Towards Local Costing of Climate Change Impacts for Decision Making in Adaptation

Alistair Hunt Department of Economics,

University of Bath

I-SEE Seminar December 15th 2009

Contents of Presentation

• Motivation for research

• Estimating economic welfare costs of CC impacts at local scale, within UK.

• Some aspects of the economics of adaptation to climate change

Motivation for Research

• Information needed to prepare against possible risks– Provide quantitative estimates of economic welfare

costs of CC impacts at local scale, within UK benefits gained from taking adaptive action.

• How to make decisions in face of substantial uncertainty – Test use of cost information in adaptation

decision-making

Projected Baseline Impacts

‘without’ Climate Change (no adaptation)

Impacts(e.g. average annual total market and non-market

damages of flood)

Time2002 2030 2050 2080

Influence of Socio-economic change - e.g. increase in

number of properties, change in occupancy rates, change in value of property / contents

Stylised Analytical Framework: No CC Impacts/Adaptation

Historical analogue (1-250 yr flood)

(NB only linear to simplify presentation)

e.g. River Flooding in UK

Physical Impact Assessment

• Use of Socio-Economic scenarios to:– Quantify magnitude of physical impacts under CC

scenarios relative to climate baseline on consistent SE scenarios

– Inform unit values ( e.g. changing with GDP growth per capita)

• Use scenarios developed for UK Climate Impacts Programme – Governance & capacity of institutions at different

levels to manage change. – Orientation of social and political values– Up to 2050s, linear extrapolation to 2080s

Use of SES : River flooding example

• Quantitative: population and household size

• Qualitative:

Socio-economic factor Socio-economic scenario GS NE LS WM Planning Policy - ve + ve ? + ve Building Design - ve + ve + ve + ve Insurance policy + ve ? ? + ve Overall net effect - ve + ve Same? + ve

Future Impacts‘with’ Climate Change & no

Adaptation(predicted change in return

period)

Projected Baseline‘without’ Climate

Change & no Adaptation

Time2002 2030 2050 2080

Gross annual average cost of climate change

Impact of climate change on return

period

Stylised Analytical Framework

Impacts(e.g. average annual total market and non-market

damages of flood)

Generic methods for linking climate variables with physical impacts

• Using historical analogues of weather extremes to identify impacts. – E.g. flooding events. – Sectors: Building, Transport

•   Simulation modeling of behavioural change– E.g. carbon enrichment– Sectors: Tourism, Health, Agriculture and Biodiversity

• Stakeholder-led and Ad-hoc projections– E.g. retailing responses to warmer summers– Sectors: Retail & Manufacturing, Water, Energy

Physical Impact Assessment

• Climate data• Basis: UKCIP02 Climate scenarios

Data presented for:• precipitation & temperature• 5 X 5 km areas• individual months • in three time-slices of 30 years covering 2010 – 2100 

Assume climate change manifests itself either by:• -         changes in means of climate variable or;• -         climate variability (extremes)

Aggregating uncertainty in impact assessment

UK projected changes in average temperature

Low emissions

High emissions

Results for UK – 2080s time-slice Annual Average Welfare Costs (£ million, 2004 prices)

(-ve denotes benefit)

Low M-L M-H H

HealthMortality - summer 3 3 4 8 Mortality - winter -8 -8 -10 -15

AgricultureCrops - mean precpn. (Eng. only) 49 NQ NQ 294 Flooding (Eng & Wales) <1 18 2 -4

BiodiversitySelected species and habitats NQ NQ NQ NQ

TransportInfrastructure subsidence 35 19 62 101 Flooding & coastal inundation 13 19 19 26 Winter disruption & maintenance -102 NQ NQ -340

Built Environment & Cultural Heritage Flooding - fluv. & coastal (Eng. & Wales) -272 -470 419 353 Flooding - intra-urban -131 -100 368 32 Subsidence (Eng. only) 162 114 213 316

Results for UK – 2080s time-slice Changes in Consumer Expenditure (£ million, 2004 prices)

TourismVisitor Spend. 14,830 11,280 12,620 28,930

EnergyHeating -1,200 -1,300 -2,100 -2,800 Cooling 300 100 300 1,200

-ve denotes reduction in consumer spend; +ve denotes increase in consumer spend

Local costing: Impacts and primary stakeholder

Impact considered Primary Stakeholder

Road maintenance in summer (subsidence) and winter (salting - ice)

Cambridgeshire County Council

Domestic property subsidence Association of British Insurers

Historic garden maintenance in Cornwall (lawn mowing and pest control)

National Trust

Health impacts of hot summers in Hampshire

Hampshire County Council

Annual Impact multipliers over baseline

(2011–2040 time period, undiscounted) Impact considered Cost multipliers

Road maintenance in summer (subsidence) and; winter (salting - ice)

