MONITORING, REPORTING, AND VERIFICATION (MRV) FOR CARBON OFFSETTING USING DEEP LEARNING TECHNIQUES

ABSTRACT Amid the escalating global climate crisis, regulations mandating companies to mitigate carbon emissions through tree planting have become increasingly prevalent. In response, this paper delves into an innovative Automated Monitoring, Reporting, and Verification (MRV) program designed to ensure transparent and accountable carbon offsetting. Our focus lies in evaluating this MRV system, which scrutinizes companies’ compliance with carbon offset requirements by monitoring their tree-planting initiatives. Leveraging advanced remote sensing technologies such as satellite imagery, LiDAR, and drones, our methodology revolutionizes vegetation detection, enabling accurate assessment of plant life conditions and precise estimation of carbon sequestration across vast areas. Furthermore, machine learning and computer vision algorithms contribute significantly to automated species detection, thereby facilitating biodiversity conservation efforts through afforestation and reforestation initiatives. In our implementation, we achieved exceptional accuracy in vegetation detection, with precision and recall rates exceeding 98%, and a mean average precision (MAP) score surpassing 95%. Additionally, employing YOLOv8 for image segmentation to generate masks for tree trunks for tree age detection with an accuracy of 98%, and YOLOv8 for image classification for species detection with an accuracy of 91%, has significantly enhanced the robustness of our methodology. The heart of carbon offsetting is meticulously examined in this research, elucidating evolving methodologies and technologies for quantifying and reporting carbon capture in trees and vegetation. We emphasize the integration of MRV techniques with carbon trading platforms to ensure transparent and verifiable carbon offset claims. Synthesizing these advancements, our research provides a comprehensive outlook on how technological innovation is reshaping carbon offsetting, fostering more effective climate change mitigation, and supporting biodiversity conservation efforts. Ultimately, our methodology guides us towards a sustainable and resilient future by offering actionable solutions to combat the global climate crisis.

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Publication Details

Journal
Journal of Green Building
Published
2025-01-01
DOI
https://doi.org/10.3992/jgb.20.3.295
Citations
1
Primary Topic
Green IT and Sustainability
Type
article
Field-Weighted Citation Impact
0.73
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article

MONITORING, REPORTING, AND VERIFICATION (MRV) FOR CARBON OFFSETTING USING DEEP LEARNING TECHNIQUES

Divija Kinger, Devashish Bhake, Abhijit Patil
1 citations
Journal of Green Building
Green IT and Sustainability
0.73
article

MONITORING, REPORTING, AND VERIFICATION (MRV) FOR CARBON OFFSETTING USING DEEP LEARNING TECHNIQUES

Divija Kinger, Devashish Bhake, Abhijit Patil
article en
1 citations

Abstract

ABSTRACT Amid the escalating global climate crisis, regulations mandating companies to mitigate carbon emissions through tree planting have become increasingly prevalent. In response, this paper delves into an innovative Automated Monitoring, Reporting, and Verification (MRV) program designed to ensure transparent and accountable carbon offsetting. Our focus lies in evaluating this MRV system, which scrutinizes companies’ compliance with carbon offset requirements by monitoring their tree-planting initiatives. Leveraging advanced remote sensing technologies such as satellite imagery, LiDAR, and drones, our methodology revolutionizes vegetation detection, enabling accurate assessment of plant life conditions and precise estimation of carbon sequestration across vast areas. Furthermore, machine learning and computer vision algorithms contribute significantly to automated species detection, thereby facilitating biodiversity conservation efforts through afforestation and reforestation initiatives. In our implementation, we achieved exceptional accuracy in vegetation detection, with precision and recall rates exceeding 98%, and a mean average precision (MAP) score surpassing 95%. Additionally, employing YOLOv8 for image segmentation to generate masks for tree trunks for tree age detection with an accuracy of 98%, and YOLOv8 for image classification for species detection with an accuracy of 91%, has significantly enhanced the robustness of our methodology. The heart of carbon offsetting is meticulously examined in this research, elucidating evolving methodologies and technologies for quantifying and reporting carbon capture in trees and vegetation. We emphasize the integration of MRV techniques with carbon trading platforms to ensure transparent and verifiable carbon offset claims. Synthesizing these advancements, our research provides a comprehensive outlook on how technological innovation is reshaping carbon offsetting, fostering more effective climate change mitigation, and supporting biodiversity conservation efforts. Ultimately, our methodology guides us towards a sustainable and resilient future by offering actionable solutions to combat the global climate crisis.

Journal of Green BuildingVol. 20(3)
K. J. Somaiya Hospital & Research Centre (IN), K J Somaiya Medical College (IN)
Openalex Percentile: Top 23%
Green IT and Sustainability
0.73
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