A new tool called the Environmental Sustainability Index (ESI) was developed to measure the social and environmental impacts of building a large hydroelectric power plant (HPP) on the Xingu River in Brazil. The ESI evaluates six components - aquatic habitats, aquatic fauna, fish, fishery, human life conditions, and navigability - across different seasons. It aggregates data from 35 variables into 16 indicators to calculate overall sustainability. The first ESI values showed the region's sustainability varies between wet and dry seasons. This innovative tool provides an integrated and reproducible method for comparing mitigation strategies and decisions over time. It was approved by environmental regulators and could generate new business for sustainable impact assessment.

1. Environment Product Line, LEME, Brazil 1
A NEW TOOL TO EVALUATE SOCIAL
AND ENVIRONMENTAL IMPACTS CAUSED
BY THE BUILDING OF A LARGE HPP
A viable way to measure sustainability is, for the first time,
added to the set of tools used to evaluate impacts in
social, economic, and environmental fields caused by the
building of a large HPP. This sweeping initiative responds
to the need for attaining a measure of sustainability,
which has been increasingly embraced as an imperative
goal, worldwide. Yet actual measurements are not merely
occasional but have not been regularly added to any ef-
fort to compare performance of cost-effective strategies,
ENVIRONMENT PRODUCT
LINE, LEME, BRAZIL
Building area of HPP Belo Monte in Xingu River (Pará state, Brazil).
Source: Analysis Team of the Environment Business Unit (LEME, Brazil), 2015.
Calculation of a sustainability index as a tool in decision-making during environmental
licensing processes: an initiative of the Analysis Team of the Environment Business Unit
(LEME, Brazil).
DETAILED DESCRIPTION OF THE INNOVATION
political decisions, or mitigation actions.
The Environmental Sustainability Index (ESI) presented
here is a measure of overall long-term ecological balance
developed and adjusted for the region surrounding a
stretch of 100 km of extension along the Xingu River in
Pará state, Brazil (Fig. 1), from now on called “Stretch of
Reduced Flow” (SRF). The SRF is a result of the general
arrangement of the HPP Belo Monte (Pará state, Brazil),
the biggest dam (11,233 MW) being built in Brazil and in
the Amazonian forest (2011 to 2015). The construction and
operation of the HPP conceived a diversion of waters from
the major reservoir (in the main channel of Xingu River), to
Betânia Souza (Project Leader); Maurício Moreira; Francisco Ribeiro;
Luciano Andrade & Cristiane Vieira (Unit Responsible)

2. Environment Product Line, LEME, Brazil 2
a secondary reservoir (in the inland), and therefore it will
cause a substantial reduction of water flow in Xingu River,
the so-called SRF (to be formed by the end of 2015).
The Environmental Analysis Team of LEME, Brazil, is re-
sponsible for the coordination of the licensing process of
this HPP. We proposed the ESI as a far-reaching, powerful
instrument that makes possible the assessment, through-
out time, of the impact of the HPP on the river natural
flow, hence on its biodiversity and human conditions be-
cause it represents a radical innovation towards a more
analytically driven approach to decision-making. For the
first time, the ongoing (since 2009) federal licensing pro-
cess of this HPP was presented with a feasible, integrat-
ed, and reproducible method (and results) to evaluate
tangible social and environmental impacts which are ac-
tually interconnected but are usually analysed only
separately.
Because there is a strong seasonality within the region,
and because we are interested on the environmental im-
pact of the formation of the SRF, we aimed for the first
ever comparison between the environmental sustainabil-
ity of rainy seasons (high waters) and of dry seasons with-
in the region in focus (Fig. 1), before and after the opera-
tion of the HPP.
Fig. 1 – The “Stretch of Reduced Flow” (SRF) along the Xingu River (Pará state, Brazil) formed by the diversion of waters during the operation of
the HPP Belo Monte.
Source: Analysis Team of the Environment Business Unit (LEME, Brazil), 2015.
Fig. 2 – Levels of calculation of the Environmental Sustainability Index (ESI)
created for the “Stretch of Reduced Flow” (SRF) region in Xingu River (Pará
state, Brazil) within the building area of HPP Belo Monte.
35 Variables 16 Indicators 6 Components ESI
1. World Economic Forum, 2001. Environmental Sustainability Index An
Initiative of the Global Leaders of Tomorrow Environment Task Force. Annual
Meeting, Davos, Switzerland. 255 p.
FIRST EVER MEASUREMENTS
OF ENVIRONMENTAL SUSTAINABILITY
OF THE SRF REGION
The method adopted to create the ESI suited to the
region in focus based on a multiple criteria approach
presented by the “Global Leaders of Tomorrow
Environment Task Force”, during the annual meeting
of the World Economic Forum (2001)1
. This approach
allowed simultaneous analysis of social, economic,
and environmental data. A high ESI rank indicates a
high level of environmental sustainability in a particu-
lar season or site; a low ESI rank in another season or
site (comparison over time or space) signals substan-
tial problems in achieving environmental sustainabil-
ity along multiple dimensions.
Our ESI score centers upon a set of six components as-
sociated with 16 core indicators, each of which combines
one to four variables for a total of 35 underlying variables
(Fig. 2 & Tab. 1). Indicators and variables were chosen
through careful review of scientific literature, available
data, and analysis of the first results. In addition, we con-
sidered further application and future comparison with
other enterprises for a full use of the method at least in a
countrywide manner, and therefore we considered com-
monality of variables due to relevance among other li-
censing processes in Brazil.
At the most basic level, we defined the scope in concep-
tual terms and we have determined that environmental
sustainability of the SRF region is a function of the varia-
bles, linked by indicators, of the following six components:
“Aquatic Habitats”, “Aquatic Fauna”, “Fish”, “Fishery”,
“Human Life Conditions”, and “Navigability” (Tab. 1).
SUCCESS OF IMPLEMENTATION

