the future for water quality...the future for water quality ian j. bateman cserge, university of...
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The Future for Water QualityIan J. BatemanCSERGE, University of East Anglia, UK
Team members include: Eric Audsley, Sandra Barns, Ian Bateman, Roy Brouwer, John Crowther, Emma Coombes, Helen Davies, Brett Day, Amelie Deflandre, Silvia Ferrini, Carlo Fezzi, David Hadley,Danyel Hampson, Steph Hime, Mike Hutchins, Andy Jones, Dave Kay, Graham Leeks, Andrew Lovett, Colin Neal, Kerry Pearn, Paulette Posen, Dan Rigby, Daniel Sandars, Dawn Turnbull, Kerry Turner, Bruce Willoughby.
Water policy & environmental
change
Impacts
Outcomes
Modelling FarmLand Use &
Incomes
Farm income
Water quality
Costs Benefits
Spatial Cost-Benefit Analysis
Household welfare
Policy compliance testing
Modelling theWater
Environment
BenefitValuation
Water Water –– The Wider PictureThe Wider Picture
Market forces & ag. policy
The ChREAM team assembled Agricultural Census data for every 2km grid square, for all of England and Wales from 1969 to 2004 and combine this with over 50,000 farm years of data from the Farm Business Survey. This gives:
• Agricultural land use hectares (wheat, barley, grass, etc.);
• Livestock numbers (dairy, beef, sheep); etc.
We then add
• Environmental and climatic variables (rainfall, temperature, machinery working days, field capacity, etc.);
• Policy determinants (NVZ, NSA, ESA, Parks, etc.)
• Input and output prices for the period
Data and modellingData and modelling
Land use model subjected to actual versus predicted testing
Climate change simulationClimate change simulation
Modelling land use change as a result of:
• climate change;
• new policy;
• world market shifts;
• etc.
Also estimating resultant farm incomes
Nitrate leaching per month
Integrated modelling: Linking land use with diffuse water pollution
Land use change & water qualityLand use change & water quality
Nitrate leaching
per month
Modelling the impacts of land use change on river water quality and ecosystems services
- and how water policy forces land use to change
Land use change & water qualityLand use change & water quality
Dairy Farms (before)
Dairy Farms (after fert.
limit)
Policy change impacts on farm incomesPolicy change impacts on farm incomese.g. impact of a fertiliser limite.g. impact of a fertiliser limit
Profit (£ / ha)
Estimating policy impacts across Estimating policy impacts across an agriculturally diverse catchmentan agriculturally diverse catchment
The Yorkshire Derwent
Cost effectiveness of alternative policy tools: Cost effectiveness of alternative policy tools: Case study of the Derwent catchment Case study of the Derwent catchment
Baseline20% Fertiliser
reduction20% Livestock
reduction20% arable to
grass
N Concentration(mgN/L)
∆ ∆ ∆ ∆ Profit (£m)
Effectiveness(£/ha per mg/L)
-2.39
-0.25.4
£77
-0.3
-1.89
£43
-1.2
-5.53
£32
• Survey over 2,000 households
• Each home located
• Locate sites visited
• Use the water quality ladder to characterise each site
• Record visit frequency• Model trade-off between
visit frequency, visit cost and water quality
• Estimate the value individuals have for changes in water quality
Site 3Site 3
Site 2Site 2
Site 1Site 1
Site 4Site 4
Site 5Site 5
Valuing water quality improvementsValuing water quality improvements
(2 visits)(2 visits)
(4 visits)(4 visits)(6 visits)(6 visits)
(1 visit)(1 visit)
(1 visit)(1 visit)
• Supplemented by various ‘stated preference’ studies asking people about what changes they would value – and how much!
• Uses novel virtual reality choice experiment approach:
Valuing water quality improvementsValuing water quality improvements
No change in water bill £5 increase in water bill
Which do you prefer?Which do you prefer?
Summary: Modelling the full effects of policy, market or environmental change