Arash Farnoosh
email: arash.farnoosh@ifpen.fr

Frederic Lantz

Jacques Percebois

Electricity generation analyses in an oil-exporting country: Transition to non-fossil fuel based power units in Saudi Arabia *

Keywords: Electricity Generation Model, Saudi Arabia, Power Generation Mix

In Saudi Arabia, fossil-fuel is the main source of power generation. Due to the huge economic and demographic growth, the electricity consumption in Saudi Arabia has increased and should continue to increase at a very fast rate. At the moment, more than half a million barrels of oil per day is used directly for power generation. Herein, we assess the power generation situation of the country and its future conditions through a modelling approach. For this purpose, we present the current situation by detailing the existing generation mix of electricity. Then we develop a optimization model of the power sector which aims to define the best production and investment pattern to reach the expected demand. Subsequently, we will carry out a sensitivity analysis so as to evaluate the robustness of the model's by taking into account the integration variability of the other alternative (non-fossil fuel based) resources. The results point out that the choices of investment in the power sector strongly affect the potential oil's exports of Saudi Arabia.

Introduction

Saudi Arabia with around one-fifth of the world's proven oil reserves is the biggest oil producer in the Organization of Petroleum Exporting Countries (OPEC). With important investments in the oil sector and low production costs, Saudi Arabia is likely to remain the world's largest net oil exporter. Thus, the Saudia oil production is 544 million of tons (Mt) in 2011 and the net exports reach 355 Mt for the same year (IEA, 2012) Over the next two decades, Saudi's power generation capacity is predicted to reach 120 gigawatt-electric (GWe) (SEC 2010). The combination of Saudi Arabia's rapidly expanding population and industrial infrastructure, along with low electricity tariffs, has increased the demand on electricity utilities (averaging 8% annual growth over the period). This dramatic load increase has led to shortages, brownouts, blackouts and power rations in various parts of the country. Electricity demand which now stands at around 50GWe, around 200 terawatt hours (TWh) of yearly production, is predicted by the government to increase from 80GWe by 2020 to more than 120GWe by 2030. 1 Figure 1, illustrates this increase of power production.

1 Electricity and cogeneration regulatory authority 2010. For the time being in Saudi Arabia, 100% of power generation is based on the fossil fuel sources (oil & gas). Figure 2 and figure 3 show respectively the share of different power units and fuels in the power generation mix of the country. Increasing oil and gas domestic consumption and the resulting impact on export revenues is not a very good option for the Saudi government due to both economic and political reasons. In this paper, we evaluate the present and future potential of using non-fossil fuel based energy in the power sector of this country.

A linear programming optimization framework was used to assess the costs and savings of expanding the role of non-fossil fuel based power sources in electricity supply. LP (Linear programming) cost minimizing is an approach that systematically evaluates potential power supply to satisfy the demand at the best societal cost. In this study, we analyse what the incremental cost would be if each sources of power generation were to integrate the electricity supply of the country. In pursuit of this objective, we provide a review of relevant non-fossil and fossil based power unit choices on the basis of resource potential, cost and economic benefits. Several choices of technologies that are or are expected to be technically and economically feasible over the next two decades have been identified and incorporated into the modelling effort.

Methodology

Electricity generation should be provided by a large set of power plants which are characterized by different technologies associated to a very large spectrum of fixed and running (variable) costs. Consequently, this leads to an optimal usage and investments so as to satisfy the current and future demand. Optimizing the overall electricity cost of production from different types of plants enables us to rank the existing production units. Indeed, when the electricity demand increases and the available power (in the lowest cost category) is not enough, producer must switch to the generation unit whose cost category is just one step above the previous one. In other words, we rank the use of power plants according to their growing variable cost (so-called "merit-order" process).

The main contribution of this study is to analyse an optimal pattern of the Saudi power generation mix through an LP model (based on the above-mentioned structure) and to reveal the impact of renewable and nuclear integration into the electric system under different penetration-range scenarios. Afterwards, the financial and economic gains (or perhaps losses) will be quantified by looking at the amount of fossil-fuel probably released and injected to the market instead of internal/national usage in the power sector.

Total electricity generation cost minimization, is one of the main modelling approaches in power generation modelling. Examples of such models include POLES2 [START_REF] Criqui | Prospective Outlook on Long-term Energy Systems[END_REF], MARKAL3 and TIMES4 [START_REF] Loulou | Documentation for the MARKAL Family of Models[END_REF]). The main idea of these models is to explain electricity prices from the marginal generation cost. In this case, assumption over the future electricity prices does not have to be made. Focusing on minimum generation cost implies minimizing the cost to be transferred to the final consumers, irrespective of the electricity price. The key advantage of this method is to analyse the producer behaviour facing with a mix of deferent types of constraints such as economic, technical and environmental ones. Our approach is similar, in the way that we develop a model where the total costs are to be minimized under certain constraints and scenarios developed in the next section.

Power Generation Means in Saudi Arabia

Before the power generation model construction, we analyse the potential of different nonfossil fuel based technologies such as geothermal, wind, solar and nuclear in Saudi Arabia.

Feasibility studies have been realized in order to identify the most suitable and reliable technologies for this region based on the technical, economic and geographical characteristics.

Due to the climate and regional properties of this country, some power units are not supposed to be useful and adapted to the national generation mix. In the following, we analyse each technology in detail and try to find out those who can be considered for the Saudi power generation mix from climatic, economic and technological point of view. Finally, the existing thermal power units in Saudi Arabia have been described.

Geothermal Energy

While not really abundant, geothermal energy potential does exist in Saudi Arabia [START_REF] Alnatheer | Environmental benefits of energy efficiency and renewable energy in Saudi Arabia's electric sector[END_REF]). This technology is not entirely a renewable resource since the geothermal wells can be depleted over time. These resources belong to two types of technology, either hydrothermal or hot dry rock. Binary and flash technologies are the main approaches generally used to extract heat from geothermal wells. Although initial investment costs of the plant are relatively high, geothermal energy could become economically competitive (on a life cycle basis) to other sources of power generation [START_REF] Boyle | Renewable Energy: Power for a Sustainable Future[END_REF]).

Some studies have suggested the potential of combined solar and geothermal power so as to provide water and electricity in Saudi Arabia [START_REF] Oktun | Geothermal energy in Saudi Arabia and its use in connection with solar energy[END_REF]. Saudi Arabia is somehow rich in terms of various geological features, with around 10 hot springs located in the regions of Gizan and Al Lith in the southern part of the country [START_REF] Taleb | Barriers hindering the utilisation of geothermal resources in Saudi Arabia[END_REF]. Some of these thermal springs could be utilized for electricity generation, even though none have yet been exploited [START_REF] Lund | Direct application of geothermal energy: 2005 worldwide review Geothermics[END_REF]. [START_REF] Alnatheer | Environmental benefits of energy efficiency and renewable energy in Saudi Arabia's electric sector[END_REF] argued that the exploitation of geothermal energy in Saudi Arabia is not cost-effective, even when compared with other renewable sources such as solar and wind power. Moreover, a set of renewable power sources scenarios were developed for Saudi Arabia in a study provided by [START_REF] Upham | Renewable Energy scenarios for the Kingdom of Saudi Arabia[END_REF] in which the prospects of geothermal energy (both power and heat) were not considered as being sufficiently viable. [START_REF] Taleb | Barriers hindering the utilisation of geothermal resources in Saudi Arabia[END_REF] identified both technical and non-technical barriers of geothermal energy utilization in Saudi Arabia. The most important reasons which are claimed are the uncertainty regarding available resources (the lack of technical feasibility studies), the lack of financial incentives and high capital cost of this technology (compared to the power generation based on oil), and the poor public acceptance of renewable energy sources in general and particularly geothermal one and lack of neither academic nor professional training in this field.

