Climate Impacts of Hydrogen and Methane Emissions Can Considerably Reduce the Climate Benefits across Key Hydrogen Use Cases and Time Scales.

Recent investments in "clean" hydrogen as an alternative to fossil fuels are driven by anticipated climate benefits. However, most climate benefit calculations do not adequately account for all climate warming emissions and impacts over time. This study reanalyzes a previously published life cycle assessment as an illustrative example to show how the climate impacts of hydrogen deployment can be far greater than expected when including the warming effects of hydrogen emissions, observed methane emission intensities, and near-term time scales; this reduces the perceived climate benefits upon replacement of fossil fuel technologies. For example, for blue (natural gas with carbon capture) hydrogen pathways, the inclusion of upper-end hydrogen and methane emissions can yield an increase in warming in the near term by up to 50%, whereas lower-end emissions decrease warming impacts by at least 70%. For green (renewable-based electrolysis) hydrogen pathways, upper-end hydrogen emissions can reduce climate benefits in the near term by up to 25%. We also consider renewable electricity availability for green hydrogen and show that if it is not additional to what is needed to decarbonize the electric grid, there may be more warming than that seen with fossil fuel alternatives over all time scales. Assessments of hydrogen's climate impacts should include the aforementioned factors if hydrogen is to be an effective decarbonization tool.

Improved assessment of baseline and additionality for forest carbon crediting.

In the California compliance cap-and-trade carbon market, improved forest management (IFM) projects generate carbon credits in the initial reporting period if their initial carbon stocks are greater than a baseline. This baseline is informed by a "common practice" stocking value, which represents the average carbon stocks of surveyed privately owned forests that are classified into the same general forest type by the California Air Resources Board. Recent work has called attention to the need for more ecologically informed common practice carbon stocking values for IFM projects, particularly those in areas with sharp ecological gradients. Current methods for estimating common practice produce biases in baseline carbon values that lead to a clustering of IFM projects in geographical areas and ecosystem types that in fact support much greater forest carbon stocks than reflected in the common practice. This phenomenon compromises additionality, or the increases in carbon sequestration or decreases in carbon emissions that would not have occurred in the absence of carbon crediting. This study seeks to expand upon recent work on this topic and establish unbiased common practice estimates along sharp ecological gradients using methods that do not rely upon discrete forest classification. We generated common practice values for credited IFM projects in the Southern Cascades using a principal components analysis on species composition over an extensive forest inventory to determine the ecological similarity between inventoried forests and IFM project sites. Our findings strengthen the results of recent research suggesting common practice bias and adverse selection. At several sites, even after controlling for private ownership, 100% of the initial carbon stocks could be explained by ecological variables. This result means that improved management did not preserve or increase carbon stocks above what was typical, suggesting that no carbon offsets should have been issued for these sites. This result reveals greater bias than that been found at project sites in this region by research that has used discrete forest categorization.

Land management can contribute to net zero.

The voluntary carbon market needs to embrace changes for the land sector.

Methane emissions from US low production oil and natural gas well sites.

Eighty percent of US oil and natural gas (O&G) production sites are low production well sites, with average site-level production ≤15 barrels of oil equivalent per day and producing only 6% of the nation's O&G output in 2019. Here, we integrate national site-level O&G production data and previously reported site-level CH4 measurement data (n = 240) and find that low production well sites are a disproportionately large source of US O&G well site CH4 emissions, emitting more than 4 (95% confidence interval: 3-6) teragrams, 50% more than the total CH4 emissions from the Permian Basin, one of the world's largest O&G producing regions. We estimate low production well sites represent roughly half (37-75%) of all O&G well site CH4 emissions, and a production-normalized CH4 loss rate of more than 10%-a factor of 6-12 times higher than the mean CH4 loss rate of 1.5% for all O&G well sites in the US. Our work suggests that achieving significant reductions in O&G CH4 emissions will require mitigation of emissions from low production well sites.

Path to net zero is critical to climate outcome.

