Methane emissions from the U.S. oil and natural gas supply chain were estimated by using ground-based, facility-scale measurements and validated with aircraft observations in areas accounting for ~30% of U.S. gas production. When scaled up nationally, our facility-based estimate of 2015 supply chain emissions is 13 ± 2 teragrams per year, equivalent to 2.3% of gross U.S. gas production. This value is ~60% higher than the U.S. Environmental Protection Agency inventory estimate, likely because existing inventory methods miss emissions released during abnormal operating conditions. Methane emissions of this magnitude, per unit of natural gas consumed, produce radiative forcing over a 20-year time horizon comparable to the CO from natural gas combustion. Substantial emission reductions are feasible through rapid detection of the root causes of high emissions and deployment of less failure-prone systems.

Multiple scientific studies suggest that methane emissions from natural gas systems could be larger than estimated in official inventories, with implications for the use of natural gas in sustainable energy systems. 2 Main Text:Natural gas emits less carbon dioxide during combustion than other fossil fuels and can be flexibly used in a variety of industries. This makes natural gas (NG) a potential "bridge fuel" during the transition to a decarbonized energy system. However, due to the high global warming potential (GWP) of methane (CH 4 ), climate benefits from NG depend on system leakage rates.Several recent estimates of leakage rates have challenged the benefits of fuel switching from coal to NG, a large near-term greenhouse gas (GHG) reduction opportunity (1-3). Policymakers require improved understanding of the leakage rates from NG systems. To this end, we review twenty years of scientific and technical literature on NG emissions. This study presents a first effort to systematically compare emissions estimates at scales ranging from devices (kg/y) to continent-wide atmospheric studies (Tg/y).We first present results from "top-down" studies which measure airborne methane concentrations. We then discuss "bottom-up" studies, which measure device-and facilitylevel leakage rates. We explore differences between study results, and discuss attribution of emissions to natural gas systems. Lastly, we examine implications for GHG emissions policies.Atmospheric studies employ aircraft (1, 4-7), tower (3, 5) and ground (3, 6-9) gas sampling, as well as remote sensing (6, 10,11). All such studies observe atmospheric concentrations, and must infer fluxes by accounting for atmospheric transport. Inference can be made using tracer-tracer correlations (2, 3, 6, 9,11,12), mass-balance (1, 13), and atmospheric modeling and inversion methods (4, 5, 7,14). Strengths and weaknesses exist with each approach (see SI).Figure 1 compiles published estimates of CH 4 leakage at all scales. It includes all known studies which a) performed measurements of emissions at some scale, and b) compared these measurements to inventories or established emissions factors. The ratio of observed emissions to the comparable emissions inventory is plotted on the x-axis, such that ratios >1 imply excess emissions observed relative to those expected.Figure 1 plots estimated CH 4 emissions from atmospheric studies above 10 10 gCH 4 /y. We include all atmospheric studies of CH 4 emissions -not just those that focus on NG -so as to bound emissions from NG. Across years, scales, and methods, these studies systematically find larger CH 4 emissions than predicted by inventories (ratios generally >1). Smaller-scale studies focusing on NG producing (1-3, 8) and consuming regions (2, 6,(9)(10)(11)14) find larger excess CH 4 emissions than national-level studies. This trend may be due to averaging effects of continental-scale atmospheric processes, or due to regional atmospheric studies focusing on areas with air quality problems, such as wintertime ozone (1, 3)....

Future energy systems may rely on natural gas as a low-cost fuel to support variable renewable power. However, leaking natural gas causes climate damage because methane (CH) has a high global warming potential. In this study, we use extreme-value theory to explore the distribution of natural gas leak sizes. By analyzing ∼15 000 measurements from 18 prior studies, we show that all available natural gas leakage data sets are statistically heavy-tailed, and that gas leaks are more extremely distributed than other natural and social phenomena. A unifying result is that the largest 5% of leaks typically contribute over 50% of the total leakage volume. While prior studies used log-normal model distributions, we show that log-normal functions poorly represent tail behavior. Our results suggest that published uncertainty ranges of CH emissions are too narrow, and that larger sample sizes are required in future studies to achieve targeted confidence intervals. Additionally, we find that cross-study aggregation of data sets to increase sample size is not recommended due to apparent deviation between sampled populations. Understanding the nature of leak distributions can improve emission estimates, better illustrate their uncertainty, allow prioritization of source categories, and improve sampling design. Also, these data can be used for more effective design of leak detection technologies.

