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Course:CONS200/2025WT2/Impact of Artificial Intelligence on Forests and Environmental Conservation: Opportunities and Challenges

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Introduction to AI Forest and Environmental Conservation

The Promise of AI in Environmental Conservation

Artificial Intelligence (AI) has emerged as a powerful environmental conservation tool, offering both promising solutions and significant challenges. AI enables real-time monitoring of ecosystems through advanced computational models and data processing techniques. Using these methods, AI can better manage forests by predicting wildfire risk, improving inventory processes, and promoting sustainable land-use practices[1], [2]. Machine learning algorithms powered by AI can analyze sizeable amounts of environmental data and be applied to identify illegal wildlife trade and monitor species populations[2]. This up-to-the-minute data can provide invaluable information that may influence environmentalists' and policymakers' decisions. Some of the live data AI provides allows for insights into climate-related impacts and optimization of conservation strategies[3]. Some of these favourable factors may look like the green light for AI usage regarding environmental applications. However, there are some concerns with the ethics and ability behind such a complex issue.

AI data centres like this draw large amounts of power to supply their computers and machines with

Challenges and Ethical Concerns with AI-Driven Conservation

AI seemingly provides the answers to environmental concerns. However, AI's sustainability has been questioned due to high energy consumption levels. This high energy consumption produces a large carbon

footprint[4]. Moreover, the lack of awareness of carbon emissions leads to biases in data collection, reinforcing ecological mismanagement and further building on existing inequalities[5]. The use of AI raises ethical concerns that can result in unintended consequences of AI-driven automation and the marginalization of local communities, which further exacerbate AI's role in conservation[6],[7]. While AI has the potential to remodel environmental protection, its integration must be accompanied by transparent governance, ethical considerations, and interdisciplinary collaboration to minimize risks and maximize benefits.

The Historical Escalation of Environmental Degradation

Past Harm and Future Hope

The greater usage of AI looks to solve a problem that stems much further than present times. Environmental degradation is an ongoing issue that has been plaguing human civilization perpetually. Historically, environmental degradation dates back to the earliest human civilizations but has significantly accelerated since the Industrial Revolution[8]. Early societies altered landscapes through deforestation, agriculture, and resource extraction, often leading to localized ecological collapse[9]. This was a precursor to a lifetime of environmental degradation for humans. The scale and intensity of environmental degradation expanded with industrialization in the 18th and 19th centuries[10]. The widespread use of fossil fuels, urbanization, and mass production led to deforestation, air and water pollution, and biodiversity loss on an unprecedented level[11][12]. Through these tragedies of the past, AI-driven conservation places itself in the centre of all these issues, culminating information in efforts to mitigate such severe environmental degradation in the

Fossil fuel usage is common across the world.

future.

In the 20th and 21st centuries, rapid industrial growth, population expansion, and consumer-driven economies have been some of the most prominent contributors to environmental damage. Issues such as climate change, ocean acidification, habitat destruction, and plastic pollution have become worldwide issues, threatening ecosystems and Earth's life support systems[13][14]. Despite efforts globally to minimize human impact on Earth's life support systems, damage continues, underscoring the urgent need for stronger environmental policies and global cooperation. This highlights the importance of considering options like AI as a tool to combat environmental degradation. Much consideration is still needed to ensure that AI usage will not exacerbate current environmental issues.

Current status of AI and Applications in Environmental Conservation

Biodiversity Monitoring and Ecosystem Analysis

AI utilization for species identification

The integration of artificial intelligence (AI) into environmental conservation has significantly advanced efforts to monitor, protect, and manage ecosystems. AI technologies, such as machine learning, computer vision, and predictive modelling, have enhanced biodiversity monitoring, habitat protection, and wildlife conservation strategies. By analyzing vast datasets from satellites, drones, and camera traps, AI enables real-time species identification, threat detection, and ecosystem analysis[15]​. These technologies allow conservationists to detect subtle changes in ecosystems that would be difficult to observe manually.

