ESTIMATING THE OPPORTUNITY COST OF LITHIUM EXTRACTION IN THE SALAR DE UYUNI, BOLIVIA

MASTER PROJECT

by

Rodrigo Aguilar-Fernandez

Dr. Jeffrey R. Vincent, Advisor

December 2009

Masters project submitted in partial fulfillment of the requirements for the Master of Environmental

Management degree in the Nicholas School of the Environment of

Duke University

2009

Estimating the Opportunity Cost of Lithium Extraction in

the Salar de Uyuni, Bolivia

Master Project

by

Rodrigo Aguilar-Fernandez

Nicholas School of the Environment

Source: Ebensperger et.al (2005); SQM (2008)

Batteries, 27%

Lubricant

Greases, 12%

Frits, 9%

Glass, 8%

Air Conditioning,

6%Aluminium, 4%

Polimers, 4%

Continuous

Casting, 3%

Pharmaceuticals,

3%

Chemical

processing, 1%

Main uses of lithium: 2008

ACKNOWLEDGMENTS

To my wife Andrea for her love, trust and patience when I needed it the most.

To my parents, Vicente and Maria del Carmen, who have provided significant support throughout my education.

To Marcelo , Chichi, and my sister Natalia, who have always been by my side.

Thank you all for your continued confidence, guidance and affection.

I ‘m also thankful to my advisor, Dr. Jeffrey Vincent, for his valuable suggestions and counseling during this project.

And lastly, to all my friends who have given me encouragement during this process.

Thank you, your support has been invaluable.

ABSTRACT

If the world plans to be moving away from oil based transport and towards hybrid and electric vehicles,

lithium supply is the key factor. The Salar de Uyuni in Bolivia holds the largest source of lithium in the world;

however, its extraction will bring a trade off with the environment. Due to the arid nature of the climate, the Salar

de Uyuni basin has a sensitive ecosystem heavily dependent on water resources. Consequently, local people’s

subsistence and well-being also depend on water resources on a daily basis. Studies conducted in the Salar de

Uyuni basin concluded that using the same spring as a production input, water consumption for lithium extraction

and crop irrigation cannot simultaneously take place. Thus, the fresh water use from the San Geronimo River

creates two mutually exclusive projects, lithium mining and quinoa crop with irrigation, generating different gains

to the economy of the region. The incremental cash flows model used in this study provides an estimate of the

benefits that each project would provide. The results indicate that even after subtracting the opportunity cost of

not conducting the quinoa irrigation project and reducing the uncertainty of the model parameters, the net

present value (NPV) of the lithium extraction project is still positive and large relative to the economy of the study

area. Nevertheless, the distributional and social differences have to be carefully assessed in the future according to

the ecosystem services and the financial model described in this study. In order to incorporate market distortions

and foreign exchange implications on the financial model, further economic research is required on both projects.

Finally, water resources and its competing uses should be recognized as an economic good, so it could be managed

more efficiently and used more equitably in this ecosystem.

1

TABLE OF CONTENTS

PARTI - INTRODUCTION AND BACKGROUND………………………………………………………………………………………………………….1

I.1 Characteristics of the study area………………………………………………………………………………………………………………………….4

I.2 Population and Economic Activity……………………………………………………………………………………………………………………….7

I.3 Minerals: The enduring treasure….………………………………………………………………………………………………………………………9

I.4 The focus of Master Project ….…………………………………………………………………………………………………………………………..10

PART II – ECOSYSTEM SERVICES IN SALAR DE UYUNI BASIN ………………………………………………………………………………..11

II.1Recreation, Culture and landscape: Ecosystem gift for local development………………………………………………………...11

II.2 Water resources in one of the world’s aridest places………………………………………………………………………………………..12

II.3 Biodiversity: The intangible key to ecosystem services……………………………………………………………………………………..16

II.4 Agriculture and Animal Husbandry…………………………………………………………………………………………………………………..17

PART III – METHODS………………………………………………………………………………………………………………………………………………19

PART IV – RESULTS…………………………………………………………………………………………………………………………………………………26

IV.1 Initial Scenario ………………………………………………………………………………………………………………………………………………..26

a) Lithium Mining Project……………………………………………………………………………………………………………………………………….26

b) Quinoa Irrigation Project……………………………………………………………………………………………………………………………………28

IV.2 Preliminary project selection…………………………………………………………………………………………………………………………..29

IV.3 Sensitivity Analysis………………………………………………………………………………………………………………………………………….30

a) Lithium Mining Project……………………………………………………………………………………………………………………………............30

b) Quinoa Irrigation Project……………………………………………………………………………………………………………………………………32

IV.4 Project selection………………………………………………………………………………………………………………………………………………35

PART V – CONCLUSIONS AND DISCUSSION …………………………………………………………………………………………………………..36

PART VI –REFERENCES ………………………………………………………………………………………………………………………………………….39

PART VII- APPENDIX……………………………………………………………………………………………………………………………………………...43

1

PART I- INTRODUCTION AND BACKGROUND

As the global energy landscape tilts away from fossil fuels towards renewables, the demand for lithium-

ion (Li-ion) battery is growing. Because of its light weight and huge energy storage capabilities, Li-ion batteries are

preferred for electronic devices, such as computers, cameras, and cell phones. Between 2003 and 2007, the world

consumption of lithium for the battery industry increased over 7% per year (Roskill, 2008). Also, Ebensperger et.al

(2005) predicts that because of the many diverse uses for lithium metal1, demand is expected to expand

considerably over the next decade reaching a up to 8.2% increased in 2010. From a global perspective, the most

important application of lithium products in 2008 covered the following applications: battery, glass & ceramics,

lubricating greases, aluminum &casting, air conditioning, pharmaceutical, and others (Ebensperger et al., 2005;

SQM, 2008; USGS, 2008). Figure 1 shows the 2008 main uses of lithium in percentages.

Figure 1: Main uses of lithium

Source: SQM Annual Report, 2008.

In particular, increased worldwide interest in greener transportation has triggered an upswing in the

market for lithium as it is a major component of batteries for electric and hybrid automobiles (Tahil, 2007;

Ebensperger et al., 2005; Nicholson, 1998). In the US, President Obama directed 2 billions of dollars of the

economic stimulus package to fund lithium battery manufacturing (Galbraith, 2009), and GM announced it would

build a plant to manufacture (Li-ion) batteries for the Chevy Volt scheduled to debut in 2011(Warren, 2009;

Lawrence 2009). Likewise, Asia and Europe are making strong commitments to electric, plug-in, and hybrid vehicles

with stated goals of starting production in 2011 (Gartner, 2009). Nissan-Renault which, together with the “Better

Place” project for electric distribution, announced the availability of electric cars in different countries such as

1 Lithium metal and compounds are widely use in lightweight aerospace alloy, ceramics and glass; carbon dioxide

absorption, water disinfection, and pharmaceuticals for treating mood disorders.

Israel and Denmark starting in 2011; and Mitsubishi’s launch of the i-Miev, a compact vehicle operating solely with

an electric motor, which the Company expects to sell outside of Japan starting in 2010 (Abuelsamid, 2009).

Lithium metal is 33rd-most abundant element on the planet and is widely distributed in trace amounts in

most rocks (pegmatite minerals), soils (brine salt flats and clay deposits) and natural waters. Large concentrations

are extracted from pegmatite (lithium-containing minerals spodumene and petalite) and brine salt flats. Lithium is

not found in elemental form due to its high reactivity, so most studies report lithium consumption or deposits in

terms of Lithium Carbonate (Li2CO3) Equivalent (LCE).2

Although the purity of extraction from pegmatites is greater, the extraction from brine salt flats is the

most economic alternative (Evans, 2008; MIR, 2008; Tahil, 2007). Figure 2 shows the world’s total reserves3 of LCE

in million (MM) tonnes from brines and pegmatite. Today the greatest part of the world’s accessible lithium

reserves (over 80%) is in the so-called “Lithium Triangle”, where the borders of Argentina, Bolivia, and Chile meet

(Evans, 2008; MIR 2008). Furthermore, the Lithium Triangle accounts for more than 50% of the world’s total

lithium metal resources (Figure 3). Lithium with extremely high strategic value has led to a race for many lithium

extraction projects on the salt flats of the world during the past two decades (Evans, 2008; Tahil, 2007; MIR, 2008).

Figure 2 : World’s Total LCE Reserves from Brines and Pegmatite by country

in (MM tonnes ; %)

Source: adapted from Evans(2008), USGS (2007), and MIR (2008)

2 Approximately 5.32 units of Lithium Carbonate (Li2CO3) equivalent converts to one unit of Lithium Metal.

3 According to the USGS (2008) “reserves” are that part of the “resources” which could be economically extracted

or produced at the time of determination. The term “reserves” need not signify that extraction facilities are in

place and operative. The term also implies that the material can be extracted with existing technology at a specific

price, usually the prevailing market price.

0.45; 0.55% 0.21; 0.26%

13.83; 16.84%

14.42; 17.56%

29.26; 35.63%

23.94; 29.15%

Chile

Bolivia

Argentina

China & Tibet

Brazil

US

Figure 3: World’s Total Lithium Metal Resources by continent

in (MM tonnes) 4

Source: adapted from Evans(2008)

Not surprisingly, in 2008 more than 55 % (65,000 tonnes) of the global production and consumption of

LCE (118,000 tonnes) came from Chile and Argentina. Because lithium is not traded as a commodity on the open

market, its price is variable depending on the deals directly between producers and manufactures. In the early

2000, the average export value for Chilean and Argentinean lithium carbonate remained around US$2,000 per ton.

That changed in 2005, when the nominal prices for lithium carbonate began to increase sharply (Figure 4). Average

export values for LCE reported by major producing countries in 2008 were more than double those seen in 2004

(Roskill, 2008).

Figure 4: LCE Average annual prices from Chile exports

in (US$ per ton)*

*Chile GDP deflator (2000=100)

Source: adapted from Roskill (2008); International Monetary Fund (2008)

4 To convert 1,000 tonnes of lithium metal to million pounds of lithium carbonate equivalent, multiply by 11.7.

0.24

0.26

2.36

3.60

14.19

6.48

Europe

Australia

Africa

Asia

North America

South America

-

1,000

2,000

3,000

4,000

5,000

6,000

7,000

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008

Nominal US$ per ton

Inflation-Adjusted US$ per ton

For all the above mentioned, today the real power player in the lithium market is Bolivia. The Salar de

Uyuni in Potosi Bolivia has close to 42% of the world's lithium reserves from brines only (Evans, 2008; USGS,

2008;MIR, 2008). Although production has not yet commenced, in May 2008, the president of Bolivia signed a

decree investing US$ 8.7 MM to set up a state owned pilot lithium extraction plant in the Salar de Uyuni with the

hopes that future profits will “fund social programs in the country” (COMIBOL, 2008).

In pursuing this, it might open further areas for production, promote further use of lithium or charge

higher taxes and introduce royalties to derive greater benefit from the economic profits of LCE exports. After all,

historically the mining sector has always been an important economic activity for Bolivia. From 1995 to 2005, the

mining sector has contributed in a range from 4.2% to 6.1 % of Bolivia’s gross domestic product (INE, 2009).

Nevertheless, in Potosi concerns remain about the environmental and social impact of the massive lithium mining.

In an impoverished but natural resource-rich Bolivia, the depletion of natural capital is typically not

accounted for. Specifically, the mining industry has traditionally been structured to externalize such environmental

costs so as to maximize profit — the industry appropriates undervalued resources and shifts the environmental

costs to others— rather than improving efficiency and innovating (Escobari, 2003; McMahon et.al., 1999).

Responses are typically short term and no sustainable. Moreover, it is certainty that when comes to evaluating

these costs, the most affected by environmental pollution and biodiversity loss from mining, are generally those

least able to understand and respond to it (e.g. remote miners' families or isolated rural communities and the

tourism business).

I.1 Characteristics of the study area

The Salar de Uyuni basin occupies a surface of 7,185 thousand hectares and is located at extreme

southwest of Bolivia. Figure 5 shows the topography and location of the basin where the gray/white indicates

elevations above 4,000 meters, green below 500 meters and the different shades of brown represent altitudes

between 500 and 4,000 meters. A characteristic of the region is the presence of large salt flats and salt lakes that

are remnants of ancient lakes. The level and area of these lakes has varied greatly over the past 200,000 years,

which is associated primarily with temporary changes in precipitation and temperature (Risache and Fritz, 1991).

Figure 5: Topography of the Salar de Uyuni Basin

Source: Molina Carpio (2007).

The Salar de Uyuni is the largest salt flat in the world and one of the twelve most important watersheds in

South America. It is at an altitude of 11,995 feet and covers an area of 1,062 thousand hectares of salt desert

(World Resources Institute, 2005). The Salar de Uyuni (Figure 5 and 6), located on the Bolivian Altiplano at 20ºS

68ºW, is surrounded by the Andes Mountains. These mountains cause a rain shadow, preventing moisture input

to the Altiplano (Highlands), producing an arid to semi-arid climate, nonetheless, where open water bodies exit

(rivers and lakes) there is a rich avifauna and vegetation cover (Pastures and marshes). Arroyo and et al. (1988)

concluded that plant species have a great dependence on the availability of water due to the arid climate; and,

only minor changes in the water budget can induce gain or decrease in vegetation cover and plant diversity.

As the evaporation rate near the salar (1300-1700 mm/yr) greatly exceeds precipitation (100-200 mm/yr),

salt crust and brines are formed throughout the year in the salar (Molina Carpio, 2007). Although the salar is

normally dry, seasonally flooding changes the volume of outflow water producing a unique pasture and marsh

pattern (Messerli, et.al., 1997).

Figure 6: The Salar de Uyuni Basin in the Bolivian Highlands (Altiplano)

Sources: Risache(1991), RAMSAR (2009) and World Resources Institute (2005).

Furthermore, many animal species live in the basin where vicunas and guanacos are most prominent

amongst the mammals (Liberman, 1995). The salt flats and the Rio Grande of Uyuni house three of the world’s six

flamingo species. During January and March (southern summer), the salar becomes a flamingo breeding ground

after the rains flooded the surface of the salar (Hurlbert, 1979; Messerli, et al., 1997). The discharge of Rio Grande

onto the salar, adjacent to where the lithium concentration is the highest, creates a permanent lagoon area used

by birds and camelids (Figure 6). Consequently, it was included in the list of the 34 biodiversity Hot Spots of the

world in the year 2000(Conservation International, 2007).

Figure 7: Landscape pictures of the Salar de Uyuni

Source: COMIBOL online (2009)

Bolivia

Chile

Salar de Uyuni

8,000)8,000)

.

.

Bolivia

Chile

Salar de Uyuni

8,000)8,000)

.

.

I.2 Population and Economic Activity

The Salar de Uyuni basin occupies approximately 61% of the Department of Potosi in Bolivia. The five

provinces in the southwest of Potosi are: Daniel Campos, Antonio Guijarro, Enrique Baldivieso, Nor Lipez and Sur

Lipez (Figure 8). The 2006 estimated population of the basin was 64,212 people (Molina Carpio, 2007). The most

populated towns in the study area are Uyuni and Kolcha “K”(right panel Figure 6). As can be seen in Table 1 the

population density is less than 1 hab/km2, much lower than the rest of the department of Potosi and the national

average. On the other hand, rates of health and human development are above the average of Potosi and below

the average of Bolivia. Table 1 shows the population, density per square kilometer, and some human development

records of the study area per province.

Figure 8: Administrative distribution of Potosi, Bolivia

Source: Molina Carpio (2007).

Table 1: Population and Human Development information

Source: Adapted from Molina Carpio (2007) based on UNDP and INE (2009)

The activities that employ a larger percentage of the economically active population in the basin are the

quinoa agriculture and camelid livestock. Even though 1% of the total area is suitable for agriculture, Quinoa

ProvincePopulation

(2006)

Density

(people/km2)

Life expectancy

(years)

Adult literacy

rateHDI

Antonio Guijarro 39,126 2.3 59.4 81.8 0.57

Daniel Campos 5,490 0.3 59.0 94.5 0.57

Enrique Baldivieso 1,690 0.8 58.5 87.7 0.53

Nor Lipez 12,171 0.5 57.4 87.7 0.54

Sur Lipez 5,522 0.4 55.4 82.4 0.48

Salar de Uyuni Basin 63,999 0.9 58.0 86.0 0.5

Department of Potosi 772,578 6.5 55.2 64.5 0.5

Bolivia 9,627,269 8.8 63.3 86.7 0.6

harvesting is the main source of income and food security for local people. In the Salar de Uyuni basin only 65% of

the land suitable for agriculture is yearly harvested (25 Th.Ha) and the rest is not cultivated because of lack of

water supply or labor (Molina Carpio, 2007).

Other activities of growing importance are tourism and mining. The study region has many tourist

attractions such as the Salar de Uyuni, the high Andes lakes and wildlife, the spectacular geological formations and

hydrothermal vents. According to Ellingson & Seidl (2006) the most visited ecotourism attraction in Bolivia is the

Salar de Uyuni. In the year 2006, 50,342 people visited Uyuni which 43% were foreigners. Figure 9 shows the influx

of visitors from 2000 -06. Relative to the year 2000, the percentage change of tourists in 2006 was 16%; foreign

visitors were 41% and bolivians were 2.7%. It was estimated that the percentage change of total tourist per year

will be around 5-8% for the following years (INE, VMT& BCB, 2009).

