Search Results (887)

Energy efficiency interventions are typically evaluated and implemented at the single-firm level, yet energy use and savings are shaped by interdependent decisions distributed across the supply chain, spanning sourcing, production, inventory, logistics, and financing. A foundational observation motivating this paper is that some energy efficiency actions are only possible through inter-firm cooperation: they require changes to partners’ processes or technologies, create benefits that accrue to different actors than those bearing the investment costs, and demand governance mechanisms (e.g., cost-sharing contract, buyer-financed supplier development, supply chain finance instruments) to be financially viable. This paper proposes CHAIN-EE (Collaborative Holistic Approach for Integrated Network Energy Efficiency), an action-oriented framework that operationalizes systems thinking into a practical roadmap for supply chain decision-makers. CHAIN-EE integrates three interconnected phases: (A) supply-chain energy diagnosis, covering boundary definition, baseline construction, and hotspot identification across nodes and flows; (B) action portfolio design, structured around a six-lever intervention taxonomy and multi-criteria evaluation embedding a cost–benefit alignment map that makes governance feasibility an explicit selection criterion; and (C) governance and continuous improvement, including incentive alignment, investment architecture and ISO 50001-compatible performance management. Evidence from four European research projects spanning the food cold chain, dairy, food-and-beverage/transport value chains, and HORECA illustrates how each phase operates in practice across different sectors and governance contexts. The paper contributes an integrative, sector-adaptable structure for supply chain energy efficiency programmes, grounded in both analytical research and applied project experience, and a targeted research agenda on cross-node rebound effects, data-enabled energy flow mapping, and multi-tier coordination mechanisms. Full article

Reliable medium–long-term wind energy projection is essential in the planning, financing, and operation of large-scale offshore wind development. This study classified projection methods into three categories: statistical/empirical and climate-signal-driven methods, dynamical models with reanalysis and regional downscaling, and machine/deep learning and hybrid methods for bias correction, downscaling, and direct data-driven projection. Then, this study reviewed the technical framework, representative studies, and comparative strengths and limitations. The main finding was that the state of the art increasingly converged on “dynamical simulation plus statistical or machine learning correction”. Next, seven main bottlenecks, along with the countermeasures, were systematically presented: (i) difficult data quality control and insufficient observational representativeness, especially offshore; (ii) divergent, even contradictory, conclusions for the same region across data sources and research groups; (iii) large uncertainty in extrapolating 10 m winds to the continually rising turbine hub height; (iv) difficulty in quantifying and communicating non-stationarity and uncertainty to decision-makers; (v) engineering conversion errors from projected “wind resource” to deliverable “electricity”; (vi) systematic biases in the marine atmospheric boundary layer, strong winds, and extreme conditions; and (vii) unresolved reliability, interpretability, and out-of-distribution generalization of AI models. Correspondingly, three mutually reinforcing strands of countermeasures were proposed: first, strengthening the observational and benchmarking foundation through unified, open, quality-controlled observation networks with data-provenance standards and shared reference datasets and intercomparison protocols; second, advancing physics–data integration and uncertainty quantification through hybrid and physics-informed correction, regime-specific bias correction of boundary-layer and extreme-wind errors, and probabilistic frameworks that delivered and clearly communicated credible intervals; and third, closing the resource-to-electricity gap by embedding power-curve convolution, wake-loss modeling, and availability and technology derating into the projection workflow, with the aim of improving medium–long-term wind energy projection accuracy. Full article

Fiscal shocks in post-land-finance China are weakening the funding basis of capital-intensive metro public-private partnership (PPP) projects, but the system-level mechanism through which public fiscal stress becomes subcontractor-level financial viability pressure remains underexplained. This study examines a section-level metro PPP project ecosystem in a sub-provincial Chinese city to trace this transmission mechanism and its financial implications. The analysis combines de-identified audit evidence and interviews with a scenario-based structural NPV model and 800,000 model-generated Monte Carlo realizations under calibrated institutional scenarios. The evidence indicates that quasi-bureaucratic SPV internal capital-market arrangements convert fiscal shortfalls into vertical and horizontal cross-subsidization practices, preserving short-term project continuity while shifting cash-flow pressure downstream. This condition is defined as a strategic resilience trap: practices that preserve short-term project continuity while potentially eroding the project ecosystem’s long-term adaptive capacity. Under calibrated assumptions, improving the contract-payment channel reduces model-generated losses by approximately 4%, suggesting that payment punctuality addresses only one part of the wider internal capital-market mechanism. Full article