We present in situ airborne measurements of methane (CH 4 ) and ethane (C 2 H 6 ) taken aboard a NOAA DHC-6 Twin Otter research aircraft in May 2014 over the Williston Basin in northwestern North Dakota, a region of rapidly growing oil and natural gas production. The Williston Basin is best known for the Bakken shale formation, from which a significant increase in oil and gas extraction has occurred since 2009. We derive a CH 4 emission rate from this region using airborne data by calculating the CH 4 enhancement flux through the planetary boundary layer downwind of the region. We calculate CH 4 emissions of (36 ± 13), (27 ± 13), (27 ± 12), (27 ± 12), and (25 ± 10) × 10 3 kg/h from five transects on 3 days in May 2014 downwind of the Bakken shale region of North Dakota. The average emission, (28 ± 5) × 10 3 kg/h, extrapolates to 0.25 ± 0.05 Tg/yr, which is significantly lower than a previous estimate of CH 4 emissions from northwestern North Dakota and southeastern Saskatchewan using satellite remote sensing data. We attribute the majority of CH 4 emissions in the region to oil and gas operations in the Bakken based on the similarity between atmospheric C 2 H 6 to CH 4 enhancement ratios and the composition of raw natural gas withdrawn from the region.

Oil and gas (O&G) well pads with high hydrocarbon emission rates may disproportionally contribute to total methane and volatile organic compound (VOC) emissions from the production sector. In turn, these emissions may be missing from most bottom-up emission inventories. We performed helicopter-based infrared camera surveys of more than 8000 O&G well pads in seven U.S. basins to assess the prevalence and distribution of high-emitting hydrocarbon sources (detection threshold ∼ 1-3 g s(-1)). The proportion of sites with such high-emitting sources was 4% nationally but ranged from 1% in the Powder River (Wyoming) to 14% in the Bakken (North Dakota). Emissions were observed three times more frequently at sites in the oil-producing Bakken and oil-producing regions of mixed basins (p < 0.0001, χ(2) test). However, statistical models using basin and well pad characteristics explained 14% or less of the variance in observed emission patterns, indicating that stochastic processes dominate the occurrence of high emissions at individual sites. Over 90% of almost 500 detected sources were from tank vents and hatches. Although tank emissions may be partially attributable to flash gas, observed frequencies in most basins exceed those expected if emissions were effectively captured and controlled, demonstrating that tank emission control systems commonly underperform. Tanks represent a key mitigation opportunity for reducing methane and VOC emissions.

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We present a theoretical framework to calculate how storage affects the energy return on energy investment (EROI) ratios of wind and solar resources. Our methods identify conditions under which it is more energetically favorable to store energy than it is to simply curtail electricity production. Electrochemically based storage technologies result in much smaller EROI ratios than large-scale geologically based storage technologies like compressed air energy storage (CAES) and pumped hydroelectric storage (PHS). All storage technologies paired with solar photovoltaic (PV) generation yield EROI ratios that are greater than curtailment. Due to their low energy stored on electrical energy invested (ESOI e) ratios, conventional battery technologies reduce the EROI ratios of wind generation below curtailment EROI ratios. To yield a greater net energy return than curtailment, battery storage technologies paired with wind generation need an ESOI e > 80. We identify improvements in cycle life as the most feasible way to increase battery ESOI e. Depending upon the battery's embodied energy requirement, an increase of cycle life to 10 000-18 000 (2-20 times present values) is required for pairing with wind (assuming liberal round-trip efficiency [90%] and liberal depth-of-discharge [80%] values). Reducing embodied energy costs, increasing efficiency and increasing depth of discharge will also further improve the energetic performance of batteries. While this paper focuses on only one benefit of energy storage, the value of not curtailing electricity generation during periods of excess production, similar analyses could be used to draw conclusions about other benefits as well. Broader context Rapid deployment of power generation technologies harnessing wind and solar resources continues to reduce the carbon intensity of the power grid. But as these technologies comprise a larger fraction of power supply, their variable nature poses challenges to power grid operation. Today, during times of power oversupply or unfavorable market conditions, power grid operators curtail these resources. Rates of curtailment are expected to increase with increased renewable electricity production. That is unless technologies are implemented that can provide grid exibility to balance power supply with power demand. Curtailment is an obvious forfeiture of energy and it increases the lifetime cost of electricity from curtailed generators. What are less obvious are the energetic costs for technologies that provide grid exibility. In this study we employ net energy analysis to compare the energetic cost of wind and solar generation curtailed at various rates to the energetic cost of those generators paired with storage. We nd that energetic cost depends on the generation technology, the storage technology, and the rate of curtailment. In some cases it is energetically favorable to store excess electricity. In other cases, it is favorable to curtail these resources. Our goal is to stimulate the identication of new and optimum uses for excess rene...

New data enable targeted policy to lessen GHG emissions