|
on Energy Economics |
| By: | David Popp |
| Abstract: | Using an original data set of both scientific articles and patents pertaining to alternative energy technologies, this paper provides new evidence on the flows of knowledge between university, private sector, and government research. Better understanding of the value of knowledge from these institutions can help decision makers target R&D funds where they are most likely to be successful. I use citation data from both scientific articles and patents to answer two questions. First, what information is most useful to the development of new technology? Does high quality science lead to commercial success? I find that this is the case, as those articles most highly cited by other scientific articles are also more likely to be cited by future patents. Second, which institutions produce the most valuable research? Are there differences across technologies? Research performed at government institutions appears to play an important translational role linking basic and applied research, as government articles are more likely to be cited by patents than any other institution, including universities. Universities play a less important role in wind research than for solar and biofuels, suggesting that wind energy research is at a more applied stage where commercialization and final product development is more important than basic research. |
| JEL: | O38 Q42 Q48 Q55 |
| Date: | 2016–08 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:22573&r=ene |
| By: | Elena Verdolini (Fondazione Eni Enrico Mattei and Centro Euro-Mediterraneo per i Cambiamenti Climatici); Francesco Vona (OFCE Sciences-Po and SKEMA Business School); David Popp (Syracuse University and NBER) |
| Abstract: | The diffusion of renewable energy in the power system implies high supply variability. Lacking economically viable storage options, renewable energy integration has so far been possible thanks to the presence of fast-reacting mid-merit fossil-based technologies, which act as back-up capacity. This paper discusses the role of fossil-based power generation technologies in supporting renewable energy investments. We study the deployment of these two technologies conditional on all other drivers in 26 OECD countries between 1990 and 2013. We show that a 1% percent increase in the share of fast-reacting fossil generation capacity is associated with a 0.88% percent increase in renewable in the long run. These results are robust to various modifications in our empirical strategy, and most notably to the use of system-GMM techniques to account for the interdependence of renewable and fast-reacting fossil investment decisions. Our analysis points to the substantial indirect costs of renewable energy integration and highlights the complementarity of investments in different generation technologies for a successful decarbonization process. |
| Keywords: | Renewable Energy Investments, Fossil Energy Investments, Complementarity, Energy and Environmental Policy |
| JEL: | Q42 Q48 Q55 O33 |
| Date: | 2016–08 |
| URL: | http://d.repec.org/n?u=RePEc:fem:femwpa:2016.51&r=ene |
| By: | Adams, R.; Jamasb, J. |
| Abstract: | In recent years, geopolitical events have raised questions about the security of European energy supplies and which electricity generation technologies present an optimal fuel mix. Likewise, private investors need to allocate their capital efficiently by devising portfolios of generation assets. This paper applies the Modern Portfolio Theory to determine an optimal portfolio with four electricity generation technologies. Using UK electricity and fuel price data and European carbon allowance prices for the period 2009-2013, we find that coal assets increase portfolio risk and decrease overall returns, whilst a combination of gas, nuclear and wind assets allows an investor to maximise risk-adjusted return. In addition, we examine the role of power purchase agreements (PPAs) to assess whether predictable revenues create more appealing portfolio characteristics. We find that such contracts reduce portfolio returns, highlighting the importance of the set prices and their possible fluctuations over time. The findings support electricity market reform that discourages coal investment and supports investment in renewable technologies. The results also suggest that PPAs could make sense for independent renewable generators, although this would require modelling of the uncertainty of variable load factors and operating costs. |
| Keywords: | Electricity, portfolio theory, technology, risk |
| JEL: | Q40 Q42 Q49 |
| Date: | 2016–08–24 |
| URL: | http://d.repec.org/n?u=RePEc:cam:camdae:1646&r=ene |
| By: | Aithal, Sreeramana; Acharya, Sridhar |
| Abstract: | The climate of the earth is influenced by first six miles above the surface and the gap between the earth's surface and six miles above is considered to be the atmosphere. The atmosphere is maintaining a temperature up to 40 to 45 degree Celsius which is suitable for the living organisms to lead a happy life. Due to increase in the emission of green house gases like CO2 the environmental temperature is gradually increasing. This is called Global warming. The emission of Co2 is increasing day by day due to deforestation or burning fossil fuels. The major contributor for global warming is industries. Conventional energy production system pollutes the environment by emitting poisonous gases. The average increase in the temperature is found to be 0.4 to 0.8 degree Celsius. If the same situation continuous, then during 2100 the average temperature may increase up to 1.4 to 5.8 degree Celsius. The average increase in the temperature year by year brings threat to the living organisms around the globe. Now it is very important to think on this issue and find out the remedy to bring down the global temperature. In energy sector the main electricity production is done using thermal energy system. In this paper the impact of green energy on green house gases is explained. In this paper a comparative study of emission of CO2 by the traditional energy production system and Renewable energy production system. The paper also suggests the methods to bring down the global warming by adopting Renewable energy sources. |
| Keywords: | Green energy, Green house, Renewable Energy, fossil fuels, Deforestation. |
| JEL: | Q54 Q55 |
| Date: | 2016–06 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:73374&r=ene |
| By: | Justice Tei Mensah (Swedish University of Agricultural Sciences) |
| Abstract: | Power cuts have become a characteristic feature of many Sub-Saharan African economies. This paper attempts to investigate the micro and macro impacts of power outages by estimating the effects on firm revenue and productivity, and output growth of the manufacturing and industrial sectors. Further, I evaluate the impact of self-generation in ameliorating the effects of electricity shortages on firm performance using a quasi-experimental approach. Results from the analysis reveal significant negative effects of electricity shortages on firm revenue and productivity, and output growth of manufacturing and industrial sectors. Finally, contrary to the notion that self-generation may be helpful to firms during outage periods, evidence from this paper suggest the reliance on self-generation is associated with productivity losses albeit short run revenue gains. |
| Keywords: | Power Outages, Sub-Saharan Africa, Electricity, Productivity, Firms |
| JEL: | D04 D24 L11 L94 O12 |
| Date: | 2016–06 |
| URL: | http://d.repec.org/n?u=RePEc:fae:wpaper:2016.20&r=ene |
| By: | John J. García; Jhonny Moncada |
| Abstract: | El mecanismo de fijacio?n del precio de oferta en el mercado ele?ctrico colombiano exhibe comportamientos estrate?gicos inherente a la estructura oligopo?lica de este mercado, no solo por su alto porcentaje hidrolo?gico, aproximadamente 80%, sino tambie?n debido a la localizacio?n geogra?fica de las plantas de generacio?n ele?ctrica cercanas a la Regio?n Andina. En esta investigacio?n se disen?a una matriz de pesos espaciales, que recoge caracteri?sticas de la localizacio?n geogra?fica de las plantas de generacio?n ele?ctrica, la cual se incorpora en un panel espacial de tipo Durbin para identificar dichos comportamientos de la geografi?a econo?mica, adema?s de las variables fundamentales que explican la formacio?n del precio en este mercado. |
| Keywords: | Precios de oferta, Mercado spot eléctrico, Panel Espacial, Colombia |
| JEL: | C23 D43 L25 |
| Date: | 2016–07–01 |
| URL: | http://d.repec.org/n?u=RePEc:col:000122:015009&r=ene |
| By: | Bjørndal, Endre (Dept. of Business and Management Science, Norwegian School of Economics); Bjørndal, Mette (Dept. of Business and Management Science, Norwegian School of Economics); Midthun, Kjetil (SINTEF Technology and Society); Tomasgard, Asgeir (Dept. of Industrial Economics and Technology Management, Norwegian University of Science and Technology) |
| Abstract: | We consider an electricity market with two sequential market clearings, for instance representing a day-ahead and a real-time market. When the first market is cleared, there is uncertainty with respect to generation and/or load, while this uncertainty is resolved when the second market is cleared. We compare the outcomes of a stochastic market clearing model, i.e. a market clearing model taking into account both markets and the uncertainty, to a myopic market model where the first market is cleared based only on given bids, and not taking into account neither the uncertainty nor the bids in the second market. While the stochastic market clearing gives a solution with a higher total social welfare, it poses several challenges for market design. The stochastic dispatch may lead to a dispatch where the prices deviate from the bid curves in the first market. This can lead to incentives for selfscheduling, require producers to produce above marginal cost and consumers to pay above their marginal value in the first market. Our analysis show that the wind producer has an incentive to deviate from the system optimal plan in both the myopic and stochastic model, and this incentive is particularly strong under the myopic model. We also discuss how the total social welfare of the market outcome under stochastic market clearing depends on the quality of the information that the system operator will base the market clearing on. In particular, we show that the wind producer has an incentive to misreport the probability distribution for wind. |
