TRUCOST’S - GaBi Software · 2015-06-16 · marketing to ‘stand out’. Trucost’s valuation...
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TRUCOST’S VALUATION METHODOLOGY Prepared by Trucost May 2015
Transcript of TRUCOST’S - GaBi Software · 2015-06-16 · marketing to ‘stand out’. Trucost’s valuation...
TRUCOST’S
VALUATION
METHODOLOGY
Prepared by Trucost
May 2015
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INTRODUCTION
This document describes Trucost’s valuation methodologies. Valuation coefficients available through
GABI are global weighted averages. Country-specific coefficients are available on request as an output
of bespoke consultancy work.
WHAT IS IT?
Natural capital valuations are financial values applied to absolute impacts that reflect the full costs to
society that a company is responsible for. Companies pay fees for the energy and water they consume
and the waste they dispose of, but natural and social capital costs reflect the true impact of these and
other impacts that are currently externalized by the company.
Businesses rely on natural and social capital to produce goods and deliver services. They depend on
natural non-renewable resources (fossil fuels and minerals) as well as natural renewable ecosystem
goods and services (freshwater and pollination). Businesses also rely on the environment for its ability
to absorb by-products of production such as pollution and waste. This ability is finite and has already
shown its limits, as with climate change caused by GHG emissions. Businesses also depend on
manufactured inputs from their suppliers and human resources.
Monetary valuation tools translate environmental and social values into the dominant language of
business and economics. They convert impacts and dependencies into costs and benefits expressed in
monetary terms. By making trade-offs and synergies visible, and giving an overall indication of value
creation or destruction to different stakeholders, valuation allows alternative practices to be assessed
and compared in an integrated and systematic way. It enables the benefits of sustainable practices to be
communicated in an easy-to-understand language.
APPLICATION EXAMPLES
Trucost’s credentials and example research include:
Delivering the world’s first public Environmental Profit and Loss Account, PUMA
(http://www.trucost.com/published-research/79/puma-environmental-profit-and-loss-
account). Other Profit and Loss Accounts include: Yorkshire Water
(http://www.trucost.com/published-research/129/yorkshirewater/ep&l) and Novo Nordisk
(http://www.trucost.com/published-research/141/novo-nordisk)
Leading the development of the Sector’s Guide (Food and Beverage and Apparel) as part of the
Natural Capital Protocol (http://www.naturalcapitalcoalition.org/natural-capital-protocol.html)
Assessing the environmental damage costs of the world’s largest 3,000 companies on behalf of
the United Nations Environment Programme Finance Initiative (UNEP FI) and the United
Nations Principles for Responsible Investment (UNEP PRI)
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Assessing the net benefit of energy production using waste wood against an appropriate
baseline candidate for Utilyx. http://www.trucost.com/published-
research/133/utylix_monetisingnaturalcapital
Applying valuations to the results of LCAs conducted by Interface to compare in a holistic way
the natural capital impact of carpet tile production in Europe and in the US.
http://www.trucost.com/published-research/131/interface/lca
Working with the Cradle to Cradle Institute to determine the impacts of Cradle to Cradle
Certified product certification, and define a Framework that assists current and future
stakeholders to carry out further analysis. This will enable companies to develop an insight into
the returns on sustainable innovation in the fields of environment, society and business, to
demonstrate the positive and negative impacts of certification to the company and at a product
level, upon these three fields. http://www.trucost.com/published-research/135/cradle-to-
cradle-report
Delivering research commissioned by the Plastics Disclosure Project (PDP) and UNEP to assess
the opportunities and risks associated with plastic mismanagement across 16 consumer goods
industries, using natural capital valuation techniques. http://www.trucost.com/published-
research/134/valuing-plastic
Launching the Water Risk Monetizer Tool with Ecolab, a publicly available online tool that
provides actionable information to help businesses around the world understand the impact of
water scarcity to their business and quantify those risks in financial terms to inform decisions
that enable growth (https://tool.waterriskmonetizer.com/)
Undertaking a study for TEEB for Business Coalition estimating the natural capital cost across a
range of business sectors at a regional level. By using an environmentally extended input-output
model (EEIO), it also estimates, at a high level, how these may flow through global supply chains
to producers of consumer goods. It demonstrates that some business activities do not generate
sufficient profit to cover their natural resource use and pollution costs. However,
businesses and investors can take account of natural capital costs in decision making to manage
risk and gain competitive advantage. http://www.trucost.com/published-research/99/natural-
capital-at-risk-the-top-100-externalities-of-business
Working with GIZ and CEBDS to provide Brazilian financial institutions with an understanding of
the relevance and magnitude of the natural capital risks they are exposed to through their
funding and investments. http://www.trucost.com/published-research/152/GIZ-Natural-
Capital-Risk-Exposure-of-the-Financial-Sector-in-Brazil-Full-Report
BUSINESS VALUE
Traditional environmental metrics as reported in product level analysis such as life cycle assessment
(LCA) studies provide a comprehensive assessment of a product’s impact on natural and social capital,
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from the use of raw materials and production processes to the product’s in-use and disposal life stages.
This enables companies to assess product design with respect to climate change, energy use, water,
land use, and other environmental indicators across product life stages.
But the traditional environmental metrics employed by LCA studies (for example, tonnes of GHG
emissions and other pollutants, cubic meters of water and hectares of land use) prevent practitioners
from comparing trade-offs among different environmental and social issues. They mask the regional
implications of product sustainability such as local water scarcity, and they present information in very
technical terms that are not broadly accessible to business people or consumers. Due to their near
universal application, LCA studies are also increasingly limited in their ability to provide a market
differentiator in some industries, such as green buildings, at a time when companies need their product
marketing to ‘stand out’.
Trucost’s valuation solution has been created to overcome these challenges by enhancing traditional
LCA impact category metrics with region-specific natural and social capital valuations. Natural and social
capital valuations convert physical impacts into a monetary value, expressing the damage caused to the
environment and society. For example, Trucost’s natural and social capital valuation of water quantifies
the cost of water use to local communities by considering, among other factors, local water scarcity.
Trucost’s natural capital valuation of land use quantifies the local cost of environmental services that
are lost when land is converted for business use.
SCOPE
The following table provides detail on the availability of coefficients for each impact category and
characterization models, as well as the relevant section of this report in terms of methodology.
TABLE 1 AN OVERVIEW OF VALUATION COEFFICIENTS
METHODOLOGY IMPACT GWP AP EP POCP ADP TOX PARTICLES WATER LAND USE
SECTION OF THIS
REPORT
CML Abiotic Depletion (ADP elements)
On request
Abiotic depletion
CML Abiotic depletion (ADP fossil)
On request
Abiotic depletion
CML Acidification Potential (AP)
On request
Acidification, Smog Formation, Toxicity Potential
CML Eutrophication Potential (EP)
On request
Eutrophication Potential
CML Freshwater Aquatic Ecotoxicity Pot. (FAETP inf.)
On request
Acidification, Smog Formation, Toxicity Potential
CML Global Warming Potential (GWP 100 years)
Global Warming Potential
CML
Global Warming Potential (GWP 100 years), excl biogenic carbon
Global Warming Potential
CML Human Toxicity Potential (HTP inf.)
On request
Acidification, Smog Formation, Toxicity Potential
CML Marine Aquatic Ecotoxicity Pot. (MAETP inf.)
On request
Acidification, Smog Formation, Toxicity Potential
CML Photochem. Ozone Creation Potential (POCP)
On request
Acidification, Smog Formation, Toxicity Potential
CML
Terrestrial Ecotoxicity Potential (TETP inf.)
On request
ILCD/PEF Acidification, accumulated exceedance
On request
ILCD/PEF
Ecotoxicity for aquatic fresh water, USEtox (recommended)
On request
ILCD/PEF
Freshwater eutrophication, EUTREND model, ReCiPe
On request
Eutrophication Potential
ILCD/PEF
Human toxicity cancer effects, USEtox (recommended)
On request
Acidification, Smog Formation, Toxicity Potential
ILCD/PEF
Human toxicity non-canc. effects, USEtox (recommended)
On request
ILCD/PEF IPCC global warming, excl biogenic carbon
Global Warming Potential
ILCD/PEF IPCC global warming, incl biogenic carbon
ILCD/PEF Particulate
On
Acidification, Smog
matter/Respiratory inorganics, RiskPoll
request Formation, Toxicity Potential
ILCD/PEF
Photochemical ozone formation, LOTOS-EUROS model, ReCiPe
On request
ILCD/PEF
Resource Depletion, fossil and mineral, reserve Based, CML2002
On request
Abiotic depletion
ILCD/PEF
Total freshwater consumption, including rainwater, Swiss Ecoscarcity
Water consumption
ReCiPe Agricultural land occupation
On request
Land use
ReCiPe Climate change, default, excl biogenic carbon
Climate change Potential
ReCiPe Climate change, incl biogenic carbon
ReciPe Fossil depletion On request
Abiotic depletion
ReCiPe Freshwater ecotoxicity
On request
Acidification, Smog Formation, Toxicity Potential
ReCiPe Freshwater eutrophication
On request
Eutrophication Potential
ReCiPe Human toxicity
On request
Acidification, Smog Formation, Toxicity
ReCiPe Marine ecotoxicity
On request
Potential
ReciPe Metal depletion On request
Abiotic depletion
ReCiPe Natural land transformation
On request
Land use
ReCiPe Particulate matter formation
On request
Acidification, Smog Formation, Toxicity Potential
ReCiPe Photochemical oxidant formation
On request
ReCiPe Terrestrial ecotoxicity
On request
ReCiPe Urban land occupation
On request
Land use
ReCiPe Water depletion
Water consumption
Traci Acidification
On request
Acidification Potential
Traci Ecotoxicity (recommended)
On request
Acidification, Smog Formation, Toxicity Potential
Traci Eutrophication
On request
Eutrophication Potential
Traci Global Warming Air, excl. biogenic carbon
Global Warming Potential
Traci Global Warming Air, incl. biogenic carbon
Traci Human Health Particulate Air
On request
Acidification, Smog Formation, Toxicity Potential
Traci Human toxicity,
Acidification, Smog
cancer (recommended)
On request
Formation, Toxicity Potential
Traci
Human toxicity, non-canc. (recommended)
On request
Acidification, Smog Formation, Toxicity Potential
Traci Resources, Fossil Fuels
On request
Abiotic depletion
Traci Smog Air
On request
Acidification, Smog Formation, Toxicity Potential
USEtox Ecotoxicity
On request
Acidification, Smog Formation, Toxicity Potential
USEtox
Human toxicity, cancer
On request
Acidification, Smog Formation, Toxicity Potential
USEtox
Human toxicity, non-canc.
On request
Acidification, Smog Formation, Toxicity Potential
FRAMEWORK FOR ASSESSMENT
The framework for assessment used to derive valuation coefficients comprises three distinct analysis
steps. It helps establish the link between impacts and changes in the condition of specific societal
groups, for example local communities, employees, businesses and the wider society.
Figure 1 illustrates the framework and the next sections detail each step.
FIGURE 1. AN OVERVIEW OF THE FRAMEWORK (ADAPTED FROM KEELER, ET AL., 2012)
UNDERSTANDING AND QUANTIFYING DRIVERS OF CHANGE The first step is to understand the drivers of change by devising appropriate key performance indicators
(KPIs) that measure the extent of impacts. This is done by performing a life-cycle analysis using GABI or
through bespoke consultancy work.
UNDERSTANDING THE CONSEQUENCES OF IMPACTS
The second step is to understand the consequence of the impact to a specific end-point. An end point is
the primary receptor of the impact – society, the environment, or the business itself. Each impact can
have several end-points. For example, water depletion (negative impact) can affect society (end point 1)
1
2
3
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through lack of drinking water and decreased food supply, and the environment (end point 2) through
decreased water availability to sustain fauna and flora. It can also affect the business itself (end point 3)
through increased water scarcity in a specific location.
Impacts are quantified in biophysical terms. Examples of metrics, or “valued attributes”, are changes in
life expectancy or changes in species richness due to the emission of pollutants. Biophysical models are
used to estimate these metrics, based on a thorough literature review, and adapted to reflect local
conditions. For example, the extent to which water pollution impacts society through decreased life
expectancy depends on local social and environmental factors such as access to drinking water and
pollutant dispersion based on hydrological patterns.
The choice of the valued attribute is informed by both the scope and requirements of the study and as
importantly by how it feeds in Step 3. One limitation of some valuation frameworks is that biophysical
(Step 2) and economic modelling (Step 3) are conducted in isolation, leading to a discrepancy in metrics.
For example, water quality metrics are often not well connected with what the society values -
recreational tourists do not value the concentration of phosphorus or other water pollutants, but rather
water clarity (Keeler, et al., 2012).
VALUING IMPACTS IN MONETARY TERMS
The third step consists of converting the biophysical metrics into monetary terms that reflect the costs
and benefits to specific beneficiaries of the change in valued attribute. The output of this step is a
valuation coefficient that reflects cost or benefit of specific practices and associated use of inputs and
emissions on natural and social capital.
One key consideration here is that regardless of the end-point (see Step 2, society, the environment or
the business itself), value is in the eye of the beholder. Costs and benefits are thus human-centric, even
in the case where the end-point is the environment. For example, the costs and benefits of a change in
biodiversity are valued based on the services that biodiversity provides to society.
Several techniques exist to assign a value to a change in valued attribute and calculate the costs and
benefits in monetary terms of a specific action. Techniques span from observing behaviour on already-
existing alternative markets as a proxy, for example how much is spent on aquatic recreational
activities, or creating artificial markets by asking population their willingness-to-pay for the existence of
wildlife habitat. Table 2 summarizes the different techniques that can be used.
TABLE 2 AN OVERVIEW OF VALUATION METHODOLOGIES
VALUATION TECHNIQUE DESCRIPTION
ABATEMENT COST The cost of removing a negative by-product for example, by reducing the
emissions or limiting their impacts.
AVOIDED COST /
REPLACEMENT COST /
Estimates the economic value of ecosystem services based on either the
costs of avoiding damages due to lost services, the cost of replacing
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SUBSTITUTE COST ecosystem services, or the cost of providing substitute services. Most
appropriate in cases where damage avoidance or replacement expenditures
have or will be made (Ecosystem Valuation, 2000)
CONTINGENT
VALUATION
A survey-based technique for valuing non-market resources. This is a stated
preference/willingness-to-pay model in that the survey determines how
much people will pay to maintain an environmental feature.
DIRECT MARKET PRICING Estimates the economic value of ecosystem products or services that are
bought and sold in commercial markets. This method uses standard
economic techniques for measuring the economic benefits from marketed
goods based on the quantity purchased and supplied at different prices. This
technique can be used to value changes in the quantity or quality of a good
or service (Ecosystem Valuation, 2000).
HEDONIC PRICING Estimates the economic value of ecosystem services that directly affect the
market price of another good or service. For example proximity to open
space may affect the price of a house.
PRODUCTION FUNCTION Estimates the economic value of ecosystem products or services that
contribute to the production of commercially marketed goods. Most
appropriate in cases where the products or services of an ecosystem are
used alongside other inputs to produce a marketed good (Ecosystem
Valuation, 2000).
SITE CHOICE / TRAVEL
COST METHOD
A revealed preference/willingness-to-pay model which assumes people
make trade-offs between the expected benefit of visiting a site and the cost
incurred to get there. The cost incurred is the person’s willingness to pay to
access a site. Often used to calculate the recreational value of a site.
