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IJERPH
Special Issues
Enabling Emerging Technologies into Green and Sustainable Development of Environment
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Enabling Emerging Technologies into Green and Sustainable Development of Environment

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Environmental Health".

Deadline for manuscript submissions: closed (2 June 2023) | Viewed by 4971

Special Issue Editors

Prof. Dr. Yang (Jack) Lu

E-Mail Website
Guest Editor
School of E-Business and Logistics, Beijing Technology and Business University, Beijing 100048, China
Interests: blockchain; artificial intelligence; IoT security; deep learning; digital transformation
Special Issues, Collections and Topics in MDPI journals
Dr. Lili Gai

E-Mail Website
Guest Editor
Department of Marketing, College of Business and Engineering, University of Texas of the Permian Basin, Odessa, TX 79762, USA
Interests: digital marketing; marketing research; retailing; international marketing
Prof. Dr. Caiming Zhang

E-Mail Website
Guest Editor
School of Labor Relations and Human Resource, China University of Labor Relations, Beijing 100048, China
Interests: big data and data mining; industry 4.0 and information integration systems; artificial intelligence and intelligent manufacturing systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The objective of this IJERPH Special Issue is to attract and publish excellent research on emerging technologies (big data analytics, artificial intelligence, deep learning, digital-based technologies, etc.) to the development of green and sustainable environment—those which have the potential to deeply impact and profoundly change activities, infrastructures, and businesses as they interact with the environment. We are interested in research concerned with the development of green environment and environmental sustainability. This includes multidisciplinary research, such as green healthcare, smart cities, and environmental accounting. We welcome works addressing healthcare, communities/cities, business/accounting, or other perspectives related to green and sustainable environment.

Papers should address the core theme of emerging technologies in relation to green and sustainable environment; potential topics include, but are not limited to:

  • Digital platforms to build healthcare ecosystems of green environment.
  • How emerging technologies help to create a green healthcare system by changing human behavior.
  • Emerging technologies, including machine learning and AI, that integrate a variety of data to build intelligent organizational processes, business models and strategy for smart cities.
  • Emerging technologies in remote-style resources and services to build smart cities.
  • Digital innovation and transformation in accounting for the development of green and sustainable environment.
  • Data security and privacy issues stemming from the use of emerging technologies related to the development of green and sustainable environment.

Prof. Dr. Yang (Jack) Lu
Dr. Lili Gai
Prof. Dr. Caiming Zhang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • green healthcare
  • sustainability of healthcare
  • green city
  • smart city
  • green accounting
  • environmental accounting
  • data security of green environment

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

17 pages, 3237 KiB  
Article
A Model for Predicting and Grading the Quality of Grain Storage Processes Affected by Microorganisms under Different Environments
by Qingchuan Zhang, Zihan Li, Wei Dong, Siwei Wei, Yingjie Liu and Min Zuo
Int. J. Environ. Res. Public Health 2023, 20(5), 4120; https://doi.org/10.3390/ijerph20054120 - 25 Feb 2023
Cited by 4 | Viewed by 2513
Abstract
Changes in storage environments have a significant impact on grain quality. Accurate prediction of any quality changes during grain storage in different environments is very important for human health. In this paper, we selected wheat and corn, which are among the three major [...] Read more.
Changes in storage environments have a significant impact on grain quality. Accurate prediction of any quality changes during grain storage in different environments is very important for human health. In this paper, we selected wheat and corn, which are among the three major staple grains, as the target grains whose storage monitoring data cover more than 20 regions, and constructed a grain storage process quality change prediction model, which includes a FEDformer-based grain storage process quality change prediction model and a K-means++-based grain storage process quality change grading evaluation model. We select six factors affecting grain quality as input to achieve effective prediction of grain quality. Then, evaluation indexes were defined in this study, and a grading evaluation model of grain storage process quality was constructed using clustering model with the index prediction results and current values. The experimental results showed that the grain storage process quality change prediction model had the highest prediction accuracy and the lowest prediction error compared with other models. Full article
(This article belongs to the Special Issue Enabling Emerging Technologies into Green and Sustainable Development of Environment)
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15 pages, 377 KiB  
Article
The Sustainability of the Fishery Industry and Environmental Development: A Study on Factor Market Distortions
by Sha Yang and Jia Wu
Int. J. Environ. Res. Public Health 2023, 20(4), 3017; https://doi.org/10.3390/ijerph20043017 - 9 Feb 2023
Cited by 3 | Viewed by 1920
Abstract
By reviewing the related research on the distortion of labor, capital, and technical factors, combined with the development and the upgrading status of the marine fishery industry, we used the macro data of the industry to measure the degree of price distortion of [...] Read more.
By reviewing the related research on the distortion of labor, capital, and technical factors, combined with the development and the upgrading status of the marine fishery industry, we used the macro data of the industry to measure the degree of price distortion of its market factors and to construct a Moore-like index and a simplified industrial structure upgrade index based on the fsQCA fuzzy set qualitative comparative analysis. The main content of this paper is related to environment and sustainable development. We found that (1) in the case of low capital factor distortion, the combination of high labor factor distortion and low marine fishery resource distortion will inhibit the rapid upgrading of the marine fishery industry structure; (2) in the case of low capital factor distortion, the combination of low labor factor distortion and high marine fishery resources will also inhibit the rapid upgrading of the marine fishery industry structure; and (3) under the combination of low labor factor distortion and low marine fishery resource factor distortion, regardless of the degree of capital factor distortion, the rapid upgrading of the marine fishery industrial structure will be inhibited; there are only differences in the timing of the impact. The impact of factor distortion on the upgrading of industrial structure lags two and three periods, respectively. Full article
(This article belongs to the Special Issue Enabling Emerging Technologies into Green and Sustainable Development of Environment)
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