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The cement industry is among the top three polluters among all industries and the examination of the nonlinear and cointegration dynamics between cement production and CO 2 emissions has not been explored. Focusing on this research gap, the study employs a novel Markov-switching autoregressive distributed lag (MS-ARDL) model and its generalization to vector error correction, the MS-VARDL model, for regime-dependent causality testing. The new method allows the determination of nonlinear long-run and short-run relations, regime duration, and cement-induced-CO 2 emission cycles in the USA for a historically long dataset covering 1900-2021. Empirical findings point to nonlinearity in all series and nonlinear cointegration between cement production and cement-induced CO 2 emissions. The phases of regimes coincide closely with NBER's official economic cycles for the USA. The second regime, characterized by expansions, lasts twice as long relative to the first, the contractionary regime, which contains severe economic recessions, as well as economic crises, the 1929 Great Depression, the 1973 Oil Crisis, the 2009 Great Recession, and the COVID-19 Shutdown and Wars, including WWI and II. In both regimes, the adverse effects of cement production on CO 2 emissions cannot be rejected with varying degrees both in the long and the short run. Markov regime-switching vector autoregressive distributed lag (MS-VARDL) causality tests confirm unidirectional causality from cement production to CO 2 emissions in both regimes. The traditional Granger causality test produces an over-acceptance of causality in a discussed set of cases. Industry-level policy recommendations include investments to help with the shift to green kiln technologies and energy efficiency. National-level policies on renewable energy and carbon capture are also vital considering the energy consumption of cement production., (© 2024. The Author(s).)

Recently, countries have been making intensive efforts to alleviate the burden on the environment and to make environmental conditions sustainable. In this context, our study aims to investigate the long-term impact of renewable energy consumption (REC) and human capital (HC) by considering the load capacity factor (LCF). We also investigate the long-term impact of economic growth (Y) and non-renewable energy consumption (NREC) on the LCF. In this context, we analyze annual data for the U.S. for the period 1965-2018 using the newly developed augmented ARDL (AARDL) approach. The long-term empirical results show the following. i) Increases in Y negatively affect LCF and deteriorate environmental quality. ii) Increases in NREC negatively affect LCF and accelerate the deterioration of environmental quality. iii) REC has no significant impact on environmental quality. iv) Increases in HC support the improvement of environmental quality. The empirical results show that contrary to expectations, renewable energy consumption does not have a significant impact on environmental quality in the U.S., whereas human capital is an important factor in improving environmental quality. In this context, US policymakers should pave the way for more investment in eco-friendly renewable energy investments and human capital to establish sustainable environmental quality. Policymakers should also take steps to reduce the use of fossil fuels., (© 2024. The Author(s).)

By considering the existence of two separate analysis families and the usage of different data frequencies, this study aims to examine the effect of method choice, data frequency, and sector-based energy consumption on carbon dioxide (CO 2 ) emissions by performing machine learning (ML) algorithms and time series econometric (TS) models simultaneously. In this situation, the study examines the United States (USA), considers sector-based energy consumption indicators as explanatory variables, uses monthly and yearly data between January 1973 and December 2021, estimates CO 2 emissions, and compares the estimation performance of the models. The empirical findings reveal that (i) the ML algorithms outperform the TS models based on R 2 and goodness of fit criteria; (ii) the estimation performance of the models increases with the high-frequency (i.e., monthly) data; (iii) the ML algorithms perform much better in case of high-frequency usage; (iv) some thresholds identify the effects of the sector-based energy consumption indicators on the CO 2 emissions; (v) electric power and transportation sectors are the most important sectors in the estimation of the CO 2 emissions for monthly and yearly data, respectively. Hence, the study provides to help the understanding role of method choice, data frequency, and sector-based energy consumption for the estimation of CO 2 emissions. Based on the results, this study proposes that US policymakers should consider the ML algorithms, use higher-frequency data, and include sector-based energy consumption indicators to have a better estimation of CO 2 emissions., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Photovoltaic is emerging as a cost-competitive source of energy generation and has experienced a decade of substantial cost decline. Recognizing that innovation in sustainable technologies can substantially contribute to the sustainable generation of energy, the federal government, universities, and industries in the USA have invested considerably in innovative solar technologies involving photovoltaic energy generation. However, the association between innovations in photovoltaic energy generation, distribution, or transmission-related technologies (IPVEGRT) and carbon dioxide emissions is unclear. The present study significantly contributes to energy economics by inspecting the nexus between IPVEGRT and carbon dioxide emissions, renewable energy consumption, the expansionary monetary policy, international collaboration in green technology development, gross domestic product per capita, and trade openness in the USA from 1990Q1 to 2018Q4. The results indicate that IPVEGRT helps reduce carbon dioxide emissions. International collaboration in green technology development and renewable energy consumption was negatively associated with carbon dioxide emissions, while expansionary monetary policy, gross domestic product per capita, and trade openness were positively associated with carbon dioxide emissions. The two-way causality between IPVEGRT and carbon dioxide emissions and between international collaboration in green technology development and carbon dioxide emissions was validated. Finally, a one-way causality between expansionary monetary policy, carbon dioxide emissions, gross domestic product per capita, and carbon dioxide emissions was validated., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

