In this study, we examine Society 5.0, defined as a future framework where advanced technologies like artificial intelligence (AI), the Internet of Things (IoT), and other digital innovations are integrated into society for sustainable resource management. Society 5.0 represents an evolution from the industrial focus of Industry 4.0, aiming for a harmonious balance between technological progress and human-centric values, consistent with the United Nations Sustainable Development Goals. Our methodology involves a detailed literature review, focusing on identifying and evaluating the roles of AI, IoT, and other emerging technologies in enhancing resource efficiency, particularly in the water and energy sectors, to minimize environmental impact. This approach allows us to present a comprehensive overview of current technological advancements and their potential applications in Society 5.0. Our study’s added value lies in its synthesis of diverse technological strategies, emphasizing the synergy between circular economy practices and sustainable economic development. We highlight the necessity for resilience and adaptability to ecological challenges and advocate for a collaborative, data-informed decision-making framework. Our findings portray Society 5.0 as a holistic model for addressing contemporary global challenges in resource management and conservation, projecting a future where technology aligns with sustainable, equitable, and human-centered development.

In an era marked by accelerating ecological degradation and widening global inequalities, prevailing patterns of resource extraction and consumption are proving increasingly unsustainable. Driven by hyper-consumption and entrenched linear production models, the global economy continues to exert immense pressure on planetary systems. This communication article calls for a fundamental paradigm shift from the long-standing assumption of resource abundance to a framework of responsible scarcity. Drawing from recent data on material throughput, on the transgression of planetary boundaries, and on the structural and geopolitical disparities underlying global resource use, this article highlights the urgent need to realign natural resource governance with ecological limits and social justice. A conceptual framework is proposed to support this transition, grounded in principles of ecological constraint, functional sufficiency, equity, and long-term resilience. The article concludes by outlining a forward-thinking research and policy agenda aimed at fostering sustainable and just modes of resource utilization in the face of growing environmental and socio-economic challenges.

Green hydrogen is a key energy carrier for industrial decarbonization; however, its large-scale deployment requires the optimization of production routes from both energetic and economic perspectives. In this study, green hydrogen production via biomass gasification and water electrolysis is comparatively evaluated using a multi-objective optimization framework based on the Differential Evolution Tabu List (DETL) algorithm. The optimization simultaneously maximizes hydrogen production while minimizing specific energy consumption and total annualized cost, explicitly capturing the trade-offs between competing technologies. Results indicate that biomass gasification outperforms water electrolysis in both energetic and economic terms. The optimal gasification configuration achieves 3625.95 kg/h of H₂ with a specific energy consumption of 39.63 kWh/kg H₂ and a total annualized cost of 2.45 MUSD/yr, whereas water electrolysis reaches 3156.78 kg/h of H₂ with 68.7 kWh/kg H₂ and a cost of 3.72 MUSD/yr. To support the interpretation of results, K-means clustering is integrated into the methodological framework, enabling the identification of representative regions within the Pareto fronts. Overall, biomass gasification provides more balanced and flexible solutions, highlighting its potential as a competitive route for sustainable hydrogen production.

The rapid expansion of artificial intelligence has intensified the demand for high-performance data centers, leading to unprecedented pressures on energy, water, and material resources. This critical review examines the emerging challenges associated with resource management in AI-driven data centers by focusing on the interplay between computational growth and environmental constraints. The analysis integrates recent advances in energy efficiency, cooling technologies, and hardware design while highlighting the increasing water footprint of thermal management systems and the material implications linked to semiconductor manufacturing and infrastructure scaling. Particular attention is given not only to the trade-offs between performance optimization and sustainability but also to the limitations of current metrics used to assess resource efficiency. The review identifies key gaps in the literature, including the lack of integrated frameworks that simultaneously address energy, water, and material flows. Finally, this review provides insights into pathways for a more sustainable AI infrastructure by synthesizing present-day knowledge, critically evaluating existing strategies, and emphasizing the need for systemic approaches that align technological innovation with resource conservation and long-term environmental resilience.