ARTIFICIAL INTELLIGENCE AND ECOLOGY: SYNERGY OF TECHNOLOGY AND SUS-TAINABLE DEVELOPMENT

Norwegian Journal of development of the International Science No 167/2025 197 ARTIFICIAL INTELLIGENCE AND ECOLOGY: SYNERGY OF TECHNOLOGY AND SUSTAINABLE DEVELOPMENT Zheenbekov E. Bachelor in Computer Science in National Louis University (Chicago, Illinois) Orcid: https://orcid.org/0009-0000-9344-9584 https://doi.org/10.5281/zenodo.17492598 Abstract Modern humanity faces a dual challenge: rapid technological progress and a growing environmental crisis. In this context, artificial intelligence (AI) is becoming not only a tool for digital revolution but also a powerful factor in transforming environmental thinking. This article examines the potential of AI for solving environmental problems: environmental monitoring, climate change forecasting, resource management, and energy system optimization. It demonstrates that AI has the potential to become the core of a new sustainable development model based on data, transparency, and predictive regulatory mechanisms. Particular attention is paid to the ethical and environmental risks associated with the high energy consumption of machine learning technologies. The paper analyzes international initiatives to create "green AI" and reveals the importance of synergy between technology, science, and ecology for the transition to a harmonious model of interaction between humans and nature. Keywords: artificial intelligence, ecology, sustainable development, green technologies, digitalization Introduction: The technological evolution of the 21st century has confronted humanity with a paradox: the more sophisticated our technologies become, the more profound our environmental problems become. Climate change, the depletion of natural resources, and the pollution of the oceans and atmosphere can no longer be solved by administrative or economic means alone. New intellectual tools are needed that can analyze, predict, and manage natural processes on a planetary scale [20, pp. 5–9; 10, pp. 452–455]. Artificial intelligence (AI) is emerging as a leading candidate for this role. Its capabilities—from processing large data sets to adaptive learning based on natural patterns—allow for the construction of complex climate models, the prediction of natural disasters, and the management of resources with unprecedented accuracy [9, pp. 220–223; 22, pp. 8–12]. In essence, AI is becoming not just an analytical technology, but a digital intermediary between humans and the biosphere, capable of maintaining ecological balance through algorithmic understanding of natural processes. The emergence of AI in ecology coincided with the transition to a new scientific paradigm—the concept of sustainable development, where the priority is not growth rates, but harmony with nature. Machine learning algorithms are used to monitor pollution, optimize energy networks, predict weather anomalies, and manage agricultural systems [4, pp. 450–454; 5, pp. 943–945]. Artificial intelligence technologies are being integrated into smart city models and create the basis for energy-efficient urban planning, where digital data serves as a tool for ecological balance [24, pp. 103– 110]. However, AI itself is not environmentally neutral. Training neural networks requires colossal energy and computing power, increasing the carbon footprint of data centers [4, pp. 449–452]. This creates a new contradiction: a tool designed to save the planet simultaneously becomes a burden on it. Therefore, alongside technological development, it is necessary to develop a “green AI” ethic focused on reducing energy consumption, using renewable energy sources, and making algorithmic decisions transparent [6, pp. 3514–3521; 25, pp. 12–15]. The purpose of this article is to explore the role of artificial intelligence in shaping an environmentally sustainable future. The research objectives include: analyzing the application of AI in ecology, examining its contribution to combating climate change, and identifying the ethical and energy limitations of its use. In the context of the global challenges of the 21st century, AI is emerging not simply as a technological achievement, but as a new form of environmental awareness, where digital systems become an extension of human intelligence aimed at preserving the planet. It is in this synthesis—science, ecology, and technology—that lies the path to creating a harmonious model of the future, in which intelligence, whether natural or artificial, serves nature rather than subjugates it.  