Transforming Manufacturing: How AI Can Drive Significant Emission Reductions

Preetam Jena

Preetam is an Energy Economist Intern at Hygge Energy and is currently pursuing his Masters in Management at Ivey Business School, London, Canada. He holds a Bachelor’s degree in Electronics and Instrumentation Engineering from the National Institute of Technology, Rourkela, India. Preetam is deeply interested in the intersection of AI, sustainability, and industrial innovation. He is passionate about exploring solutions that optimize energy efficiency, advance sustainable manufacturing practices, and contribute to a greener future.

Imagine running a manufacturing plant and knowing that every decision, every task scheduled has the potential to make a positive impact on climate change. Industries, responsible for 23% of the U.S.’s greenhouse gas emissions (EPA, 2022), are at a critical juncture. While renewable energy offers hope, its unpredictable availability often forces reliance on fossil fuels, leaving vast opportunities for emission reduction untapped.

Let’s break it down. A typical plant consuming 500 MWh of energy daily operates with a mix of renewables and fossil fuels. On high renewable days (60% renewable, 40% fossil), emissions average 204 tons of CO₂. On low renewable days (20% renewable, 80% fossil), they spike to 408 tons. Randomly scheduling tasks results in an average saving of 306 tons of CO₂ emissions daily 

While renewable energy offers hope, its unpredictable availability often forces reliance on fossil fuels.

Now, imagine flipping the script with AI. By optimizing task scheduling, maintenance and low-energy activities can be strategically shifted to periods of low renewable energy availability. This reserves peak renewable energy for energy-intensive operations, leading to a significant reduction in emissions. AI-optimized scheduling can cut daily emissions to approximately 388 tons—saving 82 tons of CO₂ every single day. Over 20 maintenance days annually, this adds up to 1,632 tons of CO₂ saved. With an average carbon credit price of $32 per ton (Visual Capitalist, 2024), this could generate approximately $52,224 in carbon credits each year.

In addition to significant carbon savings, AI-driven task scheduling offers valuable benefits for industries. It positions companies to better comply with evolving emission regulations, ensuring adherence to environmental standards and avoiding penalties. Furthermore, as governments and organizations, such as the EU, increasingly promote the demand for green products like Green Steel, AI-driven scheduling ensures that manufacturing processes align with green energy standards, opening access to emerging green markets. Lastly, the technology is highly scalable, allowing for easy implementation across multiple operational facilities with minimal adaptation, ensuring its benefits can be realized across a broader production network while enhancing both efficiency and sustainability.

AI-optimized scheduling can cut daily emissions to approximately 388 tons—saving 82 tons of CO₂ every single day.

Here’s how we make this happen at Hygge Energy. Our AI models analyze renewable generation patterns, weather forecasts, and operational demands to predict the best times to schedule tasks. They adjust dynamically to real-time conditions, refining recommendations through reinforcement learning. Surplus renewable energy is stored during peak availability, ensuring uninterrupted operations without reverting to fossil fuels.

By seamlessly aligning operations with renewable energy availability, AI transforms manufacturing from an emissions-heavy sector into a sustainability leader. It’s not just about cutting carbon—it’s about revolutionizing how industries operate, proving that efficiency and environmental responsibility can go hand in hand.