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Economic productivity depends on reliable access to electricity, but the extreme shortage events of variable wind-solar systems may be strongly affected by climate change. Here, hourly reanalysis climatological data are leveraged to examine historical trends in defined extreme shortage events worldwide. We find uptrends in extreme shortage events regardless of their frequency, duration, and intensity since 1980. For instance, duration of extreme low-reliability events worldwide has increased by 4.1 hours (0.392 hours per year on average) between 1980-2000 and 2001-2022. However, such ascending trends are unevenly distributed worldwide, with a greater variability in low- and middle-latitude developing countries. This uptrend in extreme shortage events is driven by extremely low wind speed and solar radiation, particularly compound wind and solar drought, which however are strongly disproportionated. Only average 12.5% change in compound extremely low wind speed and solar radiation events may give rise to over 30% variability in extreme shortage events, despite a mere average 1.0% change in average wind speed and solar radiation. Our findings underline that wind-solar systems will probably suffer from weakened power security if such uptrends persist in a warmer future.

Natural gas is the primary fuel used in U.S. residences, yet little is known about its consumption patterns and drivers. We use daily county-level gas consumption data to assess the spatial patterns of the relationships and the sensitivities of gas consumption to outdoor air temperature across U.S. households. We fitted linear-plus-plateau functions to daily gas consumption data in 1000 counties, and derived two key coefficients: the heating temperature threshold (Tcrit) and the gas consumption rate change per 1°C temperature drop (Slope). We identified the main predictors of Tcrit and Slope (like income, employment rate, and building type) using interpretable machine learning models built on census data. Finally, we estimated a potential 2.47 million MtCO2 annual emission reduction in U.S. residences by gas savings due to household insulation improvements and hypothetical behavioral change toward reduced consumption by adopting a 1°C lower Tcrit than the current value.

We present a near-real-time global gridded daily CO2 emissions dataset (GRACED) throughout 2021. GRACED provides gridded CO2 emissions at a 0.1° × 0.1° 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 the near-real-time daily national CO2 emissions estimates (Carbon Monitor), multi-source spatial activity data emissions and satellite NO2 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 CO2 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.

Net-zero greenhouse gas (GHG) emissions targets are driving interest in opportunities for biomass-based negative emissions and bioenergy, including from marine sources such as seaweed. Yet the biophysical and economic limits to farming seaweed at scales relevant to the global carbon budget have not been assessed in detail. We use coupled seaweed growth and technoeconomic models to estimate the costs of global seaweed production and related climate benefits, systematically testing the relative importance of model parameters. Under our most optimistic assumptions, sinking farmed seaweed to the deep sea to sequester a gigaton of CO2 per year costs as little as US$480 per tCO2 on average, while using farmed seaweed for products that avoid a gigaton of CO2-equivalent GHG emissions annually could return a profit of $50 per tCO2-eq. However, these costs depend on low farming costs, high seaweed yields, and assumptions that almost all carbon in seaweed is removed from the atmosphere (that is, competition between phytoplankton and seaweed is negligible) and that seaweed products can displace products with substantial embodied non-CO2 GHG emissions. Moreover, the gigaton-scale climate benefits we model would require farming very large areas (>90,000 km2)-a >30-fold increase in the area currently farmed. Our results therefore suggest that seaweed-based climate benefits may be feasible, but targeted research and demonstrations are needed to further reduce economic and biophysical uncertainties.

Extreme wildfires threaten human lives, air quality, and ecosystems. Meteorology plays a vital role in wildfire behaviors, and the links between wildfires and climate have been widely studied. However, it is not fully clear how fire-weather feedback affects short-term wildfire variability, which undermines our ability to mitigate fire disasters. Here, we show the primacy of synoptic-scale feedback in driving extreme fires in Mediterranean and monsoon climate regimes in the West Coast of the United States and Southeastern Asia. We found that radiative effects of smoke aerosols can modify near-surface wind, air dryness, and rainfall and thus worsen air pollution by enhancing fire emissions and weakening dispersion. The intricate interactions among wildfires, smoke, and weather form a positive feedback loop that substantially increases air pollution exposure.

We constructed a frequently updated, near-real-time global power generation dataset: CarbonMonitor-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 CarbonMonitor-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.

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 CO2 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 CO2 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.

Environmental impact assessment (EIA) is a decision support tool that analyzes the environmental and social impacts of infrastructure projects. This paper focuses on the California Environmental Quality Act (CEQA), a law requiring EIA use in California, to examine where new infrastructure is proposed and whether EIA can shape infrastructure distribution and environmental justice through the review process. We analyze the temporal and spatial distribution of more than 7000 infrastructure projects and their environmental impacts as proposed under CEQA from 2011 to 2020. Using fixed-effects negative binomial regression to model the association between the number of initiated projects and existing socioeconomic and environmental conditions by census tract, and multinomial logistic regression to investigate determinants of a project's level of environmental review, we find an unequal distribution of infrastructure. We find that socio-economically vulnerable communities and those with greater burden of environmental pollution are less likely to be the site of newly proposed infrastructure, but that proposed projects tend to be beneficial, less-polluting infrastructure like parks or schools that could help redress past injustices. Moreover, projects proposed in vulnerable communities are less likely to receive the most stringent reviews or have their impacts mitigated. These findings suggest that CEQA interacts with distributive justice in contradictory ways. They also highlight the need to separately consider environmental amenities versus harms such that EIA processes do not stand as a barrier to constructing beneficial infrastructure in environmental justice communities.

