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Uncertainty estimation is an indispensable capability for AI-enabled, safety-critical applications, e.g. autonomous vehicles or medical diagnosis. Bayesian neural networks (BNNs) use Bayesian statistics to provide both classification predictions and uncertainty estimation, but they suffer from high computational overhead associated with random number generation and repeated sample iterations. Furthermore, BNNs are not immediately amenable to acceleration through compute-in-memory architectures due to the frequent memory writes necessary after each RNG operation. To address these challenges, we present an ASIC that integrates 360 fJ/Sample Gaussian RNG directly into the SRAM memory words. This integration reduces RNG overhead and enables fully-parallel compute-in-memory operations for BNNs. The prototype chip achieves 5.12 GSa/s RNG throughput and 102 GOp/s neural network throughput while occupying 0.45 mm2, bringing AI uncertainty estimation to edge computation., Comment: 7 pages, 12 figures

With the urgent need to implement the EU countries pledges and to monitor the effectiveness of Green Deal plan, Monitoring Reporting and Verification tools are needed to track how emissions are changing for all the sectors. Current official inventories only provide annual estimates of national CO$_2$ emissions with a lag of 1+ year which do not capture the variations of emissions due to recent shocks including COVID lockdowns and economic rebounds, war in Ukraine. Here we present a near-real-time country-level dataset of daily fossil fuel and cement emissions from January 2019 through December 2021 for 27 EU countries and UK, which called Carbon Monitor Europe. The data are calculated separately for six sectors: power, industry, ground transportation, domestic aviation, international aviation and residential. Daily CO$_2$ emissions are estimated from a large set of activity data compiled from different sources. The goal of this dataset is to improve the timeliness and temporal resolution of emissions for European countries, to inform the public and decision makers about current emissions changes in Europe.

We present a near-real-time global gridded daily CO$_2$ emissions dataset (GRACED) throughout 2021. GRACED provides gridded CO$_2$ emissions at a 0.1degree*0.1degree spatial resolution and 1-day temporal resolution from cement production and fossil fuel combustion over seven sectors, including industry, power, residential consumption, ground transportation, international aviation, domestic aviation, and international shipping. GRACED is prepared from a near-real-time daily national CO$_2$ emissions estimates (Carbon Monitor), multi-source spatial activity data emissions and satellite NO$_2$ data for time variations of those spatial activity data. GRACED provides the most timely overview of emissions distribution changes, which enables more accurate and timely identification of when and where fossil CO$_2$ emissions have rebounded and decreased. Uncertainty analysis of GRACED gives a grid-level two-sigma uncertainty of value of 19.9% in 2021, indicating the reliability of GRACED was not sacrificed for the sake of higher spatiotemporal resolution that GRACED provides. Continuing to update GRACED in a timely manner could help policymakers monitor energy and climate policies' effectiveness and make adjustments quickly.

We constructed a frequently updated, near-real-time global power generation dataset: Carbon Monitor-Power since January, 2016 at national levels with near-global coverage and hourly-to-daily time resolution. The data presented here are collected from 37 countries across all continents for eight source groups, including three types of fossil sources (coal, gas, and oil), nuclear energy and four groups of renewable energy sources (solar energy, wind energy, hydro energy and other renewables including biomass, geothermal, etc.). The global near-real-time power dataset shows the dynamics of the global power system, including its hourly, daily, weekly and seasonal patterns as influenced by daily periodical activities, weekends, seasonal cycles, regular and irregular events (i.e., holidays) and extreme events (i.e., the COVID-19 pandemic). The Carbon Monitor-Power dataset reveals that the COVID-19 pandemic caused strong disruptions in some countries (i.e., China and India), leading to a temporary or long-lasting shift to low carbon intensity, while it had only little impact in some other countries (i.e., Australia). This dataset offers a large range of opportunities for power-related scientific research and policy-making.