At last, due to the above mentioned arguments and existing barriers for the development of this technology in Saudi Arabia, we don't consider the integration of geothermal energy as an option for the future energy mix of the country (neither in the optimization model nor in its associated scenarios).

Wind Energy

There are many locations in Saudi Arabia that the annual speed of wind (averaged) goes beyond 4 m/s at a height of around 20m. Al-Abbadi (2004) showed that the wind annual average speed can reach even 5.7m/s and 5.4m/s in Dhulum and Arar sites respectively for speeds higher than 5m/s for around 50% of the time. In spite of this rather high potential wind power in Saudi Arabia (compare to the other Persian Gulf CCG countries) there is not an upright future for this energy in this country, at least in the short and medium terms. In fact, the highest and most optimistic wind energy potential in Saudi Arabia was estimated to yield around 20 TWh per year (Alnaser 2009). This is a considerable amount seeing the climatic conditions of the region but compare to the other renewable options such as solar (both concentrated and photovoltaic); it does not represent even 1% of their estimated potential.

Therefore in this study we won't consider wind energy as a high potential option for the future power generation mix of the country due to its negligible potential and huge costs (currently) compare to conventional plants. Moreover, there has not been any official declaration from the government or any energy authority regarding a vast investment in this area up to now. And the existing projects are all at a very small scale (decentralized) or are just under R & D and pilot stages.

Solar Energy

Solar energy has been accepted as a key source of energy for the future in Saudi Arabia. Saudi Arabia has enormous potential for exploiting solar energy. Its geographical location, widespread unused desert land and clear skies, make it an excellent candidate for this technology. The average solar radiation falling on the Arabian Peninsula is around 2200KWh/m² per year [START_REF] Hepbasli | A Key Review on Present Status and Future Directions of Solar Energy Studies and Applications in Saudi Arabia[END_REF].

According to the Saudi Solar Radiation Atlas which is a governmental document concerning the solar radiation of the country, Saudi Arabia has vast areas subject to strong GHI

Nuclear Power

Nuclear power generation provides around 7% of the world primary energy supply and about 14.7% of the electric power generation. 8 Increasing improvements in safety means, using experience, plant availability and of course economy, made nuclear energy competitive with other means of electricity generation. For the time being 436 nuclear reactors generate around 370 GW of electric power all around the world (IEA 2011). While there are many reactors in operation in the US, Europe, Japan and China, the other regions of the world do not use this 5 Global Horizontal Irradiance which is equal to the total solar radiation. 6 Direct Normal Irradiance which is equal to direct beam radiation. technology within a significant amount. In Africa, it is only South African Republic which has two operating reactors providing only 1.8GWe of electricity. In the Middle-East only one nuclear power plant with the capacity of 1GWe is operating in Iran. Despite of the large diversity in term of design, only two types of reactor dominate nuclear power generation. 85% of operating reactors are the light water type reactors including the Russian RBMK reactor.

Majority of these reactors are Pressurized Water type reactors and the rest of it are boiling water ones. Both technologies use slightly enriched Uranium (3-5%) as fuel which does not create any potential proliferation risk. Nuclear fuel, in contrary to oil and gas resources, has extended life time and is not considered as a depleting resource.

Therefore, this technology is not a negligible source of electric power choice for Saudi Arabia as an energy source (the model will tell us more about its economic viability) and we consider it in our model's scenarios. Moreover, Saudi government has recently announced its intention to use this technology for the future power generation. According to the government officials, Saudi Arabia plans to build about 16 nuclear power reactors, with the capacity of around 20GWe, over the next 20 years by spending around $7 billion on each plant. This $112 billion investment plan (total 16 reactors) is supposed to provide one-fifth of the Saudi Arabia electricity generation for residential and industrial usage and in some cases for desalination of sea water which is very critical for this country. Most likely, the reactor locations will be along the Persian Gulf or Red Sea.

Thermal fossil-fuel-based power plants and their situation

Currently, electricity production in Saudi Arabia comes thoroughly from thermal equipment family, except coal and nuclear ones. Hence, the current electricity supply system in our optimization model is composed of only this type of power plants. Their operating principle is as following: combustion can heat a fluid which produces, in a turbine, mechanical energy converted into electrical one by a generator. There are currently three main types of thermal fossil-fuel-based power plant in Saudi Arabia: First, the gas turbines whose exhaust gases produced directly goes for the energy required to drive the alternator. Efficiency of this mode of production is relatively low (15 to 30%) and operating costs, including fuel which accounts for most of them, are very important. However, gas turbine power plant has two major advantages over competing modes of production: first the investment cost is relatively low and secondly they have the distinction of being immediately available with a very low starting time. Gas turbine is an ideal element when used for a short period, when it is necessary to significantly and rapidly increase the production capacity to meet the demand. Hence, they are very adapted to be used during peak loads. Hail-2 power plant located in the Hail in Saudi Arabia is an example of this sort of thermal unit.

Second type is the combined cycle, which consists of installing counter-pressure (steam turbine) in addition to the gas turbine so as to maximize the electricity production. Indeed, it offers the opportunity to at least triple the production of electricity for the same heat, which can lead to overall efficiency of 50 to 60%. Ras Tanura power plant located in the Ash Sharqiyah belongs to this family of thermal units.

Finally the conventional thermal stations with two versions: the thermal oil and thermal coal.

The operating principle consists of burning oil or coal to heat a fluid (most often it is the water steam) and then expansion of this fluid through a turbine that drives a generator. Despite a low overall efficiency (electricity produced is only 30-35% of energy input); it remains higher than that of Gas Turbines. In addition, operating costs are relatively low and allow to partially offsetting the heavy investment costs. However, these plants are very slow to start and ramp up, so they are not suited to respond quickly to a sudden increase in demand.

Shuaibah power plant with the capacity of 3*400 MW (gross) is an example of thermal oil units.

Figure 4 shows the locations of these power plants for all the four operating areas in Saudi Arabia: Eastern, Central, Western and Southern.

Modelling frame-work

We model the current power generation mix structure of the country by using GAMS (General Algebraic Modelling System) software. This cost minimization model contains the objective cost function that must be minimized and the demand constrains that have to be satisfied. For static short-term optimization (base year 2010), the production capacities must be respected and in the case of long-term optimization, investments are allowed. The model structure is as following: The variables of the model are the powers loaded, coming from each type of unit (i) for each season (s) in year (t).

   + × ×    ∑ ∑∑ C I p E H Min it i it ist it s i s AP D P C P 1 t st i ist it ist is - ≥ ∑ ≤ τ In

Demand

We know that the most important feature of electric power is its almost non-storability. This implies that production must be adjusted instantaneously to the consumption and ensures that equipment is functioning at full capacity at the time of peak demand, and even extreme spikes. Therefore, the load curve, which represents the continuing evolution of the power demand over time, is one of the fundamental elements of the power system optimization model. 14 mentioned before, the total electricity demand of the country will reach 80GWe in 2020 and 120GWe in 2030 (SEC 2009 andECRA 2010). Hence, future demand curves considered in the models for the year 2020 and the year 2030 increase proportionally to this demand structure up to the before-mentioned amounts.

