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Distribution Functions for Environmental and Social Risk Management

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainability in Geographic Science".

Deadline for manuscript submissions: closed (15 December 2020) | Viewed by 2031

Special Issue Editors


E-Mail Website
Guest Editor
Department of Economics, Statistics and Finance, University of Calabria, 87036 Arcavacata, Italy
Interests: statistics; parametric inference; distribution functions; survival analysis
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Economics, Statistics and Finance, University of Calabria, 87036 Arcavacata, Italy
Interests: distribution theory; copula function; inference; income distribution; stochastic frontier analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In different fields of human life, risk management and prediction are crucial activities. The environmental, social and health fields are just few examples of contexts where the quantification and prediction of risk are fundamental in order to improve human life conditions and preserve people from hazards to life, and other damages and economic losses. In this sense, the quantitative evaluation of these aspects represents a more relevant challenge than ever, and the statistical sciences are fully involved in this aim. In particular, random variables and distribution functions are powerful and effective tools for describing and analysing different aspects of risk, such as perception, extreme events and their recurrence. The selection of a suitable distribution often allows us to properly investigate the characteristics of various phenomena and, eventually, the relationship with some possible determinants. This Special Issue on “Distribution functions for environmental and social risk management” will collect research articles on:

Distribution functions for environmental and geo-hydrologic risk analysis

Prediction and prevention of risks

Distribution functions for social perception risk

Risk measures and their statistical estimation

Measurement of the dependence on extreme events

Use of copula functions for risk analysis

Prof. Francesca Condino
Prof. Domma Filippo
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

  • distribution functions
  • parametric inference
  • geo-hydrologic risk
  • social perception risk
  • environmental risk
  • extreme events
  • copula functions

Published Papers (1 paper)

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Research

16 pages, 1304 KiB  
Article
Robust Management of Systemic Risks and Food-Water-Energy-Environmental Security: Two-Stage Strategic-Adaptive GLOBIOM Model
by Tatiana Ermolieva, Petr Havlik, Yuri Ermoliev, Nikolay Khabarov and Michael Obersteiner
Sustainability 2021, 13(2), 857; https://doi.org/10.3390/su13020857 - 16 Jan 2021
Cited by 5 | Viewed by 1741
Abstract
Critical imbalances and threshold exceedances can trigger a disruption in a network of interdependent systems. An insignificant-at-first-glance shock can induce systemic risks with cascading catastrophic impacts. Systemic risks challenge traditional risk assessment and management approaches. These risks are shaped by systemic interactions, risk [...] Read more.
Critical imbalances and threshold exceedances can trigger a disruption in a network of interdependent systems. An insignificant-at-first-glance shock can induce systemic risks with cascading catastrophic impacts. Systemic risks challenge traditional risk assessment and management approaches. These risks are shaped by systemic interactions, risk exposures, and decisions of various agents. The paper discusses the need for the two-stage stochastic optimization (STO) approach that enables the design of a robust portfolio of precautionary strategic and operational adaptive decisions that makes the interdependent systems flexible and robust with respect to risks of all kinds. We established a connection between the robust quantile-based non-smooth estimation problem in statistics and the two-stage non-smooth STO problem of robust strategic–adaptive decision-making. The coexistence of complementary strategic and adaptive decisions induces systemic risk aversion in the form of Value-at-Risk (VaR) quantile-based risk constraints. The two-stage robust decision-making is implemented into a large-scale Global Biosphere Management (GLOBIOM) model, showing that robust management of systemic risks can be addressed by solving a system of probabilistic security equations. Selected numerical results emphasize that a robust combination of interdependent strategic and adaptive solutions presents qualitatively new policy recommendations, if compared to a traditional scenario-by-scenario decision-making analysis. Full article
(This article belongs to the Special Issue Distribution Functions for Environmental and Social Risk Management)
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