An Analysis of the Potential Economic Impact of Natural Gas
Production in Tanzania
MA
StTE
MASSACHUSETTS INMTMUTE.
OF TECHNOLOGY
by
Ekenedilinna Umeike
B.Eng., University of Nigeria Nsukka (2008)
Submitted to the Engineering Systems Division
and
Department of Electrical Engineering and Computer Science
in partial fulfillment of the requirements for the degrees of
Master of Science in Technology and Policy
and
Master of Science in Electrical Engineering and Computer Science
at the
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
September 2014
Massachusetts Institute of Technology 2014. All Rights Reserved.
OCT
LIBRARE
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Technology and Policy Program, Engineering Systems Division
Department of Electrical Engineering and Computer Science
July 7, 2014
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Robert J. Stoner
Initiative
Deputy Director
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Vladimir Bulovic
Professor in Emerging Technology
t o Electrical Engineering and Computer Science
Thesis Reader
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Dava
j. Newman
Professor of Aeronautics and Astronautics and Engineering Systems
Director, Technology and Policy Program
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Leslie A. Kolodziejski
Professor of Electrical Engineering
Chair, Committee on Graduate Students
Department of Electrical Engineering and Computer Science
2
An Analysis of the Potential Economic Impact of Natural Gas Production in
Tanzania
by
Ekenedilinna Umeike
Submitted to the Engineering Systems Division
and
Department of Electrical Engineering and Computer Science
on July 7 2014, in partial fulfillment of the requirements for the degrees of
Master of Science in Technology and Policy
and
Master of Science in Electrical Engineering and Computer Science
Abstract
Following substantial discoveries of natural gas in recent years, Tanzania has new options for
economic development. The country's policy makers are faced with having to make decisions about
how best to utilize the gas in order to drive economic development The options before the
government are whether to export the gas or to use it domestically. Exporting natural gas can be a
very lucrative source of government revenues which can in turn be invested in improving education,
access to healthcare or other areas to improve the general and economic well-being of the populace.
Encouraging domestic use on the other hand may not be as lucrative in terms of government
revenues, but is necessary for increasing participation along the gas value chain in particular and
stimulating in other sectors of the economy that benefit from easy access to gas or its downstream
products. In this study, I considered the options of using the gas in the production of liquefied natural
gas (LNG) intended for export, as well the domestic manufacture of urea and electricity.
I used a scenario analysis model to investigate and assess these options according to their direct
economic value as well as their revenue generating potential. These two parameters were chosen as
proxies for and economic growth and government ability to invest in public goods respectively. As
part of the assessments carried out, special attention was paid to the different scenarios associated
with meeting the government's national electrification plans as determined by their electricity
demand forecasts.
The results show that among the options considered, domestic utilization of the gas for power
production will have greatest economic value while LNG exports hold the highest revenue potential.
Furthermore, they demonstrate that even though using the natural gas for electricity production
would be the most valuable option for the Tanzanian economy per unit of gas, allowing new
generating capacity to be dominated by gas plants as has been done in some other gas rich countries
would not be economically prudent. Instead a mix of technologies will provide the best balance
between stimulating domestic gas consumption and providing cost effective electricity to consumers.
Importantly, I find that future policy must ensure that domestic utilization of gas is not sacrificed in
favor of exports even if it means reduced government revenues.
Thesis Supervisor: Robert J. Stoner
Title: Deputy Director, MIT Energy Initiative
3
4
Acknowledgement
I am grateful to all of the people I have been fortunate enough to meet during my time at MIT. I
will forever treasure and hold fond memories of the past two years. I am particularly indebted
to my thesis supervisor Dr. Robert Stoner for the guidance, patience and support that have
helped me through to this point A special thanks also to Prof. Channing Arndt and Dr. Kenneth
Strzepek for the advice on how to approach the issues explored in this study.
I am grateful to Dr. Stoner and Dr. Melanie Kenderdine for helping me to find a place at the MIT
Energy Initiative to pursue my interests. The opportunity to work and learn in the midst of likeminded and unbelievably supportive people created a wonderful working environment that I
will miss.
I would also like to say thank you to my cousin Chidube, who has very kindly and skilfully played
the roles of friend, study partner, and sounding board since we were undergraduates. Thanks
also to Ed Ballo and Barbara DeLaBarre from TPP for helping me through administrative
challenges, for casual conversation, and for all the kindness shown to me.
Finally, I would like to thank my parents and siblings for not allowing the little matter of an ocean
separating us create any distance between us or prevent them from providing the support I have
come to count on all of my life.
5
6
Contents
1
Introduction ......................................................................................................................................................... 15
1.1
M otivation .................................................................................................................................................... 15
1.2
T rends from O ther Countries ............................................................................................................... 16
1.2.1
Q atar ...................................................................................................................................................... 17
1.2.2
Trinidad and Tobago ...................................................................................................................... 18
1.2.3
A ngola ................................................................................................................................................... 19
1.2.4
N igeria .................................................................................................................................................. 20
1.3
2
3
O rganization ................................................................................................................................................ 21
Gas M onetization ............................................................................................................................................... 23
2.1
Liquefied N atural Gas (LN G) ................................................................................................................. 23
2.2
U rea Production ......................................................................................................................................... 25
2.3
Electricity Production ............................................................................................................................. 26
T anzania's Electricity Sector ......................................................................................................................... 29
3.1
O verview of the T anzanian Electricity Sector ............................................................................... 29
3.2
B ridging Electricity D eficit .................................................................................................................... 30
3.2.1
4
Pow er Generating Technologies ................................................................................................ 31
T he M odel ............................................................................................................................................................. 33
4.1
Scenario A nalysis M odels ...................................................................................................................... 33
4.2
Im plem entation ......................................................................................................................................... 34
7
4.2.1
LN G Exports Sub-m odel................................................................................................................
34
4.2.2
U rea Production Sub-m odel..................................................................................................
38
4.2.3
Electricity Production Sub-m odel.......................................................................................
40
Shortcom ings of the M odel ...................................................................................................................
44
Presentation and D iscussion of Results................................................................................................
45
4.3
5
Results...........................................................................................................................................................45
5.1
6
7
5.1.1
Revenues .............................................................................................................................................
45
5.1.2
Value Added .......................................................................................................................................
48
5.2
Effects of Global Com petition...........................................................................................................
51
5.3
Costs A ssociated w ith Generating Electricity ..........................................................................
54
5.4
Gas D em and across Monetization O ptions ...............................................................................
56
Policy Recom m endations & Conclusions .............................................................................................
57
6.1
Sum m ary of Key Findings......................................................................................................................57
6.2
Recom m endations....................................................................................................................................
6.3
Future Work................................................................................................................................................60
Bibliography ........................................................................................................................................................
8
58
61
List of Figures
Figure 2-1: Stranded Gas Reserves/High Price Import Markets.........................................................
24
Figure 2-2: Typical Gas Monetization Value Chain .................................................................................
25
Figure 2-3: The Urea Production Process. Adapted from:...................................................................
25
Figure 2-4: Conversion of gas to power .......................................................................................................
26
Figure 3-1: Electrification Rates around Tanzania.................................................................................
30
Figure 4-1: N atural Gas Price Forecast...........................................................................................................
35
Figure 4-2: LN G R evenue Flows ............................................................................................................................
38
Figure 4-3: Fuels for Electricity Generation ...............................................................................................
41
Figure 5-1: Plot of Cumulative Revenues from LNG Export.................................................................
46
Figure 5-2: Plot of Cumulative Government Income from Urea.........................................................
47
Figure 5-3: Plot of Cumulative Government Income from Electricity..............................................
48
Figure 5-4: Government Revenue across the Monetization Options................................................
49
Figure 5-5: Value Added per unit volume of gas used............................................................................
51
Figure 5-6: Investor Earnings under Mozambican and Tanzanian Fiscal Conditions................ 52
Figure 5-7: Estimated Breakeven Gas Prices for Set of Major Contemporary LNG Projects ........ 53
Figure 5-8: Selected Costs over the Operational Lives of Power Plants...........................................
55
Figure 5-9: Weighted Average Levelized Costs of Electricity across the Different Electricity
S c e n ario s .........................................................................................................................................................................
55
Figure 5-10: Gas Consumption in Selected Scenarios............................................................................
56
9
10
List of Tables
Table 4-1: Summary of Production Sharing Formula..............................................................................
36
Table 4-2: Selected LNG Exports Sub-model Assumptions ...................................................................
38
Table 4-3: Selected Urea Sub-m odel Assum ptions .................................................................................
