Geomechanical properties of rocks vary across different measurement scales,primarily due to heterogeneity.Micro-scale geomechanical tests,including micro-scale“scratch tests”and nano-scale nanoindentation tests,are ...Geomechanical properties of rocks vary across different measurement scales,primarily due to heterogeneity.Micro-scale geomechanical tests,including micro-scale“scratch tests”and nano-scale nanoindentation tests,are attractive at different scales.Each method requires minimal sample volume,is low cost,and includes a relatively rapid measurement turnaround time.However,recent micro-scale test results–including scratch test results and nanoindentation results–exhibit tangible variance and uncertainty,suggesting a need to correlate mineral composition mapping to elastic modulus mapping to isolate the relative impact of specific minerals.Different research labs often utilize different interpretation methods,and it is clear that future micro-mechanical tests may benefit from standardized testing and interpretation procedures.The objectives of this study are to seek options for standardized testing and interpretation procedures,through two specific objectives:(1)Quantify chemical and physical controls on micro-mechanical properties and(2)Quantify the source of uncertainties associated with nanoindentation measurements.To reach these goals,we conducted mechanical tests on three different scales:triaxial compression tests,scratch tests,and nanoindentation tests.We found that mineral phase weight percentage is highly correlated with nanoindentation elastic modulus distribution.Finally,we conclude that nanoindentation testing is a mineralogy and microstructure-based method and generally yields significant uncertainty and overestimation.The uncertainty of the testing method is largely associated with not mapping pore space a priori.Lastly,the uncertainty can be reduced by combining phase mapping and modulus mapping with substantial and random data sampling.展开更多
Selecting the optimal model helps decision-makers to reduce the uncertainty in the slope calculation process.The uncertainty quantification process using root-mean-square error(RMSE)has limitations.It can obscure loca...Selecting the optimal model helps decision-makers to reduce the uncertainty in the slope calculation process.The uncertainty quantification process using root-mean-square error(RMSE)has limitations.It can obscure local uncertainty features and neglect the statistical characteristics of uncertainty,which may hinder decision-makers'understanding and model selection.展开更多
The aim of this paper is to prove another variation on the Heisenberg uncertainty principle,we generalize the quantitative uncertainty relations in n different(time-frequency)domains and we will give an algorithm for ...The aim of this paper is to prove another variation on the Heisenberg uncertainty principle,we generalize the quantitative uncertainty relations in n different(time-frequency)domains and we will give an algorithm for the signal recovery related to the canonical Fourier-Bessel transform.展开更多
With the increasing integration of large-scale distributed energy resources into the grid,traditional distribution network optimization and dispatch methods struggle to address the challenges posed by both generation ...With the increasing integration of large-scale distributed energy resources into the grid,traditional distribution network optimization and dispatch methods struggle to address the challenges posed by both generation and load.Accounting for these issues,this paper proposes a multi-timescale coordinated optimization dispatch method for distribution networks.First,the probability box theory was employed to determine the uncertainty intervals of generation and load forecasts,based on which,the requirements for flexibility dispatch and capacity constraints of the grid were calculated and analyzed.Subsequently,a multi-timescale optimization framework was constructed,incorporating the generation and load forecast uncertainties.This framework included optimization models for dayahead scheduling,intra-day optimization,and real-time adjustments,aiming to meet flexibility needs across different timescales and improve the economic efficiency of the grid.Furthermore,an improved soft actor-critic algorithm was introduced to enhance the uncertainty exploration capability.Utilizing a centralized training and decentralized execution framework,a multi-agent SAC network model was developed to improve the decision-making efficiency of the agents.Finally,the effectiveness and superiority of the proposed method were validated using a modified IEEE-33 bus test system.展开更多
For uncertainty quantification of complex models with high-dimensional,nonlinear,multi-component coupling like digital twins,traditional statistical sampling methods,such as random sampling and Latin hypercube samplin...For uncertainty quantification of complex models with high-dimensional,nonlinear,multi-component coupling like digital twins,traditional statistical sampling methods,such as random sampling and Latin hypercube sampling,require a large number of samples,which entails huge computational costs.Therefore,how to construct a small-size sample space has been a hot issue of interest for researchers.To this end,this paper proposes a sequential search-based Latin hypercube sampling scheme to generate efficient and accurate samples for uncertainty quantification.First,the sampling range of the samples is formed by carving the polymorphic uncertainty based on theoretical analysis.Then,the optimal Latin hypercube design is selected using the Latin hypercube sampling method combined with the"space filling"criterion.Finally,the sample selection function is established,and the next most informative sample is optimally selected to obtain the sequential test sample.Compared with the classical sampling method,the generated samples can retain more information on the basis of sparsity.A series of numerical experiments are conducted to demonstrate the superiority of the proposed sequential search-based Latin hypercube sampling scheme,which is a way to provide reliable uncertainty quantification results with small sample sizes.展开更多
BACKGROUND Currently,there is limited research examining the relationship between anxiety,depression,coping styles,and illness uncertainty in patients with cervical cancer(CC)undergoing radiotherapy.Addressing this ga...BACKGROUND Currently,there is limited research examining the relationship between anxiety,depression,coping styles,and illness uncertainty in patients with cervical cancer(CC)undergoing radiotherapy.Addressing this gap could provide valuable insights and more reliable evidence for clinical practice targeting this patient population.AIM To analyze the anxiety,depression,and coping styles of patients with CC undergoing radiotherapy and explore their correlations with illness uncertainty.METHODS A total of 200 patients with CC undergoing radiotherapy at The First Affiliated Hospital of Soochow University between June 2018 and June 2022 were enrolled.Anxiety and depression were assessed using the Hospital Anxiety and Depression Scale(HADS),comprising subscales for anxiety(HADS-A)and depression(HADS-D).Coping styles were evaluated using the Jalowiec Coping Scale(JCS-60),comprising dimensions such as confrontive,evasive,optimistic,fatalistic,emotive,palliative,supportive,and self-reliant.Illness uncertainty was measured using the Mishel Uncertainty in Illness Scale(MUIS),encompassing ambiguity,complexity,information deficit,and unpredictability.Correlations among anxiety,depression,coping styles,and illness uncertainty were analyzed.RESULTS During radiotherapy,the mean scores were 7.12±3.39 for HADS-A,6.68±3.49 for HADS-D,1.52±0.23 for JCS-60,and 93.40±7.44 for MUIS.Anxiety(HADS-A≥8)was present in 39.5%of patients,depression(HADS-D≥8)in 41.0%,and both in 14.0%.Anxiety was significantly positively correlated with ambiguity,unpredictability,and total MUIS score(P<0.05).Depression was significantly positively correlated with ambiguity,information deficit,unpredictability,and total MUIS score(P<0.05).Most patients adopted an optimistic coping style,whereas the emotive style was least utilized.Evasive,fatalistic,and emotive coping styles were significantly positively correlated with illness uncertainty,whereas the self-reliant style was significantly negatively correlated with unpredictability(P<0.05).CONCLUSION Anxiety,depression,and coping styles in patients with CC undergoing radiotherapy correlate significantly with their level of illness uncertainty.Medical staff should address patients’psychological status and coping strategies by providing targeted information to reduce negative emotions,foster adaptive coping styles,and decrease illness uncertainty.展开更多
