Evidence map›Paper›PMID 36554387›Full record

ArticleInternational journal of environmental research and public health2022

Land-Use Carbon Emissions in the Yellow River Basin from 2000 to 2020: Spatio-Temporal Patterns and Driving Mechanisms.

Mingjie Tian, Zhun Chen, Wei Wang, Taizheng Chen, Haiying Cui

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 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

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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.

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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.

Mingjie TianThe College of Geography and Environmental Science, Henan University, Kaifeng 475000, China.
Zhun ChenSchool of Philosophy and Public Management, Henan University, Kaifeng 475000, China.
Wei WangSchool of Culture Industry and Tourism Management, Henan University, Kaifeng 475000, China.
Taizheng ChenThe College of Geography and Environmental Science, Henan University, Kaifeng 475000, China.ORCID 0000-0002-6290-0624
Haiying CuiSchool of Public Administration, Central China Normal University, Wuhan 430079, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the context of global climate governance, the study of land-use carbon emissions in the Yellow River Basin is crucial to China's "dual-carbon" goal in addition to ecological conservation and the high-quality developments. This paper computed the land-use carbon emissions of 95 cities in the Yellow River Basin from 2000 to 2020 and examined its characteristics with respect to spatio-temporal evolution and driving mechanisms. The findings are as follows: (1) The overall net land-use carbon emissions in the Yellow River Basin rose sharply from 2000 to 2020. (2) From a spatial perspective, the Yellow River Basin's land-use carbon emissions are high in the middle-east and low in the northwest, which is directly tied to the urban development model and function orientation. (3) A strong spatial link exists in the land-use carbon emissions in the Yellow River Basin. The degree of spatial agglomeration among the comparable cities first rose and then fell. "Low-Low" was largely constant and concentrated in the upper reaches, whereas "High-High" was concentrated in the middle and lower reaches with an east-ward migratory trend. (4) The rates of economic development and technological advancement have a major positive driving effect. Moreover, the other components' driving effects fluctuate with time, and significant geographical variance exists. Thus, this study not only provides a rationale for reducing carbon emissions in the Yellow River Basin but also serves as a guide for other Chinese cities with comparable climates in improving their climate governance.

Indexed as

Automobile DrivingRiversCarbonChinaCitiesEconomic DevelopmentHumansCarboncarbon emissiondriving mechanismland-usespatio-temporal patternYellow River Basin

Identifiers

PMID36554387
PMCPMC9778955

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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.