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

Deadline for manuscript submissions: closed (15 July 2023) | Viewed by 14392

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Special Issue Editors

Prof. Dr. Alessandro Fassò

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Guest Editor

Department of Economics, University of Bergamo, Bergamo, Italy
Interests: environmental data science; statistical models for spatiotemporal data; functional data analysis; air quality; atmospheric measurement uncertainty

Dr. Paolo Maranzano

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Guest Editor

Department of Economics, Management and Statistics (DEMS), University of Milano-Bicocca, 20126 Milano, Italy
Interests: environmetrics; Spatio-temporal statistics; time series modelling; air quality and pollution; energy and environmental econometrics

Special Issue Information

Dear Colleagues,

We are inviting researchers to submit original papers focused on the application of innovative statistical and econometric methodologies to challenges arising from environmental and climate change.

Original papers in the fields of geostatistics, spatial econometrics, data science, and time-series analysis, including case studies on environmental data and comprehensive review papers considering both theoretical and empirical approaches, are welcome in this Special Issue. In particular, articles focusing on the study of air, soil, and water quality in terms of pollution, studies on the effects of agricultural activities on air quality, and analyses of environmental monitoring and protection systems will be considered.

Moreover, the efforts undertaken by international institutions to address climate change and environmental protection necessitate the need to develop theoretical models and empirical studies that assess the impact of such interventions and show evidence of their effectiveness. Submissions on these topics are also of particular interest.

Relevant topics to this Special Issue include, but are not limited to:

Environmental statistics and econometrics;
Environmental data science;
Spatial and spatiotemporal statistics;
Spatial data deep learning;
Time-series analysis;
Statistical machine learning;
Air quality monitoring and control;
Air, soil, and water pollution;
Agriculture emissions.
Climate Change and mitigation policies
Policy impact assessment

We look forward to receiving your contributions.

Prof. Dr. Alessandro Fassò
Dr. Paolo Maranzano
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

environmental statistics and econometrics
spatiotemporal statistics
time series
air, soil and water pollution
climate change and mitigation policies assessment
agriculture emissions

Published Papers (6 papers)

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Research

18 pages, 524 KiB

Open AccessArticle

More Accurate Climate Trend Attribution by Using Cointegrating Vector Time Series Models

by David B. Stephenson, Alemtsehai A. Turasie and Donald P. Cummins

Sustainability 2023, 15(16), 12142; https://0-doi-org.brum.beds.ac.uk/10.3390/su151612142 - 08 Aug 2023

Viewed by 905

Abstract

Adapting to human-induced climate change is becoming an increasingly important aspect of sustainable development. To be able to do this effectively, it is important to know how much human influence has contributed to observed climate trends. Climate detection and attribution (D&A) studies achieve this by estimating scaling factors usually obtained by performing a least squares regression of the observed trending climate variable on the equivalent variable simulated by a climate model. This study proposed instead to estimate scaling factors by using the econometric approach of dynamically modelling the time series as a cointegrating Vector Auto-Regressive (VAR) time series process. It is shown that a 2nd-order cointegrating VAR(2) model is theoretically justified if the observed and simulated variables can be represented as a one-box AR(1) response to a common integrated forcing. The VAR(2) model can be expressed as a Vector Error-Correction Model (VECM) and then fitted to the data to obtain the cointegration relationship, the stationary linear combination of the two variables, from which the scaling factor is then easily obtained. Estimates of the scaling factor from the VAR(2) model are critically compared to those from Ordinary Least Squares (OLS) and Total Least Squares (TLS) for annual Global Mean Surface Temperature (GMST) data simulated by a simple stochastic model of the carbon–climate system and for historical simulations from 16 climate models in the Coupled Model Intercomparison Project 5 (CMIP5) experiment. Results from the toy model simulations show that the slope estimates from OLS are negatively biased, TLS estimates are less biased but have high variance, and the VAR(2) estimates are unbiased and have lower variance and provide the most accurate estimates with smallest mean squared error. Similar behaviour is noted in the CMIP5 data. Hypothesis tests on the VAR(2) fits found strong evidence of a cointegrating relationship with the observations for all the CMIP5 simulations. Full article

(This article belongs to the Special Issue Statistics and Econometrics of Environment and Climate Change)

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Figure 1

22 pages, 5071 KiB

Open AccessArticle

Principal Component Regression Modeling and Analysis of PM₁₀ and Meteorological Parameters in Sarajevo with and without Temperature Inversion

by Mirza Pasic, Halima Hadziahmetovic, Ismira Ahmovic and Mugdim Pasic

Sustainability 2023, 15(14), 11230; https://0-doi-org.brum.beds.ac.uk/10.3390/su151411230 - 19 Jul 2023

Viewed by 849

Abstract

The specific geographic location of Sarajevo, which is located in a valley surrounded by mountains, provides the opportunity to analyze the relation between the concentration of

P M_{10}

and meteorological parameters with and without temperature inversion. The main aim of this paper [...] Read more.

