Environmetrics最新文献

{"title":"Evaluation of ETAS and STEP Forecasting Models for California Seismicity Using Point Process Residuals","authors":"Joshua Ward,&nbsp;Maximilian Werner,&nbsp;William Savran,&nbsp;Frederic Schoenberg","doi":"10.1002/env.70014","DOIUrl":"https://doi.org/10.1002/env.70014","url":null,"abstract":"<div>\u0000 \u0000 <p>Variants of the Epidemic-Type Aftershock Sequence (ETAS) and Short-Term Earthquake Probabilities (STEP) models have been used for earthquake forecasting and are entered as forecast models in the purely prospective Collaboratory Study for Earthquake Predictability (CSEP) experiment. Previous analyses have suggested the ETAS model offered the best forecast skill for the first several years of CSEP. Here, we evaluate the prospective forecasting ability of the ETAS and STEP one-day forecast models for California from 2013 to 2017, using super-thinned residuals and Voronoi residuals. We find very comparable performance of the two models, with slightly superior performance of the STEP model compared to ETAS according to most metrics.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 4","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143871834","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":"Comparative Analysis of Bootstrap Techniques for Confidence Interval Estimation in Spatial Covariance Parameters With Large Spatial Data","authors":"Zih-Bing Chen,&nbsp;Hao-Yun Huang,&nbsp;Cheng-Xin Yang","doi":"10.1002/env.70015","DOIUrl":"https://doi.org/10.1002/env.70015","url":null,"abstract":"<p>Inconsistent estimation issues in the Matérn covariance function pose significant challenges to constructing confidence intervals using traditional methods. This paper addresses these challenges by employing the bootstrap method and comparing two straightforward approaches: the percentile bootstrap (PB) and the reverse percentile interval (RPI). We assess their efficacy through coverage rates and interval scores, focusing on accuracy and breadth. Theoretically, we prove that PB outperforms RPI, a claim substantiated by simulation experiments showing its superior coverage accuracy and interval scores. Moreover, the simulation results show strongly interdependent phenomena between parameters. Accordingly, by exploring the micro-ergodic parameter's impact, the study provides insights into these findings' underlying factors, particularly relevant for large spatial datasets. In the empirical study, our approach exhibits greater reliability and effectiveness in confidence interval estimation for large datasets with uniformly and non-uniformly distributed locations, as compared to several other methods. Furthermore, we applied the method to sea surface temperature data, demonstrating its strong applicability for analysis. This study provides theoretical insight and practical guidance for constructing confidence intervals, particularly in mitigating inconsistent estimation issues, especially in the context of the Matérn covariance function.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 3","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70015","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836087","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":"Simple Yet Effective: A Comparative Study of Statistical Models for Yearly Hurricane Forecasting","authors":"Pietro Colombo,&nbsp;Raffaele Mattera,&nbsp;Philipp Otto","doi":"10.1002/env.70009","DOIUrl":"https://doi.org/10.1002/env.70009","url":null,"abstract":"<p>In this article, we study the problem of forecasting the next year's number of Atlantic hurricanes, which is relevant in many fields of applications such as land-use planning, hazard mitigation, reinsurance and long-term weather derivative market. Considering a set of well-known predictors, we compare the forecasting accuracy of both machine learning and classical statistical models, showing that the latter may be more adequate than the first. Quantile regression models, which are adopted for the first time for forecasting hurricane numbers, provide the best results. Moreover, we construct a new index showing good properties in anticipating the direction of the future number of hurricanes. We consider different evaluation metrics based on both magnitude forecasting errors and directional accuracy.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 3","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761960","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":"A Non-Parametric Estimation Method of the Population Size in Capture-Recapture Experiments With Right Censored Data","authors":"Anabel Blasco-Moreno,&nbsp;Pedro Puig","doi":"10.1002/env.70013","DOIUrl":"https://doi.org/10.1002/env.70013","url":null,"abstract":"<div>\u0000 \u0000 <p>We present a new non-parametric approach for estimating the total number of animals or species when we only have information on the number of animals or species \u0000 that have been observed once, twice, <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>…</mi>\u0000 </mrow>\u0000 <annotation>$$ dots $$</annotation>\u0000 </semantics></math>, and the number of animals or species that have been observed <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>r</mi>\u0000 </mrow>\u0000 <annotation>$$ r $$</annotation>\u0000 </semantics></math> and more than <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>r</mi>\u0000 </mrow>\u0000 <annotation>$$ r $$</annotation>\u0000 </semantics></math> times. The approach, like the Chao estimator, gives a lower bound on population size while also providing bootstrap confidence intervals. We conducted simulations to compare our estimator to other competing ones in special scenarios with <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>r</mi>\u0000 <mo>=</mo>\u0000 <mn>2</mn>\u0000 </mrow>\u0000 <annotation>$$ r=2 $$</annotation>\u0000 </semantics></math> and 3 and found that it performed quite well. In the case of uncensored samples, we analyze which censoring point is preferable in specific examples, as well as when censoring at <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>r</mi>\u0000 <mo>=</mo>\u0000 <mn>3</mn>\u0000 </mrow>\u0000 <annotation>$$ r=3 $$</annotation>\u0000 </semantics></math> is superior to censoring at <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>r</mi>\u0000 <mo>=</mo>\u0000 <mn>2</mn>\u0000 </mrow>\u0000 <annotation>$$ r=2 $$</annotation>\u0000 </semantics></math>.