Due to the increasing emission of greenhouse gases and global warming, the development of renewable energy has become very important. The availability of fossil fuels and the low cost of their extraction compared to renewable energy projects reduce the motivation of countries, especially countries that have abundant natural resources, to develop this technology. Renewable energy deployment has become crucial in response to rising greenhouse gas emissions and global warming. Policies supporting renewable energy play a significant role in this. This study examines the effect of such policies on the deployment of renewable energy technologies, considering the role of natural resources. Two groups of countries were analysed: 20 oil developed countries and 20 oil developing countries. Given the availability of data and the achievement of balanced panels to evaluate short-term and long-term relationships between variables, in current research Data from 2010 to 2020 was used, and various panel data estimators such as Feasible Generalized Least Squares and Generalized Method of Moments were employed. The Quantile estimator was also used to assess the accuracy of the results. The findings suggest that renewable energy policies consistently lead to increased deployment of renewable energy technologies, regardless of a country's group. Of course, this positive effect is different according to the level of development in countries. Due to the higher efficiency of renewable energy policy, developed oil countries have more capacity to support renewable energy projects than oil developing countries. The abundance of natural resources in oil developed countries did not negatively impact renewable energy capacity, but in oil developing countries, the "resource curse" hindered the development of installed renewable energy.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1007/s11356-023-28851-9 | DOI Listing |
Environ Sci Pollut Res Int
January 2025
Graduate School of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8572, Japan.
Conventional power generation methods have led to adverse environmental impacts. Thus, the need for a strategic transition to alternative energy sources arises. This study presents a comprehensive approach to sustainable solar energy deployment using multi-criteria decision-making (MCDM) techniques.
View Article and Find Full Text PDFNonlinear Dynamics Psychol Life Sci
January 2025
Adelphi University, Garden City, NY.
Theories and studies of ecosystem emergence focus on macro level explanations such as government investments in research and development or those at the organizational level such as displacement of an older technological system by a new one through competition between technologies. However, mechanisms by which such shifts occur are underemphasized. This article draws on complexity theory to develop a theoretical framework to describe how emergence is generated through top down 'rules' that constrain the behavior of the system, directing it towards a desired outcome.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Mechanical Engineering, Kerman Branch, Islamic Azad University, Kerman, Iran.
This article introduces an innovative multipurpose system that integrates a solar power plant with a coastal wind farm to generate refrigeration for refinery processes and industrial air conditioning. The system comprises multiple wind turbines, solar power plants, the Kalina cycle to provide partial energy for the absorption refrigeration cycle used in industrial air conditioning, and a compression refrigeration cycle for propane gas liquefaction. An extensive energy and exergy analysis was conducted on the proposed system, considering various thermodynamic parameters such as the solar power plant's energy output, the absorption chiller's cooling load, the electricity generated by the turbines, the wind turbines' power output, and the energy efficiency and exergy of each cycle within the system.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Electrical and Computer Engineering, Hawassa University, Hawassa 05, Ethiopia.
Understanding human behavior and human action recognition are both essential components of effective surveillance video analysis for the purpose of guaranteeing public safety. However, existing approaches such as three-dimensional convolutional neural networks (3D CNN) and two-stream neural networks (2SNN) have computational hurdles due to the significant parameterization they require. In this paper, we offer HARNet, a specialized lightweight residual 3D CNN that is built on directed acyclic graphs and was created expressly to handle these issues and achieve effective human action detection.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!