| Keywords: | Market design; electricity; stochastic programming |
| JEL: | C60 L10 L94 |
| Date: | 2016–08–25 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:nhhfms:2016_011&r=ene |
| By: | Bjørndal, Endre (Dept. of Business and Management Science, Norwegian School of Economics); Bjørndal, Mette (Dept. of Business and Management Science, Norwegian School of Economics); Midthun, Kjetil (SINTEF Technology and Society); Zakeri, Golbon (Dept. of Engineering Science, University of Auckland) |
| Abstract: | We consider an electricity market organized with two settlements: one for a pre-delivery (day-ahead) market and one for real time, where uncertainty regarding production from non-dispatchable energy sources as well as variable load is resolved in the latter stage. We formulate two models to study the efficiency of this market design. In the myopic model, the day-ahead market is cleared independently of the real-time market, while in the integrated stochastic dispatch model the possible outcomes of the real-time market clearing are considered when the day-ahead market is cleared. We focus on how changes in the design of the electricity market influence the efficiency of the dispatch, measured by expected total cost or social welfare. In particular, we examine how relaxing network flow constraints and, for the stochastic dispatch model, even the balancing constraints in the day-ahead part of the dispatch models affects the overall efficiency of the system. This allows the dispatch to be infeasible day-ahead, while these infeasibilities will be handled in the real-time market. For the stochastic dispatch model we find that relaxing the network flows and balancing constraints in the dayahead part of the market provides additional flexibility that can be valuable to the system. In our examples with high up-regulation cost we find a value of "overbooking" that lead to lower total costs. In the myopic model the results are more ambiguous, however, leaving too many constraints to be resolved in the real-time market only, can lead to infeasibilities or high regulation cost. |
| Keywords: | OR in Energy; Stochastic Programming; Electricity Markets; Market Design |
| JEL: | C60 L10 L94 |
| Date: | 2016–08–25 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:nhhfms:2016_012&r=ene |
| By: | Rafael Leonardo Saavedra Mesa |
| Abstract: | En este trabajo se presentan los impactos a la demanda interna en Colombia, producidos por las transacciones internacionales de electricidad. Se hace un análisis de las transacciones con los enlaces existentes (Venezuela y Ecuador) y se proyecta el impacto que pueda producir la futura conexión con Panamá. Abstract This study presents the impacts on Colombian domestic demand produced by international transactions of electricity. It analyzes the transactions with the current transmission lines (Venezuela and Ecuador), and projects the impact than could produce a future connection with Panamá. |
| Keywords: | Transacciones internacionales de electricidad, Contratos, Demanda interna. |
| JEL: | N7 N76 O1 O13 Q4 Q41 Q43 |
| Date: | 2016–08–22 |
| URL: | http://d.repec.org/n?u=RePEc:col:000178:015014&r=ene |
| By: | Yuri Balagula |
| Abstract: | In the paper we use different methods of fractal analysis for characterization of long memory and other features of wholesale electricity prices. The connection between different characteristics of time series, such as capacity fractal dimension, Hurst exponent, spectral dimension, fractional integration order, is shown. The relation between the notions of long memory, fractional integration and persistence of a time series is considered. We calculated the fractal characteristics for wholesale electricity prices taken from electricity exchanges of Northern Europe, Italia and Ontario (Canada). The results show that the analyzed time series are persistent and reveal the long memory property. |
| Keywords: | time series, fractal analysis, fractal dimension, Hurst exponent, ARFIMA, long memory, persistence, electricity market |
| JEL: | C13 C14 C22 |
| Date: | 2016–07–04 |
| URL: | http://d.repec.org/n?u=RePEc:eus:wpaper:ec0316&r=ene |
| By: | Munoz, F. D.; van der Weijde, A. H.; Hobbs, B. F.; Watson, J-P. |
| Abstract: | We investigate the effects of risk aversion on optimal transmission and generation expansion planning in a competitive and complete market. To do so, we formulate a stochastic model that minimizes a weighted average of expected transmission and generation costs and their conditional value at risk (CVaR). We show that the solution of this optimization problem is equivalent to the solution of a perfectly competitive risk-averse Stackelberg equilibrium, in which a risk-averse transmission planner maximizes welfare after which risk-averse generators maximize profits. This model is then applied to a 240-bus representation of the Western Electricity Coordinating Council, in which we examine the impact of risk aversion on levels and spatial patterns of generation and transmission investment. Although the impact of risk aversion remains small at an aggregate level, state-level impacts on generation and transmission investment can be significant, which emphasizes the importance of explicit consideration of risk aversion in planning models. |
| Keywords: | risk aversion, stochastic programming, transmission planning, generation planning |
| JEL: | C61 D80 L94 Q40 |
| Date: | 2016–08–24 |
| URL: | http://d.repec.org/n?u=RePEc:cam:camdae:1647&r=ene |
| By: | Elena Verdolini (CMCC and FEEM); Laura Diaz Anadón (John F. Kennedy School of Government, Harvard University and University College London); Erin Baker (University of Massachusetts Amherst); Valentina Bosetti (CMCC, FEEM and Bocconi University); Lara Aleluia Reis (CMCC and FEEM) |
| Abstract: | Expert elicitation is a process for eliciting subjective probability distributions from experts about items of interest to decision makers. These methods have been increasingly applied in the energy domain to collect information on the future cost and performance of specific energy technologies and the associated uncertainty. This article reviews the existing expert elicitations on energy technologies with three main objectives: (1) to provide insights on expert elicitation methods and how they compare/complement other approaches to inform public energy decision making; (2) to review all recent elicitation exercises about future technology costs; and (3) to discuss the main results from these expert elicitations, in terms of implied rates of cost reduction and the role of R&D investments in shaping these reductions, and compare it with insights from backward looking approaches. We argue that the emergence of data on future energy costs through expert elicitations provides the opportunity for more transparent and robust analyses incorporating technical uncertainty to assess energy and climate change mitigation policies. |
| Keywords: | Energy Technologies, R&D Investments, Expert Elicitations, Uncertainty |
| JEL: | Q5 Q55 |
| Date: | 2016–07 |
| URL: | http://d.repec.org/n?u=RePEc:fem:femwpa:2016.47&r=ene |
| By: | Erin Baker (University of Massachusetts); Valentina Bosetti (Bocconi University and Fondazione Eni Enrico Mattei (FEEM)); Ahti Salo (Aalto University) |
| Abstract: | We address the problem of choosing a portfolio of policies under “deep uncertainty.” We introduce the idea of belief dominance as a way to derive a set of non-dominated portfolios and robust individual alternatives. Our approach departs from the tradition of providing a single recommended portfolio; rather, it derives a group of good portfolios. The belief dominance concept allows us to synthesize multiple expert- or model- based beliefs by uncovering the range of alternatives that are intelligent responses to the range of beliefs. This goes beyond solutions that are optimal for any specific set of beliefs to uncover other defensible solutions that may not otherwise be revealed. We illustrate our approach using an important problem in the climate change and energy policy context: choosing among clean energy technology R&D portfolios. We demonstrate how the belief dominance concept can reveal portfolios and alternatives that would otherwise remain uncovered. |
| Keywords: | Deep Uncertainty, Decision Making under Uncertainty, Robust, Dominance |
| JEL: | D8 D78 D81 |
| Date: | 2016–07 |
| URL: | http://d.repec.org/n?u=RePEc:fem:femwpa:2016.46&r=ene |
| By: | Shahbaz, Muhammad; Kumar, Mantu; Shah, Syed Hasanat; Sato, João Ricardo |
| Abstract: | This paper detects the direction of causality among carbon dioxide (CO2) emissions, energy consumption, and economic growth in Next 11 countries for the period 1972–2013. Changes in economic, energy, and environmental policies as well as regulatory and technological advancement over time, cause changes in the relationship among the variables. We use a novel approach i.e. time-varying Granger causality and find that economic growth is the cause of CO2 emissions in Bangladesh and Egypt. Economic growth causes energy consumption in the Philippines, Turkey, and Vietnam but the feedback effect exists between energy consumption and economic growth in South Korea. In the cases of Indonesia and Turkey, we find the unidirectional time-varying Granger causality running from economic growth to CO2 emissions thus validates the existence of the Environmental Kuznets Curve hypothesis, which indicates that economic growth is achievable at the minimal cost of environment. The paper gives new insights for policy makers to attain sustainable economic growth while maintaining long-run environmental quality. |
