Search Results (57)

This study conducts a comparative legal analysis of South Korea’s Framework Act on Artificial Intelligence (enacted January 2025, effective January 2026) and the EU AI Act (effective August 2024), focusing on the structural implications of their divergent regulatory philosophies for sustainable digital governance. Employing legal interpretive analysis (textual, systematic, and teleological) and comparative legal methodology, supplemented by risk-based regulation theory and the theory of hardening of soft norms, this paper examines three interconnected dimensions: the conceptual distinction between “high-impact” and “high-risk” AI, the legal nature of self-regulatory structures, and the potential distortion of civil liability attribution. The analysis reveals that Korea’s adoption of the “high-impact” concept, while strategically reducing compliance costs and avoiding stigma effects, generates significant legal gaps, including potential violations of the constitutional principle of clarity, a “liability lightning rod” phenomenon transferring responsibility from AI operators to frontline practitioners, and insufficient institutional prerequisites for effective self-regulation. In contrast, the EU’s ex-ante preventive framework provides greater legal certainty through direct enumeration of high-risk sectors and mandatory conformity assessments. Drawing on the growing body of EU AI Act scholarship, this paper proposes a five-step legislative model for dynamic regulatory adjustment tailored to Korea’s constitutional structure, encompassing statutory core criteria, periodic re-evaluation with parliamentary oversight, phased mandatory enforcement, and a presumption of conformity system, thereby offering a co-regulatory framework that balances innovation promotion with fundamental rights protection. Full article

Knowledge management platforms are increasingly important for strengthening governance, scientific collaboration, and evidence-based decision making in complex regional networks. This study analyses the prospective intention to adopt two strategic digital mechanisms of the Amazon Cooperation Treaty Organization (OCTA): the Amazon Regional Observatory (ORA) and the Network of Amazonian Research Centres (REDCIA). Adapting the Unified Theory of Acceptance and Use of Technology (UTAUT) to a pre-implementation context, the study focuses on performance expectancy, social influence, and facilitating conditions, while operationalizing these constructs through a Knowledge, Attitudes, and Practices (KAP) survey. Using a quantitative, non-experimental, cross-sectional design, penalized ordinal logistic regression models were estimated from 162 responses collected from institutional actors and experts across eight Amazonian jurisdictions. The results show that social influence is the only statistically significant predictor of intention to use in both mechanisms, whereas performance expectancy and facilitating conditions are not significant in the estimated models. These findings suggest that, in the Amazonian cooperation context, adoption is driven less by individual evaluations of utility or technical feasibility than by institutional legitimacy, peer expectations, and collaborative norms. The study contributes to the information systems literature by providing an ex ante analytical approach for assessing technology acceptance in the absence of an operational artefact. It also offers practical guidance for OCTA by highlighting the importance of change management, political endorsement, and network-based incentives to support future implementation. Full article

The recycling of pesticide packaging waste is crucial for the sustainable development of agriculture and the advancement of ecological civilization. However, the current recycling management still faces challenges. This study adopts a dynamic analytical framework of “ex-ante behavioral cognition and post-event outcome perception” to investigate the impact of digitalization on farmers’ recycling behavior of pesticide packaging waste. The analysis draws on data from the 2020 China Rural Revitalization Survey and examines two dimensions of digitalization: digital technology access and digital technology usage. The findings indicate that integrating digital technologies into farming practices significantly increases the likelihood of farmers participating in pesticide packaging waste recycling programs. These results remain robust after conducting robustness checks and addressing potential endogeneity issues. A heterogeneity analysis reveals that the promotional effect of digitalization varies significantly across different categories of rural elite status, cooperative membership, education level, pesticide spraying methods, and income structure. Mechanism testing further indicates that hazard cognition regarding pesticide packaging serves as a mediating factor in the impact of both digital technology access and usage on farmers’ recycling behavior. In contrast, farmers’ satisfaction with their living environment mediates only the effect of digital technology usage on recycling behavior. Overall, these findings provide both theoretical and empirical support for the hypothesis that digitalization can facilitate the recycling of pesticide packaging waste and enhance the ecological effectiveness of agricultural policy governance. Full article