Figure 9: Number of tourists visiting the Salar de Uyuni

Source: adapted from INE, VMT& BCB (2009)

The study area has substantial reserves of minerals, both metallic and nonmetallic (lithium and derivates).

It has the largest antimony deposits in Bolivia, as well as deposits of other metals. The San Cristobal mine started

operations in 2008 and it is considered the largest developed mining project in Bolivia in the last 10 years. The

proven and probable reserves of San Cristobal are estimated at 446 million ounces of silver, 3.45 million tonnes of

zinc and 1.27 million tonnes of lead (Molina Carpio, 2007).

It is self evident the importance of this unique ecosystem to Bolivia and the world, therefore a widespread

extraction of natural resources and excessive pressure on the ecosystem (i.e. tourism, population, mining) could

have irreparable environmental consequences. As a real world example, Figure 10 shows the destroyed landscape

and the loss of natural water courses that the salar in Chile suffered in the last 10 years.

27,935

22,973

52,914

-

10,000

20,000

30,000

40,000

50,000

60,000

2000 2001 2002 2003 2004 2005 2006

BoliviansForeigners

Total Visitors Salar

Figure 10: Loss of landscape reduce the tourist attraction of the basin

Source: Lithium mining at Salar de Atacama, Chile SQM(2008).

I.3 Minerals: enduring treasure

It is the abiotic characteristic of this ecosystem that allows minerals in sufficient quality and quantity to

make mining both desirable and feasible. Nevertheless, unlike other ecosystem services, this service is finite and of

limited capacity. The salt flats have long been a resource for local indigenous peoples. In towns on the banks of the

salar, most houses and buildings are made of bricks of the crystallized mineral. Residents work in salt, drying and

loading it in trucks to be taken to refineries in the lowlands, and carving it into trinkets to sell to tourists who visit

each year.

As mentioned earlier, lithium has become a strategic element that won fame for its use in battery-

powered cars and variety of uses. The Bolivian Pilot Plant is the first phase of the lithium extraction project where

only small amounts of LCE will be produced (400 tonnes/year of LCE). It will be run by the General Directorate of

Evaporative Resources of the Salar de Uyuni under the state owned mining company of Bolivia–COMIBOL. The

second phase called “Lithium Industrialization Plant” is reported to produce 40,000 to 60,000 tonnes per year of

LCE starting in 2014 (COMIBOL, 2008; La Razón, 2009). The positive aspects of the relationship between lithium

extraction and human well-being are mainly due to employment possibilities and the general contribution to

economic activity in the municipality and the country. With regard to the former point, it should be noted that,

the workforce available in the municipalities are mainly unqualified so the potential for participation in mining

activities is uncertain.

In spite of the poverty in Uyuni, attempts in the 1980's and 1990's by foreign companies to extract the

lithium met with resistance from the community. Likewise, many analysts now perceive numerous internal

obstacles to a full lithium industry exist, and it is unclear if Bolivia will be able to participate in the market sooner

than 2018 (Friedman-Rudovsky, 2009). Apart from the political and economical problems that Bolivia might have in

the future, the most notorious difficulty prior undertaking this enormous project is decisive information

(Viñagrande, 2009).

I.4 The focus of Master Project

The ecosystem services and the natural capital stocks that produce them are critical to the functioning of

the Earth's life-support system. They contribute to human welfare, both directly and indirectly, and therefore

represent part of the total economic value of the planet (Daily, 1997; Millennium Ecosystem Assessment, 2005;

Pagiola et al., 2004). Also, it is widely acknowledge that market transactions provide an incomplete picture of the

economic value of ecosystem services.

The acquisition of information about most ecosystems services is especially difficult because of the no

existence of a market (Champ et al., 2003; Bishop et al., 1995). Yet, no government or private agency has

considered the economic desirability and timing of the lithium extraction project which will alter the natural

environment of the salar (Lopez Canelas, 2009). In particular, no study has assessed the existence and magnitude

of differences between benefits and costs of the lithium extraction in the Salar de Uyuni Ecosystem (Zuleta, 2009;

Garzón, 2009; COMIBOL, 2009). Those services (benefits) which are not normally exchanged in markets are

generally ignored in the decision-making process. Consequently, one purpose of this paper is to provide a

preliminary assessment of the ecosystem services provided by the Salar de Uyuni basin. Second, determine the

tradeoffs of the lithium mining project and the ecosystem services by estimating a lower bound opportunity cost.

Perfect information will never be available and uncertainty will be an inherent feature of all important

decisions (Bishop et al., 1995: Pagiola et al., 2004). However, this study optimistically will provide preliminary but

useful information to enhance the ability of decision-makers to evaluate the overall magnitude of differences

between winners and losers resulting from projects that alter the use the Salar de Uyuni unique ecosystem

services.

Ultimately, this study will encourage that further economic valuation research is needed in the Salar de

Uyuni basin. Indeed, ecosystem valuation by itself provides little interest to a country owning the environmental

assets unless they can be turned into revenue flows (Bishop et al., 1995).

PART II- ECOSYSTEM SERVICES IN THE SALAR DE UYUNI BASIN

II.1 Recreation, Culture and landscape: Ecosystem gift for local development

Ecotourism is a potentially important development alternative in undeveloped countries (Ellingson &

Seidl, 2006). Many in the tourist industry classify the Salar de Uyuni as a Natural Wonder of the world because of

its area of outstanding natural beauty (New7Wonders of Nature website, 2009). Since the salt flats have an albedo

similar to that of ice sheets, the Salar de Uyuni is the brightest object on earth’s surface visible from space and

thus the most visited ecotourism attraction in Bolivia (Fricker, et al., 2005; Ellingson & Seidl, 2006).

The major attractions of the community include the mountain landscapes, train cemetery, colonial

churches, rocks formations sculpted by wind, tallest and oldest cactus, dead volcanoes, lakes and lagoons, and the

traditional villages with their historical and cultural heritage. Some of these attractions are located within the

Eduardo Avaroa Reserve located at the Sur Lipez province. Around 50,000 tourists visited the Salar de Uyuni in

2006 and sleep in a hotel made entirely of salt despite the poor infrastructure of the municipalities(towns) close to

the salar, Uyuni and Kolcha “K . Those municipalities have a population of 19,000 and 11,174 inhabitants,

respectively (INE&UDAPE, 2001). Nevertheless, the huge potential to develop with the ecotourism income is intact.

At a national level, Bolivia received 404.7 thousand visitors which generated an estimated income of US$

187.7 million in 2004 (UDAPE, 2005). According to the same report, considering the receptive tourism as a proxy

variable of the sector's contribution to GDP5, the estimated income from receptive tourism amounted in average

more than 2% of GDP in the period 1991-2004. Those figures showed tourism is an activity with a major impact on

the economy of Bolivia and should be considered as part of decision-making.

In the same manner, Uyuni and Kolcha “K” have experienced considerable income inflows and

employment changes, mainly brought about by the explosion of ecotourism over the last twenty years. Although

the majority of the population is considered below the poverty line (INE&UDAPE, 2001), the local employment has

enjoyed a gradual but constant transformation. Both Uyuni and Kolcha “K have shifted from being municipalities

where the only main activities were agriculture and livestock, to one where most of the labor force work in

construction, salt farming, craftwork and tourism. The INE and UDAPE (2001) reported that the main activities

generating employment in the municipalities are directly or indirectly related to the use of ecosystem services:

agriculture and livestock (46%), hotels and restaurants (23%), construction and mining (19%), and salt farming

(12%).

5 Because national accounts do not provide tourism information separately, UDAPE estimated the total income

from receptive tourism (Yx) according to a survey conducted to estimated the average expenditure per tourist

arriving to the country. Afterwards, they used the following expression :

Yx = [average expenditure per day x Number of tourists per year x visiting average days per tourist]

As a result, tourism seems to be a viable development option for both the communities and outsiders,

them being resident in the area or just passing through. This activity experienced sudden and unregulated growth

triggered by the arrival of entrepreneurs who set up the first campsites and tourism agencies, followed by hostels

and restaurants, and finally diverse categories of hotels and internet cafés. A new road, recently constructed

between Uyuni and the regional capital of Potosi, will improve tourists’ access in the following years bringing

employment and income to those communities (Eduardo Avaroa Reserve website, 2009).

The idea would be to balance the economic development to the mining sector and tourism which will be

ideally sustainable.

II.2 Water resources in one of the world’s aridest places

In the Salar de Uyuni water resources are not only vital to flora and fauna, but have also been the basis of

all human activities in the past and present. Messerli and et al. (1997) concluded that water resources in the Salar

de Uyuni Watershed are considered a non-renewable resource (or renewed extremely slow) and specifically

expanding mining industry may lead to ruin this sensitive ecosystem and also provide a threat to the region's water

supply. The same study established that in order to prevent damage even small changes in hydrologic conditions or

salts concentrations of ground and surface waters must be carefully assessed. The study also argues that it is not

appropriate to focus exclusively on areas with high biodiversity (e.g., around rivers or lakes). Instead, the entire

catchments, including the groundwater basins and the source area of the open water, must be assessed. As a

result, the salar has been included in the RAMSAR list of Wetlands of International Importance6.

Despite the economic importance of the mining sector in Bolivia, it has been the main caused for

irreversible environmental damage to land, rivers and communities for the last 80 years. Up until today, mines

continue to discharge hundreds of thousands of tons of toxic sludge into rivers every year (Escobari, 2003). In

Uyuni, past fights against water rights and the voracious water consumption of a mining company have been

detrimental to local farmers who use water supply from ground water reserves (Mc Mahon, 1999). After all, the

most important mining region in Bolivia, the Altiplano, up 13% of the country, has only 0.5% of available fresh

water (Escobari, 2003).

COMIBOL (2008) estimated that the Lithium Industrialization plant will consume approximately 4,000 to

4200 cubic meters per day (m3/day) of freshwater from Rio San Geronimo, and 5,000 to 5,300 m

3/day of brackish

water from Rio Grande (Figure 11).

6

It has come to be known popularly as the “Ramsar Convention”. Ramsar is the first of the modern global

intergovernmental treaties on the conservation and sustainable use of natural resources (RAMSAR, 2009).

Figure 11: Hydrological map and rainfall patterns in the Salar de Uyuni Basin

Source: adapted from Molina Carpio(2007)

Additionally, Table 2 shows the probable water consumption (m3/day) for the future lithium plant from

two different reports. The first row indicates the average water consumption estimated from an operating lithium

plant in Chile (SQM), and the second row indicates the expected future water consumption for the future lithium

plant estimated by COMIBOL (2008).

Table 2: Expected quantity of water consumption for the future lithium plant

Source: Torrez&Ramirez (2006) and COMIBOL (2008).

The same report distributed by COMIBOL (2008) states that salts precipitated in the evaporation ponds

“will be returned via brineduct to the Río Grande” (emphasis added). Hence, the quality of the withdraws to Rio

Grande could raise some concerns because local peasants use the slightly saline water mainly for animal

husbandry. Furthermore, pasture and marsh habitats show less dependence on precipitation and their presence

depends more on the availability of local freshwater and groundwater (Messerli et al., 1997). Many species in this

arid environment are limited to marsh habitats, which are very important grazing resources for the Altiplano

communities.

Rio Grande

(Brackish)

Rio

San Geronimo

SourceFresh water

(m3/day)

Brackish

(m3/day)

Torrez&Ramirez 2006 (*) 8,208 9,504

COMIBOL 2008 4,200 5,300

(*) SQM average consumption 1995-2003

It is important to highlight that the quantity of brackish water is not an immediate issue. Not only a Rio

Grande flow greatly exceeds the estimated lithium plant future daily consumption7, but also the quality of the

water is not suitable for agriculture (Molina Carpio, 2007). While this river should be monitor in the future to

assess the impacts associated with pumping, brackish water from Rio Grande will be not part of this study. On the

contrary, the quantity and consumption privileges of freshwater from Rio San Geronimo will be considered in this

study.

Knigth Piesodl Consulting8 (cited in Molina Carpio, 2007) conducted the only available hydrological study

on the Rio Jaikihua, located 15 kilometers south-east of Rio San Geronimo which is also under the same rainfall

pattern (Right panel Figure 11). The hydrological study concluded that building a dam in Rio Jaikihua, it will take

less than 18 years to consume all the potential available water from the river if pumped water reaches a 40,000

m3/day. Notice in Table 3 that all the recharged water comes directly or indirectly from precipitation representing

only 22% of pumped water and the remaining 78% represents the emptying of the aquifer. Moreover, building a

dam to pump the water will eliminate the natural flow into the Altiplano.

Table 3: Rio Jaikihua Hydrologic balance

Source: adapted from Molina Carpio (2007)

Molina Carpio (2007) also explained that given the characteristics of the region9, when the pumped water

exceeds 15 -25% of total spring recharge, not only the water availability will significantly decline throughout the

years, but also it will take the aquifer long periods of time to return to less than normal levels. For this case, Knigth

7 Rio Grande flows at 32,918 -33,574 (m3/day ) vs. 7,402 (m3/day)equal to the average expected brackish water

consumption. 8 Knight Piésold is a specialised international consulting company offering engineering and environmental services in Mining,

Environment, Hydropower, Water Resources, Roads & Construction Services(www.knightpiesold.com). 9

In terms of rainfall patterns, evapotranspiration, slope, runoff coefficient, and soil chemistry.

Table Rio Jaikihua Hydrologic balance

Daily

m3/day

Recharge

Rio Jaukihua (ephemeral) 200

Rio Toldo Basin and Rio Grande Basin (*) 3,050

Upstream Volcanic-sediments and low yield springs 4,200

Precipitation 1,400

Total recharge 8,850

Pumped water 40,000

Water removed from aquifer 31,150

Discharge into Rio Grande -

Total removable water from the river is 250 Million m3

(*) Because Rio Jaikihua is a tributary of Rio Grande, the flow is

reversed after pumping begins. Also, Rio Toldo is a tributary of Rio

Jaikihua.

Piesodl estimated that Rio Jaikihua volume will return to normal levels approximately 79 years after closing

operations.10

However, Molina Carpio (2007) then argued that because Knigth Piesodl hydrological modeling

assumed rainfall patterns higher (300 mm/year) than the average precipitations in that region (200-250 mm/year;

Figure 11), the recharge and available water of Rio Jaikihua are notably overestimated. As a result, the emptying

period of the river will be shorter and the recovery time will be longer (Molina Carpio , 2007).

Rio Jaukihua and Rio San Geronimo are the result of two hydro-geologic systems characteristic of the

study area. The first type occurs at relatively low elevations (between 3,700 and 3,900 m) and its the result of

erosion of the volcanic sediments located upstream and groundwater regulation (Molina Carpio, 2007). The

second system includes rocksprings located between 3,900 and 4,500 m, where the water precipitated seeps

through fractures and eventually emerges in low yield springs (Molina Carpio, 2007). In other words, both rivers

recharged directly from rainfall and groundwater storage (blue arrows coming from the mountain in Figure 12).

Nevertheless, Chauffaut (1998; cited in Molina Carpio, 2007) concluded that the freshwaters that flow into the

region are mostly from underground sources which were store long time ago. Chauffaut estimated that less than

20% of current runoff water comes from “recent precipitations”. The rest is water from underground sources

between 100 and 20,000 years old, according to radiocarbon-date tests. Furthermore, Molina Carpio (2007)

suggested that water stored underground should be considered a nonrenewable resource because it comes from

rainfall and ground water regulation that occurred between 90 and 19,000 years ago. 11

Figure 12: Rio San Jaikihua hydrological profile

10 250 (Mill m3) / 8850*360*10-6 (Mill m3 / year)

11 In order to support that conclusion, Molina Carpio (2007) also explained and cited other hydrological/paleoclimate studies

conducted in the area (Aravena , 1995; Pourrut etal, 1995) .

Source: adapted from Molina Carpio (2007) obtained from Knigth Piesodl Consulting (2000).

Unfortunately response to such environmental concern over an extensive lithium mining in the salar is

characterized by indifference by the government and local authorities (Lopez Canelas, 2009)

“There’s no information, no water use nor hydrological studies,… So how can they begin to project what

the long-term effects might be? This is supposedly a project to improve the region, but what if it makes

living impossible? How could it be called sustainable development?”

--- Elizabeth Lopez Canelas—Bolivian Environmental Defense League (FOBOMADE)

It must be acknowledged that there is not enough information available to be able to simulate the

complete water cycle of the salar with any precision. However, it is clear that if extensive mining takes place in the

Salar de Uyuni Watershed, water availability might become progressively more critical in the future. According to

Oliveira Costa(1993), if the highest priority is not given to the protection of water resources, especially in the most

arid mountain ecosystems of the Salar de Uyuni Watershed, natural habitats very soon will be destroyed and the

agricultural, tourist, and economic developments will be endangered.

II.3 Biodiversity: The intangible key to ecosystem services

Biodiversity plays a direct role in regulating and supporting some of the ecosystem services fundamental

for human wellbeing, e.g. services obtained directly from the ecosystems, such as food and animal husbandry. It

also plays a fundamental role in the development of other services such as tourism and agriculture since it

provides the elements that support these services (e.g. species, communities, landscapes). Biodiversity also plays

an indirect role in the development of certain activities relevant for the municipality, such co-management of

some areas of the Eduardo Avaroa Reserve. In this case, the wildlife habitat of certain species and natural

landscape means that the reserve can be developed into a tourist attraction (e.g. flamingos at Laguna Colorada or

vicunas).