All of the approaches above are equally valid, and Trucost chose valuation techniques based on data
availability and suitability. Trucost has been consistent in its application of valuation techniques across
all end-points. For example, the change in life expectancy has been valued the same regardless of
whether it is caused by malnutrition due to water depletion, or by the ingestion of contaminated food
due to water pollutants.
Value is highly contingent on local conditions. In order to estimate costs or benefits in a context when
no study exists, Trucost relies on the value transfer method. In this method, the goal is to estimate the
economic value of ecosystem services or impacts by transferring available information from completed
studies, to another location or context by adjusting for certain variables. Examples include population
density, income levels, and average size of ecosystems to name just a few.
Best practice guidelines for value transfers have been set out by UNEP in a document entitled Guidance
Manual on Value Transfer Methods for Ecosystem Services (Brander, 2004). Where possible, Trucost has
endeavoured to follow these guidelines in all of its value transfer calculations. It is important to note,
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however, that value transfers can only be as accurate as the initial study (Ecosystem Valuation, 2000). In
some instances, studies from different ecosystems and geographies have had to be ubiquitously used
throughout a valuation methodology due to data availability and data quality.
In GABI, valuation coefficients are global, weighted by Gross Domestic Product. Country-specific
coefficients are available on request.
REFERENCES
Keeler, B. L. et al., 2012. Linking Water Quality and Well-Being for Improved Assessment and Valuation
of Ecosystem Services. PNAS.
Brander, L., 2004. Guidance Manual on Value Transfer Methods for Ecosystem Services, s.l.: UNEP
Ecosystem Valuation, 2000. [Online]
Available at: http://www.ecosystemvaluation.org/
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GLOBAL WARMING POTENTIAL
INTRODUCTION
The social cost of carbon (SCC), marginal abatement cost (MAC) and the market price of carbon in
existing emissions trading schemes are common approaches that can be used to value the marginal cost
of each additional tonne of greenhouse gas (GHG) emitted (usually expressed in tonnes of carbon
dioxide equivalents (CO2e) 1. The three differ significantly in their current estimates of cost, although in
theory climate policy in its effort to balance the cost of abating pollution against the cost of pollution
damage would set emissions reduction targets that result in a MAC that is equal to the SCC. In perfect
market conditions, the price of carbon should also be equal to the SCC.
TABLE 3 AN OVERVIEW OF THE THREE COMMON APPROACHES USED TO VALUE GHGS
MARKET PRICE MARGINAL ABATEMENT
COST (MAC) SOCIAL COST OF CARBON
(SCC)
Definition The value of traded
carbon emission rights,
under policies which
constrain the supply of
emissions through the
use of permits, credits or
allowances.
The marginal abatement cost
uses the known costs of
reducing carbon to achieve
an emissions reduction
target, for example through
energy efficiency
improvements, renewable
energy, materials
substitution and/or carbon
capture and storage
technology.
The net present value of each
tonne of carbon dioxide
equivalent (CO2e) emitted
now, taking into account the
full global cost of the damage
that it imposes during its time
in the atmosphere.
1 Carbon dioxide is only one of many GHGs, such as methane, nitrous oxide and ozone. CO2e (carbon dioxide equivalents) is a measure that takes into account the emission of other GHGs when calculating the level of GHG emissions.
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MARKET PRICE MARGINAL ABATEMENT
COST (MAC) SOCIAL COST OF CARBON
(SCC)
Example
uses
Carbon pricing
instruments include
carbon taxes, emissions
trading schemes, and
crediting mechanisms.
About 40 national and
over 20 sub-national
jurisdictions are putting a
price on carbon, covering
12% of annual GHG
emissions.
Power companies can use
MAC curves to guide their
decisions about long-term
capital investment strategies
and to select among a
variety of efficiency and
generation options. Policy-
makers use MAC curves as
merit order curves, to
analyse how much
abatement can be done in an
economy at what cost, and
where policy should be
directed to achieve the
emission reductions.
The US EPA and other federal
agencies use the SCC to
estimate the climate benefits
of rulemakings, such as the
Light-Duty Vehicle
Greenhouse Gas Emission
Standards (2012-2016).
Cost
estimate
Carbon prices between
schemes occupy a
significant range, from
under US $1/tCO2 in the
Mexican carbon tax up to
$168/tCO2 in the
Swedish carbon tax.
Prices in emissions
trading schemes tend to
be lower, clustering
under $12/tCO2 (World
Bank Group, 2014).
Dependant on the mitigation
measure, abatement
technologies vary from being
net positive to $80/tCO2
(2010)
The 2013 Interagency Working
Group on the Social Cost of
Carbon (IWGSCC) updated the
U.S. SCC for 2015 from a
central value of US $24/tCO2
to $37/tCO2 using three
integrated assessment models
(IAMs): DICE-2010, FUND 3.8,
and PAGE09 (Howard, 2014).
These IAMs estimate costs per
tonne ranging between
$10/tCO2 (FUND) to
$328/tCO2 (PAGE09, 95th
percentile) (2014 US$).
Emissions trading schemes and the resulting market prices of GHGs are generally promoted for their
flexibility to reduce emissions at the lowest cost for the economy. In recent years, the reach of carbon
pricing has also been steadily increasing showing promise at a global level. Carbon pricing systems now
in operation in sub-national jurisdictions of the US and China, the world’s two largest emitters, and in
2013 alone, a total of eight new carbon markets opened (World Bank Group, 2014). However, traded
market prices currently face a number of limitations which prevents their use as a valuable pricing and
decision-making tool. They do not reflect non-traded carbon costs, nor the impact of other market-
based mechanisms such as carbon/fuel taxes, subsidies for removal of fossil fuels, or support for low
carbon technologies. They have also been historically slow to come about and fragmented, with some
nations taking concrete steps forward on carbon pricing, but others such as Australia at a setback.
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Consequently, companies are unlikely to pay for emissions across global operations. Most crucially,
market prices can be impacted by sudden economic changes. For example, the market price of carbon
under the EU ETS is currently USD 8, as the excess of allowances due to the economic slowdown since
2008, has reduced the carbon price to levels that undermine the incentive for polluters to cut emissions
(Krukowska, 2014).
The fact that the MAC method is based on the known actual costs of existing reduction efforts renders it
a valuable tool for shaping policy discussions, prioritizing investment opportunities and driving forecasts
of carbon allowance prices. Nevertheless, it too does not reflect non-traded carbon costs, thus severely
underestimating the cost of GHG emissions. Moreover, it is highly time and geography specific, with
costs of reduction fluctuating over time, by sector and by geography as technology matures with
different reduction targets translating into different MACs for each country. Moreover, estimates are
influenced by fossil fuel prices, carbon prices and other policy measures. The policies and technologies
used to support carbon abatement will therefore influence pricing.
Trucost uses the SCC, because it reflects the full global cost of the damage generated by GHG emissions
over their lifetime, and as such it is typically considered best practice. SCC is also applicable to emissions
globally, which is the case with neither the market price method nor the MAC. However, SCC valuations
are highly contingent on assumptions, in particular the discount rate chosen, emission scenarios and
equity weighting, which are discussed at length in the following sections.
Over 300 studies attempt to put a price on carbon, valuing the impact of climate change on agricultural
productivity, forestry, water resources, coastal zones, energy consumption, air quality, tropical and
extra-tropical storms, property damages from increased flood risk, and human health. However, due to
current modelling and data limitations, such as a lack of precise information on the nature of damages
and because the science incorporated into these models naturally lags behind the most recent research,
these estimates do not currently include all of the important physical, ecological, and economic impacts
of climate change recognized in the climate change literature (Ackerman and Stanton, 2010; EPA, 2013).
As noted by the IPCC Fourth Assessment Report, it is ‘very likely that [SCC] underestimates’ the
damages (IPCC, 2007).
To address these material omissions Trucost bases its SCC valuation on the Interagency Working Group
on Social Cost of Carbon (IWGSCC, 2013) values reported at the 95th percentile under a 3% discount
rate, which represents higher-than-expected impacts from temperature change further out in the tails
of SCC distribution (IWGSCC, 2013). A summary of the GHG valuation emitted in each respective year is
given in Table 4 below.
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TABLE 4 US EPA REVISED SCC, 2010-2014 ($ PER METRIC TONNE OF CO2, DOLLAR-YEAR AND
EMISSIONS-YEAR SPECIFIC)
DISCOUNT RATE 3.0 % YEAR 95TH 2010 93
2011 101
2012 107
2013 113
2014 120
SCOPE OF TRUCOST VALUATION
This methodology is intended to value the all present and future damages (or benefits) associated with
the emission of carbon dioxide equivalent gasses.
Table 5 highlights the potential impacts and benefits of emissions.
TABLE 5 IMPACTS AND BENEFITS OF GREENHOUSE GAS EMISSIONS
IMPACT MODELLING BIOPHYSICAL MODELLING ECONOMIC MODELLING
EMISSIONS
/ RESOURCE USE
IMPACT AND
DEPENDENCY END POINT
CHANGE IN VALUED
ATTRIBUTE
LINK TO ESS
(WHERE
RELEVANT)
ECOSYSTEM
SERVICE (WHERE
RELEVANT)
FINAL
BENEFICIARIES
VALUATION
APPROACH
VALUE TRANSFER
METHOD
Carbon dioxide and
carbon dioxide
equivalent emissions
Increase in
global average
temperature
All ecosystems
and people
Emissions of carbon
dioxide equivalent
gasses
Provisioning
Supporting
Regulating
Cultural
Diverse Diverse Social cost of
carbon
Integrated
assessment
modelling
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VALUATION METHODOLOGY
ALL IMPACTS
BIOPHYSICAL AND ECONOMIC MODELLING
The SCC is an estimate of the monetized damages associated with an incremental increase in GHG
emissions in a given year. To estimate the SCC, Integrated Assessment Models (IAMs) are used to
translate scenarios for economic and population growth, and resulting emissions, into changes in
atmospheric composition and global mean temperature.
The IAMs apply ‘damage functions’ that approximate the global relationships between temperature
changes and the economic costs of impacts such as changes in energy (via cooling and heating) demand;
changes in agricultural and forestry output from changes in average temperature and precipitation
levels, and CO2 fertilization; property lost to sea level rise; coastal storms; heat-related illnesses; and
some diseases (e.g. malaria and dengue fever). Finally, the models translate future damages, going as
far out as to year 2300, into present monetary value using a discount rate.
Out of the many studies that attempt to calculate a social cost of carbon, Trucost has chosen to use the
SCC estimates provided by the US Interagency Working Group’s SCC (IWGSCC, 2013) for a number of
reasons:
These SCC calculations are based on three well-established Integrated Assessment Models
(IAMs), which renders the estimate robust and credible.
The SCC calculations incorporate the timing of emission release (or reduction), which is key to
the estimation of the SCC. For example, the SCC for the year 2020 represents the present value
of the climate change damages that occur between the years 2020 and 2300 and are associated
with the release of CO2e in year 2020. Results are also presented across multiple discount rates
(2.5%, 3% and 5%) because no consensus exists on the appropriate rate to use. This allows
flexibility in the choice of discount rate according to project objectives.
They are also based on continuous improvement loops ensured through regular feedback
workshops with experts in the field, transparency and integrating the latest scientific evidence.
As a result, the latest 2013 update provides higher values than those reported in the 2010
technical support document, and incorporates updates of the new versions of each underlying
IAM.
LIMITATIONS
Despite being the most complete measure of the damage caused by GHG emissions, SCC estimates have
attracted much criticism as they omit or poorly quantify some major risks associated with climate
change. This includes social unrest and disruptions to economic growth; ocean acidification (notably
Tol’s Fund model); biodiversity, habitat and species extinction; and damages from most large-scale
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earth system feedback effects such as Arctic sea ice loss and changing ocean circulation patterns
(Howard, 2014; Kopits, 2014).
In estimating the SCC, three IAMs have received most attention in the literature. These models, which
form the foundation of the US Interagency Working Group’s SCC estimates, are: DICE -2010, FUND 3.8,
and PAGE09. Some of the limitations of these models are summarised below:
Extensive experiments with DICE by a range of researchers have shown that with small,
reasonable changes to the basic data, DICE can yield very different projections.
PAGE sets a relatively high temperature threshold for the onset of catastrophic damages.
The FUND model was found by the Heritage Foundation’s Centre for Data Analysis (CDA) to
be extremely sensitive to assumptions; so sensitive that at times it even suggests net
economic benefits to GHG emissions (Dayaratna and Kreutzer, 2014). According to the
FUND model, change in temperature up to 3°C is contributing beneficially to the
environment (IWGSCC, 2010).
SCC estimates vary across studies from below-zero to four-figure estimates, mainly due to the four
factors that have been outlined below:
Emissions scenarios: In order to derive the SCC, assumptions need to be made on future
emissions, the extent and pattern of warming, and other possible impacts of climate
change, to translate the impacts of climate change into economic consequences.
Equity weighting: A global SCC can take into account variations in the timings and locations
at which the costs of climate change impacts will be internalised, which may differ from the
locations where the GHGs are emitted. Some studies including Stern (2006) and Tol (2011)
take account of equity weightings – corrected for differences in the valuations of impacts in
poor countries.
Uncertainties: The variation in valuations is influenced by uncertainties surrounding
estimates of climate change damages and related costs. However, climate change studies
since 1995 tend to take account of net gains as well as losses due to climate change (Tol,
2011). The mean estimate of the SCC, as well as the standard deviation, have declined since
2001, suggesting either a better understanding of the impacts of climate change, or the
convergence of methodologies (Ibid).
Discount rate: The discount rate used to calculate the present value of future economic
damages resulting from GHGs emitted today can be the most significant source of variation
in estimates of the SCC (Tol, 2011). Higher discount rates result in lower present day values
for the future damage costs of climate change. Variations in discount rates are due to
differences in the parameters applied to the Ramsey equation, which is commonly used to
calculate the discount rate of the SCC. These parameters include 1) the pure rate of time
preference, which is the rate at which society discounts the utility of future generations; 2)
the growth rate of per capita consumption and 3) the elasticity of marginal utility of
consumption. For example, Stern (2006) uses a discount rate of 1.4%. As a reference point,
discount rates used by the US EPA (2013) range between 2.5% and 5%.
22
SENSITIVITY ANALYSIS
One of the key assumptions applied to IAMs is the choice of an appropriate discount rate. The very long
time scale of climate change makes the discount rate crucial at the same time as it makes it highly
controversial, with consensus is not yet fully established (IPCC, 2014).
Within standard lifecycle analysis frameworks, impacts and benefits are not discounted, and the same
value is attributed to an impact (benefit) happening today and in the future. Potential arguments for no
temporal discounting include the ethical consideration of not considering emissions that happen in the
future and impact future generations as less important as damages to the present generation, and the
‘polluter pays principle’ stating that agents causing damages should be accountable for the full extent of
the impact caused.