3. Environment Product Line, LEME, Brazil 3
COMPONENT INDICATOR VARIABLE
AQUATIC HABITATS
Water availability
01) Subterranean water level
02) Superficial discharge
03) Superficial water level
Climate
04) Rain level in Altamira
05) Rain level in Pimental
06) Rain level in Belo Monte
AQUATIC FAUNA Biodiversity of vertebrates
07) Relative abundance of turtles
08) Relative abundance of aquatic mammals
FISH
Water quality for fish 09) Dissolved oxygen concentration
Fish diversity
10) Number of species of fish
11) Abundance of fish
FISHERY
Fishery effort
12) Number of fishermen (commercial)
13) Number of fishermen (ornamental)
14) Number of days fishing (commercial)
15) Number of days fishing (ornamental)
Fishery production
16) Total weight (commercial)
17) Total weight (ornamental)
Fishery richness
18) Number of species (commercial)
19) Number of species (ornamental)
Rational exploitation of fish
20) Average length of the species with higher value
(commercial)
21) Average length of the most sold species (commercial)
22) Average length of the most sold species (ornamental)
HUMAN LIFE
CONDITIONS
Water quality for humans
23) Turbidity level
24) Escherichia coli concentration
25) Phosphorus concentration
26) Cyanobacteria concentration
Human health 27) Number of families visited by Health Agents
Education
28) Number of students in fluvial transport
29) Number of students
Productive activities
30) Number of families that presents fishery as a source of
income
Human water usage
31) Number of families in the urban areas with access to
water
32) Number of families in the rural areas with access to
water
NAVIGABILITY
Navigability dynamics 33) Number of vessels that passes by the SRF
Navigability conditions
34) Number of people that uses the SRF as a hydro way
35) Number of passengers in the fluvial public transport
Tab. 1 – Scope of the ESI created for the “Stretch of Reduced Flow” (SRF) region in Xingu River (Pará state, Brazil) within the building area of HPP Belo
Monte – list of inputs (variables).
Source: Analysis Team of the Environment Business Unit (LEME, Brazil), 2015.

4. Environment Product Line, LEME, Brazil 4
After building up the complete database, we standardized
the variables to make aggregation possible. We calculated
the Z score (value minus the average, divided by standard
deviation) for those variables for which high-observed val-
ues corresponded to high levels of environmental sustain-
ability (e.g. dissolved oxygen concentration). For variables
that high-observed values corresponded to low levels of
environmental sustainability (e.g. cyanobacteria concen-
tration in water) we also calculated the Z score (but we
standardized by dividing average minus the value, divided
by standard deviation). This effort to uniform data meets
the goal to have an index with a simple interpretation: high
scores means high levels of environmental sustainability
presented by the region in focus. At that point, we calcu-
lated the 16 indicators by taking the average of the con-
stituent variables’ Z scores. We then averaged the values
of the 16 indicators and calculated the normal percentile,
the ESI, for each season.
The calculation of the ESI allowed the first ever visualiza-
tion of the varying sustainability of the SRF region (among
seasons) in a multicriteria approach. The calculation of the
ESI used data gathered from 2012 to 2014 from the mon-
itoring projects executed as required by the ongoing fed-
eral licensing process of the HPP. Therefore, the first values
calculated showed not only comprehensive deployment
and viability of a new tool for a specific site but it also rep-
resents measures of reference of the environmental sus-
tainability status of the region before the formation of the
SRF. We highlight that, because it is such a novelty, we
simply did not have the time to reach its full potential and
largely apply the same reasoning to other customers or
sites. However, validation of its results, presented as
follows, was already highly significant with the federal en-
vironmental institution officially approving our innovative
approach and expecting for its continuity. With this in hand
and with the easiness to deploy it for any number of cus-
tomers or sites, we have an established plan to use this
tactic in future projects and even generate new business,
arousing new clients with this unique, feasible product.
We calculated the ESI of three rainy seasons and of two
dry ones (Fig. 3). Rainy seasons had higher scores for
“Aquatic Habitats”, “Human Life Conditions”, and
“Navigability”, as expected. On the other hand, higher
scores for “Fish” and “Fishery” were a result of higher cap-
ture when discharge decreases during dry seasons.
Interestingly, the ESI of the last rainy season (2014) was
lower than the ESI calculated for the dry season of 2012,
indicating that one cannot always link the dry seasons in
the Amazonian forest, with lower sustainability, as com-
monly thought.
Trade-offs between components were clear (while a com-
ponent "progressed" from one year to another, another
component "plunged") as were also parallelism among
components (when two or more components went "for-
ward" or "back" together, from one year to another). This
direct, fast, and integrated judgment is promising for the
future management of the required monitoring plan of so-
cial and environmental indicators in the region of the SRF
(during the test phase) with a view to identify problems. To
clarify how it works in general, and how the observed val-
ues are used, we display Z scores of indicators (Fig. 4), in
order to illustrate the midway outputs of the calculation of
the ESI.
Fig. 3 - Graphical representation of the ESI calculated for the
“Stretch of Reduced Flow” (SRF) region in Xingu River (Pará
state, Brazil) within the building area of HPP Belo Monte. The
figure shows the score values of social and environmental
components. The “blue area” represents the highest
observed sustainability during rainy seasons and the “orange
area” represents the highest sustainability observed during
dry ones (2012 to 2014). The grey area (intersection between
the colored areas) indicates the lowest observed
environmental sustainability of the SRF region, independently
of the season.
Source: Analysis Team of the Environment Business Unit (LEME, Brazil), 2015.
Human Life Conditions
Fish
Navigability
Fishery Aquatic Fauna
Aquatic Habitats

