Evidence map›Paper›PMID 32422930›Full record

ArticleInternational journal of environmental research and public health2020

A Multiscale Evaluation of the Coupling Relationship between Urban Land and Carbon Emissions: A Case Study of Chongqing, China.

Chuanlong Li, Yuanqing Li, Kaifang Shi, Qingyuan Yang

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In one paragraph

Article in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
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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

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Chuanlong LiCollege of Resources and Environment, State Cultivation Base of Eco-agriculture for Southwest Mountainous Land, Southwest University, Chongqing 400715, China.
Yuanqing LiChongqing Jinfo Mountain Field Scientific Observation and Research Station for Kaster Ecosystem, Ministry of Education, Southwest University, Chongqing 400715, China.
Kaifang ShiChongqing Jinfo Mountain Field Scientific Observation and Research Station for Kaster Ecosystem, Ministry of Education, Southwest University, Chongqing 400715, China.
Qingyuan YangChongqing Jinfo Mountain Field Scientific Observation and Research Station for Kaster Ecosystem, Ministry of Education, Southwest University, Chongqing 400715, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Exploring the coupling relationship between urban land and carbon emissions (CE) is one of the important premises for coordinating the urban development and the ecological environment. Due to the influence of the scale effect, a systematic evaluation of the CE at different scales will help to develop more reasonable strategies for low-carbon urban planning. However, corresponding studies are still lacking. Hence, two administrative scales (e.g., region and county) in Chongqing were selected as experimental objects to compare and analyze the CE at different scales using the spatiotemporal coupling and coupling coordination models. The results show that urban land and carbon emissions presented a significant growth trend in Chongqing at different scales from 2000 to 2015. The strength of the spatiotemporal coupling relationship between urban land and total carbon emissions gradually increased with increasing scale. At the regional scale, the high coupling coordination between urban land and total carbon emissions was mainly concentrated in the urban functional development region. Additionally, the high coupling coordination between urban land and carbon emission intensity (OI) was still located in the counties within the metropolitan region of Chongqing, but the low OI was mainly distributed in the counties in the northeastern and southeastern regions of Chongqing at the county level. This study illustrates the multiscale trend of CE and suggests differentiated urban land and carbon emission reduction policies for controlling urban land sprawl and reducing carbon emissions.

Indexed as

CarbonUrban RenewalCarbon DioxideChinaEnvironmentCarbonCarbon Dioxidecarbon emissionsChongqingcoupling relationshipscale comparisonurban land

Identifiers

PMID32422930
PMCPMC7277681

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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.