Shake now or extract later
;
a cost-benefit analysis of lowering the cap on the Groningen gas field, with special attention to earthquakes
By Peter Perey1
Master thesis MSc Economics Supervised by prof. dr. M. Mulder
University of Groningen 19 January 2018
Abstract
In this paper, the welfare effects of the lowering of the production cap of the Groningen gas field are estimated. The estimation follows from a cost-benefit analysis of the policy change. It is found that the European gas price is not affected by the lower production of the Groningen gas field. The costs consist of a loss in producer surplus, where consumer surplus is unaffected. The main benefit is the reduction in costs from the gas-induced earthquakes, where the benefits on security of supply are relatively small. The total welfare effect range from -1.8 to -7.9 billion euro excluding non-monetary benefits. Based on its findings, this paper recommends to extracts and sell the Groningen gas as quickly as possible, accompanied with a compensation package for the inhabitants of the affected region.
JEL classifications: C22, D61, Q38
Keywords: cost-benefit analysis, energy economics, energy regulations, welfare distribution
1 Student number: 2561468
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1 INTRODUCTION
On June 24 2016, the Dutch government announced the lowering of the cap of gas extraction from the Groningen gas field.2 It was a long-anticipated policy measure following a fierce debate on the impact of gas extraction on the safety of inhabitants of the region surrounding the gas field. The policy measure that was implemented in 2016, replaced an already existing cap on the production of the Groningen gas field introduced in 2004.3 The idea was to preserve the Groningen gas field as long as possible and stimulate the extraction from smaller gas fields. In Mulder & Zwart (2006a), the welfare effects of this policy were investigated for the first time. It was concluded that the cap was welfare reducing if it was binding.
What differs in the policy introduced in 2004 and the more recently new imposed cap is the motivation for the policy change. In 2004, the main purpose was to stimulate production from smaller fields to preserve the Groningen gas field for strategic reasons. On the contrary, in 2016 the main reason for the change in policy was the risk following from gas-extraction induced earthquakes. This becomes clear in the statement of Kamp (2016), where the ministry of Economic Affairs (EZ) announces the lower cap. This risk was not an element in the discussion for the cap in the research of Mulder & Zwart (2006a). The aim of this paper is to make a contribution to the debate on gas extraction in a well-organized structured fashion. In order to do this, an analysis of the social welfare effect for the Netherlands, following from the implementation of the new regulation, is made.
The welfare effect of the policy is analysed by making a societal cost-benefit analysis. All the costs and benefits that are driven by the lowering of the cap are investigated. The effects of the policy change are divided in different categories, direct and external effects. For calculation of the direct effects, the effects on the gas market, the elasticity of the gas price on the supply from Groningen is derived econometrically. For this part of the research daily data is used over a period ranging from 2/1/2013 – 29/9/2017. The external effects, effects that are not incorporated in the market, consist of effects on security of supply and earthquake-related costs. Each possible effect is introduced and examined separately and later combined. To obtain comparable outcomes, the results are calculated in present value terms. Finally, all different results are combined to find the net welfare effect of the policy change.
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DUTCH GAS MARKET & POLICY
2.1 The Dutch natural gas market
To gain a better understanding of the Dutch natural gas market, it is necessary to distinguish market and network. Gas is a natural product and because of differences in Wobbe-indices a heterogeneous good. As briefly explained by Klimstra (1986), the Wobbe-index indicates the thermic value of a gas. Broadly speaking, the product natural gas can be separated into two categories with different indices: low-and high-calorific gas. Gas with a low-calorific value contains a higher percentage nitrogen than high-calorific gas resulting in a lower Wobbe-index. Therefore, the amount of thermal energy stored in a unit low-calorific gas is lower than in the same unit of high-calorific gas. Due to the different specification there is the need for two different transport networks.
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on the source and end-users. In the Netherlands, low-calorific gas comes from the large gas field in Groningen which is the main entry point. The end-users of this product are residents of North-West Europe with the main purpose of heating. Thus, next to domestic use, low-calorific gas is exported to neighbouring countries. Entry points in the Dutch national grid for high-calorific gas are small fields, both on- and off-shore, and import from other countries. The end-users of this type of gas are the domestic industrial end-users, energy plants and again export. As shown by Siliverstovs et al. (2005), the European gas markets have become more integrated and well connected in recent years. Also, the introduction of liquified natural gas (LNG), that can be transported by ship, has contributed to the globalization of the gas market. Consequently, the Dutch gas market is not a closed market anymore. Due to the integration, prices have converged, and it is more appropriate to speak of a European gas market with uniform prices. 2.2 Policy
Since the discovery of the Groningen gas field near the village Slochteren, the Dutch gas policy changed multiple times. At the time of discovery, gas markets did not play an important role, and the overall belief was that in the near future, all energy would be retrieved from nuclear power. Therefore, in the 1960’s, the goal was to deplete and profit as much as possible. However, as stressed by Gertjan Lankhorst (2016), former CEO of GasTerra, it was also the purpose to secure domestic supply for at least 25 years. Only when resources exceeded domestic demand, there was permission to close export contracts.
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In the 1990’s the European energy markets liberalised. The goal was to foster competition which should lead to lower consumption prices. A consequence was that the whole market was no longer controllable for a single party. To prevent the rapid depletion of the Groningen gas field instead of the smaller fields, the Gaswet was introduced. According to Mulder & Zwart (2006a), this consisted of a cap on the production level of the Groningen field over a longer period of time. For the period 2005-2015 the cap was set on 425 bcm without an annual restriction. So, the producer could choose a yearly production as he liked, if the production over 10 years was 425 bcm or less. For the period of 2010-2020 the same cap of 425 bcm was in place. However, since the event of a large earthquake in 2012, this existing policy has been a topic of debate. In the next section the relationship between gas extraction and earthquakes is explained.
Figure 1: Gas extraction by fields in the Netherlands. Sources: NAM & CBS
2.3 Earthquake debate
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case, there is a small chance of minor damage around the epicentre. Later research on earthquakes induced by gas extraction shared the same view on the possible damage. De Crook et al. (2003) state that only light and non-structural damage can be expected following the earthquakes induced by gas extraction.
That these predictions were too optimistic became apparent on 16 August 2012. Near the Groningen village Huizinge there was an earthquake with a magnitude of 3.6. Immediately after the earthquake, numerous reports of damage came in. Since then, according to the NAM (2017), 38 earthquakes with a magnitude of 2.0 or higher have been measured in the area. According to the quarterly report of the National Coordinator Groningen (NCG) (2017a), the total number of damage reports with acknowledged earthquake related damage was over 50,000. Further it is reported that since the beginning of 2016, almost 150 situations are acknowledged as acutely unsafe.
