Environmetrics最新文献_第3页

Discussion on Assessing Predictability of Environmental Time Series With Statistical and Machine Learning Models

IF 1.5 3区环境科学与生态学

Environmetrics Pub Date : 2025-02-05 DOI: 10.1002/env.2900

Francesco Finazzi, Jacopo Rodeschini, Lorenzo Tedesco

引用次数: 0

Multidimensional Spatiotemporal Clustering – An Application to Environmental Sustainability Scores in Europe

IF 1.5 3区环境科学与生态学

Environmetrics Pub Date : 2025-02-04 DOI: 10.1002/env.2893

Caterina Morelli, Simone Boccaletti, Paolo Maranzano, Philipp Otto

{"title":"Multidimensional Spatiotemporal Clustering – An Application to Environmental Sustainability Scores in Europe","authors":"Caterina Morelli, Simone Boccaletti, Paolo Maranzano, Philipp Otto","doi":"10.1002/env.2893","DOIUrl":"https://doi.org/10.1002/env.2893","url":null,"abstract":"<p>The assessment of corporate sustainability performance is extremely relevant in facilitating the transition to a green and low-carbon intensity economy. However, companies located in different areas may be subject to different sustainability and environmental risks and policies. Henceforth, the main objective of this paper is to investigate the spatial and temporal pattern of the sustainability evaluations of European firms. We leverage a large dataset containing information about companies' sustainability performances, measured by MSCI ESG ratings, and geographical coordinates of firms in Western Europe between 2013 and 2023. By means of a modified version of the Chavent et al. (2018) hierarchical algorithm, we conduct a spatial clustering analysis, combining sustainability and spatial information, and a spatiotemporal clustering analysis, which combines the time dynamics of multiple sustainability features and spatial dissimilarities, to detect groups of firms with homogeneous sustainability performance. We are able to build cross-national and cross-industry clusters with remarkable differences in terms of sustainability scores. Among other results, in the spatio-temporal analysis, we observe a high degree of geographical overlap among clusters, indicating that the temporal dynamics in sustainability assessment are relevant within a multidimensional approach. Our findings help to capture the diversity of ESG ratings across Western Europe and may assist practitioners and policymakers in evaluating companies facing different sustainability-linked risks in different areas.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2893","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143111521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Multivariate Approach for Modeling Spatio-Temporal Agrometeorological Variables

IF 1.5 3区环境科学与生态学

Environmetrics Pub Date : 2025-02-04 DOI: 10.1002/env.2891

Sandra De Iaco, Claudia Cappello, Monica Palma, Klaus Nordhausen

{"title":"A Multivariate Approach for Modeling Spatio-Temporal Agrometeorological Variables","authors":"Sandra De Iaco, Claudia Cappello, Monica Palma, Klaus Nordhausen","doi":"10.1002/env.2891","DOIUrl":"https://doi.org/10.1002/env.2891","url":null,"abstract":"<p>One of the main issues facing agrometeorological studies involves measuring and modeling the evolution of different environmental variables over time; this often requires a dense monitoring network. Spatio-temporal geostatistics has the potential to provide techniques and tools to estimate the spatio-temporal multiple covariance function and define an appropriate multivariate correlation function capable of reliable predictions. This paper presents a spatio-temporal multivariate geostatistical modeling approach based on the joint diagonalization of the empirical covariance matrix evaluated at different spatio-temporal lags. The possibility to consider a reduced number of uncorrelated variables (lower than the number of observed variables) and separately model the spatio-temporal evolution of these uncorrelated components represents a substantial simplification for multivariate modeling. A space–time linear coregionalization model (ST-LCM) with appropriate parametric models for the latent components was fitted to the matrix-valued covariance function estimated for five relevant agrometeorological variables, including evapotranspiration, minimum and maximum humidity, maximum temperature, and precipitation. The analyses highlight how to identify space–time components and choose the corresponding model by evaluating some characteristics of these components, such as symmetry, separability, and type of non-separability. The predictive results of this multivariate study will be of interest for agriculture, in particular for addressing drought emergencies.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2891","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143362245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

P-min-Stable Regression Models for Time Series With Extreme Values of Limited Range

