Machine learning methods for investigating the nexus between FDI and environmental degradation: evidence from Sub-Saharan African countries

Environmental degradation has been recognized as a global issue due to high energy consumption and economic growth. This situation needs researchers to focus on, thereby, the current article examined the impact of renewable energy production (REP), energy import, renewable energy consumption (REC), gross domestic product (GDP), inflation, and foreign direct investment (FDI) on the carbon dioxide (CO2) emission in China. The study considered secondary data and extracted it from the World Bank database covering the period 1981 to 2018. The current article has examined the stationarity of the constructs using Augmented Dickey-Fuller tests and investigated the association among constructs using the quantile autoregressive distributed lag (QARDL) model. The data revealed that REP, energy import, and REC, had a significant and negative linkage with CO2 emission in China. In contrast, GDP, inflation, and FDI are linked with CO2 emission in a positive manner. The article also guided the policymakers regarding the policy development related to reducing carbon emissions using renewable energy production and consumption.

This study examines the impact of Foreign Direct Investment (FDI) on renewable energy consumption in Somalia from 1990 to 2019 using the Autoregressive Distributed Lag (ARDL) model. The findings reveal a positive, statistically significant relationship between FDI, Gross Domestic Product (GDP), trade openness, and renewable energy consumption in the long run. Specifically, a 1% increase in FDI leads to a 0.0000115% increase in renewable energy consumption. Similarly, a 1% increase in GDP leads to a 0.026041% increase in renewable energy consumption. Meanwhile, a 1% rise in trade openness enhances renewable energy consumption by 0.0000280%. Increased foreign investments and economic growth promote the adoption of renewable energy, aligning with sustainable development principles. Conversely, environmental degradation negatively impacts renewable energy consumption. Specifically, a 1% increase in environmental degradation leads to a 0.570376% decrease in renewable energy consumption. Policymakers should incentivize FDI in the renewable energy industry through tax incentives, streamlined regulations, and public-private partnerships. Strategies promoting economic growth and integrating renewable energy objectives are essential, with trade openness facilitating the importation of renewable energy technologies. A limitation of this study is the reliance on yearly data, which may not capture recent developments or short-term fluctuations. Future research should use more granular, up-to-date data to understand the dynamics in Somalia better.

Applications of machine learning in friction stir welding: Prediction of joint properties, real-time control and tool failure diagnosis

Vietnam, an emerging Southeast Asian economy, confronts the dual challenge of pursuing economic growth while ensuring environmental sustainability. In response to global efforts to mitigate CO 2 emissions, various governments have implemented various policies. The article aims to investigate the intricate relationship between innovation, renewable energy consumption (REC), foreign direct investment (FDI), gross domestic product (GDP), and CO 2 emissions in Vietnam. Using annual data from 2000 to 2023, we employ the Autoregressive Distributed Lag (ARDL) approach to analyze both short-term and long-term dynamics among these variables. Our empirical findings reveal that innovation and REC significantly reduce CO 2 emissions in the long run. At the same time, FDI shows a nuanced impact, promoting economic growth and contributing to higher emissions depending on the type and regulation of investments. Annual data from 2000 to 2023, obtained from Vietnam's General Statistics Office, the International Monetary Fund, and the World Bank, were utilized for this analysis. Empirical findings indicate that innovation contributes positively to carbon dioxide emissions in Vietnam, whereas REC is associated with a reduction in environmental degradation. On the other hand, both FDI inflows and economic development are significantly positively correlated with environmental pollution. The results underscore the importance of fostering innovation and expanding renewable energy to achieve sustainable economic growth. Policy implications suggest promoting clean technology, creating favorable conditions for sustainable FDI, and diversifying the economy to mitigate environmental impacts. This article explores the intricate relationships between innovation, renewable energy usage, FDI, GDP growth, and CO 2 emissions in Vietnam. By integrating empirical analysis with a review of existing literature, the article provides insights into the dynamic interactions among these variables and proposes policy recommendations for sustainable development in Vietnam. Ultimately, this research offers essential recommendations to support Vietnam in cultivating a green and sustainable economy during the Fifth Industrial Revolution era.

