Task 2: Report on the Costs of the Hot Summer of 2003

- 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 [email protected] 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

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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 ii 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 event 1 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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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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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.

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 18 th and 33 rd warmest on record (based on the Central England Temperature), with mean temperatures of 16.1 oC 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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(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 long- term 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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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 3 2).

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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2.1 Annex 2A: Meteorological Data for 2003

Figure 2-1: Summer 2003 Maximum Temperature Anomaly with 1961-90 mean

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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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Table 2-1. Regional Meteorological Data for Summer 2003: Actual and as anomaly with 1961-90

Region Max temp Min temp Mean temp Sunshine Rainfall Actual Anom Actual Anom Actual Anom Actual Anom Actual Anom [°C] [°C] [°C] [°C] [°C] [°C] [hours] [%] [mm] [%] 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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Figure 2-4: Weather Anomalies during 2003 Relative to 1961-90 Long-term Average, by Region

(a) Mean Temperature

UK ENG WAL SCO NI

2.5

2.0

1.5

1.0

0.5

0.0 Temp. Anomaly ( degrees C ) C degrees ( Anomaly Temp. -0.5 Winter Spring Summer Autumn

Season

(b) Sunshine Hours

UK ENG WAL SCO NI

35%

30%

25%

20%

15%

10% term Average term 5%

-5% Percentage Change Relative to Long- to Relative Change Percentage Winter Spring Summer Autumn

Season

UK ENG WAL SCO NI

10% 5% 0% -5% -10% -15%

term Average term -20% -25% -30% -35% Percentage Change Relative to Long- to Relative Change Percentage Winter Spring Summer Autumn

Season

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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 4 th and August 13 th . 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 7 th , 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 6 th August, 2003). The Telegraph (August 8 th ) 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, 4 th 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, 6 th August, 2003).

During August the main focus was on the deaths in France, estimated to be 10,000, (Guardian 4 th 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, 4 th October, 2003) reported on ONS statistics that showed “that between August 4 th and 13 th , 2,045 more people in England and Wales died that would have been expected for the time of year. The peak day was August 11 th , 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 4 th to 13 th August. We estimated the impact on mortality associated with a specified analogue heat wave

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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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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) Older adults Elderly ages (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%) Yorkshire 106 (8%) -2 (-1%) -14 (-6%) 122 (15%) Humber North West 74 (4%) -1 (0%) -9 (-2%) 84 (8%)

North East 13 (2%) 10 (8%) -10 (-6%) 13 (3%)

England 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 9 (2%) 7 (8%) 7 (-6%) 9 (3%) Ireland UK 2157 266 84 1862

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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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 No. of excess hospital Office Region admissions in >75 age group (% change over average for 10-day period) London 464 (16%) South East -53 (-1%) South West 304 (11%) Eastern 94 (3%) East Midlands 322 (14%) West Midlands 14 (1%) Yorkshire Humber 36 (1%) North West 260 (7%) North East 50 (3%) England 1,491 Wales 8 (1%) Scotland 126 (3%) Northern Ireland 33 (3%) UK 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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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) Resource costs i.e. medical costs; (ii) Opportunity costs i.e. the cost in terms of lost productivity, and (iii) 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 life- year 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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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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Table 3-3: Regional Disaggregation of Valuation of Mortality Impacts – Summer 2003 (£m) VOLYs (£) VSL (£) Region Heat induced fatalities £5,000 £15,000 £50,000 £1,200,000 London 616 3.08 9.24 30.80 739.20 South East 447 2.24 6.71 22.35 536.40 South West 282 1.41 4.23 14.10 338.40 Eastern 254 1.27 3.81 12.70 304.80 East Midlands 169 0.85 2.54 8.45 202.80 West Midlands 124 0.62 1.86 6.20 148.80 Yorkshire 122 Humber 0.61 1.83 6.10 146.40 North West 84 0.42 1.26 4.20 100.80 North East 23 0.12 0.35 1.15 27.60 England 2091 10.46 31.37 104.55 2509.20 Wales 49 0.25 0.74 2.45 58.80 Scotland 26 0.13 0.39 1.30 31.20 Northern Ireland 0 0.00 0.00 0.00 0.00 UK 2157 10.79 32.36 107.85 2588.40

Table 3-4: Regional Disaggregation of Valuation of Morbidity Impacts – Summer 2003 (£m)

No. of Government Office excess RHA Unit value Region hospital £9,12 admissions £1,854 £5,487 0 London 464 0.86 2.55 4.23 South East 0 0.00 0.00 0.00 South West 304 0.56 1.67 2.77 Eastern 94 0.17 0.52 0.86 East Midlands 322 0.60 1.77 2.94 West Midlands 14 0.03 0.08 0.13 Yorkshire Humber 36 0.07 0.20 0.33 North West 260 0.48 1.43 2.37 North East 50 0.09 0.27 0.46 England 1,491 2.76 8.18 13.60 Wales 0 0.00 0.00 0.00 Scotland 126 0.23 0.69 1.15 Northern Ireland 33 0.06 0.18 0.30 UK 1,650 3.06 9.05 15.05