Net zero greenhouse gas targets have become a central element for climate action. However, most company and government pledges focus on the year that net zero is reached, with limited awareness of how critical the emissions pathway is in determining the climate outcome in both the near- and long-term. Here we show that different pathways of carbon dioxide and methane-the most prominent long-lived and short-lived greenhouse gases, respectively-can lead to nearly 0.4 °C of warming difference in midcentury and potential overshoot of the 2 °C target, even if they technically reach global net zero greenhouse gas emissions in 2050. While all paths achieve the Paris Agreement temperature goals in the long-term, there is still a 0.2 °C difference by end-of-century. We find that early action to reduce both emissions of carbon dioxide and methane simultaneously leads to the best climate outcomes over all timescales. We therefore recommend that companies and countries supplement net zero targets with a two-basket set of interim milestones to ensure that early action is taken for both carbon dioxide and methane. A one-basket approach, such as the standard format for Nationally Determined Contributions, is not sufficient because it can lead to a delay in methane mitigation.

Quantifying methane emissions from the largest oil-producing basin in the United States from space.

Using new satellite observations and atmospheric inverse modeling, we report methane emissions from the Permian Basin, which is among the world's most prolific oil-producing regions and accounts for >30% of total U.S. oil production. Based on satellite measurements from May 2018 to March 2019, Permian methane emissions from oil and natural gas production are estimated to be 2.7 ± 0.5 Tg a-1, representing the largest methane flux ever reported from a U.S. oil/gas-producing region and are more than two times higher than bottom-up inventory-based estimates. This magnitude of emissions is 3.7% of the gross gas extracted in the Permian, i.e., ~60% higher than the national average leakage rate. The high methane leakage rate is likely contributed by extensive venting and flaring, resulting from insufficient infrastructure to process and transport natural gas. This work demonstrates a high-resolution satellite data-based atmospheric inversion framework, providing a robust top-down analytical tool for quantifying and evaluating subregional methane emissions.

A National Estimate of Methane Leakage from Pipeline Mains in Natural Gas Local Distribution Systems.

We estimate methane emissions from U.S. local distribution natural gas (NG) pipes using data collected from an advanced mobile leak detection (AMLD) platform. We estimate that there are 630,000 leaks in U.S. distribution mains, resulting in methane emissions of 0.69 Tg/year (95% cr int: 0.25, 1.23). Total emissions are calculated as the product of activity factors and emissions factors. Our analysis leveraged data on >4000 leak indications found using AMLD, combined with utility pipeline GIS information, to allow us to estimate activity factors. We derive emissions factors from AMLD emission rate estimates and correct these emissions factors based on data from in-field studies assessing AMLD emissions estimates. Finally, we quantify uncertainty in both emissions factors and activity factors and propagate the uncertainty to our total emissions estimate. In modeling leak frequency, we find a clear interaction between pipeline material and age with the leakiness of all material types increasing with age. Our national methane emissions estimate is approximately 5× greater (95% cr int: 1.7×, 8.7×) than the U.S. Environmental Protection Agency's current greenhouse gas inventory estimate for pipeline mains in local distribution systems due to both a larger estimated number of leaks and better characterization of the upper tail of the skewed distribution of emission rates.

Characterizing Elevated Urban Air Pollutant Spatial Patterns with Mobile Monitoring in Houston, Texas.

Diverse urban air pollution sources contribute to spatially variable atmospheric concentrations, with important public health implications. Mobile monitoring shows promise for understanding spatial pollutant patterns, yet it is unclear whether uncertainties associated with temporally sparse sampling and instrument performance limit our ability to identify locations of elevated pollution. To address this question, we analyze 9 months of repeated weekday daytime on-road mobile measurements of black carbon (BC), particle number (PN), and nitrogen oxide (NO, NO2) concentrations within 24 census tracts across Houston, Texas. We quantify persistently elevated, intermittent, and extreme concentration behaviors at 50 m road segments on surface streets and 90 m segments on highways relative to median statistics across the entire sampling domain. We find elevated concentrations above uncertainty levels (±40%) within portions of every census tract, with median concentration increases ranging from 2 to 3× for NO2, and >9× for NO. In contrast, PN exhibits elevated concentrations of 1.5-2× the domain-wide median and distinct spatial patterns relative to other pollutants. Co-located elevated concentrations of primary combustion tracers (BC and NOx) near 30% of metal recycling and concrete batch plant facilities within our sampled census tracts are comparable to those measured within 200 m of highways. Our results demonstrate how extensive mobile monitoring across multiple census tracts can quantitatively characterize urban air pollution source patterns and are applicable to developing effective source mitigation policies.

