- TASK 2: REPORT ON THE COSTS OF THE HOT
SUMMER OF 2003 Climate Change Impacts and Adaptation: Cross-Regional
Research Programme
Project E – Quantify the cost of impacts and adaptation
Final Report
Prepared for:
DEFRA
Prepared by:
Metroeconomica Limited (UK)
This report has been prepared by Metroeconomica Limited, Bath in conjunction with
consortium partners. Please contact Alistair Hunt, Metroeconomica on 01225 383244
or by email on ahunt@metroeconomica.com for further details.
Authors
Metroeconomica
Alistair Hunt
Richard Boyd
Tim Taylor
London School of Hygiene and Tropical Medicine (Health)
Sari Kovats
Kate Lachowyz
AEA Technology (Transport)
Paul Watkiss
Lisa Horrocks
Project E – Quantify the cost of impacts and adaptation
Defra
Table of Contents
1 INTRODUCTION.....................................................................................................3
2 CHARACTERISATION OF THE SUMMER 2003 WEATHER EVENT
IN THE UK AND EUROPE .......................................................................................5
2.1 ANNEX 2A: METEOROLOGICAL DATA FOR 2003...................................................8
3 HEALTH .................................................................................................................13
3.1 INTRODUCTION....................................................................................................13
3.2 METHOD FOR QUANTIFYING MORTALITY AND MORBIDITY IMPACTS OF THE
SUMMER 2003 HOT WEATHER EVENT ..................................................................13
3.3 RESULTS FOR QUANTIFICATION OF MORTALITY ..................................................14
3.4 RESULTS FOR QUANTIFICATION OF MORBIDITY...................................................16
3.5 RESULTS FOR MONETISATION OF HEALTH IMPACTS ...........................................17
3.6 DISCUSSION.........................................................................................................20
4 ENERGY SECTOR ................................................................................................21
4.1 INTRODUCTION....................................................................................................21
4.2 METHODOLOGY ..................................................................................................21
4.3 RESULTS .............................................................................................................26
4.4 DISCUSSION.........................................................................................................30
5 AGRICULTURE.....................................................................................................32
5.1 INTRODUCTION....................................................................................................32
5.2 METHODOLOGY ..................................................................................................32
5.3 RESULTS .............................................................................................................37
5.4 DISCUSSION.........................................................................................................39
5.5 ANNEX 5A: UK PRODUCTION AND YIELDS (1984-2004) ....................................41
6 RETAILING............................................................................................................43
6.1 INTRODUCTION....................................................................................................43
6.2 TOP-DOWN EVIDENCE .........................................................................................44
6.3 BOTTOM-UP EVIDENCE ........................................................................................47
6.4 CONCLUSIONS .....................................................................................................48
6.5 RETAILING: ANNEX 6A .......................................................................................50
7 TRANSPORT ..........................................................................................................52
7.1 INTRODUCTION....................................................................................................52
7.2 RAIL ....................................................................................................................53
7.3 ROAD ..................................................................................................................60
7.4 LONDON UNDERGROUND ....................................................................................62
7.5 AVIATION ............................................................................................................63
7.6 CYCLING AND MOTORCYCLES ............................................................................64
7.7 ADAPTATION .......................................................................................................64
7.8 DISCUSSION AND CONCLUSIONS .........................................................................65
Metroeconomica Limited
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Project E – Quantify the cost of impacts and adaptation
Defra
8 WATER RESOURCES ..........................................................................................67
8.1 INTRODUCTION....................................................................................................67
8.2 METHODOLOGY ..................................................................................................67
9 TOURISM ...............................................................................................................70
9.1 INTRODUCTION....................................................................................................70
9.2 PREVIOUS WORK .................................................................................................70
9.3 METHODOLOGY ..................................................................................................70
9.4 VALUATION OF IMPACTS .....................................................................................80
9.5 RESULTS .............................................................................................................80
9.6 DISCUSSION.........................................................................................................81
10 BUILT ENVIRONMENT ....................................................................................82
10.1 INTRODUCTION..................................................................................................82
10.2 METHODOLOGY ................................................................................................82
10.3 RESULTS ...........................................................................................................85
10.4 DISCUSSION.......................................................................................................87
11 CONCLUSIONS ...................................................................................................88
12 REFERENCES......................................................................................................90
Metroeconomica Limited
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
1 INTRODUCTION
The objective of this task is to estimate the impacts of the Summer 2003 weather event
in the UK in monetary terms. Its purpose is four-fold. First, as an example of an
extreme weather event1 that is thought likely to become more common under climate
change scenarios, this event provides a valuable source of empirical information on
potential climate change impacts. Second, since the event is very recent, it is
remembered by the wide stakeholder community - including the general public - and
any analysis of its impacts serves to act as a well-understood historical analogue of a
climate change-related event. Third, the event allows us the opportunity to identify the
extent to which proactive and reactive adaptation to mitigate the full impacts of the
event existed, and hence what lessons there may be for climate change adaptation
policy. Fourth, the task serves as an illustration of the methodological and empirical
issues associated with monetised impact analysis of climate change-related events.
The impacts of the Summer 2003 hot weather event are reported here on a sectoral
basis. Sectoral coverage includes: Health; Transport; Agriculture; Water Resources
and Water Quality; Built Environment; Tourism; Retailing and Manufacturing, and
Energy. These sectors were selected on the basis that they cover the main impacts of
the event that have been identified. The focus of the analysis is on those impacts that
can be quantified and monetised. Therefore, this report should not be seen as an
attempt to provide comprehensive coverage of impacts. There will – for example – be
many impacts that are significant in terms of their effects on welfare and yet are not
addressed here due to our having to limit ourselves to those impacts that are
quantifiable.
This sectoral approach is in line with that adopted by a previous study focused on a
summer weather event – Economic Impacts of the Hot Summer and Unusually Warm
Year of 1995, (eds. Palutikof, Subak and Agnew, 1997) – and produced for Defra, then
Department of the Environment. The Palutikof et. al. study was undertaken with
substantially more resources than the present study; our study therefore aims not to
replicate the methods or results of this study but to complement it by revisiting certain
impacts, expanding the analysis where data now allows, and adding further robustness
to the strength of the Palutikof et. al. findings.
Where possible, at the beginning of each sectoral report we provide a short summary
of the press coverage relevant to the event. This has been possible by searching the online versions of the national newspapers from July 2003 – December 2003. The
purpose of this summary is firstly to illustrate what the perceived impacts of the
weather event were thereby guiding our sectoral focus. Since, however, there are some
1
Note that there is no single definition of what constitutes an extreme event. Extremes can be quantified on the basis of i) their
frequency; ii) their intensity and exceedance of thresholds; and iii) the impacts they exert e.g. on environmental or economic
sectors (from Beniston and Stephenson, 2004).
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
differences between perceived and actual impacts and their severity, this exercise also
serves to illustrate how future adaptation responses may need to be tailored in order to
be most effective.
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
2 CHARACTERISATION OF THE SUMMER 2003
WEATHER EVENT IN THE UK AND EUROPE
Any description of impacts of a weather event relies on a calibration of impact against
weather variable(s). This section provides a characterisation of the Summer 2003
weather event in terms of its primary meteorological descriptors, against which the
impacts quantified below must be calibrated. Data has been obtained from the
Meteorological Office web-site at http://www.met-office.gov.uk/ unless otherwise
stated.
UK
Summer 2003 – defined here as the three months June, July and August - was
meteorologically notable in the UK and Europe primarily for an extremely hot period
at the end of July and in early August, in which a new record maximum temperature
for the UK was recorded. The previous UK record of 37.1 °C at Cheltenham on 3
August 1990, was beaten by a number of stations on 10 August 2003, with Brogdale
near Faversham (Kent) reporting the highest at 38.5 °C. Maximum temperature
records were also broken for individual countries within the UK (England, Scotland
and Wales). In 2003, 32 °C was exceeded on three consecutive days between 4 and 6
August and then on five consecutive days between 8 and 12 August, somewhere in the
UK (temperatures failed to reach 32 °C at any of the real-time stations on 7 August).
This compares with 1976 – another recent hot summer – when temperatures exceeded
32 °C (90 °F), somewhere in the UK, on 15 consecutive days starting on 23 June.
Met Office London had a night-time minimum temperature of 23.7 °C on 9/10 August
2003, compared to the record of 24.0 °C on 3/4 August 1990 (based on a digital data
series that goes back to 1974). St. Mawgan in Cornwall had a night-time minimum
temperature of 23.1 °C on 4/5 August 2003, its highest on record (based on a data
series that goes back to 1957).
In contrast to 1976, and to a lesser extent 1995, the hot and dry spell in 2003 occurred
principally in August. June 2003 and July 2003 were only the 18th and 33rd warmest on
record (based on the Central England Temperature), with mean temperatures of 16.1
o
C and 17.6 oC, respectively.
However, the temperature spike of this period should be seen in the broader context of
the surrounding months. Figure 2-1 (see Annex 2A) shows the Summer 2003
maximum temperature average across the UK as a deviation from the long term mean,
and makes clear that the period has significantly above average maximum
temperatures. The mean Central England Temperature for Summer 2003 (based on
temperatures that are representative of a triangular area of the UK joining Bristol,
London and Preston) was 17.3 °C, making it the fourth warmest summer period on
record. This compares with 17.8 °C and 17.5 °C for the summers of 1976 and 1995,
respectively. The mean temperature across the UK for this period was 15.8 °C
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
(minimum = 11.3 °C and maximum = 20.3 °C), 2.0 °C above the 1961-1990 long term
average.
As can be seen from Figure 2-2 the mean temperature for the year as a whole in the
UK was above average and, indeed, the year constituted the fifth warmest on record. It
is important to note, however, that neither the Summer 2003 nor the 12 month period
through to October were as warm as the same periods in 1995. For example, for the
two “high summer” months of July and August the CET anomaly for 2003 was 2.1 °C
whilst it was 3 °C in 1995.
Figure 2-3 shows the Summer 2003 rainfall as a percentage of the long term mean.
Whilst the rainfall for England and Wales for the year was below normal, (75% of the
1961-1990 long term average, with the period January to October being the eighth
driest in a series that began in 1766), it is interesting to note that in the summer
months of June and July, rainfall was above average in England and Wales, (106%
and 121% of average, respectively). The 1995 summer was drier than 2003, the two
high summer months being the driest ever recorded. 1976 was marginally wetter than
1995. The 12 month period to October 1995 was close to average but masked a
distinct pattern of a very wet winter followed by a dry spring and very dry summer.
During 2003 sunshine across the UK totaled 547.8 hours (close to 6 hours per day),
and was 109% of the 1961-1990 long term average. These rainfall and sunshine
anomalies varied by region however; Scotland received as little as 68% of the long
term average rainfall, while in contrast to the rest of the UK, Wales received slightly
less than the long term average hours of sunshine (98%).
Figure 2-4 present more detailed weather data for 2003, comparing the mean
temperature, sunshine hours and rainfall, by region and season, with the 1961-90 longterm average. As the figure shows, each region experienced above average
temperatures throughout the year, and particularly in the spring and summer.
Significantly more hours of sunshine than average where recorded during the winter
(2002-03), spring and autumn of 2003, with only moderately higher than average
values recorded during the summer.
Below average rainfall was observed, in general, across all four seasons. The summer
and autumn months were particularly dry in each region relative to the long-term
average. The spring was only slightly drier than average, as was the preceding winter.
There was also considerable regional variation in rainfall during these two seasons.
For example, rainfall in Northern Ireland and Wales was very close to the long-term
average during the spring, whilst England received only 76% of average rainfall. In
the winter, by contrast, rainfall in England was 6% above average, but below average
in the rest of the UK.
Europe
The 2003 summer was the warmest ever recorded over western and central Europe and
was most severe over Switzerland, France, southern Germany and northern Italy.
Many locations in France, Switzerland, northern Italy and southern Germany recorded
temperature anomalies (i.e. differences from historic mean) in all three summer
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
months in excess of 5ºC. In southern and eastern France, 29 sites recorded
temperatures in excess of 40ºC during the first half of August, with the record value
being 42.6ºC at Orange in the Vaucluse Department in the Rhône Valley. In Paris, the
temperature did not drop below 23ºC between August 7 and 14 and the warmest ever
minimum temperature was recorded in Paris on the night of August 11/12 with 25.5ºC
(Source: STARDEX Information Sheet 32).
Summer 2003 as a climate change event
Whilst a frequently asked question in relation to the Summer 2003 has been whether it
was caused by increasing concentrations of greenhouse gases in the atmosphere – and
resulting climate change – it should be noted that this is not quite the right question
since such events have a probability of occurring in a non-climate change world.
Rather, Stott et. al. (2004) argue that it makes more sense to investigate whether “it is
possible to estimate by how much human activities may have increased the risk of the
occurrence of such a heatwave.”
Stott et. al. estimate that there is a 90%-plus chance that anthropogenic climate change
to date has at least doubled the risk of a summer mean temperature threshold, only
exceeded in Europe in 2003, being exceeded. Furthermore, under the SRES A2
scenarios, for example, the projections suggest that 50% of years will be warmer than
2003 by the 2040s, whilst it will be seen to be an anomalously cold summer relative to
the new climate by the end of the current century.
2
www.cru.uea.ac.uk/projects/stardex/
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
2.1 Annex 2A: Meteorological Data for 2003
Figure 2-1: Summer 2003 Maximum Temperature Anomaly with 1961-90 mean
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
Figure 2-2. 2003 Mean Temperature Anomaly for UK and constituent parts.
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Figure 2-3. Summer 2003 Precipitation Anomaly with 1961-90 mean (%)
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
Table 2-1. Regional Meteorological Data for Summer 2003: Actual and as anomaly with
1961-90
Region
Max temp
Actual
[°C]
Anom
[°C]
Min temp
Actual
[°C]
Anom
[°C]
Mean temp
Actual
[°C]
Anom
[°C]
Sunshine
Actual
[hours]
Anom
[%]
Rainfall
Actual
[mm]
Anom
[%]
UK
20.3
2.2
11.3
1.7
15.8
2.0
547.8
109
176.0
75
England
21.7
2.4
12.1
1.8
16.9
2.1
602.8
111
150.9
79
Wales
19.9
1.9
11.4
1.6
15.6
1.7
506.1
98
220.2
80
Scotland
18.1
2.3
10.2
1.8
14.1
2.0
484.3
111
201.4
68
N Ireland
19.0
1.6
10.9
1.5
15.0
1.6
451.8
104
202.0
84
England & Wales
21.5
2.3
12.0
1.7
16.7
2.0
589.5
109
160.4
79
England N
20.4
2.2
11.5
1.7
15.9
2.0
573.6
115
170.6
76
England S
22.5
2.4
12.4
1.8
17.4
2.1
618.3
109
140.5
81
Scotland N
17.5
2.5
10.0
1.9
13.8
2.2
443.6
111
214.1
68
Scotland E
18.6
2.2
9.9
1.8
14.2
2.0
522.9
115
138.4
57
Scotland W
18.4
2.0
10.5
1.6
14.4
1.8
498.1
107
253.0
75
England E & NE
20.7
2.3
11.4
1.8
16.1
2.0
599.8
118
154.8
82
England NW & Wales N
19.8
2.0
11.6
1.7
15.6
1.8
530.7
106
200.1
72
Midlands
21.9
2.5
11.9
1.7
16.9
2.1
572.0
108
147.5
79
East Anglia
23.1
2.7
12.8
2.0
17.9
2.3
643.2
113
124.2
80
England SW & Wales S
20.7
1.9
12.0
1.5
16.3
1.7
557.1
100
201.3
85
England SE & Central S
23.0
2.6
12.7
1.8
17.8
2.2
670.0
112
122.9
75
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
Figure 2-4: Weather Anomalies during 2003 Relative to 1961-90 Long-term
Average, by Region
(a) Mean Temperature
UK
ENG
WAL
SCO
NI
Temp. Anomaly ( degrees C )
2.5
2.0
1.5
1.0
0.5
0.0
-0.5
Winter
Spring
Summer
Autumn
Season
(b) Sunshine Hours
Percentage Change Relative to Longterm Average
UK
ENG
WAL
SCO
NI
35%
30%
25%
20%
15%
10%
5%
0%
-5%
Winter
Spring
Summer
Autumn
Season
(c) Rainfall
Percentage Change Relative to Longterm Average
UK
ENG
WAL
NI
10%
5%
0%
-5%
-10%
-15%
-20%
-25%
-30%
-35%
Winter
Spring
Season
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
3 HEALTH
3.1 Introduction
The health impacts of the Summer 2003 weather event attracted particular media
attention. The health impacts specifically related to the heatwave period between
August 4th and August 13th. During the period itself, coverage related to accidents
caused by people trying to keep cool.
An example of typical press reports of the time shows this: “As the temperature
reached 35.4C on the roof of the London Weather Centre…it emerged that two
teenagers had died...while trying to cool down”. (Guardian, August 7th, 2003).
At this time, the newspapers were reporting guidelines issued by the Department of
Health on how to cope with the heatwave. The ten useful tips included “stay in the
shade, keep windows open, avoid physical exertion and drink lots of water.” (The
Telegraph 6th August, 2003). The Telegraph (August 8th) reported the warning issued
by medical experts that due to the increased levels of low level ozone “people with
respiratory illnesses should increase their medication and avoid exercising outdoors.”
Longer term impacts of skin cancers were also of potential concern, as shown by the
following: “Cancer Research has dispatched teams of advisers to city centres to
distribute free sun tan lotion and advice to bathers.” (Guardian, 4th August, 2003).
Incidence of reports of sickness or employees taking sick leave may also have
increased, as according to an article in the Guardian: “A survey found that up to 37%
of the population may claim to be ill in the warm weather. The research…found that
one of the top reasons to take a day off sick was “a fine summer’s day”. (Guardian, 6th
August, 2003).
During August the main focus was on the deaths in France, estimated to be 10,000,
(Guardian 4th October, 2003). In the following months it became clear that there had
been a significant mortality effect associated with the event in the UK – though not on
the same scale. (The Telegraph, 4th October, 2003) reported on ONS statistics that
showed “that between August 4th and 13th, 2,045 more people in England and Wales
died that would have been expected for the time of year. The peak day was August
11th, the day following the hottest day, when there were “1,691 deaths, which is 363
more than the average for that day in the past five years.”
3.2 Method for quantifying mortality and morbidity impacts
of the Summer 2003 hot weather event
The heat wave of 2003 was unprecedented in Western Europe. Mortality in England
and Wales increased by 16% during the 10-day heat wave of 4th to 13th August. We
estimated the impact on mortality associated with a specified analogue heat wave
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
event in England and Wales (Johnson et. al. 2005). The method and results are based
on that study.
The Met Office supplied temperatures for each Government Office Region (GOR)
during the episode. Daily values were generated for a national 5km grid by
interpolation of data from approximately 560 stations. Within each GOR, the
maximum and minimum of the daily maxima were then identified. The London region
time series, of daily data recorded at the London Weather Centre, were downloaded
from the British Atmospheric Data Centre [www.badc.nerc.ac.uk]. Data for the
Central England Temperature (CET) series were obtained from the Climatic Research
Unit, University of East Anglia and the British Atmospheric Data Centre. Temperature
anomalies were calculated by subtracting a long-term mean climatology (1971 to
2000) for the days in question from the observed data for those days.
Mortality data were extracted from databases held by ONS, for all deaths occurring on
each day in July and August 2003, and for same months in the five preceding years, by
age group (0–64, 65–74, 75 and over) and by Government Office Region (GOR).
Provisional data on emergency hospital admissions were supplied by the Department
of Health (HES). Data were obtained for the same age groups, regions and years as the
mortality data. These data are provisional and are likely to be incomplete. Emergency
hospital admissions were assigned to GORs based on the place of residence of the
person treated.
