Artificial intelligence has long since solved complex tasks and made our everyday lives easier. But do intelligent computer programs also provide new solutions for environmental and climate protection?
Unnoticed by many, artificial intelligence (AI) has long been intervening in our everyday lives—be it as a translation system, search engine, personal language assistant or robots programmed for specific activities. “The fact that AI will be an enormously important criterion for success in the future for science, society and especially the economy, is now sufficiently accepted,” says Kate Saslow, project manager for AI and foreign policy at the German Stiftung Neue Verantwortung (New Responsibility Foundation). This is largely to do with the fact that the potential applications of intelligent computer programs are extremely versatile and, as a cross-cutting technology, connect diverse areas.
What has become clear is that AI is likely here to stay, and will only develop further in the future. Instead of being stuck debating the question of whether we need or want AI—and thus missing the chance to have a say in its development—we should be discussing how and where we can use AI to serve the general good of humanity.
What is Artificial Intelligence?
Voice or face recognition, search engines, self-driving cars, computer games, social bots (chatbots that mimic human communication) or robots—all of these areas heavily involve AI.
But what exactly is artificial intelligence? So far, there is no single universally valid definition of AI. If we look for the lowest common denominator, artificial intelligence can be described as computer programs that can learn and improve themselves. They are intelligent to the extent that human cognitive decision-making structures are replicated by programming. This includes visual perception, speech recognition and generation, reasoning, decision-making and action, as well as the ability to adapt to changing environments.
Simple, “non-intelligent” algorithms (for example, navigation devices) are programmed to answer clear questions: this is the problem and this is how it is solved (e.g. “find the shortest way from A to B”). The very heterogeneous AI applications, on the other hand, are not based on classical deterministic programming, but on statistical data analysis. In a learning process—also called machine learning—the artificial system learns from examples based on training data, which it can generalise independently after the learning phase. An AI-supported translation system, for example, is fed hundreds of thousands of data in the training phase. It does not learn the examples by heart but focuses on patterns and regularities. In this case, it learns which word is translated into which word and which sentence into which sentence, which can then also be applied to new, previously unseen data.
Intelligent computer programs—a rapid development
The European market for artificial intelligence is predicted to grow 35 percent in 2024, according to Statista. The decisive factor for the rapid developments in the field of AI applications in recent years is the fast-growing computer power. This makes it possible to process the large amounts of data of rapidly advancing digitalisation. An end to these developments is not in sight. Computer scientists Dario Amodei and Danny Hernandez, who both work for OpenAI, a non-profit research company founded by, among others, Elon Musk, conclude in their study that an increase by a factor of 300,000 took place in the period from 2012 to 2017. This means that the new AI systems are currently, on average, doubling their speed every three and a half months and are thus able to process ever larger amounts of data.
Artificial Intelligence and sustainability—a good team?
AI is being treated as a key technology in many social, and especially economic, areas. Could it also transform our society towards ecological and social sustainability? In view of the urgent need for action to avert or at least contain climate collapse, a wide variety of projects, start-ups and research projects have set out to find new solutions with the help of intelligent computer programs.
In earth observation, for example, machine learning methods (such as pattern recognition and the linking of large amounts of data) together with data collected by satellites can help to make precise statements about climatic change.
The German Research Center for Artificial Intelligence (DFKI), for example, wants to combine machine learning algorithms with high-resolution satellite imagery to quickly and accurately access ground changes around the world.
Launched in 2017, the Sentinel-5P satellite is a collaboration between three European nations under the EU’s Copernicus Earth-Monitoring program. Using a specialised instrument, Sentinel-5P can highlight a wide range of pollutants, such as ozone, methane, carbon monoxide and sulphur dioxide, with unprecedented resolution and accuracy. The information will then be decoded with the help of AI to track down polluters entering our atmosphere.
Munich-based start-up Hawa Dawa is using sensors and AI to tackle the issue of air pollution in our cities: Hawa Dawa has developed a mobile measuring device that is networked via ‘Internet of Things’ technology with the Hawa Dawa software. This therefore allows it to compare weather and traffic data in real time. The software, which is equipped with artificial intelligence, constantly learns from the data compilation and can thus create forecasts as well as simulate data for areas where no sensors are installed. This creates an area-wide air quality map in real-time.
In the energy sector, AI can help to regulate the growing complexity of the decentralisation of the energy system which goes hand in hand with the switch to renewable energy sources. It also helps us use infrastructures more efficiently and increase the flexibility of the energy system by intelligently networking energy systems (smart grids), intelligent building control and energy data management.
Green traffic lights not only save time but also fuel. The savings potential, especially for larger vehicles such as trucks or buses, is enormous. The Hamburg Port Authority (HPA) is currently testing the “Green4Transport” project in the Port of Hamburg. The aim is to optimise traffic flow by networking vehicles with each other and with traffic lights.
Together with Oxford University, the Environmental Defense Fund Europe and the WWF, the UK National Grid has developed a so-called “Carbon Intensity Forecast” (a kind of “weather forecast” for clean electricity). The software can indicate the proportion of renewable and non-renewable energy in the UK’s electricity, which then produces a forecast of CO2 emissions. Consumers receive the predictions via an app, allowing them to shift their energy consumption to periods of low carbon intensity.
AI applications could also contribute to making the transport system more environmentally friendly, as well as assist in waste and recycling management.
Food waste is a huge problem, particularly in the restaurant industry. This problem is made worse because information on how much food is thrown away, and when, is rarely recorded. The company Winnow Vision, founded in London in 2013, wants to help solve this issue using artificial intelligence. Winnow has developed a smart system that recognises and records discarded food photographically. With this information, the system produces regular reports calculating the volume, value and environmental impact of the waste. With this knowledge, restaurateurs can make conscious decisions to reduce food waste in kitchens.
