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Sustainability & AI: 10 AI Applications in Sustainability in 2024

Written by
Shehmir Javaid
Shehmir Javaid
Shehmir Javaid
Industry Research Analyst
Shehmir Javaid in an industry & research analyst at AIMultiple.

He is a frequent user of the products that he researches. For example, he is part of AIMultiple's DLP software benchmark team that has been annually testing the performance of the top 10 DLP software providers.

He specializes in integrating emerging technologies into various business functions, particularly supply chain and logistics operations.

He holds a BA and an MSc from Cardiff University, UK and has over 2 years of experience as a research analyst in B2B tech.
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In the current world, it is difficult to find an industry or field which has not been touched by artificial intelligence (AI). From supply chain management to healthcare, AI is pushing every industry towards a smarter, more efficient, and more sustainable future.

The global pandemic might have shifted the focus from sustainability; however, the need for it never went away. As many companies sail out of the rough waters, they are trying to re-focus on sustainability by implementing digital solutions such as AI.

PwC numbers estimate that the use of AI can reduce worldwide greenhouse gas (GHG) emissions by 4%. Use of AI for environmental purposes can also contribute up to $5.2 trillion USD to the global economy in 2030.1 But, machine learning also has some environmental footprint. Gartner predicts that by 2025, if sustainable AI practices are not implemented, AI will consume more energy than the human workforce.2

Image shows hot use of AI can help combat against climate change while improving economic value.
Source: BCG

This article explores 10 ways AI can help business managers improve sustainability in their organization.

1.Data analysis for sustainability

 To challenge global warming, we must first be able to effectively analyze its impacts. Large language models such as GPTs are used to achieve a more sustainable future. They can:

Analyze business documents & reduce waste

With the use of AI, data such as transportation and electric use can be captured, processed, and analyzed. This can help businesses create detailed carbon footprint calculations at reduced cost.

Companies can also optimize supply-chain data analysis and interpret data by using LLMs. They can give businesses insights on their expenditure and operations.

Identify scope 3 risks

Detecting scope 3 risks can be harder than identifying scope 1 and 2. Models such as ChatGPT can help identify these risks by analyzing vast amounts of publicly available data, such as news articles, social media posts, industry reports and more.

2.Sustainable agriculture

In a world where the demand for food is in constant growth, innovations play a crucial role.3 Fortunately, Artificial Intelligence (AI) is leading the farming in a more sustainable path.

Agricultural robotics

Just like a self-driving car, AI-powered robots can move around and harvest crops when they’re ready and mature enough. This reduces waste and can improve gains.

Weather monitoring

AI can also monitor and forecast the weather. This helps farmers to predict what the weather will be like in a specific location; give them insights on when to water their crops or when it’s the best time to plant or harvest.

Land management

Another use case of AI is farm-land planning. With the help of satellite images and algorithms, and land-use data, farmers can plan where and when to plant their crops. It can also help them make sure they’re in line with regulatory compliance.

Crop and animal monitoring 

AI can help farmers in keeping their crops and animals healthy. It can use image recognition and sensors to spot the conditions of crops, whether they are attacked by bugs, or early signs of diseases in animals. Farmers can step in and fix the problem without using excessive amounts of chemicals or medicines, reducing potential losses.

3.Preparedness for natural disasters

 Flood warning

AI has the potential to significantly reduce the impact from extreme weather events, which are increasing in frequency and intensity due to climatic change. According to numbers, 250 million people are already affected by flooding annually. PwC in the same report suggests that AI-powered improvements can enable early flood warning systems, which can save over 3,000 lives and mitigate $14m economic damages between now and 2030.4

Real life example: A study shows that in Google Research uses AI to predict flooding and protect livelihoods in over 80 countries up to 7 days in advance, including countries struggling with data scarce.5

 Forest Fires

AI can also help fight forest fires and prevent losses. Cameras and sensors attached to drones, satellites, or tall towers can constantly observe forests. These devices can spot changes that can trigger fire such as unusual hot spots or rising smoke. When trained, AI can learn and distinguish the smoke from various forest smells.

Real life example: Dryad Networks implemented around 400 ‘electronic noses’ to Eberswalde forest in Brandenburg, a region most impacted by fires. These can depict the gases during the earliest phase of a fire, monitor temperature, humidity and air pressure.6

4.Biodiversity monitoring

Conserving biodiversity is one of the biggest challenges brought by the climate change problem. AI offers solutions in improving biodiversity monitoring and conservation.

Technologies such as neural networks, computer and satellite visions can help researchers detect animals in images, and to identify specific animals within a species. Using this, researchers can monitor animals such as birds, amphibians, and cetaceans, even fishes, and analyze the data swiftly through machine-learning-powered tools.7 Thus, using AI functions, scientists can make:

–   Better habitat analysis

–   more precise guesses on wildlife and species

–   analyze the climate change’s impact on animals real-time.

Real life example: Wildbook uses neural networks and computer vision algorithms to find and count animals in pictures and to identify individual animals within a group. With this knowledge, wildlife population sizes can be estimated more accurately.8

5.Fighting air pollution:

Air pollution is getting worse. This swiftly escalates to a global public health and environmental emergency that causes over seven million premature deaths every year and $8.1 trillion in health damages alone.9 AI can help reduce air pollution with:

 Real-time warnings

With data provided from air quality monitors, AI can offer insights on the impact of air quality on people and help decide on health protection policies.10 Also, by processing data from different monitors real time it can send out alerts when pollution levels pike. This way, people can act right away; stay in or wear masks.

