The Geopolitical Impact of Shale Gas:
The Modelling Approach
Willem L. Auping, HCSS/TU Delft*
Sijbren de Jong, HCSS
Enik Pruyt, TU Delft
Jan H. Kwakkel, TU Delft
The Hague Centre for Strategic Studies
Lange Voorhout 16, 2514 EE, The Hague, The Netherlands
Policy Analysis Section
Faculty of Technology, Policy and Management
Delft University of Technology
Jaffalaan 5, 2628 BX, Delft, The Netherlands
* Corresponding author; e-mail: willemauping@ hcss.nl or w.|.auping@ tudelft.nl
Conference Paper for the International System Dynamics Conference, Delft 2014
Abstract
The US’ shale gas revolution, a spectacular increase in natural gas extraction
from previously unconventional sources, has led to considerable lower gas prices
in North America. This study focusses on consequences of the shale gas revolution
on state stability of traditional oil and gas exporting countries in the vicinity of
the EU. For this purpose, we developed two separate SD models. The first model
was used for assessing the impact of shale gas and energy decoupling on oil and
gas price developments. We selected some of these price developments as input
scenarios for a second SD model. This SD model was used for assessing the
impact of energy price scenarios on countries’ economic development, and via the
development of unemployment and purchasing power, on state stability. The
conclusion of this study was that while shale gas developments may be seen as a
part of the standard energy hog-cycle, this may lead to pressure on oil prices,
which may cause instability in traditional oil and gas producing countries in the
neighbourhood of the EU. Further, the effects of energy decoupling may have an
even larger effect on putting energy prices under pressure, thus leading to social
unrest.
Keywords: Shale gas revolution, Price scenarios, Social unrest, Scenario Discovery,
Uncertainty
1 Introduction
In recent years, a spectacular rise in natural gas extraction capacity from previously
unconventional resources has dramatically changed the US’ energy landscape, making the
country independent from natural gas imports. This development is often referred to as the
2 Auping, De Jong, Pruyt, Kwakkel, 2014, The Geopolitical Impact of Shale Gas
‘shale gas revolution’. These developments were made possible by the process of hydraulic
fracturing, or ‘fracking’. As a consequence of the shale gas revolution, American gas prices
have dropped strongly, giving a competitive advantage to American industry. The rise in
similar oil resources only adds to that advantage.
Presently, the shale gas revolution is still largely an American affair, as outside North
America no commercial exploitation of shale gas resources is taking place. This can be
explained both by institutional differences between the US and other countries (e.g., resource
ownership) and geological differences. However, it seems implausible that in a global energy
system, the effects of the shale gas revolution will remain limited to the US. Although LNG
trading is limited, shale gas may substitute other, easier transportable energy sources.
Eventually, the shale gas revolution may thus have an impact on global energy markets [ref
authors].
Price fluctuations may have consequences for the economic situation of traditional oil
and gas exporting countries, of which many are heavily reliant on resource rents for
supporting there economy. Resource rents are income generated by resource extraction, often
calculated as part of GDP. Therefore, fluctuations in resource prices may influence the
development of the local economies of oil and gas exporting countries. In turn, worsening
economic situations are known to have an impact on population discontent, potentially
leading to instability (Collier and Hoeffler 2004, Ross 2004).
The complexity and uncertainty of both the global energy system and national stability
make mental simulation difficult. Hence, using a quantitative approach able to cope with this
uncertainty and the delays in the system, may be useful. System Dynamics (SD) is such an
approach (Forrester 1961, Sterman 2000, Pruyt 2013). Further, many of the factors, including
resource figures and depletion paradigms (Tilton 1996), are fundamentally uncertain. Hence,
using an approach capable of handling uncertainty in combination with SD models is
appropriate. For this purpose, the Exploratory Modelling and Analysis (EMA) methodology
can be used (Lempert, Popper, and Bankes 2003, Kwakkel and Pruyt 2013) in an approach
similar to Scenario Discovery (Bryant and Lempert 2010, Kwakkel, Auping, and Pruyt 2013).
This allows using SD models on both aggregation levels (i.e., the global energy system and
country stability systems) in an extended ‘what if analysis (Oreskes, Shrader-Frechette, and
Belitz 1994, Kleijnen 1997).
