Toward a less natural gas dependent energy mix in Spain: Crowding-out effects of shifting to biomass power generationAnális

Toward a less natural gas dependent energy mix in Spain: Crowding-out effects of shifting to biomass power generation María J. Colinet c , Jos e M. Cansino a , b , * , Jos e M. Gonz alez-Lim on a , Manuel Ord o~ nez a a University of Seville, Spain b Universidad Aut onoma de Chile, Chile c Andalusian Energy Agency (Andalusian Regional Government), Spain article info Article history: Received 30 January 2014 Received in revised form 27 July 2014 Accepted 27 July 2014 Available online 30 August 2014 Keywords: Biomass for electricity generation Natural gas generation Combined cycle technology Inputeoutput framework Fuel substitution Renewable energy abstract This paper estimates the impact of a hypothetical change in Spain's energy mix on a number of productive sectors. The change would be brought about by substituting power generation from natural gas with generation from biomass. The total amount of electricity supplied has been calculated to remain constant so that a crowding-out effect would be derived from the displacement of one technology with another. An inputeoutput (IO) framework has been used to estimate the overall economic impact on 26 productive sectors included on Spain's 2007 IO Table. Based on the available literature, the consideration of net impact improves the analysis. The results show that the overall net impact across all productive sectors of this change in the energy mix would be positive and equal to about 0.5% for the period. Higher impacts were measured for the ‘Electricity power and Electricity Supply’sector (15.4%) followed by the ‘Agriculture, Hunting, Forestry’sector (7.1%). Only the ‘Gas generation and Gas supply’sector showed a negative impact (e2.5%), which is consistent with the reduced use of natural gas. The overall calculated total impact for Spain's productive sector was equal to V8074.95 million at the 2007-equivalent value. ©2014 Elsevier Ltd. All rights reserved. 1. Introduction To raise the targets established by the European Union (EU) Directive 2009/28/EC, the authorities of Spain approved a document titled ‘Plan de Energías Renovables (“Renewable Energy Plan” in Spanish) 2011e2020’(IDAE, 2011). This legal document fixed mandatory deployment targets until 2020 for each renewable energy technology type. Complying with these mandatory targets will affect Spain's economy; therefore, assessing the economic impact becomes a relevant question. When the impact of the deployment of a particular technology is assessed, the available literature usually assumes that there will be an increase in the total installed capacity (Cald es et al., 2009; Cardenete et al., 2010; Cansino et al., 2013). However, an alternative assumption that implies no change in the total installed capacity could be more useful. In 2013, the total capacity installed in Spain was 108,148 MW. However, the demand peak was only 39,963 MW (REE, 2013). This means that the total capacity installed was 2.7 times greater than what was necessary to supply the peak. In fact, the experts recommend that the relationship between the available power and the demand peak (known as Demand Coverage Index) be 1.10 (CNE, 2012). Given that Spain has excess generation capacity, a crowding-out effect implying displacement by new technologies seems more plausible than the assumed increase in total power generation. The displacement is based on substituting Combined Cycle Plants (CCP) for Biomass Plants (BP) and accepts that the total installed capacity remains constant. The choice of the two technologies used in this analysis is explained herein. First, the literature offers evidence about the positive impact of BP on rural areas (Cardenete et al., 2010). Second, this idea is also consistent with the design of the EU Common Agriculture Policy (CAP, Council Decision 2006/144/EC). Third, the electricity supplied by BP does not depend on weather (sun or wind based technologies) and can be modulated based on the electricity demand (i.e., it is a dispatchable form of energy). BP has a low disruption risk. Section 3provides greater detail on this point. In the case of CCP, there are two additional reasons that support our hypothesis. First, the electricity generated by these plants in 2013 accounted for only 9.6% of all electricity generated (REE, 2013). Dismantling and substitution CCPs with BP would not jeopardize overall supply. Second, Spain has no natural gas resources; 99.4% *Corresponding author. University of Seville, Spain. E-mail address: [email protected] (J.M. Cansino). Contents lists available at ScienceDirect Utilities Policy journal homepage: www.elsevier.com/locate/jup http://dx.doi.org/10.1016/j.jup.2014.07.006 0957-1787/©2014 Elsevier Ltd. All rights reserved. Utilities Policy 31 (2014) 29e35 comes from imports (CORES, 2012). Reducing the installed CCP capacity means reducing Spain's dependency on foreign suppliers for this energy source. This also is one of the pillars of the EU energy strategy (European Commission, COM/2000/0769 final). In the case of BPs working 7000 hours per year (as occurs with CCP replacement), the reduction in natural gas consumption relates to a decrease in imports associated with 11,000,000 MWh, which can be valued at V290.3 million when considering an import price for natural gas of 26.4 V/MWh (CNE, 