Electricity price forecasting plays a vital role in the financial markets. This paper proposes a self-adaptive, decomposed, heterogeneous, and ensemble learning model for short-term electricity price forecasting one, two, and three-months-ahead in the Brazilian market. Exogenous variables, such as supply, lagged prices and demand are considered as inputs signals of the forecasting model. Firstly, the coyote optimization algorithm is adopted to tune the hyperparameters of complementary ensemble empirical mode decomposition in the pre-processing phase. Next, three machine learning models, including extreme learning machine, gradient boosting machine, and support vector regression models, as well as Gaussian process, are designed with the intent of handling the components obtained through the signal decomposition approach with focus on time series forecasting. The individual forecasting models are directly integrated in order to obtain the final forecasting prices one to three-months-ahead. In this case, a grid of forecasting models is obtained. The best forecasting model is the one that has better generalization out-of-sample. The empirical results show the efficiency of the proposed model. Additionally, it can achieve forecasting errors lower than 4.2% in terms of symmetric mean absolute percentage error. The ranking of importance of the variables, from the smallest to the largest is, lagged prices, demand, and supply. This paper provided useful insights for multi-step-ahead forecasting in the electrical market, once the proposed model can enhance forecasting accuracy and stability.

The ideal configuration of wastewater treatment system (WTS) for attending cities specificities has become a complex decision, due to the fact that there are several available technologies, and a diversity of characteristics presented in the scenario of each city. Considering the importance of economic analysis, especially in developing countries, this work aims to demonstrate the economic feasibility considering cost-related indicators for the ideal WTS selection for specific features in these cities. Based on a literature review, 37 main WTS and two economic cost-related indicators (Net Present Value and Annualized Net Present Value) were considered. First of all, using a multi-criteria analysis these WTS were grouped in classes using the ELECTRE TRI method, based on criteria related to efficiency, and the weights were defined by appointments from research specialists in the literature appointments. The economic analysis was performed using the Monte Carlo Simulation (MCS) method, which has been applied specifically to each WTS class, thus generating a framework of economic viability for this context. The WTS with low and high costs were appointed, considering the development level in each applied scenario. This work contributes to expanding the WTS study horizons to select an ideal system, considering the economic aspect.

Typically, the economic analysis of Renewable Energy Projects (REP) has been assessed considering Classical Methodologies of Investment Analysis (CMIA) in which only a few set of indicators are included, highlighting the Payback, Net Present Value and Internal Rate of Return. This excessive reliance on CMIA has been criticized because it neglects managerial flexibility, and it may end up leading to the project's undervaluation. This paper attempts firstly, to identify and describe the complementarity among economic evaluation methodologies for REP. Secondly, a novel framework is proposed for conducting the economic analysis to help decision-makers in identifying the most suitable methodology before proceeding with the investment. The findings of the study suggest that CMIA may be suitable for the cases of projects presenting low volatility. However, the analysis should be supplemented by a new set of indicators for projects with medium or high volatility. The Multi-Index Methodology (MIM), the Extended Multi-Index Methodology (EMIM) and the Real Options Analysis (ROA) are proposed to increase the investors’ perception towards both the project’s profitability and risk and to account for flexibility. The application of the proposed framework is demonstrated for the case of a Small Hydropower Plant (SHP) investment project. The outcomes of this paper also include the opportunities for decision-makers to flexibly respond to changes in the business and economic market using the proposed conceptual framework.

Agriculture stands as one of the major economic pillars worldwide, with food production contributing significantly to income growth. However, agricultural activities also entail risks associated with uncontrollable factors along the supply chain. To address these challenges, mathematical models have been developed for forecasting crucial variables in managing agribusiness activities. In this context, this article employs a combination of systematic bibliometric analysis and the Latent Dirichlet Allocation (LDA) method, a semi-automated approach. The main objective of this study was to automate the identification of relevant topics and construct a bibliographic portfolio (BP) covering the period 2015–2022, focusing on methodologies used in articles and other bibliometric analyses. The 30 articles included in the BP address issues related to methodologies applied in the temporal analysis of agricultural commodities. These articles were categorized based on the nature of the prediction models used, classified as (i) machine learning (ML), (ii) machine learning and artificial neural networks (ML-NN), (iii) machine learning and ensemble (ML-Ensemble), (iv) machine learning and hybrid (ML-hybrid), and (v) statistical. Regarding the results, the topic that stood out the most was termed "Forecasting Methods Applied to Agribusiness Time Series." The most utilized classes were ML-hybrid (41.95 %) and statistical (29.31 %), followed by ML-NN (14.94 %), ML (9.20 %), and ML-Ensemble (4.60 %) types. The theoretical contribution of this study lies in identifying literary gaps concerning forecasting methods applied to agribusiness, while its practical implication is to identify forecasting methodologies to support decision-making.

This paper aims to present a systematic for the analysis of the economic viability of investment projects (SAEVIP) in fixed assets. In the literature, it is possible to identify the fundamental elements to evaluate the merit of the investment project (IP). To assess the dimension 'return', indicators are analysed: net present value (NPV), NPV annualised (NPVA), index benefit/cost (IBC), return on investment annualised (ROIA), index ROIA/minimum rate of attractiveness (MRA) and return on investment (ROI). An indicator analysis for dimension 'risk' is performed by internal rate of return (IRR), Payback, index MRA/IRR, index Payback/N and Fisher point. In addition, a joint assessment of indicator of risk and return is performed. A sensitivity study is promoted on the main variables involved in economic performance of the IP (MRA, costs and revenues). To validate the SAEVIP proposal, a case study shows the wealth of information generated by the application of this systematic.

Investment appraisal of agricultural projects (APs) is particularly demanding due to several factors. Namely, APs may have longer time horizons, higher external and internal volatility, and uncertainty caused by less control of production and external conditions (e.g., climatic conditions and market demand). Indeed, these APs may face high and different risks that should be managed properly. Nevertheless, both the literature and practice do not address such complexity and uncertainty conveniently. Thus, this research aimed to develop an integrative and easy-to-use framework to support the investment appraisal of APs, which goes beyond the traditional approach based on simple and deterministic models. This framework is based on an approach that includes several capital budgeting techniques integrating extended multi-index methodology (EMIM), Monte Carlo simulation (MCS), and real options analysis (ROA). This framework allows dealing with different risk and uncertainty scenarios and managerial flexibilities, which allow alternative and additional investment options. A simpler and easier approach can be particularly important for family agribusinesses, which usually do not use sophisticated decision-making tools. An AP in an agrosilvopastoral system (i.e., agriculture, livestock, forestry) was used to present and discuss the proposed methodology considering the relevance of such systems for sustainable agriculture and their higher risk and complexity when compared to traditional approaches. The main contribution of the framework is structuring a set of steps based on several tools to carry out investment appraisal in APs.