Theoretical foundations of the interaction of artificial intelligence and ecology The modern understanding of artificial intelligence (AI) extends far beyond the boundaries of engineering and computer science. Originally emerging as an attempt to formalize human thinking within algorithmic structures, AI has now become an interdisciplinary field, uniting mathematics, neurophysiology, philosophy, linguistics, cognitive and social sciences [17, pp. 12–16; 18, pp. 25–30]. Within an ecological context, it is viewed not simply as a set of computational methods, but as an intelligent system capable of interacting with natural processes, analyzing the interrelations within the biosphere, and proposing solutions aimed at preserving the sustainability of living systems. This approach brings the concept of artificial intelligence closer to the concept of the noosphere, introduced by V. I. Vernadsky, where the human mind acts as an active geological force transforming the planet. Now this role is gradually being transferred to synthetic intelligence—a digital extension of man [14, pp. 65–69]. 198 Norwegian Journal of development of the International Science No 167/2025 Ecology as a science studies the mechanisms of sustainability and interdependence of systems—from microbial communities to the global biosphere. In this sense, the principles of ecology and the operating principles of AI have structural similarities: both systems are based on self-organization, feedback, learning, and adaptation to external conditions [1, pp. 42–46; 3, pp. 48–50]. The biosphere is a natural information network, and AI can be viewed as an artificial superstructure above it, capable of enhancing the adaptability and predictability of natural processes. Through this prism, the interaction of AI and ecology becomes a form of technological biomimetics, where digital algorithms learn from nature the principles of sustainability, efficiency, and balance. A special place in this system is occupied by the concept of "green technologies," aimed at reducing the environmental impact of production and optimizing resource consumption. Artificial intelligence is becoming a tool for the transition from reactive to proactive management of ecological systems: it analyzes emissions data, predicts pollution, models ecosystem restoration scenarios, and manages energy flows [11, pp. 107–109; 9, pp. 220–225]. For example, neural networks analyzing satellite images are capable of tracking deforestation and glacier dynamics, while machine learning algorithms can predict climate change with an accuracy previously unattainable by classical models [15, pp. 112–115]. These technologies underlie a new wave of sustainable development, where efficiency is measured not by the rate of growth, but by the degree of harmony with nature. However, the interaction of AI and ecology is impossible without ethical and philosophical reflection. The question arises: can a human-created digital intelligence become a guarantor of environmental responsibility if humans themselves are not always capable of this? The ethical dilemma of "autonomous intelligence" manifests itself in the risk that technologies designed to protect nature will begin to subordinate it to the logic of optimization, depriving it of the natural rhythms and randomness necessary for life [13, pp. 5–8; 25, pp. 18– 22]. On the other hand, AI is capable of shaping a new environmental consciousness based on data, objectivity, and empathy for planetary systems. Thus, artificial intelligence in the context of ecology is not simply a technological phenomenon, but a reflection of a profound transformation in thinking. It combines the rationality of science with the ethics of sustainability, creating the foundation for a new form of intelligence—ecological intelligence, in which digital algorithms and biospheric processes begin to act in concert. This not only expands the boundaries of science but also opens the way to the formation of a civilization capable of thinking and acting in harmony with the planet.  Application of artificial intelligence in solving environmental problems Artificial intelligence has become one of the most powerful tools for analyzing, forecasting, and managing environmental processes on a global scale. Its unique ability lies in its ability to process vast volumes of data, identify hidden patterns, and make decisions based on dynamic models. This allows not only to record the consequences of environmental changes but also to predict their development, thereby creating the conditions for a transition from response to prevention of environmental crises [6, pp. 3514–3520; 9, pp. 222– 225]. One of the key areas is environmental monitoring. Neural networks and deep learning systems that analyze satellite images, sensor data, and drone data enable highly accurate monitoring of the atmosphere, soil, and water bodies [15, pp. 112–115]. Remote sensing and machine learning technologies are used to study changes in forest structure, glacier movement, pollutant concentrations, and the rate of desertification [4, pp. 450–454]. Models developed by the European Space Agency and IBM, for example, use AI to automatically detect carbon dioxide emissions and assess the effectiveness of environmental measures. These systems provide continuous climate monitoring in real time, enabling the development of adaptive strategies for the sustainable management of natural resources. AI