The unsafe situations caused by the earthquakes have led to numerous protests and damage claims from the population in Groningen. The criticism on the passive attitude of the government and the NAM grew4. According to Kamp (2014b), the minister of Economic Affairs, there was a growing need to intervene in the production decision of the Groningen gas field. In the next section, the effect of an intervention on a production decision is discussed.
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3 THEORY
3.1 Optimal depletion
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Hotelling’s rule
Consider a two-period model, with a single producer of a non-renewable good. His revenues are the amount he sells times the price per unit minus costs. The costs are made to start operations and are not dependent on the amount that is produced and are therefore sunk. The model is described by the following set of equations:
𝛱 = 𝜋0+ 𝜋1 1+𝑟 (1) 𝜋0= 𝑝0𝑞0− 𝐶 (2) 𝜋1= 𝑝1𝑞1− 𝐶 (3) 𝑄 ≥ 𝑞0+ 𝑞1 (4)
, where Π is total profits, π0 and π1 are profits in period 0 and 1 respectively, r is the real interest rate,
p0 and p1 are prices in period 0 and 1 respectively, q0 and q1 are quantities produced in period 0 and 1
respectively, C is the operation costs and Q is the total amount of the non-renewable good available. Substituting equations (2) and (3) into (1) give:
𝛱 = 𝑝0𝑞0− 𝐶 + 𝑝1𝑞1−𝐶
1+𝑟 (5)
Since the firm is profit maximizing and costs are sunk, the restriction of equation (4) will be binding. Therefore, it is possible to rewrite equation (4) and substitute it into equation (5) to obtain:
𝛱 = 𝑝0𝑞0− 𝐶 +
𝑝1(𝑄−𝑞0)−𝐶
1+𝑟 (6)
The producer wants to know the optimal depletion for now, so he will maximize with respect to q0: 𝜕𝛱 𝜕𝑞0= 𝑝0− 𝑝1 1+𝑟= 0 → 𝑝1 𝑝0= 1 + 𝑟 (7)
The optimal depletion path for the producer is such that the growth in prices is equal to the interest rate. When prices are unaffected by the quantity sold, it can be profitable to sell all the resource now or sell everything in the future. When quantities do influence the price, the producer will set the
quantities so that equation (7) will hold. Every set of quantities that does not lead to the equality above will be suboptimal.
Now imagine a restriction on the quantities that can be produced per period. According to Hotelling’s rule this leads to a suboptimal depletion path.
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governments have better information of future trends, policies and effects outside the market. Consequently, there can be factors that are not accounted for by the market outcome. Negative (and positive) factors that are not fully incorporated in the market are externalities. These externalities can come from the production and the consumption side. Mulder & Zwart (2006b) already discuss different production and consumption externalities. They identify two externalities resulting from the resource character of natural gas. First, the presence of competition on the gas markets makes producers extract the gas too quickly, since the opportunity costs are borne by the competitors. This rapid depletion is known as the tragedy of the commons as first explained by Hardin (1968).
Secondly, they mention the lack of view on future possibilities. With uncertainty, the optimal production path is hard to determine. In their paper, Mulder & Zwart (2006b) also briefly refer to the earthquakes in the northern part of the Netherlands induced by gas production. They state that the damage is viewed as quite small, which is in line with the other information available at that point of time. Conversely, nowadays we know that the damage is not that small, and this externality is one of the main points in this paper. The consumption externalities are generally induced by the inactivity of consumers and the lack of response to price fluctuations. In conclusion, in a perfect market, the intervention of the government on a production decision is undesirable. Only in the case where externalities are not incorporated in the market, it can be beneficial to intervene. In the next section the assessment of such an intervention is described. 3.2 Cost-benefit analysis
In order to assess the welfare effect for society of a specific policy, a societal cost-benefit analysis is advised. The goal of such a cost-benefit analysis is to get a structural and systematic overview of all cost and benefits for society of a certain project or policy. Such an overview of all costs and benefits is defined as the project effect. The outcome of this project effect helps the responsible party in making a decision in an objective way. It therefore serves as a powerful tool to ex-ante investigate policy measures.
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Bureau for Economic Policy Analysis (CPB) and the Netherlands Environmental Assessment Agency (PBL) have presented a more general framework by Romijn and Renes (2013). The framework for the cost-benefit analysis conducted in this paper is based on these earlier works. In the next chapter, the method of the cost-benefit analysis conducted in this paper is defined. 3.3 Welfare distribution
The welfare effect as described above consists of different components. One of the founders of modern microeconomics, Alfred Marshall, introduced the terms producer- and consumer-surplus. Marshall (1927) used the concepts of surplus to analyse the effects of different constraints on a market. A surplus refers to the difference between the benefit and costs for a good or service. For producers this is the price they receive minus the production costs, which is the profit of the firm. For consumers, the surplus is the utility they derive from a specific good or service, minus the price they have to pay. A common term used for the utility of a specific good is the willingness to pay. According to Varian (1992), this notation follows from the fact that the utility of a good expressed in money is the maximum amount one is willing to pay, since a higher price will give negative utility. Interesting is the fact that the market price is reflected in both surpluses, although with opposite effects. Producer surplus increases with a higher price, where consumer surplus falls.
In the case of a binding restriction of a market, the market outcome will be affected. The surpluses will therefore be affected as well. One of the two groups will benefit. However, the market outcome of a restricted market will always be welfare reducing compared to a free market outcome. This means that a part of the lost surplus will not be regained. This part is referred to by Marshall (1927) as the deadweight loss. Despite the welfare reducing aspect, many restrictions on markets are present. Economically this can only be verified by the existence of external effects, which are not captured by the market.
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Figure 2: The implementation of a maximum quantity
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4 METHODOLOGY & DATA 4.1 Project effect
As described above, the cost-benefit analysis of this paper compares two different policies to obtain a project effect. The project effect is defined as the difference of the economic outcome with the project, the project alternative, and the economic outcome when this project was not in place, the null alternative. In this paper the project is a policy measure regarding the cap on gas extraction from the Groningen gas field. The policy that is investigated is the reduction of the maximum allowed production of gas to 24 billion cubicle metres (bcm) a year. This will be the project alternative used in this paper. This policy replaces the policy that was introduced by EZ (2004), where the ceiling was set on 42.5 bcm per year on average. However, the producer could choose any level of yearly production, as long as the production over 10 years would be 425 bcm or less. In this paper, this policy will be the null alternative.