| Keywords: | Energy, Growth, Emissions, Next 11 Countries |
| JEL: | A1 |
| Date: | 2016–08–08 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:73395&r=ene |
| By: | Paul J Burke; Hewen Yang |
| Abstract: | Natural gas contributes a growing share of the world’s energy mix. In this paper we use national-level data for a sample of 44 countries to estimate the price and income elasticities of natural gas demand. We present both single-equation results and results instrumenting natural gas prices with proved natural gas reserves. Our instrument includes both domestic reserves and distance-weighted reserves in other countries. We obtain estimates of the average long-run price elasticity of natural gas demand of around –1.25 and of the average long-run income elasticity of natural gas demand of +1 and higher. We also present separate estimates for final natural gas demand by industry and households. |
| Keywords: | natural gas, price, income, elasticity, demand |
| JEL: | Q41 Q31 Q43 |
| Date: | 2016 |
| URL: | http://d.repec.org/n?u=RePEc:pas:papers:2016-14&r=ene |
| By: | Johnsen, Reid (University of California, Berkeley); LaRiviere, Jacob (University of Tennessee); Wolff, Hendrik (Simon Fraser University) |
| Abstract: | This paper estimates indirect benefits of improved air quality induced by hydraulic fracturing, or "fracking". The recent increase in natural gas supply led to displacement of coal-fired electricity by cleaner natural gas-fired generation. Using detailed spatial panel data comprising the near universe of US power plants, we find that coal generation decreased by 28%. Further, fracking decreased local air pollution by an average of 4%. We show that benefits vary geographically; air pollution levels decreased by 35% in the most affected region. Back of the envelope calculations imply accumulated health benefits of roughly $17 billion annually. |
| Keywords: | fracking, coal-fired power plants, air pollution, health, electricity |
| JEL: | Q41 Q53 I18 |
| Date: | 2016–08 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp10170&r=ene |
| By: | Omar O. Chisari (Universidad Argentina de la Empresa and Conicet); Leonardo J. Mastronardi (Universidad Argentina de la Empresa); Arturo Leonardo Vásquez Cordano (Oficina de Estudios Económicos, Osinergmin); Carlos A. Romero (Universidad Argentina de la Empresa) |
| Abstract: | En este documento se evalúa el impacto sobre la economía peruana de eventos extremos. Se utiliza un modelo de equilibrio general computable de la economía peruana con énfasis en el sector energético. Dados los importantes descubrimientos de yacimientos de gas natural en el Perú y la relevancia que ha venido tomando el mismo tanto en la matriz energética como en la economía en su totalidad, resulta de relevancia analizar el impacto que generaría restricciones en el transporte que limiten su utilización tanto en el destino interno como con fines de exportación. Los resultados muestran un fuerte impacto sobre el PBI y los niveles de bienestar de los consumidores. |
| JEL: | C58 D57 D58 L95 Q35 Q41 Q54 |
| Date: | 2015–12 |
| URL: | http://d.repec.org/n?u=RePEc:ose:wpaper:33&r=ene |
| By: | Aditya Aladangady; Claudia R. Sahm |
| Abstract: | March 6, 2015{{p}}Do Lower Gasoline Prices Boost Confidence?{{p}}{{p}}Aditya Aladangady and Claudia R. Sahm{{p}}{{p}}A gallon of gasoline currently costs one third less than it did last summer, as shown by the solid line in Figure 1. Gasoline makes up 5 percent of average family spending, so lower prices at the pump should increase current purchasing power for many.1 A drop in gasoline prices may also make people more confident about their future resources--the focus of this note.{{p}}Figure 1{{p}}Figure 1: Gasoline Prices and Consumer Sentiment. See accessible link for data.{{p}}{{p}}{{p}} Source: Energy Information Administration for motor gasoline regular retail price (including taxes): http://www.eia.gov/forecasts/steo/realprices/ February is an EIA forecast, University of Michigan Surveys of Consumers for sentiment: http://www.sca.isr.umich.edu/ Leaving the Board{{p}}{{p}}Accessible version{{p}}{{p}}In fact, recent surveys show a rise in confidence that coincides with the decline in gasoline prices. As one example, we estimate that energy price declines can account for about two-thirds of the increase since last June in the Index of Consumer Sentiment (the dashed line in Figure 1).2{{p}}{{p}}Summary measures of confidence can be difficult to interpret, however, so we turn now to specific questions about expectations and buying conditions to explore the impact of gasoline prices further. First, we see that individuals' expectations about future changes in gasoline prices are informative of actual prices changes, suggesting that individuals update their views as conditions change. Specifically, the survey asks "... do you think that the price of gasoline will go up during the next twelve months, will gasoline prices go down, or will they stay about the same as they are now?" In Figure 2, the diffusion index of expected gasoline price changes for the next year from individuals (blue line) is quite volatile but appears to align fairly well with the realized year-ahead percent change in gasoline prices (gray line).3 The correlation between the two series of 0.19 is notable given the unpredictability of gasoline prices.{{p}}Figure 2{{p}}Figure 2: Gasoline Prices: Survey Expectations and Actual Price Changes Over Next Year. See accessible link for data.{{p}}{{p}} Source: Energy Information Administration for motor gasoline regular retail price (including taxes): http://www.eia.gov/forecasts/steo/realprices/, University of Michigan Surveys of Consumers for sentiment: http://www.sca.isr.umich.edu/ Leaving the Board. Quarterly data from 1982:Q2 to 2014:Q4{{p}}{{p}} Note: The one-year-ahead survey expectations for gas prices were not asked 1993-2006{{p}}{{p}}Accessible version{{p}}{{p}}As actual gasoline prices have fallen recently, expectations about gasoline prices have also softened. This suggests that individuals have a more optimistic view about their future purchasing power. To test this connection, we examine views on expected real income growth conditional on individuals' expectations about gasoline prices.{{p}}{{p}}The remaining analysis uses the individual survey responses in Michigan survey from July 2014 to February 2015 from about 4000 people. In those eight months, on average, nearly 10 percent of individuals expected gasoline prices to go down, twice the rate in 2013. Figure 3 shows that during this period individuals who expect gas prices to go down in the next twelve months (green bars) are more optimistic about their real income prospects than individuals who expect gas prices to go up (orange bars) or gas prices to stay the same (gray bars).4{{p}}Figure 3{{p}}Figure 3: Expected Change in Real Income Over Next Few Years by Gasoline Price Expectations. See accessible link for data.{{p}}{{p}} Source: University of Michigan Surveys of Consumers.{{p}}{{p}} Note: In total, 18 percent expect real income to go up, 36 percent expect real income to be unchanged, and 46 percent expect real income to go down. Differences between green and orange bars are statistically significant at 1-percent level.{{p}}{{p}}Accessible version{{p}}{{p}}Second, this inverse relationship between expectations about gasoline prices and expectations about real income appears robust throughout the distribution of current income. Figure 4 expands the left-most grouping in Figure 3--those expecting real income to increase--by income quintiles. At each quintile, those expecting gas prices to go down (green bars) are more likely to expect their real income to increase than those who expect gas prices to go up (orange bars). With the smaller subsample--only those expecting a real income increase--the differences by gasoline price expectations are less precisely estimated. Since last summer, the difference is largest (and statistically significant) in the middle of the income distribution.{{p}}Figure 4{{p}}Figure 4: Expect an Increase in Real Income by Gasoline Price Expectations Across Income Quintiles. See accessible link for data.{{p}}{{p}} Source: University of Michigan Surveys of Consumers.{{p}}{{p}} Note: In total, 18 percent expect real income to go up, 36 percent expect real income to be unchanged, and 46 percent expect real income to go down. Differences between green and orange bars are statistically significant at 1-percent level.{{p}}{{p}}Accessible version{{p}}{{p}}These data suggest that the lower gasoline prices have made a broad set of individuals more confident. Of course, whether and when this heightened optimism translates to more consumer spending is another question. While the Michigan survey does not ask about spending, it does ask whether it is a good time to buy durables.5 As shown in Figure 5, individuals who expect gasoline prices to decline further are more likely to view buying conditions as favorable.{{p}}Figure 5{{p}}Figure 5: Good time to Buy Consumer Durables by Gasoline Price Expectations Across Income Quintiles. See accessible link for data.{{p}}{{p}} Source: University of Michigan Surveys of Consumers.{{p}}{{p}} Note: The percent of who say it is a good time to buy generally rises across the income quintiles: 69, 76, 75, 79, and 81 percent. The differences between the blue and red bars are statistically significant at the 5 percent level for the first three income quintiles.