Stochastic Environmental Research and Risk Assessment最新文献

{"title":"Risk and retraction: asymmetric nexus between monetary policy uncertainty and eco-friendly investment","authors":"Lansheng Cao, Ding Jin, Sajid Ali, Muhammad Saeed Meo, Raima Nazar","doi":"10.1007/s00477-024-02812-1","DOIUrl":"https://doi.org/10.1007/s00477-024-02812-1","url":null,"abstract":"<p>Monetary policy uncertainty casts long shadows, shaping the future of financial greenscapes by influencing investment decisions and sustainability initiatives, ultimately determining the pace of our transition to a greener, renewable energy-driven economy. This research analyses the asymmetric impact of monetary policy uncertainty on green finance in the top ten advocates of green funding (USA, China, Germany, UK, France, Sweden, Japan, the Netherlands, Canada, and Spain). Moving beyond traditional panel data methods that ignore country-specific nuances, we adopt the Quantile-on-Quantile approach for a more nuanced understanding. This approach enhances accuracy by offering a global overview and detailed insights for each country individually. The findings reveal that monetary policy uncertainty curtails green finance in most of the selected economies across various quantiles. Our estimation underscores the imperative for policymakers to conduct thorough analyses and develop strategies to address the changes in monetary policy uncertainty and green finance at various levels.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"60 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219789","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 nexus between clean energy market risk and US business environment: evidence from wavelet coherence and variance analysis","authors":"Ming Li, Cem Işık, Jiale Yan, Ran Wu","doi":"10.1007/s00477-024-02810-3","DOIUrl":"https://doi.org/10.1007/s00477-024-02810-3","url":null,"abstract":"<p>Given the critical role of the clean energy market in the global economy and environmental sustainability, this paper investigates the impact of the U.S. Business Conditions Index (ADS) on the risk of segmented clean energy markets across different time scales and market conditions, as well as its spillover mechanisms. By using wavelet coherence and wavelet quantile analysis, we examine how the Aruoba–Diebold–Scotti (ADS) Business Conditions Index affects the risk levels of segmented clean energy indices under varying market conditions. To further understand this impact mechanism, we also employ the quantile Granger causality test to analyze the spillover effects of ADS on the clean energy market. The results show that the ADS index significantly influences the risk levels of segmented clean energy markets, with notable differences across various time scales and market conditions. The contributions of this study include: (1) segmenting the measurement of clean energy market risk into the Solar Index (SOLAR), Renewable Energy Index (RE), Biomass Index (BIO), Wind Energy Index (WIND), and Clean Energy Index (WILDER); (2) providing new evidence on the impact of the ADS Business Conditions Index on segmented clean energy market risk; and (3) offering new perspectives for different clean energy market participants to better navigate complex business environments and develop effective risk management strategies.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"75 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219787","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":"Identifying the coupling coordination relationship between cold chain logistics and green finance and its driving factors: evidence from China","authors":"Beifei Yuan, Fengming Tao, Hongfei Chen, Xinyi Zhu, Sha Lai, Yao Zhang","doi":"10.1007/s00477-024-02811-2","DOIUrl":"https://doi.org/10.1007/s00477-024-02811-2","url":null,"abstract":"<p>Achieving the coordination and symbiosis of cold chain logistics and green finance is notably critical for promoting regional green and sustainable development. However, The existing research on the coupling coordination relationship between cold chain logistics and green finance, as well as its driving factors, remains limited and lacks in-depth analysis. This study portrays the coupling coordination degree (CCD) from the perspectives of measurement, spatial patterns, and driving factors in China with multi-source data and the optimal parameters-based geographical detector. Results show that the CCD in China demonstrates an overall increasing trend of fluctuations, along with obvious regional differences. The spatial distribution of the CCD demonstrates a positive correlation, characterized by H-H and L-L clustering. The spatial pattern of the CCD is high in the eastern, southern regions and low in the western, northern regions, this gap is gradually narrowing between the east and west, south and north gap is widening. This spatial pattern is marked by infrastructure, economic factors, human capital, energy intensity, technological factors, and natural factors. Notably, the interactive impact among human capital, financial markets, and digital intelligence technology contributes to further integration, with the impact of individual factors ranging from 7.11 to 632.79%. It offers valuable implications for policymakers and logistics companies for sustainable development, and contributes empirical insights to emerging countries.