Excess mortality was calculated as observed deaths minus the baseline (average of
1998 to 2002) expected mortality. Excess emergency hospital admissions were
calculated in the same way. Due to the large day of week variation in hospital
admissions the baseline series was adjusted so that the appropriate day of the week in
2003 was compared with the same day of the week in each of the comparison years of
1998 to 2002. A seven-day moving average was then applied to smooth the data.
Confidence intervals (CI) were calculated for the excess values. The number of
observed deaths or emergency hospital admissions was treated as a Poisson variable;
the 95 per cent confidence limits for this value were then compared with expected
values to generate confidence limits for excess mortality and emergency hospital
admissions.
3.3 Results for quantification of Mortality
Excess deaths during the 10 day heat wave have been calculated for the 12 GOR,
except Scotland and Northern Ireland, for which mortality data are not available
(Table 3-1). However, impacts in North East Region, where the heat wave effect was
minimal, could be applied to Scotland and Northern Ireland, adjusted for population
size. Note that because of the higher temperatures affecting the South of the UK in this
period, it is in the Southern regions where the impacts (in absolute and percentage
terms) are highest.
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
Due to the large random variation in mortality, it is advisable to drop cells where the
number of excess deaths is less than 10 or excess mortality is less than 5%. The
apparent “positive” effects of the heat wave should be assumed to be “no effect”.
Mortality is usually at low levels in the summer. This positive effect should not be
included in the costs estimations since there is no good evidence that the decrease is
due to the heat wave. These adjustments are made in Table 3-3 and the less than 10
rule applies also to morbidity in Table 3-4. Note, however, that these estimates do not
account for the role of air pollution (PM10 or ozone) in the excess mortality.
Table 3-1. Attributable deaths by region and age group (% increases above average
over the 10 day heat wave in brackets)
Region
TOTAL - All Adults (0-64)
ages
Older adults Elderly
(65-75)
(<75s)
London
616 (42%)
45 (15%)
49 (17%)
522 (59%)
South East
447 (23%)
46 (15%)
56 (17%)
345 (26%)
South West
282 (21%)
37 (18%)
24 (11%)
221 (25%)
Eastern
254 (20%)
54 (27%)
-26 (-11%)
226 (27%)
East Midlands
169 (17%)
41 (23%)
-5 (-2%)
133 (21%)
West Midlands
130 (10%)
6 (2%)
10 (4%)
114 (14%)
106 (8%)
-2 (-1%)
-14 (-6%)
122 (15%)
North West
74 (4%)
-1 (0%)
-9 (-2%)
84 (8%)
North East
13 (2%)
10 (8%)
-10 (-6%)
13 (3%)
2091 (17%)
236 (11%)
74 (3%)
1781 (23%)
Wales
31 (4%)
3 (6%)
-17 (-10%)
46 (10%)
Scotland
26 (2%)
20(8%)
20 (-6%)
26 (3%)
Northern
Ireland
9 (2%)
7 (8%)
7 (-6%)
9 (3%)
2157
266
84
1862
Yorkshire
Humber
England
UK
Note that the results given in Table 3-1 compare with those presented in Palutikof et.
al. (1997) for the summer of 1995 that showed higher death rates in July and August
of 5% and 1%, respectively. The fact that – for England at least – the percentage
increase was greater in 2003 seems likely to be as a consequence of the higher daily
temperatures in the heatwave period of this summer, since acute deaths are strongly
temperature dependent.
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
3.4 Results for quantification of Morbidity
Heat waves in the UK are associated with increases in emergency hospital admissions,
but the effect is largely confined to the elderly, and is localized.
In 2003, a 16% increase in admissions in the over 75s was detected in London
(Johnson et. al., 2005). An excess was not apparent in other age groups. In the South
East, by contrast, there was a 1% decrease in admissions in the over 75 age group. The
regional results are shown in Table 3-2. Results for Wales, Scotland and Northern
Ireland were not generated in the original analysis and so have been estimated here on
the basis of transferring results for West Midlands to Wales, adjusted for population,
and for North East England to Scotland and Northern Ireland, similarly adjusted.
Table 3-2. Excess hospital admissions by region in the over 75s.
Government
Office Region
London
South East
South West
Eastern
East Midlands
West Midlands
Yorkshire Humber
North West
North East
England
Wales
Scotland
Northern Ireland
UK
No. of excess hospital
admissions in >75 age
group (% change over
average for 10-day period)
464 (16%)
-53 (-1%)
304 (11%)
94 (3%)
322 (14%)
14 (1%)
36 (1%)
260 (7%)
50 (3%)
1,491
8 (1%)
126 (3%)
33 (3%)
1658
Time series studies of the effects of ambient temperature on hospital admissions
across the whole temperature range have presented surprising results. A recent study
in London found evidence for heat-related increases in emergency admissions for only
a few specific outcomes: renal disease and respiratory disease particularly in the 75+
age-group (Kovats et. el., 2004). In Europe, higher temperatures do not appear to be
associated with increases in admissions for cardiovascular disease (Kovats et. el.,
2004; Panagiotakos et. al., 2004), although some effect is apparent in the US
(Schwartz et al., 2004).
Hospital admissions are not a perfect indicator of morbidity, as health system factors,
such as admission thresholds, will vary between countries, and over time. A small
increase in calls to NHS Direct was also apparent during the two heat waves in 2003
(early July and August) but it is not possible to convert this into a quantifiable estimate
of disease burden (Leonardi et. al., 2006). The evidence so far indicates that increases
in hospital admissions during heat waves are not as dramatic as that seen in mortality,
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
and that there is not a large “morbidity” burden associated with the heat wave in the
UK.
3.5 Results for Monetisation of Health Impacts
In health valuation there are three elements that need to be considered in estimating the
total effect of the impact on society’s welfare. These elements are:
(i)
(ii)
(iii)
Resource costs i.e. medical costs;
Opportunity costs i.e. the cost in terms of lost productivity, and
Dis-utility i.e. pain or suffering and concern and inconvenience to family
members and others.
In the case of premature death as a result of exposure to hot weather, acute mortality
most frequently affects people who are either old, ill or both. The age profile of excess
deaths presented in Table 3-1, above, supports this assumption. Two metrics are
currently used: the value of a prevented fatality (VPF) and the value of a life year
(VOLY), the latter providing a means of explicitly accommodating differing lengths
of remaining life expectancy.
Valuation of acute mortality focuses on element (iii). It is assumed that the resource
costs associated with the death would be incurred in any case when the individual dies.
It is also assumed that since acute mortality most often affects the elderly; they will be
retired from the work-force so that element ii) is not relevant. Estimates of element
(iii) rely on the use of non-market valuation techniques and consequently have a
degree of uncertainty attached to them. In this study we use the central value of a lifeyear currently recommended by the Interdepartmental Group on Costs and Benefits
(IGCB) within UK Government, of £15,000 per life-year. A reasonable range around
this value – supported by two recent studies (Chilton et. al. 2004; Alberini et. al. 2006)
is £5,000-£50,000. As a sensitivity test, a VPF of £1.2m – a value used by the
Department for Transport – is adopted. Note that this value was derived from studies
undertaken to address mortality risks in other contexts. Clearly, the application of
these values in a context different from that for which it was derived provides an
additional source of uncertainty.
Regarding the valuation of hospital admissions, recent evidence for heat impacts on
hospital admissions (Kovats et. al., 2004) suggests that admissions for respiratory
illness are correlated with heat. We therefore value respiratory hospital admissions.
The central range of values provided by the Interdepartmental Group on Costs and
Benefits (IGCB) is £1,854 – £9,120. For illustration, the mid-point of this range
(£5,487) may be used as a central value.
Monetisation of the physical impacts that are reported in Tables 3-1 and 3-2, to give
total welfare cost estimates, can be done by multiplying the number of physical cases
by the unit values for the two health end-points considered here. In the case of
mortality valuation, the number of cases can therefore be multiplied by the VSL
values. However, in trying to apply VOLYs to the data available is not so
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
straightforward. Inferring life years saved takes us beyond the direct base of evidence;
that is, inferring life years saved involves making assumptions that are speculative,
and based on indirect evidence; they cannot be taken directly from studies. We assume
here that the causes of death are similar to those attributable to air pollution, and
therefore provide the justification made by Hurley (2004) in the Clean Air For Europe
(CAFE) Cost Benefit Analysis Methodology report. He argues that there are “two
relevant facts that are well-established:
•
•
Most deaths are from cardiovascular-related causes; some are from respiratory
causes (as primary cause of death);
Most deaths occur in older people [see Tables 3-1 & 3-2].
“These characteristics imply on average a ‘short’ life expectancy among those whose
death is triggered by higher air pollution (e.g. ozone) in the immediately preceding
days. Can we estimate how short? We can make some inferences.
“Studies of the time-related patterns of daily deaths in relation to air pollution, to help
understand the extent of mortality displacement, show that a proportion would have
died very soon anyway. One way of looking at this is to consider that they would
have died from the same episode of illness, but in the absence of higher days of air
pollution would have survived a little longer. This phenomenon, known somewhat
crudely as ‘harvesting’, applies, however, to only a proportion of the earlier deaths.
“It is reasonable to consider that, in the absence of higher air pollution days, others
would have survived that episode – e.g. recovered from a heart attack – and lived for
perhaps months or years longer, before the underlying disease was brought to a point
of crisis. Such individuals will have a major effect on the average loss of life
expectancy per case, especially where (as here) average is interpreted as arithmetic
mean. Levy et. al. (2001), speculating similarly, estimated 1 year of life lost per
premature death attributable to ozone. In the light of these opinions we consider that a
best estimate of the average loss of life expectancy amongst those affected by acute
effects of air pollution is around 1 year, and so we take this as our core estimate”. A
range of 6 months to 2 years around this central value may also be employed, though
we have not done so in the results presented here. In this study, building on the work
of Hurley et. al., we derive estimates based on 1 year of life lost per premature death
and apply values as shown above. We also report the value of a statistical life for
comparative purposes.
Tables 3-3 and 3-4 report the estimated mortality and morbidity impacts respectively,
for the UK regions.
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
Table 3-3: Regional Disaggregation of Valuation of Mortality Impacts – Summer 2003
(£m)
VOLYs (£)
Region
Heat
induced
fatalities
London
South East
South West
Eastern
East Midlands
West Midlands
Yorkshire
Humber
North West
North East
England
Wales
Scotland
Northern Ireland
UK
616
447
282
254
169
124
122
84
23
2091
49
26
0
2157
VSL (£)
£5,000
3.08
2.24
1.41
1.27
0.85
0.62
£15,000
9.24
6.71
4.23
3.81
2.54
1.86
£50,000
30.80
22.35
14.10
12.70
8.45
6.20
£1,200,000
739.20
536.40
338.40
304.80
202.80
148.80
0.61
0.42
0.12
10.46
0.25
0.13
0.00
10.79
1.83
1.26
0.35
31.37
0.74
0.39
0.00
32.36
6.10
4.20
1.15
104.55
2.45
1.30
0.00
107.85
146.40
100.80
27.60
2509.20
58.80
31.20
0.00
2588.40
Table 3-4: Regional Disaggregation of Valuation of Morbidity Impacts – Summer 2003
(£m)
Government Office
Region
No. of
excess
hospital
admissions
London
South East
South West
Eastern
East Midlands
464
0
304
94
322
West Midlands
14
Yorkshire Humber
36
North West
North East
England
Wales
Scotland
Northern Ireland
UK
Final Report
RHA Unit value
£9,12
£1,854
0.86
0.00
0.56
0.17
£5,487
2.55
0.00
1.67
0.52
0
4.23
0.00
2.77
0.86
0.60
1.77
2.94
0.03
0.08
0.13
0.07
0.48
0.09
2.76
0.00
0.23
0.06
3.06
0.20
1.43
0.27
8.18
0.00
0.69
0.18
9.05
0.33
2.37
0.46
13.60
0.00
1.15
0.30
15.05
260
50
1,491
0
126
33
1,650
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
Using the central unit values for the mortality (using VOLY) and morbidity end-points
considered here, the regional and national total health welfare costs are generated and
presented in Table 3-5. As one would expect given the pattern of cases of morbidity
and mortality, the totals for mortality dominate those for morbidity. The totals also
illustrate the fact that the mean temperatures over the period were higher in the
Southern regions, with London bearing a quarter of the total welfare cost. Clearly the
result is exacerbated by the high population density in London and South East
England. The total estimated health welfare cost for the UK of the Summer 2003
heatwave is £41.4 million.
Table 3-5: Total Health Welfare costs of Summer 2003 heatwave (£m)
Government Office
Region
London
South East
South West
Eastern
East Midlands
West Midlands
Yorkshire Humber
North West
North East
England
Wales
Scotland
Northern Ireland
UK
Morbidity
total
values
2.55
0.00
1.67
0.52
1.77
0.08
0.20
1.43
0.27
8.18
0.00
0.69
0.18
9.05
Mortality
total
values
9.24
6.71
4.23
3.81
2.54
1.86
1.83
1.26
0.35
31.37
0.74
0.39
0.00
32.36
Total
health
values
11.79
6.71
5.90
4.33
4.30
1.94
2.03
2.69
0.62
39.55
0.74
1.08
0.18
41.41
3.6 Discussion
There is significant uncertainty associated with both the quantification of the
morbidity and mortality impacts of the heatwave and the monetisation of these
impacts – particularly the valuation of premature mortality. Indeed, the Palutikof et. al.
(1997) study of the 1995 summer did not monetise the physical impacts they identified
due to the uncertainties surrounding length of life expectancy losses. Nevertheless the
results presented here represent a first indication of the scale of welfare cost associated
with such a weather event.
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
4 ENERGY SECTOR
4.1 Introduction
In this case study we analyse the impact, if any, of the Summer 2003 weather on
energy use. The results are compared with those reported by Watson and Woods
(1997) for the hot and dry summer of 1995. In the 1995 study the following three
energy sub-sectors were selected for analysis: gas, electricity and motor spirit (petrol),
since it was thought that use of these fuels were most likely to exhibit a correlation
with temperature extremes. However, in contrast to gas and electricity, motor spirit
was shown to have insignificant temperature dependence. As a result, we will not
consider it in this study, restricting the analysis to gas and electricity use.
Newspaper coverage for this sector in relation to the Summer 2003 weather event was
limited. However, one potentially significant report noted that “despite the hot
weather, British Gas has advised customers to do the unthinkable and switch on their
central heating. The company said allowing systems to remain idle during the summer
could lead to breakdowns when the weather turned cold.” (Guardian, August 6th,
2003).
4.2 Methodology
This section provides a broad overview of the approach we use to quantify and value
the impact of Summer 2003 weather on gas and electricity use in the UK.
Quantification of Impacts
Watson and Woods (1997) analysed both monthly and quarterly gas and electricity
consumption data over the period 1973-1995. Monthly data is available only for total
sales, and does not distinguish between end user (defined by broad economic sectors).
In contrast, the quarterly sales data is broken down into the following end user groups:
iron and steel, other industry, transport, domestic and ‘other’ final users. In general,
Watson and Woods (1997) found a stronger correlation between quarterly
consumption data by end user (specifically, domestic and ‘other’ final users) and
temperature than between total monthly sales and temperature. Hence, we will only
work with quarterly consumption data.
Since the temperature anomalies of Summer 2003 were experienced in late July and
early August only consumption data for the third quarter (i.e. Q3 = July, August and
September) is analysed. By contrast, Watson and Woods (1997) looked at all four
quarters during 1995. Furthermore, since the use of electricity and gas in the transport
sector is (a) negligible relative to each of the other end users and (b) exhibits little
seasonal variation over all four quarters (i.e. there is no obvious relationship with
temperature), we do not consider it further.
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
Data on quarterly gas and electricity consumption for the period 1998 Quarter 3 (Q3)
to 2005 Q3 were obtained from the DTI web-site (www.dti.gov.uk/energy/).
Consumption data covering the period 1980 Q3 to 1997 Q3 were taken from the
Monthly Digest of Statistics.
Figure 4-1 and Figure 4-2 show gas and electricity consumption by end user in Q3,
over the period 1980 to 2004, respectively. In general, the data series in both figures
exhibit strong trends – in particular for electricity use. These trends will need to be
taken into account when looking for any correlation between gas and electricity
consumption and temperature.
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
Figure 4-1: Gas Consumption by End User in Q3, 1980 to 2004 (TWh)
(a) Iron and Steel Industry
(b) Other Industry
6
40
5
35
4
30
3
25
2
20
1
0
1980
1986
1992
1998
15
1980
2004
(c) Domestic
1986
1992
1998
2004
(d) Other Final User3
40
20
35
15
30
25
10
20
15
1980
1986
1992
1998
5
1980
2004
(c) All Final Users
100
95
90
85
80
75
70
65
60
55
1980
3
1986
1992
1998
2004
Public administration, commerce and agriculture.
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1986
1992
1998
2004
PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
Figure 4-2: Electricity Consumption by End User in Q3, 1980 to 2004 (TWh)
(a) Industry4
(b) Domestic
30
25
25
20
20
15
15
1980
1986
1992
1998
10
1980
2004
(c) Other Final User5
1986
1992
1998
2004
(d) All Final Users
80
30
75
25
70
65
20
60
55
15
50
10
5
1980
45
40
1980
1986
1992
1998
1986
1992
1998
2004
2004
Data on the average monthly Central England Temperature (CET)6, covering the
period 1980 to 2004, were obtained from the Met Office. The monthly average
temperature data for June, July and August were converted to a quarterly average for
Q3 in each year.
Over the period 1961 to 1990 the average CET temperature during Q3 was 15.06 oC.
In 2003 the average CET temperature in Q3 was 2.27 oC above this longer-term
average. Only in 1976 and 1995 did the average CET temperature during Q3 differ
from the longer-term average by more than this; +2.31oC in 1995 and +2.71 oC in
1976.
4
Manufacturing, construction, energy and water supply industries.
5
Commercial premises, other service sector customers, agriculture, public lighting and combined domestic/commercial
premises.
6
Central England Temperature (CET) is representative of a roughly triangular area of the United Kingdom enclosed by
Bristol, Lancashire and London. The monthly series begins in 1659, and to date is the longest available instrumental record
of temperature in the world. Since 1974 the data have been adjusted by 1-3 tenths °C to allow for urban warming. In
November 2004 the weather station Stonyhurst replaced Ringway and revised urban warming and bias adjustments were
made to daily maximum and minimum CET data.
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
Historically, the performance of an economy plays a significant role in determining
national energy use. As a result, when analysing trends in energy use over time,
energy use is typically normalised to some measure of economic output (e.g. GDP).
However, over the last 15 years, during which the contribution of the service sector to
UK economic output has increased, variations in energy use have decreasingly become
less correlated with variations in GDP. Watson and Woods (1997) consequently
decided not to normalise energy use to GDP, but rather to work directly with energy
consumption data, and remove the influence of economic growth by making a linear
fit to the data. We do likewise.
To establish if there is any correlation between average CET in Q3 and electricity and
gas use during Q3, for each of the end users shown in Figure 4-1 and Figure 4-2 (and
for total use across all end users), the following steps are taken:
•
Plot energy consumption for Q3 over time.
•
Identify the trend by fitting a linear trend line to the time series (1980-2002,
excluding 2003).
•
Create a detrended version of the original data series by subtracting the fitted trend
data from the observed data. We refer to the detrended data points as the ‘residual’
data.
•
Calculate the correlation coefficient between the residual data series and the
average CET in Q3.
To determine the impact of the temperature anomaly experienced during Summer
2003, two possible approaches are followed:
•
Use the estimated (long-term) trend line to predict energy use for Q3 2003, and
then subtract this predicted value from actual average energy use for 2003. We
refer to the difference between the two as the energy use ‘residual’ (relative to the
long-term average). This is equivalent to the “actual effect” in Watson and Woods
(1997).