Stabilizing global climate will require major transformations to energy and food systems, including drastic reductions in the use of fossil fuels, improvements in energy and material efficiencies, extensive electrification of energy end uses, sustainable intensification of agriculture, and carbon management. Although progress has been made in researching these topics in recent years, several research questions central to framing global energy and agriculture policies deserve further study. In this dissertation, I explore several relevant research topics with the goal of contributing to our understanding of the opportunities and challenges for achieving net-zero emissions energy and food systems, including (1) the characteristics of modeled net-zero CO2 emissions energy systems; (2) the potential for seaweed farming to deliver globally-scaled carbon removal and emissions mitigation; and (3) potential solutions for hard-to-abate greenhouse gas emissions from the global food system. In scenarios that successfully reach net-zero CO2 emissions by the end of this century, renewable energy sources account for 60% of primary energy at net-zero on average (compared to ~14% today), with slightly less than half of that renewable energy derived from biomass. Meanwhile, electricity makes up approximately half of final energy consumed (compared to ~20% today), highlighting the extent to which solid, liquid, and gaseous fuels remain prevalent in the scenarios even when emissions reach net-zero. Residual emissions and offsetting negative emissions are not evenly distributed across world regions, which may have important implications for negotiations on burden-sharing, human development, and equity. Sinking farmed seaweed to the deep sea is a potential method to remove carbon from the atmosphere (i.e., negative emissions), and sequestering a gigaton of CO2 per year with this method may cost as little as $480/tCO2 on average. Instead, using farmed seaweed for products that avoid a gi

Growth in satellite observations and modelling capabilities has transformed drought monitoring, offering near-real-time information. However, current monitoring efforts focus on hazards rather than impacts, and are further disconnected from drought-related compound or cascading hazards such as heatwaves, wildfires, floods and debris flows. In this Perspective, we advocate for impact-based drought monitoring and integration with broader drought-related hazards. Impact-based monitoring will go beyond top-down hazard information, linking drought to physical or societal impacts such as crop yield, food availability, energy generation or unemployment. This approach, specifically forecasts of drought event impacts, would accordingly benefit multiple stakeholders involved in drought planning, and risk and response management, with clear benefits for food and water security. Yet adoption and implementation is hindered by the absence of consistent drought impact data, limited information on local factors affecting water availability (including water demand, transfer and withdrawal), and impact assessment models being disconnected from drought monitoring tools. Implementation of impact-based drought monitoring thus requires the use of newly available remote sensors, the availability of large volumes of standardized data across drought-related fields, and the adoption of artificial intelligence to extract and synthesize physical and societal drought impacts.

Extreme wildfires are becoming more common and increasingly affecting Earth’s climate. Wildfires in boreal forests have attracted much less attention than those in tropical forests, although boreal forests are one of the most extensive biomes on Earth and are experiencing the fastest warming. We used a satellite-based atmospheric inversion system to monitor fire emissions in boreal forests. Wildfires are rapidly expanding into boreal forests with emerging warmer and drier fire seasons. Boreal fires, typically accounting for 10% of global fire carbon dioxide emissions, contributed 23% (0.48 billion metric tons of carbon) in 2021, by far the highest fraction since 2000. 2021 was an abnormal year because North American and Eurasian boreal forests synchronously experienced their greatest water deficit. Increasing numbers of extreme boreal fires and stronger climate–fire feedbacks challenge climate mitigation efforts.

Using a newly-compiled dataset of airport traffic, air pollution, and weather from 2015 to 2017, we evaluate the effect of PM2.5 pollution levels on flight departure delays in China. We instrument for PM2.5 levels using changes in atmospheric thermal inversions and wind direction. We find that a one-standard-deviation increase in the daily PM2.5 concentration levels leads to roughly 4.5–6.4 additional minutes in departure delays per flight. We estimate that reductions in China's PM2.5 pollution levels due to the Air Pollution Prevention and Control Action Plan avoided 13–18% of the increases in national average flight delays and saved US $54 to $69 million in passenger time in 2017 alone. © 2023 Elsevier Inc.

Precise and high-resolution carbon dioxide (CO2) emission data is of great importance in achieving carbon neutrality around the world. Here we present for the first time the near-real-time Global Gridded Daily CO2 Emissions Dataset (GRACED) from fossil fuel and cement production with a global spatial resolution of 0.1° by 0.1° and a temporal resolution of 1 day. Gridded fossil emissions are computed for different sectors based on the daily national CO2 emissions from near-real-time dataset (Carbon Monitor), the spatial patterns of point source emission dataset Global Energy Infrastructure Emissions Database (GID), Emission Database for Global Atmospheric Research (EDGAR), and spatiotemporal patters of satellite nitrogen dioxide (NO2) retrievals. Our study on the global CO2 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 from January 1, 2019, to December 31, 2020. This gives thorough insights into the relative contributions from each sector. Furthermore, it provides the most up-to-date and fine-grained overview of where and when fossil CO2 emissions have decreased and rebounded in response to emergencies (e.g., coronavirus disease 2019 [COVID-19]) and other disturbances of human activities of any previously published dataset. As the world recovers from the pandemic and decarbonizes its energy systems, regular updates of this dataset will enable policymakers to more closely monitor the effectiveness of climate and energy policies and quickly adapt.

