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Solar photovoltaic (PV) projects are pivotal in addressing climate change and fostering a sustainable energy future. However, the complex landscape of renewable energy investments, characterized by high upfront costs, market uncertainties, and evolving technologies, demands innovative evaluation methods. The Real Options Approach has emerged as a powerful tool, offering strategic flexibility in decision-making under uncertainty. This paper comprehensively analyzes the application of real options for evaluating solar photovoltaic projects in 2008-2023. Analysis of document descriptors (author keywords, index keywords, and noun phrases extracted from titles and abstracts) reveals that the dominant research topics in the last ten years (2014-2023) include investment optimization, strategic analysis, energy policy, optimization of energy generation and investments in wind energy. These descriptors are used to analyze the evolution of research interests on a two-year basis and reveal the yearly evolution of the research topics. Finally, the concept of emergence is used to unveil emerging research trends, providing valuable insights for researchers and practitioners in the renewable energy sector. Ultimately, this work contributes to a deeper understanding of how real options analysis empowers decision-makers to make informed choices in advancing clean and sustainable energy solutions.

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This study presents a method for modelling, predicting, and evaluating the impact of drill materials on the drilling process of hybrid palm/jute polyester composites, with the aim of enhancing hole quality regarding delamination, circularity, and cylindricity. Three drill materials, including High-Speed Steel (HSS), 5 % Cobalt-coated High-Speed Steel (HSS-Co5), and Solid Carbide drills were tested, and their impacts on drilling performance were assessed. Through thorough experimentation and statistical analysis, significant differences in results were observed between HSS drills and both HSS-Co5 and Solid Carbide drills. However, the variation in results between HSS-Co5 and Solid Carbide drill results was minimal. Additionally, the findings highlight notable disparities among drill types concerning uncertainty. The results also indicate that feed rate, drill material, and their interaction play crucial roles in determining drilling efficiency. Specifically, HSS drills consistently outperformed HSS-Co5 and Solid carbide drills, demonstrating superior performance in minimizing delamination, improving circularity, and enhancing cylindricity along with lower uncertainty.

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Renewable integration in utility grid is crucial in the current energy scenario. Optimized utilization of renewable energy can minimize the energy consumption from the grid. This demands accurate forecasting of renewable contribution and planning. Most of the researches aim to find a suitable forecasting model in terms of accuracy and error metrics. However, the uncertainty and variability in these forecasts are also significant. This work combines point forecast with interval forecast to provide comprehensive information about the forecast uncertainty. In this work, solar irradiance forecasting is carried out using artificial intelligence (AI) techniques. Forecasting is done using seasonal auto-regressive moving average with exogenous factors (SARIMAX), support vector regression (SVR), long short term memory (LSTM) techniques and performance is evaluated. SVR model exhibited the best performance with R 2 values of 0.97 and 0.96 for winter and summer respectively and 0.85 for monsoon and post-monsoon seasons. This is followed by forecast error distribution studies and uncertainty analysis. For this, SVR forecast error data is fitted using laplace distribution. Uncertainty study is carried out using confidence intervals and coverage rates. Excellent coverage rates are obtained for various confidence levels for all seasons, indicating the appropriate fitting of error distribution. For the narrow 85% confidence band, coverage rates of 89%, 95%, 90%, and 88% are obtained for winter, summer, monsoon and post-monsoon respectively. The work emphasizes the need for error-distribution studies, modeling of forecast errors and their application in providing reliable forecast intervals with the perspective of enhancing system reliability.