2013). Substituting CCP for BP plants is technologically feasible (as discussed below). The BP technology considered in this study is a mature technology on the market. This substitution implies no risk for the security of the electricity supply. Moreover, the hypothesis is consistent with the objectives for rural development established in the CAP. This change in Spain's electrical mix would contribute to reducing dependency upon foreign energy, as Spain lacks natural gas deposits. In sum, all of the above means that our proposed shift is realistic and feasible. As mentioned, the authorities of Spain have established mandatory deployment targets for all renewable energy technologies up to the year 2020. Each technology, including biomass, is listed in the document titled ‘Plan de Energías Renovables 2011e2020’(PER or Renewable Energy Plan) (IDAE, 2011) with corresponding targets. The target fixed by the PER for biomass power generation using biomass feedstock is an installed capacity of 1350 MW in 2020 (an increase of 817 MW over the amount installed in 2010). Considering the priority placed on the use of green electricity in Spain's power grid, the fully installed capacity of BP-produced electricity is used in all cases. This technology can be managed in a planned manner if raw materials are available; this differs from other RES technologies that are “variable”and dependent on natural phenomena such as rain, wind or solar radiation (Sovacool, 2009). Because CCP power output can be modulated, in this present study, we consider that the electricity generation levels using CCPs would be reduced to maintain a balance with the added power derived from BP. However, no CCPs would be dismantled. 1 This paper considers the economic impact of the change in Spain's energy mix associated with compliance with the PER (2011e2020). We estimate the impact on Spain's productive activities when deploying BP instead of CCP to generate a comparable amount of electricity. The analysis contributes to the literature by providing, to our knowledge, the first study that evaluates the net economic impact of shifting to an alternative energy technology. Moreover, our InputeOutput (IO) approach constitutes an analytical improvement by considering the crowding-out effect instead of assuming a gross increase in MW installed. These results are interesting not only for researchers but also for utility companies and policy-makers. In fact, this paper speaks directly to the authorities of Spain and the policy agenda with regard to several issues, including energy security. This paper proceeds as follows. Section 2explains the IO methodology and Section 3describes the data used in the analysis. The results and discussion are presented in Section 4, while Section 5summarizes the main conclusions. 2. Methodology The IO approach is largely supported by the available literature. The economic impact of Renewable Energy Sources (RES), such as solar energy, has frequently been estimated using IO models. In the US, for example, IO analysis has been used by Cook (1998) and Ciorba et al. (2004), while in Europe, Kulisic et al. (2007), Madlener and Koller (2007), and Allan et al. (2008) developed similar approaches. Cald es et al. (2009), Calzada et al. (2009), and the European Commission (MITRE, 2009) recently used an IO model to estimate the economic impact of RES in Spain. The basis for the methodology applied herein is the Leontief (1941) model. The starting point is the concept of a technical coefficient, a ij , indicating how the needs (z ij ) of sector jrelate to the inputs from another sector Iper unit of output (x j ) from sector j itself, which is expressed as follows: aij ¼zij xj (1) From (1),(2) is obtained: zij ¼aij$xj(2) On the other hand, the total output of sector jis the sum of intermediate consumption for the entire sector (n) of this sector's economy makers (z ij ) and products that are destined to final demand (f j ). Thus, the production of sector jcan be expressed as: xj¼zj1þzj2þ…þzjj þ…þzjn þfj(3) The production of the remaining sectors follows a similar pattern. The production of each of the nsectors is defined by the following expression: x1¼z11 þz12 þ…þz1jþ…þz1nþf1 x2¼z21 þz22 þ…þz2jþ…þz2nþf2 ……………… xj¼zj1þzj2þ…þzjj þ…þzjn þfj ……………… xj¼zj1þzj2þ…þzjj þ…þzjn þfj (4) Substituting each z ij by a ij ·x j gives: x1¼a11$x1þa12$x2þ…þa1j$xjþ…þa1n$xnþf1 x2¼a21$x1þa22$x2þ…þa2j$xjþ…þa2n$xnþf2 ……………… xj¼aj1$x1þaj2$x2þ…þajj$xjþ…þajn$xnþfj ……………… xn¼an1$x1þan2$x2þ…þanj$xjþ…þann$xnþfn (5) Solving f i , one obtains: ð1a11Þ$x1a12$x2…a1j$xj…a1n$xn¼f1 a21$x1þð1a22Þ$x2…a2j$xj…a2n$xn¼f2 ……………… aj1$x1aj2$x2…þ1ajj$xj…ajn$xn¼fj ……………… an1$x1an2$x2…anj$xj…þð1annÞ$xn¼fn (6) The expression (6) can be shown in this matrix formula: ðIAÞ$x¼f(7) where Iis the identity matrix of order nxn,Ais a matrix of order nx nfor the technical coefficients, xis a column vector of order nx1for the production of each sector, and fis the column vector of order nx 1of the final demand of each sector. If we pre-multiply the two terms in (7) by (I-A) 1 , we obtain: 1 In 2012, Spain had 51 CCPs with an installed capacity of 25,269 MW (this accounted for 25.22% of the total national installed capacity); 50.734 GWh (19.2% of the total) were generated, which is a utilization ratio of 25.1% of the CCPs' production capacity. By 2010, this had increased to 31.9%. The functioning electricity system has a rated