For more examples of how AI is helping to protect wildlife and preserve our forests, click here: Sustainability and AI.
How trustworthy is AI?
The topic of AI brings up several ethical dilemmas. Key questions include:
Autonomy: How autonomously does an AI act? The concept of autonomy of AI systems calls into question the self-determination of humans. This is a particularly key debate in the case of autonomous vehicles.
Trustworthiness and Transparency: How does an AI reach a decision? There is a certain lack of transparency in the principles of machine learning algorithms since the use of statistical correlations makes the algorithms more difficult to understand. For example, it is not always possible to deduce from an algorithm why a certain input value produces a certain output value. The reasoning that “the algorithm learned it that way from the sample data” is unsatisfactory for a deeper understanding. This can also become a problem from an environmental perspective; namely, when decisions made by AI systems cause environmental damage.
AI systems must be prevented from reproducing the discriminatory patterns that exist in society and from making unsustainable decisions. This can be done by ensuring algorithms are transparent and that the training data for algorithms is designed more inclusively.
AI as a personal shopper
Most start-ups, financial service providers and established e-commerce companies have long since integrated AI into their mobile applications, shopping systems or the data processing of their online stores. The intelligent algorithms classify and predict, suggest products to customers and make purchase recommendations.
What is worrying is that the dominance of the large IT and internet corporations (Google, Microsoft, Apple, Facebook, IBM, Amazon, etc.) is continuing in the AI sector, as in all other areas of the digital economy. These globally active providers of digital services now have a near-monopoly position and have huge pools of data at their disposal. From a sustainability perspective, the main problem is that digital corporations are using AI applications to further personalise their services. They can then create increasingly accurate forecasts about what customers want to buy, leading to more consumption. The German Federal Environment Agency study, “Consumption 4.0” states: “The lowering of technical barriers, the integration of purchase recommendations and evaluations into consumers’ everyday lives via social networks and personalised marketing can encourage consumers to make more frequent purchases, limited only by their disposable income.”
The energy hunger of machine learning
In addition to the discourse on the ethical risks of AI, the question of the life cycle assessment of AI systems is also relevant from a sustainability perspective. While some see enormous opportunities in the fact that we can tackle environmental challenges with AI, others fear it will merely create new problems. These include the further increase in our already enormous demand for electricity and massive increases in consumption.
The concerns are not unfounded: AI applications such as deep learning, simulations and forecasts require more and more computing power and will increase energy demand in data centres. As researchers at the University of Massachusetts have determined, training a single AI application for speech recognition can generate five times as much CO2 as a car emits over its entire lifetime.
However, training such a network generates services for a large number of users, which can lead to emission reductions in the long term. The emissions output of training AI should be weighed up against potential societal gains on a case-by-case basis.
Future AI applications could also have lower energy consumption, as there are still refinements that can be made, especially in the area of machine learning. “Many research groups are working on making deep learning less energy-hungry. Different hardware, new models and learning methods—all this is being investigated,” says Prof. Dr Kristian Kersting, Head of the Machine Learning Department at the Darmstadt Technical University.
Sustainable AI in policy and research
In December 2018, Germany published the German AI Strategy, which referenced sustainability concerns and opportunities of AI. The strategy outlined how evaluation criteria for the environmental impact of AI should be developed and an environmental data cloud should be established.
The plan to reach these goals included a specific funding program, “AI Lighthouses for the Environment, Climate, Nature and Resources”, in which the Federal Ministry for the Environment (BMU) looks for projects from business, science and civil society that use artificial intelligence to tackle ecological challenges. 35 projects were approved during the first funding program and 46 million EUR was allocated. Projects included Nadiki, an AI system that aids resource management and reduces CO2 emissions, and DC2heat, a digital twin that optimises the use of waste heat from data centres. Since 2018, the AI strategy has been updated and more funding has been allocated to the promotion of AI, with 5 billion EUR now allocated by 2025.
In March 2024, the European Parliament published the Artificial Intelligence Act. These guidelines have been in discussion since 2021 and focus on outlining regulations for the development of artificial intelligence. The 500-page publication recognises the potential benefits of AI for climate protection while acknowledging the need to reduce the impact of AI on environmental sustainability.
The intersection of AI and climate change is being addressed worldwide by institutions, universities and researchers alike. In August 2024, the United Nations published an impact assessment on harnessing the power of AI for climate change. Increased amounts of funding are available for projects that focus on AI in the environmental field, from the BMUV in Germany to Horizon in Europe.
Setting out frameworks for AI is crucial
With this overview, we have set out to discover how far AI applications can contribute to environmental and climate protection. Two things have become clear. As initial projects show, AI applications certainly have the potential to support sustainable development by contributing to more efficient use of resources, providing us with accurate information about the state of our planet or making forecasts easier. But assuming AI leads to a more sustainable planet is not a foregone conclusion. There are ethical concerns, electricity consumption and the dominance of corporations (which could lead to growing consumerism) to consider.
For AI applications to contribute to environmental and climate protection, suitable framework conditions must be created. This includes, for example, guidelines that keep energy consumption within limits and a political framework that guarantees data protection and transparency. This also includes ensuring that data and AI systems are not controlled by just a few global players.
To drive innovative, sustainable AI developments, it’s important to promote research and young start-ups while creating new interfaces between research communities. Currently, research communities that deal with AI and those that deal with climate and environmental problems work together far too little.
To the question of whether AI can save the world, the answer likely remains, no. AI technology alone will not save the world; other technologies won’t save the world either. But artificial intelligence can make an important contribution—if we use its power for good.
This article was originally published in December 2019. It was updated in April 2022 by Marharyta Biriukova and in August 2024 by Kezia Rice.
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