The Pace of Decarbonization: Can the Power System Transition Meet Climate Policy Goals?

To reach net zero greenhouse gas emissions by 2050, the United States will need to simultaneously expand and decarbonize its electricity supply. Aggressive clean energy policies are necessary for the pace of the transition to meet this goal. Policymakers rely on computer modeling to inform decarbonization policies, even though the models were not developed for this purpose. This paper investigates the role of electricity modeling in climate policy design through a case study of Massachusetts. The analysis compares modeling results with recent energy projects in order to highlight the strengths and weaknesses of power sector modeling as a tool to inform policy making. The results show that modeling is useful for identifying technically feasible options and for comparing them based on quantifiable indicators. Models are incapable of identifying socially optimal solutions and estimating achievable pace of decarbonization, because they omit social factors that affect decarbonization goals

What Does it Take to Reduce Massachusetts Emissions 50% by 2030? Challenges Meeting Climate Goals Under Current Legislation (S.2500)

Executive Summary: To do its part in the global fight against climate change, Massachusetts must achieve net zero greenhouse gas emissions by mid-century, and aggressive intermediate goals are essential to ensure that the state is on track for net zero. Senate bill 2500, “An Act setting next generation climate policy,” stipulates that 2030 emissions must “not be less than 50% below the 1990 emissions level.” In 2017, Massachusetts carbon dioxide emissions were 22% below 1990 levels, so the state will need to reduce annual emissions by an additional 28% of 1990 levels by 2030. If enacted, S.2500 would give the state important new tools that would significantly reduce emissions. However, our analysis suggests that additional policies beyond those in S.2500 will likely be necessary to reliably achieve the 2030 goal of cutting emissions in half from 1990 levels. With no new policies enacted (but not accounting for COVID-19), we estimate that 2030 emissions will be roughly 35% below 1990 levels (Figure 1, BAU). We use a range of policy proposals to approximate the key policies in S.2500: the Transportation and Climate Initiative cap and invest program, a net zero stretch building code, and a moderate carbon price ($29/MT rising to$48 in 2030—roughly similar to one in a recent legislative proposal) in the residential, commercial, and industrial sectors. We use published modeling results to approximate these policies and estimate that they would reduce emissions by an additional 6% below 1990 levels (~41%). This leaves an emissions reductions shortfall of ~9% (or 8 million metric tons of CO2, roughly the equivalent of 1.7 million passenger vehicles) in 2030 (see Fig. 1). To reach a 50% reduction by 2030, Massachusetts could implement a higher carbon price (e.g. $58/MT rising to$95 by 2030), which would be possible under S.2500. Some (but not all) models suggest that a higher carbon price alone would be sufficient to reach 50% of 1990 levels by 2030. Another option (not in S.2500) is to enact an ambitious clean electricity standard to reduce electricity emissions. To ensure we reach the 2030 goal, robust policies will be needed in all major sectors of the state\u27s economy, with electricity sector decarbonization particularly important (Fig. 1, Stringent case)

Carbon Neutrality Should Not Be the End Goal: Lessons for Institutional Climate Action From U.S. Higher Education

Aggressive climate action pledges from governments, businesses and institutions have increasingly taken the form of commitments to net carbon neutrality. Higher education institutions (HEIs) are uniquely positioned to innovate in this area, and over 800 U.S. colleges and universities have pledged to achieve net carbon neutrality. Eleven leading U.S. HEIs have already attained this status. Here, we examine their approaches to achieving net carbon neutrality, highlighting risks associated with treating emissions reduction approaches such as carbon offsets, renewable energy certificates, and bioenergy as best practice in isolation from broader policy frameworks. While pursuing net carbon neutrality has led to important institutional shifts toward sustainability, the mix of approaches used by HEIs is out of alignment with a broader U.S. decarbonization roadmap; in aggregate, these carbon neutral schools underutilize electrification and new zero-carbon electricity. We conclude by envisioning how HEIs can refocus climate mitigation efforts towards decarbonization (with net carbon neutrality as a possible milestone), with an emphasis on actions that will help shift policy and markets at larger scales