capacity of 2007.75 hours per year. In 2012, the operations stood at 1579.46 hours per year (See REE, 2011 and 2012). M.J. Colinet et al. / Utilities Policy 31 (2014) 29e3530 x¼ðIAÞ1$f(8) Equation (8) is the fundamental equation of the Leontief model; it shows that the production of each sector depends on the final demand and on the so-called Leontief inverse matrix. From (8), one can determine any variation that economic activities undergo in response to changes in the final demand; for example, variations associated with altering the technologies used to generate electricity. This methodology also permits the impact of a change in the magnitude for any of the considered economic activities to be calculated. For this case, the impact associated with a larger biomass power generation sector can be calculated. Introducing a larger-scale biomass electricity sub-sector must, by definition, result in changes to input requirements and supplies to the rest of the electricity sector. This methodology allows us to compare the benchmark (pre-shock on vector f) with the final stage output following the shock caused by the crowding-out effect associated with the new BP. This impact produces a double effect. The first effect is due to the fact that the investment in new BP creates a shock in the final demand, thus affecting the sector's output. This impact is calculated using (8). The second effect is associated with the fact that the new BPs haves different input needs than the CCPs they displace. In other words, the operation and maintenance costs (O&M) of the technologies are different. This causes a change in the matrix for inter-sectarian transactions that will also have an impact on the output of the sector. This variation in sector output can be calculated using a different method than those proposed by Miller and Blair (2009). The methodology used to assess this second effect follows a three-step process. The basic methodology for this calculation is found in the expression (4), because substituting one technology for another will spark changes in inter sectoral transactions. The first step implies that some needs increase when new BPs come into force. At the same time, other power sector needs decrease as CCP power generation is reduced. Thus, the net power sector needs are provided by: zf i;6¼z0 i;6þzi;BP zi;CCP (9) where, z f i;6 is the electricity sector's final needs (sector 6 in Table 4) as a function of the remaining production activities; z 0 i;6 represents the initial needs of this sector; z i;BP , represents the input needs of new BPs; z i;CCP represents CCPs needs that now decrease as a consequence of the decreased power generation using this technology. The second step introduces data from (9) into an IO Table. With the IO Table, the total resources must match the total employed by each production activity. Therefore, the monetary value of the energy provided by the new biomass sector supplies other productive sectors; the final demand must equal the value of the total resources required for their production. Consequently, it is necessary to reduce input requirements proportionally to the reduction of total sector resources. After this adjustment, because the new input needs and the total resources employed by the various production activities are not equal, it is necessary to calibrate the former as a third step in the process. Since we know the total sector input needs, these are taken as the total resources. So, respecting the technical coefficients, the new totals are calculated in relation to these coefficients. This can be done as follows: z 0 ij ¼aij$x 0 j(10) Wherez 0 ij represents the new intermediate needs of each sector; x 0 j , is the new total resources; and, a ij represents the technical coefficients. These coefficients remain unchanged with the exception of the technical coefficient for the electricity sector, which changes when one technology displaces another. Comparing the final production with the initial production allows us to calculate the impact associated with the change in the input needs. 3. Data To assess the economic impact of changing Spain's energy mix, two cost types must be taken into account. These are BP investment and O&M costs for the implementing this technology. CCP O&M costs are also necessary. This section provides a breakdown of these costs according to the 26 sectors considered. 3.1. Biomass and combined cycle plant data Data for the BP investment and O&M costs for both CCP and BP were obtained from questionnaire-based information from companies belonging to these sectors in Spain. The reliability of the information was confirmed by the Andalusian Energy Agency technical staff. For the purpose of this research, one type of BP and one type of CCP were considered, with both plants using technologies that are sufficiently developed to be considered representative of standard practice (see below). The construction period for this type of plant is about one year, with approximately a 20-year working lifetime. The standard business hypothesis has been calculated for the 2011e2020 period. CCP technology is based on a gas turbine and the subsequent use of waste heat passing through a steam cycle (Kehlhofer et al., 2009). The gas turbine combustion heat uses compressed air from a compressor coupled to the turbine itself (Brayton cycle), with expansion of gases in the turbine producing energy converted