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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) Morbidity Mortality Total Government Office total total health Region values values values London 2.55 9.24 11.79 South East 0.00 6.71 6.71 South West 1.67 4.23 5.90 Eastern 0.52 3.81 4.33 East Midlands 1.77 2.54 4.30 West Midlands 0.08 1.86 1.94 Yorkshire Humber 0.20 1.83 2.03 North West 1.43 1.26 2.69 North East 0.27 0.35 0.62 England 8.18 31.37 39.55 Wales 0.00 0.74 0.74 Scotland 0.69 0.39 1.08 Northern Ireland 0.18 0.00 0.18 UK 9.05 32.36 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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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 6 th , 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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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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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

1 20

0 15 1980 1986 1992 1998 2004 1980 1986 1992 1998 2004

40 20

35 15 30

25 10 20

15 5 1980 1986 1992 1998 2004 1980 1986 1992 1998 2004

100 95 90 85 80 75 70 65 60 55 1980 1986 1992 1998 2004

3 Public administration, commerce and agriculture.

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Figure 4-2: Electricity Consumption by End User in Q3, 1980 to 2004 (TWh)

(a) Industry 4 (b) Domestic

30 25

25 20

20 15

15 10 1980 1986 1992 1998 2004 1980 1986 1992 1998 2004

30 80 75 25 70 65 20 60

15 55 50 10 45 40 5 1980 1986 1992 1998 2004 1980 1986 1992 1998 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; +2.31 oC 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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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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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 Gas (pence per kWh) (pence per kWh)

Domestic Including taxes 7.09 1.85 Excluding taxes 6.69 1.76 Industry Including taxes 3.35 0.87 Excluding taxes 3.12 0.81

Source: Derived from International Energy Agency publication, Energy Prices and Taxes Q2 2005, obtained from the DTI web- site ( 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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Table 4-2: Correlation Coefficient between Mean CET and ‘Residual’ Electricity and Gas Sales in Q3 Electricity Gas

Iron and Steel * +0.16 Industry -0.18 +0.17 Domestic -0.57 -0.38 Other Final User -0.02 -0.22 Total Sales -0.36 -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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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 (b) Other Industry

14 15 16 17 18 14 15 16 17 18 2.50 10.00

2.00 8.00 95 6.00 1.50

4.00 1.00 95 2.00 83 0.50 - - -2.00 -0.50 03 -4.00 83 -1.00 -6.00 03 -1.50 -8.00

-2.00 -10.00

14 15 16 17 18 14 15 16 17 18 6.00 6.00

5.00 4.00

4.00 2.00 3.00 95 83 2.00 -

1.00 -2.00 -

-1.00 -4.00 95 -2.00 -6.00 03 -3.00 03 83 -4.00 -8.00

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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Figure 4-4: Scatter Plots of ‘Residual’ Electricity Sales (TWh) in Q3 by End User against Average Q3 CET (degrees C)

(a) Industry 8 (b) Domestic

14 15 16 17 18 14 15 16 17 18 2.00 1.50

1.50 1.00 1.00 03 0.50 0.50

- - 83 -0.50

95 -1.00 83 -0.50 03 -1.50 -1.00 95 -2.00

-2.50 -1.50

14 15 16 17 18 14 15 16 17 18 1.50 4.00

1.00 3.00 0.50 95 2.00 - 83

-0.50 1.00

-1.00 -

-1.50 03 03 -1.00 -2.00 83 95 -2.00 -2.50

-3.00 -3.00

8 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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Table 4-3: Financial Impact of Hot 2003 Summer on Gas Sales, by End User Anomaly Excluding Tax Including Tax Actual Predicted Actual Predicted Actual Predicted Effect Effect Effect Effect Effect Effect (TWh) (£ 2003 million) (£ 2003 million)

Iron and Steel -1.12 +0.31 -9.1 +2.5 -9.7 +2.7 Other Industry -2.00 +1.06 -16.2 +8.6 -17.4 +9.3 Domestic -2.31 -1.78 -40.7 -31.4 -42.8 -33.0 Other Final User Domestic price -5.72 -0.37 -100.7 -3.0 -105.8 -3.2 Industry price -5.72 -0.37 -46.3 -6.5 -49.8 -6.9

Table 4-4: Financial Impact of Hot 2003 Summer on Electricity Sales, by End User Anomaly Excluding Tax Including Tax Actual Predicted Actual Predicted Actual Predicted Effect Effect Effect Effect Effect Effect (TWh) (£ 2003 million) (£ 2003 million)

Other Industry +0.50 -0.30 +15.7 -9.3 +16.9 -10.0 Domestic -0.53 -0.54 -35.6 -36.5 -37.4 -38.4 Other Final User Domestic price -1.26 +0.01 -39.3 -0.3 -42.2 -0.3 Industry price -1.26 +0.01 -85.0 -0.7 -89.2 -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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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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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 11 th , 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, 5 th 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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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 2003- 04).

