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
Main uses of lithium: 2008
Batteries, 27%
Lubricant Greases, 12% Chemical processing, 1% Pharmaceuticals, Frits, 9% 3% Continuous Casting, 3% Glass, 8%
Polimers, 4% Air Conditioning, Aluminium, 4% 6% Source : Ebensperger et.al (2005); SQM (2008)
Master Project
by Rodrigo Aguilar-Fernandez
Nicholas School of the Environment
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.
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 metal 1, 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 .
1
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 2 terms of Lithium Carbonate (Li 2CO 3) Equivalent (LCE).
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 reserves 3 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 ; %)
0.45; 0.55% 0.21; 0.26%
13.83; 16.84%
23.94; 29.15%
14.42; 17.56% Chile Bolivia Argentina China & Tibet Brazil 29.26; 35.63% US
Source : adapted from Evans(2008), USGS (2007), and MIR (2008)
2 Approximately 5.32 units of Lithium Carbonate (Li 2CO 3) 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.
Figure 3: World’s Total Lithium Metal Resources by continent in (MM tonnes) 4
South America 14.19
North America 6.48
Asia 3.60
Africa 2.36
Australia 0.26
Europe 0.24
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)*
7,000
6,000 Nominal US$ per ton 5,000 Inflation-Adjusted US$ per ton
4,000
3,000
2,000
1,000
- 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008
*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.
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)
Salar de Uyuni .
.
Bolivia
Chile
8,000)
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)
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
Population Density Life expectancy Adult literacy Province HDI (2006) (people/km2) (years) rate 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 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
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
60,000 52,914
50,000
40,000 27,935 30,000
20,000 22,973 Bolivians 10,000 Foreigners Total Visitors Salar - 2000 2001 2002 2003 2004 2005 2006 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.
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 GDP 5, 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 Importance 6.
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 m3/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
Rio Grande (Brackish)
Rio San Geronimo
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
Fresh water Brackish Source (m3/day) (m3/day)
Torrez&Ramirez 2006 (*) 8,208 9,504 COMIBOL 2008 4,200 5,300
(*) SQM average consumption 1995-2003
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.
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 consumption 7, 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 Consulting 8 (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
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. Source: adapted from Molina Carpio (2007) Molina Carpio (2007) also explained that given the characteristics of the region 9, 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.
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
Table 2. Areas in thousands of Hectares (Th.Ha) and camelids in number of heads(#)
Total area Posible crop Harvested Irrigated Non-irrigated Camelids (#) Th.Ha Th.Ha Th.Ha Th.Ha Th.Ha 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 Source : adapted and translated from Molina Carpio (2007) and Soraide et al (2005).
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 “K 12 . 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 balance 14 .
Table 5: An approximation of Rio San Geronimo Hydrologic balance with and without lithium plant
Table Aproximation of Rio San Geronimo Hydrologic balance Without With Project Project Daily Daily m3/day 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 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 again 15 . 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)
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
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 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 23 23 NPV ∞23 =NPV 23* (1+r) / [(1+r) 1] from Harbeger & Jenkins, 2003. Ch.5 Pag.8.
Table 7. Cultivable and resting land
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% 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
Table Estimated Investment Costs for Quinoa Farmers 2002 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 Source : adapted from Crespo, et.al (2001) and JICA (2002).
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) 0.00 0.02 0.04 0.06 0.08 0.10 0.06 0.07 0.08 0.09 0.10 0.11 0.12 0.13 0.14 0.15 -0.02
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.
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 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 -0.02 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)
Quinoa yield with irrigation project (ton/ Ha) Quinoa yield with no irrigation project (ton/Ha) 1.081 1.907 0.4471 0.7001 5.0% 90.0% 5.0% 5.0% 90.0% 5.0% 1.0 1.0
0.8 0.8
Pert(0.35,0.59,0.76) 0.6 0.6 Pert(0.76,1.55,2.1) Minimum 0.3500 Minimum 0.7600 Maximum 0.7600 Maximum 2.1000 0.4 Mean 0.5783 Mean 1.5100 0.4 Std Dev 0.2514 Std Dev 0.0770
0.2 0.2
0.0 0.0
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.