39
Table 4-4: Selected Electricity Production Sub-model Assumptions................................................
43
Table 5-1: Some Differences in Investment Terms in Tanzania and Mozambique.....................53
11
12
List of Acronyms
CCGT - Combined Cycle Gas Turbines
CNG - Compressed Natural Gas
EWURA - Energy and Water Utilities Regulatory Authority
GTL - Gas to Liquids
LCOE - Levelized Cost of Electricity
LNG - liquefied natural gas
MMTPA - Million metric tons per annum
TANESCO - Tanzania Electric Supply Company
Tcf - trillion cubic feet
TPDC - Tanzania Petroleum Development Corporation
WAGP - West Africa Gas Pipeline
13
14
Chapter 1
Introduction
I explore monetization options available to the Tanzanian government as they seek to develop
recently discovered natural gas reserves. My objective is to compare these options quantitatively
in terms of revenue generating potential and relative economic value as a guide to policy makers.
My approach is to evaluate export and domestic consumption scenarios by measuring their
revenue generating and value adding potentials. I also specifically focus on the Tanzanian
government's plans to increase the domestic electrification rate and the role that natural gas may
play in meeting these objectives.
1.1 Motivation
The discovery of large deposits of natural resources creates game changing economic
opportunities for any nation. The extent of its impact depends to a large extent on the situation
in the country at the time the discovery is made. So if well managed, a low-income country in
the midst of a natural resource boom has the potential to record more dramatic changes to
the overall standard of living than an already industrialized, high-income counterpart
experiencing the same boom. These issues are increasingly relevant in Tanzania where natural
15
gas discoveries of up to 40tcf have been reported by (Simbakalia 2013). These issues and the
factors policy makers need to consider in addressing them are the motivation behind this study.
In deciding how best to utilize a natural resource such as natural gas, the overriding objective of
most governments would be to maximize economic benefits to the country. Economic impact
from exploiting natural gas may come directly through increased economic activity arising from
the domestic utilization of the gas as feedstock in associated downstream industries and as a
result of increased activity in other sectors such as agriculture and manufacturing that use the
products of these downstream industries. Alternatively, the government may choose to export
the gas and influence economic development through the investment of export revenues in social
projects such as improved infrastructure and education.
The government must make difficult choices that will have long term consequences for the
country's future economy. Broadly speaking the choices are whether to export the gas or use it
domestically. Important factors that will influence the government's path are the status of the
domestic market, the level of domestic industrialization as well as the level of available knowhow (Simbakalia 2013). By adopting value added and revenue generated as metrics for assessing
the monetization options, this study lays the ground work for considering these two possible
paths for economic impact of the Tanzanian gas.
1.2 Trends from Other Countries
Tanzania is a new entrant to an industry that has existed for over century and though the
particular context of its new energy sector are unique, the experiences of other developing
countries that have undergone resource booms is instructive. In this section, I briefly examine
energy sector development in Qatar, Trinidad and Tobago, Angola and Nigeria.
Some common trends among these older oil and gas producers are evident in the discussions
that follow.
16
1.2.1 Qatar
The discovery in 1971 of the massive North Field gas reservoir, which has now been proven to
have up to 900 tcf of non-associated gas, marked the beginning of a new era Qatar. However, for
reasons including technical difficulties and challenges attracting funding, it took about two
decades for proper exploitation of the reserves to begin. Part of the reason for the delay was that
the value of natural gas as a fuel was not as widely appreciated as it is today and in fact the
government of Qatar was more interested in oil production since it was already a significant
source of national income. But as oil revenues declined, the Qatari government became
increasingly interested in pursuing the development of its gas resources [ (Ibrahim and Harrigan
2012) and (Hashimoto, Elass and Eller 2004)].
According to (Hashimoto, Elass and Eller 2004) by the time gas production from the North Field
began, Qatar had a three phase plan to:
1.
Develop gas production for domestic consumption (in power, desalination, fertilizer and
petrochemicals).
2.
Build an export pipeline to deliver gas to neighbors.
3.
Build a liquefaction facility for export of LNG.
The first phase went as planned with the vast majority of the produced gas going to local power
and industrial plants (Hashimoto, Elass and Eller 2004) and the effects of this policy remain
today with virtually all of the electricity produced in the country coming from gas fired power
plants (mecometer n.d.).
For political reasons ambitious plans for a pipeline system across Gulf countries never really
took off and so that portion of Qatari strategy did not quite work out as planned. However, a
smaller project connecting Qatar, Oman and the UAE was successfully built and is now a big part
of intra-Arab gas trade [ (Hashimoto, Elass and Eller 2004) and (Fattouh and El-Katiri 2012)].
Today Qatar, with its 225,000 citizens, is the world's leading exporter of LNG and in 2012 had a
GDP of $185 billion (Rivin 2013). Furthermore, for years after the turn of the century its economy
expanded faster than that of any other making its people among the wealthiest in the world.
17
Spending fueled by proceeds from the energy sector also spurred growth in the non-oil sector
with an average growth of 20% from 2004 to 2011 (Ibrahim and Harrigan 2012).
1.2.2
Trinidad and Tobago
Trinidad and Tobago has an oil and gas industry dating back to 1866 when deposits were initially
discovered. In the time since then, the sector has grown to become an important contributor
foreign exchange incomes and GDP growth (Sergeant, Racha and John 2003). The performance
of the sector is such that the economy sometimes appears to be split into an energy economy and
a non-energy economy, with the energy economy being largely self sufficient (Artana, et al.
2007).
The country has about 27 tcf of natural gas reserves which it has been able to exploit to develop
a thriving gas sector (Kin n.d.). Just like in Qatar, the economy of Trinidad and Tobago was
primarily dependent on crude oil and the associated gas was mostly burned off. But as oil
production and prices fell, natural gas became of more interest to the government and policy
focused on diversifying away from oil and towards gas. The initial focus was on setting up
domestic petrochemical industries that would use the gas as feedstock and this eventually
expanded to include LNG exports (Racha 2001).
In addition to providing for the needs of the local petrochemical industry, early policy required
that gas producers reserve a portion of their product for use by the Trinidad and Tobago
Electricity Commission (T&TEC). Domestic demand was also further encouraged through tax
incentives and subsidies. These policies played an important role in the emergence of the country
as a major producer of ammonia and methanol [ (Sergeant, Racha and John 2003) and (Racha
2001)].
According to (Kin n.d.), the country's geographical constraints made pipelined gas deliveries to
neighboring countries infeasible. As a result this option was never pursued as a means of
monetizing Trinidadian gas.
After several attempts, the country's first LNG plant was finally commissioned in 1999 marking
the country's entry into large-scale gas exports. As of 2002, the LNG industry was already the
18
biggest user of Trinidadian gas, exporting almost half of all the gas produced in the country. The
country has since become a major player in the international LNG market [ (Sergeant, Racha and
John 2003) and (Racha 2001)].
1.2.3 Angola
Offshore oil production began in Angola began in 1968 (Hodges 2001), and though the country
was adversely affected by a civil war, offshore oil production was able to thrive between 1990
and 2003. Onshore production however was completely suspended between 1993 and 1996
(World Bank 2007). Today offshore sources still account for the majority of oil production in the
country (EIA 2014) and oil is the most important source of revenues to the Angolan government
(IEA 2006).
Similar to Trinidad and Tobago, the Angolan energy sector is almost an economy onto itself. The
linkages to other sectors of the economy are inadequate and as such they struggle to keep pace
(Ramos n.d.).
Unlike Trinidad and Tobago and Qatar that have already incorporated gas into their economies,
Angola remains a small player in the natural gas arena. The civil war hampered industrial
development in the country and so for a long time it has been unable to attract the kind of large
customers that can support the establishment of a domestic gas market. Most of the associated
gas produced has for a long time been either burned off or re-injected into oil wells. However the
transition towards gas utilization and the building a gas economy has begun but unlike in the
other countries considered previously, it started out with an export driven focus [ (EIA 2014)
and (TEA 2006)].
(EIA 2014) predicts that for the future, expanding LNG and building a domestic gas market will
be a big part of the Angola's gas policy. According to (TEA 2006), some of the options being
considered to form the basis for a new domestic market for gas are the power sector, an
aluminum smelter, an ammonia/urea plant as well as a cement plant.
19
1.2.4 Nigeria
Like all of the other countries considered in this sub-section, Nigeria started out primarily as an
oil producer following the discovery of crude oil in 1956. In the early days of the energy industry,
the simultaneous exploitation of the associated gas was not considered to be worth pursuing
because of oil was such a good source of revenues (Nwokeji 2007). Ironically, today Nigeria is
sometimes described as a gas rich country which just happens to produce some crude oil
(Okenabirhie n.d.).
