Environmetrics最新文献

{"title":"Carbon Tax Versus Renewable Energy Innovation: Theoretical Insights and Empirical Evidence","authors":"Amit Roy,&nbsp;Pu Chen,&nbsp;Willi Semmler","doi":"10.1002/env.70010","DOIUrl":"https://doi.org/10.1002/env.70010","url":null,"abstract":"<div>\u0000 \u0000 <p>In European countries, carbon pricing is often viewed as a primary strategy to combat climate change and climate risks by reducing carbon emissions and driving investment into cleaner energy sources. Decarbonization has also been suggested by directed technical change, which implements innovative renewable energy technology. We study the effectiveness of both policies for selected Northern EU countries. In a model-based investigation, we first compare optimizing and behavioral drivers of decarbonization with a focus on the two decarbonization policies. Econometrically we use local projection and the VAR method to explore the effects of both policies, carbon tax and directed technical change on GDP and emission reduction. Our results show that—although both policies are needed–significant technology-oriented policy actions on the supply side of renewable energy appear to be required to accelerate the decarbonization of the economies.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 3","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689688","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}

{"title":"Correction to “Assessing Predictability of Environmental Time Series With Statistical and Machine Learning Models”","authors":"","doi":"10.1002/env.70008","DOIUrl":"https://doi.org/10.1002/env.70008","url":null,"abstract":"<p>\u0000 <span>Newlands, N.K.</span> and <span>Lyubchich, V.</span> <span>2025</span>. “ <span>Assessing Predictability of Environmental Time Series With Statistical and Machine Learning Models</span>.” <i>Environmetrics</i> <span>36</span>(<span>2</span>), e70000. https://doi.org/10.1002/env.70000.</p><p>In the initial published version of this article, the title was incorrect. Below is the corrected article title:</p><p><b>Discussion on “Assessing Predictability of Environmental Time Series With Statistical and Machine Learning Models”</b></p><p>We apologize for this error.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143497196","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}

{"title":"New Parametric Approach for Modeling Hydrological Data: An Alternative to the Beta, Kumaraswamy, and Simplex Models","authors":"Thiago A. N. De Andrade,&nbsp;Frank Gomes-Silva,&nbsp;Indranil Ghosh","doi":"10.1002/env.70006","DOIUrl":"https://doi.org/10.1002/env.70006","url":null,"abstract":"<div>\u0000 \u0000 <p>We propose a new approach of continuous distributions in the unit interval, focusing on hydrological applications. This study presents the innovative two-parameter model called <i>modified exponentiated generalized</i> (MEG) distribution. The efficiency of the MEG distribution is evidenced through its application to 29 real datasets representing the percentage of useful water volume in hydroelectric power plant reservoirs in Brazil. The model outperforms the beta, simplex, and Kumaraswamy (KW) distributions, which are widely used for this type of analysis. The connection of our proposal with classical distributions, such as the Fréchet and KW distribution, broadens its applicability. While the Fréchet distribution is recognized for its usefulness in modeling extreme values, the proximity to KW allows a comprehensive analysis of hydrological data. The simple and tractable analytical expressions of the MEG's density and cumulative and quantile functions make it computationally feasible and particularly attractive for practical applications. Furthermore, this work highlights the relevance of the related reflected model: the <i>reflected modified exponentiated generalized distribution</i>. This contribution is expected to improve the statistical modeling of hydrological phenomena and provide new perspectives for future scientific investigations.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489971","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}

{"title":"The Effect of the North Atlantic Oscillation on Monthly Precipitation in Selected European Locations: A Non-Linear Time Series Approach","authors":"Changli He,&nbsp;Jian Kang,&nbsp;Annastiina Silvennoinen,&nbsp;Timo Teräsvirta","doi":"10.1002/env.2896","DOIUrl":"https://doi.org/10.1002/env.2896","url":null,"abstract":"<p>In this article, the relationship between the monthly precipitation in 30 European cities and towns, and two Algerian ones, and the North Atlantic Oscillation (NAO) index is characterized using the Vector Seasonal Shifting Mean and Covariance Autoregressive model, extended to contain exogenous variables. The results, based on monthly time series from 1851 up until 2020, include shifting monthly means for the rainfall series and the estimated coefficients of the exogenous NAO variable. They suggest that in the north and the west, the amount of rain in the boreal winter months has increased or stayed the same during the observation period, whereas in the Mediterranean area, there have been systematic decreases. Results on the North Atlantic Oscillation indicate that the NAO has its strongest effect on precipitation during the winter months. The (negative) effect is particularly strong in Western Europe, Lisbon, and the Mediterranean rim. In contrast, the effect in northern locations is positive for the winter months. The constancy of error variances and correlations is tested and, if rejected, the time-varying alternative is estimated. A spatial relationship between the error correlations and the distance between the corresponding pairs of cities is estimated.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2896","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143481500","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}