Meeting Potential New U.S. Climate Goals

We explore the performance of a potential addition to U.S. climate policy using authority under Section 115 of the Clean Air Act, with special attention to distributional effects among the states. This portion of the Act concerns trans-boundary air pollution, and under its provisions a national greenhouse target could be allocated among the states, with the details of state implementation optionally guided by a model rule as under other provisions of the Act. With trading allowed among the states, such a measure could lead to a national price on the covered gases. While we adopt features of a possible Section 115 implementation, the illustrative analysis is applicable to similar cap-and-trade programs that might be adopted under other authorities. We investigate the implications of such a policy using MIT’s U.S. Regional Energy Policy (USREP) model, with its electric sector replaced by the Renewable Energy Development System (ReEDS) model developed by the U.S. National Renewable Energy Laboratory. Existing federal and state climate policies are assumed to remain in place, and a national constraint on CO2 emissions is applied to achieve 45% or 50% reductions below the 2005 level by 2030. We apply the policies in a Baseline and a Low-Cost Baseline, the latter with more aggressive assumptions of technology cost improvements. The U.S. is aggregated to 18 individual states and 12 multi-state regions, and the effects of the national emissions restriction are investigated under three alternative methods by which the EPA might allocate these targets among the states. We find the cost of achieving either target to be modest - allowing for nearly identical economic growth, even without taking account of air quality and climate benefits. The alternative allocation methods generate varying per capita revenue outcomes among states and regions and drive most of the welfare impact through a direct income effect. It is assumed that states distribute permit revenue to their residents in equal lump-sum payments, which leads to net benefits to lower income households. Under the Low-Cost Baseline, carbon prices in 2030 are about ⅓ those in the Baseline, and the overall pre-benefit welfare effects are negligible. Considering climate benefits evaluated using the social cost of carbon and particulate matter air pollution health benefits, less the mitigation costs, we find net benefits in all cases, with slightly larger net benefits with the 50% reduction below 2005 emissions

Spatial ecological complexity measures in GRASS GIS

Good estimates of ecosystem complexity are essential for a number of ecological tasks: from biodiversity estimation, to forest structure variable retrieval, to feature extraction by edge detection and generation of multifractal surface as neutral models for e.g. feature change assessment. Hence, measuring ecological complexity over space becomes crucial in macroecology and geography. Many geospatial tools have been advocated in spatial ecology to estimate ecosystem complexity and its changes over space and time. Among these tools, free and open source options especially offer opportunities to guarantee the robustness of algorithms and reproducibility. In this paper we will summarize the most straightforward measures of spatial complexity available in the Free and Open Source Software GRASS GIS, relating them to key ecological patterns and processes

The Liganding of Glycolipid Transfer Protein Is Controlled by Glycolipid Acyl Structure

Glycosphingolipids (GSLs) play major roles in cellular growth and development. Mammalian glycolipid transfer proteins (GLTPs) are potential regulators of cell processes mediated by GSLs and display a unique architecture among lipid binding/transfer proteins. The GLTP fold represents a novel membrane targeting/interaction domain among peripheral proteins. Here we report crystal structures of human GLTP bound to GSLs of diverse acyl chain length, unsaturation, and sugar composition. Structural comparisons show a highly conserved anchoring of galactosyl- and lactosyl-amide headgroups by the GLTP recognition center. By contrast, acyl chain chemical structure and occupancy of the hydrophobic tunnel dictate partitioning between sphingosine-in and newly-observed sphingosine-out ligand-binding modes. The structural insights, combined with computed interaction propensity distributions, suggest a concerted sequence of events mediated by GLTP conformational changes during GSL transfer to and/or from membranes, as well as during GSL presentation and/or transfer to other proteins

Development and evaluation of low-volume tests to detect and characterize antibodies to SARS-CoV-2

Low-volume antibody assays can be used to track SARS-CoV-2 infection rates in settings where active testing for virus is limited and remote sampling is optimal. We developed 12 ELISAs detecting total or antibody isotypes to SARS-CoV-2 nucleocapsid, spike protein or its receptor binding domain (RBD), 3 anti-RBD isotype specific luciferase immunoprecipitation system (LIPS) assays and a novel Spike-RBD bridging LIPS total-antibody assay. We utilized pre-pandemic (n=984) and confirmed/suspected recent COVID-19 sera taken pre-vaccination rollout in 2020 (n=269). Assays measuring total antibody discriminated best between pre-pandemic and COVID-19 sera and were selected for diagnostic evaluation. In the blind evaluation, two of these assays (Spike Pan ELISA and Spike-RBD Bridging LIPS assay) demonstrated >97% specificity and >92% sensitivity for samples from COVID-19 patients taken >21 days post symptom onset or PCR test. These assays offered better sensitivity for the detection of COVID-19 cases than a commercial assay which requires 100-fold larger serum volumes. This study demonstrates that low-volume in-house antibody assays can provide good diagnostic performance, and highlights the importance of using well-characterized samples and controls for all stages of assay development and evaluation. These cost-effective assays may be particularly useful for seroprevalence studies in low and middle-income countries