The overall demand for electricity in Saudi Arabia has been refined by different seasons. They are defined in Table 1.

Model's parameter

Seasonal periods in Saudi Arabia S1 Summer S2

Spring and Autumn S3 Winter

Demand corresponding to each season will be the demand to be met by using the available generation capacity. The reason for which three periods have been defined is the fact that in each period, the load behaviour is quite uniform. This seasonal division is shown in the Figure 5.

8760 3 2 1 = ×       = = =

Load factors and Back-up Plants

The annual load factor of an electrical power plant demonstrates the ratio of the power generated by a plant and the theoretical maximum that could be produced over the year (8760 hours). For the nuclear and fossil-fuelled units, this annual load factor is simply determined by planned unavailability due to the maintenance or refuelling or shutdowns when the plant is not considered for dispatching. Assuming base-load generation, in this study we applied a generic 85% load factor for our nuclear and fossil-fuel based power units. Nevertheless, for solar sources, the output of the plant is impacted not only by the aforementioned unavailability factors but also by site-specific availability of solar irradiation. In this study, as it was already said in the section 3.3, we consider the load factors of 20% and 34% respectively for PV and CSP sites.

Moreover, in an attempt to cover the risk related to the intermittent production of solar power plants, we have introduced in the model a necessary investment in the fossil-fuel power plants that play the back-up role in case of insufficient capacity factor that could happen during peak consumption. In most of the regions around the world, lowest values of capacity factor for the intermittent technologies are observed during peak demand periods. On the contrary, in Saudi Arabia the capacity factor of solar technologies does not vary too much during peak hours because of the climatic characteristics of the country. Peak hours generally take place around 3 p.m. in summer when we have proper shining factor for the solar technologies.

In our model the absence of production from intermittent means is compensated by combined cycle plants and/or gas and fuel turbines which have around 100% of availability (capacity factor equals to 1) except for the ex-ante planned maintenance. So the total yearly cost of power generation, for the renewable-integrated power mix, includes these back-up costs.

Fuel costs

Fuel costs are calculated per MWh on the basis of price information available for gas, oil and uranium (IEA 2011 andWorld Bank 2011). In the case of gas price, we considered the average price of large gas producing countries like Canada, USA, Australia and Russia (6 $/MMBtu), where domestic prices of natural gas can decouple from international market prices. This averaged price could be a good representative of international gas price for Saudi power sector, although the real (strongly subsidized) domestic gas price is much lower for the Saudi power producers. And for oil, Dubai dated average price over the last 4 years has been considered (80 $/bbl), even if sometimes we use oil products in power generation which are more or less expensive than the crude itself. Despite the fact that in this study we assume stable fuel prices for the matter of simplicity; this should not be considered or interpreted as any sort of prediction of stable energy markets.

In the case of uranium the task is entirely different because the price of U3O8 (so-called yellow cake) only counts for about 5% of the total cost of power production and therefore any volatility in the price has very small impact on the total cost of electricity generation. Spotmarket plays a very limited role for the nuclear fuel (at different stages) and most of the activities are carried out under long term contracts. In the model we assume the nuclear fuel price of 7 $/MWh until fuel fabrication process, plus 2.5 $/MWh more for transport, storage and eventually reprocessing and final disposal (IEA & NEA 2010).

Technical properties and other costs

Apart from fuel costs, which have already been described, the other variable and fixed costs of each type of power plant are also essential for the decision making process of the model.

Plants' life-time and efficiency should also be incorporated in the model so as to be able to evaluate the potential amount of electricity (from technical point of view) that each power plant could produce.

Model's Scenarios

In our model we attempt to analyse the future situation of Saudi Arabia generation mix under different scenarios, respectively ten and twenty years forward. To do so, we assume the most probable scenarios for the electric mix of the country for years 2020 and 2030. Then we calculate the total yearly cost of optimal electricity generation for each specific year and scenario. Figure 10 illustrates different assumed scenarios integrated to the model. Three main scenarios, including three sub-scenarios, have been considered for the future electricity mix of the country. In first scenario, which is our business as usual and most probable scenario, we continue the power generation of Saudi Arabia by using 100% of fossil-based (Oil & Gas) power plants in years 2020 and 2030. Therefore, there is no investment or construction plan for nuclear and renewable energies. Investments only go for oil-based and gas-based thermal power plants. For this scenario we have defined three subscenarios which are as following:

Gas oriented mix (1-1): In which we consider 70% of the electricity production from gasbased power plant and the remaining 30% is provided by oil-based plants.

Fifty-fifty fossil fuel mix (1-2): This is our middle case sub-scenario in which half of the power production is provided by gas-based plants and the other half of it by oil-based ones.

This scenario is too similar to the current power generation situation of Saudi Arabia. Oil oriented mix (1-3): Finally, the last assumed sub-scenario is based on the massive usage of fuel power plants. In this scenario 70% of power is generated by Oil-based plants and the remaining part would be satisfied by Gas consuming power plants.

Our second scenario for the future mix of the country contains both fossil-based and renewable resources. We assume 30% integration of renewable sources in the total generation mix of Saudi Arabia. Only solar power plants, both PV and CSP have been integrated to the model due to their remarkable efficiencies under the climatic situation of Saudi Arabia. The rest of the electric power is afforded by the fossil-based (50%Oil & 50%Gas) thermal power plants.

Finally our third scenario contains all the possible resources of electricity generation (Fossil,

Renewable & Nuclear). In this scenario, we assume that half of the generated power is provided by non-fossil based power plants, both Nuclear and Renewable. The share of each technology in the generation mix is equal to 25% of installed capacity.

Simulation Results

To calibrate and verify the reliability of the model, we compared the results on a reference year with the observed data provided by IEA Electricity Information and BP Electricity Generation Statistics. Table 3

20

Thereafter we run the model for all the pre-defined scenarios. Figure 11 illustrates the total costs of power generation per year for different structures of generation mix.

The first scenario (and its three related sub-scenarios) shows us the cost of electricity production during years 2020 and 2030 by using only fossil fuel based power plants. The total cost of generation (minimum and optimal cost of-course) increases dramatically when the integration rate of oil rises in the national generation mix.

Moreover, the cost difference between year 2020 and year 2030 also increases when we switch to more oil dependent mixes. The results for scenario 2, in which we consider 30% of renewable share in the national generation mix, are not far from those of scenario 1-2 suggesting 50% of oil-consuming power plants in the system. However, it is essential to state that, this conclusion is only based on the pure economic insight and if we include also environmental externalities then the result would be different and renewable integration will certainly have more success. 21

Total

Finally the result concerning scenario 3, both nuclear and renewable integration to the national mix, illustrates the dramatic impact of nuclear plants on the total cost of power generation. For instance, the cost difference between scenario 3 and scenario 1-1 (which consumes mostly gas compare to more expensive oil) has been estimated by the model to be around 3.7 billion dollars in 2020 and even higher in 2030 (5.38 billion USD).