Over the 20th century, critical advancements in energy generation and agriculture have revolutionized contemporary society, propelling many of the modern advancements enjoyed by humanity today. However, these developments have come at a considerable cost to the natural environment, resulting in, among others, global climate change, ecological decline, and wildlife extinction (IPCC, 2013; Masson-Delmotte et al., 2021; F. N. Tubiello et al., 2015). Recent international efforts to mitigate severe climate warming and minimize further environmental damage, aim for swift and significant reductions in annual anthropogenic CO2 emissions worldwide the vast majority of which originate from fossil energy generation (89%), and agriculture driven land-use expansion (11%) (Friedlingstein et al., 2021; Le Quéré et al., 2018; Ramankutty, Evan, Monfreda, & Foley, 2008). Thus, climate mitigation pathways that successfully limit future warming below 2°C require the swift transformation of modern agriculture and energy generating practices (Audoly, Vogt-Schilb, Guivarch, & Pfeiffer, 2018; Popp et al., 2017; Rogelj, Popp, et al., 2018; Rogelj, Schaeffer, et al., 2015a; Rogelj, Shindell, et al., 2018). However, fossil-burning infrastructure is generally long-lived (Davis, Caldeira, & Matthews, 2010; Davis & Socolow, 2014b; Seto et al., 2016; Shearer, Fofrich, & Davis, 2017) and current climate change mitigation scenarios do not track the magnitude and patterns of necessary power plant retirements (Fofrich et al., 2020). Here, we show fossil-fired power plants retiring up to three decades earlier than historically has been the case in scenarios consistent with international climate targets (i.e., keeping global warming well below 1.5°C or 2°C), potentially jeopardizing trillions of dollars in power generating assets. If instead, power generators continue to operate as they have historically, we find the resulting emissions are incompatible with more ambitious climate m

Following record-level declines in 2020, near-real-time data indicate that global CO2 emissions rebounded by 4.8% in 2021, reaching 34.9 GtCO2. These 2021 emissions consumed 8.7% of the remaining carbon budget for limiting anthropogenic warming to 1.5 °C, which if current trajectories continue, might be used up in 9.5 years at 67% likelihood.

Air quality associated public health co-benefit may emerge from climate and energy policies aimed at reducing greenhouse gas (GHG) emissions. However, the distribution of these co-benefits has not been carefully studied, despite the opportunity to tailor mitigation efforts so they achieve maximum benefits within socially and economically disadvantaged communities (DACs). Here, we quantify such health co-benefits from different long-term, low-carbon scenarios in California and their distribution in the context of social vulnerability. The magnitude and distribution of health benefits, including within impacted communities, is found to varies among scenarios which reduce economy wide GHG emissions by 80% in 2050 depending on the technology- and fuel-switching decisions in individual end-use sectors. The building electrification focused decarbonization strategy achieves ~15% greater total health benefits than the truck electrification focused strategy which uses renewable fuels to meet building demands. Conversely, the enhanced electrification of the truck sector is shown to benefit DACs more effectively. Such tradeoffs highlight the importance of considering environmental justice implications in the development of climate mitigation planning.

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, which provides daily 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-ICLEI Track) were performed, and we estimate the overall annual uncertainty range 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.

International efforts to avoid dangerous climate change have historically focused on reducing energy-related CO2 emissions from countries with either the largest economies (e.g. the EU and the USA) and/or the largest populations (e.g. China and India). However, in recent years, emissions have surged among a different and much less-examined group of countries, raising concerns that a next generation of high-emitting economies will obviate current mitigation targets. Here, we analyse the trends and drivers of emissions in each of the 59 countries where emissions in 2010-2018 grew faster than the global average (excluding China and India), project their emissions under a range of longer-term energy scenarios and estimate the costs of decarbonization pathways. Total emissions from these 'emerging emitters' reach as much as 7.5 GtCO2/year in the baseline 2.5° scenario-substantially greater than the emissions from these regions in previously published scenarios that would limit warming to 1.5°C or even 2°C. Such unanticipated emissions would in turn require non-emitting energy deployment from all sectors within these emerging emitters, and faster and deeper reductions in emissions from other countries to meet international climate goals. Moreover, the annual costs of keeping emissions at the low level are in many cases 0.2%-4.1% of countries' gross domestic production, pointing to potential trade-offs with poverty-reduction goals and/or the need for economic support and low-carbon technology transfer from historically high-emitting countries. Our results thus highlight the critical importance of ramping up mitigation efforts in countries that to this point have been largely ignored.

