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Emissions inevitably approach zero with a “carbon law” Although the Paris Agreement's goals ( 1 ) are aligned with science ( 2 ) and can, in principle, be technically and economically achieved ( 3 ), alarming inconsistencies remain between science-based targets and national commitments. Despite progress during the 2016 Marrakech climate negotiations, long-term goals can be trumped by political short-termism. Following the Agreement, which became international law earlier than expected, several countries published mid-century decarbonization strategies, with more due soon. Model-based decarbonization assessments ( 4 ) and scenarios often struggle to capture transformative change and the dynamics associated with it: disruption, innovation, and nonlinear change in human behavior. For example, in just 2 years, China's coal use swung from 3.7% growth in 2013 to a decline of 3.7% in 2015 ( 5 ). To harness these dynamics and to calibrate for short-term realpolitik, we propose framing the decarbonization challenge in terms of a global decadal roadmap based on a simple heuristic—a “carbon law”—of halving gross anthropogenic carbon-dioxide (CO 2 ) emissions every decade. Complemented by immediately instigated, scalable carbon removal and efforts to ramp down land-use CO 2 emissions, this can lead to net-zero emissions around mid-century, a path necessary to limit warming to well below 2°C.

abstract An up-to-date assessment of environmental emissions in the US health care sector is essential to help policy makers hold the health care industry accountable to protect public health. We update nationallevel US health-sector emissions. We also estimate state-level emissions for the first time and examine associations with state-level energy systems and health care quality and access metrics. Economywide modeling showed that US health care greenhouse gas emissions rose 6 percent from 2010 to 2018, reaching 1,692 kg per capita in 2018-the highest rate among industrialized nations. In 2018 greenhouse gas and toxic air pollutant emissions resulted in the loss of 388,000 disability-adjusted life-years. There was considerable variation in state-level greenhouse gas emissions per capita, which were not highly correlated with health system quality. These results suggest that the health care sector's outsize environmental footprint can be reduced without compromising quality. To reduce harmful emissions, the health care sector should decrease unnecessary consumption of resources, decarbonize power generation, and invest in preventive care. This will likely require mandatory reporting, benchmarking, and regulated accountability of health care organizations.

Global emissions of carbon dioxide (CO2) from fossil fuels and industry increased by 2.2% per year on average between 2005 and 20151. Global emissions need to peak and decline rapidly to limit climate change to well below 2 °C of warming2,3, which is one of the goals of the Paris Agreement4. Untangling the reasons underlying recent changes in emissions trajectories is critical to guide efforts to attain those goals. Here we analyse the drivers of decreasing CO2 emissions in a group of 18 developed economies that have decarbonized over the period 2005–2015. We show that within this group, the displacement of fossil fuels by renewable energy and decreases in energy use explain decreasing CO2 emissions. However, the decrease in energy use can be explained at least in part by a lower growth in gross domestic product. Correlation analysis suggests that policies on renewable energy are supporting emissions reductions and displacing fossil fuels in these 18 countries, but not elsewhere, and that policies on energy efficiency are supporting lower energy use in these 18 countries, as well as more widely. Overall, the evidence shows that efforts to reduce emissions are underway in many countries, but these efforts need to be maintained and enhanced by more stringent policy actions to support a global peak in emissions followed by global emissions reductions in line with the goals of the Paris Agreement3.Between 2005 and 2015, several developed economies experienced decreases in CO2 emissions. In this study, emissions in 18 countries are broken down and the potential effects of energy and climate policies on emission declines are explored.

Before the outbreak of COVID-19, the transport sector as a whole accounted for 24% of annual global emissions of carbon dioxide. Decisions could be informed by modelling delegates' journeys, as we did. Because air travel would still be necessary for most participants, virtual attendance should be considered instead of long-haul trips whenever possible. By following all three steps, we calculate that travel-related carbon emissions for the AGU Fall Meeting could be reduced by more than 90% if the meeting were held biennially in Chicago, and with about one-third of the participants, those responsible for most of the emissions, attending virtually. The downside is that this would exclude many scientists based outside the United States from attending in person, potentially resulting in a two-tier conference system and conflicting with aspirations for a global scientific community.

Beijing, the capital of China, is a densely populated city with poor air quality. The impact of high pollutant concentrations, in particular of aerosol particles, on human health is of major concern. The present study uses aerosol optical depth (AOD) as proxy to estimate long-term PM2.5 and subsequently estimates the premature mortality due to PM2.5. We use the AOD from 2001 to 2012 from the Aerosol Robotic Network (AERONET) site in Beijing and the ground-based PM2.5 observations from the US embassy in Beijing from 2010 to 2011 to establish a relationship between PM2.5 and AOD. By including the atmospheric boundary layer height and relative humidity in the comparative analysis, the correlation (R2) increases from 0.28 to 0.62. We evaluate 12 years of PM2.5 data for the Beijing central area using an estimated linear relationship with AOD and calculate the yearly premature mortality by different diseases attributable to PM2.5. The estimated average total mortality due to PM2.5 is about 5100 individuals per year for the period 2001–2012 in the Beijing central area, and for the period 2010–2012 the per capita mortality for all ages due to PM2.5 is around 15 per 10 000 person-years, which underscores the urgent need for air pollution abatement.