IF 1.5 3区环境科学与生态学

Environmetrics Pub Date : 2025-01-31 DOI: 10.1002/env.2897

Leonardo Brandao Freitas Nascimento, Max Sousa Lima, Luiz H. Duczmal

{"title":"P-min-Stable Regression Models for Time Series With Extreme Values of Limited Range","authors":"Leonardo Brandao Freitas Nascimento, Max Sousa Lima, Luiz H. Duczmal","doi":"10.1002/env.2897","DOIUrl":"https://doi.org/10.1002/env.2897","url":null,"abstract":"<div>\u0000 \u0000 <p>In this paper, a <i>P-min-stable</i> regression model is proposed for a time series of extreme values observed in a limited interval. The model may be useful when the variable or indicator of interest is the minimum value of a series restricted to the unit interval and is related to other variables through a regression structure. The serial extremal dependence is induced through the marginalization of the Kumaraswamy distribution conditioned on a latent <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>α</mi>\u0000 </mrow>\u0000 <annotation>$$ alpha $$</annotation>\u0000 </semantics></math>-stable process. The model is flexible to capture trends, seasonality, and non-stationarity. Some properties of the model are presented, as well as the extremogram of the series. Procedures for estimation and inference are discussed and implemented via an Expectation-Maximization algorithm. As an illustration, the model was used to analyze the minimum relative humidity observed in the Brazilian Amazon.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143121397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

2024 Editorial Collaborators

IF 1.5 3区环境科学与生态学

Environmetrics Pub Date : 2025-01-22 DOI: 10.1002/env.2899

引用次数: 0

Modeling Anisotropy and Non-Stationarity Through Physics-Informed Spatial Regression

IF 1.5 3区环境科学与生态学

Environmetrics Pub Date : 2024-12-05 DOI: 10.1002/env.2889

Matteo Tomasetto, Eleonora Arnone, Laura M. Sangalli

{"title":"Modeling Anisotropy and Non-Stationarity Through Physics-Informed Spatial Regression","authors":"Matteo Tomasetto, Eleonora Arnone, Laura M. Sangalli","doi":"10.1002/env.2889","DOIUrl":"https://doi.org/10.1002/env.2889","url":null,"abstract":"<p>Many spatially dependent phenomena that are of interest in environmental problems are characterized by strong anisotropy and non-stationarity. Moreover, the data are often observed over regions with complex conformations, such as water bodies with complicated shorelines or regions with complex orography. Furthermore, the distribution of the data locations may be strongly inhomogeneous over space. These issues may challenge popular approaches to spatial data analysis. In this work, we show how we can accurately address these issues by spatial regression with differential regularization. We model the spatial variation by a Partial Differential Equation (PDE), defined upon the considered spatial domain. This PDE may depend upon some unknown parameters that we estimate from the data through an appropriate profiling estimation approach. The PDE may encode some available problem-specific information on the considered phenomenon, and permit a rich modeling of anisotropy and non-stationarity. The performances of the proposed approach are compared to competing methods through simulation studies and real data applications. In particular, we analyze rainfall data over Switzerland, characterized by strong anisotropy, and oceanographic data in the Gulf of Mexico, characterized by non-stationarity due to the Gulf Stream.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"35 8","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2889","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143248623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Gradient-Boosted Generalized Linear Models for Conditional Vine Copulas

IF 1.5 3区环境科学与生态学

Environmetrics Pub Date : 2024-12-05 DOI: 10.1002/env.2887

David Jobst, Annette Möller, Jürgen Groß

{"title":"Gradient-Boosted Generalized Linear Models for Conditional Vine Copulas","authors":"David Jobst, Annette Möller, Jürgen Groß","doi":"10.1002/env.2887","DOIUrl":"https://doi.org/10.1002/env.2887","url":null,"abstract":"<p>Vine copulas are flexible dependence models using bivariate copulas as building blocks. If the parameters of the bivariate copulas in the vine copula depend on covariates, one obtains a conditional vine copula. We propose an extension for the estimation of continuous conditional vine copulas, where the parameters of continuous conditional bivariate copulas are estimated sequentially and separately via gradient-boosting. For this purpose, we link covariates via generalized linear models (GLMs) to Kendall's <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>τ</mi>\u0000 </mrow>\u0000 <annotation>$$ tau $$</annotation>\u0000 </semantics></math> correlation coefficient from which the corresponding copula parameter can be obtained. In a second step, an additional covariate deselection procedure is applied. The performance of the gradient-boosted conditional vine copulas is illustrated in a simulation study. Linear covariate effects in low- and high-dimensional settings are investigated separately for the conditional bivariate copulas and the conditional vine copulas. Moreover, the gradient-boosted conditional vine copulas are applied to the multivariate postprocessing of ensemble weather forecasts in a low-dimensional covariate setting. The results show that our suggested method is able to outperform the benchmark methods and identifies temporal correlations better. Additionally, we provide an R-package called boostCopula for this method.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"35 8","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2887","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143248624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modeling Disease Dynamics From Spatially Explicit Capture-Recapture Data