• 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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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 semi- fixed 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 semi- fixed 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 non- marginal 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, non- recurring 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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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, 29 th – 32 nd 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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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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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 +0.3 -0.6 Barley +0.3 -0.3 Oats -neg -neg Sugar Beet -neg +0.2 Peas for Harvesting +0.6 -0.4 Field Beans -0.3 +0.4 Oilseed -neg +neg Linseed +neg +neg Potatoes +0.3 +neg Open Vegetables +0.5 -0.5 Protected Vegetables +0.2 -0.4 Cattle +neg -0.2 Sheep -0.3 -neg Pigs -0.5 +0.6 Poultry and Table Fowl +neg -neg Milk -0.2 +0.4 Hen Eggs -0.3 +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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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 long- term 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 +30 +23 Barley * +18 +14 Oats neg neg Sugar Beet -7 -5 Peas for Harvesting neg neg Potatoes * neg neg Field Beans * -5 -4 Oilseed neg neg Linseed neg neg Open Vegetables *** +71 +56 Protected Vegetables ** +11 +8 Cattle neg neg Sheep -22 -17 Pigs ** -6 -5 Poultry and Table Fowl neg neg Milk -10 -8 Hen Eggs ** neg neg

Sub-total (gross margin) -2 -2

Sub-total (price) +82 +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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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 60- 80%, 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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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 pigs 12 .

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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5.5 Annex 5A: UK Production and Yields (1984-2004)

(a) Wheat (b) Barley (c) Oats

2,200 9 2,100 7 145 7

8 6 6 2,100 1,900 135 7 2,000 5 5 6 1,700 125

1,900 5 4 4 1,500 115 1,800 4 3 3 3 1,300 105 1,700 2 2 Area ( 000 ha ) ha 000 ( Area Area ( 000 ha ) ha 000 ( Area Area ( 000 ha ) ha 000 ( Area Yield ( t per ha ) ha per t( Yield Yield ( t per ha ) ha per t ( Yield 2 ) ha per t ( Yield 1,100 95 1,600 1 1 1

1,500 0 900 0 85 0 1984 1994 2004 1984 1994 2004 1984 1994 2004

(d) Oilseed (e) Linseed (f) Sugar Beet

580 4 250 3 215 70

530 205 60 200 3 480 195 50 2 150 430 185 40 2 380 175 30 100 1 330 165 20 Area ( 000 ha ) ha( 000 Area Area ( 000 ha) ( 000 Area ha) ( 000 Area Yield ( tYield ha ) per 1 ( Yield t ha per ) ( Yield t ha per ) 50 280 155 10

230 0 0 0 145 0 1984 1994 2004 1984 1994 2004 1984 1994 2004

(g) Peas for Harvesting (h) Field Beans (i) Potatoes

110 5 200 5 210 50

180 45 100 200 4 40 4 160 90 190 35 140 80 3 3 30 120 180 70 25 100 2 170 60 2 20 80 15

Area ( 000 ha ) ha( 000 Area ha() 000 Area 160 Area ( 000 ha) ( 000 Area Yield ( tYield ha ) per (tYield ha) per 50 ( t Yield ha) per 60 1 1 10 40 150 40 5 30 0 20 0 140 0 1984 1994 2004 1984 1994 2004 1984 1994 2004

(j) Open Vegetables (k) Protected Vegetables (l) Cattle

200 800 4 400 14,000 1200

190 700 350 1000 180 13,000 600 3 300 170 800 500 250 160 12,000

150 400 2 200 600 140 11,000 300 150 400 130 Area ( 000 ha ) ha ( 000 Area Area ( 000 ha ) ( 000 Area 200 1 100 10,000 120 200 Value of Value Output ( mn £ ) 110 100 of Value Output ( mn£ ) 50 HomeFed) Marketing (000 HomeFed t ( dwcProd. 000 ) 100 0 0 0 9,000 0 1984 1994 2004 1984 1994 2004 1984 1994 2004

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(m) Sheep (n) Pigs (o) Milk

24,000 450 17,000 1200 3,400 7000

400 16,000 22,000 1000 3,200 6000 350 15,000 20,000 3,000 5000 300 14,000 800

18,000 250 13,000 2,800 4000 600 16,000 200 12,000 2,600 3000 150 11,000 400 14,000 2,400 2000 100 10,000 Dairy Herd ( 000 ) ( Herd 000 Dairy 12,000 200 50 9,000 2,200 1000 HomeFed ) Marketing( 000 HomeFed ) (Marketing 000 Homet Fed ( Prod. dwc 000 ) HomeFedt (dwc Prod. 000 )

10,000 0 8,000 0 ) Cow per Yield year / ( litres 2,000 0 1984 1994 2004 1984 1994 2004 1984 1994 2004

(p) Poultry and Table Fowl (q) Hen Eggs

1,800 900

1,600 850

1,400 800

1,200 750 ( 000 ) (000 1,000 700

800 650 Production( t 000 dwc )