Autoantibodies neutralizing type I IFNs are present in ~4% of uninfected individuals over 70 years old and account for ~20% of COVID-19 deaths

Publisher Copyright: © 2021 The Authors, some rights reserved.Circulating autoantibodies (auto-Abs) neutralizing high concentrations (10 ng/ml; in plasma diluted 1:10) of IFN-alpha and/or IFN-omega are found in about 10% of patients with critical COVID-19 (coronavirus disease 2019) pneumonia but not in individuals with asymptomatic infections. We detect auto-Abs neutralizing 100-fold lower, more physiological, concentrations of IFN-alpha and/or IFN-omega (100 pg/ml; in 1:10 dilutions of plasma) in 13.6% of 3595 patients with critical COVID-19, including 21% of 374 patients >80 years, and 6.5% of 522 patients with severe COVID-19. These antibodies are also detected in 18% of the 1124 deceased patients (aged 20 days to 99 years; mean: 70 years). Moreover, another 1.3% of patients with critical COVID-19 and 0.9% of the deceased patients have auto-Abs neutralizing high concentrations of IFN-beta. We also show, in a sample of 34,159 uninfected individuals from the general population, that auto-Abs neutralizing high concentrations of IFN-alpha and/or IFN-omega are present in 0.18% of individuals between 18 and 69 years, 1.1% between 70 and 79 years, and 3.4% >80 years. Moreover, the proportion of individuals carrying auto-Abs neutralizing lower concentrations is greater in a subsample of 10,778 uninfected individuals: 1% of individuals 80 years. By contrast, auto-Abs neutralizing IFN-beta do not become more frequent with age. Auto-Abs neutralizing type I IFNs predate SARS-CoV-2 infection and sharply increase in prevalence after the age of 70 years. They account for about 20% of both critical COVID-19 cases in the over 80s and total fatal COVID-19 cases.Peer reviewe

The risk of COVID-19 death is much greater and age dependent with type I IFN autoantibodies

SignificanceThere is growing evidence that preexisting autoantibodies neutralizing type I interferons (IFNs) are strong determinants of life-threatening COVID-19 pneumonia. It is important to estimate their quantitative impact on COVID-19 mortality upon SARS-CoV-2 infection, by age and sex, as both the prevalence of these autoantibodies and the risk of COVID-19 death increase with age and are higher in men. Using an unvaccinated sample of 1,261 deceased patients and 34,159 individuals from the general population, we found that autoantibodies against type I IFNs strongly increased the SARS-CoV-2 infection fatality rate at all ages, in both men and women. Autoantibodies against type I IFNs are strong and common predictors of life-threatening COVID-19. Testing for these autoantibodies should be considered in the general population

The risk of COVID-19 death is much greater and age dependent with type I IFN autoantibodies