Uzbekistan has rapidly expanded solar photovoltaic (PV) capacity as part of its transition toward a low-carbon energy system, supported by guaranteed purchase tariffs and an evolving regulatory framework. This paper evaluates the economic feasibility of solar PV investments in Uzbekistan through a combined ex post and ex ante analysis, focusing on both commercial-scale rooftop installations and a proposed large utility-scale floating photovoltaic (FPV) project. Ex post performance data from four commercial rooftop PV systems in Tashkent (20–304 kW) over the period 2024–2025 are analyzed using lifecycle investment appraisal metrics, including the Equivalent Uniform Annual Cost (EUAC)/LCOE framework, Net Present Value (NPV), and Internal Rate of Return (IRR), to benchmark real operating outcomes against modeled expectations. These results are subsequently used to calibrate ex ante simulations for a 491 MW FPV installation planned on the Sardoba reservoir, assessed using RETScreen Expert and a bespoke three-statement financial model incorporating detailed tax, financing, and operational assumptions. The findings indicate that commercial-scale rooftop PV projects in Tashkent operate close to the financial break-even point, with EUAC-based levelized costs of energy broadly aligned with current guaranteed purchase prices for PV electricity, resulting in near-zero NPVs. In contrast, the large-scale Sardoba FPV project demonstrates moderate but positive financial viability, with nominal IRRs of approximately 14–16% and payback periods under ten years at the prevailing tariff levels. Importantly, the monetized value of environmental externalities—primarily avoided CO₂ emissions—amounts to roughly from one quarter to a third of initial capital expenditure, materially enhancing the project’s overall economic value. The results suggest that while large-scale solar projects in Uzbekistan generate limited private financial rents, their societal benefits justify continued policy support, stopping short of additional direct subsidy disbursements but conducive to lower cost-of-capital measures. Full article

China’s 2026 land-use policy mandates that commercial and residential construction remain within existing urban footprints, making urban renewal the primary source of spatial capacity for future urban development. Understanding how diverse stakeholders make decisions regarding urban renewal and shape the resulting spatial configurations is therefore of critical importance. This study develops an integrated multi-agent system (MAS) and cellular automata (CA) model to simulate the spatial dynamics of urban renewal. The model explicitly incorporates the location preferences of three key agents—government, developers, and residents. Using Wuhan as a case study, we project land-use patterns for 2035 under three scenarios: a traditional development scenario, a multi-agent simulation (MAS) scenario, and an urban renewal scenario. The findings reveal that (1) urban renewal significantly improves land-use patterns by curbing urban expansion and reducing the loss of farmland and forest cover; (2) government agents exert the strongest driving force on construction land expansion (contribution coefficient: 0.1758), while residents demonstrate a discernible influence on the selection of renewal sites (0.1199), with developer preferences largely aligning with government priorities; (3) a marked preference divergence exists among agents—within central urban areas, resident selection probabilities are only 60–70% of those of government and developers, indicating a notable mismatch between market demand and planning objectives. Moreover, more than 74% of renewal areas are projected to generate economic returns below renewal costs, signaling a potential financial sustainability risk. These results suggest that while urban renewal can effectively slow urban sprawl and mitigate ecological land loss, it simultaneously necessitates multi-stakeholder governance frameworks and sustainable financing mechanisms to reconcile competing interests and reduce fiscal vulnerabilities. The proposed simulation framework offers quantitative support for spatial policy making in China’s future land stock-constrained urban development contexts. Full article

