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Record 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 on OpenAlexaff
Chris Bataille

Bibliographic record

VenueAGU Advances · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsDamagesGreenhouse gasClimate changeGlobal warmingGlobal temperatureNatural resource economicsUnited Nations Framework Convention on Climate ChangeEnvironmental scienceGeographyClimatologyEconomicsPolitical scienceKyoto Protocol

Abstract

fetched live from 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.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.406
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.005
GPT teacher head0.201
Teacher spread0.195 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2021
Admission routes1
Has abstractyes

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