The practical predictability of hail precipitation rates is significantly influenced by initial meteorological perturbations,stemming from various uncertainty sources.This study thoroughly assessed the predictability ...The practical predictability of hail precipitation rates is significantly influenced by initial meteorological perturbations,stemming from various uncertainty sources.This study thoroughly assessed the predictability of hail precipitation rates in both climatologically and flow-dependent perturbed ensembles(CEns and FEns).These ensembles incorporated initial meteorological uncertainties derived separately from two operational ensembles.Leveraging the Weather Research and Forecasting model,we conducted cloud-resolving simulations of an idealized hailstorm.The practical predictability of hail responded comparably to both climatological and flow-dependent uncertainties,which was revealed across the entire ensemble of 50 members.However,a notable difference emerged when comparing the peak hail precipitation rates among the top 10 and bottom 10 members.From a thermodynamic perspective,the primary source of uncertainty in hail precipitation lay in the significant variations in temperature stratification,particularly at-20℃and-40℃.On the microphysical front,perturbations within CEns generated greater uncertainty in the process of rainwater collection by hail,contributing significantly to the microphysical growth mechanisms of hail.Furthermore,the findings reveal a stronger dependency of hail precipitation uncertainty on thermodynamic perturbations compared to kinematic perturbations.These insights enhance the comprehension of the practical predictability of hail and contribute significantly to the understanding of ensemble forecasting for hail events.展开更多
Logs and sawnwood play an important and fundamental role in the development of China's timber industry and are also China's major imports.This study explores the impact of economic policy uncertainty(EPU)on Ch...Logs and sawnwood play an important and fundamental role in the development of China's timber industry and are also China's major imports.This study explores the impact of economic policy uncertainty(EPU)on China's log and sawnwood trade by empirically analyzing the panel data of China's major trading partner countries with these two types of forest products from 2001 to 2022.The results show that the economic policy uncertainty of trading partner countries has a significant promotion effect on China's log and sawnwood trade,while China's economic policy has a significant negative effect on China's log and sawnwood trade.In terms of products,the impact of economic policy uncertainty in trading partner countries on China's sawnwood exports is significantly positive,while the impact on log exports is negative and insignificant.The per capita income of trading partner countries has a positive and significant impact on the trade of logs and sawnwood,while China's per capita income has a negative and significant impact on the trade of logs and sawnwood.The impact of real exchange rate on trade in sawnwood and total trade in logs and sawnwood is significantly positive,while the impact on trade in logs is positive but not significant.The per capita forest area ratio has a negative and significant effect on China's log imports,sawnwood imports and total imports of both logs and sawnwood.There are differences in the extent to which economic policy uncertainty affects China's trade in logs and sawnwood with developed and developing trading partners,with the overall effect on China's trade with developed trading partners being smaller than that with developing trading partners.展开更多
Modern warfare is increasingly dependent on logistical support.The improvement in satellite imaging technology and the increase in the number of satellites in orbit have provided a technical foundation for using satel...Modern warfare is increasingly dependent on logistical support.The improvement in satellite imaging technology and the increase in the number of satellites in orbit have provided a technical foundation for using satellite observations in military logistics.Due to uncertainties in the processes of production,transport,and observation,the satellite-based observation and state estimation of military logistics exhibit characteristics of uncertainty.This paper proposes an attribute-based staged method to quantify uncertainty,addressing mixed uncertainties during satellite observations of logistics.First,Bayesian estimation is used to quantify the aleatory uncertainty in the process of single-stage logistics observation.Second,evidence theory is adopted to quantify the epistemic uncertainty caused by conflicts in multi-stage logistics observation results and the lack of understanding of production principles.Through the design of the identification framework and the dynamic optimization of basic reliability,key logistics elements are identified,enabling an accurate estimation of the state of military logistics.Finally,the application case is used to validate the effectiveness and accuracy of the proposed method.Compared to conventional evidence theory,the proposed method can make fuller use of multi-source information and reduce the relative error between the estimated value and the true value to below 0.015%.展开更多
BACKGROUND Glaucoma,a condition frequently linked to severe depression,anxiety,and sleep disturbances,affects treatment adherence while potentially compromising effectiveness.AIM To explore illness uncertainty(IU),anx...BACKGROUND Glaucoma,a condition frequently linked to severe depression,anxiety,and sleep disturbances,affects treatment adherence while potentially compromising effectiveness.AIM To explore illness uncertainty(IU),anxiety,and depressive symptoms in primary glaucoma and to discuss underlying triggers.METHODS We recruited 120 primary glaucoma cases between January 2022 and November 2023.The Mishel Uncertainty in Illness Scale(MUIS)and the Hospital Anxiety and Depression Scale(HADS)[include HADS-anxiety subscale(HADS-A)and HADS-depression subscale(HADS-D)]subscales,were used to assess IU and emotional distress(anxiety/depression),respectively.The MUIS-HADS subscale interrelationships were determined by Pearson correlation.IU-associated determinants were identified using univariate and binary logistic regression analyses.RESULTS The cohort showed a mean MUIS score of 79.73±8.97,corresponding to a moderately high IU level.The HADS-A and HADS-D scores averaged 6.57±3.89 and 7.08±5.05 points,respectively,with 15.00%of participants showing anxiety symptoms and 24.17%exhibiting depressive signs.Significant positive connections were observed between MUIS and both HADS-A(r=0.359,P<0.001)and HADSD(r=0.426,P<0.001).Univariate analysis revealed that disease duration,insomnia,monthly household income per capita,and the presence of comorbid chronic conditions were significantly associated with anxiety or depression.Multivariate analysis identified insomnia as a risk factor and higher monthly household income as a protective factor.CONCLUSION Patients with primary glaucoma experience moderate IU levels,generally low anxiety,and mild depression.Specifically,the anxiety and depression risks were 15.00%and 24.17%,respectively.A significant positive correlation existed between IU and anxiety/depression in these patients.Additionally,insomnia or lower monthly household income elevated anxiety/depression risks,enabling reliable anxiety/depression risk categorization among patients.展开更多