This study explores the impact of natural resource rents on CO2 emissions in the presence of renewable energy consumption, technological innovation, and gross domestic product (GDP) in the case of the USA from 1990 to 2020. For sustainable development policies and to slow down environmental degradation, it is important to understand the intricate connection between resource rents and carbon emissions in the US. Thus, this study hypothesizes that after controlling for renewable energy consumption, technological innovation, and GDP, among other variables, resource rents significantly contribute to carbon dioxide emissions in America. This study adds significantly to the existing literature on how resource rents affect CO2 emissions in the United States. This study employs quantile regression estimation techniques, which provide vital policy insights and academic contributions that are relevant to sustainable development and environmental conservation from both national and global perspectives. The results show that natural resource rents (NNRs) and GDP are positively associated with CO2 emissions at all quantiles. Moreover, the results indicate that technological innovation and renewable energy consumption are important in curbing CO2 emissions.

FinTech online lending to consumers has grown rapidly in the post-crisis era. As argued by its advocates, one key advantage of FinTech lending is that lenders can predict loan outcomes more accurately by employing complex analytical tools, such as machine learning (ML) methods. This study applies ML methods, in particular random forests and stochastic gradient boosting, to loan-level data from the largest FinTech lender of personal loans to assess the extent to which those methods can produce more accurate out-of-sample predictions of default on future loans relative to standard regression models. To explain loan outcomes, this analysis accounts for the economic conditions faced by a borrower after origination, which are typically absent from other ML studies of default. For the given data, the ML methods indeed improve prediction accuracy, but more so over the near horizon than beyond a year. This study then shows that having more data up to, but not beyond, a certain quantity enhances the predictive accuracy of the ML methods relative to that of parametric models. The likely explanation is that there has been data or model drift over time, so that methods that fit more complex models with more data can in fact suffer greater out-of-sample misses. Prediction accuracy rises, but only marginally, with additional standard credit variables beyond the core set, suggesting that unconventional data need to be sufficiently informative as a whole to help consumers with little or no credit history. This study further explores whether the greater functional flexibility of ML methods yields unequal benefit to consumers with different attributes or who reside in locales with varying economic conditions. It finds that the ML methods produce more favorable ratings for different groups of consumers, although those already deemed less risky seem to benefit more on balance.

FinTech online lending to consumers has grown rapidly in the post-crisis era. As argued by its advocates, one key advantage of FinTech lending is that lenders can predict loan outcomes more accurately by employing complex analytical tools, such as machine learning (ML) methods. This study applies ML methods, in particular random forests and stochastic gradient boosting, to loan-level data from the largest FinTech lender of personal loans to assess the extent to which those methods can produce more accurate out-of-sample predictions of default on future loans relative to standard regression models. To explain loan outcomes, this analysis accounts for the economic conditions faced by a borrower after origination, which are typically absent from other ML studies of default. For the given data, the ML methods indeed improve prediction accuracy, but more so over the near horizon than beyond a year. This study then shows that having more data up to, but not beyond, a certain quantity enhances the predictive accuracy of the ML methods relative to that of parametric models. The likely explanation is that there has been data or model drift over time, so that methods that fit more complex models with more data can in fact suffer greater out-of-sample misses. Prediction accuracy rises, but only marginally, with additional standard credit variables beyond the core set, suggesting that unconventional data need to be sufficiently informative as a whole to help consumers with little or no credit history. This study further explores whether the greater functional flexibility of ML methods yields unequal benefit to consumers with different attributes or who reside in locales with varying economic conditions. It finds that the ML methods produce more favorable ratings for different groups of consumers, although those already deemed less risky seem to benefit slightly more on balance.

Parameter importance assessment improves efficacy of machine learning methods for predicting snow avalanche sites in Leh-Manali Highway, India