5. Environment Product Line, LEME, Brazil 5
A NEW PRODUCT IN OUR NEW BUSINESS
UNIT READY TO DRAW NEW CONTRACTS:
SIMPLIFICATION MEANING REDUCED COSTS
Social and environmental sustainability can be measured.
While no quantity of such an intricate phenomenon can be
flawlessly calculated, the ESI presented here proved to be
remarkably powerful, valuable, and robust. Its application is a
radical change in the way we do environmental monitoring
assessment and evaluation of strategies, decisions, or ac-
tions. This instrument is one sample of how we can use our
expertise in these matters to convey more confidence to
stakeholders, clients, and environmental institutions (govern-
mental or not).
Perspectives of this method are clear: a new product line
in our new Business Unit with a brand new tool that cre-
ates a chain of comparative benchmarks of environmen-
tal settings of different sites and enterprises because it
assures the shifting of policymaking onto a more fact-
based and systematically rigorous structure. This is al-
ready added in our business model as an exclusive type
of service that only we can provide, because until now,
we are the first ones within the environmental licensing
market in Brazil with the experience and proficiency to
perform similar calculations and apply them.
Gains of this innovation are extensive. We highlight, as men-
tioned above, that we have received confirmation of deploy-
ment of the ESI in the form of the official acceptance from
the federal environmental institution upon the usage of this
new tool. We cannot express how imperative it is to acquire
this kind of validation, especially in the challenging path to
obtain a Federal License of Operation (usually, our final goal)
and to maintain it. This endorsement indicates that new
business might develop purely because customers will
search for it (thus it will generate new income as well). Value
creation for the GDF SUEZ Group to have a BU that
successfully measures social and environmental sustaina-
bility with respect to governmental licensing process can be
therefore linked to new commercial contracts ranging from
0,5 Me up to 5 Me.
Moreover, the scope of the ESI noticeably intended simpli-
fication of the entire process of environmental monitoring
which means less costs for us or our clients in the long run
(consequently making us or them more revenues) because
we tend to designate deliberation of resources solely on
variables and indicators needed for the ESI. The team of
specialists needed to execute the monitoring programs as
required by federal licensing processes reach cumulated
contract values of about 40% to 50% of the total contract.
The current business plan, also using the ESI as an outlining
tool to choose which variable should continue to be moni-
tored, is to save at least a third of that value.
Another type of simplification will also mean another de-
crease in costs: thanks to the shift towards less subjective
analysis, the final merging of results on our main types of
products (thick volumes with all-encompassing social and
environmental impacts studies) may amount to reduced
number of experts and man-hours. It is expected that a
smaller expert team will be needed to objectively analyse
the outcome of such studies, ensuing an even better and
cohesive final report.
Interestingly enough, not only the entrepreneur can benefit
from the ESI analysis and practice, but also the population
can be empowered with its findings and be more informed
when participating on debates about its own needs for de-
velopment. Finally, we point out that the ESI reveals the
prospective value of enhancements not only in the Brazilian
aptitude for data-driven pursue of sustainability and eco-
nomic growth but also the world’s capacity to do it either.
This method originated from this kind of initiative and our
application is a crucial step towards global human progress
with more sustainable environments.
Fig. 4 - Midway outputs of the calculation of the ESI suited for the “Stretch of Reduced Flow” (SRF) region in
Xingu River (Pará state, Brazil) within the building area of HPP Belo Monte. For illustrative purposes only, we
display Z scores of indicators for a rainy season and a dry one.
Source: Analysis Team of the Environment Business Unit (LEME, Brazil), 2015.
VALUE FOR THE GROUP