Overcoming climate change challenges with AI

By Nelvin Ebingiye Agbozu As climate change continues to accelerate, its global impacts — from extreme weather events to biodiversity loss are becoming increasingly severe and complex. Traditional strategies such as policy reforms, sustainability initiatives, and behavioural change, while essential, often fall short of delivering the speed and scale of transformation needed. googletag.

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Chief among them is Artificial Intelligence (AI), a technological force reshaping our capacity to understand, mitigate, and adapt to the climate crisis. AI offers unprecedented capabilities in analysing vast, complex datasets, generating real-time insights, and optimising dynamic systems. Its growing integration across climate-focused sectors signals a pivotal shift: from reactive mitigation to proactive, precision-driven strategies.

As a data scientist working at the intersection of AI and environmental systems, I have seen firsthand how digital technology is no longer a peripheral tool, it is central to climate resilience and sustainability. AI’s most immediate impact is in climate modelling and prediction, a domain traditionally dominated by physics-based simulations. While effective, these models are computationally expensive and often slow, constrained by their reliance on fixed assumptions about Earth’s dynamic systems.

Machine learning (ML) models, in contrast, learn from patterns in vast datasets such as satellite imagery, atmospheric measurements, and historical weather records to produce faster, often more nuanced forecasts. For example, Google’s DeepMind developed a deep learning model that accurately predicts short-term rainfall (nowcasting) within minutes, outperforming conventional systems used by meteorological agencies. $(document).

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Enhanced forecasts enable earlier warnings for floods, wildfires, and droughts, improving disaster preparedness, reducing loss of life, and aiding humanitarian responses. By refining our predictive capacity, AI empowers policymakers and communities to move from reactive to anticipatory climate action. The global transition to renewable energy — solar, wind, and hydro — is central to cutting emissions, but it introduces variability and intermittency into power grids.

AI is crucial in managing this complexity. Smart grid systems powered by AI can balance electricity supply and demand in real time, detect inefficiencies, and forecast renewable output based on weather conditions. For instance, AutoGrid and Uptake deploy AI to enhance grid resilience, minimising dependence on fossil fuel backups by predicting energy needs and storage requirements.

Moreover, AI-powered predictive maintenance tools reduce operational downtime in wind turbines and solar farms, cutting costs and emissions.By integrating AI into energy infrastructure, countries can accelerate their transition to decarbonised, intelligent power systems, a vital milestone in achieving net-zero goals. Agriculture is both deeply affected by and significantly contributes to climate change.

Rising temperatures, shifting precipitation patterns, and pest outbreaks threaten food security. At the same time, agriculture accounts for over 20 per cent of global greenhouse gas emissions. AI is transforming this landscape through precision agriculture.

Technologies such as AI-powered drones, sensors, and satellite analytics help farmers monitor soil health, detect crop diseases, and optimize resource use. For example, Taranis uses high-resolution aerial imagery and computer vision to identify field anomalies, allowing for targeted interventions that reduce fertilizer and pesticide use.In water-scarce regions, AI-driven irrigation systems analyse soil moisture and weather data to deliver water only when and where it is needed, preserving this critical resource.

These advances not only increase yields but also reduce environmental footprints, contributing to climate-resilient agriculture systems. Climate change accelerates habitat degradation, species extinction, and ecosystem collapse.AI helps us detect and respond to these threats with speed and scale never before possible.

Platforms like Global Forest Watch, developed by the World Resources Institute, utilise satellite data and AI to monitor global deforestation in near real time. This empowers governments and non-governmental organisations (NGOs) to enforce conservation policies and combat illegal logging activities. Meanwhile, tools like Wildlife Insights use AI-powered image recognition to identify species captured by camera traps, streamlining biodiversity monitoring across remote areas.

In marine environments, AI helps analyse sonar and satellite data to track coral bleaching and illegal fishing. By enabling real-time, cost-effective environmental monitoring, AI enhances our ability to protect vulnerable ecosystems and promote data-driven conservation strategies.Transportation is responsible for nearly a quarter of global CO2 emissions.

AI is playing a central role in decarbonising this sector through route optimisation, intelligent logistics, and EV infrastructure management. For example, UPS’s ORION platform uses AI to optimise delivery routes, reducing fuel consumption by millions of gallons annually. In urban areas, AI-driven smart traffic systems reduce congestion by dynamically adjusting traffic lights and rerouting vehicles during peak hours.

The rise of electric and autonomous vehicles further underscores AI’s potential. Intelligent charging networks can distribute electricity more efficiently across cities, while autonomous driving systems reduce fuel use through smoother driving behaviour. As urban mobility becomes smarter and cleaner, AI is enabling a systemic shift in transportation sustainability.

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push({})}); Climate finance is critical to funding adaptation and mitigation efforts globally. AI enhances the efficiency and precision of financial decision-making in this domain by analysing complex, multifactorial datasets. For instance, AI-powered Environmental, Social, and Governance (ESG) tools help investors evaluate the sustainability profiles of companies and projects.

Bloomberg’s ESG Analytics platform uses natural language processing (NLP) and machine learning to track disclosures, carbon performance, and climate risk exposures in real time.Policymakers also benefit from AI-driven simulation models that project the outcomes of proposed legislation, such as carbon taxes or subsidies for clean energy, under various scenarios. These insights foster evidence-based policy development, ensuring climate funds are directed toward high-impact areas.

While AI holds immense promise, it is not without challenges. Algorithmic bias, lack of transparency, and data privacy concerns can undermine trust and inclusivity. Moreover, training large AI models can be energy-intensive, raising questions about the environmental footprint of AI itself.

To ensure ethical AI deployment in climate contexts, developers must prioritise responsible AI principles, including fairness, accountability, and sustainability. This involves using low-carbon computing infrastructure, promoting open data standards, and fostering inclusive design practices.Global collaboration is equally vital.

The benefits of AI must be shared equitably, particularly with developing nations that are often most affected by climate impacts but least resourced to deploy advanced technologies. Initiatives like the United Nations’ AI for Good and the Paris Agreement’s AI for Climate Action exemplify the kind of multi-stakeholder cooperation needed. AI is not a magic wand, but it is a strategic catalyst.

• Agbozu, a data scientist, writes from Bayelsa State $(document).ready(function(){(adsbygoogle = window.adsbygoogle || []).

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