The eye is generally considered to be the most important sensory organ of humans. Diseases and other degenerative conditions of the eye are therefore of great concern as they affect the function of this vital organ. With proper early diagnosis by experts and with optimal use of medicines and surgical techniques, these diseases or conditions can in many cases be either cured or greatly mitigated. Experts that perform the diagnosis are in high demand and their services are expensive, hence the appropriate identification of the cause of vision problems is either postponed or not done at all such that corrective measures are either not done or done too late. An efficient model to predict eye diseases using machine learning (ML) and ranker-based feature selection (r-FS) methods is therefore proposed which will aid in obtaining a correct diagnosis. The aim of this model is to automatically predict one or more of five common eye diseases namely, Cataracts (CT), Acute Angle-Closure Glaucoma (AACG), Primary Congenital Glaucoma (PCG), Exophthalmos or Bulging Eyes (BE) and Ocular Hypertension (OH). We have used efficient data collection methods, data annotations by professional ophthalmologists, applied five different feature selection methods, two types of data splitting techniques (train-test and stratified k-fold cross validation), and applied nine ML methods for the overall prediction approach. While applying ML methods, we have chosen suitable classic ML methods, such as Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), AdaBoost (AB), Logistic Regression (LR), k-Nearest Neighbour (k-NN), Bagging (Bg), Boosting (BS) and Support Vector Machine (SVM). We have performed a symptomatic analysis of the prominent symptoms of each of the five eye diseases. The results of the analysis and comparison between methods are shown separately. While comparing the methods, we have adopted traditional performance indices, such as accuracy, precision, sensitivity, F1-Score, etc. Finally, SVM outperformed other models obtaining the highest accuracy of 99.11% for 10-fold cross-validation and LR obtained 98.58% for the split ratio of 80:20.

The fundamental approaches to industrial growth, government interventions, and market structure have ignored the main consequences of the common idea of “growth first, clean later”. This study investigates the relationship between carbon emissions (CO2) and international collaboration in climate change mitigation technologies (ICCCMT), renewable energy consumption (REC), exports (EXP), imports (IMP), gross domestic product (GDP), foreign direct investment (FDI), natural resources rents (NRR), and domestic innovation in climate change mitigation technologies (DICCMT) for a panel of the Organization for Economic Cooperation and Development (OECD) countries over the period of 1990-2020. We adopted a series of econometric techniques for the visualization of the available data. We used second-generation econometrics estimations to verify cross-sectional dependence, co-integration, and stationary between the variables.Based on our findings, the study reveals that ICCCMT, REC, and DICCMT have a positive effect and contribute to CO2 mitigation of the 30 OECD economies. Furthermore, the findings reveal that ICCCMT can promote renewable energy consumption, thus the increase of REC will significantly mitigate CO2 emissions. The outcomes from the panel dynamic GMM model confirmed a positive relationship between CO2 emissions, FDI, exports, imports, and GDP. The study indicates that these variables can adversely affect climate change mitigation.

Tuberculosis (TB) remains one of the most pressing public health issues, especially in developing countries. The high incidence rate and the spread of multidrug-resistant strains of “Mycobacterium tuberculosis” pose significant challenges to modern medicine. India is one of the countries with the highest TB burden, making the optimization of disease spread prediction methods crucial for the effective implementation of prevention and treatment measures. The application of machine learning (ML) methods enables the automation of large-scale data analysis and the identification of key risk factors. This study aims to develop effective machine learning models for assessing the risk of TB spread in India based on socio-economic, demographic, and medical factors. A dataset containing 148 records from the period 2019–2022, categorized by Indian states, was used for analysis. Key variables included the number of detected TB cases, treatment success rates, mortality rates among patients, and the tobacco and alcohol consumption status of patients. The study involved data preprocessing, correlation analysis, and the application of machine learning methods. Several models were tested: linear regression, regularized models (Lasso and Ridge), support vector machine (SVM), k-nearest neighbors (KNN), random forest, and decision tree. The analysis showed that the best accuracy was achieved by the SVM model with optimized parameters, demonstrating the highest coefficient of determination and the lowest root mean square error. The comparison of other models revealed significant advantages of SVM over linear regression and decision trees, which exhibited low generalization capability. The most influential factors in predicting TB spread were determined using the Permutation Importance method. The most significant factors included geographic location (state), the number of registered TB cases among children, the number of women with TB, the mortality rate among patients, and the infrastructure available for treating drug-resistant TB. It was also found that social factors, such as tobacco and alcohol consumption among patients, influence the disease spread, although their contribution is less significant. The study confirmed the effectiveness of applying machine learning methods to predict tuberculosis spread. The optimized SVM model provided the best accuracy and generalization capability. Factor importance analysis revealed that regional characteristics, demographic indicators, and mortality rates have the greatest impact on disease spread. The obtained results can be used to improve TB control strategies, particularly through targeted interventions in high-risk regions. The use of ML methods enhances disease control efficiency, which is an essential step in the global fight against tuberculosis.