into electrical energy with an alternator. Natural gas is the primary fuel used. The gases from turbine reach temperatures well over 600  C; a recovery boiler uses these gases to produce steam which, in turn, can be used to drive a steam turbine to produce energy that is converted into electrical energy (Rankine Cycle). The type of CCP envisaged for the scenario in this study corresponds to a 800 MW capacity. The BP technology used is based on a classical thermodynamic Rankine cycle with a combustion boiler, steam turbine, extraction and air condensation. The boiler pipes and natural water circulation include a super-heater, evaporator and economizer bank (Van Loo and Koppejan, 2008). “Orujillo”(the pomace or residual pulp) derived from olive oil production) is the biomass fuel used. This product is burned in the boiler to produce high pressure, superheated steam that feeds the turbine, where it expands and produces electrical energy by driving an alternator connected to the turbine shaft. The BP type considered for this research corresponds to a 25 MW capacity. The use of pomace as a raw material does not involve secondary issues such as indirect land use changes associated with the use of certain biofuels. 2 Total new installed capacity for BPs demands 5 million tons of biomass to operate at full capacity (500 ktep). Only in southern Spain (Andalusian region) are 3958 ktep per year available. 38.6% of this amount (1526 ktep per year) comes from olive oil production residue (Colinet and Lobo, 2013). 2 For a recent publication concerning indirect land use change, see Popp et al. (2012). M.J. Colinet et al. / Utilities Policy 31 (2014) 29e35 31 Tables 1 and 2 show the investment and O&M costs associated with the implementation of BP. The main economic activity benefiting from the implementation of a BP is seen in the ‘Metallic product’sector, which receives 32.7% of the total spending on investment. The ‘Construction’(15.5%) sector also benefits significantly, along with ‘Metallurgy’(14.7%). Because BP uses a raw material, the main economic activity benefiting from the operation of a BP is the ‘Agriculture, Hunting, Forestry’sector, which absorbs 77.8% of the total O&M expenses (Cardenete et al., 2010; Soli~ no, 2010; Soli~ no et al., 2012). Spending on ‘Financial Intermediation’represents 14.7%. Table 3 shows the O&M costs associated with a CCP. The O&M costs for a CCP are much less than those of a BP, representing only 18.9% of the cost per MW installed in BP. In the case of a CCP, the economic activities most directly related to O&M costs are financial services (38.4%) and ‘Gas generation, gas and supply’(36.5%). In accordance with our assumption, when 1 MW of BP-derived electricity is placed on the electricity grid, 1 MW of CCP is crowded-out. This directly affects the O&M costs of each technology. In the case of BP, O&M costs increase, while those for CCP decrease. The explanation for the elevated importance of financial service expenditures for both plant types is that the demand for electricity is generally assured, which reduces investment risk. Consequently, in a non-credit crunch scenario, leverage is high. Due to the assumption that the installed CCP will continue operating but at a lower intensity, dismantling costs are irrelevant for this research. 3.2. Spanish economic data IO Tables have served as the basis for an ample number of economic impact analyses. However, the publication of these tables by most statistical agencies is not usually offered on an annual basis. Therefore, for this paper, a symmetrical IO table for Spain was constructed (using a variety of procedures) from the supply and use tables with the basic IO Framework prices in Spain for 2007. The reason why the 2007 IO Table was chosen rather than the most recent for 2009 is that the origin and destination tables for 2007 present a biased electric sector with regard to the gas sector. This is better suited to the objective of this article. We have chosen the D model (Eqn. (7))( Eurostat, 2008), which considers a fixed structure for product sales in which each product has its own sales structure irrespective of the productive activity generated. 3 Furthermore, it requires a relatively simple mechanical procedure with these tables being closer to national statistics and not appearing as negatives in the IO Table. An IO Table contains rows and columns to represent economic sectors and productive activities, respectively. The 75 productive activities appearing in the source and destination tables for Spain have been categorized according to the 26 sectors considered in this article (Table 4). 