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$), consistent to the cash flow model profile of the mining project. Similarly, the discounted cash in and cash out flows to equity for the 23-year Lithium Mining Project are presented in Figure 16.
Figure 16. Estimated Cash Flows to Equity (Th. US$)
300,000
250,000
200,000
150,000
100,000
50,000
- 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 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.
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, fuels 22 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$)
$60,000 Exporting Expenses
Transaction costs and Insurance $50,000
Operations (Freight, transportation and packing) and Wages $40,000 General Administrative Expenses
Operating and Maintenance Labor
$30,000 Other Production costs and contingencies
Transportation Costs (loading, rail, trucks, etc) $20,000
Maintenance Materials
$10,000 Fuels (plants, power generation and utilities)
Operating supplies and Materials
$- A. Operating Materials, B. Labor C. Working Capital D. Other expenses Supplies and Fuels Operating (Cash) Operating In terms of average sales, LCE represented nearly 60% and Potassium Chloride 38% of total sales during the 23-year project. The obvious result at sales is the direct dependence on market prices. Though the private lithium mining is going to assume the capital cost and risk of the project, it will still generate positive NPV.
Additionally, the cash model calculated the income and exporting taxes incurred by the private company. The cash flow model for the government perspective was based on 35% of income taxes and 4.5% of exporting taxes and royalties (COMIBOL, 2009). Figure 18 provides initial estimates of income and exporting taxes that the government could receive during mining operations. 23
21 Labor from the mineral and private sector. Assuming that the mean % change of wages from 1996-2008 is good estimators for both sectors. 22 Maintenance materials, chemicals, flotation reagents, mobile equipment supplies, etc; transportation expenses (fuels, loading, rail, trucks). 23 Refer to Appendix C and D for detailed information on how income and exporting taxes were calculated.
Figure 18. Estimated Taxes revenues flows for the government (Th. US$)
72,000
63,000
54,000
45,000
36,000
27,000
18,000
9,000
- 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
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$) at 10% discount rate. b ) Quinoa Irrigation Project
Today 161 households, located 20 Km west of the Rio San Geronimo (Figure 11), harvest 612 hectares of quinoa crops each year without irrigation (Project “COLLCHA K”; Water Ministry of Bolivia, 2008; PROAGRO/GTZ ). In order to evaluate the irrigation project net present benefits, the total cash flows model of the irrigation project was estimated by the incremental change of the cash flows with and without the project for: a) quinoa famers, b) private quinoa industry, and c) government. Those cash flows were added to obtain the possible estimated benefits that the Salar de Uyuni basin provided.
First, a set of parameters were extracted based on the literature found on quinoa harvesting and past irrigation projects conducted in the same region. The initial set of parameters for the cash flow models are described in Appendix E. Second, a detail spreadsheet of annual quinoa harvesting, production, costs, consumption and sales were developed to imitate an operating plan for the next 23-years (Appendix F). Third, quinoa famers and private quinoa industry cash flows were estimated based on historic patterns on domestic and exporting sales, real prices, taxes, and cost structures. The results from each economic actor cash flows were computed separately.
Since the quinoa harvesting in the Salar de Uyuni basin is mainly focused on the domestic and international markets 24 , both quinoa farmers and exporting industry directly benefit from this project. Focusing only inside the quinoa irrigation project scope, Figure 19 illustrates that the project incremental NCF is positive for farmers and quinoa production industry perspective, but negative for the government during the loan payment period, followed by positive NCF afterwards.
Figure 19. Estimated Incremental Net Cash flows from each perspective (Th. US$)
1,500
1,200
900
600
300
- 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 (300)
(600) Net Cash Flow to Quinoa Farmers Net Cash Flow to Private Quinoa Production Sector Net Cash Flow to Government
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$) relative the economy of the study area. From the
24 Crespo et.al (2001) and Soraide et.al (2005) reported that approximately 63% (average) of the quinoa harvested in the Salar de Uyuni Basin is exported to USA , Europe, Peru and Ecuador each year. While domestic and farmer consumption represent 27% and 18%, respectively.
perspective of the Salar de Uyuni basin economy, the Lithium mining would have to be developed despite foregoing benefits of the quinoa irrigation project.
