World

Socio-ecological systems (SESs) exhibit nonlinear feedback across environmental, social, and economic processes, requiring integrative analytical tools capable of representing such coupled dynamics. This study presents a quantitative framework that integrates a compartmental model of a global human–ecosystem with two complementary optimization approaches (Fisher Information (FI) and Multi-Objective Optimization (MOO)) to evaluate policy strategies for sustainability. The model represents biophysical and socio-economic interactions across 15 compartments, incorporating feedback loops between greenhouse gas (GHG) accumulation, temperature anomalies, and trophic–economic dynamics. Six policy-relevant decision variables were selected (wild plant mortality, sectoral prices (agriculture, livestock, and industry), base wages, and resource productivity) and optimized under temporal (25-year) and magnitude (±10%) constraints to ensure policy realism. FI-based optimization enhances system stability, whereas the MOO framework balances environmental, social, and economic objectives using the Ideal Point Method. Both approaches prevent the systemic collapse observed in the baseline scenario. The FI and MOO strategies reduce terminal global temperature by 11.4% and 15.0%, respectively, relative to the baseline (35 °C → 31.0 °C under FI; 35 °C → 29.7 °C under MOO). Resource-use efficiency, measured through the resource requirement coefficient (λ), improves by 8–10% under MOO (0.6767 → 0.6090) and by 6–7% under FI (0.6668 → 0.6262). These outcomes offer actionable guidance for long-term climate policy at national and international scales. The MOO framework provided the most balanced outcomes, enhancing environmental and social performance while maintaining economic viability. Overall, the integration of optimization and information-theoretic approaches within SES models can support evidence-based public policy design, offering actionable pathways toward resilient, efficient, and equitable sustainability transitions.

Our world is rapidly urbanizing, while 2.5 billion people are projected to shift from rural to urban areas by 2050, with close to 90% occurring in Asia and Africa. In Southeast Asia, particularly, the Phnom Penh capital city of Cambodia is experiencing this rapid urbanization, facing significant challenges in improving the quality of urban life and achieving social sustainability. Hence, this research aims to assess this capital on social sustainability dimensions to find out the strong and weak points of its 14 districts in order to reveal the improvement potential. The research developed and applied an urban social sustainability index based on national development priorities, SDG11, the New Urban Agenda, and other SDGs that related to human wellbeing and social inclusiveness. The AHP was used to prioritize indicators to develop a priority index, while the standard score was used to apply the index to assess the 14 districts of Phnom Penh. The data for this index application were sourced from Phnom Penh’s commune database. The results showed that the highest-scoring district for urban social sustainability was Chamkarmon, followed by Boeng Keng Kang and Doun Penh. The findings revealed that Prek Pnov was weak in income generation and welfare, while Kamboul was weak in gender inclusion compared to other districts. Prampir Makara was strong, and Sen Sok was weak in resilience to vulnerability. Boeng Keng Kang was strong in welfare and sanitation, while Kamboul was weak in sanitation. Doun Penh was strong, and Mean Chey was weak in water supply.

In many jurisdictions, property registration and transfers remain constrained by inefficient, paper-based processes that depend on multiple intermediaries and bureaucratic approvals. This paper proposes a decentralized, blockchain-based property platform designed to streamline these processes using Non-Fungible Tokens (NFTs) and artificial intelligence (AI) agents to modernize public-sector asset management. The work addresses the persistent inefficiencies of paper-based property registration and ownership transfer by embedding legal and administrative logic within smart contracts and automating compliance through an intelligent conversational interface. The system was implemented using Ethereum-based ERC-721 standards, React for the user interface, and Langfuse-powered AI integration for guided user interaction. The pilot implementation presents secure, transparent, and auditable property-transfer transactions executed entirely on-chain, while hybrid IPFS-based storage and decentralized identifiers preserve privacy and legal validity. Comparative analysis against existing national initiatives indicates that the proposed architecture delivers decentralization, citizen control, and interoperability without compromising regulatory requirements. The system reduces bureaucratic overhead, simplifies transaction workflows, and lowers user error risk, thereby strengthening accountability and public trust. Overall, the paper outlines a viable foundation for legally aligned, AI-assisted digital property registries and offers a policy-oriented roadmap for integrating blockchain-enabled systems into public-sector governance infrastructures.