ArticleInternational journal of environmental research and public health2022
Policy Analysis on Recycling of Solid Waste Resources in China-Content Analysis Method of CNKI Literature Based on NVivo.
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.
What it found
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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.
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.
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Who cites it
1 citing paper in PubMed.
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The recycling of solid waste resources can effectively alleviate resource shortages and environmental pollution and can promote the sustainable development of an ecological economy and green economy. Therefore, China has set up a series of recycling policies. In order to promote the development of China's solid waste resource recycling industry, and to be able to set up and implement correct policy documents according to real-time dynamics, this study uses NVivo software to analyze the content of 10 Core Journals' documents screened from the China HowNet database and sets up three node classifications and four partition dimensions to build a three-dimensional model for qualitative analysis and emotional analysis. The analysis determines the existing weaknesses of China's solid waste resource recycling policy under four dimensions and puts forward prospects for its future from these four aspects of system, capacity, measures and environment.
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Registered trials
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.