Rationale: GM-CSF (granulocyte-macrophage colony-stimulating factor) has emerged as a promising target against the hyperactive host immune response associated with coronavirus disease (COVID-19). Objectives: We sought to investigate the efficacy and safety of gimsilumab, an anti-GM-CSF monoclonal antibody, for the treatment of hospitalized patients with elevated inflammatory markers and hypoxemia secondary to COVID-19. Methods: We conducted a 24-week randomized, double-blind, placebo-controlled trial, BREATHE (Better Respiratory Education and Treatment Help Empower), at 21 locations in the United States. Patients were randomized 1:1 to receive two doses of intravenous gimsilumab or placebo 1 week apart. The primary endpoint was all-cause mortality rate at Day 43. Key secondary outcomes were ventilator-free survival rate, ventilator-free days, and time to hospital discharge. Enrollment was halted early for futility based on an interim analysis. Measurements and Main Results: Of the planned 270 patients, 225 were randomized and dosed; 44.9% of patients were Hispanic or Latino. The gimsilumab and placebo groups experienced an all-cause mortality rate at Day 43 of 28.3% and 23.2%, respectively (adjusted difference = 5% vs. placebo; 95% confidence interval [-6 to 17]; P = 0.377). Overall mortality rates at 24 weeks were similar across the treatment arms. The key secondary endpoints demonstrated no significant differences between groups. Despite the high background use of corticosteroids and anticoagulants, adverse events were generally balanced between treatment groups. Conclusions: Gimsilumab did not improve mortality or other key clinical outcomes in patients with COVID-19 pneumonia and evidence of systemic inflammation. The utility of anti-GM-CSF therapy for COVID-19 remains unclear. Clinical trial registered with www.clinicaltrials.gov (NCT04351243).

This corrigendum resolves an error in figure 17 and clarifies the scope of the cement sector in figure 2. Figure 17 in the original published manuscript depicts a Kaya identity for the agriculture, forestry and other land uses (AFOLU) sector. We unintentionally excluded land-use CO2 emissions from total greenhouse gas (GHG) emissions in this identity, and depicted only agricultural GHG emissions.

In support of the global stocktake of the Paris Agreement on climate change, this study presents a comprehensive framework to process the results of an ensemble of atmospheric inversions in order to make their net ecosystem exchange (NEE) carbon dioxide (CO2) flux suitable for evaluating national greenhouse gas inventories (NGHGIs) submitted by countries to the United Nations Framework Convention on Climate Change (UNFCCC). From inversions we also deduced anthropogenic methane (CH4) emissions regrouped into fossil and agriculture and waste emissions, as well as anthropogenic nitrous oxide (N2O) emissions. To compare inversion results with national reports, we compiled a new global harmonized database of emissions and removals from periodical UNFCCC inventories by Annex I countries, and from sporadic and less detailed emissions reports by non-Annex I countries, given by national communications and biennial update reports. No gap filling was applied. The method to reconcile inversions with inventories is applied to selected large countries covering ∼90% of the global land carbon uptake for CO2 and top emitters of CH4 and N2O. Our method uses results from an ensemble of global inversions produced by the Global Carbon Project for the three greenhouse gases, with ancillary data. We examine the role of CO2 fluxes caused by lateral transfer processes from rivers and from trade in crop and wood products and the role of carbon uptake in unmanaged lands, both not accounted for by NGHGIs. Here we show that, despite a large spread across the inversions, the median of available inversion models points to a larger terrestrial carbon sink than inventories over temperate countries or groups of countries of the Northern Hemisphere like Russia, Canada and the European Union. For CH4, we find good consistency between the inversions assimilating only data from the global in situ network and those using satellite CH4 retrievals and a tendency for inversions to diagnose higher CH4 emissi

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.

Achieving net-zero CO2 emissions has become the explicitgoal of many climate-energy policies around the world. Although many studies have assessed net-zero emissions pathways, the common features and tradeoffs of energy systems across global scenarios at the point of net-zero CO2 emissions have not yet been evaluated. Here, we examine the energy systems of 177 net-zero scenarios and discuss their long-term technological and regional characteristics in the context of current energy policies. We find that, on average, renewable energy sources account for 60% of primary energy at net-zero (compared to ∼14% today), with slightly less than half of that renewable energy derived from biomass. Meanwhile, electricity makes up approximately half of final energy consumed (compared to ∼20% today), highlighting the extent to which solid, liquid, and gaseous fuels remain prevalent in the scenarios even when emissions reach net-zero. Finally, residual emissions and offsetting negative emissions are not evenly distributed across world regions, which may have important implications for negotiations on burden-sharing, human development, and equity.

If future net-zero emissions energy systems rely heavily on solar and wind resources, spatial and temporal mismatches between resource availability and electricity demand may challenge system reliability. Using 39 years of hourly reanalysis data (1980-2018), we analyze the ability of solar and wind resources to meet electricity demand in 42 countries, varying the hypothetical scale and mix of renewable generation as well as energy storage capacity. Assuming perfect transmission and annual generation equal to annual demand, but no energy storage, we find the most reliable renewable electricity systems are wind-heavy and satisfy countries' electricity demand in 72-91% of hours (83-94% by adding 12 h of storage). Yet even in systems which meet >90% of demand, hundreds of hours of unmet demand may occur annually. Our analysis helps quantify the power, energy, and utilization rates of additional energy storage, demand management, or curtailment, as well as the benefits of regional aggregation.

