Insights for Organisations

AI & Sustainability: Artificial Intelligence for Tackling Environmental Challenges

Paul Brown
03.11.23

The 2023 United Nations COP 28 conference will take place in the UAE, bringing the world together to tackle climate change. One of this year’s themes is technology and innovation, which will be key to tackling environmental challenges. Among other topics, such as big data, clean energy, and energy storage, the topic of artificial intelligence will take centre stage – but why is that?

In an era marked by unprecedented environmental challenges, the emergence of artificial intelligence (AI) has ushered in a new frontier in our collective efforts to address and mitigate the pressing issues of our planet.

According to research by PwC UK, the economic impact of AI in agriculture, water, energy, and transport has the potential to make a significant mark, contributing up to $5.2 trillion USD to the global economy in 2030. As for the environmental impact, AI application could reduce global greenhouse gas emissions by 4% by 2023. Not to mention, it could also create an astonishing 38.2 million new jobs!

From climate modelling to wildlife protection, let’s delve into how AI is helping tackle environmental challenges and forge a greener future for our planet and people…

What’s in this article?

Executive summary

The impact of AI in addressing environmental challenges is profound. Climate modelling and prediction harness the power of AI to understand the complex dynamics of climate change. AI-driven systems optimise renewable energy sources, enhancing efficiency and sustainability in the energy sector. And in smart buildings, AI manages energy consumption, reducing costs and emissions.

In the farming world, precision agriculture leverages sensors and data analytics to maximise crop yields while conserving resources. While, in wildlife conservation, AI is able to monitor endangered species and preserve habitats. Similarly, AI-enabled marine ecosystem monitoring safeguards ocean health and the associated risks.

Air and water quality are improved through AI-driven sensor networks, protecting public health and ecosystems. Additionally, waste management and recycling are revolutionised, reducing landfill waste and increasing resource efficiency. Carbon capture and sequestration (CCS) benefits from AI’s ability to optimise materials and conditions, reducing carbon emissions and enhancing environmental sustainability. Not to mention, AI supports disaster preparedness and response with early warning systems. Ultimately, artificial intelligence technology  is at the forefront of efforts to create a greener, more sustainable future and hiring the top talent to facilitate this couldn’t be more important.

10 Ways AI technology supports sustainability

  1. Climate modelling and prediction
  2. Renewable energy
  3. Energy efficiency in buildings
  4. Precision agriculture
  5. Wildlife conservation and monitoring
  6. Air and water quality monitoring
  7. Waste management and recycling
  8. Marine ecosystem protection
  9. Disaster preparedness and response
  10. Carbon capture and sequestration (CCS)

1. Climate modelling and prediction

The temperature on Earth has increased by an average of 0.08 degree Celsius per decade since 1880, and the rate of warming is more than double since 1981, presenting a severe climate crisis. Using AI for modelling and predicting climate change could prove significant in combating it. Machine learning algorithms analyse vast datasets to identify patterns and provide more accurate climate models. It can also draw upon multiple data points, including temperature, greenhouse gas emissions, and ocean currents – something unmanageable for humans alone. As a result, this helps us better understand the impacts of climate change and prepare for them accordingly.

2. Renewable energy

AI plays a pivotal role in renewables and the energy sector as a whole, supporting the development and transition towards more sustainable energy production. For instance, weather-driven energy production requires accurate prediction of weather conditions in order to enable renewable energy facilities to anticipate periods of high energy production and plan accordingly. Another instance is for grid balancing. AI systems are instrumental in grid management and balancing the supply and demand of renewable energy. They can predict energy demand fluctuations and adjust the supply accordingly. Read all about AI in the energy sector for more information.

3. Energy efficiency in buildings

AI-driven smart building systems leverage data from a network of sensors to revolutionise energy efficiency within structures, both residential and commercial. By continuously monitoring occupancy patterns and environmental conditions, these systems autonomously adjust heating, cooling, and lighting, ensuring resources are used optimally. Real-time adaptation reduces energy consumption, cutting operational costs and greenhouse gas emissions.

4. Precision agriculture

AI is revolutionising agriculture by empowering farmers to make data-driven decisions, and therefore optimising crop management and enhancing both productivity and sustainability. The integration of sensors and data analytics in farming practices allows for a more precise and efficient approach to cultivation. For example, sensors placed in fields and on machinery collect an array of data, including soil moisture, temperature, nutrient levels, and crop health. This real-time data is then processed by machine learning models, enabling farmers to gain deep insights into the condition of their crops and soil. As a result, farmers are able to maximise crop yield while minimising resource usage.

Yet, one of the most significant benefits is the early detection of crop diseases and pests. AI can identify signs of disease or infestation in plants well before they are visible to the naked eye. This early warning system allows for timely intervention, reducing the need for chemical pesticides and preventing crop loss. Pesticides are responsible for an estimated 11,000 human fatalities, 385 million cases of poisoning every year, and play a huge part in the decrease in biodiversity, so this advancement will be life-changing.

5. Wildlife conservation and monitoring

Wildlife conservation is critical for several reasons, such as protecting the animals and the ecosystems they live in, which is declining at an alarming rate. AI is playing a vital role in the conservation of wildlife and their habitats through the deployment of AI-enabled cameras and sensors. These tools are designed to monitor, track, and safeguard endangered species by providing invaluable data on wildlife populations and their behaviours.

