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Record W3162218702 · doi:10.5194/egusphere-egu21-9269

Emergency responses to the climate crisis: The case of direct air capture of CO2

2021· article· en· W3162218702 on OpenAlexaff
Ryan Hanna, Ahmed Abdulla, Yangyang Xu, David G. Victor

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsCarleton University
Fundersnot available
KeywordsFinancial crisisClimate changeEconomicsBusinessNatural resource economicsPolitical scienceMacroeconomicsBiology

Abstract

fetched live from OpenAlex

Global emissions of CO2 have been rising at 1–2% per year, and the gap between emissions and what is needed to stop warming at aspirational goals like 1.5ºC is growing. To stabilize warming at 1.5ºC, most studies find that societies must rapidly decarbonize their economy while also removing CO2 previously emitted to the atmosphere. In response to these realities, dozens of national governments, thousands of local administrative governments, and scores of scientists have made formal declarations of a climate crisis that demands a crisis response. In times of crisis, such as war or pandemics, many barriers to policy expenditure and implementation are eclipsed by the need to mobilize aggressively around new missions; and policymaking forged in crisis often reinforces incumbents such as industrial producers. Though highly motivated to slow the climate crisis, governments may struggle to impose costly polices on entrenched interest groups and incumbents, resulting in less mitigation and therefore a greater need for negative emissions. We model wartime-like crash deployment of CO2 direct air capture (DAC) as a policy response to the climate crisis, calculating (1) the crisis-level financial resources which could be made available for DAC; (2) deployment of DAC plants paired with all combinations of scalable energy supplies and the volumes of CO2 each combination could remove from the atmosphere; and (3) the effects of such a program on atmospheric CO2 concentration and global mean surface temperature. Government expenditure directed to crises has varied, but on average may be about 5% of national GDP. Thus, we calculate that an emergency DAC program with annual investment of 1.2–1.9% of global GDP (anchored on 5% of US GDP; $1–1.6 trillion) removes 2.2–2.3 GtCO2 yr–1 in 2050, 13–20 GtCO2 yr–1 in 2075, and 570–840 GtCO2 cumulatively over 2025–2100. Though comprising several thousand plants, the DAC program cannot substitute for conventional mitigation: compared to a future in which policy efforts to control emissions follow current trends (SSP2-4.5), DAC substantially hastens the onset of net-zero CO2 emissions (to 2085–2095) and peak warming (to 2090–2095); yet warming still reaches 2.4–2.5ºC in 2100. Only with substantial cuts to emissions (SSP1-2.6) does the DAC program hold temperature rise to 2ºC. Achieving such massive CO2 removals hinges on near-term investment to boost the future capacity for upscaling. With such prodigious funds, the constraints on DAC deployment in the 2–3 decades following the start of the program are not money but scalability. Early deployments are important because they help drive the technology down its learning curve (indeed, in the long run, initial costs matter less than performance ceilings); they are also important because they increase the potential for future rapid upscaling. Deployment of DAC need not wait for fully decarbonized power grids: we find DAC to be most cost-effective when paired with electricity sources already available today: hydropower and natural gas with renewables; fully renewable systems are more expensive because their low load factors do not allow efficient amortization of capital-intensive DAC plants.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.033
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0120.005
Scholarly communication0.0070.007
Open science0.0020.007
Research integrity0.0130.010
Insufficient payload (model declined to judge)0.0110.002

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 head0.011
GPT teacher head0.239
Teacher spread0.228 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations0
Published2021
Admission routes1
Has abstractyes

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