The specific geographic location of Sarajevo, which is located in a valley surrounded by mountains, provides the opportunity to analyze the relation between the concentration of

P M_{10}

and meteorological parameters with and without temperature inversion. The main aim of this paper was to develop forecasting models of the hourly average of

P M_{10}

values in the Sarajevo urban area based on meteorological parameters measured in Sarajevo and on the Bjelasnica mountain with and without temperature inversion by using principal component regression (PCR). Also, this research explored and analyzed the differences in the values of the meteorological parameters and

P M_{10}

in Sarajevo with and without temperature inversion, and the difference in temperatures between Sarajevo and Bjelasnica with temperature inversion using statistical hypothesis testing with a total of 240 hypothesis tests performed. The measurements of meteorological parameters were taken from 2020 to 2022 for both Sarajevo (630 m) and the Bjelasnica mountain (2067 m), which allowed for the identification of time periods with and without temperature inversion, while measurements of

P M_{10}

were taken only in Sarajevo. Data were collected during the heating season (November, December, January, February and March). Since analyses have shown that only January and November had time periods with and without temperature inversion during each hour of the day, a total of seven cases were identified: two cases with and five cases without temperature inversion. For each case, three PCR models were developed using all principal components, backward elimination and eigenvalue principal component elimination criteria (

λ < 1

). A total of 21 models were developed. The performance of the models were evaluated based on the coefficient of determination

R^{2}

and the standard error

S E

. The backward elimination models were shown to have high performances with the highest value of

R^{2} =

97.19 and the lowest value of

S E = 1.32

. The study showed that some principal components with eigenvalues

λ < 1

were significantly related to the independent variable

P M_{10}

and thus were retained in the PCR models. In the study, it was shown that backward elimination PCR was an adequate tool to develop

P M_{10}

forecasting models with high performances and that it could be useful for authorities for early warnings or other action to protect citizens from very harmful pollution. Hypothesis tests showed different relations of meteorological parameters and

P M_{10}

with and without temperature inversion. Full article

(This article belongs to the Special Issue Statistics and Econometrics of Environment and Climate Change)

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Figure 1

20 pages, 1192 KiB

Open AccessArticle

Solar Self-Sufficient Households as a Driving Factor for Sustainability Transformation

by Franz Harke and Philipp Otto

Sustainability 2023, 15(3), 2734; https://0-doi-org.brum.beds.ac.uk/10.3390/su15032734 - 02 Feb 2023

Viewed by 1803

Abstract

We present a model to estimate the technical requirements, including the photovoltaic area and battery capacity, along with the costs, for a four-person household to be 100% electrically self-sufficient in Germany. We model the hourly electricity consumption of private households with quasi-Fourier series and an autoregressive statistical model based on data from Berlin in 2010. Combining the consumption model and remote-sensed hourly solar irradiance data from the ERA5 data set, we find the optimal photovoltaic area and battery capacity that would have been necessary to be self-sufficient in electricity from July 2002 to June 2022. We show that it is possible to build a self-sufficient household with today’s storage technology for private households and estimate the costs expected to do so. Full article

(This article belongs to the Special Issue Statistics and Econometrics of Environment and Climate Change)

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Figure 1

17 pages, 283 KiB

Open AccessArticle

Evaluating the Causal Effects of Emissions Trading Policy on Emission Reductions Based on Nonlinear Difference-In-Difference Model

by Lianyan Fu, Lin Zhou, Peili Wu, Zhichuan Zhu, Zhuoxi Yu and Dehui Wang

Sustainability 2022, 14(23), 15726; https://0-doi-org.brum.beds.ac.uk/10.3390/su142315726 - 25 Nov 2022

Viewed by 3311

Abstract

Based on panel data from 30 provinces, cities, and autonomous regions from 2001 to 2019, this paper uses the nonlinear difference-in-difference (

D I D

) method to estimate the distribution of causal effects of emissions trading policy on emission reduction in Chinese [...] Read more.

