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Advances in Statistical Methods for Environmental Applications

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Environmental Sustainability and Applications".

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 447

Special Issue Editors


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Guest Editor
Department of Management and Quantitative Studies, Parthenope University of Naples, 80133 Naples, Italy
Interests: analysis and forecasting of time series; dependence structure; estimation of the tail dependence; road transport emissions
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Management and Quantitative Studies, Parthenope University of Naples, 80133 Naples, Italy
Interests: environmental quality; living conditions; income distribution; economic inequality

Special Issue Information

Dear Colleagues,

Environmental issues have been at the forefront of both public and scientific debate in the last few years, especially during the recent terrible pandemic. Indeed, several scientific articles have focused on various environment aspects to increase the knowledge and foster pro-environmental behaviors.

However, the real success of a scientific article is based on it having accurate data analysis that makes use of good quality data and rigorous approaches to support the conclusions.

Progress in statistical methodology and the availability of many user-friendly software provide many researchers with the opportunity to make a relevant contribution to the existing literature.

The purpose of this Special Issue of Sustainability is to collect works that present interesting empirical applications in environmental topics using advanced statistical methods. Fields of application can include air, water, energy, urban areas, green spaces, waste, biodiversity and climate change issues. They can also cover energy-saving behaviors from individuals and households and investment decisions to prevent and control industrial pollution by firms. The list of statistical methods includes, but is not limited to, machine learning techniques, decision trees, quantile regression, copula function.

We invite you to submit an original research article on this topic. Your manuscript must not have been considered for publication elsewhere.

Prof. Dr. Giovanni De Luca
Prof. Dr. Andrea Regoli
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. Sustainability is an international peer-reviewed open access semimonthly 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 2400 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

  • environment
  • pollution
  • renewable energy
  • statistical learning
  • big data analytics

Published Papers

There is no accepted submissions to this special issue at this moment.
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