This research aimed to assess and implement the long- and short-run relationship of agriculture and environmental sustainability with control variables. Purposely, this research consolidated theoretical and conceptual principles to create a systematic structure in agriculture for the development of both sectors, i.e., agricultural and the environment. On this ground statement, this research was motivated to contemplate the relationship between carbon dioxide emission, agricultural production, gross domestic product, renewable energy consumption, and foreign direct investment using annual data series of Latin American and Caribbean countries from 1971 to 2018. Autoregressive distributed lag (ARDL) was used as an econometric methodology to examine the relationship among the variables. Agriculture is the most vulnerable sector in Latin American and Caribbean countries, and the economy is heavily dependent on it. The main results of this research indicated that agriculture and CO2 emissions were positively related to each other for the long and short run, which means that agricultural activities increased the CO2 emission levels. At the same time, the control variables showed mixed associations with environmental degradation as gross domestic product (GDP) was positively significant and renewable energy consumption was negatively significant. The error correction (ECt−1) term was negatively significant, confirming the long-run relationship and the speed of adjustment from short- to long-run equilibrium. Agricultural production and GDP led to increments in CO2 emissions, while renewable energy consumption negatively contributed to toxic emissions. The speed of adjustment in Latin American and Caribbean countries was nippy. It required 2.933 periods for the transformation from the short periodic phase to the long term. A comprehensive approach is the research debate rigorously and holistically based on divergent sectors of an economy and their relationship with environmental sustainability. The econometric method, symbolic system, and conceptual existence were designed originally.

Cancer has been characterized as a heterogeneous disease consisting of many different subtypes. The early diagnosis and prognosis of a cancer type have become a necessity in cancer research, as it can facilitate the subsequent clinical management of patients. The importance of classifying cancer patients into high or low risk groups has led many research teams, from the biomedical and the bioinformatics field, to study the application of machine learning (ML) methods. Therefore, these techniques have been utilized as an aim to model the progression and treatment of cancerous conditions. In addition, the ability of ML tools to detect key features from complex datasets reveals their importance. A variety of these techniques, including Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Support Vector Machines (SVMs) and Decision Trees (DTs) have been widely applied in cancer research for the development of predictive models, resulting in effective and accurate decision making. Even though it is evident that the use of ML methods can improve our understanding of cancer progression, an appropriate level of validation is needed in order for these methods to be considered in the everyday clinical practice. In this work, we present a review of recent ML approaches employed in the modeling of cancer progression. The predictive models discussed here are based on various supervised ML techniques as well as on different input features and data samples. Given the growing trend on the application of ML methods in cancer research, we present here the most recent publications that employ these techniques as an aim to model cancer risk or patient outcomes.

Prediction of tunneling-induced ground settlements is an essential task, particularly for tunneling in urban settings. Ground settlements should be limited within a tolerable threshold to avoid damages to aboveground structures. Machine learning (ML) methods are becoming popular in many fields, including tunneling and underground excavations, as a powerful learning and predicting technique. However, the available datasets collected from a tunneling project are usually small from the perspective of applying ML methods. Can ML algorithms effectively predict tunneling-induced ground settlements when the available datasets are small? In this study, seven ML methods are utilized to predict tunneling-induced ground settlement using 14 contributing factors measured before or during tunnel excavation. These methods include multiple linear regression (MLR), decision tree (DT), random forest (RF), gradient boosting (GB), support vector regression (SVR), back-propagation neural network (BPNN), and permutation importance-based BPNN (PI-BPNN) models. All methods except BPNN and PI-BPNN are shallow-structure ML methods. The effectiveness of these seven ML approaches on small datasets is evaluated using model accuracy and stability. The model accuracy is measured by the coefficient of determination (R2) of training and testing datasets, and the stability of a learning algorithm indicates robust predictive performance. Also, the quantile error (QE) criterion is introduced to assess model predictive performance considering underpredictions and overpredictions. Our study reveals that the RF algorithm outperforms all the other models with the highest model prediction accuracy (0.9) and stability (3.02 × 10−27). Deep-structure ML models do not perform well for small datasets with relatively low model accuracy (0.59) and stability (5.76). The PI-BPNN architecture is proposed and designed for small datasets, showing better performance than typical BPNN. Six important contributing factors of ground settlements are identified, including tunnel depth, the distance between tunnel face and surface monitoring points (DTM), weighted average soil compressibility modulus (ACM), grouting pressure, penetrating rate and thrust force.

Foreign direct investment, Green Technological Innovation and Energy Poverty: Empirical evidences from Sub-Saharan African countries