Sharmila G.,
Sathiyasri U.,
Smriti Nandi,
Suriya S.,
Guruprasanth D. V.,
- Assistant Professor, Department of Computer Science Engineering, Manakula Vinayagar Institute of Technology, Kalitheerthalkuppam, Puducherry, India.
- Student, Department of Computer Science Engineering, Manakula Vinayagar Institute of Technology, Kalitheerthalkuppam,, Puducherry, India.
- Student, Department of Computer Science Engineering, Manakula Vinayagar Institute of Technology, Kalitheerthalkuppam, Puducherry, India.
- Student, Department of Computer Science Engineering, Manakula Vinayagar Institute of Technology, Kalitheerthalkuppam, Puducherry, India.
- Student, Department of Computer Science Engineering, Manakula Vinayagar Institute of Technology, Kalitheerthalkuppam, Puducherry, India.
Abstract
Climate change stands out as one of the biggest challenges for our planet today, as it poses a threat unprecedented to global ecosystems and human societies. This phenomenon is primarily driven by the increase in atmospheric greenhouse gases, particularly carbon dioxide (CO2), which trap heat in the atmosphere and cause rising global temperatures. These sources are largely industrial processes, transportation, and energy production, all of which rely very much on power generation and cement manufacturing sectors. Unavoidably, the growing concern over climate change effects-from extreme weather events to the increase in sea levels and destruction of ecosystems underline the pressing necessity for a revolution that could provide such an innovation toward effects mitigation and finding a sustainable route toward future development. The application of these AI-based techniques leads to the optimized process of carbon capture with reduced energy consumption and high overall system efficiency. This will integrate AI to create greater operational viability in technologies that include CCUS; besides, it supports the general transition towards an economy with low carbon as well. Industries are facing new pressures of regulations and other social expectations in terms of minimizing carbon emissions. Thus, there is a need to develop the ability to achieve effective carbon management strategies while ensuring implementation. This will then help to support the kind of global efforts needed towards climate change mitigation and facilitate the development of sustainable practice, which can lower industry emissions considerably. All results presented here indicate that innovations regarding carbon management through integration of AI and other cutting-edge technologies will bring about transformative change. Effective carbon-capture and utilization solutions therefore can help address one of the world’s biggest problems.
Keywords: Climate Change, Global Warming, Greenhouse Gases, Carbon Dioxide (CO2) , Industrial Processes, Carbon Capture, Reduced Energy Consumption , Utilization , CCUS (Carbon Capture, Utilization, and Storage) , Carbon Management Strategies.
[This article belongs to Journal of Remote Sensing & GIS ]
Sharmila G., Sathiyasri U., Smriti Nandi, Suriya S., Guruprasanth D. V.. AI-Driven Carbon Capture and Utilization: Advancing Sustainable Solutions for Climate Change Mitigation. Journal of Remote Sensing & GIS. 2025; 16(01):40-48.
Sharmila G., Sathiyasri U., Smriti Nandi, Suriya S., Guruprasanth D. V.. AI-Driven Carbon Capture and Utilization: Advancing Sustainable Solutions for Climate Change Mitigation. Journal of Remote Sensing & GIS. 2025; 16(01):40-48. Available from: https://journals.stmjournals.com/jorsg/article=2025/view=194986
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Journal of Remote Sensing & GIS
| Volume | 16 |
| Issue | 01 |
| Received | 08/01/2025 |
| Accepted | 16/01/2025 |
| Published | 25/01/2025 |
| Publication Time | 17 Days |
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