is also actively used in predicting and preventing natural disasters such as fires, floods, droughts, and hurricanes. Deep learning algorithms can analyze meteorological data, geoinformation maps, and historical patterns to generate accurate forecasts of extreme events [10, pp. 455–458; 22, pp. 11–13]. For example, the FireNet system, based on neural networks, identifies fires from satellite images in seconds, and FloodML models predict the likelihood of flooding using long-term hydrological data. The use of AI in disaster forecasting significantly reduces the response time of civil defense services and reduces human and economic losses. In natural resource management, artificial intelligence is used to optimize water, energy, and land systems. Neural network algorithms can analyze the balance between resource consumption and replenishment, identify areas of inefficient use, and propose rational allocation scenarios [11, pp. 110–114]. In the energy sector, AI regulates the supply and distribution of energy in smart grids, predicts consumption, and increases the share of renewable sources. In water systems, algorithms optimize irrigation, preventing water overuse and soil degradation, and in agricultural technology, they control the dosage of fertilizers and pesticides depending on plant health and weather conditions [5, pp. 944–947]. This integrated approach not only improves the efficiency of economic activity but also reduces human impact on natural ecosystems. The use of AI in waste processing and the construction of "smart" disposal systems is particularly important. Machine vision systems are used on automated sorting lines to recognize and separate materials by type: plastic, glass, metal, and paper [7, pp. 85–88]. Computer algorithms are trained to distinguish even contaminated and mixed waste, which improves the quality of recycling. In "smart cities," AI controls garbage trucks, predicts container filling, and minimizes emissions during waste collection and transportation [24, pp. 103–106]. Such systems, implemented in Singapore and Amsterdam, have reduced logistics costs by up to 25% and CO₂ emissions by 15–20%. Norwegian Journal of development of the International Science No 167/2025 199 An equally significant area is the use of AI for ecosystem modeling and biodiversity conservation. Mathematical models based on machine learning make it possible to predict population behavior, assess the risk of species extinction, and develop ecosystem restoration programs [12, pp. 128–131]. For example, AI helps track animal migration and identify poaching zones using thermal imaging drones and acoustic sensors. WWF and Google Earth Engine projects use algorithms that analyze sound data from tropical forests to identify illegal logging and hunting. These developments contribute to the formation of a global digital biomonitoring network, where every ecosystem process can be recorded and assessed with high accuracy [20, pp. 9– 12]. Taken together, the application of AI in ecology is forming a new strategy for planetary governance based on data and predictive analytics. Artificial intelligence is becoming the core of intelligent systems capable of monitoring climate processes, regulating resource balance, and protecting the biosphere from destruction. This signals humanity's gradual shift from observing nature to consciously cooperating with it, where technology no longer opposes ecosystems but becomes an integral part of them.  The Impact of Artificial Intelligence on Energy and Climate Resilience The use of artificial intelligence (AI) in energy and climate is ushering in a new era of technological interaction between humans and nature. It is becoming an intelligent tool capable of optimizing energy production, reducing carbon emissions, increasing the efficiency of urban systems, and minimizing anthropogenic impacts on ecosystems. Essentially, AI is becoming the link between technological progress and sustainable development, helping to build a "smart energy" based on predictive algorithms and the rational allocation of resources [11, pp. 107–110; 20, pp. 12– 16]. One of the key areas is the application of AI in the energy sector to optimize energy generation, storage, and consumption. Machine learning algorithms analyze data on weather, consumption levels, equipment status, and market trends, which helps improve the efficiency of energy systems and integrate renewable energy sources (RES) such as solar, wind, and hydroelectric power [7, pp. 88–92]. Intelligent models predict peak loads and automatically distribute energy between consumers, minimizing losses and production costs. Examples of such solutions include Google's DeepMind Energy Management system, which reduced data center energy consumption by 40%, and Siemens MindSphere, used to optimize wind farms and solar power plants [5, pp. 944–947]. AI also plays a crucial role in reducing carbon dioxide (CO₂) emissions. Intelligent industrial and transport process