600 600 1984 1994 2004 1984 1994 2004 Productionfor Human Consumption

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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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Table 6-1 Possible linkages between the climate and the retail sector Climate Variable Impact Increased Mean Temperature in Summer 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) Increased Mean Temperature in Winter Change in commodities consumed Reduced sickness in workforce Change in price and quantity supplied of some commodities Changes in costs (e.g. reduced heating) Increased Precipitation in Winter Reduced frequency of shopping trips Change in consumption for some goods (e.g. raincoats, umbrellas) Changes in price and quantity o agricultural commodities Reduced precipitation in summer Increased frequency of shopping trips Change in consumption for some goods (e.g. DIY) Increased Sun Days Increased frequency of shopping trips Change in consuption for some goods Increased Flooding Direct impact on sales through closure of shops Impact on transport of commodities Increase in "Extreme Events" 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) Non-UK Climate Change 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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Table 6-2: Impact of Summer Climate Variation on Sales of Textiles, Clothing and Footwear

Sales of Textiles, Sales of Sales of Dependent Clothing and Household Predominantly Variables Footwear Goods Food Stores Code EARA EARB EAQW

Regressors Constant 161.345 ** 130.1332 *** 172.7135 ** CET -0.088642 ** -0.015801 EWR -0.019175 * 0.0078088 *** EWRSQ 1.23E-05 * EWR(-1) 0.0062059 ** SUN 0.0078794 *** 0.024702 *** -0.0046973 * SUNSQ -7.64E-05 *** 2.18E-05 ** DISPINC 0.0038864 -0.0032796 0.0013997

R-square 0.9937 0.9957 0.99938 DW 1.998 1.9475 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 Estimate July Aug Sep Textiles, 2003 model 164.557 165.116 164.923 clothes and 1961-90 model 164.764 164.704 164.593 footwear Difference -0.207 0.412 0.331 2003 model 129.710 129.157 129.107 Household 1961-90 model 129.567 129.751 129.347 Goods Difference 0.144 -0.594 -0.241 2003 model 173.738 173.855 173.772 1961-90 model 173.790 173.762 173.720 Mainly food Difference -0.052 0.092 0.052

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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 July August September Total Textiles, clothes and footwear -4.9 9.8 7.6 12.4 Household goods 3.4 -14.0 -5.5 -16.1 Mainly food -4.0 7.0 3.8 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 9 th , 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, 9 th 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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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 slow- down 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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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) hot weather extremes; b) cold weather extremes; c) 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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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 [email protected] , or by fax to 01225 461678

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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 Positive (benefits) Negative (Costs) Air Increased internal flights. £1 million Reduced payloads. Reduced overseas flights. Loss of overseas holidays £10million

Rail Increased revenue from trips £10 million Rail buckle £1 million Speed restrictions £1 million Increased lineside fires £1 million Road 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 Water Reduced delays to offshore shipping £1 Closure of canals due to water shortages– loss million of income £1 million. Reduction in sales to farmers. Total £20 million £36 million

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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• 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 Rail 13 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 Watchman deployed to monitor track Free Temperature (SFT), or roughly equivalent to ambient air temperature above 35 °C

Rail temperature more than 37 °C above SFT, or Initial speed restriction (30 or 60 mph roughly equivalent to ambient air temperature above depending on location) 40 °C

Rail temperature more than 42 °C above SFT, or More stringent speed restriction (20 mph) roughly equivalent to ambient air temperature above 45 °C

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 Speed restriction (45 or 90 mph depending will be above 36 °C on location)

24-hour forecast indicates ambient air temperature More stringent speed restriction (30 or 60 will be above 41 °C 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 1976 1990 1991–94 1995 1996– 2003– 2004 a 2003 04

Number of 132 73 32 (on 133 36 (on 137 42 reported rail average) average) buckles

Weather b Hot Warm Hot Hot summer summer summer summer

Average summer temperature in Central England 17.8 °C 16.2 °C 15.6 °C 17.4 °C 15.9 °C 17.3 °C 16.2 °C Temperature dataset 14 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 weather- related 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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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 3.5.6 15 ). 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 journeys 16 . 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) Resource Cost Perceived Cost Market Price Vehicle £/hour £/hour £/hour

Rail – working time 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 non- work 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 (non- work)

Weekly average - Percentage 16.5% 37.8% 45.7% of Distance Travelled by Occupants

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 Passenger Unit Values Total

Minutes Delay Hours* 2004 prices/hr Work 34076 38.74 1,319,978

Commuting 78065 5.28 412,357

Other 94380 4.67 441,166

Total 135,000 2,173,501

* 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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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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Table 7-6 . Incidence of railway lineside and station fires in Great Britain

Period 2002-03 2003-04 2004 a

Reportable lineside / 84 119 92 station fires 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 revenues 18 . 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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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 Transport 19 . 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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Table 7-7. UK Regional road subsidence costs – Summer 2003 Total Central Damage cost Govt. Region (£m) contribn.

SE 18.69 9.82 E 13.24 8.09 EM 7.40 5.36 SW 1.30

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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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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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 Britain 20 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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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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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: time delays for maintenance

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 break-downs from overheating

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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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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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.