To achieve net-zero emissions by 2050, we need economic means of sequestering carbon dioxide (CO 2 ) and reducing greenhouse gas emissions (GHG). We analyze the sequestration potential of the Intermountain West (I-West) region, US, as a primary energy transition hub through analysis of wellbore retrofit potential and emission reduction in both fugitive gas abatement and flare gas. We selected the I-West region due to its abundant energy sources and oil and gas production legacy. Preliminary analysis hints that well retrofits can breathe new life into a well at a fraction of the cost of a new drill. With millions of potential candidates in the US, even a modest fraction (1% or less) suitable for retrofit could accelerate the shift to large-scale CO 2 sequestration. Fugitive gas, the unintentional release of wellbore gases such as methane, is a significant emissions source. Through conservative analysis, it is estimated that wellhead leakage alone may account for 5 million tonnes of carbon dioxide equivalent (CO 2 e) emissions. We conclude by assessing the CO 2 emissions from flaring, which is the burning of associated gas during well operations, conservative analysis indicates flaring contributes another 2 million tonnes of CO 2 emissions to the region. We find that with targeted retrofit and better controls on emissions sources, the I-West region can make a significant impact in the nation's push to become net-zero. This study outlines economic feasibility and actionable items to achieve the critical reductions in emissions and increases in sequestration necessary to attain net zero., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Published by Elsevier Ltd.)

Consistent with the increasing environmental interest, the clean energy transition is highly critical to achieving decarbonization targets. Also, energy security has become an important topic under the shadow of the energy crisis,. Accordingly, countries have been trying to stimulate clean energy use to preserve the environment and ensure energy security. So, considering the leading role of economic size and volume of energy use, the study examines the USA to define whether energy transition helps decrease energy security risk (ESR) and curb CO 2 emissions. So, the study applies a disaggregated level analysis by performing quantile-based models for the period from 2001/Q1 through 2022/Q4. The results demonstrate that (i) the energy transition index decreases environmental ESR at higher quantiles and reliability ESR at lower and middle quantiles, whereas it is not beneficial in declining economic and geopolitical ESR; (ii) energy transition curbs CO 2 emissions in building and transport sectors at lower quantiles, whereas it does not help decrease CO 2 emissions in industrial and power sectors; (iii) energy transition is mostly ineffective on ESR, whereas it is highly effective in curbing CO 2 emissions in all sectors except for transport across various quantiles as time passes; (iv) the results differ according to the aggregated and disaggregated levels; (v) the results are consistent across main and alternative models. Hence, the study highlights the dominant effect of energy transition in curbing sectoral CO 2 emissions rather than easing ESR. Accordingly, the study discusses various policy implications for the USA., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Wildfires are becoming more frequent due to the global climate change. Large amounts of greenhouse gases emitted by wildfires can lead to increases in extreme climate events. Accurately estimating the greenhouse gas carbon dioxide (CO2) emissions from wildfires is important for mitigation of climate change. In this paper, we develop a novel method to estimate wildfire CO2 emissions from the relationship between local CO2 emissions and XCO2 anomalies. Our method uses the WRF-Chem assimilation system from OCO-2 XCO2 retrievals which coupled with Data Assimilation Research Testbed (DART). To validate our results, we conducted three experiments evaluating the wildfire CO2 emissions over the conterminous United States. The four-month average wildfire emissions from July to October in 2015∼2018 were estimated at 4.408 Tg C, 1.784 Tg C, 1.514 Tg C and 2.873 Tg C, respectively. Compared to the average of established inventories CT2019B, FINNv1.5 and GFASv1.2 fire emissions, our estimates fall within one standard deviation, except for 2017 due to lacking of OCO-2 XCO2 retrievals. These results suggest that the regional carbon assimilation system, such as WRF-Chem/DART, using OCO-2 XCO2 retrievals has a great potential for accurately tracking regional wildfire emissions. [ABSTRACT FROM AUTHOR]

