Francisco T. G. Lima Verde Neto1, Paulo A. C. Rocha1, Jenyffer da S. G. Santos2, Angel P. Garcia2, Daniel Albiero2*ABSTRACTKEYWORDSINTRODUCTIONINTRODUCTIONMATERIAL AND METHODSMATERIAL AND METHODSFIGURE 1. Main screen of GETEM.Therefore, data for the geothermal gradient were used for 11 different values, varying from 20 °C/km to 110 °C/km at three different depths from 1500 to 3000 m, assuming a soil temperature of 25 °C. The inputs for the GETEM are listed in Table 2.TABLE 2. Temperature inputs on GETEM for the Gradient x Depth Analysis.We compared eight sites that are well known for their high geothermal gradients, as shown in Table 3, and they were then compared at different depths from 1000 to 3000 m.. Geological data for well-known locations across the northeastern Region (Carneiro et al., 2017).E 3. Geological data for well-known locations across the northeastern Region (Carneiro et al., 2017).TABLE 3. Geological data for well-known locations across the northeastern Region (Carneiro etTABLE 4. Temperature inputs on GETEM for depth analysis for specific sites.(*) The geothermal gradient approach for this site would likely reach a temperature greater than the critical temperature for the water (374, 15 °C). Therefore, we excluded this data from the calculations.RESULTS AND DISCUSSIONTABLE 5. Results from the GETEM simulations (US$/MWh), taking the binary cycle as the default.GETEM simulations (US$/MWh), taking the binary cycle as the default.GETEM simulations (US$/MWh), taking the binary cycle as the default.TABLE 6. Results for the GETEM simulations (US$/MWh), with the default software settings.TABLE 7. Results for GETEM simulations using the software default settings.The LCOE between geothermal energy and other energy sources was then compared using these data (Figure 4).table_2FIGURE 4. LCOE variationTABLE 8. Qualitative comparison between renewable energy sources (Long, 2009).table_3technology, which relies heavily on well construction costs and technology.technology, which relies heavily on well construction costs and technology.LE 9. Installed capacity of electricity generation (GW). Source: (Hanbury & Vasquez (2018).TABLE 10. Emissions for different energy sources. Source: Tomasini-Montenegro et al. (2017).REFERENCESFUTURE PERSPECTIVE AND CHALLENGESCONCLUSIONSCONCLUSIONSCONCLUSIONSRenewable and Sustainable Energy Reviews 133 (2020) 110351Contents lists available at ScienceDirectRenewable and Sustainable Energy Reviewsjournal homepage: http://www.elsevier.com/locate/rserRenewable energy policy effectiveness: A panel data analysis across Europe and Latin AmericaA R T I C L E I N F OA B S T R A C T1. Introduction1. Introduction1. Introduction1. Introduction2. The multiple drivers of renewable energy investment2.1. Contrasting results on the impacts of support policy2.1. Contrasting results on the impacts of support policyu}%2?& e!9&  + e H ^ m |  Q Francisco T. G. Lima Verde Neto1, Paulo A. C. Rocha1, Jenyffer da S. G. Santos2, Angel P. Garcia2, Daniel Albiero2* Francisco T. G. Lima Verde Neto1, Paulo A. C. Rocha1, Jenyffer da S. G. Santos2 Angel P. Garcia2, Daniel Albiero2* 2*Corresponding author. Universidade Estadual de Campinas (UNICAMP)/Campinas - SP, Brasil. E-mail: daniel.albiero@gmail.com | ORCID ID: https://orcid.org/0000-0001-6877-8618ABSTRACTKEYWORDS This paper proposes geothermal energy as an alternative solution to the water–energy dilemma in the northeastern region of Brazil (NEB). The main application of this study was to provide a theoretical basis to support a different approach to policies minimizing water scarcity and ensuring sustainability. The analysis developed in this study compares the levelized cost of electricity (LCOE) for many different energy sources. The novelty of this study is the use of geothermal energy in the context of the Brazilian Northeast, focusing on water desalination processes, which are expensive in terms of energy. Therefore, this study is highly important because it offers the potential of addressing the energy/economic barrier related to water desalination in environments with economically viable geothermal energy. This is the case in Northeast Brazil with potential for reuse of abandoned oil wells. In the form of enhanced geothermal systems (EGS), geothermal energy is a competitive energy source compared to other sources in the Brazilian Energy Matrix, especially when considering factors in addition to the economic benefits. In the form of EGS, geothermal energy is a suitable option for addressing water scarcity in the northeast region in a sustainable and low-emission manner. This is a strategic opportunity for NEB in the context of energy production and freshwater production through desalination. renewable energies, water–energy nexus, levelized cost, engineered geothermal systems, desalination.INTRODUCTION Droughts are one of the main contributing factors to the social and economic problems and structural deficiencies of the region. Water scarcity and low rainfall in the NEB are substantial issues. This is predominantly because most of the groundwater in the region is saline, with desalination being a potential solution to the problem (Silva et al., 2018). Renewable