A comprehensive analysis of real options in solar photovoltaic projects: A cluster-based approach.

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.

Simple solutions for improving thermal comfort in huts in the highlands of Peru.

In the Peruvian mountains, hundreds of thousands of rural households living in poverty live in cold indoor environments, close to 0 °C. Indoor cold causes thousands of respiratory diseases and excess of winter deaths. In this study, we numerically calculated the impact of simple low-cost refurbishments on discomfort time during a year. Using EnergyPlus and Python, we modelled a typical one-room hut used as bedroom built with a metal-sheet roof, adobe walls, dirt floors, and high infiltration rates. Then, 9 individual solutions were studied, and their combination resulted in 215 different hut designs. The model was calibrated with field measurements to estimate the infiltration. All the numerical calculations included an uncertainty analysis based on Monte Carlo method, and a sensitivity analysis to assess the impact of reducing infiltration on discomfort time. The base case had a discomfort time of 44% of time. The calibration of infiltration resulted in a mean hourly air exchange rate equal to 29.1 h-1 (SD = 17.0 h-1). Five different designs formed the Pareto front that optimized discomfort time and costs. The solution with the lowest discomfort time during a year, 37% of the time, was adding insulation to the roof (U = 0.83 W/m2•K) and the door (U = 1.00 W/m2•K); and its cost was 286USD. In this solution, when infiltrations were reduced to 4.1 h-1 (SD = 4.1 h-1) discomfort time decreased until 16%. These results benefit those households that nowadays invest their limited resources to improve their living conditions but without technical guidance.

The Influence of Anthropogenic and Environmental Disturbances on Parameter Estimation of a Dengue Transmission Model.

Some deterministic models deal with environmental conditions and use parameter estimations to obtain experimental parameters, but they do not consider anthropogenic or environmental disturbances, e.g., chemical control or climatic conditions. Even more, they usually use theoretical or measured in-lab parameters without worrying about uncertainties in initial conditions, parameters, or changes in control inputs. Thus, in this study, we estimate parameters (including chemical control parameters) and confidence contours under uncertainty conditions using data from the municipality of Bello (Colombia) during 2010-2014, which includes two epidemic outbreaks. Our study shows that introducing non-periodic pulse inputs into the mathematical model allows us to: (i) perform parameter estimation by fitting real data of consecutive dengue outbreaks, (ii) highlight the importance of chemical control as a method of vector control, and (iii) reproduce the endemic behavior of dengue. We described a methodology for parameter and sub-contour box estimation under uncertainties and performed reliable simulations showing the behavior of dengue spread in different scenarios.

Determining nitrate and sulfate pollution sources and transformations in a coastal aquifer impacted by seawater intrusion-A multi-isotopic approach combined with self-organizing maps and a Bayesian mixing model.

Over the past few decades, the La Paz aquifer system in Baja California Sur, Mexico, has been under severe pressure due to overexploitation for urban water supply and agriculture; this has caused seawater intrusion and deterioration in groundwater quality. Previous studies on the La Paz aquifer have focused mainly on seawater intrusion, resulting in limited information on nitrate and sulfate pollution. Therefore, pollution sources have not yet been identified sufficiently. In this study, an approach combining hydrochemical tools, multi-isotopes (δ2HH2O, δ18OH2O, δ15NNO3, δ18ONO3, δ34SSO4, δ18OSO4), and a Bayesian isotope mixing model was used to estimate the contribution of different nitrate and sulfate sources to groundwater. Results from the MixSIAR model revealed that seawater intrusion and soil-derived sulfates were the predominant sources of groundwater sulfate, with contributions of ~43.0% (UI90 = 0.29) and ~42.0% (UI90 = 0.38), respectively. Similarly, soil organic nitrogen (~81.5%, UI90 = 0.41) and urban sewage (~12.1%, UI90 = 0.25) were the primary contributors of nitrate pollution in groundwater. The dominant biogeochemical transformation for NO3- was nitrification. Denitrification and sulfate reduction were discarded due to the aerobic conditions in the study area. These results indicate that dual-isotope sulfate analysis combined with MixSIAR models is a powerful tool for estimating the contributions of sulfate sources (including seawater-derived sulfate) in the groundwater of coastal aquifer systems affected by seawater intrusion.

An alternative model to explain the vectorial capacity using as example Aedes aegypti case in dengue transmission.

