United Nations University (UNU)

Relational values contribute to the pluralistic valuation of nature by providing a unique perspective beyond the dichotomy of intrinsic and instrumental values. This study examines how the Nagara River Basin in central Japan is relationally valued by locals and the factors influencing their evaluation. The Nagara River Basin is home to approximately 830,000 people and deeply connected to the local economy and culture. We subjected data from an online survey of 2031 respondents living in the river basin to exploratory factor analysis and structural equation modeling to explore the factors that impact the level of relational values. We found that the residents of the basin valued the river in two relational ways: their own self-identity and well-being (RV-self), and the community or the social relationships that are built around the river (RV-others). Structural equation modeling revealed that RV-self was significantly correlated with the number of activities that the respondents engaged in around the river, and special knowledge of the traditional interactions between people and the Nagara River. Furthermore, RV-others was significantly correlated with the number of activities engaged in by the respondents around the river, childhood experiences in nature, social interactions around the river, and general knowledge regarding the Nagara River. These findings provide insight into how relational values can be developed, suggesting a direction for future research on how these factors can be used to develop pro-environmental behavior and promote the well-being of local communities.

Soil erosion is a critical challenge to the sustainability of soil and water resources, particularly in volcanic regions like the Telagawaja Sub-Watershed, Bali. The area is defined by andesitic-basalt rocks (Qvab), basaltic tuff (Qvbt), and ancient volcanic formations from the Tertiary period (Tomub), coupled with tropical climate conditions with an average rainfall of 219.21 mm per month, temperatures ranging from 20 to 27 °C, and intensive agriculture, making it highly prone to erosion. This study compares soil erosion predictions from conventional data sources, derived from field observations and laboratory tests, with remote sensing data obtained from multisensor satellites (e.g., CHIRPS, Sentinel-2, and DEM Alos Palsar), using the Universal Soil Loss Equation (USLE). Erosion estimates based on conventional data range between 0.37 and 4,657 t ha−1 yr−1, while remote sensing estimates vary from 0 to 49,384 t ha−1 yr−1. Both approaches highlight that very light erosion (<15 t ha−1 yr−1) dominates the region, covering 45.90% and 29.21% of the area, respectively. The comparison reveals a 21.03% agreement between methods, with 78.97% exhibiting differences in erosion classification. Conventional methods tend to produce more uniform outcomes, whereas remote sensing generates more spatially detailed, pixel-based maps, especially in areas with complex topography and vegetation variability. The study underscores the value of integrating both techniques for generating more precise erosion zonation maps, crucial for effective watershed management and soil conservation strategies.

UNESCO’s dual designations of Biosphere Reserves (BRs) and Global Geoparks (UGGps) offer unique opportunities for integrating biodiversity and geodiversity conservation with sustainable development. This study investigates the synergies and conflicts between these designations at Mount Hakusan, Japan, the country’s only site with overlapping BR and UGGp territories. Employing qualitative methods, including semi-structured interviews and document analysis, the research highlights key governance challenges, conservation impacts, and community engagement outcomes. Findings highlight key synergies, including enhanced global recognition, collaborative educational initiatives, and the potential for interdisciplinary conservation efforts. However, the study also identifies significant conflicts, such as governance inefficiencies, budgetary constraints, and tensions between biodiversity conservation and geotourism priorities. While the BR framework emphasizes ecosystem protection, the UGGp’s focus on geotourism can create challenges in balancing conservation with economic development. Furthermore, low public awareness and limited youth engagement pose barriers to fully realizing the potential benefits of both designations. Recommendations are provided for integrated management strategies, emphasizing multi-stakeholder collaboration, and enhancing community participation to align conservation and tourism objectives. The study enhances the understanding of managing Multi-Internationally Designated Areas (MIDAs) and offers practical strategies to support global conservation and sustainable development initiatives.