Foraminifera are shell-bearing microorganisms that are commonly found in marine deposits on the seabed.They are important indicators in many analyses,are used in climate change research,monitoring marine environments,...Foraminifera are shell-bearing microorganisms that are commonly found in marine deposits on the seabed.They are important indicators in many analyses,are used in climate change research,monitoring marine environments,evolutionary studies,and are also frequently used in the oil and gas industry.Although some research has focused on automating the classification of foraminifera images,few have addressed the uncertainty in these classifications.Although foraminifera classification is not a safety-critical task,estimating uncertainty is crucial to avoid misclassifications that could overlook rare and ecologically significant species that are informative indicators of the environment in which they lived.Uncertainty estimation in deep learning has gained significant attention and many methods have been developed.However,evaluating the performance of these methods in practical settings remains a challenge.To create a benchmark for uncertainty estimation in the classification of foraminifera,we administered a multiple choice questionnaire containing classification tasks to four senior geologists.By analyzing their responses,we generated human-derived uncertainty estimates for a test set of 260 images of foraminifera and sediment grains.These uncertainty estimates served as a baseline for comparison when training neural networks in classification.We then trained multiple deep neural networks using a range of uncertainty quantification methods to classify and state the uncertainty about the classifications.The results of the deep learning uncertainty quantification methods were then analyzed and compared with the human benchmark,to see how the methods performed individually and how the methods aligned with humans.Our results show that human-level performance can be achieved with deep learning and that test-time data augmentation and ensembling can help improve both uncertainty estimation and classification performance.Our results also show that human uncertainty estimates are helpful indicators for detecting classification errors and that deep learning-based uncertainty estimates can improve calibration and classification accuracy.展开更多
Interpreting is a fast-paced activity where interpreters must make quick choices when faced with uncertainty. This study looks at how professional interpreters handle linguistic uncertainty in English-Chinese sight tr...Interpreting is a fast-paced activity where interpreters must make quick choices when faced with uncertainty. This study looks at how professional interpreters handle linguistic uncertainty in English-Chinese sight translation, with a focus on the strategies they use. By analyzing transcription data alongside instructor evaluations, we found that interpreters relied most on creative interpretation and omission, while strategies like paraphrasing, simplification, transformation, addition, and generalization appeared less often. The results show a clear preference for strategies that keep communication flowing without adding unnecessary cognitive load. These findings support the Processing Economy Hypothesis, which suggests interpreters naturally seek efficient ways to process language while maintaining meaning. The study also highlights practical implications for interpreter training, emphasizing the value of flexible, economy-oriented strategies to help interpreters stay fluent under pressure.展开更多
Response analysis of structures involving non-probabilistic uncertain parameters can be closely related to optimization.This paper provides a review on optimization-based methods for uncertainty analysis,with focusing...Response analysis of structures involving non-probabilistic uncertain parameters can be closely related to optimization.This paper provides a review on optimization-based methods for uncertainty analysis,with focusing attention on specific properties of adopted numerical optimization approaches.We collect and discuss the methods based on nonlinear programming,semidefinite programming,mixed-integer programming,mathematical programming with complementarity constraints,difference-of-convex programming,optimization methods using surrogate models and machine learning techniques,and metaheuristics.As a closely related topic,we also overview the methods for assessing structural robustness using non-probabilistic uncertainty modeling.We conclude the paper by drawing several remarks through this review.展开更多
When assessing seismic liquefaction potential with data-driven models,addressing the uncertainties of establishing models,interpreting cone penetration tests(CPT)data and decision threshold is crucial for avoiding bia...When assessing seismic liquefaction potential with data-driven models,addressing the uncertainties of establishing models,interpreting cone penetration tests(CPT)data and decision threshold is crucial for avoiding biased data selection,ameliorating overconfident models,and being flexible to varying practical objectives,especially when the training and testing data are not identically distributed.A workflow characterized by leveraging Bayesian methodology was proposed to address these issues.Employing a Multi-Layer Perceptron(MLP)as the foundational model,this approach was benchmarked against empirical methods and advanced algorithms for its efficacy in simplicity,accuracy,and resistance to overfitting.The analysis revealed that,while MLP models optimized via maximum a posteriori algorithm suffices for straightforward scenarios,Bayesian neural networks showed great potential for preventing overfitting.Additionally,integrating decision thresholds through various evaluative principles offers insights for challenging decisions.Two case studies demonstrate the framework's capacity for nuanced interpretation of in situ data,employing a model committee for a detailed evaluation of liquefaction potential via Monte Carlo simulations and basic statistics.Overall,the proposed step-by-step workflow for analyzing seismic liquefaction incorporates multifold testing and real-world data validation,showing improved robustness against overfitting and greater versatility in addressing practical challenges.This research contributes to the seismic liquefaction assessment field by providing a structured,adaptable methodology for accurate and reliable analysis.展开更多
Cropland nitrate leaching is the major nitrogen(N) loss pathway, and it contributes significantly to water pollution. However, cropland nitrate leaching estimates show great uncertainty due to variations in input data...Cropland nitrate leaching is the major nitrogen(N) loss pathway, and it contributes significantly to water pollution. However, cropland nitrate leaching estimates show great uncertainty due to variations in input datasets and estimation methods. Here, we presented a re-evaluation of Chinese cropland nitrate leaching, and identified and quantified the sources of uncertainty by integrating three cropland area datasets, three N input datasets, and three estimation methods. The results revealed that nitrate leaching from Chinese cropland averaged 6.7±0.6 Tg N yr^(-1)in 2010, ranging from 2.9 to 15.8 Tg N yr^(-1)across 27 different estimates. The primary contributor to the uncertainty was the estimation method, accounting for 45.1%, followed by the interaction of N input dataset and estimation method at 24.4%. The results of this study emphasize the need for adopting a robust estimation method and improving the compatibility between the estimation method and N input dataset to effectively reduce uncertainty. This analysis provides valuable insights for accurately estimating cropland nitrate leaching and contributes to ongoing efforts that address water pollution concerns.展开更多