An alternative approach is to use a positive temporal discounting which places less significance on
future impacts (benefits) than on present ones. This stems from the concept of pure time preference,
stating that individuals prefer benefits occurring in the present rather than in the future; that future
generations will be richer and a dollar is worth less to them as a result; and recognising the opportunity
cost of capital. The Stern Report used a social discount rate of 1.4% in its analysis of the future cost of
carbon, which was considered low at the time of publication, compared to Nordhaus, who currently
uses a discount rate of 3% in the near term (Bell, 2011). To illustrate the sensitivity of estimates to
discount rates, using a discount rate of 1%, the discounted value of $1 m 300 years [from today] is
around $50,000 today. But if the discount rate is 5%, the current value is less than 50 cents (Burtraw
and Sterner, 2009). This range of discount rates, which span those commonly used in calculating the
SCC, lead to differences in net present value after three hundred years that vary by a factor of one
hundred thousand (Bell, 2011).
Some consensus is also building for using declining rates over time (IPCC, 2014). Literature suggests that
if there is a persistent element to the uncertainty in the rate of return to capital or in the growth rate of
the economy, it will result in an effective discount rate that declines over time (RFF, 2012). This
approach would yield a higher present value to the long-term impacts of climate change and thus a
higher value for the SCC (Arrow et al., 2014).
In light of existing disagreement, the US Interagency Working Group displays the average SCC for
discount rates of 5%, 3% and 2.5%, with 3% being the central value (IWGSCC, 2013). It also recommends
presenting the results undiscounted (using a discount rate of 0%).
23
REFERENCES
Ackerman, F., Stanton, E., 2010. The Social Cost of Carbon. Economics Review, 53. Stockholm
Environment Institute, USA. Available at:
http://www.paecon.net/PAEReview/issue53/AckermanStanton53.pdf
Arrow, K., Revesz, R., Howard, P., Goulder, L., Kopp, R., Livermore, M., Oppenheimer, M., Sterner, T.,
2014. Global warming: Improve Economic Models of Climate Change. Nature, comment. Available at:
http://www.nature.com/news/global-warming-improve-economic-models-of-climate-change-1.14991
Bell, R., 2011. The “Social Cost of Carbon” and Climate Change Policy. World Resources Institute.
Available at: http://www.wri.org/blog/2011/07/%E2%80%9Csocial-cost-carbon%E2%80%9D-and-
climate-change-policy
Burtraw, D., Sterner, T., 2009. Climate Change Abatement: Not ‘Stern’ Enough? Available at:
http://www.rff.org/Publications/WPC/Pages/09_04_06_Climate_Change_Abatement.aspx
Dayaratna, K.D., Kreutzer, D.W., 2014. Unfounded FUND: Yet Another EPA Model Not Ready for the Big
Game. Backgrounder #2897 on Energy and Environment. Available at:
http://www.heritage.org/research/reports/2014/04/unfounded-fund-yet-another-epa-model-not-
ready-for-the-big-game
EPA, 2013. The Social Cost of Carbon. Available at:
http://www.epa.gov/climatechange/EPAactivities/economics/scc.html
Howard, P., 2014. Omitted Damages: What’s Missing From the Social Cost of Carbon. The Cost of
Carbon Project, a joint project of the Environmental Defense Fund, the Institute for Policy Integrity, and
the Natural Resources Defense Council. Available at:
http://costofcarbon.org/files/Omitted_Damages_Whats_Missing_From_the_Social_Cost_of_Carbon.pd
f
IPCC, 2007. IPCC Fourth Assessment Report: Climate Change 2007. Climate Change 2007: Working
Group III: Mitigation of Climate Change. 2.4 Cost and benefit concepts, including private and social cost
perspectives and relationships to other decision-making frameworks.
IPCC, 2014. IPCC Fifth Assessment Report. Working Group III.
IWGSCC, 2010. Technical Support Document: Social Cost of Carbon for Regulatory Impact Analysis.
Interagency Working Group on Social Cost of Carbon, United States Government. Available at:
http://www.whitehouse.gov/sites/default/files/omb/inforeg/for-agencies/Social-Cost-of-Carbon-for-
RIA.pdf
IWGSCC, 2013. Technical Support Document: Technical Update of the Social Cost of Carbon for
Regulatory Impact Analysis. Interagency Working Group on Social Cost of Carbon, United States
Government. [online] Available at:
http://www.whitehouse.gov/sites/default/files/omb/assets/inforeg/technical-update-social-cost-of-
carbon-for-regulator-impact-analysis.pdf
24
Kopits, E., 2014. The Social Cost of Carbon in Federal Rulemaking. National Centre for Environmental
Economics, U.S. EPA. Available at: http://www.hks.harvard.edu/m-
rcbg/cepr/Papers/2014/SCC_HKS%20Energy%20Policy%20Seminar_03%2031%2014_forweb.pdf
Krukowska, E., 2014. Europe Carbon Permit Glut Poised to Double by 2020: Sandbag. Bloombert
Business News. Available at: http://www.bloomberg.com/news/articles/2014-10-14/europe-carbon-
permit-glut-poised-to-double-by-2020-sandbag-says
RFF (Resources for the Future), 2012. How Should Benefits and Costs Be Discounted in an
Intergenerational Context? The Views of an Expert Panel. Available at:
http://www.rff.org/RFF/Documents/RFF-DP-12-53.pdf
Stern, N., 2006. Stern Review Report on the Economics of Climate Change. Cambridge: Cambridge
University Press.
Tol, R., 2011. The Social Cost of Carbon. Annual Review of Resource Economics, Annual Reviews, 3(1), p.
419-443.
World Bank Group, 2014. State and Trends of Carbon pricing. [online] URL:
http://www.ecofys.com/files/files/world-bank-ecofys-2014-state-trends-carbon-pricing.pdf
25
ACIDIFICATION, SMOG FORMATION, TOXICITY
POTENTIAL
INTRODUCTION
One of the major issues that the world is facing today is environmental pollution, caused by the
emission of pollutants to different media, for example air, freshwater and natural land. Each pollutant is
associated with different, but overlapping types of impacts. Some effects are caused directly by the
primary pollutant emitted (for example health impacts of particulates), and some are caused by
secondary pollutants formed in the atmosphere from pollutants that act as precursors.
SCOPE OF TRUCOST VALUATION
This methodology values the impact of organic, inorganic and heavy metal pollutants on human health,
and the impact of heavy metal and organic pollutants on ecosystems.
TABLE 6: IMPACTS AND BENEFITS OF AIR, LAND, WATER EMISSIONS
IMPACT MODELLING BIOPHYSICAL MODELLING ECONOMIC MODELLING
EMISSIONS
/ RESOURCE USE
IMPACT AND
DEPENDENCY END POINT
CHANGE IN VALUED
ATTRIBUTE
LINK TO ESS
(WHERE
RELEVANT)
ECOSYSTEM
SERVICE (WHERE
RELEVANT)
FINAL
BENEFICIARIES
VALUATION
APPROACH
VALUE TRANSFER
METHOD
Selected air, land
and water
pollutants, including
heavy metals and
pesticides
Concentration
of air pollutants
Terrestrial
ecosystems
Change in the
potentially affected
fraction of species
Supporting Biodiversity Diverse
Multiple -
Contribution of
biodiversity to
the delivery and
value of
provisioning,
regulating and
cultural services
Geophysical and
social conditions,
Species density,
Average Ecosystem
value
Freshwater
ecosystems
Marine
ecosystems
People
Change in DALYs
due to ingestion Provisioning
Safe food Diverse
Contingent
valuation
(willingness-to-
pay)
Revenue and Income
elasticity
Safe drinking
water Diverse
Change in DALYs
due to inhalation NA NA Diverse
Sulphur dioxide,
Particulate Matter,
Nitrogen oxide,
ammonia to air
Concentration
of air pollutants People
Change in DALYs
due to inhalation NA NA Diverse
Contingent
valuation
(willingness-to-
pay)
Revenue and Income
elasticity
27
VALUATION METHODOLOGY
The link between pollution and human health impacts has been widely investigated. For example, the
2010 Global Burden of Disease study (Lim et al, 2012) found that 8 million deaths could be attributed to
pollution exposure globally. Similarly, a study conducted by Greenpeace and Peking University found
that air pollution kills more people than smoking in China, with 79 out of 100,000 people dying
prematurely as a result of air pollution in Beijing (Jing, 2015).
IMPACT ON HUMAN HEALTH
BIOPHYSICAL MODELLING
Studies on the health impacts of pollution use a technique called Impact Pathway Analysis (IPA)
(Desaigues et al. 2006), which translates exposures to pollutants into physical effects using dose-
response functions (DRF) from peer-reviewed studies. Dose-response functions describe the change in
the number of cases of a specific disease caused by a change in emission of (or exposure to) a substance.
In order to quantify the actual burden of each disease, the number of cases is often converted into
Disability-Adjusted Life Years (DALY). According to the World Health Organization (WHO, 2014), a DALY
can be thought of as one year of life in full health, with DALY values of less than one representing a year
of life spent in sub-optimal health. The DALY can be used to represent the total health burden
associated with a disease, including both the Years of Life Lost (YLL) due to premature death and Years
Lost Due to Disability (YLD) due to morbidity (WHO, 2014).
In order to calculate the quantity of DALYs lost due to the emission of organic pollutants and
heavy metals to air, land and water, Trucost used a model called EUSES-LCA2.0 (National
Institute of Public Health and the Environment, 2004). This model calculates the quantity of
DALYs lost per unit of emission for over 3,000 chemicals emitted to freshwater, seawater,
natural/agricultural and industrial land, and rural/urban/natural air. EUSES-LCA takes into
account cancer and non-cancer diseases caused by the ingestion (food and water) and
inhalation of chemicals. Trucost adapted EUSES-LCA2.0 to take into account local conditions, at
a continental level, by changing parameters related to geography (land and water areas),
climate (temperature, wind speed) and human exposure (population density, diet).
EUSES-LCA does not provide DALY losses associated with the emission of common inorganic air
pollutants such as sulphur dioxide, nitrogen oxide and PM10. Adaptation of EUSES-LCA to model
these substances would result in higher than acceptable uncertainty due to the different
characteristics of organic and inorganic substances. European estimates were found for the
most common inorganic air pollutants (Zelm, et al., 2008) and adjusted using population
density in order to derive country-specific DALY estimates for these pollutants. All other factors,
including dispersion conditions, are held constant.
28
ECONOMIC MODELLING
Once the quantity of DALYs lost is calculated, several valuation methods can be used to put a
monetary value on the DALY, such as the cost of illness and value of a statistical life (VSL) approaches.
Cost of illness is a purely economic approach to valuing mortality and morbidity that includes medical
expenses spent to recover initial health conditions; the value of labour time lost due to illness or
premature deaths; and the value of leisure time lost due to illness or premature deaths.
The VSL is a common concept used in policy-making representing the sum of an individual’s willingness-
to-pay (WTP) for small risk reductions (including mortality risk from air pollution) that together add up
to one statistical life. VSL is based not on how much a fatally ill person would be prepared to pay for a
miracle recovery, but is based on whether the average person considers a particular cost to be justified
in relation to a reduction in the risk of mortality. VSL studies are used to derive the WTP for reduced
mortality effects from air pollution in the literature.
A related concept is the Value of a Life Year (VOLY), which can be estimated directly from a WTP study,
or calculated by converting the VSL into a discounted stream of annual life year values over the
remaining lifetime of the subject. In contrast to the VSL which is uniformly applied to all ages, the VOLY
can be used to take account of the duration of life lost due to premature death. The VOLY is therefore
useful in the valuation of premature mortality due to pollution exposures since the majority of such
deaths are expected to occur among the elderly.
Trucost decided to use the VOLY to value DALYs, as it encompasses most aspects relating to illness
and expresses the value to the wider population rather than the purely economic cost of
treatment/illness. Trucost used the results of a study conducted in the context of the New Energy
Externalities Development for Sustainability (NEEDS) project (Desaigues, et al., 2006). Surveys were
conducted in nine European countries to elicit people’s WTP for an increase in life expectancy.
VALUE TRANSFER
The VOLY used to value DALYs is based on European estimates. This value was adjusted for use in other
countries on the basis of income and income elasticity. When income elasticity is between 0 and 1, the
good is considered a necessity, with demand for the good less responsive to changes in income. When
income elasticity is higher than 1 the good is considered a luxury. Thus the assumed income elasticity of
WTP for mortality risk reduction is used to adjust the VOLY for countries with differing average income.
A value of 0.5 was assigned to income elasticity for this study based on Desaigues, et al. (2006). The
higher the income levels in a country the higher the value. Yet, instead of using country-specific value,
Trucost then calculated a global median across all the countries in its dataset and applied this value to
every country. This avoids the ethical problem of assigning a higher value for a life in a richer country.
LIMITATIONS
Trucost uses EUSES-LCA 2.0 instead of the more robust toxicity consensus model, USEtox.
DALYs calculated for inorganic pollutants are based on a different methodology and are
transferred based on population density only.
29
The value of one DALY provided are based on European estimates.
SENSITIVITY ANALYSIS
DALY valuation is sensitive to the income elasticity coefficient applied in the value transfer. In this study,
Trucost used a coefficient of 0.5, with a result of US$ 46,528 per DALY, and performed sensitivity
analysis for coefficients at 0.4 (DALY = $52,270), 0.6 (DALY = $41,417) and 0.85 (DALY = $30,963).
ECOSYSTEM AND BIODIVERSITY IMPACTS
To value impacts on biodiversity, a study must define biodiversity, quantify biodiversity losses due to
emissions of pollutants through dispersion and deposition models, and then place a monetary value on
these losses. According to the Millennium Ecosystem Assessment (MA, 2005), ‘biodiversity is the
variability among living organisms from all sources, including terrestrial, marine and other aquatic
ecosystems and the ecological complexes of which they are part; this includes diversity within species,
between species, and of ecosystems’. This definition draws the attention to the many aspects of
biodiversity and its link to the concept of ecosystems.
The Convention on Biological Diversity identifies pollution as a key driver of biodiversity loss and
identifies ‘reduc[ing] pollution and its impact on biodiversity’ as one of its targets (Target 7.2) (MA,
2005). However, the impacts of polluting substances on terrestrial and freshwater ecosystems have
been omitted from many valuation studies due to the lack of available information and modelling
uncertainties. The majority of studies have therefore been unable to assign general, per tonne costs to
air pollution in relation to their effects on most ecosystems.
BIOPHYSICAL MODELLING
In spite of the difficulties listed above, certain studies have attempted to quantify changes in
biodiversity due to the emission of pollutants, within the discipline of life cycle analysis (Goedkoop, M.
& Spriensma, R., 2000). These studies have mainly focused on two concepts, the potentially affected
fraction of species on the one hand, and the potentially disappeared fraction of species on the other.
These measures relate to the concept of species richness, as they express the proportion of species
affected or disappeared due to a change such as emission of polluting substances.
EUSES-LCA2.0 estimates the potentially affected fraction of species due to the emission of
pollutants to air, land and water. Affected species need not disappear. Trucost made the
assumption that 10% of species affected will disappear based on Eco-Indicator (1999). The
output of this analysis step is the potentially disappeared fraction of species due to the
emission of each pollutant to a specific media at a continental level.
Impact on ecosystems has not been included for inorganic pollutants.
30
ECONOMIC MODELLING
According to TEEB (2010), ‘the value of biodiversity derives from its role in the provision of ecosystem
services, and from peoples’ demand for those services’. Placing a monetary value on biodiversity
involves understanding the link between measures of biodiversity and ecosystem services. When
multiplied by the value of the ecosystem services, the marginal value of biodiversity change can be
calculated.
