Evidence map›Paper›PMID 35954666›Full record

ArticleInternational journal of environmental research and public health2022

A Document Analysis of Peak Carbon Emissions and Carbon Neutrality Policies Based on a PMC Index Model in China.

Chenrui Lu, Bing Wang, Tinggui Chen, Jianjun Yang

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 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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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3 · Its place in the literature

Who cites it

10 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

4 authors.

Chenrui LuSchool of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou 310018, China.
Bing WangSchool of Artificial Intelligence and Electronic Commerce, Hangzhou College of Commerce, Zhejiang Gongshang University, Hangzhou 311500, China.
Tinggui ChenSchool of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou 310018, China.ORCID 0000-0001-6061-2517
Jianjun YangDepartment of Computer Science and Information Systems, University of North Georgia, Oakwood, GA 30566, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the commitment to peaking carbon emissions by 2030 and achieving carbon neutrality by 2060, the text analysis of policies related to peak carbon emissions and carbon neutrality has become a hot research topic in China. However, current domestic and foreign research mainly focuses on the impact and enlightenment of carbon emission measurement and other aspects and rarely optimizes the road to carbon neutrality through the analysis of policy texts. Based on both domestic and international research results, this paper takes 11 central government, ministry, province, and city policies as the research object, uses the PMC index model to calculate the PMC indices of the 11 representative documents, and draws surfaces. The results indicate that nearly half of the policies have excellent scores, but some policies still have deficiencies in terms of guarantee incentives and policy coverage. Given these shortcomings, this paper proposes that the government should provide technical assistance to industrial enterprises in addition to certain subsidies to reduce the energy consumption of enterprises in production and achieve sustainable development. While popularizing and developing low-carbon technology, enterprises should pay attention to personnel training and management, and use the digital economy to empower the transition to eco-friendly production. Based on the above research, the main contributions of this paper are as follows: to make theoretical and practical preparations for carbon neutralization and to provide suggestions for optimizing policies.

Indexed as

CarbonIndustryCarbon DioxideChinaEconomic DevelopmentPolicyCarbonCarbon Dioxidecarbon neutralitypeak carbon emissionsPMCpolicy evaluation

Identifiers

PMID35954666
PMCPMC9368600

What OpenQuestion holds

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LicenceCC BY
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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.