Unfortunately Nigeria is often cited as an example of how not to develop a natural resource. As
with some other oil rich countries, Nigeria's economic development has been concentrated
largely on the oil industry to the neglect of other sectors (Ramos n.d.) and detriment of the
economy at large.
As far back as the 1960s, there were efforts to utilize Nigerian gas domestically but
infrastructural difficulties hampered these efforts. Around the same time negotiations for the
export of LNG from Nigeria to the United Kingdom were delayed because of disagreements over
price. Before any agreements could be reached, the North Sea discoveries were made and the
British demand for Nigerian gas faded away (Pearson 1970). However, after a few decades of
mostly flaring gas, the Nigeria LNG company became operational in 1999 as part of export
focused attempts to reduce the environmental impacts of flaring and to monetize Nigeria's gas
deposits [ (Okenabirhie n.d.) and (NLNG n.d.)]. In 2011 the commissioning of the West Africa Gas
Pipeline (WAGP) followed. With this pipeline Nigeria began exporting gas to nearby neighbors
Ghana with plans to extend delivery to neighbors Benin and Togo as well (Shell 2011).
Though some gas was already used domestically for power generation for example, the early
emphasis on exports resulted in inadequate attention being given to developing domestic
consumption. To address this shortcoming, the Nigerian government instituted a domestic
supply obligation (DSO) to guarantee availability of gas for local use as part of its plan to deepen
the domestic gas market and support industrialization (Okenabirhie n.d.).
(Okenabirhie n.d.) identified fertilizer, aluminum, methanol as well as additional power plants
are among the options proposed for deepening domestic gas market In addition to guaranteeing
domestic gas supply, a new pricing policy was adopted to encourage domestic gas demand. The
20
Nigerian government created the strategicdomestic sector, strategicindustrialsector as well as
the strategiccommercialsector as categories into which industries will be classified. The price to
be paid for gas depends on which category the customer falls under (Omisakin n.d.).
A couple of things are evident from the foregoing discussions. There appear to be three phases
-
that major oil and gas producers may pass through as their industries develop. The phases are
the development of a domestic market, exports to neighboring countries, as well as exports to
the wider international community. The order in which they may occur appears to depend to
some degree on prevailing conditions within the producing countries when production ensues.
In addition, the economies of developing countries in the midst of oil and gas booms have
exhibited a tendency to become dominated by the industry. Some sectors of the economy such
as energy intensive sectors and others that provide services to the energy sector benefit from
linkages. One downside to the disproportionate dependency on one sector is that in the event
that the sector suffers, it can drag down the dependent sectors and possibly the entire economy
as well.
Furthermore, the circumstances affecting a government's choices are not entirely within its
control. For example, just as Nigeria had no control over the effects of discoveries in the North
Sea on demand for its gas in the United Kingdom, Tanzania's gas industry is emerging at a time
when shale gas developments in the United States and China appear likely to permanently
change the international gas market (EIU 2014). This will inevitably factor into long term
thinking about Tanzania's natural gas.
1.3
Organization
The rest of the thesis is organized as follows:
Chapter two describes the gas monetization options that this paper focuses on, that is, the export
of LNG, urea production and the use of gas in generating electricity. Naturally, attention is
focused on the role of gas.
21
In line with the added focus on Tanzania's electricity sector, the third chapter provides a brief
overview of the country's electricity system and the plans to improve performance - a driver for
the scenarios considered in the model.
The fourth chapter is an account of the scenario analysis model on which the findings of this
paper are based. There is a discussion about scenario analysis models and their applicability to
the issues addressed in this paper. Following this discussion are descriptions of the different submodels in the model.
Chapter five presents and discusses the results and is followed by a final chapter in which key
results are summarized and policy recommendations are presented.
22
Chapter 2
Gas Monetization
Though numerous monetization options including CNG for domestic transportation, as well as
GTL production exist, I evaluate three that are significant for Tanzania - LNG, urea and electricity
generation. The choice of these options are informed by the experiences in other countries as
discussed previously. They also feed into the broader developmental
objectives and
commitments of the government Domestic urea manufacture for instance could support the
Tanzanian government's commitment to the Abuja declaration for increasing fertilizer use
(NEPAD 2011), while the gas to power option is consistent with the objectives outlined in (TZ
MEM 2013) to improve access to electricity. Further justification for evaluating these options lies
in the fact that actual proposals to pursue options in these areas already exist [ (2b1st Consulting
2012)and (Daly 2014)] increasing the likelihood that they will be pursued.
2.1 Liquefied Natural Gas (LNG)
Figure 2-1 shows some locations of low cost stranded gas and gas markets around the world that
they are typically transported to. With its increasingly improving technology and declining costs,
LNG offers a means by which natural gas producers can transport gas to distant customers via
specialized tankers (Black 2010). LNG exports are especially useful for producers that lack a
23
sizable domestic or regional market that they can service as is the case with many developing
countries including Tanzania. The marketfor LNG has evolved from a 3 MMTPA industry globally
in the 1970s to 106 MMTPA in 2001 and is expected to continue growing, doubling between
1999 and 2020 (Coyle, Durr and Shah 2003).
PYc0 kPor Markftt
Low Cost Strarded Gas
High
*
Figure2-1: Stranded Gas Reserves/High Price Import Markets
LNG is produced by cooling natural gas to -161 degrees Celsius. In its liquefied form, the gas fills
approximately 1/600th of the space it occupies in gaseous form. This fact makes it economical
to develop gas reserves that would otherwise have been too expensive to explore (Coyle, Durr
and Shah 2003).
Components of the LNG value chain include - gas production and field
processing, onshore gas treatment, and gas conversion via liquefaction. Others are LNG shipping,
receiving terminal and end use as a fuel (Coyle, Durr and Shah 2003).
Gas is sourced from nearby gas fields and piped to production and conversion facilities where
processing and liquefaction take place. The liquefied gas is transported in specialized vessels
that keep their cargo at very low temperatures during transport to buyers. At the buyer's end,
regasification is carried out at receiving facilities from where it can be distributed locally via
pipeline to end users. On the supply and demand sides of this value chain, significant investments
in infrastructure are required and will often result in job creation.
The value chain is illustrated in figure 2-2.
24
At Source Location
Gas Field
m
Gas Production
Facility
Gas Conversion
Facility
Transportation to
Buyer
Distribution to
Receiving
Users
Facilities
At Consumer Location
Figure 2-2: Typical Gas Monetization Value Chain (Coyle, Durrand Shah 2003)
As a result of the dominance of long-term supply contracts, breaking into the LNG market is not
straightforward even if a new producer's gas is cheaper than competing gas. Timing of projects
are important as it can allow new projects to compete for opportunities that open up as preexisting long-term contracts reach expiry (Paltsev, et al. 2013).
2.2
Urea Production
Global demand for urea is approximately 165 million tons annually and up to 90 percent of that
is used as fertilizer. It is the most commonly used fertilizer (Zwart 2013). Besides its use as a
fertilizer, urea is used in the production of urea-formaldehyde (UF) resins, melamine, and
potassium cyanate as well as urea nitrate. Further demand growth is expected to come with the
growing demand for biofuels (MEEA 2010).
Gas Field
Figure2-3: The Urea ProductionProcess. Adapted from: (Toyo EngineeringCorporation2012)
25
Regions with large natural gas endowments tend to be home to large urea exporters reflecting
the importance of natural gas to the manufacturing process (Yara 2012). The largest consumers
and manufacturers of urea are in Asia and there is a thriving international trade between
countries with excess production and those with unmet demand (FactFish 2014). Twenty-seven
percent of urea manufactured in 2010 was traded internationally (Yara 2012) demonstrating its
viability as a trade commodity.
2.3
Electricity Production
Burning gas to generate electricity is an increasingly popular choice, especially given the
relatively low carbon emissions levels and the trend towards limiting emissions. Gas can either
be delivered to distant power stations where electricity is produced or used in nearby power
stations to generate electricity which can then be transmitted by wire to consumers. The decision
about how to proceed will usually depend on economic and technical factors such as location of
demand centers, and transmission losses that affect financial viability and technical feasibility.