We examine technology ETF and uncertainty index(VIX,GVZ,and OVZ)spillover dynamics and quantile frequency interconnectedness across market states.This study is the first to use quantile-frequency spillover,quadruple w...We examine technology ETF and uncertainty index(VIX,GVZ,and OVZ)spillover dynamics and quantile frequency interconnectedness across market states.This study is the first to use quantile-frequency spillover,quadruple wavelet coherence,and wavelet quantile correlation methodologies to facilitate these analyses.The total connectedness index value is 70%,which is much higher in both the upper and lower quantiles.Under normal market conditions,short-term connectedness significantly exceeds long-term connectedness.Levels of ETF-uncertainty indicator connectedness increase under extreme market conditions;most technology ETFs are net spillover transmitters and uncertainty indices net spillover receivers,indicating the contagion risk of ETF investments.We show that while greater ETF-uncertainty index connectedness may benefit portfolio diversification,large fluctuations in technology EFTs can result in financial instability due to high market volatility.In the long term,the joint effects of uncertainty indices on ETFs are significant,with negative correlations between ETFs and uncertainties at different frequencies,supporting the potential role of uncertainty indices in hedging technology ETF portfolio risks.Dynamic portfolio rebalancing,scenario analysis,and stress testing may help to manage the effects of high connectedness.展开更多
This paper proposed a new systematic approach-functional evidential reasoning model(FERM) for exploring hazardous chemical operational accidents under uncertainty. First, FERM was introduced to identify various causal...This paper proposed a new systematic approach-functional evidential reasoning model(FERM) for exploring hazardous chemical operational accidents under uncertainty. First, FERM was introduced to identify various causal factors and their performance changes in hazardous chemical operational accidents, along with determining the functional failure link relationships. Subsequently, FERM was employed to elucidate both qualitative and quantitative operational accident information within a unified framework, which could be regarded as the input of information fusion to obtain the fuzzy belief distribution of each cause factor. Finally, the derived risk values of the causal factors were ranked while constructing multi-level accident causation chains to unveil the weak links in system functionality and the primary roots of operational accidents. Using the specific case of the “1·15” major explosion and fire accident at Liaoning Panjin Haoye Chemical Co., Ltd., seven causal factors and their corresponding performance changes were identified. Additionally, five accident causation chains were uncovered based on the fuzzy joint distribution of the functional assessment level(FAL) and reliability distribution(RD),revealing an overall increase in risk along the accident evolution path. The research findings demonstrated that FERM enabled the effective characterization, rational quantification and accurate analysis of the inherent uncertainties in hazardous chemical operational accident risks from a systemic perspective.展开更多
Engineering tests can yield inaccurate data due to instrument errors,human factors,and environmental interference,introducing uncertainty in numerical model updating.This study employs the probability-box(p-box)method...Engineering tests can yield inaccurate data due to instrument errors,human factors,and environmental interference,introducing uncertainty in numerical model updating.This study employs the probability-box(p-box)method for representing observational uncertainty and develops a two-step approximate Bayesian computation(ABC)framework using time-series data.Within the ABC framework,Euclidean and Bhattacharyya distances are employed as uncertainty quantification metrics to delineate approximate likelihood functions in the initial and subsequent steps,respectively.A novel variational Bayesian Monte Carlo method is introduced to efficiently apply the ABC framework amidst observational uncertainty,resulting in rapid convergence and accurate parameter estimation with minimal iterations.The efficacy of the proposed updating strategy is validated by its application to a shear frame model excited by seismic wave and an aviation pump force sensor for thermal output analysis.The results affirm the efficiency,robustness,and practical applicability of the proposed method.展开更多
Geochemical survey data are essential across Earth Science disciplines but are often affected by noise,which can obscure important geological signals and compromise subsequent prediction and interpretation.Quantifying...Geochemical survey data are essential across Earth Science disciplines but are often affected by noise,which can obscure important geological signals and compromise subsequent prediction and interpretation.Quantifying prediction uncertainty is hence crucial for robust geoscientific decision-making.This study proposes a novel deep learning framework,the Spatially Constrained Variational Autoencoder(SC-VAE),for denoising geochemical survey data with integrated uncertainty quantification.The SC-VAE incorporates spatial regularization,which enforces spatial coherence by modeling inter-sample relationships directly within the latent space.The performance of the SC-VAE was systematically evaluated against a standard Variational Autoencoder(VAE)using geochemical data from the gold polymetallic district in the northwestern part of Sichuan Province,China.Both models were optimized using Bayesian optimization,with objective functions specifically designed to maintain essential geostatistical characteristics.Evaluation metrics include variogram analysis,quantitative measures of spatial interpolation accuracy,visual assessment of denoised maps,and statistical analysis of data distributions,as well as decomposition of uncertainties.Results show that the SC-VAE achieves superior noise suppression and better preservation of spatial structure compared to the standard VAE,as demonstrated by a significant reduction in the variogram nugget effect and an increased partial sill.The SC-VAE produces denoised maps with clearer anomaly delineation and more regularized data distributions,effectively mitigating outliers and reducing kurtosis.Additionally,it delivers improved interpolation accuracy and spatially explicit uncertainty estimates,facilitating more reliable and interpretable assessments of prediction confidence.The SC-VAE framework thus provides a robust,geostatistically informed solution for enhancing the quality and interpretability of geochemical data,with broad applicability in mineral exploration,environmental geochemistry,and other Earth Science domains.展开更多
This study contributes to renewable energy policy modeling by developing a dynamic decision-making framework that incorporates uncertainty,irreversibility,and the value of information.It responds to the growing need f...This study contributes to renewable energy policy modeling by developing a dynamic decision-making framework that incorporates uncertainty,irreversibility,and the value of information.It responds to the growing need for structured tools to guide investments amid climate volatility,technological change,and economic risk.Grounded in decision theory—especially the work of Von Neumann,Morgenstern,and Savage—the framework models renewable energy investments using subjective probabilities and quasi-option value under evolving climate conditions.The empirical component focuses on ARAMCO’s renewable energy strategy,a corporate case illustrating how fossil-fuel-dependent entities can pivot toward sustainability.The analysis uses Net Present Value(NPV)modeling and real options analysis under different discount rates and carbon pricing scenarios to assess financial feasibility.Results show that lower discount rates and moderate carbon prices improve investment attractiveness,while high carbon pricing significantly reduces project viability.The study also highlights the policy relevance of this framework.Government subsidies,adaptive regulation,and public-private partnerships emerge as critical enablers of resilient investments.It further suggests that aligning ESG reporting standards with carbon pricing policies can strengthen market signals and encourage private capital flow into renewables.By integrating theoretical modeling with corporate investment realities,this chapter offers a replicable tool for policymakers and investors.Future research should expand its application across sectors and geographies to validate generalizability and improve planning in the transition toward low-carbon economies.展开更多
基金support of this project through the Southwest Regional Partnership on Carbon Sequestration(Grant No.DE-FC26-05NT42591)Improving Production in the Emerging Paradox Oil Play(Grant No.DE-FE0031775).
