Energy Economics

Energy Economics Abstract Energy economics is a vast topic. Many applied and agricultural economists today work on fuel economy, fossil fuel energy issues, energy sector economic analysis, electricity sector economic and policy issues, techno-economic analyses of energy alternatives as well as agricultural energy issues such as biofuels, and energy use in agriculture. Energy economists also employ a wide variety of modeling and analytical tools. We cover six broad topics: externalities, policy analysis, energy demand and supply analyses, electricity pricing, biofuels, and techno-economic analysis. Applied and agricultural economists have made and will continue to make major contributions to the literature and policy analysis in these areas. Energy is a pervasive and powerful force in the economy. Energy economics is an applied sub-discipline of economics covering all aspects of supply, demand, pricing, policy, and externalities associated with energy production and consumption. Thus, energy economics is a vast topic, and we cannot possibly cover all aspects in this article. Rather, we will pick some important topics in the domain of energy economics and briefly describe the problems and issues that are addressed, how they are analyzed, and the current and future roles of agricultural economists working in this space. Energy and climate change are inextricably intertwined. While we cannot completely avoid discussing the links between energy and climate change in this article, we will leave most of the discussion of climate change economics to the paper in this issue covering that topic (see McCarl and Hertel 2018). The two topics are so closely linked because fossil energy is the main source of greenhouse gas (GHG) emissions that cause climate change. Thus, many energy policies are also climate policies, for example, the Corporate Average Fuel Economy (CAFE) standards, the Clean Power Plan (CPP), biofuels policy, carbon tax, and more. The other important point to make before embarking on the details of this article is that we are not limiting ourselves to agricultural energy issues such as biofuels, energy use in agriculture, etc. Many agricultural economists today work on fuel economy, fossil fuel energy issues, energy sector economic analysis, electricity sector economic and policy issues, techno-economic analyses of energy alternatives, etc. Thus, we will examine a much broader scope than just energy and agriculture, but will cover that area in more detail and reference some recent review articles in that area. In doing economic analysis on these topics, energy economists employ a wide variety of modeling and analytical tools ranging from micro- or firm-level evaluation of energy options to economy-wide assessments of alternative energy futures. Energy economists use regional and national models to assess alternative energy futures and evaluate the consequences of alternative energy policies. Econometric analysis is used to estimate supply and demand elasticities that can then be used in other models for forecasting, or for policy analysis. Math programming models are commonly used for energy and agricultural policy analysis, and to estimate the impacts of technology or climate-induced agricultural yield shocks. It is not the purpose of this paper to review all the techniques that have been or could be used in energy economics. Rather, we will illustrate some of the research areas and tools that have been used in each area. We will cover six broad topics: externalities, policy analysis, demand and supply analyses, energy pricing (particularly electricity), biofuels, and techno-economic analysis. A key point is that applied agricultural economists have made and will continue to make major contributions to the literature and policy analysis in all these areas. We will also highlight that in many cases, multi-disciplinary research is needed to adequately address important energy issues. Externalities Energy economists often get involved in valuing environmental costs and benefits. The methods vary but include the market analogy method wherein values can be derived from similar markets, as well as indirect market methods such as hedonic pricing wherein the value of a non-market item such as scenic beauty, airport noise, etc. can be derived from market values that include those traits. We will not provide an exhaustive review of non-market valuation techniques as they are covered in another paper in this volume. However, it is important to note that in developing energy and climate values, the use of non-market methods often is needed. Energy economics is closely linked to environmental economics, water economics, and climate change economics. All of these topics involve (in one way or another) market failures or externalities. Energy economists work on issues dealing with pollution from energy production and consumption. Energy and water are both inputs into agricultural production, and agricultural production also can contaminate waterways (Tegtmeier and Duffy 2004; National Research Council 2008; Moss 2008). Externalities can be particularly important in the case of utilities and electricity generation (Pearce 2002). An example of negative externalities would be that wind power leads to noise pollution, adverse health effects, loss of visual amenities, impacts on wildlife, and falling ice (Timilsina, van Kooten, and Narbel 2013). There are also benefits associated with wind power; McCubbin and Sovacool (2013) use a CO–Benefits Risk Assessment (COBRA) model to compare wind and natural gas electricity generation, and find that the human health and climate benefits range between 1.5 cents/kWh and 11.8 cents/kWh compared to natural gas generation. Agricultural economists have been engaged in incorporating external costs to obtain social costs for future electrical capacity planning. Owen (2004) suggests that future capacity planning of electricity generation should give preference to the lowest social cost when the utility is public or quasi-public, and environmental taxes can be used to internalize external costs in the case of private utilities. Policy Analysis Energy policy analysis takes many forms and is accomplished with many different tools and models. At the economy-wide level, computable general equilibrium models have been used to evaluate the impacts of different energy policies or impacts of new technologies. Energy sector models—often using math programming—have been used to evaluate the impacts of energy policies such as CAFE, Renewable Fuel Standard (RFS), Clean Power Plan (CPP), carbon tax, and many others. Sarica and Tyner (2013) use a hybrid macro-energy sector model to evaluate the impacts of all these different policies. These authors find, as is often the case, that the carbon tax is most efficient. These authors also find that the CPP is a very low-cost means of reducing emissions, whereas the CAFE policy is very expensive, with the carbon tax equivalent reaching about $300/ton by 2045. Reducing electricity emissions is the low hanging fruit, and increased fuel economy at high levels becomes very expensive. Firm-level or regional models have been used to compare the impacts of alternative energy policies, while spreadsheet-based