In the field of SD, many examples exist of models regarding energy systems. The most
well-known example is of course the Limits to Growth and earlier related studies (Meadows
et al. 1972). Further, early models already focus on energy transitions (Naill 1977, Sterman
1981), and extemalities of energy economics (Fiddaman 1997). However, in our knowledge
no SD study used energy models as a scenario generator for price developments. These
scenarios are in essence sets of outcome indicators from particular runs forming an internally
consistent narrative about plausible future developments.
Although much of the conflict literature does not naturally fit SD thinking, examples
exist of modelling social unrest in SD. For example, Wils, Kamiya, and Choucri (1998)
present a model which can be used to examine the development of internal and external
pressure related to resource use. Further, Anderson (2011) used an SD model for looking at
the effects of counterinsurgency policy in relation to public support and other factors. Finally,
Pruyt and Kwakkel ((in press)) compared three SD models about the rise of activism,
extremism, and terrorism. In none of these models, however, extemal price scenarios were
used for ‘stress testing’ state stability.
In this paper, we present a multi-model approach (Pruyt and Kwakkel (in press),
Auping, Pruyt, and Kwakkel 2012) using a global energy model to generate energy price
scenarios, which function as input for testing via country stability model whether these
scenarios may lead to an increase in instability. Hence, we explore the long term effects of
Auping, De Jong, Pruyt, Kwakkel, 2014, The Geopolitical Impact of Shale Gas 3
shale gas and other unconventional energy sources on the global energy mix, and
consequentially plausible energy scenarios. This model fits within an opportunity costs
paradigm (Tilton 1996). We present a selection of these energy scenarios, where the focus lies
on taking those price scenarios that fall outside the scope of more traditional forecasts of
energy prices by for example the EIA and BP (2013, 2012).
These scenarios are than used as input for a country stability model, focussing on
economic discontent (i.e., ‘greed’ in Collier and Hoeffler 2004). As such, the price scenarios
are used for ‘stress testing’ country stability for traditional oil and gas exporting countries,
more specific those countries in the vicinity of the European Union (EU). These countries are
Algeria, Azerbaijan, Egypt, Kazakhstan, Qatar, Russia, and Saudi A rabia.
The setup of this paper is as follows. In next section, we will start with explaining the
research approach chosen in this study. We will then present an extended discussion on the
model structure of both the global energy model and the country stability model. In the
following section we will explain the metrics for choosing 14 price scenarios. Following on
this, we will present the results on country stability by taking Russia as an example. Finally,
we will discuss the results of this approach and draw conclusions on the geopolitical
consequences of the shale gas revolution.
2 Methodology
In this section, we will start with explaining the setup of the study and the use of SD and
EMA in an approach similar to Scenario Discovery. After this, we will present the global
energy model and country stability model used in this study! and a short discussion on model
validity.
2.1 System Dynamics and Exploratory Modelling and Analysis
SD (Forrester 1961, Sterman 1981, Pruyt 2013) is a modelling method which is particularly
useful for simulating systems which are characterized by strong feedback loops, delays and
stock-flow structures.
Exploratory Modelling and Analysis (EMA) is a research methodology that uses
computational experiments to analyse deeply uncertain issues (Bankes 1993, Pruyt and
Kwakkel 2013). EMA consists of quantitative modelling of the set of plausible models and
uncertainties, the process of exploiting the information contained in such a set through (a
large number of) computational experiments, the analysis of the results of these experiments,
and the testing potential policies for robustness (Bankes 1993, Pruyt and Kwakkel 2013).
EMA can be useful when relevant information exists that can be exploited by building
models, but where this information is insufficient to specify a single model that accurately
describes the real system behaviour. In this circumstance, models can be constructed that are
consistent with the available information, but such models are not unique, which is an
important reason for specifying different functions, model structures, or multiple models.
These models, combined with parametric uncertainties, allow for computational experiments
that reveal how the world would behave if any particular combination of assumptions would
be correct. By conducting many such computational experiments, the implications of the
various guesses can be explored.
The result of such an approach is in essence an extended ‘what if? analysis (Oreskes,
Shrader-Frechette, and Belitz 1994, Kleijnen 1997) and close to the Scenario Discovery
Parts of the model description have already been published in the report recently published by the authors [ref
authors]
4 Auping, De Jong, Pruyt, Kwakkel, 2014, The Geopolitical Impact of Shale Gas
approach (Bryant and Lempert 2010). In order to allow assessing the effects of long delays in
the system, such as developments in extraction capacity in the global energy model or
demographic effects in the country stability model, we simulated both models for the time
period between 2010 and 2060.