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4.2 Research design
All the different effects that make up the project effect can be combined if the results are reported in present value. Results are in present value when they are accounted for time-preferences. In the end, there will be clear estimates for each expected effect and an overall net present value effect of the project. All the effects are the differences between the outcome for the project alternative and the null alternative. The direct effects, effects on the gas market, are differentiated in effects on producer- and consumer-surplus. The external effects that are considered in this paper, are the effects on security of supply, consisting of reliability of supply and strategic storage, and most importantly the effect on earthquakes in the northern part of the Netherlands. These effects are all combined in the cost-benefit analysis to estimate the net project effect. In formula form this becomes:
𝑁𝑃𝑉 = 𝑃𝑉(𝛥𝑃𝑆 + 𝛥𝐶𝑆 + 𝑆𝑜𝑆 + 𝐸𝑄), (8)
where NPV is the net present value of the project effect, PV is the present value, PS is producer surplus, CS is consumer surplus, SoS refer to the effects on security of supply and EQ are the effects on the earthquakes.
4.2.1 Discount factor
Since the effects should be displayed in present day terms, it is needed to find a net present value (NPV) of the project effect. To be able to calculate the present value of predicted future market outcomes, a proper discount factor is needed. The discount factor is based on a risk-free rate that accounts for the time preference and a risk premium. In van Ewijk et al. (2015), the proper discount factor for social cost-benefit analysis in the Netherlands is analysed. They recommend a standard discount factor of 3 per cent, compared to a discount factor of 5.5 percent in earlier recommendations. Reason for this decline is the fall of the risk-free long-term interest rate. This has declined from circa 2 to circa 0 percent, where the risk-premium of 3 percent was unaffected. In this paper, the recommendations of Ewijk et al. (2015) are followed. However, to make the analysis more robust, the analysis is also conducted with a discount factor of 1 and 5 percent.
4.3 Producer surplus
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in profit levels is determined by the difference in quantities and price. The difference for each year is described by formula (9):
𝛥𝑃𝑆𝑡= 𝛥𝑞𝑝𝑡 = 𝛥𝑝𝑡∙ 𝑞𝑡+ 𝑝𝑡∙ 𝛥𝑞𝑡+ 𝛥𝑝𝑡∙ 𝛥𝑞𝑡, (9)
where qt and pt denote annual produced quantity and price for year t. This formula consists of
a price-, volume- and interaction-effect. To obtain the values for these quantities, different optimal depletion paths for both alternatives have to be established.
4.3.1 Optimal depletion paths
The optimal depletion path of the Groningen gas field is determined by the producer given the circumstances he is not able to influence. As explained above, the idea of different alternatives is that the only difference is the policy that is investigated for the cost-benefit analysis. With these different alternatives, different optimal depletion paths are relevant. Since we assume that the operator of the Groningen gas field is a profit-maximizing firm, it is logical that the depletion path chosen by the operator under the old restriction was the optimal one. The most recent depletion plan is retrieved from NAM (2013) and is assumed to be the depletion path under the null alternative. In this paper, the end year of production is assumed to be 2050, which is different from the view of the NAM. This is caused by the intentions of the ministry of Economic Affairs to abandon gas consumption in this year.
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4.3.2 Price effect
The other variable that is included in formula (9) is the gas price. The relevant gas price in this paper is the TTF day-ahead spot price. This price is reached when the supply- and demand-curves intersect with a corresponding price and quantity. Therefore, every daily reported spot price is determined by the underlying supply- and demand-curves. Hence, each movement of the price is caused by a movement of one or both curves. As can be seen in formula (9), to analyse the difference in profits a price effect needs to be calculated. The price effect is defined as the sole effect of the new cap, the project alternative, on the price. In this paper the price effect of the new restrictions on production is analysed in an empirical way in chapter 5.
4.4 Consumer surplus
The effects on the consumer surplus are similar to the effects on producer surplus. The difference is in the quantity effect. Given the nature of the demand profile for natural gas, there is no reason to believe that the different alternatives have an influence on total demand. The level of gas production is not relevant for both industrial and residential users. On top of that, the level of household gas consumption can be seen as price inelastic in the short-run due to the lack of substitutability in heating sources. One could argue that the elasticity of residential demand is zero in the short run. However, in the long-run some price elasticity can be expected. Indeed, other studies report some price elasticity for household users. For industrial demand the assumption of zero elasticity is not realistic. In the short term the industry may have trouble to switch from energy source due to long-term contracts, but in the long run they gave perfect substitution options. Therefore, in the calculation of the change of consumer surplus, there will be some elasticity on demand. Mulder & Zwart (2006a) use a price elasticity of -0.25, where Jeeninga & Boots (2001) report a value of -0.1 for the short-run and -0.2 for the long run. Based on these values, this paper assumes a price elasticity of total demand of -0.2. The present value of the effect on consumer surplus is calculated similarly to the PV of the effect on producer surplus, by discounting the welfare effect of each year. The difference in consumer surplus for a given year is described in formula (10):
𝛥𝐶𝑆𝑡 = (𝛥𝑝𝑡∙ 𝑞𝑡𝐷+ 𝑝
𝑡∙ ∆𝑞𝑡𝐷 + 𝛥𝑝𝑡∙ ∆𝑞𝑡𝐷), (10)
Where CSt , 𝑞𝑡𝐷 and 𝑝𝑡 are the consumer surplus, total demand and price level in year t and 𝛥
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4.5 Security of supply
In their cost-benefit analysis of the previous cap on Groningen, Mulder & Zwart (2006a) recognize two different possible benefits regarding security of supply. First, they mention the benefits for reliability of gas supply. Due to its ability to act as swing supplier, the Groningen gas field can act as short-term back-up when other sources of supply fail. The lifespan of this back-up facilitating role of Groningen can be extended by capping, or in the case of this paper further restricting, the production. The postponement of the building costs of a replacement for this back-up is the benefit. Eventually, Mulder & Zwart (2006a) estimated this effect in their study to be a maximum of 20 million euro5.
The second benefit regarding security of supply mentioned by Mulder & Zwart (2006a) is the potential of the field to act as strategic storage. In this case, the benefit of lowering the cap on Groningen would be the postponement of the costs of building strategic storages and implementing them. The authors also mention the technical aspect of the gas field in relation to strategic storages. They mention the storages must be in place before the flexibility of the field is too low. According to Mulder & Zwart (2006a), something to expect when the total reserves are below 400 bcm.
