Predicting the supply of mitigation services by landholders

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Predicting the supply of mitigation services by
landholders

• Associate Professor John Rolfe
• Central Queensland University

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Outline of the talk

• Understanding where incentive mechanisms might be
  used
• Predicting the opportunity costs of potential
  mitigation actions
• Case study 1 Desert Uplands
• Experimental auctions
• Land value analysis
• Case study 2 Fitzroy basin
• Choice Modelling
• Experimental auction

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Acknowledgements

• Results drawn from two National Market Based
  Instruments Pilot Projects funded by Australian
  and State governments
• Co-researchers include Jill Windle, Juliana
  McCosker, Stuart Whitten and Andrew Reeson

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Who bears costs of salinity and water quality
impacts?
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Relative size of costs

• If most of the costs were private, on-farm costs,
  perhaps little intervention needed - but
• There may be substantial public costs on-farm and
  off-farm
• Because impacts are usually diffuse and
  multi-party, difficult to sort out private
  off-farm impacts with property rights
• Risks of on-farm private impacts may be poorly
  assessed and a market failure could lead to
  public costs being incurred

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Addressing these costs

• Where there are public benefits in mitigation
  actions, then enforcement or ongoing incentives
  may be needed to change behaviour
• Where there are private benefits available from
  mitigation, then may only need information or
  short term incentives to change behaviour

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Tools to address these costs

• Regulation appropriate in some cases, but large
  hidden costs relating to compliance,
  administration and opportunity costs
• Suasion and information provision have limited
  benefit
• Developing interest in Market Based Incentives
  (MBIs)

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Types of MBIs

• Price based instruments
• Taxes, subsidies
• Competitive tenders
• Quantity based instruments
• Cap-and-trade mechanisms
• Offsets (including mitigation banks)
• Bubble schemes
• Market friction instruments
• Insurance mechanisms
• Access to capital, trading opportunities

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Predicting the supply of mitigation actions

• Important information need when designing MBIs
  (particularly quantity based mechanisms)
• Need predictive information to set caps and
  reserve prices in auctions
• Need predictive information to get support for
  policy implementation
• Need information to get funding for competitive
  tenders

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Trade opportunities can be estimated from supply
functions

• Normal to predict trading activity by estimating
  then interacting supply and demand functions

Price of mitigation action
Quantity of mitigation action
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Potential trade between sectors

• Potential trade in mitigation actions can be
  predicted from difference in supply functions

Price of mitigation action
Sector 1
Sector 2
Quantity of mitigation action
Q1
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Potential trade within sector

• Potential trade in mitigation can be identified
  from shape of supply function
• Identifies variation in opportunity costs

Price of mitigation action
Quantity of mitigation action
Q1
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Estimating opportunity costs

• Important to assess economic impacts of changing
  management at property level
• Four main options to do this
• Farm production models
• Analysis of expected impacts on land prices
  (expectations about future profitability)
• Experimental auctions (assessing expectations of
  landholders)
• Quantitative surveys (eg Choice Modelling)

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Case study 1

• Desert Uplands region of central-western
  Queensland
• About size of Tasmania
• Beef cattle, extensive grazing
• Low productivity country, but generally good
  condition
• Some fragmentation from clearing
• Fragile in many areas
• Increased pressure from grazing

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Scenario of interest

• Landholders enter voluntary agreement to have
  minimum level of biomass 40 - over certain
  areas of grazing country
• Could be over particular area or for corridor
  across property
• Expect that lower stocking rates would be needed
  to achieve condition

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Used two approaches to assess scenario of interest

• Simple production models
• Estimated returns per acre
• Multiplied by change in stocking rate
• Multiplied by area involved
• Experimental auctions
• Asked landholders to design conservation areas
  and submit bids
• Assessed bids to identify drivers of bid values

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Simple production models
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Experimental workshops

• Held 3 hour workshop with small group of
  landholders in Barcaldine and Jericho
• Each allocated a dummy property to treat as
  their own
• Had to indicate the area that they would manage,
  and a bid for being paid
• Several rounds held in each workshop
• Small cash prizes awarded to most efficient bids
• Efficiency estimated by calculating environmental
  benefits and dividing by price
• Like BushTender process with single management
  action

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Hypothetical bias

• Put pressure on workshop participants to deliver
  cost-effective bids
• Provided cash prizes after each round for the
  most cost-effective bids
• Repeated the rounds 3 or 4 times
• Tried to guard against artificially low bids
• Asked participants to base bids on their own
  property operations
• Said that our results might be used by government
  to allocate funding to the area

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Cost-effectiveness of pooled bids
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Regression analysis of bids
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Outcomes - 1

• Comparison shows that in experimental auction
  process
• Area of yellowjacket and ironbark not significant
• Value/acre of other vegetation types much higher
  than in simple model
• A number of other factors important
• Property characteristics (size, of vegetation)
• Interest in being paid for providing services
• Bidding round (effect of competition

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Outcomes - 2

• Both approaches used to estimate the value of
  conserving an option
• 1000 acres of gidgee scrub
• 1000 acres of box
• 1000 acres of ironbark
• 1000 acres of yellowjacket
• 1000 acres of cleared country (regrowth)
• Value under simple model 3440
• Value from experimental auction / regression
  model 15, 028

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Why did the experimental auctions predict higher
values than simple production models?