Building on near-real-time and spatially explicit estimates of daily carbon dioxide (CO2) emissions, here we present and analyze a new city-level dataset of fossil fuel and cement emissions. Carbon Monitor Cities provides daily, city-level estimates of emissions from January 2019 through December 2021 for 1500 cities in 46 countries, and disaggregates five sectors: power generation, residential (buildings), industry, ground transportation, and aviation. The goal of this dataset is to improve the timeliness and temporal resolution of city-level emission inventories and includes estimates for both functional urban areas and city administrative areas that are consistent with global and regional totals. Comparisons with other datasets (i.e. CEADs, MEIC, Vulcan, and CDP) were performed, and we estimate the overall uncertainty to be 21.7%. Carbon Monitor Cities is a near-real-time, city-level emission dataset that includes cities around the world, including the first estimates for many cities in low-income countries. A more complete description of this dataset is published in Scientific Data (https://doi.org/10.1038/s41597-022-01657-z).

Precise and high-resolution carbon dioxide (CO$_2$) emission data is of great importance of achieving the carbon neutrality around the world. Here we present for the first time the near-real-time Global Gridded Daily CO$_2$ Emission Datasets (called GRACED) from fossil fuel and cement production with a global spatial-resolution of 0.1$^\circ$ by 0.1$^\circ$ and a temporal-resolution of 1-day. Gridded fossil emissions are computed for different sectors based on the daily national CO$_2$ emissions from near real time dataset (Carbon Monitor), the spatial patterns of point source emission dataset Global Carbon Grid (GID), Emission Database for Global Atmospheric Research (EDGAR) and spatiotemporal patters of satellite nitrogen dioxide (NO$_2$) retrievals. Our study on the global CO$_2$ emissions responds to the growing and urgent need for high-quality, fine-grained near-real-time CO2 emissions estimates to support global emissions monitoring across various spatial scales. We show the spatial patterns of emission changes for power, industry, residential consumption, ground transportation, domestic and international aviation, and international shipping sectors between 2019 and 2020. This help us to give insights on the relative contributions of various sectors and provides a fast and fine-grained overview of where and when fossil CO$_2$ emissions have decreased and rebounded in response to emergencies (e.g. COVID-19) and other disturbances of human activities than any previously published dataset. As the world recovers from the pandemic and decarbonizes its energy systems, regular updates of this dataset will allow policymakers to more closely monitor the effectiveness of climate and energy policies and quickly adapt.

As the COVID-19 virus spread over the world, governments restricted mobility to slow transmission. Public health measures had different intensities across European countries but all had significant impact on peoples daily lives and economic activities, causing a drop of CO2 emissions of about 10% for the whole year 2020. Here, we analyze changes in natural gas use in the industry and built environment sectors during the first half of year 2020 with daily gas flows data from pipeline and storage facilities in Europe. We find that reductions of industrial gas use reflect decreases in industrial production across most countries. Surprisingly, natural gas use in buildings also decreased despite most people being confined at home and cold spells in March 2020. Those reductions that we attribute to the impacts of COVID-19 remain of comparable magnitude to previous variations induced by cold or warm climate anomalies in the cold season. We conclude that climate variations played a larger role than COVID-19 induced stay-home orders in natural gas consumption across Europe.

In October of 2020, China announced that it aims to start reducing its carbon dioxide (CO2) emissions before 2030 and achieve carbon neutrality before 20601. The surprise announcement came in the midst of the COVID-19 pandemic which caused a transient drop in China's emissions in the first half of 2020. Here, we show an unprecedented de-carbonization of China's power system in late 2020: although China's power related carbon emissions were 0.5% higher in 2020 than 2019, the majority (92.9%) of the increased power demand was met by increases in low-carbon (renewables and nuclear) generation (increased by 9.3%), as compared to only 0.4% increase for fossil fuels. China's low-carbon generation in the country grew in the second half of 2020, supplying a record high of 36.7% (increased by 1.9% compared to 2019) of total electricity in 2020, when the fossil production dropped to a historical low of 63.3%. Combined, the carbon intensity of China's power sector decreased to an historical low of 519.9 tCO2/GWh in 2020. If the fast decarbonization and slowed down power demand growth from 2019 to 2020 were to continue, by 2030, over half (50.8%) of China's power demand could be provided by low carbon sources. Our results thus reveal that China made progress towards its carbon neutrality target during the pandemic, and suggest the potential for substantial further decarbonization in the next few years if the latest trends persist.