4.6 Effect on earthquake related damage
The influence of a lower production level on the number and severity of earthquakes is difficult to predict. Actually, since the production of the Groningen gas field has declined in 2015, the total number of earthquakes monitored by the NAM remained on a same level. However, this can also be the result of better monitoring devices that can detect the smaller earthquakes. In fact, when only earthquakes of a magnitude of 2.0 and higher are considered, there is a decline from a peak of 13 in 2013 to 3 and 4 in 2016 and 2017 respectively as can be seen in figure 3. So, a first cautious conclusion can be that not the total number, but the severity of the earthquakes has declined. This decline in severity can have beneficial welfare effects. These effects are described in chapter 6.
5 Note that Mulder & Zwart (2006a) also obtain an higher value for the scenario where they use a 7% discount
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5 Price effect
5.1 VariablesTo be able to identify the price effect mentioned in section 4.3.2, it is needed to include all underlying factors influencing the dependent variable. Hulshof et al. (2016) already investigated market fundamentals describing the natural gas price. The variables used in their research are used here as well, with some alterations. The quantitative data used for the analysis of the gas price includes data from 2/1/2013 – 29/9/2017. It consists of daily data for weekdays only, except the industry index which is available monthly. The variables used can be found in table 1 and are described below. In figure 4, the TTF day-ahead gas price is depicted.
Table 1: Variable definitions
Description Source Frequency (unit)
TTF gas price Title Transfer facility day-ahead spot market price
Bloomberg L.P. Daily (€/MWh)
Brent oil price Brent crude oil European spot market price
Bloomberg L.P. Daily (€/barrel
Heating degree days Heating degree days in the Netherlands
Bloomberg L.P. Daily (degree Celsius)
Gas storage Weighted deviation from
average filling degree of past years in the
Netherlands
Bloomberg L.P. Daily (percentage points)
Industry index Industrial activity index of manufacturing in the Netherlands
Eurostat Monthly (indices)
Gas Production Daily total gas production in the Netherlands
Bloomberg L.P. Daily (MCM)
5.1.1 The price of oil
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for the crude oil is converted to €/barrel to eliminate exchange rates. The daily Brent oil spot price is also depicted in figure 4.
Figure 4: Daily TTF gas and Brent oil price. Source: Bloomberg L.P.
5.1.2 Residential demand
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Figure 5: Heating Degree Days in the Netherlands. Source: Bloomberg L.P.
5.1.3 Industrial demand
Another type of demand is industrial demand, which depends on the output. In times of economic growth, an increase in the consumption of goods and services can increase the demand for natural gas. Especially in areas where natural gas is a part of the inputs, like manufacturing, this is visible. To capture the influence of the economic activity the approach of Hulshof et al. (2016) is followed, but with the data on the Netherlands instead of the weighted average of several countries. The industry index is shown in figure 6, where it becomes apparent that economic activity is rather low in the sample period.
Figure 6: Industry index for manufacturing in the Netherlands. Source: Eurostat
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5.1.4 Storage
According to the theory of gas storage, the level of inventories filled influences the difference between spot and future prices (Fama & French, 1987; Brennan, 1958). Brown and Yücel (2008) stated that the natural gas price is affected by the storage. Storages can act as swing suppliers, since the majority of the fields cannot easily adapt to demand fluctuations (Groningen gas field is the main exception). Thus, storages are filled in low demand periods and used in periods of peak demand. This will limit the effect of scarcity in peak demand and could have a dampening effect on the price. Cartea & Williams (2008) argue that most storages are operating on a yearly basis and therefore the deviation from the usual cycle is most relevant for the spot price. Following the calculations of Hulshof et al. (2016), Cartea & Williams (2008) and Brown and Yücel (2008), a storage differential is calculated. The formula for this differential is showed in formula (11). ∅𝑡 = 𝑆𝑡− ( 1 𝑛𝑆𝑡−365+ … . + 1 𝑛𝑆𝑡−𝑛∗365), (11)
where øt denotes the storage differential from the average over the past n years at day t and St
the percentage of capacity filled at a given day t. In this paper, a value of two years is taken for n due to data limitations. Figure 7 shows a structural lower filling degree from the beginning of January 2015, probably caused by the increase of storage facility and the time it takes to fill.
Figure 7: Difference between current and seasonal filling degree of Dutch gas storage facilities. Own calculations based on data from Bloomberg L.P.
5.1.5 Production
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level of the Groningen field would be the most interesting variable. Unfortunately, only daily data on the total gas production in the Netherlands is available. However, since the Groningen field is the only field that can differ in production on a daily basis, it is still considered as a good alternative. As seen in figure 8, the seasonal swing supply of the Groningen field is still visible in this data set.
Figure 8: Daily total gas production in the Netherlands. Source: Bloomberg L.P.
5.2 Empirical model
In this paper, a linear regression model is constructed to analyse the different price effects. The dependent variable is the TTF natural gas spot price (𝑃𝑡𝑇𝑇𝐹). In this way, it is possible to identify
the effects of different market fundamentals on the natural gas price. The explanatory variables are the Brent crude oil price (𝑃𝑡𝐵𝑟𝑒𝑛𝑡) , heating degree days in the Netherlands (𝐻𝐷𝐷𝑡), an
industry index for manufacturing (𝐼𝑁𝐷𝑡), the gas storage differential (𝑆𝑡𝑜𝑟𝑡), the daily gas production (𝑃𝑅𝑂𝐷𝑡) and finally dummies to control for the day-of-the-week effect (𝐷𝑞𝑑𝑎𝑦, q=1,2,.,5). The model is given in formula form in formula (12).
𝑑. ln 𝑃𝑡𝑇𝑇𝐹= 𝛼 + 𝛽1(𝑑. ln 𝑃𝑡𝐵𝑟𝑒𝑛𝑡) + 𝛽2(𝑑. 𝐻𝐷𝐷𝑡) + 𝛽3(𝑑. ln 𝐼𝑁𝐷𝑡) + 𝛽4(𝑑. 𝑆𝑡𝑜𝑟𝑡) + 𝛽5(𝑑. ln 𝑃𝑅𝑂𝐷𝑡) + 𝛽6(𝐷𝑞𝑑𝑎𝑦) + 𝜀𝑡. (12) 5.3 Statistical tests
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Dickey-Fuller (ADF) test. The results are reported in table 2. The industry index, HDD and production level are stationary in levels. The natural gas price, oil price and storage differential are non-stationary in levels, but stationary in first differences. Therefore, the model is transformed into a first-differences model where all the variables are taken in their first differences (with exception of the dummies). The result for the White test indicates that the null hypothesis of homoscedastic errors is rejected. The results are therefore estimated with robust standard errors.