Sensitivity & Break-even Analysis

In order to perform a reasonable sensitivity analysis, we have chosen to test the impact of changes in the discount rate on a total generation cost calculation. The reason behind this choice is the fact that the discount rate has more significant impact on the generation cost for capital intensive centralized generation units and at the same time it is the most uncertain factor in the case of Saudi Arabia. Oil and gas technologies and their associated O&M and fuel costs are already very well known in Saudi Arabia and therefore we are looking for a factor (which is the discount rate in our study) that can remarkably impact the new power units such as renewable and nuclear ones. Sensitivity analysis over the other underlying parameters of generation cost, such as fuel costs, has not been considered in our study because of their rather negligible influence over the total cost of nuclear & solar power units.

In the particular case of solar plants, generally load factor variation has the most important weight in the total cost sensitivity analysis and to a lesser extent the construction cost.

However, in the case of Saudi Arabia as the load factor is somehow stable (due to the regional climatic condition and important share of CSP) we focus more on the construction and initial investment costs.

The discount rate that we have considered in our model is equal to 8% based on a set of governmental studies and information regarding investments in power sector in Saudi Arabia (KACARE 2010). Sensitivity analysis has been performed for all the three scenarios. The impact of several discount rates on total annual generation cost for these three scenarios is shown in the Figure 12 for discount rates ranging from 5% to 15%.

Logically, within higher capital cost, the total cost for all scenarios increases. On one hand, we see a relative stability of fossil-based (gas and fuel) power plants cost and therefore their almost insensitivity to cost of capital changes. On the other hand, nuclear power units, in spite of having a lower initial investment cost ratio rather than solar technologies, are the most sensitive units to discount rate changes, too simply because they have much longer construction times than any other power unit. The construction time for a nuclear plant in the model is equal to 5 years while for solar plants is only 1 year. This high sensitivity of nuclear power units compare to solar ones can be easily revealed by comparing the sensitivity results

(comparative growth rate of each chart) of the two non-fossil based plants integration scenarios (2 & 3). Therefore, financing structure and capital costs are of significant importance to investments in nuclear capacity.

Break-even analysis has also been performed for aforementioned scenarios at different discount rates. The outcome will help us to make a more rational (from economic point of view) technology choice for the national power generation. As it is shown in figure 12, at the discount rate of 8%, our pure fossil based scenario intersects the 30% renewable penetration scenario. It means that at the discount rates greater than 8%, a fossil-based generation mix is more economic than that of scenario 2. However, scenario 3 (with both nuclear and renewable penetration) remains the most economical solution. This situation continues until the discount rate of 13%. Thereafter, the fossil-based scenario becomes again the best scenario (economically speaking) compare to the other two. It is important to remark that with a higher integration of gas plants in to the system this second break-even point could be pushed even more to the left. In other words, higher percentage of gas power plants in the pure fossil-based national mix will promote the first scenario (under the current gas price assumptions ofcourse).

There is another interesting observation for our two non-fossil based scenarios at the discount rate of 12%. From this point the distance between the two scenarios becomes narrower. It shows the fact that after 12% of discount rate the profitability of scenario 3 over the 2 nd one becomes less and less significant. It confirms the higher sensitivity of nuclear power plants to discount rates than that of renewable energies such as solar in our case.

Economic Analysis and Interpretation

Figure 13 shows us the important share of oil consumption in the Saudi Arabia's total oil production. In 2010, around one third of the total oil consumption went for power generation via fuel power plants.

Figure 12 24 This amount will proportionally increase if Saudi Arabia continues to generate electricity under its current production structure. That means, keep using oil-consuming power plants for 55% of the total electricity generation of the country.

Under the before-mentioned demand increase scenarios, total oil consumption of Saudi Arabia for power generation will reach 1.5 mbd and 2.25 mbd, respectively in 2020 and in 2030. These numbers can become even higher if the share of oil-fired power plants goes beyond 55% of the national generation mix. As a matter of fact, Saudi Arabia can release at least 1mbd of crude oil by decarbonising its power generation. For instance, under scenario 2, (30% of renewable integration into the generation mix) Saudi Arabia will be able to put aside around 1.05 mb per day in 2020. This number could be easily doubled if the generation mix moves toward scenario 3 and even tripled by going beyond 25% of nuclear integration.

Eventually, switching from first group scenarios (1-1, 1-2 & 1-3) to non-fossil fuel based scenarios will not only reduce the generation cost of electricity but will also remarkably increase the oil export revenue of Saudi Arabia.

Conclusion

The results of the simulations of the power sector in Saudia Arabia shows us that for various scenarios of fossil-based power plant replacement, by both renewable & nuclear ones, we can observe a remarkable cost reduction in the total power generation cost of Saudi Arabia. The same thing does not happen in the case of generation mix extension by using only renewable power plants.

By exporting the amount of oil extracted out of the generation mix (released thanks to the fuel power plant replacement) Saudi Arabia can make a massive financial and political benefits.

Financial benefits, not only because of the considerable reduction in the total generation cost of electricity, but also, by raising the amount of crude oil export. Political benefits, due to an increase in their spare capacity of oil production (providing more flexibility for Saudi Arabia in terms of oil production) and consequently, an increase in its role in the OPEC and international oil market.

At the end we should emphasize on the fact that these benefits could be realized only in the case that we give an opportunity cost to the fuel that we use in the power plants. Without this hypothesis (e.g. cheaper fuel cost compare to the international market price due to subsidies) the major part of the variable cost will be vanished in the model and the benefits would become negligible.

Figure 1 :

 1 Figure 1: Power generation growth forecast in GW for Saudi Arabia (Source: SEC/KACARE 2010)

Figure 3 :Figure 2 :

 32 Figure 3: Electricity generation by fuel in Saudi Arabia (Source: OECD/IEA 2011)

  5 and fractions of DNI 6 which are respectively ideal for Photovoltaic (PV) and Concentrating Solar Power (CSP) technologies. Just for giving an example, within about 2000 KWh/m²/y of DNI, it has been estimated that the potential annual energy yield of CSP technology in Saudi Arabia is around 124,560 TWh. 7 This amount represents around 650 times the total electricity consumption of the country in 2009. This reflects the fact that CSP technology must be considered between the most suitable renewable technologies in the Saudi's future energy mix. Hence, in this study and in our model's scenarios we do consider solar option in the future electricity generation mix of the country. Load factors considered for both PV and CSP technologies in the model are respectively equal to 0.2 and 0.34 (K.A. CARE 2010).

Figure 4 :

 4 Figure 4: Existing power plants in Saudi Arabia (Source: Saudi Electricity Company 2009)

  which, P ist is the Power loaded on the grid by each equipment of type i, for the season s in year t (MW) C it : The capacity of the equipment of type i in year t (MW) H s: Length of the season s (hours) I it: Investment cost of each unit of production ($/kW) E it: Variable cost of production for each equipment i ($/kWh) D st: Called power on the grid for the season s (MW) τ is: Coefficient of availability in each season for each equipment i AP t: Supply of the must-run or auto-producers (MW) if there is any

Figure 6

 6 Figure 6 represents the load curve of Saudi Arabia during year 2009. This demand structure

Figure 6 :

 6 Figure 6: Annual load curve for Saudi Arabia in 2009 (Source: Electricity & Co-generation Regulatory Authority)

Figure 5 :

 5 Figure5: Seasonal periods' definition over an annual loading curve of Saudi Arabia's power sector(ECRA 2010)

  Figure11

Figure 13 :

 13 Figure 13: Saudi Arabia's oil production since 1965 (Source: BP, 2011)

Table 1 :

 1 Seasonal definition

-economic data for each type of power plant

  Table 2 provides the techno-economic properties of various thermal power plants used in the model. These values are derived from the studies of IEA and NEA (2010) on power generation costs.