This chapter synthesizes the findings from the 12 case studies presented in this volume. It is designed to answer the following questions: (1) How can we conceptualize the connections between business and biodiversity in the context of managing socio-ecological production landscapes and seascapes (SEPLS)?; (2) How can we measure, evaluate, and monitor impacts and dependency of businesses on biodiversity and nature’s contributions to people through managing SEPLS?; and (3) How can we address challenges and seize opportunities in SEPLS to effectively manage business impacts on biodiversity, ecosystems, and human well-being? In addition to addressing these questions based on the synthesis of the case study findings, the chapter provides recommendations for policymakers and other stakeholders to ensure that the processes and outcomes are ecologically and socially sound and equitable in promoting more sustainable businesses. These recommendations are examples of ethical, equitable, and actionable ways of ensuring that businesses are sustainable while conserving biodiversity. In that way, businesses can contribute to achieving multiple sustainable development goals (SDGs) simultaneously, including not merely those specific for life below water and on land but also those related to poverty reduction, food security, good health and well-being, gender equality, quality education, capacity development, employment, climate action, responsible consumption and production, and partnerships and institutional development.

The prolonged armed conflict in Colombia, spanning over the last five decades, has significantly impacted its agricultural areas and led to the widespread displacement and disruption of farming activities. The agricultural sector is crucial for Colombia as it contributes to food security, the economy, and the Nation’s employment rate. However, the agricultural sector is challenged by the environment and its natural resources, especially water in water abstraction and soil in terms of degradation and land cover change. Additionally, climate change exacerbates these challenges by altering precipitation patterns, increasing the frequency of extreme weather events, and further stressing water and soil resources, making sustainable management even more critical. The Resource Nexus approach comes into play to cope with and mitigate such challenges. Combined with social equity to advance the sustainability of agriculture, the Nexus approach demonstrates pathways towards the achievement of the Sustainable Development Goal (SDG) 2 (Zero Hunger)in synergies with other SDGs, like SDG 5 (Gender Equality), SDG 6 (Clean Water and Sanitation), SDG 10 (Reduced Inequality), SDG 12 (Responsible Consumption and Production), SDG 13 (Climate Action), and SDG 15 (Life on Land). This paper addresses the dual challenge of improving natural resources management and population vulnerability reduction in the frame of environmental conflicts and population inequalities that severely affect the resilience of food systems. In line with principles of inclusion and gender equity, the methodology developed here aims to identify Colombia's productive regions that would benefit from enhanced management at the landscape level, the Resource Nexus approach. With the use of geographic information systems (GIS), this research spatially evaluates the (i) impact of land-use changes and the land-use fragmentation due to resource overuse, (ii) the provision of ecosystem services under different uses of natural resources and suggests ecosystem services planning as a methodology for municipal ecosystem-based management, (iii) climate change and the anthropogenic impacts on agricultural productivity in Colombia at the municipality scale. The results indicate significant environmental changes over the past few decades, including reduced natural forests and increased agricultural land. This shift has coincided with a decrease in freshwater availability. Additionally, there is a concerning trend of agricultural expansion into protected areas, highlighting the ongoing pressures on natural resources and the need for sustainable management practices. This study underscores the value of the science-policy interface to ensure increased social equity, economic growth, and resource conservation.

Development partnerships that overcome the intrinsic limitations of individual organizations are essential to achieve the goals of the 2030 Agenda for Sustainable Development. However, one in five strategic partnerships fails, incurring higher costs for lesser impact. Assuming that partnership performance is proportionate to co-financed project ratings, we used the corresponding evaluation reports and project ratings from multiple multilateral organizations to automate the identification and analysis of critical predictive factors for partnership success. This approach ultimately helps form more selective partnerships. Our automation engine integrates text mining, sentiment and semantic analysis, and supervised Machine Learning with explainable and opaque algorithms. To achieve this goal, we compared four alternative Machine Learning models. By applying Shapley’s additive explanation values to trained models, we ranked factors by criticality, identifying their Top-10 rank. With bootstrap sampling, instead, we computed confidence intervals. Our rating predictions were rather good. Explainable AI models achieved a Matthews Correlation Coefficient of 0.749. Opaque algorithms reached 0.780. Both compare well with the current success rates of 81% for project co-financing partnerships and 75% for knowledge partnerships. Our prototype engine may help organizations improve partnership appraisal efficiency and improve collaboration. Future work will include refining factor formulations, deploying models with incremental learning, and introducing causality analysis.