control systems analyze production cycles, identify inefficient areas, and propose emission reduction scenarios [10, pp. 456–458; 22, pp. 10–14]. In the metallurgy and chemical industries, machine learning algorithms regulate reaction temperatures and pressures, ensuring energy savings. In the transportation industry, AI is used to optimize routes, predict traffic jams, regulate traffic lights, and manage electric vehicle networks, which can significantly reduce the carbon footprint of cities [8, pp. 215–218]. The concept of "smart cities" embodies the synthesis of technology and ecology. In such cities, AI is integrated into transport, lighting, water supply, and waste management systems, transforming the urban environment into a self-learning ecosystem [24, pp. 103– 110]. Projects have already been implemented in Singapore, Helsinki, and Dubai where algorithms analyze traffic patterns, air pollution levels, and building temperatures, automatically regulating ventilation, energy supply, and lighting. These technologies reduce energy consumption by 20–30% and improve the quality of the urban environment [8, pp. 217–221]. AI enables a balance between technological growth and environmental sustainability, transforming cities from sources of pollution into active elements of climate regeneration. In agriculture, artificial intelligence is being used to reduce the impact on ecosystems and improve the sustainability of agricultural production. Machine vision systems determine plant health, analyze soil moisture, temperature, and nutrient levels, allowing for precise application of water, fertilizers, and pesticides [5, pp. 944–947]. This minimizes the chemical load on the environment and reduces resource consumption. In Japan and the Netherlands, autonomous farms are being implemented, where AI controls greenhouses, lighting, and microclimate, ensuring high yields with a minimal carbon footprint [9, pp. 224–226]. Thus, digital farming is becoming part of a global "smart ecology" strategy, where precision replaces redundancy, and data becomes a tool for maintaining natural balance. However, along with these environmental benefits comes a new challenge: the balance between digitalization and the carbon footprint of AI technologies themselves. Training large neural networks requires enormous amounts of computation, which is accompanied by colossal energy consumption for data centers [4, pp. 448–452]. According to estimates from the University of Massachusetts Amherst, training a single GPT-type model can emit as much CO₂ as the lifetime of five cars. This raises the question of developing a Green AI approach, which aims to reduce energy costs by optimizing algorithm architecture, using energy-efficient processors, and using renewable energy sources [6, pp. 3514–3518]. Thus, the impact of artificial intelligence on energy and climate sustainability is twofold. On the one hand, AI opens the way to an energy-efficient and environmentally friendly future, where every system is regulated by accurate data. On the other hand, it requires a critical rethinking of our own energy costs and the development of an ethical model for technological growth. Only in this balance—between digital progress and environmental responsibility—can artificial intelligence become not a burden, but a tool for harmonizing energy and climate, forming the foundation for a sustainable civilization of the future.  Problems and risks of using artificial intelligence in ecology 200 Norwegian Journal of development of the International Science No 167/2025 Despite the enormous potential of artificial intelligence (AI) for sustainable development, its application is fraught with numerous contradictions and limitations. While AI is designed to help humanity overcome the environmental crisis, the technologies that underpin it are themselves becoming the source of new challenges—energetic, ethical, social, and philosophical. These risks require a comprehensive understanding, as their resolution will determine whether AI can become a tool for ecological balance or become yet another mechanism for exacerbating global inequalities and human-induced pressure on the planet [19, pp. 132–136; 25, pp. 18–22]. One of the most pressing issues is the high energy consumption and carbon footprint of data centers that power machine learning systems. Modern artificial intelligence models—especially those based on deep neural networks—require colossal computing resources. Researchers at the University of Massachusetts Amherst estimate that training a single GPT model emits up to 300 tons of CO₂, which is comparable to operating dozens of cars throughout their entire life cycle [4, pp. 450–453]. The data centers where these models are trained consume billions of kilowatt-hours of electricity annually, often from non-renewable sources. As a result, artificial intelligence, designed to combat climate change, is itself becoming an additional factor in global warming. A solution may lie in the