energy technologies are a sustainable alternative to fossil fuels. The development of these technologies has been highly responsive to general energy policy guidelines and environmental and social goals, such as diversifying energy carriers, improving access to clean energy, and reducing pollution and dependence on fossil and imported fuels (Turkenburg, 2000; Vogt et al., 2021). Most of the energy used in desalination processes is derived from fossil fuels, which are becoming increasingly depleted and emit carbon dioxide. Incorporating renewable energy sources into the desalination process can increase Among these alternatives, enhanced geothermal systems (EGS) involve injecting fluids at low temperatures, which results in the same flow through high-temperature 1 Universidade Federal do Ceará (UFC)/Fortaleza - CE, Brasil. 2 Universidade Estadual de Campinas (UNICAMP)/Campinas - SP, Brasil. Area Editor: Henrique Vieira de Mendonça Received in: 4-18-2022 Accepted in: 12-22-2022 E 1 Universidade Federal do Ceará (UFC)/Fortaleza - CE, Brasil. 2 Universidade Estadual de Campinas (UNICAMP)/Campinas - SP, Brasil. Area Editor: Henrique Vieira de Mendonça Received in: 4-18-2022 Accepted in: 12-22-2022 Engenharia Agrícola, Jaboticabal, v.43, special issue, e20220160, 2023 Edited by SBEA Francisco T. G. Lima Verde Neto, Paulo A. C. Rocha, Jenyffer da S. G. Santos, et al. regions of the Earth’s crust. This fluid is also produced at another point and used as a heat source for diverse applications, such as binary cycles for energy production. EGS have been developed on at least 18 sites in countries, including Australia, Germany, the USA, China, and the Philippines. Research efforts in different regions have generally focused on government efforts to develop this technology, with an expectation of supplying approximately 70 GWe of power by 2050 (Lu, 2018).Cost-effective desalination can be achieved on resources at 90 °C temperatures and can be improved if technological developments are made (Loutatidou & Arafat, 2015). Brazil. The energy and economic viability of this proposal was also demonstrated.MATERIAL AND METHODS The analysis developed in this study was used to compare the levelized cost of electricity (LCOE) for many different energy sources. The data for EGS were obtained from the Geothermal Electric Technology Evaluation Model (GETEM) free software (DOE, 2018). It consists of three main information categories, namely, the resource temperature, resource depth, and the method of extraction (Hydrothermal or EGS). Abandoned oil and gas wells can be used to implement a desalination system that uses abandoned wells as a heat source to desalinate seawater (Noorollahi et al., 2017). Ali et al. (2018) noted that geothermal resources can be used for different desalination processes because geothermal resources are an uninterrupted source of thermal energy. In some places, geothermal water can be cost- effective, costing as low as 1 Euro/m³ (Tomaszewska et al., 2018), including in regions with water scarcity, such as the Gulf Coast, Sub Saharan, and Middle East, and North Africa (where the cost reaches 1.61–2.0 US$ /m³). It can represent a solution for providing freshwater with low emissions and a competitive cost, and it is cheaper than methods that use PV cells (Chandrasekharam et al., 2018). This model can be used to estimate the leveled cost of electricity (LCOE) for a user-defined geothermal resource type, temperature, and depth. With this information, the GETEM is used to estimate the generation cost using a set of default inputs based on several resource scenarios defined and evaluated by the DOE Geothermal Technologies Office (GTO). The costs, performance, and LCOE based on these default inputs are displayed in the model as default scenarios. A GETEM user can develop an alternative scenario by revising the selected default inputs up to ~109 total for the power plant, well field, exploration, confirmation, operation and maintenance, geothermal pumping, reservoir performance, and economic parameters used. The model then displays the values used in the default scenario. These values can be retained for scenario evaluation or can be revised. As the inputs are revised, the LCOE for the revised scenario (shown at the top of the page) will change.Figure 1 shows the main screen of the GETEM used in this work, as seen on the sheet “Start Here”: Therefore, the purpose of this study was to analyze geothermal energy as a suitable option for NEB. The main contribution of this study was to examine the potential of using abandoned oil wells as geothermal energy generators to enhance water desalination systems in northeastern nanFIGURE 1. Main screen of GETEM. The geothermal data was sourced from Carneiro et al. (2017), and we performed essential statistical characterization, namely, the mean, minimum, and maximum, as shown in Table 1, to infer the influence of resource depth and geothermal gradient on the LCOE. TABLE 1. Geological data for 89 sites across the northeastern region (Carneiro et al., 2017). Engenharia Agrícola, Jaboticabal, v.43, special issue, e20220160, 2023 Geological data for 89 sites Across the Northeastern Region