Introduction
Artificial Intelligence (AI) has become a potent force in the era of digital governance and worldwide catastrophes, such as pandemics and climate change. AI systems are currently being utilised to help decision-making in fields including public health monitoring, agriculture, humanitarian aid distribution, and conflict risk assessment. These systems promise faster insights, predictive capabilities, and real-time feedback loops (Sirmaçek et al., 2023). However, the precision and contextual accuracy of the data these systems rely on provide a delicate foundation beneath their complexity.
Environmental security encompasses the safeguarding of natural resources and ecosystems to ensure the stability and well-being of societies (Schaafsma, 2021). The need for strong environmental security has never been greater in a time of growing environmental degradation and climate concerns. This is highlighted by the rising frequency and severity of extreme weather events including floods, food insecurity, and wildfires. For instance, AI systems can examine large datasets to forecast the probability of some environmental disasters, optimise resource allocation, and enhance disaster response mechanisms to mitigate potential disasters (ft.com).
The efficacy of AI in environmental security, nonetheless, is not solely dependent on technological advancements. Human expertise plays a crucial role in interpreting AI outputs, making context-specific decisions, and ensuring ethical considerations are upheld. More culturally aware and efficient environmental management techniques may result from the fusion of AI findings with indigenous and local knowledge. Moreover, the deployment of AI systems must be approached with caution, considering the environmental footprint associated with data centers and computational processes. The energy consumption and resource demands of AI technologies necessitate a balanced approach that weighs the benefits against potential environmental costs (news.mit.edu; teenvogue.com)
This article delves into the symbiotic relationship between AI and human efforts in fortifying environmental security. It explores how collaborative approaches can enhance resilience against environmental threats, ensuring sustainable and equitable resource management in the face of global challenges.
AI Strengths in Environmental Resource Management
As mentioned earlier, AI has revolutionized environmental monitoring by enabling predictive analytics to forecast environmental changes and potential disasters, using vast datasets from satellites, sensors, and historical records, enabling preemptive measures (Olawade et al., 2024). AI systems can significantly optimise natural resource distribution by evaluating consumption trends and forecasting demand, leading to better resource allocation, reduced waste, and increased sustainability (Padhiary et al., 2025). This applies to water, energy, land, and even food distribution. It can be used to forecast hunger risk zones to allow for the distribution of food aid more efficiently. In India and Kenya, AI has been used to forecast crop water and fertilizer needs, resulting in about 30% water savings and higher crop yield per hectare (agrinextcon.com, worldagritechusa.com).
In agriculture, its algorithms evaluate soil health, weather patterns, and crop requirements to determine the best planting times and irrigation strategies, leading to more efficient water use and higher agricultural yields (ibid, 2025). For example, in September last year, The Guardian reported the use of AI apps among coffee farmers in Sorwot village in Kericho- Kenya, which allowed them to use less fertilizer than would have been used, thereby saving money (theguardian.com).
Similarly, in energy management, AI forecasts energy consumption patterns, allowing for the integration of renewable energy sources while decreasing dependency on fossil fuels (Khan et al., 2023). Ghana’s Bui Power Authority (BPA) incorporating solar panels into the northern national grid is a significant step toward clean energy adoption. AI could further enhance this step by forecasting energy consumption using historical electricity usage, sunlight hours, cloud cover, and temperature among others for future electricity demand peaks, when to charge or discharge and solar energy production variability.
In urban settings, AI also aids in waste management by predicting waste generation patterns and optimising collection routes, thereby reducing fuel consumption and emissions for preemptive preparations (Nwokediegwu et al., 2024). Artificial intelligence can do this by predicting seasonal waste spikes from festivities or holidays and population demographics among others.
Artificial intelligence helps to conserve biodiversity by monitoring animal populations and detecting illicit actions such as felling and poaching (Oladede, 2025). Computer vision and deep learning algorithms use photos from camera traps and drones to identify species and follow their movements. This real-time monitoring contributes to the protection of endangered species and the implementation of conservation regulations. In essence, this can be useful in the tracking of illegal mining in Ghana so that existing species of fish and wildlife will be conserved if any since AI models can detect anomalies in water quality data, prompting immediate investigation and remediation.
AI also monitors air and water quality by analysing data from sensors, enabling timely responses to pollution incidents (ibid, 2024). For instance, Accra was the first African city to join the BreatheLife campaign, positioning itself as a leader among cities on the continent aiming to combat air pollution. The city’s average air pollution levels are reported to be five times higher than WHO guidelines, underscoring the urgency of implementing effective pollution control measures (unep.org).
The Human Role
While Artificial Intelligence offers powerful tools for environmental resource management, the human element remains indispensable. Human expertise ensures that AI-generated insights are interpreted correctly, adapted to local contexts, and implemented ethically and effectively.
First, AI systems can process vast datasets to identify patterns and predict environmental changes but human experts are essential for interpreting these outputs within the appropriate ecological, social, and political contexts. A study on climate resilience governance by Mehryar, Yazdanpanah & Tong emphasizes that expert judgment and domain knowledge is needed for translating AI-driven insights into actionable strategies and policies.
Second, local communities possess valuable knowledge about their environments, which can enhance AI applications. Collaborative environmental governance models advocate for integrating diverse knowledge systems, including indigenous and traditional knowledge, into environmental decision-making processes. This integration ensures that AI solutions are culturally sensitive and more likely to be accepted by local populations.
