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Prediction and Decision-Making Methods in Environmental Sciences
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Prediction and Decision-Making Methods in Environmental Sciences

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

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 6151

Special Issue Editors

Prof. Dr. Yong Liu

E-Mail Website
Guest Editor
School of Business, Jiangnan University, Wuxi 214121, China
Interests: decision analysis; supply chain management; corporate social responsibility
Dr. Jeffrey Yi-Lin Forrest

E-Mail Website
Guest Editor
School of Business, Pennsylvania State System of Higher Education (Slippery Rock campus), 1 Morrow Way, Slippery Rock, PA 16057, USA
Interests: decision analysis; mathematical and general systems theory and applications; statistics; regional economics; nonlinear analysis and applications; management science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Environmental protection and sustainable development are conducive to the promotion of the unification of ecological, economic, and social benefits as well as the sustainable, stable, and healthy development of the national economy.

Environmental prediction and decision-making involve a wide range of factors and strong comprehensiveness, involving many fields of natural science and social science, etc., as well as a unique research object. To fully discover the features of the underlying environmental problems and make scientific and reasonable decisions, it is necessary to construct some novel prediction and decision-making models and methods. There are various approaches, such as grey prediction, BP, the Fuzzy multiple attribute decision-making method, grey decision models, rough set, data envelopment analysis (DEA), game theory, etc.  For example, we can use the grey relational analysis method to construct an index system for urban waterlogging disasters, we can use DEA to measure and analyze the efficiency of green technology innovation, and we can exploit game theory to discuss corporate social responsibility in low-carbon emission reduction. For this Special Issue, we invite authors to submit original research and review articles exploring the issues and applications of environmental decision-making and sustainable development.

Prof. Dr. Yong Liu
Dr. Jeffrey Yi-Lin Forrest
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

  • grey prediction models (GM(1,1), BP, Fuzzy prediction) and multiple-attribute decision methods;
  • fuzzy multiple-attribute decision method;
  • grey decision method (grey relational analysis, grey cluster analysis, grey target decision analysis);
  • statistical analysis method;
  • health management methods;
  • green technology innovation;
  • garbage classification management;
  • green supply chain;
  • corporate social responsibility

Published Papers (3 papers)

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Research

23 pages, 3088 KiB  
Article
Decision Analysis of Manufacturer-Led Closed-Loop Supply Chain Considering Corporate Social Responsibility
by Qi Zhang, Yong Liu and Zhiyang Liu
Int. J. Environ. Res. Public Health 2022, 19(22), 15189; https://doi.org/10.3390/ijerph192215189 - 17 Nov 2022
Viewed by 1148
Abstract
With the rapid development of the economy, a growing number of consumers and enterprises are paying attention to corporate social responsibility (CSR). Meanwhile, there exist a variety of conflicts in closed-loop supply chain management. To analyse and deal with the decision problems of [...] Read more.
With the rapid development of the economy, a growing number of consumers and enterprises are paying attention to corporate social responsibility (CSR). Meanwhile, there exist a variety of conflicts in closed-loop supply chain management. To analyse and deal with the decision problems of the manufacturer-led closed-loop supply chain with CSR, by using the manufacturer Stackelberg game, we construct some basic models considering CSR, and exploit them to analyse the optimal decisions of supply chains with and without CSR under centralized and decentralized decision making and explore the influence of CSR on supply chain, and then we establish a coordination mechanism through two-part tariff. Full article
(This article belongs to the Special Issue Prediction and Decision-Making Methods in Environmental Sciences)
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18 pages, 606 KiB  
Article
Digital Economy, Environmental Regulation and Corporate Green Technology Innovation: Evidence from China
by Chenggang Wang, Tiansen Liu, Yue Zhu, Meng Lin, Wenhao Chang, Xinyu Wang, Dongrong Li, He Wang and Jinsol Yoo
Int. J. Environ. Res. Public Health 2022, 19(21), 14084; https://doi.org/10.3390/ijerph192114084 - 28 Oct 2022
Cited by 23 | Viewed by 2590
Abstract
Background: As human beings enter the digital age, the impact of the digital economy on environmental regulation and corporate green technology innovation (CGTI) is expanding. In order to effectively strengthen the efficacy of environmental regulation and improve the green technology innovation ability of [...] Read more.
Background: As human beings enter the digital age, the impact of the digital economy on environmental regulation and corporate green technology innovation (CGTI) is expanding. In order to effectively strengthen the efficacy of environmental regulation and improve the green technology innovation ability of corporate, this paper conducts in-depth research on the influence process of the digital economy and environmental regulation on the CGTI. Methods: Based on the mediating variable environmental regulation, this paper explores the influence process of the digital economy on CGTI. Combined with empirical analysis methods such as the fixed-effect model, mediating effect model, spatial model and regression analysis, the authors reveal the influence process of the digital economy on CGTI. Results: The digital economy can directly promote the improvement of the green technology innovation level of CGTI. The digital economy can indirectly affect the CGTI through the mediating variable of environmental regulation, marginal effect and spatial spillover effect. Conclusions: The digital economy and CGTI had a significant spatial correlation among different regions in China. In different regions of China, there are significant differences in the relationship between the digital economy, environmental regulation and CGTI. Full article
(This article belongs to the Special Issue Prediction and Decision-Making Methods in Environmental Sciences)
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25 pages, 5068 KiB  
Article
An Optimized Damping Grey Population Prediction Model and Its Application on China’s Population Structure Analysis
by Xiaojun Guo, Rui Zhang, Houxue Shen and Yingjie Yang
Int. J. Environ. Res. Public Health 2022, 19(20), 13478; https://doi.org/10.3390/ijerph192013478 - 18 Oct 2022
Cited by 5 | Viewed by 1795
Abstract
Population, resources and environment constitute an interacting and interdependent whole. Only by scientifically forecasting and accurately grasping future population trends can we use limited resources to promote the sustainable development of society. Because the population system is affected by many complex factors and [...] Read more.
Population, resources and environment constitute an interacting and interdependent whole. Only by scientifically forecasting and accurately grasping future population trends can we use limited resources to promote the sustainable development of society. Because the population system is affected by many complex factors and the structural relations among these factors are complex, it can be regarded as a typical dynamic grey system. This paper introduces the damping accumulated operator to construct the grey population prediction model based on the nonlinear grey Bernoulli model in order to describe the evolution law of the population system more accurately. The new operator can give full play to the principle of new information first and further enhance the ability of the model to capture the dynamic changes of the original data. A whale optimization algorithm was used to optimize the model parameters and build a smooth prediction curve. Through three practical cases related to the size and structure of the Chinese population, the comparison with other grey prediction models shows that the fitting and prediction accuracy of the damping accumulated–nonlinear grey Bernoulli model is higher than that of the traditional grey prediction model. At the same time, the damping accumulated operator can weaken the randomness of the original data sequence, reduce the influence of external interference factors, and enhance the robustness of the model. This paper proves that the new method is simple and effective for population prediction, which can not only grasp the future population change trend more accurately but also further expand the application range of the grey prediction model. Full article
(This article belongs to the Special Issue Prediction and Decision-Making Methods in Environmental Sciences)
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