286 American Academic Scientific Research Journal for Engineering, Technology, and Sciences ISSN (Print) 2313-4410, ISSN (Online) 2313-4402 https://asrjetsjournal.org/index.php/American_Scientific_Journal/index Cloud Carbon Footprint Tracker for Sustainable Practices Aamerkhan Golandaz a *, Ashish Gawande b a IIoT Security Architect, SLB, Texas, USA b IIoT Cloud Engineer, SLB, Texas, USA a Email: aamer.golandaz@gmail.com b Email: ashish.gawande@gmail.com Abstract In today’s digital era, businesses heavily rely on cloud-based solutions to support their operations, leading to substantial carbon emissions due to the extensive use of cloud resources. As organizations increasingly adopt cloud computing, the environmental impact of these infrastructures has become a growing concern. To address this issue, this paper presents a Carbon Footprint Tracker for Cloud Resources, a comprehensive solution that leverages cloud analytics and artificial intelligence (AI) to measure, analyze, and minimize carbon emissions. Our proposed system integrates seamlessly with Azure Cost Management APIs, Azure Monitor, and advanced Machine Learning models to provide organizations with real-time insights into their cloud consumption and its associated carbon footprint. By analyzing usage patterns and optimizing resource allocation, the system offers data-driven recommendations to enhance sustainability. Additionally, it generates detailed sustainability reports, enabling businesses to track their environmental impact and make informed decisions toward greener cloud strategies. Through this innovative approach, enterprises can effectively reduce their carbon footprint, improve operational efficiency, and align with global sustainability goals. By embracing eco-friendly cloud practices, organizations can contribute to a more sustainable future, ensuring responsible and energy-efficient cloud usage. Keywords: Type Carbon Footprint Tracker; Azure Cost Management; Sustainability Reporting; Machine Learning; Artificial Intelligence; Cloud Computing; Carbon Emission Reduction; Energy Efficient Computing. 1. Introduction Cloud computing has revolutionized the way businesses operate by providing unparalleled efficiency, scalability, and flexibility in managing digital infrastructure. However, this technological advancement has also led to significant energy consumption in data centers, contributing to rising carbon emissions [1,2]. ------------------------------------------------------------------------ Received: 1/25/2024 Accepted: 3/12/2025 Published: 3/28/2025 ------------------------------------------------------------------------ * Corresponding author. https://asrjetsjournal.org/index.php/American_Scientific_Journal/index American Academic Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) - Volume 101, No 1, pp 286-295 287 As enterprises increasingly shift their workloads to the cloud, the environmental impact of cloud computing has become a critical concern. The demand for compute-intensive applications, large-scale data storage, and continuous cloud resource utilization results in high electricity consumption and carbon footprint, which organizations often overlook [3]. Despite growing awareness of sustainability, many enterprises lack visibility into their cloud-based carbon footprint. Cloud service providers offer various resources and pricing models, but businesses struggle to monitor, analyze, and optimize cloud usage from an environmental perspective [4]. Without clear insights into energy consumption and its associated emissions, organizations find it challenging to adopt effective sustainability strategies [5]. To address these challenges, this paper introduces a Cloud-Powered Carbon Footprint Tracker, an AI-driven solution that leverages cloud analytics and machine learning to measure and optimize carbon emissions in cloud environments. By integrating with Azure Cost Management APIs [6], Azure Monitor [8], Grafana, and other cloud telemetry tools, this system provides real-time insights into cloud carbon impact and offers actionable recommendations to minimize emissions [3][4]. Through automated sustainability reporting and optimization strategies, organizations can effectively reduce their environmental footprint while maintaining operational efficiency [9]. This paper explores how leveraging artificial intelligence [6], cloud monitoring tools, and predictive analytics can help businesses transition toward eco-friendly cloud computing practices. By enhancing transparency and promoting energy-efficient resource management, the proposed solution empowers enterprises to align with global sustainability goals, reduce operational costs, and contribute to a greener digital ecosystem [2,5]. 1.1. Related Work  Cloud Sustainability Initiative As cloud computing continues to power modern enterprises, major cloud service providers have recognized the urgent need to address its environmental impact. Industry leaders such as Microsoft Azure, Amazon Web Services (AWS), and Google Cloud have developed comprehensive sustainability initiatives aimed at reducing energy consumption, optimizing resource utilization, and minimizing carbon emissions across cloud environments [1,2]. These initiatives focus on leveraging renewable energy sources, improving data center efficiency, and providing tools for organizations to track and mitigate their cloud carbon footprint [3]. One notable example is Microsoft's Emissions Impact Dashboard, which allows businesses to calculate, monitor, and reduce their cloud-based CO₂ emissions [9]. This tool provides organizations with detailed insights into their carbon footprint, enabling them to make data-driven decisions to optimize their cloud workloads