It is not difficult to choose the lithium mining project based on the economic concepts presented here, however, the preliminary model has a set of fixed assumptions that certainty will change over time and the value of both projects. Thus, sensitivity analysis was conducted on the parameters of the model to compare the results for both projects. Those results are described in the next section.
IV.3 Sensitivity Analysis a ) Lithium Mining Project
The sensitivity analysis performed on the cash flow model tried to reduce the uncertainty of the real prices growth rates in the future, as well as the gross benefits estimation of the project from both perspectives. The mean expected NPV from the private equity perspective was 691,180 (Th.US$) 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$). Only the latter perspective constituted the gross benefits provided by the Salar de Uyuni basin after 23-year of lithium mining. Figure 20 and 21 show the frequency distribution of the NPV from both perspectives after the model was run 10,000 times.
Figure 20. NPV frequency distribution- Lithium Mining Company (MM US$) 200 400 600 800 1000 1200 1400 1600
Figure 21. NPV tax-revenues of Government frequency distribution- (MM US$) 200 400 600 800 1000 1200
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 0.0 0.2 0.4 0.6 -0.6 -0.4 -0.2
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.
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
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
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$) of Infinite 23-year Quinoa Irrigation Projects 7000 -4000 18000 29000 40000 -15000
Given the cash flow and the sensitivity analysis provided good estimates for future costs and benefits of the quinoa irrigation project, there is 90% probability that the expected NPV of the benefits of quinoa irrigation Project over the long run will be between -1,105 and 21,971 thousands US$.
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
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 0.0 0.2 0.4 0.6 0.8 1.0 -0.4 -0.2
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
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$) – 0 200 400 600 800 -200 1000 1200
Summary Statistics of the NPV for both projects in (Th. US$)
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
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.
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 km 2) 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
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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)
APPENDIX B: Lithium Mining Estimated Operating Plan Source: COMIBOL, 2009; Ebensperger et al., 2005; Harbeger & Jenkins, 2003 ; Zuleta, 2009. Table Estimated Annual Production
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. Output Quantities and inventories (tons)
Production of Lithium Carbonate 23,000 23,230 23,462 23,697 23,934 24,173 24,415 24,659 24,906 25,155 25,406 25,660 25,917 26,176 26,438 26,702 26,969 27,239 27,511 27,787 Production of Potassium Chloride 102,000 103,020 104,050 105,091 106,142 107,203 108,275 109,358 110,451 111,556 112,671 113,798 114,936 116,086 117,246 118,419 119,603 120,799 122,007 123,227 Production of Boric Acid 6,500 6,565 6,631 6,697 6,764 6,832 6,900 6,969 7,039 7,109 7,180 7,252 7,324 7,398 7,472 7,546 7,622 7,698 7,775 7,853