Table 8-1: Overall Reservoir Stocks 1990-2004 1

England and Wales percentage full 2 Mean 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 (1988-2004)

January 87 73 80 85 91 95 95 61 79 91 96 96 95 86 95 80 February 90 90 87 84 96 97 98 71 76 93 97 96 94 94 95 94 March 92 93 91 88 90 96 98 82 92 92 97 97 95 96 92 92 April 93 92 93 92 85 97 97 85 92 97 97 95 95 95 92 94 May 92 88 91 95 92 96 93 86 87 97 97 97 97 92 89 95 June 90 82 86 91 95 92 88 88 88 94 95 96 92 97 93 91 July 86 3 77 85 83 90 87 80 82 88 95 92 94 85 95 87 85 August 80 3 70 82 76 87 76 69 73 81 93 83 89 81 91 81 78 September 74 3 59 74 80 82 69 53 63 74 88 77 83 78 86 70 82 October 71 51 65 84 81 72 47 55 71 87 80 88 77 77 60 84 November 75 59 65 85 81 75 52 63 69 93 82 95 86 83 53 88 December 80 3 67 78 89 80 86 57 77 76 93 85 97 88 92 61 86 Annual average 84 75 81 86 87 86 77 74 81 93 90 94 89 90 81 87

Source : Centre for Ecology and Hydrology, Wallingford

1 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. 2 Percentage of useable capacity 3 Revision made in 2004

Table 8-2: Drought orders in England and Wales

United Kingdom Number 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 EA Region ²

North West 3 2312 0 0 0 0 0 01 0 North East 21 18 0 0 0 0 0 0 0 0 Midlands 1 2 0 0 0 0 0 0 0 0 Anglian 001000000 0 Thames 0000000000 Southern 1 5 2 0 0 0 0 0 0 0 South West 7 5 0 0 0 0 0 0 0 0

Welsh 4 0 2 0 0 0 0 0 0 0 0

England and Wales 53 44 3 0 0 0 0 0 1 0

Scotland 0 0 0 0 0 1 0 01 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

Source: WSR; DEFRA; SEERAD; DRD (NI) Water Service

1 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). 2 Water Authority Region before 1989. Includes water supply companies. 3 1996 figure includes one order made for environmental purposes. 4 The 1984 figure includes three orders made by the Welsh Office jointly with the Department of the Environment (Midlands Region).

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Table 8-3: Leakage in England and Wales (Ml)

1992/3 1993/4 1994/5 1995/6 1996/7 1997/8 1998/9 1999/2000 2000/1 2001/2 2002/3 2003/4 2004/5

Distribution input 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 3,600 3,693 3,866 3,685 3,295 2,955 2,618 2,432 2,365 2,527 2,606 2,625 2,584 per cent of input 22 23 23 22 20 19 17 16 16 16 17 17 17

Supply pipe losses 1,181 1,195 1,246 1,295 1,233 1,034 933 875 878 888 999 1,024 1,024 per cent of input 7 7 8 8 8 7 6 6 6 6 6 7 7

Total leakage 4,781 4,888 5,112 4,980 4,528 3,989 3,551 3,306 3,243 3,414 3,605 3,649 3,608 per cent of input 29 30 31 29 28 25 24 22 22 22 23 23 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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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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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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Figure 9-1: Tourism Trips and Nights – England – 1998 to 2004

40 Trips 30 Nights Millions 20

v r b ul y r g r p e J a ov p No F Ma Au N Sep 8 Jan 9 A 1 Oct M 3 Jan 4 A 8 0 00 2 2 3 0 99 99 20 00 00 00 1 1998 199Jun 1 1999 Sep200 2000 Dec2001 2 200 200 2 2003 2Jun 20 2004 Month

