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Record W4391736111 · doi:10.1007/s10644-024-09617-w

Club convergence of sustainable development: fresh evidence from developing and developed countries

2024· article· en· W4391736111 on OpenAlexaboutno aff
Konstantinos Eleftheriou, Peter Nijkamp, Michael Polemis

Bibliographic record

VenueEconomic Change and Restructuring · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Productivity
Canadian institutionsnot available
FundersUniversity of OxfordUniversity of CambridgeHellenic Academic Libraries Link
KeywordsClubDeveloping countryConvergence (economics)Sustainable developmentBusinessPolitical scienceEconomic growthEconomicsBiology

Abstract

fetched live from OpenAlex

Abstract Sustainability is a process that characterizes in a broad sense a nation’s ecological performance and may display a time-varying pattern. Such dynamic trajectories may vary among different countries and prompt not only intriguing questions on space–time convergence but also on the possibility of club convergence. The scope of this study is to investigate the long-run convergence pattern of 137 countries, as presented by their sustainable development index (SDI) over the period 1990–2019. The statistical–econometric analysis used to identify convergence across (groups of) countries is based on the advanced Phillips and Sul (JAE 24:1153–1185, 2009; ECTA 75:1771–1855, 2007) method. The empirical findings from our study allow us to identify two SDI convergence clubs of countries. The first and the biggest club includes mainly the developing African and Asian countries; whereas, the second club includes many OECD countries including inter alia the US, Canada, and Australia. Our analysis brings to light that the transition paths of these two clubs show a significant divergence pattern; this a-symmetry calls also into question the effectiveness of global green policies, such as the clean development mechanism as foreseen in the Kyoto protocol.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.255
Threshold uncertainty score0.932

Codex and Gemma teacher scores by category

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

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 head0.067
GPT teacher head0.237
Teacher spread0.170 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations14
Published2024
Admission routes1
Has abstractyes

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