Climate change is one of the most pressing global challenges of our time, demanding urgent and coordinated action across all sectors. As nations, businesses, and civil society organisations increasingly recognise the need for climate action, the role of technology has gained prominence, especially artificial intelligence (AI).

While most of us are aware of the basics pertaining to AI, this article is aimed at specifically exploring how AI can shape Net Zero. AI holds immense potential to transform the way of addressing climate change, offering advanced data processing, decision-making, and optimisation capabilities that enhance decarbonisation. As we face complex global challenges like emissions management, renewable energy integration, and ecosystem protection, AI has emerged as a key tool in achieving a sustainable future.

Role in Climate Change Mitigation

One of the greatest challenges in decarbonisation is the volume and complexity of the data involved in tracking emissions, understanding climate risks, and planning mitigation efforts. AI, particularly through machine learning (ML), offers advanced techniques to manage large datasets, making it possible to build better climate models, forecast weather patterns, and even predict the future impacts of current emissions.

For instance, AI can improve carbon footprint assessments by analysing the life-cycle emissions of products and services. This information can then guide policy decisions, corporate strategies, and consumer behaviour, helping to reduce overall carbon emissions. AI-powered tools can also facilitate the optimisation of energy use in buildings, transportation, and manufacturing, helping industries become more energy-efficient.

One notable application of AI in energy management is in smart grids. Renewable energy sources, such as wind and solar, are intermittent by nature, making it difficult to predict supply and match it with demand. AI can help by analysing past weather patterns and real-time data to forecast energy production more accurately, thereby reducing the reliance on backup fossil fuel-powered generators. Companies like DeepMind have successfully applied AI to wind energy, improving efficiency by as much as 20%.

Carbon Removal and Climate Adaptation

AI’s capabilities also extend to carbon removal technologies. From analysing the best locations for carbon capture and storage (CCS) facilities to optimising the performance of direct air capture systems, AI can enhance the efficiency and scalability of these technologies. In natural environments, AI can be used to monitor forest biomass and assess the carbon sequestration potential of different ecosystems, aiding in reforestation and conservation efforts.

Beyond mitigation, AI plays a significant role in climate adaptation. Climate-induced disasters like floods, hurricanes, and wildfires have increased in frequency and intensity. AI can help governments and communities better prepare for and respond to these events. For example, deep learning models can forecast extreme weather events and predict their impacts, providing timely warnings to mitigate damage.

Google’s Flood Hub, an AI-driven tool, has been instrumental in issuing flood alerts worldwide. In agriculture, AI-driven precision farming techniques help farmers make informed decisions about

crop planting and irrigation in response to climate variability, thereby improving yields and reducing water consumption.

Risks Associated with AI in Climate Action

While AI holds enormous promise, it is not without its drawbacks. One major concern is the carbon footprint of AI itself. Training AI models, especially deep learning models, can be energy-intensive. The data centres required to store and process vast amounts of information consume significant amounts of electricity, much of which may still come from non-renewable sources.

The extraction of minerals needed to build the hardware for AI systems, such as lithium, cobalt, and rare earth elements, presents additional environmental and social challenges. Mining these materials often leads to habitat destruction and contributes to environmental degradation in vulnerable areas.

Moreover, AI’s benefits may not be equally distributed. AI development is often concentrated in developed nations, leaving developing countries with less access to its advantages. These countries, often the hardest hit by climate change, may not have the capital or infrastructure to implement AI solutions at the scale needed to tackle their unique challenges. Ensuring equitable access to AI-driven climate solutions will require international cooperation and investment in capacity-building.

Financial Feasibility and Future Directions

AI’s contributions to Net Zero are promising, but they also need to be financially feasible. While AI technologies can lead to significant cost savings through optimised energy use, better resource management, and more effective disaster response, initial investment costs can be high. Governments, private sector companies, and international organisations need to collaborate to provide the necessary funding and regulatory frameworks to support the integration of AI in climate solutions.

Additionally, AI has the potential to reshape the carbon market by improving carbon credit assessments and automating trading platforms. AI can also assist financial institutions in making climate-risk assessments, helping them align their portfolios with net-zero objectives. The integration of AI into financial markets could drive capital toward green technologies and initiatives, further accelerating the transition to a low-carbon economy.

Conclusion: Balancing Promise and Risks

Artificial intelligence is emerging as a powerful ally in the fight against climate change. From optimising energy use and reducing emissions to supporting adaptation strategies and biodiversity conservation, AI offers solutions that can accelerate progress toward a sustainable future. However, its deployment must be carefully managed to minimise its environmental footprint and ensure equitable access across the globe.

As we move forward, the focus should be on enhancing AI’s energy efficiency, promoting sustainable hardware development, and ensuring that its benefits are widely shared. By balancing the immense potential of AI with the need for careful oversight, AI can become a cornerstone of global climate action, helping humanity rise to the challenge of mitigating climate change while building a resilient, low-carbon future.

This will require collaboration across sectors, innovation in both AI and climate technologies, and thoughtful policymaking to maximise the benefits of this powerful technology