文摘Geomechanical properties of rocks vary across different measurement scales,primarily due to heterogeneity.Micro-scale geomechanical tests,including micro-scale“scratch tests”and nano-scale nanoindentation tests,are attractive at different scales.Each method requires minimal sample volume,is low cost,and includes a relatively rapid measurement turnaround time.However,recent micro-scale test results–including scratch test results and nanoindentation results–exhibit tangible variance and uncertainty,suggesting a need to correlate mineral composition mapping to elastic modulus mapping to isolate the relative impact of specific minerals.Different research labs often utilize different interpretation methods,and it is clear that future micro-mechanical tests may benefit from standardized testing and interpretation procedures.The objectives of this study are to seek options for standardized testing and interpretation procedures,through two specific objectives:(1)Quantify chemical and physical controls on micro-mechanical properties and(2)Quantify the source of uncertainties associated with nanoindentation measurements.To reach these goals,we conducted mechanical tests on three different scales:triaxial compression tests,scratch tests,and nanoindentation tests.We found that mineral phase weight percentage is highly correlated with nanoindentation elastic modulus distribution.Finally,we conclude that nanoindentation testing is a mineralogy and microstructure-based method and generally yields significant uncertainty and overestimation.The uncertainty of the testing method is largely associated with not mapping pore space a priori.Lastly,the uncertainty can be reduced by combining phase mapping and modulus mapping with substantial and random data sampling.
基金supported by the National Key Research and Development Program of China:Core Technologies and Software Systems of Geospatial Intelligence(Project No.:2021YFB3900900).
文摘Selecting the optimal model helps decision-makers to reduce the uncertainty in the slope calculation process.The uncertainty quantification process using root-mean-square error(RMSE)has limitations.It can obscure local uncertainty features and neglect the statistical characteristics of uncertainty,which may hinder decision-makers'understanding and model selection.
文摘The aim of this paper is to prove another variation on the Heisenberg uncertainty principle,we generalize the quantitative uncertainty relations in n different(time-frequency)domains and we will give an algorithm for the signal recovery related to the canonical Fourier-Bessel transform.
基金funded by Jilin Province Science and Technology Development Plan Project,grant number 20220203163SF.
文摘With the increasing integration of large-scale distributed energy resources into the grid,traditional distribution network optimization and dispatch methods struggle to address the challenges posed by both generation and load.Accounting for these issues,this paper proposes a multi-timescale coordinated optimization dispatch method for distribution networks.First,the probability box theory was employed to determine the uncertainty intervals of generation and load forecasts,based on which,the requirements for flexibility dispatch and capacity constraints of the grid were calculated and analyzed.Subsequently,a multi-timescale optimization framework was constructed,incorporating the generation and load forecast uncertainties.This framework included optimization models for dayahead scheduling,intra-day optimization,and real-time adjustments,aiming to meet flexibility needs across different timescales and improve the economic efficiency of the grid.Furthermore,an improved soft actor-critic algorithm was introduced to enhance the uncertainty exploration capability.Utilizing a centralized training and decentralized execution framework,a multi-agent SAC network model was developed to improve the decision-making efficiency of the agents.Finally,the effectiveness and superiority of the proposed method were validated using a modified IEEE-33 bus test system.
基金co-supported by the National Natural Science Foundation of China(Nos.51875014,U2233212 and 51875015)the Natural Science Foundation of Beijing Municipality,China(No.L221008)+1 种基金Science,Technology Innovation 2025 Major Project of Ningbo of China(No.2022Z005)the Tianmushan Laboratory Project,China(No.TK2023-B-001)。
文摘For uncertainty quantification of complex models with high-dimensional,nonlinear,multi-component coupling like digital twins,traditional statistical sampling methods,such as random sampling and Latin hypercube sampling,require a large number of samples,which entails huge computational costs.Therefore,how to construct a small-size sample space has been a hot issue of interest for researchers.To this end,this paper proposes a sequential search-based Latin hypercube sampling scheme to generate efficient and accurate samples for uncertainty quantification.First,the sampling range of the samples is formed by carving the polymorphic uncertainty based on theoretical analysis.Then,the optimal Latin hypercube design is selected using the Latin hypercube sampling method combined with the"space filling"criterion.Finally,the sample selection function is established,and the next most informative sample is optimally selected to obtain the sequential test sample.Compared with the classical sampling method,the generated samples can retain more information on the basis of sparsity.A series of numerical experiments are conducted to demonstrate the superiority of the proposed sequential search-based Latin hypercube sampling scheme,which is a way to provide reliable uncertainty quantification results with small sample sizes.
基金Supported by National Natural Science Foundation of China,No.81602792The Natural Science Foundation of the Jiangsu Higher Education Institutions of China,No.23KJB310023+5 种基金Jiangsu Provincial Medical Key Discipline,No.ZDXK202235The Maternal and Child Health Research Project of Jiangsu Province,No.F202210The Project of State Key Laboratory of Radiation Medicine and Protection,Soochow University,No.GZK1202101Suzhou Science and Technology Development Plan Project,No.KJXW2020008BOXI Natural Science Cultivation Foundation of China of The First Affiliated Hospital of Soochow University,No.BXQN202107Clinical Diagnosis and Treatment Technology Innovation Project Youth Characteristic Technology Project of The First Affiliated Hospital of Soochow University,No.2100201.