Real Estate Online Platforms (REOPs) are responsible for providing property-related information to their users. However, most of these users are not satisfied with the information provided to them. This thesis highlights the REOP users’ needs and regrets and the pertinent disruptive digital technologies (DDTs) to address these needs. Two models are developed for assessing the REOPs users’ perception and the two-way relationship between them. Also, the barriers to adoption of the DDTs from a managerial perspective are examined.For assessing the users’ perception, Smart Real Estate Technology Adoption Model (SRETAM) is developed, whereas Risk, Service, Information, System TAM (RSISTAM) is developed to assess the two-way relationship between the perceptions. Concepts of KANO and SISQual are used to assess the perceptions, whereas Decision-Making Trial and Evaluation Laboratory (DEMATEL) is used for assessing the potential two-way relationships. The barriers to adoption of the DDTs are analysed through Fault Tree Analysis. These models, coupled with the users’ needs and DDT adoption barriers, constitute the novel framework adopted in this study.Eight key regrets of the users are identified from the published literature and meta-analyses: complicated buy-sell process, lack of information, housing costs, house size, mortgage, agents, inspections and emotional decision-making. Nine key technologies can help address the REOP users’ needs and regrets. In terms of REOP users’ perception, 31 key factors have been identified, among which 19 are very important based on responses from 407 respondents. Graphical statistics, attractive design, immersive and novel content attract REOPs users, whereas tracing user location, learning tutorials and hyperlinks discourage them. Among possible relations between RSISTAM constructs, nine are categorised as two-way. There are 21 key barriers to adopting DDTs in the Australian real estate sector identified through a survey of 102 real estate

[EN] This paper examines how a U.S. Air Force (USAF) faculty team reimagined and redesigned an in-person Leader Development Course (LDC) to a virtual version (vLDC). Using the Design Thinking Process for Innovation (DTPI) and action research methods, a new, virtual course was imagined, designed, tested, and improved over a six-cycle-process. Data was collected via multiple sources from 121 participants (19 faculty/staff and 102 students) and analyzed using manual coding and NVivo Software. Results are organized into 22 categories under four themes (general course design, student experience, instructor experience and faculty development, and technology experience) showing a progressive refinement with key lessons learned that led to the final creation of the new virtual course. Of the five key features in action research (actions matter, context-specific research, multiple cycles and phases, inclusion of people as research target, and reflections), participants reported that multiple cycles and reflections were most important in relation to the DTPI so that change could be enacted that reflected participant voices in the design process of the virtual course. The application of the DTPI using action research methods produced results and lessons learned in the design process that contribute to the theory and practice on developing and teaching in a virtual learning environment. The study fills a gap in the scholarly field and informs other institutions on the process, failures, and successes of course redesign to a virtual version.

[Figure: see text].

To control the spread of the 2019 novel coronavirus (COVID-19), China imposed nationwide restrictions on the movement of its population (lockdown) after the Chinese New Year of 2020, leading to large reductions in economic activities and associated emissions. Despite such large decreases in primary pollution, there were nonetheless several periods of heavy haze pollution in eastern China, raising questions about the well-established relationship between human activities and air quality. Here, using comprehensive measurements and modeling, we show that the haze during the COVID lockdown was driven by enhancements of secondary pollution. In particular, large decreases in NOx emissions from transportation increased ozone and nighttime NO3 radical formation, and these increases in atmospheric oxidizing capacity in turn facilitated the formation of secondary particulate matter. Our results, afforded by the tragic natural experiment of the COVID-19 pandemic, indicate that haze mitigation depends upon a coordinated and balanced strategy for controlling multiple pollutants.

Atmospheric methane removal (e.g. in situ methane oxidation to carbon dioxide) may be needed to offset continued methane release and limit the global warming contribution of this potent greenhouse gas. Because mitigating most anthropogenic emissions of methane is uncertain this century, and sudden methane releases from the Arctic or elsewhere cannot be excluded, technologies for methane removal or oxidation may be required. Carbon dioxide removal has an increasingly well-established research agenda and technological foundation. No similar framework exists for methane removal. We believe that a research agenda for negative methane emissions-'removal' or atmospheric methane oxidation-is needed. We outline some considerations for such an agenda here, including a proposed Methane Removal Model Intercomparison Project (MR-MIP). This article is part of a discussion meeting issue 'Rising methane: is warming feeding warming? (part 1)'.

