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Benefit: Cost Ratio
David PannellSchool of Agricultural and Resource Economics
University of Western Australia
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Is the project worth doing? How to judge? Assume aim is to maximise the value of
environmental outcomes “Value” can include
Environmental, social and economic Tangible and intangible
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Resources are limited Not every good project can be funded Best strategy is to choose highest ratio
of benefits to costs Dividing by costs is crucial Reveals projects that give best value for money
(benefits per dollar spent) Works even if projects are of different sizes
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Benefit score
BCR = ─────────────────────────────────────── Project cost
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B C B/C Rank
2 1 2 1
9 8 1.12 2
2 6 0.33 3
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Measuring benefits For ranking to work, benefits need to be
measured in a consistent way Not necessarily in dollars Our approach is to define a scoring
system and apply it consistently
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(Asset value) (Prop’n expected impact of project on value)
BCR = ──────────────────────────────────────────────────
Project cost
50 0.2 = 10
10 is the benefit score for the project. Indicates that the project generates benefits equivalent to 10% of the value of a 100-point asset.
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BCR = ──────────────────────────────────────────────────
Project cost
Potential project benefits
E(prop’n of required adoption)
(1 Risk of failure
) Discountfactor fortime lags
V W V: asset value
W: effectivenessof works
A B A: adoption
B: compliance
F P GF: feasibility
P: socio-politicalG: long-term funding
1/(1 + r)L
L: time lag to benefitsr: discount rate
C + PV(M+E) G C: project cost
M: annual maintenance costE: polluter-pays compliance costs
PV: summed present value over 20 yearsG: long-term funding
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The benefits are “expected” in a statistical sense – weighted by probabilities
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Simulations of other metrics In terms of expected value of
environmental benefits, the INFFER metric is about 100% better than commonly used metrics Some add where they should multiply Many ignore costs Most omit one or more of the benefits factors
(often feasibility and adoption)
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Data quality Process uses best available data and
knowledge Can be based on expert opinion if
necessary Can update with improved science or
modelling if it becomes available Step 3 elicits
quality of information data gaps strategy to deal with data gaps
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The asset and spin-offs INFFER quantifies benefits that are
directly related to the natural asset Other benefits (e.g. increased social
capital from doing the project) can be captured qualitatively and reported in Project Assessment Report
The asset can be defined more broadly to capture 2 or more sub-assets
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Simplification The current version of INFFER asks for only
one response for each parameter In reality, there might be heterogeneity
within the asset High feasibility to protect one part Low feasibility for another part
Response should be an overall average
The price of simplicity
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Heterogeneous asset Average
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Simplification Assumes overall
benefits are proportional to level of adoption or compliance
Might be non-linear Usually too little info to
know 0
1
2
3
4
5
6
7
8
9
10
0 2 4 6 8 10
Adoption
Ben
efit
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How is it used
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How is it used? Quantitative data is collected by
completion of the Project Assessment Form (PAF) Qualitative information provides context and
helps select quantitative values
PAF is completed for multiple assets Projects ranked on the basis of BCR
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How is it used? Would not expect
mechanistic application of this ranking
Priorities also influenced by funder priorities, opportunities, quality of information, …
Project BCR Rank
Lake X 5 1
River Y 1.5 3
Park Z 2.2 2
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Can also compare versions of the same project
Different scales Different on-ground actions Different policy mechanisms
Example: Gippsland Lakes
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Frequently Asked Questions
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How does INFFER compare projects for different types of assets?
On basis of overall value for money The V score is not specific to a
particular type of asset The other elements of the benefit ratio
are all proportions or probabilities Allows consistent comparison across
asset types
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V seems subjective. Is that a problem?
It is subjective. Values are. V makes explicit what we already do
implicitly transparency Consistency in scoring is important
Relate it carefully to table of V examples Have a group that reviews all V scores for
consistency
V is usually not the most uncertain factor Often W or A
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How important is accuracy of the numbers
We rarely have highly accurate numbers It matters, but great precision is not
needed W = 0.1 vs W = 0.8 makes a big difference W = 0.11 vs W = 0.13 doesn’t
Missing out a variable matters a lot The design of the BCR metric matters a
lot Data inaccuracy matters a bit
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How does INFFER compare a one-year project with a five-year project?
On the basis of overall value for money. We ask, which of the two projects has the greatest environmental benefits per dollar spent?
In both cases, we ask for information about the need for ongoing expenditure (beyond the project) and factor that in.
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How does INFFER compare large and small projects?
On the basis of overall value for money. We ask, which of the two projects has the greatest environmental benefits per dollar spent?
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How does INFFER deal with projects that require investment over a long time frame?
In the Project Assessment Form, we ask for an estimate of ongoing annual maintenance costs, which are factored into the assessment of cost effectiveness. Maintenance costs are converted to a “present value” using standard discounting methods.
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How does INFFER deal with projects where there is a long time frame until the benefits
are generated? It’s designed for a project of say 5 years, with
benefits over longer time frame. E.g. project may avert degradation that is not
expected to happen for decades, or it may take years for current actions to repair an already-degraded asset.
We collect information about the likely time lag until benefits. Then, the BCR includes a discount factor. Projects with more immediate benefits get higher weight (other things equal).
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For more details
See the BCR page and FAQs at
http://www.inffer.org
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Acknowledgements Affiliations of the INFFER team
University of Western Australia Department of Primary Industries, Victoria North Central Catchment Management Authority Future Farm Industries CRC
Other key funders Australian Research Council (Federation Fellow Program) Department of the Environment, Water, Heritage and the
Arts (CERF Program) Department of Sustainability and Environment , Victoria
