Digital Innovation Meets Climate Change – Opportunities and Risks

We are now living in the age of Big Data, driven by advances in computerisation, digital technology, and artificial intelligence (AI). Just 25 years ago, technologies such as the internet and computer chips were not yet widely accessible. Handwritten letters, dial alarm clocks, carbon paper, corded telephones, and transactions requiring physical signatures are now largely relics of the past.

Today’s increasingly interconnected systems, combined with the ability to process vast amounts of data in real time, are reshaping how society functions. The rapid expansion of data, fuelled by the internet, social media, Internet of Things (IoT) devices, e-commerce, and cloud computing, has created both opportunities and challenges.

As data volumes grow, technology companies continue to develop tools and applications to manage and interpret this information. This is where AI plays a crucial role.

Data is no longer generated solely by consumers; businesses also produce large volumes of data to gain insights and improve decision-making. Many organisations rely on AI and machine learning to process and organise this data into actionable intelligence.

The global expansion of data centres, required to handle complex computations and train AI models, further highlights the scale and potential of this digital transformation.

Digital tools supporting climate action

Digital technologies are increasingly being used to understand and respond to climate change. They enable the collection and analysis of large datasets, from historical weather records to real-time climate monitoring, improving the accuracy of predictions and assessments of environmental and socio-economic impacts.

For example, the National Oceanic and Atmospheric Administration’s(NOAA) Weather and Climate Toolkit (WCT) is a free, open-source platform for visualising and converting weather and climate data. It allows users to analyse data from radar, satellites, and modelling systems, while supporting interoperability with tools such as ArcGIS, Google Earth, and MATLAB.

Cities around the world are also leveraging data and AI-driven modelling to address climate-related challenges, including extreme heat, flooding, droughts, and sea-level rise.

AI in action – Smarter cities and climate resilience

Several cities are already applying AI and advanced simulations to build more resilient urban environments.

In Singapore, digital twin technology is being used to tackle urban heat. Data from meteorological observation sites, combined with high-resolution satellite imagery, are used to measure the cooling effects of vegetation.

These datasets are integrated into computational and geospatial models to simulate environmental interactions and inform urban planning decisions.

In Boulder, Colorado, officials have adopted a City Simulator tool following devastating floods more than a decade ago. This platform integrates geographic information system (GIS) data, weather patterns, and demographic information to model future flood scenarios. By simulating conditions projected for 2050, planners can better prepare for increasingly severe weather events.

Meanwhile, researchers at the Massachusetts Institute of Technology (MIT) have developed Tree-D Fusion, a programme that simulates the growth of approximately 600,000 trees across North America under varying environmental conditions.

This tool helps urban planners optimise tree placement to maximise shade, reduce urban heat islands, conserve water, and enhance resilience to extreme weather.

The environmental cost of digitalisation

While digitalisation and AI offer promising climate solutions, they also come with environmental costs.

The digital sector is a growing contributor to global emissions. According to the European Climate Pact, data centres and data transmission networks accounted for around 1% of global energy-related GHG emissions in 2023. The rise of generative AI and increased computing demands are driving higher electricity consumption and placing additional strain on energy systems.

AI systems also require significant amounts of water for cooling, with some large-scale models consuming millions of litres during both training and operation. In addition, the rapid turnover of digital devices, such as laptops, smartphones, and tablets, contributes to mounting electronic waste.

Balancing the benefits of digital innovation with its environmental impact will be critical in ensuring that technology supports, rather than undermines, global climate goals.