China’s extensive experience with Public–Private Partnerships (PPPs) over the past decade offers valuable lessons for international infrastructure governance. Central to the PPP concept is the imperative to deliver Value for Money (VfM), and VfM assessment serves as a critical tool to determine whether the PPP modality is appropriate for a given project. While China’s Ministry of Finance (MoF) introduced a qualitative VfM assessment framework in 2015, its conceptual ambiguity, indicator overlap, and procedural inconsistencies have limited its effectiveness in guiding PPP decisions. This paper critically examines the existing Chinese qualitative VfM framework through comparative analysis with two international approaches: the UK HM Treasury’s three-dimensional VfM approach and the UN ESCAP’s developmental framework for Asia–Pacific countries. Through pair-wise and structural comparison, the study identifies key gaps in the Chinese model, including the absence of a preliminary applicability screening stage and insufficiently specified treatment of output clarity, scope stability, interface coordination, private-sector capability, revenue sustainability, and asset-life/contract-term alignment. Drawing on international insights, a refined VfM framework is proposed, featuring a pre-screening mechanism, a refined indicator system, and enhanced scoring methodology. The framework is applied to a complex, large-scale PPP case in China through a retrospective reassessment to illustrate its operability and diagnostic usefulness. Results show that the refined approach preserves the original positive VfM judgement while improving analytical clarity, procedural transparency, and the traceability of expert scoring. The paper concludes by discussing how a more structured qualitative VfM procedure can contribute to evaluation governance in PPP decision-making, while also identifying the need for further testing across additional PPP cases in China and beyond. Full article

Ports and industrial regions increasingly call clean-fuel projects “hubs,” although the market functions behind those projects are often still being developed. In Ulsan, this language has shifted from the Northeast Asian Oil Hub to an oil–gas hub and then to a hydrogen–ammonia vision. This article does not ask whether the infrastructure matters. It asks whether the “hub” label is supported by publicly visible evidence. It develops a public-evidence framework for calibrating claims against evidence and adds a public-label validation layer to sustainability-transition and port-governance analysis. The framework is applied to public documents on Ulsan’s oil, oil–gas, and hydrogen–ammonia projects. The oil-hub narrative began with the use of stockpiling assets and tankage leasing. The North Port later became an oil/LNG terminal through project vehicles, terminal-use agreements, EPC contracts, financing, and commercial operation. These records confirm terminal implementation, not the existence of a trading hub. The South Port is better understood as a low-carbon infrastructure vision, while the April 2026 ammonia-bunkering operation is treated as a port-system fuel-supply milestone. By linking public labels to evidence of implementation, recurring use, coordination routines, market institutions, and external recognition, the framework treats hub naming as a sustainability–accountability issue tied to SDG-related infrastructure claims and ESG disclosure integrity, rather than merely as project branding. Full article

Low-rise terraced housing dominates residential construction in non-metropolitan Taiwan, yet empirical evidence on priority divergence between developers and homebuyers in such markets remains scarce. Using a case study of Changhua County, we applied the Analytic Hierarchy Process (AHP) to quantify priority structures of 35 construction managers and 58 homebuyers. Both groups evaluated an identical five-dimensional hierarchy encompassing fourteen directly surveyed factors across location selection, housing price, financing, construction risk, and building planning. Consistency ratios were 0.028 (developers) and 0.019 (homebuyers). The results indicate substantial differences in relative priorities. Developers prioritized Construction Risk (0.368), with Government Regulations (A42) ranking first globally (0.152). Homebuyers prioritized Location Selection (0.412), with Transportation Convenience (A11) ranking first (0.223). The Location Selection dimension gap (0.221 points) and Construction Risk gap (0.251 points) represent the largest divergences. Factor-level rank inversions are pronounced: Transportation Convenience ranks first for homebuyers but fifth for developers; Government Regulations ranks first for developers but fourteenth for homebuyers. The findings suggest that supply-side and demand-side stakeholders may place different emphasis on project feasibility and residential use, with implications for site selection, floor plan design, and buyer communication in regional markets. Full article