{{p}}{{p}}Accessible version{{p}}{{p}}Interestingly, the differences in reported buying conditions in the bottom half of the income distribution are even more pronounced than the differences in expected real income. One possible explanation for the stronger responses at lower income levels is that gasoline represents a larger share of family budgets. As shown by the bars in Figure 6, the gasoline expenditure shares from the Consumer Expenditure Survey peak in the middle income quintile and are lowest for top income quintile. Nonetheless, the solid line in Figure 6, indicates that the mean spending on gasoline does rise with income, meaning that while those with low income may see a larger fraction of their income freed up, falling gasoline prices cause the largest real income boost in dollar terms for those with higher income. The extent to which this income boost translates into new spending may also differ across individuals. In particular, those whose spending had previously been limited may exhibit a higher marginal propensity to consume.{{p}}Figure 6{{p}}Figure 6: Gasoline Expenditures in 2013 by Income Quintile. See accessible link for data.{{p}}{{p}} Source: Consumer Expenditure Survey.{{p}}{{p}}Accessible version{{p}}{{p}}All told, it appears that the decline in gasoline prices since last summer have both made individuals more optimistic about their future real incomes and bolstered their assessment of current buying conditions. Together with the direct savings from lower gasoline prices that are currently being realized, the extra confidence may also boost consumer spending going forward. Importantly, the impact of lower gas prices on confidence appears broad-based across income groups and may therefore lead to increased spending for a wide range of individuals.{{p}}{{p}}1. Expenditure shares for gasoline from Consumer Expenditure Survey, 2013, for example: http://www.bls.gov/cex/2013/combined/quintile.pdf Return to text{{p}}{{p}}2. Index of Consumer Sentiment combines the relative score (percent giving favorable replies minus percent giving unfavorable replies plus 100) to five questions about current and expected economic conditions. Our estimate of the energy-price effect is from a monthly regression model of sentiment that also includes CPI inflation, percent change in stock prices, unemployment rate, and change in payroll employment as covariates. The estimation range includes January 1978 to February 2015. Return to text{{p}}{{p}}3. The diffusion index is the percent expecting an increase in gasoline prices minus the percent expecting a decrease in gasoline prices plus 100. Return to text{{p}}{{p}}4. Specifically, the income question asks: "During the next year or two, do you expect that your (family) income will go up more than prices will go up, about the same, or less than prices will go up?" In Figure 2, the bars of the same color sum to 100 percent. The relative heights of the bars of the same color reflect differences in expectations about real income. In general, households are pessimistic about their income prospects. See earlier analysis of income expectations in Sahm (2013): http://www.federalreserve.gov/econresdata/notes/feds-notes/2013/why-have-americans-income-expectations-declined-so-sharply-20130926.html Return to text{{p}}{{p}}5. Specifically, the buying conditions question asks: "About the big things people buy for their homes--such as furniture, a refrigerator, stove, television, and things like that. Generally speaking, do you think now is a good or a bad time for people to buy major household items?" Return to text{{p}}{{p}} Disclaimer: FEDS Notes are articles in which Board economists offer their own views and present analysis on a range of topics in economics and finance. These articles are shorter and less technically oriented than FEDS Working Papers.{{p}}Search Working Papers{{p}}{{p}}{{p}}Last update: March 6, 2015 |
| Date: | 2015–03–06 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfn:2015-03-06&r=ene |
| By: | Ryan Decker; Aaron Flaaen; Maria D. Tito |
| Abstract: | Print{{p}}March 22, 2016{{p}}Unraveling the Oil Conundrum: Productivity Improvements and Cost Declines in the U.S.{{p}}Shale Oil Industry{{p}}Ryan Decker, Aaron Flaaen, and Maria Tito 1{{p}}Oil prices have declined by roughly 70 percent since peaking in the middle of 2014. The U.S. oil rig count--a common measure of drilling{{p}}activity--peaked in late 2014 and has since declined by about 60 percent. Yet U.S. production of crude oil continued rising until the{{p}}middle of 2015 and has since fallen by only 6 percent from its peak. The dramatic advance of U.S. oil production seen in the last{{p}}decade--driven primarily by new discoveries of shale oil and innovation in drilling and extraction technology--has not been as responsive{{p}}to the deterioration of oil markets as some analysts predicted.2{{p}}Why have large declines in prices and in the rig count not triggered a more dramatic decline in production? At what price level would a{{p}}large share of U.S. shale oil production lose economic viability? In this note, we explore these questions with a focus on the U.S. shale{{p}}oil industry in the Bakken, Eagle Ford, and Permian Basin regions.3 We describe large productivity improvements in drilling and fracking{{p}}methods that have allowed production to remain strong despite falling rig usage. We then document cost declines that have preserved{{p}}profitability for many firms even in the midst of historically low prices.{{p}}The Resilience of Production{{p}}The dramatic increase in total U.S. oil extraction between 2011 and mid-2015 was driven almost entirely by unconventional production in{{p}}geologies such as shale; these areas now account for more than half of U.S. output. The recent decrease in extraction has likewise{{p}}been concentrated in unconventional production.4 This decline has been moderate in light of the large collapse in prices and in the rig{{p}}count. One explanation for the modest response of production to the deterioration of the oil market is the high pace of productivity{{p}}growth seen by the shale extraction industry in recent years.{{p}}Figure 1. Rig Counts and Wells per Rig, Bakken Region{{p}} Source: U.S. Energy Information Administration (EIA), Drilling Productivity Report, March 2016{{p}}Accessible version{{p}}Improvements in the number of wells a rig can drill each month and in the average length of each well have contributed to the{{p}}productivity gains in drilling. As shown in Figure 1, which plots data for the Bakken region, the number of wells drilled per rig in a given{{p}}month has risen steadily since 2011, and it accelerated further after the rig count began falling in 2014.5 The main driver of this greater{{p}}rig efficiency is the adoption of pad drilling technology whereby a rig can drill multiple wells from the same spot without the need for{{p}}expensive and time-consuming disassembly, relocation, and reassembly. Additionally, each well has become much larger, as the{{p}}average well length has doubled from roughly one to two miles (American Petroleum Institute (API) 2015, Quarterly Well Completion{{p}}Report. IHS Global Inc.).{{p}} FRB: FEDS Notes: Unraveling the Oil Conundrum: Productivity Impro... https://www.federalreserve.gov/econresdata/notes/feds-notes/2016/unrav...{{p}}1 of 8 3/28/2016 12:50 PM{{p}}Increased and more efficient use of water, sand, and other proppants in the fracking process has further enhanced the productivity of oil{{p}}wells, particularly early in the well lifecycle.6 As a result, in addition to the increase in the number of new wells per rig, the extraction from{{p}}these new wells in their first month of production has roughly tripled since early 2008.7 These improvements can be best illustrated by{{p}}examining well decline curves, which track productivity over a well's lifecycle.{{p}}Figure 2. Average Well Decline Curve by Cohort{{p}} Source: Authors' calculation from Dept. of Mineral Resources, North Dakota{{p}} Note: Average well decline curves for Bakken.{{p}}Accessible version{{p}}To facilitate a more disaggregated analysis, we use well-level data for the Bakken region from the North Dakota Department of Mineral{{p}}Resources. These data provide monthly production by well in addition to other well characteristics and account for roughly 95 percent of{{p}}total Bakken oil production. Figure 2 plots well decline curves for selected well cohorts, defined by year of drilling, in the Bakken region.{{p}}The y-axis reports average well output in barrels per day. The x-axis tracks months of operation--the well lifecycle. The changes in{{p}}productivity of wells in early months of production are striking: output during the first full month of production has roughly doubled since{{p}}2007. The series of upward shifts in productivity paths seems to persist throughout the lifecycle across subsequent well cohorts. While{{p}}innovations in fracking technology are primarily thought to shift production forward in the well lifecycle (rather than to increase total{{p}}lifetime output), well-level data suggest that output in later-producing months has actually increased in recent years.{{p}}Figure 3. Well Decline, Controlling for Well Size{{p}} Note: Shaded areas represent 95% confidence intervals around the point estimates.{{p}}Accessible version{{p}}How much of this increase in productivity is driven solely by expansions of well size, as opposed to innovations in fracking technology?{{p}}Using data for the Bakken region, we regress well-level output on a series of monthly indicators that are distinct for each cohort year.{{p}} FRB: FEDS Notes: Unraveling the Oil Conundrum: Productivity Impro... https://www.federalreserve.gov/econresdata/notes/feds-notes/2016/unrav...