Geothermal Gradient (°C/km) Minimum 7,0 Mean 31,3 Maximum 123,0 Minimum Mean Maximum Geothermal energy: an alternative to the water–energy dilemma in Northeastern BrazilTherefore, data for the geothermal gradient were used for 11 different values, varying from 20 °C/km to 110 °C/km at three different depths from 1500 to 3000 m, assuming a soil temperature of 25 °C. The inputs for the GETEM are listed in Table 2.TABLE 2. Temperature inputs on GETEM for the Gradient x Depth Analysis. TABLE 2. Temperature inputs on GETEM for the Gradient x Depth Analysis. Depth (m) Geothermal Gradient (°C/km) 1500 2000 2500 20 55 65 75 30 70 85 100 40 85 105 125 50 100 125 150 60 115 145 175 70 130 165 200 80 145 185 225 90 160 205 250 100 175 225 275 110 190 245 300 120 205 265 325We compared eight sites that are well known for their high geothermal gradients, as shown in Table 3, and they were then compared at different depths from 1000 to 3000 m. TABLE 3. Geological data for well-known locations across the northeastern Region (Carneiro et al., 2017). Site State Geothermal Gradient (°C/km) Specific Heat Flow (mW/m²) ANT. NAVARRO PB 65,0 195 CAMINDE CE 76,2 229 PARAMOTI CE 79,1 237 QUIXADA CE 82,7 248 CRATEUS CE 86,2 259 FORTALEZA CE 99,8 299 CARIDADE CE 99,9 300 BRE. M. DEUS PE 123,0 370. Geological data for well-known locations across the northeastern Region (Carneiro et al., 2017).E 3. Geological data for well-known locations across the northeastern Region (Carneiro et al., 2017).TABLE 3. Geological data for well-known locations across the northeastern Region (Carneiro et The inputs on GETEM are shown in Table 4: TABLE 4. Temperature inputs on GETEM for depth analysis for specific sites. Depth (m) Site 1000 1500 2000 2500 3000 ANT. NAVARRO 90,0 122,5 155,0 187,5 220,0 CAMINDE 101,2 139,3 177,4 215,5 253,6 PARAMOTI 104,1 143,7 183,2 222,8 262,3 QUIXADA 107,7 149,1 190,4 231,8 273,1 CRATEUS 111,2 154,3 197,4 240,5 283,6 FORTALEZA 124,8 174,7 224,6 274,5 324,4 CARIADE 124,9 174,9 224,8 274,8 324,7 BRE. M. DEUS 148,0 209,5 271,0 332,5 394,0* (*) The geothermal gradient approach for this site would likely reach a temperature greater than the critical temperature for the water (374, 15 °C) Therefore we excluded this data from the calculationsTABLE 4. Temperature inputs on GETEM for depth analysis for specific sites.(*) The geothermal gradient approach for this site would likely reach a temperature greater than the critical temperature for the water (374, 15 °C). Therefore, we excluded this data from the calculations. Engenharia Agrícola, Jaboticabal, v.43, special issue, e20220160, 2023 Francisco T. G. Lima Verde Neto, Paulo A. C. Rocha, Jenyffer da S. G. Santos, et al.RESULTS AND DISCUSSION This technical recommendation is based on the mechanisms of binary power plants, which are mainly dependent on the water phase diagram. At higher temperatures, water emerges on the surface with high enthalpy, and there would be a substantial amount of energy to be extracted from the steam. Therefore, flash power plants would be more suitable for use than binary power plants. We initially worked through the inputs established in Table 2 with the combinations of GETEM simulations, only selecting the binary power plant option. This was despite the technical recommendation that for temperatures above 200 °C, the best option would be flash power plants. The results are presented in Table 5. TABLE 5. Results from the GETEM simulations (US$/MWh), taking the binary cycle as the default. Depth (m) Geothermal Gradient (°C/km) 1500 2000 2500 20 27132,2 3587,9 2258,6 30 2258,3 1219,6 787,4 40 1055,8 581,6 391,4 50 590,1 339,7 252,7 60 369,2 233,5 186,4 70 267,7 188,9 149,2 80 202,9 148,5 120 90 171,3 123,3 112,1 100 140,4 104,6 112,6 110 123,2 98,9 136,7 120 107,2 96,8 173,2TABLE 5. Results from the GETEM simulations (US$/MWh), taking the binary cycle as the default.GETEM simulations (US$/MWh), taking the binary cycle as the default. The need for clean, reliable, and cost-competitive energy is at the core of the Indonesian energy policy challenge. One of the solutions that has been provided by the World Bank is a loan to develop the potential of geothermal energy. LCOE is one of the many criteria that can be used to assess the decision to promote an energy source. However, if we try to monetize all the impacts, which can vary substantially for each energy source, we can have different results. This is because the criteria to monetize such effects and impacts would ignore fundamental value judgments and essential themes, such as wildlife and ecosystems. This may create an illusory method to compare that keeps policies and stakeholders outside the decision process. For some values with a geothermal gradient more significant than 100 °C/km, the LCOE increased with a specific gradient. This observation can be explained because of the efficiency parameters for the binary cycle, and it is in line with the technical recommendation of GETEM if we consider that for geothermal gradients above 90 (in Table 2), the resource temperatures would be above 200 °C. Figure 2 shows the influence of the geothermal gradient on the LCOE for different established resource depths. This information