Blockchain is a fast-changing field that is highly useful in such areas as finance, supply chain management, voting systems, and healthcare. As a consequence, software developers are increasingly creating Blockchain-Based Applications (BBAs) and Smart Contracts (SCs). However, the development of BBAs has been associated with various problems, especially in the process of updating and debugging such systems with a high degree of reliability. This is due to the immutability of deployed SCs. In this paper, we conduct an in-depth analysis of 61 published BBA articles between 2017 and 2025 to identify some causes of these challenges. Our results indicate that there is inadequate adaptation of the Software Development Life Cycle (SDLC) for BBAs. In particular, few BBA projects—only 32% of the reviewed projects—address the analysis phase, and only 29% deal with the design phase, frequently ignoring formal modeling methods. Based on these observations, we propose a new, context-adaptive methodology that facilitates BBA developers passing through the requirements, analysis, design, and implementation processes. Formal modeling techniques—such as Use Case Maps (UCMs), Finite State Machines (FSMs), and extended Unified Modeling Language (UML) class and sequence diagrams—are used within the methodology to document BBA structural and behavioral features and maintain complete traceability between requirements and implementation. In order to overcome the blockchain-specific drawbacks of traditional UML, we present formal stereotype extensions of UML class diagrams, where a four-compartment structure is introduced to differentiate state variables, functions, events, and access modifiers on SCs. We also provide analogous extensions to UML sequence diagrams using differentiated arrow notations to distinguish between function calls and event emissions to support accurate modeling of decentralized transaction flows. These extensions are described with a rationale and are formally defined and justified by mapping rules. Our methodology is justified by two case studies that prove its applicability in different fields of blockchain. The initial case study thus designs and executes a system of a halal chicken meat supply chain on Ethereum, showing the complete traceability of requirements that are based on UCM-based requirements and FSM-generated algorithms to implement SCs. The second case study applies the methodology to a decentralized Electronic Health Record (EHR) management system, and it shows coverage and completeness modeling. The methodology was evaluated through two case studies using a structured questionnaire and quantitative metrics, including traceability accuracy, reduction-in-error indicators, SC defect and gas-analysis results, modeling overhead measurements, and static security analysis with Slither. It is also evaluated based on a group of seven literature-based qualitative evaluation criteria that include workflow expressiveness, reusability, technical concept coverage, intelligibility, completeness, tool support, and blockchain limitation modeling. Full article

Background/Objectives: Health systems across Europe are increasingly challenged by population ageing, multimorbidity, and persistent inequities in access to care. Community Nursing has emerged as a promising approach to strengthening preventive, community-based services, yet evidence on stakeholder-perceived needs during early implementation remains limited. This study aimed to explore how different types of needs are perceived and articulated during the early implementation of Community Nursing in Austria. Methods: An interpretive descriptive qualitative study was conducted as part of an Austrian Community Nursing pilot project. Semi-structured interviews were carried out with eleven stakeholders, including informal caregivers, network partners (e.g., local healthcare providers), and local political decision-makers. Data were analyzed using qualitative content analysis, guided deductively by Bradshaw’s Taxonomy of Needs and complemented by inductive sub-category development. Interviews were conducted via telephone or video call (Zoom) and ranged from approximately 30 min to an hour. Results: Normative needs reflected expectations for preventive services, continuity of care, advocacy, and sustainable organizational structures. Felt needs centered on trust, emotional security, and relational continuity. Expressed needs became visible through active use of Community Nursing services, including preventive programs, transitional care support, and administrative navigation. Comparative needs highlighted geographic inequities and differences between municipalities with and without access to Community Nursing. Across stakeholder groups, concerns regarding long-term financing and sustainability were prominent. Conclusions: The findings suggest that Community Nursing addresses multiple stakeholder-perceived needs simultaneously, particularly by providing relational, accessible, and preventive support. However, sustained impact depends on stable funding and systemic integration beyond pilot phases. These results offer transferable insights for the development and scaling of community-based nursing models in ageing societies. Full article