文摘BACKGROUND Currently,there is limited research examining the relationship between anxiety,depression,coping styles,and illness uncertainty in patients with cervical cancer(CC)undergoing radiotherapy.Addressing this gap could provide valuable insights and more reliable evidence for clinical practice targeting this patient population.AIM To analyze the anxiety,depression,and coping styles of patients with CC undergoing radiotherapy and explore their correlations with illness uncertainty.METHODS A total of 200 patients with CC undergoing radiotherapy at The First Affiliated Hospital of Soochow University between June 2018 and June 2022 were enrolled.Anxiety and depression were assessed using the Hospital Anxiety and Depression Scale(HADS),comprising subscales for anxiety(HADS-A)and depression(HADS-D).Coping styles were evaluated using the Jalowiec Coping Scale(JCS-60),comprising dimensions such as confrontive,evasive,optimistic,fatalistic,emotive,palliative,supportive,and self-reliant.Illness uncertainty was measured using the Mishel Uncertainty in Illness Scale(MUIS),encompassing ambiguity,complexity,information deficit,and unpredictability.Correlations among anxiety,depression,coping styles,and illness uncertainty were analyzed.RESULTS During radiotherapy,the mean scores were 7.12±3.39 for HADS-A,6.68±3.49 for HADS-D,1.52±0.23 for JCS-60,and 93.40±7.44 for MUIS.Anxiety(HADS-A≥8)was present in 39.5%of patients,depression(HADS-D≥8)in 41.0%,and both in 14.0%.Anxiety was significantly positively correlated with ambiguity,unpredictability,and total MUIS score(P<0.05).Depression was significantly positively correlated with ambiguity,information deficit,unpredictability,and total MUIS score(P<0.05).Most patients adopted an optimistic coping style,whereas the emotive style was least utilized.Evasive,fatalistic,and emotive coping styles were significantly positively correlated with illness uncertainty,whereas the self-reliant style was significantly negatively correlated with unpredictability(P<0.05).CONCLUSION Anxiety,depression,and coping styles in patients with CC undergoing radiotherapy correlate significantly with their level of illness uncertainty.Medical staff should address patients’psychological status and coping strategies by providing targeted information to reduce negative emotions,foster adaptive coping styles,and decrease illness uncertainty.
基金supported by the National Natural Science Foundation of China(Grant Nos.42005005 and 42030607)the Science and Technology Department of Shaanxi Province(Grant No.2024JC-YBQN-0248)+2 种基金the Education Department of Shaanxi Province(Grant No.23JK0686)a Xi'an Science and Technology Project(Grant No.22GXFW0131)the Young Talent fund of the University Association for Science and Technology in Shaanxi(Grant No.20210706)。
文摘The practical predictability of hail precipitation rates is significantly influenced by initial meteorological perturbations,stemming from various uncertainty sources.This study thoroughly assessed the predictability of hail precipitation rates in both climatologically and flow-dependent perturbed ensembles(CEns and FEns).These ensembles incorporated initial meteorological uncertainties derived separately from two operational ensembles.Leveraging the Weather Research and Forecasting model,we conducted cloud-resolving simulations of an idealized hailstorm.The practical predictability of hail responded comparably to both climatological and flow-dependent uncertainties,which was revealed across the entire ensemble of 50 members.However,a notable difference emerged when comparing the peak hail precipitation rates among the top 10 and bottom 10 members.From a thermodynamic perspective,the primary source of uncertainty in hail precipitation lay in the significant variations in temperature stratification,particularly at-20℃and-40℃.On the microphysical front,perturbations within CEns generated greater uncertainty in the process of rainwater collection by hail,contributing significantly to the microphysical growth mechanisms of hail.Furthermore,the findings reveal a stronger dependency of hail precipitation uncertainty on thermodynamic perturbations compared to kinematic perturbations.These insights enhance the comprehension of the practical predictability of hail and contribute significantly to the understanding of ensemble forecasting for hail events.
文摘Logs and sawnwood play an important and fundamental role in the development of China's timber industry and are also China's major imports.This study explores the impact of economic policy uncertainty(EPU)on China's log and sawnwood trade by empirically analyzing the panel data of China's major trading partner countries with these two types of forest products from 2001 to 2022.The results show that the economic policy uncertainty of trading partner countries has a significant promotion effect on China's log and sawnwood trade,while China's economic policy has a significant negative effect on China's log and sawnwood trade.In terms of products,the impact of economic policy uncertainty in trading partner countries on China's sawnwood exports is significantly positive,while the impact on log exports is negative and insignificant.The per capita income of trading partner countries has a positive and significant impact on the trade of logs and sawnwood,while China's per capita income has a negative and significant impact on the trade of logs and sawnwood.The impact of real exchange rate on trade in sawnwood and total trade in logs and sawnwood is significantly positive,while the impact on trade in logs is positive but not significant.The per capita forest area ratio has a negative and significant effect on China's log imports,sawnwood imports and total imports of both logs and sawnwood.There are differences in the extent to which economic policy uncertainty affects China's trade in logs and sawnwood with developed and developing trading partners,with the overall effect on China's trade with developed trading partners being smaller than that with developing trading partners.
文摘Modern warfare is increasingly dependent on logistical support.The improvement in satellite imaging technology and the increase in the number of satellites in orbit have provided a technical foundation for using satellite observations in military logistics.Due to uncertainties in the processes of production,transport,and observation,the satellite-based observation and state estimation of military logistics exhibit characteristics of uncertainty.This paper proposes an attribute-based staged method to quantify uncertainty,addressing mixed uncertainties during satellite observations of logistics.First,Bayesian estimation is used to quantify the aleatory uncertainty in the process of single-stage logistics observation.Second,evidence theory is adopted to quantify the epistemic uncertainty caused by conflicts in multi-stage logistics observation results and the lack of understanding of production principles.Through the design of the identification framework and the dynamic optimization of basic reliability,key logistics elements are identified,enabling an accurate estimation of the state of military logistics.Finally,the application case is used to validate the effectiveness and accuracy of the proposed method.Compared to conventional evidence theory,the proposed method can make fuller use of multi-source information and reduce the relative error between the estimated value and the true value to below 0.015%.
文摘BACKGROUND Glaucoma,a condition frequently linked to severe depression,anxiety,and sleep disturbances,affects treatment adherence while potentially compromising effectiveness.AIM To explore illness uncertainty(IU),anxiety,and depressive symptoms in primary glaucoma and to discuss underlying triggers.METHODS We recruited 120 primary glaucoma cases between January 2022 and November 2023.The Mishel Uncertainty in Illness Scale(MUIS)and the Hospital Anxiety and Depression Scale(HADS)[include HADS-anxiety subscale(HADS-A)and HADS-depression subscale(HADS-D)]subscales,were used to assess IU and emotional distress(anxiety/depression),respectively.The MUIS-HADS subscale interrelationships were determined by Pearson correlation.IU-associated determinants were identified using univariate and binary logistic regression analyses.RESULTS The cohort showed a mean MUIS score of 79.73±8.97,corresponding to a moderately high IU level.The HADS-A and HADS-D scores averaged 6.57±3.89 and 7.08±5.05 points,respectively,with 15.00%of participants showing anxiety symptoms and 24.17%exhibiting depressive signs.Significant positive connections were observed between MUIS and both HADS-A(r=0.359,P<0.001)and HADSD(r=0.426,P<0.001).Univariate analysis revealed that disease duration,insomnia,monthly household income per capita,and the presence of comorbid chronic conditions were significantly associated with anxiety or depression.Multivariate analysis identified insomnia as a risk factor and higher monthly household income as a protective factor.CONCLUSION Patients with primary glaucoma experience moderate IU levels,generally low anxiety,and mild depression.Specifically,the anxiety and depression risks were 15.00%and 24.17%,respectively.A significant positive correlation existed between IU and anxiety/depression in these patients.Additionally,insomnia or lower monthly household income elevated anxiety/depression risks,enabling reliable anxiety/depression risk categorization among patients.
