Sectors such as aviation may require low-carbon liquid fuels to dramatically reduce emissions. This analysis characterizes the economic viability of electrofuels, synthesized from CO₂ from direct air capture (DAC) and hydrogen from electrolysis of water, powered primarily by solar or wind electricity. This optimization-based techno-economic analysis suggests that using today's technology, hydrocarbon electrofuels would cost upward of $4/liter of gasoline equivalent (lge), potentially falling to $1.7-1.8/lge in the next decade and <$1/lge by 2050. Only in the latter case are electrofuels potentially less costly than using petroleum fuels offset with DAC with sequestration. Achieving low-end electrofuel costs is contingent on substantial reductions in the capital cost of DAC, electrolyzers, and renewable electricity generation. However, the system also requires sufficient operational flexibility to efficiently power this capital-intensive equipment on variable electricity. Such forms of flexibility include various types of storage, supplementary natural gas and grid electricity interconnections (penalized with a steep carbon price), curtailment, and the ability to modestly adjust fuel synthesis and DAC operating levels over time scales of several hours to days.

Electricity rate subsidies are an important policy mechanism to help low-income households afford necessary energy services, bringing substantial quality of life benefits to roughly 30% of California households. Resulting increases in consumption may unintentionally increase costly peak demand while also raising emissions of greenhouse gases and criteria air pollution from electricity generation, potentially motivating additional measures to shift consumption to hours with fewer unintended consequences. In a difference-in-differences study of interval data from over 30 000 northern California dwellings, we find that enrollment in the California Alternate Rates for Energy subsidy is associated with a ∼13% increase in electricity consumption, varying modestly across regions and seasons, increasing by an additional ∼3% during summer peak times. We find that peak demand costs associated with CARE are about $45 M per year but could fall by ∼1/3 with peak shifting to level demand.

Associated gas flaring during crude oil production is an important contributor to global warming. Satellite technology has made global flaring monitoring possible with high spatial resolution. In this study, we construct a granular database to geographically match global oil and gas fields with remote sensing flaring data from the Visible Infrared Imaging Radiometer Suite from 2012 to 2019. The geographic information system database contains over 50 000 oil and gas fields and around 4700 infrastructure sites (e.g. refineries, terminals) in 51 countries and regions, representing 96% of global oil production and 89% of natural gas production. Over 2900 fields and 140 infrastructure sites in 47 countries contain matching flares. The annual matched flare volume covers 89%-92% of the satellite-estimated flaring volume of these countries and 85%-87% of total worldwide volume detected by the satellite. In 2019, a set of 263 'high-flare' fields (which flare more than 0.1 billion cubic meters per year) account for 67% of the total matched satellite-estimated volume. These fields are mainly concentrated in the Persian Gulf, West and East Siberia, Eastern Venezuela Basin, Permian and Williston Basins in the United States, the Gulf of Mexico, and West and North Africa. Accounting for asymmetric instrument uncertainty suggests that country-level flaring rates are accurate to within -8% to +29%, the global average within 1%.

Natural gas distribution systems within municipalities supply a substantial fraction of energy consumed in the United States. As decarbonization of the natural gas system necessitates new modes of operation outside original design purposes, for example, increased hydrogen or biogas blending, it becomes increasingly important to understand in advance how existing infrastructure will respond to these changes. Such an analysis will require detailed information about the existing asset base, such as local soil composition, plastic type, and other characteristics that are not systematically tracked at present or have substantial missing data. Opportunistic sampling, for example, collecting measurements at assets that are already undergoing maintenance, has the potential to substantially reduce the cost of gathering such data but only if the results are representative of the full asset base. To assess prospects for such an approach, we employ a dataset including the entire service line and leak database from a large natural gas distribution utility (∼66,700 km of service pipelines and over 530,000 leaks over decades of observations). This dataset shows that service lines affected by excavation damage produce an approximately random sample of plastic and steel service lines, with similar distributions of component age, operating pressure, and pipeline diameter, as well as a relatively uniform spatial distribution. This means that opportunistic measurements at these locations will produce a first-order estimate of the relative prevalence of key characteristics across the utility's full asset base of service lines. We employ this approach to estimate the plastic type, which is unknown for roughly 80% of plastic service lines in the database. We also find that while 32% of leaks across all components occur in threaded steel junctions, excavation damage accounts for 75% of hazardous grade 1 leaks in plastic service lines and corrosion accounts for 47% in steel service lines. Insights from this sampling approach can thus help natural gas utilities collect the data they need to ensure a safe and reliable transition to a lower-emission system.

Abstract: Residential energy efficiency programs play an important role in combating climate change. More precise quantification of the magnitude and timing of energy savings would bring large system benefits, allowing closer integration of energy efficiency into resource adequacy planning and balancing variable renewable electricity. However, it is often difficult to quantify the efficacy of an energy efficiency intervention, because doing so requires consideration of a hypothetical counterfactual case in which there was no intervention, and randomized control trials are often implausible. Although quasi-experimental econometric evaluation sometimes works well, we find that for a set of energy efficiency rebate programs in Northern California, a naïve interpretation of econometric measurement finds that rebate participation is associated with an average increase in electricity consumption of 7.2% [4.5%, 10.1%], varying in magnitude and sign depending on the type of appliance or service covered by the rebate. A subsequent household survey on appliance purchasing behavior and analysis of utility customer outreach data suggest that this regression approach is likely measuring the gross impact of buying a new appliance but fails to adequately capture a counterfactual comparison. Indeed, it is unclear whether it is even possible to construct a suitable counterfactual for econometric analyses of these rebate programs using data generally available to electric utilities. We view these results as an illustration of a limitation of econometric methods of program evaluation and the importance of weighing engineering modeling and other imperfect methods against one another when attempting to provide useful evaluations of real-world policy interventions.