4. Results and discussion From the data contained in Tables 1e3, the IO analysis provides results showing the crowding-out effect of CCP by BP. On the one hand, the impact originates in the shock in demand associated with the necessary investment for the construction of the BP. On the other hand, the net variation of the O&M (positive variation due to the start-up of the BP and negative variation due to the reduction of the CCP activity) causes an economic effect, which induces change in the input needs of the electric sector, modifying the transaction matrix. The shock pattern could be considered as follows. Characteristics of O&M costs for renewable technologies (such as biomass) help explain why most European Union Member States have introduced measures to promote green electricity (Cansino et al., 2010). The most widespread promotional measure is the feed-in tariff scheme. This scheme has actually been suspended in Spain Table 1 Investment cost of BP power generation (Euros/MW). Agriculture, hunting, forestry Fishing and aquaculture Energy extracts Other extracts Coke, refined petroleum and nuclear fuel Electricity power and electricity supply 18,904.4 Gas generation and gas supply 8493 Water generation and water supply Food, beverages and tobacco Textiles and textile products, leather and footwear Wood and products of wood and cork Chemicals and chemical products 48,517.2 Construction materials 94,697.4 Metallurgy 183,987.6 Metallic products 407,069 Machinery, Nec 123,288.6 Vehicles 6286.6 Other forms of transport 7152 Other manufacturing 89,090 Construction 193,881.8 Commerce Hotels and restaurants Transport and communications 14,134.6 Financial intermediation 49,604.2 Other retail trade Services not oriented to sales Total 1,245,106.6 Source: Own elaboration. Table 2 O&M costs of BP power generation (Euros/MW). Agriculture, hunting, forestry 3,870,484.1 Fishing and aquaculture Energy extracts Other extracts Coke, refined petroleum and nuclear fuel Electricity power and electricity supply Gas generation and gas supply Water generation and water supply Food, beverages and tobacco Textiles and textile products, leather and footwear Wood and products of wood and cork Chemicals and chemical products 93,051.4 Construction materials 18,610.3 Metallurgy Metallic products 55,830.9 Machinery, Nec Vehicles Other forms of transport Other manufacturing 87,734.2 Construction 55,830.9 Commerce Hotels and restaurants Transport and communications 55,830.9 Financial intermediation 732,176.1 Other retail trade Services not oriented to sales Total 4,969,548.6 Source: Own elaboration. 3 That is to say, certain goods may be manufactured by different productive activities. M.J. Colinet et al. / Utilities Policy 31 (2014) 29e3532 since the Royal Decree 1/1202 (RD 1/2012) came into force to reduce public spending, but it is expected to once again take effect in 2017 (Hern andez, 2012). For this reason, our research considers a temporary investment pattern for the 817 MW of additional BP- produced power. 4 Approximately one year is needed to build and put the BP into operation; therefore, annually, for the 2017e2019 period, one third of the total target (272 MW) could be implemented. The pattern used enables the PER (2011e2020) target to be reached by the 2020 deadline. These results are shown separately in Tables 5 and 6.Table 5 shows the annual, investment spending impact for 2017, which will be repeated in 2018 and 2019. Table 6 shows the impact caused by the new input needs, due to the use of BP instead of CCP. The demand shock associated with the investment required to install the BP is positive for all economic activities. Moreover, the total growth of economic activities for the years 2017e2019 is equivalent to 0.03%. Economic sectors with the greatest impact were those of ‘Other manufacturing’,‘Machinery’and ‘Energy extracts’, with each of these activities being closely linked to investment projects such as the one considered. The total impact on Spain's economy 5 is V1387.22 million at the 2007-equivalent value. Crowding-out provokes a positive total effect on economic activities when input needs vary due to the substitution of BPs for CCPs. As presented in Tables 2 and 3, not all economic activities show a positive effect. Crowding-out causes a logical and negative effect on ‘Gas generation and Gas supply’due to the decreased use of natural gas. When the additional BP power is placed onto the electricity grid (817 MW by 2020), the overall effect on the economic activity of this sector is equal to a reduction of the 2007-equivalent value by 2.5%. Table 3 O&M costs of CCP power generation (Euros/MW). 1MW Agriculture, hunting, forestry Fishing and aquaculture Energy extracts Other extracts Coke, refined petroleum and nuclear fuel Electricity power and electricity supply 88,536.00 Gas generation and gas supply 343,728.00 Water generation and water supply Food, beverages and tobacco Textiles and textile products, leather and footwear Wood and products of wood and cork Chemicals and chemical products 36,911.70 Construction materials 7382.34 Metallurgy Metallic products 22,147.02 Machinery, Nec Vehicles Other forms of transport Other manufacturing 34,802.46 Construction 22,147.02 Commerce Hotels and restaurants Transport and communications 22,147.02 Financial intermediation 361,617.48 Other retail trade Services not oriented to sales Total 939,419.0 Source: Own elaboration. Table 4 IOT 07 productive activity structure and correspondence with the branches of the IO framework for Spain 2007. ITO07 Activities 1. Agriculture, hunting, forestry 1, 2 2. Fishing and aquaculture 3 3. Energy extracts 4, 5 4. Remaining extracts 6, 7 5. Coke, refined petroleum and nuclear fuel 8 6. Electricity power and electricity supply 9 7. Gas generation and gas supply 10 8. Water generation and water supply 11 9. Food, beverages and tobacco 12e16 10. Textiles and textile products, leather and footwear 17e19 11. Wood and products of wood and cork 20 12. Chemicals and chemical products 23, 24 13. Construction materials 25, 27, 28 14. Metallurgy 29 15. Metallic products 30 16. Machinery, Nec 31e35 17. Vehicles 36 18. Other forms of transport 37 