Inventories of Lithium Carbonate 3,450 3,485 3,519 3,555 3,590 3,626 3,662 3,699 3,736 3,773 3,811 3,849 3,888 3,926 3,966 4,005 4,045 4,086 4,127 4,168 Inventories of Potassium Chloride 17,340 17,513 17,689 17,865 18,044 18,225 18,407 18,591 18,777 18,965 19,154 19,346 19,539 19,735 19,932 20,131 20,333 20,536 20,741 20,949 Inventories of Boric Acid 650 657 663 670 676 683 690 697 704 711 718 725 732 740 747 755 762 770 777 785
Volumen of Sales Lithium Carbonate 19,550 19,746 19,943 20,142 20,344 20,547 20,753 20,960 21,170 21,382 21,595 21,811 22,029 22,250 22,472 22,697 22,924 23,153 23,385 23,619 Volumen of Sales Potassium Chloride 84,660 85,507 86,362 87,225 88,098 88,979 89,868 90,767 91,675 92,591 93,517 94,452 95,397 96,351 97,314 98,288 99,271 100,263 101,266 102,279 Volumen of Sales Boric Acid 5,850 5,909 5,968 6,027 6,088 6,148 6,210 6,272 6,335 6,398 6,462 6,527 6,592 6,658 6,724 6,792 6,860 6,928 6,997 7,067
B. Output Prices
Price index for Lithium Carbonate 1 1.01 1.02 1.03 1.04 1.05 1.06 1.07 1.08 1.09 1.10 1.12 1.13 1.14 1.15 1.16 1.17 1.18 1.20 1.21 1.22 1.23 1.24 Price index forPotassium Chloride 1 1.01 1.02 1.03 1.04 1.05 1.06 1.07 1.08 1.09 1.10 1.12 1.13 1.14 1.15 1.16 1.17 1.18 1.20 1.21 1.22 1.23 1.24 Price index forBoric Acid 1 1.01 1.02 1.03 1.04 1.05 1.06 1.07 1.08 1.09 1.10 1.12 1.13 1.14 1.15 1.16 1.17 1.18 1.20 1.21 1.22 1.23 1.24
Price for Lithium Carbonate (US$ per ton) 5,355 5,463 5,517 5,572 5,628 5,684 5,741 5,799 5,857 5,915 5,974 6,034 6,095 6,155 6,217 6,279 6,342 6,405 6,469 6,534 6,599 6,665 Price for Potassium Chloride (US$ per ton) 762 777 785 793 801 809 817 825 833 842 850 858 867 876 884 893 902 911 920 930 939 948 Price for Boric Acid (US$ per ton) 582 593 599 605 611 618 624 630 636 643 649 656 662 669 675 682 689 696 703 710 717 724
C. Input Index Prices
Wage of Production Workers 1 1.06 1.12 1.19 1.26 1.33 1.41 1.49 1.58 1.68 1.77 1.88 1.99 2.11 2.23 2.36 2.50 2.65 2.81 2.97 3.15 3.33 3.53 Price of Operating Materials, Supplies and Fuels 1 1.05 1.10 1.15 1.20 1.26 1.32 1.38 1.44 1.51 1.58 1.66 1.74 1.82 1.90 1.99 2.09 2.18 2.29 2.39 2.51 2.62 2.75 Administrative and General Expenses 1 1.03 1.07 1.10 1.14 1.18 1.22 1.26 1.30 1.34 1.38 1.43 1.48 1.53 1.58 1.63 1.68 1.74 1.79 1.85 1.91 1.98 2.04 Working capital 1 1.04 1.08 1.12 1.16 1.20 1.24 1.29 1.34 1.39 1.44 1.49 1.55 1.60 1.66 1.72 1.79 1.85 1.92 1.99 2.07 2.14 2.22 D. Depreciation in US$ per year Plants, Ponds , Buildings and Storage 19,005 17,104 15,394 13,854 12,469 11,222 10,100 9,090 8,181 7,363 6,627 5,964 5,367 4,831 4,348 3,913 3,522 3,169 2,853 2,567 Book value of Plants, Ponds , Buildings and Storage 171,043 153,938 138,544 124,690 112,221 100,999 90,899 81,809 73,628 66,265 59,639 53,675 48,307 43,477 39,129 35,216 31,695 28,525 25,673 23,105
Machinery, trucks, pumping equipment and other infraestructure 3,718 7,691 6,537 5,557 4,723 4,015 3,412 2,901 2,466 2,096 1,781 1,514 1,287 1,094 930 790 672 571 485 413 Book value of Machinery, trucks, pumping equipment 51,272 43,582 37,044 31,488 26,764 22,750 19,337 16,437 13,971 11,876 10,094 8,580 7,293 6,199 5,269 4,479 3,807 3,236 2,751 2,338
Table Estimated Annual Operation Costs in Th. US$
