Unraveling AI for a Greener Planet: Harnessing Artificial Intelligence in Climate Action

Curated by Clean Technology Hub Research Partnership Group (RPG)

Written by Erick Kiprono Cheruiyot

1. Introduction

The global fight against climate change is one of the most pressing of our time. Global temperatures are projected to rise by 1°C (1.8°F) above pre industrial levels, 1-m projected sea level rise by year 2300 due to greenhouse gas emissions, elevated carbon dioxide (CO2; average 476–521 ppm) and methane (CH4; average 19–71 ppm). The challenge goes beyond the typical impacts of sea level rise and extreme weather events; There is limited evidence on the impact of climate change. The impacts of climate change are inevitable, and new problems emerge, such as the introduction of novel viruses such as SARS-CoV-2.

Over the past 10 years, there have been efforts and strategies put into realizing global goals to reduce greenhouse gas (GHG) emissions. Some of the efforts include the 2018 Royal Society-National Academy of Sciences Forum on Climate Change and Ecosystems and the 2018 International Panel on Climate Change (IPCC) Special Report on 1.5°C. However, the scalability, effectiveness, and magnitude of strategies need to be explored, better evaluated and understood. Tools are needed to accomplish this.

Artificial intelligence (AI) remains now as a critical and important ingredient in tackling climate change complexities. Climate change actions and strategies encompass policy reviews, innovation, behaviour changes, research, simulations, and forecasting, among other things. Innovative simulation models using machine learning, for example, have been used to predict short-term and long-term climate patterns. In this context, AI emerges as a potent tool, offering groundbreaking capabilities to address these complex issues. This blog explores the nexus between AI and climate action, highlighting how AI technologies are revolutionizing efforts to combat climate change, enhance resilience, and promote sustainability.

Figure 1: Machine Learning & Climate change

2. The Role of AI in Climate Action

Artificial intelligence is a powerful catalyst for change in various fields, including climate science and environmental management. AI’s ability to analyze vast amounts of data, identify patterns, and make predictions positions it as an invaluable asset in understanding and mitigating climate change.

2.1. Predictive Modeling and Climate Dynamics

AI’s predictive modeling capabilities are crucial in understanding climate dynamics. Machine learning algorithms can process extensive climate data to predict future climate scenarios with high precision. These models help scientists and policymakers anticipate changes, such as temperature rises, precipitation patterns, and extreme weather events. By providing accurate forecasts, AI enables proactive planning and mitigation strategies, reducing the adverse impacts of climate change.

Figure 2: Predictive Models and Climate Change

This is demonstrable in the case of temperature prediction which researchers, policymakers and operators of early warning signal platforms may utilize. For example, it is possible to build an AI prediction model for temperature in Nairobi, Kenya, based on historical climate data (15/6/2024–30/6/2024). Using R studio and Open AI’s GPT-3, a Random Forest regression model can be employed to predict the temperature (temp) in Nairobi using the following predictors: humidity, windspeed, sea level pressure, cloud cover, dew, precipitation, and visibility. Building the model using 100 trees, it explains 39.85% of variation in the temperature . Researchers may go further to identify the importance of each predictor by using what is called the Inc Node Purity metric :

• Sea level pressure was the most significant predictor with an Inc Node Purity of 3.5754, suggesting that it contributes the most to predicting temperature.
• Dew followed with an Inc Node Purity of 2.8248.
• Humidity also played a significant role with an Inc Node Purity of 1.9881.
• Wind speed and Cloud cover had a moderate importance.
• Precipitation and Visibility had the least importance in this model, with Inc Node Purity values of 0.7724and 0.6327, respectively.

Improvements in the analysis may be made by :

1. Increasing the number of trees to potentially improve model stability and accuracy.
2. Exploring additional predictors that may have a significant impact on temperature.
3. Performing hyperparameter tuning to optimize the model settings for better performance.

Table 1: Variable Importance

2.2.1. Real Application

Predictive models such as Random Forest have been applied in India to predict air temperature. The scientist carried out air temperature prediction using Particle Swarm Optimization Algorithm, Decision Tree, Multi-layer Perception Neural Network and Random Forest Models. Random Forest Model is the most suitable due to high accuracy, and minimum Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) when compared to the other models. Therefore, integration of AI and other machine learning algorithms is vital in addressing the anticipated climate action deliverables.

2.2 Optimizing Energy Use and Reducing Emissions

One of the most significant contributions of AI to climate action is optimizing energy use. AI systems can analyze energy consumption patterns and identify inefficiencies in real-time. For instance, the smart grid: How AI is powering today’s energy technologies — SAP. Smart grids powered by AI can balance energy loads, integrate renewable energy sources, and reduce wastage. Furthermore, AI-driven optimization algorithms are employed in various industries to minimize emissions. From enhancing fuel efficiency in transportation to optimizing industrial processes, AI helps reduce the carbon footprint across sectors.