Real-life example: IQAir application has a ranking that shows in real time which towns have the most pollution in the air. The Plume Labs app gives full maps that show where pollution is worst. The app also tells what the air quality will be like every hour, as levels can change a lot during the day. 11

Predictive Models

By using AI, scientists can design predictive models. These models can forecast information such as air-pollutant concentration.12 In another example, engineers from Cornell designed a model that can calculate the fine particulate matter (PM2.5) that is the soot, dust, and exhaust from trucks and cars that gets into people’s lungs. Using these models, future negative impacts can be avoided.

6.Less defective production

According to Forbes, around 17 billion purchased items are returned globally.13 This is due to reasons such as product defects or customer dissatisfaction. This is equal to about 4.7 million metric tons of CO2 emitted yearly. A 10% reduction in these returns would save enough energy to power about 57000 homes in the USA for a year.

AI-enabled computer vision systems can help resolve this issue at the manufacturing end by reducing defective production. Computer vision-enabled quality control systems installed on the conveyor belt or production line can inspect the quality of the product more accurately and efficiently than manual inspection.

See how it works:

This reduction in defective products can ultimately reduce product returns and GHG emissions of the organization related to reverse logistics and other return processes.

7.Better leak detection in production

A building can waste around 25 to 30% of the water consumed on average. In a world where many countries are facing droughts and water shortages, this is an unsustainable practice. 14

AI-powered computer vision can help detect leaks of water and other harmful chemicals within a production plant and alert authorities to take quick action. This can help businesses reduce their environmental impact. 

See how it works:

8.Safer workplace

Sustainability is a combination of three parts. These parts are environmental, social and govermental. A business needs to focus on all three parts to be truly sustainable. Working at a production plant/factory can be dangerous since it involves using heavy machinery. 15

AI-enabled computer vision systems can help improve worker safety by ensuring compliance with safety rules. This can help improve the social sustainability of a business by making the business more secure for its workers.

Smart cameras can be installed at certain points of the manufacturing facility to inspect if the workers are following rules and wearing safety equipment. The system can also identify other risks in the facility and notify the relevant operations or safety manager for further action.

https://youtu.be/vQyfYi1qUgI

You can check our data-driven lists of:

9.Reduced energy consumption

Even though investments in renewable energy have increased significantly in the past few years, renewable energy still only accounts for about 12.5% of the world’s total energy production.16 Therefore, saving energy is an effective way of improving the environmental sustainability of a business. 

AI can help in this area. AI can study the patterns of energy consumption and provide insights on reducing and improving consumption while not compromising the company’s productivity.

According to a recent study, AI-enabled models can help businesses improve energy efficiency by 10-40%.17This efficiency results in a significant reduction in carbon emissions and costs for the organization. Google uses Deepmind AI to reduce its data center energy consumption by 30%. 18

10.Optimized and sustainable logistics

AI can also help improve the sustainability of the distribution and logistics operations of a business, which account for a significant chunk of the total corporate carbon footprint.

AI-power software can provide optimized routes for the delivery of products by incorporating sustainability as a key factor. Route optimization systems have become a necessity for logistics firms since they provide significant financial and environmental benefits. 

Watch how AI and digital twin technologies are enabling sustainable last-mile delivery

To learn more about how AI is revolutionizing the logistics sector, check out this quick read.

Challenges:

Artificial intelligence (AI) looks promising in helping protect the environment, but it also presents some challenges:

Computing energy:  Advanced AI models need significant computing power, which means they use a lot of energy.19This influences both operational prices and carbon emissions. Thus, using energy-intensive AI technologies in the service of environmental sustainability can be paradoxical. 

Labor abuses: Large language models such as ChatGPT can require labels to keep the model away from toxic texts. To get these labels, OpenAI sent tens of thousands of particle texts to a firm in Kenya. The data labelers employed by the company are paid only around $1.32 and $2 per hour. 20 This raises questions about whether workers’ rights are being usurped in the development of AI tools for a sustainable future.

Bias: AI models learn from data, and if the data is biased or or represent only a particular part of reality, the models can come up with results that aren’t correct. For instance, an AI model trained in location-specific data can fail generating data for other areas.

Ethics: Decisions made based on the results of AI can have big effects on society and the world. It is used to keep an eye on environments, track species, or make predictions that could affect communities. Therefore, questions can appear around privacy and ownership of data.

Best practices to mitigate challenges:

 Energy-Efficient AI: The priority should be using algorithms and devices that use less energy. As in the case of MIT researchers, research groups can work on designing models that keep a balance between how well AI works and how much energy it uses. 21 AI computing infrastructure can be powered by renewable energy sources, which can help lower the carbon footprint even more.

Reduce Bias:  AI models should use rigorous methods for collecting, testing, and validating data to make sure they are countable and to avoid bias. It’s important to include a wide range of data and consider how conditions can vary in different locations.

Ethical Guidelines: For AI to be used to protect the environment, ethical guidelines and policies need to be designed and followed. This includes having clear rules about who owns the data, how to keep it private, and how to use AI in an ethical way.

Stakeholder Engagement: Involve stakeholders in the decision-making process; specifically the groups that will be affected by the results of AI. This means making sure that everyone knows how AI models work and what data they use.

Further reading

If you have any questions, feel free to contact us:

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Shehmir Javaid
Industry Research Analyst
Shehmir Javaid in an industry & research analyst at AIMultiple. He is a frequent user of the products that he researches. For example, he is part of AIMultiple's DLP software benchmark team that has been annually testing the performance of the top 10 DLP software providers. He specializes in integrating emerging technologies into various business functions, particularly supply chain and logistics operations. He holds a BA and an MSc from Cardiff University, UK and has over 2 years of experience as a research analyst in B2B tech.

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