Wildlife Conservation and Anti-Poaching Efforts

One major application is wildlife conservation, where AI-driven models assist in identifying species and detecting poaching activities. Platforms such as Conservation AI leverage computer vision techniques to classify animals and assess threats based on images collected from camera traps and drones[15]. Similarly, AI-powered monitoring systems have been implemented in forests to detect illegal logging and track biodiversity changes through satellite imagery[1].

Marine and Climate Applications

In marine ecosystems, AI contributes to ocean conservation by monitoring water quality, tracking marine biodiversity, and identifying pollution patterns through automated data analysis[16]. Additionally, AI-based climate models predict environmental changes, enabling conservationists to develop adaptive strategies to mitigate the effects of climate change[17].

Ethical and Logistical Considerations

Despite its potential, the use of AI in conservation raises ethical and logistical challenges. Concerns have been raised regarding AI-driven surveillance technologies, which, while beneficial for monitoring biodiversity, may inadvertently impact indigenous communities and land rights[6]. The sustainability of AI applications is also debated, as they require large computational resources, which can contribute to energy consumption and emissions[5].

Potential benefits

Non-Invasive and Scalable Monitoring

One of AI’s greatest strengths is its ability to support non-invasive observation, allowing researchers to monitor species and ecosystems without direct interference[15]. This reduces stress on wildlife and allows for long-term data collection in remote or sensitive areas, where human presence might otherwise disrupt natural behavior. Camera traps, drones, and acoustic sensors (powered by AI image or sound recognition algorithms) can operate continuously and autonomously, offering extensive and detailed biodiversity data without human presence and intrusion[18]​. As a result, AI enables conservationists to collect data more frequently, while reducing the interference with natural ecosystems.

Predictive and Analytical Power

A significant impact of AI is its ability to process large datasets at unprecedented speeds, which is particularly useful in species identification and biodiversity assessments. AI-based tools have enabled scientists to conduct rapid evaluations of tree species conservation status, significantly expanding the scope of biodiversity research[3]. In forest management, AI facilitates the detection of deforestation patterns, improving the efficiency of conservation policies[1]. AI also enhances predictive analytics, which helps conservationists anticipate threats such as habitat destruction, illegal poaching, or climate-related ecosystem changes. Tools powered by AI can analyze large datasets quickly, enabling accurate biodiversity assessments, and by identifying trends and patterns across time and space, AI systems help researchers prioritize the most vulnerable species and ecosystems for intervention[3].

Acceleration of Species Risk Assessment and Red Listing

IUCN red list (Darker shades) vs automated assessment (lighter shades) of possibly threatened and not threatened tree species in different biomes.

Utilizing AI's strong predictive and analytical power, it has been used to estimate conservation status for thousands of plant and animal species, often much faster than traditional expert-led assessments[3]. By quickly evaluating large datasets, including species occurrence, habitat loss, climate projections, and ecological traits, AI can be used to determine the likelihood of extinction risk in a timely manner[19] This rapid capability is critical for prioritizing conservation actions, particularly for lesser-known species that are classified as data-deficient (DD) by the International Union for Conservation of Nature (IUCN) and species that are in imminent danger of going extinct[18]​. As more species can be assessed efficiently, global efforts like the IUCN Red List can be updated more frequently, allowing for more timely interventions.

Policy and Resource Optimization

By identifying trends in real time, AI supports better resource allocation and policy design. Conservation organizations can prioritize action based on immediate, data-informed needs[20]. For example, AI can help determine where anti-poaching patrols should be deployed or which regions require urgent restoration efforts, based on dynamic environmental data. This improves operational efficiency and ensures limited resources are directed where they can have the greatest ecological and economic impact[21]. Moreover, AI-generated insights can inform policy by providing strong, data-supported arguments for conservation legislation or protected area designations.

