Is Greenfield investment improving welfare: A quantitative analysis for Latin American and Caribbean developing countries.

Greenfield investment is considered the backbone of emerging economies and developing countries. This research is carried out to investigate the causal impact of Greenfield investment as a target variable and some other controlled variables for the sample of 23 Latin American and Caribbean (LA&C) developing countries. The period is 1998-2017, and Levin, Lin and Chu (LLC) and System-Generalized Method of Moment (Sys-GMM) techniques are employed for analytical analysis. The Sys-GMM technique estimates show that Greenfield investment has a significant positive impact on these countries' economic growth, health, education, and welfare. Furthermore, controlled variables remittances have a significant and positive impact, while foreign aid has a negative effect on the dependent variables. The rest of the other controlled variables show mixed results. From the analysis, it is suggested that Greenfield investment has improved per capita income, education and health sector that further enhanced the welfare of the society. In addition, new foreign investment creates job employment and brings innovations that improve labour skills. On the other hand, foreign aid must be avoided, which harms the economic activities of developing countries. Therefore, it is concluded that governments of Latin American and Caribbean developing countries adopt more friendly policies to attract Greenfield investment.

A novel data balancing approach and a deep fractal network with light gradient boosting approach for theft detection in smart grids.

Electricity theft is the largest type of non-technical losses faced by power utilities around the globe. It not only raises revenue losses to the utilities but also leads to lethal fires and electric shocks at distribution side. In the past, field operation groups were sent by the utilities to conduct inspections of suspicions electric equipments stated by the public. Advanced metering infrastructure based recent development in the smart grids makes it easy to detect electricity thefts. However, the conventional supervised learning techniques have low theft detection performance mainly due to imbalance datasets available for training. Therefore, in this paper, we develop a novel theft detection model with twofold contribution. A unique hybrid sampling technique named as hybrid oversampling and undersampling using both classes (HOUBC) is proposed to balance the dataset. HOUBC first performs undersampling and then oversampling using both the majority (normal) and minority (theft) classes. A new deep learning method, fractal network is applied with light gradient boosting method to extract and learn important characteristics from electricity consumption profiles for identifying electricity thieves. The proposed model relies on smart meter's data for theft detection and hence, a rapid and widespread adaption of this model is feasible, which shows its main advantage. The performance of the model is evaluated with real-world smart meter's data, i.e., state grid corporation of China. Comprehensive simulation results describe the effectiveness of the proposed model against conventional schemes in terms of electricity theft detection.

Optimization of a near-zero-emission energy system for the production of desalinated water and cooling using waste energy of fuel cells.

In the present study, a biomass-based multi-purpose energy system that can generate power, desalinated water, hydrogen, and ammonia is presented. The gasification cycle, gas turbine, Rankine cycle, PEM electrolyzer, ammonia production cycle using the Haber-Bosch process, and MSF water desalination cycle are the primary subsystems of this power plant. On the suggested system, a thorough thermodynamic and thermoeconomic evaluation has been conducted. For the analysis, the system is first modeled and investigated from an energy point of view, after which it is similarly studied from an exergy point of view before the system is subjected to economic analysis (exergoeconomic analysis). The system is evaluated and modeled using artificial intelligence to aid in the system optimization process after energy, exergy, and economic modeling and analysis. The resulting model is then optimized using a genetic algorithm to maximize system efficiency and reduce system expenses. EES software does the first analysis. After that, it sends the data to MATLAB program for optimization and to see how operational factors affect thermodynamic performance and overall cost rate. To find the best solution with the maximum energy efficiency and lowest total cost, multi-objective optimization is used. In order to shorten computation time and speed up optimization, the artificial neural network acts as a middleman in the process. In order to identify the energy system's optimal point, the link between the objective function and the choice factors has been examined. The results show that increasing the flow of biomass enhances efficiency, output, and cost while raising the temperature of the gas turbine's input decreases cost while simultaneously boosting efficiency. Additionally, according to the system's optimization results, the power plant's cost and energy efficiency are 37% and 0.3950$/s, respectively, at the ideal point. The cycle's output is estimated at 18900 kW at this stage.