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 A. Operating Materials, Supplies and Fuels Operating supplies and Materials (chemicals, flotation reagents, mobile equipment supplies, etc) 4,853 5,320 5,570 5,832 6,106 6,393 6,694 7,008 7,338 7,683 8,044 8,422 8,817 9,232 9,666 10,120 10,596 11,094 11,615 12,161 12,733 Fuels (plants, power generation and utilities) 10,963 12,017 12,582 13,174 13,793 14,441 15,120 15,830 16,575 17,354 18,169 19,023 19,917 20,853 21,833 22,860 23,934 25,059 26,237 27,470 28,761 Maintenance Materials 3,197 3,505 3,670 3,842 4,023 4,212 4,410 4,617 4,834 5,061 5,299 5,548 5,809 6,082 6,368 6,667 6,981 7,309 7,652 8,012 8,389 Transportation Costs (loading, rail, trucks, etc) 11,191 12,268 12,844 13,448 14,080 14,742 15,435 16,160 16,920 17,715 18,548 19,419 20,332 21,288 22,288 23,336 24,433 25,581 26,783 28,042 29,360 Other Production costs and contingencies 1,564 1,715 1,796 1,880 1,968 2,061 2,158 2,259 2,365 2,476 2,593 2,715 2,842 2,976 3,116 3,262 3,416 3,576 3,744 3,920 4,104
B. Labor Operating and Maintenance Labor 2,855 3,202 3,391 3,591 3,802 4,027 4,264 4,516 4,782 5,065 5,363 5,680 6,015 6,370 6,746 7,144 7,565 8,012 8,484 8,985 9,515 General Administrative Expenses 874 933 964 996 1,029 1,062 1,098 1,134 1,171 1,210 1,250 1,291 1,334 1,378 1,423 1,470 1,519 1,569 1,620 1,674 1,729
C. Working Capital Operating (Cash) Operations (Freight, transportation and packing) and Wages 7,423 7,982 8,277 8,584 8,901 9,231 9,572 9,926 10,294 10,675 11,070 11,479 11,904 12,344 12,801 13,275 13,766 14,275 14,803 15,351 15,919 Transaction costs and Insurance 4,682 5,035 5,221 5,414 5,615 5,822 6,038 6,261 6,493 6,733 6,982 7,241 7,509 7,786 8,074 8,373 8,683 9,004 9,337 9,683 10,041
D. Other expenses Operating Exporting Expenses 994 1,068 1,108 1,149 1,191 1,235 1,281 1,329 1,378 1,429 1,482 1,536 1,593 1,652 1,713 1,777 1,842 1,911 1,981 2,055 2,131
Estimated Annual Operation Costs - 53,046 55,424 57,910 60,509 63,227 66,069 69,042 72,150 75,400 78,799 82,354 86,073 89,961 94,029 98,283 102,734 107,389 112,258 117,352 122,681 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
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APPENDIX C: Income Statement Lithium Mining Project Source: COMIBOL, 2009; Ebensperger et al., 2005; Harbeger & Jenkins, 2003 .
Table Cost of Goods Sold of Lithium Carbonate (Th. US$) 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031
Average Direct Cost of Production (US$ per ton) 1,005 1,131 1,138 1,151 1,170 1,192 1,219 1,250 1,285 1,322 1,363 1,407 1,453 1,502 1,554 1,609 1,666 1,726 1,789 1,854
Computation of Cost of Lithium Carbonate Sold Book Value of Inventories at Beginning of Year - 3,468 3,941 4,006 4,092 4,199 4,323 4,466 4,624 4,799 4,989 5,194 5,414 5,649 5,899 6,164 6,444 6,740 7,053 7,382 Direct Labor Cost 1,729 1,831 1,939 2,053 2,174 2,303 2,439 2,583 2,735 2,896 3,067 3,248 3,440 3,643 3,858 4,085 4,326 4,581 4,852 5,138 Direct Materials Expense 21,112 22,119 23,175 24,281 25,440 26,655 27,929 29,264 30,663 32,129 33,666 35,276 36,965 38,734 40,589 42,534 44,572 46,708 48,948 51,296 Direct Machinery Depreciation 2,007 4,153 3,530 3,001 2,550 2,168 1,843 1,566 1,331 1,132 962 818 695 591 502 427 363 308 262 223 Less Book Value of Inventories at End of Year (3,468) (3,941) (4,006) (4,092) (4,199) (4,323) (4,466) (4,624) (4,799) (4,989) (5,194) (5,414) (5,649) (5,899) (6,164) (6,444) (6,740) (7,053) (7,382) (7,728)
