Week 9- Valuing the environment Flashcards

1
Q

Avoidance cost def and e.g

A

Cost people incur to avoid the negative consequences of an environmental change
e.g Valuing clean municipal water by the cost to buy water elsewhere if local waterways were degraded (valuing the inconvenience)

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2
Q

Restoration cost e.g

A

valuing ecosystem based on cost to recreate the ecosystem if it was seriously damage
e.g cost of cleaning up oil spill bt reintroducing exterminated plants and animals

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3
Q

Replacement cost e.g

A

valuing clean municipal water by the cost to use human made technology to do what watersheds do naturally to purify water.

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4
Q

Revealed preference methods

A

Methods based on actual market transaction data (use values):

  • Hedonic pricing
  • travel cost
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5
Q

Hedonic pricing

A

value of a good bc of its characteristics, rather than the good itself e.g Housing price and air quality correlation

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6
Q

Hedonic pricing can obtain which values

A

Use values:

-direct and indirect

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7
Q

Travel cost can obtain which values

A

Use value: direct only

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8
Q

Stated preference can obtain which values

A

Non use and Use

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9
Q

Contingent Valuation method (CVM)

A

elicits WTP values from surveys to preserve (or facilitate) a decrease (or increase) in the quantity or quality of a good.

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10
Q

Choice Modelling (CM)

A

presents respondents with alternative states of the world (policies), defined by choice sets.

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11
Q

Strategic bias

A

inflating one’s WTP in hope of affecting results of the analysis.

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12
Q

Approaches to valuing non-market goods

A
  • cost values (e.g avoidance, restoration, replacement)
  • Revealed preference (Use Values): (e.g Travel cost (direct use), hedonic pricing (direct and indirect).
  • Stated preference: CVM and CM (Use and non-use)
  • Benefit transfer
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13
Q

Benefit transfer

A

Apply values from completed studies to a different area where a new decision must be made

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14
Q

How to minimise bias with Benefit transfer

A
  • Transfer demand function (value function) not mean WTP
  • Similar study sites (e.g ecologically) and in terms of policy environment.
  • More rigorous existing study (better data)
  • similarity in non-market goods valued
  • similar socio-economic characteristics
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15
Q

Revealed preference vs Stated preference in using behavioral changes to value an environmental resource

A

-revealed preference does, state preference does not

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16
Q

e. g contingent non-market valuation example of koalas in Queensland.
- 200,000 koalas in QLD
- QLDer’s WTP $200Mill/y
- 6.7ha/koala

A

=$1000/koala/y
=$150/ha/y
Policies-
A: $150/ha/y SUBSIDY to private forest owners to maintain koala habitat
B: TAXES charge land developers $150/ha/y to clear koala habitat
C: MARKETABLE LAND CLEARING PERMITS - Capped total area of habitat that can be cleared. Developers compete for rights to acquire permits to clear land.
policies are efficient as long as costs (i.e property costs) do not exceed benefits ($200M/y)

17
Q

Bequest Value

A

Benefit from knowing that our children/future generations will have access to these resources.

18
Q

Existence Value

A

Value that we hold to know that the resource exists (not actually use resource) e.g pleases you to know that koalas in bris exists even though you may not have seen one

19
Q

Direct Use Value e.g

A

Extractive: Minerals, logs

non-extractive: Recreation/tourism

20
Q

Indirect use values e.gs

A

Ecosystem services:

  • Wetland water purification
  • carbon sequestration
21
Q

Option value e.g

A

By refraining of using the resource now, there is the option to harvest it in the future if necessary (e.g if desperate can log nation park)

22
Q

Pros and cons of cost values

A

Pros:
-More direct and easier to measure than stated or revealed preference approaches
Problem with avoidance cost: disutility e.g carrying bottled water home instead of tap utility
Problems with restoration and replacement cost: does replacement/restoration equate original environmental service.

23
Q

Travel cost model basic premise

A

travel cost to a site can be regarded as the price of access to the site.

24
Q

Objective of TCM

A

CS of recreationists

25
Q

Full TCM demand curve

A

y-travel cost ($)
x-visitation (Q)
trying to calculated CS, above actual fuel costs

26
Q

TCM 2 assumptions

A
  1. People demand less recreation as travel cost increases

2. recreationists react to higher travel costs the same way they would to entry fees

27
Q

TCM 2 assumptions

A
  1. People demand less recreation as travel cost increases

2. recreationists react to higher travel costs the same way they would to entry fees

28
Q

Hedonic wage method (value of a human life)

A
  • worker compensated $1000/year for a 0.1% additional risk of dying.
    i. e collective willingness to accept being exposed to a 0.1% risk of dying (1/1000 people) is $1000x1000people = 1M
  • This is a statistical life
29
Q

Hypothetical bias

A
  • people not taking survey seriously, bc of belief that not will come of it
30
Q

Information bias

A

e.g highlighting environmental implications and omitting economic info

31
Q

Interviewer bias

A

Interviewer elicits responses to reflect their standpoint

32
Q

Payment vehicle bias

A
  • one willingness to pay changes based on fund organisation e.g one won’t be willing to contribute money to federal gov but will for a non-for profit organisation
33
Q

Steps in applying choice modelling

A
  1. Characterisation of the decision problem (e.g geographical scope, focus groups)
  2. Attribute selection
  3. Experimental design development (reducing attribute combinations)
  4. Questionnaire creation
  5. Data collection issues
  6. Model estimation
  7. Policy analysis
34
Q

CVM vs CM

A
  • in CM, respondents don’t perceive that they’re actually stating WTP
  • CM avoids bias
  • CVM only elicits 1 (or 2) WTP estimates, where CM have choice sets; far more information about respondents preferences
35
Q

Weeknesses of CM

A
  • may have higher cognitive burden
  • Framing issues (i.e choice sets don’t represent policy options preferred by the respondent)
  • Increased contingencies
  • complex data analysis (with increased contingencies)