•
Create scatter plots of the estimated ‘residual’ gas and electricity use data against
average CET for Q3. Fit a linear regression line to the scatter plots (these are
shown below). Use the regression equations to directly estimate the energy use
‘residual’ for Q3 2003 (as a function of the actual average CET in Q3 2003). This
is equivalent to the “predicted effect” in Watson and Woods (1997).
For both approaches forecasting errors (lower and upper 95% confidence interval) are
calculated.
While we report all the results below, when calculating the economic impact of
Summer 2003 on energy use, we only include values where: (a) the correlation
between CET in Q3 and the ‘residual’ data exhibit a strong association and (b) the
estimated ‘anomaly’ is outside the forecasting errors of the regression lines.
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
Valuation of Impacts
The impact of the Summer 2003 temperature anomaly is valued using the price data
presented in Table 4-1. These are the prices paid by end users. Thus we are not
actually estimating welfare losses or gains associated with Summer 2003 weather, but
rather changes in the value of electricity and gas sales. Of course, the price paid for an
additional unit of electricity by a household amounts to a transfer to government
(additional tax revenue) and energy suppliers (additional sales revenue), and in
purchasing and distributing that extra unit, energy suppliers incur additional (variable)
costs. Sufficient data is not publicly available however, to allow us to estimate gross
margins for energy suppliers.
Table 4-1: UK Domestic and Industrial Electricity and Gas Prices in 2003 (current
prices)
Electricity
(pence per kWh)
Domestic
Including taxes
Excluding taxes
Industry
Including taxes
Excluding taxes
Gas
(pence per kWh)
7.09
6.69
1.85
1.76
3.35
3.12
0.87
0.81
Source: Derived from International Energy Agency publication, Energy Prices and Taxes Q2 2005, obtained from the DTI website (www.dti.gov.uk/energy/).
4.3 Results
Table 4-2 shows the correlation between ‘residual’ (as defined above) electricity and
gas sales by end user during Q3, and average Q3 CET. The correlations for domestic
electricity and gas use are substantially higher than for the other end user groups. This
is not surprising given household space heating requirements, which are strongly
temperature dependent. Given that gas is used significantly more than electricity for
space heating, we might expect the correlations for gas use to be (much) greater than
those for electricity use. This is what Watson and Woods (1997) found, particularly in
winter 1995, but also for summer 1995. However, the correlations for domestic energy
use in Table 4-2 show the reverse with domestic electricity use showing greater
temperature dependence than gas use during summer months.
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
Table 4-2: Correlation Coefficient between Mean CET and ‘Residual’ Electricity and
Gas Sales in Q3
Electricity
Gas
Iron and Steel
Industry
Domestic
Other Final User
Total Sales
*
-0.18
-0.57
-0.02
-0.36
+0.16
+0.17
-0.38
-0.22
-0.10
Note: * included in Industry category.
The scatter plots shown in Figure 4-3 and Figure 4-4 allow for further assessment of
the relationship between gas and electricity use, respectively, and average Q3 CET.
Over the period 1980-2004 the three warmest summers were 1983, 2003 and 1995.
Points for these anomalously warm years are labeled in the figures.
Looking at gas use by sector first, the relationship between mean summer CET and
sales to the iron and steel sector and other industry is very weak (the linear regression
line is virtually flat). However, for both the domestic sector and other final users, an
increase in summer temperatures from the mean trend reduces gas consumption (as
indicated by the downward sloping line as mean CET increases). Nonetheless, the
impact of the hot summers in 1995 and 2003 is not so clear; both lying very close to
the 1980-2004 mean trend. 1983, the third hottest summer over the period, lies slightly
more below the mean trend, but significantly, domestic gas use is lower still during
several other, colder summers.
Summer gas use by other final users between 1980 and 2004 is the lowest during
2003. Interestingly, gas use in this sector in 1995 and 1983 lies above the mean trend.
It is difficult to hypothesise why.
As with gas sales, the relationship between mean summer CET and electricity sales to
industry is weak. Of the individual sectors considered, only sales to the domestic
sector exhibit relatively strong temperature dependence. Domestic electricity sales
during the hot summers of 1995 and 2003 were amongst the lowest over the period
1980-2004. This does not support the view that domestic use of air-conditioning for
cooling is on the rise, but rather that when the summer weather is relatively warm
there is less demand for heating and people spend more time outdoors, pursuing
leisure activities.
Table 4-3 and Table 4-4 show the actual and predicted effects of the Summer 2003
temperature anomaly on gas and electricity sales, respectively. Recall that the actual
effect is given by ‘residual’ energy consumption (the difference between the actual use
and the long-term trend) multiplied by the relevant price. The predicted effect is given
by the ‘residual’ energy consumption (estimated from the linear regression lines in
Figure 4-3 and Figure 4-4) multiplied by the relevant price.
The results are presented in current 2003 prices. For other final users the effects are
valued using both domestic and industrial energy prices from Table 4-1, since the
price is likely to lie somewhere in between.
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
Figure 4-3: Scatter Plots of ‘Residual’ Gas Sales (TWh) in Q3 by End User against
Average Q3 CET (degrees C)
(a) Iron and Steel Industry
14
15
16
17
(b) Other Industry
14
18
15
16
17
18
10.00
2.50
8.00
2.00
95
6.00
1.50
4.00
1.00
95
2.00
83
0.50
-
-2.00
03
-0.50
-4.00
83
-1.00
-6.00
03
-1.50
-8.00
-2.00
-10.00
(c) Domestic
14
15
16
(d) Other Final User7
17
14
18
15
16
17
18
6.00
6.00
5.00
4.00
4.00
2.00
3.00
2.00
-
83
95
1.00
-2.00
-1.00
-4.00
95
-2.00
-6.00
03
-3.00
83
-8.00
-4.00
(c) All Final Users
14
15
16
17
18
20.00
15.00
10.00
5.00
95
-
83
-5.00
-10.00
03
-15.00
7
Public administration, commerce and agriculture.
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03
PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
Figure 4-4: Scatter Plots of ‘Residual’ Electricity Sales (TWh) in Q3 by End User
against Average Q3 CET (degrees C)
(a) Industry8
14
15
16
(b) Domestic
17
14
18
15
16
17
18
1.50
2.00
1.50
1.00
1.00
03
0.50
0.50
-
83
-
-0.50
-1.00
83
95
-0.50
03
-1.50
-1.00
95
-2.00
-1.50
-2.50
(c) Other Final User9
14
15
16
(d) All Final Users
17
18
14
1.50
15
16
17
18
4.00
1.00
3.00
0.50
95
-
2.00
83
1.00
-0.50
-1.00
-1.50
03
03
-1.00
-2.00
83
-3.00
8
95
-2.00
-2.50
-3.00
Manufacturing, construction, energy and water supply industries.
9
Commercial premises, other service sector customers, agriculture, public lighting and combined domestic/commercial
premises.
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
Table 4-3: Financial Impact of Hot 2003 Summer on Gas Sales, by End User
Anomaly
Actual
Predicted
Effect
Effect
(TWh)
Iron and Steel
Other Industry
Domestic
Other Final User
Domestic price
Industry price
Excluding Tax
Actual
Predicted
Effect
Effect
(£ 2003 million)
Including Tax
Actual
Predicted
Effect
Effect
(£ 2003 million)
-1.12
-2.00
-2.31
+0.31
+1.06
-1.78
-9.1
-16.2
-40.7
+2.5
+8.6
-31.4
-9.7
-17.4
-42.8
+2.7
+9.3
-33.0
-5.72
-5.72
-0.37
-0.37
-100.7
-46.3
-3.0
-6.5
-105.8
-49.8
-3.2
-6.9
Table 4-4: Financial Impact of Hot 2003 Summer on Electricity Sales, by End User
Anomaly
Actual
Predicted
Effect
Effect
(TWh)
Other Industry
Domestic
Other Final User
Domestic price
Industry price
Excluding Tax
Actual
Predicted
Effect
Effect
(£ 2003 million)
Including Tax
Actual
Predicted
Effect
Effect
(£ 2003 million)
+0.50
-0.53
-0.30
-0.54
+15.7
-35.6
-9.3
-36.5
+16.9
-37.4
-10.0
-38.4
-1.26
-1.26
+0.01
+0.01
-39.3
-85.0
-0.3
-0.7
-42.2
-89.2
-0.3
-0.7
4.4 Discussion
Before discussing the results some words of caution are warranted. With the possible
exception of domestic electricity sales, and domestic gas sales at the limit, the
correlation coefficients given in Table 4-2 are too low to draw any firm conclusions
about the impacts of summer weather anomalies on energy use. This is at least true
over the period 1980-2004; noting that Watson and Woods (1997) found much higher
correlations for the period they studied, 1972-1995. Furthermore, for both electricity
and gas use, across all sectors considered, our estimated ‘residuals’ fall within the
forecasting errors of the regression lines (defined by the lower and upper 95%
confidence intervals). As a result, it is highly questionable as to whether the estimated
temperature-effects shown in Table 4-3 and Table 4-4 represent anything more than
forecasting errors, as opposed to true anomalies.
Bearing these words of caution in mind, we only discuss the impact of Summer 2003
temperatures on energy use by the domestic sector. Domestic electricity sales in
Summer 2003 are very close to the mean trend (the data point for 2003 virtually sits
on the regression line shown in Figure 4-4). The same effect is illustrated in Table
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
4-4, where the actual effect is nearly identical to the predicted effect. Compared to an
average year, the hot weather during 2003 saved households just over £37 million in
electricity bills, while energy suppliers lost close to £36 million in revenue (but also
saved an unknown amount in variable costs). Note that these two amounts should not
be summed, since payments by households are just transfers to suppliers. Domestic
gas consumption in Summer 2003 is slightly lower the mean trend (as shown in
Figure 4-3), and this effect is demonstrated in Table 4-3, where the actual reduction in
gas use is slightly greater than the predicted reduction. The temperature anomaly in
Summer 2003 is estimated to have saved households close to £43 million in gas bills,
relative to an average year. At the same time, suppliers of gas lost just under £41
million in revenue. The total savings in domestic energy bills (i.e. the financial
benefit to households) in the UK due to the hot summer weather in 2003, compared
with an average year, is thus about £80 million.
For the purpose of comparison, Watson and Woods estimated that, relative to an
average year, the actual effect of the hot summer of 1995 resulted in savings of about
£74 million for domestic users of gas. However, the domestic sector spent an
additional £34 million on electricity, compared with an average year. Thus, the net
effect estimated by Watson and Woods is a saving of £40 million.
While the temperature anomaly during summer 1995 is slightly greater than that
experienced in Summer 2003, the larger saving in household gas bills in 1995 is
probably due to differences in the reference point. The average year (based on mean
Q3 CET), as measured over 1972-1995, is colder than the average year, as measured
over 1980-2004. As a result, the mean Q3 CET in 1995 is 1.8 oC above average year
(1972-1995), whereas the mean Q3 CET in 2003 is only 1.5 oC above average year
(1980-2004).
The difference in the estimated impact on household electricity use between this study
and that of Watson and Woods cannot be explained by differences in the reference
point. The most likely explanation is that the 1995 consumption figure used by
Watson and Woods was provisional (they used published data from 1996). According
to the data we obtained from the DTI, domestic electricity use in 1995 is actually
lower than that in 1994 and 1996, and in fact is the lowest data point relative to the
mean trend (recall Figure 4-4).
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
5 AGRICULTURE
5.1.1 Introduction
In this case study we analyse the possible impact of the Summer 2003 weather on
agricultural output in the UK. The results are compared with those reported by Subak
(1997) for the hot and dry summer of 1995, although comparisons are limited due to
differences in the approach used to both quantify and value possible impacts.
Newspaper coverage of agriculture and the Summer 2003 hot weather event focused
primarily on the negative impact of the hot and dry conditions on European crop
production:
“France is expected to lose more than 20% of its grain harvests. Italy is expected to
lose 13% of its wheat, and Britain 12%. Across the EU as a whole, wheat production
is down 10 million tonnes, or about 10%.” (Guardian, 11 September, 2003).
This lost output, plus lost livestock, “translated into economic losses in Europe of £7
billion, according to the European insurance industry” (Guardian, December 11th,
2003). Positive effects on the UK fruit and viticulture industries were, however, noted;
for example “The scorching weather has produced the ideal growing conditions for
green and red Discovery Apples” (The Telegraph, 5th August, 2003). The wine
industry also expected to gain, with reports that consumers should “expect the finest
English wine ever, vineyard owners said yesterday”, (The Telegraph, 6th August,
2003).
5.2 Methodology
This section provides a broad overview of the approach we use to quantify and value
the impact of Summer 2003 weather on UK agriculture.
Quantification of Impacts
We use a similar approach as that employed by Subak (1997) for the summer of 1995,
in which annual UK yields for a number of crop and livestock categories in 2003 are
compared with predicted yields. We have chosen to examine a large number of
agricultural products in the first instance, as opposed to focusing on a few of the more
(economically) important products.
Losses (deficits) and gains (surpluses) are estimated as the difference between actual
annual yields in 2003 and annual yields as predicted by linear regression equations (up
to 2002). That is:
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
Yield deficit (or surplus) equals actual yield in 2003 less Equation 1
predicted yield in 2003 (from the regression equation).
Specifically, the following steps are taken:
•
Plot relevant annual agricultural output statistic over time.
•
Identify the trend by fitting a linear trend line to the time series (excluding 200304).
•
Create a detrended version of the original data series by subtracting the fitted trend
data from the observed data. (We refer to the detrended data points as the
‘residual’ data.)
•
Regress the estimated ‘residual’ data on average CET and total precipitation in Q3
(up to 2002), and use the estimated equations to directly estimate the ‘residual’ for
Q3 2003 (as a function of the actual average CET and total precipitation in Q3
2003)10.
For most arable crops and livestock populations we work with time series data
covering 1984-2002. Time-series data for potatoes, milk and hen eggs are longer
(1973-2002). The complete time series for the period 1984-2004 for those products
considered in this case study are provided at Annex 5A. The data was obtained from
the UK ONS.
To establish if there is any correlation between either mean Q3 CET or total
precipitation in Q3, and output for each of the products listed in Annex 5A (see page
41), we calculate Pearson correlation coefficients. We also derive forecasting errors
(lower and upper 95% confidence interval) for the fitted linear-trend lines.
If the estimated surplus or deficit for each product is attributable in part to weather
conditions during Summer 2003, then it is reasonable to expect that: (a) the correlation
between mean CET or total precipitation and the ‘residual’ data exhibit a strong
association and (b) the estimated deficit or surplus to be outside the forecasting errors
of the regression lines.
Valuation of Impacts
The financial impacts of the Summer 2003 climate anomaly on agriculture
(specifically, farms, since indirect effects upstream and downstream of the farm ‘gate’
are not considered) can be calculated using the accounting conventions of fixed costs,
gross and net margins, expressed either per hectare, per head or per unit of output (e.g.
10
Subak, in contrast, compared actual yields / output in 1995 with those predicted by the estimated linear trend line.
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
tonne of wheat)11. Obviously, how these monetary measures of farm output are
determined is a critical building block of the costing process.
Gross margins measure the value of output, including direct subsidies, less variable
cost, such as seeds and fertiliser in the case of crops. Variable costs are directly related
to each unit of output, and can be avoided if the activity that generates the output is
not undertaken. Gross margins show the financial loss to a farm business if one less
unit of an activity is pursued, ceteris paribus. That is, assuming other so-called ‘fixed
costs’, such as labour, machinery, buildings and land remain unchanged.
When determining the impact of the Summer 2003 climate anomaly on farms it is
therefore important to establish whether the event will impact on the gross margin
only, or whether it will also affect these ‘fixed costs’. Identification of the most
appropriate measure of unit cost to use is made even complicated, however, since the
standard definition of gross margin ignores a number of costs which behave more like
the variable costs used in the definition of the gross margin, as opposed to pure ‘fixed
costs’. These so-called ‘semi-fixed costs’ include: direct labour (e.g. labour for
milking and some harvesting), the use of contractors, machinery operating costs and
some livestock sheltering costs. By contrast, pure ‘fixed costs’ reflect overall average
labour, machinery and buildings costs per unit of activity, and include depreciation of
machinery and buildings.
Thus, there are three potential indicators of the value of farm output, and thus the
value-added at risk to climate change: (1) gross margin (2) gross margin less semifixed costs and (3) gross margin less total fixed costs. Which of these provides the best
indicator of forgone value-added – as indicated above – depends on the magnitude and
permanence of the impact being assessed.
Gross margin provides the largest estimate of financial cost, and is most relevant
where it can be reasonably assumed that labour, machinery, and building costs remain
unaffected by the climate event. The use of gross margin is thus appropriate where
there is a one-off, non-recurring impact, such as a temporary reduction in yield. In
cases where there is a permanent, but still marginal impact, such as that associated
with a change in stocking numbers, it is likely that the use of labour and machinery,
and possibly buildings will change. In these cases, gross margin adjusted for semifixed costs, as opposed to simply gross margin, provides a ‘better’ estimate of the
financial cost of the weather-related impact. Where there are permanent and nonmarginal impacts, involving changes in the cropping-livestock mix and/or intensity,
which will affect the whole farm business, the financial cost of the change is best
captured by gross margin adjusted for total fixed costs. Indeed, in these circumstances,
the total cost of the change is best modelled using the “Total Farm Budget” model –
see pages 4-15 to 4-19 of Metroeconomica (2004).
Given that the impacts of Summer 2003 are most likely to involve a one-off, nonrecurring change in yield, we value the impacts using gross margin. For the reasons
11
In the case of livestock, gross margins per head are first estimated, and then converted to gross margins per hectare
according to the typical number of stock per hectare.
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
given in Subak (1997), it is necessary to use a baseline measure of value that is not
responsive to weather. The baseline gross margin is assumed to prevail under recent
relevant pricing conditions, but not including the Summer 2003 event. For this study,
we use the average gross margins per product over the period 1999-2002, which are
shown in Table 5-1.
The estimated financial impact for each crop considered is given by:
Financial impact (£) due to 2003 summer weather equals the Equation 2
estimated yield deficit (or surplus) (tonnes per ha) times the area
cropped in 2003 (ha) times the gross margin (£ per ha).
The estimated financial impact for each livestock population considered is given by:
Financial impact (£) due to 2003 summer weather equals the Equation 3
yield deficit (or surplus) (tonnes dressed carcass weights) times
the unadjusted gross margin (£ per head) divided by the average
dressed carcass weight per head.
Similar algorithms are used for milk and hen eggs (not shown). For open and protected
vegetables the financial and economic impact is estimated directly from the regression
equations, since the dependent variable is already in monetary units (“value of
production”).
Table 5-1: Average Gross Margins for Selected Agricultural Products in UK (1999-2002)
(2003 prices)
Wheat
Barley
Oats
GM = £65 per tonne
GM = £70 per tonne
GM = £75 per tonne
Oilseed Rape
Linseed
Sugar Beet
GM = £135 per tonne
GM = £210 per tonne
GM = £20 per tonne
Peas for Harvesting
Field Beans
Potatoes
GM = £100 per tonne
GM = £120 per tonne
GM = £30 per tonne
Cattle
Sheep
Pigs
GM = £115 per head
GM = £30 per head
GM = £20 per head
Poultry and Table Fowl
Milk
Hen Eggs
GM = £0.60 per bird
GM = £0.10 per litre
GM = £0.30 per dozen
Source: Nix, J., Farm Management Pocketbook, 29th – 32nd Edition.
