This study uses a quantitative methodology to investigate how the rise of digital money has affected efforts to increase green energy use in China. This work contributes to the body of knowledge by using a number of empirical methods, such as regression analysis, parametric quantile estimation, stability diagnostic tests, and sensitivity analysis. This study's results further demonstrate the importance of digital financing in easing the adoption of renewable energy sources throughout China. Financing alternatives for renewable energy projects have increased as a result of digital finance's integration of digital technology with financial services. A wider range of investors has been attracted through crowdfunding, peer-to-peer lending, and other alternative financing models made possible by digital platforms, allowing the development of small and medium-sized renewable energy projects that may have had trouble securing funding through more traditional channels. The impact of digital finance on energy management and optimization is also investigated. As a result, renewable energy sources have been more widely adopted due to increased energy efficiency, better grid integration, and more efficient energy delivery. This study presents substantial evidence of the beneficial benefits of digital finance on renewable energy use in China using rigorous empirical methodologies such as regression analysis, parametric quantile estimation, stability diagnostic tests, and sensitivity analysis. The results highlight the significance of using digital money to boost the use of renewable energy, lessen reliance on fossil fuels, and help create a greener, more sustainable future.
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http://dx.doi.org/10.1007/s11356-023-29504-7 | DOI Listing |
Sci 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.
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January 2025
Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Bengaluru, India.
The growing integration of renewable energy sources within microgrids necessitates innovative approaches to optimize energy management. While microgrids offer advantages in energy distribution, reliability, efficiency, and sustainability, the variable nature of renewable energy generation and fluctuating demand pose significant challenges for optimizing energy flow. This research presents a novel application of Reinforcement Learning (RL) algorithms-specifically Q-Learning, SARSA, and Deep Q-Network (DQN)-for optimal energy management in microgrids.
View Article and Find Full Text PDFJ Environ Manage
January 2025
The Business School, RMIT University, Viet Nam. Electronic address:
This study analyzes the impact of state-level renewable energy policies and incentives on the corporate information environment in the US. It considers these renewable energy policies and incentives as exogenous measures of firm-level renewable energy exposure. The findings indicate that such policies and incentives significantly increase firms' adoption of renewable energy, confirming their suitability as proxies for external shocks.
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