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Record W2254276833 · doi:10.1162/glep_a_00336

Death and Environmental Taxes: Why Market Environmentalism Fails in Liberal Market Economies

2015· article· en· W2254276833 on OpenAlexaboutno aff
Robert MacNeil

Bibliographic record

VenueGlobal Environmental Politics · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Geoengineering
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmentalismEconomicsPoliticsCarbon taxGlobalizationPolitical economyMarket economyInternational political economyClimate policyNeoliberalism (international relations)Ecological modernizationMarket failureGreenhouse gasEconomic policyEconomyPolitical scienceLawNeoclassical economics

Abstract

fetched live from OpenAlex

This article aims to explain why market-based climate policies (carbon levies and emissions trading) have had limited success at the national level in “liberal-market economies” like Australia, Canada, and the United States. This situation is paradoxical to the extent that market environmentalism is often thought to be a concept tailored to the political traditions and policy paradigms in these states. I argue this occurs because precisely in such economies, workers have been the least protected from the market and the effects of globalization, leading to a squeeze on incomes and public services, and providing fertile ground for a virulently antitax politics. When coupled with the disproportionately carbon-intensive lifestyles in these states and the strength of fossil fuel interests, it becomes extremely easy and effective for opponents of climate policy to frame carbon prices as an onerous tax on workers and families. The article explores how this strategy has functioned at a discursive level and considers what this situation implies for climate policy advocates in carbon-intensive, neoliberal polities.

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)0.006
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0060.028
Scholarly communication0.0070.009
Open science0.0010.005
Research integrity0.0060.009
Insufficient payload (model declined to judge)0.0060.001

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 head0.014
GPT teacher head0.203
Teacher spread0.189 · 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 designNot applicable
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

Citations53
Published2015
Admission routes1
Has abstractyes

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