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"38 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219788","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":"A statistical test of phase difference via wavelet method and its application to the spread of air pollution","authors":"Zhen Wan, Keyao Wei, Yingcun Xia","doi":"10.1007/s00477-024-02801-4","DOIUrl":"https://doi.org/10.1007/s00477-024-02801-4","url":null,"abstract":"<p>Accurate measurement and inference of phase difference between two time series are critical across several fields, including signal processing, economic dynamics, and air pollution research. Wavelet methods offer advantages over traditional approaches by allowing time–frequency localization and adaptability to non-stationary signals, which makes them widely used for phase difference estimation. However, existing methods do not provide a statistical test to determine whether a measured phase difference reflects a true underlying relationship between the signals or is merely an artifact of measurement errors or randomness. In this paper, we propose a bootstrap method to fill this gap. Our method is particularly suited to the analysis of non-standard data distributions and complex temporal dependencies. Extensive simulations demonstrate its desirable power and control of type-I error. Furthermore, we apply the method to study air pollution dispersion in China and elucidate the factors influencing phase differences.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"5 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219790","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":"A comprehensive risk assessment framework for mooring risks at hydrocarbon berths using fuzzy rule-based Bayesian network and multi-attribute decision-making","authors":"Hakan Demirel, Veysi Başhan, Melih Yucesan, Muhammet Gul","doi":"10.1007/s00477-024-02809-w","DOIUrl":"https://doi.org/10.1007/s00477-024-02809-w","url":null,"abstract":"<p>Mooring operations -especially at hydrocarbon berths- are critical components of the marine and offshore industry. They usually involve securing the berths of ships carrying valuable cargo and ensuring the safety of personnel, assets, and the environment. For this purpose, a comprehensive risk assessment framework for mooring operations at hydrocarbon berths is proposed in this study. This framework helps evaluate risks using a rule-based Bayesian network. In the assessment of mooring risks, four risk parameters of severity, occurrence, detection, and maintenance are considered to construct the BN structure of each mooring risk. These parameters are weighted with the aid of the fuzzy Best Worst Method. Hereafter, a fuzzy rule-based system is constructed by incorporating BN to determine a risk priority score. The framework also develops mitigation strategies to maintain effective risk management for safe and secure maritime transportation. Sensitivity analyzes and comparison studies were conducted to test the validity of the proposed comprehensive risk management framework. The study reveals that the most critical risk is associated with Technical Failures (Q1) in the cluster pertaining to the automation of Quick Release Hooks. This risk stems from technical malfunctions in automation systems, encompassing sensors and control mechanisms, potentially resulting in the unintended release of mooring lines. The second highest priority risk is linked to Human Error (M1) in the mooring risks cluster, attributed to human errors such as inadequate training, miscommunication, and procedural mistakes during mooring operations, posing risks of accidents and damage to ships and infrastructure. Conversely, the least significant risk, Redundancy (Q5), focuses on redundancy. This risk is associated with automation and underscores the importance of implementing redundancy mechanisms to ensure the safe continuation of mooring operations in the face of system failures. In conclusion, the proposed comprehensive risk assessment framework offers a systematic approach to evaluate and prioritize mooring risks at hydrocarbon berths. The study’s findings emphasize the critical importance of addressing technical malfunctions in the automation of Quick Release Hooks and human errors during mooring operations. By identifying the most significant risks and developing mitigation strategies, this framework contributes to enhancing the safety and security of maritime transportation, particularly in the context of hydrocarbon berths.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"2 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219791","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":"Toward robust pattern similarity metric for distributed model evaluation","authors":"Eymen Berkay Yorulmaz, Elif Kartal, Mehmet Cüneyd Demirel","doi":"10.1007/s00477-024-02790-4","DOIUrl":"https://doi.org/10.1007/s00477-024-02790-4","url":null,"abstract":"<p>SPAtial EFficiency (SPAEF) metric is one of the most thoroughly used metrics in hydrologic community. In this study, our aim is to improve SPAEF by replacing the histogram match component with other statistical indices, i.e. kurtosis and earth mover’s distance, or by adding a fourth or fifth component such as kurtosis and skewness. The existing spatial metrics i.e. SPAtial efficiency (SPAEF), structural