Extreme climate events including heat waves and droughts are projected to become more frequent under future climate change conditions. However, the mechanisms between soybean yields and climate factors, specifically involving variable rainfall and high heat episodes, are still unclear, particularly with respect to spatial trends in the United States (US) Midwest. A recently modified version of the model GLYCIM was used to evaluate rainfed soybean production across 12 states at a 10 km spatial resolution for three time periods (2011-2020, 2051-2060, 2091-2099) under Representative Concentration Pathway (RCP) scenarios 4.5 and 8.5. Results showed that except for the northernmost Midwest counties, most of the current rainfed cropping system in the Midwest would suffer a 24.6-47.4 % yield loss without considering the CO 2 fertility effect. Incorporating the effect of elevated CO 2 showed a smaller yield loss of 11.6-29.5 %. The increased frequency of extreme degree days (EDD) or accumulation of hourly temperatures above 30 °C associated with increased vapor pressure deficit (VPD) played a key role in contributing to water deficits and resultant crop losses under these future climate conditions. Although a relatively weak relationship between summer rainfall and crop yield was observed, decreased rainfall caused VPD to increase which induced crop water deficits. These findings suggest that it is crucial to consider VPD along with high temperature and low rainfall trends simultaneously for development of potential management or breeding-based adaptative strategies for soybean., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

This investigation explores whether sustainable finance and renewable energy could facilitate U.S. carbon neutrality. We perform the time-varying parameter-stochastic volatility-vector auto-regression (TVP-SV-VAR) model to obtain the changing relations among U.S. sustainable finance (SF), renewable energy (RE) and carbon dioxide emission (CO2). The empirical outcomes reveal a short-term negative effect from RE to CO2, indicating that renewable energy consumption could promote U.S. carbon neutrality. This effect is asymmetrical, and it could be observed that RE increase has a greater effect on CO2 than RE reduction. Also, the development of sustainable finance could facilitate U.S. carbon neutrality, and the direct impact is longer and more significant than RE, but the indirect effect of SF on CO2 by influencing RE is hysteretic. Besides, the asymmetric effect reveals that the negative direct impact of SF increase on CO2 is smaller than SF reduction, and the latter's indirect effect is more rapid than the former. Against the backdrop of global warming and frequent extreme weather, the above conclusions have meaningful practical applications for the U.S. to achieve carbon neutrality targets through developing sustainable finance and renewable energy., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

