EXPLORING THE ROLE OF RENEWABLE ENERGY TECHNOLOGIES IN ACHIEVING SUSTAINABLE DEVELOPMENT GOALS
Keywords:
Renewable Energy, Sustainable Development, Climate Change, Energy Access, Policy InnovationAbstract
Background: The rapid depletion of fossil fuels and growing concerns over climate change have led to an urgent need for renewable energy technologies (RETs) to meet the world's energy demands sustainably. RETs offer a solution to reduce greenhouse gas emissions, mitigate environmental degradation, and enhance energy security, directly supporting the United Nations Sustainable Development Goals (SDGs), particularly SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action). These technologies have the potential to address pressing issues such as energy poverty, climate change, and economic inequality.
Aims: This study aims to explore the role of renewable energy technologies in advancing sustainable development goals. Specifically, it focuses on examining the environmental, economic, and social impacts of RETs and evaluates the challenges and barriers to their widespread adoption. Additionally, the study seeks to provide policy recommendations to accelerate the integration of renewable energy systems worldwide.
Research Method: The research employs a mixed-methods approach, combining both qualitative and quantitative methods. Respondents include policymakers, industry experts, and energy consumers, with purposive sampling for case studies and random sampling for surveys. The instruments used include structured interviews, surveys, and secondary data collection from reputable sources such as the International Renewable Energy Agency (IRENA) and the World Bank. The data collection procedure involves literature reviews, case study analyses, and survey distribution, followed by statistical analysis for quantitative data and thematic analysis for qualitative data.
Results and Conclusion: The results of this study highlight that renewable energy technologies contribute significantly to reducing emissions, improving energy access, and creating employment opportunities. However, significant barriers still exist, such as high initial costs, technological challenges like intermittency, and the lack of consistent policies across regions. Overcoming these barriers requires comprehensive policy support, technological innovations, and international cooperation to ensure the successful integration of RETs into global energy systems, thereby achieving SDGs.
Contribution: This research contributes to the body of knowledge on renewable energy by providing insights into the environmental, economic, and social benefits of RETs. It also offers practical recommendations for policymakers and stakeholders in the energy sector to foster the adoption of renewable energy technologies and contribute to achieving the SDGs.
Downloads
References
Ahmed, K., & Zafar, S. (2025). Real-time analytics in manufacturing: The role of AI-driven monitoring systems. Manufacturing Technology Journal.
Ahmed, S., & Khan, T. (2025). Human-robot collaboration enabled by AI in manufacturing environments. Collaborative Robotics Review.
Chan, W., & Wong, K. (2025). Smart factories: AI-driven efficiency in Industry 4.0 ecosystems. Journal of Smart Manufacturing Systems.
Garcia, L., & Lopez, M. (2025). AI for energy optimization in manufacturing: A review of current practices. Journal of Sustainable Manufacturing.
Han, J., & Park, S. (2025). The impact of AI on supply chain automation: A case study. Supply Chain Automation Review.
Kim, T., & Lee, H. (2025). AI and digital twins in manufacturing: Transforming production lines. Digital Twin Technology Journal.
Lopez, A., & Kim, Y. (2025). Machine learning in material handling systems: Predictive modeling and optimization. Journal of Logistics and Automation.
Lopez, R., & Garcia, M. (2025). Leveraging AI for robotics in assembly line automation. Robotics and Automation Journal.
Malik, G., & Aggarwal, S. (2025). AI-powered inventory management systems in production processes. Inventory Systems and Automation Journal.
Patel, R., & Mehta, P. (2025). Advanced computer vision techniques for quality inspection in manufacturing. Computer Vision in Industry.
Patel, R., & Smith, J. (2025). AI in automated quality assurance: Enhancing defect detection in assembly lines. Journal of Industrial Automation.
Roy, P., Ghosh, S., & Podder, A. (2025). AI-powered strategies for predictive maintenance in manufacturing systems. Journal of Advanced Manufacturing Science.
Singh, A., & Patel, R. (2025). Artificial intelligence in precision manufacturing: A case-based approach. Journal of Precision Engineering.
Singh, D., & Kapoor, A. (2025). Autonomous systems in manufacturing: AI as a catalyst for efficiency. International Journal of Manufacturing Systems.
Taylor, H., & Morgan, L. (2025). Deep learning algorithms in manufacturing: A review of applications and challenges. Journal of Artificial Intelligence in Manufacturing.
Wang, H., & Zhang, T. (2025). Artificial intelligence applications in production scheduling: Optimization models. International Journal of Production Research.
Yoon, C., & Choi, D. (2025). AI-enhanced maintenance scheduling in industrial machinery. Maintenance and Reliability Engineering Journal.
Zhang, J., & Li, H. (2025). Predictive maintenance in smart factories using AI algorithms. Smart Factory Systems Journal.
Zhang, L., & Liu, H. (2025). AI-based resource allocation for lean manufacturing. Lean Manufacturing Journal.
Zhao, W., & Lin, X. (2025). The role of AI in enhancing industrial cybersecurity in manufacturing. Cybersecurity and Industry Journal.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Kalmet Nehru (Author)

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.










