The threat of global warming has increased due to industrialization, urbanization, population expansion, and changes in lifestyle among the Group of Seven(G7) Carbon dioxide emissions (CO₂) directly affect how much electricity can be generated from various sources. This research aims to identify environmental hazards associated with various energy sources. Analyzing the impact of various energy sources on CO₂ emissions from electricity and heat production using data from the G7. The data is analyzed using quantile regression (QR), generalized method of moments (GMM), random effects (RE), and fixed effects (FE). Our results indicate a substantial positive impact on CO₂ emissions regardless of the technology used to generate coal and gas power. Coal-fired power plants have a larger impact on the environment than other sources of emissions. Also, all coal and gas coefficients are significant in FE, RE, GMM, and QR. Oil coefficients have a negative impact on environmental degradation and are significant for FE, RE, and D-GMM regressions. Hydroelectric and renewable energy production can reduce CO₂ emissions in all regression models. Nuclear energy has a beneficial impact on the environment, but the coefficients are only significant for S-GMM and the last quantile. However, the most significant result of this study is the identification of a cause-and-effect relationship between CO₂ emissions and energy production. Carbon dioxide (CO₂) emissions can be lowered by shifting away from fossil fuels and toward renewable and hydroelectric sources. The research also suggests several renewable and alternative electricity production policies for sustainable energy.

This study examines the effects of health expenditure, energy consumption, CO₂ emissions, population size, and income on health outcomes in 46 Asian nations between 1997 and 2019. Cross-sectional dependence (CSD) and slope heterogeneity (SH) tests are utilized due to the close linkages between Asian nations as a result of commerce, tourism, religion, and international agreements. The research uses unit root and cointegration tests of the second generation after validating CSD and SH issues. Due to the results of the CSD and SH tests, it is clear that conventional methods of estimation are inappropriate, so a new panel method, the inter autoregressive distributive lag (CS-ARDL) model, is used instead. In addition to CS-ARDL, the study’s results were checked with a common correlated effects mean group (CCEMG) method and an augmented mean group (AMG) method. According to the CS-ARDL study, higher rates of energy use and healthcare spending lead to better health outcomes for Asian countries over the long run. CO₂ emissions are shown to be harmful to human health, according to the study. The influence of a population’s size on health outcomes is shown to be negative in the CS-ARDL and CCEMG, but favorable in the AMG. Only the AMG coefficient is significant. In most instances, the results of the AMG and CCEMG corroborate the results of the CS-ARDL. Among all the factors influencing life expectancy in Asian countries, healthcare spending is the most influential. Hence, to improve health outcomes, Asian countries need to take the required actions to boost health spending, energy consumption, and long-term economic growth. To achieve the best possible health outcomes, Asian countries should also reduce their CO₂ emissions.

Agriculture is crucial for the existence of humankind. Agriculture provides a significant portion of the income for many people all around the world. Additionally, it provides a large number of work possibilities for the general public. Numerous farmers desire for a return to the old-fashioned techniques of farming, which provides little profit in today’s market. Long-term economic growth and prosperity are dependent on the success of agriculture and associated companies in the United States. Agribusiness crop yields may be increased by carefully selecting the right crops and putting in place supportive infrastructure. Weather, soil fertility, water availability, water quality, crop pricing, and other factors are taken into consideration while making agricultural predictions. Machine learning is critical in crop production prediction because it can anticipate crop output based on factors such as location, meteorological conditions, and season. It is advantageous for policymakers and farmers alike to be able to precisely estimate crop yields throughout the growing season since it allows them to anticipate market prices, plan import and export operations, and limit the social cost of crop losses. The use of this tool assists farmers in making informed decisions about which crops to grow on their land. In this study, a machine learning framework for agricultural yield prediction is presented. Crop information is collected in an experiment’s data set. Then, feature selection is performed using the Relief algorithm. Features are extracted using the linear discriminant analysis algorithm. Machine learning predictors, namely, particle swarm optimization-support vector machine (PSO-SVM), K-nearest neighbor, and random forest, are used for classification.

Innovation has the potential to act as a double-edged sword in impacting economic growth. While it serves as a powerful driver of economic advancement, it also carries risks alongside its benefits. Recognizing this duality, our study aims to fill the identified gap and add comprehensiveness to the literature by assessing the individual impact of innovation indicators on economic growth in the top five Southeast Asian countries based on GDP: Indonesia, Thailand, Singapore, Malaysia, and Vietnam. The innovation aspect comprises 21 indicators from the Global Innovation Index (GII), grouped into seven categories: institution, human capital and research, infrastructure, market sophistication, business sophistication, knowledge and technology outputs, and creative outputs. Both panel analysis and country-specific assessments consistently conclude that innovation significantly influences economic growth. However, delving into the categorized indicators reveals intriguing insights. While all the indicators demonstrate a notable impact, most of them are found to hinder rather than foster economic growth. This compelling empirical evidence underscores that innovation in the selected countries has yet to be optimized, highlighting the urgent need to implement innovation-friendly policies, including removing innovation barriers, targeting investment in key sectors, and fostering education and skills development. This holistic approach aims to cultivate an environment conducive to innovation, thereby solidifying innovation's role as one of the primary drivers of economic growth.

This research examines the effect of career development, organizational commitment, and organizational support on employee retention among employees. The sample of 70 employees was selected from a population of 391 employees of a particular university using purposive sampling. A questionnaire was used to collect data on the four variables, which were analyzed using statistical techniques such as validity and reliability testing, multiple regression analysis, hypothesis testing, and coefficient of determination. The results indicate a significant simultaneous effect of career development, organizational commitment, and organizational support on employee retention. These findings support the expectancy theory and the social exchange theory, suggesting that organizations can improve employee retention by providing opportunities for career development, promoting organizational commitment, and providing support to their employees. The study highlights the importance of creating a positive work environment and fostering employee satisfaction for higher levels of employee retention.