Consumers
Gas Field
Power Plant
Transmission & Distribution
Figure2-4: Conversion of gas to power
Gas power plants are able to serve base load and peaking functions in a power system. They have
short lead times and are flexible and generally quite reliable. Monetization by electrification
offers two immediate benefits - it is usually a reliable gas consumer, thus deepening the domestic
market for gas and given the widely recognized links between electricity use and economic
26
output, its increased availability could trigger or at the very least support further economic
development
27
28
Chapter 3
Tanzania's Electricity Sector
Despite the fact that Tanzania's economy has managed an impressive growth rate of 6 - 7 percent
in recent years, popular opinion holds that economic growth is hampered by the crisis in the
country's power sector (Norton Rose Fulbright 2013). As such improving access to electricity is
a priority issue for the country's government To this end, an official government electricity
demand forecast and plans for how to meet it was drawn up and articulated in the Power Sector
Master Plan that was produced by the Tanzanian Ministry of Energy and Minerals.
3.1 Overview of the Tanzanian Electricity Sector
The electric power system in Tanzania is operated by the state owned, vertically integrated
monopoly Tanzania Electric Supply Company (TANESCO). TANESCO has an installed capacity of
1028 MW serving a population of approximately forty-four million people. Fifty-five percent of
the existing capacity is hydro with the balance made up of thermal capacity. A far cry from
Sweden's 17,000 kWh for instance, the per capita electricity consumption in Tanzania is less than
100 kWh annually (Larsson 2007). Furthermore, there is a severe access deficit in the country,
29
with less than a fifth of the country's population having access to electricity and unsurprisingly
the situation is particularly severe in rural areas (Norton Rose Fulbright 2013).
Electricity demand is forecasted to grow at over 10 percent annually thus necessitating
significant investment in capacity expansion. Fortunately Tanzania has untapped hydro, coal and
natural gas resources that could allow it meet its future electricity needs (Larsson 2007) if
properly deployed.
One challenge facing the power sector in Tanzania is that electricity has been artificially cheap.
The low prices impose financial pressure on TANESCO, ultimately resulting in operating losses
for the company and affecting the financial viability of the power system as currently operated
(Norton Rose Fulbright 2013). Fortunately, TANESCO was recently able to secure approval to
raise tariffs and so could be on the way to financial stability (EWURA 2013).
Ruaw.
4%
Uir I:% Sai'mal
So%
4
1.4
4
it1
- V,
i
Figure3-1: ElectrificationRates around Tanzania (Larsson 2007)
3.2 Bridging Electricity Deficit
The Tanzanian government set itself a target to increase per capita consumption of electricity
from 81 kWh to 200 kWh between 2011 and 2016. In addition to increasing electricity
consumption per individual, the government also intends to increase the electrification rate to
30
75 percent by 2035 (TZ MEM 2013) from under 20 percent. If the government plan is followed
faithfully, then capacity additions will be rapid in the early years followed by a more moderate
pace in the later years. The plan requires almost non-stop investment in new electricity and
related infrastructure over about two decades. The largest capacity additions occur between
2014 and 2023, and are only a fraction of overall investments as transmission and distribution
will have to grow as well to keep pace with expected electricity supply.
3.2.1 Power Generating Technologies
According to details provided in (TZ MEM 2013), the government expects future electricity
capacity additions to come from fuel resources that are domestically available and so to be
predominantly from new coal, hydro and gas power plants. These choices are intended to take
advantage of domestically available resources. They come with different environmental
implications, associated costs and construction lead times.
Coal is the most widely available fossil fuel in the world and coal fired power plants provide for
over 40% of global electricity needs. However, it is considered to be a dirty fuel, with over a
hundred pollutants released into the atmosphere when coal is burned. It is a major source of the
world's carbon dioxide emissions and though technology for limiting emissions exist, they come
at a very high cost [ (Schissel, Smith and Wilson 2008), (Freese, Clemmer and Nogee 2008) and
(IEA-CIAB 2010)].
Hydropower is a zero-emission resource that takes advantage of the "natural energy of flowing
water to provide clean, fast, flexible electricity generation" (DOE 2004). According to (IRENA
2012), it"is the most mature, reliable and cost-effective renewable power generation technology
available" and 16% of the world's electricity and 80% of the renewable electricity are produced
from hydro power sources. By controlling the flow of water, hydropower plants are very flexible,
able to respond quickly peak demand and unexpected events in the power system and as a result
can play an important role system stability (DOE 2004).
Natural gas power plants on the other hand have low carbon emissions levels and capital costs
when compared to other fossil fuel based power generation technologies. They also have the
31
added benefit of short construction lead times when compared to coal plants for example which
can take more than twice as long to construct Gas fired power plants are uniquely able to meet
baseload requirements while also being able to respond flexibly to fluctuating demand from
consumers and supply from renewable sources. Gas fired power plants are either combined
cycle, steam turbine or gas turbine plants [ (AEP n.d.) and (MITEI 2011)]. CCGT plants are
currently the most popular kind of gas fired power plants. They generate electricity in the same
way as regular gas based plants but also make use of the waste heat to generate some more
electricity as well. As a result, they are significantly more efficient than older technologies
(Alawode and Omisakin 2011).
The ability of the Tanzanian government to faithfully follow its ambitious electrification plan and
either pay for or attract the required private investments remains to be seen. The capital costs
of setting up the different power generating technologies and the construction lead times will be
big factors in determining how successful the plans will be. With that in mind, the lower capital
costs per kilowatt, shorter construction lead times and the domestic abundance of natural gas
could all work together to make gas power plants an increasingly attractive option in the country.
In fact, given these relative advantages, investing in gas power plants could prove to be the only
way that Tanzania is able to meet the financial requirements of its ambitious electrification
targets.
32
Chapter 4
The Model
Given the scant information on decisions made by the Tanzanian government with regards to
developing the gas sector, I have developed a model that enables analyses of a range of possible
future scenarios. The model is split into sub-models for LNG export, electricity production and
urea manufacture - the three monetization options considered. The model was implemented
entirely in Microsoft Excel.
4.1
Scenario Analysis Models
Scenario analysis is well suited for considering long term issues. It is used in the corporate world
and the public sector as a tool for formulating strategies for future business and social
development efforts. By examining possible future outcomes, it serves an important role in the
mitigation of risks (Maack 2001).
Scenarios describe possible versions of the future and the elements that lead to those outcomes.
The degree of detail associated with each scenario depends on what it is intended to describe
(Kosow and Gabner 2008). They are particularly useful at times when there is uncertainty about
what will happen (Postma and Liebl 2005) as is the case in Tanzania. Qualitatively, good
scenarios are plausible, consistent as well as distinct and relevant to the issue at hand (Maack
33
2001). Several different kinds of scenarios are used depending on what the objective of the
analysis is. (IAA 2013) identified a couple of them including:
"
Historical scenarios
*
Synthetic scenarios
"
Single-event scenarios and
"
Multi-event scenarios
Various approaches are followed to carry out scenario analysis and as such there are no rigid
implementation rules. The approaches generally tend to involve an iterative process that
includes preparations, scenario building, writing scenarios and then using the results of analysis
to plan, implement and then improve strategy (Maack 2001).
Synthetic scenarios were developed for this study. They consider events that could play out in
the future. They tend to be very dependent on assumptions (IAA 2013) and as such for
correctness, assumptions have to be carefully made. The scenarios developed for this study are
discussed shortly.
4.2 Implementation
4.2.1 LNG Exports Sub-model
The LNG sub-model tracks exploration costs, capital investments as well as the revenue flows to
be earned by participating parties if the LNG option is pursued. In this thesis the focus is on
government revenues so private sector revenues are not reported on.
Perhaps the most important assumption in this sub-model is the capacity of the proposed LNG
trains. It is important because together with the price of the gas it has the most direct effect on
the amount of revenue that can be earned. Consistent with the assumptions made by (Castelo
2013) in the DNEAP model for neighboring Mozambique, this sub-model of my model assumes
that each of the LNG trains to be built will have a capacity of 5mmtpa. Also based on the DNEAP
model, I assumed that each LNG train will cost about $6 billion and take six years to build. There
34
are three scenarios with four, six and eight LNG trains respectively. Construction work on new
trains is assumed to commence in two year intervals. Such a speedy of expansion is not
unprecedented as is evident from the pace at which the Nigeria LNG company was able to expand
its production capacity (NLNG n.d.).
The facilities are assumed to operate at full capacity over the duration of time considered in the
model. In reality, actual production levels will probably be driven by a combination of long term
supply contracts as well as the less predictable opportunities for gas sales on the spot market
(Hartley 2013) and so will not always result in full capacity utilization. This assumption is a
useful way to determine the absolute maximum gas production and revenue generation levels
possible in each of the scenarios in this sub-model.