Trucost’s approach to valuing a change in the potentially disappeared fraction of species follows a three
step process:
Step 1: Quantify the relationship between species richness and one selected ecosystem
function and calculate the difference before and after pollutant emissions. Ecosystem functions
are the biological, geochemical, and physical processes that take place within an ecosystem.
Primary productivity (the capacity of ecosystems to absorb light) was chosen over other
ecosystem processes due to data availability, and its direct link with key ecosystem services as
outlined in the literature. This follows the approach outlined by Costanza et al. (2006) who
reports a correlation between species richness and net primary productivity at three spatial
scales.
Step 2: Quantify the relationship between net primary productivity and ecosystem service value
for terrestrial and aquatic ecosystems. A value for the provisioning, regulating and cultural
services provided by terrestrial and aquatic ecosystems was first calculated based on the
analysis of De Groot et al. (2012). De Groot et al. (2012) calculated minimum, maximum,
median, average and standard deviation for each service provided by key terrestrial and aquatic
ecosystems via a meta-analysis of selected value estimates in the Ecosystem Service Value
database (Van der Ploeg & de Groot, 2010), which compiles ecosystem service valuation studies
available in the literature. Trucost then performed a regression analysis of average net primary
productivity and average ecosystem service value per m2 per country, and found an exponential
relationship.
Step 3: Calculation of the percentage of final ecosystem service value correlated with net
primary productivity and application of this percentage to the average ecosystem service value
in a given region. Trucost calculated the percentage difference in pre and post change
ecosystem service value at a country and substance level, and applied this percentage to the
average value of one m2 of natural ecosystem in a given region. The average ecosystem service
value of one terrestrial and aquatic m2 was calculated following the methodology described in
step 2.
VALUE TRANSFER
As outlined in Steps 1 to 3, country-specific or regional-specific variables are inputted in the model at
several stages.
Potentially disappeared fraction of species: Continent-specific
Species of richness: Country-specific
31
NPP: Country-specific
Ecosystem service value used in the regression analysis: Country-specific
LIMITATIONS
Trucost used EUSES-LCA 2.0 instead of the more robust toxicity consensus model, USETOX.
No scientific information is readily available on the conversion from potentially affected fraction
and potentially disappeared fraction.
Irreversible damage in ecosystems and biodiversity is not taken into account.
Impacts on ecosystems have not been included for inorganic substances (sulphur dioxide,
nitrogen oxide, and particulate matter).
Biodiversity valuation takes into account only one measure of biodiversity.
Biodiversity valuation is not linked to a particular ecosystem service, but to total provisioning,
regulating and cultural services, through one single measure of ecosystem functioning, net
primary production.
SENSITIVITY ANALYSIS
Value of the potentially disappeared fraction of species is dependent on the relationship between
species richness and net primary productivity on the one hand, and net primary productivity and
ecosystem service value on the other. Trucost performed sensitivity analysis on the end result by
varying each coefficients used in the regression analysis by 10%. Results vary in-line with the variation in
coefficients (a 10% change in the coefficient leads to a 10% change in results).
Another source of variation in the results is the average ecosystem service value of one m2 in the region
of interest. As explained in Step 3, the percentage of ESV lost due to biodiversity loss is applied to the
average value of one m2 in the region of interest. Trucost used averages based on ecosystem
repartition and global value per type of ecosystem, but recommends using more specific value when
available.
32
REFERENCES
Costanza, R., Fisher, B., Mulder, K., Liu, S. and Christopher, T. (2006). Biodiversity and ecosystem
services: A multi-scale empirical study of the relationship between species richness and net primary
production. Ecological Economics.
De Groot, R., Brander, L., van der Ploeg, S., Costanza, R., Bernard, F., Braat, L., Christie, M., Crossman,
N., Ghermandi, A., Hein, L., Hussain, S., Kumar, P., McVittie, A., Portela, R., Rodriguez, L. C., ten Brink, P.,
van Beukering, P. (2012). Global estimates of the value of ecosystems and their services in monetary
units. Ecosystem Services. 1, 50-61.
Desaigues, B., Ami, D. & Hutchison, M., 2006. Final report on the monetary valuation of mortality and
morbidity risks from air pollution, Paris: NEEDS.
Goedkoop, M. & Spriensma, R., 2000. The Ecoindicator 99 - A Damage oriented method for Life Cycle
Impact Assesment, Netherlands: product ecology consultants.
Jing, L. (2015). Air pollution is bigger killer in China than smoking, says new Greenpeace study. Available:
http://www.scmp.com/news/china/article/1701625/air-pollution-bigger-killer-mainland-china-smoking-
says-new-greenpeace. Last accessed 17 March 2015.
National Institute of Public Health and the Environment, (2004). European Union System for the
Evaluation of Substances 2.0 (EUSES 2.0). Bilthoven.
Lim, S. S., Vos, T., Flaxman, A. D., Danaei, G., Shibuya, K., Adair-Rohani, H., AlMazroa, M. A., Amann, M.,
Anderson, H. R., Andrews, K. G., Aryee, M., Atkinson, C., Bacchus, L. J., Bahalim, A. N., Balakrishnan, K.,
Balmes, J., Barker-Collo, S., Baxter, A., Bell, M. L., Blore, J. D., Blyth, F., Bonner, C., Borges, G., Bourne, R.,
Boussinesq, M., Brauer, M., Brooks, P., Bruce, N. G., Brunekreef, B., BryanHancock, C., Bucello, C.,
Buchbinder, R., Bull, F., Burnett, R. T., Byers, T. E., Calabria, B., Carapetis, J., Carnahan, E., Chafe, Z.,
Charlson, F., Chen, H., Chen, J. S., Cheng, A. T., Child, J. C., Cohen, A., Colson, K. E., Cowie, B. C., Darby,
S., Darling, S., Davis, A., Degenhardt, L., Dentener, F., Des Jarlais, D. C., Devries, K., Dherani, M., Ding, E.
L., Dorsey, E. R., Driscoll, T., Edmond, K., Ali, S. E., Engell, R. E., Erwin, P. J., Fahimi, S., Falder, G.,
Farzadfar, F., Ferrari, A., Finucane, M. M., Flaxman, S., Fowkes, F. G. R., Freedman, G., Freeman, M. K.,
Gakidou, E., Ghosh, S., Giovannucci, E., Gmel, G., Graham, K., Grainger, R., Grant, B., Gunnell, D.,
Gutierrez, H. R., Hall, W., Hoek, H. W., Hogan, A., Hosgood III, H. D., Hoy, D., Hu, H., Hubbell, B. J.,
Hutchings, S. J., Ibeanusi, S. E., Jacklyn, G. L., Jasrasaria, R., Jonas, J. B., Kan, H., Kanis, J. A., Kassebaum,
N., Kawakami, N., Khang, Y., Khatibzadeh, S., Khoo, J., Kok, C., Laden, F., Lalloo, R., Lan, Q., Lathlean, T.,
Leasher, J. L., Leigh, J., Li, Y., Lin, J. K., Lipshultz, S. E., London, S., Lozano, R., Lu, Y., Mak, J., Malekzadeh,
R., Mallinger, L., Marcenes, W., March, L., Marks, R., Martin, R., McGale, P., McGrath, J., Mehta, S.,
Memish, Z. A., Mensah, G. A., Merriman, T. R., Micha, R., Michaud, C., Mishra, V., Hanafiah, K. M.,
Mokdad, A. A., Morawska, L., Mozaffarian, D., Murphy, T., Naghavi, M., Neal, B., Nelson, P. K., Nolla, J.
M., Norman, R., Olives, C., Omer, S. B., Orchard, J., Osborne, R., Ostro, B., Page, A., Pandey, K. D., Parry,
C. D., Passmore, E., Patra, J., Pearce, N., Pelizzari, P. M., Petzold, M., Phillips, M. R., Pope, D., Pope III, C.
A., Powles, J., Rao, M., Razavi, H., Rehfuess, E. A., Rehm, J. T., Ritz, B., Rivara, F. P., Roberts, T., Robinson,
C., Rodriguez-Portales, J. A., Romieu, I., Room, R., Rosenfeld, L. C., Roy, A., Rushton, L., Salomon, J. A.,
33
Sampson, U., Sanchez-Riera, L., Sanman, E., Sapkota, A., Seedat, S., Shi, P., Shield, K., Shivakoti, R., Singh,
G. M., Sleet, D. A., Smith, E., Smith, K. R., Stapelberg, N. J., Steenland, K., Stöckl, H., Stovner, L. J., Straif,
K., Straney, L., Thurston, G. D., Tran, J. H., Van Dingenen, R., van Donkelaar, A., Veerman, J. L.,
Vijayakumar, L., Weintraub, R., Weissman, M. M., White, R. A., Whiteford, H., Wiersma, S. T., Wilkinson,
J. D., Williams, H. C., Williams, W., Wilson, N., Woolf, A. D., Yip, P., Zielinski, J. M., Lopez, A. D., Murray,
C. J. & Ezzati, M. (2012) A comparative risk assessment of burden of disease and injury attributable to 67
risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden
of Disease Study 2010. The Lancet. 380 (9859), 2224-2260.
Millennium Ecosystem Assessment, (2005). Biodiversity Synthesis. Ecosystems and Human Well-being.
Goedkoop, M., Heijungs, R., Huijbregts, M., De Schryver, A., Struijs, J. and van Zelm, R. (2013). Report 1:
Characterisation. Recipe 2008.
The Economics of Ecosystems and Biodiversity, (2010). Biodiversity, ecosystems and ecosystem services,
The Economics of Ecosystems and Biodiversity: The Ecological and Economic Foundations
Van der Ploeg, S., de Groot R. S. (2010) The TEEB Valuation Database – a searchable database of 1310
estimates of monetary values of ecosystem services. Foundation for Sustainable Development,
Wageningen, the Netherlands.
WHO, 2014. World Health organization. [Online]
Available at: http://www.who.int/healthinfo/global_burden_disease/metrics_daly/en/
Zelm, R.v. et al., 2008. European characterization factors for human health damage of PM10 and ozone
in life cycle impact assessment. Elvesier-Atmospheric Environment, Volume 42, pp. 441-453.
34
EUTROPHICATION POTENTIAL
INTRODUCTION
Eutrophication describes the impact of pollutants, specifically nitrogen and phosphorus, on water
bodies. The nutrient enrichment, which results from these pollutants, creates algae blooms and water
toxicity leading to negative impacts on aquatic ecosystems. Water bodies are generally classified into
four categories known as trophic classes, ranging from oligotrophic, with clear water and high
biodiversity, to hypereutrophic, with frequent algal blooms and little or no fish (Carlson, 1977).
Eutrophication can affect rivers, lakes, reservoirs and coastal waters. The enriched waters, when
warmed in summer, can lead to algae blooms, particularly from phosphorus emissions due to the
application of agricultural fertilizers. Algae have a short lifespan and the process of decay uses dissolved
oxygen. These algae blooms can be so severe that they use up all of the oxygen in the water body
resulting in hypoxia, which kills fish and other organisms (Anderson et al., 2002). Harmful algae blooms
(HABs) occur when toxic algae grows in the water (Anderson et al., 2002). These blooms can give off an
unpleasant smell, reduce water clarity and harm the health of those animals that use it as a source of
drinking water. Algae blooms can occur in both inland and coastal waters.
Academic literature uses a number of methods to assign a monetary value to the impacts of
eutrophication. Popular methods have tended to focus on valuing: the impacts on human health due to
unsafe drinking water using a dose-response methodology (Hansen and Anderson, 2008); the reduction
in recreational activities through willingness to pay (WTP) or travel cost methods (Carson and Mitchell,
1993; Dodds et al., 2009; Pretty et al., 2003); the loss of property values using hedonic pricing (Egan et
al., 2009; Gibbs et al., 2002; Ge et al., 2013); the loss of biodiversity through percentage of species
effected and the recovery cost associated with national endangered species plans (Dodds et al., 2009);
and the provision of clean water through water treatment costs (Pretty et al., 2003).
Valuation requires a country-specific estimate of the cost of the impacts caused by the eutrophication
potential of pollutants per unit mass [1 kg]. Therefore, to be able to compare across nations it is
necessary to define a consistent waterbody into which the pollutant is most likely to cause economic
damage. For example, pollutants in rivers will disperse according the flow rate, and pollutants in oceans
quickly become diluted making the economic costs are more difficult to estimate on a national level. To
support this, the United States Environmental Protection Agency (US EPA) does not currently assign an
economic cost to coastal eutrophication (US EPA, 2009). The effect of nitrates on animal species is also
likely to be greater for freshwater species than marine (Carmargo, 2006). Furthermore, the average
amount of time water remains in a lake is 8.5 years (Globox, 2010), and hence the most significant
economic costs of pollutants in surface water are likely to relate to their impact on lakes.
SCOPE OF TRUCOST VALUATION
The present methodology includes impacts on human health and impacts on ecosystems due to
eutrophication. Table provides a high level overview of the ecosystem services linked to those impacts,
for example: eutrophication will affect provisioning services, generating impacts on human health due
35
to water consumption; eutrophication will also influence other types of ecosystem services provided by
water bodies such as recreation. Please note, that with regards to water provisioning services two
impact pathways are considered: safe drinking water from water treatment and unsafe drinking water
on human health impacts. Table 7 also provides the biophysical modelling (quantification of impacts
within the scope of the valuation) and economic modelling (actual valuation of impacts) undertaken in
this methodology.
TABLE 7: OVERVIEW OF VALUATION METHODOLOGY FOR KEY POLLUTANTS
IMPACT MODELLING BIOPHYSICAL MODELLING ECONOMIC MODELLING
EMISSIONS
/ RESOURCE USE
IMPACT AND
DEPENDENCY END POINT
CHANGE IN VALUED
ATTRIBUTE
LINK TO ESS
(WHERE
RELEVANT)
ECOSYSTEM
SERVICE (WHERE
RELEVANT)
FINAL
BENEFICIARIES
VALUATION
APPROACH
VALUE TRANSFER
METHOD
Nitrate to water,
phosphate to water
Eutrophication Freshwater
ecosystems
Change in secchi
depth
Provisioning,
cultural and
supporting
Diverse Population Hedonic pricing
Average freshwater
bodies volume and
perimeter,
population density
Concentration
of nitrates and
phosphates
People Change in water
treatment costs Diverse Diverse Population Restoration cost
Average freshwater
bodies volume and
perimeter
Concentration
of nitrates and
phosphates
People Change in DALY due
to ingestion Provisioning
Safe water
drinking Population
Willingness-to-
pay
Average freshwater
bodies volume and
perimeter,
population density,
population structure,
population with
access to safe
drinking water
37
Natural Capital Impacts in Agriculture
SUPPORTING BETTER BUSINESS DECISION-MAKING VALUATION METHODOLOGY
IMPACT ON ECOSYSTEM SERVICES
For the purposes of creating a robust and adaptable model, valuation techniques that rely on
measurable and continuous input variables, such as unit mass (or concentration) of a pollutant or secchi
depth, will produce the best results (Poor et al., 2007). Contingent valuation, travel cost and hedonic
pricing methods have most commonly been used to value eutrophication impacts.
More recent studies have tended to use the travel cost method as a more reliable valuation tool.