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Composite indicators (CIs) are being utilized more frequently to assess and monitor environmental systems. The revised leachate pollution index (r-LPI) is one such composite indicator used to quantify the pollution potential of landfill leachate on a scale of 5-100. The development of CIs involves several steps, and each of these steps has various methodological choices, each of which could lead to different results. Thereby, the reliability of the quantified pollution potential of leachate may be questioned. This study investigated the techniques for developing the r-LPI, examining decisions related to parameter selection, normalization technique, weighting approach, sub-indicator weights, and their aggregation. As the index developer made the decisions, each of these stages was fraught with uncertainty. The uncertainty in the various stages of the development of r-LPI was quantified using the Monte Carlo-based uncertainty analysis and the sensitivity analysis approach. Uncertainty analysis is a helpful but seldom-used step of index development that identifies the model's most dependable sections. Sensitivity analysis was carried out to ascertain the degree of impact the input parameters have on the r-LPI values. The combined use of sensitivity and uncertainty analysis in this study for the formulation of r-LPI affirmed the transparency, credibility, and accuracy of the index.

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This article introduces a model-based robust control framework for electrohydraulic soft robots. The methods presented herein exploit linear system control theory as it applies to a nonlinear soft robotic system. We employ dynamic mode decomposition with control (DMDc) to create appropriate linear models from real-world measurements. We build on the theory by developing linear models in various operational regions of the system to result in a collection of linear plants used in uncertainty analysis. To complement the uncertainty analyses, we utilize H ∞ ("H Infinity") synthesis techniques to determine an optimal controller to meet performance requirements for the nominal plant. Following this methodology, we demonstrate robust control over a multi-input multi-output (MIMO) hydraulically amplified self-healing electrostatic (HASEL)-actuated system. The simplifications in the proposed framework help address the inherent uncertainties and complexities of compliant robots, providing a flexible approach for real-time control of soft robotic systems in real-world applications.

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Pharmaceuticals and personal care products (PPCPs) have raised increasing concern worldwide due to their continuous release and potential hazards to the ecosystem and human health. This study optimized the entropy weight model (EW-WRSR) that combines entropy weight with multi-criteria decision analysis to investigate pollution patterns of PPCPs in the coasts and estuaries. The results revealed that occurrences of PPCPs from the 1940s to the present were consistent with using PPCPs, different types of human activities, and local urban development. This helped better understand the history of PPCP contamination and evaluate the uncertainty of EW-WRSR. The model predicted hotspots of PPCPs that were consistent with the actual situation, indicating that PPCPs mainly enter the nearshore ecosystem by the form of sewage discharge and residual aquaculture. This study can provide method that identifying highly contaminated regions on a global scale.

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Exploring the challenges posed by uncertainties in numerical modeling for hazardous material storage, this study introduces methodologies to improve monitoring networks for detecting subsurface leakages. The proposed approaches were applied to the Korea CO2 Storage Environmental Management (K-COSEM) test site, undergoing calibration, validation and uncertainty analysis through hydraulic and controlled-CO2 release tests. The calibration phase involved inter-well tracer and multi-well pumping tests, leveraging the Parameter ESTimation (PEST) model to determine the aquifer flow and solute transport properties of the K-COSEM site. To tackle uncertainties with limited observation data, we adopted Latin Hypercube simulation. Our uncertainty analysis confirmed model accuracy in simulating observed CO2 breakthrough curves. We also explored a probabilistic method to identify the environmental change point (EnCP) through correlation analysis with the distance from the CO2 injection well, revealing a linear trend and pinpointed potential preferential flow pathways by assessing detection probabilities. Evaluating CO2 detection capabilities was crucial for optimizing monitoring well placement, highlighting strategic well selection based on detection probabilities. This study advances managing uncertainties in hydrogeological modeling, underscoring the importance of sophisticated models in designing monitoring networks for hazardous leak detection in complex subsurface conditions.