One of the most significant contributions of AI in wildlife conservation is its ability to collect data non-intrusively and continuously. This minimises human disturbance in natural habitats while providing conservationists with a vast and detailed dataset that was previously impossible to obtain. The data collected supports evidence-based conservation efforts, helping to make informed decisions on land management, protected area designations, and the implementation of policies that safeguard our planet’s biodiversity.

6. Air and water quality monitoring

Air pollution emissions have declined in recent years, yet it still remains the largest environmental health risk across Europe, leading to an estimated 238,000 deaths in 2020 alone. As for water quality, this is heavily impacted by multiple factors, notably agricultural runoff containing chemical fertilisers and pesticides, which is toxic to humans and causes eutrophication of water resources.

AI-driven sensor networks have emerged as a cornerstone in the ongoing effort to monitor and improve air and water quality. These sophisticated systems offer a real-time, data-driven approach to environmental protection, yielding a wealth of benefits for public health and ecological sustainability.

In urban areas, where air pollution is a significant concern, networks of sensors continuously measure pollutants such as particulate matter, nitrogen dioxide, and volatile organic compounds. AI algorithms process this data to create real-time air quality maps, enabling authorities to issue timely warnings and adjust traffic management and industrial operations to mitigate pollution.

Likewise, in aquatic environments, these sensor networks track water quality parameters like pH, dissolved oxygen, and contaminants. AI analyses the data, which is crucial for safeguarding aquatic ecosystems and ensuring safe drinking water sources. Moreover, early detection of contamination events, like chemical spills or algal blooms, allows rapid response measures to be taken to minimise damage.

7. Waste management and recycling

AI-driven systems are transforming waste management by providing smarter, more efficient solutions. These systems can optimise waste collection routes by analysing historical data and real-time inputs, like traffic conditions and current fill levels in bins. This results in significant cost savings, reduced fuel consumption, and a decrease in greenhouse gas emissions from collection vehicles.

AI helps reduce landfill waste by identifying opportunities for recycling and composting. Machine learning algorithms can sort through vast amounts of data to improve the separation of recyclables from non-recyclables, ultimately reducing the volume of waste sent to landfills.

By enhancing recycling processes, AI can increase the recycling rate by efficiently sorting materials at recycling facilities. Advanced robotics and AI vision systems are capable of sorting materials with greater precision than manual labour, increasing the quality and value of recycled materials.

8. Marine ecosystem protection

Marine ecosystems currently face decline in many forms, from marine heatwaves and loss of biodiversity to rising sea levels and acidification. AI is emerging as a powerful ally in the quest to monitor and preserve marine ecosystems, and combat these issues. Autonomous underwater vehicles (AUVs) equipped with advanced AI technologies are being deployed to explore and document the health of our oceans, facilitating crucial conservation efforts.

These AI-driven AUVs can conduct detailed surveys of marine environments, capturing high-resolution images and collecting vast amounts of data. They are capable of detecting subtle changes in coral reefs, which are vital for monitoring the health of these fragile ecosystems. By identifying signs of bleaching, disease, or damage, AI helps conservationists take prompt actions to protect these critical habitats.

9. Disaster preparedness and response

Climate change is causing a surge in natural disasters with the world experiencing a five-fold increase over the last 50 years. According to the World Meteorological Organization (WMO), early detection and warning systems for these disasters and extreme weather conditions can aid in saving lives – and AI is key to doing so.

AI is transforming the way we prepare for and respond to natural disasters, such as hurricanes, wildfires, and more. By harnessing advanced algorithms, AI can analyse historical data, current conditions, and real-time data streams to provide early warnings and facilitate rapid, informed responses. This capability is pivotal in reducing environmental damage and safeguarding human lives. For instance, in the case of wildfires, AI-driven systems can monitor environmental conditions, including temperature, humidity, wind speed, and historical fire behaviour. Machine learning algorithms can issue predictive fire risk assessments, helping authorities allocate firefighting resources more effectively and making it possible to evacuate at-risk communities before the fire reaches them.

10. Carbon capture and sequestration (CCS)

Carbon capture and sequestration (CCS) processes aim to capture CO2 emissions and store them underground to prevent their release into the atmosphere, and AI technology is instrumental to this.

AI optimises CCS processes by aiding in the selection of capture materials and the fine-tuning of process conditions. Machine learning models analyse a wide range of factors, such as the type of industrial emissions, the geology of the storage site, and operational parameters, to determine the most effective CCS approach for each unique scenario.

AI’s predictive capabilities enhance the efficiency of carbon capture by optimising the solvent or adsorbent used to capture CO2, ensuring the maximum amount of carbon is removed from industrial emissions. This not only reduces carbon emissions but also minimises the environmental footprint of CCS by optimising resource usage.

Furthermore, AI assists in the continuous monitoring of underground storage sites to ensure the long-term integrity of the stored CO2. By analysing data from sensors and predicting potential issues, AI can help prevent leaks and ensure the safe, long-term storage of captured carbon.

Hire top AI talent for a greener future with FDM

At the heart of this AI innovation stands the skilled technology talent that will pave the way for sustainability across industries. In fact, as mentioned above, PwC reports that AI could create 38.2 million net new jobs across the global economy –  a digital skills gap that is yet to be fulfilled. Are you looking to hire the brightest, most innovative minds to join your teams and support your effort towards a greener future?

At FDM, we recruit diverse talent and provide them with expert training to equip them with the necessary skills to thrive in an AI-driven environment. Whichever skills our clients desire, we can source and upskill the perfect candidates to merge seamlessly into their teams and make a real difference!

Learn more about FDM’s technical consultant services or get in touch for more information.