The diurnal cycle CO$_2$ emissions from fossil fuel combustion and cement production reflect seasonality, weather conditions, working days, and more recently the impact of the COVID-19 pandemic. Here, for the first time we provide a daily CO$_2$ emission dataset for the whole year of 2020 calculated from inventory and near-real-time activity data (called Carbon Monitor project: https://carbonmonitor.org). It was previously suggested from preliminary estimates that did not cover the entire year of 2020 that the pandemics may have caused more than 8% annual decline of global CO$_2$ emissions. Here we show from detailed estimates of the full year data that the global reduction was only 5.4% (-1,901 MtCO$_2$, ). This decrease is 5 times larger than the annual emission drop at the peak of the 2008 Global Financial Crisis. However, global CO$_2$ emissions gradually recovered towards 2019 levels from late April with global partial re-opening. More importantly, global CO$_2$ emissions even increased slightly by +0.9% in December 2020 compared with 2019, indicating the trends of rebound of global emissions. Later waves of COVID-19 infections in late 2020 and corresponding lockdowns have caused further CO$_2$ emissions reductions particularly in western countries, but to a much smaller extent than the declines in the first wave. That even substantial world-wide lockdowns of activity led to a one-time decline in global CO$_2$ emissions of only 5.4% in one year highlights the significant challenges for climate change mitigation that we face in the post-COVID era. These declines are significant, but will be quickly overtaken with new emissions unless the COVID-19 crisis is utilized as a break-point with our fossil-fuel trajectory, notably through policies that make the COVID-19 recovery an opportunity to green national energy and development plans.

The COVID-19 pandemic has disrupted human activities, leading to unprecedented decreases in both global energy demand and GHG emissions. Yet a little known that there is also a low carbon shift of the global energy system in 2020. Here, using the near-real-time data on energy-related GHG emissions from 30 countries (about 70% of global power generation), we show that the pandemic caused an unprecedented de-carbonization of global power system, representing by a dramatic decrease in the carbon intensity of power sector that reached a historical low of 414.9 tCO2eq/GWh in 2020. Moreover, the share of energy derived from renewable and low-carbon sources (nuclear, hydro-energy, wind, solar, geothermal, and biomass) exceeded that from coal and oil for the first time in history in May of 2020. The decrease in global net energy demand (-1.3% in the first half of 2020 relative to the average of the period in 2016-2019) masks a large down-regulation of fossil-fuel-burning power plants supply (-6.1%) coincident with a surge of low-carbon sources (+6.2%). Concomitant changes in the diurnal cycle of electricity demand also favored low-carbon generators, including a flattening of the morning ramp, a lower midday peak, and delays in both the morning and midday load peaks in most countries. However, emission intensities in the power sector have since rebounded in many countries, and a key question for climate mitigation is thus to what extent countries can achieve and maintain lower, pandemic-level carbon intensities of electricity as part of a green recovery.

After steep drops and then rebounds in transportation-related CO$_2$ emissions over the first half of 2020, a second wave of COVID-19 this fall has caused further -- but less substantial -- emissions reductions. Here, we use near-real-time estimates of daily emissions to explore differences in human behavior and restriction policies over the course of 2020.

Activity reductions in early 2020 due to the Coronavirus Disease 2019 pandemic led to unprecedented decreases in carbon dioxide (CO2) emissions. Despite their record size, the resulting atmospheric signals are smaller than and obscured by climate variability in atmospheric transport and biospheric fluxes, notably that related to the 2019-2020 Indian Ocean Dipole. Monitoring CO2 anomalies and distinguishing human and climatic causes thus remains a new frontier in Earth system science. We show, for the first time, that the impact of short-term, regional changes in fossil fuel emissions on CO2 concentrations was observable from space. Starting in February and continuing through May, column CO2 over many of the World's largest emitting regions was 0.14 to 0.62 parts per million less than expected in a pandemic-free scenario, consistent with reductions of 3 to 13 percent in annual, global emissions. Current spaceborne technologies are therefore approaching levels of accuracy and precision needed to support climate mitigation strategies with future missions expected to meet those needs.

Changes in CO$_2$ emissions during the COVID-19 pandemic have been estimated from indicators on activities like transportation and electricity generation. Here, we instead use satellite observations together with bottom-up information to track the daily dynamics of CO$_2$ emissions during the pandemic. Unlike activity data, our observation-based analysis can be independently evaluated and can provide more detailed insights into spatially-explicit changes. Specifically, we use TROPOMI observations of NO$_2$ to deduce ten-day moving averages of NO$_x$ and CO$_2$ emissions over China, differentiating emissions by sector and province. Between January and April 2020, China's CO$_2$ emissions fell by 11.5% compared to the same period in 2019, but emissions have since rebounded to pre-pandemic levels owing to the fast economic recovery in provinces where industrial activity is concentrated.