2.2 Global energy model
The balancing of supply and demand can be seen as the combination of two balancing
feedback loops (Figure 1). The first feedback mechanisms is between supply and price. With
a price increase, more supply will be possible, while with an increase in supply, the price will
decrease. The second feedback is between demand and price, where more demand will
generally result in a higher price, while a higher price eventually leads to a decline in demand.
Eventually, as the reaction of both demand and supply on the price is delayed, supply and
demand will adjust according to the witnessed price levels.
In modelling efforts it is sometimes necessary to introduce exogenous trends, which
are almost without exception deeply uncertain. It is thus necessary to explore the
consequences of different plausible evolutions of this important factor. For this reason, we
needed an exogenous driver for the GDP growth. However, as energy is vital to the
functioning of the economy, we assumed that energy prices may also influence GDP growth,
which is an endogenous effect. The overall GDP growth is thus partly endogenous. Given the
functioning of SD models, the economic growth will lead to exponential growth of the energy
demand, as energy intensity forms a linear relation between GDP and energy demand.
a
Depletion Costs
+
2 +
Energy intensity
WW +
Supply Price Demand
am Endogenous GDP Exogenous GDP
growth growth
Figure 1. Balancing of supply and demand
The effects of political instability on energy supply, a potential feedback from the instability
models, is not considered here. Furthermore, policy measures aimed at changing the
composition of the energy mix — in essence the definition of an energy transition — are not
considered, besides one driver for the development of renewable energy capacity.
The fully quantified global energy model is subdivided in 5 sub-models, which are
mutually linked (see Figure 2). As is visible in this diagram, we look at the demand
development, supply development, prices of the different primary energy sources, costs
development of the supply, and trade between the different regions. The development of
demand, supply, and the prices of the different primary energy sources are important given
the feedbacks between supply and demand via the price effect. The costs extraction costs sub-
model is important for modelling the effects of depletion on extraction costs. Finally, as a
greater availability of gas may lead to a larger share of LNG on the market, it is important to
consider trade between the different regions of the tradable resources, in this case gas (LNG),
Auping, De Jong, Pruyt, Kwakkel, 2014, The Geopolitical Impact of Shale Gas 5
oil, coal, and renewables. Trade of the two remaining primary energy sources, nuclear and
hydro, is thus not considered.
In the model, 4 different regions are defined: Northem America (US and Canada),
Europe and adjacent regions (Europe, non-European CIS, Middle East, and North A frica), Far
East (China, India, Japan, and South Korea), and the rest of the world. The first two regions
are defined with the availability of overland gas pipelines in mind. The Far East is presently a
major user of LNG.
Exogenous GDP
growth
ea
Endogenous
GDP growth
Higher
demand
increase
prices Increasing.
prices
reduce
demand
a Extraction
Energy costs Increasing costs
demand \ ‘
Resource
pricing
f Higher prices allow
Higher increasing capacity
costs lead
tohigher More supply
prices causes lower
prices
reduce possible
Extraction
capacity
‘Cumulative extraction
High local apriand decreases the quality of (nan
may cause imports renewables) energy reserves
Resources can be
exported when
available
Imports increase
local supply
Availability
determines the
demand mix
Figure 2. Sector diagram of the energy prices model. Sub models are displayed in a box. External
trends are shown in blue and italics. Relevant initial conditions in red with a light blue background.
2.2.1 Detailed description
Energy demand
In the demand sub-model (see Figure 3), demand is calculated for different energy sources.
We distinguish six different primary energy sources: oil, gas, coal, hydro, nuclear, and
renewables. Demand fluctuates as a result of three different drivers:
1. Substitution: supply, allocated on the basis of absolute prices, determines an ideal
energy demand mix. Part of the energy demand is then substituted to let the energy
mix change into the direction of the calculated ‘ideal’ energy mix.
2. GDP: energy demand is directly affected by a change in GDP. Decoupling (i.e., an
increase in energy efficiency leading to lower growth in energy demand than the
economic growth) leads us to apply a small discount on this change. A gain, this small
discount translates into exponential growth, or in this case more specifically,
exponential decay.