The gas prices used in the model are those forecasted for Japan as reported by (Medlock III 2014)
less $4 to account to for the cost of liquefaction and transportation. Asian prices are used because
like Mozambique which has already secured long term supply deals with Asian buyers (Team
2014), Tanzanian LNG exports will probably find a market in Asia. The forecasted prices as
illustrated below in figure 4-1 reflect the expectation that prices in Japan will decline from the
highs triggered by the 2011 Fukushima nuclear disaster and the consequent shutting down of all
Japan's nuclear power stations. According to (Medlock III 2014), the price decline is expected to
come about as a result of increased global gas supply as new producers like Tanzania join the
market, gradual restoration of the nuclear capacity or a combination of both factors.
Natural Gas Price Forecast ($/MMBtu)
12
10
8
6
4
2
0
-t
-4j
-n
v-1
kD
-1J
-
-4
r-
F W - : - G Pr4c F cas tn mr W M 0 ML
4 r4
N
r
q
r4j
N'
Figure4-1: Natural Gas Price Forecast
35
N
The sources of government income from LNG depend on the specific rules in the country and the
nature of government participation in the industry. In Tanzania, the rules as described in (TPDC
2013) suggest that the government reserves the right to participate directly in the industry
through joint ventures in which the state owned TPDC and private investors both hold equity in
developing the gas. However, for simplicity the scenarios considered in this paper do not explore
joint ventures. The model uses the rules in (TPDC 2013) as assumptions for determining revenue
flows between government and private investors.
The first source of government revenue is royalty payments. Royalty payments are a percentage
of the value of the gross gas production from a contract area and are made before any recovery
costs are deducted. In the case of Tanzania it is 7.5 percent. Furthermore, from the net
production investors are entitled to some of the gas produced up to a 50 percent limit to recover
qualifying expenses incurred up that time. Any unrecovered expenses are rolled over to the
following year (TPDC 2013).
The gas left after allowed cost recoveries have been deducted is called profit gas. The profit gas
is split between the government and the operators of the gas field according to a pre-agreed
sharing formula. The sharing formula from (TPDC 2013) is used in the model. It is shown in table
4-1 below.
Government Share of Profit Gas
Average Daily Production (MMscfd)
0-149.9
60%
150-299.9
300-449.9
500-599.9
600-749.9
>750
65%
70%
75%
80%
85%
Table 4-1: Summary of ProductionSharingFormula
The government earns additional revenue from the corporate income tax that the gas companies
are required to pay. The 30% rate used in the model is as specified in the Tanzanian Income Tax
11 of 2004 for corporations (KPMG 2012).
36
Taxable income is the sum of a company's revenues from gas sales and recovery gas less
exploration costs, operations and maintenance costs, depreciation, and royalties. As per usual
the model is designed such that if investors make losses, such as in the years before gas
production starts and revenues are earned, tax paid is zero. The algorithm for determining tax
paid in a particular year is of the form:
If Ti x TR <0 then CIT=0
Else CIT = TI x TR.
Where CIT = Corporate Income Tax,
TR = Tax Rate, and
TI = Taxable Income.
In accordance with methods adopted by (Castelo 2013), the pre-production cumulative CAPEX
is depreciated at 20% annually. After production begins, depreciation for a particular year is
calculated by applying the depreciation rate to the difference between the cumulative CAPEX for
that year and that from four years earlier.
Pre - production Depreciation(in yr X) = 20% x CC(in yr X)
Post productionDepreciation(in yr K) = 20% x [CC (in yr K) - CC (in yr K - 4)]
Where CC = Cumulative CAPEX
Figure 3-2 illustrates the various paths through which revenues from Tanzanian gas production
will flow for both investors and government.
37
Total Government Revenue
= Royalties + Govt. Share of Profit Gas + CorporateIncome Tax
Gross Production
Net Production
Royalties
Cost Gas
Profit Gas
Government Profit Gas
Company Profit Gas
]
Corporate Income Tax
I
Tanzania Government
Revenue
Company Revenue
Figure4-2: LNG Revenue Flows [Adaptedfrom (Castelo 2013)]
Some of the other assumptions used in this sub-model are:
Royalties
7.5%
Corporate Income Tax
30%
Cost Recovery Limit
50%
Table 4-2: Selected LNG Exports Sub-model Assumptions
4.2.2
Urea Production Sub-model
Just like the LNG sub-model, the urea sub-model tracks capital investments and revenue flows if
urea production is pursued as a monetization option.
As in the other sub-models of the model, three scenarios were modeled for this sub-model as
well. The scenarios explore outcomes that may arise in the event that one, two or three urea
38
plants are constructed respectively. Consistent with other plants being built on the continent,
each plant in this sub-model is assumed to have an annual urea production capacity of 1.3 million
tons and to cost about $2 billion to construct. Similarly the model assumes that construction time
is three years and that construction of each new plant commences two years after the previous
one was completed (IFC 2012). Though 1.3 million tons of urea is roughly twice the estimated
2011 urea consumption in East Africa (FactFish 2014), it is reasonable to expect that exports to
the wider international market would be part of any plans for urea production in Tanzania and
so there would be a market for all of the output. As in the previous sub-model, as a simplification
capacity utilization in all of the scenarios is assumed to be 100 percent.
Total gas required was computed as the sum of the gas required to produce the ammonia input
as well as to produce the urea itself. As in the LNG sub-model, taxable income was computed as
company revenue less qualifying expenses. The depreciation method applied in the LNG submodel was also adopted in this sub-model as well.
The urea plants are assumed to be owned by private investors with no direct equity participation
by the government or its agencies. Also, it is assumed that the plant operators will purchase gas
as feedstock at the international gas price and pay taxes. As a result, government revenues
stemming from investments in urea plants are expected to come directly from gas sales and the
resulting corporate income taxes.
Some other assumptions used in this sub-model are:
Volume of natural gas required per ton of ammonia
32.37 MMBtu
Gas required per ton of urea produced
28.13 MMBtu
OPEX
$40/ton
Table 4-3: Selected Urea Sub-model Assumptions
The model also assumes that current urea prices in Tanzania represent competitive
international prices since there is no local manufacturing capacity and the country's urea supply
is met by imports. Then starting with the Tanzania urea price (converted to 2010 dollars) for
January 2014 as reported by AMITSA, the Regional Agricultural Input Market Information and
Transparency System, the model computes future urea prices. For the calculation, the model
39
assumes that urea prices will change at the same rate as the LNG price since natural gas is the
most expensive input in the manufacture of nitrogen based fertilizers (US GAO 2003). This means
that a plot of the urea price forecast would be have the shape as that for gas price forecasts.
4.2.3
Electricity Production Sub-model
The electricity sub-model tracks capital investments and revenue flows and related costs while
also considering the different roles natural gas could play in bridging the electricity deficit in
Tanzania. Consistent with a more specific focus on Tanzania's electricity sector, this sub-model
also considers carbon emissions as well as spending on fuel over the economic lifespan of each
new power plant. The nameplate capacities of the plants in the individual scenarios differ but,
consistent with assumed capacity factors, the total energy output across each of them is equal to
that projected in (TZ MEM 2013).
The first scenario is based on the government plan detailed in (TZ MEM 2013). In this scenario,
natural gas power plants have the smallest share of planned new generating capacity compared
to either coal or hydro.
The second scenario is one that may be considered somewhat unusual at first glance. Following
the same timetable proposed in (TZ MEM 2013), only gas fired power plants are built. In other
words, instead of the mix of technologies and fuels proposed in the original government plan,
this scenario considers outcomes that could arise from building only CCGT plants instead.
The domination of a power system by a single technology and fuel is not unprecedented. It has
happened in several countries around the world as demonstrated in figure 4-3 below. Though
this scenario may never actually play out, it is a useful extreme to investigate because it allows
us to consider an upper limit for gas use in addressing Tanzania's electricity needs as specified
in the government's own plans.
The final scenario is a low carbon scenario in which no coal plants are built. Instead only natural
gas and hydro plants are built. Tanzania may want to start early to avoid carbon emissions for
reasons ranging from wanting to avoid possible future carbon taxes, future international
conventions to simply doing its part to be environmentally responsible.
40
The model assumes that the operational lives of gas, coal and hydro plants are twenty, twentyfive and fifty years respectively. However, as before, these simplifying assumptions once again
make the computation of an absolute upper limit of possible fuel requirements and levels of
carbon emissions more straight forward.