implementation of the Green AI concept—the development of energy-efficient architectures, quantum computing, and the use of renewable energy in data center infrastructure [6, pp. 3516–3521]. No less serious are the ethical and legal dilemmas arising from the use of autonomous systems without human intervention. In environmental projects, AI is increasingly gaining the ability to make decisions independently—for example, to regulate emissions, redistribute resources, or operate robotic installations at mining and processing facilities [13, pp. 7–10]. However, the question of liability for possible errors or unpredictable consequences remains unresolved. Who bears legal and moral responsibility for damage caused to a natural system as a result of an incorrect algorithm—the developer, the operator, or the state? The lack of international regulatory standards makes these issues particularly pressing. Therefore, the United Nations and UNESCO insist on the need to develop a code of ethics for autonomous systems designed to interact with the biosphere [25, pp. 15–17]. The next challenge relates to digital inequality and the limited availability of "green" technologies in developing countries. AI development requires significant investment, access to computing power, trained specialists, and communications infrastructure, which is unavailable to most countries in the Global South [20, pp. 17–19]. As a result, a new type of environmental inequality is emerging—a technological one. Countries with advanced AI systems gain the ability to manage natural processes, while others become dependent on external digital solutions. This situation threatens to undermine the principles of global environmental justice and could lead to technological colonialism, where control over environmental data becomes a form of power. At the same time, the risk of relying on artificial intelligence in strategic decision-making is increasing. Algorithms are increasingly used to assess natural risks, design infrastructure, plan land use, and even formulate climate policy [12, pp. 128–130]. However, absolute reliance on algorithms can lead to the loss of the human capacity for critical analysis. AI models reality based on data, but data is always limited and subject to distortion. The danger is that an environmental strategy based solely on algorithmic forecasts may ignore the social and cultural aspects of the interaction between humans and nature. Therefore, it is necessary to maintain the principle of human-centeredness—AI should assist humans in decision-making, not replace them in areas of moral responsibility [19, pp. 140–142]. Against this backdrop of challenges, a fundamental contradiction arises between technological growth and the limitations of the biosphere. Human history shows that each new technological wave first creates the illusion of control over nature and then increases dependence on it. Artificial intelligence is no exception: the more it automates processes, the more difficult it becomes to manage its consequences. The widespread adoption of digital systems leads to increased consumption of rare earth metals, electricity, and water for server cooling [11, pp. 110–113]. There is a danger that "ecological AI" will become a tool for hypertechnological growth, contrary to the principles of sustainability. Nevertheless, recognizing these risks is the first step to overcoming them. The future of artificial intelligence in ecology must be built on the principles of ethical digital responsibility, energy efficiency, and social justice. Only with strict regulation, international cooperation, and the inclusion of philosophical and ethical dimensions in technology policy will AI be able to fulfill its original mission—not to subjugate nature, but to contribute to its preservation.  Prospects and development directions of "green AI" The current stage of artificial intelligence (AI) technology development is characterized by a gradual transition from the quantitative growth of computing power to a qualitative rethinking of its environmental efficiency. A new scientific and technological paradigm is emerging—Green AI—which is focused on reducing energy consumption, increasing the resilience of algorithms, and integrating AI into the processes of environmentally responsible planetary system management [6, pp. 3514–3518; 20, pp. 14–17]. In a context where digital infrastructure is already comparable in energy consumption to the aviation industry, the development of energy-efficient algorithms is becoming not only a technical but also an ethical priority. The core idea of the Green AI concept is to create algorithms that require minimal computing resources while maintaining high accuracy and functionality. Reducing data volumes, applying hybrid architectures, utilizing energy-efficient processors, and transitioning to distributed learning systems make it possible to re- Norwegian Journal of development of the International Science No 167/2025 201 duce the