Mongolian rangelands face interacting ecological and livelihood pressures, including livestock pressure, vegetation change, soil-carbon dynamics, household income variability, and inefficiencies in livestock by-product recovery. This paper examines whether observed administrative and household data, field-observed pilot-area audit evidence, satellite-derived/backcast vegetation indicators, model-reconstructed ecological trajectories, econometric associations, machine-learning diagnostics, Monte Carlo uncertainty outputs, and scenario-based carbon-finance calculations are consistent with a study-specific ecological–economic feedback framework in Mongolian pastoral rangelands. The analysis combines observed livestock and household data, satellite-derived vegetation indicators, field-anchored soil organic carbon (SOC) information, climate controls, and pilot-area by-product audit evidence in a dual-scale framework comprising nine pasture-user groups in Öndörshireet Soum, Töv Aimag, and a national soum-level panel for 2002–2024. SOC, above-ground biomass (AGB), and below-ground biomass (BGB) trajectories are treated as model-reconstructed series rather than independently observed annual field measurements. Fixed-effects panel models are used to estimate conditional associations, while machine-learning models assess predictive consistency within reconstructed data structures. Under the fitted full specification, the best-performing national-panel model reports an out-of-sample R² of 0.942 for model-reconstructed SOC; this value is interpreted as high internal predictive consistency within the reconstructed SOC panel, not as independent validation of observed annual SOC change. Because the SU/SOC ratio mechanically contains SOC, the full-specification predictive results are subject to leakage risk, and leakage-free validation is needed for a more conservative assessment of predictive performance. Panel estimates suggest that vegetation condition is positively associated with ln(household income), while the by-product waste ratio is negatively associated with ln(income), conditional on fixed effects and model specification. Scenario-based carbon-finance outputs, framed with reference to Verra’s VM0042 Improved Agricultural Land Management methodology, vary materially with compliance, carbon price, weighted average cost of capital, and revenue-sharing assumptions; these outputs are illustrative sensitivity calculations and do not demonstrate VM0042 compliance, project eligibility, project-registration readiness, verified emission reductions, or credit-issuance readiness. The findings are associational, reconstruction-dependent, and scenario-based. They support an analytical framework rather than establish a closed causal loop. Full article

Background: Heat exposure is a leading climate-related health threat, yet whether the heat–disease burden relationship is moderated by national adaptive capacity remains poorly quantified at the global level. We examined associations between heat exposure and cause-specific disability-adjusted life year (DALY) burden across climate vulnerability strata and projected future burden to 2050 under IPCC AR6 warming scenarios. Methods: We constructed a country–year panel spanning 187 countries and 34 years (1990–2023) by merging ERA5 reanalysis temperature data; GBD 2023 DALY rates for cardiovascular diseases (CVD), chronic kidney disease (CKD), and chronic respiratory diseases (CRD); ND-GAIN adaptive-capacity scores; and WHO GHO health system indicators. Countries were stratified into adaptive-capacity tertiles (Low: n = 63; Medium: n = 62; High: n = 62). We used two-way fixed-effects panel regression with country-clustered standard errors, a formal Chow test of slope equality, lagged exposure models, and a benefit-of-adaptation counterfactual. Future DALY burden was projected to 2030, 2045, and 2050 using country-specific ERA5 warming trends scaled to IPCC AR6 SSP scenario multipliers. Findings: The heat–CVD dose–response was 26 times larger in Low versus High adaptive-capacity countries (β = −346.2 vs. −13.1 DALY years per 100,000 per °C). The Chow test confirmed statistically significant slope heterogeneity across tertiles for all three outcomes (CVD: F = 22.0, p < 0.0001; CKD: F = 14.9, p < 0.0001; CRD: F = 9.4, p < 0.0001). CKD burden rose 47·8% globally between 1990 and 2023, with the strongest within-country heat–CKD association in Medium adaptive-capacity countries (β = −61.5, p < 0.0001). These findings were robust to lagged exposure specifications. Under SSP5-8.5 by 2050, Low adaptive-capacity countries face a projected CVD DALY rate change 23 times larger than High adaptive-capacity countries (−16.2% vs. −0.7%). Upgrading Low adaptive-capacity countries to High tertile standards would avert 15.6% of projected CVD DALY burden under SSP5-8.5 by 2050. Conclusions: Adaptive capacity substantially moderates the health consequences of heat exposure. The quantified benefit of adaptation investment—expressed as averted DALY burden—provides a direct metric for health-system strengthening and climate adaptation financing, particularly in low-income settings facing the steepest projected burden increases. These results position adaptive capacity as a critical social determinant of planetary health, linking Earth-system boundary transgression to inequitably distributed human disease burden across the global community. Full article