This article in MRS Bulletin and the framework set out in the introductory article articulate a scenario of renewable electrons and electrification of end use appliances and industrial processes as a plausible paradigm to realize a carbon-free energy economy. The subsequent articles cover specific sectoral or chemical applications of those renewable electrons (e.g., for hydrogen, transportation, building use, electrochemical storage, and within the chemical industry). This article addresses the intersections among and across those sectors. We describe the importance of considering integrated systems and systems of systems as we consider pathways to a decarbonized energy economy. Further, we review and summarize key insights into the innovation challenges that reside at the particular integration interfaces among sectors, and highlight the opportunity for advances in materials and processes that will be critical to successful achievement of economy-wide, low-carbon energy systems.Graphical abstract

Global greenhouse gas (GHG) emissions can be traced to five economic sectors: energy, industry, buildings, transport and AFOLU (agriculture, forestry and other land uses). In this topical review, we synthesise the literature to explain recent trends in global and regional emissions in each of these sectors. To contextualise our review, we present estimates of GHG emissions trends by sector from 1990 to 2018, describing the major sources of emissions growth, stability and decline across ten global regions. Overall, the literature and data emphasise that progress towards reducing GHG emissions has been limited. The prominent global pattern is a continuation of underlying drivers with few signs of emerging limits to demand, nor of a deep shift towards the delivery of low and zero carbon services across sectors. We observe a moderate decarbonisation of energy systems in Europe and North America, driven by fuel switching and the increasing penetration of renewables. By contrast, in rapidly industrialising regions, fossil-based energy systems have continuously expanded, only very recently slowing down in their growth. Strong demand for materials, floor area, energy services and travel have driven emissions growth in the industry, buildings and transport sectors, particularly in Eastern Asia, Southern Asia and South-East Asia. An expansion of agriculture into carbon-dense tropical forest areas has driven recent increases in AFOLU emissions in Latin America, South-East Asia and Africa. Identifying, understanding, and tackling the most persistent and climate-damaging trends across sectors is a fundamental concern for research and policy as humanity treads deeper into the Anthropocene.

Models show that to avert dangerous levels of climate change, global carbon dioxide emissions must fall to zero later this century. Most of these emissions arise from energy use. Davis et al. review what it would take to achieve decarbonization of the energy system. Some parts of the energy system are particularly difficult to decarbonize, including aviation, long-distance transport, steel and cement production, and provision of a reliable electricity supply. Current technologies and pathways show promise, but integration of now-discrete energy sectors and industrial processes is vital to achieve minimal emissions. Net emissions of CO2 by human activities - including not only energy services and industrial production but also land use and agriculture - must approach zero in order to stabilize global mean temperature. Energy services such as light-duty transportation, heating, cooling, and lighting may be relatively straightforward to decarbonize by electrifying and generating electricity from variable renewable energy sources (such as wind and solar) and dispatchable ("on-demand") nonrenewable sources (including nuclear energy and fossil fuels with carbon capture and storage). However, other energy services essential to modern civilization entail emissions that are likely to be more difficult to fully eliminate. These difficult-to-decarbonize energy services include aviation, long-distance transport, and shipping; production of carbon-intensive structural materials such as steel and cement; and provision of a reliable electricity supply that meets varying demand. Moreover, demand for such services and products is projected to increase substantially over this century. The long-lived infrastructure built today, for better or worse, will shape the future.Here, we review the special challenges associated with an energy system that does not add any CO2 to the atmosphere (a net-zero emissions energy system). We discuss prominent technological opportunities and barriers fo

International efforts to avoid dangerous climate change aim for large and rapid reductions of fossil fuel CO2 emissions worldwide, including nearly complete decarbonization of the electric power sector. However, achieving such rapid reductions may depend on early retirement of coal- and natural gas-fired power plants. Here, we analyze future fossil fuel electricity demand in 171 energy-emissions scenarios from Integrated Assessment Models (IAMs), evaluating the implicit retirements and/or reduced operation of generating infrastructure. Although IAMs calculate retirements endogenously, the structure and methods of each model differ; we use a standard approach to infer retirements in outputs from all six major IAMs and—unlike the IAMs themselves—we begin with the age distribution and region-specific operating capacities of the existing power fleet. We find that coal-fired power plants in scenarios consistent with international climate targets (i.e. keeping global warming well-below 2 °C or 1.5 °C) retire one to three decades earlier than historically has been the case. If plants are built to meet projected fossil electricity demand and instead allowed to operate at the level and over the lifetimes they have historically, the roughly 200 Gt CO2 of additional emissions this century would be incompatible with keeping global warming well-below 2 °C. Thus, ambitious climate mitigation scenarios entail drastic, and perhaps un-appreciated, changes in the operating and/or retirement schedules of power infrastructure.

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.

Changes in CO2 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 CO2 emissions during the pandemic. Unlike activity data, our observation-based analysis deploys independent measurement of pollutant concentrations in the atmosphere to correct misrepresentation in the bottom-up data and can provide more detailed insights into spatially explicit changes. Specifically, we use TROPOMI observations of NO2 to deduce 10-day moving averages of NO x and CO2 emissions over China, differentiating emissions by sector and province. Between January and April 2020, China's CO2 emissions fell by 11.5% compared to the same period in 2019, but emissions have since rebounded to pre-pandemic levels before the coronavirus outbreak at the beginning of January 2020 owing to the fast economic recovery in provinces where industrial activity is concentrated.