13 – 15

(-) 1.3 – 1.6

Domestic property subsidence 12 - 15

Historic garden maintenance in Cornwall (lawn mowing and pest control)

1.2 – 1.5

Health impacts of hot summers in Hampshire

16 - 18

Future Impacts‘with’ Climate Change

& no Adaptation

Projected Baseline‘without’ Climate

Change & no Adaptation

Time2002 2030 2050 2080

Gross benefit of adaptation for

comparison with costs of adaptation

Future Impacts (‘with’ Climate Change) after

Adaptation(e.g. reduction in predicted

return period)

Residual Impacts of Climate Change

Stylised Analytical FrameworkImpacts

(e.g. average annual total market and non-market cost

of flood)

Application to Flood Management

• Riverine flood risks in Shrewsbury, Shropshire – Impacts

• Direct physical damage to residential and non-residential property

• Forgone output from short-term disruption to non-residential properties.

• Direct impacts on human health (mortality, injuries and stress).

Total damage costs associated with different flood

frequencies in Shrewsbury (£'000s)     Average waiting time (yrs) between events/frequency per year

  Average waiting time (yrs) between events   3 5 10 15 25 50 100 150 Infinity

  Frequency per year 1 0.33 0.2 0.1 0.067 0.04 0.02 0.01 0.007 0

Damage category  

Residential property 5 12 78 84 98 188 326 352 352

Ind/commercial (direct) 7 146 376 440 570 1217 1514 1558 1558

Car damage 76 128 256 256 256 256 290 306 306

Infrastructure damage 12 25 29 31 36 48 77 79 79

Health 8 15 29 55 108 115 122 133 133

                   

Total damage (000) 107 325 767 866 1068 1824 2329 2427 2427

Area (damage X frequency) 35.62 28.78 54.62 55.07 55.07 36.48 20.73 7.93 16.18

Application to Flood Management

• Riverine flood risks in Shrewsbury, Shropshire – Adaptation

• Key problem: uncertainty in impacts may result in inappropriate level or type of adaptation

May be better to adopt a portfolio of options that reflect the decision-makers’ preferences relating to (economic?) optimisation versus reducing the chances of getting it wrong (variance from the “optimal”)

Flood management decision-making: portfolio analysis

• Instead of appraisal of single flood response options using the economic efficiency criterion, a group of options are collectively appraised.

• Economic efficiency criterion (Net Present Value) is the measure of portfolio return. Also measure NPV variance as indicator of uncertainty

May be better able to capture variations in effectiveness of responses across a wider range of possible (climatic and socio-economic) futures.

Potential Flood Management Options Option Type Specific Options

Managing the Rural Landscape to reduce runoff

Rural infiltration

Rural catchment storage

Rural conveyance

Managing the Urban Landscape Urban storage

Urban infiltration

Urban conveyance

Managing Flood Events Pre-event measures

Forecasting and warning systems

Flood fighting actions

Collective damage avoidance

Individual damage avoidance e.g. property resistance

Managing Flood Losses Land use management

Flood-proofing

Land use planning

Building codes

Insurance, shared risk and compensation

Health and social measures

River Engineering River conveyance

Engineered flood storage

Flood water transfer

“Hard” defences

Economic returns to flood management options

• 3 options: hard defence; property resistance; warning system

• CBA for each option– Three degrees of implementation (20%, 50%, 100%)– Constant-scale economies in costs assumed– Four (consistent) CC/SE scenario combinations– Portfolios created from combinations of two options

and three options, each option disaggregated according to degree of implementation

Two-option Portfolio Analysis

0

2000

4000

6000

8000

10000

12000

14000

0 20000000 40000000 60000000 80000000 100000000

Variance

EN

PV

Three-option Portfolio Analysis

0

2000

4000

6000

8000

10000

12000

14000

0 10000000 20000000 30000000 40000000 50000000 60000000

Variance

EN

PV

Results

• Economic efficiency – variance trade-off exists for both 2 and 3 option portfolios

• Sub-optimal portfolios can be identified

• Hard defences generally contribute most to higher NPV and higher variance; property resistance option has opposite effect.

Conclusions

• Seems possible to scope out identified climate change impacts against specified climate scenarios, though socio-economic scenarios add significant (even more!) complexity

• Adaptation assessment may be enriched by use of portfolio analysis – incorporates uncertainty more explicitly into decision-making. But reliant on reliable, quantitative data relating to both the costs and benefits of identified adaptation options.

Conclusions

• Future research priorities may, inter alia, include:– Applying portfolio analysis within a portfolio of

alternative decision rules – Improving representation of non-market

values within decision rules– Application of non-market valuation

techniques to evaluation of “softer”, behavioural-based, adaptation options