Satellite observations reveal extreme methane leakage from a natural gas well blowout.

Methane emissions due to accidents in the oil and natural gas sector are very challenging to monitor, and hence are seldom considered in emission inventories and reporting. One of the main reasons is the lack of measurements during such events. Here we report the detection of large methane emissions from a gas well blowout in Ohio during February to March 2018 in the total column methane measurements from the spaceborne Tropospheric Monitoring Instrument (TROPOMI). From these data, we derive a methane emission rate of 120 ± 32 metric tons per hour. This hourly emission rate is twice that of the widely reported Aliso Canyon event in California in 2015. Assuming the detected emission represents the average rate for the 20-d blowout period, we find the total methane emission from the well blowout is comparable to one-quarter of the entire state of Ohio's reported annual oil and natural gas methane emission, or, alternatively, a substantial fraction of the annual anthropogenic methane emissions from several European countries. Our work demonstrates the strength and effectiveness of routine satellite measurements in detecting and quantifying greenhouse gas emission from unpredictable events. In this specific case, the magnitude of a relatively unknown yet extremely large accidental leakage was revealed using measurements of TROPOMI in its routine global survey, providing quantitative assessment of associated methane emissions.

Climate Impacts of Hydropower: Enormous Differences among Facilities and over Time.

To stabilize the climate, we must rapidly displace fossil fuels with clean energy technologies. Currently hydropower dominates renewable electricity generation, accounting for two-thirds globally, and is expected to grow by at least 45% by 2040. While it is broadly assumed that hydropower facilities emit greenhouse gases on par with wind, there is mounting evidence that emissions can be considerably greater, with some facilities even on par with fossil fuels. However, analyses of climate impacts of hydropower plants have been simplistic, emphasizing the aggregated 100-year impacts from a one-year pulse of emissions. Such analyses mask the near-term impacts of methane emissions central to many current policy regimes, have tended to omit carbon dioxide emissions associated with initial plant development, and have not considered the impact of the accumulation of gases in the atmosphere over time. We utilize an analytic approach that addresses these issues. By analyzing climate impacts of sustained hydropower emissions over time, we find that there are enormous differences in climate impacts among facilities and over time. If minimizing climate impacts are not a priority in the design and construction of new hydropower facilities, it could lead to limited or even no climate benefits.

Mapping Air Pollution with Google Street View Cars: Efficient Approaches with Mobile Monitoring and Land Use Regression.

Air pollution measurements collected through systematic mobile monitoring campaigns can provide outdoor concentration data at high spatial resolution. We explore approaches to minimize data requirements for mapping a city's air quality using mobile monitors with "data-only" versus predictive modeling approaches. We equipped two Google Street View cars with 1-Hz instruments to collect nitric oxide (NO) and black carbon (BC) measurements in Oakland, CA. We explore two strategies for efficiently mapping spatial air quality patterns through Monte Carlo analyses. First, we explore a "data-only" approach where we attempt to minimize the number of repeated visits needed to reliably estimate concentrations for all roads. Second, we combine our data with a land use regression-kriging (LUR-K) model to predict at unobserved locations; here, measurements from only a subset of roads or repeat visits are considered. Although LUR-K models did not capture the full variability of on-road concentrations, models trained with minimal data consistently captured important covariates and general spatial air pollution trends, with cross-validation R2 for log-transformed NO and BC of 0.65 and 0.43. Data-only mapping performed poorly with few (1-2) repeated drives but obtained better cross-validation R2 than the LUR-K approach within 4 to 8 repeated drive days per road segment.