models have been used to do techno-economic analysis and embed alternative energy policies in the analysis. One example is the evaluation of solar energy policies such as the federal tax credit, depreciation allowance, net metering, and time-of-day pricing (Jung and Tyner 2014; Sesmero, Jung, and Tyner 2016). There are two possible methods to transition away from fossil fuels: generic incentives and technology-specific incentives. Generic incentives include policies such as carbon taxes and cap and trade. Goulder and Schein (2013) show that using a producer and consumer surplus model that comparably designed carbon tax, cap and trade, and hybrid policies yield very similar incentives to reduce emissions. Ultimately, the results will depend on the design of the market-based instruments. Rozenberg, Vogt-Schilb, and Hallegatte (2014), using a macroeconomic growth model, show that transitioning away from dirty capital built before climate policies is optimal for achieving stringent climate targets. Technology-specific incentives reduce emissions differently from generic incentives, but can be as successful under some conditions. Economists tend to prefer a carbon tax that puts a price on the emission externality. Such a tax is low cost in terms of implementation, provides clear guidance to private-sector actors, and covers all economic sectors. However, governments thus far have generally preferred targeted emission policies such as biofuel incentives, CPP, fuel economy standards, etc. Agricultural economists will continue to play a major role in evaluating the consequences of these and other policy alternatives. The Clean Power Plan proposed by the Obama administration was aimed at reducing CO2 emissions from the electricity sector. In the case of the CPP, a mass-based approach, measured in total short tons of CO2, is deemed to be preferable to a rate-based policy, measured in pounds per megawatt hour, under the CPP (Oliver, Khanna, and Chen 2016). This study, which uses a dynamic electricity generation model of multiple regions, also finds that rate-based policy is 25% more expensive than a regional mass-based policy, and a rate-based policy is 40% more expensive than a national mass-based policy. Similarly, Bushnell et al. (2015) use an electricity generation model of multiple regions to find that mass-based approaches lead to greater efficiency, and a mix of mass-based approach and rate standards leads to an inefficient ordering of generation resources. There are also other benefits of the CPP. Carbon standards also provide local and regional health co-benefits, and a policy that considers demand-side energy efficiency leads to the greatest health benefits, which was found using scenario analysis (Driscoll et al. 2015). However, Hogan (2015) uses an electricity market model to show that a national carbon tax would reduce CO2 emissions and produce little or no unwanted distortion in the electricity market. Shale gas development has had both positive and negative impacts. Some positive impacts have been felt in small towns, for example increased employment, economic expansion, and lease payments to the holders of mineral rights (Muehlenbachs, Spiller, and Timmins 2015). The study used an econometric model of hedonic pricing. Taheripour, Tyner, and Sarica (2014) use a combination of a partial equilibrium energy model and a CGE model to find that with the shale resource expansion in the United States, gross domestic product from 2008 to 2035, on average, is projected to be 3.5% higher each year than without the shale boom. There are also other impacts of CAFE standards. Whitefoot and Skerlos (2012) use a simulation model and econometric framework to find that the footprint-based CAFE standards lead to an increase in the sales-weighted average vehicle size by 2% to 32%, which leads to a decrease in fuel economy of 1–4 mpg and increases CO2 emissions by 5% to 15%. CAFE standards affect the profit of domestic producers and lead to surplus changes in used car markets that disproportionately affect low-income households (Jacobsen 2013). The study used an econometric model. Electricity Pricing With retail net metering, the energy utility buys customer-generated electricity at the retail instead of its normal wholesale purchase price, which is advantageous for renewable electricity. Retail net metering imposes a cost on non-solar utility customers because it increases the cost the utility pays for electricity (Darghouth, Barbose, and Wiser 2011). The study also finds that solar and wind energy-supplying customers are being subsidized by higher fees from other customers. At low levels of renewable generation penetration, these issues may not impose significant costs on the utility or on non-solar or wind customers. However, at higher levels of renewable generation penetration, there could be significant costs to non-solar or wind customers, as well as potentially all customers. An example would be the case of Germany, where household electricity prices have approximately doubled since 2000, and there are also issues of the burden of higher energy prices being borne by relatively poorer households (Böhringer, Landis, and Angel Tovar Reaños 2017). Agricultural economists have worked on both the efficiency and equity issues associated with renewable electricity. Wind and solar generation have different levels of predictability based on factors such as technology, forecasting, and weather. These renewable energy resources are always interruptible. Thus, there must be a backup generation source that can come on quickly. There have been studies that examine high levels of penetration and costs of intermittency of solar and wind. For example, Hirth (2013), using a stylized model of the northwestern European electricity market to estimate integration costs of wind energy, finds integration costs up to 50% of total generation costs, at penetration rates of 30% to 40%. Brouwer et al. (2014) use a unit commitment and economic dispatch (UECD) system simulation model to examine high penetration of renewable electricity sources, and find that when wind generation increases to over 30% there is an oversupply of wind power; they also find an increase in direct system cost by 1–6 €/per megawatt-hour (MWh). (Gowrisankaran, Reynolds, and Samano 2016) combine integration costs, variability costs, and backup generation costs from a model of electricity generation, demand, and system operations to come up with the total social cost for a 20% solar generation share. There is a total intermittency cost of $46 per MWh. As such, there are much larger costs imposed on the power system by renewables at higher levels of renewable energy penetration. Agricultural economists are involved in electricity sector and energy sector evaluations of these pricing and interruptible energy issues. An important part of electricity supply is energy storage systems, as they are a potential solution to intermittency. Potential storage systems include compressed air energy storage, pumped hydro, and advanced battery technologies (Dunn, Kamath, and Tarascon 2011). However, these systems would be costly and have not been implemented at a large scale (Baker et al. 2013). Technological advancement and decreases in costs