for sustainability. By integrating real-time monitoring, carbon accounting, and sustainability reporting, the dashboard supports enterprises in adopting energy-efficient cloud strategies [5]. Similarly, AWS and Google Cloud have introduced carbon footprint tracking tools and sustainability programs to support enterprises in adopting energy-efficient cloud strategies. AWS provides a Customer Carbon Footprint Tool, which offers American Academic Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) - Volume 101, No 1, pp 286-295 288 visibility into an organization's cloud-related emissions [7], while Google Cloud’s Carbon Footprint Report helps businesses assess their environmental impact and adopt greener computing practices [8]. Beyond tracking emissions, these cloud providers are also investing heavily in renewable energy projects, carbon offset programs, and AI-driven optimizations to enhance energy efficiency [4]. Microsoft, for instance, has committed to becoming carbon-negative by 2030, while AWS and Google Cloud aim for net-zero emissions through sustainable energy initiatives [5,7,8]. These efforts collectively drive the adoption of eco-friendly cloud computing and encourage organizations to align their digital transformation strategies with global sustainability goals. By leveraging these cloud sustainability initiatives, businesses can reduce operational costs, enhance energy efficiency, and contribute towards a greener future, ensuring that cloud computing remains both innovative and environmentally responsible [1,3].  Carbon Tracking in Enterprises Carbon footprint tracking has become a vital part of enterprise sustainability strategies, yet many existing tools fail to provide detailed visibility into cloud-based workloads. Traditional carbon accounting solutions offer broad estimates of emissions but lack real-time integration with cloud infrastructure, making it difficult for organizations to assess the environmental impact of specific workloads [1]. Without granular insights into compute, storage, and network usage, enterprises struggle to identify inefficiencies, optimize cloud resources, and implement targeted emission reduction strategies 1.2. System Architecture  Cloud Usage Data Collection Efficient carbon footprint tracking requires accurate and real-time data collection from cloud environments. The proposed system leverages multiple Azure services to extract, analyze, and monitor cloud resource utilization, ensuring precise measurement of carbon emissions [2]. Azure Cost Management API Retrieves real-time cloud service consumption data, including compute, storage, and networking costs. This data provides a baseline for carbon impact calculations based on energy-intensive workloads [7]. Azure Monitor Offers detailed performance insights into resource utilization across virtual machines, databases, and cloud services. It helps track power consumption trends and detect underutilized resources that contribute to unnecessary emissions [8]. Azure Resource Graph Enables comprehensive analysis of virtual machine configurations, energy consumption patterns, and resource allocations. This helps in identifying inefficient deployments and optimizing cloud infrastructure for sustainability [1]. American Academic Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) - Volume 101, No 1, pp 286-295 289 Grafana – A visualization and monitoring tool that integrates with Azure Monitor, Prometheus, and Thanos. We will use Grafana to visualize cloud resource consumption and CO₂ emissions, providing real-time tracking for sustainability efforts [7]. By integrating these data collection mechanisms, the system provides a holistic view of cloud resource usage, allowing enterprises to monitor their carbon footprint in real time and take proactive steps toward eco-friendly cloud optimization[3].  Carbon Emission Calculation To accurately measure the carbon footprint of cloud-based workloads, the system employs a formula-driven approach based on resource consumption and energy efficiency factors [2][4]. The total emissions are calculated by aggregating the impact of compute, storage, and network usage. Total Carbon Emissions=Compute Impact + Storage Impact + Network Impact (1) Compute impact = VM Hours × Data Center Power Efficiency Factor. (2) Storage impact = GB Stored × Storage Energy Factor. (3) Network impact = Data Transferred × Energy Intensity (4) Where: Compute Impact measures the energy consumption of virtual machines based on their active runtime and the efficiency of the underlying data center infrastructure [3]. Storage Impact accounts for the power required to maintain and manage stored data, considering the energy efficiency of different storage classes (e.g., hot, cold, archive) [2] evaluates the energy required for data transmission across networks, incorporating factors such as data center interconnects, internet bandwidth, and cloud provider efficiencies [5]. By applying this calculation model, the system provides precise carbon footprint estimates for cloud workloads, allowing enterprises to identify high-impact areas and optimize resource allocation for sustainability[1].  