In arid climates like the Salar de Uyuni basin, it must be highlight that by no means a region without fauna

and flora (Messerli, et al., 1997).Thus, biodiversity is a very important element since it regulates the functioning of

the ecosystem. For example, biodiversity defines the ecosystem biomass (the totality of living organisms in the

ecosystem), which allows the ecosystem to function under a variety of conditions. This determines the conditions

of adaptability and resilience that maintain a diversity of living organisms even in the seemingly inhospitable arid

Altiplano ecosystem. However, because the adaptation to this harsh environment is extremely sensitive, even

minor human interventions can direct to irreversible change or loss (Hurlbert, 1979; Messerli, et al., 1997).

Messerli (1997) also concluded that “some species and associates could form a certain reservoir for migration in

the event of future climatic change…..but most important is that people, plants, and animals depend on the

universal life-limiting factor: water! “.

Unfortunately, it is difficult to define one general condition for biodiversity in the Salar de Uyuni basin

today. While some elements are protected others are under constant pressure due to their traditional uses: for

example, some Altiplano plant species used for fuel (llaretas) or crafts (cardón). However, there have been no

assessments as yet to determine their current situation. There is no systematic information to identify trends and

changes over time or in space in the biodiversity of the municipality. Monitoring has only taken place for some

“charismatic” species (e.g. flamingos) in some sectors of the Eduardo Avaroa Reserve. The same is true for water, a

vital resource for biodiversity in the area due to its scarcity.

According to Lopez Canelas (2009) some local people are concerned about lithium extraction in the

region. They said that if excessive lithium mining occurs that could lead to huge trenches or the destruction of

large areas of the salar. It is almost certain that covering its surface with brine extraction facilities and evaporation

ponds (Figure 9) will irreversibly damage the surface of the salt flat and the Rio Grande delta, used by wild

flamingos as an annual breeding ground. On the other hand, pressure over land use is mainly an issue in areas with

larger settlements, such Uyuni, where marsh lands are being handed over to tourism development projects.

II.4 Agriculture and Animal Husbandry

Agricultural and Animal husbandry practice in the Salar de Uyuni basin occurs at a subsistence level and

contribute to local families’ food and income. About 38.6 thousand hectares of land in the basin is suitable for

agriculture (<1% of the total) and roughly 11% is natural grassland mainly used as pasture for camelids. Table 4

shows the area of agricultural land and the number of heads of cattle by province. Largely due to the limitations of

the ecosystem and lack of capital for implementing large-scale agricultural production (i.e. water irrigation), 40% of

the households have 3 to 4 hectares of yearly cultivated crops. For local peasants, agriculture represents 65 to

85% of their income.

Table 4: Agriculture and Livestock in Salar de Uyuni basin

Source: adapted and translated from Molina Carpio (2007) and Soraide et al (2005).

Table 2. Areas in thousands of Hectares (Th.Ha) and camelids in number of heads(#)

Total area

Th.Ha

Posible crop

Th.Ha

Harvested

Th.Ha

Irrigated

Th.Ha

Non-irrigated

Th.HaCamelids (#)

Antonio Guijarro 1,489.0 14.95 11.52 1.04 10.48 128.3

Daniel Campos 1,210.6 4.84 7.72 0.48 7.24 23.3

Enrique Baldivieso 161.4 4.00 1.05 0.02 1.03 9.1

Nor Lipez 2,089.2 14.47 4.74 0.36 4.38 68.4

Sur Lipez 2,235.5 0.29 0.27 0.20 0.07 42.3

Total basin 7,185.7 38.6 25.3 2.1 23.2 271.4

There are various factors that constrain the ability of agriculture land pastoral activities to grow as

productive activities in the region. One of these factors is the ecosystem and its limitations: water supply is a factor

limiting activities, as already mentioned in this study; this limitation is compounded by soil conditions that do not

allow for intensive activity. Soil is predominately alkaline in the basin with high salt content, making it unsuitable

for intensive agricultural practices (such as fruit cultivation), except for the cereal quinoa and potatoes (Jacobsen

and Mujica, 2001).

Apart from these environmental factors, social, economic and cultural conditions have tended to favor

other economic and productive activities, particularly tourism and mining. The migration of the younger

population from the countryside to towns and cities further limits the possibilities for growth in agricultural and

pastoral sectors.

In spite of the above, 20.7 thousand hectares of quinoa are cultivated each year (Crespo et al., 2001),

approximately 82% of the total harvested crops (Columnn 3 of Table 4). The Salar de Uyuni basin is the principal

organic quinoa producer region in Bolivia, with an annual contribution of 60 % of the national production

corresponding to 13,485 tonnes (Crespo et al., 2001; Soraide et al, 2005). According to the last census, 66% of the

population (42,255) is involved in quinoa agriculture.

Quinoa cereal is highly appreciated for its nutritional value, as its protein content is very high. Unlike

wheat or rice (which are low in lysine), quinoa contains a balanced set of essential amino acids, making it an

unusually complete meal. Quinoa has more iron, phosphorus, and calcium than wheat, corn or white rice. It is also

a good source of dietary fiber and magnesium. Quinoa is gluten free and considered easy to digest. Its seed can

also be used to make a high protein drink (Quinua Real, 2009).

Currently, Bolivia is the second largest producer of quinoa in the world and the only one that can produce

organic quinoa. Bolivia constitutes the principal world exporter of quinoa, contributing 43% of the total quinoa

production in the world and 0.14% of the Bolivian GDP (FAO, 2009; CAMEX, 2009). There are approximately more

than 35,000 hectares harvested crops producing 23,000 tonnes of quinoa each year. As an average, the official

exports reached 4,000 tonnes a year during 1998 and 2002. This product involves about 70 thousand small

producers and exports of U.S. $ 5.1 million per year (U.S. $ 2.7 official $ 2.4 and unofficial). Due to the international

market demand for organic food, quinoa crop harvesting has become an important commercial product.

According to CAMEX, during the year 2003 the exports have registered increases of more than US$ 700,000, this

increase is accompanied with an increase of the number of national enterprises that export this grain.

The importance of quinoa agriculture is important for the Salar de Uyuni basin and Bolivia because of the

following:

Food safety: The quinoa is native to the Salar de Uyuni Basin and used in stews, salads or croquettes, the breakfast

cereal and soup. Quinoa is essentially used as food and to a lesser extent for medicinal purposes; there are

different ways to eat: grains and flakes. Out of the 70 thousand agro units of production, 55 thousand occur

irregularly and subsistence, 13 thousand produced continuously for sale and consumption, and only 2 thousand to

sell produce to market (Soraide et.al, 2005). Quinoa is considered a substitute product of any kind of meat and

resembles the qualities of milk. It is a very important source of income for local peasants. Also, there is a fledgling

industry in Bolivia of products such as quinoa or pasta, cereal preparations and bars quinoa with chocolate.

Agriculture sector: it is the main product of the Bolivian highlands producing an average of 25.6 thousand tons of

cereal per year with an average yield of 660 kg / ha. Quinoa provides 2.35% of agricultural domestic product of

peasant origin. As a byproduct, quinoa is used to feed animals and firewood (Molina Carpio, 2007).

Foreign trade: legal exports account for 4.5% of the Bolivian exports clearly peasants. Export $ 2.7 million are

legally registered and approximately $ 2.4 million smuggled out of Peru (Crespo et al., 2001).

PART III- METHODS

The initial phase of this study was devoted to gathering information and analyzing the current state of the

Salar de Uyuni basin. This was accomplish by analyzing past and present research on every relevant aspect in the

study area. That is, main economic activities, ecological and environmental characteristics, social and demographic

indicators (COMIBOL, 2009; INE, VMT& BCB, 2009; CAMEX, 2009; Messerli et al., 1997; Molina Carpio, 2007, etc).

The focus of this phase led to gather information particularly on the lithium mining project and the water

consumption in the Salar de Uyuni basin. Despite the significance of both topics in that region for Bolivia,

quantitative and qualitative information was very limited in detail and disaggregation. In particular, there are no

studies conducted recently on the economic profile of the region and the ecological interactions. Additionally, this

phase also included identifying and contacting experts in that region which provided the author with valuable

information, opinion and guidance (personal communication with, Curi, Lopez Canelas, Zuleta). Part of the results

of this phase was summarized in the previous sections.

Once the information was gathered and analyzed, the author was only able to focus its study in water

resources competing use, mainly because the lithium mining activities are not yet systematically perceived as a

threat for other ecosystem services (i.e. recreation and biodiversity). Moreover, information on that subject is still

deficient. For instance, research on the interactions between tourism development and water consumption is null

in the Salar de Uyuni basin. Thus, the only two important elements that have a clear competing use and relatively

consistent information were the water consumption of the mining sector and crop irrigation.

As mentioned earlier, not only lithium has become a strategic element with promising source of income

for the Salar de Uyuni basin, but also the fresh water resources converted into crop irrigation. The future lithium

mining plant will use freshwater from the San Geronimo river which could alternatively be utilized for quinoa

irrigation (Figure 11). In fact, the Water Ministry of Bolivia has a quinoa irrigation project for the same municipally

of Kolcha “K12

. Although the name of the river is not mentioned in the report, the approximate area that the

project might benefit is 612 Hectares of quinoa crops.13

Because there is no hydrologic study conducted on Rio San Geronimo, the hydrologic balance provided by

Knigth Piesodl will represent a rough and overestimated approximation. Given that Rio Jaukihua and Rio San

Geronimo have the same precipitation patterns and hydro-geologic formation system; it will be assumed in this

study that both rivers have similar hydrologic balance14

.

Table 5: An approximation of Rio San Geronimo Hydrologic balance with and without lithium plant

Source: adapted from Molina Carpio (2007)

Notice in Table 5 that the expected water consumption of the future Lithium Plant from Rio San Geronimo

is more than the overestimated recharge. Moreover, if a 20-year lithium mining operation takes place, Rio San

Geronimo would require at least 21 years to be suitable for water extraction again15

. Therefore, following Molina

Carpio (2007), Chauffaut(1998) and Messerli et al., (1997) conclusions, water consumption for lithium mining and

12

Irrigation Project “COLLCHA K”; featured by Water Ministry of Bolivia (Ministerio de Agua de Bolivia) and PROAGRO/GTZ at

http://www.riegobolivia.org/proyectos.html?accion=edit&id=11

13 According to Table 7 and irrigation Project “COLLCHA K”

14 With the exception of Rio Toldo recharge which In fact is not part of Rio San Geronimo basin (Figure 11).

15 6204*360*20(m3) / 5800*360 (m3 / year)

Table Aproximation of Rio San Geronimo Hydrologic balance

Without

Project Daily

m3/day

With Project

Daily

m3/day

Recharge

Rio San Geronimo (ephemeral) 200 200

NA

Upstream Volcanic-sediments and low yield springs 4,200 4,200

Precipitation 1,400 1,400

Total recharge 5,800 5,800

Pumped water(*) - 6,204

Discharge into the Altiplano 6,048 -

(*) average of the expected freshwater consumption from table 2

crop irrigation cannot take place at the same time and long recovery period should take place after extensive

extraction periods.16

The competing project of providing irrigation for quinoa harvesting could increase the income of local

peasants and the exporting sales of quinoa. Like private investment in extensive lithium mining can generate

important tax revenues flows for the local government and many other multiplicative economic effects in the

whole economy (COMIBOL, 2009; Ebensperger et al., 2005; Ellingson et al. 2006; Evans, 2008; Tahil, 2007; MIR,

2008); a medium scale water irrigation project for quinoa crops can also generate positive effects for the local and

national economy (CAMEX, 2009; Crespo, et.al, 2004; JICA, 2002; Molina Carpio, 2007; Soraide et al., 2005;

Victorio, 1999). A study conducted by ESMAP, UNDP, and the World Bank in the study region (explicated in Crespo,

et.al, 2004) concluded that water irrigation increased the quinoa yield by almost 180% in average. Therefore, if

quinoa production is expanded it could be assumed: 1) local quinoa Farmer gross income will grow, 2) domestic

supply, exporting and national sales will increase, and 3) domestic and exporting sales taxes will rise (Soraide et al.,

2005; JICA, 2002).

In the arid Salar de Uyuni basin, the fresh water use from the San Geronimo River creates two mutually

exclusive projects (i.e. lithium mining and quinoa crop with irrigation) generating different net gains to the

economy of the region and the country. In order to estimate the gains and losses from the economy as a whole,

cash flows for each project were constructed for alternatives economic actors. The lithium mining project

considered two economic actors: a private mining company and the government. Likewise, the quinoa irrigation

project considered a) the quinoa famers, b) private quinoa industry accounting for domestic and export sales, and

c) the government. The estimation of the cash flows for those economic actors facilitated to approximate the

benefits and costs of each project (Harbeger & Jenkins, 2003). This study assumed that if any of the projects is

conducted, the net benefits of the not-chosen project will constitute a lower-bound opportunity cost of

undertaking the chosen project.

The cash flow model for the lithium mining considered a 23-year timeframe, from 2009 to 2031, two

years of construction followed by 20 years of operations and 1 year of closure. Because the lithium extraction,

where the concentration is the highest, will last approximately 20 years, and much of the information was

estimated in early 2008, that time frame was chosen. Due to the intense competitive rivalry, much of the technical

information regarding operating costs and investments is considered proprietary by the current industry players,

and thus they are unwilling to share such information. However, COMIBOL provide the author fairly disaggregate

estimates of operating costs, construction investments and technical background for the future lithium mining

plant. For instance, Table 6 shows that the total investment in the project will be around US$ 273.5 million. The

16

For this ecosystem, extensive extraction means when the water extraction reaches 15-25% of the total spring

recharge.

production recognized three main exporting products: Lithium Carbonate, Potassium Chloride, and Boric Acid.

Additionally, the cash flow model for the private company considered 100% equity for the initial investments,

annual real prices growth of outputs and inputs, and most of the initial investment period. On the other hand, the

cash flow model for the government considered 35% of income taxes and 4.5 % of exporting fees plus royalties

estimated by COMIBOL schemes and current regulations.

Table 6. Estimated investment Costs of the Lithium mining Project

Source: adapted from COMIBOL (2009).

Unlike the lithium mining project, the irrigation project has an initial investment incurred by government

to build the irrigation infrastructure. That is, the government would have to incur in a long term loan of US$ 1.54

million at 11% of annual interest rate for 10 years. Yet, this project will benefit 161 households and 612 hectares of

quinoa crops (Water Ministry of Bolivia, 2008). The irrigation land size was computed based on distribution of

cultivable land and resting/rotation crops summarized in Table7 which is a common sustainable harvesting practice

in the Salar de Uyuni basin. The timeframe for this project is certainty larger, but for comparison reasons, the

author 23-year time frame was assumed. However, it could be argued that the irrigation project could be

replicated infinite number of times in the future. Thus, the net present value of the entire project was computed

using an infinite formula to account for this assumption. 17

17

NPV∞23=NPV23* (1+r)23 / [(1+r)23 - 1] from Harbeger & Jenkins, 2003. Ch.5 Pag.8.

Table Estimated Investment Capital for the Lithium mining Project (*)

Investment Closure

Th.US$ Th.US$

A. Materials & Equipment

Lithium Carbonate Plant and Equipment 58,240

Potassium Chloride Plant and Equipment 45,873

Boric Acid Plant and Equipment 41,481

Solar Evaporation Ponds 16,330

Service Facilities(water supply systems, Electrical power (10 MW) plant ,etc) 11,648

Buildings (Office, maintenance, water-house, laboratory, medical, wharehouse, communications, etc) 6,765

Storage Facilities (input materials, by-products, inventories and finished outputs) 9,710

B. Machinery

Production wells and brine delivery System 10,537

Trucks and loading machines 5,080

Harvesting and pumping Equipment(pipelines, pumps, etc) 9,167

C. Labor Construction

Wages for construction, engineering and consultants 13,361

D. Working Capital Construction

Importing and freight Tariffs 25,580

Working Capital 10,185

E. Other expenses Construction

Start up expenses (hiring, marketing, legal representation, multiple stakeholders meetings) 3,654

Land and transaction costs (municipal, government, etc) 1,797

Other infraestructure costs and contingencies 4,111

F. Mining closure and other expenses 2,800

Total project Investment 273,519 2,800

(*) Calculations based in 2009 nominal prices and bi-annual labor wages

Table 7. Cultivable and resting land

Source: Crespo, et.al (2001).

Because of the unavailability of detailed information, costs were taken from the previously mentioned

quinoa harvesting studies. The total cash flows model of the irrigation project for each economic actor was

estimated by the incremental change of the net benefits/costs with and without the irrigation project. In other

words, the quinoa famers, the private quinoa industry and the government cash flows with the irrigation project

were subtracted from the cash flows without the project. Subsequently, adding the three net benefits represented

an overall estimate of the possible benefits of the San Geronimo River irrigation project on quinoa harvesting and

production. Table 8. Estimated investment Costs for Quinoa Farmers per cycle

Source: adapted from Crespo, et.al (2001) and JICA (2002).

