Causal relationships between energy consumption, foreign direct investment and economic growth: Fresh evidence from dynamic simultaneous-equations models

This paper examines the interrelationships between energy consumption, foreign direct investment and economic growth using dynamic panel data models in simultaneous-equations for a global panel consisting of 65 countries. The time component of our dataset is 1990–2011 inclusive. To make the panel data analysis more homogenous, we also investigate this interrelationship for a number of sub-panels which are constructed based on the income level of countries. In this way, we end up with three income panels; namely, high income, middle income, and low income panels. In the empirical part, we draw on growth theory and augment the classical growth model, which consists of capital stock, labor force and inflation, with foreign direct investment and energy. Generally, we shows mixed results about the interrelationship between energy consumption, FDI and economic growth

The nexus between foreign investment, domestic capital and economic growth: Empirical evidence from the MENA region

The objective of this paper is to estimate an econometric model for analyzing the interrelationship between foreign direct investment and domestic capital and economic growth in 13 MENA countries by using a ‘growth model’ framework and simultaneous-equations models estimated by the Generalized Method of Moments (GMM) during the period 1990–2010. Our empirical results show that there is bi-directional causal relationship between foreign investment and economic growth, between domestic capital and economic growth, and there is uni-directional causal relationship from foreign direct investment to domestic capital for the region as a whole

Causal relationships between energy consumption, foreign direct investment and economic growth: Fresh evidence from dynamic simultaneous-equations models

This paper examines the interrelationships between energy consumption, foreign direct investment and economic growth using dynamic panel data models in simultaneous-equations for a global panel consisting of 65 countries. The time component of our dataset is 1990–2011 inclusive. To make the panel data analysis more homogenous, we also investigate this interrelationship for a number of sub-panels which are constructed based on the income level of countries. In this way, we end up with three income panels; namely, high income, middle income, and low income panels. In the empirical part, we draw on growth theory and augment the classical growth model, which consists of capital stock, labor force and inflation, with foreign direct investment and energy. Generally, we shows mixed results about the interrelationship between energy consumption, FDI and economic growth

Artificial Intelligence-Based Diabetes Diagnosis with Belief Functions Theory

We compared various machine learning (ML) methods, such as the K-nearest neighbor (KNN), support vector machine (SVM), and decision tree and deep learning (DL) methods, like the recurrent neural network, convolutional neural network, long short-term memory (LSTM), and gated recurrent unit (GRU), to determine the ones with the highest precision. These algorithms learn from data and are subject to different imprecisions and uncertainties. The uncertainty arises from the bad reading of data and/or inaccurate sensor acquisition. We studied how these methods may be combined in a fusion classifier to improve their performance. The Dempster–Shafer method, which uses the formalism of belief functions characterized by asymmetry to model nonprecise and uncertain data, is used for classifier fusion. Diagnosis in the medical field is an important step for the early detection of diseases. In this study, the fusion classifiers were used to diagnose diabetes with the required accuracy. The results demonstrated that the fusion classifiers outperformed the individual classifiers as well as those obtained in the literature. The combined LSTM and GRU fusion classifiers achieved the highest accuracy rate of 98%

Do Environmental Innovation and Green Energy Matter for Environmental Sustainability? Evidence from Saudi Arabia (1990–2018)

Climate change and global warming, caused by excessive carbon emissions from transportation and other environmentally hazardous activities, are serious problems for many countries nowadays. Therefore, while some countries are not making optimal use of their resources, others are working hard to preserve a green and clean environment in order to foster long-term growth. Governments and policymakers throughout the world are finally starting to take the risks of climate change and global warming seriously. This paper extends previous literature related to environmental design practices by investigating the impacts of environmental innovation and the deployment of green energy on decreasing carbon dioxide (CO2) emissions for Saudi Arabia during the period 1990–2018. Different CO2 emission measures are incorporated in the analysis, namely per capita CO2 emissions, CO2 intensity, CO2 emissions from liquid fuel use, and CO2 emissions from heat and electricity generation. Overall, the outcomes of the autoregressive distributed lag (ARDL) technique demonstrate the presence of a long-term association between our two main variables (green energy use and environmental innovation) and the different measures of CO2 emissions, except CO2 emissions from liquid fuels consumption for green energy use and CO2 intensity for environmental innovation. In another sense, the use of renewable energies and technologies linked to environmental patents proves to be a good alternative if they do not contribute to environmental pollution. On the basis of the results, this study offers several policy recommendations