3. Relative price changes: if prices go up, demand decreases and vice versa. Going from
the short term to the long term, this effect becomes more pronounced. This effect is
faster in response to prices than the economic growth reduction effect mentioned
above.
6 Auping, De Jong, Pruyt, Kwakkel, 2014, The Geopolitical Impact of Shale Gas
As was mentioned earlier, economic growth is viewed as at least partly exogenous to the
system. More specific, potential economic growth trends are explored with a quasi-random set
of waves, which superposed form the exogenous part of the economic growth variable. The
potential feedback of energy prices is added to this dynamic value. In the case of rising energy
prices, this means that economic growth is negatively affected.
Figure 3. View of the demand sub-model
Extraction capacity
The (extraction) capacity sub-model (see Figure 4) calculates capacities for (extraction) of
each of the six energy types. If long term profit margins allow, new capacity is developed and
added, where the profit margins are calculated by subtracting the costs from the price, which
both have own dynamics. However, this capacity will only become available after a delay.
Short term price effects lead to capacity being either mothballed or brought back online.
For energy sources that can be stockpiled (oil, coal, and to some extent renewables),
stocks are calculated. These stocks are then used to determine the relative scarcity of the
resource in question, and through that, its price. The difference in price dynamics between
stockpiled resources and non-stockpiled resources is very large. When stockpiling is possible,
overcapacity can be accumulated. The consequence of this accumulation of resources is that
the throughput time of stocks increases. As stockpiling is expensive, this has a downward
effect on the price. This effect is easily much larger than the actual relative overcapacity
which can be calculated by comparing production (or extraction) capacity with the demand,
especially as both demand and supply react delayed on price changes. Hence, it is to be
expected that resources sold from stockpiles show larger volatility than resources of which
essentially production or transport capacity is sold.
The availability of shale gas in the US is incorporated in the model by changing the
proposed new gas extraction capacity in Northern America proportional to the increase in
shale gas capacity witnessed in the past years. In other regions, the proposed new capacity is
within normal proportions, relative to present price levels. As such, we assume that the
American situation is part of the initial situation in which we start simulating the primary
Auping, De Jong, Pruyt, Kwakkel, 2014, The Geopolitical Impact of Shale Gas 7
energy sources system. Other potential non-conventional fossil energy sources are considered
to be part of the normal continuum of increasing availability at higher prices.
INSTALLED ENERGY
(EXTRACTION) CAPACITY
SUPPLY CHAIN
Figure 4. View of the (extraction) capacity sub-model
Extraction costs
Costs are influenced by two drivers: (1) the Energy Retum On Energy Invested (EROEI) in
the case of non-renewable resources, which decreases when reserves are depleted; and (2)
leaming effects in the case of all resources (see Figure 5). Both are calculated via the
‘cumulative extracted fuel’ (or, ‘other energy resources’). Non-renewable sources will thus
initially become cheaper, only to become more expensive after depletion sets in. In the case of
renewable resources, learning effects will cause costs to decrease as they are used more often.
8 Auping, De Jong, Pruyt, Kwakkel, 2014, The Geopolitical Impact of Shale Gas
Figure 5. View of the costs sub-model
Energy pricing
Prices are calculated in different ways, depending on the region and the type of energy. See
Figure 6 for a graphic representation. For stockpiled resources, stocks are compared to the
energy demand by dividing the stock by the shortest throughput time, in order to calculate the
available capacity of the stock. The resultant relative shortage or surplus is subsequently
multiplied by the unit costs in order to calculate a price. Another mechanism for calculating
the price is comparing the (extraction) capacity of the energy source to the demand. The last
option is known as a ‘cost-plus’ mechanism, which adds a percentage to the unit cost of the
production capacity.
Figure 6. View of the prices sub-model
Auping, De Jong, Pruyt, Kwakkel, 2014, The Geopolitical Impact of Shale Gas 9
Trade
Figure 7. V iew of the trade sub-model
In this sub-model (see Figure 7), a local surplus and/or shortage of tradable resources (oil,
gas, coal, and to some extent renewables) in one region is matched to the existence of a
surplus and/or shortage in other regions, causing ex- and imports. The availability of (LNG)
infrastructure is considered to be a limiting factor only with respect to gas.