With the development of automation and informatization in the steelmaking industry, the human brain gradually fails to cope with an increasing amount of data generated during the steelmaking process. Machine learning technology provides a new method other than production experience and metallurgical principles in dealing with large amounts of data. The application of machine learning in the steelmaking process has become a research hotspot in recent years. This paper provides an overview of the applications of machine learning in the steelmaking process modeling involving hot metal pretreatment, primary steelmaking, secondary refining, and some other aspects. The three most frequently used machine learning algorithms in steelmaking process modeling are the artificial neural network, support vector machine, and case-based reasoning, demonstrating proportions of 56%, 14%, and 10%, respectively. Collected data in the steelmaking plants are frequently faulty. Thus, data processing, especially data cleaning, is crucially important to the performance of machine learning models. The detection of variable importance can be used to optimize the process parameters and guide production. Machine learning is used in hot metal pretreatment modeling mainly for endpoint S content prediction. The predictions of the endpoints of element compositions and the process parameters are widely investigated in primary steelmaking. Machine learning is used in secondary refining modeling mainly for ladle furnaces, Ruhrstahl–Heraeus, vacuum degassing, argon oxygen decarburization, and vacuum oxygen decarburization processes. Further development of machine learning in the steelmaking process modeling can be realized through additional efforts in the construction of the data platform, the industrial transformation of the research achievements to the practical steelmaking process, and the improvement of the universality of the machine learning models.

Examining the kinetics involved in the Argon Oxygen Decarburization (AOD) process, especially in the hotspot and emulsion zones within distinct reactors, can offer a deeper understanding of the refining mechanism in stainless-steelmaking. A predictive dynamic model has been formulated to estimate the effects of different refining processes, encompassing decarburization, desiliconization, demanganization, and chromium removal. The model includes a sub-model for heat loss calculation. The FactSage™ software, along with its macro programming capability, was utilized to incorporate thermochemical and kinetic information into the model. The model forecasts that the predominant chromium removal occurs within the hotspot zone, while carbon, silicon, and manganese removals occur in both the hotspot and emulsion zones. The predictions regarding the transient compositions of steel and slag, as well as the temperature of the steel bath, align with the plant data (Average of five heats), showcasing consistency.

A Computational Fluid Dynamics (CFD) model of the Argon Oxygen Decarburization (AOD) process with side gas injection is developed to investigate the influence of the gas nature (argon or oxygen) on the jet penetration depth, the bubble column behavior, and the local steel-gas interactions around the tuyere. The developed AOD model relies on separate mass, momentum and energy transport equations for the steel and gas phases. Additionally, the model addresses the heat generated from the carbon monoxide formation, distribute it on gas/steel interface. The gas properties (e.g. gas density) are calculated based on the ideal gas law and are influenced by the reaction heat source. Notably, this study represents the first consideration of the impact of the gas composition on the gas phase properties, including the gas-steel interfacial tension and the drag force. This approach can be utilized to simulate the different decarburization stages. This research suggests that different semi-empirical jet penetration models exhibit higher accuracy for Ar injection in liquid steel, while tending to overestimate O 2 jet penetration depth. The findings also highlight the significant influence of gas composition on gas dynamics within the bath and at the refractory interface, emphasizing the need for further investigation.

The aerosol optical depth (AOD) trend between 2001 and 2010 is estimated globally and regionally from observations and results from simulations with the EMAC (ECHAM5/MESSy Atmospheric Chemistry) model. Although interannual variability is applied only to anthropogenic and biomass-burning emissions, the model is able to quantitatively reproduce the AOD trends as observed by the MODIS (Moderate Resolution Imaging Spectroradiometer) satellite sensor, while some discrepancies are found when compared to MISR (Multi-angle Imaging SpectroRadiometer) and SeaWIFS (Sea-viewing Wide Field-of-view Sensor) observations. Thanks to an additional simulation without any change in emissions, it is shown that decreasing AOD trends over the US and Europe are due to the decrease in the emissions, while over the Sahara Desert and the Middle East region, the meteorological changes play a major role. Over Southeast Asia, both meteorology and emissions changes are equally important in defining AOD trends. Additionally, decomposing the regional AOD trends into individual aerosol components reveals that the soluble components are the most dominant contributors to the total AOD, as their influence on the total AOD is enhanced by the aerosol water content.

Gas-blowing technology is widely used in converter steelmaking to homogenize liquid steel and accelerate chemical reactions, with Argon oxygen decarburization (AOD) being the dominant process for stainless steelmaking. Due to the harsh environment, it is advisable to study the phenomenon using small-scale physical models and numerical simulations before conducting industrial-scale trials. This paper presents a practical computational fluid dynamics (CFD) approach for simulating the AOD process, with chemical reactions considered. This approach can simulate the entire process in a reasonable time using a standard workstation. The simulation employs a Finite Volume Method CFD approach to handle mass, momentum, and energy transfer, and a local equilibrium assumption is utilized. The study shows that a practical approach can be used to model the initial stage of decarburization in the AOD process with a reduced accuracy in mass transport calculations. The accuracy of the simulation is validated using industrial data, and good agreement is found.