Figure 9-2: Expenditure by Domestic Tourists – England – 1998 to 2003

3,000

2,500

2,000

1,500 Expenditure

£million 1,000

500

0 1998Jul 1999Jul 2000Jul 2001Jul 2002Jul 2003Jul 2004Jul 1998Jan 1999Jan 2000Jan 2001Jan 2002Jan 2003Jan 2004Jan 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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It should be noted that the structure of our data does not allow for the testing of random effects 21 . 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 + βn.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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• 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 Bed nights Expenditures Regressors Coef. Std. Coef. Std. Coef. Std. Err. Err. Err. Constant 0.5829 (***) 0.1330 2.8683 (***) 0.4513 -330.32 (***) 26.2607 Mean temperature 0.0133 (***) 0.0039 0.0407 (***) 0.0133 0.9138 0.7723 Sunshine hours 0.0015 (***) 0.0002 0.0045 (***) 0.0008 0.2909 (***) 0.0457 Real income 0.0001 0.0001 -0.0002 (*) 0.0001 0.1514 (***) 0.0090 Summer (dummy) 0.1478 (***) 0.0271 0.4580 (***) 0.0919 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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• 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 + βn.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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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 time- invariant 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 (∆ = X t – X t-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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Table 9-2 Dynamic Models – Arellano-Bond panel data estimator Trips Bed nights Expenditures Regressors Coef. Std. Coef. Std. Coef. Std. Err. Err. Err. Constant 0.0228 (***) 0.0038 0.0701 (***) 0.0183 4.6527 (***) 1.3945 Lagged ∆ of the -0.1339 (**) 0.0635 -0.0498 0.0509 -0.1192 (**) 0.0535 dependent variable ∆ of mean temperature 0.0095 (***) 0.0032 0.0288 (**) 0.0120 1.5881 (**) 0.7200 ∆ of sunshine hours 0.0024 (***) 0.0004 0.0071 (***) 0.0015 0.3253 (***) 0.0504 ∆ of rain fall -0.0003 (***) 0.0001 -0.0015 (**) 0.0006 -0.1276 (**) 0.0500 ∆ of real income -0.0005 0.0003 -0.0017 0.015 -0.4233 (*) 0.2240 ∆ of summer periods 0.1681 (***) 0.0279 0.4672 (***) 0.0923 26.95 (***) 5.1512 ∆ of heat wave (Jun03- 0.0431 (***) 0.0159 0.1381 (***) 0.0520 14.4758 (***) 2.8398 Aug03) dummy N 690 690 690 H0: No autocorrel. of Pr > z = 0.0067 Pr > z = 0.0202 Pr > z = 0.0087 order 1 H0: No autocorrel. of Pr > z = 0.3000 Pr > z = 0.2794 Pr > z = 0.1406 order 2 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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Table 9-3 Dynamic models including further lag effects – Arellano-Bond panel data estimator Trips Bed nights Expenditures Regressors Coef. Std. Coef. Std. Coef. Std. Err. Err. Err. Constant 0.0892 (***) 0.0314 0.0254 0.1383 -15.4354 13.3662 Lagged ∆ of the t 0.6486 (***) 0.1176 0.7693 (***) 0.1090 0.7143 (***) 0.1162 dependent variable t 0.0476 (***) 0.0149 0.1167 (***) 0.0437 5.5819 (***) 1.8680 t-1 0.0199 (***) 0.0071 0.0558 (*) 0.0325 6.6604 (***) 1.9017 t-2 -0.022 (***) 0.0078 -0.075 (***) 0.0260 -1.4134 1.2643 ∆ of mean temperature t-3 -0.060 (***) 0.0163 -0.162 (***) 0.0498 -8.663 (***) 2.6086 t-4 0.052 (***) 0.0160 0.1928 (***) 0.0612 16.778 (***) 5.3621 t-5 0.0223 0.0166 0.1187 (*) 0.0707 7.0220 (**) 2.8440 t-6 0.0216 0.0202 0.1154 0.0720 12.328 (**) 5.1581 t 0.002 (***) 0.0006 0.0073 (***) 0.0025 0.5856 (***) 0.1749 t-1 0.0003 0.0002 0.0009 0.0009 -0.0137 0.0656 t-2 0.0013 (**) 0.0005 0.0049 (***) 0.0019 -0.0297 0.1226 ∆ of sunshine hours t-3 0.0017 (***) 0.0005 0.0054 (***) 0.0015 0.2117 (**) 0.0957 t-4 -0.002 (***) 0.0004 -0.006 (***) 0.0014 -0.518 (***) 0.1469 t-5 -0.004 (***) 0.0007 -0.012 (***) 0.0032 -0.607 (***) 0.1318 t-6 -0.0008 0.0005 -0.0026 0.0021 -0.359 (**) 0.1692 t -0.0003 0.0004 -0.0005 0.0013 0.0719 0.0805 t-1 -0.001 (***) 0.0002 -0.004 (***) 0.0007 -0.165 (***) 0.0287 t-2 -0.0002 0.0001 -0.0006 0.0005 -0.195 (***) 0.0649 ∆ of rain fall t-3 0.0012 (***) 0.0002 0.0042 (***) 0.0010 0.2604 (***) 0.0518 t-4 0.0002 0.0004 0.0013 0.0017 0.1628 0.1089 t-5 0.0005 0.0003 0.0024 (**) 0.0011 0.2104 (***) 0.0711 t-6 -0.0002 0.0002 0.0003 0.0005 -0.0245 0.0390 ∆ of real income t 0.0005 0.0004 0.0011 0.0016 0.1211 (*) 0.0712 ∆ of summer periods t -0.036 (***) 0.0456 -1.010 (***) 0.1679 -54.87 (***) 9.9139 ∆ of heat wave t 0.2929 (***) 0.0753 1.196 (***) 0.2861 87.785 (***) 18.9300 (Jun03-Aug03) dummy N 345 345 345 H0: No autocorrel. of order Pr > z = 0.0076 Pr > z = 0.0317 Pr > z = 0.0294 1 H0: No autocorrel. of order Pr > z = 0.0231 Pr > z = 0.0722 Pr > z = 0.1260 2 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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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 11 th 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

Impact Summer 03 Impact Model - Model - (Observed Summer 03 prediction prediction minus (predicted with real with 1961- mean minus Month Observed values 90 means model) mean) July 2,068.89 1,721.23 1,722.20 346.69 -0.97 August 2,482.67 2,050.03 2,026.61 456.06 23.41 September 1,655.11 2,411.86 2,416.68 -761.57 -4.82 Total impact (England) 41.18 17.63 Total impact (UK) 53.05 22.70

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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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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 1995 23 . 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, 27 th 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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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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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 similar- sized effect in the two hot summers.