5. Risks and Challenges of AI in Environmental Conservation

Increasing Energy Consumption and Carbon Footprint

One of the risks that AI carries can be found in the increasing amounts of energy consumption and enlarged carbon footprint it leaves behind. As the use of AI requires IT resources, these facilities require a great amount of energy consumption to remain functional. That being said, the true relationship between AI and energy consumption likely lies within the industrial and technological structure of each region[22]. As a growing carbon footprint goes hand in hand with increases in energy consumption, the potential for a damaging carbon footprint also lies in the way in which AI tools are used. It is essential to use these tools in a way that their carbon footprint does not diminish the benefits that come from the usage of AI[23]. Essentially, though the usage of AI may seem to increase energy consumption and create a more damaging carbon footprint, it comes down to the ways in which these tools are used in order to remain sustainable and reliable.

Ethical and Socioeconomic Implications

Besides the potential increases in energy consumption and a larger carbon footprint, AI also brings the risks of ethical and socioeconomic implications. With the efficiency that accompanies AI also comes the responsibility of using it ethically. In order to combat potential ethical implications, it is important for conservation organizations and researchers to be transparent when reporting their usage of AI technology[24]. By remaining truthful and integrous, the vast amounts of data, reports, and systems can be maintained in a more ethical manner. Additionally, the usage of AI touches on topics of socioeconomic relations like labor markets and policies. As AI becomes more prominent and machines are able to do increasingly more complex jobs, it is important to tread carefully and observe the potential patterns of consumption hidden behind the usage of AI[24]. Moreover, the applications of policies play a key role in maintaining AI as a sustainable and efficient tool towards conservation.

Technological Limitations and Biases of AI Technology

It is also important to take note of the technological limitations and biases of AI technology. Putting aside the primary limitation of energy consumption, AI can also be inaccurate at times, making it difficult to implement in the field of conservation. With this in mind, it is crucial to acknowledge the limits of AI technology and work with it within those boundaries to make it truly useful. Similarly, the biases, risks, and consequences that AI and those who use it may carry must also be taken into consideration[24].

Colonialism and Traditional Values

Another aspect of the risks and challenges of AI in conservation is the subject of colonialism and traditional values. One example of colonialism through AI is if data recorded from the Global South is primarily sent to the Global North for the purpose of AI models[25]. It is important to be ware of AI colonialism while upholding the traditional values that different regions maintain. In doing so, we minimize the risk of enforcing colonialist practices and can maintain a functional and beneficial use for AI in conservation.

Prioritizing Technology Over Technique

Finally, there are the risks of over-prioritizing technology over technique. With AI comes the challenge of ensuring the programs and systems built from it remain consistent and coherent. The efficiency that comes with AI could lead to disregard for actual techniques when combating conservation issues. For example, AI could generate summaries that don’t take into account the specific local nature of conservation problems[24]. It is also hard to properly measure the full range and effects of AI on conservation problems, so it is important to retain proper technique when applying this technology, prioritizing proper technique over technological speed and efficiency.

Path Forward: Potential Solutions and Recommendations

Ethical Considerations and Responsible AI

The integration of AI in conservation must be governed by strong ethical guidelines to ensure that its benefits are realized without reinforcing existing inequalities or environmental harm. A major ethical concern is the potential for AI to induce biases, particularly if the data used to train AI models is incomplete or unrepresentative of local ecosystems [6]. This bias can lead to misallocation of resources or misguided conservation efforts that neglect vulnerable communities or ecosystems. Therefore, ethical AI in conservation must prioritize inclusivity, transparency, and fairness in both data collection and decision-making processes. Furthermore, AI should not be viewed as a replacement for traditional knowledge or human expertise; instead, it should complement and support local conservation practices[5]. To foster responsible AI use, ethical frameworks and regulatory policies need to be established at the national and international levels to prevent misuse and ensure AI applications contribute positively to conservation outcomes.