2.3 Country stability model
The impact of oil and gas prices on country stability is largely a one way process (see Figure
8). However, as instability will impact the development of the GDP and the resource
extraction capacity, the effect is self-enforcing. Some other, minor feedbacks occur in
impacting stability with resource prices. Examples are the effects of population size on the
fertility and mortality levels, which may cause a deadlock situation with high population and
little development. A nother example is the effect that immigration will have on the workforce,
and the effect the available workforce has on immigration. A last one may occur when the
regime is susceptible for the discrepancy between on the one hand the democratic
expectations that the population may have, and the present regime type on the other.
However, instability may again counteract this development when the government reacts in a
more autocratic way to a crisis in the country.
Within the process of prices influencing instability, however, many factors counteract
in either making specifically price de- or increases lead to more instability. Price increases
will have a positive effect on government finances, create more employment, but it has an
adverse effect on purchasing power. It will depend on the specific conditions in a country,
whether the positive or the negative effects will be dominant.
10 Auping, De Jong, Pruyt, Kwakkel, 2014, The Geopolitical Impact of Shale Gas
Size of security
La
Government
type
m Democratic ig
Education level @xPectations ~~,"
< Disagreement with
government type
:
Government
finances
i
+ Food and fuel ‘
Labor + Purchasin,
* subsidies 7
productivity (power
Wage levels
na
Discontent
Gpp
——> GDP =
-t
\
Immigrants >&V
msgs ee eas
~® potential ad +
workforce —__ + (Youth)
Fertility — + ———* unemployment wee
+
4. disagreeing
Instability
Population size
1 —
Resource
rents
‘ a
FA _ Oiland gas
—
Prices for oil ——— + extraction capacity
and gas
mn
Cost development of oil
and gas extraction
Figure 8. The impact of resource prices on instability. Government decisions have brown arrows,
important uncertain relations have dotted arrows, feedbacks from instability have bold arrows, and
important variables have bold typeface.
Several factors may act as buffers for avoiding potential instability, especially in the case of
decreasing price levels. The first one is the availability of financial reserves for the
govemment. When governments use resource rents to build up large financial buffers, these
will allow them to maintain fuel and food subsidies in periods of low prices. Not maintaining
these subsidies may lead to an unstable situation in the country. Another buffer is the
availability of immigrants in the country. With many immigrants, the government may have
the opportunity to repatriate the immigrants in order to reduce the unemployment under the
domestic workforce.
Auping, De Jong, Pruyt, Kwakkel, 2014, The Geopolitical Impact of Shale Gas 11
The fully quantified energy prices model is subdivided in 5 sub models, which are mutually
linked (Figure 9). These sectors generally contain several of the factors visible in Figure 8,
and as intra-sector feedbacks are not shown in the sector diagram, less feedbacks are visible.
The sector diagram further makes clear that we do not take the effects of nationalism
and ethnic conflict into account. We thus focused solely on the direct and indirect effects
caused by resource rents in society, instead of taking all potential causes of instability into
account.
Instability hampers
economic growth
Instability
| Influences
functioning of
government
Population
expectations
may influence Institutions may
institutions fye! discontent
Part of population is
working and productive
Disagreeing
a
insta
Economic development ¥
decreases mortalty and
fertility
Instability
Youth unemployment and
decreasing purchasing power
Extraction capacity Tere
makes resource rents
possible
Instability hampers
resource extraction
(growth)
Figure 9. Sector diagram of the instability model
2.3.1 Sector description
Economy
Figure 10. View of the economy sub-model
In this sub-model (see Figure 10), the economic effect is calculated. First, the GDP is
calculated by means of an exogenous economic growth structure representing the effects of
12 Auping, De Jong, Pruyt, Kwakkel, 2014, The Geopolitical Impact of Shale Gas
external economic circumstances on the national economy. Second, we look at the effects of
the changes in resource rents on economic growth. Growth is thus partly endogenously
influenced. Resource rents are calculated by multiplying available fuel resources (limited to
oil and gas in this model) with the relevant price scenarios. The availability of fuel resources
(in this case restricted to oil and gas) is calculated by using a resource supply chain from
undiscovered resources to the extraction and finally internal use or export.
The effect on the workforce is calculated through the GDP. This has consequences for both
youth unemployment and wage levels. Finally, purchasing power is calculated by looking at
the relative change in food and fuel dependency.
Resources
Two things are calculated in the resources sub-model (see Figure 11). First, the development
of the extraction capacity for both oil and gas is modelled with a delay on proposed new
capacity. Further, the costs of extraction (energy return on energy invested) are calculated in
order to mimic the depletion of resources. Compared with the price scenarios, this will
determine how much new capacity is being developed.