	TechnoPlant type	Nuclear Plant CCGT Plant Fuel Plant	Solar PV	CSP
	Efficiency (%)	33	57	38	*	*
	Investment cost ($/Kwe)	2050	534	364	3400	3000
	Life cycle (years)	60	30	30	25	25
	Fix O&M cost ($/Kwe)	46	8	8	50	60
	Variable O&M cost ($/MWh)	0.8	1	0.3	0.5	0.5

Table 2 (

 2 Source: IEA 2010 Median Case)

Table 3 :

 3 shows the amount of power production in our base case (reference year 2010) generation mix and those of BP and IEA. Model's base case result validation: power production in 2010

		Power
	Source	generation
	BP Statistics	240 TWh
	IEA Electricity Information	240.3 TWh
	Model Base Case	239 TWh

Prospective Outlook on Long term Energy Systems

MARKet ALlocation

The Integrated MARKAL-EFOM System

Acknowledgement

The authors are heavily indebted to Abdulrahman ALGHABBAN and Said NACHET for their helpful comments and advices. Many people have provided helpful comments and suggestions on the analysis, and we are pleased to take this opportunity to thank them. All remaining errors are ours. The views expressed herein are strictly those of the authors and are

not to be considered as representing those of the IFP Energies Nouvelles or University of Montpellier 1.

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email: bremond.vincent@gmail.com

Emmanuel Hache
email: emmanuel.hache@ifpen.frmarc.joets@ipag.fr

Marc Joëts
email: marc.joets@ipag.fr.

Vincent Brémond

On the link between oil and commodity prices: a panel VAR approach

La collection "Les cahiers de l'économie" a pour objectif de présenter des travaux réalisés à IFP Energies nouvelles et à IFP School, travaux de recherche ou notes de synthèse en économie, finance et gestion. La forme peut être encore provisoire, afin de susciter des échanges de points de vue sur les sujets abordés. Les opinions émises dans les textes publiés dans cette collection doivent être considérées comme propres à leurs auteurs et ne reflètent pas nécessairement le point de vue d' IFP Energies nouvelles ou d' IFP School.

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Introduction

Context

From the beginning of the 21st century, commodities markets have experienced major changes, with a steady and continuous upward trend until mid-2008, when prices collapsed in the wake of the financial crisis, before reengaging in an upward movement from 2009 to 2011. This represents an apparent break from the pattern observed during the 1980s and the 1990s when prices fell around 1% per year on average. Although the timing and the magnitude have been quite different in the various different segments of the commodities markets (energy, non-ferrous metals, agricultural raw materials and beverages), the price increase that began in late 2001 had spread into all commodities markets by 2004-2005 in the context of steady world economic growth. Indeed, from 2004 to 2008, the world registered an average economic growth higher than 5%, the strongest number observed since the early 1970s. This strong world economic growth has increased demand for industrial and energy commodities. It was particularly fuelled by the Chinese economy, which became the world's leading consumer in the main non-ferrous metals and agro-industrial raw materials markets. Between 2001 and[START_REF] Radetzki | The anatomy of three commodity booms[END_REF] accounted for all additional demand in the lead market, 80% of that in the cotton and zinc markets, and some 50% in the copper and aluminium markets. This Chinese boom had a particular impact, as it followed the 1990s, which were marked by a lack of investment and a strong downsizing trend in key industrial activities such as the non-ferrous metals sector.

Commodities prices have tripled, on average, between 2001 and 2012 with a first price boom from 2001 to 2008 (interrupted by the world financial crisis in the third quarter of 2008) and then followed by a price collapse from 2008 to 2009. Since then, commodities markets have experienced a new upward trend from 2009 to 2011 with a huge increase in price volatility.

In the history of commodities markets, three periods of sharp market price increases are generally identified [START_REF] Radetzki | The anatomy of three commodity booms[END_REF]). The first one followed the Great Depression of the 1930s: it reflected an upward adjustment in the commodities markets due to the sharp decline observed during [1929][1930][1931][1932] especially in food commodities. The second one, which was limited to a few quarters from 1949 to 1952, was the consequence of the Korean War and the need to build emergency storage capacity from the United States in critical raw materials such as rubber which was produced in Asian areas. During this period, the prices of raw materials 1 increased on average by 45%, with a surge in prices of agro-industrial products (+84%) and, to a lesser extent, of metals and minerals (+34%). The third one, during the 1970s, is clearly marked by external factors (first and second oil shocks) and severe weather conditions in certain production areas between 1971 and 1974 which also led to an increase in the prices of agricultural raw materials. Overall, during this period, energy prices quadrupled and the prices of raw materials tripled, on average. Thus, during the 20th century, price volatility was more driven by strong movements in agricultural products as a result of severe weather conditions or geopolitical uncertainties, or by structural changes in industrial organisation (new pricing system in the light of the OPEC creation, dismantlement of cartels in non-ferrous metals industries, etc.). before re-engaging in an upward movement from 2009 to 2011. This represents an apparent break from the one observed during the 1980s and the 1990s when prices fell around 1% per year on average. The price increase that began in 2001 was first largely driven by higher prices for non-ferrous metals and this trend spread to the other commodities markets, illustrating the socalled co-movement phenomenon. Moreover, it happened in a new economic and financial context. On the one hand, due to the energy crisis, the relation between non-energy commodity prices and crude oil prices has grown in importance in recent years, especially for agricultural and beverage commodities (maize, soybeans and sugar) which are used for the production of alternatives to crude oil. The food versus fuel debates are nowadays a key issue for policy makers and the relations between agricultural commodities and crude oil are being widely studied in the academic literature. On the other hand, some authors [START_REF] Coleman | The role of market speculation in rising oil and gas prices: A need to put the cop back on the beat[END_REF][START_REF] Masters | Testimony before the Committee on Homeland Security and Governmental Affairs[END_REF][START_REF] Masters | Testimony before the Committee on Homeland Security and Governmental Affairs[END_REF]White, 2008a, 2008b) claim that the new commodity price dynamic is widely linked to the movement of "financialisation" of the commodities markets with the introduction of new financial tools such as the Exchange Traded Fund (ETF), or can be explained during certain periods of financial instability by speculative trading [START_REF] Hache | Speculative trading and oil price dynamic: A study of the WTI market[END_REF]. In this new context, the comovement question seems to be highly relevant.