Fuels for Electricity
100.00%
90.00%
80.00%
70.00%
60.00%
50.00%
40.00%
30.00%
20.00%
10.00%
0.00%
U N. Gas
U Oil
U Coal
E Hydro
Figure4-3: Fuelsfor ElectricityGeneration (data gatheredfromMacro Economy Meter - http://mecometer.com/)
Currently, electricity prices in Tanzania are proposed by TANESCO, the state owned power
utility, and are subject to approval by EWURA, the regulatory authority. In late 2013, TANESCO
proposed a tariff increase that would have approximately doubled the price of electricity
between late 2013 and 2015, including an initial 67.87 percent increase. Instead EWURA
approved a 39.19 percent increase effective from January 2014 until 2016 unless a cost of service
study to be carried out in 2015 finds that an earlier increase is justified (EWURA 2013). The
energy prices used in the model begin at the level approved by EWURA for the energy charge
and then assumes that prices increase between 2016 and 2019 to the levels in TANESCO's
original application. Afterwards they remain constant.
The model uses energy prices, thus excluding any charges associated transmission and
distribution. In this way power generation is considered as an economically distinct activity and
41
the model focuses on the electricity related activity that can most directly be associated with
natural gas use.
As per the norm in Tanzania, the model's energy prices are differentiated across the three tariff
categories - general usage, low voltage and high voltage. Electricity consumption across the
three tariff groups in the model follows ratios forecasted in (TZ MEM 2013).
Even though domestic Tanzania gas prices may eventually be lower than export prices, the
international gas price used in the LNG and urea sub-models is also used in the electricity submodel as well since it represents the competitive value of the gas to Tanzania. In this sub-model,
it also assumed that new investments in new generating capacity will be private sector driven.
Consequently, similar to the case in the urea sub-model, government revenues that flow directly
from the use of gas for electricity will comprise of revenues from the domestic sale of
government's share of profit gas and taxes paid by the generating companies.
Unlike in the other sub-models, depreciation of the generating plants is computed using the
straight line method. Straight line method of depreciation is the method generally adopted for
depreciation under cost of service regulation of the electricity sector (Gomez 2013). The model
assumes that the salvage value of generating plants is a quarter of its original value. The annual
depreciation was computed using the formula:
Annual DepreciationExpense =
Cost of Fixed Asset - Salvage Value
Economic Life of Asset (Years)
The Levelized Cost of Energy (LCOE) is a useful means of distinguishing the relative cost of
electricity produced by power plants (Kost, et al. 2013) and is a useful means of comparing the
three scenarios considered in this sub-model. The levelized cost of producing electricity at each
plant, in each scenario was computed using:
= (1 +r)t
+ t
I+
X(1
E
En
t=1 (1 + rOt
42
Where,
I = Investment Expenditure
At = Annual Total Costs in year t
Et= Produced quantity of electricity in the respective year in GWh
r = real interest rate
n = operational lifetime in years
t = year of lifetime
Some other assumptions made in this sub-model are summarized in table 3 below:
Natural Gas Heat Rate
Thermal Plant Capacity Factors
8,513.82 Btu/KWh
0.75
Fixed O&M Coal Steam Thermal
Fixed O&M Gas Turbine
Fixed O&M CCGT
Fixed O&M Hydro
70.33 USD/KW/yr
9 USD/KW/yr
7 USD/KW/yr
16 USD/KW/yr
Variable O&M Coal Steam Thermal
0.0075 USD/KWh
Variable O&M Gas Turbine
Variable O&M CCGT
0.0056 USD/KWh
0.003 USD/KWh
Table 4-4: Selected Electricity ProductionSub-model Assumptions
With the results obtained for each plant, the average LCOE in each scenario weighted by the
capacity of each of the power plants in the respective scenarios was computed to get three
weighted average LCOEs.
Furthermore, in order to compare the various costs associated with each of the scenarios in this
sub-model, a discounted cash flow analysis of projected fixed O&M costs, variable O&M costs and
fuel costs were computed for the assumed economic lifespans of each power plant. For simplicity
the capital cost associated with the construction of each plant was assumed to be an overnight
cost and so unlike the other costs was not discounted.
Discounted cash flow (DCF) analysis is a method by which investment opportunities are assessed
in so as to determine the present value of future cash flows (Investopedia n.d.). It makes it
possible to compare projects on an even basis regardless of differences in "capital scales, risk
characteristics, and timelines to delivery" (Paltsev, et al. 2013).
43
Discounted cash flows were computed using:
Discounted Cash Flow =
Cash Flow
(1 + r
Where,
r = discount rate
n = year of operation
4.3 Shortcomings of the Model
As is the case with all models, the one developed for this paper is imperfect. Generally speaking,
a shortcoming of scenarios analysis is that there are inevitably unknown and sometimes
unknowable variables (Postma and Liebl, How to improve scenario analysis as a strategic
management tool? 2005). As a consequence, assumptions simply have to be made.
My model for Tanzania included many simplifying assumptions such as that of non-stop
production at the various plants considered and overnight costs for power plants. There is also
the fact that since there are no confirmed plans about things like the capacities of future LNG and
urea plants, the scenarios modelled are largely hypothetical and so may never happen as
assumed. That said, it also important to note that even confirmed plans can often change and so
even when they exist, there will always be a degree of uncertainty about future outcomes.
The scenarios model for Tanzania remains useful because it achieves its objective of providing a
relative measure of the usefulness and economic impact of each monetization option considered.
44
Chapter 5
Presentation and Discussion of Results
Government revenues are a measure of what the government can do domestically for
development through public investments in infrastructure for instance. And even though
revenue maximization would be a legitimate goal for the Tanzanian government to pursue, the
results discussed in this chapter demonstrate that larger revenues do not necessarily translate
into higher economic value for the country. The results show that though the government will in
the long term earn the most revenue from exporting LNG, among the options considered actual
maximum direct value to the Tanzanian economy for each unit volume of gas will come from
generating electricity.
5.1 Results
5.1.1 Revenues
In the model the Tanzanian government either exports gas or is able to sell it domestically at the
same price as it would obtain in the international market. The reason for this, as indicated earlier,
is that international prices are usually the most competitive and so reflect the actual value of the
gas to the Tanzania even if some of it becomes opportunity costs given up through subsidies.
45
................
- ........
. ... ................
......
.....
. .... .......................
...............................
. .. .......
....
....
....
.
.............................
..-- -.
.................
..........
Based on the sharing formula and associated payments to the government proposed in (TPDC
2013), the model projects very significant government income in all of the three LNG export
scenarios. Consistent with expectations, figure 5-1 shows that by 2035 annual revenues are
highest in the scenario with 8 LNG trains. The results demonstrate that even with investors
deducting cost gas for cost recovery, the government can expect to begin earning steadily rising
revenue right from the year exports begin. Starting with a revenue of about half a billion dollars
in the first year of operation in all of the scenarios, fifteen years later annual revenues will range
from just over $6 billion with four LNG trains to approximately $9 billion with eight LNG trains.
The results also show that cumulative government income after fifteen years of operation could
be almost $66 billion with eight trains, a figure that is only marginally higher than revenue from
six trains. Annual government revenue with eight trains only surpasses revenue in the case of
four trains after about eight years of production in 2028 and at about the same time in the case
of six trains. These results reflect the effect of the additional investment on the rate at which
government revenues grow since investors have more costs to recover. Greater costs to be
recovered by investors result in delayed returns for government but ultimately, the government
will eventually earn more with higher production volumes
Cumulative Government Revenue (LNG)
70000
C
0
60000
50000
40000
w 30000
U 20000
CC 10000
0
2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035
-4trains
-
6trains
-
8trains
Figure S-1: Plot of Cumulative Revenues from LNG Export
46
............
The results show no surprises in outcomes for the manufacture of urea in Tanzania. Projected
government incomes from urea are significantly smaller than those that it can expect to earn
over a similar period from exporting LNG. This outcome is not unexpected given the vastly
smaller amounts of gas involved and the fact that the government is less economically involved
in the in the industry. The more urea plants there are, the more revenue the government will
earn but as with everything there is a limit to how much production capacity the market can
support.
After seventeen years of operation, annual revenues vary between $540 million in the one plant
scenario to $1,571 million three plant scenario. Similarly the cumulative government revenues
earned from a urea industry will range from approximately $8 billion to $17 billion for the
production scenarios considered.
Cumulative Government Income (Urea)
)
20000
18000
16000
14000
12000
10000
'
8000
6000
c: 4000
2000
0
2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035
-
3 Urea Plant
-
2 Urea Plants
-
1 Urea Plant
Figure 5-2: Plot of Cumulative Government Incomefrom Urea
Electricity capacity in all three scenarios is intended to meet the same demand projections and
so the same amount of electrical energy is produced and sold at the tariff approved by the
regulator. As a result, the same gross revenue is earned for the generation of electricity
regardless of the scenario. However, government revenues vary from one scenario to another
because of the differences in revenues generated from fuel sales and in the taxes collected.