{{p}}2 of 8 3/28/2016 12:50 PM{{p}}The resulting cohort-specific well-decline coefficients capture the change in output along each month of operation compared to a{{p}}baseline year, for which we choose 2007. The coefficients for the cohort of 2015 are shown in blue in Figure 3. (Notice they are roughly{{p}}equal to the difference in the well decline curves between 2015 and 2007 in Figure 2.) Next, we add a measure of well size to the{{p}}regression, specifically, the lateral distance of a well that undergoes perforations in preparation for fracking. As shown in grey in Figure{{p}}3, these adjusted coefficients are not largely different from the raw coefficients without the well-size adjustment. This analysis points to{{p}}innovations apart from increases in well size as the main contributing factors to the output gains evident from Figure 2.{{p}}Figure 4. Decomposition of Production Changes{{p}} Source: Drilling Productivity Report, EIA, March 2016.{{p}}Accessible version{{p}}Other lifecycle and composition issues can provide further insights into aggregate production. Figure 4 decomposes the change in{{p}}monthly oil production from all regions into the positive contribution of new wells (the blue line) and the negative contribution from{{p}}existing wells (the red line), the latter being almost invariably negative as a result of the natural lifecycle declines depicted in Figure 2.{{p}}Consequently, aggregate production increases whenever output from new wells (the blue line) exceeds the natural output declines{{p}}among existing wells (the red line). Despite productivity improvements, output from new wells began falling in mid-2015 as the number{{p}}of well completions dropped significantly. This pushed the positive contribution from new wells below the negative contribution from{{p}}existing wells, resulting in aggregate production declines. It is noteworthy, though, that the drag from existing well declines peaked{{p}}shortly thereafter, mitigating the fall in aggregate output. Completions of larger wells and productivity improvements among legacy wells{{p}}have softened the negative pull from existing wells.{{p}}Figure 5. Rigs Needed for Flat Production{{p}} Source: Drilling Productivity Report, EIA, March 2016.{{p}}Accessible version{{p}} FRB: FEDS Notes: Unraveling the Oil Conundrum: Productivity Impro... https://www.federalreserve.gov/econresdata/notes/feds-notes/2016/unrav...{{p}}3 of 8 3/28/2016 12:50 PM{{p}}Figure 5 provides another way of understanding composition effects behind production, the number of rigs needed to maintain a{{p}}constant production level, that is, rigs needed for flat production (RNFP). This measure is constructed by calculating the number of new{{p}}rigs necessary to offset observed declines from existing wells, using the prior month's estimate of new production per rig. As rig{{p}}efficiency has increased and legacy-well decline curves have shifted up, and as total production has gradually fallen, the RNFP has{{p}}declined. This framing highlights a decreasing usefulness of the rig count as a sole indicator of overall activity.{{p}}General equilibrium mechanisms have also affected the oil production climate. Lower drilling activity combined with productivity{{p}}enhancements have reduced the demand for workers, and wages of workers in the Natural Resources and Mining sector have declined{{p}}by 10 percent or more in the three main shale regions.8 Service costs have fallen by about 30 percent in the shale regions (Curtis 2015),{{p}}with anecdotal reports even indicating that some service firms are performing tasks for free to retain market share. Diesel fuel and other{{p}}energy sources are key production inputs, and their costs have fallen mechanically with oil prices. Using cost share estimates from the{{p}}Energy Information Administration (EIA) from 2009, we construct a back-of-the-envelope estimate that labor, services, and fuel cost{{p}}reductions have collectively reduced overall production costs by more than 10 percent.9 Other costs have fallen with broad market{{p}}conditions as well, most notably royalties and taxes (which often depend nonlinearly on prices).10{{p}}Additional productivity improvements and cost reductions have resulted from the aggressive reallocation of drilling and operating activity{{p}}toward high-productivity plays and rigs as well as from the failure or acquisition of low-productivity, high-cost firms.11 Considering the{{p}}recent productivity improvements, how should we expect aggregate production to evolve in coming months and years? The EIA,{{p}}accounting for the various productivity and lifecycle factors described above, forecasts total U.S. oil production to gradually fall to about{{p}}8.2 million barrels per day in early-2017, flattening out thereafter (U.S. Energy Information Administration 2016b).{{p}}Economic Viability of Shale Production{{p}}In recent years, companies have made extensive efforts to reduce drilling and production costs. Cost estimates vary widely, reflecting{{p}}substantive variation in regional and company-specific costs, ambiguity about the details of cost estimation, and general uncertainty. Our{{p}}goal is to generate a range of estimates that can inform our view of the economic viability of continued shale oil production.{{p}}We focus on two important cost types. The first are often referred to as "cash costs", and for our purposes they include the costs of{{p}}operating a lease (such as pumping, equipment rental, servicing, and materials) as well as administrative costs and per-barrel taxes.{{p}}Roughly speaking, these can be thought of as the average per-barrel cost of ongoing production at existing wells. At the margin,{{p}}production from an existing well is profitable so long as prices can cover cash costs and transportation expenses. The second cost{{p}}measure is the long-cycle breakeven, an estimate of the oil price that would make new drilling profitable. The long-cycle breakeven can{{p}}be thought of as the cost that is most relevant to long-term production growth.{{p}}Publicly traded oil companies report cash costs to the SEC in quarterly and annual filings. We collected these costs from 10K reports for{{p}}2013 and 2014. We also obtained the costs for the first nine months of 2015 from 10Q reports. We use data for about 25 companies with{{p}}large operations in the three main shale regions and that focus primarily on oil production.12 We report these costs in Figure 6.{{p}}Figure 6. Cash Costs{{p}} Source: SEC filings and investor reports. 2015 results only reported through Q3. Weights based on barrels oil produced{{p}}Sum of operating costs, G&A expenses, and production taxes (excludes interest){{p}}Accessible version{{p}}Each blue dot in Figure 6 represents a firm, and the red line shows the production-weighted average across companies. The weighted{{p}}average includes all companies for which we have all three years of data, though some small, high-cost companies are omitted from the{{p}}scatter. Figure 6 shows that cash costs vary widely across companies; various sources also indicate wide cost dispersion across wells{{p}}within firms. The between-firm dispersion has declined some over time, as has the average cost, falling from about $18 per barrel to{{p}} FRB: FEDS Notes: Unraveling the Oil Conundrum: Productivity Impro... https://www.federalreserve.gov/econresdata/notes/feds-notes/2016/unrav...{{p}}4 of 8 3/28/2016 12:50 PM{{p}}about $14 per barrel (without considering survivorship bias). The decline represents both aggressive cost-reduction efforts by firms and{{p}}the general equilibrium cost reductions discussed above. With transportation costs ranging from $5 to $10 (depending on region and{{p}}resources), the data suggest that for many firms the cost of operating existing wells is still somewhat below the current oil spot price.{{p}}Figure 7. Long-Cycle Breakeven Estimates{{p}} Source: Federal Reserve Bank of Kansas City firm survey, profitable price for drilling{{p}} Note: Blue bars represent the distribution of breakeven prices in a given quarter.{{p}}Accessible version{{p}}In the longer term, an important cost threshold is the long-cycle breakeven. This threshold includes drilling and transportation costs (as{{p}}well as internal cost of capital hurdles) and therefore reflects the price at which new wells are economically profitable. Estimates of{{p}}long-cycle breakeven costs vary widely. In Figure 7, we report results from quarterly surveys of oil companies administered by the{{p}}Federal Reserve Bank of Kansas City (and, as such, include operations primarily in the Niobrara region). Respondents were asked to{{p}}name the price at which new drilling could be expected to be profitable. Each bar captures the overall distribution of breakeven prices{{p}}reported in each quarter (we only include firms that responded in all three time periods). As in Figure 6, the red line represents the{{p}}average across firms (in this case we use a simple average since production data are not available from the survey).{{p}}Again, there exists wide variation in costs across companies. But, as with cash costs, dispersion has narrowed some, and average costs{{p}}have fallen significantly since 2014. Breakeven prices averaged about $60 per barrel in the third quarter of 2015. Given the pace of cost{{p}}reduction, by early 2016, costs are likely to have declined further. Estimates for other regions tend to be somewhat lower as well. Table 1{{p}}reports long-cycle breakeven estimates from a variety of sources.13{{p}}Table 1. Breakeven Estimates{{p}}Region Date Source Estimate{{p}}Bakken 2015q3 NDIC $30-$45{{p}}Bakken 2015q3 Minn. FRB $53{{p}}Eagle/Permian 2016q1 Bloomberg Intelligence $23-$59{{p}}Multiple regions* 2015q3 KC FRB $60{{p}}Multiple regions Recent Wells Fargo $25-$46{{p}}All regions Recent Rystad Energy $36{{p}}This note was revised on March 28 to correct a data heading in Table 1. Return to text.{{p}}Wells Fargo and Bloomberg Intelligence provide estimates for some regions that go as low as the mid-$20 range. Other regions have{{p}}costs approaching $60. The estimates suggest that current prices are too low for much long-term economic viability of shale oil{{p}}production. Given the lifecycle curves shown in Figure 2, aggregate production is not likely to remain strong or rise again until the market{{p}}sees substantial price growth.