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Biofuels have emerged as a promising and eco-friendly alternative to conventional fossil fuels. Biofuel sourced from rice straw (RS) and municipal solid waste (MSW), which are abundant residues from agricultural and municipal activities, present a sustainable solution to address waste management challenges. Utilizing life cycle assessment, this study quantifies the environmental advantages by assessing the reduction in greenhouse gas emissions, energy consumption, and other environmental impacts linked with employing these waste materials for biofuel production. Employing a cradle-to-gate approach as the system boundary for bioethanol production, with the functional unit set as per liter of bioethanol produced, the analysis reveals that the global warming potential (GWP) for ethanol from MSW is 4.4 kg CO2 eq., whereas for RS, it is 2.1 kg CO2 eq. per functional unit. The total environmental impacts were primarily due to enzymatic hydrolysis and electricity consumption for ethanol production from MSW and RS. Despite advancements, fossil fuel consumption remains a potential energy source for biofuel production. The cumulative energy demand stands at 18.6 MJ for RS and 71.5 MJ for MSW per functional unit, underscoring the potential to significantly reduce overall impacts by transitioning to a more environmentally sustainable energy source. The uncertainty analysis acknowledges the inherent uncertainties associated with data, assumptions, and methodologies, highlighting the crucial need for ongoing research and updates to enhance the accuracy of future assessments. This analysis forms the foundation for well-informed decision-making, providing valuable insights for policymakers, industry stakeholders, and consumers.

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An Addendum to the AAPM's TG-51 protocol for the determination of absorbed dose to water is presented for electron beams with energies between 4 MeV and 22 MeV ( 1.70 cm ≤ R 50 ≤ 8.70 cm $1.70\nobreakspace {\rm cm} \le R_{\text{50}} \le 8.70\nobreakspace {\rm cm}$ ). This updated formalism allows simplified calibration procedures, including the use of calibrated cylindrical ionization chambers in all electron beams without the use of a gradient correction. New k Q $k_{Q}$ data are provided for electron beams based on Monte Carlo simulations. Implementation guidance is provided. Components of the uncertainty budget in determining absorbed dose to water at the reference depth are discussed. Specifications for a reference-class chamber in electron beams include chamber stability, settling, ion recombination behavior, and polarity dependence. Progress in electron beam reference dosimetry is reviewed. Although this report introduces some major changes (e.g., gradient corrections are implicitly included in the electron beam quality conversion factors), they serve to simplify the calibration procedure. Results for absorbed dose per linac monitor unit are expected to be up to approximately 2 % higher using this Addendum compared to using the original TG-51 protocol.

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Real-world rotordynamic systems exhibit inherent uncertainties in manufacturing tolerances, material properties, and operating conditions. This study presents a Monte Carlo simulation approach using MSC Adams View and Adams Insight to investigate the impact of these uncertainties on the performance of a Laval/Jeffcott rotor model. Key uncertainties in bearing damping, bearing clearance, and mass imbalance were modeled with probabilistic distributions. The Monte Carlo analysis revealed the probabilistic nature of critical speeds, vibration amplitudes, and overall system stability. The findings highlight the importance of probabilistic methods in robust rotordynamic design and provide insights for establishing manufacturing tolerances and operational limits.

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Investigating the effects of spatial scales on the uncertainty and sensitivity analysis of the social vulnerability index (SoVI) model output is critical, especially for spatial scales finer than the census block group or census block. This study applied the intelligent dasymetric mapping approach to spatially disaggregate the census tract scale SoVI model into a 300-m grids resolution SoVI map in Davidson County, Nashville. Then, uncertainty analysis and variance-based global sensitivity analysis were conducted on two scales of SoVI models: (a) census tract scale; (b) 300-m grids scale. Uncertainty analysis results indicate that the SoVI model has better confidence in identifying places with a higher socially vulnerable status, no matter the spatial scales in which the SoVI is constructed. However, the spatial scale of SoVI does affect the sensitivity analysis results. The sensitivity analysis suggests that for census tract scale SoVI, the indicator transformation and weighting scheme are the two major uncertainty contributors in the SoVI index modeling stages. While for finer spatial scales like the 300-m grid's resolution, the weighting scheme becomes the uttermost dominant uncertainty contributor, absorbing uncertainty contributions from indicator transformation.