Table Land size per Household in Nor Lipez province

Available land per household (Ha) % Families

Total property

01 to 10 39.5%

11 to 20 46.5%

21 to 30 11.5%

31 to 40 2.5%

Cultivable Land

2.1 to 3 12.0%

3.1 to 4 40.5%

4.1 to 5 37.5%

5.1 to 6 7.5%

6.1 to 7 2.5%

Resting Land

05 to 10 87.8%

15 to 20 9.7%

25 to 30 2.5%

Table Estimated Investment Costs for Quinoa Farmers2002 2009

US$ per Ha US$ per Ha

A. Labor

Soil preparation and Seeding 54 78

Harvest 41 59

Cultural ritual work 17 24 -

B. Input materials -

Tools and materials 16 24

Seeds 5 7

Guano - 5 tons (transportation included) 38 55

No Irrigation assuming 100 m3 per cycle (*) 85 123

Other services inputs 11 16

Irrigation assuming 100 m3 per cycle 13 19 -

C. Machinery and transportation -

Crawler service fee 18 26

Harvest transportation 8 11

Threshing 13 18 -

D. Other expenses -

Organic Certification 16 22

Total Investment Costs 333 483 (*) water is get from water trucks 3 times per cycle, thus it including trasportation and fuel costs

Both project cash flow models were used to evaluate how the benefits/costs and the net present values

(NPV) may evolve under various circumstances, by developing possible scenarios of how each of the main

parameters may change over time. Hence, sensitivity analysis was conducted on input and output prices each of

the projects. This analysis was done using both discounted benefits and costs for each model. Analysis with the

model was restricted to fluctuations of real prices of the inputs and outputs (e.g. Lithium Carbonate prices,

farmers’ quinoa price, yield efficiency), and the discount factor to compute the NPV. With @RISK and Excel any

uncertain parameter situation was modeled using Monte Carlo Simulation with 10,000 iterations. The output of

the model was interpreted in the RESULTS section of this document.

In the lithium mining project, the following rates of growth were considered variable each year: Price of

Lithium Carbonate, Potassium Chloride and Boric Acid; Wage of Production Workers, Costs of Operating Materials,

Supplies and Fuels, Administrative and General Expenses, and Working capital . Because information is not

available to fit an appropriate distribution for any of those variables, a Pert Distribution will be assumed to

simulate the rates of growth per year. 18

For example, the real rate of growth for miners wages was expected to

follow a Pert Distribution with a minimum and maximum of -1% and 9%, respectively. The mean was set equal

5.9% which is the average percentage change of real wages in the bolivian mining sector from 1996-2008(INE,

2009 and UDAPE, 2009). 19

Figure 13. Cumulative ascending probabilities: a) % Rate of growth of miners’ wages (left plot) b) expected

Discount Rates (right plot)

Source: own plots based on UDAPE (2009) and Lopez, H. (2008).

18 The PERT distribution uses the most likely value (mode) and it is designed to generate a distribution that more closely

resembles realistic probability distribution by providing minimum and maximum values. Depending on the values provided, the

PERT distribution can provide a close fit to the normal or lognormal distributions. The shape parameter is calculated from the

defined most likely value. 19 Assuming that the mean % change of wages is a good estimator of the real level of miners wages from 1996-2008. Appendix

G shows the cumulative ascending probabilities for the rest of variables used in the cash flow model for the lithium mining

project.

-0.0

2

0.0

0

0.0

2

0.0

4

0.0

6

0.0

8

0.1

0

0.0

6

0.0

7

0.0

8

0.0

9

0.1

0

0.1

1

0.1

2

0.1

3

0.1

4

0.1

5

Figure 13 displays the cumulative ascending probabilities where y-axis shows the probability of a value

less than any x-axis value. Thus, the probability that the discount rate will be less than 9.5% is 0.4.

The annual percentage rates of growth of inputs and output prices, quinoa yield efficiency, and the

discount rate were considered variables in the quinoa irrigation project for every year. All of these parameters

were assumed to follow a Pert Distribution according to the reports provided on quinoa agriculture and production

(CAMEX, 2009; Crespo, et.al, 2004; JICA, 2002; Soraide et al., 2005; Victorio, 1999). For instance, the annual rate of

change of the price paid to the farmer was assumed to have a mean of 4%, a maximum of 15% and minimum of -

5% (Crespo, et.al, 2004; Soraide et al., 2005). Figure 14 illustrates the expected annual rates of change of the price

paid to the farmer per ton of quinoa, and Figure 15 the annual quinoa yield with and without the irrigation

project.20

Figure 14. Cumulative ascending probabilities of %Rate of growth Farmers and export prices to USA market

Source: own plots based on CAMEX (2009), Crespo, et.al(2004) and Soraide et al.(2005).

Figure 15. Cumulative ascending probabilities of Quinoa yield efficiency with/without irrigation (Tons/Ha)

Source: own plots based on Crespo, et.al (2004) and Soraide et al.(2005).

20

Appendix H shows the cumulative ascending probabilities for the rest of variables used in the cash flow model

for irrigation project.

-0.0

2

0.0

0

0.0

2

0.0

4

0.0

6

0.0

8

0.1

0

0.1

2

0.1

4

5.0% 90.0% 5.0%

0.4471 0.7001

0.0

0.2

0.4

0.6

0.8

1.0

Quinoa yield with no irrigation project (ton/Ha)

Pert(0.35,0.59,0.76)

Minimum 0.3500

Maximum 0.7600

Mean 0.5783

Std Dev 0.0770

5.0% 90.0% 5.0%

1.081 1.907

0.0

0.2

0.4

0.6

0.8

1.0

Quinoa yield with irrigation project (ton/ Ha)

Pert(0.76,1.55,2.1)

Minimum 0.7600

Maximum 2.1000

Mean 1.5100

Std Dev 0.2514

In the absence of any inductive technique research to value irrigation water, the output of the quinoa

irrigation model is a computation of NPV of a simple farm crop cost and return budget of net return for every

household crop per year, which assumes a specific input mix and product yield (Young, 2005).

PART IV- RESULTS

IV.1 Initial Scenario

a ) Lithium Mining Project

Based on technical information about lithium mining operations, costs and timing, the author was able to

construct cash flows for the 23-year project from a private company perspective. The economic, financial, and

technical parameters were the starting point to obtain the annual operating plan and the cash flows of the mining

plant. The initial parameters used for the model are shown in the Appendix A and detailed information on the

annual operating plan is presented in the Appendix B. The initial results of the cash flow model demonstrated that

lithium mining is a profitable activity. The cash flow profile of the project with the initial conditions can be seen in

Figure 16 where the NPV of the Net Cash Flows (NCF) was 421,585 (Th. US$), consistent to the cash flow model

profile of the mining project. Similarly, the discounted cash in and cash out flows to equity for the 23-year Lithium

Mining Project are presented in Figure 16.

Figure 16. Estimated Cash Flows to Equity (Th. US$)

The declining shape of the discounted cash flows was expected due the effects of the chosen at 10%

discount rate per year for this phase. Because the NCF were positive from first year of operations (year 2) until the

mine closure, the initial investment payback period approximately occurred in the fifth year.

-

50,000

100,000

150,000

200,000

250,000

300,000

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Discounted CASH IN at 10% Discounted CASH OUT at 10% Total CASH IN Total CASH OUT

The cash model used here assumes 1% increase in LCE real price per year, while wages and general

expenses were assumed to rise 5.9% and 3.3%, respectively (INE, 2009 and UDAPE, 2009)21

. As it can be seen in

Figure 17, the majority of the operating costs derived from operating materials, supplies, fuels22

and labor. In

average, 63% of the operating expenses were materials, supplies including fuels and transportation, while labor

represented 10%.

Figure 17. Estimated Operating Costs Break Down(Th. US$)

In terms of average sales, LCE represented nearly 60% and Potassium Chloride 38% of total sales during

the 23-year project. The obvious result at sales is the direct dependence on market prices. Though the private

lithium mining is going to assume the capital cost and risk of the project, it will still generate positive NPV.

Additionally, the cash model calculated the income and exporting taxes incurred by the private company.

The cash flow model for the government perspective was based on 35% of income taxes and 4.5% of exporting

taxes and royalties (COMIBOL, 2009). Figure 18 provides initial estimates of income and exporting taxes that the

government could receive during mining operations.23

21

Labor from the mineral and private sector. Assuming that the mean % change of wages from 1996-2008 is good

estimators for both sectors. 22

Maintenance materials, chemicals, flotation reagents, mobile equipment supplies, etc; transportation expenses

(fuels, loading, rail, trucks). 23 Refer to Appendix C and D for detailed information on how income and exporting taxes were calculated.

$-

$10,000

$20,000

$30,000

$40,000

$50,000

$60,000

A. Operating Materials,

Supplies and Fuels

B. Labor C. Working Capital

Operating (Cash)

D. Other expenses

Operating

Exporting Expenses

Transaction costs and Insurance

Operations (Freight, transportation and

packing) and Wages

General Administrative Expenses

Operating and Maintenance Labor

Other Production costs and contingencies

Transportation Costs (loading, rail, trucks, etc)

Maintenance Materials

Fuels (plants, power generation and utilities)

Operating supplies and Materials

Figure 18. Estimated Taxes revenues flows for the government (Th. US$)

It is clear that the lithium mining project could provide an important inflow of tax-income to Bolivia,

ultimately representing the potential benefits that the Salar de Uyuni basin might provide if lithium (and

derivatives) is extract. According to the initial results of this model, the NPV of the tax-revenues was 394,662 (Th.

US$) at 10% discount rate.

b ) Quinoa Irrigation Project

Today 161 households, located 20 Km west of the Rio San Geronimo (Figure 11), harvest 612 hectares of

quinoa crops each year without irrigation (Project “COLLCHA K”; Water Ministry of Bolivia, 2008; PROAGRO/GTZ). In

order to evaluate the irrigation project net present benefits, the total cash flows model of the irrigation project

was estimated by the incremental change of the cash flows with and without the project for: a) quinoa famers, b)

private quinoa industry, and c) government. Those cash flows were added to obtain the possible estimated

benefits that the Salar de Uyuni basin provided.

First, a set of parameters were extracted based on the literature found on quinoa harvesting and past

irrigation projects conducted in the same region. The initial set of parameters for the cash flow models are

described in Appendix E. Second, a detail spreadsheet of annual quinoa harvesting, production, costs, consumption

and sales were developed to imitate an operating plan for the next 23-years (Appendix F). Third, quinoa famers

and private quinoa industry cash flows were estimated based on historic patterns on domestic and exporting sales,

real prices, taxes, and cost structures. The results from each economic actor cash flows were computed separately.

-

9,000

18,000

27,000

36,000

45,000

54,000

63,000

72,000

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Discounted Taxes-Income at 10% Total Taxes

Since the quinoa harvesting in the Salar de Uyuni basin is mainly focused on the domestic and

international markets24

, both quinoa farmers and exporting industry directly benefit from this project. Focusing

only inside the quinoa irrigation project scope, Figure 19 illustrates that the project incremental NCF is positive for

farmers and quinoa production industry perspective, but negative for the government during the loan payment

period, followed by positive NCF afterwards.

Figure 19. Estimated Incremental Net Cash flows from each perspective (Th. US$)

Farmers direct input costs decreased (i.e water) nearly 40% because they would not need to buy water

from water tankers anymore. Also, quinoa farmers and industry estimated sales increased by 120%. This was

expected, and is consistent with assumption of increasing crop yield as a result of the irrigation project. On the

other hand, the government domestic sales/income taxes (14%) and exporting fees (1.5%) were offset by the loan

amortization payments during the first 10 years.

The initial model estimated that the 23-year quinoa irrigation project would have a NPV of 9,459(Th. US$).

Yet, assuming that the project can repeated many times in the future, the estimated NPV is now 10,784 (Th. US$)

at 10% discount rate. The Salar de Uyuni basin economy development would definitely increase with the project,

specially the quinoa farmers.

IV.2 Preliminary project selection

Based on the preliminary results discuss above, the lithium mining project had greater benefits in present

US$ values for the Salar de Uyuni basin. Even after subtracting the opportunity cost of not conducting the quinoa

irrigation project, NPV is still enormous (383,878 Th. US$) relative the economy of the study area. From the

24

Crespo et.al (2001) and Soraide et.al (2005) reported that approximately 63% (average) of the quinoa harvested in the Salar

de Uyuni Basin is exported to USA , Europe, Peru and Ecuador each year. While domestic and farmer consumption represent

27% and 18%, respectively.

(600)

(300)

-

300

600

900

1,200

1,500

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

Net Cash Flow to Quinoa Farmers Net Cash Flow to Private Quinoa Production Sector

Net Cash Flow to Government

perspective of the Salar de Uyuni basin economy, the Lithium mining would have to be developed despite

foregoing benefits of the quinoa irrigation project.

It is not difficult to choose the lithium mining project based on the economic concepts presented here,

however, the preliminary model has a set of fixed assumptions that certainty will change over time and the value

of both projects. Thus, sensitivity analysis was conducted on the parameters of the model to compare the results

for both projects. Those results are described in the next section.

IV.3 Sensitivity Analysis

a ) Lithium Mining Project

The sensitivity analysis performed on the cash flow model tried to reduce the uncertainty of the real

prices growth rates in the future, as well as the gross benefits estimation of the project from both perspectives.

The mean expected NPV from the private equity perspective was 691,180 (Th.US$) and St. Dev of 180,553(Th.US$),

whereas the government’s expected NPV mean was 560,344(Th.US$) and St. Dev of 109,315 (Th.US$). Only the

latter perspective constituted the gross benefits provided by the Salar de Uyuni basin after 23-year of lithium

mining. Figure 20 and 21 show the frequency distribution of the NPV from both perspectives after the model was

run 10,000 times.

Figure 20. NPV frequency distribution- Lithium Mining Company (MM US$)

20

0

40

0

60

0

80

0

10

00

12

00

14

00

16

00

Figure 21. NPV tax-revenues of Government frequency distribution- (MM US$)

Obviously, the expected NPV from both perspectives are linked because of the income and sales taxes

scheme. The government tax revenues will increase if the lithium mining sales are not offset by the operating costs

or debt, which according to the model, it will never happen because the expected NPV is never negative.

Not all the parameters equally impacted the cash flow model and it is important to determine which

parameters had greater impact on NPV estimates. The regression of the NPV estimates against the parameters of

the model provides that information. Figure 22 illustrates the parameters that had impacted the NPV estimates of

the lithium mining benefits using either a multivariate stepwise regression analysis, or a rank order correlation

analysis.25

Figure 22. Coefficient estimates of NPV vs. model parameters

25

The excel Add-in @Risk automatically performed both analysis on the output variables and their associated

inputs in the model. The coefficients are ranked by their impact on the output using Multivariate Stepwise

Regression and Rank Order Correlation. Refer to Appendix G for more detail on the coefficient ranking procedure.

20

0

40

0

60

0

80

0

10

00

12

00

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

Although the Price of Lithium Carbonate (annual rate of growth) positively impacted the NPV estimates in

every year, the highest impacts occurred during the first year. After that, the impact gradually declined over the

years. This tendency was determined by the discount factor which has an obvious impact on the expected NPV

estimates. Figure 22 and 23 confirmed that well known fact. Approximately, real discount rates between 7 to 8%

tend to place weight in both first and second halves of the project’s cash flows declining over time estimating

higher NPV. On the contrary, real discount rates greater than 8% estimated lower NPV because more weight was

placed on the first half of the project life.

Figure 23. Discount rate vs. Expected NPV

b ) Quinoa Irrigation Project

In examining the sensitivity analysis performed on the incremental cash flow model for the 23-year

quinoa irrigation project, it is important to understand the estimated NPV for each perspective. The expected NPV

summary statistics of each perspective are presented in Table 9.

Table 9. Estimated Incremental NPV- Quinoa Irrigation Project

The estimated expected NPV for the 23-year quinoa irrigation project is 8,824 (Th.US$) and standart

deviation of 5,943 (Th.US$). However, assuming that the project can repeated many times in the future, the

infinite estimated NPV represents the estimated benefits that the Salar de Uyuni would provide if the quinoa

irrigation project is chosen.

Figure 24. Frequency distribution NPV (Th. US$) of Infinite 23-year Quinoa Irrigation Projects

Given the cash flow and the sensitivity analysis provided good estimates for future costs and benefits of

the quinoa irrigation project, there is 90% probability that the expected NPV of the benefits of quinoa irrigation

Project over the long run will be between -1,105 and 21,971 thousands US$.

As can be seen in Table 9, the expected NPV of the irrigation project which directly benefits the Salar de

Uyuni basin is similar as the previously computed. Yet, the variability of this figure is outstanding and resulted from

the nature of agriculture. Negative NPV may occur primarily caused to the intrinsic dependent on natural process

and random weather patterns. For this model, that uncertainty was introduced by the yearly quinoa yield

Expected Incremental NPV (Th. US$)

Mean Median Std Dev Min Max

Farmer NPV 6,109 6,002 1,706 1,523 13,300

Industry NPV 3,528 3,552 4,223 (9,460) 19,503

Government NPV (644) (644) 222 (1,324) 208

NPV Salar de Uyuni 23-year -Irrigation project 8,824 8,799 5,943 (8,593) 29,502

NPV Salar de Uyuni -Infinite Irrigation projects 10,183 9,989 6,989 (10,114) 36,564

-15

00

0

-40

00

70

00

18

00

0

29

00

0

40

00

0

efficiency. Furthermore, the farmer’s NPV was never negative probably reflecting that no matter how low the yield

return was, farmers will always sell quinoa to supply some part of the local market and own consumption(food

security). However, low yield efficiencies directly impact the well being of 161 families. That is, if the “median”

and “min” scenario (Table 9) were assumed to happen, the average daily income would drop from $ 1.0 to $ 0.2

per person.26

The quinoa industry not only relies on the yield return, but also on the domestic and exporting prices.