19. Other manufacturing 21, 22, 26, 38, 39 20. Construction 40 21. Commerce 41e43 22. Hotels and restaurants 44, 45 23. Transport and communications 46e52 24. Financial intermediation 53e55 25. Remaining retail trade 56e60, 62, 64, 67, 69, 71, 74 26. Services not oriented to sales 61, 63, 65, 66, 68, 70, 72, 73, 75 Source: Own elaboration. Table 5 Impacts associated with investment costs of new BP (in millions of Euros). Initial output 272 MW Final output Variation (%) Agriculture, hunting, forestry 44,435.0 44,436.5 0.0034 Fishing and aquaculture 3025.0 3025.0 0.0011 Energy extracts 1374.0 1375.0 0.0711 Remaining extracts 5321.0 5324.7 0.0698 Coke, refined petroleum and nuclear fuel 34,419.0 34,422.4 0.0097 Electricity power and electricity supply 40,051.0 40,070.4 0.0485 Gas generation and gas supply 11,166.0 11,173.4 0.0659 Water generation and water supply 6216.0 6216.7 0.0108 Food, beverages and tobacco 93,503.0 93,504.7 0.0018 Textiles and textile products, leather and footwear 21,987.0 21,988.4 0.0065 Wood and products of wood and cork 11,300.0 11,304.3 0.0384 Chemicals and chemical products 67,589.0 67,617.1 0.0416 Construction materials 34,159.0 34,196.4 0.1094 Metallurgy 40,544.0 40,631.8 0.2166 Metallic products 47,631.0 47,766.9 0.2853 Machinery, Nec 64,765.0 64,810.4 0.0701 Vehicles 62,654.0 62,657.4 0.0054 Other forms of transport 13,753.0 13,755.7 0.0198 Other manufacturing 62,778.0 62,826.3 0.0769 Construction 323,774.0 323,861.4 0.0270 Commerce 180,165.0 180,183.5 0.0103 Hotels and restaurants 117,011.0 117,012.7 0.0014 Transport and communications 154,511.0 154,547.4 0.0236 Financial intermediation 78,373.0 78,399.3 0.0336 Remaining retail trade 351,131.0 351,165.6 0.0099 Services not oriented to sales 199,833.0 199,834.6 0.0008 Total 2,071,468.0 2,072,107.9 0.0309 Source: Own elaboration. 4 The approval of the new Law for the Electricity Sector 24/2013 could change the temporary investment patterns taken for granted in this article. The new Law foresees specific retribution for certain renewable power plants. Nevertheless, the Law fails to clarify what those characteristics may be. 5 Based on to Santamaría (2012) and Weitzman (2011), a discount rate equal to 3% is applied. M.J. Colinet et al. / Utilities Policy 31 (2014) 29e35 33 On the other hand, crowding-out provokes a positive effect on ‘Agriculture, Hunting, Forestry,’which is also logical due to the raw materials used by the new BPs. When the full 817 MW of new BP- derived power is available, the overall effect on the economic activity of this sector is equal to an increase of 7.1% above the 2007- equivalent value. The main effect on economic activities caused by crowding-out is on ‘Electricity power and Electricity Supply,’which is due to the higher costs of the electricity generated by BP technology, as shown on Table 2. When the economic activities of all sectors are considered together, the total net variation resulting from the addition of 817 MW of new BP-derived power to the electricity grid is equal to 0.5%. This equates to a total impact on Spain's economy of V 6697.73 million at the 2007-equivalent value. Logically, the net impact is less than the gross estimated impact based on similar analyses, as indicated in the study by Cardenete et al. (2010). The possible impact on electricity prices is an important issue. Currently, in Spain only 40% of the consumer electricity price is market based. The rest of the price is determined under the Public Authorities (Law 24/2013). The relevant market is the Iberian one (Spain and Portugal). On this market, the share of the electricity generated from biomass is only 0.07% (Spanish Ministry of Industry (2013)). The cost of a feed-in tariff system is financed through private debt, supported by sovereign guarantee, and without any kind of impact on electricity prices (FADE, 2014). For the forthcoming years, the most likely pattern for this price is not dissimilar from previous years; therefore, we assume that there will be no relevant impact on electricity demand or output based on prices. 5. Conclusions The economic impact due to the deployment of new energy technologies can be assessed by using an IO framework. However, most of the available literature focuses on gross rather than net impacts. Net economic impacts clearly are relevant when policymakers consider mandating a change the energy mix. In this paper, we carried out an analysis within an IO framework by estimating the economic crowding-out effect associated with the displacement of CCPs by BPs. Our results were significantly enhanced by considering net impact. Within this improved methodological context, our major finding is that the economic crowding-out effect is positive and equal to about 0.5% of total output for the period when the net variation of O&M costs on demand are considered. The greatest growth is seen in the economic sector for ‘Electricity Power and Electricity Supply’(15.4%), followed by the ‘Agriculture, Hunting, Forestry’sector at 7.1%. Only ‘Gas generation and Gas supply’suffered a negative economic impact (2.5%), reflecting the fuel shift. By taking into account the joint impacts from investment costs associated with new BPs and the net variation of O&M costs, the overall effect on Spain's productive sector is equal to V 8074.95 million at the 2007-equivalent value. This amount should be considered a maximum because the inclusion of any