Atmospheric microplastics (AMPs) can absorb and scatter radiation, which can be quantified by radiative forcing. Although the radiative forcing of AMPs is commonly positive at the global scale, regional environmental variables affect the radiative forcing of aerosols, potentially reversing its directions and cause opposite radiative impacts in the atmosphere. In this study, the total suspended particles were collected within one year in Tianjin, China, and the monthly average concentration of AMPs of 200.0–463.9 items/m³ was detected. Accordingly, the direct radiative forcing (DRF) of AMPs was calculated as −0.03 to 0.03 W/m² at the top of the atmosphere and −0.09 to 0.72 W/m² at the Earth's surface, respectively. The surface albedo significantly affects the direction of the DRF of AMPs. A low surface albedo leads to a cooling effect of AMPs in the atmosphere in Tianjin, while a high surface albedo causes a warming effect in the atmosphere. The DRF calculated under different surface albedo indicates that the potential impact of AMPs on atmospheric temperature is relatively complex. The temperature changes caused by AMPs in grassland and bare soil areas may be opposite to those in areas covered by snow and ice.

The Sustainable Development Goals (SDGs), adopted globally in 2015, necessitate localization where national and sub-national entities adapt and integrate these goals to address socioeconomic and environmental issues in different contexts. This research examines how local governments in Ghana integrate the SDGs into their Medium-Term Development Plans (MTDPs) and assesses the association between these localized efforts and key socio-economic development outcomes. By analyzing the MTDPs of 138 local governments across 14 regions, the research finds that 78% of the 169 SDGs targets have been localized in Ghana. Using the Spearman correlation analysis, the study reveals a strong association between localized SDGs targets and improved socio-economic outcomes, particularly in areas of healthcare access, education, gender equality, and economic opportunities. The results highlight the multi-dimensional and interconnected impacts of the SDGs, underscoring the need for a nexus approach to achieving sustainable development. Nonetheless, goals related to climate action and environmental sustainability show less localization, indicating a need for targeted interventions. The study showcases the potential of SDGs localization to drive sustainable development, reinforcing the importance of empowering local governments in tailoring global goals to local contexts. The study’s insights contribute to the broader discourse on SDGs localization, offering valuable lessons for similar initiatives in other sub-Saharan African countries.

Traditional coastal flood risk prediction often overlooks critical geographic features, underscoring the need for accurate risk prediction in coastal cities to ensure resilience. This study enhances the prediction of coastal flood occurrence by utilizing the Geospatial Artificial Intelligence (GeoAI) approach. This approach employed models—random forest (RF), k-nearest neighbor (kNN), and artificial neural networks (ANN)—and compared them to the IPCC risk framework. This study used El Salvador as a demonstration case. The models incorporated seven input variables: extreme sea level, coastline proximity, elevation, slope, mangrove distance, population, and settlement type. With a recall score of 0.67 and precision of 0.86, the RF model outperformed the other models and the IPCC approach, which could avoid imbalanced datasets and standard scaler issues. The RF model improved the reliability of flood risk assessments by reducing false negatives. Based on the RF model output, scenario analysis predicted a significant increase in flood occurrences by 2100, mainly under RCP8.5 with SSP5. The study also highlights that the continuous mangrove along the coastline will reduce coastal flood occurrences. The GeoAI approach results suggest its potential for coastal flood risk management, emphasizing the need to integrate natural defenses, such as mangroves, for coastal resilience.

Governing artificial intelligence (AI) poses intricate difficulties, requiring a well-balanced approach involving local and global legal frameworks. local governance enables the implementation of customized legislation that caters to specific cultural, economic, and sociological requirements, thereby ensuring that AI technologies align with national interests and values. Global governance, in contrast, facilitates international collaboration, uniformity, and the control of risks that cross borders, creating a unified structure capable of tackling the global consequences of AI advancement and implementation. This chapter examines the need to combine local and global governance methods to establish a robust regulatory framework for AI. By balancing local and global perspectives, we can ensure the responsible, ethical, and efficient development and utilization of AI technology, benefiting individual states and the international community. Implementing this balanced governance model is vital in navigating the rapidly changing AI landscape, fostering innovation, and protecting the public's interests worldwide.

Artificial Intelligence (AI) systems can be classified into two functional modes according to Daniel Kahneman's dual-process theory: fast thinking (System 1) and slow thinking (System 2). AI's ability to think quickly allows it to carry out rapid, automated activities like identifying patterns and providing immediate answers, akin to human instincts. Slow thinking refers to using methodical and calculated methods, enabling AI to manage complicated decision-making and strategic planning effectively. This chapter investigates integrating cognitive processes in AI across several industries, such as healthcare, finance, and politics. It evaluates the effectiveness of this integration and explores the ethical considerations associated with it. This work seeks to deepen comprehension of AI's capabilities and guide its growth towards more advanced, dependable, and ethically sound implementations by investigating the dual-process theory in AI.