Industry negative expected NPV revealed that if low yield efficiency occurred, not enough supply would negatively

affect the industry performance, established by 2 to 4 operating companies in the region (Soraide et.al., 2005).

Based on the previously discussed, the most significant drivers of quinoa irrigation project are captured

on the regression coefficients of the NPV vs. model parameters.

Figure 25. Coefficient estimates of expected NPV vs. model parameters

Figure 25 confirms that the most important drivers are quinoa yield efficiency, discount rate, inputs

materials, farmer and exporting prices rates of change. Thus, if any substantial cost reduction in the quinoa

production chain is to be achieved, it must be accomplished by a decrease in tools, materials, guano and seeds at

the farmer’s level (input materials). Also, the quinoa agriculture performance is very important for the whole

economy in every year.

26

An average of 201 households and 3.5.people per household were assumed in the Salar de Uyuni Basin in 23-year project

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

IV.4 Project selection

The cash flows model used in this study provided an approximation of the benefits that each project

would provide after reducing the uncertainty. The project with the higher NPV (after the sensitivity analysis) was

the lithium mining; however, the estimated foregone benefits of the irrigation project should be considered.

Figure 26. Density distribution of NPV for both projects (MM US$) –

Figure 26 displays that the distribution of the estimated NPV for both projects in thousands of US$ will

never overlap. In other words, the minimum possible NPV of the lithium mining will never exceed the NPV of the

quinoa irrigation project.

-20

0 0

20

0

40

0

60

0

80

0

10

00

12

00

Summary Statistics of the NPV for both projects in (Th. US$)

Mean Median Std Dev Min Max

Lithium Mining 560,344 550,471 109,316 232,021 1,149,360

Quinoa Irrigation project 10,183 9,989 6,989 (10,114) 36,564

PART V- CONCLUSIONS AND DISCUSSION

Since humans are an integral part of ecosystems, the ecosystem services are the benefits people obtain

from ecosystems (Daily, 1997). This study identified and described five ecosystems services in the Salar de Uyuni

basin: 1) Recreation, Culture and landscape; 2) Water resources, 3) Biodiversity, and 4) Agriculture and Animal

Husbandry. Today, all of them are very important for local people subsistence and well-being on a daily basis,

whereas minerals could be the key for regional economic development in the future (e.g. Improvements on

education and health systems, road infrastructure). Primarily, the Salar de Uyuni basin is an exceptional yet hostile

environment due to the extreme shortage of water resources.

The constituents of well-being are situation-dependent reflected on local geography, culture and

ecological circumstances (Daily, 1997; Pagiola et.al, 2004). Also, well-being is at the opposite end of a continuum

from poverty, which has been defined as “a pronounced deprivation of well-being” (Millennium Ecosystem

Assessment, 2005). Human well-being in the Salar de Uyuni has multiple components including basic material for

living, health, social relations and food security; however, their well-being is also affected by poverty, globalization,

trade, market, legal and policy framework. In any case, people in the study area are heavily dependent on the

services provided by this ecosystem, especially, water resources.

Other characteristics identified in this study are the high degree of social complexity (high levels of

poverty and legitimate demands from the indigenous peoples, low concentration of government development

initiatives, possible large-scale mining, rapid growing tourism industry); and lack of scientific information for better

understanding of the relationships with regard to the services this ecosystem provides. Unfortunately, the author

faced a constant information challenge throughout this research.

Moreover, there are a perceptible conflicts between users of the ecosystem —particularly the local

people, tourism operators and mining companies— firstly over water availability and secondly over equality of

access to the emerging economic opportunities. Again, without scientific knowledge it is impossible to identify the

magnitude of the threads and determine the right courses of actions to minimize those conflicts. Water and its

competing uses should be recognized and estimated as an economic good, so it could be managed more efficiently

and use more equitable(Young, 2005). Therefore, if we allow capacity of ecosystems to provide these services to

deteriorate without knowing it, sooner or later the Salar de Uyuni’s people may suffer.

From this primary assessment, apparently, the only feature in the Salar de Uyuni basin that has the world

talking today is the lithium resources.

Lithium carbonate is the key property in today’s battery technology of choice – lithium-ion. If the

transportation sector plans to be moving away from oil based transport and towards hybrid and electric vehicles,

lithium supply is the key factor (Evans, 2008; Tahil, 2007; Ebensperger et al., 2005; Zuleta, 2009). While the Salar

de Uyuni in Bolivia holds the largest single source of lithium in the world, the accuracy of the statement has been

challenged, nonetheless, the point remains that the resource is huge (Evans, 2008 ; Ebensperger et al.,2005; Tahil,

2007; Zuleta, 2009). Risacher (1991) revised the reserve estimate for the upper salt for the whole salar, covering

nearly 7,000 thousand hectares, and calculated 8.9 millions tones of Li in a brine with an average grade of 542

mg/lt (0.045% Li) 27

. Also, Risacher made a separate reserve calculation for the delta of the Rio Grande (240 km2)

with grades that excesses 1,000 mg/lt.

Bolivia’s national mining department, (COMIBOL) has been given the responsibility to bring Uyuni to

production of LCE plant within the next three years (private or state own capital investment). As with many highly

valued resources, lithium mining will bring a trade off with the environment in Salar de Uyuni which has a highly

sensitive biodiversity due to the arid nature of the climate (Hurlbert, 1979; Messerli, et al., 1997; Molina Carpio,

2007). Yet, no government or private agency has considered the economic trade-offs of altering the natural

environment of the salar (Curi, 2009; Lopez Canelas, 2009; Zuleta, 2009). In particular, no study has assessed the

magnitude of differences between probable benefits of the lithium extraction and the foregone benefits of the

next best use of freshwater resources from Rio San Geronimo (i.e. quinoa agriculture).

Not only mining lithium has become a strategic element with promising source of income for the Salar de

Uyuni basin, but also the fresh water resources converted into quinoa crop irrigation. This study built a cash flow

model for both competing uses of fresh water from the Rio San Geronimo, to estimate the gains and losses from

the economy as a whole derived from implementing one of the projects. Based on the relatively simple cash flow

model presented here, the lithium mining project had greater benefits in present US$ values for the Salar de Uyuni

Basin. Even after subtracting the opportunity cost of not conducting the quinoa irrigation project and reducing the

uncertainty of the model parameters, NPV is still positive and large relative the economy of the study area.

Nevertheless, the cash flow model and parameters presented in this study are just a crude illustration of

the extremely complex social and economic interactions of an economic system. In one hand, this study brings

together known figures reported in most cases during the early 2000 and in other cases up to 2007. On the other

hand, the estimates of this simple model may be bias associated with tax distortions, cost of capital, exchange

rates, social discount rates or other market distortions. Also, the model relies heavily on prices changes from year

to year which are even more uncertain if projected more than 15 years from now.

Until recently, lithium went primarily into ceramics and glass. Now batteries make up one-fifth of the

world’s end-use market for the mineral. But shortages could stop an emerging industry or dramatically reshape it

27

Tahil(2007), Ebensperger et al.(2005) and Evans(2008) and others commentators on lithium reserves and resources including

the United States Geological Survey (USGS) have quoted the reserves estimates in the Salar de Uyuni as 5.5 million tonnes of

lithium (Li) in a brine grading 423 mg/lt (0.035%).

within a decade. Evans (2008) and US Geological Survey offer a more conservative estimate, forecasting that

demand will begin to drive lithium prices up in the next 10 to 15 years.

Higher lithium prices could also give the nascent U.S. battery industry a steeper climb to the top. The U.S.

consumes more lithium than any other country, despite having only 760,000 tons of the world’s 13.8 million tons

of identified lithium resources (those of known quantity, quality and grade), according to the U.S. Geological

Survey. While most U.S. lithium imports now come from Chile and Argentina, China has brought new supply online

in the last few years. Additionally, it could be argue that the larger the production Bolivia throws into the market,

the larger the reduction in lithium prices that Lithium mining company will have to face as a result.

Finally, it is important to highlight that the reserves of lithium alone will not define the future of industrial

projects, nor the economic development in the region and the country, what matters is a timely and fine plan to

participate in foreign markets as soon as possible. However, the distributional effects of both projects have to be

carefully assessed according to the ecosystem services profile and the cash flow model presented in this master

project.

PART VI- REFERENCES

Abuelsamid S. (July 6, 2009). Renault-Nissan and EDF to start 100 EV field test in Paris in 2010. Better Place.

Retrieved July 18, 2009, from www.betterplace.com/news/

Arroyo, Kalin M. T., Squeo, E A., Armesto,J. J., and Villagran, C.1988.Effects of aridity on plant diversity in the

northern Chilean Andes: Results of a natural experiment. Ann. Missouri Botanical Gardens, 75: 55-78.

Bishop, R. Bingham, G. & et al. 1995. Issues in ecosystem valuation: improving information for decision making.

Ecological Economics Journal. 14, 73-90.

Cámara de Exportadores de Bolivia-CAMEX(Bolivian Exporting Bureau). 2009. Cuadernos Sectoriales: La Quinua.

La Paz- Bolivia. Retrieved July 29, 2009 from www.camexbolivia.com

Champ, Patricia A. Boyle, Kevin J. Brown, Thomas C.(2003). A primer on Nonmarket Valuation. The economics of

Non-Market Goods and Resources. Kluwer Academic Publishers. 2003 Edition. Chapter 12, pp 445-500.

COMIBOL-Corporación Minera de Bolivia (National Mining Department) 2008. COMIBOL Annual Report. Retrieved

July 6, 2009, from www.comibol.gov.bo/comibol.html

COMIBOL-Corporación Minera de Bolivia (National Mining Department) and Dirección Nacional de Recursos

Evaporíticos. 2009. Press Releases

:www.evaporiticosbolivia.org/indexi.php?Modulo=NotasPrensa&Opcion=LstGeneral and personal

communication and e-mail exchanges May-July, 2009.

Conservation International.2007.Tropical Andes. Biodiversity Hotspots. Retrieved July 25, 2009, from

http://www.biodiversityhotspots.org/xp/hotspots/andes/Pages/default.aspx

Crespo, F., E. Brenes, and K. Madrigal. 2001. El cluster de la quinua en Bolivia: Diagnóstico competitivo y

recomendaciones estratégicas (The Quinoa Cluster in Bolivia: A Competitive Diagnostic and Strategic

recommendations). Proyecto Andino de Competitividad-CAF. INCAE. La Paz, Bolivia.

Curi, Marianela, head director of Sustainable Forest Management Project in Bolivia, BOLFOR. The Nature

Conservancy. Personal communication and various e-mail exchanges July-August, 2009.

Daily, Gretchen C. 1997. Nature's services : societal dependence on natural ecosystems. Washington, DC: Island

Press. ISBN: 1559634758.

Ebensperger, Arlene. Maxwell, Philip. Moscoso, Christian. 2005. The lithium industry: Its recent evolution and

future prospects. Australian School of Mines, Curtin University of Technology. Perth, WA 6845, Australia.

Eduardo Avaroa Reserve, Bolivia (REA). Characteristics of REA National Park .Accessed Jun 29, 2009, from

www.boliviarea.com/index.php?option=com_content&task=view&id=5&Itemid=6

Ellingson, Lindsey. Seidl, Andrew. 2006. Comparative analysis of non market valuation techniques for the Eduardo

Avaroa Reserve, Bolivia. Ecological Economics.

Escobari, Jorge. 2003. Problemática Ambiental en Bolivia (Environmental Challenges in Bolivia). Unidad de Análisis

de Políticas Sociales y Económicas-UDAPE (Unit of Policy, Social and Economic Analysis) La Paz Bolivia.

Retrieved May 8, 2009, from www.udape.gov.bo

Evans, R. Keith.2008. An Abundance of Lithium. Retrieved May 10, 2009, from

www.worldlithium.com/An_Abundance_of_Lithium_1_files/An%20Abundance%20of%20Lithium.pdf

Fricker, H. Borsa, A. Minster, B. Carabajal, C. Quinn, K. Bills, B. 2005. Assessment of ICES at performance at the

Salar de Uyuni, Bolivia. Geophysical Journal International. VOL. 32, L21S06.

Friedman-Rudovsky, Jean. 2009. For Lithium Car Batteries, Bolivia Is in the Driver's Seat. Time Magazine.

Galbraith K. February 17, 2009. Obama Signs Stimulus Packed With Clean Energy Provisions. The New York Times.

Garrett and Martin Laborde. 1983. Salting Out Process for Lithium Recovery. Sixth International Symposium on

Salt, Salt Institute. Vol. II.

Gartner J. July 7, 2009. Lithium China Gearing Up for EV Dominance. Reuters.

Garzón, Dionisio J. April 23, 2009. Minería, sueños y realidades (Mining, dreams and realities). La Razon Editorial

(independent Bolivian Information Agency).

Harbeger, Arnold. Jenkins, Glenn. Cost-Benefit Analysis of Investment Decisions. University of Chicago. 2003

Edition. Chapters 3-10.

Hurlbert, Stuart. Keith, James O. 1979. Distribution and Spatial Patterning of Flamingos in the Andean Altiplano.

University of California Press on behalf of the American Ornithologists' Union. Vol. 96, No. 2, pp. 328-342.

Instituto Nacional de Estadística- Bolivia (INE- National Statistic Institute)-Unidad de Análisis De Políticas Sociales y

Económicas (UDAPE-Unit of Policy, Social and Economic Analysis). Potosí: Estadísticas e Indicadores de

Pobreza según Sección Municipal, 200(Poverty indices). Cuadro Nº 3.06.02.08.

Instituto Nacional de Estadística- Bolivia- INE (National Statistic Institute). 2009. GDP per economic sector annual

report.

Instituto Nacional de Estadística- Bolivia-INE, Banco Central de Bolivia-BCB y Viceministerio de Turismo de Bolivia

-VMT. (2009). Encuesta Gasto del Turismo Receptor y Emisor: 2007(Survey on the Receptor and Emisor

Tourism Spending in Bolivia: 2007)

Jacobsen, S.-E. and A. Mujica. 2001. Avances en el conocimiento de resistencia a factores abióticos adversos en la

quinua (Chenopodium quinoa Willd).. Memorias, Primer Taller Internacional sobre Quinua –Recursos

Geneticos y Sistemas de Producción, 10–14 May 1999.Universidad Nacional Agraria La Molina, Lima, Peru.

Jacobsen, S.-E. and Z. Portillo Edition

Jennifer Rietbergen-McCracken and Hussein Abaza.2000. Environmental valuation: a worldwide compendium of

case studies. London : Earthscan. # ISBN: 1853836958

Japan International Cooperation Agency (JICA-Agencia de Cooperación Internacional del Japón).2002.Proyecto de

mejoramiento del proceso industrial y comercialización de Quinua (Improvements on the Industrial and

Commercialization processes of Quinoa). MAGER Publishers.

La Razón (Jun 14, 2009). Economic Supplement. Retrieved Aug 9, 2009, from

http://www.la-razon.com/versiones/20090808_006813/nota_257_858225.htm

Lawrence U. January 17, 2009. Lithium-Ion Batteries Could Become Cheaper. The New York Times.

Liberman, M. 1995. Los Bosques de Polylepis Tarapacana en el Parque Nacional del Nevado Sajama(Polylepis

Tarapacana Forests in the Sajama National Park). II International Symposium on Sustainable Mountain

Development. Field guide, Bolivia, pp.61-69.

Lopez Canelas, Elizabeth.2009. Bolivian Environmental Defense League (FOBOMADE). Personal communication

via E-mail.

Lopez, Humberto.2008. The Social Discount Rate: Estimates for Nine Latin American Countries. The World Bank

Latin America and the Caribbean Region Offce of the Chief Economist.

Mc Mahon, G., Evia, J.L., et. al. 1999. An Environmental Study of Artisanal, Small, and Medium Mining in Bolivia,

Chile, and Peru. Technical Paper N° 429, World Bank. Retrieved from www.naturalresources.org.

Millennium Ecosystem Assessment. 2005. Concepts of Ecosystem Value and Valuation Approaches. Island Press.

Chapter 6.

Meridian International Research (MIR). 2008. The Trouble with Lithium 2: Under the Microscope.

www.meridian-intres.com/Projects/Lithium_Microscope.pdf

Messerli, Bruno. Grosjean, Martin, Vuille, Mathias. 1997. Water Availability, Protected Areas, and Natural

Resources in the Andean Desert Altiplano. Mountain Research and Development. Vol. 17, No. 3, The

United Nations University. Managing Fragile Ecosystems in the Andes, pp. 229-238. Published by:

International Mountain Society Stable.

Molina Carpio, Jorge. 2007. Agua recurso hídrico en el Sudoeste de Potosí (Water hydric resource in the south-

east of Potosi). Comité para la Gestión Integral del Agua en Bolivia Coordinación General: Centro de

Estudios Superiores Universitarios Universidad Mayor de San Simón - CESU UMSS. Publish by Foro

Boliviano sobre Medio Ambiente y Desarrollo-FOBOMADE.