Capacity Building and Stakeholder Engagement

For AI to be effectively integrated into conservation efforts, it is important to increase the capacity of local organizations, governments, and communities. Capacity building should focus on training conservation professionals in AI technologies, data management, and analytical techniques, enabling them to use these tools to their full potential [20]. Equally important is the engagement of stakeholders, including local communities, policymakers, and indigenous groups, who must be involved in the design and implementation of AI-powered conservation initiatives. This ensures that AI tools are adapted to local needs and that the conservation strategies implemented are culturally appropriate and socially beneficial[2]. Additionally, stakeholder engagement helps prevent conflicts that may arise from the imposition of AI solutions that overlook local context or priorities. Collaborative partnerships between governments, NGOs, and the private sector will be essential to foster a collective approach to AI-driven conservation.

Funding and Investment Strategies

Effective AI-driven conservation efforts require significant financial investment, particularly in developing regions where infrastructure and technology adoption may be limited. Funding and investment strategies should focus on creating sustainable models that support long-term AI integration into conservation programs. Governments, international organizations, and private investors should collaborate to provide funding for research, technology development, and capacity-building initiatives[17]. Public-private partnerships could be particularly effective in scaling AI applications while ensuring alignment with environmental and social sustainability goals. Investment strategies should also prioritize the development of low-cost AI solutions that are accessible to conservation organizations in lower-income regions[3]. To maximize the impact of AI on conservation, it is important to not only direct funding toward technological development but also ensure that there is sufficient investment in education, training, and community engagement to support these technologies.

Conclusion: Navigating the Opportunities and Challenges of AI in Conservation

The Good

AI presents great potential in addressing some of society's most pressing environmental challenges. AI's capacity to process large datasets, audit biodiversity, predict ecological threats, and streamline conservation strategies has proved largely beneficial in forests, marine, and wildlife systems[3]. Through a 'hands-off' monitoring approach and quick analytical capabilities, AI has broadened its ability to assist conservation efforts.

Areas of AI That Require More Consideration

This promising future of AI is also paired with complex challenges. AI's overall environmental cost is large due to its high-energy infrastructure, with additional ethical concerns surrounding data governance, bias, and the marginalization of communities that cannot be overlooked[6]. Additionally, placing too much emphasis on AI risks over-prioritizing automation over local knowledge or ecological nuance highlights the need for cautious usage of AI in environmental conservation[7].

AI in the Future

To fully utilize AI for environmental conservation purposes, its implementation should include considerations like sustainability, equity, and collaboration. Transparency between data practices and policymakers' uses for the data will be crucial in ensuring that the benefits of using AI do not come at the cost of the ecosystems being protected. As society continues to face environmental degradation issues, AI should be used as a tool alongside current conservation strategies to make informed, ethical, and innovative solutions to humanity's dynamic problems.

References

Please use the Wikipedia reference style. Provide a citation for every sentence, statement, thought, or bit of data not your own, giving the author, year, AND page. For dictionary references for English-language terms, I strongly recommend you use the Oxford English Dictionary. You can reference foreign-language sources but please also provide translations into English in the reference list.

Note: Before writing your wiki article on the UBC Wiki, it may be helpful to review the tips in Wikipedia: Writing better articles.[26]