IF 1.5 3区环境科学与生态学

Environmetrics Pub Date : 2024-12-02 DOI: 10.1002/env.2888

Fabian R. Ketwaroo, Eleni Matechou, Matthew Silk, Richard Delahay

{"title":"Modeling Disease Dynamics From Spatially Explicit Capture-Recapture Data","authors":"Fabian R. Ketwaroo, Eleni Matechou, Matthew Silk, Richard Delahay","doi":"10.1002/env.2888","DOIUrl":"https://doi.org/10.1002/env.2888","url":null,"abstract":"<p>One of the main aims of wildlife disease ecology is to identify how disease dynamics vary in space and time and as a function of population density. However, monitoring spatiotemporal and density-dependent disease dynamics in the wild is challenging because the observation process is error-prone, which means that individuals, their disease status, and their spatial locations are unobservable, or only imperfectly observed. In this paper, we develop a novel spatially-explicit capture-recapture (SCR) model motivated by an SCR data set on European badgers (<i>Meles meles</i>), naturally infected with bovine tuberculosis (<i>Mycobacterium bovis</i>, TB). Our model accounts for the observation process of individuals as a function of their latent activity centers, and for their imperfectly observed disease status and its effect on demographic rates and behavior. This framework has the advantage of simultaneously modeling population demographics and disease dynamics within a spatial context. It can therefore generate estimates of critical parameters such as population size; local and global density by disease status and hence spatially-explicit disease prevalence; disease transmission probabilities as functions of local or global population density; and demographic rates as functions of disease status. Our findings suggest that infected badgers have lower survival probability but larger home range areas than uninfected badgers, and that the data do not provide strong evidence that density has a non-zero effect on disease transmission. We also present a simulation study, considering different scenarios of disease transmission within the population, and our findings highlight the importance of accounting for spatial variation in disease transmission and individual disease status when these affect demographic rates. Collectively these results show our new model enables a better understanding of how wildlife disease dynamics are linked to population demographics within a spatiotemporal context.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2888","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143110329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Calibrating Satellite Maps With Field Data for Improved Predictions of Forest Biomass

IF 1.5 3区环境科学与生态学

Environmetrics Pub Date : 2024-11-28 DOI: 10.1002/env.2892

Paul B. May, Andrew O. Finley

{"title":"Calibrating Satellite Maps With Field Data for Improved Predictions of Forest Biomass","authors":"Paul B. May, Andrew O. Finley","doi":"10.1002/env.2892","DOIUrl":"https://doi.org/10.1002/env.2892","url":null,"abstract":"<div>\u0000 \u0000 <p>Spatially explicit quantification of forest biomass is important for forest-health monitoring and carbon accounting. Direct field measurements of biomass are laborious and expensive, typically limiting their spatial and temporal sampling density and therefore the precision and resolution of the resulting inference. Satellites can provide biomass predictions at a far greater density, but these predictions are often biased relative to field measurements and exhibit heterogeneous errors. We developed and implemented a coregionalization model between sparse field measurements and a predictive satellite map to deliver improved predictions of biomass density at a 1 <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msup>\u0000 <mi>km</mi>\u0000 <mn>2</mn>\u0000 </msup>\u0000 </mrow>\u0000 <annotation>$$ {mathrm{km}}^2 $$</annotation>\u0000 </semantics></math> resolution throughout the Pacific states of California, Oregon and Washington. The model accounts for zero-inflation in the field measurements and the heterogeneous errors in the satellite predictions. A stochastic partial differential equation approach to spatial modeling is applied to handle the magnitude of the satellite data. The spatial detail rendered by the model is much finer than would be possible with the field measurements alone, and the model provides substantial noise-filtering and bias-correction to the satellite map.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143120339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Varying Precision Beta Prime Autoregressive Moving Average Model With Application to Water Flow Data

IF 1.5 3区环境科学与生态学

Environmetrics Pub Date : 2024-11-25 DOI: 10.1002/env.2886

Kleber H. Santos, Francisco Cribari-Neto

{"title":"A Varying Precision Beta Prime Autoregressive Moving Average Model With Application to Water Flow Data","authors":"Kleber H. Santos, Francisco Cribari-Neto","doi":"10.1002/env.2886","DOIUrl":"https://doi.org/10.1002/env.2886","url":null,"abstract":"<div>\u0000 \u0000 <p>We introduce a dynamic model tailored for positively valued time series. It accommodates both autoregressive and moving average dynamics and allows for explanatory variables. The underlying assumption is that each random variable follows, conditional on the set of previous information, the beta prime distribution. A novel feature of the proposed model is that both the conditional mean and conditional precision evolve over time. The model thus comprises two dynamic submodels, one for each parameter. The proposed model for the conditional precision parameter is parsimonious, incorporating first-order time dependence. Changes over time in the shape of the density are determined by the time evolution of two parameters, and not just of the conditional mean. We present simple closed-form expressions for the model's conditional log-likelihood function, score vector, and Fisher's information matrix. Monte Carlo simulation results are presented. Finally, we use the proposed approach to model and forecast two seasonal water flow time series. Specifically, we model the inflow and outflow rates of the reservoirs of two hydroelectric power plants. Overall, the forecasts obtained using the proposed model are more accurate than those yielded by alternative models.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"35 8","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143253408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0