In the face of intensifying global challenges like climate change, ecosystem degradation, and resource scarcity, the potential for artificial intelligence (AI) to catalyze sustainable development has garnered widespread attention. The rapidly evolving field of AI-enabled sustainability solutions holds promise across diverse domains, from renewable energy optimization to precision farming and smart city infrastructure. As the European Union (EU) accelerates its transition towards a net-zero emissions economy by 2050, a comprehensive understanding of the synergies between AI and sustainability is crucial to inform policymaking, investment decisions, and research priorities.
This article presents a systematic review of literature reviews examining the intersection of AI and sustainable development, with a specific focus on the EU context. By synthesizing insights from previous conceptual and empirical studies, it aims to uncover the current state of the art, identify key trends and gaps, and chart a course for future advancements. The review adopts a balanced perspective, exploring both the sustainability of AI (addressing the environmental and social impacts of AI systems) and AI for sustainability (leveraging AI to enhance environmental, social, and economic sustainability).
Sustainable Development Frameworks
The overarching framework for this review is grounded in established sustainable development concepts, including the United Nations’ Sustainable Development Goals (SDGs), Environmental Impact Assessment (EIA) methodologies, and circular economy principles. The 17 SDGs provide a comprehensive roadmap for achieving a more equitable and environmentally sustainable future, encompassing dimensions such as climate action, responsible consumption and production, and sustainable cities and communities. EIA, on the other hand, offers a structured approach to evaluating the environmental implications of policies, programs, and technological interventions. Meanwhile, the circular economy model promotes the reuse, repair, and recycling of resources, moving away from the traditional linear “take-make-waste” paradigm.
AI Applications in Sustainable Development
The application of AI across these sustainable development frameworks spans a diverse range of domains. In the energy sector, for instance, AI-powered predictive analytics and optimization algorithms can enhance the integration of renewable energy sources, optimize grid balancing, and improve energy efficiency in buildings and industrial processes. In resource management, AI-enabled autonomous systems and decision support tools can support precision agriculture, monitor biodiversity, and optimize waste management and recycling operations. The integration of AI with the Internet of Things (IoT), satellite imagery, and other data sources has unlocked new possibilities for real-time monitoring, early warning systems, and data-driven policymaking.
Systematic Review Methodology
To gain a comprehensive understanding of the current state of research at the intersection of AI and sustainable development, this review adopted a systematic approach. The literature search process involved querying multiple academic databases, including Scopus, Web of Science, and IEEE Xplore, using a combination of keywords such as “artificial intelligence,” “sustainability,” “sustainable development,” and their variations. The search was limited to peer-reviewed journal articles and conference proceedings published in the English language.
The review criteria included a focus on publications that synthesized and critically analyzed existing literature, either through conceptual or empirical approaches. This allowed the identification of high-quality review articles that could provide a holistic perspective on the field. The selected studies were then subjected to a thematic analysis, which involved categorizing the content along key dimensions, such as the specific sustainability challenges addressed, the types of AI technologies and algorithms employed, and the evaluation methodologies utilized.
Sustainable Development Challenges
The review of the literature reveals that the application of AI in sustainable development has primarily focused on two key challenge areas: climate change mitigation and ecosystem conservation.
In the domain of climate change mitigation, AI-based solutions have been explored to optimize the integration of renewable energy sources, improve energy efficiency in buildings and industrial processes, and support the transition to a circular economy. For instance, AI-powered predictive analytics can forecast energy demand patterns and optimize the dispatch of renewable energy assets, while autonomous vehicles and smart city infrastructure can reduce transportation-related emissions.
Regarding ecosystem conservation, AI systems have demonstrated potential in biodiversity monitoring, land use planning, and environmental impact assessment. By leveraging computer vision, remote sensing, and natural language processing techniques, AI-based systems can rapidly process large volumes of data from satellites, drones, and sensor networks to detect changes in habitat, track species populations, and identify potential threats to ecosystems.
AI-enabled Sustainable Solutions
The review of the literature further reveals a growing emphasis on the development and deployment of AI-enabled sustainable solutions, which can be broadly categorized into three main areas: predictive analytics, autonomous systems, and decision support tools.
Predictive analytics leverage machine learning algorithms to forecast resource demands, detect anomalies, and anticipate the environmental impacts of human activities. For example, AI-powered energy demand forecasting models can help grid operators optimize the integration of renewable energy sources, while early warning systems for natural disasters can enhance disaster preparedness and resilience.
Autonomous systems, such as smart city infrastructure and precision agriculture technologies, employ AI-based control systems and robotics to automate various sustainability-related tasks. These include optimizing energy and resource use, automating waste sorting and recycling processes, and precisely managing irrigation and fertilizer application in agricultural settings.
Decision support tools harness the power of AI to assist policymakers, urban planners, and sustainability managers in making informed, data-driven decisions. By integrating AI-powered predictive analytics, optimization algorithms, and natural language processing, these tools can help evaluate the environmental and socioeconomic impacts of policies, identify optimal resource allocation strategies, and facilitate stakeholder engagement.
Conclusion
The systematic review of literature reviews on the intersection of AI and sustainable development reveals a growing body of research that explores the multifaceted relationships between these two domains. The review highlights the significant potential of AI to address pressing global challenges, from climate change mitigation to ecosystem conservation, through the development of innovative predictive analytics, autonomous systems, and decision support tools.
As the EU accelerates its transition towards a sustainable, low-carbon future, the insights gained from this review can inform policymaking, investment strategies, and research priorities. By fostering cross-disciplinary collaboration between the fields of AI, sustainability, and the broader information systems discipline, the European Union can harness the transformative power of AI to drive tangible progress towards the achievement of the Sustainable Development Goals.
The review also underscores the need for continued research and development in this rapidly evolving field, with a particular emphasis on exploring the economic dimensions of AI-enabled sustainability solutions, aligning with the SDGs, and engaging diverse stakeholders. By addressing these emerging research directions, the European Future Energy Forum can play a pivotal role in shaping the trajectory of AI-powered sustainable development, ultimately contributing to a more resilient and equitable future for all.