could make energy storage less expensive than adding additional standby generation capacity. Demand-side management includes load management, energy conservation, fuel substitution, and load building. Bhattacharyya (2011) contends that demand side management is important as reduction in demand lowers pressure on system expansion, improves utilization of available infrastructure, and improves market operation. Palensky and Dietrich (2011) argue that with better demand management, there are benefits of increased energy efficiency. Again, the applied nature of our discipline and the tools commonly employed make agricultural economists well-equipped to handle these important questions. Energy Supply and Demand Energy supply and demand elasticities are important for modeling as well as forecasting. Just as agricultural economists have accomplished estimations of supply and demand elasticities for agricultural commodities, they have also done research in energy supply and demand analysis. Like for the agricultural commodity elasticity estimates, the results are often used directly in partial or general equilibrium models for economic and policy analyses. Dahl (2012) uses calculations based on previous literature to find that the income elasticity of gasoline demand ranges between 1.26 and 0.66, and the income elasticity of diesel demand ranges between −0.13 and 0.38. Krichene (2002), using an econometric model of simultaneous supply and demand, finds that for crude oil, the long-run price elasticity of demand is −0.005 and the long-run price elasticity of supply is 0.10. Further, Krichene finds that for natural gas, the long-run price elasticity of demand is −1.10 and the long run price elasticity of supply is 0.80. Burke and Yang (2016) find that the long-run price elasticity of demand for natural gas is −1.25 and the long-run income elasticity of demand for natural gas is above 1 using an econometric model of demand for natural gas. There have also been studies that have examined residential demand elasticities. Bernard, Bolduc, and Yameogo (2011), using an econometric model for panel data, find that the long-run household price elasticity of demand for electricity is −1.35 in Canada. In another study on residential elasticities in Japan, Okajima and Okajima (2013) use an econometric model for panel data and find that the short-run household price elasticity of demand for electricity is −0.397, and that the long-run household price elasticity of demand for electricity is −0.487. In one application of energy elasticities, Coady et al. (2015) use own price elasticities for IMF global energy subsidy calculations. Another application of elasticities would be to examine rebound effects of energy policies. Sorrell, Dimitropoulos, and Sommerville (2009) use an econometric approach to find that the direct rebound effect ranges between 10% and 30%. However, O’Rear, Sarica, and Tyner (2015) use a partial equilibrium model to evaluate the rebound effect and find it is small. Energy supply and demand elasticities are also contained in and are important to CGE models (Beckman, Hertel, and Tyner 2011). Biofuels Agricultural economists have done most of the economic and policy analysis related to biofuels. That has ranged from technology assessments of new technologies to partial equilibrium analyses of biofuels policy issues (Tyner and Taheripour 2008; Khanna and Crago 2012) to agricultural sector models used for a wide range of biofuels policy issues (Babcock, Barr, and Carriquiry 2010; Beach, Zhang, and McCarl 2012). Taheripour, Cui and Tyner (2018) provide a review of many of the major studies related to biofuel economics and policy. Agricultural economists also have used computable general equilibrium (CGE) analysis, particularly for land use change issues associated with biofuel policies (Tyner and Taheripour 2008; Khanna and Crago 2012). Sajedinia and Tyner (2017) provide a review of the use of CGE models for land use change, analysis of the food-fuel issue, biofuel economic welfare impacts, and related topics. Studies have examined the impacts of biofuel production on land use. Tyner and Taheripour (2008) use general equilibrium modes to show that ethanol and biodiesel production potentially leads to induced land use changes and that there is an important link between energy and agricultural markets. Taheripour et al. (2012) show that extensive margin disaggregation by productivity level leads to estimates of land requirements for ethanol production that is 25% lower than previous studies using a computable energy equilibrium model. Taheripour and Tyner (2013) use a computable energy equilibrium model to find that compared to the previous models, less global cropland expansion is induced by ethanol production. Also, the share of global cropland expansion is lower for the United States, and the share of forest in global cropland expansion is lower. Taheripour, Zhao, and Tyner (2017) use a computable energy equilibrium model and find that land use change emissions from miscanthus bio-gasoline production become negative because of the high level of carbon sequestration in soil with this crop. Khanna and Crago (2012) compare partial equilibrium and general equilibrium models to find that crop-specific induced land use change effects depend on increased demand for a feedstock and the demand for substitute feedstocks. Beach, Zhang, and McCarl (2017) use a dynamic nonlinear programming model to find that a volume mandate and a carbon price increase demand for cellulosic feedstocks and increase bioelectricity production. A variety of methodologies have been used to come up with different conclusions about the impacts of biofuel production on land use. One side effect of bioenergy production is an increase in the strength of the link between agricultural and energy markets (Tyner 2010). Babcock (2012), with the use of a multimarket partial equilibrium model of the agricultural sector, finds that ethanol contributes significantly to higher crop prices and to higher food prices. Hertel, Tyner, and Birur (2010) use a computable energy equilibrium model to find that high oil prices were the cause of a biofuels boom in the United States. Taheripour, Hertel, and Tyner (2011) find that biofuel expansion leads to a decrease in livestock production in countries other than the European Union and United States due to the international transmission of grain prices with the use of a computable generable equilibrium model. Beckman et al. (2011) use a computable energy equilibrium model to find that mandates reduce the impact of energy market volatility on agricultural markets. Abbott (2014) uses a model of U.S. corn markets to find that for the period 2005–2009, ethanol may have caused about one-third of the increase in corn prices. Some studies have looked at the effects of biofuel mandates. For example, De Gorter and Just (2010) developed a stylized model of the gasoline and ethanol markets and find that a mandate is significantly better than consumption subsidies on biofuels in terms of social costs. McPhail and Babcock (2012) find that RFS and the blend wall reduce the price elasticity of demand for corn and gasoline and increase the price variability using stochastic partial equilibrium