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Multiple uncertainties such as water quality processes, streamflow randomness affected by climate change, indicators' interrelation, and socio-economic development have brought significant risks in managing water quantity and quality (WQQ) for river basins. This research developed an integrated simulation-optimization modeling approach (ISMA) to tackle multiple uncertainties simultaneously. This approach combined water quality analysis simulation programming, Markov-Chain, generalized likelihood uncertainty estimation, and interval two-stage left-hand-side chance-constrained joint-probabilistic programming into an integration nonlinear modeling framework. A case study of multiple water intake projects in the Downstream and Delta of Dongjiang River Basin was used to demonstrate the proposed model. Results reveal that ISMA helps predict the trend of water quality changes and quantitatively analyze the interaction between WQQ. As the joint probability level increases, under strict water quality scenario system benefits would increase [3.23, 5.90] × 109 Yuan, comprehensive water scarcity based on quantity and quality would decrease [782.24, 945.82] × 106 m3, with an increase in water allocation and a decrease in pollutant generation. Compared to the deterministic and water quantity model, it allocates water efficiently and quantifies more economic losses and water scarcity. Therefore, this research has significant implications for improving water quality in basins, balancing the benefits and risks of water quality violations, and stabilizing socio-economic development.

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Accurate identification of urban waterlogging areas and assessing waterlogging susceptibility are crucial for preventing and controlling hazards. Data-driven models are utilized to forecast waterlogging areas by establishing intricate relationships between explanatory variables and waterlogging states. This approach tackles the constraints of mechanistic models, which are frequently complex and unable to incorporate socio-economic factors. Previous research predominantly employed single-type data-driven models to predict waterlogging locations and evaluation of their effectiveness. There is a scarcity of comprehensive performance comparisons and uncertainty analyses of different types of models, as well as a lack of interpretability analysis. The chosen study area was the central area of Beijing, which is prone to waterlogging. Given the high manpower, time, and economic costs associated with collecting waterlogging information, the waterlogging point distribution map released by the Beijing Water Affairs Bureau was selected as labeled samples. Twelve factors affecting waterlogging susceptibility were chosen as explanatory variables to construct Random Forest (RF), Support Vector Machine with Radial Basis Function (SVM-RBF), Particle Swarm Optimization-Weakly Labeled Support Vector Machine (PSO-WELLSVM), and Maximum Entropy (MaxEnt). The utilization of diverse single evaluation indicators (such as F-score, Kappa, AUC, etc.) to assess the model performance may yield conflicting results. The Distance between Indices of Simulation and Observation (DISO) was chosen as a comprehensive measure to assess the model's performance in predicting waterlogging points. PSO-WELLSVM exhibited the highest performance with a DISOtest value of 0.63, outperforming MaxEnt (0.78), which excelled in identifying areas highly susceptible to waterlogging, including extremely high susceptibility zones. The SVM-RBF and RF models demonstrated suboptimal performance and exhibited overfitting. The examination of waterlogging susceptibility distribution maps predicted by the four models revealed significant spatial differences due to variations in computational principles and input parameter complexities. The integration of four WSAMs based on logistic regression has been shown to significantly decrease the uncertainty of a single data-driven model and identify the most flood-prone areas. To improve the interpretability of the data model, a geographical detector was incorporated to demonstrate the explanatory capacity of 12 variables and the process of waterlogging. Building Density (BD) exhibits the highest explanatory power in relation to explain waterlogging susceptibility (Q value = 0.202), followed by Distance to Road, Frequency of Heavy Rainstorms (FHR), DEM, etc. The interaction between BD and FHR results in a nonlinear increase in the explanatory power of waterlogging susceptibility. The presence of waterlogging susceptibility risk in the research area can be attributed to the interactions of multiple factors.