{"title":"Discussion on Assessing Predictability of Environmental Time Series With Statistical and Machine Learning Models","authors":"Ansgar Steland","doi":"10.1002/env.70003","DOIUrl":"https://doi.org/10.1002/env.70003","url":null,"abstract":"&lt;p&gt;I congratulate the authors for their interesting and insightful paper. Their findings contribute to the ongoing discussion of the pros and cons of machine learning methods and statistical approaches in environmetrics. In my discussion, I want to address some issues in the design of the comparison study and the interpretation of the results, and add some general thoughts. Moreover, I report about my own analyses of the Indiana citizen science data set used by the authors. Specifically, I developed a non-linear ARIMA regression model with improved heteroscedasticity-consistent uncertainty estimation, which turns out to substantially outperform the best method of (Bonas et al. &lt;span&gt;2025&lt;/span&gt;). I also applied a couple of machine learning approaches not examined there, which I regard as quite accessible general purpose machine learning methods. One of those methods is recommended by the early 2025 state-of-the-art AI large language models. I report about the interesting results in the last section.&lt;/p&gt;&lt;p&gt;The authors argue that for non-linear and non-stationary processes, machine learning methods are typically superior due to their non-parametric structure. But here one should recall that the classical but still popular class of feedforward artificial neural networks trained by backpropagation is non-linear least squares regression with a certain non-linear regression function known up to a parameter vector. In its entirety, this was mainly recognized and elaborated by the econometrician Halbert White, see White (&lt;span&gt;1992&lt;/span&gt;). And, of course, there are many methods classified as traditional statistical approaches which are non-parametric as well. The differences are more with respect to the dimensionality and sample size, and how the methods deal with it. Many machine learning methods such as artificial neural networks fit high-dimensional overparametrized parametric models, whereas statistical methods usually work with specifically chosen stochastic models aiming at parsimony. Such ML methods process high-dimensional inputs as given, and the purpose of the first stage of a model is to learn features from the input data, sometimes being agnostic with respect to the type and meaning of the input variables. The guiding principle is that one only fixes the basic topology (e.g., fully-connected layers or convolutional layers), and the model can extract optimal features provided the learning sample is large enough. This is in contrast to statistical approaches, which tend to design a model for specific types of inputs and deal with high dimensionality by suitable preprocessing steps, human-controlled feature generation and/or specific assumptions and related estimation methods, especially sparse models and sparse estimation, which combine estimation and variable selection. A further distinction is that statistical models and modeling using stochastic processes often impose a certain structure, which is derived from domain knowledge (e.g., physic","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143475634","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}

{"title":"Semiparametric Copula-Based Confidence Intervals on Level Curves for the Evaluation of the Risk Level Associated to Bivariate Events","authors":"Albert Folcher,&nbsp;Jean-François Quessy","doi":"10.1002/env.70005","DOIUrl":"https://doi.org/10.1002/env.70005","url":null,"abstract":"&lt;p&gt;If &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;(&lt;/mo&gt;\u0000 &lt;mi&gt;X&lt;/mi&gt;\u0000 &lt;mo&gt;,&lt;/mo&gt;\u0000 &lt;mi&gt;Y&lt;/mi&gt;\u0000 &lt;mo&gt;)&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ left(X,Yright) $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; is a random pair with distribution function &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;F&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;X&lt;/mi&gt;\u0000 &lt;mo&gt;,&lt;/mo&gt;\u0000 &lt;mi&gt;Y&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;mo&gt;(&lt;/mo&gt;\u0000 &lt;mi&gt;x&lt;/mi&gt;\u0000 &lt;mo&gt;,&lt;/mo&gt;\u0000 &lt;mi&gt;y&lt;/mi&gt;\u0000 &lt;mo&gt;)&lt;/mo&gt;\u0000 &lt;mo&gt;=&lt;/mo&gt;\u0000 &lt;mi&gt;ℙ&lt;/mi&gt;\u0000 &lt;mo&gt;(&lt;/mo&gt;\u0000 &lt;mi&gt;X&lt;/mi&gt;\u0000 &lt;mo&gt;≤&lt;/mo&gt;\u0000 &lt;mi&gt;x&lt;/mi&gt;\u0000 &lt;mo&gt;,&lt;/mo&gt;\u0000 &lt;mi&gt;Y&lt;/mi&gt;\u0000 &lt;mo&gt;≤&lt;/mo&gt;\u0000 &lt;mi&gt;y&lt;/mi&gt;\u0000 &lt;mo&gt;)&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ {F}_{X,Y}left(x,yright)=mathbb{P}left(Xle x,Yle yright) $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;, one can define the level curve of probability &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;p&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ p $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; as the values of &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;(&lt;/mo&gt;\u0000 &lt;mi&gt;x&lt;/mi&gt;\u0000 &lt;mo&gt;,&lt;/mo&gt;\u0000 &lt;mi&gt;y&lt;/mi&gt;\u0000 &lt;mo&gt;)&lt;/mo&gt;\u0000 &lt;mo&gt;∈&lt;/mo&gt;\u0000 &lt;msup&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;ℝ&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msup&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ left(x,yright)in {mathbb{R}}^2 $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; such that &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;F&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;X&lt;/mi&gt;\u0000 &lt;mo&gt;,&lt;/mo&gt;\u0000 &lt;mi&gt;Y&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;mo&gt;(&lt;/mo&gt;\u0000 &lt;mi&gt;x&lt;/mi&gt;\u0000 &lt;mo&gt;,&lt;/mo&gt;\u0000 &lt;mi&gt;y&lt;/mi&gt;\u0000 ","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143456021","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}