Cost of Lithium Carbonate Sold 21,380 27,630 28,579 29,248 30,059 31,001 32,068 33,254 34,554 35,967 37,490 39,122 40,865 42,718 44,684 46,765 48,965 51,286 53,733 56,310 -
Table Computation of Sales and exporting taxes for Sold of Lithium Carbonate (Th. US$) 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031
Gross Sales before taxes 106,795 108,942 111,131 113,365 115,644 117,968 120,339 122,758 125,225 127,742 130,310 132,929 135,601 138,327 141,107 143,943 146,837 149,788 152,799 155,870 Exporting fees and other taxes 4,806 4,902 5,001 5,101 5,204 5,309 5,415 5,524 5,635 5,748 5,864 5,982 6,102 6,225 6,350 6,477 6,608 6,740 6,876 7,014
Net Sales 101,989 104,039 106,130 108,264 110,440 112,659 114,924 117,234 119,590 121,994 124,446 126,948 129,499 132,102 134,757 137,466 140,229 143,048 145,923 148,856 0
Table Total Operating Costs of Lithium Carbonate (Th. US$) 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 Costs Cost of Goods Sold 21,380 27,630 28,579 29,248 30,059 31,001 32,068 33,254 34,554 35,967 37,490 39,122 40,865 42,718 44,684 46,765 48,965 51,286 53,733 56,310 Interest Expense Administration and General Expenses 504 520 538 555 574 593 612 632 653 675 697 720 744 768 794 820 847 875 904 934 Depreciation on Plants, Ponds , Buildings and Storage 10,263 9,236 8,313 7,481 6,733 6,060 5,454 4,909 4,418 3,976 3,578 3,220 2,898 2,609 2,348 2,113 1,902 1,712 1,540 1,386
Total Cost 32,147 37,387 37,429 37,285 37,366 37,654 38,134 38,795 39,625 40,617 41,765 43,063 44,507 46,095 47,826 49,698 51,714 53,872 56,177 58,630 0
Table Net Income of Lithium Carbonate (Th. US$)
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031
Gross Income before taxes 69,843 66,652 68,701 70,979 73,074 75,006 76,790 78,439 79,965 81,377 82,681 83,885 84,992 86,007 86,932 87,768 88,515 89,175 89,746 90,226 Income taxes 24,445 23,328 24,045 24,842 25,576 26,252 26,876 27,454 27,988 28,482 28,938 29,360 29,747 30,102 30,426 30,719 30,980 31,211 31,411 31,579
Net Income after taxes 45,398 43,324 44,656 46,136 47,498 48,754 49,913 50,986 51,977 52,895 53,743 54,525 55,245 55,905 56,506 57,049 57,535 57,964 58,335 58,647 0
APPENDIX D: Estimated Total Cash Flows to Equity –Lithium Mining Project Source: Ebensperger et al., 2005; Harbeger & Jenkins, 2003 .
Table Total Cash Flow to Equity (Th. US$)
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. CASH IN Sales - - 176,062 179,600 183,210 186,893 190,649 194,481 198,391 202,378 206,446 210,596 214,829 219,147 223,551 228,045 232,629 237,304 242,074 246,940 251,903 256,967 - Less Exporting fees and other taxes - - (7,923) (8,082) (8,244) (8,410) (8,579) (8,752) (8,928) (9,107) (9,290) (9,477) (9,667) (9,862) (10,060) (10,262) (10,468) (10,679) (10,893) (11,112) (11,336) (11,563) - Change in Accounts Receivable - - (27,317) (549) (560) (571) (583) (595) (607) (619) (631) (644) (657) (670) (683) (697) (711) (725) (740) (755) (770) (786) - Government Interest Subsidy ------Loans Proceeds ------Asset Liquidations ------Land ------19 Buildings ------20,102 Machinery ------2,034 Inventories ------47,738 Accounts Receivable ------39,870 Accounts Payable ------(9,672) Cash Balances ------25,960
TOTAL CASH IN - - 140,822 170,969 174,406 177,911 181,487 185,135 188,856 192,652 196,525 200,475 204,504 208,615 212,808 217,086 221,449 225,900 230,441 235,073 239,798 244,618 126,050