FUEL EXTRACTION
costs
Figure 11. View of the resources sub-model
Auping, De Jong, Pruyt, Kwakkel, 2014, The Geopolitical Impact of Shale Gas 13
Population
POPULATION
EDUCATION
Figure 12. View of the population sub-model
In the population sub-model (see Figure 12), the demographics of the country are modelled.
The population is influenced by births, deaths and migration and is subdivided in age cohorts
of 5 year each in order to create some precision in the population development. The fertility of
women is modelled endogenously with a correlation to the GDP per adult. The death rate is
calculated relative to the changes in life expectancy. This variable is influenced both by an
exogenous trend factor, as well as by the negative influence (severe) instability has on life
expectancy.
Further, the education level of the population is modelled in order to calculate
democratic expectations, related to the level of education. This is done both for the total
population, as well as the youth. Since the youth was found to be higher educated than the
average population in all countries we investigated, this is likely to generate potential for
youth frustration.
14 Auping, De Jong, Pruyt, Kwakkel, 2014, The Geopolitical Impact of Shale Gas
Instability
Figure 13. View of the instability sub-model
The instability sub-model (see Figure 13) calculates the amount and level of disagreement
with the current situation by the population. The main factors for calculating disagreement are
unemployment, purchasing power changes, and changes in the difference between the
government type and the expectations of the government type. The amount of disagreement at
the highest level (i.e., willingness to use violence against the government) is compared to the
size and force of military capabilities.
2.4 Institutions
In the institutions sub-model (see Figure 14), we calculate potential shifts in the country’s
state form. The model follows the polity scores between -10 (pure autocracy, e.g., Saudi
Arabia) and +10 (pure democracy, e.g., North-western European countries). Governments
may, or may not, decide to follow the democratic expectations of the population. The polity
score has an influence on the stability of the government. Further, the absence of violence
decreases in the case of instability. A lower value for the absence of violence leads
consequentially to a lower value for government legitimacy. Finally, government finances are
calculated in order to be able to know when for example fuel subsidies will become
untenable.
Auping, De Jong, Pruyt, Kwakkel, 2014, The Geopolitical Impact of Shale Gas 15
INSTITUTIONS
Figure 14. View of the institutions sub-model
2.5 Model validation
As usual in the field of SD, we approach model validity as whether the models were fit for
purpose (Oreskes, Shrader-Frechette, and Belitz 1994, Sterman 2000, Lane 1995). In this
case, the models had distinct uses. The global energy model was aimed as price scenario
generator, where most emphasis was on scenarios outside the bandwidth of more conventional
oil and gas price forecasts, like the forecasts of the EIA (2012) or BP (2013). In the country
stability model, the purpose was to be able to assess whether certain price scenarios would
positively or negatively affect country stability.
The model validity tests we performed can be split in approaches during the building
of the model, and after completing it. Ex-ante, we assured validity by literature research,
expert meetings, and unit checks on model equations. It is important to notice here, that the
use of SD assumes system continuity, the importance of accumulation in the system, and
causal relations between system elements. Literature about resources and resource scarcity
intrinsically fits these assumptions very well. As a consequence, we were able to largely
follow, or easily interpret, available literature about resource scarcity. However, the literature
on state stability largely followed different paradigms, making model specification more
difficult and model resemblance of the used literature less direct.
After finishing the first rounds of modelling, we applied checks on units and equations
in the models, and performed modelling workshops with experts. These led to the conclusion
that the models were fit for purpose.
16 Auping, De Jong, Pruyt, Kwakkel, 2014, The Geopolitical Impact of Shale Gas
3 Oil and gas prices selection
Figure 15. The global energy mix development (left), regional energy shares (middle), and regional
gas prices (right) for the first scenario, which had the highest relative coal share found in the scenario
set.
For the scenario selection, we applied two different metrics. The first metric looks at
situations in which an individual energy type would have its largest share over all runs
generated with the global energy model. In the second metric, we selected those models with
most volatility. This last metric made use of a roughness measure [ref by one of authors]
calculating the length of the curve composing the price development. We selected the five
scenarios with the highest roughness.