Moreso, designing AI systems with a human-centered approach ensures that they align with user needs and values. Human-centered design emphasises boosting human self-efficacy and promoting social interaction, which enables the acceptance and effectiveness of AI tools. Participatory environmental management approaches further involve stakeholders in the design and implementation of AI solutions, fostering ownership and trust (Santos & Carvalho, 2025).
The use of AI in environmental settings brings up ethical issues such as data privacy, bias, and the potential displacement of human responsibilities. An elaborate framework for ethical AI integration in human services organizations (Perron, Goldkind&Victor, 2025) recommends assessing AI applications from a variety of risk angles, taking into account variables such as data sensitivity and possible effects on client welfare. The ethical application of AI in environmental management can be guided by such frameworks.
A field study in Wisconsin examined the deployment of an AI tool for detecting illegal agricultural waste dumping. The study found that while the AI system provided accurate detections, human interpretation was necessary to assess the usefulness of the information. Different organisations involved in the study had varying assessments based on their goals and regulatory frameworks, highlighting the importance of human context in AI implementation (Rothbacher et al., 2025).
The Risks of Over-Reliance on AI
AI system implementation and operation need significant computing resources, which raises energy consumption and degrades the environment. Large volumes of power, frequently from fossil fuels, are consumed by data centres that serve AI applications, which increase greenhouse gas emissions (news.mit.edu ; teenvogue.com). Additionally, these centres require significant water resources for cooling purposes.
AI systems are only as unbiased as the data they are trained on. Over-reliance on AI can inadvertently perpetuate existing biases in training datasets, leading to skewed or discriminatory outcomes. Moreover, AI can be exploited to generate and disseminate misinformation. A report by The Guardian warns that AI could facilitate the spread of climate disinformation through deep fakes and misleading content, undermining efforts to address climate change.
AI systems, especially those based on complex algorithms, often operate as “black boxes” or opaque, making it challenging to understand their decision-making processes. This opacity can lead to systemic risks, where incorrect or biased AI outputs go unchallenged due to a lack of transparency. An article in Technological Forecasting and Social Change discusses the systemic sustainability risks created by AI, emphasising the need for robust governance and transparency mechanisms (Galaz et al., 2021).
The environmental impacts of AI are not uniformly distributed. Regions hosting large data centres may experience disproportionate environmental burdens, such as increased energy consumption and water usage (steptoe.com). It is thus important to highlight the need for equitable distribution of AI’s environmental costs and advocate for system management approaches that mitigate environmental inequity (Hajiesmaili et al., 2024).
Studies have shown that integrating AI into environmental management raises ethical concerns, particularly regarding data privacy, surveillance, and the potential displacement of human labour. Khalid et al., 2024 ; Perron, Goldkind&Victor, 2025, to mention a few, echo the importance of perceived environmental responsibility in AI-driven risk management, suggesting that without ethical considerations, AI applications may lead to unintended negative consequences.
Conclusion
As we navigate the intersection of artificial intelligence (AI) and environmental resource management, a balanced and ethically grounded roadmap is essential.
AI systems should be incorporated into human-in-the-loop processes, involving community stakeholders and experts to ensure ethical, culturally sensitive, and contextually aware decision-making on important issues. Human rangers in Kenya’s wildlife conservation programs for example, employ AI-powered prediction technologies to stop poaching, but field professionals make the ultimate interpretation and decision (Nauman, 2025). This is particularly important because AI systems are neither morally nor culturally sensitive nor contextually aware.
Advancing digital literacy and capacity building, particularly in the Global South, impedes the effective adoption and oversight of AI tools. It is imperative to support interdisciplinary training in AI, data science, and environmental studies through universities, public-private partnerships, and NGOs while establishing AI-for-Environment innovation hubs in West African universities to incubate locally relevant solutions. In addition, expanding technical and ecological education programmes to empower stakeholders should be of deep concern to working with AI systems.
Invest in sustainable AI infrastructure to reduce the environmental footprint of AI by promoting research into low-power algorithms, supporting carbon-neutral data centers, and encouraging decentralized AI computing. This approach aims to minimise the carbon cost per model trained (Strubel, 2020).
Establish international and national regulatory frameworks for the ethical use of AI in environmental management, ensuring clear governance to prevent unintended harm. This includes establishing AI environmental impact assessments, enforcing data privacy standards, and creating redress mechanisms for misuse. The African Union’s Agenda 2063 can be used as a foundational platform to advocate for AI governance that promotes environmental equity and transparency.
Another recommendation is to ensure AI models used in environmental policy and management are interpretable and auditable, as opaque models can erode public trust and complicate decision-making. In Nigeria for instance, explainable AI has been deployed in analysing satellite imagery for deforestation detection (earthranger.com).
In a nutshell, AI is a multiplier, not a miracle. Its potential to enhance environmental security lies not in replacing human judgment, but in amplifying it. For AI to deliver meaningful impact in managing natural resources and mitigating environmental shocks, it must be paired with empowered, informed, and engaged human actors. Policymakers, scientists, local communities, and civil society all play critical roles in shaping how AI is developed, deployed, and governed. Without their input, even the most advanced systems risk missing the mark or causing harm. To ensure our shared future, we must build adaptive, ethical, and inclusive frameworks by combining technological precision with human wisdom, not just smart systems alone.
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