Table 2: ADF-Test results
Variable Levels 1st difference
LN(TTF spot price) -2.050 -36.382***
LN(Brent oil price) -1.562 -37.432***
LN(Industry index) -3.229** -34.828***
Heating degree days -6.249*** -35.571***
Storage differential -1.102 -30.674***
LN(Production) -4.774*** -48.337***
Note: *,**,*** refer to 10%, 5%, 1% respectively.
As Hulshof et al. (2016) suggest, an endogeneity bias for the oil price, storage differential and in this case the production can arise through reverse causality. To control for this the Hausmann endogeneity test is conducted to see if the variables are exogenous. The instruments used are the first order lags. In the case of the oil price, the relevance is low, however it is very difficult to obtain better instruments. The test results in table 3 show that all variables are exogenous.
Table 3: Hausman endogeneity test results using the first order lag as instrument
Variable Hausman F-statistic Relevance instrument
d. LN(Brent oil price) 0.05 4.22
d. Storage differential 0.92 23.76
d. LN(Production) 0.02 124.36
Note: *,**,*** refer to 10%, 5%, 1% respectively.
5.4 Regression results
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This effect is in line with the expectations and findings of for example Hulshof et al. (2016). The first difference in heating degree also has a significant positive sign. The coefficient indicates that an average temperature of 1 degree Celsius below the temperature of the day before leads to a 0.001% increase of the TTF gas price. Again, this is in line with expectations, as the demand curve shifts to the right. This result is again comparable with the small positive effect found by Hulshof et al. (2016). The first difference in the industry index, storage differential and gas production show no significant result. Finally, the weekday dummies show some day-of-the-week effects on the TTF gas price. Since the coefficient of the gas production does not significantly differ from 0, it can be concluded that the difference in gas production does not affect the difference in the TTF gas price. This is somewhat in line with the findings of the European Commission (2016) who found that lowering of the cap on Groningen had limited impact on European Hub prices.
Table 4: Regression results
d. LN(TTF gas price) Coefficient Standard error
d. LN(Brent oil price) 0.145*** 0.032
d. HDD 0.001*** 0.001 d. LN(Industry) 0.054 0.158 d. Storage differential 0.001 0.001 d. LN(Production) 0.008 0.008 Dummy_Tuesday -0.003 0.003 Dummy_Wednesday -0.003 0.002 Dummy_Thursday -0.008*** 0.003 Dummy_Friday -0.011*** 0.003 Constant 0.005** 0.002 R2 0.05 N 1,208
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6 Earthquake related effects
6.1 IntroductionAs mentioned in section 4.6, the total welfare effect that is related to the gas-induced earthquakes consists of different components. Following the notation of de Kam (2016), two different types of damage can be distinguished. First, there is material damage at buildings that need to be repaired. In theory, this restoration will be paid for by the Dutch Petroleum Company (NAM)6. The effects on material damage are discussed in section 6.2. The second type of damage is the loss of value to the buildings. This loss of value is referred to as non-monetary economic effects by Koster & van Ommeren (2015). They show the existence of the two separate types of damage by finding loss of value, which is reflected in the house prices, even if the owners of the house are compensated for the material damage. The effects on immaterial damage will be further elaborated in section 6.3. Additionally, van der Voort & Vanclay (2015) mention the social impacts of the earthquakes on the inhabitants of the affected region. This will be explained further in section 6.4.
6.2 Material damage
To analyse the effect of the lowering of the gap on the material damage, historical information is used. Since the earthquake near Huizinge in 2012, homeowners can report damage for inspection. In the first years, reports were handled by the NAM, but after numerous complaints regarding conflicts of interest they are removed from the process. In table 5, the yearly data on damage reports and compensations are displayed. All the data is retrieved from the NAM and from the third quarterly report of the NCG (2017b). From these historical data, several trends and predictions can be made.
6 The “in theory” is added because there are many cases where the house owners and the NAM are in conflict
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Table 5: Yearly number of damage reports and average damage compensation of earthquake-induced damage in the Groningen area. Sources: NAM & NCG
Number of damage reports Average damage compensation (€) Total amount (million €) 2012 2,485 12,000 29.8 2013 9,705 12,000 116.5 2014 17,889 6,500 116.3 2015 28,680 4,000 114.7 2016 17,925 2,000 35.9 2017* 2,945 1,700 5 Total 79,629 418.1
* numbers are over the period Jan-Sep 2017
Most important to note is that there is no direct causation of number of damage reports and number of earthquakes in the same year. Most of the damage is only noticed after a large period of time and it will never become clear when this damage is exactly done. However, it seems that the largest peak of damage reports is over, and the amount is declining fast. Important to note is that the table only includes the damage reports where the NAM and the house owners both accepted the compensation. There is still a large amount of house owners in conflict, but according to the NCG (2017b) most of those are from the period before the lower cap on the Groningen gas field. Another important characteristic that can be seen in the table is the downward trend in damage compensation. The simple reason is that the most urgent, and therefore costly, cases of damage are mostly reported in the first years. Later, minor damages became more apparent, leading to an increase in damage reports and decrease in average compensation.
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Due to the unpredictable character of gas induced earthquakes, as explained by BOA (1993), it is appropriate to analyse different possible scenarios regarding yearly damage. As base year 2016 is taken, since the historical data suggests that most of the damage reports regarding historic damage are handled previously. Three different scenarios of reduction in yearly material damage are considered in this paper. The different scenarios and their accompanying NPV are presented in table 6. The results for the discount factor of 5% are displayed in the appendix (see table A2). Note that in the lowest scenario, there is still a positive effect assumed. This is caused by the fact that according to van Eck et al. (2006), induced seismicity is related to the production rate. Further, the highest scenario does not imply a reduction to zero damage costs per year, since extraction will still take place.
Table 6: Welfare effects of reduction in earthquake related material damage in three different scenarios (in million euro; discount rate is 3%).