The key role that atmospheric carbon dioxide plays in climate warming makes it particularly relevant to understand the human and natural processes that increase or decrease carbon in the atmosphere. Contributing fluxes from the ocean, land, and various anthropogenic sources have been estimated by previous research but large uncertainties remain. As the only component directly under our control, the human contribution is critical to understand for policy decisions seeking to mitigate and prevent climate impacts. In this dissertation I have focused on a few aspects of understanding future fossil fuel emissions on both short and long timescales. I started with the long term, exploring carbon feedbacks on fossil fuel emissions. In my first research chapter I conceptualize and estimate the size of several economic mechanisms that generate a carbon-climate feedback, using the Kaya Identity to separate a net economic feedback into components associated with population, GDP, heating and cooling, and the carbon intensity of energy. In a fossil fuel intensive future scenario, I found that such decreases in economic activity due to warming reduced fossil fuel emissions by 13% this century, lowering atmospheric CO2 by over 100 ppm in 2100. The natural carbon-climate feedback, in contrast, increased atmospheric CO2 over this period by a similar amount, and thus the net effect including both feedbacks was nearly zero. Importantly, although these impacts of climate warming on the economy may offset weakening land and ocean carbon sinks, a loss of economic productivity will have high societal costs, potentially increasing wealth inequity and limiting resources available for effective adaptation.The uncertainty in my estimation of a potential economic carbon feedback effect is high, however, motivating a need for improved understanding of these feedbacks within more sophisticated models. To that end, in my second study I have extended a framework previously developed for calculating

Many studies have estimated the adverse effects of climate change on crop yields, however, this literature almost universally assumes a constant geographic distribution of crops in the future. Movement of growing areas to limit exposure to adverse climate conditions has been discussed as a theoretical adaptive response but has not previously been quantified or demonstrated at a global scale. Here, we assess how changes in rainfed crop area have already mediated growing season temperature trends for rainfed maize, wheat, rice, and soybean using spatially-explicit climate and crop area data from 1973 to 2012. Our results suggest that the most damaging impacts of warming on rainfed maize, wheat, and rice have been substantially moderated by the migration of these crops over time and the expansion of irrigation. However, continued migration may incur substantial environmental costs and will depend on socio-economic and political factors in addition to land suitability and climate.

The COVID-19 pandemic is impacting human activities, and in turn energy use and carbon dioxide (CO2) emissions. Here we present daily estimates of country-level CO2 emissions for different sectors based on near-real-time activity data. The key result is an abrupt 8.8% decrease in global CO2 emissions (-1551 Mt CO2) in the first half of 2020 compared to the same period in 2019. The magnitude of this decrease is larger than during previous economic downturns or World War II. The timing of emissions decreases corresponds to lockdown measures in each country. By July 1st, the pandemic's effects on global emissions diminished as lockdown restrictions relaxed and some economic activities restarted, especially in China and several European countries, but substantial differences persist between countries, with continuing emission declines in the U.S. where coronavirus cases are still increasing substantially.

We use 36 years (1980-2015) of hourly weather data over the contiguous United States (CONUS) to assess the impact of low-cost energy storage on highly reliable electricity systems that use only variable renewable energy (VRE; wind and solar photovoltaics). Even assuming perfect transmission of wind and solar generation aggregated over CONUS, energy storage costs would need to decrease several hundred-fold from current costs (to ∼$1/kWh) in fully VRE electricity systems to yield highly reliable electricity without extensive curtailment of VRE generation. The role of energy storage changes from high-cost storage competing with curtailment to fill short-term gaps between VRE generation and hourly demand to near-free storage serving as seasonal storage for VRE resources. Energy storage faces "double penalties" in VRE/storage systems: with increasing capacity, (1) the additional storage is used less frequently and (2) hourly electricity costs would become less volatile, thus reducing price arbitrage opportunities for the additional storage.

We constructed a near-real-time daily CO2 emission dataset, the Carbon Monitor, to monitor the variations in CO2 emissions from fossil fuel combustion and cement production since January 1, 2019, at the national level, with near-global coverage on a daily basis and the potential to be frequently updated. Daily CO2 emissions are estimated from a diverse range of activity data, including the hourly to daily electrical power generation data of 31 countries, monthly production data and production indices of industry processes of 62 countries/regions, and daily mobility data and mobility indices for the ground transportation of 416 cities worldwide. Individual flight location data and monthly data were utilized for aviation and maritime transportation sector estimates. In addition, monthly fuel consumption data corrected for the daily air temperature of 206 countries were used to estimate 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 by the unfolding impacts of the COVID-19 pandemic. The Carbon Monitor near-real-time CO2 emission dataset shows a 8.8% decline in CO2 emissions globally from January 1st to June 30th in 2020 when compared with the same period in 2019 and detects a regrowth of CO2 emissions by late April, which is mainly attributed to the recovery of economic activities in China and a partial easing of lockdowns in other countries. This daily updated CO2 emission dataset could offer a range of opportunities for related scientific research and policy making.

A Correction to this paper has been published: https://doi.org/10.1038/s41467-020-20254-5.

We use a global transport model and satellite retrievals of the carbon dioxide (CO2) column average to explore the impact of CO2 emissions reductions that occurred during the economic downturn at the start of the Covid-19 pandemic. The changes in the column averages are substantial in a few places of the model global grid, but the induced gradients are most often less than the random errors of the retrievals. The current necessity to restrict the quality-assured column retrievals to almost cloud-free areas appears to be a major obstacle in identifying changes in CO2 emissions. Indeed, large changes have occurred in the presence of clouds and, in places that were cloud-free in 2020, the comparison with previous years is hampered by different cloud conditions during these years. We therefore recommend to favor all-weather CO2 monitoring systems, at least in situ, to support international efforts to reduce emissions.