{"title":"Discussion on “Assessing Predictability of Environmental Time Series With Statistical and Machine Learning Models”","authors":"Paolo Maranzano,&nbsp;Paul A. Parker","doi":"10.1002/env.70001","DOIUrl":"https://doi.org/10.1002/env.70001","url":null,"abstract":"<p>We contribute to the discussion of the insightful article “Assessing predictability of environmental time series with statistical and machine learning models” by Bonas et al. (2024), in which the authors commend their effort in comparing a wide range of methodologies for the challenging task of predicting environmental time series data. We focus our discussion on two topics of interest to us. First, we consider extensions of the explored methodologies that allow for heteroscedastic error terms. Second, we consider non-Gaussianity and fitting models on transformed data. For both of these points, we will make use of the authors' supplied code and data in order to extend their examples. Ultimately, we find that modeling of heteroscedasticity error terms has the potential to improve both point and interval estimates for these environmental time series. We also find that the use of transformations to handle non-Gaussianity can improve interval estimates.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143431605","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}

{"title":"Stacking Weights and Model Space Selection in Frequentist Model Averaging for Benchmark Dose Estimation","authors":"Jens Riis Baalkilde,&nbsp;Niels Richard Hansen,&nbsp;Signe Marie Jensen","doi":"10.1002/env.70002","DOIUrl":"https://doi.org/10.1002/env.70002","url":null,"abstract":"<p>In dose-response modeling, several models can often yield satisfactory fits to the observed data. The current practice in risk assessment is to use model averaging, which is a way to combine multiple models in a weighted average. A key parameter in risk assessment is the benchmark dose, the dose resulting in a predefined abnormal change in response. Current practice when applying frequentist model averaging is to use weights based on the Akaike Information Criterion (AIC). This paper introduces stacking weights as an alternative for dose-response modeling and generalizes a Diversity Index from dichotomous to continuous responses for model space selection. Three simulation studies were conducted to evaluate the new methods. They showed that, in three realistic scenarios, recommended strategies generally performed well, with stacking weights outperforming AIC weights in several cases. Strategies involving model selection were less effective. However, in a challenging scenario, none of the methods performed well. Due to the promising results of stacking weights, they have been added to the R package “bmd.”</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143431614","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}

{"title":"“Assessing Predictability of Environmental Time Series With Statistical and Machine Learning Models”","authors":"Nathaniel K. Newlands,&nbsp;Vyacheslav Lyubchich","doi":"10.1002/env.70000","DOIUrl":"https://doi.org/10.1002/env.70000","url":null,"abstract":"<p>The relative merits of machine learning and statistical methods are discussed recently by Bonas et al. 2004, who raise important open questions for the statistical community regarding the value-added benefits of data science and the future role of environmental statistics. Specifically, they identify three major knowledge gaps where statistics is seen as crucial to strengthening inference in machine learning (ML): to provide an ML model-based framework amenable to explainability, to determine the best approach for quantifying uncertainty in relation to complex environmental dynamics, and to comprehensively identify ML's value-added benefits. We continue this discussion by exploring these general questions and sharing our perspective and insights from our modeling of marine and terrestrial ecosystem dynamics. We propose several lines of inquiry where environmental statisticians and data scientists could collaboratively advance predictive analytics.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70000","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143431613","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}

{"title":"Does the Quality of Political Institutions Matter for the Effectiveness of Environmental Taxes? An Empirical Analysis on CO2 Emissions","authors":"Donatella Baiardi,&nbsp;Simona Scabrosetti","doi":"10.1002/env.2895","DOIUrl":"https://doi.org/10.1002/env.2895","url":null,"abstract":"<p>Focusing on a sample of 39 countries in the period 1996–2017, we analyze whether the relationship between environmental taxes and CO₂ emissions depends on the quality of political institutions. Our results show that an increase in the environmental tax revenue is related to a reduction in CO₂ emissions only in countries with more consolidated democratic institutions, higher civil society participation, and less corrupt governments. Moreover, the relationship between CO₂ emissions and revenue neutral shifts to different tax sources depends not only on the quality of political institutions, but also on the kind of externality the policymaker aims at correcting.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2895","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143389315","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}