Evidence map›Paper›PMID 36231741›Full record

ArticleInternational journal of environmental research and public health2022

Spatial and Temporal Distribution and the Driving Factors of Carbon Emissions from Urban Production Energy Consumption.

Liyuan Fu, Qing Wang

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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.

Liyuan FuSchool of Economics, Liaoning University, Shenyang 110036, China.
Qing WangSchool of Economics, Liaoning University, Shenyang 110036, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Urban production energy consumption produces a large amount of carbon emissions, which is an important source of global warming. This study measures the quantity and intensity of carbon emissions in 30 provinces of China based on urban production energy consumption from 2005-2019, and uses the Dagum Gini coefficient, kernel density estimation, carbon emission classification and spatial econometric model to analyze the spatial and temporal distribution and driving factors of quantity and intensity of carbon emissions from China and regional production energy consumption. It was found that the growth rate of carbon emission quantity and carbon emission intensity of production energy consumption decreased year by year in each province during the study period. The imbalance of carbon emission was strong, with different degrees of increase and decrease, and there were big differences between eastern and western regions. The classification of carbon emissions differed among provinces and there was heterogeneity among regions. The quantity and intensity of carbon emissions of production energy consumption qwre affected by multiple factors, such as industrial structure. This study provides an in-depth comparison of the spatial and temporal distribution and driving factors of quantity and intensity of carbon emissions of production energy consumption across the country and regions, and provides targeted policies for carbon emission reduction across the country and regions, so as to help achieve China's "double carbon" target quickly and effectively.

Indexed as

CarbonCarbon DioxideChinaEconomic DevelopmentGlobal WarmingIndustryCarbonCarbon Dioxidecarbon emission classificationcarbon emissions from production energy consumptiondriving factorregional imbalance

Identifiers

PMID36231741
PMCPMC9566382

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.