simulation. Chen et al. (2014) use a numerical simulation model of the fuel and agriculture sectors and find that global social welfare decreases with the RFS and LCFS because of efficiency costs. Taheripour and Tyner (2014) use a computable general equilibrium model to find that the Renewable Fuel Standard impact on gasoline price is negligible. Zhou and Babcock (2017) use a numerical optimization model to show that more E85 pumps lower RIN prices and in turn lower the cost of complying with mandates. Using an econometric demand model, Pouliot and Babcock (2017) determine that the United States would consume 285 million gallons of E85 if it were priced at the same cost-per-mile basis as E10, but 1 billion gallons of E85 if it were priced 20% less than the cost-per-mile basis of E10. These studies by agricultural economists represent major contributions to the debates on the Renewable Fuel Standard (RFS) and biofuels policy in general. Other studies looked at the effects of taxes and subsidies on biofuels. De Gorter and Just (2009) use a theoretical welfare framework to find that a tax credit increases direct welfare for corn farmers and slightly increases welfare for gasoline consumers. Khanna et al. (2011), using a crop productivity model, find that the Biomass Crop Assistance Program and volumetric tax credits lead to biofuel production that exceeds the minimum required by the RFS, and helps transition toward cellulosic biofuels. Chen et al. (2011) find that volumetric tax credits improve the competitiveness of cellulosic biofuels. Babcock, Moreira, and Peng (2013) use a stochastic model that calculates market clearing prices to show that biodiesel tax credits lead to U.S. biodiesel production exceeding the levels of the mandate, and also lower sugarcane ethanol imports. A variety of methodologies have been used in these analyses, and again, agricultural economists have provided very important input into policy debates. Some studies look at the effects of import tariffs on biofuels. Chen and Khanna (2012) use a multi-market, multi-period, price-endogenous mathematical programming model to find that policies such as VEETC and import tariffs delay the transition to advanced biofuels and negatively impact food crop prices. Babcock, Barr, and Carriquiry (2010) use a stochastic model and find that eliminating the ethanol import tariff would not impact corn and ethanol markets in the United States due to a combination of strong demand for ethanol in Brazil and a saturated U.S. ethanol market. Further studies have examined the environmental impacts of biofuels, and various methodologies have been used to come up with different conclusions about the effects of taxes and subsidies on biofuels. For example, Smith et al. (2008) use calculations and geographic information systems to determine that the economic mitigation potential of agricultural feedstocks is estimated to be 640 Mt CO2-eq. yr−1 at 0–20 US$ t CO2-eq.−1. McCarl (2008) uses lifecycle accounting and the economics of feedstocks to show that feedstocks and pathways have different GHG offset effects with grain ethanol offering the least, electricity offering the most, and cellulosic ethanol falling in the middle. Elobeid et al. (2013) use a multimarket agricultural sector PE model and find that increased carbon emissions, water quality degradation, pollution, and sediment loads can potentially offset the environmental benefits of biofuels. Still further studies have examined feedstock choices. Beach and McCarl (2010) use a dynamic nonlinear programming model to find that in the case of mandate increases, crop residues and switchgrass are the primary feedstocks. However, this study did not consider energy sorghum and miscanthus as potential feedstocks. McCarl and Zhang (2011) include energy sorghum and miscanthus as potential feedstocks and find that miscanthus becomes the most widely-used feedstock to meet mandate increases. Beach, Zhang, and McCarl (2012) use a dynamic nonlinear programming model that includes feedstock storage costs to look at feedstocks choices. Initially, due to storage costs there is a shift away from cellulosic ethanol production to corn ethanol, but over time miscanthus is used more and becomes the most widely-used feedstock by 2035. Techno-economic Analysis Another important area is microeconomic work related to evaluation of specific technologies, and the development of methodologies to link sub-sectors in meaningful ways. This is necessary for effective energy policy analysis. Benefit-cost analysis is a very important tool in this regard, and option value models also can be useful. Making greater use of stochastic techno-economic analysis will aid in quantifying the inherent riskiness of the technologies (Bittner, Tyner, and Zhao 2015; Jung and Tyner 2014; Zhao, Yao, and Tyner 2016; Bann et al. 2017; Yao et al. 2017). There have been many techno-economic analyses (TEA) that have examined biofuels. Brown et al. (2013) calculate the minimum fuel selling price using techno-economic analysis for ethanol and biodiesel produced from fast pyrolysis and hydroprocessing, and find it to be $2.57/gal. Pearlson, Wollersheim, and Hileman (2013) calculate the baseline cost for hydroprocessed esters and fatty acids (HEFA) fuel production using techno-economic analysis and find that it varies between $1.01 L–1 and $1.16 L–1 due to facility size. Bond et al. (2014) use TEA and find that the catalytic process for conversion of whole biomass into drop-in aviation fuels leads to jet fuel-range liquid hydrocarbons with a minimum selling price of $4.75 per gallon. Thilakaratne et al. (2014) calculate the minimum fuel selling price using techno-economic analysis for mild catalytic pyrolysis of woody biomass and find it to be of $3.69 per gallon with a 10% internal rate of return. Stochastic TEA models also can be used for policy analysis. Bittner, Tyner, and Zhao (2015) compare the cost of capital subsidies to a reverse auction using a policy overlay on a micro-level stochastic TEA model and find that the reverse auction achieves far more risk reduction at the same government policy cost. While techno-economic analysis has mainly been used to evaluate biofuels, some studies have examined other energy topics. Yang, Wei, and Chengzhi (2009) use techno-economic analysis to determine the optimum size of the battery bank and the PV array for a given load and a desired loss of power supply for a standalone hybrid PV–Wind system. Ma et al. (2015) examined a pumped storage-based standalone PV generation system using system techno-economic analysis and find that the proposed models and optimization algorithm is effective and can be used in small autonomous systems in remote areas. Rincón et al. (2014) examine biomass steam turbines and biomass gasification-combined cycles using agro-economic and techno-economic analysis and find that the biomass-integrated gasification combined cycle system was better to meet heating and electricity requirements. Techno-economic analysis has become widely used not only for assessing the potential viability of new technologies and pathways, but also for evaluating the impacts of various policies on stimulating investments in the technologies. Conclusions In this article we have reviewed many of the areas in energy economics in which agricultural economists have made significant contributions. Being applied economists, working on real-world energy economic and policy issues has been attractive for agricultural economists. In the future, issues relating to energy, climate change, environment, and water will grow in importance, and we can expect agricultural economists to continue to make significant contributions. References Abbott P. 2014. 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Abstract

