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The Future for Water Quality Ian J. Bateman CSERGE, University of East Anglia, UK. Team members include: Eric Audsley, Sandra Barns, Ian Bateman, Amy Binner, Roy Brouwer, John Crowther, Emma Coombes, Helen Davies, Brett Day, Amelie Deflandre, Silvia Ferrini, Carlo Fezzi,
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The Future for Water QualityIan J. Bateman CSERGE, University of East Anglia, UK Team members include: Eric Audsley, Sandra Barns, Ian Bateman, Amy Binner, 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 – The Wider Picture Market forces & ag. policy Water policy & environmental change Outcomes Modelling Farm Land Use & Incomes Modelling the Water Environment Benefit Valuation Impacts Farm income Water quality Household welfare Policy compliance testing Costs Benefits Spatial Cost-Benefit Analysis
Data and modelling • 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 Land use model subjected to actual versus predicted testing
Land use change & water quality Climate change simulation Nitrate leaching per month • Modelling land use change as a result of: • climate change; • new policy; • world market shifts; • etc. • Also estimating resultant farm incomes Integrated modelling: Linking land use with diffuse water pollution Modelling the impacts of land use change on river water quality and ecosystems services - and how water policy forces land use to change
Dairy Farms (after fert. limit) Dairy Farms (before) Policy change impacts on farm incomese.g. impact of a fertiliser limit Farm Gross Margin (£ / ha)
Estimating policy impacts across an agriculturally diverse catchment The Yorkshire Derwent
Cost effectiveness of alternative policy tools: Case study of the Derwent catchment 5.4 -0.2 -0.3 -1.2 -2.39 -1.89 -5.53 £43 £32 £77
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 Valuing water quality improvements (2 visits) Site 4 (6 visits) Site 1 (4 visits) Site 5 (1 visit) Site 2 (1 visit) Site 3
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 improvements Which do you prefer? No change in water bill £5 increase in water bill
Summary: Modelling the full effects of policy, market or environmental change