Since the 1990s, governments’ growing interest in sustainability has promoted widespread adoption of life cycle costing (LCC) in investment appraisal. LCC relies on the estimates of financial variables that contain acknowledged uncertainty. Despite numerous probabilistic studies in the literature, the influence of uncertainty is still unclear because conventional approaches are data-intensive and/or computationally complex. In this light, the thesis aims to develop an improved approach with second order moment thinking, and explicitly identify the influence for the purpose of addressing the inherent uncertainty.First, the thesis respectively examines the single-variable uncertainty concerning the underlying variables (cash flows, interest rates, timing of cash flows, asset lifetime). For each variable being probabilistic, second order moment theory is employed to establish a formulation for obtaining the expected value and variance of present worth. Four formulations are presented/established; the moments of the variables, the squares of timing, and the logarithms of interest rates are involved in measuring uncertainty. The formulations are subsequently implemented on numerical studies, which utilise unit cash flows, and three case studies: a vertical greening system in Italy, an extensive green roof in Malaysia, and a green roof in the US. The numerical studies are proven to generate typical trends applicable to real cases. The thesis finds that i) the need to consider cash flow uncertainty primarily lies in the estimation of the present worth variance of an investment, and investors ignoring such uncertainty might underestimate the influence of interest rates on the variance; ii) the inclusion of interest rate uncertainty causes interest rates to positively impact present worth variance. A turning point rate exists and gives the largest variance; iii) timing uncertainty could be influential and potentially leads to the rejection of a viable investment option. This

Within the Culex pipiens mosquito complex, there are six contemporarily recognized taxa: Cx. quinquefasciatus, Cx. pipiens f. pipiens, Cx. pipiens f. molestus, Cx. pipiens pallens, Cx. australicus and Cx. globocoxitus. Many phylogenetic aspects within this complex have eluded resolution, such as the relationship of the two Australian endemic taxa to the other four members, as well as the evolutionary origins and taxonomic status of Cx. pipiens pallens and Cx. pipiens f. molestus. Ultimately, insights into lineage relationships within the complex will facilitate a better understanding of differential disease transmission by these mosquitoes. To this end, we have combined publicly available data with our own sequencing efforts to examine these questions.

As the Earth warms, carbon sinks on land and in the ocean will weaken, thereby increasing the rate of warming. Although natural mechanisms contributing to this positive climate-carbon feedback have been evaluated using Earth system models, analogous feedbacks involving human activities have not been systematically quantified. Here we conceptualize and estimate the magnitude of several economic mechanisms that generate a carbon-climate feedback, using the Kaya identity to separate a net economic feedback into components associated with population, GDP, heating and cooling, and the carbon intensity of energy production and transportation. We find that climate-driven decreases in economic activity (GDP) may in turn decrease human energy use and thus fossil fuel CO2 emissions. In a high radiative forcing scenario, such decreases in economic activity reduce fossil fuel emissions by 13% this century, lowering atmospheric CO2 by over 100 ppm in 2100. The natural carbon-climate feedback, in contrast, increases atmospheric CO2 over this period by a similar amount, and thus, the net effect including both feedbacks is nearly zero. Our work highlights the importance of improving the representation of climate-economic feedbacks in scenarios of future change. Although the effects of climate warming on the economy may offset weakening land and ocean carbon sinks, a loss of economic productivity will have high societal costs, potentially increasing wealth inequity and limiting resources available for effective adaptation.

The global urbanization rate is accelerating; however, data limitations have far prevented robust estimations of either global urban expansion or its effects on terrestrial net primary productivity (NPP). Here, using a high resolution dataset of global land use/cover (GlobeLand30), we show that global urban areas expanded by an average of 5694 km2 per year between 2000 and 2010. The rapid urban expansion in the past decade has in turn reduced global terrestrial NPP, with a net loss of 22.4 Tg Carbon per year (Tg C year-1). Although small compared to total terrestrial NPP and fossil fuel carbon emissions worldwide, the urbanization-induced decrease in NPP offset 30% of the climate-driven increase (73.6 Tg C year-1) over the same period. Our findings highlight the urgent need for global strategies to address urban expansion, enhance natural carbon sinks, and increase agricultural productivity.

Substantial quantities of air pollution and related health impacts are ultimately attributable to household consumption. However, how consumption pattern affects air pollution impacts remains unclear. Here we show, of the 1.08 (0.74-1.42) million premature deaths due to anthropogenic PM2.5 exposure in China in 2012, 20% are related to household direct emissions through fuel use and 24% are related to household indirect emissions embodied in consumption of goods and services. Income is strongly associated with air pollution-related deaths for urban residents in which health impacts are dominated by indirect emissions. Despite a larger and wealthier urban population, the number of deaths related to rural consumption is higher than that related to urban consumption, largely due to direct emissions from solid fuel combustion in rural China. Our results provide quantitative insight to consumption-based accounting of air pollution and related deaths and may inform more effective and equitable clean air policies in China.