carbon footprint of AI without sacrificing performance [4, pp. 448–452; 11, pp. 118–120]. Major technology companies—Google, IBM, and Microsoft—are already implementing automatic server cooling systems powered by renewable energy sources, while research centers are developing TinyML technologies, which train models directly on low-power devices. Thus, Green AI is becoming not just a scientific field, but a norm of future technological ethics aimed at minimizing the environmental footprint of digitalization. One of the most promising approaches is the use of biomimetic principles, where artificial intelligence algorithms are inspired by the mechanisms of natural systems. In nature, self-organization, adaptation, and energy efficiency are fundamental laws of survival. These same principles underlie the development of neural architectures capable of learning with minimal expenditure of energy and computational resources [15, pp. 114–117; 12, pp. 130–132]. For example, evolutionary algorithms that mimic the process of natural selection allow AI models to select optimal solutions in a manner analogous to biological evolution. Such approaches form the field of neuroecological design, where artificial systems adopt nature's capacity for resilience and balance. The development of "green AI" is impossible without the support of international organizations. Large-scale global initiatives are currently underway aimed at integrating artificial intelligence into sustainable development strategies. The UNESCO Recommendation on the Ethics of Artificial Intelligence (2021) defines the principles of "ecologically responsible AI," emphasizing the importance of energy efficiency, equitable access, and protection of the biosphere [25, pp. 12–15]. The United Nations Environment Programme (UNEP) is implementing the AI for the Planet project, which uses AI to analyze climate data, manage resources, and prevent natural disasters [20, pp. 8–10]. The European Green Deal emphasizes the need to combine digitalization and decarbonization, requiring each algorithm to be environmentally certified for its energy consumption and emissions. These initiatives point to the emergence of a global standard for ecological intelligence, where the effectiveness of technology is assessed not only by speed but also by sustainability. The synergy between AI and renewable energy will play a special role in the future. Intelligent control systems are already optimizing the operation of solar and wind power plants, regulating energy supply based on weather conditions and peak loads [7, pp. 89–91]. Algorithms for forecasting supply and demand in the energy sector help minimize losses and stabilize power grids, reducing dependence on hydrocarbons. The combination of AI with energy storage technologies, smart grids, and distributed power sources creates the foundation for a next-generation energy ecosystem, where digital intelligence becomes the guarantor of ecological balance. However, the ultimate goal of developing "green AI" goes beyond technical efficiency. It is linked to the formation of a nooecological society—a model in which intelligence, nature, and culture coexist in a state of mutual equilibrium. In this paradigm, AI is viewed not as a tool for dominating nature, but as a mediator capable of coordinating human activity within the constraints of the biosphere [14, pp. 68–70; 21, pp. 154– 158]. The nooecological approach unites the rationality of science, the moral compass of philosophy, and the artistic perception of the world, creating the foundation for a new type of civilization—a civilization of symbiotic intelligence. In the future, the development of "green AI" will become an integral part of the global sustainable development strategy. The transition from exploitative technologies to life-sustaining ones will allow humanity to move beyond the "human versus nature" paradigm and toward a "human with nature" model. Artificial intelligence, inspired by the laws of the biosphere, could become a tool that, rather than destroying the world, helps it maintain balance—quietly, precisely, and intelligently. Conclusion The modern development of artificial intelligence marks not just a technological revolution, but the beginning of a new ecological era, in which intelligence— both natural and artificial—becomes the primary tool for preserving the planet. AI, integrated into the ecological system, is capable not only of analyzing and predicting processes but also of shaping a new type of thinking, where scientific rationality is combined with the ethics of sustainability and respect for the biosphere. The areas discussed in this article—from environmental monitoring to energy system optimization and the creation of smart cities—show that artificial intelligence is becoming the heart of the technological transition to sustainable development. Its potential lies in its ability to see the world as a single network of interconnected systems, in which every mistake has