Evidence map›Paper›PMID 36361363›Full record

ArticleInternational journal of environmental research and public health2022

Ecological Effect Assessment of Low-Carbon City Construction in China.

Juan Yin, Jin Guo

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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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.

Juan YinCollege of Intelligent Science and Control Engineering, Jinling Institute of Technology, Nanjing 211169, China.
Jin GuoSchool of Business, Nanjing Normal University, Nanjing 210023, China.ORCID 0000-0002-1502-7770

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper takes the second batch of low-carbon pilot cities in China as the research object and selects the Urban Health Ecological Index to measure the green development level of cities, aiming to explore and evaluate the theoretical mechanism and policy effect of low-carbon pilot projects to promote the coordinated development of urban economy, society and the environment. The research conclusions show that: ① The low-carbon city pilot project is conducive to support the pilot cities to build a low-carbon industrial system, advocate a low-carbon lifestyle, establish a low-carbon evaluation system, and then play a positive role in promoting the green development level of the city; ② By applying the Propensity Score Matching-Difference in Differences (PSM-DID) model, the empirical analysis finds that after the implementation of the pilot policy, the green development level of low-carbon pilot cities has been significantly improved, and this conclusion is still stable in the parallel trend test, counterfactual test and sample expansion test; ③ In terms of regional heterogeneity, the low-carbon pilot projects have a more significant policy effect on promoting the green development of provincial capitals and eastern cities. Strict administrative supervision in provincial capitals and good economic foundations in eastern cities have had a positive moderating effect on the policy effect of low-carbon pilot projects. Finally, this paper discusses how to realize the ecological effects of low-carbon city pilot projects and put forward some relevant policy suggestions.

Indexed as

CarbonIndustryChinaCitiesEconomic DevelopmentPilot ProjectsCarbonlow-carbon pilot policyPSM-DID modelurban green development

Identifiers

PMID36361363
PMCPMC9656669

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.