  1. ↑ 1.0 1.1 1.2 Raihan, Asif (2023). "Artificial intelligence and machine learning applications in forest management and biodiversity conservation". Natural Resources Conservation and Research. 6.
  2. ↑ 2.0 2.1 2.2 Shivaprakash, Kadukothanahally Nagaraju; Swami, Niraj; Mysorekar, Sagar; Arora, Roshni; Gangadharan, Aditya; Vohra, Karishma; Jadeyegowda, Madegowda; Kiesecker, Joseph M (2022). "Potential for artificial intelligence (AI) and machine learning (ML) applications in biodiversity conservation, managing forests, and related services in India". Sustainability. 14: 7154 – via MDPI.
  3. ↑ 3.0 3.1 3.2 3.3 3.4 3.5 Silva, Sandro Valerio; Andermann, Tobias; Zizka, Alexander; Kozlowski, Gregor; Silvestro, Daniele (April 28, 2022). "Global Estimation and Mapping of the Conservation Status of Tree Species Using Artificial Intelligence". Frontiers in Plant Science. 13 – via Frontiers.
  4. ↑ Dwivedi, Yogesh K.; Hughes, Laurie; Kar, Arpan Kumar; Baaabdullah, Abdullah M; Grover, Purva; Abbas, Roba; Andreini, Daniela; Abumoghli, Iyad; Wade, Michael (April 2022). "Climate change and COP26: Are Digital Technologies and information management part of the problem or the solution? an editorial reflection and call to action". International Journal of Information Management. 63: 102456 – via Science Direct.
  5. ↑ 5.0 5.1 5.2 Kopka, Alexander; Grashof, Nils (February, 2022). "Artificial intelligence: Catalyst or barrier on the path to sustainability?". Technological Forecasting and Social Change. 175 – via Elsevier Science Direct. Check date values in: |date= (help)
  6. ↑ 6.0 6.1 6.2 6.3 Parris-Piper, Naomi; Dressler, Wolfram H.; Satizábal, Paula; Fletcher, Robert (February, 2023). "Automating violence? The anti-politics of 'smart technology' in biodiversity conservation". Biological Conservation. 278 – via Elsevier Science Direct. Check date values in: |date= (help)
  7. ↑ 7.0 7.1 Sandbrook, Chris (December 2024). "Beyond the Hype: Navigating the Conservation Implications of Artificial Intelligence". Conservation Letters. 18 – via Society for Conservation Biology.
  8. ↑ Ahuti, Singh (December 2015). "Industrial Growth and Environmental Degradation" (PDF). International Education and Research Journal. 1: 5–7 – via Environmental Science.
  9. ↑ Sanders, William T.; Webster, David (August 1994). "Preindustrial man and Environmental Degradation". Biodiversity and landscapes: a paradox of humanity: 77–104.
  10. ↑ Jonsson, Fredrik Albruttion (September 2012). "The Industrial Revolution in the Anthropocene". The Journal of Modern History. 84: 679–696 – via JSTOR.
  11. ↑ Rafindadi, Abdulkadir Abdulrashid; Yusof, Zarinah; Zaman, Khalid; Kyophilvong, Phouphet; Akhmat, Ghulam (June 2014). "The relationship between air pollution, fossil fuel energy consumption, and water resources in the panel of selected Asia-Pacific countries". Environmental Science and Pollution Research International. 21: 11395–11400 – via PubMed.
  12. ↑ Rastandeh, Amin; Jarchow, Meghann (July 2020). "Urbanization and biodiversity loss in the post-COVID-19 era: complex challenges and possible solutions". Cities and Health. 5: 37–40.
  13. ↑ Hall-Spencer, Jason M.; Harvey, Ben P. (April 2019). "Ocean Acidification Impacts on Coastal Ecosystem Services due to Habitat Destruction". Emerging Topics in Life Sciences. 3: 197–206 – via PubMed.
  14. ↑ Warner, K.; Hamza, M.; Oliver-Smith, A.; Renaud, F.; Julca, A. (2010). "Climate change, environmental degradation, and migration". Natural Hazards. 55: 689–715.
  15. ↑ 15.0 15.1 15.2 Fergus, Paul; Chalmers, Carl; Longmore, Steven; Wich, Serge (November 11, 2024). "Harnessing Artificial Intelligence for Wildlife Conservation". Conservation. 4 – via MDPI.