This paper based on quantile stochastic frontier framework constructs quantile eco-efficiency measures. Using the estimates from the quantile stochastic frontier, the eco-performance of the U.S. states for nitrogen oxides (NO X ), carbon dioxide (CO 2 ), and sulfur dioxide (SO 2 ) emissions is evaluated. A decoupling analysis involving the evaluation of the nonsynchronous change among states' economic output and environmental degradation levels is also performed. The findings suggest that U.S. states have followed a decoupling process among their GDP and emission levels over the period 1990-2017. In addition, a quantile eco-productivity change estimator is presented alongside with its main components (i.e. quantile eco-technical change and quantile eco-efficiency change). Our findings suggest that over the examined period states' eco-productivity levels have been improved driven both from their eco-technical and eco-efficiency change levels., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Inland waters (rivers, lakes, and reservoirs) have been recognized as hotspots of methane (CH 4 ) emissions. However, the magnitude and spatiotemporal pattern of CH 4 emissions and their underlying mechanisms remain largely unknown due to a lack of process-based quantification of CH 4 production, consumption, and evasion within the aquatic ecosystem. Here we developed a process-based aquatic CH 4 module within the framework of the Dynamic Land Ecosystem Model (DLEM) to explicitly simulate inland water carbon fluxes and the associated CH 4 processes. We further applied this model to assess the inland-water CH 4 emissions across the conterminous United States (CONUS) as affected by the climate variability, land use, fertilizer nitrogen (N) application, atmospheric N deposition, and rising atmospheric CO 2 concentration during 1860-2019. The inland water CH 4 emissions across the CONUS had doubled from the 1860s (1.65±0.18 Tg CH 4 -C∙yr -1 ) to the 2010s (3.73±0.36 Tg CH 4 -C∙yr -1 ). In the 2000s, inland water accounts for 8% of the regional CH 4 budget that offsets 11∼14% of the terrestrial C uptake across the CONUS. Our study showed that the small headwater streams (1 st -3 rd order) account for 49% of the diffusive CH 4 , and reservoirs constitute 50% of the ebullitive CH 4 emissions during the 2010s. Climate change and variability played a dominant role in the increased CH 4 emissions from rivers and lakes. This study implies that effective mitigation strategies to reduce CH4 emissions should pay much attention to global climate change and headwater stream management., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

This paper investigates carbon emission peak in China based on a comparative analysis of energy transition in China and the United States (US). The LMDI model is adopted to decompose carbon emissions into several driving factors in 2000-2018 for China and the US. Gray forecasting and NAR neural network are combined to predict peak time and identify optimal transition paths. The factor decomposition indicates that energy intensity is the main inhibitory factor for increased carbon emissions, while economic growth and population size are contributors for increased carbon emissions. There are significant differences in the impact of structure effect on carbon emissions in the two countries. The industry decomposition indicates that industry development is a critical inhibitor for increased carbon emissions after 2014 in China. The growth of transport and agriculture are basically contributing to increase carbon emissions in China and the US. The forecast results illustrate that China could complete carbon emission peak by 2030 under the baseline scenario, with a peak volume of 11354.72Mt CO 2 . Under the industrial structure adjustment scenario, the carbon peak year may be advanced to 2028. While adjusting industrial structure and energy consumption structure at the same time, China could achieve carbon emission peak at 9918.21Mt CO 2 in 2025., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Over the last half century in the United States, the per-hectare volume of wood in trees has increased, but it is not clear whether this increase has been driven by forest management, forest recovery from past land uses, such as agriculture, or other environmental factors such as elevated carbon dioxide, nitrogen deposition, or climate change. This paper uses empirical analysis to estimate the effect of elevated carbon dioxide on aboveground wood volume in temperate forests of the United States. To accomplish this, we employ matching techniques that allow us to disentangle the effects of elevated carbon dioxide from other environmental factors affecting wood volume and to estimate the effects separately for planted and natural stands. We show that elevated carbon dioxide has had a strong and consistently positive effect on wood volume while other environmental factors yielded a mix of both positive and negative effects. This study, by enabling a better understanding of how elevated carbon dioxide and other anthropogenic factors are influencing forest stocks, can help policymakers and other stakeholders better account for the role of forests in Nationally Determined Contributions and global mitigation pathways to achieve a 1.5 degree Celsius target., (© 2022. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

The visual analysis of carbon neutrality research can help better understand the development of the research field and explore the difficulties and hot spots in the research, thus making contributions to "carbon emission reduction," environmental protection and human health. This paper makes a visual quantitative analysis of 2,819 research papers published in top international journals from 2008 to 2021 in the WOS core database. It is found that China, the United States, Britain, and Germany are leading the way in carbon neutrality research. The research hotspots are mainly divided into three dimensions: (1) biomass energy and the negative effects it might bring; (2) ways and methods of electrochemical reduction of carbon dioxide; (3) catalysts and catalytic environment. The research mainly went through the conceptual period of 1997-2007, the exploration period of bioenergy from 2008 to 2021, the criticized period of bioenergy sources from 2011 to 2013, and the carbon dioxide electroreduction period from 2013 to the present. In the future, the research direction of biomass energy is to find one kind of biomass energy source which can be stored in a low-carbon way, produced in large quantities at a low cost, and will not occupy forestland. The electrolysis of water to produce hydrogen and the synthesis of fuel with CO 2 are two major research directions at present, whose aims are to find the suitable catalyst and environment for the reaction. Besides, more research can be done on "carbon neutrality" policies so as to reduce carbon dioxide emissions from the source, develop a low-carbon economy and protect human health., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Mao, Zhu, Ju, Bao and Xu.)