Agriculture receives substantial subsidies. These need to be deducted from farm
income to obtain a more reliable estimate of the real contribution that climate-induced
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
changes in farm output make to the national economy. One way to derive economic,
as opposed to financial, impacts is to remove the various Exchequer payments (net of
any refunds from EU) using adjustment factors like those provide in Table 5-2. We
use these factors to convert our estimates of the financial impacts of Summer 2003 on
farm output to economic values.
Table 5-2: Economic Adjustment Factors for Temporary, One-off Changes in
Agricultural Output
Agricultural Product
Adjustment Factor
Cereals
22
Oilseeds
29
Peas and Beans
35
Other crops
22
Beef
33
Dairy
22
Sheep
12
Source: Based on MAFF (1999) FCDPAG3
Notes: High value horticultural crops, field vegetables, potatoes and commodities subject to quotas, such as milk and sugar beet,
are treated the same as cereals.
Limitations of Approach
Given the regional variations in climatic conditions exhibited, basing our analysis on
UK-level yield data will mask possibly significant regional differences in impacts.
However, collecting and analysing regional agricultural datasets, which would help
better control for non-climatic influences on yield and productivity, is beyond the
resources of this case study. It is thus assumed that any estimated deficits or surpluses
in yield in 2003 are solely attributable to climatic factors, except for the underlying
linear trend, which is assumed to be caused by changes in operational practice on the
farm (e.g. use of additional inputs).
This is not to say, however, that the weather conditions during the summer are the sole
cause of the estimated deficits or surpluses. Temperature, rainfall and sunshine during
spring 2003 and the preceding autumn and winter will also affect yield and output in
2003. We return to this below.
It is beyond the scope of this case study to comment separately on the impacts of
Summer 2003 weather conditions on the presence of pests and diseases, and what
affect they had on yield. These affects are assumed to be captured within our estimate
of the deficit / surplus for each product considered. The response of farmers (‘on-farm
decisions’) to the weather conditions, in terms of adjusting inputs (e.g. additional use
of pesticides or irrigation, increased drying, etc.), is also not considered. As a result,
any additional cost incurred by farmers in taking adaptive measures is omitted from
the analysis. It is also beyond the scope of this case study to capture the impact of the
hot, dry conditions on product quality.
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
5.3 Results
Table 5-3 shows the correlation coefficients between ‘residual’ yields / output (as
defined above) and mean CET and total precipitation in Q3. Of the 17 products
considered, only peas for harvesting, open vegetables and pigs exhibit even a
moderate (absolute value) correlation between ‘residual’ yields and both mean Q3
CET and total Q3 precipitation. For oilseed, linseed, oats, and poultry and table fowl
the correlation coefficients are very weak, indicating little dependence between yields
/ output and both mean CET or total precipitation in Q3 for the products considered.
The coefficients for most of the products show a positive / negative or negative /
positive pattern with respect to Q3 CET and total Q3 precipitation. This could lead to
reinforcing temperature and precipitation effects on yield / output, given above
average temperature and below average levels of total precipitation in Summer 2003.
For example, we would expect the yield / output of wheat, barley, harvesting peas,
vegetables, cattle and poultry and table fowl to be above the long-term average;
whereas, we would expect the yield / output of field beans, sugar beet, oilseed, pigs,
milk and hen eggs to be below the long-term average. Temperature and precipitation
effects on yield / output in Summer 2003 would have worked against each other for
products with correlation coefficients that exhibit a positive / positive or negative /
negative pattern (potatoes, linseed, oats and sheep).
Table 5-3: Pearson Correlation Coefficient (r) between Q3 Mean CET and Precipitation
and ‘Residual’ Annual Agricultural Output
Mean CET
Total
Precipitation
Wheat
Barley
Oats
Sugar Beet
Peas for Harvesting
Field Beans
Oilseed
Linseed
Potatoes
Open Vegetables
Protected Vegetables
Cattle
Sheep
Pigs
Poultry and Table Fowl
Milk
Hen Eggs
+0.3
+0.3
-neg
-neg
+0.6
-0.3
-neg
+neg
+0.3
+0.5
+0.2
+neg
-0.3
-0.5
+neg
-0.2
-0.3
-0.6
-0.3
-neg
+0.2
-0.4
+0.4
+neg
+neg
+neg
-0.5
-0.4
-0.2
-neg
+0.6
-neg
+0.4
+0.2
Notes: rounded to the nearest 0.1; “neg” = less than 0.2 (absolute value).
For the crops considered, open vegetables show the largest impact (in absolute terms),
with predicted financial gains in excess of £70 million compared with the long-term
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
average. Even though wheat yields are only estimated to be 0.25 tonnes per ha up
relative to the long-term average, the financial impact is relatively high (plus £30
million) as a result of the extensive area used for wheat production in the UK during
2003. Barley yields are also up by a similar amount (plus 0.24 tonnes per ha), with
corresponding financial gains equal to about £18 million. Of the other crops
considered, protected vegetables also benefited from the weather conditions in
Summer 2003, although the gains are small. Sugar beets and field beans all show
small losses; the impact on oats, oilseeds, potatoes, harvesting peas and linseeds is
negligible.
Of the livestock and related products considered, none exhibited a noticeable positive
impact, compared with the long-term average. The predicted weight of both home-fed
sheep and pigs is below the long-term average, resulting in financial losses of roughly
£22 and £6 million, respectively. Predicted milk yields are close to 50 litres per cow
below the long-term average; the value of lost output is about £10 million. The
predicted impact of Summer 2003 weather on cattle, poultry and table fowl and hen
eggs is negligible.
In aggregate, across those products considered, we estimate that, relative to the longterm average, the value of agricultural ‘deficits’ and ‘surpluses’ (as defined above) in
2003 is about negative £2 million (for those products valued on the basis of gross
margin) and plus £82 million (for those products valued on the basis of price). We use
the term ‘sub-total’, since only a sub-set of all agricultural products are included in
analysis. Given the different valuation bases, the two figures should not be summed.
Table 5-4: Estimated Financial and Economic Impact of 2003 Summer Weather on UK
Agricultural Output in 2003 (£ million, 2003 prices, based on gross margin)
Financial Impact Economic Impact
Wheat
*
Barley
Oats
Sugar Beet
Peas for Harvesting
*
Potatoes
*
Field Beans
Oilseed
Linseed
***
Open Vegetables
**
Protected Vegetables
Cattle
Sheep
**
Pigs
Poultry and Table Fowl
Milk
**
Hen Eggs
Sub-total (gross margin)
Sub-total (price)
+30
+18
neg
-7
neg
neg
-5
neg
neg
+71
+11
neg
-22
-6
neg
-10
neg
-2
+82
+23
+14
neg
-5
neg
neg
-4
neg
neg
+56
+8
neg
-17
-5
neg
-8
neg
-2
+64
Notes: Open and protected vegetables are valued on the basis of price, whereas all other products are valued on the basis of gross
margin. “Neg” = less than 1 (absolute value). * = significant at 10% level; ** = significant at 5% level. *** = significant at 2.5%
level.
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
5.4 Discussion
As expected, wheat and barley show a yield surplus. Subak also found yield surpluses
for these crops in 1995. High temperatures and sunshine during the ‘bulking’ period
means that potatoes struggle to grow properly. Subak estimated yield deficits for
potatoes, but we find no noticeable effect. Brassicas are adversely affected by heat and
water stress, as are root vegetables (roots are shorter than normal and smaller in
diameter). With the exception of tomatoes and cucumbers, which exhibited gains,
Subak found losses for the other individual vegetables considered (overall, net losses
were estimated at about £20 million in 1995). In contrast, we find significant financial
gains, relative to the long-term average. There are three possible explanations for the
contrasting results. First, the approaches followed are different. Subak compared
actual annual yields with predicted yields based on the long-term linear trend, whereas
we directly predict yield deficits or surpluses as a function of climate variables.
Second, in contrast to the other products we considered, the gains for vegetables are
valued on the basis of price as opposed to gross margin, due to data limitations. The
real value (as discussed above) of our predicted gains will thus be overstated by 6080%, depending on the products’ profit margins. Finally, Subak also considered
changes in variable cost as farmers responded to 1995 weather conditions. In some
cases, increases in variable cost were sufficient to offset gains in the value of output,
thus resulting in net financial losses, despite estimated increases in yield.
We estimated financial losses for sugar beets and field beans in 2003 relative to the
long-term average. Subak, likewise, estimated losses for sugar beets in 1995, but did
not consider field beans.
By May or June, most dairy and beef farms hope to rely almost entirely on grass for
forage. However, hot and dry weather impairs grass growth, thus necessitating the use
of supplemental feed (increasing costs). Nonetheless, grazing cows and beef cattle will
tend to suffer reduced forage intakes, which will impact their health and production.
Excess heat has also been established as having additional direct effects on fertility
and milk production. The predicted impact of Summer 2003 on beef cattle is
negligible; as expected, milk production shows a moderate decline. Impacts of
summer 1995 weather on milk production and cattle populations were not explored by
Subak.
It is generally accepted that temperature has a major influence on the productivity of
pigs, by influencing their rate and efficiency of absorbing nutrition. We show a small
decrease in pig production for 2003 (down 24 kt dwc relative to long-term average).
Subak found that pig populations in 1995 were about 1% below predicted levels.
Growth rates in poultry and table fowl are reduced by heat stress, and mortality rates
are considerably higher. Subak estimated poultry populations to be between 2-4%
lower than predicted. However, we find no noticeable impact of Summer 2003 on
predicted bird numbers or hen egg production.
With the possible exception of harvesting peas, open vegetables and pigs, the
correlation coefficients given in Table 4-2 are too low to draw any firm conclusions
about the impacts of summer weather anomalies on yield surpluses or deficits.
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
Furthermore, for all the agricultural products considered, our estimated ‘residuals’ fall
within the forecasting errors of the regression equations (defined by the lower and
upper 95% confidence intervals), with the exception of pigs and milk. Overall, while
we provide estimates of the financial and economic impact of yield surpluses and
deficits in 2003, it is not possible to conclude with any confidence that these gains /
losses are wholly attributable to the weather conditions that prevailed in the
summer of 2003. The only product were the estimated surplus or deficit is likely to be
attributable to weather conditions during Summer 2003 is pigs12.
12
The correlation between both mean CET and total precipitation and the ‘residual’ data exhibit a moderate association
and (b) the estimated deficit is outside the forecasting errors of the regression equation. Moreover, the estimated
coefficients are significant at the 5% level.
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
5.5 Annex 5A: UK Production and Yields (1984-2004)
(b) Barley
6
1,900
5
1,800
4
3
1,700
2
1,600
Area ( 000 ha )
2,000
Yield ( t per ha )
7
Area ( 000 ha )
2,100
7
1,900
6
8
5
1,700
4
1,500
3
1,300
2
1,100
1
900
0
2004
1
1,500
1984
1994
0
2004
1984
(d) Oilseed
1994
7
145
6
135
5
125
4
115
3
105
2
95
1
85
1984
(e) Linseed
580
4
Yield ( t per ha )
9
2,100
Area ( 000 ha )
2,200
(c) Oats
Yield ( t per ha )
(a) Wheat
0
2004
1994
(f) Sugar Beet
250
3
530
215
70
205
60
195
50
185
40
175
30
165
20
155
10
1
Yield ( t per ha )
1
100
Area ( 000 ha )
330
2
150
Yield ( t per ha )
2
380
Area ( 000 ha )
430
Yield ( t per ha )
Area ( 000 ha )
200
3
480
50
280
1994
0
1984
(g) Peas for Harvesting
(h) Field Beans
5
110
210
2
60
50
140
3
120
100
2
80
60
1
40
40
Area ( 000 ha )
70
0
2004
20
1984
800
190
700
180
600
170
500
160
400
150
140
300
130
200
120
100
110
100
1984
Final Report
1994
0
2004
25
170
20
15
160
1994
0
2004
5
140
1984
(k) Protected Vegetables
4
Value of Output ( £ mn )
200
30
10
400
350
3
300
250
2
200
150
1
100
50
0
1984
1994
- 41 -
0
2004
1994
0
2004
(l) Cattle
1200
14,000
Home Fed Marketing ( 000 )
(j) Open Vegetables
35
180
150
Value of Output ( £ mn )
1994
Area ( 000 ha )
1984
190
1
40
30
45
4
Yield ( t per ha )
3
50
200
160
Area ( 000 ha )
80
Yield ( t per ha )
4
Area ( 000 ha )
5
180
90
0
2004
1994
(i) Potatoes
200
100
Area ( 000 ha )
1994
145
1984
0
2004
Yield ( t per ha )
1984
0
2004
1000
13,000
800
12,000
600
11,000
400
10,000
200
9,000
1984
1994
0
2004
Home Fed Prod. ( 000 t dwc )
230
PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
250
16,000
200
150
14,000
100
12,000
50
10,000
1984
1994
0
2004
1000
15,000
13,000
600
12,000
10,000
200
9,000
8,000
1984
1,600
1,400
1,200
1,000
800
600
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2004
0
2004
1994
850
800
750
700
650
600
1994
- 42 -
7000
3,200
6000
3,000
5000
2,800
4000
2,600
3000
2,400
2000
2,200
1000
1984
900
1984
3,400
2,000
(q) Hen Eggs
Production for Human Consumption
( 000 )
Production ( 000 t dwc )
1,800
1994
400
11,000
(p) Poultry and Table Fowl
1984
800
14,000
Dairy Herd ( 000 )
18,000
1200
16,000
Home Fed Prod. ( 000 t dwc )
300
Home Fed Marketing ( 000 )
350
20,000
Home Fed Prod. ( 000 t dwc )
400
22,000
(o) Milk
17,000
450
24,000
Home Fed Marketing ( 000 )
(n) Pigs
2004
1994
0
2004
Yield per Cow ( litres / year )
(m) Sheep
PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
6 RETAILING
6.1 Introduction
Climate variation may have significant impacts on retail sales. Changes in weather may result
in changes in consumer behaviour, either in terms of frequency of shopping trips or in terms
of the goods consumed. Table 6-1 presents a number of possible linkages between climate
variation and the retail sector.
One impact that is associated with retailing is the effect on individual product lines whose
consumption is likely to be closely associated with temperature. For example, Palutikof et al
(1997) found an impact of the 1995 heatwave on the retailing sector – particularly clothing
and footwear sectors. More generally, there may be an impact on the retailing sector of the
economy as a result of households changing their typical purchasing schedules, and perhaps
delaying or forsaking expenditures in favour of spending time on outdoor recreation pursuits.
In order to investigate these effects in more detail for Summer 2003 we use two distinct
methods. First, we use a top-down, statistics-driven approach as in Palutikof et al (1997),
with regression analysis of selected sectoral retailing statistics and climatic data. Second, we
review case study material relating to retail sector stakeholders, gathered from newspaper
reports. A survey of large retailers was conducted, but no results were obtained – possibly
due to stakeholder fatigue.
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
Table 6-1 Possible linkages between the climate and the retail sector
Climate Variable
Increased Mean Temperature in Summer
Increased Mean Temperature in Winter
Increased Precipitation in Winter
Reduced precipitation in summer
Increased Sun Days
Increased Flooding
Increase in "Extreme Events"
Non-UK Climate Change
Impact
Change in commodities consumed
Reduced productivity of workforce
Change in price and quantity supplied of some
commodities (e.g. fruit/veg)
Changes in costs (e.g. increased energy demand for
cooling)
Change in commodities consumed
Reduced sickness in workforce
Change in price and quantity supplied of some
commodities
Changes in costs (e.g. reduced heating)
Reduced frequency of shopping trips
Change in consumption for some goods (e.g. raincoats,
umbrellas)
Changes in price and quantity o agricultural
commodities
Increased frequency of shopping trips
Change in consumption for some goods (e.g. DIY)
Increased frequency of shopping trips
Change in consuption for some goods
Direct impact on sales through closure of shops
Impact on transport of commodities
Impacts on stocks held - consequential costs for
retailers
Dramatic changes in demand for certain goods
Potential impacts on retailers' purchasing in following
years leading to oversupply (cob-web type effect)
Impacts on demand for UK traded goods
Impacts on supplies for imported goods
Increases in costs (e.g. transport)
6.2 Top-down evidence
Review of previous studies
Previous studies on the influence of climatic factors on retail sales have found a range of
impacts of climate variation on retail. For the UK, Palutikof et. al. (1997) found an impact of
the 1995 heatwave on the retailing sector – particularly clothing and footwear sectors. They
used regression analysis of sectoral retailing statistics and climatic data for monthly analysis
of impacts. In a study for the US, Jorgensen et. al. (2004) used an input-output modeling
approach to estimate impacts on different sectors. This highlights the impacts that climate
change may have in terms of costs of inputs. Starr (2000) used monthly data on retail sales
and weather to estimate linkages. She finds unusual weather has a modest but significant
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effect on monthly sales, but “lagged effects often offset original effects, so that weather’s
influence tends to wash out at a quarterly frequency”.
In addition to the above, UK regional impacts studies have highlighted the impact on retail
sector as a potential impact but little quantification has been done.
Methodology and Data
Data on monthly retail sales indices is available for the UK in the Monthly Digest of
Statistics. This provided a timeseries going back to 1986. A longer time series does exist for
the UK, but for the purposes of this study it was felt that this would be sufficient to show
current impacts of climate on sales, given changing consumption habits. Regression analysis
was carried out on the determination of the value of sales for 3 main sectors: textiles,
clothing and footwear (EARA), household goods (EARB) and predominantly food stores
(EAQW). Monthly data on climatic conditions were found from the Met Office. Data on
disposable income on a quarterly basis was obtained from the ONS.
Results
The results for the impact of climate variation on the retail sector in the UK are shown in
Table 6-2 below.
For textiles, clothing and footwear, a number of lag effects were tested but found to be
insignificant. The results show that an increase in temperature leads to a reduction in sales of
these goods, with low levels of rainfall reducing sales and high levels increasing them.
Sunshine has a positive impact. The impact of disposable income is found to be insignificant,
perhaps showing the lack of variation in the dataset.
Sales of household goods are also affected by climate variation, is shown in the second
column of Table 6-2. This shows that there is a non-linear relationship between sunshine and
sales of household goods, and that rainfall increases sales of household goods, both in the
present month and in the following month. Temperature was found to be insignificant and so
was excluded. Disposable income again is shown to be insignificant, which may reflect the
quality of the data. Under standard assumptions of the consumption function, one would
expect a positive relationship between disposable income and the level of sales for household
goods.
Sales of food are also impacted by weather changes. Rainfall was found to be insignificant,
but temperature was found to have a negative impact and sunshine was found to have a
nonlinear impact, with a negative impact at lower levels and a positive impact at higher
levels. Again disposable income is found to be insignificant.
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
Table 6-2: Impact of Summer Climate Variation on Sales of Textiles, Clothing and Footwear
Dependent
Variables
Code
Regressors
Constant
CET
EWR
EWRSQ
EWR(-1)
SUN
SUNSQ
DISPINC
R-square
DW
Sales of Textiles,
Clothing and
Footwear
EARA
Sales of
Predominantly
Food Stores
EAQW
Sales of
Household
Goods
EARB
161.345 **
-0.088642 **
-0.019175 *
1.23E-05 *
130.1332 ***
172.7135 **
-0.015801
0.0078088 ***
0.0062059 **
0.024702 ***
-7.64E-05 ***
-0.0032796
0.0078794 ***
0.0038864
0.9937
1.998
0.9957
1.9475
-0.0046973 *
2.18E-05 **
0.0013997
0.99938
1.9927
Estimating the Impact of Summer 2003
The impact of Summer 2003 on sales of the three sectors examined above can be identified by
using the values of temperature, sunshine and rainfall in 2003 and feeding these into models
based on the parameters identified. This can be compared to the sales index that may have
been anticipated if the conditions of the 1961-90 average had prevailed. The results of this are
presented in Table 6-4 below.