similarity (SSIM) and spatial pattern efficiency metric (SPEM) were compared with newly proposed metrics to assess their converging performance. The mesoscale hydrologic model (mHM) of the Moselle River is used to simulate streamflow (Q) and actual evapotranspiration (AET). The two-source energy balance AET during the growing season is used as monthly reference maps to calculate the spatial performance of the model. The moderate resolution imaging spectroradiometer based leaf area index is utilized by the mHM via pedo-transfer functions and multi-scale parameter regionalization approach to scale the potential ET. In addition to the real monthly AET maps, we also tested these metrics using a synthetic true AET map simulated with a known parameter set for a randomly selected day. The results demonstrate that the newly developed four-component metric i.e. SPAtial Hybrid 4 (SPAH4) slightly outperforms conventional three-component metric i.e. SPAEF (3% better). However, SPAH4 significantly outperforms the other existing metrics i.e. 40% better than SSIM and 50% better than SPEM. We believe that other fields such as remote sensing, change detection, function space optimization and image processing can also benefit from SPAH4.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"76 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219792","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":"Dynamic Bayesian networks for spatiotemporal modeling and its uncertainty in tradeoffs and synergies of ecosystem services: a case study in the Tarim River Basin, China","authors":"Yang Hu, Jie Xue, Jianping Zhao, Xinlong Feng, Huaiwei Sun, Junhu Tang, Jingjing Chang","doi":"10.1007/s00477-024-02805-0","DOIUrl":"https://doi.org/10.1007/s00477-024-02805-0","url":null,"abstract":"<p>Ecosystem services (ESs) refer to the benefits that humans obtain from ecosystems. These services are subject to environmental changes and human interventions, which introduce a significant level of uncertainty. Traditional ES modeling approaches often employ Bayesian networks, but they fall short in capturing spatiotemporal dynamic change processes. To address this limitation, dynamic Bayesian networks (DBNs) have emerged as stochastic models capable of incorporating uncertainty and capturing dynamic changes. Consequently, DBNs have found increasing application in ES modeling. However, the structure and parameter learning of DBNs present complexities within the field of ES modeling. To mitigate the reliance on expert knowledge, this study proposes an algorithm for structure and parameter learning, integrating the InVEST (Integrated Valuation of Ecosystem Services and Trade-Offs) model with DBNs to develop a comprehensive understanding of the spatiotemporal dynamics and uncertainty of ESs in the Tarim River Basin, China from 2000 to 2020. The study further evaluates the tradeoffs and synergies among four key ecosystem services: water yield, habitat quality, sediment delivery ratio, and carbon storage and sequestration. The findings show that (1) the proposed structure learning and parameter learning algorithm for DBNs, including the hill-climb algorithm, linear analysis, the Markov blanket, and the EM algorithm, effectively address subjective factors that can influence model learning when dealing with uncertainty; (2) significant spatial heterogeneity is observed in the supply of ESs within the Tarim River Basin, with notable changes in habitat quality, water yield, and sediment delivery ratios occurring between 2000–2005, 2010–2015, and 2015–2020, respectively; (3) tradeoffs exist between water yield and habitat quality, as well as between soil conservation and carbon sequestration, while synergies are found among habitat quality, soil retention, and carbon sequestration. The land-use type emerges as the most influential factor affecting the tradeoffs and synergies of ESs. This study serves to validate the capacity of DBNs in addressing spatiotemporal dynamic changes and establishes an improved research methodology for ES modeling that considers uncertainty.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"5 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219812","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":"Simultaneous identification of groundwater contamination source information, model parameters, and boundary conditions under an unknown boundary mode","authors":"Zibo Wang, Wenxi Lu, Zhenbo Chang, Yukun Bai, Yaning Xu","doi":"10.1007/s00477-024-02795-z","DOIUrl":"https://doi.org/10.1007/s00477-024-02795-z","url":null,"abstract":"<p>Boundary conditions play a crucial role in groundwater contamination source identification (GCSI), but they may be complex and reliable estimates are difficult to obtain in advance in actual situations. If the estimated values deviate significantly from the actual situation, the GCSI results will be inaccurate. However, very few studies have attempted to identify the boundary conditions in GCSI, and even when they are identified, they are often considered too simple. The boundary mode (<i>B</i>_<i>mode</i>) is assumed to be known, but in reality, it is often unknown and is more complex than initially assumed. Previous practices based on this assumption may not accurately reflect actual situations. Therefore, this study focused on the concentration boundaries, and the boundary