The development of globalization has separated the production and consumption of products spatially, and the international trade of products has become a carrier of embodied carbon trade. This paper adopted the perspective of value-added trade to calculate the amount of embodied carbon trade of China from 2006 to 2015 and perform a structural decomposition analysis of the changes in China's embodied carbon trade. This study found that: (1) China's embodied carbon exports are much larger than its embodied carbon imports, and there are differences between countries. China imported the largest amount of embodied carbon from South Korea, and it exported the largest amount of embodied carbon to the United States. (2) The structural decomposition analysis shows that changes in the value-added carbon emission coefficient during the study period would have caused China's embodied carbon trade to decrease, and changes in value-added trade would have caused China's embodied carbon trade to increase. Therefore, countries trading with China need to strengthen their cooperation with China in energy conservation, emission reduction, and product trade. In order to accurately reflect China's embodied carbon trade, it is necessary to calculate embodied carbon trade from the perspective of value-added trade., Competing Interests: The authors have declared that no competing interests exist.

This paper provides a review and comparison of strategies to increase forest carbon, and reduce species losses for climate change mitigation and adaptation in the United States. It compares forest management strategies and actions that are taking place or being proposed to reduce wildfire risk and to increase carbon storage with recent research findings. International agreements state that safeguarding biodiversity and ecosystems is fundamental to climate resilience with respect to climate change impacts on them, and their roles in adaptation and mitigation. The recent Intergovernmental Panel on Climate Change report on impacts, mitigation, and adaptation found, and member countries agreed, that maintaining the resilience of biodiversity and ecosystem services at a global scale is "fundamental" for climate mitigation and adaptation, and requires "effective and equitable conservation of approximately 30 to 50% of Earth's land, freshwater and ocean areas, including current near-natural ecosystems." Our key message is that many of the current and proposed forest management actions in the United States are not consistent with climate goals, and that preserving 30 to 50% of lands for their carbon, biodiversity and water is feasible, effective, and necessary for achieving them. [ABSTRACT FROM AUTHOR]

Methane (CH4) is a powerful greenhouse gas with a global warming potential 84 times higher than carbon dioxide (CO2) over 20 years. CH4 is produced from many natural and anthropogenic sources which can be further classified as biogenic or thermogenic in origin. The largest biogenic sources result from anaerobic decay such as wetlands, melting permafrost, or the breakdown of organic matter in the guts of ruminant animals. Thermogenic CH4 is generated during the breakdown of organic matter at high temperatures and pressure within the Earth's crust, a process which also produces more complex trace hydrocarbons such as ethane (C2H6) and propane (C3H8). Emissions of thermogenic CH4 are dominated by the fossil fuel energy sector, and the presence of elevated C2H6 along with CH4 can be used to distinguish oil and gas emissions from biogenic sources. This work outlines the development and deployment of an Unmanned Aerial System (UAS) outfitted with a fast (1 Hz) and sensitive (1–2 ppb s-1) CH4 & C2H6 sensor and ultrasonic anemometer. The UAV platform is a vertical-takeoff, hexarotor vehicle capable of vertical profiling to 120 m altitude and plume sampling across scales up to 1 km. This system has been used for direct quantification of point sources, as well as distributed emitters such as landfills, with source rates as low as 0.04 kg h-1 and up to 1500 kg h-1. Simultaneous measurements of CH4 and C2H6 mixing ratios, vector winds, and positional data allows for source classification (biogenic versus thermogenic), differentiation, and emission rates without the need for modeling or a priori assumptions about winds, vertical mixing, or other environmental conditions. The UAS has been deployed throughout the Southwest United States for system validation and targeted quantification of various sources emitting at or below the detection limits of other aircraft and satellite systems. This system offers a direct, repeatable method of horizontal and vertical profiling of emission plumes at scales that provide complementary information for regional aerial surveys as well as local ground-based monitoring. [ABSTRACT FROM AUTHOR]