It can be seen from the table that the impact of the climate conditions in Summer 2003 was
particularly acute in the household goods sector, with negative impacts in August and
September. For textiles the impacts are broadly positive, whilst for mainly food retailers the
impacts were not as significant as for the others.
Table 6-3: Impacts on Index Values of Summer 2003 climate anomaly (Index value 2000=100)
Sector
Textiles,
clothes and
footwear
Household
Goods
Mainly food
Final Report
Estimate
2003 model
1961-90 model
Difference
2003 model
1961-90 model
Difference
2003 model
1961-90 model
Difference
July
164.557
164.764
-0.207
129.710
129.567
0.144
173.738
173.790
-0.052
Aug
Sep
165.116
164.923
164.704
164.593
0.412
0.331
129.157
129.107
129.751
129.347
-0.594
-0.241
173.855
173.772
173.762
173.720
0.092
0.052
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
To estimate the value of these impacts, it is necessary to enter values into the indices
identified above. These show the differential impact by sector of between -£16.1 million
to £12.4 million depending on the sector. The total estimated was £3.2 million for this
selection of sectors.
Table 6-4: Value of impacts of Summer 2003 climate anomaly (£mn)
Sector
Textiles,
clothes and
footwear
Household
goods
Mainly food
July
August
September
Total
-4.9
9.8
7.6
12.4
3.4
-4.0
-14.0
7.0
-5.5
3.8
-16.1
6.9
6.3 Bottom-up evidence
We reviewed the newspaper reports on the impact of the Summer 2003 hot weather event on
retailers. The following paragraphs summarise the available evidence.
Beer sales
Newspaper reports at the time of the heatwave related to predicted patterns of demand. For
example, by August 9th, share dealers had “figured that the ongoing heatwave should lead to
record sales of beer, wine and spirits. Scottish & Newcastle, the Fosters and John Smiths
company, frothed up 10.5 to 385.5p”. (Telegraph, 9th August 2003).
Food and Barbecues
On August 5th, the Guardian reported that Tesco “predicts a 100% increase in ice cream
sales this week… ‘There will be 500 lorries on the road this week transporting ice cream
alone’…. Over the weekend Tesco shifted 500,000 punnets of British strawberries and 11m
bags of ready-washed salad. Sales of Pimms were three times higher than at this time last
year. Sainsburys has doubled its ice cream sales, shifting 400,000 Mars ice creams last week,
and ordering in extra stocks. B&Q, the UK’s largest DIY and garden store, reported a
“significant” leap in sales of barbeques, paddling pools, gazebos and garden swings”
Anticipatory Adaptation
More formal evidence of “anticipatory adaptation” came from a newspaper report on August
8th, 2003 (Guardian) which quoted Chris Carden of Asda, thanking the retail trade’s private
network of weather forecasters. “‘It’s a few months back that we got advice about a very
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
warm spell being likely towards the end of the summer’, he said. Orders went out promptly
for barbecue grills and bulk-buy preparations were made for every accessory from Iceberg
lettuce to suntan cream….As a result the Leeds-based retailers have coped this week with
sale increases of 365% for home barbecues, 250% for suntan cream, 80% and growing for
beer, and 62% for paddling pools.
“The heatwave and its habits have tested supermarket predictors to the limits, stores putting
some instant-reaction systems for consistently hot weather in action for the first time…. Staff
were given a temporary summer restaurant in Asda’s store at Ashton-under-Lyme, after
scores volunteered to work in the refrigerated section because of the cool. Tables and chairs
have been moved into the shop’s walk-in fridge so staff can have meals in the cool…. The
hot weather has also seen hefty losers on supermarket shelves, notably the self-tanning
solution… umbrellas… and bubblebath liquid.” “Dixons and Currys were baffled to
discover that they had sold 100 electric blankets.” Guardian, August 7th 2003.
Overall Retail Sales
Later in the year, it was possible to identify how the heat-wave had affected retail sales in
more quantitative terms, as retailers posted their quarterly and half-year results. For example,
the Guardian, September 18, 2003 reported on the experience of Morrison’s supermarket.
“Morrison’s, the UK’s fifth largest food retailer…said the heatwave in July and August
boosted sales in recent weeks, with like-for-like sales up by 9.6% in the first five weeks of
the second half. ‘We have enjoyed good trading in the exceptional summer weather. People
have been encouraged to get out and about and our in-store cafes and petrol filling stations
have benefited particularly.’ Morrison’s said.” For other companies, the weather had a
detrimental effect. The Guardian (17th September) reports that “Kingfisher suffered a slowdown in summer sales because of the heatwave.” A negative effect was also felt by the M&S
food business, (Guardian, October 8th, 2003) though sales at Dixons, Britain’s biggest
consumer electronics retailer, held up despite the summer heatwave (Guardian, September
10th, 2003). This falls broadly in line with our quantitative estimates above.
These findings were supplemented by a questionnaire – Annex 6A to this sectoral report (see
page 50) - that was submitted to a group of large retailers. No useful data was collected,
perhaps due to stakeholder fatigue.
6.4 Conclusions
Overall, the impacts of the Summer 2003 on retailing were marginal at best. Though some
sectors may have experienced positive increases in sales (e.g. beer, textiles and food), other
sectors had negative impacts – such as the household goods sector. As in the 1995 study, the
retail impacts are rather small compared to other impacts.
For three selected sectors, the total estimated gain was £3.2 million for this selection of
sectors. However, the distribution across sectors is interesting with household goods fairing
worst, with an estimated loss of £16.1 million in sales for the period in question.
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Further research is needed in this area, particularly in terms of evaluating the role that
adaptation for the retail sector may have.
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
6.5 Retailing: Annex 6A
Questionnaire relating to the Costs and Benefits to Retailers of Unusually Warm Summers and other
Weather Extremes
This questionnaire forms part of work to cost the impacts of climate change for the UK
Department for Environment, Food and Rural Affairs. We are seeking to investigate the
impact that changes in weather have on business practice and sales.
In general terms we are looking to establish the importance of weather
patterns in determining what your business sells, and when.
If you are able to provide any quantitative evidence to support your answers we would hope to
make estimates of the costs or benefits of weather extremes.
We would be very grateful for any information you could give in relation to the following
series of questions. Also, if there is anything you would like to say on this topic that has not
been addressed in these questions, please do so below.
Q1. Does your business use weather forecasting information to help your stocking planning?
Q2. Sales of which product types are most susceptible to
a)
b)
c)
hot weather extremes;
cold weather extremes;
other weather extremes?
Q3. Are your product stocking patterns influenced by the length of periods of warm weather?
Q4. Are planned stocking patterns calibrated in any quantitative way against temperature
levels or precipitation amounts? If so, would you be able to supply these to us?
Q5. Do weather temperature extremes have any consequences for the transport and storage
aspects of your business? What are these?
Q6. The Summer of 2003 was notable primarily for an extremely hot period at the end of July
and in early August, in which a new record maximum temperature for the UK was recorded.
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
Did this weather event have any consequences for your business additional to those reflected
in the questions above?
Q7. If there were consequences for your business resulting from the Summer 2003 hot
weather, were there any “lessons learnt” in the event of a repeat of this type of weather event?
Q8. What, if any, were the specific costs or benefits to your business of the Summer 2003
period of hot weather? On balance, was it a good or bad thing for your business?
Q9. Do you have any other comments on the impact of extreme weather events on your
business?
Many thanks for your time. Please return this questionnaire by email to
ahunt@metroeconomica.com, or by fax to 01225 461678
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
7 TRANSPORT
7.1 Introduction
The UKCIP has identified transport as one of the main sectors likely to be affected by
future climate change (McKenzie Hedger et al, 2000). The exceptionally warm weather
in 1994–1995 had major impacts on the transport infrastructure and provision of
transport services. The findings of the impact of the long hot summer of 1995 (Thornes,
1997) are summarised in Box 7-1 below.
Box 7-1 Economic Impacts of the Long Hot Summer of 1995
The impacts of the hot summer of 1995 had a significant impact on the transport sector, for the road, rail
and water transport areas. These included major problems from rail buckling and rail-side fires, problems
with wheel rutting of roads, canal water shortages, and increased pedal and motor cycle accidents.
However, it also led to increases in internal flights and domestic rail journeys. The event also resulted in
new specifications for road design, guidance on conserving water in canals, and fire protection for the rail
network.
The economic costs and benefits are summarised below.
Mode
Air
Positive (benefits)
Increased internal flights. £1 million
Rail
Increased revenue from trips £10 million Rail buckle £1 million
Speed restrictions £1 million
Increased lineside fires £1 million
Increased fuel sales (not quantified)
Increase in pedal cycle accidents £12m
Savings in winter maintenance £8m
Poor resurfacing work
Wheel rutting of roads - road rutting repairs
£10m
Reduced delays to offshore shipping £1 Closure of canals due to water shortages– loss
million
of income £1 million. Reduction in sales to
farmers.
£20 million
£36 million
Road
Water
Total
Negative (Costs)
Reduced payloads. Reduced overseas flights.
Loss of overseas holidays £10million
Source: Economic Impacts of the Hot Summer and Unusually Warm Year of 1995 (Thornes, 1997). Chapter 11. In
UEA.
The present case study summarises the impacts on transport of the exceptionally hot
period during Summer 2003.
Based on reviews of relevant studies (e.g. AEA, 2004: Wilson and Burtwell, 2002; DfT,
2004; RS, 2005, plus regional studies) and the experiences of the 1995 summer heatwave, the following transport effects were identified for consideration:
•
Impact on the rail network from buckled rails, or speed restrictions due to potential risk of
rail buckling, increasing journey times and reducing the frequency of some services;
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
•
•
•
•
•
•
•
•
•
Impact on the rail network from the risk of fires;
Impacts on the road network from the risk of wheel rutting and subsidence, and the
increased incidence of road service and maintenance leading to delays;
Increased passenger discomfort, customer and staff heat stress for all modes, but
particularly for the London underground (including possible heat exhaustion for
vulnerable passengers);
Changes in demand for cooling (energy use) on public transport and road vehicles;
Overheating of equipment both on rail and road infrastructure (e.g. signals) and
trains/underground;
Disruption to airports;
Increased risk of vehicles overheating, including cars and diesel locomotives;
Changes in modal switch (towards cycling) and changes in accident risks;
Increased demands on transport (from increased tourism).
For road and rail modes, the potential effects relate both to passenger and freight
transport.
After a further review of the literature and anecdotal evidence related to the 2003 heatwave event a number of priority areas for analysis were identified. These are set out by
mode below.
7.2 Rail
The high temperatures in the summer of 2003 led to widespread speed restrictions on
the rail network due to real and potential rail buckling.
The issue of rail buckling concerns high rail temperatures in excess of the design
maximum for track (e.g. ≥ 36 °C for some parts of the system). The problem of rail
buckling can be managed by differential speed limits, but also managed locally by
differential speed limits at a lower critical rail temperature for sections of track that are
less than full strength. The guidance from Network Rail13 for hot weather is shown in
Table 7-1. Note that rail temperatures are usually 15–18 °C higher than ambient air
temperatures
13
From Railtrack Company Specification, 2002, quoted in Atkins, 2005.
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Table 7-1: Network Rail Guidance relating to high rail temperatures for rail buckling.
Cause
Restriction
Rail temperature more than 32 °C above its Stress
Free Temperature (SFT), or roughly equivalent to
ambient air temperature above 35 °C
Watchman deployed to monitor track
Rail temperature more than 37 °C above SFT, or
roughly equivalent to ambient air temperature above
40 °C
Initial speed restriction (30 or 60 mph
depending on location)
Rail temperature more than 42 °C above SFT, or
roughly equivalent to ambient air temperature above
45 °C
More stringent speed restriction (20 mph)
Based on observations and judgment of watchman
Closure of rail track
Additional precautions are imposed in exceptionally hot weather, between 12:00 and 20:00, applying
on a geographical (route) basis, not site-specific (so can affect large areas)
24-hour forecast indicates ambient air temperature
will be above 36 °C
Speed restriction (45 or 90 mph depending
on location)
24-hour forecast indicates ambient air temperature
will be above 41 °C
More stringent speed restriction (30 or 60
mph depending on location)
There are two major impacts here:
•
The physical damage to the rail, which gives rise to two costs: i.e. repair costs and the
time delays (additional costs of travel time) during repairs;
• The time delays (additional costs of travel time) from speed restrictions to prevent rail
buckling.
Rail buckling (costs)
The incidence of reported rail buckling shows a strong link with particularly warm summer
weather. Historic data for ‘warmer’ summers are presented in the Table 7-2 (data from
Atkins, 2005; Thornes, 1997; HSE, 2004).
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Table 7-2: Incidence of rail buckling in Great Britain for warm summers.
Year
Number of
reported rail
buckles
Weather
b
Average summer
temperature in
Central England
Temperature
14
dataset
1976
1990
132
73
Hot
summer
Warm
summer
17.8 °C
16.2 °C
1991–94
32 (on
average)
1995
133
1996–
2003
36 (on
average)
Hot
summer
15.6 °C
17.4 °C
2003–
04
137
2004
a
42
Hot
summer
15.9 °C
17.3 °C
16.2 °C
a
The reporting period for rail transport statistics changed from financial to calendar year in 2004. Between 1
April and 31 December 2004, 32 incidences of track buckle were reported: this figure has been scaled up to
provide a comparable 12-month period figure.
b
Summers classified as “hot” or “warm” based on qualitative comparison with other recent summer at the time.
There is a strong correlation between the reported rail buckles in hot summers (1976, 1995,
2003). However, rail buckling is dependent on the condition and maintenance of the track, as
well as the effects of elevated temperatures, and so attribution of these impacts to hot weather
is extremely difficult. This makes the derivation of casual relationships more difficult,
because of confounding factors such as maintenance.
The cost related to the excess rail buckles in 1995, over and above the average of the
preceding 3 years, was estimated at £1m by Railtrack (Thornes, 1997). We have been unable
to obtain an estimate of the average cost to Network Rail for repair or replacement of buckled
rail (and associated service disruption) in 2003, partly because the actual costs for rail repair
depend strongly on site-related factors. In the absence of this information, an indicative value
can be derived based on the cost estimate for 1995. The number of rail buckles reported in
2003 is similar to 1995, and so we assume that costs in 2003 will be of a similar magnitude. It
is stressed that the additional cost of repair or replacement of damaged rail due to the hot
weather will depend on the existing schedule for routine rail replacement. If the buckles
occurred in older rail that was already due for replacement, the additional cost of hot weatherrelated buckling may be minimal (though there would be some cost due to changes in
maintenance timetabling - specifically, the cost of repair is only being brought forward, so the
actual incremental cost of repair is the difference between the immediate cost of repair and the
present value cost of repair under routine maintenance).
14
The Central England Temperature (CET) dataset is a monthly mean timeseries of observed temperatures representing a roughly
triangular area of the UK enclosed by Bristol, Lancashire and London. See www.metoffice,gov.uk
/research/hadleycentre/obsdata/cet.htm. Temperatures reported in Table 2 are the average of June, July and August monthly
temperatures for the years indicated.
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
Within this case study, it has not been possible to assess the potential impacts (and costs)
from additional time delays from maintenance. These would add to the estimated time delays
in the next section.
Speed restrictions and time delays
The heat-wave of August 2003 led to precautionary heat related speed restrictions across
some of the network. This increased journey times on the network for individuals, and
potentially also for freight.
Between 14 May and 18 September 2003, there were 165,000 delay minutes from hot
weather-related on UK railways, according to Network Rail calculations (Atkins, 2005), in
comparison with only 30,000 delay minutes for a similar period in summer 2004. As a first
approximation, we assume there were 135,000 delay minutes attributable to the summer of
2003 (165,000 less 30,000). The excess delays are attributed to the August 2003 event.
The valuation of the time delays associated with the Summer 2003 heat-wave can be
calculated using the standard approach for travel time savings in transport appraisal. There is
a long history of valuing travel time savings in the UK in cost-benefit analysis. The Green
Book, Appraisal and Evaluation in Central Government, provides guidance on appraisal and
evaluation in Government – and there is specific information on Transport Appraisal in the
New Approach to Appraisal (NATA) and the multi-modal guidance from DfT.
For the analysis here, we have used the Values of Time and Operating Costs from DfT
Transport Analysis Guidance (TAG Unit 3.5.615). This provides the latest values of time,
occupancy figures, purpose splits, GDP growth rates and vehicle operating costs
recommended by the Department for Transport (DfT) for use in economic appraisals of
transport projects in Great Britain and replaces the previous Transport Economics Note.
Time spent traveling is distinguished between;
•
•
•
Travel in the course of Work,
Commuting (travel to and from normal place of work) and
Other (travel for other non-work purposes).
The guidance sets out that time spent traveling during the working day is a cost to the
employer's business. It is assumed that savings in travel time convert non-productive time to
productive use and that, in a free labour market, the value of an individual's working time to
the economy is reflected in the wage rate paid (TAG Unit 3.5.6.). Note, this assumes that all
savings in working time can be used for the production of output by the employee, and that
the value of this output is measured by the total labour cost to the employer. The value of
working time applies only to journeys made in the course of work. This excludes commuting
journeys, which are discussed below.
15
http://www.webtag.org.uk/webdocuments/3_Expert/5_Economy_Objective/3.5.6.htm
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The majority of journeys do not take place during working hours, but in the traveller's own
time. However, people implicitly put a value on their own time, and will trade-off cheaper,
slower journeys against faster, more expensive ones (TAG Unit 3.5.6.). This 'willingness to
pay' to save travel time will vary, depending on such factors as the income of the individual
traveller, the value of the journey purpose and its urgency, and the comfort and attractiveness
of the journey itself. Different values may therefore be attributed to time spent on the same
activity by different people, whose incomes and journey characteristics may vary and time
spent by the same individual on different journeys or parts of journeys16. Values are provided
for non-working time applying to non-work journey purposes, including travel to and from
work and other travel (e.g. leisure).
The perceived value of working time is the value as perceived by the employer. Businesses
perceive costs in the factor cost unit of account and therefore the perceived cost and the
resource cost are the same for values of working time (TAG Unit 3.5.6.). The resource cost is
given by the gross wage rate plus non-wage labour costs (including national insurance,
pensions and other costs). For non-work time, individual consumers perceive costs in the
market price unit of account and therefore the perceived cost and the market price are the
same for 'commuting' and 'other' purposes. The values of working time for rail, commuting
and other time (general) from the DfT guidance (TAG Unit 3.5.6.) are estimated for different
types of vehicle occupant and are given below.
Table 7-3: Values of Time per person per journey type (£ per hour, 2002 prices and values)
Vehicle
Rail – working time
Resource Cost
£/hour
Perceived Cost
£/hour
Market Price
£/hour
30.57
30.57
36.96
Commuting
4.17
5.04
5.04
Other
3.68
4.46
4.46
Source: Webtag Unit 3.5.6
Notes: The basic sources of wage rate data are the New Earnings Survey of the Office for National Statistics, the
National Travel Survey (NTS) of the Department for Transport and the 2000 Labour Cost Survey.
For the results here, we have used resource market prices, for consistency with other areas of
analysis.
To assess the impact of the delay minutes, the analysis also needs to estimate the train
occupancy, i.e. the number of passengers affected by the delays. This is needed in order to
determine the total minutes of time lost to all individuals using the rail network – i.e. 135,000
delay minutes for all trains multiplied by the average number of individuals per train. No
16
One important specific application of this second type of variability is that time spent walking to/from and waiting for public
transport services is commonly valued much more highly than time spent actually travelling. There is consistent evidence that
people will pay more to save walking and waiting time than they will for an equivalent saving in ride time. This approach should
normally be adopted for multi-modal transport appraisal.
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general occupancy rates are given in the NATA guidance (as it is assumed that specific rail
data will be available). To address this we have used average national data from DfT
statistics (Transport Statistics Great Britain)17 on the average passenger load factors for the
UK rail network, which gives a value of 92 passengers per train.
