Artificial Intelligence (AI) is rapidly transforming industries worldwide, while sustainability has become one of the defining challenges of our time. As governments and businesses seek ways to reduce greenhouse gas emissions, improve resource efficiency, and adapt to climate change, AI is increasingly recognized as a technology that can support these objectives.
Organizations including the United Nations, the International Energy Agency (IEA), the OECD, and the European Commission recognize AI’s potential to support the Sustainable Development Goals (SDGs). McKinsey estimates that AI applications could reduce global greenhouse gas emissions by 5-10% by 2030 if widely implemented across sectors such as energy, transport, manufacturing, agriculture, and buildings. McKinsey
Within the European Union, AI is also becoming increasingly relevant to the objectives of the Fit for 55 package, which aims to reduce net greenhouse gas emissions by at least 55% by 2030 compared with 1990 levels. Achieving this target requires not only expanding renewable energy generation but also making Europe’s energy systems, industries, and infrastructure significantly more efficient. Digital technologies,including AI are expected to play an important supporting role in this transition.BCG UNDP
While artificial intelligence is not a climate solution on its own, it can help optimize complex systems, improve decision-making, and reduce unnecessary resource consumption across multiple sectors.
Improving energy efficiency
Energy efficiency remains one of the most cost-effective ways to reduce greenhouse gas emissions. According to the International Energy Agency, buildings account for approximately 30% of global final energy consumption and 26% of global energy-related CO₂ emissions. IEA Meta Itelligence
AI-powered building management systems are increasingly being used to optimize heating, cooling, ventilation, lighting, and electricity consumption. These systems continuously analyze occupancy levels, indoor temperatures, weather forecasts, and electricity demand, automatically adjusting building operations in real time.
Research indicates that AI-driven building optimization can reduce energy consumption by 10-20%, depending on the type of building and its operating conditions.
One widely cited example comes from Google, whose DeepMind AI reduced cooling energy consumption in one of its data centres by approximately 40%, lowering the facility’s overall electricity use by around 15%.
As the European Union tightens energy performance requirements for buildings under the Fit for 55 package and the revised Energy Performance of Buildings Directive (EPBD), intelligent energy management systems are expected to become increasingly important.
Supporting renewable energy integration
Europe’s transition toward renewable energy creates new operational challenges.
Unlike conventional power plants, renewable electricity generation depends on weather conditions. Wind speeds, cloud cover, and seasonal variations make electricity production less predictable, requiring increasingly sophisticated grid management. Nature Food.
Artificial intelligence helps address these challenges by improving:
- renewable energy forecasting,
- electricity demand prediction,
- grid balancing,
- battery management,
- and energy storage optimization.
According to the International Renewable Energy Agency (IRENA), AI-enhanced forecasting models can improve solar and wind generation accuracy by 20-30% compared with traditional forecasting methods.SDG
More accurate forecasting reduces the need for fossil-fuel backup generation while improving overall grid reliability both key objectives of the Fit for 55 initiative as Europe continues increasing the share of renewable energy in its electricity mix.Science Direct EPA
AI in sustainable industry
Industrial production accounts for approximately 25% of global greenhouse gas emissions, making efficiency improvements particularly valuable.
Artificial intelligence is increasingly used to optimize industrial operations through predictive maintenance, quality control, process optimization, and supply chain management.
The World Economic Forum estimates that predictive maintenance can reduce equipment downtime by 30-50% while extending machinery lifespans by 20-40%. Better equipment performance not only improves productivity but also reduces unnecessary energy use, raw material consumption, and maintenance-related emissions. DEV
Research published in Nature Communications suggests that AI-assisted manufacturing can significantly improve energy efficiency without compromising production output. These benefits are particularly relevant for energy-intensive industries such as steel, cement, and chemicals, which are among the sectors facing the greatest decarbonization challenges under the European Green Deal. Science Direct
Supporting more sustainable agriculture
Agriculture contributes approximately one-third of global anthropogenic greenhouse gas emissions, according to research published in Nature Food. At the same time, the sector faces increasing pressure from climate change, water scarcity, and rising demand for food production.
Artificial intelligence is becoming an important component of precision agriculture.
Machine learning systems analyze soil moisture, weather forecasts, satellite imagery, and crop conditions to optimize irrigation schedules, fertilizer application, and crop management.
The Food and Agriculture Organization (FAO) estimates that precision irrigation systems can reduce water consumption by 20-30% without negatively affecting crop yields.
Similarly, AI-supported fertilizer management allows farmers to apply nutrients more precisely, reducing nitrogen runoff and lowering nitrous oxide emissions. According to the IPCC, nitrous oxide has a global warming potential approximately 273 times greater than carbon dioxide over a 100-year period, making more efficient fertilizer use an important climate mitigation measure. Science Direct
AI and the Fit for 55 transition
The European Union’s Fit for 55 package is not solely about replacing fossil fuels with renewable energy. It also focuses on making Europe’s economy smarter, more efficient, and less resource-intensive.
Artificial intelligence can support several key policy objectives by:
- improving energy efficiency,
- enabling smarter electricity grids,
- reducing industrial waste,
- optimizing transport and logistics,
- supporting precision agriculture,
- and strengthening environmental monitoring.
Rather than replacing climate policy, AI complements existing regulatory measures by helping organizations achieve sustainability targets more efficiently and with better data-driven decision-making. EP
Conclusion
Artificial intelligence is increasingly becoming a practical tool for supporting sustainability across multiple sectors. Evidence suggests that AI can improve energy efficiency, optimize renewable energy integration, reduce industrial emissions, enhance agricultural resource management, and strengthen environmental monitoring.
These applications align closely with the objectives of the European Union’s Fit for 55 package, which seeks to accelerate the transition toward a low-carbon economy while maintaining economic competitiveness.
However, the sustainability story of AI is not entirely positive. Behind every AI model lies a rapidly expanding digital infrastructure that consumes electricity, water, raw materials, and computing resources. As AI adoption accelerates, understanding its own environmental footprint becomes just as important as recognizing its potential benefits.
In Part II of this series, we examine the hidden environmental costs of artificial intelligence, including data centre energy consumption, water use, hardware production, electronic waste, and the policy measures needed to ensure that AI contributes to a genuinely sustainable future.