Studies by Dodds et al. (2009) and Pretty et al. (2003) use the travel cost method to assess WTP for
improved water clarity or to avoid site closures. This relies on site or regionally specific data that may be
difficult to obtain, such as the frequency of closure. The valuation is also influenced by other factors
such as travel distance, age and having access to a car (Vesterinen et al., 2010).
Hedonic pricing studies of recreational losses are similarly dependent on local factors and produce a
huge range of results. There is also the danger of double counting when including the loss of property
prices, which would be driven by the ability to use the water body for recreational purposes. This is
supported by a meta-analysis study of more than 100 studies which revealed that the hedonic model
tends to produce larger valuations than the travel cost or contingent value methods (Ge et al., 2013).
This valuation will include (but will not be limited) to value impacts on recreation and biodiversity.
Therefore, this section of the valuation methodology focuses on the decrease of property prices, which
incorporates both recreation and biodiversity.
BIOPHYSICAL MODELLING
Studies using the hedonic method estimate the effect of eutrophication on waterfront property prices.
Waterfront property prices are significantly affected by water clarity (Gibbs et al., 2002). Secchi depth is
the most widely used measure of water clarity due to its ease of use. The biophysical modelling
methodology proposed here requires a link between secchi depth and phosphorus level, a relationship
that has been investigated since the 1970s, as for example in a review by Canfield and Bachman (1981).
Trucost selected the results of a more recent study of over 170 lakes in Iowa, which produced the
following relationship between Secchi depth and total phosphorus (Downing et al., 2010):
𝑆𝑒𝑐𝑐ℎ𝑖 𝐷𝑒𝑝𝑡ℎ = 29.93 (𝑇𝑜𝑡𝑎𝑙 𝑃ℎ𝑜𝑠𝑝ℎ𝑜𝑟𝑢𝑠)−0.78 (1)
This study improves on previous estimates and has an approximate R2 value of 0.6 (p-value<0.001).
In order to estimate the eutrophication effects due to pollutant inputs at a country level, Trucost
calculated the increase in phosphorous equivalent concentration in an average lake associated with the
use of one kilogram of each input pollutant. A baseline phosphorus concentration value of 60μg/L was
assumed for comparison. This value was selected because it is the median concentration of a eutrophic
lake, as defined in the Trophic State Index (Carlson, 1977). Hence, the value reflects a water body where
eutrophication is already occurring and thus allows Trucost to calculate the marginal cost of an increase
in eutrophication due to a pollutant. The phosphorus concentration increase was calculated by
assuming the pollutant most likely cause economic damage in a lake. Using GIS data and the Global
38
Lakes and Wetlands Database (Lehner and Döll, 2004), the median area of a lake and the average
perimeter of a median lake were calculated for each country. These parameters were used to determine
the impacts of eutrophication on a hypothetical lake in each country. The volume of this hypothetical
lake was calculated using the average water depth for each nation derived from the GLOBOX data set
(Wegener Sleeswijk, 2010; Globox, 2010). The increase in pollutant concentration was then calculated
and used as an input for the valuation.
Trucost then converted the change in pollutant concentration into secchi depth and used the water
clarity change as a percentage of one secchi depth meters as the value for pricing.
ECONOMIC MODELLING
Trucost used data from three studies (Krysel et al, 2003; Gibbs et al, 2002; Michael et al, 1996) in the US
comprising a total of 44 estimates of water frontage price (per foot) decreases due to a 1m reduction in
secchi depth and calculated the median value. The US dollar value from each study was converted into
2013 US dollar values with inflation data taken from the World Bank (2015a).
Trucost then used purchasing power parity (PPP) conversion rates for 2013 from the World Bank
(2015b) to adjust the value for each country and calculated the price per waterfront meter. Finally, the
value per meter of waterfront for each country is applied to the perimeter of the hypothetical lake to
establish the hedonic cost of eutrophication at a country-level.
LIMITATIONS
Scope of ecosystem damage impacts
Many models use WTP to describe a loss of recreational income from a waterbody, which is partially
reflected in property prices but not fully, as local tourism and hospitality are also negatively affected.
However, given that hedonic pricing models tend to produce larger valuations than contingent valuation
and travel cost methods, as they include a greater number of impacts (Ge et al., 2013), the hedonic
method was considered to better reflect the true cost of eutrophication.
Hedonic modelling is used as a tool for estimating the economic impacts of a decrease in biodiversity
and loss of cultural services (such as recreation). As a result, direct links between pollutant
concentration and loss of biodiversity have not been included. Trucost recognises the importance of
those impacts and identifies them as potential developments.
Quantification of biophysical impacts
The link between secchi depth and phosphorus level is an approximation and thus introduces
uncertainty in the method. The sensitivity of the method to these coefficients is analysed, and Trucost
will continue to monitor developments in this area.
39
IMPACT ON HUMAN HEALTH
Water pollution can directly impact human health when unsafe drinking water is consumed. However,
water is also treated to prevent the negative impacts of polluted water consumption and this comes
with an economic cost. The global average for percentage of drinking water that is safe is 83% (Globox,
2010). Therefore, to encompass the impact on human health it is necessary to look at the costs both
safe and unsafe drinking water.
BIOPHYSICAL MODELLING
UNSAFE DRINKING WATER
The human health effects of unsafe water consumption were estimated based on dose response
functions (DRF) describing the relationship between Years of Life Lost (YLL) and nitrate in drinking
water. YLL has been used as a proxy for Disability Adjusted Life Years (DALY) lost, which is a more
complete measure of the health impacts of disease including YLL due to premature death and Years of
Lost to Disability (YLD) due to morbidity (WHO, 2014), due to a lack of available data on YLD and to
maintain consistency with the valuation of other human health impacts. Trucost used the data from the
EXIOPOL study to calculate the median YLL per 100,000 for males and for females due to nitrate
pollution in drinking water. Population data, obtained from the World Bank, allowed the YLL to be made
country-specific via adjustments for the demographic breakdown of each nation by gender.
Using GIS data and the Global Lakes and Wetlands Database (Lehner and Döll, 2004), the median area of
a lake and the average perimeter of a median lake were calculated for each country. The volume of this
hypothetical lake was calculated using the average water depth for a nation using GLOBOX data set
(Wegener Sleeswijk, 2010; Globox, 2010). The increase in pollutant concentration was then calculated.
It was necessary to estimate the catchment area from the hypothetical lake to determine the
proportion of the national population were most likely to be affected by drinking unsafe water caused
by eutrophication. Trucost assumed a 3 km catchment radius for each hypothetical lake. This radius was
selected from a study that found that the majority of the world’s population live within 3 km from a
freshwater source (Kummu et al., 2011). The population density of each country was applied to
calculate how many people live in the catchment area.
Finally, multiplying the increase in concentration from the unit mass by the DRF gives the YLL per unit
mass of pollutant. Trucost used YLL as a proxy for DALYs as no information on YLD from eutrophic
drinking water consumption could be located. The YLL will be taken as equivalent to DALY as the input
for the valuation.
SAFE DRINKING WATER
The economic model requires an input of phosphorus yield in a watershed. The data and relationship
reported by the Nature Conservancy (McDonald and Shemie, 2014) was used to determine the
incremental change in dollar value from an initial sediment yield to the increased sediment yield
associated with the application of the eutrophying pollutant. As this relationship is associated with
phosphorus yield in the watershed area, it was necessary to convert unit mass of phosphorus to yield
40
based on the average watershed area in the United States (USGS, 2008). Trucost selected the United
States as most of the 100 city watersheds studied in the report are US based (McDonald and Shemie,
2014).
The unit mass (1 kg of Phosphorus) was divided by the median watershed area in the US. This is the
additional phosphorus yield applied to the watershed.
ECONOMIC MODELLING
UNSAFE DRINKING WATER
Once the quantity of DALYs lost is calculated, several valuation methods can be used to put a
monetary value on DALY, such as the cost of illness, the value of a statistical life (VSL) and the Value
of a Life Year (VOLY). Cost of illness is a purely economic approach to valuing mortality and morbidity
that can include medical expenses spent to recover from the initial health conditions; the value of
labour time lost due to illness or premature deaths; and the value of leisure time lost due to illness or
premature deaths.
Value of statistical life is a common concept used in policy-making. It is the sum of an individual’s WTP
for small risk reductions (such as mortality risk) that together add up to one statistical life. It is based
not on how much a fatally ill person would be prepared to pay for a miracle recovery, but is based on
whether the average person considers a particular cost to be justified in relation to a reduction in the
risk of mortality. A related concept is the Value of a Life Year (VOLY), which can be estimated directly
from a contingent valuation study, or calculated by converting the VSL into a discounted stream of
annual life year values over the remaining lifetime of the subject. In contrast to the VSL which is
uniformly applied to all ages, the VOLY can be used to take account of the duration of life lost due to
premature death. The VOLY is therefore useful in the valuation of premature mortality due to pollution
exposures since the majority of such deaths are expected to occur among the elderly.
Trucost decided to use the VOLY to value DALYs, as it encompasses most aspects relating to illness
and expresses the value to the wider population rather than the purely economic cost of
treatment/illness. Trucost used the results of a study conducted in the context of the New Energy
Externalities Development for Sustainability (NEEDS) project (Desaigues, et al., 2006). Surveys were
conducted in nine European countries to elicit people’s WTP for an increase in life expectancy.
The VOLY used to value of DALYs is based on European estimates. This value was adjusted for use in
other countries on the basis of income and income elasticity. When income elasticity is between 0 and
1, the good is considered a necessity, with demand for the good less responsive to changes in income.
When income elasticity is higher than 1 the good is considered a luxury. Thus the assumed income
elasticity of WTP for mortality risk reduction is used to adjust the VOLY for countries with differing
average income.
A value of 0.5 was assigned to income elasticity for this study based on Desaigues, et al (2006). The
higher the income levels in a country the higher the value. Yet, instead of using country-specific value,
Trucost calculated a global median across all the countries in its dataset and applied this value to every
41
country. This avoids the ethical problem of assigning a higher value for a life in a richer country. The
median value used in this study is US$ 46,528 per DALY.
SAFE DRINKING WATER
McDonald and Shemie (2014) presented the relationship between phosphorus yield (as defined by
tonne per square kilometre of the watershed) and treatment cost. However, the relationship is not
linear and in fact is a power function. Hence, estimation of the incremental increase in cost due to an
increase in phosphorus yield is extremely sensitive to the phosphorus yield prior to any increase.
Trucost selected the median phosphorus yield in McDonald and Shemie (2014) for the initial
phosphorus yield (PYi) value. Using the equation from the regression (R2=0.0635; p-value>0.8) the total
cost of water treatment prior to adding a unit mass of pollutant was calculated.
The initial phosphorus yield was then increased by the additional phosphorus yield (PYa) from the unit
mass of pollutant and the method above was applied to calculate the total cost of water treatment after
the unit mass has been applied in the watershed. The difference between these costs represents the
change in treatment cost due to the additional unit mass of phosphorus.
LIMITATIONS
UNSAFE DRINKING WATER
Scope of human health impacts
The water consumption methodology does not include the impacts on human health due to diseases
that result from lack of water for domestic use. As mentioned in the literature, lack of access to water
for domestic purposes can lead to hygiene and sanitation problems such as diarrheal diseases and
nematode infections. The impact of water consumption has on cultural services (for example,
recreation) and on regulating services (for example, waste assimilation), has been excluded in this
methodology.
The values of VSL provided are based on European estimates
Performing value transfer to other countries is subject to error. First, the nature of WTP is influenced by
factors like education, and knowledge about impacts of pollutants on health, which were all held
constant in the benefit transfer (OECD, 2010). Second, there is uncertainty associated with income
elasticity. Sensitivity analysis was conducted using 0.4, 0.6 and 0.85 as income elasticity.
SAFE DRINKING WATER
Granularity of economic impacts
Despite the global trend toward rising costs due to poorer quality water, it is extremely difficult to
develop a model to assign an economic value to the impact of eutrophication on water treatment costs,
especially as plants often do not vary the quantity of chemicals applied with the water quality (Gartner
et al., 2013). The water treatment cost is estimated using data from 100 global cities; however the
majority are located in the United States.
42
Quantification of economic impacts
There is a weak correlation between the water treatment cost and the concentration of the pollutant.
This is due to other factors, such as the type of technology used and the labour costs, having a stronger
influence on costs than input water quality.
Correlation with other economic modelling
The impacts on human health and property prices are based on phosphorus or nitrate leaked into water
but this is based on phosphorus yield which is applied to the land in the watershed. Trucost recognises
the importance of those impacts and identifies them as potential developments.
SENSITIVITY ANALYSIS
The model employed to measure the decrease in property prices depends on four approximated input
parameters (average perimeter of hypothetical lake, median lake area, average depth of lakes, pollutant
concentration baseline) and two coefficients of the regression analysis, which relates secchi depth to
phosphorus concentration. As the baseline concentration of phosphorus is the most sensitive
parameter, results could be produced for a mesotrophic baseline lake, a eutrophic and a hypereutrophic
lake to obtain more accurate results. Ideally, the site-specific baseline concentration would be used for
each application of the methodology if this data were available.
The model used to determine impacts on human health depends on five approximated input
parameters (percentage of unsafe drinking water, median lake area, average depth of lakes, population
density and percentage of the population that is female) and two coefficients for the biophysical
modelling (YLL for males and YLL for females) taken as the median result from EXIOPOL (Hansen and
Anderson, 2008). Trucost performed sensitivity analysis by varying each of these parameters and
coefficients by ±10%. The health impact model does not include any variables that are particularly
sensitive and as such, errors in the input variables do not have significant impacts on the final output
result.
The relationship between phosphorous yield and treatment costs defined in the Urban Water Blueprint
report (McDonald & Shemie, 2014) has a low R2 value, which is expected because there are many
factors affecting water treatment costs and the contribution to the variation in the costs of phosphorus
yield alone is likely to be small. As the median phosphorus yield was selected for the initial phosphorus
yield value, it is prudent to see how the selection of alternate values will change the water treatment
cost (in the units ‘dollar per kg of phosphorus’). The results show that as the initial phosphorus value
gets closer to zero (in less polluted areas) the phosphorus water treatment cost goes up dramatically.
Conversely, as the initial phosphorus yield increases, the marginal value of one kilogram of phosphorus
shrinks close to zero. It is expected that the price per unit mass of pollutant changes according to the
existing phosphorus yield load in a given watershed.
43
REFERENCES
Anderson, D. M., Glibert, P. M., Burkholder, J. M. (2002) Harmful algal blooms and eutrophication:
nutrient sources, composition, and consequences. Estuaries, Vol. 25, no. 4, pp. 704-726.
Canfield, D. E., R. W. Bachman (1981) Prediction of Total Phosphorus Concentrations, Chlorophyll a, and
Secchi Depths in Natural and Artificial Lakes. Can. J. Fish. Aquat. Sci., Vol. 38, pp. 414-423.
Carlson, R.E. (1977) A trophic state index for lakes. Limnology and Oceanography, Vol. 22, no. 2, pp.
361-369.
Carson, R. T., Mitchell, R. C. (1993) The value of clean water: the public's willingness to pay for boatable,
fishable, and swimmable quality water. Water resources research, Vol. 29, no. 7, pp. 2445-2454.