47
-- - -
- _- - - - __ .
...
.....
.....
..................
......
..
...
...............
....
...
..
.
............
The results demonstrate that at assumed prices, the government will earn a lot more revenue
from selling gas for electricity production than for urea production.
Cumulative Government Revenue (Electricity)
80000
70000
60000
0 50000
40000
30000
20000
10000
0
-
PSMP
-
Gas Only
-
Low Carbon
Figure5-3: Plot of Cumulative Government Income from Electricity
Bringing the results from the different sub-models together, figure 5-4 shows plots of the annual
and cumulative revenues that will be earned by the government from the different monetization
options based on the assumptions made in the model. The plots show how the three LNG
scenarios fare against the most gas intensive scenarios from the other sub-models. They
illustrate just how significantly urea lags behind the other two options as a source of public
revenues and how rapidly LNG exports can rise to close the revenue gap with electricity
production despite the gap in start times.
5.1.2 Value Added
Although the cumulative revenues earned by the government are an important component of
factors to be considered in decision making, considered in isolation they may give a biased
measure of the potential economic value of the gas produced and used in the different options.
It is vulnerable to the effects introduced by the different market sizes for different options.
48
Annual Govemnmeot Revenue Across Monetization
s02
cn
02622
Options (4 tiamns)
9/22
09200n
Cumulative Government Revenue Across Monetization Options
(4 trains!
Annual
0
0
nco
" *iceJV
Cumulative
Revenue
Government
10 1
Government
Across Monetization
X-
Options
(6
trains)
'W
Revenue
Annual Government
A141 XM
Across Monetizatonr
Revenue
Across Monetization
9
i..w
7
o2012,RV
Lf-s
Z,').
iO
7002
-
Options 16 trains!
'L2.
20v
'12S
Dveosoj 1-sso
-
-n
-cO
to
Cmu
Figure 5-4: Government Revenue across the Monetization Options
Loe
t
(8
trains)
?'70 x n
ative Govemment Revenue Across
..............
1,2
-((Sc 2v- i.Oiv,
Options
0500
!0-000
Monetizat-on
Options iS train s
As a result of these effects, revenue outcomes will usually favor the option with the largest
market. To account for this likely bias and to get a dollar measure of actual economic impact,
value added per unit volume of gas produced was also calculated in the model for each of the
monetization options.
"Value added based productivity measures reflect an industry's capacity to contribute to
economy wide income and final demand" (OECD 2001) and so is a good proxy for an activity's
contribution to the gross domestic product. For the purposes of this study, to even out the effects
of the different market sizes for each of the monetization options, the value added by each option
was divided by the corresponding volume of gas to give the value added per unit volume of
natural gas used in creating that value.
The value addition considered in the model is limited to the direct or primary impacts of each
option considered. The domestic production of urea and generation of electricity using natural
gas do not necessarily introduce new downstream value adding effects. For instance, all of
Tanzania's current urea consumption is satisfied through imports and once the urea is available,
regardless of how it was supplied, most of the same downstream effects will occur whether it is
in transportation services or agriculture. In the same way, value is added to the economy per
kilowatt regardless of how it generated. Downstream value will be added to the economy as a
result of increased electricity supply regardless of whether it is generated by gas, coal or hydro
power plants.
It is also worth noting that it was assumed that domestic industries will be set up close to the
source of gas. As a consequence, value addition by gas distribution activities such as through
pipelines were assumed to be negligible and so were not calculated.
According to Prof. Channing Arndt of the United Nations University, economists often associate
coefficients with certain industries and economic activities when trying to estimate value added
by those industries or activities. As such, in the model, value added by each of the monetization
options considered was computed as a percentage of gross revenue generated by the option
being considered. To determine what would be an appropriate coefficient I consulted Prof.
Arndt. After our discussion we agreed that based on results from similar industries around the
world, 20% would be an appropriate coefficient to apply across all the monetization options
considered in the model.
50
.......
...............
......................
....
........
...
.
....
. .....
.
Value Added per TBtu of Gas Used
6
5
4
3
2
1
2020 2021
2022
-
2023
2024
LNG Scenario 2
2025 2026
-
2027
2028
2029
Electricity (Gas Only)
-
2030 2031
2032
2033
2034 2035
Urea Scenario 1
Figure5-5: Value Added per unitvolume ofgas used
The results obtained and illustrated in fig 5-5 demonstrate that for each unit of gas produced,
electricity returns by far the most value to the economy and that LNG returns the least value. In
other words, if the Tanzanian government wants to rank priority areas that should receive gas
in terms of the value they add to the economy, electricity would be top ranked and LNG exports
despite the larger sized markets would be the lowest ranked.
5.2
Effects of Global Competition
The results so far presented are of projections based on the best available information about the
fiscal conditions under which operators in Tanzania would operate. In reality some of these
conditions may not hold as a result of competition for investors from neighboring Mozambique
where even larger reserves of gas have been discovered [ (Gloystein and Vukmanovic 2013) and
(Blas 2013)].
For instance, while royalty payments in Mozambique can be as low as 2 percent, the Tanzanians
currently propose to charge a 7.5 percent rate. Similarly, while investors in Mozambique are able
to recover their costs every year up to a limit of 65 percent, cost recovery in Tanzania is 50
percent. Additionally, for the first eight years of operation gas producers in Mozambique pay
corporate taxes at a 24 percent rate before it rises to 32 percent. In Tanzania, the rate is 30
percent [ (Melina and Xiong 2013) and (TPDC 2013)]. Furthermore, unlike Tanzania which
51
.
.........
.
..........
proposes to share profit gas on the basis of daily production levels as illustrated in table 3-1,
Mozambicans have adopted an R-factor which is calculated as the ratio of "the concessionaire's
cumulative cash inflows net of operating costs and tax, to its cumulative capital expenditures"
(Melina and Xiong 2013).
Under Mozambican conditions as described in (Castelo 2013) and (Melina and Xiong 2013),
investors will earn billions of dollars more in revenue than they would under the Tanzanian
conditions suggested in (TPDC 2013) and used in my model. Figure 5-6 illustrates how wide the
revenue gap could be for investors.
Cumulative Investor Earnings Tanzanian vs Mozambican Terms (4 LNG
Trains)
100000
90000
80000
70000
0
60000
50000
40000
30000
20000
10000
0
2020 2021 2022 2023 2024 2025 2026
-
2027 2028 2029 2030 2031 2032 2033 2034 2035
Mozambican Conditions
-
Tanzanian Conditions
Figure5-6: InvestorEarnings under Mozambican and Tanzanian Fiscal Conditions
Interested in speeding up cost recovery and earning higher returns, it is likely that investors
would use the more favorable terms available in nearby Mozambique and the threat of diverting
investments as leverage in negotiations with Tanzanian authorities. The Tanzanian government
will be expected to match and possibly surpass the incentives offered in Mozambique and doing
so will mean accepting less revenue.
Assuming that levels of production and prices are the same, then the gross revenues and
consequently the value added to the economy as estimated in my model will remain unchanged.
52
Royalties
Corporate Income Tax
Cost Recovery Limit
Tanzania
7.5%
30.0%
Mozambique
2.0%
24% (for first 8 years) and then 32%
50.0%
65.0%
Table 5-1: Some Differences in Investment Terms in Tanzania and Mozambique
Furthermore, there are several other competing projects at various stages of development across
the world. Each of these are viable at different gas prices. (Paltsev, et al. 2013) explored the
breakeven costs of possible Cypriot LNG developments and compared them to those of possible
competitors. The comparison also demonstrated that Tanzania's breakeven cost is lower than
those of several potential competitors as illustrated in figure 5-7 below.
16 r
* FOB Cost (Breakeven)
E Shipping Cost
12
a
10
8
1.
6
4
(D
cc
.~
'Op *E
Ulm
~UZ4
0j -;;
C 3
~Z
-a
E
'm0*
0
-
0
0p
0
2.
Figure5-7: Estimated Breakeven Gas Pricesfor Set of Major Contemporary LNG Projects
Fiscal conditions such as taxes and other levies imposed by the government will also go some
way in determining what the breakeven cost for an LNG project will be (Paltsev, et al. 2013) and
53
consequently how price competitive it can be. As a result, the Tanzanian government will also
have to take the range of breakeven costs of competitors into account when it considers setting
or adjusting these conditions.