Vectorial capacity (VC), as a concept that describes the potential of a vector to transmit a pathogen, has had historical problems related to lacks in dimensional significance and high error propagation from parameters that take part in the model to output. Hence, values estimated with those equations are not sufficiently reliable to consider in control strategies or vector population study. In this paper, we propose a new VC model consistent at dimensional level, i.e., the definition and the equation of VC have same and consistent units, with a parameter estimation method and mathematical structure that reduces the uncertainty in model output, using as a case of study an Aedes aegypti population of the municipality of Bello, Colombia. After a literature review, we selected one VC equation following biological, measurability and dimensional criteria, then we rendered a local and global sensitivity analysis, identifying the mortality rate of mosquitoes as a target component of the equation. Thus, we studied the Weibull and Exponential distributions as probabilistic models that represent the expectation of mosquitoes infective life, intending to include the best distribution in a selected VC structure. The proposed mortality rate estimation method includes a new parameter that represents an increase or decrease in vector mortality, as it may apply. We noticed that its estimation reduces the uncertainty associated with the expectation of mosquitoes' infective life expression, which also reduces the output range and variance in almost a half.

A Different Approach to Estimate Temperature-Dependent Thermal Properties of Metallic Materials.

Thermal conductivity, λ, and volumetric heat capacity, ρcp, variables that depend on temperature were simultaneously estimated in a diverse technique applied to AISI 1045 and AISI 304 samples. Two distinctive intensities of heat flux were imposed to provide a more accurate simultaneous estimation in the same experiment. A constant heat flux was imposed on the upper surface of the sample while the temperature was measured on the opposite insulated surface. The sensitivity coefficients were analyzed to provide the thermal property estimation. The Broydon-Fletcher-Goldfarb-Shanno (BFGS) optimization technique was applied to minimize an objective function. The squared difference objective function of the numerical and experimental temperatures was defined considering the error generated by the contact resistance. The temperature was numerically calculated by using the finite difference method. In addition, the reliability of the results was assured by an uncertainty analysis. Results showing a difference lower than 7% were obtained for λ and ρcp, and the uncertainty values were above 5%.

An integrated mechanistic modeling of a facultative pond: Parameter estimation and uncertainty analysis.

Imitating natural lakes, pond treatment systems inherit a high complexity with interconnected web of biochemical reactions and complex hydraulic processes. As such, its simulation requires a large and integrated model, which has been a challenge for pond engineers. In this study, we develop an all-encompassing model to gain a quantitative and comprehensive understanding of the hydraulic, physicochemical and microbiological conversion processes in the most common pond, a facultative pond. Moreover, to deal with an evitable issue of large mechanistic models as overparameterization leading to poor identifiability, a systematic parameter estimation was implemented. The application of sensitivity analysis reveals the most influential parameters on pond performance. Particularly, physical parameters, such as vertical eddy diffusivity, water temperature, and maximum growth rate of heterotrophs induce the most changes of organic matters while microbial assimilation and ammonia volatilization appear to be main processes for nutrient removal. In contrast, the efficiency of phosphate precipitation and nutrient biological removal via polyphosphate accumulating organisms and denitrifying bacteria is limited. Identifiability problems are addressed mainly by the characterization of light dependence of algal growth, interaction between water temperature and its coefficient, and the growth of autotrophic bacteria while based on the determinant measures, the most important parameter subsets affecting model outputs are related to physical processes and algal activity. After the establishment of the influential and identifiable parameter subset, an automatic calibration with the data collected from Ucubamba pond system (Ecuador) demonstrates the effect of high-altitude climatic conditions on pond behaviors. An aerobic prevailing condition is observed as a result of high light intensity causing accelerated algal activities, hence, leading to the limitation of hydrolysis, anaerobic processes, and the growth of anoxic heterotrophs for denitrification. Furthermore, the output of uncertainty analysis indicates that a large avoidable uncertainty as a result of vast complexity of the applied model can be reduced greatly via a systematic approach for parameter estimation.

Determination of physicochemical properties of petroleum derivatives and biodiesel using GC/MS and chemometric methods with uncertainty estimation.

The physicochemical properties of a substance, such as a fuel, can vary significantly with composition. Determining these properties with ASTM standard methods is both expensive and time-consuming, which has led to a desire to use chemometric modeling as an alternative. In this study, we compare the accuracy and robustness of two chemometric models, partial least squares (PLS) regression and support vector machine (SVM) with uncertainty estimation to determine how the physicochemical properties depend on the composition. A set of hydrocarbon mixtures, including crude oil, oil, gasoline, and biofuel/biodiesel, were collected. GC-MS data were taken, and physicochemical properties were measured for these mixtures using ASTM standard methods. PLS and SVM were used to develop predictive models of the physicochemical properties. Uncertainty in the estimated property values was estimated using a bootstrapping technique. With this uncertainty estimate, it is possible to assess the trustworthiness of any prediction, which ensures that the chemometric models can be applied for general purposes. SVM was found to be generally better for predicting the physicochemical properties, although we expect that with a more comprehensive data set the performance of the PLS models can be improved. We show in this work that PLS and SVM can be used to generate a predictive model of physicochemical properties based on GC-MS data. Combined with uncertainty analysis, these models provide robust predictions that can be used for regulatory, economic, and safety purposes.