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Wastewater-based epidemiology (WBE) and wastewater surveillance have become a valuable complementary data source to collect information on community-wide exposure through the measurement of human biomarkers in influent wastewater (IWW). In WBE, normalization of data with the de facto population that corresponds to a wastewater sample is crucial for a correct interpretation of spatio-temporal trends in exposure and consumption patterns. However, knowledge gaps remain in identifying and validating suitable de facto population biomarkers (PBs) for refinement of WBE back-estimations. WBE studies that apply de facto PBs (including hydrochemical parameters, utility consumption data sources, endo- and exogenous chemicals, biological biomarkers and signalling records) for relative trend analysis and absolute population size estimation were systematically reviewed from three databases (PubMed, Web of Science, SCOPUS) according to the PRISMA guidelines. We included in this review 81 publications that accounted for daily variations in population sizes by applying de facto population normalization. To date, a wide range of PBs have been proposed for de facto population normalization, complicating the comparability of normalized measurements across WBE studies. Additionally, the validation of potential PBs is complicated by the absence of an ideal external validator, magnifying the overall uncertainty for population normalization in WBE. Therefore, this review proposes a conceptual tier-based cross-validation approach for identifying and validating de facto PBs to guide their integration for i) relative trend analysis, and ii) absolute population size estimation. Furthermore, this review also provides a detailed evaluation of the uncertainty observed when comparing different de jure and de facto population estimation approaches. This study shows that their percentual differences can range up to ±200 %, with some exceptions showing even larger variations. This review underscores the need for collaboration among WBE researchers to further streamline the application of de facto population normalization and to evaluate the robustness of different PBs in different socio-demographic communities.

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In disease surveillance, capture-recapture methods are commonly used to estimate the number of diseased cases in a defined target population. Since the number of cases never identified by any surveillance system cannot be observed, estimation of the case count typically requires at least one crucial assumption about the dependency between surveillance systems. However, such assumptions are generally unverifiable based on the observed data alone. In this paper, we advocate a modeling framework hinging on the choice of a key population-level parameter that reflects dependencies among surveillance streams. With the key dependency parameter as the focus, the proposed method offers the benefits of (a) incorporating expert opinion in the spirit of prior information to guide estimation; (b) providing accessible bias corrections, and (c) leveraging an adapted credible interval approach to facilitate inference. We apply the proposed framework to two real human immunodeficiency virus surveillance datasets exhibiting three-stream and four-stream capture-recapture-based case count estimation. Our approach enables estimation of the number of human immunodeficiency virus positive cases for both examples, under realistic assumptions that are under the investigator's control and can be readily interpreted. The proposed framework also permits principled uncertainty analyses through which a user can acknowledge their level of confidence in assumptions made about the key non-identifiable dependency parameter.

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Water environmental capacity (WEC) is an indicator of environment management. The uncertainty analysis of WEC is more closely aligned with the actual conditions of the water body. It is crucial for accurately formulating pollution total emissions control schemes. However, the current WEC uncertainty analysis method ignored the connection between water quality and discharge, and required a large amount of monitoring data. This study analyzed the uncertainty of the WEC and predicted its economic value based on Copula and Bayesian model for the Yitong River in China. The Copula model was employed to calculate joint probabilities of water quality and discharge. And the posterior distribution of WEC with limited data was obtained by the Bayesian formula. The results showed that the WEC-COD in the Yitong River was 9009.67 t/a, while NH3-N had no residual WEC. Wanjinta Highway Bridge-Kaoshan Town reach had the most serious pollution. In order to make it have WEC, the reduction of COD and NH3-N was 5330.47 t and 3017.87 t. The economic value of WEC-COD was 5.97 × 107 CNY, and the treatment cost was 2.04 × 108 CNY to make NH3-N have residual WEC. The economic value distribution of WEC was extremely uneven, which could be utilized by adjusting the sewage outlet. In addition, since the treated water was discharged into the Sihua Bridge-Wanjinta Highway Bridge reach, the WEC-COD and the economic value were 19,488.51 t/a and 8.24 × 107 CNY. Increasing the flow of rivers could effectively improve WEC and economic value. This study provided an evaluation tool for guiding river water environment management.

