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Record W3015490194 · doi:10.1073/pnas.2004093117

Why carbon pricing is not sufficient to mitigate climate change—and how “sustainability transition policy” can help

2020· article· en· W3015490194 on OpenAlexaff
Daniel Rosenbloom, Jochen Markard, Frank W. Geels, Lea Fuenfschilling

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGreenhouse gasSustainabilityFraming (construction)Carbon priceClimate changeEconomicsCarbon taxClimate change mitigationPaceEnvironmental economics

Abstract

fetched live from OpenAlex

Carbon pricing is often presented as the primary policy approach to address climate change. We challenge this position and offer “sustainability transition policy” (STP) as an alternative. Carbon pricing has weaknesses with regard to five central dimensions: 1) problem framing and solution orientation, 2) policy priorities, 3) innovation approach, 4) contextual considerations, and 5) politics. In order to address the urgency of climate change and to achieve deep decarbonization, climate policy responses need to move beyond market failure reasoning and focus on fundamental changes in existing sociotechnical systems such as energy, mobility, food, and industrial production. The core principles of STP can help tackle this challenge. Many are eager to take more substantive policy steps to address climate change, but pricing carbon pricing alone won’t be sufficient. Image credit: Shutterstock/Shawn Goldberg. Realizing deep decarbonization at the pace necessary to mitigate the worst impacts of climate change has emerged as a pressing challenge for policymakers (1). As a result, the debate about appropriate policy responses has intensified. Many experts and societal actors see carbon pricing as the primary way forward (2⇓–4). Some even use it to argue against other policies, such as fuel efficiency standards. Viewed as the most efficient approach to cut greenhouse gas (GHG) emissions, carbon pricing incentivizes actors to seek the lowest-cost abatement options for their specific circumstances. Consequently, many economists argue that carbon pricing should be the cornerstone of a climate policy response. We question this reasoning. Carbon pricing faces five major issues that limit its use for accelerating deep decarbonization. First, carbon pricing frames climate change as a market failure rather than as a fundamental system problem. Second, it places particular weight on efficiency as opposed to effectiveness. Third, it tends to stimulate the optimization of existing systems rather than transformation. Fourth, it … [↵][1]1To whom correspondence may be addressed. Email: daniel.rosenbloom{at}utoronto.ca. [1]: #xref-corresp-1-1

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)0.009
metaresearch head score (Gemma)0.021
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 score0.013
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.021
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0040.014
Scholarly communication0.0130.019
Open science0.0020.005
Research integrity0.0120.012
Insufficient payload (model declined to judge)0.0100.002

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 head0.133
GPT teacher head0.289
Teacher spread0.157 · 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

Citations334
Published2020
Admission routes1
Has abstractyes

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