Abstract Energy economics is a vast topic. Many applied and agricultural economists today work on fuel economy, fossil fuel energy issues, energy sector economic analysis, electricity sector economic and policy issues, techno-economic analyses of energy alternatives as well as agricultural energy issues such as biofuels, and energy use in agriculture. Energy economists also employ a wide variety of modeling and analytical tools. We cover six broad topics: externalities, policy analysis, energy demand and supply analyses, electricity pricing, biofuels, and techno-economic analysis. Applied and agricultural economists have made and will continue to make major contributions to the literature and policy analysis in these areas. Energy is a pervasive and powerful force in the economy. Energy economics is an applied sub-discipline of economics covering all aspects of supply, demand, pricing, policy, and externalities associated with energy production and consumption. Thus, energy economics is a vast topic, and we cannot possibly cover all aspects in this article. Rather, we will pick some important topics in the domain of energy economics and briefly describe the problems and issues that are addressed, how they are analyzed, and the current and future roles of agricultural economists working in this space. Energy and climate change are inextricably intertwined. While we cannot completely avoid discussing the links between energy and climate change in this article, we will leave most of the discussion of climate change economics to the paper in this issue covering that topic (see McCarl and Hertel 2018). The two topics are so closely linked because fossil energy is the main source of greenhouse gas (GHG) emissions that cause climate change. Thus, many energy policies are also climate policies, for example, the Corporate Average Fuel Economy (CAFE) standards, the Clean Power Plan (CPP), biofuels policy, carbon tax, and more. The other important point to make before embarking on the details of this article is that we are not limiting ourselves to agricultural energy issues such as biofuels, energy use in agriculture, etc. Many agricultural economists today work on fuel economy, fossil fuel energy issues, energy sector economic analysis, electricity sector economic and policy issues, techno-economic analyses of energy alternatives, etc. Thus, we will examine a much broader scope than just energy and agriculture, but will cover that area in more detail and reference some recent review articles in that area. In doing economic analysis on these topics, energy economists employ a wide variety of modeling and analytical tools ranging from micro- or firm-level evaluation of energy options to economy-wide assessments of alternative energy futures. Energy economists use regional and national models to assess alternative energy futures and evaluate the consequences of alternative energy policies. Econometric analysis is used to estimate supply and demand elasticities that can then be used in other models for forecasting, or for policy analysis. Math programming models are commonly used for energy and agricultural policy analysis, and to estimate the impacts of technology or climate-induced agricultural yield shocks. It is not the purpose of this paper to review all the techniques that have been or could be used in energy economics. Rather, we will illustrate some of the research areas and tools that have been used in each area. We will cover six broad topics: externalities, policy analysis, demand and supply analyses, energy pricing (particularly electricity), biofuels, and techno-economic analysis. A key point is that applied agricultural economists have made and will continue to make major contributions to the literature and policy analysis in all these areas. We will also highlight that in many cases, multi-disciplinary research is needed to adequately address important energy issues. Externalities Energy economists often get involved in valuing environmental costs and benefits. The methods vary but include the market analogy method wherein values can be derived from similar markets, as well as indirect market methods such as hedonic pricing wherein the value of a non-market item such as scenic beauty, airport noise, etc. can be derived from market values that include those traits. We will not provide an exhaustive review of non-market valuation techniques as they are covered in another paper in this volume. However, it is important to note that in developing energy and climate values, the use of non-market methods often is needed. Energy economics is closely linked to environmental economics, water economics, and climate change economics. All of these topics involve (in one way or another) market failures or externalities. Energy economists work on issues dealing with pollution from energy production and consumption. Energy and water are both inputs into agricultural production, and agricultural production also can contaminate waterways (Tegtmeier and Duffy 2004; National Research Council 2008; Moss 2008). Externalities can be particularly important in the case of utilities and electricity generation (Pearce 2002). An example of negative externalities would be that wind power leads to noise pollution, adverse health effects, loss of visual amenities, impacts on wildlife, and falling ice (Timilsina, van Kooten, and Narbel 2013). There are also benefits associated with wind power; McCubbin and Sovacool (2013) use a CO–Benefits Risk Assessment (COBRA) model to compare wind and natural gas electricity generation, and find that the human health and climate benefits range between 1.5 cents/kWh and 11.8 cents/kWh compared to natural gas generation. Agricultural economists have been engaged in incorporating external costs to obtain social costs for future electrical capacity planning. Owen (2004) suggests that future capacity planning of electricity generation should give preference to the lowest social cost when the utility is public or quasi-public, and environmental taxes can be used to internalize external costs in the case of private utilities. Policy Analysis Energy policy analysis takes many forms and is accomplished with many different tools and models. At the economy-wide level, computable general equilibrium models have been used to evaluate the impacts of different energy policies or impacts of new technologies. Energy sector models—often using math programming—have been used to evaluate the impacts of energy policies such as CAFE, Renewable Fuel Standard (RFS), Clean Power Plan (CPP), carbon tax, and many others. Sarica and Tyner (2013) use a hybrid macro-energy sector model to evaluate the impacts of all these different policies. These authors find, as is often