AI’s double-edged role: Sustainability tool or environmental threat?

The review finds that artificial intelligence has already been deployed in a wide range of sustainability-focused applications with measurable contributions to the SDGs. In energy management, AI enables smart grids, predictive load balancing, and integration of renewable sources, cutting waste and improving efficiency. Similar technologies have been applied to optimize heating, ventilation, and cooling systems in buildings, reducing energy footprints in urban areas.

Artificial intelligence has been widely praised as a game-changing tool in the fight for sustainability, but new research warns of significant hidden costs. A systematic review published in Sustainability analyzes the promises and risks of AI technologies across sectors from agriculture to energy, raising urgent questions about their long-term environmental, economic, and ethical impacts.

Titled “Artificial Intelligence for Sustainability: A Systematic Review and Critical Analysis of AI Applications, Challenges, and Future Directions”, the paper evaluates 138 studies published between 2018 and 2024. It connects AI adoption to the United Nations Sustainable Development Goals (SDGs) while also highlighting the dangers of what the author calls an “AI green paradox,” where technological gains may be overshadowed by growing ecological and social costs.

How is AI advancing sustainability goals?

The review finds that artificial intelligence has already been deployed in a wide range of sustainability-focused applications with measurable contributions to the SDGs. In energy management, AI enables smart grids, predictive load balancing, and integration of renewable sources, cutting waste and improving efficiency. Similar technologies have been applied to optimize heating, ventilation, and cooling systems in buildings, reducing energy footprints in urban areas.

Agriculture is another sector where AI has shown transformative potential. Precision farming techniques use AI-driven sensors and predictive models to reduce reliance on fertilizers and pesticides, improve irrigation practices, and boost crop yields. These improvements not only increase efficiency but also minimize ecological harm in regions where agriculture places heavy pressure on natural resources.

Environmental monitoring has emerged as a third major field of application. AI models track deforestation, predict floods, monitor pollution, and assist in biodiversity conservation, offering early warnings that help policymakers and communities respond more effectively to crises. The circular economy is also benefitting, as robotics and AI-based systems automate waste sorting, enhance material recovery, and support smart city logistics from traffic management to eco-friendly urban planning.

In manufacturing, AI aligns with Industry 4.0 by enabling predictive maintenance, optimizing production lines, and designing products with sustainability criteria in mind. These contributions demonstrate that AI can be an enabler of significant environmental progress when deployed responsibly.

What are the ecological and ethical risks of AI adoption?

The review also brings to light the profound risks tied to the rapid adoption of AI. One of the central challenges is the ecological burden of the technology itself. AI models require massive energy input for training and operation, consuming vast amounts of electricity and water for data centers. This results in high carbon emissions and a growing volume of electronic waste. The study warns of an “AI green paradox” where tools designed to save the planet contribute to its degradation.

The analysis also underscores data and algorithm risks. AI systems frequently suffer from biases, lack of transparency, and the so-called black box problem, where decision-making processes are difficult to interpret or audit. Without high-quality and accessible data, these systems can reinforce inequalities, fail to detect environmental risks accurately, or produce unreliable results.

Socio-economic risks compound the problem. High development and implementation costs restrict AI access to wealthy nations and corporations, raising fears of a widening digital divide. Unequal access means that low- and middle-income countries may be excluded from the sustainability benefits of AI while still facing the global environmental consequences of its energy consumption. Workforce disruption is another pressing concern, as automation threatens traditional jobs without parallel investments in reskilling.

The combination of ecological damage, algorithmic opacity, and unequal access amounts to what the study terms “digital pollution” and “green debt.” If unaddressed, these risks could erode public trust in AI and undermine its contribution to sustainability.

What future directions can ensure responsible AI for sustainability?

The paper offers a series of strategies aimed at turning AI into a tool for sustainable development rather than a source of unintended harm. One of the key recommendations is the advancement of “Green AI,” which focuses on designing energy-efficient algorithms, building renewable-powered data centers, and developing circular economy approaches to recycle AI hardware.

Data governance is highlighted as an essential priority. Policies that ensure fair access to datasets, improve data quality, and mandate transparency in algorithm design are seen as necessary to address bias and accountability. Open-source AI platforms are also proposed as a way to democratize access, allowing smaller firms, governments, and researchers to contribute to sustainability solutions.

The review also calls for education and training programs to create a new generation of specialists who can design and deploy AI responsibly. Building “Green AI engineers” who combine technical expertise with sustainability values is presented as vital for the technology’s long-term future.

Furthermore, regulatory action is called for at both national and international levels. Legal frameworks must enforce transparency, accountability, and ethical oversight to prevent misuse and ensure AI aligns with social and environmental priorities. The author argues that without clear governance, AI risks becoming a driver of ecological harm rather than a solution.