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Enregistrement W3127125962 · doi:10.1029/2020av000364

There Are Several Pathways to Net‐Zero CO<sub>2</sub> Emissions and It's Past Time to Get Moving

2021· article· en· W3127125962 sur OpenAlexaff
Chris Bataille

Notice bibliographique

RevueAGU Advances · 2021
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueAtmospheric and Environmental Gas Dynamics
Établissements canadiensSimon Fraser University
Organismes subventionnairesnon disponible
Mots-clésDamagesGreenhouse gasClimate changeGlobal warmingGlobal temperatureNatural resource economicsUnited Nations Framework Convention on Climate ChangeEnvironmental scienceGeographyClimatologyEconomicsPolitical scienceKyoto Protocol

Résumé

récupéré en direct d'OpenAlex

Over the last decade, many global studies have shown plausible pathways for keeping global temperature increases below 1.5–2°C/2.7–3.6°F above preindustrial levels (IPCC, 2014b, 2018), above which scientists have been warning significant damages are highly likely (IPCC, 2014a, 2014b; U.S. Global Change Research Program, 2017, 2018). The +2°C and +1.5°C temperature goals roughly correspond with net-zero CO2 emissions and deep reductions in other greenhouse gases (GHGs) by 2070 and 2050, followed in the +1.5°C case by 5–20 GtC per year net-negative emissions for the foreseeable future. These global studies are not, however, sufficiently granular for national or regional infrastructure planning and policymaking, which is where the power for climate action lies. Williams et al. (2021) maps out eight regionally detailed scenarios for the United States to achieve net-zero CO2 emissions in time to hold global temperatures below 2°C (3.6F), 1.5°C (2.7F), and 1°C (1.8F) over preindustrial levels later this century if other countries make similar efforts; we currently stand at +1.1°C (2.0F). To the author's knowledge, this work is the first broad exploration of US scenarios compliant with the Paris Agreement that includes all energy-using sectors and natural land sinks since the US Mid-Century Strategy was submitted to the United Nations Framework Convention on Climate Change in November 2016. The study also includes the first that returns to +1.0°C via a 500 Mt CO2 net negative land sink by 2050, a level of ambition beyond most discussion by undoing existing climate damages. Williams et al. (2021) directly address several key sectoral debates, especially the roles of variable wind and solar versus on-demand clean power options (e.g., geothermal, hydrogen turbines, nuclear, or fossil fuels with carbon capture and storage [CCS]), and human-made versus agricultural and land use sinks. While not the end of the story, this project provides a firm foundation for establishing state and federal stakeholder dialogue and adaptive policy moving forward. There is wide agreement on the key strategies for deep decarbonization: (1) demand but not necessarily end-use service reductions (e.g., reduced home heating needs through efficiency and electric heat pumps), (2) decarbonization of the end-use electricity, liquid and gaseous fuels, and feedstocks used by households and firms, and (3) the use of land-use and human-made negative emissions measures, including biomass combustion or direct air CO2 capture (Keith et al., 2018) followed by CCS, to directly return CO2 to the geosphere (Baker et al., 2020; Bataille et al., 2016; Clarke et al., 2014; Grubler et al., 2018; Van Vuuren et al., 2018; Williams et al., 2012). What is newly done by Williams et al. (2021) is the systematic modeling of eight different but related scenarios, specifically for the United States, at a sufficient level of state detail to begin infrastructure planning and sectoral policymaking. These include a least cost pathway, low fossil fuel and renewable cost variations, a low land for renewables variation, delayed electrification, lower overall demand, 100% renewable primary energy, and 500 Mt of net negative emissions to allow a return to +1.0°C. These scenarios address siloed literature debates and provide a systematic treatment of “known unknowns” through diverse scenarios, critical to building confidence for establishing robust, adaptive policy (Waisman et al., 2019). In particular, the authors address the role of variable renewable generation and clean firm power to temporally match electricity demand and supply via multiple scenarios. They find 100% renewables with hydrogen firm power backup is possible, but it is not the least expensive option and needs more land. In the least cost option, firm clean power is provided by hydropower, remaining nuclear units, and fossil methane turbines running infrequently, with their emissions absorbed by biorefineries with CCS and long-lived chemical feedstock sinks. The recent dramatic and continuing fall in the cost of wind and solar photovoltaics, often below the running cost of coal and natural gas plants, means variable renewables provide 80% or more of primary energy in all the scenarios described, and even 90% in the least cost scenario. Industry may bear further exploration, as it still consumes significant amounts of fossil-fuel crude oil and methane in the least cost scenario, and expensively but renewably sourced biomass liquid fuels and gases in the 100% renewable scenario; it's mainly business-as-usual with cleaner feedstocks and CCS. Since the Paris Agreement, which pushed the global target for this century from a maximum −80% reduction to net-zero & negative, transformational technical if not yet commercial means have been established to reduce all industrial emissions to very low or negative levels. These include full thermal or electrocatalytic electrification, use of alternative low GHG heat sources (e.g., solar, biofuels, nuclear), use of hydrogen made via electrolysis or from methane with CCS, use of low carbon feedstocks, and direct CCS where appropriate. The methods used will depend on accelerated innovation and commercialization, regional clean energy resources and amenable CCS geology (Bataille, 2019; Bataille et al., 2018; Friedmann et al., 2019; Leeson et al., 2017; Rissman et al., 2020). Future national net-zero studies should ideally reflect this range of options. One of the intriguing findings, the relatively small costs of 0.2%–1.2% of GDP for reaching net-zero compared to the reference case, not including the benefits of avoided climate damages, suggests another line of inquiry. The authors use a 2% discount rate (which can be thought of as the interest rate paid for loans), a typical rate when valuing society-wide opportunity costs of capital and time. Most CO2 mitigation actions across the economy require more upfront capital but reduce fuel costs over the long term; a lower discount rate values the long-term benefits and costs more, and vice versa. Private, risk-adjusted rates of capital faced by firms and households when borrowing capital are typically at least several percent more than the social discount rate (Murphy & Jaccard, 2011). The upshot is while the net costs to society for reaching net-zero before accounting for reduced climate damages are likely low, from a firm and household perspective their relative individual costs will likely be higher, and this will reduce their incentive to act, e.g. for a household to replace their furnace with an electric heat pump. Given the long lifetimes of residences, buildings, infrastructure and industrial facilities, policymakers must address these challenges, and soon, if the net-zero targets are to be reached. More detailed economic and policy analyses, technically enriched by studies like this which provide a physical map to net-zero, are needed to address these questions. To help start this conversation, the paper's scenarios also serve as the quantitative foundation for America's Zero Carbon Action Plan, a climate policy road map for the new US administration and ambitious states, with contributions from influential American and international technical and policy specialists (https://www.unsdsn.org/Zero-Carbon-Action-Plan). Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,406
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,005
Tête enseignante GPT0,201
Écart entre enseignants0,195 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2021
Routes d'admission1
Résumé présentoui

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