the case, that the carbon tax is most efficient. These authors also find that the CPP is a very low-cost means of reducing emissions, whereas the CAFE policy is very expensive, with the carbon tax equivalent reaching about $300/ton by 2045. Reducing electricity emissions is the low hanging fruit, and increased fuel economy at high levels becomes very expensive. Firm-level or regional models have been used to compare the impacts of alternative energy policies, while spreadsheet-based models have been used to do techno-economic analysis and embed alternative energy policies in the analysis. One example is the evaluation of solar energy policies such as the federal tax credit, depreciation allowance, net metering, and time-of-day pricing (Jung and Tyner 2014; Sesmero, Jung, and Tyner 2016). There are two possible methods to transition away from fossil fuels: generic incentives and technology-specific incentives. Generic incentives include policies such as carbon taxes and cap and trade. Goulder and Schein (2013) show that using a producer and consumer surplus model that comparably designed carbon tax, cap and trade, and hybrid policies yield very similar incentives to reduce emissions. Ultimately, the results will depend on the design of the market-based instruments. Rozenberg, Vogt-Schilb, and Hallegatte (2014), using a macroeconomic growth model, show that transitioning away from dirty capital built before climate policies is optimal for achieving stringent climate targets. Technology-specific incentives reduce emissions differently from generic incentives, but can be as successful under some conditions. Economists tend to prefer a carbon tax that puts a price on the emission externality. Such a tax is low cost in terms of implementation, provides clear guidance to private-sector actors, and covers all economic sectors. However, governments thus far have generally preferred targeted emission policies such as biofuel incentives, CPP, fuel economy standards, etc. Agricultural economists will continue to play a major role in evaluating the consequences of these and other policy alternatives. The Clean Power Plan proposed by the Obama administration was aimed at reducing CO2 emissions from the electricity sector. In the case of the CPP, a mass-based approach, measured in total short tons of CO2, is deemed to be preferable to a rate-based policy, measured in pounds per megawatt hour, under the CPP (Oliver, Khanna, and Chen 2016). This study, which uses a dynamic electricity generation model of multiple regions, also finds that rate-based policy is 25% more expensive than a regional mass-based policy, and a rate-based policy is 40% more expensive than a national mass-based policy. Similarly, Bushnell et al. (2015) use an electricity generation model of multiple regions to find that mass-based approaches lead to greater efficiency, and a mix of mass-based approach and rate standards leads to an inefficient ordering of generation resources. There are also other benefits of the CPP. Carbon standards also provide local and regional health co-benefits, and a policy that considers demand-side energy efficiency leads to the greatest health benefits, which was found using scenario analysis (Driscoll et al. 2015). However, Hogan (2015) uses an electricity market model to show that a national carbon tax would reduce CO2 emissions and produce little or no unwanted distortion in the electricity market. Shale gas development has had both positive and negative impacts. Some positive impacts have been felt in small towns, for example increased employment, economic expansion, and lease payments to the holders of mineral rights (Muehlenbachs, Spiller, and Timmins 2015). The study used an econometric model of hedonic pricing. Taheripour, Tyner, and Sarica (2014) use a combination of a partial equilibrium energy model and a CGE model to find that with the shale resource expansion in the United States, gross domestic product from 2008 to 2035, on average, is projected to be 3.5% higher each year than without the shale boom. There are also other impacts of CAFE standards. Whitefoot and Skerlos (2012) use a simulation model and econometric framework to find that the footprint-based CAFE standards lead to an increase in the sales-weighted average vehicle size by 2% to 32%, which leads to a decrease in fuel economy of 1–4 mpg and increases CO2 emissions by 5% to 15%. CAFE standards affect the profit of domestic producers and lead to surplus changes in used car markets that disproportionately affect low-income households (Jacobsen 2013). The study used an econometric model. Electricity Pricing With retail net metering, the energy utility buys customer-generated electricity at the retail instead of its normal wholesale purchase price, which is advantageous for renewable electricity. Retail net metering imposes a cost on non-solar utility customers because it increases the cost the utility pays for electricity (Darghouth, Barbose, and Wiser 2011). The study also finds that solar and wind energy-supplying customers are being subsidized by higher fees from other customers. At low levels of renewable generation penetration, these issues may not impose significant costs on the utility or on non-solar or wind customers. However, at higher levels of renewable generation penetration, there could be significant costs to non-solar or wind customers, as well as potentially all customers. An example would be the case of Germany, where household electricity prices have approximately doubled since 2000, and there are also issues of the burden of higher energy prices being borne by relatively poorer households (Böhringer, Landis, and Angel Tovar Reaños 2017). Agricultural economists have worked on both the efficiency and equity issues associated with renewable electricity. Wind and solar generation have different levels of predictability based on factors such as technology, forecasting, and weather. These renewable energy resources are always interruptible. Thus, there must be a backup generation source that can come on quickly. There have been studies that examine high levels of penetration and costs of intermittency of solar and wind. For example, Hirth (2013), using a stylized model of the northwestern European electricity market to estimate integration costs of wind energy, finds integration costs up to 50% of total generation costs, at penetration rates of 30% to 40%. Brouwer et al. (2014) use a unit commitment and economic dispatch (UECD) system simulation model to examine high penetration of renewable electricity sources, and find that when wind generation increases to over 30% there is an oversupply of wind power; they also find an increase in direct system cost by 1–6 €/per megawatt-hour (MWh). (Gowrisankaran, Reynolds, and Samano 2016) combine integration costs, variability costs, and backup generation costs from a model of electricity generation, demand, and system operations to come up with the total social cost for a 20% solar generation share. There is a total intermittency cost of $46 per MWh. As such, there are much larger costs imposed on the power system by renewables at higher levels of renewable energy penetration. Agricultural economists are involved in electricity sector and energy sector