{{p}}Simple marginal cost reasoning has limits in this industry. There is considerable anecdotal evidence that firms may choose to{{p}} FRB: FEDS Notes: Unraveling the Oil Conundrum: Productivity Impro... https://www.federalreserve.gov/econresdata/notes/feds-notes/2016/unrav...{{p}}5 of 8 3/28/2016 12:50 PM{{p}}temporarily operate at prices below their costs for a variety of reasons. Many firms bought price hedges that have allowed them to{{p}}continue selling some oil above spot prices, though some of these contracts will expire by the end of 2016. Shut-in costs--the costs of{{p}}shutting down producing wells--are significant. Some firms may choose to operate below cost to retain market share or a core of skilled{{p}}workers, to maintain cash flow for debt servicing or dividends, or to avoid losing production rights under certain types of lease terms.{{p}}Some firms may have continued ability to survive on credit, though this option may be running out. Oil company bonds are trading at{{p}}large spreads, there have already been waves of defaults, and new issuance is down. Banks that lend to oil producers accept proven{{p}}reserves as collateral, but periodically this collateral is marked to market. By mid-2015, U.S. onshore producers were devoting an{{p}}average of 80 percent of cash flow to debt servicing costs (U.S. Energy Information Administration 2015). While outside the scope of the{{p}}present study, these financial issues are likely to be a major driver of producer dynamics going forward. Financial markets may also be a{{p}}mechanism that transmits oil market deterioration to the broader economy.14{{p}}In summary, the lack of a large production response to falling prices is likely due primarily to both rapid productivity gains and steadily{{p}}falling costs of drilling and production. Additionally, aggressive reallocation efforts by firms have provided further boosts to aggregate{{p}}productivity. While some of the factors boosting production--such as reallocation and labor hoarding--are likely to be temporary, other{{p}}factors represent permanent improvements in exploration and drilling methods. These productivity improvements have led to{{p}}considerable declines in cash costs for operating wells, and in breakeven thresholds for new wells. These costs may yet fall further.{{p}}However, current breakeven estimates are generally higher than spot prices, suggesting that production growth may be muted for some{{p}}time.{{p}}References{{p}}American Petroleum Institute. 2015. Quarterly Well Completion Report 31(4). IHS Global.{{p}}Bokowy, Tom and Ryan Hatcher. 2015. "ND Bakken Tax Trigger: Per Barrel Impact." The Bakken Magazine March 3, 2015. At{{p}} http://thebakken.com/articles/1029/nd-bakken-tax-trigger-per-barrel-impact (accessed March 14, 2016).{{p}}Cakir Melek, Nida. 2015. "What Could Lower Prices Mean for U.S. Oil Production?" Federal Reserve Bank of Kansas City Economic{{p}}Review First Quarter 2015. At https://www.kansascityfed.org/~/media/files/publicat/econrev/econrevarchive/2015/1q15wcakirmelek.pdf{{p}}(accessed March 14, 2016).{{p}}Carroll, Joe. 2016. "Texas Shale Drillers Lure $2 Billion in New Equity to Permian." BloombergBusiness February 1, 2016. At{{p}} http://www.bloomberg.com/news/articles/2016-02-02/texas-shale-drillers-lure-2-billion-in-new-equity-to-permian (accessed March 14,{{p}}2016).{{p}}Curtis, Trisha. 2015. "US Shale Oil Dynamics in a Low Price Environment." The Oxford Institute for Energy Studies paper no. WPM 62.{{p}}At https://www.oxfordenergy.org/wpcms/wp-content/uploads/2015/11/WPM-62.pdf (accessed March 14, 2016).{{p}}The Economist. 2016. "The Oil Conundrum." January 23, 2016. The Economist. At http://www.economist.com/news/briefing/21688919-{{p}}plunging-prices-have-neither-halted-oil-production-nor-stimulated-surge-global-growth (accessed March 14, 2016).{{p}}Egan, Matt. 2016. "Big Banks Brace for Oil Loans to Implode." CNN Money January 17, 2016. At http://money.cnn.com/2016/01{{p}}/18/investing/oil-crash-wall-street-banks-jpmorgan/ (accessed March 15, 2016).{{p}}The Federal Reserve Bank of Kansas City. "Energy Survey." At https://www.kansascityfed.org/research/indicatorsdata/energy{{p}}(accessed March 14, 2016).{{p}}Glazer, Emily. 2016. "J.P. Morgan Sounds Fresh Warning on Energy-Loan Losses." The Wall Street Journal February 23, 2016. At{{p}} http://www.wsj.com/articles/j-p-morgan-sounds-fresh-warning-on-energy-losses-1456243537 (accessed March 14, 2016).{{p}}Grunewald, Rob. 2016. "From Red-Hot to Lukewarm in the Bakken." Federal Reserve Bank of Minneapolis fedgazette. January 28,{{p}}2016. At https://www.minneapolisfed.org/publications/fedgazette/from-red-hot-to-lukewarm-in-the-bakken (accessed March 14, 2016).{{p}}Loder, Asjylyn. 2016. "Shale Faces March Madness as $1.2 Billion in Interest Comes Due." BloombergBusiness February 18, 2016. At{{p}} http://www.bloomberg.com/news/articles/2016-02-18/shale-faces-march-madness-as-1-2-billion-in-interest-comes-due (accessed March{{p}}14, 2016).{{p}}Petroff, Alana and Tal Yellin. 2015. "What It Costs to Produce Oil." CNN Money. At http://money.cnn.com/interactive/economy/the-cost-to-{{p}}produce-a-barrel-of-oil/index.html?iid=EL (accessed March 14, 2016).{{p}}Pioneer Natural Resources. 2014. 2014 Annual Report. At http://investors.pxd.com/phoenix.zhtml?c=90959&p=irol-reportsAnnual{{p}}(accessed March 14, 2016).{{p}}Raine, Chris. 2016. "U.S. Shale Still Covers Production Costs at $30 Oil." Oil Daily, Energy Intelligence Group, February 11, 2016.{{p}}U.S. Energy Information Administration. 2010. "Oil and Gas Lease Equipment and Operating Costs 1994 through 2009." September 28,{{p}}2010. At http://www.eia.gov/pub/oil_gas/natural_gas/data_publications/cost_indices_equipment_production/current/coststudy.html{{p}}(accessed March 14, 2016).{{p}}U.S. Energy Information Administration. 2015. "Debt Service Uses a Rising Share of U.S. Onshore Oil Producers' Operating Cash Flow."{{p}}Today in Energy September 18, 2015. At https://www.eia.gov/todayinenergy/detail.cfm?id=22992 (accessed March 14, 2016).{{p}} FRB: FEDS Notes: Unraveling the Oil Conundrum: Productivity Impro... https://www.federalreserve.gov/econresdata/notes/feds-notes/2016/unrav...{{p}}6 of 8 3/28/2016 12:50 PM{{p}}U.S. Energy Information Administration. 2016. Drilling Productivity Report March. At https://www.eia.gov/petroleum/drilling/ (accessed{{p}}March 14, 2016).{{p}}U.S. Energy Information Administration. 2016. Short-Term Energy Outlook March. At http://www.eia.gov/forecasts/steo/ (accessed March{{p}}14, 2016).{{p}}1. We thank Travis Adams, Emily Wisniewski, and Morgan Smith for excellent research assistance. Mine Yucel of the Federal Reserve Bank of Dallas and Rob{{p}}Grunewald of the Federal Reserve Bank of Minneapolis each provided several helpful conversations and data. Jason Brown, Nida Cakir Melek, and Chad{{p}}Wilkerson of the Federal Reserve Bank of Kansas City shared extensive background information, insights, and data of relevance to shale oil economics. We{{p}}thank Deepa Datta, Rob Vigfusson, and Gustavo Suarez for several helpful conversations. Finally, Kim Bayard and Norm Morin provided extensive resources{{p}}and guidance for this project. Return to text{{p}}2. One notable exception is Cakir Melek (2015) which stated that "even with a 50 percent decline in rig counts [in calendar year 2015], improvements in{{p}}efficiency could increase production in 2015." Return to text{{p}}3. The output from the Bakken, Eagle Ford, and Permian regions accounts for more than 85 percent of total production of shale oil. Return to text{{p}}4. Unconventional production, which now represents 55 percent of total output, amounted to 5.0 million barrels per day (mbd) in February 2016, down from the{{p}}peak of 5.5 mbd in March 2015. Conventional production has instead oscillated around 4 mbd over the last year (U.S. Energy Information Administration{{p}}2016a). Return to text{{p}}5. U.S. Energy Information Administration (2016a). Return to text{{p}}6. See, for example, The Economist (2016). Return to text{{p}}7. The data refer to all regions of unconventional production as reported by the U.S. Energy Information Administration (2016a). Return to text{{p}}8. Data are from the Bureau of Labor Statistics' Quarterly Census of Employment and Wages (QCEW). Region-level wages are computed as the employment-weighted{{p}}average of county-level average weekly wages. Following Grunewald (2016), the Bakken region comprises the following counties: Billings, Burke,{{p}}Divide, Dunn, Golden Valley, McKenzie, Mountrail, Stark, Williams, Richland, Roosevelt, and Sheridan. We define Eagle Ford and Permian Basin regions using{{p}}county lists from the Railroad Commission of Texas. The Eagle Ford region included the following counties: Atascosa, Bastrop, Bee, Brazos, Burleson, De{{p}}Witt, Dimmitt, Fayette, Frio, Gonzales, Grimes, Karnes, La Salle, Lavaca, Lee, Leon, Live Oak, Madison, Maverick, McMullen, Milam, Robertson, Walker,{{p}}Webb, Wilson, and Zavala. The Permian Basin includes the following counties: Andrews, Borden, Cochrane, Coke, Crane, Crosby, Dawson, Dickens, Ector,{{p}}Gaines, Garza, Glasscock, Hale, Hockley, Howard, Irion, Jeff Davis, Kent, Kimble, Lamb, Loving, Lubbock, Lynn, Martin, Midland, Mitchell, Nolan, Pecos,{{p}}Reagan, Reeves, Scurry, Sterling, Terry, Tom Green, Upton, Ward, Winkler, and Yoakum. Return to text{{p}}9. The EIA Cost Study was discontinued after 2009. Using QCEW data for Natural Resources and Mining wages in Bakken, Eagle Ford, and Permian Basin{{p}}data, we estimate that labor costs have fallen by about 10 percent. Our service cost estimate is based on the numbers suggested by Curtis (2015). According{{p}}to EIA, per-gallon diesel prices fell from $3 to $1.84 during 2015 (STEO date), or by more than 30 percent (U.S. Energy Information Administration 2010).