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection fatality rate (IFR) doubles with every 5 y of age from childhood onward. Circulating autoantibodies neutralizing IFN-α, IFN-ω, and/or IFN-β are found in ∼20% of deceased patients across age groups, and in ∼1% of individuals aged 4% of those >70 y old in the general population. With a sample of 1,261 unvaccinated deceased patients and 34,159 individuals of the general population sampled before the pandemic, we estimated both IFR and relative risk of death (RRD) across age groups for individuals carrying autoantibodies neutralizing type I IFNs, relative to noncarriers. The RRD associated with any combination of autoantibodies was higher in subjects under 70 y old. For autoantibodies neutralizing IFN-α2 or IFN-ω, the RRDs were 17.0 (95% CI: 11.7 to 24.7) and 5.8 (4.5 to 7.4) for individuals <70 y and ≥70 y old, respectively, whereas, for autoantibodies neutralizing both molecules, the RRDs were 188.3 (44.8 to 774.4) and 7.2 (5.0 to 10.3), respectively. In contrast, IFRs increased with age, ranging from 0.17% (0.12 to 0.31) for individuals <40 y old to 26.7% (20.3 to 35.2) for those ≥80 y old for autoantibodies neutralizing IFN-α2 or IFN-ω, and from 0.84% (0.31 to 8.28) to 40.5% (27.82 to 61.20) for autoantibodies neutralizing both. Autoantibodies against type I IFNs increase IFRs, and are associated with high RRDs, especially when neutralizing both IFN-α2 and IFN-ω. Remarkably, IFRs increase with age, whereas RRDs decrease with age. Autoimmunity to type I IFNs is a strong and common predictor of COVID-19 death.The Laboratory of Human Genetics of Infectious Diseases is supported by the Howard Hughes Medical Institute; The Rockefeller University; the St. Giles Foundation; the NIH (Grants R01AI088364 and R01AI163029); the National Center for Advancing Translational Sciences; NIH Clinical and Translational Science Awards program (Grant UL1 TR001866); a Fast Grant from Emergent Ventures; Mercatus Center at George Mason University; the Yale Center for Mendelian Genomics and the Genome Sequencing Program Coordinating Center funded by the National Human Genome Research Institute (Grants UM1HG006504 and U24HG008956); the Yale High Performance Computing Center (Grant S10OD018521); the Fisher Center for Alzheimer’s Research Foundation; the Meyer Foundation; the JPB Foundation; the French National Research Agency (ANR) under the “Investments for the Future” program (Grant ANR-10-IAHU-01); the Integrative Biology of Emerging Infectious Diseases Laboratory of Excellence (Grant ANR-10-LABX-62-IBEID); the French Foundation for Medical Research (FRM) (Grant EQU201903007798); the French Agency for Research on AIDS and Viral hepatitis (ANRS) Nord-Sud (Grant ANRS-COV05); the ANR GENVIR (Grant ANR-20-CE93-003), AABIFNCOV (Grant ANR-20-CO11-0001), CNSVIRGEN (Grant ANR-19-CE15-0009-01), and GenMIS-C (Grant ANR-21-COVR-0039) projects; the Square Foundation; Grandir–Fonds de solidarité pour l’Enfance; the Fondation du Souffle; the SCOR Corporate Foundation for Science; The French Ministry of Higher Education, Research, and Innovation (Grant MESRI-COVID-19); Institut National de la Santé et de la Recherche Médicale (INSERM), REACTing-INSERM; and the University Paris Cité. P. Bastard was supported by the FRM (Award EA20170638020). P. Bastard., J.R., and T.L.V. were supported by the MD-PhD program of the Imagine Institute (with the support of Fondation Bettencourt Schueller). Work at the Neurometabolic Disease lab received funding from Centre for Biomedical Research on Rare Diseases (CIBERER) (Grant ACCI20-767) and the European Union's Horizon 2020 research and innovation program under grant agreement 824110 (EASI Genomics). Work in the Laboratory of Virology and Infectious Disease was supported by the NIH (Grants P01AI138398-S1, 2U19AI111825, and R01AI091707-10S1), a George Mason University Fast Grant, and the G. Harold and Leila Y. Mathers Charitable Foundation. The Infanta Leonor University Hospital supported the research of the Department of Internal Medicine and Allergology. The French COVID Cohort study group was sponsored by INSERM and supported by the REACTing consortium and by a grant from the French Ministry of Health (Grant PHRC 20-0424). The Cov-Contact Cohort was supported by the REACTing consortium, the French Ministry of Health, and the European Commission (Grant RECOVER WP 6). This work was also partly supported by the Intramural Research Program of the National Institute of Allergy and Infectious Diseases and the National Institute of Dental and Craniofacial Research, NIH (Grants ZIA AI001270 to L.D.N. and 1ZIAAI001265 to H.C.S.). This program is supported by the Agence Nationale de la Recherche (Grant ANR-10-LABX-69-01). K.K.’s group was supported by the Estonian Research Council, through