High nitrous oxide fluxes from rice indicate the need to manage water for both long- and short-term climate impacts.

Global rice cultivation is estimated to account for 2.5% of current anthropogenic warming because of emissions of methane (CH4), a short-lived greenhouse gas. This estimate assumes a widespread prevalence of continuous flooding of most rice fields and hence does not include emissions of nitrous oxide (N2O), a long-lived greenhouse gas. Based on the belief that minimizing CH4 from rice cultivation is always climate beneficial, current mitigation policies promote increased use of intermittent flooding. However, results from five intermittently flooded rice farms across three agroecological regions in India indicate that N2O emissions per hectare can be three times higher (33 kg-N2O⋅ha-1⋅season-1) than the maximum previously reported. Correlations between N2O emissions and management parameters suggest that N2O emissions from rice across the Indian subcontinent might be 30-45 times higher under intensified use of intermittent flooding than under continuous flooding. Our data further indicate that comanagement of water with inorganic nitrogen and/or organic matter inputs can decrease climate impacts caused by greenhouse gas emissions up to 90% and nitrogen management might not be central to N2O reduction. An understanding of climate benefits/drawbacks over time of different flooding regimes because of differences in N2O and CH4 emissions can help select the most climate-friendly water management regimes for a given area. Region-specific studies of rice farming practices that map flooding regimes and measure effects of multiple comanaged variables on N2O and CH4 emissions are necessary to determine and minimize the climate impacts of rice cultivation over both the short term and long term.

Assessment of methane emissions from the U.S. oil and gas supply chain.

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 CO2 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.

Impacts of increasing typhoons on the structure and function of a subtropical forest: reflections of a changing climate.

Due to their destructive and sporadic nature, it is often difficult to evaluate and predict the effects of typhoon on forest ecosystem patterns and processes. We used a 21-yr record of litterfall rates to explore the influence of typhoon frequency and intensity, along with other meteorological variables, on ecosystem dynamics in a subtropical rainforest. Over the past half century there has been an increasing frequency of strong typhoons (category 3; >49.6 m s-1; increase of 1.5 typhoons/decade) impacting the Fushan Experimental Forest, Taiwan. At Fushan strong typhoons drive total litterfall mass with an average of 1100 kg ha-1 litterfall typhoon-1. While mean typhoon season litterfall has been observed to vary by an order of magnitude, mean litterfall rates associated with annual leaf senescence vary by <20%. In response to increasing typhoon frequency, total annual litter mass increased gradually over the 21-year record following three major typhoons in 1994. Monthly maximum wind speed was predictive of monthly litterfall, yet the influence of precipitation and temperature was only evident in non-typhoon affected months. The response of this subtropical forest to strong typhoons suggests that increasing typhoon frequency has already shifted ecosystem structure and function (declining carbon sequestration and forest stature).

High-Resolution Air Pollution Mapping with Google Street View Cars: Exploiting Big Data.

Air pollution affects billions of people worldwide, yet ambient pollution measurements are limited for much of the world. Urban air pollution concentrations vary sharply over short distances (≪1 km) owing to unevenly distributed emission sources, dilution, and physicochemical transformations. Accordingly, even where present, conventional fixed-site pollution monitoring methods lack the spatial resolution needed to characterize heterogeneous human exposures and localized pollution hotspots. Here, we demonstrate a measurement approach to reveal urban air pollution patterns at 4-5 orders of magnitude greater spatial precision than possible with current central-site ambient monitoring. We equipped Google Street View vehicles with a fast-response pollution measurement platform and repeatedly sampled every street in a 30-km2 area of Oakland, CA, developing the largest urban air quality data set of its type. Resulting maps of annual daytime NO, NO2, and black carbon at 30 m-scale reveal stable, persistent pollution patterns with surprisingly sharp small-scale variability attributable to local sources, up to 5-8× within individual city blocks. Since local variation in air quality profoundly impacts public health and environmental equity, our results have important implications for how air pollution is measured and managed. If validated elsewhere, this readily scalable measurement approach could address major air quality data gaps worldwide.