Sustainable finance has evolved rapidly in tandem with digital transformation and the circular economy; however, the simultaneous integration of these three domains remains fragmented. This study analyzes the intellectual structure of the field through a bibliometric analysis of a curated corpus of 2537 articles indexed in Scopus between 2015 and 2025, of which 2471 were classified into three thematic trajectories: sustainable finance combined with digital transformation (D1), sustainable finance combined with the circular economy (D2), and triadic convergence (D3). The classification followed a deductive, rule-based procedure, with documents independently coded by the two authors and discrepancies resolved by consensus. VOSviewer was used to construct networks of keyword co-occurrence, co-citation, and bibliographic coupling, identifying four thematic clusters. A complementary keyword-overlap projection was then used to articulate the deductive classification with the inductive clusters. The results reveal a rapidly expanding field, geographically concentrated in China, in which the dyadic trajectories anchor predominantly in a single conceptual cluster, while triadic convergence (D3), which appears only in 2021 and accounts for 2.7% of the classified corpus, is the only trajectory whose documents distribute across three clusters simultaneously. This pattern provides empirical support for interpreting triadic convergence as an emerging frontier rather than a consolidated stream. The findings are interpreted under the lens of economicità, an Italian accounting concept that frames sustainability as a condition for the firm’s long-term economic equilibrium. Full article

This study examines the relationship between Environmental, Social, and Governance (ESG) performance and financing and pricing outcomes in green and conventional bond markets in China over the period of 2017–2024. Drawing on signaling theory, information asymmetry theory, and market segmentation theory, the study argues that the role of ESG performance differs across bond types because green and conventional bonds operate within different institutional and informational environments. Using a comparative analysis of green and conventional bonds, the findings show that ESG performance is more strongly and consistently associated with conventional bond characteristics, particularly in relation to issuance amount, yield to maturity, and credit spreads. In contrast, ESG effects in green bonds are weaker and less consistent, suggesting that investors place greater emphasis on certification mechanisms, environmental project objectives, and sustainability-related bond characteristics than on broader issuer-level ESG disclosures. The findings also suggest that ESG information does not affect all debt instruments in the same way or always functions as a purely risk-reducing signal. In the Chinese market, stronger ESG exposure may also be associated with transition risks, regulatory pressures, and sector-specific sustainability challenges, particularly in conventional bond markets. Overall, the results indicate that the financial relevance of ESG performance depends not only on firm characteristics but also on the institutional and informational environment of the financial instrument itself. The findings remain robust across alternative model specifications and sensitivity analyses, providing additional confidence in the reported differences between green and conventional bond markets. The study contributes to the sustainable finance literature by showing that the pricing relevance of ESG information is instrument-specific rather than uniform across debt markets. It also provides practical implications for regulators, investors, and issuers by highlighting the importance of disclosure quality, transparency standards, and external verification mechanisms in strengthening investor confidence and reducing potential greenwashing risks in sustainable finance markets. Full article

This study examines how the relationship between perceived financial accounting disclosures (FAD) and investor reactions to negative financial news (IRNFN) is conditioned by two individual-level moderators among 310 retail investors holding shares in project-based organisations (PBOs) listed on the Dubai Financial Market and Abu Dhabi Securities Exchange. The two moderators are framing bias susceptibility, a cognitive predisposition to be influenced by presentational form, and AI channel reliance (AICR), the extent to which investors rely on AI-mediated information channels—including algorithmic news aggregators, robo-advisory tools, AI-curated social media feeds, and automated sentiment-scored financial alerts—for receiving and interpreting corporate disclosures. Drawing on Behavioural Finance Theory and the Theory of Planned Behaviour, the study investigates whether the strength of the FAD–IRNFN association depends on these cognitive and informational processing conditions. The measurement model was estimated using confirmatory factor analysis in AMOS 25, and the moderation hypotheses were tested through path analysis with mean-centred composite scores and bias-corrected bootstrap inference, with a latent interaction robustness check reported in parallel. AI channel reliance emerged as a substantial moderator of the FAD–IRNFN relationship, while framing bias provided a smaller, marginally significant moderating effect. The findings are consistent with the theoretical expectation that, in AI-mediated information environments, the perceived quality and presentation of complex disclosures are associated with stronger, rather than weaker, investor reactions to negative news. Because the design is cross-sectional and based on self-reported data, the results are interpreted as associations rather than causal effects, with implications for disclosure regulation, corporate communication, and AI platform design in the UAE and comparable emerging markets. Full article