AI-Driven Optimization Suggestions To minimize cloud-related carbon emissions, the system incorporates AI-driven analytics that provide actionable recommendations for optimizing resource usage [4]. By leveraging machine learning models and cloud telemetry data, the system enhances efficiency while maintaining performance [5]. Detect Underutilized Resources – The AI model continuously analyzes cloud usage patterns to identify idle or American Academic Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) - Volume 101, No 1, pp 286-295 290 underutilized virtual machines, storage, and networking resources. It then suggests scaling down, consolidating, or shutting down unused services to reduce unnecessary energy consumption [3,5]. Recommend Energy-Efficient Regions – Based on real-time data center energy efficiency factors and regional carbon intensity, the system suggests migrating workloads to lower-carbon Azure regions. This helps enterprises leverage renewable-powered data centers and optimize sustainability [1,7]. Predictive Workload Scheduling – Using historical usage trends and machine learning algorithms, the system predicts optimal scheduling times to run workloads when energy demand and carbon intensity are lower [4]. This ensures efficient cloud utilization while reducing environmental impact. By implementing these AI-driven optimization strategies, enterprises can achieve cost savings, improved sustainability, and enhanced cloud efficiency, aligning their digital operations with global carbon reduction goals.  Sustainability Dashboard The Sustainability Dashboard serves as a central interface for organizations to monitor, analyze, and optimize their cloud-related carbon emissions [8]. By providing real-time visualization, historical tracking, and actionable insights, the dashboard helps businesses make data-driven sustainability decisions [7]. Real-Time CO₂ Emission Visualization – Displays a live dashboard with dynamic graphs and charts showcasing current carbon emissions based on Azure cloud usage [7]. Historical Trend Reports – Generates detailed sustainability reports, enabling businesses to track long-term carbon footprint trends, seasonal variations, and emission reduction progress [2]. These reports help in compliance tracking, corporate sustainability reporting, and strategic planning. Energy Consumption Insights – Provides AI-powered recommendations to optimize cloud resource usage, such as scaling down idle services, adjusting workload schedules, or migrating to energy-efficient regions [4]. This empowers organizations to reduce energy consumption while maintaining cloud performance. By leveraging the Sustainability Dashboard, enterprises gain end-to-end visibility into their cloud carbon impact, ensuring proactive sustainability management and alignment with global environmental goals. 1.3. Development & Implementation The development and implementation of the Carbon Footprint Tracker will require a robust backend system, intelligent AI models for recommendations, and an interactive frontend for visualization. Below is the breakdown of the implementation process:  Backend Development (Go & Azure) American Academic Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) - Volume 101, No 1, pp 286-295 291 Develop APIs using Go (Golang) – To interact with Azure's cloud resources, APIs will be developed using Go (Golang). These APIs will extract essential data from Azure Cost Management APIs, providing information on cloud service consumption, usage patterns, and associated costs [6]. Implement Cloud Usage Monitoring via Azure SDK for Go – Using the Azure SDK for Go, cloud usage metrics will be monitored in real time. This allows for tracking of compute, storage, and networking resource utilization, which will be essential for calculating carbon emissions [8]. Store Sustainability Data in Azure CosmosDB or PostgreSQL – The collected data and emission metrics will be stored in Azure CosmosDB or PostgreSQL, depending on the system’s scalability needs. Both databases will offer high performance, low latency, and scalability, ensuring that large amounts of cloud usage data are efficiently stored and accessible for analysis [5,7].  AI Model for Recommendations Use Azure Machine Learning to Train Models – Historical cloud usage data will be fed into Azure Machine Learning to train models that can predict carbon emissions, detect inefficient usage patterns, and recommend optimizations. These models will be continuously trained using updated data to improve their prediction accuracy [3]. Implement Clustering Algorithms – Clustering algorithms (such as K-means or DBSCAN) will be used to identify inefficient usage patterns across cloud resources. This helps to recognize underutilized virtual machines, over-provisioned storage, and other areas of inefficiency, which could be optimized for lower emissions [2,4]. Deploy Models Using Azure Functions for Real-Time Recommendations – The trained models will be deployed using Azure Functions to provide real-time, actionable recommendations for carbon footprint reduction. The serverless nature of Azure Functions will ensure that the recommendations are delivered instantly, without any latency, based on the latest data [1,5].  Frontend & Visualization Use Grafana or Power BI to Create an Interactive Sustainability Dashboard – For data visualization, tools like Grafana or Power BI will be employed to create an interactive sustainability dashboard. This dashboard will display real-time and historical data on CO₂ emissions, cloud resource usage, and optimization opportunities, allowing stakeholders to visualize the carbon impact and track progress [7]. Develop a UI Displaying CO₂ Emissions Per Cloud Resource using Grafana – The frontend UI will show detailed CO₂ emissions per cloud resource (e.g., virtual machines, storage, data transfer) in an easily digestible format. Users will be able to drill down into specific resource types and gain insights into which services contribute most to emissions [9]. Assumptions American Academic Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) - Volume 101, No 1, pp 286-295 292 Carbon intensity factor (kgCO₂/kWh) – 0.5 Approximate CPU power consumption in kWh – 0.0002 Figure 1: CPU-Based CO₂ Emissions for 6 hours