global consequences, and every decision can change the environmental future. However, along with these opportunities, AI also brings new risks: high energy costs, ethical dilemmas, and the threat of technological inequality. These challenges are forcing humanity to rethink the very concept of progress. In this context, the concept of Green AI is becoming not just a research area, but a moral compass for a digital civilization striving to minimize its carbon footprint and harmonize its interactions with nature. The prospects for the development of "green AI" are linked not so much to the improvement of computing technologies as to the formation of a nooecological society—a synthesis of intelligence, culture, and nature. Artificial intelligence should not be above nature, but at its service, becoming a mediator between the biosphere and the technosphere, a tool for the conscious management of planetary processes. The final point of this article is this: humanity stands on the threshold of a new paradigm—a paradigm of intelligent coexistence with the Earth, where artificial intelligence becomes a partner with nature. Its true mission is not to dominate, but to understand; not to 202 Norwegian Journal of development of the International Science No 167/2025 consume, but to preserve. And it is in this understanding that hope for a sustainable, harmonious, and meaningful future for the planet is born. References: 1. Agaev, R. M. Artificial Intelligence and Sustainable Development: Environmental Challenges and Solutions. Moscow: Nauka, 2022. 184 p. 2. Alyoshina, I. A., & Titova, E. V. Green Technologies and Digitalization in the Management of Natural Systems. Saint Petersburg: Polytechnic University Press, 2021. 198 p. 3. Barinova, N. V., & Kozlov, I. Yu. Environmental Safety and the Role of Artificial Intelligence in Reducing Technogenic Risks. Economics and Ecology of Territories, 2020, No. 3, pp. 45–54. 4. Belkhir, L., & Elmeligi, A. Assessing ICT Global Emissions Footprint: Trends to 2040 and Recommendations. Journal of Cleaner Production, 2018, Vol. 177, pp. 448–463. 5. Benke, K., & Tomkins, B. Future Food-Production Systems: Smart Farming and AI. Food Security, 2017, Vol. 9(5), pp. 941–947. 6. Bhatia, N., & Jain, S. Artificial Intelligence in Sustainable Development: A Review. Sustainability, 2022, Vol. 14(6), pp. 3514–3528. 7. Bobrov, A. G. Artificial Intelligence and Energy: Problems of Environmental Optimization. Yekaterinburg: Ural Federal University Press, 2021. 152 p. 8. Calzada, I. (ed.). Smart City Citizenship: The Futurability of Society in the AI Era. London: Elsevier, 2021. 256 p. 9. Choudhary, A., & Kumar, S. Artificial Intelligence for Environmental Sustainability. Environmental Science and Policy, 2021, Vol. 127, pp. 220–231. 10. Clark, T., & Fox, J. AI and Climate Change: How Artificial Intelligence Can Help Tackle the Environmental Crisis. Nature Climate Change, 2022, Vol. 12, pp. 452–460. 11. D’Amato, D., Fores, S., & Droste, N. Green Growth and AI: Interlinkages between Environmental Policies and Smart Systems. Ecological Economics, 2020, Vol. 169, pp. 106–121. 12. Goodall, N. Machine Learning and Environmental Policy: Opportunities and Challenges. Environmental Modelling & Software, 2021, Vol. 143, p. 105134. 13. IEEE Global Initiative. Ethically Aligned Design: A Vision for Prioritizing Human Well-Being with Autonomous and Intelligent Systems. New York: IEEE, 2020. 210 p. 14. Kiselev, S. A., & Platonova, Yu. M. Artificial Intelligence and the Ecology of Consciousness: From the Technosphere to the Noosphere. Moscow: Bauman MSTU Press, 2020. 176 p. 15. Li, D., & Xu, J. AI and Environmental Monitoring: Integrating Big Data and Satellite Systems. Remote Sensing of Environment, 2021, Vol. 255, p. 112137. 16. Linardatos, P., Papastefanopoulos, V., & Kotsiantis, S. Explainable AI: Balancing Accuracy and Transparency in Climate Modeling. Applied Sciences, 2022, Vol. 12(2), pp. 631–639. 17. McCarthy, J. What Is Artificial Intelligence? Stanford: Stanford University Press, 2018. 104 p. 18. Nilsson, N. J. Artificial Intelligence: A New Synthesis. San Francisco: Morgan Kaufmann, 2019. 513 p. 19. O’Neill, C. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. New York: Crown Publishing, 2016. 272 p. 20. United Nations Organization. Artificial Intelligence for the Planet: Harnessing AI for the Earth. Geneva: UNEP, 2019. 94 p. 21. Rakitina, E. P. Environmental Ethics in the Age of Artificial Intelligence. Novosibirsk: SibAK, 2022. 160 p. 22. Rolnick, D., Donti, P., Kaack, L., et al. Tackling Climate Change with Machine Learning. arXiv preprint arXiv:1906.05433. Cambridge (MA): MIT Press, 2020. 56 p. 23. Sedykh, V. I. Artificial Intelligence in Natural Resource Management. Moscow: Infra-M, 2021. 228 p. 24. Thompson, S., & Brierley, E. Artificial Intelligence for Sustainable Cities. Cities, 2021, Vol. 114, p. 103132. 25. UNESCO. Recommendation on the Ethics of Artificial Intelligence. Paris: UNESCO Publishing, 2021. 66 p.