Achieving urban climate neutrality and interim mitigation targets requires rapid demand-side emission reductions, yet current user-centred interventions remain fragmented, are often concentrated on low-impact actions, and rarely provide a traceable basis for comparing outcomes, validity conditions, and equity implications across contexts. This paper reframes demand-side mitigation as a design problem of “use infrastructures”: integrated configurations of communication, product-technology, services, interaction, and governance that make low-carbon choices practicable within everyday routines. We introduce the Design Evidence Link (DEL) as a traceability device supporting ex ante configuration (selection and orchestration of levers) and ex post verification (monitoring, attribution of outcomes, and trade-off control). Through a design-led comparative analysis of nine international cases in high-impact sectors (household energy, ground mobility, food systems, and circular economy/materials), we derive and consolidate a shared extraction and coding protocol that links determinants (barriers and enablers) to design requirements and decision-grade metrics (carbon impact, adoption, continuity, and equity), explicitly qualifying uncertainty and evidence levels. Cross-case results show that effective interventions rely less on isolated information and more on coordinated action packages that reduce cognitive and economic frictions, enhance data credibility through standards and accountability, and embed follow-up mechanisms that support behavioural continuity. DEL also surfaces recurring validity conditions and failure modes (digital exclusion, trust erosion, rebound, and lock-in), translating them into operational criteria for policy and design. Compared with behaviour-change or theory-of-change framings, DEL focuses on the observable orchestration of integrated conditions of use and on the explicit grading of evidence. It should therefore be read as a structured analytical–operational framework for ex ante and ex post assessment, whose transferability remains conditional on source quality, contextual prerequisites, and the limits of the selected cases. Full article

Digital transformation in public institutions is increasingly understood as a socio-technical and organizational process rather than a purely technological upgrade. This study presents the design of an ICT-based digital transformation roadmap aimed at improving administrative efficiency and citizen service delivery in a municipal public utility in Ecuador. A mixed-methods diagnostic approach was adopted, combining qualitative evidence from direct observation and a semi-structured interview with the head of the IT department, and quantitative data from a structured online survey administered to citizens. Baseline Key Performance Indicators (KPIs) were established using institutional records, service logs, and workflow analysis conducted over a three-month diagnostic window. Post-implementation KPI values are explicitly treated as ex ante projections, derived from process redesign analysis, benchmarking with comparable public utilities, and scenario-based assumptions, rather than empirically observed outcomes. The empirical results demonstrate high citizen readiness and acceptance of proposed digital services, including remote service portals, electronic invoicing, and automated support channels. The projected operational improvements—such as reductions in response and administrative processing times and increased digital transaction rates—are therefore presented as expected performance scenarios. A risk and alternative scenario analysis further examines how organizational constraints, resource availability, governance capacity, and change-management factors may moderate these outcomes. The study contributes a transparent and replicable framework for diagnosing digital readiness and planning ICT-driven transformation initiatives in resource-constrained public utilities, while emphasizing the need for future longitudinal validation using post-implementation data. Full article

The increasing need to regenerate public housing stock highlights the importance of adopting integrated evaluation tools capable of supporting transparent, sustainable, and public value-oriented investment decisions. This study compares two alternative intervention strategies—renovation with extension and demolition followed by reconstruction—by applying a Cost–Benefit Analysis (CBA) model developed in two phases. In the first phase, the analysis focuses on social benefits, with the aim of assessing their contribution to collective well-being. The second phase incorporates potential energy-related benefits, estimated on the basis of performance improvements associated with the two design scenarios. The results demonstrate that the integrated consideration of economic, social, and energy–environmental dimensions affects the relative performance differences between the examined strategies, offering a more comprehensive evaluation framework than conventional approaches based solely on monetary costs. The proposed model, which is replicable in Mediterranean contexts, contributes to the ongoing international debate on ex ante evaluation tools and provides operational insights to support urban regeneration policies oriented towards more effective, equitable, and policy-consistent solutions, in line with the objectives of the European Green Deal and the 2030 Agenda. The two-phase structure allows decision-makers to distinguish between short-term social effects and long-term energy-related benefits, offering a transparent support tool for public investment choices under fiscal constraints. Full article