evaluations of these pricing and interruptible energy issues. An important part of electricity supply is energy storage systems, as they are a potential solution to intermittency. Potential storage systems include compressed air energy storage, pumped hydro, and advanced battery technologies (Dunn, Kamath, and Tarascon 2011). However, these systems would be costly and have not been implemented at a large scale (Baker et al. 2013). Technological advancement and decreases in costs could make energy storage less expensive than adding additional standby generation capacity. Demand-side management includes load management, energy conservation, fuel substitution, and load building. Bhattacharyya (2011) contends that demand side management is important as reduction in demand lowers pressure on system expansion, improves utilization of available infrastructure, and improves market operation. Palensky and Dietrich (2011) argue that with better demand management, there are benefits of increased energy efficiency. Again, the applied nature of our discipline and the tools commonly employed make agricultural economists well-equipped to handle these important questions. Energy Supply and Demand Energy supply and demand elasticities are important for modeling as well as forecasting. Just as agricultural economists have accomplished estimations of supply and demand elasticities for agricultural commodities, they have also done research in energy supply and demand analysis. Like for the agricultural commodity elasticity estimates, the results are often used directly in partial or general equilibrium models for economic and policy analyses. Dahl (2012) uses calculations based on previous literature to find that the income elasticity of gasoline demand ranges between 1.26 and 0.66, and the income elasticity of diesel demand ranges between −0.13 and 0.38. Krichene (2002), using an econometric model of simultaneous supply and demand, finds that for crude oil, the long-run price elasticity of demand is −0.005 and the long-run price elasticity of supply is 0.10. Further, Krichene finds that for natural gas, the long-run price elasticity of demand is −1.10 and the long run price elasticity of supply is 0.80. Burke and Yang (2016) find that the long-run price elasticity of demand for natural gas is −1.25 and the long-run income elasticity of demand for natural gas is above 1 using an econometric model of demand for natural gas. There have also been studies that have examined residential demand elasticities. Bernard, Bolduc, and Yameogo (2011), using an econometric model for panel data, find that the long-run household price elasticity of demand for electricity is −1.35 in Canada. In another study on residential elasticities in Japan, Okajima and Okajima (2013) use an econometric model for panel data and find that the short-run household price elasticity of demand for electricity is −0.397, and that the long-run household price elasticity of demand for electricity is −0.487. In one application of energy elasticities, Coady et al. (2015) use own price elasticities for IMF global energy subsidy calculations. Another application of elasticities would be to examine rebound effects of energy policies. Sorrell, Dimitropoulos, and Sommerville (2009) use an econometric approach to find that the direct rebound effect ranges between 10% and 30%. However, O’Rear, Sarica, and Tyner (2015) use a partial equilibrium model to evaluate the rebound effect and find it is small. Energy supply and demand elasticities are also contained in and are important to CGE models (Beckman, Hertel, and Tyner 2011). Biofuels Agricultural economists have done most of the economic and policy analysis related to biofuels. That has ranged from technology assessments of new technologies to partial equilibrium analyses of biofuels policy issues (Tyner and Taheripour 2008; Khanna and Crago 2012) to agricultural sector models used for a wide range of biofuels policy issues (Babcock, Barr, and Carriquiry 2010; Beach, Zhang, and McCarl 2012). Taheripour, Cui and Tyner (2018) provide a review of many of the major studies related to biofuel economics and policy. Agricultural economists also have used computable general equilibrium (CGE) analysis, particularly for land use change issues associated with biofuel policies (Tyner and Taheripour 2008; Khanna and Crago 2012). Sajedinia and Tyner (2017) provide a review of the use of CGE models for land use change, analysis of the food-fuel issue, biofuel economic welfare impacts, and related topics. Studies have examined the impacts of biofuel production on land use. Tyner and Taheripour (2008) use general equilibrium modes to show that ethanol and biodiesel production potentially leads to induced land use changes and that there is an important link between energy and agricultural markets. Taheripour et al. (2012) show that extensive margin disaggregation by productivity level leads to estimates of land requirements for ethanol production that is 25% lower than previous studies using a computable energy equilibrium model. Taheripour and Tyner (2013) use a computable energy equilibrium model to find that compared to the previous models, less global cropland expansion is induced by ethanol production. Also, the share of global cropland expansion is lower for the United States, and the share of forest in global cropland expansion is lower. Taheripour, Zhao, and Tyner (2017) use a computable energy equilibrium model and find that land use change emissions from miscanthus bio-gasoline production become negative because of the high level of carbon sequestration in soil with this crop. Khanna and Crago (2012) compare partial equilibrium and general equilibrium models to find that crop-specific induced land use change effects depend on increased demand for a feedstock and the demand for substitute feedstocks. Beach, Zhang, and McCarl (2017) use a dynamic nonlinear programming model to find that a volume mandate and a carbon price increase demand for cellulosic feedstocks and increase bioelectricity production. A variety of methodologies have been used to come up with different conclusions about the impacts of biofuel production on land use. One side effect of bioenergy production is an increase in the strength of the link between agricultural and energy markets (Tyner 2010). Babcock (2012), with the use of a multimarket partial equilibrium model of the agricultural sector, finds that ethanol contributes significantly to higher crop prices and to higher food prices. Hertel, Tyner, and Birur (2010) use a computable energy equilibrium model to find that high oil prices were the cause of a biofuels boom in the United States. Taheripour, Hertel, and Tyner (2011) find that biofuel expansion leads to a decrease in livestock production in countries other than the European Union and United States due to the international transmission of grain prices with the use of a computable generable equilibrium model. Beckman et al. (2011) use a computable energy equilibrium model to find that mandates reduce the impact of energy market volatility on agricultural markets. Abbott (2014) uses a model of U.S. corn markets to find that for the period 2005–2009, ethanol may have caused about one-third of the increase in