Topics In Demand
Notification
New

No notification found.

Carbon Footprint Tracking and Reduction Using AI
Carbon Footprint Tracking and Reduction Using AI

May 20, 2025

18

0

As global climate goals tighten, industries across sectors are under growing pressure to reduce their environmental impact. In particular, tracking and minimizing carbon footprints has become a business imperative, not just a CSR initiative. However, traditional methods for carbon accounting are often manual, error-prone, and lack real-time responsiveness.

Enter artificial intelligence (AI): a transformative technology that is revolutionizing how organizations monitor, analyze, and reduce their greenhouse gas (GHG) emissions. From real-time data analysis to intelligent optimization of operations, AI is enabling businesses to not only track their environmental impact but also actively reduce it.

Why Carbon Footprint Tracking Matters

The manufacturing sector alone is responsible for nearly 23% of global GHG emissions (World Bank, 2023). With Scope 3 emissions—those from indirect sources such as suppliers and product use—accounting for over 70% of a company’s carbon footprint (CDP Global Supply Chain Report, 2022), effective tracking requires deep visibility into complex supply chains and operations.

Moreover, regulatory frameworks such as the EU Carbon Border Adjustment Mechanism (CBAM) and the SEC’s proposed climate disclosure rules in the U.S. are making carbon reporting not just a best practice but a compliance requirement.

The Role of AI in Carbon Tracking

AI technologies are transforming traditional carbon accounting in the following ways:

1. Automated Data Aggregation

AI-powered platforms can automatically pull data from IoT sensors, ERP systems, and utility bills to aggregate carbon-relevant metrics across multiple facilities and geographies. This eliminates human error and speeds up reporting cycles.

Companies using AI-driven sustainability platforms reduce data collection time by up to 80%, according to a 2022 McKinsey report.

2. Carbon Emission Forecasting

Machine learning models analyze historical and real-time data to forecast future emissions based on production schedules, material sourcing, and logistics. This helps companies stay ahead of compliance thresholds and proactively plan emission reduction strategies.

Example: Siemens uses predictive analytics in its industrial plants to anticipate high-emission production cycles and adjust processes in advance.

3. Scope 3 Visibility

AI enables a more accurate estimation of Scope 3 emissions by integrating supplier data, logistics tracking, and lifecycle assessments. Natural Language Processing (NLP) can also parse supplier reports, invoices, and contracts to extract emission-relevant data.

According to credible sources, AI-powered ESG platforms improve Scope 3 data accuracy by up to 65% compared to spreadsheet-based methods.

4. Optimization of Energy Usage

AI algorithms can monitor and optimize energy consumption in real time, identifying inefficiencies in equipment and production processes. Reinforcement learning models are especially effective in dynamically adjusting operational parameters for minimal energy use.

For instance, Google DeepMind’s AI reduced energy usage for data center cooling by 40%, setting a precedent for industrial applications.

5. Carbon Reduction Recommendations

Generative AI and advanced analytics tools can generate actionable recommendations—from switching suppliers with lower emissions to redesigning supply chains. AI can also run simulations to test the impact of various sustainability strategies before implementation.

Case Study Snapshot: Unilever

Unilever has implemented an AI-powered carbon measurement system across its supply chain to achieve its target of net-zero emissions by 2039. Using AI, the company now receives real-time dashboards on emissions by product line, helping prioritize changes with the highest impact.

Benefits of AI-Driven Carbon Management

  • Real-time visibility across all emission scopes

  • Faster compliance with ESG regulations

  • Data-driven sustainability decisions

  • Operational cost savings through energy optimization

  • Increased brand trust among consumers and investors

Challenges to Consider

  • Data availability and quality remain a hurdle, especially for Scope 3.

  • Integration with legacy systems can slow adoption.

  • AI explainability is key, especially when emissions calculations inform investor and regulatory reports.

  • High initial costs of AI implementation, though ROI is strong over time.

Conclusion

AI is not a silver bullet, but it’s a powerful tool in the arsenal of climate-conscious businesses. As carbon tracking becomes integral to operations and regulation, companies that adopt AI early are likely to lead the next phase of sustainable transformation.

By combining automation, predictive analytics, and machine learning, AI is transforming carbon management from a reporting chore into a competitive advantage.


That the contents of third-party articles/blogs published here on the website, and the interpretation of all information in the article/blogs such as data, maps, numbers, opinions etc. displayed in the article/blogs and views or the opinions expressed within the content are solely of the author's; and do not reflect the opinions and beliefs of NASSCOM or its affiliates in any manner. NASSCOM does not take any liability w.r.t. content in any manner and will not be liable in any manner whatsoever for any kind of liability arising out of any act, error or omission. The contents of third-party article/blogs published, are provided solely as convenience; and the presence of these articles/blogs should not, under any circumstances, be considered as an endorsement of the contents by NASSCOM in any manner; and if you chose to access these articles/blogs , you do so at your own risk.


XLNC Technologies is a dynamic consulting firm specializing in Robotic Process Automation (RPA), Electronic Data Interchange (EDI), Generative Artificial Intelligence (Gen AI), Cyber Security, and a wide range of IT solutions and services. With a clientele spread across 50 countries worldwide, we are dedicated to delivering intelligent automation and innovative solutions within the framework of industry best practices. Our goal is to become a recognized brand across various industries. At XLNC Technologies, we prioritize a people-centric approach to automation. We provide extensive support and training to employees before and throughout their automation journey, ensuring high-quality automation services and fostering a conducive, automation-friendly organizational environment. Recognized by the CIO as one of the top 10 APAC RPA companies, we are a premier implementation partner with Automation Anywhere and proud recipients of the “Automation Anywhere Growth Partner of the Year” award

© Copyright nasscom. All Rights Reserved.