New7Wonders of Nature. 2009. Nominees: South America. Accessed Jul 12, 2009, from

www.new7wonders.com/nature/en/nominees/southamerica/c/SalardeUyuniLake/

Oliveira Costa, J .P. 1993. Programa Integrado de Conservación Ambiental y Desarrollo Sustentable de la

Cordillera de los Andes(Environmental Conservation and Sustainable Development in the Andes

Mountains). Anexo 2. Instituto Universitario de Conservación Nacional -IUCN, La Paz, Bolivia, pp. 44-48.

Pagiola, S., Konrad Von Ritter, Bishop, J. 2004. Assessing the Economic Value of Ecosystem Conservation. The

World Bank Environment Department. Environment Department Paper No.101.

Pavlovic-Zuvic, P. & et al. 1983. Recovery of Potassium Chloride, Potassium Sulfate and

Boric Acid from the Salar de Atacama Brines. Sixth International Symposium on Salt, Salt Institute. Vol. II.

Piers Nicholson and Keith Evans. 1998. Evaluating New Directions for the Lithium Market. Journal of Minerals

Economics.

Quinoa Real is a producers / retailers / distributors. Information extracted from webpage at

www.quinoareal.com.br.

RAMSAR , The Convention on Wetlands.(2009). http://www.ramsar.org/index_about_ramsar.htm

Risache, François and Fritz, Bertrand. 1991. Quaternary geochemical evolution of the salars of Uyuni and Coipasa,

Central Altiplano, Bolivia. Chemical Geology. Vol 90, 211-231.

Roskill Information Services. 2007. Retrieved Jun 8, 2009, from http://www.roskill.com/reports/lithium

Selvaradjou, S-K., L. Montanarella, O. Spaargaren and D. Dent. 2005. European Digital Archive of Soil Maps

(EuDASM) - Soil Maps of Latin America and Caribbean Islands. EUR 21822 EN. Office of the Official

Publications of the European Comunities. Luxembourg.

Soquimich-SQM. 2008. Annual report. Retrieved May 28 , 2009, from

http://www.sqm.com/aspx/Lithium/Default.aspx

Soraide, David. Carvajal, Mirko.Claver Mamani, Pedro. Choque Marca, Willy. 2005. Estudio Linea Base 2001-2004,

Programa Quinua Altiplano Sur( Base Research 2001-2004, Southern Highlands Quinoa Program)

Fundacion Autapo-Educacion para el Desarrollo.

Tahil, William. 2007. The Trouble with Lithium Implications of Future PHEV Production for Lithium Demand.

Meridian International Research.

Torres Henriquez, Jorge, Ramirez, Maria Virginia. 2006. Gestión del Conocimiento en SQM Salar(Information

Management in SQM Salar). Tesis para optar al Grado de Magister en Gestion y Direccion de Empresas

(Thesis for Master's Degree in Business Management and Administration).Universidad de Chile. Facultad

de Ciencias Fisicas Y Matematicas. Departamento de Ingenieria Industrial.

Unidad de Análisis de Políticas Sociales y Económicas-UDAPE (Unit of Policy, Social and Economic Analysis). 2005.

Estructura del Sector Turismo En Bolivia.La Paz- Bolivia.

US Geological Survey.2008. Mineral Commodities Summary, Accessed May 23, 2009 from Duke University

Libraries.

http://minerals.usgs.gov/minerals/pubs/commodity/lithium/mcs-2008-lithi.pdf

Victorio, Giusti. 1999. Mejoramiento de las Tecnologías Tradicionales de Poscosecha de Quinua en el Altiplano

Boliviano (Improvements on the Traditional technology of Quinoa harvesting in the Bolivian Highlands).

Proyecto FAO-Poscosecha. La Paz- Bolivia.

Viñagrande, Honorato. 2001. La Maldición De La Riqueza y Las Ansias Desatadas por El Litio Boliviano( The

Resources Curse and the anxiety caused by the Bolivian Lithium). García y Morales, Enzarzados a Causa De

La Revuelta Indígena Capital Madrid. Monitor de Latinoamérica.

Warren, M. 2009. AUTOS: Volt Electric Cars Start Pre-Production. SPEEDtv. Retrieved Jul 1, 2009, from

www.automotive.speedtv.com/article/autos-volt-electric-cars-start-pre-production/

Water Ministry of Bolivia (Ministerio de Agua de Bolivia). 2008. Proyecto: Riego Colcha K( Colcha K Irrigation

Project). PROAGRO/GTZ. Retrieved Sept 1, 2009, from www.riegobolivia.org/proyectos

World Resources Institute. 2005. Watersheds of the World 2005. Earth Trends Data Tables: Water Resources and

freshwater Ecosystems.

Young, Robert A. Determining the economic value of water: concepts and methods. Washington, DC : Resources

for the Future, c2005. ISBN: 189185397X (hardcover : alk. paper).

Zuleta Calderon Juan Carlos. Ph.D. Independent lithium economics analyst based in Bolivia. Various e-mail

exchanges June-July, 2009.

PART VII- APPENDIX

APPENDIX A: Economic, technical, financial and tax parameters during annual operations

Source: COMIBOL, 2009; Ebensperger et al., 2005; Tahil, 2007; MIR, 2008; Mc Mahon et al., 1999; UDAPE, 2009;

INE 2009; Zuleta, 2009.

Table of parameters assumed during annual operations

A. Economic B. Technical

Prices (*) Output (***)

Initial Price of Lithium Carbonate (US$ per ton) 5,302 Initial Lithium Carbonate Production (tons) 23,000

Initial Price of Potassium Chloride (US$ per ton) 754 Initial Potassium Chloride Production (tons) 102,000

Initial Price of Boric Acid (US$ per ton) 576 Initial Boric Acid Production (tons) 6,500

Annual growth rates (**) Growth Rate of Lithium Carbonate Production (%) 1.0%

Growth Rate of Potassium Chloride Production (%) 1.0%

Price of Lithium Carbonate (%) 1.0% Growth Rate of Boric Acid Production (%) 1.0%

Price of Potassium Chloride (%) 1.0%

Price of Boric Acid (%) 1.0% End of the year Inventories and materials in process (% output)

Lithium Carbonate 15%

Wage of Production Workers( %) 5.9% Potassium Chloride 17%

Price of Operating Materials, Supplies and Fuels (%) 4.7% Boric Acid 10%

Administrative and General Expenses (%) 3.3%

Working capital (%) 3.7%

C. Financial D. Taxes

Accounts Receivable (% of Sales) Income Tax Rate (%) 35.0%

Lithium Carbonate 20% Exporting fees and other taxes (%) 4.5%

Potassium Chloride 8%

Boric Acid 5% (***) Following 2000-2008 SQM production trend and Technical information from (COMIBOL, 2009)

Accounts Payable (% of Total Direct Labor and Operating Materials&Fuels ) 10%

Depreciation Rates (%)

Plants, Ponds , Buildings and Storage 10%

Machinery, trucks, pumping equipment and other infraestructure 15%

Discount rate private investment 10.0%

Discount rate Government 10.0%

(*) Average nominal Prices (FOB) from SQM (2008) and Roskill (2008)

(**) Expected prices growth ; Average annual change in Consumer Price Index of Bolivia and growth of real wages from 1997-2008 INE&UDAPE (2009)

1

AP

PE

ND

IX B

: Li

thiu

m M

inin

g E

stim

ate

d O

pe

rati

ng

Pla

n

So

urc

e:

CO

MIB

OL,

20

09

; E

be

nsp

erg

er

et

al.

, 2

00

5;

Ha

rbe

ge

r &

Je

nki

ns,

20

03

; Z

ule

ta,

20

09

.

Ta

ble

Est

ima

ted

An

nu

al P

rod

uct

ion

01

23

45

67

89

10

111

21

314

151

61

718

19

2021

22

2009

201

020

112

012

2013

201

420

152

016

2017

201

820

192

020

2021

2022

202

320

24

2025

2026

202

720

2820

29

203

02

031

A.

Ou

tpu

t Q

uan

titi

es

and

inve

nto

rie

s (t

on

s)

Pro

du

ctio

n o

f Li

thiu

m C

arb

on

ate

23

,000

23,2

30

23

,462

23,6

97

23

,93

4

2

4,1

73

24

,41

5

2

4,6

59

24

,90

6

2

5,1

55

25

,40

6

25

,66

0

2

5,9

17

26,1

76

26

,43

8

26

,702

2

6,9

69

27

,23

9

27

,511

27,

787

Pro

du

ctio

n o

f P

ota

ssiu

m C

hlo

rid

e

102

,000

10

3,0

20

104

,050

10

5,0

91

106

,14

2

107,

203

1

08,2

75

10

9,3

58

110

,45

1

111,

556

1

12,6

71

11

3,7

98

114,

936

116

,08

6

11

7,2

46

118

,419

119,

603

1

20,7

99

1

22,0

07

123,

227

Pro

du

ctio

n o

f B

ori

c A

cid

6,

500

6,5

65

6,

631

6,6

97

6,

764

6,8

32

6,

900

6,9

69

7,

039

7,1

09

7,

180

7,2

52

7,

324

7,39

8

7,4

72

7,

546

7,6

22

7,6

98

7,

775

7,

853

Inv

en

tori

es

of

Lith

ium

Ca

rbo

nat

e

3,45

0

3

,485

3,51

9

3

,555

3,59

0

3,

626

3,66

2

3,

699

3,73

6

3,

773

3,81

1

3

,84

9

3,8

88

3,

926

3

,96

6

4,00

5

4,

045

4

,08

6

4,12

7

4,16

8

Inv

en

tori

es

of

Po

tass

ium

Ch

lori

de

17

,340

17,5

13

17

,689

17,8

65

18

,04

4

1

8,2

25

18

,40

7

1

8,5

91

18

,77

7

1

8,9

65

19

,15

4

19

,34

6

1

9,5

39

19,7

35

19

,93

2

20

,131

2

0,3

33

20

,53

6

20

,741

20,

949

Inv

en

tori

es

of

Bo

ric

Aci

d

650

657

663

670

676

683

690

697

704

711

718

725

732

74

0

747

755

7

62

77

0

77

7

78

5

Vo

lum

en

of

Sale

s Li

thiu

m C

arb

on

ate

19

,550

19,7

46

19

,943

20,1

42

20

,34

4

2

0,5

47

20

,75

3

2

0,9

60

21

,17

0

2

1,3

82

21

,59

5

21

,81

1

2

2,0

29

22,2

50

22

,47

2

22

,697

2

2,9

24

23

,15

3

23

,385

23,

619

Vo

lum

en

of

Sale

s P

ota

ssiu

m C

hlo

rid

e

84,6

60

85

,507

86,3

62

87

,225

88,0

98

88,

979

89,8

68

90,

767

91,6

75

92,

591

93,5

17

94,4

52

95,

397

96

,35

1

97,3

14

98,2

88

99,

271

100

,26

3

101

,266

10

2,27

9

Vo

lum

en

of

Sale

s B

ori

c A

cid

5,

850

5,9

09

5,

968

6,0

27

6,

088

6,1

48

6,

210

6,2

72

6,

335

6,3

98

6,

462

6,5

27

6,

592

6,65

8

6,7

24

6,

792

6,8

60

6,9

28

6,

997

7,

067

B.

Ou

tpu

t P

rice

s

Pri

ce in

de

x f

or

Lith

ium

Ca

rbo

nat

e1

1.0

11.

021.

03

1.04

1.0

51.

061.

07

1.08

1.0

91.

101.

12

1.1

31

.14

1.1

51

.16

1.1

71.

181.

20

1.2

11.

221.

231.

24

Pri

ce in

de

x f

orP

ota

ssiu

m C

hlo

rid

e

11.

01

1.02

1.0

31.

041.

05

1.06

1.0

71.

081.

09

1.10

1.1

21

.13

1.1

41.

15

1.1

61

.17

1.18

1.2

01

.21

1.22

1.23

1.2

4

Pri

ce in

de

x f

orB

ori

c A

cid

1

1.0

11.

021.

03

1.04

1.0

51.

061.

07

1.08

1.0

91.

101.

12

1.1

31

.14

1.1

51

.16

1.1

71.

181.

20

1.2

11.

221.

231.

24

Pri

ce f

or

Lith

ium

Ca

rbo

nat

e (

US

$ p

er

ton

)5

,355

5,46

3

5

,517

5,57

2

5

,628

5,68

4

5,

741

5,79

9

5,

857

5,91

5

5,

974

6,03

4

6

,09

5

6,1

55

6,

217

6

,27

9

6,34

2

6,

405

6

,46

9

6,53

4

6,59

9

6

,665

Pri

ce f

or

Po

tass

ium

Ch

lori

de

(U

S$

pe

r to

n)

762

77

7

7

85

79

3

8

01

80

9

8

17

82

5

8

33

84

2

8

50

85

8

86

7

8

76

884

89

3

90

2

911

920

930

939

9

48

Pri

ce f

or

Bo

ric

Aci

d (

US

$ p

er

ton

)5

82

593

599

605

611

618

624

630

636

643

649

656

662

669

67

5

682

689

6

96

70

3

71

0

71

7

724

C.

Inp

ut

Ind

ex

Pri

ces

Wag

e o

f P

rod

uct

ion

Wo

rke

rs1

1.0

61.

121.

19

1.26

1.3

31.

411.

49

1.58

1.6

81.

771.

88

1.9

92

.11

2.2

32

.36

2.5

02.

652.

81

2.9

73.

153.

333.

53

Pri

ce o

f O

pe

rati

ng

Ma

teri

als,

Su

pp

lie

s a

nd

Fu

els

11.

05

1.10

1.1

51.

201.

26

1.32

1.3

81.

441.

51

1.58

1.6

61

.74

1.8

21.

90

1.9

92

.09

2.18

2.2

92

.39

2.51

2.62

2.7

5

Ad

min

istr

ativ

e a

nd

Ge

ne

ral

Exp

en

ses

11.

03

1.07

1.1

01.

141.

18

1.22

1.2

61.

301.

34

1.38

1.4

31

.48

1.5

31.

58

1.6

31

.68

1.74

1.7

91

.85

1.91

1.98

2.0

4

Wo

rkin

g c

apit

al1

1.0

41.

081.

12

1.16

1.2

01.

241.

29

1.34

1.3

91.

441.

49

1.5

51

.60

1.6

61

.72

1.7

91.

851.

92

1.9

92.

072.

142.

22

D.

De

pre

ciat

ion

in U

S$ p

er

year

Pla

nts

, Po

nd

s ,

Bu

ild

ings

an

d S

tora

ge

19,0

05

17

,104

15,3

94

13

,854

12,4

69

11,

222

10,1

00

9,0

90

8,

181

7,3

63

6,

627

5,9

64

5,

367

4,83

1

4,3

48

3,

913

3,5

22

3,1

69

2,

853

2,

567

Bo

ok

val

ue

of

Pla

nts

, Po

nd

s , B

uil

din

gs

and

Sto

rag

e

171

,043

15

3,9

38

138

,544

12

4,6

90

112

,22

1

100,

999

90

,89

9

8

1,8

09

73

,62

8

6

6,2

65

59

,63

9

53

,67

5

4

8,3

07

43,4

77

39

,12

9

35

,216

3

1,6

95

28

,52

5

25

,673

23,

105

Ma

chin

ery

, tr

uck

s, p

um

pin

g e

qu

ipm

en

t an

d o

the

r in

frae

stru

ctu

re3,

718

7,6

91

6,

537

5,5

57

4,

723

4,0

15

3,

412

2,9

01

2,

466

2,0

96

1,

781

1,5

14

1,

287

1,09

4

930

790

6

72

57

1

48

5

41

3

Bo

ok

val

ue

of

Mac

hin

ery

, tru

cks,

pu

mp

ing

eq

uip

me

nt

51,2

72

43

,582

37,0

44

31

,488

26,7

64

22,

750

19,3

37

16,

437

13,9

71

11,

876

10,0

94

8,5

80

7,

293

6,19

9

5,2

69

4,

479

3,8

07

3,2

36

2,

751

2,

338

Tab

le E

stim

ate

d A

nn

ual

Op

era

tio

n C

ost

s in

Th

. U

S$

01

23

45

67

89

10

111

21

314

151

61

718

19

2021

A. O

pe

rati

ng

Mat

eri

als,

Su

pp

lie

s an

d F

ue

ls

Op

era

tin

g su

pp

lie

s an

d M

ate

rial

s (c

he

mic

als

, fl

ota

tio

n r

ea

ge

nts

, m

ob

ile

eq

uip

me

nt

su

pp

lie

s, e

tc)