5.3 Costs Associated with Generating Electricity
The cost of meeting future electricity needs in Tanzania is an integral part of plans to address the
lack of access to reliable electricity in the country. Figure 5-7 shows the overall costs of meeting
the Tanzanian government's own forecasts for future demand using the approaches specified in
the different scenarios in the model. According to the results, the gas only scenario is marginally
costlier than the PSMP scenario and the low carbon scenario would be the cheapest.
Perhaps the most useful feature of these results is the structure of the costs associated with each
scenario. They show that at $18.2 billion, the discounted capital cost of implementing the PSMP
scenario is higher than the discounted costs of the low carbon and gas only scenarios which are
$15 billion and $11.7 billion respectively.
Also notable is the role that fuel expenditures play in overall costs. With absolutely no renewable
sources included, it is not surprising that that fuel expenditures play an especially significant role
in the gas only scenario where they account for more than half of costs.
Furthermore, consistent with the high penetration of coal power plants in the PSMP scenario and
the high operating costs of coal power plants assumed in (TZ MEM 2013), the fixed operations
and maintenance costs in that scenario are several times the magnitude of those in the other two
scenarios.
54
..............
Selected Costs Over Operational Life
40000
35000
30000
25000
0
20000
15000
10000
5000
0
PSMP
U Capital Costs
Gas Only
U Fixed O&M Costs
U Variable O&M Costs
Low Carbon
i Fuel Expenditure
Figure5-8: Selected Costs over the OperationalLives of PowerPlants
The LCOE was computed for each power plant proposed by the Tanzanian government in the
PSMP as well as for the substitute plants under the alternative scenarios considered in the model.
To ease comparison, the average LCOE for each scenario weighted by the name plate capacity of
the respective power plants and the result is shown in figure 5-8.
Weighted Average Levelized Cost of Electricity
0.074
0.072
0.070
4Ln
0.068
0.066
0.064
0.062
0.060
PSMP
Gas Only
Low Carbon
Figure5-9: Weighted Average Levelized Costs of Electricity across the Different Electricity Scenarios
55
...........
.--l.........
--.
---
...
...........
..........
.
They indicate that the gas only scenario has a higher average LCOE than the other scenarios. The
extent of the difference is probably due at least in part to the dominance of fuel expenditures in
that scenario and the fact that as a result of the comparatively short economic lives of gas power
plants, costs are spread over a shorter period.
5.4 Gas Demand across Monetization Options
One of the most important issues facing Tanzanian authorities and investors in gas exploration
is the need to find a market for the gas. Tanzania has a small economy and so one may expect
that, like in Nigeria and Angola before them, opportunities for domestic demand will be limited
even under the most optimistic scenarios of domestic consumption. Consistent with those
expectations, the results from the model demonstrate that even the most conservative LNG
exports scenario offers a much larger market for Tanzanian gas than the most gas intensive
electricity and urea scenarios respectively, and eventually both of them combined. This result is
illustrated in figure 5-9 below.
Gas Consumption Across Monetization Options
1200
1000
t=
0
a)
800
600
E
400
>
200
2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035
0 LNG Scenario 1 (4 Trains)
U Electricity (Gas Only)
E Urea Scenario 3
Figure5-10: Gas Consumption in Selected Scenarios
56
Chapter 6
Policy Recommendations & Conclusions
I have addressed the question of whether Tanzania should export its gas or use it domestically,
and if used domestically, in what way. In previous chapters I presented a description of the
natural gas related opportunity and options facing Tanzania. I then explored different possible
futures for the country by exploring scenarios to determine what the outcomes could be. In this
chapter I therefore present key findings, policy recommendations and then conclude with
suggestions for further work that can be done to address questions that this paper raises.
6.1 Summary of Key Findings
1.
Though the initial set up costs and lead times are high, the export of natural gas as LNG
holds the greatest potential of the options I considered for direct government revenue
generation although revenue flows are initially slowed by investors having to recoup
their invested capital.
II.
LNG exports quickly outstrip the domestic utilization options of the natural gas
considered in the model. These results confirm that Tanzania's domestic markets cannot
support production of large volumes of natural gas in the short to medium term.
57
III.
There is no correlation between revenue generating potential and value added per unit
volume of gas. Under the assumed conditions and within the timeframe considered. Per
unit volume of gas consumed, electricity is about four times as valuable to the Tanzanian
economy as LNG export and is the most valuable of the options considered.
IV.
If electricity capacity expansion happens at the pace targeted by the government in the
Power Sector Master Plan, then in the short term, annual revenue generation from
electricity does not lag far behind LNG. This is important because it implies that at least
in the early years, just as was done in Qatar, Tanzania could potentially focus efforts at
promoting its electricity sector without a huge opportunity cost in foregone gas export
revenues.
V.
Though the capital cost of meeting projected electricity demand is least under the gas
only scenario, it turns out to be the most expensive choice in terms of the levelized cost
of electricity generated if gas is sold domestically at international prices.
There is the added downside that gas power plants have the shortest useful economic life
of the generating technologies proposed and so new plants would in theory be required
within the lifetime of hydro plants built in other scenarios.
VI.
Thanks to the combination of the government's ability determine domestic gas prices and
the dominant share of fuel costs in the overall power system costs, the government's
ability to influence domestic electricity prices is greatest under the gas only scenario.
6.2 Recommendations
Given the limited size of Tanzania's economy, the ideal outcome of policies intended to drive the
utilization of natural gas reserves on the scale now known to exist in the country should in the
long term, like in Trinidad and Tobago, be focused on "developing broad based domestic
consumptive industries and also a direct market in LNG" (Baksh 2008). In other words, the
government must provide the right conditions for investors to set up value adding industries
58
that will use gas as a feedstock for producing other goods and services in Tanzania alongside gas
export facing LNG investments.
The findings of this study make a strong case for delaying or at the very least slowing the pace of
exports if necessary so as to ensure gas availability for domestic use. To this end, instituting a
domestic supply obligation similar to the one in place in Nigeria would be an important part of
future policy. With regards to how the gas should be used domestically, I am convinced by the
results that in the short term, gas to power developments be given priority ahead of urea
production.
Furthermore, the overwhelming importance of gas prices to government's revenue were evident
from proportion of revenues that are expected to come from gas sales compared to taxes. Ideally
the government should avoid subsidies, but it may be worthwhile to accept some loss in revenue
as a fair price to pay to incentivize investors in domestic industries and to encourage wider local
demand. Subsidizing energy consumption is certainly not uncommon, but there is a growing
trend towards removing or at least reducing them (IEA n.d.), because of difficulties sustaining
them. The terms and duration of any subsidies adopted should be very clearly defined in
Tanzanian policy together with rules and procedures for how they will be reviewed or removed.
Predictability and transparency should help promote public and investor cooperation at review
times.
Beyond issues around government revenues, gas prices are an important part of the long-term
viability of any of the monetization options considered. Prices that appear attractive today may
not be as attractive in the future. Any plans or projections should include contingencies for
unfavorable gas prices which may be very low in the case of LNG exports or very high in the case
of electricity or urea production (Paltsev, et al. 2013).
Earlier discussions demonstrated that energy resource rich countries have had a tendency to
develop power systems around a single resource. Findings in this paper demonstrate that in the
absence of subsidies for domestic gas, allowing this to happen in Tanzania with gas would be a
mistake. Results suggest that to balance the objectives of promoting domestic gas consumption
through use in electricity production, provide affordable electricity to consumers and possibly
minimizing carbon emissions without sacrificing much government revenue, a combination of
gas and hydro power plants along the lines of the low carbon scenario would be the best choice.
59
6.3
Future Work
For future work I propose a study that incorporates more monetization options and considers a
longer time window than in this study. Furthermore, possible secondary and tertiary linkages
between the different monetization options, industries, the tax base as well as the wider
economy may be incorporated into analyses. I believe that factors like the job generation
potential (distinguished between temporary and permanent), worker earnings and the effects of
sustained government spending would also be worth considering.
These additional complexities may necessitate a different modeling method better suited for
more detailed economic analyses. For instance, a systems dynamics model or a computable
general equilibrium (CGE) model could provide a means of accounting for the interactions
between different sectors of the economy, the effects of government spending on long-term
economic activity, as well as a way to account for the effect of increased economic activity on
government tax revenues and consequently on government's capacity to spend.
It would also be interesting to investigate what specific levels of export versus domestic gas
utilization would allow government meet revenue targets through gas sales, maximize job and
wealth creation for the populace and still potentially provide sufficient income tax revenues for
government from the now better employed people to make up for the revenue losses that
domestic facing policies may cause in the short term.
60
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