corn prices. Some studies have looked at the effects of biofuel mandates. For example, De Gorter and Just (2010) developed a stylized model of the gasoline and ethanol markets and find that a mandate is significantly better than consumption subsidies on biofuels in terms of social costs. McPhail and Babcock (2012) find that RFS and the blend wall reduce the price elasticity of demand for corn and gasoline and increase the price variability using stochastic partial equilibrium simulation. Chen et al. (2014) use a numerical simulation model of the fuel and agriculture sectors and find that global social welfare decreases with the RFS and LCFS because of efficiency costs. Taheripour and Tyner (2014) use a computable general equilibrium model to find that the Renewable Fuel Standard impact on gasoline price is negligible. Zhou and Babcock (2017) use a numerical optimization model to show that more E85 pumps lower RIN prices and in turn lower the cost of complying with mandates. Using an econometric demand model, Pouliot and Babcock (2017) determine that the United States would consume 285 million gallons of E85 if it were priced at the same cost-per-mile basis as E10, but 1 billion gallons of E85 if it were priced 20% less than the cost-per-mile basis of E10. These studies by agricultural economists represent major contributions to the debates on the Renewable Fuel Standard (RFS) and biofuels policy in general. Other studies looked at the effects of taxes and subsidies on biofuels. De Gorter and Just (2009) use a theoretical welfare framework to find that a tax credit increases direct welfare for corn farmers and slightly increases welfare for gasoline consumers. Khanna et al. (2011), using a crop productivity model, find that the Biomass Crop Assistance Program and volumetric tax credits lead to biofuel production that exceeds the minimum required by the RFS, and helps transition toward cellulosic biofuels. Chen et al. (2011) find that volumetric tax credits improve the competitiveness of cellulosic biofuels. Babcock, Moreira, and Peng (2013) use a stochastic model that calculates market clearing prices to show that biodiesel tax credits lead to U.S. biodiesel production exceeding the levels of the mandate, and also lower sugarcane ethanol imports. A variety of methodologies have been used in these analyses, and again, agricultural economists have provided very important input into policy debates. Some studies look at the effects of import tariffs on biofuels. Chen and Khanna (2012) use a multi-market, multi-period, price-endogenous mathematical programming model to find that policies such as VEETC and import tariffs delay the transition to advanced biofuels and negatively impact food crop prices. Babcock, Barr, and Carriquiry (2010) use a stochastic model and find that eliminating the ethanol import tariff would not impact corn and ethanol markets in the United States due to a combination of strong demand for ethanol in Brazil and a saturated U.S. ethanol market. Further studies have examined the environmental impacts of biofuels, and various methodologies have been used to come up with different conclusions about the effects of taxes and subsidies on biofuels. For example, Smith et al. (2008) use calculations and geographic information systems to determine that the economic mitigation potential of agricultural feedstocks is estimated to be 640 Mt CO2-eq. yr−1 at 0–20 US$ t CO2-eq.−1. McCarl (2008) uses lifecycle accounting and the economics of feedstocks to show that feedstocks and pathways have different GHG offset effects with grain ethanol offering the least, electricity offering the most, and cellulosic ethanol falling in the middle. Elobeid et al. (2013) use a multimarket agricultural sector PE model and find that increased carbon emissions, water quality degradation, pollution, and sediment loads can potentially offset the environmental benefits of biofuels. Still further studies have examined feedstock choices. Beach and McCarl (2010) use a dynamic nonlinear programming model to find that in the case of mandate increases, crop residues and switchgrass are the primary feedstocks. However, this study did not consider energy sorghum and miscanthus as potential feedstocks. McCarl and Zhang (2011) include energy sorghum and miscanthus as potential feedstocks and find that miscanthus becomes the most widely-used feedstock to meet mandate increases. Beach, Zhang, and McCarl (2012) use a dynamic nonlinear programming model that includes feedstock storage costs to look at feedstocks choices. Initially, due to storage costs there is a shift away from cellulosic ethanol production to corn ethanol, but over time miscanthus is used more and becomes the most widely-used feedstock by 2035. Techno-economic Analysis Another important area is microeconomic work related to evaluation of specific technologies, and the development of methodologies to link sub-sectors in meaningful ways. This is necessary for effective energy policy analysis. Benefit-cost analysis is a very important tool in this regard, and option value models also can be useful. Making greater use of stochastic techno-economic analysis will aid in quantifying the inherent riskiness of the technologies (Bittner, Tyner, and Zhao 2015; Jung and Tyner 2014; Zhao, Yao, and Tyner 2016; Bann et al. 2017; Yao et al. 2017). There have been many techno-economic analyses (TEA) that have examined biofuels. Brown et al. (2013) calculate the minimum fuel selling price using techno-economic analysis for ethanol and biodiesel produced from fast pyrolysis and hydroprocessing, and find it to be $2.57/gal. Pearlson, Wollersheim, and Hileman (2013) calculate the baseline cost for hydroprocessed esters and fatty acids (HEFA) fuel production using techno-economic analysis and find that it varies between $1.01 L–1 and $1.16 L–1 due to facility size. Bond et al. (2014) use TEA and find that the catalytic process for conversion of whole biomass into drop-in aviation fuels leads to jet fuel-range liquid hydrocarbons with a minimum selling price of $4.75 per gallon. Thilakaratne et al. (2014) calculate the minimum fuel selling price using techno-economic analysis for mild catalytic pyrolysis of woody biomass and find it to be of $3.69 per gallon with a 10% internal rate of return. Stochastic TEA models also can be used for policy analysis. Bittner, Tyner, and Zhao (2015) compare the cost of capital subsidies to a reverse auction using a policy overlay on a micro-level stochastic TEA model and find that the reverse auction achieves far more risk reduction at the same government policy cost. While techno-economic analysis has mainly been used to evaluate biofuels, some studies have examined other energy topics. Yang, Wei, and Chengzhi (2009) use techno-economic analysis to determine the optimum size of the battery bank and the PV array for a given load and a desired loss of power supply for a standalone hybrid PV–Wind system. Ma et al. (2015) examined a pumped storage-based standalone PV generation system using system techno-economic analysis and find that the proposed models and optimization algorithm is effective and can be used in small autonomous systems in remote areas. Rincón et al. 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