An assumption has to be made on the split of working, commuter and other trips disrupted by
the 2003 event. We have used average data, based on the National Travel Survey (1999 2001) (used in appraisal guidance) which produces journey purpose splits for work and nonwork travel (commuting and other), based on distance travelled and trips made - these purpose
splits are necessary in order to calculate values of time per vehicle for the average train. We
have used the all week average (average of daily times, split by time, and week and weekend
travel), shown below.
Table 7-4: Proportion of Travel in Work and Non-Work Time for Rail
Time
Work
Commuting
Other (nonwork)
Weekly average - Percentage
of Distance Travelled by
Occupants
16.5%
37.8%
45.7%
This allows analysis of the delay minutes split by occupant. These delay minutes are then
multiplied by the TAG values (market prices) to derive the total damages, shown below.
Table 7-5: Valuation of Delay Minutes
Delay
Minutes
Passenger
Delay Hours*
Unit Values
2004 prices/hr
Total
Work
34076
38.74
1,319,978
Commuting
78065
5.28
412,357
Other
94380
4.67
441,166
Total
2,173,501
135,000
* assuming 92 passengers per train, and the split of average work and non-work time from Table 4, and
converting from minutes to hours.
The valuation of the 2003 delay minutes, in 2004 prices for travel time, is estimated at £2.2
million. The values may underestimate the delays, as we have had to assume weekly average
occupancy, and have used national occupancy estimates (when in fact many of the delays
were in London and the South-East where occupancies are higher). This value can be
compared with a cost of time lost through rail speed restrictions during the summer of 1995,
which were reported at £1m (Thornes, 1997).
The numbers exclude additional waiting time and exclude additional factors from the heat
affecting journey conditions. Journey ambience is important in determining willingness to
17
http://www.dft.gov.uk/stellent/groups/dft_transstats
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
pay for travel time, but has not been included here. These omissions are likely to mean the
values above underestimates the total costs of the travel time delays.
The values above do not capture delays to rail freight. Rail freight is significant, though much
occurs at off peak times, and is slower moving than passenger transport (though there are
exceptions, e.g. the postal rail services). This is a potentially major omission, and again
implies the values above are underestimates.
The value can be compared to the regional analysis of Atkins (2005). This study estimated
the economic costs associated with rail delays in Summer 2003 in four Network Rail Areas
around London (which captured 26% of the national delay minutes). Building in assumptions
for the number and type (commuter vs. leisure) of customer on delayed trains, and using
appropriate values for time lost from the DfT’s Transport Analysis Guidance, they calculated
the losses from rail delays in the four sample areas of the rail network in and around London
to be £727,000. This is approximately proportional to the national values estimated above –
the slight differences are due to the different proportions of work/non-time and passenger
occupancy rates between the national average and the four areas considered by Atkins.
In addition to the economic costs of rail delays to passengers, Network Rail incurred financial
costs of £6.5 million in compensation payable to Train Operating Companies for speed
restrictions. [Note that this represents a transfer payment from one private entity to another;
not a resource cost, and therefore should not be included in total costs.]
Other Potential Effects
A number of other potential effects have been considered.
Hotter, drier weather may potentially influence the occurrence and severity of lineside fires.
These incidents affect rail operations, because they can lead to the railway being disrupted or
closed as a precautionary measure. Additional line-side fires were reportedly a significant
issue in 1995, leading to costs estimated at £1 million (Thornes, 1997). The table shows the
number of reported lineside and station fires between 2002 and 2004 (HSE, 2004). During
2003-04, reported lineside fires increased by 42 % compared to the previous year. Reported
fires reduced again in the following year, indicating that the increase in 2003 may have had
some connection with the hotter summer. However, the actual causes of these incidents is not
known and so attribution of these events (and any associated potential costs) to the weather
event is difficult to prove. In the absence of available data on the costs associated with
lineside fires in 2003 (and exactly when during the year they occurred), we have been unable
to exactly quantify the additional costs related to the hot weather.
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
Table 7-6 . Incidence of railway lineside and station fires in Great Britain
Period
Reportable lineside
station fires
/
2002-03
2003-04
2004
84
119
92
a
a
The reporting period for rail transport statistics changed from financial to calendar years in 2004. Between 1
April and 31 December 2004, 69 lineside and station fires were reported: this figure has been scaled up to
provide a comparable 12-month period figure.
It has also been suggested that high temperatures potentially give rise to degraded signalling
systems. The most vulnerable signalling assets are considered to be those located within
signalling centres. These are provided with air conditioning systems to prevent thermal
overload of control systems. Threshold values for failure are in the region of 40°C. We have
not been able to source any incidence of failures from the 2003 event.
The high air temperatures will have increased power consumption by air conditioning
equipment.
Some previous climate change scoping studies have raised the issue that excessively high
temperature could lead to diesel engines overheating (electric traction failure). This is not
expected to be a significant risk for systems that are designed and maintained to a good
standard and we have found no evidence of such effects in 2003.
There is also a potential increase in fuel use to provide carriage air conditioning. This is
likely to have been a real effect, though we have not been able to source data on the potential
magnitude for 2003.
Finally, it might also be possible that the event of 2003 stimulated increased passenger
numbers, as more people sought to take advantage of the weather, making additional leisure
domestic trips, for example, to the coast. We have not been able to find sufficiently disaggregated data to investigate this issue, but it was a major benefit in 1995, with an estimated
£10 million of extra revenues18. Further work to explore these benefits is needed.
7.3 Road
The high temperatures in the summer of 2003 also gave rise to deformations in the surface of
many roads, with high profile damage to roads in the South East (for example, it was reported
in newspapers at the time that sections of the surface of the M25 had melted).
The type of road influences the susceptibility to high temperatures, and asphalt and concrete
will behave in different ways. Black surfaces did melt and lead to wheel rutting during the
summer of 2003 (DfT, 2004). This causes the aggregate to subside and the road to lose its
grip (road-stone polishing).
18
As revenues are a transfer from passengers to operators; the cost of supplying the service needs to be removed.
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
There are other impacts possible from the warmer temperatures. It would be expected that
cars with air conditioning would have higher fuel use during the period (though how overall
transport demand changed during the period is unknown). It is also possible that vehicles
were more susceptible to break-down – particularly from over heating. We have investigated
this latter issue, but not found available data that would allow an evaluation of the importance
of this potential effect. This could be followed up with the break-down service providers.
Finally, it might also be possible that the event of 2003 stimulated increased passenger trips,
from people travelling to the coast for example. We have not been able to find sufficiently
dis-aggregated data to investigate this issue.
We quantify the costs associated with road subsidence. Incidence is confined to roads in the
management of local authorities only. This is because the A-roads and Motorways are built to
a different construction specification and are therefore less vulnerable to subsidence.
Supporting this assumption, we understand that no additional funds were requested by the UK
Highways Agency for subsidence repair work following the summer of 2003.
We do not have estimates of time loss values and other WTP to avoid damage e.g. to vehicles,
as a result of the road subsidence. Consequently, we use restoration costs to proxy for impact
costs – an assumption supported by the fact that, under current legislation at least, these costs
are incurred by the public authorities. Data relating to Summer 2003 was obtained from the
UK Department for Transport19. The regionally disaggregated totals are presented in Table
7-7. Only English regions are reported to have suffered significant damage in 2003. These
costs are split between local authority and central government on the basis of the following:
local authorities have access to emergency running costs cover under the 'Bellwin Scheme' in
the Local Government and Housing Act 1989. This can cover capital costs of reconstruction
(where this is cheaper than repair and can be done within two months of the emergency)
within an envelope of up to 85% of the overall costs of dealing with the event. Most
significant damage to the highway will be something that takes more than two months to
complete, so the DfT considers contingency funding in such cases. DfT policy is to make a
contribution to the capital costs of such reconstruction, though the local authority is expected
to spend at least 15% of its annual capital road maintenance grant in addressing the issue. In
this instance a number of counties, including Wiltshire, Surrey, Bedfordshire, Suffolk and
Norfolk were not eligible for DfT additional support.
19
Edward Bunting (pers. comm..)
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
Table 7-7. UK Regional road subsidence costs – Summer 2003
Region
Total
Central
Damage cost
Govt.
(£m) contribn.
SE
E
EM
SW
18.69
13.24
7.40
1.30
9.82
8.09
5.36
Total
40.62
23.27
7.4 London Underground
The heat and lack of ventilation in underground carriages is a source of concern during
periods of intense summer heat. Ventilation is a particular concern when trains are not
moving (as trains create ventilation when moving). The problem of temperatures on the
London underground is a continued concern (i.e. every summer). The problem was, however,
particularly acute during 2003.
Temperature measurements undertaken in Summer 2003 revealed a maximum temperature of
41.5 °C recorded in a train and 36.2 °C recorded on a station. Average train temperatures
were at least 27.0 °C and were almost 2.0 °C warmer on deep level lines (Atkins, 2005).
During July 2003, 4,000 passengers were trapped on London Underground in broken down
trains for at least 90 minutes, and subjected to combined temperatures and humidity
approaching 40 °C. Ten people were taken to hospital suffering from heat exhaustion and 627
were treated at the scene.
There are some reported statistics on the incidence of health effects during the heat wave itself
compared to other years (Atkins, 2005). The average rate of fainting during July and August
2003 was 0.92 incidents per day – compared to a rate of 0.82 during the year. Fainting
represented around one-sixth of all health and safety incidents recorded, and the proportion of
fainting to other incidents did not increase significantly during July and August. Based on
these levels of reported health effects, combined with typical valuation estimates (e.g. for an
emergency room visit or hospital admission), including medical care, lost time at work, and
dis-utility, we conclude that the costs of the higher temperatures in Summer 2003 on human
health in the Underground were extremely low (thousands of pounds) though there could be a
small additional increase in delay times from these incidents.
The valuation of passenger discomfort (i.e. for all passengers) is potentially much greater.
According to survey work undertaken in 2003, the mean temperature range for thermal
comfort varied between 21 and 26 °C in trains, and 17 and 25 °C in stations (BRE, 2004).
Mean observed temperatures were outside these ranges, at 28 °C in trains and 26 °C in
stations: out of those surveyed 66 % of passengers in trains and 50% of passengers in stations
indicated a preference for cooler conditions.
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
LUL data show passenger demand increases in warmer periods. However, evidence from a
weekly forecasting model for the August 2003 event found Underground revenues fell by 1
to 1.5% during the two weeks of the heat-wave (Atkins, 2005), which would be broadly
equivalent to £0.5 million (based on average revenues and trip data for the Underground
(Transport for London, 2004)). However, it is unclear if this loss in demand to the
Underground is merely a substitution effect (to other above ground modes) or due to lower
overall transport demand caused by extreme temperatures.
7.5 Aviation
The deformation of runways due to high temperature is considered unlikely, as the asphalt
used is far denser than that used for motorways and less likely to deform.
Higher temperatures reduce the density of the air, thus increasing the fuel needed and, in
some limited cases, the runway length needed for take-off by old planes with full payloads
(DfT, 2004). In practice this may mean that flights will run at slightly less than full capacity.
There is anecdotal evidence that some flights by Concorde were affected by the high
temperatures during the 2003 event, leading to additional refuelling stops (see Box 7-2).
Box 7-2 Impact on Concorde Flights
During the period of peak temperatures in Summer 2003, British Airways were forced to plan for a
refueling stop on the BA001 flagship route to New York.
For any aircraft, engine performance is impaired as temperatures increase. For Concorde, engine
performance is critical on take-off; for a given weight, the length of runway required for take-off is
increased at higher temperatures. Under the Summer 2003 heatwave conditions, it was necessary to
reduce the weight of the aircraft in order to keep within the limits of the runway at Heathrow airport.
British Airways reduced the amount of fuel on-board the aircraft, and were forced to land at Gander
in Newfoundland to re-fuel on the way to New York.
In previous years, similar hot weather would not have required similar actions because the aircraft
was rarely full, but in 2003 with only a few months until Concorde was taken out of service, every
flight was full, and so the only option to reduce the weight for a successful take-off was to carry less
fuel.
from www.concordesst.com
In relation to demand changes, we have not found any evidence for a potential decrease in
international flights during the period. This was a major effect linked to the unusually warm
year of 1995 when airlines and holiday tour operators suffered a loss of business because
more people stayed in the UK. The different response to the two events may be linked to the
much longer period of above average temperatures during 1994–95, which had a more
sustained effect on holiday plans than the shorter period of extreme temperatures in August
2003.
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
7.6 Cycling and Motorcycles
There is a strong correlation between monthly average temperatures and road casualties from
pedal cycle or motorcycle use, reflecting the seasonality of these forms of transport. However,
statistics available for road casualties in Great Britain20 do not indicate any significant
increases in accidents for pedal cyclists or motorcycles during Summer 2003 in comparison
with 2002 or 2004.
This is in contrast to the analysis for the hot summer of 1995, when 12 additional deaths of
pedal cyclists were considered to be related to the higher temperatures and included in the
economic valuation (Thornes 1997). Since the early 1990s, cycling casualties have steadily
reduced, so it may be that improved safety measures have provided adequate protection for
cyclists in recent years.
7.7 Adaptation
There is significant potential for the transport system to adapt to the average changes
identified, and reduce the risk of vulnerability to extreme heat events. The key aspects of
adaptation/management are to increase resilience, resistance and adaptive capacity, for the
transport infrastructure, which might include for example:
Adaptation could significantly reduce many of the above impacts above. The project team
notes that many of the potential problems can be managed – and are indeed managed
effectively, in other countries where more extreme temperatures than those encountered in the
UK occur.
The scale of the risks from extreme weather to rail infrastructure is strongly linked to current
maintenance. Network Rail have indicated that many of the problems faced by the rail
network during the hot weather in August 2003 had more to do with general failures in
management practices and monitoring at that time than with the weather conditions
themselves (John Dora, pers. comm.). Significant changes have since been instigated, such as
maintenance of the network being brought back “in house”. This means that in 2006, the risks
to the rail sector from extreme hot weather are considered much lower, and if similar weather
recurred, disruption to services is expected to be minimal, or even non-existent. However
there may be some confusion over this situation as a footnote in Atkins (2005), attributed to
Network Rail, states that recently re-laid rail has a lower critical temperature and therefore
unusually warm weather in spring could result in speed restrictions applied at ambient
temperatures which are not high by summer standards. We also note that the procedures and
plans implemented after 1995 did not prevent a repeat of the levels of rail buckling and delays
that occurred.
20
From Road Casualties in Great Britain monthly statistics available from www.dft.gov.uk
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
In relation to road, there is the potential for up-grading road surfaces further from the current
British Standard (which was revised after the very hot summer of 1995). An alternative
preventative adaptation measure to avoid road subsidence and surface damage is tree felling.
Trees remove moisture from the soil and if close to the road actually deform the road. In some
situations there may be a need to fell trees that are close to roads in an effort to maintain a
safe network. Again, any appraisal of this measure will have to take into account the present
value cost of tree felling and the present value benefits, as described above.
7.8 Discussion and Conclusions
The analysis shows that there were significant impacts on the transport network from the
extreme summer temperatures of 2003.
The costs (and benefits) covered in this case study are summarised in the Table 7-8. In the
absence of available data either on impacts or on the potential costs of impacts, there are a
number of categories for which we can provide no costs estimates. The overall valuation is
therefore a minimum estimate. We have found no evidence for economic benefits in the
transport sector from the hot weather in Summer 2003 in this valuation case study. There are
potentially benefits relating to increases in demand for transport, although in many cases these
changes represent a modal shift (e.g. from trains to cars, rather than additional transport
demand) and are thus a transfer rather than net benefit.
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
Table 7-8 Summary of Costs and Benefits of Summer 2003 heatwave in Great Britain.
Mode
Impacts in Summer 2003
Valuation
Rail
Speed restrictions: passenger delay
-£ 2.2 million (a)
Rail buckles: additional maintenance
-£1.3 million (b)
Other
Not quantified
Speed restrictions: time waiting for train
services
Speed restrictions: freight delay
Rail
buckling:
maintenance
time
delays
for
Increased line-side fires
Damage to other infrastructure (e.g.
signals)
Changes to journey ambience
Changes in demand
Road
Subsidence
-£40.6 million (c)
Other
Not quantified
Increased fuel use for air conditioning
Incidence
of
overheating
break-downs
from
Changes in demand
Underground
Changes in demand
-£0.5 million
Health effects
-< £0.01 million
Other
Not quantified
Passenger discomfort
Pedal cycle
No discernible impact on accidents
Not quantified
(a) Based on estimate of net 130,000 delay minutes during 2003, combined with NATA guidance for
valuation.
(b) Based on costs of 1995 for similar level of rail buckles, updated to 2004 prices.
(c) Based on DfT data
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
8 WATER RESOURCES
8.1 Introduction
In this case study we analyse the impact, if any, of the Summer 2003 weather on water
resources in the UK. Waughray (1997) investigated the impact of the hot summer of 1995 on
water resource management and supply, finding that the impact was unequally distributed,
with three water regions putting over 5% more water into supply in 1995/6 than in 1994/5,
whilst two regions put less water into supply than in 1994/5. The exceptional costs of
supplying water in summer 1995 were estimated at £96.1 million, with 71% of these costs
being borne by Yorkshire Water Plc and North West Water Ltd.
8.2 Methodology
This section provides a broad overview of the approach we use to quantify and value the
impact of Summer 2003 weather on water resources in the UK.
Quantification of Impacts
Waughray (1997) used a range of techniques to attempt to isolate the impact of summer 1995
on water resource management. This included examination of drought orders, which were
found not to be a firm indicator of whether a region is prone to water shortages or not.
Waughray also examined data on operational costs of water supply between 1974/5 and
1995/6, finding a number of inconsistencies in the cost data over the time series which made
analysis impossible. To arrive at an estimate of the cost of summer 1995, Waughray used data
on the volume and cost of public water into supply in 1995/6 and compared in to that of the
previous year. Exceptional costs of £96.1 million were reported in England and Wales in
1995/96, with a noticeable regional distribution as 71% of these costs fell on Yorkshire Water
plc and North West Water Ltd.
For Summer 2003, a range of data is available on water resources available and supplied.
First, one can examine the stocks of water available in reservoirs, as shown in Table 8-1
below. We can see that in Summer 2003 and the period immediately afterwards there is a
marked reduction in reservoir stocks – with stocks being at their lowest levels since 1995.
In terms of drought orders, it can be seen from Table 8-2 that there were only two drought
orders in 2003, compared to 63 in 1995. Given the similar decline in stocks, this indicates a
significant problem in the use of drought orders to show the impacts on water resource
availability and management of extreme events.
The company accounts presented in OFWAT (2004) report that total operating expenditure in
2003-4 was £2.8 billion, which was 1.5% higher than that of 2002-3. This difference is
attributed to increased costs relating to bad debts and Environment Agency charges.
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PROJECT E: Quantifying the cost of impacts and adaptation: Summer 2003
There was a rise in reported unplanned supply disruptions in 2003-4, due largely to
disruptions in the Thames Water region. OFWAT (2004) suggests that “The high number for
Thames Water is not in itself exceptional”. There was, however, a 12% increase in bursts
(OFWAT, 2004), though this is not reflected in the supply losses reported in Table 8-3, which
shows only a marginal increase of 2.5% from the 2002-3 figure.