NPV Scenario 1: 5 million euro cost reduction 111 Scenario 2: 15 million euro cost reduction 332 Scenario 3: 25 million euro cost reduction 553
6.3 Immaterial damage
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As described above, the immaterial damage can be calculated as the price difference of houses due to the earthquakes. In his paper, based on further work of Koster, de Kam (2016) calculated that the total loss of value of the houses in the area is a minimum of €954 million divided over 180,000 houses. This is a minimum since the data inputs used are transactions on sold houses, where the houses that are not sold are likely to have a higher loss of value. The distribution of the average loss in value per municipality is very skewed, ranging from €73 to €19,867. By combining the findings of both papers, it is found that the costs calculated by de Kam (2016) are a result of the signal effect of non-monetary costs in the future.
However, the findings of de Kam (2016) are criticized by other researches. For example, Bosker et al. (2016) argue that the finding of de Kam is overestimated, since he did not account for regular loss of value for the houses. They estimate that the average decline in value due to the earthquakes is circa 2 percent in contrast to the 3 percent estimated by de Kam (2016). The findings of Bosker et al. (2016) are comparable with an earthquake induced decline in housing prices of 2.2 percent by Atlas voor Gemeenten (2017). Therefore, it can be concluded that the precise effect on the decline in house prices is hard to estimate and is involved with lots of insecurity. On the other hand, all papers agree on the fact that the effect strongly differs within the region.
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Table 7: Yearly share of the municipalities of Loppersum and Slochteren in the total amount of earthquakes. Source: NAM
2012 2013 2014 2015 2016 2017
Share of Loppersum & Slochteren
69% 66% 61% 53% 52% 47%
The large differences in the loss of house value within the region, make it necessary to differentiate between regions. The total region that is affected by gas-extraction induced earthquakes used in this paper is determined by two different sources. First, data of the NAM on damage reports per municipality is used. Second, all municipalities used in the paper of de Kam (2016) that are not mentioned by the NAM are added to the total region. In this paper, it is assumed that every house in an affected municipality is potentially threatened by gas-induced earthquakes. This specification leads, according to data of CBS, to a total of almost 300,000 houses divided over 24 municipalities7. The municipalities are divided into 3 different areas. The core area, the most affected area, consists of 11 municipalities as specified by the CBS, mentioned in de Kam (2016). The other 8 municipalities used in de Kam (2016) are classified as the first ring. The remaining 5 municipalities are classified as the outer area. All municipalities and their corresponding number of houses can be found in the appendix (see tables A3, A4 and A5).
The variance in the effect of earthquakes on the different areas is visible in the percentage of damage reports compared to the number of houses. The results of this variance can be seen in table 8 below. It is clear that the percentage of damage reports is rapidly decreasing with the distance from the most affected area. The results mean that people living in the core area have on average an eight times higher chance to have earthquake related damage than people living in the area defined as the first ring. Consequently, the effect of the lower cap on Groningen will have a significantly higher potential effect on the core area than the other areas.
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Table 8: Number of houses and damage reports in the earthquake affected region, per area. Sources: CBS (houses) & NAM (damage reports)
Number of houses Number of damage reports Percentage Core area 77,184 67,415 87% 1st ring 165,124 17,750 11% Outer area 52,747 1,027 2% Total 295,055 86,192*
*The difference between the number of damage reports reported here and in table 5 stems from the fact that this number includes damage reports where no agreement on compensation is reached.
A possible future adjustment of the signal effect, as mentioned above, is exactly what can be expected with the announcement of the new imposed cap on gas extraction. The interesting fact is that the real impact of the cap is not that important since irrational behaviour is allowed in the model. Hence, only the perception of households on the future risks is important. Therefore, the lowering of the cap can have a positive effect on the value of houses in the earthquake-region. As explained, the total monetary effect of earthquakes on house prices is heavily debated and hard to monetarise. Therefore, following the paper of de Joode et al. (2004), this effect will be presented as Pro Memorie (PM) in the cost-benefit analysis. Considering the large differences in the total region, this paper assumes that the different effects are comparable with the difference in percentage of damage reports between the three areas. Thus, the effects in the core area will be eight times as strong as the effect in the area labelled as the first ring.
6.4 Social impacts
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7 Results Cost-benefit analysis
7.1 Effect on producer surplusAs explained in formula (9), the change in producer surplus is influenced by several factors. The quantity and change in quantity are determined by the depletion paths under the different alternatives. These depletion paths are presented in chapter 7.1.1. Next, the price effects, calculated in chapter 5 are presented in 7.1.2. The underlying price scenarios that are needed are reported in 7.1.3. Finally, the results are combined to obtain the effect on the producer surplus in section 7.1.4.
7.1.1 Depletion path
As described above, the different alternatives have their different depletion strategy. The strategies, constructed as described in section 4.3.1, are shown in figure 9. The delta q for each year is calculated by substracting the null alternative from the project alternative. As can be seen in figure 9 the first years the delta will be negative. In 2026, the two alternatives are equal, and the delta is zero. In the period 2026-2050, the delta is positive, implying a higher production level in the new situation.
Figure 3: Different future depletion strategies for the Groningen gas field. Source null alternative: NAM
7.1.2 Price effect
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7.1.3 Price scenarios
For both the alternatives, there is an underlying price scenario needed. This scenario is unaffected by the alternative. The International Energy Agency (IEA) estimated different global forecasts in their energy technology perspective (ETP) 2017. These forecasts lead to different future European gas price scenarios. They distinguish three different price paths for the gas price.
The first development path is the Reference Technology Scenario which can be seen as a baseline forecast based on the country-specific agreements that are existing. These agreements are a significant shift of the historical path, but still not enough to achieve global objectives. A slightly more ambitious is the 2DS, which is the two-degree scenario. The scenario is based on the global agreement to limit the average global increase of temperature by two degrees Celsius. The path of the development and deployment of the energy system is designed in such manner that there is at least a 50% chance the scenario is met. This scenario has been used in the ETP for several years now. Finally, a new technology path is the B2DS, the beyond two-degree scenario. In this scenario the clean energy alternatives are fully embodied in the system and used on a worldwide scale. This alternative is practically in line with the Paris Agreement aspirations. All these scenarios are converted into EUR/MWh in table 9. In this paper it is assumed that the prices develop in a linear trend from the points given in table 9. The different price paths are graphically depicted in figure 10.