Desaigues, B., Ami, D., Bartczak, A., Braun-Kohlová, M., Chilton, S., Farreras, V., Hunt, A., Hutchison, M.,
Jeanrenaud, C., Kaderjak, P., Máca, V., Markiewicz, O., Metcalf, H., Navrud, S., Nielsen, J.S., Ortiz, R.,
Pellegrini, S., Rabl, A., Riera, R., Scasny, M., Stoeckel, M.-E., Szántó, R., Urban, J., (2006). Final Report on
the Monetary Valuation of Mortality and Morbidity Risks from Air Pollution. Deliverable RS1b of NEEDS
Project.
Dodds, W. K., Bouska, W. W., Eitzmann, J. L., Pilger, T. J., Pitts, K. L., Riley, A. J., Thornbrugh, D. J. (2009).
Eutrophication of US freshwaters: analysis of potential economic damages. Environmental Science &
Technology, Vol. 43, no. 1, pp. 12-19.
Downing, J.A., Poole, K., Filstrup, C. T. (2010) Black Hawk Lake Diagnostic/Feasibility Study. Iowa
Department of Natural Resources (IDNR) and Iowa State University (ISU). Prepared by the Limnology
Laboratory at ISU.
Egan, K. J., Herriges, J. A., Kling, C. L., Downing, J. A. (2009) Valuing water quality as a function of water
quality measures. American Journal of Agricultural Economics, Vol. 91, no. 1, pp. 106-123.
Gartner T., Mulligan J., Schmidt R, Gunn J. (2013) Natural Infrastructure. Washington D.C.: WRI
Ge, J., Kling, C., Herriges, J. (2013) How Much is Clean Water Worth? Valuing Water Quality
Improvement Using A Meta-Analysis (No. 36597). Iowa: Iowa State University.
Gibbs, J. P., Halstead, J. M., Boyle, K. J, Huang, J. (2002) A Hedonic Analysis of the Effects of Lake Water
Clarity on New Hampshire Lakefront Properties. Agricultural and Resource Economics Review. 31 (1),
39-46.
Globox (2010) Data derived from: ‘GLOBOX’ Available at:
http://www.cml.leiden.edu/software/software-globox.html (Accessed: 19 January 2014).
Hansen, M. S., Andersen M. S. (2008) Dose-response Function Paper. EXIOPOL Deliverable DII. 2. a 1.
Krysel C., Boyer E. M., Parson C., & Welle P. (2003) Lakeshore property values and water quality:
Evidence from property sales in the Mississippi Headwaters Region. Walker, MN: Mississippi
Headwaters Board.
44
Kummu M., De Moel, H., Ward, P. J., Varis, O. (2011) How close do we live to water? A global analysis of
population distance to freshwater bodies. PloS one, Vol. 6, no. 6, pp. e20578.
Lehner, B., Döll, P. (2004) Development and validation of a global database of lakes, reservoirs and
wetlands. Journal of Hydrology, Vol. 296, no. 1, pp. 1-22.
McDonald, R., Shemie, D. (2014) Urban Water Blueprint: Mapping conservation solutions to the global
water challenge, Washington, D.C.: The Nature Conservancy.
Michael, H. J., Boyle, K. J., & Bouchard, R. (1996) MR398: Water Quality Affects Property Prices: A Case
Study of Selected Maine Lakes. Maine: MAINE AGRICULTURAL AND FOREST EXPERIMENT STATION
OECD (2010). Valuing Lives Saved from Environmental, Transport and Health Policies: A Meta-Analysis of
Stated Preferences Studies. Working Party on National Environmental Policies. Paris: OECD.
Poor, P. J., Pessagno, K. L., Paul, R. W. (2007) Exploring the hedonic value of ambient water quality: A
local watershed-based study. Ecological Economics, Vol. 60, no. 4, pp. 797-806.
Pretty, J. N., Mason, C. F., Nedwell, D. B., Hine, R. E., Leaf, S., Dils, R. (2003) Environmental Costs of
Freshwater Eutrophication in England and Wales. Environmental Science & Technology. Vol. 3, no. 2, pp.
201-208.
US Environmental Protection Agency (2009). Valuing the protection of ecological systems and services.
EPA Sci. Advis. Board Rep., Washington, DC
US Geological Survey. (2008) Watershed Index. [Online] Available:
http://edna.usgs.gov/watersheds/html_index.htm, Accessed: 6th February, 2015.
Vesterinen, J., Pouta, E., Huhtala, A., Neuvonen, M. (2010) Impacts of changes in water quality on
recreation behaviour and benefits in Finland. Journal of environmental management. Vol. 91, no. 4, pp.
984-994.
Wegener Sleeswijk, A. (2010) GLOBOX–A spatially differentiated multimedia fate and exposure
model. Environmental Science and Pollution Research. Vol. 13, no. 2, pp. 143-143.
WHO (2014). Metrics: Disability-Adjusted Life Year (DALY). [Online], Available:
http://www.who.int/healthinfo/global_burden_disease/metrics_daly/en
World Bank Group. (2015). Inflation, GDP deflator (annual %).Available:
http://data.worldbank.org/indicator/NY.GDP.DEFL.KD.ZG. Accessed: 17th March 2015.
World Bank Group. (2015b). PPP conversion factor, GDP (LCU per international $) .Available:
http://data.worldbank.org/indicator/PA.NUS.PPP. Accessed: 17th March 2015.
45
WATER CONSUMPTION
INTRODUCTION
Water availability can be affected when the demand for water exceeds the water available in a certain
period of time. This situation usually occurs in locations where there is a combination of low rainfall and
high population density, or in locations with strong agricultural and industrial operations. An
unsustainable rate of water abstraction can affect access to water for the local population, provoke the
intrusion of salt water in groundwater sources and in the more extreme situations, can lead to the
disappearance of water bodies and wetlands (European Environment Agency, 2015).
As stated by the United Nations Department of Economics and Social Affairs (UN Water, 2015a), global
water withdrawals are associated with: agriculture (70%), industry (20%) and domestic use (10%).
Water is key to ensure food security, as crops and livestock rely heavily on water (UN Water, 2015b).
According to the FAO report ‘The state of the world’s land and water resources for food and
agriculture’, water scarcity is increasing, leading to the salinization of water bodies and the
deterioration of water-related ecosystems. For example, all continents have been subjected to
ecosystem degradation, resulting in loss of biodiversity and decrease of recreational and cultural values
(FAO, 2011).
UN Water (2012) determines that uncertainties around water availability and water demand are
increasing, which imposes a risk to the welfare of society and the environment. In addition, UN Water
recognises that effective water management strategies will need ‘an explicit recognition of the
economic values of water and its different benefits’.
The WBCSD (2013) publication, ‘Business guide to water valuation,’ combines the Total Economic Value
(TEV) Framework with the Ecosystem Services Framework to identify the benefits provided by water.
Direct use values include provisioning services (including water supply for drinking, agricultural and
industry purposes) and cultural services (for example, recreation). Indirect use values include regulating
services (for example, waste assimilation) and habitat services (for example, species diversity). Water
scarcity has an impact on these benefits provided by water.
SCOPE OF TRUCOST VALUATION
The scope of the water valuation methodology includes the impacts of water consumption on human
health and ecosystems. Table 8 provides a high level overview of the ecosystem services linked to those
impacts (for example, water consumption will affect provisioning services, generating impacts on
human health; water consumption will also influence the flow of habitat services, generating impacts on
ecosystems). Table 8 also details the biophysical modelling (quantification of impacts within the scope
of the valuation) and economic modelling (actual valuation of impacts) undertaken in this methodology.
Natural Capital Impacts in Agriculture
SUPPORTING BETTER BUSINESS DECISION-MAKING
TABLE 8: OVERVIEW OF VALUATION METHODOLOGY FOR WATER CONSUMPTION
IMPACT MODELLING BIOPHYSICAL MODELLING ECONOMIC MODELLING
EMISSIONS
/ RESOURCE USE
IMPACT AND
DEPENDENCY END POINT
CHANGE IN VALUED
ATTRIBUTE
LINK TO ESS
(WHERE
RELEVANT)
ECOSYSTEM
SERVICE (WHERE
RELEVANT)
FINAL
BENEFICIARIES
VALUATION
APPROACH
VALUE TRANSFER
METHOD
Water consumption Water
depletion
People
Change in DALYs
due to malnutrition
caused by
decreased water
availability
Provisioning Food Population Willingness-to-
pay
Proportion of
population
vulnerable to
malnutrition,
revenue and income
elasticity
Terrestrial
ecosystems
Change in the
potentially affected
fraction of species
Supporting Biodiversity Diverse
Multiple -
Contribution of
biodiversity to
the delivery and
value of
provisioning,
regulating and
cultural services
Geophysical and
social conditions,
Species density,
Average Ecosystem
value
Natural Capital Impacts in Agriculture
SUPPORTING BETTER BUSINESS DECISION-MAKING
47
VALUATION METHODOLOGY
IMPACT ON HUMAN HEALTH
Water shortages for irrigation can lead to lower crop yields and thus ultimately to malnutrition.
According to the International Food Policy Research Institute (IFPRI, 2014), irrigation is a key factor in
ensuring future food supply and is the largest water user.
The impacts on human health due to water consumption included in this methodology are limited to
those linked to the lack of water for irrigation, which leads to malnutrition. Water scarcity has been
considered an explanatory variable for the quantification of impacts on human health due to water
consumption. Country-specific water scarcity was determined using GIS data published by the World
Resources Institute (WRI, 2013a). In addition, water scarcity was adjusted for inter-annual and seasonal
variability using WRI data (WRI 2013b, WRI 2013c).
BIOPHYSICAL MODELLING
The quantification methodology for human health impacts due to water consumption was developed
using an estimate of the Disability Adjusted Life Years (DALY) lost per unit of water consumed as
reported in the Ecoindicator-99 (Goedkoop and Spriensma, 2001). According to the World Health
Organization (WHO, 2014), a DALY can be thought of as one year of life in full health, with DALY values
of less than one representing a year of life spent in sub-optimal health. The DALY can be used to
represent the total health burden associated with a disease, including both the Years of Life Lost (YLL)
due to premature death and Years Lost Due to Disability (YLD) due to morbidity.
In order to quantify human health impacts, a characterisation factor (referred to as CF malnutrition)
developed by Pfister (2011) was applied, describing human health impact in DALYs per m3. This
parameter is country-specific and depends on several variables. Trucost recalculated CF malnutrition by
sourcing and consolidating information from different data sources. The most up to date data points
were used to estimate each of the variables detailed below:
Water stress index, which is a measure of water scarcity, was established using WRI data (WRI,
2013a; WRI, 2013b; WRI, 2013c).
The share of total water withdrawals used for agricultural purposes was determined using FAO
data (FAO, 2014).
The Human Development Factor which depends on the Human Development Index (UNDP,
2013).
Per-capita water requirement to prevent malnutrition which is independent of location and was
sourced from Pfister (2011).
The damage factor for malnourished people which is independent of location and was sourced
from Pfister (2011).
48
ECONOMIC MODELLING
Once the quantity of DALYs lost is calculated, several valuation methods can be used to put a
monetary value on DALY, such as the cost of illness and the value of a statistical life (VSL). Cost of
illness is a purely economic approach to valuing mortality and morbidity that can include medical
expenses spent to recover from the initial health conditions; the value of labour time lost due to illness
or premature deaths; and the value of leisure time lost due to illness or premature deaths.
The VSL is a common concept used in policy-making. It is the sum of an individual’s willingness-to-pay
(WTP) for small risk reductions (such as mortality risk) that together add up to one statistical life. It is
based not on how much a fatally ill person would be prepared to pay for a miracle recovery, but is based
on whether the average person considers a particular cost to be justified in relation to a reduction in the
risk of mortality. A related concept is the Value of a Life Year (VOLY), which can be estimated directly
from a WTP study, or calculated by converting the VSL into a discounted stream of annual life year
values over the remaining lifetime of the subject. In contrast to the VSL which is uniformly applied to all
ages, the VOLY can be used to take account of the duration of life lost due to premature death. The
VOLY is therefore useful in the valuation of premature mortality due to pollution exposures since the
majority of such deaths are expected to occur among the elderly.
Trucost decided to use the VOLY to value DALYs, as it encompasses most aspects relating to illness
and expresses the value to the wider population rather than the purely economic cost of
treatment/illness. Trucost used the results of a study conducted in the context of the New Energy
Externalities Development for Sustainability (NEEDS) project (Desaigues, et al., 2006). Surveys were
conducted in nine European countries to elicit people’s WTP for an increase in life expectancy.
VALUE TRANSFER
The VOLY used to value DALYs is based on European estimates. This value was adjusted for use in other
countries on the basis of income and income elasticity. When income elasticity is between 0 and 1, the
good is considered a necessity, with demand for the good less responsive to changes in income. When
income elasticity is higher than 1 the good is considered a luxury. Thus the assumed income elasticity of
WTP for mortality risk reduction is used to adjust the VOLY for countries with differing average income.
A value of 0.5 was assigned to income elasticity for this study based on Desaigues et al. (2006). The
higher the income levels in a country the higher the value. Yet, instead of using a country-specific value,
Trucost calculated a global median across all the countries in its dataset and applied this value to every
country. This avoids the ethical problem of assigning a higher value for a life in a richer country. The
median value used in this study is US$ 46,528 per DALY.
LIMITATIONS
The water consumption methodology does not include the impacts on human health due to
diseases that result from lack of water for domestic use. Lack of access to water for domestic
purposes can lead to hygiene and sanitation problems such as diarrheal diseases and nematode
infections. The impact of water consumption has on cultural services (for example, recreation)
49
and on regulating services (for example, waste assimilation), has been excluded in this
methodology.
The value of one DALY provided are based on European estimates. Performing value transfer to
other countries is subject to error and there is uncertainty associated with income elasticity in
relation to WTP for increased life expectancy. Sensitivity analysis was conducted using
alternative values for income elasticity of 0.4, 0.6 and 0.85.
SENSITIVITY ANALYSIS
DALY valuation is sensitive to the income elasticity coefficient applied in the value transfer between
countries. In this study, Trucost used a coefficient of 0.5, with a result of $ 46,528 per DALY, and
performed sensitivity analysis for coefficients at 0.4 (DALY = 52,270 $), 0.6 (DALY = $ 41,417) and 0.85
(DALY = $ 30,963) to assess how the value of DALY would be affected when using different income
elasticity levels.
IMPACT ON ECOSYSTEMS AND BIODIVERSITY
To value impacts on biodiversity, a study must define biodiversity, quantify biodiversity losses due to
water consumption, and then place a monetary value on those losses. According to the Millennium
Ecosystem Assessment (MA, 2005), ‘biodiversity is the variability among living organisms from all
sources, including terrestrial, marine and other aquatic ecosystems and the ecological complexes of
which they are part; this includes diversity within species, between species, and of ecosystems’. This
definition draws attention to the many aspects of biodiversity and its link to the concept of ecosystems.
The Convention on Biological Diversity identifies water withdrawals from rivers (leading to habitat
change) as a key driver of biodiversity loss and highlights this in one of its goals (Goal 5): ‘Pressures from
habitat loss, land use change and degradation, and unsustainable water use reduced’ (MA, 2005). As
mentioned in Life Cycle Impact Assessment studies, water consumption can decrease ecosystem quality
of both aquatic and terrestrial ecosystems (van Zelm et al., 2010; Mila i Canals, et al. 2009).