is especially useful because when searching for opportunities to start a geothermal power plant, an objective criterion can be established to start exploratory research based on this criterion, predominantly when focusing on energy/economic efficiency issues. These are essential for evaluating a project with a low-carbon transition perspective (Albiero et al., 2015). The International Energy Association expects that geothermal electricity generation will grow from 87 TWh in 2017 to 277–555 TWh in 2040. Depending on the policy scenario (IEA, 2018), a 218–538% increase will occur. This places geothermal electricity generation with a faster rate than biomass and represents a strategic opportunity for the energy industry. Comparing 2017 to 2016, geothermal electricity was the only renewable energy source that had capacity growth.The importance of geothermal energy as a player in the energy industry has increased worldwide. Countries, such as Costa Rica, El Salvador, Iceland, Kenya, and the Philippines, produce a substantial portion, comprising approximately 20% of their electricity from geothermal energy (Fridleifsson et al., 2008). Geothermal energy does not have the uncertainties associated with renewable energy, and it can be an essential source of carbon-free development in countries, such as Indonesia, where a trilemma for developing countries is present. We performed GETEM simulations using GETEM standards, i.e., using binary power plants for resources below 200 °C and flash power plants for resources with temperatures greater than 200 °C. The results are shown in Table 6 and Figure 2. Engenharia Agrícola, Jaboticabal, v.43, special issue, e20220160, 2023 Geothermal energy: an alternative to the water–energy dilemma in Northeastern Brazil The decrease in LCOE has shown a behavioral change, especially when the conversion technology changes. This has indicated a difference between the costs of operating a flash power plant and a binary power plant. In some cases (2500 m; gradient greater than 80 °C/km), the LCOE values are lower for binary power plants than for flash power plants. in a mixture of steam and liquid. Binary power plants use the geofluid to heat a chosen working fluid in a closed cycle (DiPippo, 2015). In this study, the same type of power plants were analyzed to promote the use of this technology in Brazil, given that for temperatures of geofluid lower than 150 °C, it is “difficult, although not impossible, to build a flash-steam power that can efficiently and economically put such a resource to use” (DiPippo, 2015). y p p p p Flash power plants are a simple way of generating energy because they are used when the geofluid is producedTABLE 6. Results for the GETEM simulations (US$/MWh), with the default software settings. TABLE 6. Results for the GETEM simulations (US$/MWh), with the default software settings. Depth (m) Geothermal Gradient (°C/km) 1500 2000 2500 20 27132,2 3587,9 2258,6 30 2258,3 1219,6 787,4 40 1055,8 581,6 391,4 50 590,1 339,7 252,7 60 369,2 233,5 186,4 70 267,7 188,9 149,2 80 202,9 148,5 144,4 90 171,3 123,3 118,2 100 140,4 104,6 103,3 110 123,2 98,9 92 120 107,2 96,6 82,6 The examples in Table 4 meet the criteria of having a geothermal gradient greater than 60 °C. Taking this into account, we have obtained the results shown in Table 7 and Figure 3. Engenharia Agrícola, Jaboticabal, v.43, special issue, e20220160, 2023 Francisco T. G. Lima Verde Neto, Paulo A. C. Rocha, Jenyffer da S. G. Santos, et al. It is important to clarify the definition of the criterion 60 °C/km. Figure 4 Table 7 show that for every depth simulated, geothermal gradients greater than 60 °C/km provide LCOE lower than the sources that are currently operating in Brazil. Therefore, this “breaking point” highlights an opportunity to start an exploratory campaign to find geothermal resources.TABLE 7. Results for GETEM simulations using the software default settings. TABLE 7. Results for GETEM simulations using the software default settings. LCOE (US$/MWh) Depth (m) ANT. NAVARRO CAMINDE PARAMOTI QUIXADA CRATEUS FORTALEZA CARIDADE BRE. M. DEUS 1000 736,3 488,2 445,0 392,8 354,5 255,8 255,3 169,6 1500 310,0 230,8 206,4 192,5 181,5 150,0 149,8 129,2 2000 207,9 158,6 150,9 141,7 132,6 127,8 127,6 93,6 2500 167,6 155,4 146,7 137,7 130,1 103,6 103,4 79,9 3000 171,0 131,4 125,2 118,1 112,1 92,9 92,8 - The LCOE between geothermal energy and other energy sources was then compared using these data (Figure 4). E between geothermal energy and other energy sources was then compared using these data (Figure 4).The LCOE between geothermal energy and other energy sources was then compared using these data (Figure 4). Engenharia Agrícola, Jaboticabal, v.43, special issue, e20220160, 2023 Geothermal energy: an alternative to the water–energy dilemma in Northeastern Braziltable_2FIGURE 4. LCOE variation In Europe, geothermal energy can be cost- competitive and may win the renewable cost challenge. However, further research is required to reduce the investment risks (Clauser & Ewert, 2018). Geothermal systems can be a source of baseload energy production because they are not weather dependent and can be a renewable option for developing