1
Table 8-1: Overall Reservoir Stocks 1990-2004
percentage full
England and Wales
Mean
(1988-2004)
January
February
March
April
May
June
July
August
September
October
November
December
Annual average
87
90
92
93
92
90
86
80
74
71
75
80
84
73
90
93
92
88
82
77
70
59
51
59
67
75
3
3
3
3
80
87
91
93
91
86
85
82
74
65
65
78
81
85
84
88
92
95
91
83
76
80
84
85
89
86
91
96
90
85
92
95
90
87
82
81
81
80
87
95
97
96
97
96
92
87
76
69
72
75
86
86
95
98
98
97
93
88
80
69
53
47
52
57
77
61
71
82
85
86
88
82
73
63
55
63
77
74
79
76
92
92
87
88
88
81
74
71
69
76
81
91
93
92
97
97
94
95
93
88
87
93
93
93
96
97
97
97
97
95
92
83
77
80
82
85
90
96
96
97
95
97
96
94
89
83
88
95
97
94
95
94
95
95
97
92
85
81
78
77
86
88
89
86
94
96
95
92
97
95
91
86
77
83
92
90
95
95
92
92
89
93
87
81
70
60
53
61
81
Source : Centre for Ecology and Hydrology, Wallingford
1
2
3
Covers a selection of representative reservoirs throughout England and Wales which have a total capacity of 1,531,928 Ml.
Data relate to the start of the month.
Percentage of useable capacity
Revision made in 2004
Table 8-2: Drought orders in England and Wales
United Kingdom
EA Region
Number
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
²
North West
North East
Midlands
Anglian
Thames
Southern
South West
3
23
21
1
0
0
1
7
12
18
2
0
0
5
5
0
0
0
1
0
2
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
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
Welsh
4
0
2
0
0
0
0
0
0
0
0
53
44
3
0
0
0
0
0
1
0
0
0
0
0
0
1
0
0
1
2
Northern Ireland
10
0
0
0
0
0
0
0
0
0
United Kingdom
63
44
3
0
0
1
0
0
2
2
England and Wales
Scotland
Source: WSR; DEFRA; SEERAD; DRD (NI) Water Service
1
2
3
4
Drought orders in England and Wales were initially made under Section 1 of the Drought Act 1976, then under Section 131 of the Water Act 1989 and
are now made under section 73 of the Water Resources Act 1991(which, as amended by the Environment Act 1995, now allows the Environment Agency
to apply for drought orders for environmental purposes).
Water Authority Region before 1989. Includes water supply companies.
1996 figure includes one order made for environmental purposes.
The 1984 figure includes three orders made by the Welsh Office jointly with the Department of the Environment (Midlands Region).
Final Report
2
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
- 68 -
80
94
92
94
95
91
85
78
82
84
88
86
87
PROJECT E: Quantifying the cost of impacts and adaptation:Summer 2003
Table 8-3: Leakage in England and Wales (Ml)
1992/3
Distribution input
1993/4
1994/5
1995/6
1996/7
1997/8
1998/9
1999/2000
2000/1 2001/2
2002/3
2003/4 2004/5
16,252
16,236
16,590
17,027
16,365
15,683
15,056
15,058
14,991
15,326
15,404
15,658
15,378
Distribution losses
per cent of input
3,600
22
3,693
23
3,866
23
3,685
22
3,295
20
2,955
19
2,618
17
2,432
16
2,365
16
2,527
16
2,606
17
2,625
17
2,584
17
Supply pipe losses
per cent of input
1,181
7
1,195
7
1,246
8
1,295
8
1,233
8
1,034
7
933
6
875
6
878
6
888
6
999
6
1,024
7
1,024
7
Total leakage
per cent of input
4,781
29
4,888
30
5,112
31
4,980
29
4,528
28
3,989
25
3,551
24
3,306
22
3,243
22
3,414
22
3,605
23
3,649
23
3,608
23
Source: OFWAT
Discussion
The extreme conditions of Summer 2003 and subsequent months had significant
impacts on water reserves, with levels of reservoirs falling to their lowest levels since
1995. However, no significant impacts can be identified for the Summer 2003
weather event on the costs incurred to water companies, with the exceptional
increases in costs in 2003-4 being attributed largely to increases in costs relating to
bad debts and Environment Agency charges. No major impacts were identified in
terms of exceptional disruptions, though burst pipe incidence in 2003-4 was
significantly above that of 2002-3. However, losses of water through leakage were
only marginally above 2002-3 levels.
As a consequence of the above, we have not been able to identify any significant cost
items of the Summer 2003 event on water resource management.
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PROJECT E: Quantifying the cost of impacts and adaptation:Summer 2003
9 TOURISM
9.1 Introduction
In this case study we analyse the impact, if any, of the Summer 2003 weather on
domestic tourism in the UK. Agnew (1997) investigated the impact of the hot summer
of 1995 on tourism, focussing on the impacts on domestic tourism. This was chosen
as it was felt that the impact on UK tourists was likely to be most significant due to
access to information about climatic conditions. Whilst the internet and the expansion
in media means that information flows about weather were more readily available in
2003 than in 1995, the impact is still likely to be most acutely experienced by the
choices of UK residents – given the planning time for long-haul holidays. We extend
Agnew’s analysis by examining the influence on tourism at regional level – showing
that regional level variation in temperatures can have a significant impact on tourism
expenditures. We use panel data techniques to analyse the tourism data and find a
significant relationship between average temperatures and hours of sun and tourism.
The lag structure has been tested and it has been found that the weather in previous
months affects the tourism decision.
9.2 Previous work
Previous studies on the influence of hot summers on tourism have shown a positive
impact on tourist numbers, but a negative impact on tourist expenditures. For the
1995 hot summer, Agnew (1997) estimated there was a reduction in tourist
expenditures of £238.9 million as a consequence of the climate variation experienced
over the whole of 1995 – with the major impacts coming in the months April to
September (£217.5 million). This was based on quarterly data from 1980 to 1995.
Warm summer weather may lead to a number of impacts on tourism, including:
•
•
•
•
Changes in preferences for holiday type and/or activities, with a positive shift
towards outdoor pursuits such as those that may take place in the Highlands.
Coastal and rural recreation is likely to improve, with indoor tourist locations and
urban centres being negatively impacted;
Change in preference of holiday destination – with finer weather more UK
residents and overseas residents may decide to visit the Highlands;
Impacts on environmental quality – climate change may have negative impacts on
water based activities (through increased incidence of algal blooms) and visits to
national parks (through increased risk of fires).
Investment in tourist-related services – as tourist volumes increase in the
Highlands in the summer, there will be spin-offs to services including
accommodation and retailers of recreational clothing and equipment.
(based on Agnew, 1997)
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Based on a survey of tourist boards following the same event, the hot summer of
1995, Giles and Perry (1998) argue that weather may have a larger impact on tourist
decisions due to a structural change in the tourism sector – with a move towards
family group and short-break holidays, which were felt to be more likely to be
influenced by climate related factors. In addition they suggest “the general population
in the UK appear to be making more spontaneous holiday decisions”. These factors
influence the extent to which climate change and hot summers are likely to influence
domestic tourism patterns.
Looking at the impact of hot summers on tourism, the WISE report finds that there is
a positive relationship between weather and domestic tourism (Palutikof and Agnew,
1999). A summer warming of 1 oC is estimated to increase domestic holidays by 0.8
to 4.7%. This provides a range of potential outcomes for tourism destinations, though
in the longer term the interactions between climate and tourism are likely to be more
complex, given the nature of the tourism product and the influence of climate change
on competing destinations – in the case of the Scottish Highlands, for instance, the
impacts of climate change on the Alps and Pyrenees are likely to have some effect on
demand for hill walking.
As part of the WISE study, Agnew and Palutikof (2006) examine the influence of
climatic variables on monthly UK tourist data for the period 1980 to 1996. They show
that climate influences are particularly important in some months and not in others.
Domestic tourism is particularly sensitive to climate in March and April and is not
sensitive at all in February and October. Based on their regressions, they estimate a
net increase of £309 million for tourism expenditure due to the hot weather in 1995.
This is an important result, and contrasts with the results of Agnew (1997). Given that
this is based on monthly data, this result is likely more robust.
In a more complex study, Hamilton et al (2004) build on a simulation model of
international tourism generate scenarios of international tourism departures and
arrivals for 2000-2075, with inclusion of the impact of climate change on the
desirability of visiting tourism locations. This shows that for the UK the impact will
be to reduce outbound tourism and slightly reduce inbound tourism (the balance being
broadly positive for the tourism industry as a whole).
9.3 Methodology
This section provides a broad overview of the approach we use to quantify and value
the impact of Summer 2003 weather on claims for subsidence damage in the UK.
Quantification of Impacts
Agnew (1997) used aggregate monthly data on tourism in Great Britain and examined
the influence of climatic variables on these. Aggregating to quarterly data for the
period 1979-1995 and regressing for individual quarters, Agnew finds that sunshine
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PROJECT E: Quantifying the cost of impacts and adaptation:Summer 2003
and temperature are more important in predicting bed occupancy rates than rainfall
(with the exception of the summer period). For expenditure, temperature is most
significant – except the summer period when expenditure is determined by sunshine
in January, February and March and rainfall in that period. This lag structure is
understandable in that dull winters increase expenditure on tourism. Agnew finds that
weather accounts for a reduction in total tourist expenditure of £238.9 million, of a
total observed variation of £445 million.
For the present study, we are able to take advantage of a more extensive data series at
a regionally disaggregated level. We take data from the UK Tourism Survey from
1995 to 2004 for tourism expenditures, bed nights and trips. There were some issues
with consistency of regions reported in this period – we had to adjust the data to fit
different regions for England. The final regions used were: West Midlands, East of
England, East Midlands, London, North West, Cumbria, North East, South East,
South West and Yorkshire. Data for Wales and Scotland were not available on such a
consistent basis and so were excluded from the analysis. The tourism data is annual,
but the data on the proportion of trips per month were used to transform the annual
data into monthly data.
Weather data were obtained on a monthly basis from the Met Office but for different
regions: East & North East (E&NE), North West (NW), Midlands (Mid), East Anglia,
South West (SW) and South East (SE). This data series goes back to 1998.
We matched the weather regions to the tourism data as follows. Weather figures for
the Midlands were assumed the same for East and West Midlands, East Anglia for
East of England; South East for South East and London, North West for North West
and Cumbria, North East for North East and Yorkshire, and South West for South
West. Figure 9-1 below shows the data used for trips and nights for England, while
Figure 9-2 shows the data on expenditures.
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PROJECT E: Quantifying the cost of impacts and adaptation:Summer 2003
Figure 9-1: Tourism Trips and Nights – England – 1998 to 2004
60
50
Millions
40
Trips
Nights
30
20
10
19
98
19 J a
9 n
19 8 J u
98 n
19 N o
9 v
19 9 A
99 p r
20 S e
00 p
20 F eb
0
20 0 J
00 ul
20 D e
01 c
20 Ma
0 y
20 1 O
0 2 ct
20 M
0 2 ar
20 A u
03 g
20 J a
0 n
20 3 J
0 3 un
20 N o
0 v
20 4 A
04 p r
Se
p
0
Month
Figure 9-2: Expenditure by Domestic Tourists – England – 1998 to 2003
3,000
£ million
2,500
2,000
1,500
Expenditure
1,000
500
2004 Jul
2004 Jan
2003 Jul
2003 Jan
2002 Jul
2002 Jan
2001 Jul
2001 Jan
2000 Jul
2000 Jan
1999 Jul
1999 Jan
1998 Jul
1998 Jan
0
Month
To estimate the impact that temperature, precipitation and sun hours have on tourism,
we used panel data techniques. First, simple correlation analysis showed a lack of
correlation between rainfall and tourism variables. It also showed that trips and nights
were not correlated with income, though expenditure is. We first attempted a linear
static model, assuming fixed effects. The results of this analysis are shown in Table 1.
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PROJECT E: Quantifying the cost of impacts and adaptation:Summer 2003
It should be noted that the structure of our data does not allow for the testing of
random effects21. In all cases rainfall was tested and found to be insignificant and so
was excluded. The static model can be expressed in mathematical form as follows:
Yit = α + β 0.tempit + β 1.sunshineit + β 2.incomeit + β n.summerit + ut , (1)
Where:
i
represents UK regions;
t
represents months between Jan 1998 and Dec 2004;
Y
represents the number of trips, bed nights and tourists’
expenditure;
temp
is the mean temperature of period (t) in region (i);
sunshine
is the average sunshine hours of period (t) in region (i);
income
is real disposable income;
summer
is a dummy variable for months July – September;
Table 9-1 shows the determinants of trips. Key points from this are:
•
the insignificance of disposable income;
•
the higher the temperature the greater the number of trips (a 1 oC increase in
temperature is associated with an increase of 13,000 trips in that month);
•
the higher the number of sunny days the greater the number of trips (one extra
sunny hour leads to 1,500 extra trips);
21
Baltagi (2001) argues that the fixed-effect model is an appropriate specification if the analysis is focused on a given
number (N) of units so that the statistical inference is conditional on the particular set of (N) unities., which in our case
are UK regions (N=10). On the other hand, random-effect models require the assumption of uncorrelated explanatory
variables and the time-invariant unobservable component of the model, which is assumed to be random (e.g.
Wooldridge, 2002; Greene, 1993). In other words, the random-effect model would require that the units were randomly
selected from a large number of possibilities, which is the case when the unit is individuals or households.
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PROJECT E: Quantifying the cost of impacts and adaptation:Summer 2003
•
the dummy summer term shows that people tend to take more holidays in the
summer, whatever the weather.
Table 9-1: Linear static models
Trips
Regressors
Coef.
Bed nights
Std.
Err.
0.1330
0.0039
0.0002
0.0001
0.0271
Coef.
Std.
Err.
0.4513
0.0133
0.0008
0.0001
0.0919
Expenditures
Coef.
Std.
Err.
Constant
0.5829(***)
2.8683(***)
-330.32(***) 26.2607
Mean temperature
0.0133(***)
0.0407(***)
0.9138
0.7723
(***)
Sunshine hours
0.0015
0.0045(***)
0.2909(***)
0.0457
Real income
0.0001
-0.0002(*)
0.1514(***)
0.0090
(***)
(***)
Summer (dummy)
0.1478
0.4580
26.85(***)
5.3475
R-square (within)
0.4123
0.3593
0.4481
N
828
828
828
F-test that all betas are 0
0.0000
0.0000
0.0000
Notes: (*) Significant at 10%; (**) Significant at 5%; (***) Significant at 1%
Table 9-1 also shows the same regression for bed nights. This shows the following:
•
Income is significant only at the 10% level. The sign is negative, suggesting that
people stay away for a shorter time with increased incomes. This may be
reflecting the relative income and substitution effects for leisure.
•
Mean temperature in the month is associated with an increase in the number of
bed nights. A 1 oC increase in temperature leads to 40,000 additional bed nights.
•
Increased sunshine hours are associated with increase number of bed nights. An
increase by one additional hour of sun leads to a 4,500 increase in the number of
bed nights.
The impact of temperature on expenditures: sunshine hours, mean temperature and
income are found to be significant, with all having positive linkages. The main
findings are:
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PROJECT E: Quantifying the cost of impacts and adaptation:Summer 2003
•
temperature has a positive impact on expenditure (but with a low significance
level – only at the 20% level). The coefficient should hence be treated with some
care.
•
Sunshine hours are significant and positive. An hour’s increase in sunshine leads
to an additional £290,000 of expenditure.
•
Dummies for Summer and Summer 2003 are significant. The 2003 dummy shows
that, even after considering climatic variation, the (monthly) revenues in 2003
were £26.8 million higher than the average of the period 1998-2003.
•
Income has a positive impact on tourism expenditures. This contrasts with the
negative impact on bed nights found above, suggesting that though higher
incomes lead to less nights away from home there may be increased spending
while on holiday.
The static model described above is relevant since it captures the effect of the weather
data on the tourism indicators within the same period of time, which in our data is one
month. However, it seems reasonable to assume that some decisions towards tourism
(e.g. holiday trips) might have been determined according to weather conditions
observed in the previous months. In order to account for this possible lagged effect,
researchers have to estimate dynamic models such as the distributed lag model, which
in its linear form can be defined as:
Yit = α + β 0. Xit + β 1. Xit − 1 + β 2. Xit − 2 + β n. Xit − n + ut ,
(2)
Where:
Y
Number of trips, bed nights and tourists’ expenditure;
X
Represents all independent variables or regressors in (1);
u
The error term.
Distributed lag models, however, include some practical estimation problems that
have to be addressed (Gujarati, 1999). For example, the number of lags to be included
in the model in general can not be determined through economic theory; the degrees
of freedom are reduced when several lags are included in the model, which,
depending on the sample size, can compromise the model’s estimation; and finally,
the multicollinearity problem can arise with the use of many lags of the same
independent variable since most variables tend to be linearly correlated with their
own lags. In order to reduce the number of lags in a model and the potential
multicollinearity problem, the auto-regressive model (AR)22 can be suggested:
22
Examples of auto-regressive models are the Koyck model, the adaptive expectation model and the partial adjustment
model (Gujarati, 1999).
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PROJECT E: Quantifying the cost of impacts and adaptation:Summer 2003
Yit = α + β 0. Xit + β 1.Yit − 1 + ut ,
(3)
Dynamic panel data models are characterised by the presence of the lag of the
dependent variable among the independent variables, a characteristic that introduces
estimation problems such as autocorrelation. This characteristic suggests that the OLS
estimator is biased and inconsistent. In addition, the fixed-effect (within) estimator
will be biased and its consistency will depend on the sample size being large (Baltagi,
2001, page 130). An alternative procedure that overcomes the problems above is
given by the first-difference transformation of the lagged variables. After the timeinvariant unobservable component of the model is removed by first differencing the
variables in the model, the difference of the dependent variable can be used as an
instrumental variable of the lagged dependent variable since it is not correlated with
the error term (Baltagi, 2001; Wooldridge, 2002; Greene, 1993). Arellano and Bond
(1991) derived an estimator, called the Arellano-Bond dynamic panel-data estimator,
using the generalised method of moments with instrumental variables that consists of
including lagged levels of the dependent variable and the differences of the
independent variables as instruments in the model. The Arellano-Bond dynamic
panel-data estimator was used to estimate all our dynamic models. It has to be
observed that the parameters estimated in these models refer to changes or variations
(∆ = Xt – Xt-1) in the dependent and independent variables:
∆Yit = α + β 0.∆Xit + β 1.∆Yit − 1 + ∆ut ,
(4)
We then estimate a dynamic model for each of the tourism parameters. The results for
trips are shown in Table 2. This shows similar results to the static model, though
rainfall is here found to be significant, with the expected negative sign. Table 2 shows
the same analysis for bed nights. Again these results are similar to the static model.
For expenditure the main findings are:
•
That spending rises with temperature and sun hours.
•
That rainfall negatively impacts on expenditure.
•
That Summer 2003 was particularly significant compared to the previous
period.
•
Income has an impact on expenditure, though it is not significant at the 95%
level. The sign is negative, which is not expected, though the impact of the
lagged dependent variable may explain this.
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PROJECT E: Quantifying the cost of impacts and adaptation:Summer 2003
Table 9-2 Dynamic Models – Arellano-Bond panel data estimator
Trips
Regressors
Constant
Lagged ∆ of the
dependent variable
∆ of mean temperature
∆ of sunshine hours
∆ of rain fall
∆ of real income
∆ of summer periods
∆ of heat wave (Jun03Aug03) dummy
N
H0: No autocorrel. of
order 1
H0: No autocorrel. of
order 2
Coef.
Bed nights
0.0228(***)
-0.1339(**)
Std.
Err.
0.0038
0.0635
0.0095(***)
0.0024(***)
-0.0003(***)
-0.0005
0.1681(***)
0.0431(***)
0.0032
0.0004
0.0001
0.0003
0.0279
0.0159
Coef.
Expenditures
0.0701(***)
-0.0498
Std.
Err.
0.0183
0.0509
Coef.
4.6527(***)
-0.1192(**)
Std.
Err.