• Production models too simple
• Did not take into account location factors (creek
  lines, water points, fences)
• Did not account for risk and uncertainty
• Did not consider extra management costs (extra
  mustering, fire breaks)
• Experimental auction results included more
  factors
• transaction costs (for negotiating and monitoring
  agreements)
• Engagement costs (pain and suffering for dealing
  with the government)

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Case study 2

• Case study of interest Fitzroy Basin in Central
  Queensland
• Major catchment draining into Great Barrier Reef
  lagoon
• High levels of sediment and nutrient export
  gt80 coming from agriculture
• Key agriculture industries are grazing and
  dryland farming
• Also limited impacts from urban, industrial and
  mining activities
• Results of project may be more generally
  applicable to catchments with water quality issues

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Assessing potential supply of agricultural
mitigation is complex

• Range of mitigation actions
• Focused on riparian buffers in case study
• Range of management actions
• E.g. size of buffer, type and period of exclusion
• Variations in biophysical attributes and
  ecological impacts
• E.g. stream order, soil type, existing cover
• Variations in landholder characteristics,
  attitudes and experience
• Contract design options

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Predicting supply useful for mechanism design
application

• Mechanism design
• Type of information needed to make initial broad
  choices about MBIs
• E.g likely takeup rates, incentives needed,
  overall budget
• Mechanism application
• Type of detailed information needed to design a
  particular application
• E.g key factors that impact on takeup and bids

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Tested three approaches

• A. Comprehensive CM survey
• Pilot survey collecting detailed information has
  been tested in field
• B. General CM survey
• Pilot survey collecting summary information has
  been tested in workshops with landholders
• C. Experimental auctions
• Auction process has been tested in workshops with
  landholders

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Comprehensive CM is too complex to operate

• Designed to fulfil both mechanism design and
  application roles
• Landholders asked to complete a series of choice
  sets
• 4 attributes (payment, stream length, contract
  length, contracting body)
• 4 alternatives (3 options status quo)
• Required management level fixed
• Additional information about necessary capital
  costs requested for each option selected
• Pilot survey achieved low response rate

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Example of followup

• If you chose an option other than option A, will
  you need to
• A. fence some part of your river frontage area?
  Yes / No
• If Yes, how many kilometres? ________
• B. put in extra watering points? Yes / No
  
• If, yes, how many? _____________

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General CM is appropriate

• Used simple CM format
• Asked participants to answer for a stream section
  on their property
• 3 alternatives, including status quo
• 3 attributes (price, buffer width, minimum
  biomass target)
• Did not include capital costs in choice sets
• Other issues covered by a single question in a
  survey to participants
• E.g. preferred contract arrangements, necessary
  capital costs

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Example of general choice set
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Summary of initial model
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How to use CM results

• Model gives information about how supply will
  change with management conditions
• Need to compare this to environmental gains
  associated with conditions
• Select most cost-efficient conditions
• E.g. cost of buffer width is 3.70/m/km
• At what width do costs outweigh benefits?
• Additional data will allow better models to be
  developed

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Direct questions reveal variation in capital costs
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Variation in capital costs
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Experimental workshops reveal variation in bids

• Workshop participants given a dummy property
• Key property attributes were constant across
  maps, but shuffled to appear different
• Asked to mark in a buffer zone they would
  consider and the bid amount needed
• Prizes for most cost-effective bids
• Simple metric
• converted buffer details to tons of sediment
  averted
• 5 year contract

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Initial round focused on opportunity cost (no
capital)
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Workshops explored opportunity costs capital
costs
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Implications for devolved grants

• Results show that dealing only with capital costs
  will not attract large number of bids

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Final summary

• Potential use of different MBIs for dealing with
  different issues
• Choice modelling and experimental auctions have
  complementary roles in predicting potential
  supply of mitigation
• CM useful for making initial broad choices about
  MBIs
• Gives understanding about broad tradeoffs
• E. A. useful to design particular mechanism
• Additional 2-way communication benefits