Table 9: Future European gas price scenarios (prices in euro/MWh). Source: IEA
2020 2030 2040 2050
Reference technology scenario 20 29 33 35
2 Degree scenario 20 27 28 29
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Figure 4: Graphical representation of future European gas price scenarios. Source: IEA
7.1.4 Total effect producer surplus
With all the different components known, the present value of the change in producer surplus can be estimated with the use of formula (9). The estimates are reported below in table 10. The different price scenarios and different discount factors influence the outcome of the project. As mentioned above, a profit maximizing producer will always choose the optimal depletion path. Since the optimal depletion path after the implementation of the new cap was obtainable with the old regulation as well, a positive NPV is not realistic. Therefore, it can be concluded that the reference technology scenario is not likely to be realistic. The same holds for a discount rate of 1%. Resulting, the reference technology scenario and the 1% discount rate are dropped as realistic assumptions.
Looking at the result in table 10, it is clear to see that the loss in producer surplus is affected by the climate goals of Europe. This follows from the fact that the most ambitious climate goals show a quicker abandoning of fossil fuels like natural gas. Of course, this is reflected in future prices, making the option to sell now relatively more beneficial. Thus, the more ambitious Europe’s climate goals are, the costlier the new cap on Groningen will be.
Table 10: Net change in producer surplus in different future European gas price scenarios and different discount factors (in million euro).
1% discount rate 3% discount rate 5% discount rate
Reference technology scenario 11,892 625 -6,455
2 Degree scenario 7,323 -2,410 -8,490
Beyond 2-degree scenario 1,759 -5,889 -10,634
0 5 10 15 20 25 30 35 40 2016 2024 2032 2040 2048 G as p rice (E U R/MWh )
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7.2 Effect on consumer surplus
The effect on the consumer surplus is obvious due to the price effect of zero. This means that the demand profiles of the two alternatives are the same as well. This indicates that there is no clear welfare effect on the consumers due to the lowering of the cap on Groningen. Both industrial and residential consumers do not experience a project effect. However, there might be some implications of the effect. As explained above, the government of the Netherlands earned billions with the extraction of gas and used this for the government budget. Due to the cap, this revenue stream has declined substantially. It is logical to think that the government will make use of other tools to increase their earnings on the natural gas. A prime example is the taxation of natural gas. However, it is hard to determine if this is due to the project effect. 7.3 Security of supply
In this paper, the benefits regarding security of supply are based on the results reported by Mulder & Zwart (2006a). Although the line of reasoning is still applicable, the total lifespan of these benefits is significantly smaller. As mentioned above, with decreasing total reserves the benefits will become less. Because the reserves in the field have decreased significantly since their paper, the benefits mentioned by Mulder & Zwart (2006a) are higher than appropriate for this paper. The reserve minimum of 400 bcm is taken as point where there will be no benefits from extra security of supply. According to CBS (2018), the amount above 400 bcm in the Groningen gas field is halved since 2006. As a result, the benefits of the new cap are expected to have a maximum of half the benefits as mentioned by Mulder & Zwart (2006a). It can be argued that the benefits of security of supply are influenced by future gas prices. This effect is strengthened by the demand effect, since the storages and flexibility needed are influenced by demand. Therefore, the benefits for the beyond degree scenario will be lower than in the 2-degree scenario. The results are reported below in table 11 for a discount factor of 3%. The results with a discount factor of 5% can be found in appendix table A6.
Table 11: Benefits for security of supply by lowering the cap with different future European gas price scenarios (in million euro; discount rate is 3%).
2-degree scenario Beyond 2-degree scenario
Reliability of supply < 5 < 5
Strategic storage < 50 < 30
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7.4 Earthquakes
As explained in the previous chapter, the total effect on gas-extraction induced earthquakes is accompanied with many uncertainty. However, it is possible to specify the different components of this effect. In the case of the material damage, the establishment of some scenarios could partially monetarise the effect. The maximum benefit of this effect is estimated at 553 million euro. Further, both the effect on the immaterial damage and social impacts create a PM effect. In the next section these results are combined with the other results of the other components of the cost-benefit analysis. This gives the opportunity to determine a break-even point of the PM effect for the policy to be welfare improving.
7.5 Cost-benefit analysis
With all the results, the net project effect can be calculated. The results for the 3% discount factor are reported below in table 12. The results for the 5% discount factor can be found in table A7, in the appendix. The costs result from the direct market where the producer loses surplus. The consumer surplus is unaffected, implying that other suppliers take over the role of the Groningen gas field for the same price. The benefits come from externalities, with a moderate role for security of supply. This moderate role is due to the technical difficulties to act as a swing supplier if the reserves are below a certain point. By far the largest benefits are expected due to the earthquake effects. However, the large uncertainty for these effect makes it hard to predict the precise effect a priori.
Table 12: Welfare effects of a lower cap (24 bcm/year) on Groningen, in two future European gas price scenarios (in million euro; discount rate is 3%).
2-degree scenario Beyond 2-degree scenario Costs
Change producer surplus 2,410 8,490
Change consumer surplus 0 0
Benefits
Reliability of supply < 5 < 5
Strategic Storage < 50 < 30
Material damage < 553 < 553
Immaterial damage & social impacts PM PM
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As can be seen in table 12, the net effect depends on the PM effect on the immaterial damage and social impacts. Although it is hard to predict this effect, the effect can be used to calculate a break-even point of the policy reform. For the lowering of the cap on Groningen to be beneficial, the benefits on immaterial damage and social impacts must outweigh the negative calculated monetary costs. In the case of the 2-degree scenario this must equal 1.8 billion euro and for the beyond 2-degree scenario this increases to 7.9 billion. The region where the benefits can be made is limited to the earthquake region.
As described in chapter 6, the impact of earthquakes differs strongly between areas. It is assumed in this paper that the percentage of damage reports is a good reflection of how much an area is influenced by the earthquakes. The same relative impact can be expected for the benefits. To calculate what the minimum effect per household in the core area must be for the policy change to be welfare improving, the relative impact per area must be indexed. If the core area has an index value of 1, the indices of the first ring and outer area are 0.12 and 0.02 respectively. Combined with the total number of houses per area, the break-even benefits per area can be calculated. The results of the minimal required effect per area with a discount rate of 3% are reported in table 13. The results with a discount rate of 5% are presented in the appendix (see table A8). So, in the most favourable scenario, the minimal required benefit per household in the core area is over 18 thousand euro. According to the house prices presented in de Kam (2016), this equals to roughly 12 percent of the average house value in that region.
