Search Results (516)

Valuation methodologies vary across industries because firms differ in capital intensity, asset life, earnings stability, and exposure to risk. This study examines the valuation approaches used by South African equity analysts across the diversified mining, platinum group metals mining, gold mining, retail, and banking sectors over the 2018–2026 period, with non-financial firm coverage extending to 2024 and banking sector coverage extending to 2026. Using qualitative document analysis of 201 equity research reports covering 24 Johannesburg Stock Exchange-listed companies, including 19 non-financial firms and the five largest South African banks, the study identifies clear clustering of valuation methods by industry. The findings show that resource-based sectors are predominantly valued using intrinsic approaches such as life-of-mine discounted cash flow (DCF) and risk-adjusted net present value (NPV), while retail firms are primarily valued using earnings-based multiples. Gold mining exhibits a hybrid valuation pattern, and banking institutions are valued using balance-sheet- and profitability-based approaches anchored on book value, return on equity, and dividend flows. Overall, the results suggest that valuation practices in the sampled equity research reports are strongly industry-specific and broadly aligned with the underlying economic characteristics of the sectors analysed. The study contributes to the limited empirical literature on professional valuation practice in African capital markets and provides insights relevant to analysts, investors, and regulators. Full article

Dynamic material stock–flow analysis is widely used to assess long-lived built-environment systems, but data scarcity makes its application challenging because stock, inflow, and outflow evidence often do not form a coherent accounting structure. This study develops a reconstruction framework for dynamic material stock–flow modeling under fragmented and incomplete data conditions. The framework integrates data structure diagnosis, harmonization, historical inflow reconstruction, stock anchoring through calibration, dynamic reconstruction, and credibility assessment via empirical consistency checking, uncertainty propagation, and sensitivity analysis. The study uses Vietnam’s residential metabolism case to formalize and test a reconstruction methodology with incomplete historical inflow records, intermittent benchmark stock observations, heterogeneous classifications, and limited evidence on demolition outflows. Comparative results show that omitting historical inflow reconstruction distorts cohort depth and delayed outflow behavior, while omitting stock anchoring leads to persistent underestimation of stock levels relative to benchmarks. The analysis further shows that credible interpretation depends not only on internal stock–flow consistency, but also on compatibility with benchmarks and robustness under plausible parameter variation. The study concludes that, under data scarcity, dynamic stock–flow modeling should be approached as system reconstruction, providing a transparent and reproducible methodological basis for long-lived material systems. Full article

Industrial areas, which represent a specific type of urbanised area with an extremely high concentration of material reserves, can be considered key anthropogenic raw material reservoirs in the context of urban mining. Industrial areas, characterised by a high material density and a specific composition of structural systems, show extraordinary potential for providing secondary raw materials with high material and energy value. This increases the need for their systematic evaluation. The aim of the present study was to define the role of the selected industrial area as a strategic node for secondary raw material extraction, to identify the structure and quality of “urban deposits” in the selected location of the Ostrava–Karviná region (CZ), and to provide an analytical framework for its integration into circular planning processes. The methodological approach is based on a combination of pre-demolition audit, material flow mapping, spatial analysis, and structural element characterisation. It is becoming apparent that industrial areas have a high material density and contain significant amounts of recyclable metals, reinforced concrete elements, etc. These stocks are often concentrated in structural systems with predictable geometries, such as serial assembly prefabricated and steel frames, allowing for more accurate estimates of recoverable volumes. The results show that the incorporation of industrial areas into the process of urban mining can significantly reduce the consumption of primary raw materials, mitigate the environmental impacts associated with the extraction of raw materials, and, at the same time, promote the regeneration of industrial areas (or brownfields) through the planned decomposition of structures. The inclusion of urban mining in urban development strategies and the regeneration of industrial sites leads to the prediction that urban mining is one of the key elements for achieving a material-efficient and low-carbon urban environment. Full article