Grants PRG117 and PRG377. R.H. was supported by an Al Jalila Foundation Seed Grant (Grant AJF202019), Dubai, United Arab Emirates, and a COVID-19 research grant (Grant CoV19-0307) from the University of Sharjah, United Arab Emirates. S.G.T. is supported by Investigator and Program Grants awarded by the National Health and Medical Research Council of Australia and a University of New South Wales COVID Rapid Response Initiative Grant. L.I. reports funding from Regione Lombardia, Italy (project “Risposta immune in pazienti con COVID-19 e co-morbidità”). This research was partially supported by the Instituto de Salud Carlos III (Grant COV20/0968). J.R.H. reports funding from Biomedical Advanced Research and Development Authority (Grant HHSO10201600031C). S.O. reports funding from Research Program on Emerging and Re-emerging Infectious Diseases from Japan Agency for Medical Research and Development (Grant JP20fk0108531). G.G. was supported by the ANR Flash COVID-19 program and SARS-CoV-2 Program of the Faculty of Medicine from Sorbonne University iCOVID programs. The 3C Study was conducted under a partnership agreement between INSERM, Victor Segalen Bordeaux 2 University, and Sanofi-Aventis. The Fondation pour la Recherche Médicale funded the preparation and initiation of the study. The 3C Study was also supported by the Caisse Nationale d’Assurance Maladie des Travailleurs Salariés, Direction générale de la Santé, Mutuelle Générale de l’Education Nationale, Institut de la Longévité, Conseils Régionaux of Aquitaine and Bourgogne, Fondation de France, and Ministry of Research–INSERM Program “Cohortes et collections de données biologiques.” S. Debette was supported by the University of Bordeaux Initiative of Excellence. P.K.G. reports funding from the National Cancer Institute, NIH, under Contract 75N91019D00024, Task Order 75N91021F00001. J.W. is supported by a Research Foundation - Flanders (FWO) Fundamental Clinical Mandate (Grant 1833317N). Sample processing at IrsiCaixa was possible thanks to the crowdfunding initiative YoMeCorono. Work at Vall d’Hebron was also partly supported by research funding from Instituto de Salud Carlos III Grant PI17/00660 cofinanced by the European Regional Development Fund (ERDF/FEDER). C.R.-G. and colleagues from the Canarian Health System Sequencing Hub were supported by the Instituto de Salud Carlos III (Grants COV20_01333 and COV20_01334), the Spanish Ministry for Science and Innovation (RTC-2017-6471-1; AEI/FEDER, European Union), Fundación DISA (Grants OA18/017 and OA20/024), and Cabildo Insular de Tenerife (Grants CGIEU0000219140 and “Apuestas científicas del ITER para colaborar en la lucha contra la COVID-19”). T.H.M. was supported by grants from the Novo Nordisk Foundation (Grants NNF20OC0064890 and NNF21OC0067157). C.M.B. is supported by a Michael Smith Foundation for Health Research Health Professional-Investigator Award. P.Q.H. and L. Hammarström were funded by the European Union’s Horizon 2020 research and innovation program (Antibody Therapy Against Coronavirus consortium, Grant 101003650). Work at Y.-L.L.’s laboratory in the University of Hong Kong (HKU) was supported by the Society for the Relief of Disabled Children. MBBS/PhD study of D.L. in HKU was supported by the Croucher Foundation. J.L.F. was supported in part by the Evaluation-Orientation de la Coopération Scientifique (ECOS) Nord - Coopération Scientifique France-Colombie (ECOS-Nord/Columbian Administrative department of Science, Technology and Innovation [COLCIENCIAS]/Colombian Ministry of National Education [MEN]/Colombian Institute of Educational Credit and Technical Studies Abroad [ICETEX, Grant 806-2018] and Colciencias Contract 713-2016 [Code 111574455633]). A. Klocperk was, in part, supported by Grants NU20-05-00282 and NV18-05-00162 issued by the Czech Health Research Council and Ministry of Health, Czech Republic. L.P. was funded by Program Project COVID-19 OSR-UniSR and Ministero della Salute (Grant COVID-2020-12371617). I.M. is a Senior Clinical Investigator at the Research Foundation–Flanders and is supported by the CSL Behring Chair of Primary Immunodeficiencies (PID); by the Katholieke Universiteit Leuven C1 Grant C16/18/007; by a Flanders Institute for Biotechnology-Grand Challenges - PID grant; by the FWO Grants G0C8517N, G0B5120N, and G0E8420N; and by the Jeffrey Modell Foundation. I.M. has received funding under the European Union’s Horizon 2020 research and innovation program (Grant Agreement 948959). E.A. received funding from the Hellenic Foundation for Research and Innovation (Grant INTERFLU 1574). M. Vidigal received funding from the São Paulo Research Foundation (Grant 2020/09702-1) and JBS SA (Grant 69004). The NH-COVAIR study group consortium was supported by a grant from the Meath Foundation.Peer reviewe