{{p}}Labor, services, and fuel costs comprise 21 percent, 15 percent, and 17 percent of total costs, respectively. Holding cost shares and other costs constant, we{{p}}find a total cost decline of 12 percent. Return to text{{p}}10. Royalties are typically set at about 20 percent of total sales. As such, royalties fall mechanically with prices. A price decline from $100 to $30 per barrel{{p}}implies an effective decline in royalties of $14 per barrel. For information about taxes, see Bokowy and Hatcher (2015). Return to text{{p}}11. Abundant anecdotal evidence of within-firm reallocation efforts can be found in annual and quarterly SEC filings of public oil companies. For example, from{{p}}Pioneer Natural Resources' 2014 Annual Report: "Drilling activity is being high-graded to the areas and intervals in the play with the highest estimated ultimate{{p}}recoveries and net revenue interests" (Pioneer Natural Resources 2014, 2-3). Return to text{{p}}12. We include only companies with operations focusing primarily in the Bakken, Eagle Ford, and/or Permian Basin regions and companies that have large{{p}}operations elsewhere but separately report data for Bakken, Eagle Ford, and/or Permian Basin regions. We omit companies for which products other than{{p}}crude oil account for more than half of total barrel of oil equivalent (BOE) production. Costs are expressed in per-BOE terms and are not reported separately{{p}}for crude oil and other products. In 2015, the firms in our sample account for just under a quarter of total Bakken, Eagle Ford, and Permian Basin production.{{p}}Return to text{{p}}13. FRB estimates are from quarterly surveys of respective regional Federal Reserve Banks. Bloomberg Intelligence estimates are from William Foiles'{{p}}breakeven model, retrieved February 2016. Rystad Energy estimates are based on proprietary data (UCube) from more than 15,000 fields across 20 nations;{{p}}their results are reported by Petroff and Yellin (2015). Wells Fargo estimates are reported in Raine (2016). Return to text{{p}}14. U.S. oil producers face large interest bills in coming months (Loder 2016). Some producers have expressed concern about the spring 2016 rebasing of oil{{p}}reserve collateral (Federal Reserve Bank of Kansas City). A few firms have sought to obtain financing through new equity offerings (Carroll 2016). Financial{{p}}markets indicate that investors are concerned about banks with heavy exposure to the energy sector (Egan 2016, Glazer 2016). Return to text{{p}}Please cite this note as:{{p}}Decker, Ryan A., Aaron Flaaen, and Maria D. Tito (2016). "Unraveling the Oil Conundrum: Productivity Improvements and Cost Declines{{p}}in the U.S. Shale Oil Industry," FEDS Notes. Washington: Board of Governors of the Federal Reserve System, March 22, 2016,{{p}} http://dx.doi.org/10.17016/2380-7172.1736.{{p}} Disclaimer: FEDS Notes are articles in which Board economists offer their own views and present analysis on a range of topics in{{p}}economics and finance. These articles are shorter and less technically oriented than FEDS Working Papers.{{p}}Last update: March 28, 2016{{p}}Home | Economic Research & Data{{p}} FRB: FEDS Notes: Unraveling the Oil Conundrum: Productivity Impro... https://www.federalreserve.gov/econresdata/notes/feds-notes/2016/unrav...{{p}}7 of 8 3/28/2016 12:50 PM{{p}}Accessibility Contact Us Disclaimer Website Policies FOIA PDF Reader{{p}} FRB: FEDS Notes: Unraveling the Oil Conundrum: Productivity Impro... https://www.federalreserve.gov/econresdata/notes/feds-notes/2016/unrav...{{p}}8 of 8 3/28/2016 12:50 PM |
| Date: | 2016–03–22 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfn:2016-03-22&r=ene |
| By: | Kirill Borissov; Mikhail Pakhnin |
| Abstract: | We consider two models of economic growth with exhaustible natural resources, exogenous technical progress and agents heterogeneous in their time preferences. In the first model we assume private ownership of natural resources. We show that every competitive equilibrium in this model converges to a balanced-growth equilibrium. The long-run extraction rate and the rate of growth are determined by the discount factor of the most patient agents. The second model assumes public ownership of natural resources. The resource revenue is equally distributed among agents, who choose the resource extraction rate by voting. We define an intertemporal voting equilibrium and show that it also converges to a balanced-growth equilibrium. The long-run voting equilibrium extraction rate and the rate of growth are determined by the median discount factor. Our results suggest that, other things being equal, the growth rate in the case of private ownership is higher than that of public ownership if the most patient agents do not constitute the majority in population; otherwise there is no difference in the growth rates between the two regimes. However, in the long run private ownership leads to a higher level of inequality than public ownership. If we take into account the detrimental effect of inequality on economic growth, then the public property regime will likely result in a higher long-run rate of growth compared to the private property regime. |
| Keywords: | economic growth, exhaustible resources, heterogeneous agents, voting |
| JEL: | Q32 E13 D91 O40 |
| Date: | 2016–06–07 |
| URL: | http://d.repec.org/n?u=RePEc:eus:wpaper:ec0216&r=ene |
| By: | Carlos Ballesteros |
| Abstract: | The paper develops a Dynamic Stochastic General Equilibrium (DSGE) model, which assesses the macroeconomic and labor market effects derived from simulating a positive shock to the stochastic component of the mining-energy sector productivity. Calibrating the model for the Colombian economy, this shock generates a whole increase in formal wages and a raise in tax revenues, expanding total consumption of the household members. These facts increase non-tradable goods prices relative to tradable goods prices, then real exchange rate decreases (appreciation) and occurs a displacement of productive resources from the tradable (manufacturing) sector to the non-tradable sector, followed by an increase in formal GDP and formal job gains. This situation makes the formal sector to absorb workers from the informal sector through the non-tradable formal subsector, which causes informal GDP to go down. As a consequence, in the net consumption falls for informal workers, which leads some members of the household not to offer their labor force in the informal sector but instead they prefer to keep unemployed. Therefore, the final result on the labor market is a decrease in the number of informal workers, of which a part are in the formal sector and the rest are unemployed. |
| Keywords: | Mining and energy boom, dutch disease, formal and informal sectors, unemployment, DSGE model |
| JEL: | E0 E1 E2 E3 |
| Date: | 2016–08–01 |
| URL: | http://d.repec.org/n?u=RePEc:col:000122:015011&r=ene |
| By: | Marcel Kohler |
| Abstract: | The destabilising economic impact of South Africa’s dependence on imported crude oil is a key motivation behind the country’s drive to develop a biofuel industry. Much concern has been raised over the impact of biofuels production on price of food for the country's poor. It is this concern that has seen the prohibition of maize and the favouring of sugar cane as a feedstock in South Africa's Biofuels Industrial Strategy. This paper sets out to analyse the economic feasibility of producing bioethanol from sugar based on the industry's efforts to diversify its market base. The study suggests that bioethanol production is financially viable at an average US$102/bbl for the period 2005-2015, based on estimates that producers typically pay the equivalent of US$67/bbl for sugar cane feedstock, incur approximately US$20/bbl in operating & maintenance costs and require the equivalent of US$15/bbl to recoup capital investments. To kick-start the commercial production of fuel grade ethanol in South Africa, producers require mandated subsidisation. State support for bioethanol producers in the form of a guaranteed minimum selling price for bioethanol of 95 percent of the basic fuel price, exemption from fuel taxes in addition to specific capital investment allowances are required. |
| Keywords: | Biofuels, Costing, South Africa |
| JEL: | Q42 |
| Date: | 2016–08 |
| URL: | http://d.repec.org/n?u=RePEc:rza:wpaper:630&r=ene |
| By: | Shahbaz, Muhammad; Shahzad, Syed Jawad Hussain; Ahmad, Nawaz; Alam, Shaista |
| Abstract: | The present paper re-examines the asymmetric impact of financial development on environmental quality in Pakistan for the period 1985Q1 to 2014Q4. A comprehensive index of financial development is generated using Bank- and Stock market-based financial development indicators. The results show that inefficient use of energy adversely affects the environmental quality. This suggests adoption of energy efficient technology at both production and consumption levels. These technologies would be helpful to improve environmental quality, enhance the productivity in long-run and save energy. Bank-based financial development also impedes the environment. The government should encourage lenders to ease the funding for energy sector and allocate financial resources for environment friendly businesses rather than wasting them in consumer financing. |
| Keywords: | Financial development, Growth, Energy, CO2 emissions |
| JEL: | A1 A10 |
| Date: | 2016–08–10 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:73394&r=ene |
| By: | Matthew Adler (Duke University School of Law); David Anthoff (Energy and Resources Group, University of California); Valentina Bosetti (Bocconi University); Greg Garner (The Pennsylvania State University); Klaus Keller (The Pennsylvania State University and Carnegie Mellon University); Nicolas Treich (INRA, University of Toulouse) |