dashboard sample Figure 2: Memory-Based CO₂ Emissions for 6 hours dashboard sample American Academic Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) - Volume 101, No 1, pp 286-295 293 Figure 3: Network Traffic Impact for 6 hours  Setting to create Alerts for CO₂ Emissions in Grafana Using Prometheus & Thanos To monitor cloud resource consumption and ensure sustainable cloud usage, it is essential to set up real-time alerts in Azure monitoring or Grafana. Alerts will help to detect high carbon emissions, optimize cloud resources and take necessary actions to reduce environmental impact. Sample rule in Grafana to trigger alert for high CO2 emission sum(rate(container_cpu_usage_seconds_total[$__interval)) * 0.0002 * 0.5 > 50 0.0002 → Converts CPU time to kWh (approximate power consumption per second of CPU usage). (5) 0.5 → Converts kWh to kg CO₂ using carbon intensity factors. > 50 → Triggers an alert when emissions exceed 50 kg CO₂. Alerts can be sent via Slack, Email, or Microsoft Teams 1.4. Results & Expected Impact  Cloud Sustainability Insights Initial simulations and prototype testing indicate that organizations can reduce their cloud emissions by up to 25-30% through optimized resource allocation [3,4]. By identifying underutilized resources, adjusting workload schedules, and migrating to more energy-efficient cloud regions, businesses can significantly reduce their carbon footprint [1,2]. Real-time tracking and actionable recommendations allow for continuous improvement in sustainability practices, making it easier to achieve long-term energy efficiency and emission reduction goals [5]. American Academic Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) - Volume 101, No 1, pp 286-295 294  Cost Reduction Optimizing cloud resource usage not only contributes to sustainability but also leads to substantial cost savings [7,8]. By identifying and shutting down idle resources, consolidating workloads, and leveraging energy-efficient regions, organizations can minimize unnecessary cloud consumption, reducing overall operational costs [2]. The system's predictive analytics further helps businesses plan their workloads for optimal resource utilization, ensuring they only pay for the resources they truly need, which results in cost-efficient cloud management [5]  Contribution to Sustainability Goals The tool aligns with global sustainability standards and supports organizations in meeting their environmental, social, and governance (ESG) objectives [1,3]. By providing accurate carbon emission tracking and sustainability reporting capabilities, the tool helps businesses comply with industry regulations and adopt eco- friendly cloud strategies. Ultimately, the system will contribute to the global reduction of carbon emissions, helping businesses play their part in achieving net-zero goals and supporting broader climate change mitigation efforts [2,7]. 1.5. Conclusions The Carbon Footprint Tracker for Businesses effectively bridges the gap between cloud efficiency and sustainability by utilizing real-time analytics, AI-driven recommendations, and cloud resource optimization techniques. Through detailed monitoring and actionable insights, organizations can reduce their carbon emissions, optimize cloud costs, and align with sustainability goals. This tool not only helps businesses become more eco-conscious but also contributes to broader climate change mitigation efforts by providing a tangible way to measure and reduce their environmental impact. Looking ahead, future developments will focus on expanding multi-cloud support to include platforms like AWS and Google Cloud Platform (GCP). This will ensure the tool’s applicability across diverse cloud environments, allowing businesses to optimize their entire cloud footprint, regardless of the provider. Additionally, integration with carbon offset programs will be explored, enabling organizations to offset their remaining emissions and further accelerate their journey toward net-zero goals. With these advancements, the Carbon Footprint Tracker will evolve into a comprehensive solution that supports sustainable cloud practices across all layers of cloud infrastructure. 1.6. Future Enhancements The Carbon Footprint Tracker for Businesses bridges the gap between cloud efficiency and sustainability by leveraging real-time analytics and AI-driven recommendations. Future work will explore multi-cloud support (AWS, GCP) and integration with carbon offset programs to further enhance sustainability efforts. 1.7. Construction of references American Academic Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) - Volume 101, No 1, pp 286-295 295 References [1] Liu, Y., Xu, Z., & Lu, W. (2020). Towards green cloud computing: A comprehensive survey on sustainability, energy efficiency, and resource management. In Future Generation Computer Systems (pp. 145-164). [2] Zhou Z., & Zhang X. (2018). Green cloud computing: A survey on energy efficiency, sustainability, and resource management. Energy Reports, 4, 107-118. [3] Yousef M., & Keshav S. (2021). Carbon footprint reduction in cloud computing through resource optimization and energy efficiency. IEEE/ACM CLOUD, 290-299. [4] Khan I., & Ameen A. (2020). AI and machine learning for cloud energy optimization: An approach to sustainability. Journal of Cloud Computing: Advances, Systems, and Applications, 9(1), 1-13. [5] IBM Research. (2022). Cloud Optimization Using AI and Predictive Analytics. IBM Journal of Research & Development, 66(4), 23-38. [6] Aamerkhan G., Umerkhan G., Mohammed A.A. Enhancing Cybersecurity Through Artificial Intelligence: Techniques, Challenges, and Future Directions [7] Microsoft. (2023). 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