基金funded by the Norwegian Research Council(IKTPLUSS-IKT og digital innovasjon,project no.332901).
文摘Foraminifera are shell-bearing microorganisms that are commonly found in marine deposits on the seabed.They are important indicators in many analyses,are used in climate change research,monitoring marine environments,evolutionary studies,and are also frequently used in the oil and gas industry.Although some research has focused on automating the classification of foraminifera images,few have addressed the uncertainty in these classifications.Although foraminifera classification is not a safety-critical task,estimating uncertainty is crucial to avoid misclassifications that could overlook rare and ecologically significant species that are informative indicators of the environment in which they lived.Uncertainty estimation in deep learning has gained significant attention and many methods have been developed.However,evaluating the performance of these methods in practical settings remains a challenge.To create a benchmark for uncertainty estimation in the classification of foraminifera,we administered a multiple choice questionnaire containing classification tasks to four senior geologists.By analyzing their responses,we generated human-derived uncertainty estimates for a test set of 260 images of foraminifera and sediment grains.These uncertainty estimates served as a baseline for comparison when training neural networks in classification.We then trained multiple deep neural networks using a range of uncertainty quantification methods to classify and state the uncertainty about the classifications.The results of the deep learning uncertainty quantification methods were then analyzed and compared with the human benchmark,to see how the methods performed individually and how the methods aligned with humans.Our results show that human-level performance can be achieved with deep learning and that test-time data augmentation and ensembling can help improve both uncertainty estimation and classification performance.Our results also show that human uncertainty estimates are helpful indicators for detecting classification errors and that deep learning-based uncertainty estimates can improve calibration and classification accuracy.
基金this paper was supported by Humanities and Social Science Youth Foundation of Ministry of Education,China[23YJC740018]。
文摘Interpreting is a fast-paced activity where interpreters must make quick choices when faced with uncertainty. This study looks at how professional interpreters handle linguistic uncertainty in English-Chinese sight translation, with a focus on the strategies they use. By analyzing transcription data alongside instructor evaluations, we found that interpreters relied most on creative interpretation and omission, while strategies like paraphrasing, simplification, transformation, addition, and generalization appeared less often. The results show a clear preference for strategies that keep communication flowing without adding unnecessary cognitive load. These findings support the Processing Economy Hypothesis, which suggests interpreters naturally seek efficient ways to process language while maintaining meaning. The study also highlights practical implications for interpreter training, emphasizing the value of flexible, economy-oriented strategies to help interpreters stay fluent under pressure.
文摘Response analysis of structures involving non-probabilistic uncertain parameters can be closely related to optimization.This paper provides a review on optimization-based methods for uncertainty analysis,with focusing attention on specific properties of adopted numerical optimization approaches.We collect and discuss the methods based on nonlinear programming,semidefinite programming,mixed-integer programming,mathematical programming with complementarity constraints,difference-of-convex programming,optimization methods using surrogate models and machine learning techniques,and metaheuristics.As a closely related topic,we also overview the methods for assessing structural robustness using non-probabilistic uncertainty modeling.We conclude the paper by drawing several remarks through this review.
文摘When assessing seismic liquefaction potential with data-driven models,addressing the uncertainties of establishing models,interpreting cone penetration tests(CPT)data and decision threshold is crucial for avoiding biased data selection,ameliorating overconfident models,and being flexible to varying practical objectives,especially when the training and testing data are not identically distributed.A workflow characterized by leveraging Bayesian methodology was proposed to address these issues.Employing a Multi-Layer Perceptron(MLP)as the foundational model,this approach was benchmarked against empirical methods and advanced algorithms for its efficacy in simplicity,accuracy,and resistance to overfitting.The analysis revealed that,while MLP models optimized via maximum a posteriori algorithm suffices for straightforward scenarios,Bayesian neural networks showed great potential for preventing overfitting.Additionally,integrating decision thresholds through various evaluative principles offers insights for challenging decisions.Two case studies demonstrate the framework's capacity for nuanced interpretation of in situ data,employing a model committee for a detailed evaluation of liquefaction potential via Monte Carlo simulations and basic statistics.Overall,the proposed step-by-step workflow for analyzing seismic liquefaction incorporates multifold testing and real-world data validation,showing improved robustness against overfitting and greater versatility in addressing practical challenges.This research contributes to the seismic liquefaction assessment field by providing a structured,adaptable methodology for accurate and reliable analysis.
基金supported by the National Key Research and Development Program of China (2023YFD1902703)the National Natural Science Foundation of China (Key Program) (U23A20158)。
文摘Cropland nitrate leaching is the major nitrogen(N) loss pathway, and it contributes significantly to water pollution. However, cropland nitrate leaching estimates show great uncertainty due to variations in input datasets and estimation methods. Here, we presented a re-evaluation of Chinese cropland nitrate leaching, and identified and quantified the sources of uncertainty by integrating three cropland area datasets, three N input datasets, and three estimation methods. The results revealed that nitrate leaching from Chinese cropland averaged 6.7±0.6 Tg N yr^(-1)in 2010, ranging from 2.9 to 15.8 Tg N yr^(-1)across 27 different estimates. The primary contributor to the uncertainty was the estimation method, accounting for 45.1%, followed by the interaction of N input dataset and estimation method at 24.4%. The results of this study emphasize the need for adopting a robust estimation method and improving the compatibility between the estimation method and N input dataset to effectively reduce uncertainty. This analysis provides valuable insights for accurately estimating cropland nitrate leaching and contributes to ongoing efforts that address water pollution concerns.
基金supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea(NRF-2024S1A5A2A01028034).