With the continuous advancement of sustainable agriculture, drone technology has become a focus of attention. Current research primarily relies on classical models for questionnaire surveys and analyses within specific regions, rather than implementing macro-level investigations that incorporate innovative algorithms. This study designed a survey questionnaire to investigate Chinese farmers’ preferences for agricultural drones and their technology adoption mechanisms under sustainable agriculture context. The Ant Colony Optimization-Decision Tree (ACO-DT) model and SHAP (Shapley Additive exPlanations) value analysis are applied to analyze the contribution of different indicators to technology adoption. The ACO-DT model outperformed traditional machine learning models with approximate accuracy 0.85, recall 0.98, and F1 Score 0.90, effectively identifying potential drone users compared to other traditional machine learning models. The SHAP analysis showed “Time Required for Promotion” (average SHAP value exceeds 1.25) and “Understanding of UAV Agriculture” (average SHAP value is about 1.0) were core influencing factors. Specifically, high-cognition farmers preferred shorter promotion cycles, while low-cognition group favored longer cycles to reduce decision-making uncertainty. Practically, the study enriches agricultural technology adoption research methodologically and offers references for advancing smart agriculture and optimizing rural production factors. Full article

The dual-channel structure resulted from manufacturer encroachment could alter the incentives of downstream retailer to ex ante communicate demand forecast. And different types of channel competition need to be investigated in this dual-channel information sharing scenario. This paper aims to investigate retailer’s ex ante imperfect demand information sharing strategy given that upstream manufacturer has set up direct sales channel (manufacturer encroachment). The imperfect information sharing means the demand information shared is uncertain and has some error relative to the real-world demand condition. It examines two types of channel competition: quantity competition and price competition. Additionally, this study discusses the encroaching manufacturer’s incentives for adjusting channel substitution. The paper adopts a stylized game theoretic model to describe interactions between retailer and the encroaching manufacturer. Contrary to conventional wisdom, the paper shows that under manufacturer encroachment, it is always possible for ex ante demand information sharing. Specifically, in the Cournot competition scenario where retailer channel and the encroaching manufacturer direct channel compete in quantity, the encroaching manufacturer could encourage demand information communication through side payment. Furthermore, in the Bertrand competition scenario, retailer may voluntarily share demand information. In addition, in either quantity or price competition, the encroaching manufacturer has incentives to adjust channel substitution for profit maximization. Full article

Unlocking SDG-relevant capital depends on coherent and robust impact performance metrics that enable ex ante decision-making across investment options and ex post assessment of both forecasted and realized impact. This study proposes a “synthetic” approach for measuring the impact performance of investments that can be adopted by impact investors and that complements standard impact reporting. We identify five criteria relevant for impact performance measurement—intentionality, measurability, feasibility, incrementality, and comparability—and use them to benchmark a sample of 84 metrics developed by academics and practitioners in the credit finance sector, which attracts the largest volume of impact investments. While over half of the metrics satisfy the criteria of intentionality, measurability, and feasibility—necessary for impact reporting—none meet all five, which are required for robust impact performance measurement. This highlights a significant gap between current practices and what is required to assess impact performance. Based on our findings, we propose a limited set of impact performance metrics suited to credit finance, underlined by a sector-specific theory of change. These metrics, and those that we plan to develop for other sectors, as well as for SDG themes like employment, gender, and climate, are essential to scale up the capital needed to meet the SDGs. Full article

Recognizing the negative consequences of auto-oriented urban transportation, Chinese cities began developing Urban Street Design Guidelines (USDGs) in 2016. The literature on urban transportation design from a decision-making perspective is very limited. As the first systematic evaluation of the pioneering effort by cities in China, this study analyzes local USDG documents and interviews key practitioner stakeholders from ten large cities by adapting a leading policy evaluation tool of urban street design for sustainable transportation based on the Complete Streets Policy Framework. A total of 11 USDGs adopted between 2016 and 2020 were evaluated to represent the wide range of urban contexts in China. The evaluation revealed an average performance of only 30.9% of the total possible score. Despite strong aspirations, local USDGs face significant implementation challenges, lack consideration of disadvantaged communities, and need clarify modal priorities in diverse contexts. Targeted improvements could contribute to more effective and sustainable urban street building and management in China’s cities. As an ex-ante assessment, this study provides a key reference for the future analyses of the outcomes of local USDGs. Full article