BIOPHYSICAL MODELLING
Impacts of water consumption on ecosystem quality were measured based on Net Primary Productivity
(NPP). NPP is the rate at which plants store energy as food matter, excluding the energy dissipated
through plant respiration (FAO, 1987). It can be expressed as biomass per unit area (for example g m-2
year-1). NPP was considered here as a proxy for ecosystem quality, as it is closely related to the
vulnerability of vascular plant species biodiversity (Pfister, 2011). In addition, it is assumed that damage
to vascular plants is representative of damage to all fauna and flora species in an ecosystem (Delft,
2010).
NPP can be affected by several parameters, such as temperature, radiation and water availability
(Nemani et al., 2003). The objective of the biophysical modelling is to determine the fraction of NPP
which is limited only by water availability. This was estimated based on the country-specific parameter
NPP wat lim defined in Pfister (2011). However, as the effects of water consumption on ecosystem
quality depend on local water availability, NPP wat lim was adjusted for water scarcity. Precipitation
50
was used as a proxy for water scarcity, with country-specific precipitation data sourced from Aquastat
(FAO, 2014). In that sense, countries with the same NPP wat lim but higher water scarcity (lower
precipitation) will be affected by water consumption related ecosystem damage to a greater extent.
Thus, the parameter NPP wat lim adjusted reflects the percentage of 1 m2 that will be affected by the
consumption of 1 m3 of water in a year (units are m2 year m-3).
ECONOMIC MODELLING
According to TEEB (2010), ‘the value of biodiversity derives from its role in the provision of ecosystem
services, and from peoples’ demand for those services’. Placing a monetary value on biodiversity
involves understanding the link between measures of biodiversity and ecosystem functions on the one
hand and ecosystem functions and services on the other. When multiplied by the value of the services,
the marginal value of biodiversity change can be calculated. In this methodology, biodiversity is
represented by species richness (the number of species in an ecosystem) and ecosystem function is
represented by NPP.
Trucost’s approach to valuing a change in ecosystem quality follows a four step process:
Step 1: Quantify the relationship between species richness and NPP in order to calculate the
NPP of a given ecosystem before water consumption. Ecosystem functions are the biological,
geochemical, and physical processes that take place within an ecosystem. Primary productivity
(the capacity of ecosystems to absorb light) was chosen among other ecosystem processes due
to data availability, and its direct link with key ecosystem services as outlined in the literature.
This follows the approach outlined by Costanza et al. (2007) who reports a correlation between
species richness and NPP at three spatial scales.
Step 2: Quantify NPP after water consumption. In order to calculate the post change NPP,
Trucost used the parameter NPP wat lim adjusted to estimate the change in NPP that is
attributable to water consumption. By using the percentage of NPP affected by water
availability, the NPP remaining after water consumption was determined.
Step 3: Quantify the relationship between NPP and ecosystem service value (ESV) for terrestrial
ecosystems. A value for the provisioning, regulating and cultural services by terrestrial
ecosystems was first calculated based on the analysis of De Groot et al. (2012). De Groot et al.
(2012) calculated the minimum, maximum, median, average and standard deviation for each
service provided by key terrestrial ecosystems using the Ecosystem Service Value database (Van
der Ploeg and de Groot, 2010), which compiles ecosystem service valuation studies available in
the literature. Trucost then performed a regression analysis between the average NPP and
average ESV per m2 per country and found an exponential relationship.
Step 4: Calculation of the percentage of final ESV that is correlated with NPP and application of
this percentage to the average ESV in a given region. Trucost calculated the percentage
difference pre and post water consumption in average ESV at a country level, and applied this
percentage to the average value of one m2 of natural ecosystem in a given region. The average
ESV of one terrestrial m2 was calculated following the methodology as described in step 3,
51
combining the average ESV of one m2 per ecosystem type based on De Groot et al. (2012) and
ecosystem repartition per country (Olson et al, 2004).
VALUE TRANSFER
As outlined in Steps one to four country-specific variables are inputted in the model at several stages.
NPP wat lim adjusted : Country-specific
Species richness: Country-specific
NPP: Country-specific
ESV used for regression analysis: Country-specific
LIMITATIONS
Ecosystem damages due to water consumption provided in Pfister (2011) are limited to
terrestrial ecosystems only. Aquatic ecosystems are excluded from the scope, even though
aquatic organisms could also affected by water consumption.
Adjustments were required to account for water scarcity in the quantification of ecosystem
damage, using precipitation as a proxy (conversion of NPP wat lim to NPP wat lim adjusted). In
addition, a more robust and comprehensive method to quantify ecosystem quality could be
developed in the future, extending beyond NPP, which is ultimately related to vascular plants.
Due to the complexity of the indirect relationship between water scarcity and ecosystem
quality, temporal variability in water availability (inter-annual and seasonal) has not been
considered in the methodology. Irreversible damage in ecosystems and biodiversity is not taken into account.
Biodiversity valuation takes into account only one measure of biodiversity.
Biodiversity valuation is not linked to a particular ecosystem service, but to total provisioning,
regulating and cultural services, through one single measure of ecosystem functioning, NPP.
SENSITIVITY ANALYSIS
Value of the NPP wat lim adjusted is dependent on the relationship between species richness and NPP,
and the relationship between NPP and ESV. Trucost performed sensitivity analysis on the end result by
varying each of the coefficients used in the regressions by 10%. Results vary in-line with the variation in
coefficients (a 10% change in the coefficient leads to a 10% change in results).
Another source of variation in the results is the average value of one m2 in the region of interest. As
explained in Step 4, the percentage of ESV lost due to biodiversity loss is applied to the average value of
one m2 in the region of interest. Trucost used averages based on ecosystem repartition and global value
per type of ecosystem but recommends using more specific value when available.
52
REFERENCES
Costanza, R., Fisher, B., Mulder, K., Liu, S. and Christopher, T. (2007). Biodiversity and ecosystem
services: A multi-scale empirical study of the relationship between species richness and net primary
production. Ecological Economics. Vol. 61, pp. 478-491.
De Groot, R., Brander, L., van der Ploeg, S., Costanza, R., Bernard, F., Braat, L., Christie, M., Crossman,
N., Ghermandi, A., Hein, L., Hussain, S., Kumar, P., McVittie, A., Portela, R., Rodriguez, L. C., ten Brink, P.,
van Beukering, P. (2012). Global estimates of the value of ecosystems and their services in monetary
units. Ecosystem Services. Vol. 1, no. 1, pp. 50-61.
Delft (2010). Shadow Prices Handbook - Valuation and Weighting of Emissions and Environmental
Impacts. CE Delft.
Desaigues, B., Ami, D., Bartczak, A., Braun-Kohlová, M., Chilton, S., Farreras, V., Hunt, A., Hutchison, M.,
Jeanrenaud, C., Kaderjak, P., Máca, V., Markiewicz, O., Metcalf, H., Navrud, S., Nielsen, J.S., Ortiz, R.,
Pellegrini, S., Rabl, A., Riera, R., Scasny, M., Stoeckel, M.-E., Szántó, R., Urban, J., (2006). Final Report on
the Monetary Valuation of Mortality and Morbidity Risks from Air Pollution. Deliverable RS1b of NEEDS
Project.
European Environment Agency (2015). Water resources. Impacts due to over-abstraction. [Online],
Available at: http://www.eea.europa.eu/themes/water/water-resources/impacts-due-to-over-
abstraction
FAO (1987). Site Selection For Aquaculture: biological productivity of water bodies. United Nations
Development Programme, Food and Agriculture Organization of the United Nations. [Online], Available:
http://www.fao.org/docrep/field/003/AC176E/AC176E05.htm
FAO (2011). The state of the world’s land and water resources for food and agriculture. Managing
systems at risk. Summary report. Rome: Food and Agriculture Organization of the United Nations.
FAO (2014). AQUASTAT database, Food and Agriculture Organization of the United Nations (FAO).
[Online], Available: http://www.fao.org/nr/water/aquastat/data/query/index.html?lang=en
Goedkoop, M., Spriensma, R., (2001). The Ecoindicator 99. A Damage oriented method for Life Cycle
Impact Assesment. Product Ecology Consultants.
IFPRI (2014). Maintaining food security under growing water scarcity. Going beyond agricultural water
productivity. Washington DC: International Food Policy Research Institute.
Mila i Canals, L., Chenoweth, J., Chapagain, A., Orr, S., Anton, A., Clift, R. (2009). Assessing freshwater
use impacts in LCA: Part I-inventory modelling and characterisation factors for the main impact
pathways. International Journal of Life Cycle Assessment, Vol. 14, no. 1, pp. 28-42
Millennium Ecosystem Assessment, (2005). Ecosystems and Human Well-being. Biodiversity Synthesis.
Washington DC: World Resource Institute
53
Nemani, R., Keeling, C., Hashimoto, H., Jolly, W., Piper, S., Tucker, C., Myneni, R., Running, S. (2003)
Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science Vol.
300, no. 5625, pp. 1560-1563
Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V. N., Underwood, E. C.,
D'Amico, J. A., Itoua, I., Strand, H. E., Morrison, J. C., Loucks, C. J., Allnutt, T.F., Ricketts, T. H., Kura, Y.,
Lamoreux, J.F., Wettengel, W. W., Hedao, P., Kassem K.R. (2004) Terrestrial Ecoregions of the World: A
New Map of Life on Earth. BioScience. Vol. 51, no. 11, pp. 933-938.
Pfister, S. (2011). Environmental evaluation of freshwater consumption within the framework of life
cycle assessment. DISS. ETH NO. 19490. ETH ZURICH.
TEEB (2010). The Economics of Ecosystems and Biodiversity Ecological and Economic Foundations.
Chapter 2: Biodiversity, ecosystems and ecosystem services. Earthscan, London and Washington:
Pushpam Kumar
UN Water (2012). Managing water under uncertainty and risk. The United Nations World Water
Development Report 4, Vol. 1. Paris: United Nations Educational, Scientific and Cultural Organization
UN Water (2015a). Water for food. [Online], Available:
http://www.unwater.org/fileadmin/user_upload/unwater_new/docs/water_for_food.pdf
UN Water (2015b). Water and food security. [Online], Available:
http://www.un.org/waterforlifedecade/food_security.shtml
UNDP (2013). Human Development Reports. Human development statistical tables. [Online], Available:
http://hdr.undp.org/en/data.
Van der Ploeg, S., R.S. de Groot (2010). The TEEB Valuation Database – a searchable database of 1310
estimates of monetary values of ecosystem services. Wageningen, the Netherlands: Foundation for
Sustainable Development
Van Zelm, R., Schipper, A., Rombouts, M., Snepvangers, J., Huijbregts, M. (2010) Implementing
groundwater extraction in life cycle impact assessment: Characterization factors based on plant species
richness for the Netherlands. Environmental Science & Technology. Vol. 45, no. 2, pp. 629-35.
WBCSD (2013). Business guide to water valuation. An introduction to concepts and techniques.
Switzerland: WBCSD.
WHO (2014). Metrics: Disability-Adjusted Life Year (DALY). [Online], Available:
http://www.who.int/healthinfo/global_burden_disease/metrics_daly/en
WRI (2013a). Aqueduct Global Maps 2.0. Working paper. Baseline Water Stress. Washington DC: World
Resource Institute.
WRI (2013b). Aqueduct Global Maps 2.0. Working paper. Inter-annual Variability. Washington DC:
World Resource Institute.
54
WRI (2013c). Aqueduct Global Maps 2.0. Working paper. Seasonal Variability. Washington DC: World
Resource Institute.
55
LAND USE CHANGE
INTRODUCTION
Land use change is increasing in importance in the business and policy arena as a key direct driver of
habitat and ecosystem change that is degrading the stock of natural capital on which society relies (MA,
2005a). The impacts of land use change are increasingly associated with the loss of ecosystem services,
a concept promulgated by the Millennium Ecosystem Assessment (MA, 2005a). The quantification and
valuation of ecosystem services is gaining traction as it is commensurable with traditional economic
metrics used in corporate and political decision-making. This document outlines the Trucost
methodology for quantifying and valuing the ecosystem service costs of land use change at the country
level.
SCOPE OF TRUCOST VALUATION
Trucost’s methodology focuses on the loss of all ecosystem services due to the conversion of land from
its natural ecosystem to an alternative land use or state. The new state of the land, be it a pasture for
cattle grazing, a rice paddy or a corn field, is assumed to provide no ecosystem services and therefore
the net benefits of agricultural activity are not covered in this valuation – only the value of the
ecosystem services lost due to land conversion.
VALUATION METHODOLOGY
Trucost’s methodology is split into two components – biophysical modelling and economic modelling.
Biophysical modelling describes how Trucost calculates the ecosystem services that are lost by
converting each ecosystem to an alternative land use, as well as the area of land converted from its
natural state. Economic modelling describes how Trucost calculates the value of the ecosystem services
that have been lost. Each component is described in more detail below. This methodology is limited to
ecosystem services that are provided by terrestrial ecosystems.
TABLE 9: OVERVIEW OF METHODOLOGY FOR LAND USE AND LAND USE CHANGE
IMPACT MODELLING BIOPHYSICAL MODELLING ECONOMIC MODELLING
EMISSIONS/
RESOURCE USE
IMPACT AND
DEPENDENCY END POINT
CHANGE IN
VALUED
ATTRIBUTE
LINK TO ESS
(WHERE
RELEVANT)
ECOSYSTEM
SERVICE (WHERE
RELEVANT)
FINAL
BENEFICIARIES VALUATION APPROACH
VALUE TRANSFER
METHOD
Land use and land
use change
Natural
ecosystem
area and
associated
ecosystem
services
Terrestrial
ecosystems
Hectares of
natural
ecosystems
replaced with
alternative land
uses
Provisioning
Regulating
Cultural
All ecosystem
services provided
by displaced
ecosystems e.g.
climate
regulation, water
flow regulation,
and waste
treatment
Diverse
Meta-analysis of value
estimates derived from
various market and non-
market (e.g. benefit
transfer, avoided cost
and hedonic pricing)
valuation
methodologies
Share of ecosystem
area replaced with
alternative land uses
at a country-level
57
IMPACT ON ECOSYSTEM SERVICES
BIOPHYSICAL MODELLING
To estimate the loss of ecosystem services associated with the conversion of land from its natural state,
it is necessary to map a set of ecosystem services to each specific ecosystem type. The monetary value
of ecosystem services provided by an ecosystem can then be estimated by combining the value of the
various ecosystem services it provides.
MAPPING ECOSYSTEM SERVICES TO ECOSYSTEMS
A number of sources are available to inform the mapping of ecosystem services to specific well-
functioning ecosystems, and to define values for these ecosystem services. These include:
Ecosystem Service Valuation Database (van der Ploeg & de Groot, 2010)
Costanza (1997)
De Groot (2012)
Costanza (2014)
For the purposes of this study, Trucost has used de Groot et al. (2012) as a basis for mapping material
ecosystem services to ecosystems. The de Groot et al. (2012) study is based on a sample of 665 original
value estimates (benefit transfer studies were excluded), extracted the Ecosystem Service Valuation
Database, that met a series of selection criteria detailed in the paper. De Groot et al. (2012) was
preferred as the study presents ecosystem service values in ‘international dollars’ suitable for global
application. This also aligns with Trucost’s other valuation methodologies, and means that the step of
mapping ecosystem services between different studies does not have to be attempted. This step would
involve the loss of some granularity in the final results. Table 10 outlines the ecosystems and the
ecosystem services that have been considered in this study. The cells in red indicate where values were
available in the source data but Trucost chose not to include them, and green cells indicate where an
additional value was calculated. Both cases are described in greater detail below.