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The adjoint function of connection number has unique advantages in solving uncertainty problems of water resource complex systems, and has become an important frontier and research hotspot in the uncertainty research of water resource complex problems. However, in the rapid evolution of the adjoint function, some problems greatly limit the application of the adjoint function in the research of water resources. Therefore, based on bibliometric analysis, development, practical application issues, and prospects of the hot directions are analyzed. It is found that the development of the connection number of water resource set pair analysis can be divided into three stages: (1) relatively sluggish development before 2005, (2) a period of rapid advancement in adjoint function research spanning from 2005 to 2017, and (3) a subsequent surge post-2018. The introduction of the adjoint function of connection number promotes the continuous development of set pair analysis of water resources. Set pair potential and partial connection number are the crucial research directions of the adjoint function. Subtractive set pair potential has rapidly developed into a relatively independent and important trajectory. The research on connection entropy is comparatively less, which needs to be further strengthened, while that on adjacent connection number is even less. The adjoint function of set pair potential can be divided into three major categories: division set pair potential, exponential set pair potential, and subtraction set pair potential. The subtraction set pair potential, which retains the original dimension and quantity variation range of the connection number, is widely used in water resources and other fields. Coupled with the partial connection number, a series of new connection number adjoint functions have been developed. The partial connection number can be mainly divided into two categories: total partial connection number, and semi-partial connection number. Among these, the calculation expression and connotation of total partial connection numbers have not yet reached a consensus, accompanied by the slow development of high-order partial connection numbers. Semi-partial connection number can describe the mutual migration movement between different components of the connection number, which develops rapidly. With the limitations and current situation described above, promoting the exploration and application of the adjoint function of connection number in the field of water resources and other fields of complex systems has become the focus of future research.

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Tailings dam breaches (TDBs) and subsequent flows can pose significant risk to public safety, the environment, and the economy. Numerical runout models are used to simulate potential tailings flows and understand their downstream impacts. Due to the complex nature of the breach-runout processes, the mobility and downstream impacts of these types of failures are highly uncertain. We applied the first-order second-moment (FOSM) methodology to a database of 11 back-analyzed historical tailings flows to evaluate uncertainties in TDB runout modelling and conducted a sensitivity analysis to identify key factors contributing to the variability of the HEC-RAS model output, including at different locations along the runout path. The results indicate that prioritizing resources toward advancements in estimating the values of primary contributors to the sensitivity of the selected model outputs is necessary for more reliable model results. We found that the total released volume is among the top contributors to the sensitivity of modelled inundation area and maximum flow depth, while surface roughness is among the top contributors to the sensitivity of modelled maximum flow velocity and flow front arrival time. However, the primary contributors to the sensitivity of the model outputs varied depending on the case study; therefore, the selection of appropriate rheological models and consideration of site-specific conditions are crucial for accurate predictions. The study proposes and demonstrates the FOSM methodology as an approximate probabilistic approach to model-based tailings flow runout prediction, which can help improve the accuracy of risk assessments and emergency response plans. Supplementary Information: The online version contains supplementary material available at 10.1007/s10230-024-00970-w.