4,8

53

5,32

0

5

,570

5,83

2

6

,106

6,39

3

6,

694

7,00

8

7,

338

7,68

3

8,

044

8,42

2

8

,81

7

9,2

32

9,

666

10

,12

0

10

,596

1

1,0

94

11

,61

5

12

,161

12,

733

Fue

ls (

pla

nts

, po

we

r ge

ne

rati

on

an

d u

tili

tie

s)1

0,9

63

12

,017

12,5

82

13

,174

13,7

93

14

,44

1

1

5,1

20

15

,83

0

1

6,5

75

17

,35

4

1

8,1

69

19

,02

3

19

,91

7

2

0,8

53

21,8

33

22

,86

0

23

,934

2

5,0

59

26

,23

7

27

,470

28,

761

Ma

inte

nan

ce M

ate

rial

s3,

197

3,

505

3,6

70

3,

842

4,0

23

4,

212

4,4

10

4,

617

4,8

34

5,

061

5,2

99

5,

548

5,8

09

6,

082

6,36

8

6,6

67

6,

981

7,3

09

7,6

52

8,

012

8,

389

Tra

nsp

ort

ati

on

Co

sts

(lo

adin

g, r

ail,

tru

cks,

etc

)1

1,1

91

12

,268

12,8

44

13

,448

14,0

80

14

,74

2

1

5,4

35

16

,16

0

1

6,9

20

17

,71

5

1

8,5

48

19

,41

9

20

,33

2

2

1,2

88

22,2

88

23

,33

6

24

,433

2

5,5

81

26

,78

3

28

,042

29,

360

Oth

er

Pro

du

ctio

n c

ost

s an

d c

on

tin

gen

cie

s1,

564

1,

715

1,7

96

1,

880

1,9

68

2,

061

2,1

58

2,

259

2,3

65

2,

476

2,5

93

2,

715

2,8

42

2,

976

3,11

6

3,2

62

3,

416

3,5

76

3,7

44

3,

920

4,

104

B. L

ab

or

Op

era

tin

g a

nd

Mai

nte

nan

ce L

abo

r2,

855

3,

202

3,3

91

3,

591

3,8

02

4,

027

4,2

64

4,

516

4,7

82

5,

065

5,3

63

5,

680

6,0

15

6,

370

6,74

6

7,1

44

7,

565

8,0

12

8,4

84

8,

985

9,

515

Ge

ne

ral

Ad

min

istr

ati

ve E

xp

en

ses

874

933

964

996

1,0

29

1,

062

1,0

98

1,

134

1,1

71

1,

210

1,2

50

1,

291

1,3

34

1,

378

1,42

3

1,4

70

1,

519

1,5

69

1,6

20

1,

674

1,

729

C. W

ork

ing

Cap

ital

Op

era

tin

g (

Ca

sh)

Op

era

tio

ns

(Fre

igh

t, t

ran

spo

rtat

ion

an

d p

ack

ing

) a

nd

Wag

es

7,4

23

7,98

2

8

,277

8,58

4

8

,901

9,23

1

9,

572

9,92

6

1

0,2

94

10

,67

5

1

1,0

70

11

,47

9

11

,90

4

1

2,3

44

12,8

01

13

,27

5

13

,766

1

4,2

75

14

,80

3

15

,351

15,

919

Tra

nsa

ctio

n c

ost

s an

d In

sura

nce

4,6

82

5,03

5

5

,221

5,41

4

5

,615

5,82

2

6,

038

6,26

1

6,

493

6,73

3

6,

982

7,24

1

7

,50

9

7,7

86

8,

074

8

,37

3

8,68

3

9,

004

9

,33

7

9,68

3

10,

041

D. O

the

r e

xp

en

ses

Op

era

tin

g

Exp

ort

ing

Exp

en

ses

994

1,06

8

1

,108

1,14

9

1

,191

1,23

5

1,

281

1,32

9

1,

378

1,42

9

1,

482

1,53

6

1

,59

3

1,6

52

1,

713

1

,77

7

1,84

2

1,

911

1

,98

1

2,05

5

2,13

1

Esti

mat

ed

An

nu

al O

pe

rati

on

Co

sts

-

5

3,0

46

55

,42

4

5

7,9

10

60

,50

9

6

3,2

27

66

,06

9

6

9,0

42

72

,15

0

7

5,4

00

78

,79

9

8

2,3

54

86

,07

3

89

,96

1

94

,02

9

98

,28

3

10

2,7

34

10

7,3

89

11

2,2

58

11

7,3

52

12

2,6

81

Initial Costs measured in nominal prices

550 work force both employees and eventual workers. Based on Comibol bi-annual projections.

Ponds function 365 days but plant operates 300 days

AP

PE

ND

IX C

: In

com

e S

tate

me

nt

Lith

ium

Min

ing

Pro

ject

So

urc

e:

CO

MIB

OL,

20

09

; E

be

nsp

erg

er

et

al.

, 2

00

5;

Ha

rbe

ge

r &

Je

nki

ns,

20

03

.

T

able

Co

st o

f G

oo

ds

Sold

of

Lit

hiu

m C

arb

on

ate

(Th

. US$

)

01

23

45

67

89

10

11

12

13

14

15

16

17

18

19

20

21

22

20

09

20

10

20

11

20

12

20

13

20

14

20

15

20

16

20

17

20

18

20

19

20

20

20

21

20

22

20

23

20

24

20

25

20

26

20

27

20

28

20

29

20

30

20

31

Ave

rage

Dir

ect

Co

st o

f P

rod

uct

ion

(U

S$

per

to

n)

1,0

05

1

,13

1

1,1

38

1

,15

1

1,1

70

1,1

92

1

,21

9

1,2

50

1

,28

5

1,3

22

1,3

63

1,4

07

1,4

53

1,5

02

1,5

54

1,6

09

1,6

66

1,7

26

1,7

89

1,8

54

Co

mp

uta

tio

n o

f C

os

t o

f Li

thiu

m C

arb

on

ate

So

ld

Bo

ok

Va

lue

of

Inv

ento

ries

at

Beg

inn

ing

of

Yea

r-

3,4

68

3

,94

1

4,0

06

4

,09

2

4

,19

9

4,3

23

4

,46

6

4,6

24

4

,79

9

4

,98

9

5

,19

4

5

,41

4

5

,64

9

5

,89

9

6

,16

4

6

,44

4

6

,74

0

7

,05

3

7

,38

2

Dir

ect

Lab

or

Co

st1

,72

9

1,8

31

1

,93

9

2,0

53

2

,17

4

2

,30

3

2,4

39

2

,58

3

2,7

35

2

,89

6

3

,06

7

3

,24

8

3

,44

0

3

,64

3

3

,85

8

4

,08

5

4

,32

6

4

,58

1

4

,85

2

5

,13

8

Dir

ect

Ma

teri

als

Exp

ens

e2

1,1

12

22

,11

9

2

3,1

75

2

4,2

81

25

,44

0

26

,65

5

2

7,9

29

29

,26

4

3

0,6

63

32

,12

9

33

,66

6

35

,27

6

36

,96

5

38

,73

4

40

,58

9

42

,53

4

44

,57

2

4

6,7

08

4

8,9

48

51

,29

6

Dir

ect

Ma

chin

ery

De

pre

cia

tio

n2

,00

7

4,1

53

3

,53

0

3,0

01

2

,55

0

2

,16

8

1,8

43

1

,56

6

1,3

31

1

,13

2

9

62

8

18

6

95

5

91

5

02

4

27

3

63

30

8

2

62

22

3

Les

s B

oo

k V

alu

e o

f In

ven

tori

es

at

En

d o

f Y

ear

(3,4

68

)

(3

,94

1)

(4,0

06

)

(4

,09

2)

(4,1

99

)

(4

,32

3)

(4,4

66

)

(4,6

24

)

(4,7

99

)

(4,9

89

)

(5

,19

4)

(5,4

14

)

(5

,64

9)

(5,8

99

)

(6

,16

4)

(6

,44

4)

(6

,74

0)

(7,0

53

)

(7

,38

2)

(7,7

28

)

Co

st o

f L

ith

ium

Ca

rbo

na

te S

old

21

,38

0

2

7,6

30

28

,57

9

29

,24

8

3

0,0

59

3

1,0

01

32

,06

8

3

3,2

54

34

,55

4

3

5,9

67

3

7,4

90

3

9,1

22

4

0,8

65

4

2,7

18

4

4,6

84

4

6,7

65

4

8,9

65

51

,28

6

53

,73

3

5

6,3

10

-

Tab

le C

om

pu

tati

on

of

Sa

les

and

ex

po

rtin

g ta

xe

s fo

r So

ld o

f Li

thiu

m C

arb

on

ate

(Th

. US

$)

01

23

45

67

89

10

11

12

13

14

15

16

17

18

19

20

21

22

20

09

20

10

20

11

20

12

20

13

20

14

20

15

20

16

20

17

20

18

20

19

20

20

20

21

20

22

20

23

20

24

20

25

20

26

20

27

20

28

20

29

20

30

20

31

Gro

ss

Sa

les

bef

ore

ta

xes

10

6,7

95

1

08

,94

2

11

1,1

31

1

13

,36

5

11

5,6

44

1

17

,96

8

12

0,3

39

12

2,7

58

12

5,2

25

12

7,7

42

1

30

,31

0

13

2,9

29

13

5,6

01

13

8,3

27

14

1,1

07

14

3,9

43

14

6,8

37

1

49

,78

8

15

2,7

99

1

55

,87

0

Exp

ort

ing

fee

s a

nd

oth

er

tax

es

4,8

06

4

,90

2

5,0

01

5

,10

1

5,2

04

5,3

09

5

,41

5

5,5

24

5

,63

5

5,7

48

5,8

64

5,9

82

6,1

02

6,2

25

6,3

50

6,4

77

6,6

08

6,7

40

6,8

76

7,0

14

Ne

t Sa

les

10

1,9

89

10

4,0

39

10

6,1

30

10

8,2

64

11

0,4

40

11

2,6

59

11

4,9

24

11

7,2

34

11

9,5

90

12

1,9

94

12

4,4

46

12

6,9

48

12

9,4

99

13

2,1

02

13

4,7

57

13

7,4

66

14

0,2

29

14

3,0

48

14

5,9

23

14

8,8

56

0

Tab

le T

ota

l Op

era

tin

g C

ost

s o

f Li

thiu

m C

arb

on

ate

(T

h. U

S$)

01

23

45

67

89

10

11

12

13

14

15

16

17

18

19

20

21

22

20

09

20

10

20

11

20

12

20

13

20

14

20

15

20

16

20

17

20

18

20

19

20

20

20

21

20

22

20

23

20

24

20

25

20

26

20

27

20

28

20

29

20

30

20

31

Co

sts C

ost

of

Go

od

s So

ld2

1,3

80

27

,63

0

2

8,5

79

2

9,2

48

30

,05

9

31

,00

1

3

2,0

68

33

,25

4

3

4,5

54

35

,96

7

37

,49

0

39

,12

2

40

,86

5

42

,71

8

44

,68

4

46

,76

5

48

,96

5

5

1,2

86

5

3,7

33

56

,31

0

Inte

rest

Exp

ens

e

Ad

min

istr

ati

on

an

d G

ener

al

Exp

en

ses

50

4

5

20

53

8

5

55

57

4

5

93

61

2

63

2

65

3

67

5

6

97

7

20

7

44

7

68

7

94

8

20

8

47

87

5

9

04

93

4

De

pre

cia

tio

n o

n P

lan

ts,

Po

nd

s , B

uil

din

gs a

nd

Sto

rage

1

0,2

63

9,2

36

8

,31

3

7,4

81

6

,73

3

6

,06

0

5,4

54

4

,90

9

4,4

18

3

,97

6

3

,57

8

3

,22

0

2

,89

8

2

,60

9

2

,34

8

2

,11

3

1

,90

2

1

,71

2

1

,54

0

1

,38

6

Tota

l C

ost

32

,14

73

7,3

87

37

,42

93

7,2

85

37

,36

63

7,6

54

38

,13

43

8,7

95

39

,62

54

0,6

17

41

,76

54

3,0

63

44

,50

74

6,0

95

47

,82

64

9,6

98

51

,71

45

3,8

72

56

,17

75

8,6

30

0

Tab

le N

et

Inco

me

of

Lit

hiu

m C

arb

on

ate

(Th

. US$

)

01

23

45

67

89

10

11

12

13

14

15

16

17

18

19

20

21

22

20

09

20

10

20

11

20

12

20

13

20

14

20

15

20

16

20

17

20

18

20

19

20

20

20

21

20

22

20

23

20

24

20

25

20

26

20

27

20

28

20

29

20

30

20

31

Gro

ss

Inco

me

be

fore

ta

xes

69

,84

3

6

6,6

52

68

,70

1

70

,97

9

7

3,0

74

7

5,0

06

76

,79

0

7

8,4

39

79

,96

5

8

1,3

77

8

2,6

81

8

3,8

85

8

4,9

92

8

6,0

07

8

6,9

32

8

7,7

68

8

8,5

15

89

,17

5

89

,74

6

9

0,2

26

Inco

me

ta

xes

24

,44

5

2

3,3

28

24

,04

5

24

,84

2

2

5,5

76

2

6,2

52

26

,87

6

2

7,4

54

27

,98

8

2

8,4

82

2

8,9

38

2

9,3

60

2

9,7

47

3

0,1

02

3

0,4

26

3

0,7

19

3

0,9

80

31

,21

1

31

,41

1

3

1,5

79

Ne

t In

com

e a

fte

r ta

xes

45

,39

84

3,3

24

44

,65

64

6,1

36

47

,49

84

8,7

54

49

,91

35

0,9

86

51

,97

75

2,8

95

53

,74

35

4,5

25

55

,24

55

5,9

05

56

,50

65

7,0

49

57

,53

55

7,9

64

58

,33

55

8,6

47

0

AP

PE

ND

IX D

: E

stim

ate

d T

ota

l C

ash

Flo

ws

to E

qu

ity

–Li

thiu

m M

inin

g P

roje

ct

So

urc

e:

Eb

en

spe

rge

r e

t a

l.,

20

05

; H

arb

eg

er

& J

en

kin

s, 2

00

3.

T

ab

le T

ota

l C

ash

Flo

w t

o E

qu

ity

(T

h. U

S$)

01

23

45

67

89

10

11

12

13

14

15

16

17

18

19

20

21

22

20

09

20

10

20

11

20

12

20

13

20

14

20

15

20

16

20

17

20

18

20

19

20

20

20

21

20

22

20

23

20

24

20

25

20

26

20

27

20

28

20

29

20

30

20

31

A.

CA

SH

IN

Sa

les

-

-

17

6,0

62

1

79

,60

0

18

3,2

10

1

86

,89

3

19

0,6

49

1

94

,48

1

19

8,3

91

20

2,3

78

20

6,4

46

2

10

,59

6

21

4,8

29

2

19

,14

7

22

3,5

51

2

28

,04

5

23

2,6

29

2

37

,30

4

24

2,0

74

2

46

,94

0

25

1,9

03

25

6,9

67

-

Less

Ex

po

rtin

g f

ee

s a

nd

oth

er

tax

es

-

-

(7,9

23

)

(8,0

82

)

(8

,24

4)

(8,4

10

)

(8,5

79

)

(8

,75

2)

(8

,92

8)

(9,1

07

)

(9,2

90

)

(9,4

77

)

(9

,66

7)

(9,8

62

)

(1

0,0

60

)

(10

,26

2)

(1

0,4

68

)

(10

,67

9)

(1

0,8

93

)

(11

,11

2)

(1

1,3

36

)

(1

1,5

63

)

-

Ch

an

ge i

n A

cco

un

ts R

ec

eiv

ab

le-

-

(2

7,3

17

)

(5

49

)

(5

60

)

(5

71

)

(5

83

)

(5

95

)

(6

07

)

(61

9)

(6

31

)

(6

44

)

(65

7)

(6

70

)

(68

3)

(6

97

)

(71

1)

(7

25

)

(74

0)

(7

55

)

(77

0)

(7

86

)

-

Go

ve

rnm

en

t In

tere

st

Sub

sid

y-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

Loa

ns

Pro

cee

ds

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

As

set

Liq

uid

ati

on

s-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

La

nd

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

19

B

uil

din

gs-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

2

0,1

02

M

ac

hin

ery

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

2,0

34

In

ve

nto

rie

s -

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

4

7,7

38

A

cco

un

ts R

ec

eiv

ab

le-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

3

9,8

70

A

cco

un

ts P

ay

ab

le-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

(9

,67

2)

C

as

h B

ala

nc

es

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

25

,96

0

TO

TA

L C

AS

H I

N-

-

1

40

,82

2

17

0,9

69

1

74

,40

6

17

7,9

11

1

81

,48

7

18

5,1

35

1

88

,85

6

1

92

,65

2

1

96

,52

5

20

0,4

75

2

04

,50

4

20

8,6

15

2

12

,80

8

21

7,0

86

2

21

,44

9

22

5,9

00

2

30

,44

1

23

5,0

73

2

39

,79

8

2

44

,61

8

1

26

,05

0

B.