baseload capacity in energy production in countries, such as Turkey (Melikoglu, 2017).TABLE 8. Qualitative comparison between renewable energy sources (Long, 2009). TABLE 8. Qualitative comparison between renewable energy sources (Long, 2009). Energy Source Capacity Factor (%) Reliability Environmental Impact Main Use Geothermal 86 - 95 Reliable and Continuous Minimum Use of Soil Electrical Energy Biomass 83 Reliable Use of Fertile Lands Transportation, Heat, Electrical Energy Hydroelectric 30-35 Weather Related Dam Construction Electrical Energy Wind 25-40 Weather Related Large Occupation Electrical Energy Solar 24-33 Weather Related Large Occupation Electrical Energy Engenharia Agrícola, Jaboticabal, v.43, special issue, e20220160, 2023 Francisco T. G. Lima Verde Neto, Paulo A. C. Rocha, Jenyffer da S. G. Santos, et al. GETEM has presented a more detailed description of costs (Figure 5).table_3technology, which relies heavily on well construction costs and technology. For EGS, well field capital and exploratory costs are nearly one-third of all costs impacting the LCOE. These data are critical because according to the Brazilian National Petroleum Agency, data on the overall production for every oil and gas well onshore in the Northeast Region can be obtained. There are approximately 3800 wells that did not produce either oil or gas from a total of approximately 10500 wells (ANP, 2018). There are extensive opportunities for exchange between the oil and gas industry and an eventual geothermal energy power plant using EGS Geothermal energy can be used to smooth the substantial change in services and rig demand caused by the volatility of oil prices. The maintenance of jobs in the region, especially in places that are highly dependent on the oil and gas industries, should also be considered by policymakers. Some regions in Brazil have the potential use of technologies needed to develop EGS in parallel with other O&G, such as shale gas, especially in the Sergipe– Alagoas basin (Péres et al., 2016) (Figure 6). The installed capacity of the NEB electricity generation should also be considered (Table 9). The installed capacity has 31% reliance on thermal sources, with this number increasing to 79% in the state of Paraíba and to greater than 50% in the states of Ceará, Pernambuco, and Maranhão (Hanbury & Vasquez, 2018). Given that thermal energy generally consumes a substantial amount of freshwater to generate energy, geothermal energy can be considered suitable for regions with water-supply issues, as noted by Chandrasekharam et al. (2018). In the context of freshwater production through desalination, traditional thermal energy is economically attractive. However, geothermal energy has a level of emissions that is three orders of magnitude lower than that of coal (Hanbury & Vasquez, 2018). The emissions for geothermal energy and EGS are generally lower than the emissions from fossil fuel energy sources (Table 10).The high values for emissions for geothermal energy (Bayer et al., 2013) originated from the specifics of different technologies, with higher values associated with non- Engenharia Agrícola, Jaboticabal, v.43, special issue, e20220160, 2023 Geothermal energy: an alternative to the water–energy dilemma in Northeastern Brazil condensable gas in flash power plants. Closed cycles such as EGS tend to have zero emissions associated with the geofluid in the operational phase. Most of the CO2 emissions from EGS are derived from the consumption of diesel in construction and operations (Tomasini- Montenegro et al., 2017). TABLE 9. Installed capacity of electricity generation (GW). Source: (Hanbury & Vasquez (2018). Total Hydro Thermal Wind Solar Nuclear Nordeste 32505 11568 10089 10157 691 0 Maranhão 3388 662 2505 221 0 0 Piauí 1834 119 68 1408 240 0 Ceará 3715 1 1934 1775 5 0 Rio Grande do Norte 4161 0* 511 3533 117 0 Paraíba 775 5 613 157 0* 0 Pernambuco 3500 764 1964 762 10 0 Alagoas 4044 3725 319 0* 0* 0 Sergipe 1707 1581 91 35 0* 0 Bahia 9381 4711 2085 2267 319 0 *negligible valuesLE 9. Installed capacity of electricity generation (GW). Source: (Hanbury & Vasquez (2018).TABLE 10. Emissions for different energy sources. Source: Tomasini-Montenegro et al. (2017).REFERENCES TABLE 10. Emissions for different energy sources. Source: Tomasini-Montenegro et al. (2017). Source Emissions (kgC/MWh) 122 (4-740) 1100 (190-1300) 847(520-1160) 50 (15-800) 500 (250-1234) 31(0-410) 20 (4-100) 7,55-57,5 REFERENCES Albiero D, Cajado DM, Fernandes ILC, Monteiro LA, Esmeraldo GGSL (2015) Tecnologias Agroecológicas para o Semiárido. Available: https://www.repositoriobib.ufc.br/000021/000021cd.pdf. Accessed Dez 17, 2022. Xavier RS, Galvão CB, Rodrigues RL, Garcia AP, Albiero D (2022) Mechanical properties of lettuce (Lactuca sativa L.) for horticultural machinery design. Scientia Agricola 79: 5. DOI: https://doi.org/10.1590/1678-992X-2020-0249 Ali A, Tufa RA, Macedonio F, Curcio E, Drioli E (2018) Membrane technology in renewable-energy-driven desalination. Renewable and Sustainable Energy Reviews 81: 1 - 21. DOI: https://doi.org/10.1016/j.rser.2017.07.047FUTURE PERSPECTIVE AND CHALLENGES ANP (2018) Dados Estatísticos. In: Central Data Centers. Available: https://www.gov.br/anp/pt-br/centrais-de- conteudo/dados-estatisticos. Accessed Mar 03, 2021. The prospects for the use of EGS for water desalination in the Northeast of Brazil are attractive because in addition to it being a clean and available renewable energy source that is relatively easy to use through the reuse of abandoned oil wells, it enables a low-carbon transition policy. 