Gas price scenarios Gas price scenarios
8500
Price ($/obtu)
Price ($/obtu)
17500
*
021s 2070 2075 2030 _ 2035 2040 20a 7050 001s 2070 2025 2030 2035 2040 7045 7050
Time (year) Time (year)
6500
Oil price scenarios Oil price scenarios
Price ($/obtu)
Price ($/obtu)
02015 2020 2025 2030 2035 7040 7045 _F0s0 02015 2020 2025 2030 2035 7040 7045 7050
Time (year) Time (year)
Figure 16. The gas and oil price scenarios as used in the shale gas study. Scenarios 1 to 9 were based
on having a highest relative share of one (found in this study) of particular energy sources, where
scenarios 10 to 14 were selected for their volatility.
Auping, De Jong, Pruyt, Kwakkel, 2014, The Geopolitical Impact of Shale Gas 17
One scenario is visible in Figure 15. This shows the situation with highest coal share in 2050.
In each scenario, developments of the relative energy mix, demand shares in each region, and
gas and oil prices are combined in generating an internally consistent narrative. These allow
assessing under what circumstances certain price developments take place. The price
scenarios eventually selected by this process are visible in Figure 16.
The volatility of the prices can be explained by looking carefully at the structure of the
energy system. First, it is clear that the oil price is more volatile than the European gas price
scenarios. This is motived by the fact that the European gas price is more or less a weighted
average of all other major energy prices. Another explanation is the difference in market
structure. Gas availability is primarily determined by either local resources, or the availability
of infrastructure. Hence, gas is a commodity being sold as a capacity. Accumulation of this
capacity is very difficult, as gas storage is difficult and prone to losses. In contrast, with oil
trade, oil is made available in barrels, making available oil a stock quantity. Therefore, with
oil accumulation of over production is possible, leading to significantly higher volatility of oil
prices.
Second, with regard to this volatility it is clear that delays in the system play an
important role in generating temporarily price plateaus, situations in which, for example,
production does not meet theoretic demand. In such a case, prices are constantly high, but
new capacity is being developed. A fter the development period, relatively much new capacity
will become available, while substitution and reduction effects were also building up in
strength. This situation will then lead to a collapse in prices after a prolonged period with
relatively stable, high prices. The shale gas revolution can thus be seen as such a
development, fitting into the normal hog cycle (Sterman 2000, 791, Meadows 1969) of energy
commodities.
Third, by the analysis of the total set of price scenarios, it became clear that
decoupling (i.e., reduction of the energy intensity of GDP) is a very important factor in
explaining lower energy prices. As decoupling causes the demand for all primary energy
commodities to drop, it will affect all commodity prices. The consequence is that
conventional energy sources will reach lower price levels, while renewable energy sources
will have more problems in becoming accepted. Increased energy efficiency is thus
counteracting of the energy transition.
4 Results for state stability
In Figure 17, we compared the influence of the selected scenarios from Figure 16 and two
IEA scenarios with either constantly growing or declining price scenarios, with the ‘business
as usual’ scenario of the IEA, which showed a constant price (2012). The assessment was thus
mostly qualitative, where we looked for all time steps in any of the 100 scenarios generated
per country whether a scenario caused more or less social unrest compared to the reference
case. In the desirable cases, all time steps showed less instability; in the mostly desirable cases
the average effect was less instability, but at some moments the country was more instable.
For mostly undesirable and undesirable cases, the opposite was true. The fact that only mostly
undesirable and mostly desirable cases were seen, even with regard to the moderate
decreasing and moderate increasing price scenarios, is a consequence of the complexity of the
system, with several counteracting feedback loops.
18 Auping, De Jong, Pruyt, Kwakkel, 2014, The Geopolitical Impact of Shale Gas
Scenario effects for Russia:
Internal instability
Number of cases
Scenario 1
Scenario
Scenario
Mostly desirable
Mostly undesirable
HE Undesirable
gs
Figure 17. Scenario effects representation from the country stability model
Further assessment of the different consequences of the price scenarios gave the following
insights. First, as instability is a self-reinforcing effect, price scenarios with early drops in the
prices show higher instability throughout the runs. In these situations, lower prices caused
increased (youth) unemployment leading to discontent and eventually increased instability.
This effect was strengthened especially when a worsened governmental financial situation
asked for slashing of food and fuel subsidies, creating an immediate decrease in purchasing
power. When the instability limits economic recovery, this is a difficult situation to resolve
fora country.