conditions were also considered unknown variables, along with contamination source information and model parameters. To alleviate the problem of identifying the boundary conditions under an unknown <i>B</i>_<i>mode</i>, we proposed for the first time to treat the <i>B</i>_<i>mode</i> as an unknown variable. Thus, the source information, model parameters, <i>B</i>_<i>mode</i>, and corresponding parameters in the boundary concentration (<i>BC</i>) function were identified simultaneously. The Differential Evolution Adaptive Metropolis with a Snooker Update and Sampling from a Past Archive (DREAM_(ZS)) algorithm and a Kriging surrogate model were used as the primary means of solution. We designed four different synthetic cases to test the effectiveness of the above ideas. When identifying the <i>B</i>_<i>mode</i>, the obtained <i>BC</i> mostly fitted well with the true <i>BC</i>. It was therefore considered feasible for identifying the <i>B</i>_<i>mode</i>. The performance of the DREAM_(ZS) algorithm was found to be superior to the traditional DREAM algorithm and was more efficient.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"8 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219793","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":"Lite approaches for long-range multi-step water quality prediction","authors":"Md Khaled Ben Islam, M. A. Hakim Newton, Jarrod Trevathan, Abdul Sattar","doi":"10.1007/s00477-024-02770-8","DOIUrl":"https://doi.org/10.1007/s00477-024-02770-8","url":null,"abstract":"<p>Forecasting accurate water quality is very important in aquaculture, environment monitoring, and many other applications. Many internal and external factors influence water quality. Therefore, water quality parameters exhibit complex time series characteristics. Consequently, long-range accurate prediction of water quality parameters suffers from poor propagation of information from past timepoints to further future timepoints. Moreover, to synchronise the prediction model with the changes in the time series characteristics, periodic retraining of the prediction model is required and such retraining is to be done on resource-restricted computation devices. In this work, we present a low-cost training approach to improve long-range multi-step water quality prediction. We train a short-range predictor to save training effort. Then, we strive to achieve and/or improve long-range prediction using multi-step iterative ensembling during inference. Experimental results on 9 water quality datasets demonstrate that the proposed method achieves significantly lower error than the existing state-of-the-art approaches. Our approach significantly outperforms the existing approaches in several standard metrics, even in the case of future timepoints at long distances.\u0000</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"60 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219795","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":"Research on runoff interval prediction method based on deep learning ensemble modeling with hydrological factors","authors":"Jinghan Huang, Zhaocai Wang, Jinghan Dong, Junhao Wu","doi":"10.1007/s00477-024-02780-6","DOIUrl":"https://doi.org/10.1007/s00477-024-02780-6","url":null,"abstract":"<p>Precise prediction of runoff is not only conducive to the prevention of floods and droughts but also to the rational use of water resources. Due to the frequency of weather extremes and the complexity of runoff variability, achieving accurate runoff predictions is challenging. This research develops a deep-learning ensemble model for interval prediction based on meteorological and hydrological factors. The model can be divided into four stages: feature extraction, decomposition, point prediction, and interval prediction. First, Pearson's correlation coefficient filters out key driving variables affecting runoff. Next, the original data are decomposed by variational modal decomposition (VMD) to intrinsic modal function (IMF); Then, each IMF is decomposed by complementary ensemble empirical modal decomposition (CEEMD) to capture more data details. Following, the runoff point prediction portion is realized by the attention mechanism fusion gated recurrent unit (AM-GRU). In this study, data from Dunhuang and Panjiazhuang stations, located in the upper and lower reaches of the Shule River in China, were used to validate and analyze the VMD-CEEMD-ISSA-AM-GRU (VCIAG) model. The results show that (1) the VCIAG model has the best fitting effect which the NSE values of Dunhuang and Panjiazhuang stations are 0.97 and 0.96, respectively. (2) In the multi-period prediction in advance, the highest prediction accuracy is achieved when the prediction period is 1 day and the accuracy of the prediction decreases gradually as the prediction period becomes longer. (3) In flood early warning, the VCIAG performs well at both stations, which suggests that the proposed model can take precautionary measures in advance before the floods come. (4) In terms of interval prediction, the VCIAG model has the narrowest prediction interval width and the highest prediction accuracy, which enhances the application value of the model.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"38 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219806","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}