A potato model intercomparison across varying climates and productivity levels.

A potato crop multimodel assessment was conducted to quantify variation among models and evaluate responses to climate change. Nine modeling groups simulated agronomic and climatic responses at low-input (Chinoli, Bolivia and Gisozi, Burundi)- and high-input (Jyndevad, Denmark and Washington, United States) management sites. Two calibration stages were explored, partial (P1), where experimental dry matter data were not provided, and full (P2). The median model ensemble response outperformed any single model in terms of replicating observed yield across all locations. Uncertainty in simulated yield decreased from 38% to 20% between P1 and P2. Model uncertainty increased with interannual variability, and predictions for all agronomic variables were significantly different from one model to another (P < 0.001). Uncertainty averaged 15% higher for low- vs. high-input sites, with larger differences observed for evapotranspiration (ET), nitrogen uptake, and water use efficiency as compared to dry matter. A minimum of five partial, or three full, calibrated models was required for an ensemble approach to keep variability below that of common field variation. Model variation was not influenced by change in carbon dioxide (C), but increased as much as 41% and 23% for yield and ET, respectively, as temperature (T) or rainfall (W) moved away from historical levels. Increases in T accounted for the highest amount of uncertainty, suggesting that methods and parameters for T sensitivity represent a considerable unknown among models. Using median model ensemble values, yield increased on average 6% per 100-ppm C, declined 4.6% per °C, and declined 2% for every 10% decrease in rainfall (for nonirrigated sites). Differences in predictions due to model representation of light utilization were significant (P < 0.01). These are the first reported results quantifying uncertainty for tuber/root crops and suggest modeling assessments of climate change impact on potato may be improved using an ensemble approach.

Uncertainty analysis of daily potable water demand on the performance evaluation of rainwater harvesting systems in residential buildings.

The objective of this paper is to perform a sensitivity analysis of design variables and an uncertainty analysis of daily potable water demand to evaluate the performance of rainwater harvesting systems in residential buildings. Eight cities in Brazil with different rainfall patterns were analysed. A numeric experiment was performed by means of computer simulation of rainwater harvesting. A sensitivity analysis was performed using variance-based indices for identifying the most important design parameters for rainwater harvesting systems when assessing the potential for potable water savings and underground tank capacity sizing. The uncertainty analysis was performed for different scenarios of potable water demand with stochastic variations in a normal distribution with different coefficients of variation throughout the simulated period. The results have shown that different design variables, such as potable water demand, number of occupants, rainwater demand, and roof area are important for obtaining the ideal underground tank capacity and estimating the potential for potable water savings. The stochastic variations on the potable water demand caused amplitudes of up to 4.8% on the potential for potable water savings and 9.4% on the ideal underground tank capacity. Average amplitudes were quite low for all cities. However, some combinations of parameters resulted in large amplitude of uncertainty and difference from uniform distribution for tank capacities and potential for potable water savings. Stochastic potable water demand generated low uncertainties in the performance evaluation of rainwater harvesting systems; therefore, uniform distribution could be used in computer simulation.

Probabilistic tools for assessment of pest resistance risk associated to insecticidal transgenic crops

One of the main risks associated to transgenic crops expressing Bacillus thuringiensis (Bt) toxins is the evolution of pest resistance. The adoption of Bt crops requires environmental risk assessment that includes resistance risk estimation, useful for definition of resistance management strategies aiming to delay resistance evolution. In this context, resistance risk is defined as the probability of the Bt toxin resistance allele frequency (RFreq) exceeding a critical value (CriticalFreq). Mathematical simulation models have been used to estimate (RFreq) over pest generations. In 1998, Caprio developed a deterministic simulation model with few parameters that can be used to obtain RFreq point estimates from point information about model parameters and decision variables involved in that process. In this work, the resistance risk was estimated using Caprio´s model, by incorporating uncertainty to the resistance allele initial frequency (InitialFreq). The main objective was to evaluate the influence of different probability distribution functions on the risk estimates. The simulation results showed that the influence of InitialFreq input distributions on the risk estimates changes along pest generations. The risk estimates considering input Normal distribution for InitialFreq are similar to those ones obtained considering Triangular distribution if their variances are equal. The use of Uniform distribution instead the Normal or Triangular due to the lack of information about InitialFreq leads to an overestimation of risk estimates for the initial generations and sub estimation for the generations after the one for which the critical frequency is achieved.