Literature review

Since the founding paper of [START_REF] Pindyck | The excess Co-Movement of Commodity Prices[END_REF], the relations between commodity prices through the co-movement analysis have been often studied. The first studies were dedicated to the excess co-movement phenomenom. Pindyck and Rotemberg brought to light the excess of comovement for a set of seven commodities (namely, wheat, cotton, copper, gold, crude oil, lumber, and cocoa), defined as when a co-movement between unrelated commodities 2 prices remains ''...in excess of anything that can be explained by the common effects...''. They explain that raw commodities may have a common trend because of direct effects (an increase of industrial production leading to an increase in industrial commodities' demand for the production process and non-industrial commodities' demand due to the increase of revenues) or indirect effects (through the expectations for commodities, affecting the storage process and then the current prices) resulting from macroeconomic changes. They include in their model macroeconomic variables (nominal interest rate, industrial production, consumer price index, etc.) in order to take into account common effects. They estimate the commodity price equation using Ordinary Least Squares (OLS), where the explicative variables are "common" to all the commodities, and then study the correlation between the errors. If the correlations between the errors are different from 0, excess co-movement is detected, according to the authors. They conclude by explaining that excess co-movement can be due to: (i) herd behaviour from the traders; (ii) liquidity constraints; (iii) frequency of data. Nevertheless, few studies have supported similar conclusions. [START_REF] Palaskas | Is there Excess Co-movement of Primary Commodity Prices: A Cointegration Test?[END_REF], considering a dataset of nine commodity prices (cocoa, coffee, wheat, cotton, rubber, copper, lead, crude oil, and silver), conclude that if the co-movement can be detected (via cointegration tests), an excess of co-movement, unlike the findings of Pindyck and Rotemberg, is extremely rare. [START_REF] Leybourne | The excess Comovement of Commodity Prices Revisited[END_REF] compare these two studies, and, after splitting the concept of excess co-movement into two sub-definitions (namely "strong" and "weak" excess co-movement), estimate empirically their own model with twelve commodities (cocoa, coffee, copper, cotton, gold, lumber, aluminium, crude oil, soya, sugar, wheat), and finally conclude that excess comovement does not occur frequently. [START_REF] Deb | The excess Co-movement of Commodity Prices Reconsidered[END_REF], using GARCH processes on two sample periods (1960-1985 and 1974-1992), and focusing on four possible definitions of the excess of co-movement, find that no excess comovements are found in the first sample, and that in the second sample, the excess of co-movement is not found with tests of the "correct size and good power".

Using two models (a macro model and an equilibrium model that takes inventory into account), [START_REF] Ai | On the Comovement of Commodity Prices[END_REF] draw similar conclusions 3 , supporting the results that the excess co-movement hypothesis is not validated 4 . They demonstrate that the price co-movements are explained by demand and supply side factors and also by fundamentals 5 .

As the majority of studies have rejected the excess of co-movement hypothesis, recent studies have focused more and more on the co-movement relations. Actually, [START_REF] Cashin | The Myth of Comoving Commodity Prices[END_REF], with a concordance statistic 6 , conclude that co-movement does not exist between two unrelated commodity prices, while it is common for two related commodity prices 7 . [START_REF] Saadi | Le phénomène des mouvements joints des prix internationaux de matières premières[END_REF] applies cointegration and causality tests to mining (aluminium, copper, tin, nickel, lead, zinc) and agricultural (cocoa, coffee, cotton, rubber) commodity prices and finds evidence of co-movement for the majority of pairs of prices over the study period, which runs from 1970:01 to 1998:12.

Yang ( 2004), using a Hansen and Johansen (1993) procedure, advances the fact that governmental policy matters, too. They find a long-run equilibrium relation between four agricultural US futures commodity prices (corn, oat, soybeans, and wheat) from mid-1996, while the Federal Agricultural Improvement and Reform Act was implemented the same year, leading to a more liberalised agricultural market 8 .

Turning to the studies that specifically consider oil, [START_REF] Natanelov | Is there co-movement of agricultural commodity futures prices and crude oil[END_REF] estimate the co-movement relation between oil prices and a set of agricultural commodities (cocoa, coffee, corn, soybeans, soybean oil, wheat, rice, and sugar) and gold futures prices. Using a Johansen cointegration test for the period 2002-2010, the authors find that four commodity prices are cointegrated with crude oil (cocoa, wheat, coffee, and gold) with a causality link running from crude oil prices to cocoa and gold prices, and from wheat and coffee prices to crude oil prices. However, in five cases, the hypothesis of no cointegration is not rejected. Campiche et al. (2007), using the same methodological tools (i.e., a Johansen cointegration test) and about the same set of data (agricultural commodity 5. Moreover, the authors conclude that "there is] raising doubts on the role of speculation per se in causing the large price movements".

6. Measuring the time share with two series in the same state (i.e., both in increase or decrease).

7. Cashin et al. consider successively the datasets of [START_REF] Pindyck | The excess Co-Movement of Commodity Prices[END_REF] for unrelated commodity prices and [START_REF] Deb | The excess Co-movement of Commodity Prices Reconsidered[END_REF] for related commodity prices.

8. Unlike the 1981-1996 period, when cointegration is not detected.

prices), find no cointegration evidence for the period 2003-2005, whereas they do find cointegration between crude oil prices and corn and soybean prices in [START_REF] Radetzki | The anatomy of three commodity booms[END_REF]-2007[START_REF] Baffes | Oil Spills on Other Commodities[END_REF], with an OLS estimation, examines the pass-through relations between crude oil prices and (i) commodity indexes and (ii) individual commodity prices9 . Regarding the results for the indexes, the highest elasticities with oil prices are obtained when fertiliser and beverage prices are considered (when raw material prices are almost inelastic). Individual elasticities for 35 commodity prices with oil price are also provided.

Joëts and Mignon (2011) estimate long-run relations between energy forward prices (i.e. oil, gas, coal, and electricity) at distinct maturities and find that, using a non-linear panel cointegration methodology (i.e. PSTR models), oil prices are positively linked to gas and coal.

Nazlioglu and Soytas ( 2012) contribute to the debate on the relation between oil prices and agricultural commodity prices10 by implementing a panel data cointegration test, as well as a Granger causality test. Considering 24 different agricultural prices, the authors validate the hypothesis of cointegration with the tests developed by [START_REF] Pedroni | Panel cointegration. Asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis[END_REF]. Their estimation of the long-run parameters is realised through the Fully Modified Ordinary Least Squares (FMOLS) and Dynamic Ordinary Least Squares (DOLS) procedure developed by Kao and Chiang (2000) and Mark and Sul (2003). Their estimations reveal that, except for cotton, coffee, and sugar, where the impact is null, an increase in oil prices results in an increase in agricultural commodity prices.

As far as we know, with the exception of [START_REF] Nazlioglu | Oil Price, agricultural commodity prices, and the Dollar: A panel cointegration and causality analysis[END_REF], there is no study of the co-movement between commodities with the use of panel data tools. The majority of studies have been made with bivariate time-series procedures. Moreover, while [START_REF] Nazlioglu | Oil Price, agricultural commodity prices, and the Dollar: A panel cointegration and causality analysis[END_REF] focused on agricultural commodities with first generation panel unit root test which have the restrictive assumption to not account for cross-section dependence, we rely on several kinds of commodities using second generation unit root and cointegration panel tests 11 . Our approach considers potential cross-section dependence between commodity prices. In this article, we will (i) use panel data methodology, reinforcing the robustness of our results, and (ii) focus on a very large dataset, including more than the agricultural commodity prices generally used. The rest of this article is organised as follows. Section 2 describes the dataset and econometric methodologies used. The empirical results are then provided in Section 3. Finally, Section 4 concludes the article.