Iterative decision making is deeply embedded in the design process of healthcare facilities. A significant body of literature and practices, most notably Evidence-based Design, explicitly seeks to better inform decisions as a key pathway to achieving improved outcomes. The objective of this systematic review is to explore multiple dimensions of decision making in the healthcare design literature, including interprofessional stakeholder engagement, decision flow elements, and multidisciplinary methodologies aimed at improving decision quality during healthcare facility design processes. This review offers a comprehensive review of 114 papers from the Web of Science, CINAHL, MEDLINE, and Art and Architecture Source. Decisions made during healthcare facility design processes are characterized as complex, highly interdependent, and difficult to reverse with significant implications for human and operational outcomes. The published literature emphasizes decision support generated from ex ante or ex post research. Despite numerous references to the importance of decision making, there are considerable gaps in the study of interprofessional group decision-making dynamics. The adoption and application of decision analysis tools and integrated decision flows are emerging. This review synthesizes current perspectives and methods aimed at improving decision making during the design of healthcare facilities and proposes a potential framework for future investigations of design decision quality. Full article

This study examines whether adopting blockchain technology can enhance maritime supply chain performance by improving information sharing in the presence of mismatches between service capacity and demand. We analyze a maritime supply chain with one port and one carrier. Depending on whether the port and the carrier adopt blockchain technology to share forecast information, we consider two scenarios: neither party adopts the technology, or both the port and the carrier adopt it. We find that when the port’s ex ante expected demand is relatively low, the adoption of blockchain technology not only incentivizes the port to expand its service capacity but also increases the actual demand from the carrier. In addition, when the port has a high forecasting accuracy, it prompts both the port and the carrier to make more stable decisions on the service capacity and freight rates under demand uncertainty. Finally, while the port and the carrier exhibit conflicting incentives to adopt blockchain technology, these tensions can nonetheless be reconciled. This alignment becomes possible due to blockchain’s spillover effect: by enabling information sharing, it facilitates a closer match between the port’s service capacity and the carrier’s realized demand. Full article

The rapid urbanization of contemporary cities represents one of the most complex challenges of the 21st century, with profound implications for the environmental, social, and economic sustainability of territories. In this context, urban regeneration emerges as a strategic approach to territorial transformation. The complexity of urban dynamics requires the adoption of innovative paradigms and systemic approaches capable of guiding decision-making processes toward eco-sustainable and resilient solutions. This research develops advanced decision support tools for urban regeneration, using the city of Potenza (Italy) as a case study. The main objective is to identify key indicators to evaluate the effectiveness of urban regeneration interventions in advance (ex-ante). The methodology develops a composite economic-financial risk index capable of providing an accurate picture of existing conditions while adapting to the territorial specificities of the analyzed area. This index, which uses the Analytic Hierarchy Process (AHP) technique to integrate elementary economic-financial indicators in order to assess the sustainability level of urban redevelopment projects, is able to synthesize complex economic variables into a single parameter of immediate comprehension, strategically guiding investments toward a sustainable urban development model. The analysis of results highlights a peculiar territorial configuration: semi-central areas present the greatest criticalities, while there is a progressive decrease in risk both toward the central core and toward peripheral and extra-urban areas. The study represents a significant methodological contribution to future urban regeneration initiatives at the local level, promoting an integrated vision of sustainable urban development for the benefit of current and future generations. Full article