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 3","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761959","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":"From Climate Chat to Climate Shock: Non-Linear Impacts of Transition Risk in Energy CDS Markets","authors":"Emanuele Campiglio,&nbsp;Luca De Angelis,&nbsp;Paolo Neri,&nbsp;Ginevra Scalisi","doi":"10.1002/env.70012","DOIUrl":"https://doi.org/10.1002/env.70012","url":null,"abstract":"<p>It is still unclear to what extent transition risks are being internalized by financial investors. In this paper, we provide a novel investigation of the impact of media-based measures of transition risks on the credit risk of energy companies, as measured by their credit default swaps (CDS) indices. We include both European and North American markets in the 2010–2020 period. Using linear and non-linear local projections, we find that a transition risk shock affects CDS indices only when combined with tangible physical climate-related impacts. We also find evidence of non-linear cross-border effects, with North American energy companies particularly affected by European dynamics. We suggest that the public reaction in the wake of severe climate-related disasters, which might push policymakers to adopt more decisive climate action, contributes to making the transition-related debate salient in the eyes of credit market actors.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 3","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70012","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143749465","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":"Effects of Climate Change on House Prices in Outdoor Tourism Destinations: A Case Study of Southwestern Colorado","authors":"Kadie S. Clark,&nbsp;J. Isaac Miller","doi":"10.1002/env.70007","DOIUrl":"https://doi.org/10.1002/env.70007","url":null,"abstract":"<div>\u0000 \u0000 <p>We estimate the historical effects of climate change on real estate prices in the San Juan Mountain Region of Southwestern Colorado, an area strongly influenced by outdoor recreation-based tourism, and we use these estimates to make projections for future house prices in the region based on multiple anthropogenic climate forcing scenarios. We find that local warm-season minimum and cold-season temperature and local warm-season maximum temperature have significantly positive long-run relationships with global anthropogenic climate forcing. Moreover, once we control for non-climate factors that affect the housing market, we find that local cold-season precipitation and local warm-season maximum temperature have significant but opposite effects on local house prices. Scenario-based projections suggest that these two effects largely negate each other under any climate scenario, so that effects of climate change on house prices are expected to continue through the end of the century as they have over the past few decades.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 3","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143690027","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":"Joint Modeling of Wind Speed and Wind Direction Through a Conditional Approach","authors":"Eva Murphy,&nbsp;Whitney Huang,&nbsp;Julie Bessac,&nbsp;Jiali Wang,&nbsp;Rao Kotamarthi","doi":"10.1002/env.70011","DOIUrl":"https://doi.org/10.1002/env.70011","url":null,"abstract":"<p>Atmospheric near surface wind speed and wind direction play an important role in many applications, ranging from air quality modeling, building design, wind turbine placement to climate change research. It is therefore crucial to accurately estimate the joint probability distribution of wind speed and direction. In this work, we develop a conditional approach to model these two variables, where the joint distribution is decomposed into the product of the marginal distribution of wind direction and the conditional distribution of wind speed given wind direction. To accommodate the circular nature of wind direction, a von Mises mixture model is used; the conditional wind speed distribution is modeled as a directional dependent Weibull distribution via a two-stage estimation procedure, consisting of a directional binned Weibull parameter estimation, followed by a harmonic regression to estimate the dependence of the Weibull parameters on wind direction. A Monte Carlo simulation study indicates that our method outperforms two other approaches in estimation efficiency: one that utilizes periodic spline quantile regression and another that generates data from the commonly used Abe-Ley distribution for cylindrical data. We illustrate our method by using the output from a regional climate model to investigate how the joint distribution of wind speed and direction may change under some future climate scenarios. Our method indicates significant changes in the variation of wind speed with respect to some directions.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 3","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689690","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":"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}