CA

SH

OU

T

As

set

Co

ntr

ibu

tio

ns

La

nd

80

9

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

Bo

ric

Ac

id P

lan

t a

nd

Eq

uip

men

t1

16

,47

5

29

,99

7

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

M

ac

hin

ery

16

,10

9

8

,67

4

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

A

cco

un

ts R

ec

eiv

ab

le-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

A

cco

un

ts P

ay

ab

le-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

C

as

h B

ala

nc

es

14

,16

3

2

1,2

44

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

In

itia

l In

ven

tori

es

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

Dir

ec

t La

bo

r C

os

t1

3,3

61

-

3

,20

2

3,3

91

3

,59

1

3,8

02

4,0

27

4

,26

4

4,5

16

4,7

82

5,0

65

5

,36

3

5

,68

0

6

,01

5

6,3

70

6,7

46

7,1

44

7

,56

5

8

,01

2

8

,48

4

8,9

85

9,5

15

-

Dir

ec

t M

ate

ria

l E

xpe

ns

e4

4,4

53

-

3

9,0

96

40

,96

1

4

2,9

16

44

,96

4

4

7,1

11

49

,36

2

5

1,7

20

5

4,1

92

5

6,7

82

59

,49

8

6

2,3

44

6

5,3

27

68

,45

3

7

1,7

30

7

5,1

66

78

,76

6

8

2,5

41

8

6,4

97

90

,64

4

9

4,9

92

-

Ad

min

istr

ati

ve

an

d G

en

era

l E

xp

en

se

3,6

54

-

93

3

96

4

9

96

1

,02

9

1

,06

2

1,0

98

1

,13

4

1

,17

1

1

,21

0

1,2

50

1,2

91

1,3

34

1

,37

8

1

,42

3

1

,47

0

1,5

19

1,5

69

1,6

20

1

,67

4

1

,72

9

-

Oth

er

Co

sts

an

d c

on

tin

gen

cie

s/ c

los

ure

-

4

,11

1

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

7,7

79

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

Inte

res

t Ex

pen

se-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

Pri

nc

ipa

l R

epa

ymen

ts-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

Ch

an

ge i

n A

cco

un

ts P

ay

ab

le-

-

(4

,00

3)

(1

90

)

(1

99

)

(2

08

)

(2

18

)

(2

29

)

(2

39

)

(25

1)

(2

63

)

(2

76

)

(28

9)

(3

03

)

(31

7)

(3

32

)

(34

8)

(3

65

)

(38

2)

(4

01

)

(42

0)

(4

40

)

-

Ch

an

ge i

n C

as

h B

ala

nc

es

-

-

(8,2

27

)

48

2

4

99

5

18

5

37

55

7

57

8

5

99

6

21

64

4

6

68

69

3

7

18

74

5

7

72

80

1

8

31

86

1

8

93

9

26

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

Do

me

stic

Im

po

rtin

g T

ari

ffs

14

3

2

92

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

Tra

ns

ac

tio

n c

os

ts4

45

73

9

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

Inco

me

ta

xes

-

-

41

,63

2

3

6,2

93

37

,04

6

3

8,3

93

39

,61

7

4

0,7

31

41

,74

4

42

,66

6

43

,50

4

4

4,2

64

44

,95

1

45

,56

8

4

6,1

20

46

,60

8

47

,03

4

4

7,4

00

47

,70

4

47

,94

8

4

8,1

31

48

,25

1

-

TO

TA

L C

AS

H O

UT

20

9,6

12

65

,05

87

2,6

32

81

,90

08

4,8

49

88

,49

89

2,1

37

95

,78

39

9,4

52

10

3,1

59

10

6,9

19

11

0,7

43

11

4,6

44

11

8,6

34

12

2,7

22

12

6,9

20

13

1,2

38

13

5,6

86

14

0,2

73

14

5,0

10

14

9,9

07

15

4,9

73

7,7

79

CA

SH

FL

OW

: E

QU

ITY

(20

9,6

12

)(6

5,0

58

)6

8,1

89

89

,06

98

9,5

57

89

,41

38

9,3

51

89

,35

38

9,4

05

89

,49

38

9,6

06

89

,73

28

9,8

60

89

,98

19

0,0

86

90

,16

69

0,2

11

90

,21

59

0,1

68

90

,06

28

9,8

90

89

,64

41

18

,27

2

Dis

cou

nt

fact

or

1.0

00

.91

0.8

30

.75

0.6

80

.62

0.5

60

.51

0.4

70

.42

0.3

90

.35

0.3

20

.29

0.2

60

.24

0.2

20

.20

0.1

80

.16

0.1

50

.14

0.1

2

Dis

cou

nte

d N

et

Cas

h F

low

(20

9,6

12

)

(5

9,1

43

)

56

,35

5

6

6,9

19

61

,16

8

5

5,5

18

50

,43

6

4

5,8

52

41

,70

8

37

,95

4

34

,54

7

3

1,4

50

28

,63

2

26

,06

4

2

3,7

23

21

,58

5

19

,63

3

1

7,8

48

16

,21

7

14

,72

6

1

3,3

62

12

,11

4

14

,52

9

NP

V L

ith

ium

Pla

nt

=4

21

,58

5

1

APPENDIX E: –Initial paramentes for Quinoa Irrigation Project

Source: CAMEX, 2009; Crespo et al (2001); Soraide et.al, (2005); Water Ministry of Bolivia, 2008.

Table Amounts and Consumption of Conventional Quinoa in 2002 ( No irrigation)

Th.Tons Th. US $

Domestic Consumption 17.5 9400

Peasants Families 14.2 7,400

Urban market 3.3 2000

Exports

USA and Europe 1.8 2,700

Peru and Ecuador(unofficial) 2.8 2,400

Gross production of Quinua Bolivia 22.1 14,500

Gross production of Quinua Salar de Uyuni Basin 13.49 8,848

Crespo et al., 2001 & Soraide et.al, 2005.

Table Real prices and annual growth of Quinua (Average 2003 prices)

Lower Upper

Nominal prices (US$ per Ton)

Agricultor 441 559

Domestic market 650 750

Peru and Ecuador(FOB -official) 800 1200

USA market (FOB) 1200 1600

Average Annual Growth (%)

Agricultor 3.5%

Domestic market 2.5%

USA market 3.5%

Peru and Ecuador(official) 2.8%

Crespo et al., 2001 & Soraide et.al, 2005.

Table Distribution of the Harvested Quinoa at the Salar de Uyuni Basin

Lower Upper

Domestic Consumption

Peasants Families 16% 17%

Urban market 21% 28%

Exports

USA and Europe 29% 29%

Peru and Ecuador(official) 34% 35%

Crespo et al., 2001 & Soraide et.al, 2005.

Table Average Quinoa yield (Tons per hectare)

Lower Upper

Quinoa yield with no irrigation project 0.5 0.58

Quinoa yield with irrigation project 1.4 1.625

Total crops near San Geronimo( Ha) 0.3 0.612

APPENDIX F: Estimated Operating Plan–Quinoa Irrigation Project

Source: CAMEX, 2009; Crespo et al ( 2001); Soraide et.al, (2005); Water Ministry of Bolivia, 2008.

Table Quinoa Estimated Annual Production, Consumption and prices

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031

A. Quinoa Harvesting and Consumption (Th. tons)

I. Total Quinua Harvesting without irrigation 0.355 0.355 0.355 0.355 0.355 0.355 0.355 0.355 0.355 0.355 0.355 0.355 0.355 0.355 0.355 0.355 0.355 0.355 0.355 0.355 0.355 0.355 0.355

Domestic Consumption

Peasants Families 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06Urban market 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07

Exports

USA and Europe 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10

Peru and Ecuador 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12

II. Total Quinua Harvesting with irrigation 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99

Domestic Consumption

Peasants Families 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08Urban market 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28

Exports

USA and Europe 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29

Peru and Ecuador 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35

B. Quinoa Prices and indexes

I. Quinoa Price indexes

Domestic Consumption

Peasants Families 1 1.04 1.09 1.13 1.18 1.23 1.28 1.33 1.39 1.45 1.51 1.57 1.64 1.71 1.78 1.85 1.93 2.01 2.10 2.19 2.28 2.37 2.47

Urban market 1 1.06 1.11 1.17 1.24 1.31 1.38 1.45 1.53 1.62 1.71 1.80 1.90 2.01 2.12 2.23 2.36 2.48 2.62 2.77 2.92 3.08 3.25

Exporting Quinoa Price indexes

USA and Europe 1 1.07 1.13 1.21 1.29 1.37 1.46 1.55 1.65 1.76 1.88 2.00 2.13 2.27 2.41 2.57 2.74 2.92 3.11 3.31 3.52 3.75 4.00 Peru and Ecuador 1 1.06 1.13 1.20 1.27 1.35 1.43 1.52 1.62 1.72 1.82 1.94 2.06 2.19 2.32 2.47 2.62 2.78 2.95 3.14 3.33 3.54 3.76

II. Domestic Consumption Quinoa Prices (US$ per Ton)Peasants Families 850 885 923 961 1,002 1,044 1,088 1,133 1,181 1,230 1,282 1,336 1,392 1,451 1,511 1,575 1,641 1,710 1,782 1,857 1,935 2,016 2,101 Urban market 1,190 1,255 1,324 1,397 1,474 1,555 1,641 1,731 1,826 1,927 2,033 2,144 2,262 2,387 2,518 2,657 2,803 2,957 3,120 3,291 3,472 3,663 3,865

Exporting Quinoa Prices (US$ per Ton)USA and Europe 2,380 2,535 2,699 2,875 3,062 3,261 3,473 3,698 3,939 4,195 4,468 4,758 5,067 5,397 5,747 6,121 6,519 6,943 7,394 7,874 8,386 8,931 9,512 Peru and Ecuador 1,700 1,805 1,917 2,036 2,162 2,297 2,439 2,590 2,751 2,921 3,102 3,295 3,499 3,716 3,946 4,191 4,451 4,727 5,020 5,331 5,662 6,013 6,385

C. Input Index Prices

Labor 1 1.05 1.11 1.17 1.23 1.30 1.37 1.45 1.52 1.61 1.69 1.78 1.88 1.98 2.09 2.20 2.32 2.45 2.58 2.72 2.86 3.02 3.18

Input materials 1 1.03 1.06 1.10 1.13 1.16 1.20 1.24 1.28 1.32 1.36 1.40 1.44 1.49 1.53 1.58 1.63 1.68 1.73 1.79 1.84 1.90 1.96

Machinery and transportation 1 1.04 1.08 1.12 1.16 1.20 1.25 1.30 1.35 1.40 1.45 1.51 1.56 1.62 1.69 1.75 1.82 1.89 1.96 2.03 2.11 2.19 2.27

Other expenses 1 1.03 1.05 1.08 1.10 1.13 1.16 1.19 1.22 1.25 1.28 1.31 1.34 1.38 1.41 1.45 1.48 1.52 1.56 1.60 1.64 1.68 1.72

Table Quinoa Agriculture Estimated Annual Costs in Th. US$

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

Labor 99 105 110 116 123 129 136 143 151 159 168 177 187 197 207 218 230 243 256 270 284 300 316

Input materials (No irrigation) 137 142 146 151 155 160 165 170 175 181 187 192 198 204 211 217 224 231 238 245 253 261 269

Machinery and transportation 34 36 37 38 40 41 43 44 46 48 50 52 54 56 58 60 62 65 67 70 72 75 78

Other expenses 14 15 15 15 16 16 16 17 17 18 18 19 19 20 20 21 21 22 22 23 23 24 24

Input materials (Irrigation) 74 76 78 81 83 86 88 91 94 97 100 103 106 109 113 116 120 124 127 131 135 140 144

Table Quinoa Estimated Annual Sales in Th.US$

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

A. Crops without irrigation

Peasant Family Sales 253 264 275 287 299 311 324 338 352 367 382 398 415 433 451 470 489 510 531 554 577 601 626

Domestic Value added Sales 89 94 99 104 110 116 122 129 136 144 152 160 169 178 188 198 209 220 233 245 259 273 288

Exporting Sales

USA and Europe 245 261 278 296 315 336 357 381 405 432 460 490 522 556 592 630 671 715 761 811 863 919 979

Peru and Ecuador 205 218 231 246 261 277 294 313 332 353 374 398 422 448 476 506 537 570 606 643 683 726 771

B. Crops with Irrigation

Peasant Family Sales 777 810 844 880 916 955 995 1,037 1,080 1,126 1,173 1,222 1,274 1,327 1,383 1,441 1,502 1,565 1,630 1,699 1,770 1,844 1,922

Domestic Value added Sales 331 350 369 389 411 433 457 482 509 537 566 597 630 665 701 740 780 823 869 916 967 1,020 1,076

Exporting Sales

USA and Europe 686 731 779 829 883 940 1,002 1,067 1,136 1,210 1,288 1,372 1,461 1,556 1,658 1,765 1,880 2,002 2,132 2,271 2,419 2,576 2,743

Peru and Ecuador 592 628 667 709 753 799 849 902 957 1,017 1,080 1,147 1,218 1,293 1,374 1,459 1,549 1,645 1,747 1,856 1,971 2,093 2,223

Table Quinoa Production Estimated Annual Costs in Th. US$

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

Direct Labor 164 173 182 192 202 213 225 237 249 263 277 292 308 324 342 360 380 400 422 445 469 494 521

Materials and Equipment 245 252 260 268 276 285 294 303 312 322 332 342 353 364 375 387 399 411 424 437 451 464 479

Machinery and transportation 53 55 57 59 62 64 66 69 72 74 77 80 83 86 89 93 96 100 104 108 112 116 121

Administrative and General Expenses 27 28 28 29 30 31 31 32 33 34 35 35 36 37 38 39 40 41 42 43 44 45 46

Table Loan amorization Irrigation Project in Th. US$

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

Interest Payments 169 159 148 136 122 106 89 70 49 26

Principal Payments 92 102 113 126 140 155 172 191 212 236

Loan Balance 1,448 1,345 1,232 1,106 966 811 639 448 236 0

APPENDIX G: Lithium Mining Project- cumulative ascending probabilities of prices in annual rates of growth (%)

Source: COMIBOL, 2009; Ebensperger et al., 200; UDAPE, 2009; INE 2009; Zuleta, 2009.

Output Prices in annual growth rates

Operation Costs in annual growth rates

-0.1

5

-0.1

0

-0.0

5

0.0

0

0.0

5

0.1

0

0.1

5

-0.1

5

-0.1

0

-0.0

5

0.0

0

0.0

5

0.1

0

0.1

5

5.0% 90.0% 5.0%

-0.0584 0.0784

0.0

0.2

0.4

0.6

0.8

1.0

Price of Boric Acid

Pert(-0.1,0.01,0.12)

Minimum -0.1000

Maximum 0.1200

Mean 0.0100

Std Dev 0.0416

5.0% 90.0% 5.0%

0.0318 0.0872

0.0

0

0.0

1

0.0

2

0.0

3

0.0

4

0.0

5

0.0

6

0.0

7

0.0

8

0.0

9

0.1

0

0.1

1

0.0

0.2

0.4

0.6

0.8

1.0

Wage of Production Workers

Pert(0.01,0.0635,0.1)

Minimum 0.0100

Maximum 0.1000

Mean 0.0607

Std Dev 0.0169

5.0% 90.0% 5.0%

0.0212 0.0761

0.0

0

0.0

1

0.0

2

0.0

3

0.0

4

0.0

5

0.0

6

0.0

7

0.0

8

0.0

9

0.1

0

0.1

1

0.0

0.2

0.4

0.6

0.8

1.0

Price of Operating Materials, Supplies and Fuels

Pert(0.01,0.043,0.1)

Minimum 0.0100

Maximum 0.1000

Mean 0.0470

Std Dev 0.0167

APPENDIX H: Quinoa irrigation Project- cumulative ascending probabilities of prices in annual rates of growth (%)

Source: CAMEX, 2009; Crespo et al ( 2001); Soraide et.al, (2005); Water Ministry of Bolivia, 2008 Output Prices annual growth rates

Inputs annual growth rates

0.0

0

0.0

1

0.0

2

0.0

3

0.0

4

0.0

5

0.0

6

0.0

7

0.0

8

0.0

9

0.1

0

0.1

1

-0.0

4

-0.0

2

0.0

0

0.0

2

0.0

4

0.0

6

0.0

8

0.1

0

0.1

2

0.0

1

0.0

2

0.0

3

0.0

4

0.0

5

0.0

6

0.0

7

0.0

8

0.0

9

0.0

0

0.0

1

0.0

2

0.0

3

0.0

4

0.0

5

0.0

6

0.0

7

0.0

8

APPENDIX H: Multivariate Stepwise Regression and Rank Order Correlation Source: @Risk Manual

Even though @Risk uses two methods for calculating and rank coefficients, the author could not find any option to display the

coefficients or regression data base, nor the Stepwise regression equation or p-values of the coefficients.

@Risk uses Multiple regression to fit multiple input data sets to a planar equation that could produce the output data set. The

values then are returned by @RISK, are normalized variations of the regression coefficients.

According to @Risk Manual, the Stepwise regression is a technique for calculating regression values with multiple input values

and it’s the technique preferred for large numbers of inputs because it removes all variables that provide an insignificant contribution

from the model. The coefficients listed in the tornado report (Figures 22 and 25) are normalized regression coefficients associated with

each input. A regression value of 0 indicates that there is no significant relationship between the input and the output, while a

regression value of 1 or -1 indicates a 1 or -1 standard deviation change in the output for a 1 standard deviation change in the input.

@Risk computes a Rank order correlation which calculates the relationship between two data sets by comparing the rank of

each value in a data set. To calculate rank, the data is ordered from lowest to highest and assigned numbers (the ranks) that

correspond to their position in the order. This method is preferable to linear correlation when we do not necessarily know the

probability distribution functions from which the data were drawn. For example, if data set A was normally distributed and data set B

was log normally distributed, rank order correlation would produce a better representation of the relationship between the two data

sets.

0.0

0

0.0

1

0.0

2

0.0

3

0.0

4

0.0

5

0.0

6

0.0

7

0.0

8 5

10

15

20

25

30

35

40

45

50

55