By using space-based measurements of the column-averaged dry air mole fraction of carbon dioxide (XCO2) from the Orbiting Carbon Observatory-2 (OCO-2) and CO and NO2 from the Tropospheric Monitoring Instrument (TROPOMI), this study investigates the seasonal variation in the characteristics of CO2, CO, and NO2 across major states in the United States. Beyond correlating these trends with natural factors, significant emphasis is placed on human activities, including heating demands, energy usage, and the impacts of the COVID-19 pandemic. Concentration enhancements in observations influenced by anthropogenic emissions from urban regions relative to background values are calculated to estimate gas emissions. Our investigation reveals a strong correlation between NO2 and CO2 emissions, as evidenced by a correlation coefficient (r) of 0.75. Furthermore, we observe a correlation of 0.48 between CO2 and CO emissions and a weaker correlation of 0.37 between CO and NO2 emissions. Notably, we identify the NO2 concentration as a reliable indicator of CO2 emission levels, in which a 1% increase in NO2 concentration corresponds to a 0.8194% (±0.0942%) rise in annual mean CO2 emissions. Enhancement ratios among NO2, CO, and XCO2 are also calculated, uncovering that high ΔNO2: ΔXCO2 ratios often signify outdated industrial structures and production technologies, while low ΔCO: ΔXCO2 ratios are linked to states that utilize clean energy sources. This approach offers a deeper understanding of the effect of human activities on atmospheric gas concentrations, paving the way for more effective environmental monitoring and policy-making. [ABSTRACT FROM AUTHOR]

This paper studies the relationship between income inequality and carbon emissions in the United States between 1929 and 2019. We contribute to the literature by studying if and how the relationship has varied over time. To this end we estimate a band spectrum regression model and a smooth-varying coefficients model. Our results show that higher inequality was associated with lower emissions during the early part of the sample and higher emissions towards the end of the sample. • Whether the level of inequality affects carbon dioxide emssions is unclear. • We test the association between income inequalty and carbon dioxide emissions. • We find that the relationship varies over time. • Changes in consumption patterns may partially explain the time varying effects. [ABSTRACT FROM AUTHOR]

Streams and rivers are major sources of greenhouse gases (GHGs) to the atmosphere, as carbon and nitrogen are converted and outgassed during transport. Although our understanding of drivers of individual GHG fluxes has improved with numerous site‐specific studies and global‐scale compilations, our ability to parse out interrelated physical and biogeochemical drivers of gas concentrations is limited by a lack of consistently collected, temporally continuous samples of GHGs and their associated drivers. We present a first analysis of such a dataset collected by the National Ecological Observatory Network across 27 streams and rivers across ecoclimatic domains of the United States. Average concentrations of CO2 ranged from 36.9 ± 0.88 to 404 ± 33 μmol L−1, CH4 from 0.003 ± 0.0003 to 4.99 ± 0.72 μmol L−1, and N2O from 0.015 to 0.04 μmol L−1 and spanned ranges of previous global compilations. Both CO2 and CH4 were strongly affected by physical drivers including mean air temperature and stream slope, as well as by dissolved oxygen and total nitrogen concentrations. N2O was exclusively correlated with total nitrogen concentrations. Results suggested that potential for gas exchange dominated patterns in gas concentrations at the site level, but contributions of in‐stream aerobic and anaerobic metabolism, and groundwater also likely varied across sites. The highest gas concentrations as well as highest variability occurred in low‐gradient, warmer, and nonperennial systems. These results are a first step in providing unprecedented, continuous estimates of GHG flux constrained by temporally variable physical and biogeochemical drivers of GHG production. [ABSTRACT FROM AUTHOR]