governmental compensation payments to CCP owners has not yet been considered. For example, we have not accounted for possible payments to maintain the CCPs that might resume production if an increase in electricity demand cannot be met by other technologies. Our results are relevant for policy-makers because they can inform decisions about the energy mix. The results show that a hypothetical but feasible substitution of CCP by BP would contribute to attaining two national objectives. First, reducing natural gas usage in power generation diminishes Spain's dependence on foreign supplies. Second, the positive impact on the agricultural sector is consistent with the objective for rural development, which is one of the pillars of the Common Agricultural Policy. The results are also relevant for the utilities involved, whether BP or CCP owners. Although the methodology applied in this work is based on the IO approach using IO tables, it follows a fixed-coefficient Table 6 Impact associated with net O&M cost variations (in millions of Euros). Initial output 272 MW 545 MW 817 MW Final output Variation (%) Final output Variation (%) Final output Variation (%) Agriculture, hunting, forestry 44,435.0 45,489.1 2.4 46,543.1 4.7 47,597.2 7.1 Fishing and aquaculture 3025.0 3025.0 0.0 3025.0 0.0 3025.0 0.0 Energy extracts 1374.0 1374.0 0.0 1374.0 0.0 1374.0 0.0 Remaining extracts 5321.0 5321.0 0.0 5321.0 0.0 5321.0 0.0 Coke, refined petroleum and nuclear fuel 34,419.0 34,419.0 0.0 34,419.0 0.0 34,419.0 0.0 Electricity power and electricity supply 40,051.0 42,111.6 5.1 44,172.2 10.3 46,232.8 15.4 Gas generation and gas supply 11,166.0 11,072.4 0.8 10,978.8 1.7 10,885.2 2.5 Water generation and water supply 6216.0 6216.0 0.0 6216.0 0.0 6216.0 0.0 Food, beverages and tobacco 93,503.0 93,503.0 0.0 93,503.0 0.0 93,503.0 0.0 Textiles and textile products, leather and footwear 21,987.0 21,987.0 0.0 21,987.0 0.0 21,987.0 0.0 Wood and products of wood and cork 11,300.0 11,300.0 0.0 11,300.0 0.0 11,300.0 0.0 Chemicals and chemical products 67,589.0 67,604.3 0.0 67,619.6 0.0 67,634.9 0.1 Construction materials 34,159.0 34,162.1 0.0 34,165.1 0.0 34,168.2 0.0 Metallurgy 40,544.0 40,544.0 0.0 40,544.0 0.0 40,544.0 0.0 Metallic products 47,631.0 47,640.2 0.0 47,649.3 0.0 47,658.5 0.1 Machinery, Nec 64,765.0 64,765.0 0.0 64,765.0 0.0 64,765.0 0.0 Vehicles 62,654.0 62,654.0 0.0 62,654.0 0.0 62,654.0 0.0 Other forms of transport 13,753.0 13,753.0 0.0 13,753.0 0.0 13,753.0 0.0 Other manufacturing 62,778.0 62,792.4 0.0 62,806.8 0.0 62,821.2 0.1 Construction 323,774.0 323,783.2 0.0 323,792.3 0.0 323,801.5 0.0 Commerce 180,165.0 180,165.0 0.0 180,165.0 0.0 180,165.0 0.0 Hotels and restaurants 117,011.0 117,011.0 0.0 117,011.0 0.0 117,011.0 0.0 Transport and communications 154,511.0 154,520.2 0.0 154,529.3 0.0 154,538.5 0.0 Financial intermediation 78,373.0 78,473.9 0.1 78,574.8 0.3 78,675.7 0.4 Remaining retail trade 351,131.0 351,131.0 0.0 351,131.0 0.0 351,131.0 0.0 Services not oriented to sales 199,833.0 199,833.0 0.0 199,833.0 0.0 199,833.0 0.0 Total 2,071,468.0 2,074,650.2 0.2 2,077,832.5 0.3 2,081,014.7 0.5 Source: Own elaboration. M.J. Colinet et al. / Utilities Policy 31 (2014) 29e3534 technology and therefore, it is likely to be valid only for short-term scenarios. As such, the results presented herein have a higher predictive value for short periods of time, no more distant than the 2020 horizon. Predicting a longer-term scenario would require a different approach involving a dynamic general equilibrium or recursive model. Acknowledgements The authors acknowledge financial support received from the C atedra de Economía de la Energía y del Medio Ambiente (Department for Energy Economy and the Environment) at the University of Seville-Fundaci on Roger Torn e. The second, third and fourth authors also acknowledge financial support from SEJ 132 project of the Andalusian Regional Government. The second author acknowledges support from Universidad Aut onoma de Chile, Chile. The standard disclaimer applies. References Allan, G.J., Bryden, I., McGregor, P.G., Stallard, T., Swales, J.K., Turner, K., Wallace, R., 2008. Concurrent and legacy economic and environmental impacts from establishing a marine energy sector in Scotland. Energy Policy 36, 2734e2753. Cald es, N., Varela, M., Santamaria, M., Saez, R., 2009. Economic impact of solar thermal electricity deployment in Spain. Energy Policy 37 (5), 1628e1636. http://dx.doi.org/10.1016/j.emplo.2008.12.022. Calzada, G., Merino, R., Rallo, J.R., 2009. Study of the Effects on Employment of Public Aid to Renewable Energy Sources. Working Paper. Universidad Rey Juan Carlos. http://www.worldcoal.org/bin/pdf/original_pdf_file/wci_briefing_note_ report_effects_employment_public_aid_renewables(08_07_2009).pdf (accessed 24.02.10.). Cansino, J.M., Pablo-Romero, M. del P., Roman, R., Y~ niguez, R., 2010. Tax incentives to promote green electricity: an overview of Eu-27 countries. Energy Policy 38 (10), 6000e6008. Cansino, J.M., Cardenete, M.A., Gonzalez-Limon, J.M., Pablo-Romero, M. del P., 2013. Economic impacts of solar thermal electricity technology deployment on Andalusian productive activities: a CGE approach. Ann. Regional Sci. 50, 25e47. Cardenete, M.A., Gonzalez-Limon, J.M., Pablo-Romero, M. del P., Roman, R., 2010. Impacto Sobre el Desarrollo Econ omico Local en Andalucía del Uso de la Biomasa Como Energía Renovable. Econ. Agrar. Recur. Nat. 