Table 10-2: Insurance Claims as a Result of Summer 2003 anomaly (£m, 2004 prices)

3-yr average Model Actual comparison

2003 Q3+Q4 218.1 307 390*

1961-1990 ave 184.3 184.3 266**

Total 33.8 122.7 124

* 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 Insurance claim costs of subsidence (£m, 2004 prices) Low High London 5 19 South East 20 74 South West 3 12 Eastern 3 12 East Midlands 1 3 West Midlands 1 3 Yorks & Humb. 0 0 North West 0 0 North East 0 0 England 33 124 Wales 0 0 Scotland 0 0 Northern Ireland 0 0 UK 33 124

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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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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

Health 41 (14 - 2604) Not monetised

Energy* 80 40

Agriculture (Arable crops)** 88 212

Transport 26.6 19

Retail +3.2 Negative

Water - 114

Tourism*** +38 (23-53) +133

Built Environment 124 180-240

*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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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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12 REFERENCES

AEA Technology (2004). Railway Safety Implications of Weather, Climate and Climate Change: Final Report to The Rail Safety and Standards Board. 04/T096/RSRP/05/SPERserv/269.

Agnew, M. D. (1997) "Tourism" In: J. P. Palutikof, S. Subak, and M. D. Agnew, eds. Economic Impacts of the Hot Summer and Unusually Warm Year of 1995. Report prepared at the request of the Department of Environment. Published by the University of East Anglia, Norwich, UK.

Agnew, M. D. and J. P. Palutikof (2006) “Impacts of Short-Term Climate Variability in the UK on Demand for Domestic and International Tourism”. Climate Research 31 109-120.

Alberini, A., A. Hunt and A. Markandya, (2006). The Willingness to Pay for Mortality Risk Reductions: An EU 3-Country Survey . EC DG Research.

Arellano, M. and S. Bond (1991), "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations", The Review of Economic Studies 58 277-97.

Atkins (2005). Climate Change and London’s Transport Systems: Technical Report for the London Climate Change Partnership , published by the GLA.

Baltagi, B.H. (2001), Econometric Analysis of Panel Data, second edition, Chichester: John Wiley and Sons, Ltd.

Beniston, M., and D.B. Stephenson, (2004). “Extreme Climatic Events and Their Evolution Under Changing Climatic Conditions”, Global and Planetary Change , 44 , 1-9.

BRE (2004). Understanding Thermal Comfort on London Underground Trains and Stations – Summer Survey . Report No. 211738, for London Underground Limited.

Chilton S., Covey J., Jones-Lee M., Loomes G. & Metcalf H. (2004). Valuations of Health benefits Associated with Reductions in Air Pollution . London: Defra. Transport for London (2004). London Travel Report 2004 . Available online at http://www.tfl.gov.uk/tfl/pdfdocs/ltr/london-travel-report-2004.pdf

DfT (2004). The Changing Climate: Impact on the Department for Transport . Sarah Wooller of the In-House Policy Consultancy, DfT.

DfT (2004b). Road Casualties in Great Britain: 2004 Annual Report . Published by Transport Statistics.

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Giles, A.R. and A.H. Perry (1998) "The Use of a Temporal Analogue to Investigate the Possible Impact of Projected Global Warming on the UK Tourist Industry" Tourism Management 19 :75-80.

Greene, W. (1993), Econometric Analysis , Third Edition, New Jersey: Prentice-Hall, Inc.

Gujarati, D (1999), Essentials of Econometrics , Second edition, Singapore: Irwin/McGraw-Hill. Hamilton, J.M., D.J. Maddison and R.S.J. Tol (2004), Climate Change and International Tourism: A Simulation Study. In : Amelung, B., K. Blazejczyk, A. Matzarakis and D. Viner, Climate Change and Tourism: Assessment and Coping Strategies. NATO Science Series. Dordrecht: Kluwer Academic Publishers, in press. HSE (2004). Health and Safety Executive’s Annual Report on the Safety Record of the Railways in Great Britain During 2004. Available from www.hse.gov.uk/railways/statistics.htm

Hurley, F., A. Hunt, H. Cowie, M. Holland, B. Miller, S. Pye, P. Watkiss (2005). Methodology Paper (Volume 2) For Service Contract For Carrying Out Cost-Benefit Analysis of Air Quality Related Issues, In Particular In the Clean Air For Europe (CAFE) Programme. Published at: europa.eu.int/comm/environment/air/cafe/activities/cba.htm

Johnson, H., Kovats, R.S., McGregor, G. R. Stedman, J. R Gibbs, M. Walton, H., Cook, L., and E. Black, (2005) "The Impact of the 2003 Heatwave on Mortality and Hospital Admissions in England", Health Statistics Quarterly , (2005) 6-11. Jorgensen, D.W., Goettle, R.J., B.H. Hurd and Joel B. Smith et al (2004) "US Market Consequences of Global Climate Change". Report for Pew Center on Global Climate Change. Available online at www.pewclimate.org/docUploads/Market%5FConsequences%2Dreport%2Epdf Kovats, R. S., Hajat, S., and P. Wilkinson, (2004) "Contrasting Patterns of Mortality and Hospital Admissions During Heatwaves in London, UK", Occupational and Environental Medicine , 61 (2004) 893-898.