Based on panel data from 30 provinces, cities, and autonomous regions from 2001 to 2019, this paper uses the nonlinear difference-in-difference (

D I D

) method to estimate the distribution of causal effects of emissions trading policy on emission reduction in Chinese industrial enterprises, and examines the heterogeneity of the effects. The empirical results show that (1) the emissions trading policy has a significant effect on industrial

S O_{2}

emissions reduction in China, where the reduction effect is larger in non-pilot areas than in pilot areas; (2) the policy effects are not proportional to the regional

S O_{2}

emissions intensity, and the emissions trading policy is not more effective in regions with higher industrial

S O_{2}

emissions intensities. One advantage of this paper is the use of nonlinear

D I D

to estimate the emissions reduction effect, which eliminates the bias problem caused by the strict linearity assumption of the classical

D I D

method. Another advantage is that the combination of the random forest method avoids the subjectivity in the selection of control variables and uses distribution effects for multilevel comparisons. This method improves the validity of estimating the effect of emissions trading policy and provides targeted policy suggestions for the effective promotion of system implementation, all of which have academic and application value. Full article

(This article belongs to the Special Issue Statistics and Econometrics of Environment and Climate Change)

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19 pages, 2874 KiB

Open AccessArticle

Regional Flood Frequency Analysis of the Sava River in South-Eastern Europe

by Igor Leščešen, Mojca Šraj, Biljana Basarin, Dragoslav Pavić, Minučer Mesaroš and Manfred Mudelsee

Sustainability 2022, 14(15), 9282; https://0-doi-org.brum.beds.ac.uk/10.3390/su14159282 - 28 Jul 2022

Cited by 6 | Viewed by 2881

Abstract

Regional flood frequency analysis (RFFA) is a powerful method for interrogating hydrological series since it combines observational time series from several sites within a region to estimate risk-relevant statistical parameters with higher accuracy than from single-site series. Since RFFA extreme value estimates depend on the shape of the selected distribution of the data-generating stochastic process, there is need for a suitable goodness-of-distributional-fit measure in order to optimally utilize given data. Here we present a novel, least-squares-based measure to select the optimal fit from a set of five distributions, namely Generalized Extreme Value (GEV), Generalized Logistic, Gumbel, Log-Normal Type III and Log-Pearson Type III. The fit metric is applied to annual maximum discharge series from six hydrological stations along the Sava River in South-eastern Europe, spanning the years 1961 to 2020. Results reveal that (1) the Sava River basin can be assessed as hydrologically homogeneous and (2) the GEV distribution provides typically the best fit. We offer hydrological-meteorological insights into the differences among the six stations. For the period studied, almost all stations exhibit statistically insignificant trends, which renders the conclusions about flood risk as relevant for hydrological sciences and the design of regional flood protection infrastructure. Full article

(This article belongs to the Special Issue Statistics and Econometrics of Environment and Climate Change)

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19 pages, 3795 KiB

Open AccessEditor’s ChoiceArticle

How COVID-19 Affected GHG Emissions of Ferries in Europe

by Gianandrea Mannarini, Mario Leonardo Salinas, Lorenzo Carelli and Alessandro Fassò

Sustainability 2022, 14(9), 5287; https://0-doi-org.brum.beds.ac.uk/10.3390/su14095287 - 27 Apr 2022

Cited by 7 | Viewed by 3333

Abstract

Unprecedented socioeconomic conditions during the COVID-19 pandemic impacted shipping. We combined ferry CO

_{2}

Unprecedented socioeconomic conditions during the COVID-19 pandemic impacted shipping. We combined ferry CO

_{2}

emissions in Europe (from the EU-MRV) with port call data and vessel parameters, and analysed them using mixed-effects linear models with interactions. We found a generalized reduction in unitary emissions in 2020, confirming its causal relation with COVID-19. Furthermore, for larger ferries, additional and COVID-19-related reductions between 14% and 31% occurred, with the larger reductions for those built before 1999. Ferries operating in the Baltic and Mediterranean Seas experienced comparable reductions in their unitary emissions, but in the North Sea per-ship emissions decreased by an additional 18%. Per-ship emissions at berth, while showing increases or decreases depending on ferry type, did not significantly change at the fleet level. We believe that our methodology may help assess the progress of shipping toward decarbonisation in the presence of external shocks. Full article

(This article belongs to the Special Issue Statistics and Econometrics of Environment and Climate Change)

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