  16. ↑ Isabelle, Diane A.; Westerlund, Mika (February 9, 2022). "A Review and Categorization of Artificial Intelligence-Based Opportunities in Wildlife, Ocean and Land Conservation". Sustainability. 14 – via MDPI.
  17. ↑ 17.0 17.1 Adanma, Uwaga Monica; Ogunbiyi, Emmanuel Olurotimi (May 21, 2025). "Artificial intelligence in environmental conservation: evaluating cyber risks and opportunities for sustainable practices". Computer Science & IT Research Journal. 5 – via Computer Science & IT Research Journal.
  18. ↑ 18.0 18.1 Ditria, Ellen M.; Buelow, Christina A.; Gonzalez-Rivero, Manuel; Connolly, Rod M. (July, 2022). "Artificial intelligence and automated monitoring for assisting conservation of marine ecosystems: A perspective". Frontiers in Marine Science. 9 – via Frontiers. Check date values in: |date= (help)
  19. ↑ Bachman, Steven P.; Brown, Matilda J. M.; Leão, Tarciso C. C.; Lughadha, Eimear Nic; Walker, Barnaby E. (March 4, 2024). "Extinction risk predictions for the world's flowering plants to support their conservation". New Phytologist. 242 – via New Phytologist Foundation.
  20. ↑ 20.0 20.1 Ullah, Fazal; Saqib, Saddam; Xiong, You-Cai (November 25, 2024). "Integrating artificial intelligence in biodiversity conservation: bridging classical and modern approaches". Biodiversity and Conservation. 34: 45–65 – via Spring Nature Link.
  21. ↑ DeSantis, Nicole; Supples, Christina; Philips, Lea; Pigot, Julien; Ervin, Jamison; Wade, Toby (June, 2025). "Leveraging AI for enhanced alignment of national biodiversity targets with the global biodiversity goals". Nature-Based Solutions. 7 – via Elsevier Science Direct. Check date values in: |date= (help)
  22. ↑ Kopka, Alexander; Grashof, Nils (February, 2022). "Artificial intelligence: Catalyst or barrier on the path to sustainability?". Technological Forecasting and Social Change. 175 – via Elsevier Science Direct. Check date values in: |date= (help)
  23. ↑ Dwivedi, Y. K., Hughes, L., Kar, A. K., Baabdullah, A. M., Grover, P., Abbas, R., Andreini, D., Abumoghli, I., Barlette, Y., Bunker, D., Chandra Kruse, L., Constantiou, I., Davison, R. M., De’, R., Dubey, R., Fenby-Taylor, H., Gupta, B., He, W., Kodama, M., … Wade, M. (2022). Climate change and COP26: Are Digital Technologies and information management part of the problem or the solution? an editorial reflection and call to action. International Journal of Information Management, 63, 102456. https://doi.org/10.1016/j.ijinfomgt.2021.102456
  24. ↑ 24.0 24.1 24.2 24.3 Sandbrook, C. (2024). Beyond the hype: Navigating the conservation implications of artificial intelligence. Conservation Letters, 18(1). https://doi.org/10.1111/conl.13076
  25. ↑ Sam A. Reynolds, Sara Beery, Neil Burgess, Mark Burgman, Stuart H.M. Butchart, Steven J. Cooke, David Coomes, Finn Danielsen, Enrico Di Minin, América Paz Durán, Francis Gassert, Amy Hinsley, Sadiq Jaffer, Julia P.G. Jones, Binbin V. Li, Oisin Mac Aodha, Anil Madhavapeddy, Stephanie A.L. O'Donnell, William M. Oxbury, Lloyd Peck, Nathalie Pettorelli, Jon Paul Rodríguez, Emily Shuckburgh, Bernardo Strassburg, Hiromi Yamashita, Zhongqi Miao, William J. Sutherland (2025). The potential for AI to revolutionize conservation: a horizon scan. Trends in Ecology & Evolution, 40(2), 191-207. https://doi.org/10.1016/j.tree.2024.11.013
  26. ↑ En.wikipedia.org. (2018). Writing better articles. [online] Available at: https://en.wikipedia.org/wiki/Wikipedia:Writing_better_articles [Accessed 18 Jan. 2018].


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