We constructed a near-real-time daily CO2 emission dataset, namely the Carbon Monitor, to monitor the variations of CO2 emissions from fossil fuel combustion and cement production since January 1st 2019 at national level with near-global coverage on a daily basis, with the potential to be frequently updated. Daily CO2 emissions are estimated from a diverse range of activity data, including: hourly to daily electrical power generation data of 29 countries, monthly production data and production indices of industry processes of 62 countries/regions, daily mobility data and mobility indices of road transportation of 416 cities worldwide. Individual flight location data and monthly data were utilised for aviation and maritime transportation sectors estimates. In addition, monthly fuel consumption data that corrected for daily air temperature of 206 countries were used for estimating the emissions from commercial and residential buildings. This Carbon Monitor dataset manifests the dynamic nature of CO2 emissions through daily, weekly and seasonal variations as influenced by workdays and holidays, as well as the unfolding impacts of the COVID-19 pandemic. The Carbon Monitor near-real-time CO2 emission dataset shows a 7.8% decline of CO2 emission globally from Jan 1st to Apr 30th in 2020 when compared with the same period in 2019, and detects a re-growth of CO2 emissions by late April which are mainly attributed to the recovery of economy activities in China and partial easing of lockdowns in other countries. Further, this daily updated CO2 emission dataset could offer a range of opportunities for related scientific research and policy making.

The considerable cessation of human activities during the COVID-19 pandemic has affected global energy use and CO2 emissions. Here we show the unprecedented decrease in global fossil CO2 emissions from January to April 2020 was of 7.8% (938 Mt CO2 with a +6.8% of 2-{\sigma} uncertainty) when compared with the period last year. In addition other emerging estimates of COVID impacts based on monthly energy supply or estimated parameters, this study contributes to another step that constructed the near-real-time daily CO2 emission inventories based on activity from power generation (for 29 countries), industry (for 73 countries), road transportation (for 406 cities), aviation and maritime transportation and commercial and residential sectors emissions (for 206 countries). The estimates distinguished the decline of CO2 due to COVID-19 from the daily, weekly and seasonal variations as well as the holiday events. The COVID-related decreases in CO2 emissions in road transportation (340.4 Mt CO2, -15.5%), power (292.5 Mt CO2, -6.4% compared to 2019), industry (136.2 Mt CO2, -4.4%), aviation (92.8 Mt CO2, -28.9%), residential (43.4 Mt CO2, -2.7%), and international shipping (35.9Mt CO2, -15%). Regionally, decreases in China were the largest and earliest (234.5 Mt CO2,-6.9%), followed by Europe (EU-27 & UK) (138.3 Mt CO2, -12.0%) and the U.S. (162.4 Mt CO2, -9.5%). The declines of CO2 are consistent with regional nitrogen oxides concentrations observed by satellites and ground-based networks, but the calculated signal of emissions decreases (about 1Gt CO2) will have little impacts (less than 0.13ppm by April 30, 2020) on the overserved global CO2 concertation. However, with observed fast CO2 recovery in China and partial re-opening globally, our findings suggest the longer-term effects on CO2 emissions are unknown and should be carefully monitored using multiple measures.

Using innovative longitudinal data from a survey of unemployment insurance (UI) recipients, we test several implications of a canonical job search model for reservation wages during unemployment spells. First, consistent with the model, we find that reservation wages fall faster when UI benefit durations are shorter. However, workers set their initial reservation wages higher, and adjust them slower, relative to model predictions. Second, workers' expectations--elicited at the beginning of their unemployment spell--about how their reservation wage will evolve if they remain unemployed are largely congruent with reservation wage realizations, as assumed in the canonical model. Third, our data on expectations and realizations suggest that dynamic selection over the unemployment spell is inconsequential for our results. Fourth, higher wages on workers' lost jobs, relative to predictions from a Mincerian wage regression, hasten the expected and realized declines in reservation wages over the unemployment spell. Finally, reservation wages are a more powerful predictor of re-employment wages than wages on the previous job. [ABSTRACT FROM AUTHOR]