Table 13: Minimal required benefit per household for immaterial damage and social impacts per area (in euros; discount rate is 3%)
2-degree scenario Beyond 2-degree scenario
Core area 18,621 80,075
First ring 2,247 9,855
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8 Conclusion
The aim of this paper is to make a contribution to the debate on gas extraction in Groningen in a well-organized structured fashion. In order to do so, a cost-benefit analysis of the lowering of the production cap on the Groningen gas field is made.
This paper has found that there is no significant price effect of a lower cap on Groningen on the European gas price. This means that there is no change in consumer surplus, since other suppliers will enter the market. Therefore, the costs in this cost-benefit analysis are based on the loss in profits of the producer. The total costs of lowering the cap range from 2410 million euro in the 2-degree scenario to 8490 in the beyond 2-degree scenario.
The benefits of security of supply consist of two different effects, the effect on reliability of supply and the effect on strategic storage. Combined, the maximum benefits range from 55 million euro in the 2-degree scenario to 35 million euro in the beyond 2-degree scenario. The benefits of lower cost caused by gas-extraction induced earthquakes are accompanied with a high degree of uncertainty. In the most favourable scenario, the monetary benefits of lower material damage are estimated at 553 million euro. Furthermore, a PM effect is taken into account for the benefits regarding immaterial damage and social impacts.
Altogether, the monetary effect of the policy change is estimated at -1.8 billion euro in the 2-degree scenario and -7.9 billion euro in the beyond 2-2-degree scenario. For the lower cap on Groningen to be beneficial, the PM effect must offset this negative monetary effect. Changing the discount factor does alter the results, but does not change the conclusions.
Regarding the research of the price elasticity of the gas price on the supply of the Groningen field, several improvements could be made. The research in this paper was bounded to data on the Netherlands only, where it is plausible that, due to integration of the gas market, factors across different countries influence the gas price. Furthermore, data availability limited the research of the market to week-days only with a relative short time span of less than 5 years. Finally, due to data limitations, the regression could only be done in first differences where the regression in levels is preferred. Altogether, this leads to the suggestion for further research of the price elasticity. Research with data over a longer time span after the implementation of the new cap is advised.
39
houses. Unfortunately, the NAM is not willing to provide house specific data for analyses of the negative effects on the region. Furthermore, due to the unpredictable character of man-induced earthquakes, the possible future outcomes are uncertain at best. Therefore, further research for elaborate future scenarios is strongly advised to make the analyses more robust. Nevertheless, the results published in this paper give a clear outcome of the welfare effects of the implemented policy measurement. For the policy change to be beneficial for society, the minimal benefit per household in the most affected area must be over 18 thousand euro (in the most favourable scenario). The unlikelihood of this number can be seen in the fact that this is more than 12 percent of the average total value of houses in that region. In a conversation with honorary professor of public housing and land market G.R.W. de Kam (personal communication, January 12, 2017), he agreed that this number is improbable. Thus, the findings of this paper indicate that the policy of a cap of 24 bcm per year for production of the Groningen gas field is welfare reducing for the Netherlands.
However, the problem is the distribution effect of costs and benefits. Where the benefits of gas extraction are distributed across the country, the burden of earthquake -risk and -damage is limited to the Northern part of the Netherlands. This uneven distribution makes that inhabitants of the earthquake affected region should be compensated for their extra burden when production remains high. However, with this extra compensation in mind, it is still credible that the lowering of the cap on Groningen is welfare reducing. Concluding, based on the findings in this paper, the best policy recommendation is to extract and sell the gas in the Groningen field as quickly as safety conditions allows, with an extra compensation for the inhabitants of the earthquake affected region. This should include quicker and more generous damage compensation, investments in safety and help for inhabitants that want to move out of the area. Acknowledgements
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Appendix
Table A1: Correlation explanatory variables
LN (Brent oil price) HDD LN (Industry) Storage differential LN (Production)
Tuesday Wednesday Thursday Friday LN(Brent oil price) HDD -0.009 LN(Industry) 0.568 0.229 Storage differential 0.515 -0.192 0.322 LN(Production) 0.518 0.657 0.533 0.209 Tuesday -0.002 0.009 0.003 0.001 -0.002 Wednesday -0.003 0.010 -0.007 -0.002 0.004 -0.254 Thursday 0.003 -0.001 -0.001 -0.006 0.002 -0.253 -0.252 Friday 0.013 -0.014 0.018 0.009 -0.003 -0.250 -0.249 -0.248
Table A2: Welfare effects of reduction in earthquake related material damage in three different scenarios (in million euro; discount rate is 5%).
NPV Scenario 1: 5 million euro cost reduction 86 Scenario 2: 15 million euro cost reduction 258 Scenario 3: 25 million euro cost reduction 430
Table A3: Number of houses and damage reports in the core area, per municipality. Sources: CBS (houses) & NAM (damage reports)
Municipality Number of houses Number of damage reports
45
Table A4:Number of houses and damage reports in the core area, per municipality. Sources: CBS (houses) & NAM (damage reports)
Municipality Number of houses Number of damage reports
Groningen (gemeente) 100,866 13,959 Grootegast 5,006 40 Haren 8,923 749 Kollumerland 5,522 0 Oldambt 18,385 1,511 Pekela 5,753 63 Veendam 12,787 521 Zuidhorn 7,882 907 Total 165,124 17,750
Table A5: Number of houses and damage reports in the outer area, per municipality. Sources: CBS (houses) & NAM (damage reports)
Municipality Number of houses Number of damage reports
Aa en Hunze 11,165 321 Bellingwedde 4,118 39 Leek 8,611 60 Noordenveld 14,647 138 Tynaarlo 14,206 469 Total 52,747 1027
Table A6: Benefits for security of supply by lowering the cap with different price scenarios (in million euro; discount rate is 5%).
2-degree scenario Beyond 2-degree scenario
Reliability of supply < 10 < 10
Strategic storage < 250 < 150
Total benefits security of supply < 260 < 160
Table A7: Welfare effects of a lower cap (24 bcm/year) on Groningen, in two future European gas price scenarios (in million euro; discount rate is 5%).
2-degree scenario Beyond 2-degree scenario Costs
Change producer surplus 5,889 10,634
Change consumer surplus 0 0
Benefits
Reliability of supply < 10 < 10
Strategic Storage < 250 < 150
Material damage < 430 < 430
Immaterial damage & social impacts PM PM
46
Table A8: Minimal required benefit per household for immaterial damage and social impacts per area (in euros; discount rate is 5%)
2-degree scenario Beyond 2-degree scenario
Core area 52,684 101,781
First ring 6,484 12,526