Synthetic and real-world data are used in AI training, creating potential problems. A governance framework is proposed for the ethical and successful merging of synthetic and authentic (measured) data in AI training. The framework covers technological, legal, and ethical issues to set norms and standards for synthetic data production, validation, and integration with measured data. Technically, the framework requires transparent disclosure of synthetic data proportion and characteristics, rigorous quality assurance mechanisms to validate synthetic data against real-world counterparts, and ongoing monitoring of AI models trained on fused data to detect and address biases or inconsistencies. It supports unambiguous synthetic data restrictions to comply with privacy and data protection legislation. Ethically, it emphasizes the need to overcome synthetic data biases and ensure that AI models trained on fused data do not perpetuate discrimination. By applying this governance framework, stakeholders may ensure the ethical integration of synthetic and measured data in AI training, increasing confidence in AI systems and reducing threats to individuals and society.

The chapter delves into the complex balance between truth and deception in artificial intelligence (AI) systems, a critical issue as AI continues influencing society. The potential for AI to uncover truths through data analysis and propagate deception, either unintentionally or intentionally, poses significant ethical and practical challenges. It also discusses the use of AI in industries like healthcare, finance, and interstate conflict, emphasizing the importance of truth vs deception. The chapter suggests a framework for embedding ethical considerations into AI development and operational processes, including rigorous testing protocols, transparent AI systems, and robust oversight mechanisms. The chapter concludes that balancing truth and deception in AI requires collaboration from policymakers, end-users, and ethicists, fostering an interdisciplinary approach to AI governance to harness AI benefits while minimizing risks.

Quantum computing, a disruptive force in computation, challenges traditional digital computing. This chapter explores the intricate governance systems required to balance digital and quantum computing development and integration. The governance of this dual computational environment must address legal frameworks, technology interoperability, security procedures, ethics, and socio-economic effects. Technological interoperability requires standards and protocols to integrate digital and quantum systems, maximizing their benefits. The security of quantum computing is of utmost importance, given its ability to bypass cryptographic protocols. Therefore, governance must safeguard data integrity and privacy with robust security measures. The social impacts of quantum computing, such as employment loss and the digital divide, must be carefully considered to prevent exacerbating inequality. Ultimately, reconciling digital and quantum computing necessitates a comprehensive strategy that balances regulatory, technical, security, ethical, and socio-economic factors.

Integrating artificial intelligence (AI) into governance processes presents opportunities and challenges. The chapter explores the role of AI in memorizing and reasoning, aiming to create an ethical, transparent, and efficient governance framework. It highlights the importance of robust data management procedures and AI's cognitive capacity in governance contexts. The chapter suggests governance principles and regulatory methods to create efficient, independent AI systems that balance memorization versus thinking and are aligned with societal norms and values.

Artificial intelligence (AI) governance frameworks must balance innovation, ethics, public safety, and fairness as AI technologies evolve. The chapter summarizes this book and emphasizes the necessity for flexible, inclusive, and forward-thinking AI governance policies. Multi-stakeholder cooperation between governments, commercial sectors, academia, and civil society is crucial to robust and adaptable AI regulatory frameworks. This chapter recommends a dynamic and balanced governance structure that can adapt to technical advances to ensure that AI maximizes benefits while minimizing risks.

High-performance computing (HPC) has significantly impacted various fields, including scientific research, machine learning, and artificial intelligence (AI). The balance between Central Processing Units (CPUs) and Graphics Processing Units (GPUs) is crucial for HPC. CPUs are adaptable and robust, while GPUs offer exceptional performance in parallel processing, making them ideal for tasks requiring extensive data throughput, such as AI. This chapter explores the governance balance between CPU and GPU utilization, analyzing their advantages and limitations and suggesting strategies to enhance their collective performance. It emphasizes the importance of allocating hardware resources for each task, using hybrid computing frameworks, and optimizing software to achieve an optimal balance of efficiency. We can optimize computational efficiency, minimize processing times, and tackle complex computational challenges by utilizing the synergistic capabilities of CPUs and GPUs.