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DOI: gy https://doi.org/10.1016/j.rser.2017.01.164 https://doi.org/10.1016/j.rser.2017.01.164 Engenharia Agrícola, Jaboticabal, v.43, special issue, e20220160, 2023Renewable and Sustainable Energy Reviews 133 (2020) 110351Contents lists available at ScienceDirectRenewable and Sustainable Energy Reviewsjournal homepage: http://www.elsevier.com/locate/rserRenewable energy policy effectiveness: A panel data analysis across Europe and Latin America Germ´an Bersalli a,b,*, Philippe Menanteau b, Jonathan El-Methni c a Institute for Advanced Sustainability Studies, Berliner Straße 130, 14467, Potsdam, Germany b Univ. Grenoble Alpes, CNRS, INRA, Grenoble INP, GAEL, 1241 rue des R´esidences, 38400, Saint-Martin-d’H`eres, France c Universit´e de Paris, CNRS, MAP5 UMR 8145, F-75006, Paris, FranceA R T I C L E I N F OA B S T R A C T Keywords: Renewable energy Decarbonisation Electricity Latin America Europe Panel data Auctions Renewable energy (RE) technologies for electricity generation are a central pillar of energy sector decarbon- isation strategies worldwide. Public policies to promote their diffusion have been in place in developed econ- omies since 1980, and, since the 2000s, a growing number of emerging countries began implemented such policies. The Latin American countries have been proactive in RE promotion, but few attempts have been made to evaluate the results. This article proposes an econometric analysis of the effectiveness of RE policies, based on panel data for 20 Latin American and 30 European countries, over 20 years. The results converge for the in- fluence of promotion policies in general: they have a positive and statistically significant effect on RE investment, being the principal determinant in both regions. Nevertheless, on their own, tax incentives are insufficient to assure the deployment of RE technologies. This article also highlights specificities in policy approaches and motivations across both regions and explains why auction became the main instrument in Latin American countries.1. Introduction RE, which were first introduced at the end of the 1980s in Denmark. The numerous policy adjustments and reforms in Europe combined with different country-level approaches, provide valuable data for an eco- nomic analysis that considers differences in socio-economic contexts and should help design and implement similar policies in other world areas. There is a growing body of literature assessing RE policies based on cross-country analyses [4–11], case studies [12–16], and, more recently, econometrics [17–25]. A substantial increase in the share of Renewable Energy (RE) sources in the energy mix is crucial to achieving a level of anthropogenic CO2 emissions compatible with the Paris Climate Agreement. Thus, RE technologies for electricity generation are considered one of the fun- damentals of global energy sector decarbonisation strategies [1]. Public policies to promote their diffusion were implemented in the developed countries from the 1980s, within a niche market strategy, and, since the 2000s, a growing number of emerging and developing countries have also introduced support policies [2]. Following a sharp drop in the cost of several RE technologies, especially wind and solar photovoltaic (PV) [3], these policies aim to promote their large-scale diffusion to achieve a dominant share of the electricity mix. However, there are several open questions regarding the effectiveness, economic efficiency, and equity of selected promotion policies and, especially, in emerging and developing countries where empirical evidence is scarce. Among the emerging and developing economies, those in Latin America have been proactive in promoting RE since the 2000s. How- ever, very little research has aimed at evaluating the results of these policies. There are some valuable cases studies [26–30], but no attempts to perform broader statistical or econometric analysis representative of the Latin American region. Among the few studies that include some countries in Latin America is Pfeiffer and Mulder’s (2013) study [31] of 108 developing economies. Moreover, European and Latin-American countries have implemented the same kind of instruments, which fa- cilitates comparisons between both regions.Europe was a pioneer in the implementation of policies to promote Abbreviations: AUC, Auctions; EU, European Union; FE, Fixed-effects model; FIT, Feed-in Tariffs; PV, Photovoltaic; RE, Renewable Energy; RPS, Renewables Portfolio Standards. * Corresponding author. Institute for Advanced Sustainability Studies, Berliner Straße 130, 14467, Potsdam, Germany. E-mail addresses: german.bersalli@iass-potsdam.de (G. Bersalli), philippe.menanteau@univ-grenoble-alpes.fr (P. Menanteau), jonathan.el-methni@ parisdescartes.fr (J. El-Methni). Available online 19 September 2020 1364-0321/© 2020 Elsevier Ltd. All rights reserved. https://doi.org/10.1016/j.rser.2020.110351 Received 29 August 2019; Received in revised form 5 June 2020; Accepted 3 September 2020 G. Bersalli et al. Renewable and Sustainable Energy Reviews 133 (2020) 110351 incentives are also used to complement price or quantity-based in- struments, but the cases in which they represent the main (the only) support policy are scarce. The objectives of the present article are, firstly, to perform an orig- inal ex-post assessment of the effectiveness of the support policies implemented in Latin America, comparing them with the European experience, and, secondly, to analyse the influence of energy-system and macroeconomic determinants on RE diffusion. We conducted an econometric analysis based on panel data of 50 countries (30 in Europe and 20 in Latin America) during the period 1995–2015. The criterion of effectiveness is understood as the policy’s capacity to trigger new in- vestment in RE which is the primary objective of such measures [7].1 To consider a comparable and homogeneous unit of effectiveness, we selected the annual increase in installed capacity (in MW/inhabitant) of RE technologies as the dependent variable of our econometric model. Although from a theoretical perspective, price and quantity in- struments may be equivalents [39], numerous case studies and cross-country analyses show that, in practice, the effectiveness of policy instruments can differ substantially. Evaluation of European RE policy suggests that effectiveness and efficiency are higher in countries using price instruments than in those that have implemented quantity in- struments [9,10,40,41]. Nevertheless, the effectiveness of these policies depends heavily on each technology’s maturity and the specific “design elements” of each policy instrument [42].This article contributes to the literature on energy policy assessment by integrating an econometric analysis based on panel data that includes most of the Latin American countries, a region sparsely studied despite its ambitious objectives concerning RE deployment. The model allows us to test novel variables to capture the effects of energy-related and eco- nomic factors on RE diffusion. The results of this econometric analysis are then compared with previous case studies to understand the effects of policies in different contexts. From a political point of view, a com- parison between different decarbonisation pathways in Europe and Latin America should facilitate future cooperation in energy and climate matters, an objective of the European Commission, the Mercosur, and other multilateral organizations. The number of countries that have implemented policies and the hindsight on these actions is sufficient for econometric studies to pro- vide additional analytical elements. These studies differ in their meth- odology and their findings for policy effectiveness. Popp et al. [43] analysed the influence of the ratification (or not) of the Kyoto Protocol. According to the authors, although ratification is not in itself a direct incentive for investment, it can serve to signal a country’s commitment to climate policy and, hence, to future carbon prices. Their model, that included data from the OECD countries between 1991 and 2004, showed a positive and significant relationship between the ratification of the Kyoto Protocol and investment, but no significant influence of direct support policies. Marques and Fuinhas [23] included a broader range of policy vari- ables to evaluate RE effectiveness in a set of 23 European countries from 1990 to 2007. First, the authors constructed a variable for the number of policy measures in each country (the accumulated Number of RE Pol- icies and Measures). They then tested the influence of different types of policies according to the general classification proposed by the IEA Global Renewable Energy Policies and Measures Database: Information and Education Policies, Economic Instruments (including FIT, FIP, etc.), R&D, Regulatory Instruments (including quotas, standards, etc.), Voluntary Approaches and Policy Support.Their results showed a pos- itive and significant impact of public policies in general. However, if considered individually, only subsidies and policy support (e.g., stra- tegic planning and creation of institutional frameworks) showed a major influence. Overall, Marques and Fuinhas found that regulatory in- struments and all other types of policies were not significant for pro- moting RE. The rest of this article is structured as follows. Section 2 reviews the existing literature about RE investment determinants and allows selec- tion of the main explanatory variables included in our model. Section 3 describes the data and the econometric model. Section 4 presents and discusses de results, and finally, Section 5 summarises the main con- clusions and policy implications.2. The multiple drivers of renewable energy investment Analysis of international experiences shows that RE diffusion is a complex process involving several elements [32]. In addition to support policies, other technical, economic, and