Leonardi, G., Hajat, S., Kovats, R. S., Smith, G. E., Cooper, D., and Gerard, E. (2006) "Syndromic Surveillance Use to Detect the Early Effects of Heat Waves: An Analysis of NHS Direct Data in England". Social & Preventitive Medicine ; 51 (4) .

Levy, J. I., T. J. Carrothers, J. T. Tuomisto, J. K. Hammitt, J. S. Evans (2001). “Assessing the Public Health Benefits of Reduced Ozone Concentrations.” Environmental Health Perspectives 109 pp.1215-26.

MAFF (1999) Flood and Coastal Defence Project Appraisal Guidance: Economic Appraisal, MAFF, London.

McKenzie Hedger, M., M. Gawith, I. Brown, R. Connell, and T.E. Downing, (eds.) (2000). Climate Change: Assessing the Impacts - Identifying Responses. The First

Final Report - 91 - PROJECT E: Quantifying the cost of impacts and adaptation:Summer 2003

Three Years of THE UK Climate Impacts Programme. UK Climate Impacts Programme, available at http://www.ukcip.org.uk/resources/publications/pub_dets.asp?ID=16

Metroeconomica (2004) Costing the Impacts of Climate Change. Report for UK Climate Impacts Programme . Available online at http://www.ukcip.org.uk/resources/publications/pub_dets.asp?ID=54 Nix, J., (various) Farm Management Pocketbook, 29 th – 32 nd Edition. Wye College, London OFWAT (2004) Financial Performance and Expenditure of the Water Companies in England and Wales 2003-04 Report . OFWAT.

Palutikof, J.P. and Agnew, M.D., (1999) “Report of University of East Anglia.” Weather Impacts on Natural, Social and Economic Systems (WISE): Final Report Contract ENV4-CT97-0448. University of East Anglia, Norwich, UK.

Palutikof, J. P., Subak, S., and Agnew, M.D, (1997). Economic Impacts of the Hot Summer and Unusually Warm Year of 1995 . Report prepared at the request of the Department of Environment. Published by the University of East Anglia, Norwich, UK.

Panagiotakos, D. B., Chrysohou, C. and C. Pitsavos, (2004) "Climatological Variations in Daily Hospital Admissions for Acute Coronary Syndromes", International Journal of Cardiology , 94 (2004) 229-233.

Schwartz, J., Samet, J. and J. A. Patz, (2004)" The effects of Temperature and Humidity on Hospital Admissions for Heart Disease", Epidemiology , 15 (2004) 755- 761. Starr, M., (2000). The Effects of Weather on Retail Sales. FEDS Working Paper No. 2000-08. Available at SSRN: http://ssrn.com/abstract=221728 or DOI: 10.2139/ssrn.221728 Stott, P. A., Stone, D. A., and Allen, M. R. (2004) “Human Contribution to the European Heatwave of 2003” Nature, 432 , 610-614

Subak, S. (1997) "Agriculture" In: J. P. Palutikof, S. Subak, and M. D. Agnew, eds. Economic Impacts of the Hot Summer and Unusually Warm Year of 1995. Report prepared at the request of the Department of Environment. Published by the University of East Anglia, Norwich, UK.

Thornes, J.E. (1997) “Transport”. In: J. P. Palutikof, S. Subak, and M. D. Agnew, eds. Economic Impacts of the Hot Summer and Unusually Warm Year of 1995. Report prepared at the request of the Department of Environment. Published by the University of East Anglia, Norwich, UK.

Watson, S. J. & J. C. Woods, (1997), "Energy," In: J. P. Palutikof, S. Subak, and M. D. Agnew, eds. Economic Impacts of the Hot Summer and Unusually Warm Year of 1995. Report prepared at the request of the Department of Environment. Published by the University of East Anglia, Norwich, UK. pp. 75-85

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Wilson, M. I., and M. H. Burtwell, (2002) Prioritising Future Construction Research and Adapting to Climate Change: Infrastructure (Transport and Utilities) TRL: Limited Project Report PR/IS/13/02 for the CRISP Commission.

Wooldridge, J.M (2002), Econometric Analysis of Cross Section and Panel Data , Cambridge: Massachusetts Institute of Technology Press.

Waughray, D. (1997) "Water" In: J. P. Palutikof, S. Subak, and M. D. Agnew, eds. Economic Impacts of the Hot Summer and Unusually Warm Year of 1995. Report prepared at the request of the Department of Environment. Published by the University of East Anglia, Norwich, UK.

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