Las roturas de presas de relaves (TDBs) y los flujos subsiguientes pueden suponer un riesgo significativo para la seguridad pública, el medio ambiente y la economía. Los modelos numéricos de desbordamiento se utilizan para simular posibles flujos de relaves y comprender su impacto aguas abajo. Debido a la naturaleza compleja de los procesos de rotura-desbordamiento, la movilidad y los impactos aguas abajo de este tipo de fallos tienen mucha incertidumbre. Se aplicó la metodología del segundo-momento de primer-orden (FOSM) a una base de datos de 11 flujos históricos de relaves analizados retrospectivamente para evaluar las incertidumbres en la modelización del desbordamiento de TDB y se realizó un análisis de sensibilidad para identificar los factores clave que contribuyen a la variabilidad de los resultados del modelo HEC-RAS, incluso en diferentes ubicaciones a lo largo de la trayectoria de fuga. Los resultados indican que es necesario priorizar los recursos hacia avances en la estimación de los valores de los principales contribuyentes a la sensibilidad de los resultados del modelo seleccionado para obtener resultados más fiables del modelo. El volumen total liberado se encuentra entre los principales contribuyentes a la sensibilidad del área de inundación modelizada y la profundidad máxima del flujo, mientras que la rugosidad de la superficie se encuentra entre los principales contribuyentes a la sensibilidad de la velocidad máxima del flujo modelizado y el tiempo de llegada del frente de flujo. Sin embargo, los principales factores que contribuyen a la sensibilidad de los resultados del modelo varían dependiendo del caso de estudio; por lo tanto, la selección de modelos reológicos apropiados y la consideración de las condiciones específicas del emplazamiento son cruciales para obtener predicciones precisas. El estudio propone y muestra la metodología FOSM como un enfoque probabilístico aproximado para la predicción de la extensión de flujos de relaves basada en modelos, que puede ayudar a mejorar la precisión de las evaluaciones de riesgos y los planes de respuesta a emergencias.

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The present study assessed the environmental impacts due to bitumen production in India using life cycle assessment approach. The impacts were calculated for production of 1 t of bitumen and system boundary covered extraction of resources, processing at refinery, transportation of bitumen and storage at the production site. In this study, five scenarios were considered to estimate the impacts reduction assuming different future electricity mix and thermal energy source. Crude oil extraction phase had contributed highest (91%) followed by refinery phase (4%), then transportation (3%) and at last storage of bitumen (2%). The normalization results found that the bitumen production had highest impacts on abiotic depletion fossil and lowest impact on eutrophication. Scenario S4 had the least environmental impacts and provided the overall reductions of 33% compared to the baseline scenario. Scenario S4 reduced the impacts significantly on acidification (51%), eutrophication (30%), and human toxicity (71%), but the reductions were not significant on global warming (11%) and increased the impacts on abiotic depletion fossil (1%). The results of sensitivity analysis found that thermal energy obtained from hard coal consumed during bitumen production is the most sensitive parameter for all the impact categories. The uncertainty analysis showed that the results of this study are reliable and had standard deviation less than 5% for all the impact categories. The findings of the present study will help the decision makers and concerned authorities to reduce the environmental impacts from bitumen production in India.

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This paper examines the uncertainty of greenhouse gas (GHG) emissions during monorail construction. Firstly, a deterministic analysis is conducted. Subsequently, the obtained data are evaluated using the data quality indicator (DQI), and a Markov chain Monte Carlo (MCMC) simulation method is employed to assume different parameter distributions. The results of the deterministic calculation indicate that the calculated emissions per unit area of the station amount to 1.97 ton CO2e/m2, while the calculated emissions per unit section length reach 7.55 ton CO2e/m2. To simulate parameter distribution, we utilize a Beta distribution with good shape applicability. Furthermore, we establish scenarios involving system boundary reduction, low-emission factors, and reduced material and energy inputs in order to analyze scenario uncertainties. Regarding model uncertainty, this paper assumes that the material and energy quantity data conform to the normal, log-normal, uniform, and triangular distributions, respectively, subsequently analyzing the uncertainty distributions. This paper analyzes the GHG emission uncertainty evaluation of 16 monorail stations and sections during the construction period, which is divided into parameter, scenario, and model uncertainty. We provide a concrete framework for studying uncertainties related to GHG emissions at stations and sections during the monorail construction period. The scenario analysis results will help to make decisions about the choice of parameters, system boundaries, and other settings. It provides new guidance for emission reduction policies, such as reducing the use of steel-related products or using alternative environmentally friendly materials, considering emission reduction factors more comprehensively and setting emission reduction factors according to uniform distribution principle as far as possible.

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