1.3945
0.0535
0.0288(**)
0.0071(***)
-0.0015(**)
-0.0017
0.4672(***)
0.1381(***)
0.0120
0.0015
0.0006
0.015
0.0923
0.0520
1.5881(**)
0.3253(***)
-0.1276(**)
-0.4233(*)
26.95(***)
14.4758(***)
0.7200
0.0504
0.0500
0.2240
5.1512
2.8398
690
Pr > z = 0.0067
690
Pr > z = 0.0202
690
Pr > z = 0.0087
Pr > z = 0.3000
Pr > z = 0.2794
Pr > z = 0.1406
Notes: Standard errors are robust.
(*) Significant at 10%; (**) Significant at 5%; (***) Significant at 1%
We analyse the impact of lagged weather variables using a dynamic model with a time
effect. The results of these are reported in Table 9-3. These are rather difficult to
interpret. Indeed, the presence of second order autocorrelation (which cannot to our
knowledge be corrected for with current methods) means that the estimated
coefficients may be inconsistent. All of the regressions have this property.
We were able to estimate the lag effects up to six months, but the data were not
sufficient to estimate further lags. Given the difficulty in analysing this data what we
can say from these tables is the following:
•
All the models show significant lagged effects of weather variables.
•
It is difficult to interpret the signs of the lags, as there is some evidence in the
literature that changing signs of lagged variables using this type of estimation is to
be expected (e.g. Baltagi, 2001). However, we find that the results are significant
with of up to 5 months for different weather variables. This correlates to a certain
extent with the analysis done by Agnew (1997), which showed that dull days in the
winter can have an influence on tourism expenditures.
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PROJECT E: Quantifying the cost of impacts and adaptation:Summer 2003
Table 9-3 Dynamic models including further lag effects – Arellano-Bond
panel data estimator
Trips
Regressors
Constant
Lagged ∆ of the
dependent variable
∆ of mean temperature
∆ of sunshine hours
∆ of rain fall
Coef.
Bed nights
t
0.0892(***)
0.6486(***)
Std.
Err.
0.0314
0.1176
t
t-1
t-2
t-3
t-4
t-5
t-6
t
t-1
t-2
t-3
t-4
t-5
t-6
t
t-1
t-2
t-3
t-4
t-5
t-6
t
t
t
0.0476(***)
0.0199(***)
-0.022(***)
-0.060(***)
0.052(***)
0.0223
0.0216
0.002(***)
0.0003
0.0013(**)
0.0017(***)
-0.002(***)
-0.004(***)
-0.0008
-0.0003
-0.001(***)
-0.0002
0.0012(***)
0.0002
0.0005
-0.0002
0.0005
-0.036(***)
0.2929(***)
0.0149
0.0071
0.0078
0.0163
0.0160
0.0166
0.0202
0.0006
0.0002
0.0005
0.0005
0.0004
0.0007
0.0005
0.0004
0.0002
0.0001
0.0002
0.0004
0.0003
0.0002
0.0004
0.0456
0.0753
∆ of real income
∆ of summer periods
∆ of heat wave
(Jun03-Aug03)
dummy
N
H0: No autocorrel. of order
1
H0: No autocorrel. of order
2
Coef.
Expenditures
0.0254
0.7693(***)
Std.
Err.
0.1383
0.1090
Coef.
-15.4354
0.7143(***)
Std.
Err.
13.3662
0.1162
0.1167(***)
0.0558(*)
-0.075(***)
-0.162(***)
0.1928(***)
0.1187(*)
0.1154
0.0073(***)
0.0009
0.0049(***)
0.0054(***)
-0.006(***)
-0.012(***)
-0.0026
-0.0005
-0.004(***)
-0.0006
0.0042(***)
0.0013
0.0024(**)
0.0003
0.0011
-1.010(***)
1.196(***)
0.0437
0.0325
0.0260
0.0498
0.0612
0.0707
0.0720
0.0025
0.0009
0.0019
0.0015
0.0014
0.0032
0.0021
0.0013
0.0007
0.0005
0.0010
0.0017
0.0011
0.0005
0.0016
0.1679
0.2861
5.5819(***)
6.6604(***)
-1.4134
-8.663(***)
16.778(***)
7.0220(**)
12.328(**)
0.5856(***)
-0.0137
-0.0297
0.2117(**)
-0.518(***)
-0.607(***)
-0.359(**)
0.0719
-0.165(***)
-0.195(***)
0.2604(***)
0.1628
0.2104(***)
-0.0245
0.1211(*)
-54.87(***)
87.785(***)
1.8680
1.9017
1.2643
2.6086
5.3621
2.8440
5.1581
0.1749
0.0656
0.1226
0.0957
0.1469
0.1318
0.1692
0.0805
0.0287
0.0649
0.0518
0.1089
0.0711
0.0390
0.0712
9.9139
18.9300
345
Pr > z = 0.0076
345
Pr > z = 0.0317
345
Pr > z = 0.0294
Pr > z = 0.0231
Pr > z = 0.0722
Pr > z = 0.1260
Notes: Standard errors are robust.
(*) Significant at 10%; (**) Significant at 5%; (***) Significant at 1%
Because of the problems with autocorrelation and the difficulty in determining signs of
coefficients, we use the dynamic models (Table 9-2) for further analysis of the impacts
of Summer 2003 on tourism in the UK.
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PROJECT E: Quantifying the cost of impacts and adaptation:Summer 2003
9.4 Valuation of Impacts
The impact of the Summer 2003 temperature anomaly is valued using the
dynamic function shown in Table 9-2. This reflects the impacts on tourism due to
the anomaly experienced in Summer 2003 but not the full economic cost, which
would also include the impact on tourists in terms of increased welfare from being
on holiday in better weather and so forth.
Taking the average 1961-90 average as the base, we can estimate the impact that
the anomaly has on the value of tourism in England. We then extrapolate the
values for Scotland and Wales based on the total tourism expenditure reported in
2003.
9.5 Results
The results of this analysis are presented in Table 9-4 below. Using the dynamic
model, we estimate that the impacts of the Summer 2003 heat wave at between
£17.6 million and £41.2 million for England. This is dramatically different from
the change observed between 2003 and 2002 data, perhaps because of issues such
as foot and mouth disease and the September 11th attacks, which both impacted on
tourism. We can scale this up for Scotland and Wales on the basis of observed
2003 data, total UK spending was £26,482 million compared to an England total
of £20,560 million. Assuming that the impacts on tourism in Scotland and Wales
were similar to those in England, this gives an estimate of an impact of £22.7
million to £53.05 million of the effect of Summer 2003 on expenditures on
domestic tourism.
Table 9-4: Tourism Expenditure – Impact of Summer 03 Anomaly
Month
July
August
September
Total impact
(England)
Total impact
(UK)
Final Report
Model prediction
with real
Observed
values
2,068.89
1,721.23
2,482.67
2,050.03
1,655.11
2,411.86
Model prediction
with 196190 means
1,722.20
2,026.61
2,416.68
- 80 -
Impact
Summer 03
(Observed
minus
mean
model)
346.69
456.06
-761.57
Impact
Summer 03
(predicted
minus
mean)
-0.97
23.41
-4.82
41.18
17.63
53.05
22.70
PROJECT E: Quantifying the cost of impacts and adaptation:Summer 2003
9.6 Discussion
Before discussing the results some words of caution are warranted. First, expenditures
do not fully represent the welfare gains or losses due to the Summer 2003 weather
event to tourists in the UK. Second, we have not identified the longer impacts of
Summer 2003 on domestic tourism in the UK – it is possible that lag effects may be
significant. However, the results do show that climate does impact on domestic
tourism – and this is a significant result. Temperature, rainfall and hours of sunshine
all have significant effects, with the expected signs.
The results show less variation due to the summer of 2003 compared to the summer
of 1995 demonstrated by Agnew and Palutikof (2006) who found a positive impact
on tourist expenditure of £309 million, with an overall impact in the summer months
of £133 million. This difference could be due to a number of factors, including:
•
•
Differing tourist markets between 2003 and 1995. Tourism has undergone
dramatic changes in terms of increased international and domestic competition.
Differing climatic conditions, with summer 1995 being significantly hotter and
hence there may be non-linearity. This cannot be estimated from the models.
Climate change is likely to have a significant impact on tourism flows in the UK and
worldwide. This research has shown that climatic variables can have significant
impacts on domestic tourism – including lagged impacts of extended periods of 5
months or more. This shows that weather conditions in previous months affect the
decision whether or not to take a holiday domestically.
The panel data models developed in this paper suggest that there is much scope for
use of regional data for analysis of the influence of climatic variables on tourism.
Further analysis of regional datasets across Europe and worldwide would enhance the
evidence base on the influence that climate has on the decisions of tourists as to
destinations domestically.
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PROJECT E: Quantifying the cost of impacts and adaptation:Summer 2003
10 BUILT ENVIRONMENT
10.1 Introduction
In this case study we analyse the impact, if any, of the Summer 2003 weather on
insurance claims for domestic property subsidence. Palutikof (1997) examined the
impact of temperature and precipitation for the warm year of 1995 on a limited time
series of subsidence insurance claims, with data existing on an annual basis. They
found it difficult to determine a significant relationship between the meteorological
and claims data. They therefore estimated the impact on claims on the basis of
comparing 1995 with the mean of the previous three years, and found that subsidence
losses increased by £170 million in 199523. Aggregate insurance claims data is now
available on a quarterly basis, which should allow us to increase the sensitivity of
regression analysis now possible. This enables us to better estimate the subsidence
impact of the hot summer in 2003.
Newspaper coverage emphasized the subsidence impact on the built environment. For
example, “Surveyors and structural engineers are reporting a huge surge in claims over
the past four weeks as Britain’s homeowners pay the price for the summer’s heatwave.
Cameron Durley, one of the biggest firms of structural engineers in the UK, and which
acts for most of the big insurers, says they are up four or five fold over the past month
alone. Consulting engineer John Pryke & Partners warns that 2003/2004 is likely to
become the worst year for ten to fifteen years and is predicting an avalanche of claims
will hit over the coming months. Victorian and Edwardian homes, built on shallower
foundations than modern homes are under threat. Most at risk are those built on the
clay subsoil that stretches from Oxford across Southern and Eastern England. Trees
near properties are often the cause of the worst problems.” (Guardian, 27th September,
2003).
10.2 Methodology
This section provides a broad overview of the approach we use to quantify and value
the impact of Summer 2003 weather on claims for property subsidence damage in the
UK.
23
Reductions in burst pipe losses as a result of the mild winter were also reported as being reduced by £124 million,
giving a net climate impact on building insurance claims of £19 million.
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PROJECT E: Quantifying the cost of impacts and adaptation:Summer 2003
Quantification of Impacts
Palutikof et. al. (1997) used annual data on insurance claims for domestic subsidence
related damage going from 1975 to 1995. Palutikof et. al. highlights the fact that due
to the limited time series of data available and that they were undertaking the analysis
only a year after the event itself, many claims related to the event were unlikely to
have been settled or even made at the time of writing.
To investigate the impact of Summer 2003, we are able to take advantage of a more
extensive data series. The fact that our study has been conducted later after the event
than that by Palutikof et. al. gives some hope that this data set is more complete. Data
are available on a quarterly basis from 1991 to 2003 from the Association of British
Insurers (ABI). Figure 10-1 shows the total claims (in £ million) whilst Figure 10-2
shows the total number of claims for the period. The number of claims are clearly
higher in the period to 1993, which Palutikof et. al. attributed to the hot dry summers
of 1989 and 1990. There was a noticeable increase in both the total claim and the
number of claims in 2003.
Figure 10-1: Gross Incurred Insurance Claims Relating to Subsidence 1991-2003 (£
million)
200
180
160
140
120
100
80
60
40
20
0
1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Year
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Figure 10-2: Number of Insurance Claims for Subsidence 1991-2003
25,000
20,000
15,000
10,000
5,000
0
1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Data on the average monthly Central England Temperature (CET)24 and precipitation,
covering the period 1989 to 2004, were obtained from the Met Office. The monthly
average temperature data for all quarters were converted to quarterly averages.
To estimate the impact that temperature and precipitation have on claims for building
subsidence we used regression analysis. Autocorrelation was detected in the dataset
for the standard ordinary least squares regression, so the Cochrane-Orcutt procedure
was used to correct for AR(4) errors – given that we are dealing with quarterly data
this seemed the most appropriate.
Various lag structures were tested, as were squared terms (to detect non-linearities).
The best fitting model is presented in Table 10-1. This shows a non-linear impact of
temperature, showing that at low temperatures an increase in the season’s (quarter)
CET leads to a reduction in claims, while at higher temperatures there is a positive
impact on the number of claims. Precipitation (denoted by the variable RAIN) shows a
negative impact, as would be expected. CET and rainfall in the previous quarter are
also shown to be significant. Longer lags were tested but found to be insignificant. It
should be noted that this analysis was only conducted on 52 data sets – a longer time
series would improve the robustness of the results.
24
Central England Temperature (CET) is representative of a roughly triangular area of the United Kingdom enclosed by
Bristol, Lancashire and London. The monthly series begins in 1659, and to date is the longest available instrumental
record of temperature in the world. Since 1974 the data have been adjusted by 1-3 tenths °C to allow for urban warming.
In November 2004 the weather station Stonyhurst replaced Ringway and revised urban warming and bias adjustments
were made to daily maximum and minimum CET data.
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Table 10-1: Determinants of Insurance Claims for Subsidence
Regressor
Coefficient
Constant
173.6867 (***)
CET
-9.1517 (**)
CET-squared
0.36092 (**)
RAIN
-0.38231(**)
CET(-1)
2.3477 (***)
RAIN(-1)
-0.46226 (**)
R-sq
0.69005
Valuation of Impacts
The impact of the Summer 2003 temperature anomaly is valued using the econometric
function shown above. This reflects the additional insurance losses due to the anomaly
experienced in Summer 2003 but – note – not the full economic cost, which would
also include the impact on property owners in terms of stress and inconvenience.
Taking the average 1961-90 average as the base, we can estimate the impact that the
anomaly has on the value of insurance claims.
10.3 Results
The results of this analysis are presented in Table 10-2 below. The model predicts
claims of £218.1 million for Quarters 3 and 4 of 2003, which, according to the model,
would be the only ones impacted by the anomaly. This compares to a real world value
of £307 million. Our model predicts 69% of the variation in the claims time series,
which may be also driven by other factors such as changing socio-economic factors
including the culture of claiming for insurance.
If we implemented the model for the 1961-1990 average temperature and precipitation
this gives us estimates of “normal” weather conditions, with an estimate of £184.3
million over the same period. The model prediction therefore, for the value of the
Summer 2003 weather anomaly, is £33.8 million. If we compare the actual value of
£307 million with the model-generated value under normal weather the costs of
Summer 2003 in terms of impacts on insurance losses are £122.7 million. A third
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PROJECT E: Quantifying the cost of impacts and adaptation:Summer 2003
estimate – of £124 million - is provided by using the method adopted in the Palutikof
study i.e. to compare the claim value in 2003 with the average for the three preceding
years. This compares to the losses estimated by Palutikof of £170 million (in 2004
prices) for the Summer 1995 anomaly. The results overall suggest a broadly similarsized effect in the two hot summers.
Table 10-2: Insurance Claims as a Result of Summer 2003 anomaly (£m, 2004 prices)
Model
3-yr
average
comparison
Actual
2003 Q3+Q4
218.1
307
390*
1961-1990 ave
184.3
184.3
266**
33.8
122.7
124
Total
* Total for 2003, all quarters; ** Average for 2000-2002
The claims data was only available at the aggregate level, for Great Britain. The
analysis therefore does not include Northern Ireland. There is also no regional
dissaggregation. However, we would like to get some initial idea of the regional
impacts. Therefore, we split the impacts on the basis of areas of clay soil since the
majority – though not all – of property subsidence occurs in areas of clay soils. The
impacts are further attributed according to the populations of the regions containing
clay soils. We apportion the impact cost of £124 million between the regions, as
presented in Table 10-3. Future analysis should improve on this crude attributive
mechanism by using regional claims data, if available.
Table 10-3: Insurance claim costs of subsidence – Regional disaggregation
Region
London
South East
South West
Eastern
East Midlands
West Midlands
Yorks & Humb.
North West
North East
England
Wales
Scotland
Northern Ireland
UK
Final Report
Insurance claim costs of subsidence
(£m, 2004 prices)
Low
5
20
3
3
1
1
0
0
0
33
0
0
0
33
High
19
74
12
12
3
3
0
0
0
124
0
0
0
124
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PROJECT E: Quantifying the cost of impacts and adaptation:Summer 2003
10.4 Discussion
Before discussing the results some words of caution are warranted. First, the time
series analysis is based on only 52 observations. This suggests that care is needed as
new observations may affect the analysis significantly. In addition, the modelled
results are significantly different from the real world data, suggesting that there may
be other factors impacting upon insurance claims in this period.
A second point is that the estimates presented here do not constitute estimates of
welfare costs. That is, they do not include,for example, uninsurable losses or the
impacts on wellbeing of residents and owners. Rather, they represent the cost of
repairing damages. The welfare costs (avoided) are presumably higher.
However, the results do show that climate does impact on the amount of subsidence –
and this is a significant result. It is important to note in this context that the impact in
the summer is determined by the temperature and rainfall in the spring as well as in
the summer, and these are as important as the temperatures in the summer itself.
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PROJECT E: Quantifying the cost of impacts and adaptation:Summer 2003
11 CONCLUSIONS
This report has attempted to quantify the welfare impacts of the hot weather event of
2003 in the UK. We have adopted an approach that addresses perceived to be
important impacts in the following sectors: Health; Energy; Agriculture; Transport;
Retail; Water; Tourism and Built Environment. Where possible, regionally
disaggregated results have been provided. In not every case has it been possible to
estimate welfare costs (or benefits). Table 11-1 summarises the national level results
and are presented – for ease of comparison - alongside the results for Summer 1995,
estimated by Paluikof. et. al. (1997), where possible. As far as possible, the impacts in
the Palutikof et. al study common to those in our study are selected. It should be noted
that due to definitional differences of the weather event, methodological differences
and data limitations in both studies, the comparison is not always like-for-like.
Nevertheless, it is interesting to set the two sets of results alongside each other and see
that the values given in the two studies are generally not too dissimilar from each
other. Note that since changes in consumer expenditures or producer costs – rather
than net welfare impacts - are calculated in a number of sectors it does not make sense
to sum these sectoral totals.
Table 11-1. Welfare Costs (Benefits) of the Hot Weather Event of Summer 2003 in UK
compared to 1995 Hot Weather Event
Sector
2003
1995
£m
£m
41 (14 - 2604)
Not monetised
Energy*
80
40
Agriculture (Arable crops)**
88
212
Transport
26.6
19
Retail
+3.2
Negative
Water
-
114
+38 (23-53)
+133
124
180-240
Health
Tourism***
Built Environment
*Benefits to consumers; **Costs to producers; *** Increased consumer spending – comparison
with Agnew and Palutikof (2006)
The results shown in Table 11-1 bely the high degree of uncertainty inherent in the
estimates. This uncertainty is perhaps greatest in the estimation of health costs. Our
range for health costs predominantly reflects the uncertainty in the monetization of
mortality impacts, specifically whether a value of a life year (VOLY) or Value of a
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PROJECT E: Quantifying the cost of impacts and adaptation:Summer 2003
Prevented Fatality (VPF) is used. The higher end of the range reflects the use of the
latter unit value. Notably, Palutikof et. al. felt that the uncertainty in valuation of
mortality was too great to express health costs in monetary terms. Nevertheless, health
valuation of this type is used in other areas of environmental policy analysis and so is
included here.
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PROJECT E: Quantifying the cost of impacts and adaptation:Summer 2003
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