| Abstract: | The social cost of carbon (SCC) is a monetary measure of the harms from carbon emission. Specifically, it is the reduction in current consumption that produces a loss in social welfare equivalent to that caused by the emission of a ton of CO2. The standard approach is to calculate the SCC using a discounted-utilitarian social welfare function (SWF)—one that simply adds up the well-being numbers (utilities) of individuals, as discounted by a weighting factor that decreases with time. The discounted-utilitarian SWF has been criticized both for ignoring the distribution of well-being, and for including an arbitrary preference for earlier generations. Here, we use a prioritarian SWF, with no time-discount factor, to calculate the SCC in the integrated assessment model RICE. Prioritarianism is a well-developed concept in ethics and theoretical welfare economics, but has been, thus far, little used in climate scholarship. The core idea is to give greater weight to well-being changes affecting worse off individuals. We find substantial differences between the discounted-utilitarian and non-discounted prioritarian SCC. |
| Keywords: | Prioritarianism, Social Welfare Function, Social Cost of Carbon |
| JEL: | Q54 I30 |
| Date: | 2016–08 |
| URL: | http://d.repec.org/n?u=RePEc:fem:femwpa:2016.55&r=ene |
| By: | Vera Angelova; Giuseppe Attanasi; Yolande Hiriart |
| Abstract: | We compare the performance of liability rules for managing environmental disasters when third parties are harmed and cannot always be compensated. A firm can invest in safety to reduce the likelihood of accidents. The firm’s investment is unobservable to authorities. Externality and asymmetric information call for public intervention to define rules aimed at increasing prevention. We determine the investment in safety under No Liability, Strict Liability and Negligence, and compare it to the first best. Additionally, we investigate how the (dis)ability of the firm to fully cover potential damages affects the firm’s behavior. An experiment tests the theoretical predictions. In line with theory, Strict Liability and Negligence are equally effective; both perform better than No Liability; investment in safety is not sensitive to the ability of the firm to compensate potential victims. In contrast with theory, prevention rates absent liability are much higher and liability is much less effective than predicted. |
| JEL: | D82 K13 K32 Q58 |
| Date: | 2016–08 |
| URL: | http://d.repec.org/n?u=RePEc:hum:wpaper:sfb649dp2016-028&r=ene |
| By: | Johannes Emmerling (Fondazione Eni Enrico Mattei (FEEM) and Centro Euromediterraneo sui Cambiamenti Climatici (CMCC)); Vassiliki Manoussi (Fondazione Eni Enrico Mattei (FEEM)); Anastasios Xepapadeas (Athens University of Economics and Business) |
| Abstract: | Climate Engineering, and in particular Solar Radiation Management (SRM) has become a widely discussed climate policy option to study in recent years. However, its potentially strategic nature and unforeseen side effects provide major policy and scientific challenges. We study the role of the SRM implementation and its strategic dimension in a model with two heterogeneous countries with the notable feature of model misspecification on the impacts from SRM. We find that deep uncertainty leads to a reduction in SRM deployment both under cooperation and strategic behavior, which is a more relevant issue if countries act strategically. Furthermore, we demonstrate that the heterogeneity in impacts from SRM has an asymmetric effect on the optimal policy and could typically lead to unilateral SRM implementation. We also consider heterogeneous degrees of ambiguity aversion, in which case the more confident country only will use SRM. |
| Keywords: | Climate Change, Solar Radiation Management, Uncertainty, Robust Control, Differential Game |
| JEL: | Q53 Q54 |
| Date: | 2016–08 |
| URL: | http://d.repec.org/n?u=RePEc:fem:femwpa:2016.52&r=ene |
| By: | Matthew R. Sisco (Center for Research on Environmental Decisions, Columbia University); Valentina Bosetti (Bocconi University and Fondazione Eni Enrico Mattei); Elke U. Weber (Center for Research on Environmental Decisions, Columbia University) |
| Abstract: | We analyzed the effects of 10,748 weather events on attention to climate change between December 2011 and November 2014 in local areas across the United States. Attention was gauged by quantifying the relative increase in Twitter messages about climate change in the local area around the time of each event. Coastal floods, droughts, wildfires, strong wind, hail, excessive heat, extreme cold, and heavy snow events all had detectable effects. Attention was reliably higher directly after events began, compared to directly before. This suggests that actual experiences with extreme weather events are driving the increases in attention to climate change, beyond the purely descriptive information provided by the weather forecasts directly beforehand. Financial damage associated with the weather events had a positive and significant effect on attention, although the effect was small. The abnormality of each weather event’s occurrence compared to local historical activity was also a significant predictor. In particular and in line with past research, relative abnormalities in temperature (“local warming”) generated attention to climate change. In contrast, wind speed was predictive of attention to climate change in absolute levels. These results can be useful to predict short-term attention to climate change for strategic climate communications, and to better forecast long-term climate policy support. |
| Keywords: | Climate Attention, Social Media, Extreme Weather |
| JEL: | Q54 C81 D80 |
| Date: | 2016–08 |
| URL: | http://d.repec.org/n?u=RePEc:fem:femwpa:2016.53&r=ene |
| By: | Marcelo Ochoa (Federal Reserve Board of Governors); Dana Kiku (University of Ilinois); Ravi Bansal (Duke University) |
| Abstract: | We use the forward-looking information from the US and global capital markets to estimate the economic impact of long-run temperature fluctuations. We find that global warming has a significant negative effect on asset valuations and that temperature risks carry a negative price. We also find that the negative elasticity of equity prices to temperature risks have been increasing over time, which suggests that the impact of climate change on the macro-economy has been rising. We use our empirical evidence to calibrate a long-run risks model with temperature-induced disasters in future output and growth and quantify the social cost of carbon emissions. The model simultaneously matches the projected temperature path, the observed consumption growth dynamics, discount rates provided by the risk-free rate and equity market returns, and the estimated temperature elasticity of equity prices. We show that a preference for early resolution of uncertainty and long-run impact of temperature on growth imply a significant social cost of carbon emissions. |
| Date: | 2016 |
| URL: | http://d.repec.org/n?u=RePEc:red:sed016:542&r=ene |
| By: | Dipak Dasgupta (The Energy & Resource Institute (TERI)); Etienne Espagne (Centre d’Etudes Prospectives et d’Information Internationale (CEPII)); Jean-Charles Hourcade (Centre International de Recherche sur l’Environnement et le Développement (CIRED)); Irving Minzer (Johns Hopkins University, School of Advanced International Studies (SAIS)); Seyni Nafo (African Group at the UNFCCC); Baptiste Perissin-Fabert (Commissariat Général au Développement Durable (CGDD)); Nick Robins (Inquiry into the Design of a Sustainable Financial System (UNEP)); Alfredo Sirkis (Centro Brasil no Clima (CBC)) |
| Abstract: | Finance has been critical to the development of interest and momentum concerning the Paris Agreement, which emerged from COP21. However, a quick scan of the accord could lead many to derive a disappointing picture because of the absence of practical commitments to financial devices that can limit the risks of climate change. We support the opposite view that the text marks a new departure by committing countries to “making financial flows consistent with a pathway towards low greenhouse gas emissions and climate-resilient development ». This was matched by parallel developments such as the Financial Stability Board’s launch of a new Task Force on climate disclosure. We argue that, further steps now need to be taken within the broader context of financing the new model of prosperity laid out in the UN Sustainable Development Goals (UN, September 2015). At a time of increasing financial uncertainty and inadequate investment in the real economy, putting in place a framework for financing the transition to a low-carbon, resilient model of development is now an economic imperative – and an immense opportunity. Mitigating the systemic risks of climate change while putting the global financial system on a path toward balanced and sustainable development, is in the long-term strategic interests of both industrialized and developing countries and we suggest what practical steps can be accomplished in a near future in this direction. |
| Keywords: | COP 21, Paris Agreement, Climate Finance |
| JEL: | Q5 Q58 F53 |
| Date: | 2016–07 |
| URL: | http://d.repec.org/n?u=RePEc:fem:femwpa:2016.50&r=ene |
| By: | Samuel Fankhauser; Aditi Sahni; Annie Savvas; John Ward |
| Abstract: | Climate change cannot be addressed unless developed and developing countries alike invest heavily in low-carbon technologies and climate-resilient practices. Access to finance has therefore become central to climate change policy. In this Viewpoint we review likely climate investment needs and ask where the main financing gaps might be. We argue that besides the usual analysis of mitigation and adaptation needs, it is important to also gauge the ability of investors to mobilize the required funds. Some investors, whether public and private, will find it harder than others to raise capital, and so a rough |
| Keywords: | adaptation financing; climate change; climate policy; climate finance |
| JEL: | F3 G3 |
| Date: | 2016 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:64179&r=ene |