Evidence map›Paper›PMID 34236609›Full record

ArticleEnvironmental science and pollution research international2021

Industrialization, urbanization, and carbon emission efficiency of Yangtze River Economic Belt-empirical analysis based on stochastic frontier model.

Caiqing Zhang, Panyu Chen

Abstract read
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Article in Environmental science and pollution research international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

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  4. The Impact of Intermediate Goods Imports on Energy Efficiency: Empirical Evidence from Chinese Cities.International journal of environmental research and public health · 2022
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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

2 authors.

Caiqing ZhangDepartment of Economic Management, North China Electric Power University, Baoding, 071000, China.
Panyu ChenDepartment of Economic Management, North China Electric Power University, Baoding, 071000, China. cpyhd0013@163.com.ORCID http://orcid.org/0000-0002-4368-7629

Funding

National Natural Science Foundation of China 71471061
6 · The paper itself

Abstract

Carbon emission efficiency directly determines the level of green economic development. Based on the panel data of China's Yangtze River Economic Belt (YEB) from 2008 to 2017, this paper uses the stochastic frontier analysis (SFA) model to analyze the overall carbon emission efficiency level, influencing factors, and changing trends, with a view to discussing the relationship between economic development and carbon emission efficiency. The results suggest, first, the overall carbon emission efficiency of the YEB is on an upward trend, but there is still much room for improvement. Second, the impact of industrialization and urbanization on carbon emission efficiency follows a U-shaped. As industrialization and urbanization progress, the impact on carbon emission efficiency shows a downward and then upward trend. Third, due to the rebound effect, technological progress has a slight negative impact on carbon emission efficiency. Energy consumption structure, government intervention, and foreign trade are all negative incentive factors. Therefore, efforts to improve carbon emission efficiency in the YEB should focus on transforming the economic growth model, adjusting the industrial structure, improving the energy consumption structure, and innovating green technology. The research results can provide a reference for the government policymakers to develop a green economy.

Indexed as

RiversUrbanizationCarbonChinaEconomic DevelopmentEfficiencyIndustrial DevelopmentCarbonCarbon emission efficiency;Industrialization;Stochastic frontier analysis;Urbanization;Yangtze River Economic Belt

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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.