Shrinking household size and CO

Yanyan Yu, Hao Wang, Hejing Wang, Weiwei Huang, Tana Qian

Abstract read

In one paragraph

Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. 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

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Yanyan YuSchool of Management, Beijing Institute of Technology, Beijing 100081, China.

Hao WangSchool of Management, Beijing Institute of Technology, Beijing 100081, China.

Hejing WangSchool of Management, Beijing Institute of Technology, Beijing 100081, China.

Weiwei HuangSchool of Economics and Management, Zhejiang Normal University, Jinhua, Zhejiang 321004, China.

Tana QianEX Research Institute, 2-17-22 takada toshimaku, Tokyo 171-0033, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Owing to variations in expenditure structure among residents of different household sizes, their impacts on production and consumption activities within the economic system lead to significant variations in carbon footprint characteristics. Based on machine learning and input-output models, we quantify the carbon footprints of residents across different household sizes from 2010 to 2020 and analyze the underlying mechanisms. Per capita expenditure and population scale in one-person households are the main drivers for the increase in direct and indirect CO

Indexed as

carbon footprintexpenditure structurehousehold sizeindustrial structureinput-output model

Identifiers

PMID42602639

DOI10.1016/j.isci.2026.117096

PMCPMC13474243

What OpenQuestion holds

Textmetadata

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.