Emergency responses to the climate crisis: The case of direct air capture of CO2
Notice bibliographique
Résumé
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.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,012 | 0,005 |
| Communication savante | 0,007 | 0,007 |
| Science ouverte | 0,002 | 0,007 |
| Intégrité de la recherche | 0,013 | 0,010 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,002 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».