Figure 3: Artificial Intelligence in Optimizing Energy Efficiency

2.3. Enhancing Climate Resilience

Building resilience to climate impacts is another critical area where AI plays a transformative role. AI technologies can monitor environmental conditions and detect early warning signs of natural disasters, such as floods, hurricanes, and wildfires. By providing timely alerts, AI helps communities prepare and respond more effectively, minimizing loss of life and property. Additionally, AI-driven tools can assess vulnerabilities in infrastructure and suggest adaptive measures to enhance resilience.

3. Revolutionary Impact on Sustainability Efforts

The intersection of AI and climate action promises to unlock new solutions, fostering a sustainable future. By leveraging AI, we can accelerate progress towards sustainability goals in unprecedented ways.

Sustainable Agriculture and Food Security: AI is revolutionizing agriculture by promoting sustainable practices. Precision farming, AI and drones are reshaping agriculture and allows farmers to optimize resource use, such as water and fertilizers, thereby reducing environmental impact. AI-driven systems can monitor crop health, predict yields, and suggest best practices for pest and disease management. These advancements not only enhance productivity but also contribute to food security by ensuring stable and sustainable agricultural practices.

Conservation and Biodiversity Management: Conservation efforts benefit significantly from AI’s capabilities in data analysis and pattern recognition. AI Powered drones and remote sensing technologies monitor wildlife populations, track endangered species, and assess ecosystem health. Machine learning algorithms analyze this data to identify threats, such as poaching and habitat loss, enabling targeted conservation strategies. By enhancing our understanding of ecosystems, AI supports biodiversity preservation and the sustainable management of natural resources.

Sustainable Urban Planning: Urbanization poses significant challenges to sustainability, but AI offers innovative solutions for sustainable urban planning. Smart cities, driven by AI, utilize data from various sources to optimize infrastructure, transportation, and resource management. AI algorithms can predict traffic patterns, reduce congestion, and improve public transportation systems. Additionally, AI helps design energy-efficient buildings and manage waste more effectively, contributing to greener and more livable urban environments.

4. AI-Climate Action Nexus: A Driver of Transformative Change.

As climate risks escalate, the discourse on the AI-climate action nexus gains momentum. The integration of AI in climate action strategies is recognized as a driver of transformative change, with the potential to address climate change comprehensively.

Mitigating Climate Change: AI’s ability to process and analyze large datasets enables the identification of emission sources and the development of targeted mitigation strategies. For example, AI can optimize supply chains to reduce emissions, enhance renewable energy integration, and improve energy storage solutions. By supporting data-driven decision-making, AI helps accelerate the transition to a low-carbon economy.

Promoting Sustainable Practices: AI promotes sustainable practices by providing actionable insights and enabling efficient resource management. In industries such as manufacturing, transportation, and agriculture, AI-driven solutions enhance operational efficiency while minimizing environmental impact. By fostering sustainable practices, AI contributes to long-term environmental and economic sustainability.

Building Climate Resilience: The adaptive capabilities of AI are crucial in building resilience to climate impacts. AI-powered systems can model the effects of various adaptation strategies, assess their effectiveness, and recommend the best approaches. This proactive approach helps communities and organizations prepare for and adapt to changing climate conditions, reducing vulnerability and enhancing resilience.

5. Research and Future Directions

While AI is a promising tool for climate action, future research and applications have to identify the most effective ways to leverage AI for climate mitigation, adaptation, and sustainability. This can be done by:

1. Understanding AI’s Potential: Research focuses on exploring the full potential of AI in addressing climate change. This includes developing advanced AI models for climate prediction, optimizing renewable energy systems, and enhancing carbon capture technologies. By understanding AI’s capabilities, researchers aim to maximize its impact on climate action.
2. Addressing Ethical and Social Implications: The integration of AI in climate action also raises ethical and social considerations. Research aims to address issues such as data privacy, algorithmic bias, and the equitable distribution of AI benefits. Ensuring that AI technologies are developed and deployed responsibly is crucial to their acceptance and effectiveness in climate action.
3. Fostering Collaboration and Innovation: Collaborative efforts between governments, industries, and research institutions are essential to harness AI’s potential in climate action. Research initiatives focus on fostering innovation through interdisciplinary collaboration, knowledge sharing, and the development of open-source AI tools. By working together, stakeholders can drive transformative change and accelerate progress towards climate goals.

5. Conclusion

The intersection of artificial intelligence and climate action presents a powerful and innovative approach to addressing the global climate crisis. AI’s capabilities in predictive modeling, optimizing energy use, enhancing sustainable agriculture, and managing biodiversity offer promising solutions for mitigating emissions, improving sustainability, and adapting to climate impacts. As we continue to explore and understand this nexus, fostering collaboration across various sectors will be essential to unlocking the full potential of AI in creating a sustainable and resilient future. By leveraging AI’s transformative power, we can make significant strides in combating climate change and ensuring a healthier planet for future generations.

Erick Kiprono is a member of CTH Research Partnership Group (RPG). He is an MSc. Student at the Department of Civil and Environmental Engineering, Meru University of Science and Technology, Kenya with interest in climate action and Artificial intelligence.