The global carbon cycle is experiencing continued perturbations via increases in atmospheric carbon concentrations, which are partly reduced by terrestrial biosphere and ocean carbon uptake. Greenhouse gas satellites have been shown to be useful in retrieving atmospheric carbon concentrations and observing surface and atmospheric CO 2 seasonal-to-interannual variations. However, limited attention has been placed on using satellite column CO 2 retrievals to evaluate surface CO 2 fluxes from the terrestrial biosphere without advanced inversion models at low latency. Such applications could be useful to monitor, in near real time, biosphere carbon fluxes during climatic anomalies like drought, heatwaves, and floods, before more complex terrestrial biosphere model outputs and/or advanced inversion modelling estimates become available. Here, we explore the ability of Orbiting Carbon Observatory-2 (OCO-2) column-averaged dry air CO 2 (XCO 2) retrievals to directly detect and estimate terrestrial biosphere CO 2 flux anomalies using a simple mass-balance approach. An initial global analysis of surface–atmospheric CO 2 coupling and transport conditions reveals that the western US, among a handful of other regions, is a feasible candidate for using XCO 2 for detecting terrestrial biosphere CO 2 flux anomalies. Using the CarbonTracker model reanalysis as a test bed, we first demonstrate that a well-established mass-balance approach can estimate monthly surface CO 2 flux anomalies from XCO 2 enhancements in the western United States. The method is optimal when the study domain is spatially extensive enough to account for atmospheric mixing and has favorable advection conditions with contributions primarily from one background region. We find that errors in individual soundings reduce the ability of OCO-2 XCO 2 to estimate more frequent, smaller surface CO 2 flux anomalies. However, we find that OCO-2 XCO 2 can often detect and estimate large surface flux anomalies that leave an imprint on the atmospheric CO 2 concentration anomalies beyond the retrieval error/uncertainty associated with the observations. OCO-2 can thus be useful for low-latency monitoring of the monthly timing and magnitude of extreme regional terrestrial biosphere carbon anomalies. [ABSTRACT FROM AUTHOR]

High spatial resolution carbon dioxide (CO2) flux inversion systems are needed to support the global stocktake required by the Paris Agreement and to complement the bottom-up emission inventories. Based on the work of Zhang, a regional CO2 flux inversion system capable of assimilating the column-averaged dry air mole fractions of CO2 (XCO2) retrieved from Orbiting Carbon Observatory-2 (OCO-2) observations had been developed. To evaluate the system, under the constraints of the initial state and boundary conditions extracted from the CarbonTracker 2017 product (CT2017), the annual CO2 flux over the contiguous United States in 2016 was inverted (1.08 Pg C yr−1) and compared with the corresponding posterior CO2 fluxes extracted from OCO-2 model intercomparison project (OCO-2 MIP) (mean: 0.76 Pg C yr−1, standard deviation: 0.29 Pg C yr−1, 9 models in total) and CT2017 (1.19 Pg C yr−1). The uncertainty of the inverted CO2 flux was reduced by 14.71% compared to the prior flux. The annual mean XCO2 estimated by the inversion system was 403.67 ppm, which was 0.11 ppm smaller than the result (403.78 ppm) simulated by a parallel experiment without assimilating the OCO-2 retrievals and closer to the result of CT2017 (403.29 ppm). Independent CO2 flux and concentration measurements from towers, aircraft, and Total Carbon Column Observing Network (TCCON) were used to evaluate the results. Mean bias error (MBE) between the inverted CO2 flux and flux measurements was 0.73 g C m−2 d−1, was reduced by 22.34% and 28.43% compared to those of the prior flux and CT2017, respectively. MBEs between the CO2 concentrations estimated by the inversion system and concentration measurements from TCCON, towers, and aircraft were reduced by 52.78%, 96.45%, and 75%, respectively, compared to those of the parallel experiment. The experiment proved that CO2 emission hotspots indicated by the inverted annual CO2 flux with a relatively high spatial resolution of 50 km consisted well with the locations of most major metropolitan/urban areas in the contiguous United States, which demonstrated the potential of combing satellite observations with high spatial resolution CO2 flux inversion system in supporting the global stocktake. [ABSTRACT FROM AUTHOR]