socio-political variables may have a significant impact on the diffusion rate of RE technologies. A growing corpus of econometric work is investigating the effects of different drivers. However, only a few [18,19,31,33] include in their analyses the developing and emerging countries although these econo- mies account for more than half of total RE investment2 [2]. Aguirre & Ibikunle [44] whose approach was similar to that followed by Marques & Fuinhas (2012), analysed the OECD and BRIC countries from 1990 to 2010. They found a low level of effectiveness of promotion policies and even a negative relationship between tax incentive policies and diffusion of RE. However, the studies by both Marques & Fuinhas (2012) and Aguirre & Ibikunle (2014) included all RE sources, including biomass (in all its forms) and large hydroelectric power plants. These last two traditional RE sources are generally not targeted by support policies and, therefore, their estimation results may have been possibly biased.2.1. Contrasting results on the impacts of support policy Given the cost gap between RE and conventional sources, in- vestments in green energy have relied heavily on the implementation of support policies. These policies, traditionally, fall into two broad cate- gories: price-based and quantity-based [34] Among price-based initia- tives, guaranteed purchase or Feed-in Tariffs (FIT) and Feed-in Premiums (FIP) are intended to ensure return on investment in a stable and predictable framework. The second type of initiative includes quantity-based policies that set targets for the integration of RE into the energy mix and rely on Auctions (AUC) or Renewable Portfolio Standard (RPS) schemes, which include flexibility mechanisms such as green certificates [35–38]. Recently, the “quantity” instruments, especially auctions, tend to replace the “price” instruments in both regions. Tax Pfeiffer & Mulder [31]took again a different approach based on dummy variables to assess a panel of 108 developing countries between 1980 and 2010. Their results indicated that the likelihood of investing in RE was 10% higher after the Kyoto Protocol and even 30% higher in countries with support policies. Regarding policy effectiveness, coun- tries that implemented economic or regulatory instruments showed respectively a 27% and 52% higher likelihood of investing in RE. In terms of amount invested, Pfeiffer and Mulder found a more significant effect where regulatory instruments were used compared to economic instruments, which contrasts with most of the findings for these aspects (see, e.g., Ref. [9]). 1 We also consider the definition in Mitchell et al. (2011): effectiveness is ‘the extent to which intended objectives are met, for instance the actual increase in the output of renewable electricity generated or shares of renewable energy in total energy supplies within a specified time period’. 1 We also consider the definition in Mitchell et al. (2011): effectiveness is ‘the extent to which intended objectives are met, for instance the actual increase in the output of renewable electricity generated or shares of renewable energy in total energy supplies within a specified time period’.2 The recent development in RE investment has varied by regions, rising in China, Latin America, and the Middle East and Africa while falling in Europe, the United States, Asia-Oceania (excluding China), Japan, and India (REN21, 2018). Cadoret and Padovano [20] measured two other policy variables in a panel that includes 26 European countries from 2004 to 2011. The first i 2 The recent development in RE investment has varied by regions, rising in China, Latin America, and the Middle East and Africa while falling in Europe, the United States, Asia-Oceania (excluding China), Japan, and India (REN21, 2018). Renewable and Sustainable Energy Reviews 133 (2020) 110351 G. Bersalli et al. between energy prices and the contribution of RE to energy supply in regions with high economic growth, although this was not significant for low-growth economies. reflects the country’s level of commitment (as a percentage) to the 2020 European RE targets.3 The second variable concerns environmental taxes, as a percentage of environmental duties in total tax revenues, according to the Eurostat classification. They found no statistically sig- nificant influence from this second variable, which, they believe, might be because environmental tax revenues are not intended for environ- mental protection or the dissemination of new technologies, but are budgetary instruments. It would be optimal to include in our model the costs of the electricity produced by the different sources in each country. However, the only data available are the overall electricity prices by country. These are endogenous since they include the cost of electricity produced by RE. Therefore most previous models included proxy variables such as per capita gas and coal production and, if available, the prices of these sources for each country. Polzin et al. [22]used ordinal variables to represent the different policies in place in the OECD countries between 2000 and 2011. 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