Data, unit root, and cointegration tests

We consider daily data over the period from July 11, 2000 to July 15, 2011. We rely on thirty commodity series: crude oil West Texas Intermediate (WTI), and 29 other commodity prices 12 . We split the commodity data into eight panel groups according to their characteristics: namely, Energy, Precious Metals, Non-Ferrous Metals, Agro-Industrials, Food, Oleaginous, Exotic, and Livestock. Using such a large sample of commodity prices allows us to take into account possible heterogeneity in the relations between the markets. All series are expressed in Log form and depicted in the Appendix. To control for the economic and financial environment that might impact all commodity price series, we rely on the Standard & Poor's 500 (SP500) equity indexwhich has the advantage of being available at a daily frequency. This variable also allows treating oil and commodity prices as financial assets and controls for the recent financial turmoil.

In order to see whether oil prices can impact the dynamic of commodity prices, we first investigate the unit root properties of our panel groups. We employ second-generation tests that are based on the assumption of crosssectional dependence between the panel members 13 . Indeed, the application of the CD test developed by Pesaran (2004)-based on the average of the pairwise correlation coefficients of the OLS residuals from the individual regressionsshows that such cross-sectional correlations exist in our sample (see Table 5 in the Appendix). The [START_REF] Choi | Combination unit root tests for cross-sectionally correlated panels[END_REF] unit root test is used to study the stationarity of our series. It relies on an error-components panel model and removes the cross-sectional dependence by eliminating (i) individual effects using the Elliott, Rothenberg and Stock (1996) methodology (ERS), and (ii) the time trend effect by centering on the individual mean. As shown in Table 2 the 12. The data were extracted from DataStream. 13. According to Pesaran (2004), cross-sectional dependence can arise for several reasons, such as spatial spillovers, financial contagion, socioeconomic interactions, and common factors.

Choi test concludes in favor of the unit root hypothesis: i.e., that all price series, except the Livestock panel, are I (1) (i.e. stationary in first logdifference) 14 . (Elliott, Rothenberg, and Stock., 1996) for each series are determined by 012; all tests are computed with individual effects and time trends specifications; under the unit root hypothesis, the Choi statistics are standard normal when T and N converge jointly to infinity.

Turning now to the cointegration analysis, we first rely on first-generation tests, namely the seven tests proposed by [START_REF] Pedroni | Critical values for cointegrations tests in the heterogeneous panels with multiple regressors[END_REF][START_REF] Pedroni | Panel cointegration. Asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis[END_REF] which are all based on the null hypothesis of no cointegration. Among the seven Pedroni tests, four are based on the within dimension (panel cointegration tests) and three on the between dimension (group-mean panel cointegration tests). Group-mean panel cointegration statistics are more general in the sense that they allow for heterogeneous coefficients under the alternative hypothesis. We estimate, for each panel, two long-run relations: (i) a relation between all the commodity prices of the considered panel, and (ii) the same relation adding the SP500 as a proxy for the financial and economic environement 15 .The results are not clear-cut, but the hypothesis of no cointegration seems to be privileged for almost all series (as expected for the Food and Non-Ferrous Metals panels), and so we do not have co-movements between WTI and commodity prices in the long-run. As with the unit root test, we also apply second-generation cointegration tests allowing for cross-sectional dependence.

14. For WTI and SP500, the augmented Dickey-Fuller test has been used to check the unit root properties of each time series. The results reveal that both series are I (1).

15. The results are available upon request to the authors.

The four panel error correction based tests proposed by [START_REF] Westerlund | Testing for error correction in panel data[END_REF] rely on structural dynamics and are a panel extension of the Banerjee et al.

(1998) tests developed in the time series context. Among Westerlund's four tests, two consider a homogeneous cointegrating relation under the alternative, while the two others allow for a heterogeneous long-term relation. The results shown in Table 3 show that all the panel groups are not cointegrated with the WTI, which confirms the results of the first generation tests. Considering the fact that our conclusions support the no cointegration hypothesis, the long term relation between WTI crude oil and the commodities panels is not validated. However, short run co-movements between prices may exist, resulting from investors' hedging strategies. To investigate this phenomenon, we test for cross-causality between markets using the new panel Granger causality test proposed by [START_REF] Dumitrescu | Testing for Granger Non-causality in Heterogeneous Panels[END_REF]. Following the seminal work of Granger (1969) on time series causality, we say that a variable x causes a variable y if we are able to better predict y using all available information than in the case where the information set used does not include x. In order to allow for the property of heterogeneity in the panel data framework 16 , Dumitrescu and Hurlin (2011) propose extending the Granger causality approach by adding cross-sectional units to the time series dimension. Hence, they develop a test of no causality which accounts for Homogenous Non-Causality, i.e., no causal relation for any of the units of the panel) under the null. Under the alternative, they specify the heterogenous hypothesis defined as the cross-sectional average of the Wald statistics associated with the individual Granger causality tests. Two subgroups of cross-sectional units are therefore defined: one characterised by causal relations from x to y 17 and another subgroup for which there is no causal relation. From the Panel Granger-causality test, Table 4 reveals that in the majority of the cases, the WTI crude oil price Granger-causes the commodity prices. These causal relations appear to be different according to the panel group considered. Indeed, almost all series, except Exotic, seem to be influenced by WTI price movements. The relation between oil and energy prices is not surprising since crude oil is often viewed as a key factor upon which energy markets are set. Just as with the crude oil market, Agro-Industrial and Non-Ferrous Metals are often considered to be good proxies for, respectively, global activity, the industrial and real estate sectors, then causal relations could be the consequence of international economic fluctuations. Turning to the Food and Oleaginous panel groups, causality is the consequence of input-output relations. Reverse causality from commodity groups to crude oil is not validated according to the statistic. Because it is relevant that short run dynamics exist from WTI to the commodity markets, we propose to estimate the Panel VAR model and analyse the impulse response functions. 17. The regression model is not constrained to be the same across units.

Panel Vector autoregression and impulse responses

In order to assess the link between WTI crude oil prices, the stock market, and commodity prices, we consider the following panel VAR model 18 .

where i indicates the type of commodity, t runs from 1 to T, is the vector of endogeneous variables, is the vector of error terms, is the commodity-group specific intercept matrix, and is the matrix polynomial in the lag operator.

The vector of endogenous variables is given in turn by where i denotes the individual dimension composed by commodity prices, and t=1,...T the time.

, , and denote the commodity prices 19 , the crude oil prices, and the stock market index, respectively.

The usefulness of the panel VAR approach is that it combines a multivariate framework, which takes into account endogenous variables, with panel data dimensions, which takes into account unobserved individual heterogeneity. Furthermore, to overcome the well-known problem of correlation between the regressors and the fixed effects in dynamic panel specifications, we use the Generalised Method of Moments (GMM) method as an estimation procedure, using the codes of [START_REF] Love | Financial development and dynamic investment behavior: Evidence from a panel VAR[END_REF].

The impulse response functions are given in the Appendix and indicate the impact of a WTI price shock on commodity markets. First, the panels Energy, Precious Metals, and Oleaginous exhibit very similar reactions (i.e., a positive response at period t+1). Non-Ferrous Metals have a slower reaction, in terms of both time and magnitude, indicating a less close relation. Concerning the magnitude of the results, it is worth noting that the response at one day for the 18. As mentioned in the description of the data, the SP500 is included in our panel VAR regressions to control for the economic and financial environment, something which might affect all commodity prices.