TABLE 10: ECOSYSTEM SERVICES ASSESSED IN TRUCOST’S METHODOLOGY BASED ON DE GROOT ET AL. (2012)
Ecosystem
Provisioning services Regulating services Cultural services
Habitat or
supporting
services
Foo
d
Wat
er
Raw
mat
eri
als
Gen
etic
re
sou
rce
s
Med
icin
al
reso
urc
es
Orn
amen
tal
reso
urc
es
Air
qu
alit
y re
g.
Clim
ate
reg.
Dis
turb
ance
mo
der
atio
n
Wat
er f
low
reg
.
Was
te t
reat
men
t
Ero
sio
n
pre
ven
tio
n
Nu
trie
nt
cycl
ing
Po
llin
atio
n
Bio
logi
cal c
on
tro
l
Aes
thet
ic
info
rmat
ion
Rec
reat
ion
Insp
irat
ion
Spir
itu
al
exp
erie
nce
Co
gnit
ive
dev
elo
pm
ent
Nu
rser
y se
rvic
e
Gen
etic
div
ersi
ty
Coastal wetlands - Y Y Y Y - - Y Y - Y Y Y - - - Y - - - - -
Grasslands - Y Y - Y - - Y - - Y Y - - - - Y - - - - -
Inland wetlands - Y Y - Y Y - Y Y Y Y Y Y - Y Y Y Y - - - -
Temperate forest - Y Y - - - - Y - - Y Y Y - - - Y - - - - -
Tropical forest - Y Y Y Y - Y Y Y Y Y Y Y Y Y - Y - - - - -
Woodlands - - Y - - Y - Y - - Y Y - Y - - Y - - - - -
59
ECOSYSTEM AREA
The terrestrial area covered by each ecosystem in each country was calculated by mapping the
ecosystem categories in Table to GIS datasets representing country administrative boundaries and
global ecoregions.
Country boundaries, or administrative areas, were derived from the GADM v2.0 dataset (GADM,
2012). The data was downloaded as a shapefile and used in conjunction with ecoregion data derived
from Olson et al. (2004), which showed the size and distribution of over 800 terrestrial ecoregions
around the world. Once these datasets were spatially joined, Trucost was able to calculate the area
of each ecoregion in each country.
The next step involved manually mapping ecoregions to the ecosystems defined in Error! Reference
source not found.. This required investigation of the type of flora contained within each ecoregion,
and creating a set of rules for assigning ecoregions to ecosystems in this study. A key difficulty in
executing this step is the lack of a specific definition for each ecosystem category. Olson et al. (2004)
defines ecoregions as “relatively large units of land containing a distinct assemblage of natural
communities and species, with boundaries that approximate the original extent of natural
communities prior to major land-use change”. Thus ecoregions are large areas with similar ecological
characteristics and can be more easily defined. However, the boundaries of the ecoregions are not
static and are subject to constant change over time. This may mean that historically classified
ecoregions may no longer be valid at certain times of the year, or in future years. The ecoregion
maps produced by Olson et al. (2004) are based upon hundreds of previous studies, which have
been further refined for use as part of a global dataset, and thus offer greater confidence in their
validity for the purposes of this study.
The benefit of using this ecoregion dataset is that it provides a global coverage and represents the
approximate original extent of the natural communities of species prior to major land use change.
The dataset further classifies the ecoregions into 14 biomes and 8 biogeographic realms. This
classification has assisted in the mapping process.
Trucost calculated the size of agricultural areas using GIS datasets provided by Portmann et al.
(2010). This provided Trucost with raster datasets describing the number of hectares of certain crops
grown within certain regions. Trucost used this information in conjunction with the GIS datasets
mentioned above to calculate the areas in which crops are grown, and to identify the ecosystem
displaced by each crop area. Trucost was able to attribute, for each crop in the study, the area of
each ecosystem that has been lost, and the agricultural practices that are using the land in its place.
ECONOMIC MODELLING
A number of options for calculating the value of ecosystem services were available to Trucost in this
study. At a high-level, these options included:
Using a combination of values from the Ecosystem Service Valuation Database (van der
Ploeg, 2010).
Performing a separate meta-analysis to estimate the values of ecosystem services globally.
60
Using values directly provided by Costanza et al. (1997), de Groot et al. (2012) or Costanza et
al. (2014).
Using a hybrid of one or more of the approaches mentioned above.
As a result of reviewing the options available, Trucost chose to use the ecosystem service values
detailed in de Groot et al. (2012) on the basis that the values had been adjusted to account for
Purchasing Power Parity and since the meta-regression methodology applied was considered more
robust than the Constanza et al (2014) method. Costanza et al (2014) was constrained by the need to
follow the same methodology as in the 1997 study to ensure comparability. Costanza also included
the valuation of supporting services which may be partially or completely captured within the
valuation of other ecosystem services, potentially leading to double counting (TEEB, 2010). TEEB
(2010) recommends that supporting ecosystem services not be included in meta-analysis studies of
this type, and as such, Trucost has excluded the values in de Groot et al (2012) that refer to either
habitat or supporting services.
Trucost also excluded or modified some other ecosystem service values included in de Groot et al.
(2012). Food provisioning services were excluded from the calculation on the basis that this service
would be provided by the agro-ecosystems that replace the natural ecosystems (and are the subject
of this study). Furthermore, de Groot et al. (2012) do not present a waste treatment service value
for woodland ecosystems. Trucost considered that it was likely that this service would be provided
by woodland ecosystems and a proxy value was calculated based on the average value of waste
treatment ecosystem service provided by similar ecosystems (tropical, temperate and boreal
forests).
Trucost considers land use change to be any occupation of land that exists in place of natural
ecosystems, and thus the average value of ecosystem services is used instead of the marginal value.
This takes into account the fact that the timing of land conversion is unknown with respect to the
timespan from when there was zero ecosystem service scarcity to present day levels of scarcity.
Once these final ecosystem service values were calculated, Trucost used this information in
conjunction with the data outlined in the biophysical modelling section to calculate the value of the
ecosystem services lost due to land use change for agricultural purposes.
LIMITATIONS
Scope of the biophysical modelling
There is not a complete coverage of ecosystem services for each of the ecosystems. On the whole,
ecosystem services are only valued where one or more primary valuation studies has been published
for that ecosystem. Where no monetary valuation exists for a particular ecosystem or service, that
ecosystem or service has been excluded from this methodology.
Biophysical modelling techniques
Mapping Olson et al. (2004) ecoregions to the ecosystems used by de Groot et al. (2012) is a
complex task. Rules were established to map ecosystems as accurately as possible but this still
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leaves room for error. Furthermore, the Ecosystem definitions applied in de Groot et al. (2012) are
not consistent with those used in Olson et al. (2004) to define the ecoregion extent.
The agricultural area dataset provided by Portmann et al. (2010) comes in a raster form
(representing land use in a grid of pixels). This data format is useful for a top down analysis such as
this, but loses granularity when focused on specific geographical locations (particularly where the
location is represented by a small number of pixels).
It is assumed that ecosystems such as tropical rainforest for instance, provide the same ecosystem
services wherever it exists around the world. The methodology does not take into account the
change in quantity or quality of the same ecosystem services in different locations.
Ecosystem service valuations
The valuations calculated by de Groot et al. (2012) are a meta-analysis of global ecosystem services.
Hence, ecosystem values that reflect very specific, local characteristics are used in multiple locations
around the globe with some adjustment for site specific characteristics and study characteristics.
Furthermore, a number of valuation methods have been used in this meta-analysis in unequal
proportions. This means that some ecosystem service values will have a weighting towards a certain
valuation method that may be subject to an upward or downward bias.
Finally, taking the average of ecosystem services to fill data gaps (as is the case for water treatment
services from woodland) is a crude attempt to account for the ecosystem services that are not
currently captured in the valuation literature. It has been done to ensure that the ecosystem
services provided are at least captured to some degree, and are of a similar magnitude to the
valuations for similar ecosystems.
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REFERENCES
Costanza, R., d’Arge, R., de Groot, R., Farber, S., Grasso, M., Hannon, B., Limburg, K., Naeem, S.,
O’Neill, R. V., Paruelo, J., Raskin, R. G., Sutton, P., van den Belt, M. (1997) The value of the world’s
ecosystem services and natural capital. Nature. 387, 253-260.
Costanza, R., de Groot, R. S., Sutton, P., van der Ploeg, S., Anderson, S. J., Kubiszewski, I., Farber, S.,
Turner, R. K. (2014) Changes in the global value of ecosystem services. Global Environmental Change.
26, 152-158.
De Groot, R., Brander, L., van der Ploeg, S., Costanza, R., Bernard, F., Braat, L., Christie, M.,
Crossman, N., Ghermandi, A., Hein, L., Hussain, S., Kumar, P., McVittie, A., Portela, R., Rodriguez, L,
C., ten Brink, P., van Beukering, P. (2012) Global estimates of the value of ecosystem s and their
services in monetary units. Ecosystem Services. 1, 50-61.
GADM. (2012) Global Administrative Areas. Available online: http://www.gadm.org/version2
[Accessed on: 01.11.14]
MA. (2005a) Millennium Ecosystem Assessment. Ecosystems and Human Well-being: Synthesis.
Island Press, Washington, DC.
Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V. N., Underwood, E. C.,
D'Amico, J. A., Itoua, I., Strand, H. E., Morrison, J. C., Loucks, C. J., Allnutt, T.F., Ricketts, T. H., Kura,
Y., Lamoreux, J.F., Wettengel, W. W., Hedao, P., Kassem K.R. (2004) Terrestrial Ecoregions of the
World: A New Map of Life on Earth. BioScience 51:933-938.
Portmann, F. T., Siebert, S., Döll, P. (2010) MIRCA2000 – Global monthly irrigated and rainfed crop
areas around the year 2000. A new high-resolution data set for agricultural and hydrological
modelling. Global Biogeochemical Cycles. 24, GB 1011, doi: 10.1029/2008GB003435.
TEEB. (2010) Integrating the ecological and economic dimensions in biodiversity and ecosystem
service valuation. The Economics of Ecosystems and Biodiversity: The Ecological and Economic
Foundations.
Van der Ploeg, S., de Groot R. S. (2010) The TEEB Valuation Database – a searchable database of
1310 estimates of monetary values of ecosystem services. Foundation for Sustainable Development,
Wageningen, the Netherlands.
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ABIOTIC DEPLETION
INTRODUCTION Abiotic resources are defined as resources that come from non-living materials. They include metals,
minerals and fossil fuels. Trucost follow the methodology of Ponsioen, Vieira & Goedkoop (2013,
2014) to value the impacts of resource consumption on society.
VALUATION METHODOLOGY
ALL IMPACTS
BIOPHYSICAL MODELLING
A schematic of the cause and effect pathways from fossil resource use to surplus cost can be seen in
Figure 2. It is assumed that the fossil resources that are most easy to extract are extracted first.
Continued fossil fuel extraction comes with increased cost. As conventional sources are depleted,
production techniques change or resources are extracted from more costly locations. The additional
cost is represented by the marginal cost increase. The method used for metal and mineral resources
is similar, except that the decrease in ore grade is used to calculate the marginal cost increase,
instead of pure resource scarcity.
FIGURE 2: CAUSE AND EFFECT PATHWAYS OF FOSSIL RESOURCE USE TO THE INDICATOR
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ECONOMIC MODELLING The surplus cost is calculated based on projected production, marginal cost increase and discount
rate for each resource.
Scenarios of resource production were taken from research of the Intergovernmental Panel on
Climate Change, as stated in Ponsioen, Vieira & Goedkoop (2013). These scenarios project the
amount of resources that will be produced in any given year in the future. The amounts are driven
by economic growth predictions, population growth predictions, technological development and
substitution.
The marginal cost increase is defined as ‘the long term average increase in cost after producing a
certain amount of resource, based on the concept that first the least costly resources are extracted’.
It is derived from the relationship between production costs for each production technique and
cumulative production of fossil resource.
For crude oil and natural gas, Ponsioen, Vieira & Goedkoop (2013) used IEA data on cost and
resource availability per production technique. For coal, cost and resource availability data per
country were used. The cost-cumulative production curve for crude oil can be seen in Figure 3 as an
example. A line could be drawn through the cost-cumulative production curve to estimate the
marginal cost increase for each resource.
FIGURE 3: COST-CUMULATIVE PRODUCTION DATA FOR CRUDE OIL. FOR EACH PRODUCTION
TECHNIQUE, A MINIMUM AND MAXIMUM COST ESTIMATE WAS GIVEN.
Results are calculated based on a discount rate of 15%, 3% and 0%. Trucost used the surplus cost
calculated based on a 3% discount rate.
No value transfer is performed in this analysis. Values are global. If production costs per production
technique, data on projected production and estimated reserves were available at a country-level,
country-specific coefficients could be derived.
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SENSITIVITY ANALYSIS Ponsioen, Vieira & Goedkoop (2013) carried out Monte Carlo Simulations assuming uniform
distributions between the minima and maxima of the cost ranges for each production technique or
country. The cumulative production per production technique or country were split up into even
amounts and randomly assigned a cost value. The graph was then reordered and an example can be
seen in Figure 4.
FIGURE 4: COST-CUMULATIVE PRODUCTION CURVE FOR OIL BASED ON MONTE CARLO
SIMULATIONS
LIMITATIONS This method was initially developed for the Recipe model and used by Trucost to complete
its set of valuation indicators. It should be noted that the calculation of abiotic depletion
impacts differ greatly from one model to another, which can yield very different results
(Klinglmair, Sala & Brandao, 2013). This is a core limitation of this valuation coefficient and
Trucost advises users to chose Recipe when monetising this impact category.
No regional or national differentiation applied in the Ponsioen, Vieira & Goedkoop study.
This is mainly due to data availability and the fact that the data are given per production
technique rather than per country for oil and natural gas. Ponsioen, Vieira & Goedkoop
stated that they didn’t apply regional differentiation because doing this would encompass
regional geopolitical issues that were beyond the scope of the present LCA. However, it is
entirely possible that production costs will differ by region and country depending on the
local economic situation (for example, labour costs). Production amounts will vary by nation
or region, but this data is not available in the IPCC report.
Uncertainty in the data estimates. Uncertainty in the production costs were addressed by
using the maxima and minima estimates shown in Figure 16. In the Monte Carlo Simulations
used by Ponsioen, Vieira & Goedkoop, uniform distributions between the maxima and
minima cost estimates were assumed when assigning a cost to each production amount.
However, there was no uncertainty quoted for the production amount. It is more difficult to
predict the amount of reserves available. This has so far been addressed by using the
different IPCC scenarios that predict the production amounts.
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REFERENCES Klinglmair, M., Sala, S., Brandao, M. (2013). Assessing resource depletion in LCA: a review of
methods and methodological issues. The International Journal of Life Cycle Assessment.
Ponsioen,T.,Vieira,M. & Goedkoop,M. (2014). Surplus Cost as a Life Cycle Impact Indicator for Fossil
Resource Scarcity. The International Journal of Life Cycle Assessment, 4(19),pp.872-881
Vieira, M.(2014). Fossil and mineral resource scarcity – Course Materials. S.l:LC Impact.
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