Evidence map›Paper›PMID 37972354›Full record

ArticleEnvironmental science & technology2023

Factors Driving China's Carbon Emissions after the COVID-19 Outbreak.

Xinlu Sun, Zhifu Mi

Open access · hybridAbstract read
In one paragraph

Article in Environmental science & technology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
14.3field-weighted citation impact, top 2% of its field
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

3 citing papers in PubMed, 25 citations in OpenAlex.

  1. China's Low-Carbon Energy Transition Over the Past Two Decades: Experiences and Implications.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
  2. Article
  3. Article
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

2 authors at 1 institution in 1 country.

Xinlu SunThe Bartlett School of Sustainable Construction, University College London, London WC1E 7HB, U.K.
Zhifu MiThe Bartlett School of Sustainable Construction, University College London, London WC1E 7HB, U.K.ORCID 0000-0001-8106-0694
University College London · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The outbreak of the coronavirus disease 2019 (COVID-19) may exert profound impacts on China's carbon emissions via structural changes. Due to a lack of data, previous studies have focused on quantifying the changes in carbon emissions but have failed to identify structural changes in the determinants of carbon emissions. Here, we use China's latest input-output table and apply structural decomposition analyses to understand the dynamic changes in the determinants of carbon emissions from 2012 to 2020, specifically the impact of COVID-19 on carbon emissions. We find that final demand per capita contributed to emissions growth at a slower pace, but production structure drove a greater carbon emissions increase than before the pandemic. Export-led emissions growth rebounded, and investment-led emissions were more concentrated in the construction sector. The carbon intensity of several heavy industries increased, e.g., the nonmetallic products sector, the metal products sector, and the petroleum, coking, and nuclear fuel sector. In addition, lower production efficiency and increased reliance on carbon-intensive inputs indicated a deterioration in production structure. For policy implications, efforts should be undertaken to increase investment in low-carbon industries and increase the proportion of consumption in GDP to shift investment-led growth to consumption-led growth for an inclusive and green recovery from the pandemic.

Indexed as

COVID-19CarbonCarbon DioxideChinaEconomic DevelopmentHumansIndustryCarbonCarbon DioxideCO2 emissionsgreen recoveryinput−output analysispandemic impactsstructural decomposition analysis

Identifiers

PMID37972354
PMCPMC10702430
OpenAlexW4388718727

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.