Um dos principais riscos associados às culturas inseticidas que expressam toxinas da bactéria Bacillus thuringiensis (Bt) é a evolução de resistência em pragas alvo. A adoção das culturas Bt requer avaliações prévias de impacto ambiental que incluem quantificação desse risco, informação útil para definição de estratégias de manejo para retardar o processo de evolução da resistência. O risco de resistência é definido como a probabilidade de a freqüência do alelo de resistência à toxina Bt (RFreq) na população da praga alvo ser superior a um valor crítico (CriticalFreq). Modelos matemáticos de simulação têm sido utilizados para estimar RFreq ao longo das gerações da praga. Em 1998, Caprio desenvolveu um modelo determinístico, com poucos parâmetros, que produz estimativas pontuais de RFreq a partir de informações também pontuais sobre os parâmetros e variáveis de decisão envolvidos no processo. Neste trabalho, o risco de resistência foi estimado utilizando o modelo de Caprio, incorporando-se incerteza ao parâmetro freqüência inicial do alelo de resistência (InitialFreq). Avaliou-se o efeito de diferentes distribuições de InitialFreq sobre as estimativas de risco. Observou-se que essas estimativas são afetadas pela distribuição de InitialFreq de modo diferenciado ao longo das gerações. As estimativas obtidas considerando a distribuição Normal são similares àquelas considerando a distribuição Triangular quando as referidas distribuições têm a mesma variância. O uso da distribuição Uniforme, em vez da Normal ou Triangular, leva à superestimação das estimativas de risco de resistência nas gerações iniciais e subestimação nas gerações subseqüentes àquela em que a CriticalFreq é atingida.

Probabilistic tools for assessment of pest resistance risk associated to insecticidal transgenic crops

One of the main risks associated to transgenic crops expressing Bacillus thuringiensis (Bt) toxins is the evolution of pest resistance. The adoption of Bt crops requires environmental risk assessment that includes resistance risk estimation, useful for definition of resistance management strategies aiming to delay resistance evolution. In this context, resistance risk is defined as the probability of the Bt toxin resistance allele frequency (RFreq) exceeding a critical value (CriticalFreq). Mathematical simulation models have been used to estimate (RFreq) over pest generations. In 1998, Caprio developed a deterministic simulation model with few parameters that can be used to obtain RFreq point estimates from point information about model parameters and decision variables involved in that process. In this work, the resistance risk was estimated using Caprio´s model, by incorporating uncertainty to the resistance allele initial frequency (InitialFreq). The main objective was to evaluate the influence of different probability distribution functions on the risk estimates. The simulation results showed that the influence of InitialFreq input distributions on the risk estimates changes along pest generations. The risk estimates considering input Normal distribution for InitialFreq are similar to those ones obtained considering Triangular distribution if their variances are equal. The use of Uniform distribution instead the Normal or Triangular due to the lack of information about InitialFreq leads to an overestimation of risk estimates for the initial generations and sub estimation for the generations after the one for which the critical frequency is achieved.

Um dos principais riscos associados às culturas inseticidas que expressam toxinas da bactéria Bacillus thuringiensis (Bt) é a evolução de resistência em pragas alvo. A adoção das culturas Bt requer avaliações prévias de impacto ambiental que incluem quantificação desse risco, informação útil para definição de estratégias de manejo para retardar o processo de evolução da resistência. O risco de resistência é definido como a probabilidade de a freqüência do alelo de resistência à toxina Bt (RFreq) na população da praga alvo ser superior a um valor crítico (CriticalFreq). Modelos matemáticos de simulação têm sido utilizados para estimar RFreq ao longo das gerações da praga. Em 1998, Caprio desenvolveu um modelo determinístico, com poucos parâmetros, que produz estimativas pontuais de RFreq a partir de informações também pontuais sobre os parâmetros e variáveis de decisão envolvidos no processo. Neste trabalho, o risco de resistência foi estimado utilizando o modelo de Caprio, incorporando-se incerteza ao parâmetro freqüência inicial do alelo de resistência (InitialFreq). Avaliou-se o efeito de diferentes distribuições de InitialFreq sobre as estimativas de risco. Observou-se que essas estimativas são afetadas pela distribuição de InitialFreq de modo diferenciado ao longo das gerações. As estimativas obtidas considerando a distribuição Normal são similares àquelas considerando a distribuição Triangular quando as referidas distribuições têm a mesma variância. O uso da distribuição Uniforme, em vez da Normal ou Triangular, leva à superestimação das estimativas de risco de resistência nas gerações iniciais e subestimação nas gerações subseqüentes àquela em que a CriticalFreq é atingida.