文摘We examine technology ETF and uncertainty index(VIX,GVZ,and OVZ)spillover dynamics and quantile frequency interconnectedness across market states.This study is the first to use quantile-frequency spillover,quadruple wavelet coherence,and wavelet quantile correlation methodologies to facilitate these analyses.The total connectedness index value is 70%,which is much higher in both the upper and lower quantiles.Under normal market conditions,short-term connectedness significantly exceeds long-term connectedness.Levels of ETF-uncertainty indicator connectedness increase under extreme market conditions;most technology ETFs are net spillover transmitters and uncertainty indices net spillover receivers,indicating the contagion risk of ETF investments.We show that while greater ETF-uncertainty index connectedness may benefit portfolio diversification,large fluctuations in technology EFTs can result in financial instability due to high market volatility.In the long term,the joint effects of uncertainty indices on ETFs are significant,with negative correlations between ETFs and uncertainties at different frequencies,supporting the potential role of uncertainty indices in hedging technology ETF portfolio risks.Dynamic portfolio rebalancing,scenario analysis,and stress testing may help to manage the effects of high connectedness.
基金supported by the National Key Research&Development Program of China(2021YFB3301100)the National Natural Science Foundation of China(52004014)the Fundamental Research Funds for the Central Universities(ZY2406).
文摘This paper proposed a new systematic approach-functional evidential reasoning model(FERM) for exploring hazardous chemical operational accidents under uncertainty. First, FERM was introduced to identify various causal factors and their performance changes in hazardous chemical operational accidents, along with determining the functional failure link relationships. Subsequently, FERM was employed to elucidate both qualitative and quantitative operational accident information within a unified framework, which could be regarded as the input of information fusion to obtain the fuzzy belief distribution of each cause factor. Finally, the derived risk values of the causal factors were ranked while constructing multi-level accident causation chains to unveil the weak links in system functionality and the primary roots of operational accidents. Using the specific case of the “1·15” major explosion and fire accident at Liaoning Panjin Haoye Chemical Co., Ltd., seven causal factors and their corresponding performance changes were identified. Additionally, five accident causation chains were uncovered based on the fuzzy joint distribution of the functional assessment level(FAL) and reliability distribution(RD),revealing an overall increase in risk along the accident evolution path. The research findings demonstrated that FERM enabled the effective characterization, rational quantification and accurate analysis of the inherent uncertainties in hazardous chemical operational accident risks from a systemic perspective.
基金supported by the National Natural Science Foundation of China(Grant No.U23B20105).
文摘Engineering tests can yield inaccurate data due to instrument errors,human factors,and environmental interference,introducing uncertainty in numerical model updating.This study employs the probability-box(p-box)method for representing observational uncertainty and develops a two-step approximate Bayesian computation(ABC)framework using time-series data.Within the ABC framework,Euclidean and Bhattacharyya distances are employed as uncertainty quantification metrics to delineate approximate likelihood functions in the initial and subsequent steps,respectively.A novel variational Bayesian Monte Carlo method is introduced to efficiently apply the ABC framework amidst observational uncertainty,resulting in rapid convergence and accurate parameter estimation with minimal iterations.The efficacy of the proposed updating strategy is validated by its application to a shear frame model excited by seismic wave and an aviation pump force sensor for thermal output analysis.The results affirm the efficiency,robustness,and practical applicability of the proposed method.
基金supported by the National Natural Science Foundation of China(Nos.42530801,42425208)the Natural Science Foundation of Hubei Province(China)(No.2023AFA001)+1 种基金the MOST Special Fund from State Key Laboratory of Geological Processes and Mineral Resources,China University of Geosciences(No.MSFGPMR2025-401)the China Scholarship Council(No.202306410181)。
文摘Geochemical survey data are essential across Earth Science disciplines but are often affected by noise,which can obscure important geological signals and compromise subsequent prediction and interpretation.Quantifying prediction uncertainty is hence crucial for robust geoscientific decision-making.This study proposes a novel deep learning framework,the Spatially Constrained Variational Autoencoder(SC-VAE),for denoising geochemical survey data with integrated uncertainty quantification.The SC-VAE incorporates spatial regularization,which enforces spatial coherence by modeling inter-sample relationships directly within the latent space.The performance of the SC-VAE was systematically evaluated against a standard Variational Autoencoder(VAE)using geochemical data from the gold polymetallic district in the northwestern part of Sichuan Province,China.Both models were optimized using Bayesian optimization,with objective functions specifically designed to maintain essential geostatistical characteristics.Evaluation metrics include variogram analysis,quantitative measures of spatial interpolation accuracy,visual assessment of denoised maps,and statistical analysis of data distributions,as well as decomposition of uncertainties.Results show that the SC-VAE achieves superior noise suppression and better preservation of spatial structure compared to the standard VAE,as demonstrated by a significant reduction in the variogram nugget effect and an increased partial sill.The SC-VAE produces denoised maps with clearer anomaly delineation and more regularized data distributions,effectively mitigating outliers and reducing kurtosis.Additionally,it delivers improved interpolation accuracy and spatially explicit uncertainty estimates,facilitating more reliable and interpretable assessments of prediction confidence.The SC-VAE framework thus provides a robust,geostatistically informed solution for enhancing the quality and interpretability of geochemical data,with broad applicability in mineral exploration,environmental geochemistry,and other Earth Science domains.
文摘This study contributes to renewable energy policy modeling by developing a dynamic decision-making framework that incorporates uncertainty,irreversibility,and the value of information.It responds to the growing need for structured tools to guide investments amid climate volatility,technological change,and economic risk.Grounded in decision theory—especially the work of Von Neumann,Morgenstern,and Savage—the framework models renewable energy investments using subjective probabilities and quasi-option value under evolving climate conditions.The empirical component focuses on ARAMCO’s renewable energy strategy,a corporate case illustrating how fossil-fuel-dependent entities can pivot toward sustainability.The analysis uses Net Present Value(NPV)modeling and real options analysis under different discount rates and carbon pricing scenarios to assess financial feasibility.Results show that lower discount rates and moderate carbon prices improve investment attractiveness,while high carbon pricing significantly reduces project viability.The study also highlights the policy relevance of this framework.Government subsidies,adaptive regulation,and public-private partnerships emerge as critical enablers of resilient investments.It further suggests that aligning ESG reporting standards with carbon pricing policies can strengthen market signals and encourage private capital flow into renewables.By integrating theoretical modeling with corporate investment realities,this chapter offers a replicable tool for policymakers and investors.Future research should expand its application across sectors and geographies to validate generalizability and improve planning in the transition toward low-carbon economies.