Second, it became clear that buffers were essential in avoiding increased instability.
Important buffers are sovereign wealth funds and labour migrants. The first is quite clear: in
the case of a recession, financial buffers allow government to continue spending without
risking having to slash food and fuel subsidies. The second is often used by autocratic regimes
trying to avoid increased unemployment of the original population. In these cases, labour
migrants are a buffer that can be disposed of in case of a worsened economic situation,
postponing a dangerous situation with high levels of (youth) unemployment.
Finally, the correlational effects of the regime type and stability are very important
(Marshall and Cole 2011). Regime types generally follow population expectations with a
delay. The transition between a autocracy and a democracy is an unstable period. Research by
the Center for Systemic Peace has shown that these transitional democracies, or ‘anocracies”
are 5 times more unstable than autocracies and 10 times more unstable than democracies.
Of the countries considered in this study, being Algeria, Azerbaijan, Egypt,
Kazakhstan, Qatar, Russia, and Saudi Arabia, especially Russia and Algeria are most at risk
for instability in periods with low energy prices. As most countries in our study, they generate
most resource rents from the more price volatile oil resources. Further, both countries have
only limited buffers and an inherently unstable regime type.
5 Discussion and conclusions
In this paper, we have presented two SD models, where the first model simulated the global
energy system, and the second model country stability. We used these models to assess the
potential impact of the US’ shale gas revolution on state stability in traditional oil and gas
exporting countries in the vicinity of Europe.
Auping, De Jong, Pruyt, Kwakkel, 2014, The Geopolitical Impact of Shale Gas 19
The approach we used in this study, using a global energy model to produce scenarios
which functioned as input for ‘stress testing’ of countries on the effects on their stability,
proved very useful. It allowed to determine which drivers caused, indirectly, heightened
instability in countries necessary of European energy policy.
The approach can be expended in several ways. First, we could make more use of
computerised learning algorithms, like the Patient Rule Induction Method (PRIM), to select
the input variables which, alone or in combination, often lead to either increase instability or
the lowering of prices. Second, the country analysis could be expended with countries outside
the direct vicinity of Europe, like Venezuela. Third, another promising direction for further
research would be closing the loop by feeding the extraction capacity from the country
stability model back to the global energy model. Fourth, labour migrants may form a buffer in
safeguarding country stability, but may also cause grievances which have currently been left
outside the scope of this research.
Regarding the energy part of the case, the following conclusions can be drawn. First,
the oil price is structurally more volatile than the gas price. Therefore, a situation in which
(shale) gas partly replaces oil, for instance as feedstock in chemical plants, may lead to a
larger decline in income than purely a lower gas price could possible create. Second, shale gas
fits in the normal hog cycle of energy commodities, increasing the plausibility of shale gas
being part of developments leading to periods with considerably lower energy prices. Third,
the effects of decoupling energy demand and GDP on energy prices are easily much larger
than the effects caused by shale gas, as decoupling leads to an overall decline in demand
(growth).
Regarding state stability, it can first be concluded that the case where shale gas and
other unconventional energy sources lead to lower oil prices would have most undesirable
effects on traditional oil and gas exporting countries, as these countries practically all generate
most resource rents by oil. In these cases, a reduction in resource rents by lower oil prices
may lead to increased youth unemployment and, when subsidies need to be cut, worsened
purchasing power. Second, as social unrest has a negative effect on economic development,
avoiding unrest is most desirable. Third, countries with limited sovereign wealth funds as part
of GDP or labour immigrants, are more vulnerable, as they lack buffer capacity for
(temporarily) avoiding social unrest caused by economic downtum. Fourth, regime type is
also very important for the stability of a country, as countries transitioning from an autocratic
regime type to a democratic regime type are more unstable than either full autocracies or full
democracies. Taking these four points into account and by careful deliberation over our model
results, we were able to conclude that especially Algeria and Russia are at risk for these
developments.
Finally, the insights generated with the country stability model especially make clear
which economic vulnerabilities countries have. In the long run, increased instability in the
neighbourhood of Europe is an undesirable effect. However, as the present crisis in Ukraine
demonstrates, these insights may be useful in determining which economic sanctions would
harm countries like Russia most. Hence, the model insights proved to be more extensive than
could have been expected in advance.
20 Auping, De Jong, Pruyt, Kwakkel, 2014, The Geopolitical Impact of Shale Gas
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