index to model crop
growth and a create robust historical dataset
 Re/insurance market is ready and primed for the product
 Parametric covers - advantage of low cost and fast payments
 Target buyers include
 Large commercial farmers and co-operatives
 Buyers of crops, including off-sellers, food processors, wholesalers and retailers
 Banks providing loans to farmers and co-operatives
 A proportion of all premiums paid go to ARC Agency
 ARC Ltd have the option to participate on the cover
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32
6. Conclusion
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Conclusion
Insurance protects Africa against the impact of climate change
Climate change is happening
 Africa particularly at risk- the effects are uncertain, but uncertainty itself is a threat
 Increase in frequency and severity of floods and droughts likely
Consequences:
 Food security, health issues, eco-system degradation, population displacement
 All compromising the security and safety of Africa’s peoples
Role of insurance to protect the vulnerable has never been clearer
 Even ignoring climate change, a growing population is highly exposed to climate risk
The African Risk Capacity (ARC) shows what can be achieved
 Delivering emergency response support immediately after crop fails
 Encourages the development of a culture of risk understanding, risk management and
contingency planning in governments, increasing the resilience of society as a whole
 Private sector has access to ARC’s index and data to design crop failure coverage
 Policy replication allows NGOs to better leverage their aid budgets
Protect the vulnerable and help Africa prosper despite higher climate risks
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Thank you
David Simmons
Managing Director: Capital, Science and Policy Practice
Willis Towers Watson
[email protected]
+44 20 3124 8917
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35
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