19. The variable considers one commodity panel group at a time.

energy panel group is about 0.002, a little less than for the precious metals.

The response of Oleaginous is about 0.0014. As for the agro-industrial panel group, no significant response appears. For the food panels, the response is negative but quite limited in terms of magnitude. This result is not a surprise for the energy group, given that energy prices are often contractually linked to the oil price. Considering the responses of precious metals, oleaginous, and non-ferrous metals, the influence of exchange traded funds can be an explanation, as well as hedging behaviour on the part of traders. Two conclusions emerge from our analysis: (i) considering the rapidity of the return to equilibrium, the hypothesis of market efficiency receives strengthened support, (ii) the fact that various panels have the same reaction emphasises the importance of exchange traded funds. Regarding the responses to a shock from the proxy for global economic activity, all group panels, except for the energy panel group, have the same kind of reaction: i.e., a positive response at one day, followed by a fast return to equilibrium. One can interpret this result as evidence for the fact that these commodity prices remain driven by factors outside the sphere of influence of the price of oil. The magnitudes of the responses for precious and non-ferrous metals are quite similar and high, with a value of 0.0025. This highlights the large influence of global activity upon these variables. For the three other groups, the magnitude is much weaker: between 0.001 and 0.0015, reflecting nonetheless a mixed influence of global activity because of the more specific composition of these panels.

Conclusion

In this article, we focussed on the relation between oil and commodity prices, relying on second-generation panel unit root and cointegration tests. Using a collection of data with a wide scope (energy, precious metals, nonferrous metals, agro-industrials, food, oleaginous, exotic, and livestock), we first conclude that the hypothesis of long-run relations between oil and the panel data groups is not validated. We then do not find evidence of comovement in commodity prices in the long run. However, considering that short-run relations might exist due to hedging strategies such as cross hedging between correlated markets or new portfolio allocations, we then implemented a short-term analysis, using a panel Granger causality test and a panel VAR methodology. From the panel Granger causality test, we observed that the WTI crude oil price Granger-causes the commodity prices (except for the exotic commodities), while a reverse causality is not validated. From the Panel VAR methodology we find that three panel groups, namely energy, precious metals, and oleaginous, have a similar reaction at a lag of one day to a shock from the price of oil, while non-ferrous metals have a slower reaction. Our results support two main conclusions: (i) the speed of the return to long-term equilibrium for each commodity group emphasises the efficiency of the markets and the importance of the existing fundamental market forces, and (ii): fast co-movements are at play in the short run for these groups of commodities due to hedging behaviour and the influence of ETFs.

  077) Notes: (a) Between parentheses: p-values with cross-sectional dependence based on bootstrapped distribution (300 bootsrtrap replications). (b) Tests are computed with individual effects and time trends. (c) The Bartlett kernel is used for the semi parametric corrections. (d) The lleads and lags in the error correction test are chosen using the Akaike criterion. (e) The number of common factors is determined by the criterion (See Bai and Ng, 2004) with five as the maximum number of factors.

Figure 2 .

 2 Figure 2. Non-ferrous Metals and Agro-industrials panel groups

Figure 4 .

 4 Figure 4. Exotic and Livestock panel groups

Table 1 :

 1 Indices of Commodity Prices

	2001	2003	2005	2007	2008	2009	2010	2011	2012
	All primary commodity
	58.3	65.0	100	135.1	172.3	120.7	152.2	192.2	186.2
	Non-Fuel
	75.8	81.8	100	140.5	151.1	127.3	160.9	189.5	170.9
	Food
	80.4	88.5	100	127.2	156.9	133.9	149.2	178.6	175.5
	Beverages
	65.6	85.5	100	123.3	152.0	154.4	176.2	205.5	167.4
	Industrial
	72.6	75.3	100	154.3	145.7	118.7	169.9	197.8	167.1
	Agricultural raw materials
	95.1	95.4	100	114.2	113.4	93.9	125.1	153.5	134.0
	Metal
	56.3	60.7	100	183.3	169.0	136.5	202.3	229.7	191.0
	Energy
	48	55.2	100	131.9	184.7	116.8	147.1	193.8	195.2
		Notes: Source, International Monetary Fund
	The 2001-2011 boom and bust appears quite different. Indeed commodities
	markets experienced major changes with a steady and continuous upward
	trend mid-2008 when prices collapsed in the wake of the financial crisis,
	1. Measured by the International Monetary Fund (IMF) index.

Table 2 :

 2 Second-generation panel unit root tests

	Choi
	Z	L*

Notes: Between parentheses: p-values. For Choi's test, the optimal lag orders in the individual ERS statistics

Table 3 :

 3 Second-generation panel cointegration tests

		Westerlund
		Group-mean statistics	Panel statistics
	EnergyG	0.736 (0.730) -0.536 (0.200) -1.066 (0.380) -1.507 (0.370)
	MetalpG	1.698 (0.080) -2.361 (0.070) -0.245 (0.600) 0.052 (0.720)
	MetalnfG	-0.732 (0.260) -1.612 (0.110) -0.080 (0.600) -0.928 (0.320)
	AgroG	-0.047 (0.493) 0.064 (0.440) -0.284 (0.403) -0.437 (0.333)
	AlimG	0.095 (0.580) 0.327 (0.593) -0.409 (0.423) -0.546 (0.360)
	OleaG	1.083 (0.830) 0.869 (0.750) 0.526 (0.750) 0.182 (0.610)
	ExoG	-1.514 (0.080) -1.939 (0.057) -1.463 (0.123) -2.121 (0.

Table 4 :

 4 Panel Granger causality test Statistics are reported. The test statistic converges under the null to a standard normal distribution when T and N tend sequentially to infinity.

	Energy	12.29	16.09	5.35	1.07	-3.34	-1.11
	Precious metals 60.92	141.89	47.22	3.93	2.28	0.75
	Non-Ferrous	4.74	5.22	1.73	2.41	-1.76	-0.58
	metals
	Agro-	12.13	19.37	6.44	3.17	0.37	0.12
	Industrials
	Food	5.73	6.70	2.22	1.74	-3.08	-1.02
	Oleaginous	27.61	42.63	14.18	2.77	-0.38	-0.12
	Exotic	3.10	0.21	0.07	1.79	-2.55	-0.08
	Notes:

Pindyck and Rotemberg explained that the commodities are unrelated because "none … are substitutes or complements, none are co-produced, and none is used as a major input for the production of another".

The authors consider five agricultural commodities: wheat, barley, corn, oats, and soybeans, from 1957:01 to 2002:09.

The equilibrium model gives better results than the macro models.

In three different periods.

The exchange rate of the USD is also considered.

Our approach considers potential cross-section dependence between commodity prices.

The authors distinguish between the heterogeneity of the regression model and that of the causal relation from x to y.

Appendix

The "Cahiers de l'Économie" Series

The "Cahiers de l'économie" Series of occasional papers was launched in 1990 with the aim to enable scholars, researchers and practitioners to share important ideas with a broad audience of stakeholders including, academics, government departments, regulators, policy organisations and energy companies.

All these papers are available upon request at IFP School. All the papers issued after 2004 can be downloaded at: www.ifpen.fr

The list of issued occasional papers includes:

# 1. D. PERRUCHET, J.-P. CUEILLE