10 (2), 159e182. Ciorba, U., Pauli, F., Menna, P., 2004. Technical and economical analysis of an induced demand in the photovoltaic sector. Energy Policy 32, 949e960. CNE, 2012. Propuesta del mecanismo por el que se establece el servicio de garantía de suministro. http://www.cne.es/cne/doc/publicaciones/cne51_12.pdf (last accessed 15.01.14.). CNE, 2013. Informe mensual de supervisi on del mercado mayorista de gas. http:// www.cne.es/cne/doc/publicaciones/IAP_GAS_Mayorista_Mar2013.pdf (accessed 22.04.14.). Colinet, M.J., Lobo, J., 2013. El olivar, fuente de energía. In: Grupo de Estudios Avanzados sobre el Territorio y Medio Ambiente. Andalucía: el olivar, Seville, pp. 147e162. Cook, C., 1998. The Maryland solar roofs program: state and industry partnership for PV residential commercial viability using the state procurement process. In: Proceedings of the Second World Conference on Photovoltaic Solar Energy Conversion, Vienna, 6e10 July, pp. 3425e3428. CORES, 2012. Corporaci on de Reservas Estrat egicas de productos petrolíferos. http://www.cores.es/sites/default/files/archivos/estadisticas/est-gas-balance- 2012.pdf (accessed 05.01.14.). Council Decision of 20 February 2006 on Community strategic guidelines for rural development (programming period 2007 to 2013). (2006/144/EC) Available at: http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri¼CELEX:32006D0144: EN: HTML (last accessed 9th, 2014). European Commission, 2000. Green Paper etowards a European Strategy for the Security of Energy Supply COM/2000/0769 Final. Available at: http://eur-lex. europa.eu/LexUriServ/LexUriServ.do?uri¼CELEX:52000DC0769:EN. HTML (last accessed 09.01.14.). European Commission, 2009. Monitoring and Modelling Initiative on the Targets for Renewable Energies (MITRE). Country Report, Spain. http://mitre/ energyprojets.net (accessed 19.01.10.). European Parliament and the Council, 2009. Directive 2009/28/EC of the European Parliament and of the Council, of 23 April 2009, on the Promotion of the Use of Energy from Renewable Sources and Amending and Subsequently Repealing Directives 2001/77/EC and 2003/30/EC, Brussels. EUROSTAT, 2008. Eurostat Manual of Supply, Use and InputeOutput Tables, Methodologies and Working Papers. European Communities, Luxembourg. FADE, 2014. FONDO DE AMORTIZACI  ON DEL D EFICIT EL ECTRICO. https://www.fadefund.com (accessed July, 2014). Hern andez, G., 2012. ¿Hasta 2017? Enova 13, 7e10. IDAE, 2011. Resumen del Plan de Energías Renovables 2011e2020. Disposable at: www.idae.es (last accessed 10.02.13.). Kehlhofer, R., Rukes, B., Hannemann, F., Stirnimann, F.X., 2009. Combined-cycle Gas &Steam Turbine Power Plants. Pennwell Books. Kulisic, B., Loizou, Rozakis S., Segon, V., 2007. Impacts of biodiesel production on Croatian economy. Energy Policy 35 (12), 6036e6045. Law for the Electricity Sector 24/2013. BOE n  310 de 27/12/2013, 105198e105294. Available at: www.boe.es. Leontief, W., 1941. The Structure of the American Economy, 1919e1929. Harvard University Press, Cambridge, Mass. Madlener, R., Koller, M., 2007. Economic and CO 2 mitigation impacts of promoting biomass heating systems: an inputeoutput study for Vorarlberg, Austria. Energy Policy 35 (12), 6021e6035. Miller, R.E., Blair, P.D., 2009. InputeOutput Analysis. Foundations and Extensions, second ed. Cambridge University Press. Popp, A., Krause, M., Dietrich, J.P., Lotze-Campen, H., Leimbach, M., Beringer, T., Bauer, N., 2012. Additional CO 2 emissions from land use change dforest conservation as a precondition for sustainable production of second generation bio-energy. Ecol. Econ. 74, 64e70. RD 1/2012. Royal Decree 1/2012 January 27, by which the suspension of pre-assigned retributions are suspended and the elimination of economic incentives for new electricity production facilities using co-generation, renewable energy sources and waste. BOE (Official State Gazette) n  24 de 28/1/2012, 8068e8072. REE, 2011. El Sistema el ectrico espa~ nol 2011. http://www.ree.es/sistema_electrico/ pdf/infosis/Inf_Sis_Elec_REE_2011_v3.pdf (last accessed 16.03.13.). REE, 2012. El sistema el ectrico espa~ nol Avance del informe 2012. http://www.ree. es/sistema_electrico/pdf/infosis/Avance_REE_2012.pdf (last accessed 16.03.13.). REE, 2013. El sistema el ectrico espa~ nol Avance del informe 2013. http://www.ree.es/ sites/default/files/downloadable/avance_informe_sistema_electrico_2013.pdf (last accessed 09.01.14.). Santamaría, M., 2012. An alisis coste-beneficio de los biocarburantes en Espa~ na (Ph D dissertation). Universidad of Alcal a de Henares. Soli~ no, M., 2010. External benefits of biomass-e in Spain: an economic valuation. Bioresour. Technol. 101 (6), 1992e1997. Soli~ no, M., Farizo, B.A., V azquez, M.A., Prada, A., 2012. Generating electricity with forest biomass: consistency and payment timeframe effects in choice experiments. Energy Policy 41, 798e806. Sovacool, B.K., 2009. The intermittency of wind, solar, and renewable electricity generators: technical barrier or rhetorical excuse? Util. Policy 17, 288e296. Spanish Ministry of Industry, 2013. Energy in Spain. Available at: http://www. minetur.gob.es/energia/balances/Balances/LibrosEnergia/Energia_2011.pdf. Van Loo, S., Koppejan, J., 2008. The Handbook of Biomass Combustion and Co-firing. Earthscan. Weitzman, M.L., 2011. Gamma discounting. Am. Econ. Rev. 91, 260e271. M.J. Colinet et al. / Utilities Policy 31 (2014) 29e35 35