Numerous studies address the estimation of energy consumption at intermodal terminals, with a primary focus on existing facilities. However, a significant research gap lies in the lack of reliable methods and tools for the ex ante estimation of energy consumption in transshipment systems. Such tools are essential for assessing the energy demand and intensity of intermodal terminals during the design phase. This gap presents a challenge for intermodal terminal designers, power grid operators, and other stakeholders, particularly in an era of growing energy needs. The authors of this paper identified this issue in the context of a real business case while planning potential intermodal terminal locations along new railway lines. The need became apparent when power grid designers requested energy consumption forecasts for the proposed terminals, highlighting the necessity to formulate and mathematically solve this problem. To address this challenge, a three-stage model was developed based on a pre-designed intermodal terminal. Stage I focused on establishing the fundamental assumptions for intermodal terminal operations. Key parameters influencing energy intensity were identified, such as the size of the transshipment yard, the types of loading operations, the number of containers handled, and the selection of handling equipment. These parameters formed the foundation for further analysis and modeling. Stage II focused on determining the optimal number of machines required to handle a given throughput. This included determining the specific parameters of the equipment, such as speed, span, and efficiency coefficients, as well as ensuring compliance with installation constraints dictated by the terminal layout. Stage III focused on estimating the energy consumption of both individual handling cycles and the total consumption of all handling equipment installed at the terminal. This required obtaining detailed information about the operational parameters of the handling equipment, which directly influence energy consumption. Using these parameters and the equations outlined in Stage III, the energy consumption for a single loading cycle was calculated for each type of handling equipment. Based on the total number of loading operations and model constraints, the total energy consumption of the terminal was estimated for various workload scenarios. In this phase of the study, numerous test calculations were performed. The analysis of testing parameters and the specified terminal layout revealed that energy consumption per cycle varies by equipment type: rail-mounted gantry cranes consume between 5.23 and 8.62 kWh, rubber-tired gantry cranes consume between 3.86 and 7.5 kWh, and automated guided vehicles consume approximately 0.8 kWh per cycle. All handling equipment, based on the adopted assumptions, will consume between 2200 and 13,470 kWh per day. Based on the testing results, a methodology was developed to aid intermodal terminal designers in estimating energy consumption based on variations in input parameters. The results closely align with those reported in the global literature, demonstrating that the methodology proposed in this article provides an accurate approach for estimating energy consumption at intermodal terminals. This method is also suited for use in ex ante cost–benefit analysis. A sensitivity analysis revealed the key variables and parameters that have the greatest impact on unit energy consumption per handling cycle. These included the transshipment yard’s dimensions, the mass of the equipment and cargo, and the nominal specifications of machinery engines. This research is significant for present-day economies heavily reliant on electricity, particularly during the energy transition phase, where efficient management of energy resources and infrastructure is essential. In the case of Poland, where this analysis was conducted, the energy transition involves not only switching handling equipment from combustion to electric power but, more importantly, decarbonizing the energy system. This study is the first to provide a methodology fully based on the design parameters of a planned intermodal terminal, validated with empirical data, enabling the calculation of future energy consumption directly from terminal technical designs. It also fills a critical research gap by enabling ex ante comparisons of energy intensity across transport chains, an area previously constrained by the lack of reliable tools for estimating energy consumption within transshipment terminals. Full article

As the adoption of Electric Vehicles (EVs) accelerates, driven by increasing urbanization and the push for sustainable infrastructure, the need for innovative solutions to support this growth has become more pressing. Vehicle-to-Grid (V2G) technology presents a promising solution by enabling EVs to engage in bidirectional interactions with the electrical grid. Through V2G, EVs can supply energy back to the grid during peak demand periods and draw power during off-peak times, offering a valuable tool for enhancing grid stability, improving energy management, and supporting environmental sustainability. Despite its potential, the large-scale implementation of V2G faces significant challenges, particularly from a technological and regulatory standpoint. The success of V2G requires coordinated efforts among various stakeholders, including vehicle manufacturers, infrastructure providers, grid operators, and policymakers. In addition to the technical barriers, such as battery degradation due to frequent charging cycles and the need for advanced bidirectional charging systems, regulatory frameworks must evolve to accommodate this new energy paradigm. This review aims to provide a comprehensive analysis of V2G technology, focusing on different perspectives—such as those of users, vehicles, infrastructures, and the electricity grid. This study will also explore ex ante, ex post, and ongoing assessment studies, alongside the experiences of pioneer cities in implementing V2G. Full article