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Record W2888257790 · doi:10.3386/w24923

Per Capita Income, Consumption Patterns, and CO₂ Emissions

2018· preprint· en· W2888257790 on OpenAlexaff
Justin Caron, Thibault Fally

Bibliographic record

VenueNational Bureau of Economic Research · 2018
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicEnergy, Environment, Economic Growth
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsPer capitaConsumption (sociology)Agricultural economicsPer capita incomeEconomicsEnvironmental scienceNatural resource economicsGeographyBusinessDemographyPopulation

Abstract

fetched live from OpenAlex

This paper investigates the role of income-driven differences in consumption patterns in explaining and projecting energy demand and CO 2 emissions. We develop and estimate a general-equilibrium model with non-homothetic preferences across a large set of countries and sectors, and trace embodied energy consumption through intermediate use and trade linkages. Consumption of energy goods is less than proportional to income in rich countries, and more income-elastic in low-income countries. While income effects are weaker for embodied energy, we find a significant negative relationship between income elasticity and CO 2 intensity across all goods. These income-driven differences in consumption choices can partially explain the observed inverted-U relationship between income and emissions across countries, the so-called environmental Kuznet curve. Relative to standard models with homothetic preferences, simulations suggest that income growth leads to lower emissions in high-income countries and higher emissions in some low-income countries, with only modest reductions in world emissions on aggregate.

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 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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.153
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.003

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 head0.219
GPT teacher head0.407
Teacher spread0.188 · 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; both teacher heads agree on what is shown here.

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

Citations32
Published2018
Admission routes1
Has abstractyes

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