B. CASH OUT Asset Contributions Land 809 ------Boric Acid Plant and Equipment 116,475 29,997 ------Machinery 16,109 8,674 ------Accounts Receivable ------Accounts Payable ------Cash Balances 14,163 21,244 ------Initial Inventories ------Direct Labor Cost 13,361 - 3,202 3,391 3,591 3,802 4,027 4,264 4,516 4,782 5,065 5,363 5,680 6,015 6,370 6,746 7,144 7,565 8,012 8,484 8,985 9,515 - Direct Material Expense 44,453 - 39,096 40,961 42,916 44,964 47,111 49,362 51,720 54,192 56,782 59,498 62,344 65,327 68,453 71,730 75,166 78,766 82,541 86,497 90,644 94,992 - Administrative and General Expense 3,654 - 933 964 996 1,029 1,062 1,098 1,134 1,171 1,210 1,250 1,291 1,334 1,378 1,423 1,470 1,519 1,569 1,620 1,674 1,729 - Other Costs and contingencies/ closure - 4,111 ------7,779 ------Interest Expense ------Principal Repayments ------Change in Accounts Payable - - (4,003) (190) (199) (208) (218) (229) (239) (251) (263) (276) (289) (303) (317) (332) (348) (365) (382) (401) (420) (440) - Change in Cash Balances - - (8,227) 482 499 518 537 557 578 599 621 644 668 693 718 745 772 801 831 861 893 926 ------Domestic Importing Tariffs 143 292 ------Transaction costs 445 739 ------Income taxes - - 41,632 36,293 37,046 38,393 39,617 40,731 41,744 42,666 43,504 44,264 44,951 45,568 46,120 46,608 47,034 47,400 47,704 47,948 48,131 48,251 -
TOTAL CASH OUT 209,612 65,058 72,632 81,900 84,849 88,498 92,137 95,783 99,452 103,159 106,919 110,743 114,644 118,634 122,722 126,920 131,238 135,686 140,273 145,010 149,907 154,973 7,779
CASH FLOW: EQUITY (209,612) (65,058) 68,189 89,069 89,557 89,413 89,351 89,353 89,405 89,493 89,606 89,732 89,860 89,981 90,086 90,166 90,211 90,215 90,168 90,062 89,890 89,644 118,272
Discount factor 1.00 0.91 0.83 0.75 0.68 0.62 0.56 0.51 0.47 0.42 0.39 0.35 0.32 0.29 0.26 0.24 0.22 0.20 0.18 0.16 0.15 0.14 0.12
Discounted Net Cash Flow (209,612) (59,143) 56,355 66,919 61,168 55,518 50,436 45,852 41,708 37,954 34,547 31,450 28,632 26,064 23,723 21,585 19,633 17,848 16,217 14,726 13,362 12,114 14,529
NPV Lithium Plant = 421,585
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
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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 0.06 Urban 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 0.08 Urban 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 0.00 0.05 0.10 0.15 0.00 0.05 0.10 0.15 -0.15 -0.10 -0.05 -0.15 -0.10 -0.05
Price of Boric Acid -0.0584 0.0784 5.0% 90.0% 5.0% 1.0
0.8
0.6 Pert(-0.1,0.01,0.12)
Minimum -0.1000 Maximum 0.1200 Mean 0.0100 0.4 Std Dev 0.0416
0.2
0.0
Operation Costs in annual growth rates
Wage of Production Workers Price of Operating Materials, Supplies and Fuels 0.0318 0.0872 0.0212 0.0761 5.0% 90.0% 5.0% 1.0 5.0% 90.0% 5.0% 1.0
0.8 0.8
0.6 0.6 Pert(0.01,0.0635,0.1) Pert(0.01,0.043,0.1)
Minimum 0.0100 Minimum 0.0100 Maximum 0.1000 Maximum 0.1000 Mean 0.0607 Mean 0.0470 Std Dev 0.0169 Std Dev 0.0167 0.4 0.4
0.2 0.2
0.0 0.0 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10 0.11 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10 0.11
0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10 0.11
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 0.00 0.02 0.04 0.06 0.08 0.10 0.12 -0.04 -0.02
Inputs annual growth rates
0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09
5 10 15 20 25 30 35 40 45 50 55
0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08
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.