Evidence map›Paper›PMID 39341880›Full record

ArticleScientific reports2024

Forecasting national CO

Lorenzo Costantini, Francesco Laio, Manuel Sebastian Mariani, Luca Ridolfi, Carla Sciarra

Erratum issuedAbstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Lorenzo CostantiniCENTAI, Turin, Italy. lorenzo.costantini@centai.eu.ORCID https://orcid.org/0000-0002-8346-1907
Francesco LaioDIATI, Politecnico di Torino, Turin, 10129, Italy.ORCID https://orcid.org/0000-0003-3931-1482
Manuel Sebastian MarianiURPP Social Networks, University of Zurich, Zurich, CH-8050, Switzerland.ORCID https://orcid.org/0000-0003-1032-5821
Luca RidolfiDIATI, Politecnico di Torino, Turin, 10129, Italy.ORCID https://orcid.org/0000-0003-2947-8641
Carla SciarraDIATI, Politecnico di Torino, Turin, 10129, Italy.ORCID https://orcid.org/0000-0002-2729-5617

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Urgent climate action, especially carbon emissions reduction, is required to achieve sustainable goals. Therefore, understanding the drivers of and predicting [Formula: see text] emissions is a compelling matter. We present two global modeling frameworks-a multivariate regression and a Random Forest Regressor (RFR)-to hindcast (until 2021) and forecast (up to 2035) [Formula: see text] emissions across 117 countries as driven by 12 socioeconomic indicators regarding carbon emissions, economic well-being, green and complexity economics, energy use and consumption. Our results identify key driving features to explain emissions pathways, where beyond-GDP indicators rooted in the Economic Complexity field emerge. Considering current countries' development status, divergent emission dynamics appear. According to the RFR, a -6.2% reduction is predicted for developed economies by 2035 and a +19% increase for developing ones (referring to 2020), thus stressing the need to promote green growth and sustainable development in low-capacity contexts.

Identifiers

PMID39341880
PMCPMC11439049

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.