The rapid growth of liquefied natural gas (LNG) exports underscores the importance of CO2 monitoring for LNG export terminals. We present a method for measuring LNG terminal CO2 emissions using remote sensing imaging spectroscopy. The method is first validated using 47 power plant emission events with in situ measured data, then applied to 22 emission events in Sabine Pass and Cameron. The power plant data set shows a robust correlation between our estimates and in situ data, with R2 0.9146 and the average error −2%. At Sabine Pass, eight point sources are identified with emission rates from 219.69 ± 54.95 to 1,083.22 ± 308.06 t/hr. At Cameron, three point sources are identified with emission rates from 91.64 ± 25.81 to 265.61 ± 67.80 t/hr. The liquefaction carbon intensity estimates also align with past study ranges. This illustrates that remote sensing can validate environmental reporting and CO2 inventories for industrial facilities.

Abstract. Sentinel-2 satellite imagery has been shown by studies to be capable of detecting and quantifying methane emissions from oil and gas production. However, current methods lack performance calibration with ground-truth testing. This study developed a multi-band–multi-pass–multi-comparison-date methane retrieval algorithm that enhances Sentinel-2 sensitivity to methane plumes. The method was calibrated using data from a large-scale controlled-release test in Ehrenberg, Arizona, in fall 2021, with three algorithm parameters tuned based on the true emission rates. Tuned parameters are the pixel-level concentration upper-bound threshold during extreme value removal, the number of comparison dates, and the pixel-level methane concentration percentage threshold when determining the spatial extent of a plume. We found that a low value of the upper-bound threshold during extreme value removal can result in false negatives. A high number of comparison dates helps enhance the algorithm sensitivity to the plumes in the target date, but values in excess of 12 d are neither necessary nor computationally efficient. A high percentage threshold when determining the spatial extent of a plume helps enhance the quantification accuracy, but it may harm the yes/no detection accuracy. We found that there is a trade-off between quantification accuracy and detection accuracy. In a scenario with the highest quantification accuracy, we achieved the lowest quantification error and had zero false-positive detections; however, the algorithm missed three true plumes, which reduced the yes/no detection accuracy. In contrast, all of the true plumes were detected in the highest detection accuracy scenario, but the emission rate quantification had higher errors. We illustrated a two-step method that updates the emission rate estimates in an interim step, which improves quantification accuracy while keeping high yes/no detection accuracy. We also validated the algorithm's ability to detect true positives and true negatives in two application studies.

Methane leakage from point sources in the oil and gas industry is a major contributor to global greenhouse gas emissions. The majority of such emissions come from a small fraction of “super-emitting” sources. We evaluate the emission detection and quantification capabilities of Kairos Aerospace’s airplane-based hyperspectral imaging methane emission detection system for methane fluxes of 18–1,025 kg per hour of methane (kgh(CH4 )). In blinded controlled releases of methane conducted over 4 days in San Joaquin County, CA, Kairos detected 182 of 200 valid nonzero releases, including all 173 over 15 kgh(CH4 ) per meter per second (mps) of wind and none of the 12 nonzero releases below 8.3 kgh(CH4 )/mps. Nine of the 26 releases in the partial detection range of 5–15 kgh(CH4 )/mps were detected. There were no false positives: Kairos did not detect methane during any of the 21 negative controls. Plume quantification accuracy depends on the wind measurement technique, with a parity slope of 1.15 (s ¼ 0.037, R2 ¼ 0.84, N ¼ 185) using a cup-based wind meter and 1.45 (s ¼ 0.059, R2 ¼ 0.80, N ¼ 157) using an ultrasonic anemometer. Performance is comparable even with only modeled wind data. For emissions above 15 kgh/mps, quantification error scales as roughly 30%–40% of emission size, even when using wind reanalysis data instead of ground-based measurements. This reflects both uncertainty in wind measurements and in Kairos’ estimates. These findings suggest that at 2 mps winds under favorable environmental conditions in the United States, Kairos could detect and quantify over 50% of total emissions by identifying super-emitting sources.

Satellites are increasingly seen as a tool for identifying large greenhouse gas point sources for mitigation, but independent verification of satellite performance is needed for acceptance and use by policy makers and stakeholders. We conduct to our knowledge the first single-blind controlled methane release testing of satellite-based methane emissions detection and quantification, with five independent teams analyzing data from one to five satellites each for this desert-based test. Teams correctly identified 71% of all emissions, ranging from 0.20 [0.19, 0.21] metric tons per hour (t/h) to 7.2 [6.8, 7.6] t/h. Three-quarters (75%) of quantified estimates fell within ± 50% of the metered value, comparable to airplane-based remote sensing technologies. The relatively wide-area Sentinel-2 and Landsat 8 satellites detected emissions as low as 1.4 [1.3, 1.5, 95% confidence interval] t/h, while GHGSat's targeted system quantified a 0.20 [0.19, 0.21] t/h emission to within 13%. While the fraction of global methane emissions detectable by satellite remains unknown, we estimate that satellite networks could see 19-89% of total oil and natural gas system emissions detected in a recent survey of a high-emitting region.