This study investigates the short-term and long-term impacts of gross domestic product (GDP), inflation, foreign capital flows, trade balance and interest rate on stock market performance in Saudi Arabia for the period 1990–2023. The autoregressive distributed lag (ARDL) approach and error correction model (ECM) are employed to empirically examine the short-run and long-run relationships. The ARDL-ECM technique is effective for analyzing cointegration and assessing adjustment processes. Additionally, impulse response function (IRF) analysis based on the vector autoregression (VAR) model, estimated using these macroeconomic indicators, is applied in this paper. This study provides novel insights and addresses emerging gaps in the literature concerning Saudi Arabia as a developing economy. The long-term relationship in the bounds test results confirms its existence. In the long run, inflation and interest rate exert a statistically significant negative effect on stock market performance, while the trade balance has a significant positive impact. GDP and foreign capital inflows do not exhibit statistically significant long-run effects. Short-run dynamics indicate persistence in stock market performance along with significant effects from inflation and interest rate changes, while GDP and foreign capital inflows remain statistically insignificant in the long-run scenario. Forecast error variance decomposition (FEVD) results show that approximately 68.5% of the variation in market performance is explained by its own shocks, followed by foreign capital flows (16.3%) and inflation (8.4%). While foreign capital flow does not exhibit statistical significance in the ARDL long-run estimates, its contribution in variance decomposition highlights its role as an important source of external shocks. These findings are relevant to various stakeholders, including investors and policymakers. Additionally, policy emphasis should be placed on controlling inflation and maintaining stable interest rates while improving trade balance conditions. Although foreign capital flow does not show a direct long-run effect, its role in influencing market variability suggests the need for a stable and well-regulated investment environment. Full article

Universities worldwide are increasingly committing to carbon neutrality; however, most institutional climate strategies treat operational emissions forecasting and ecosystem-based carbon sequestration as separate analytical domains, leading to inconsistencies in accounting boundaries, temporal alignment, and verification practices. This study develops and demonstrates an integrated LEAP–InVEST framework that explicitly links energy-system modeling with spatial ecosystem carbon accounting within a unified monitoring, reporting, and verification (MRV)-aligned structure. The framework combines the Low Emissions Analysis Platform (LEAP) for scenario-based greenhouse gas emissions modeling with the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model for spatial carbon storage assessment. A key methodological contribution lies in reconciling emission flows and carbon stock changes by converting carbon stock variations into annualized removal flows, thereby enabling consistent estimation of gross emissions, carbon removals, and net emissions while avoiding double counting across scopes. Using a university campus in Taiwan as a case study, a baseline inventory was established following ISO 14064-1 standards, and future emissions trajectories were simulated under Business-as-Usual and mitigation pathways through 2040. In parallel, land-use and land-cover data were used to quantify historical and projected carbon stocks across forest, grassland, agricultural, and built-up areas. Results indicate that electricity consumption constitutes the dominant emissions source, and that energy efficiency improvements, photovoltaic deployment, and green power procurement provide the largest mitigation potential. Although ecosystem carbon stocks remain substantial, their annual sequestration capacity offsets only a limited portion of projected emissions, reinforcing the importance of prioritizing emissions reduction before applying nature-based removals. The proposed framework provides a transferable methodological approach for institutional carbon neutrality planning by integrating emissions reduction and carbon sequestration within a coherent analytical system. By aligning energy modeling, ecosystem dynamics, and MRV principles, the framework enhances the transparency, credibility, and robustness of net-zero pathway assessment and is applicable to universities and compact urban systems seeking data-driven and verifiable decarbonization strategies. Full article

Water resources serve as a rigid constraint for urban sustainable development, yet existing studies still lack sufficient understanding of the decoupling effect and its nonlinear mechanism in urban water resource utilization. This study comprehensively employs the spatiotemporal dynamic matrix, decoupling model, and explainable machine learning methods to conduct an empirical analysis of 70 small cities in Guangxi, China. Findings: (1) From the integrated perspective of stock and flow, the dynamic patterns of water use are diversified. (2) The decoupling status is generally positive, with over 60% of counties decoupling, primarily characterized by weak decoupling. However, over 30% of counties are still in an unhealthy negative decoupling state, indicating that the problem of extensive use of water resources is still prominent. (3) Water resource endowment, population, urbanization, water supply facilities, and land use complexity are key factors affecting decoupling relationships. The effects of these factors exhibit nonlinear patterns such as L, N, U, inverted U, and parabolic patterns, accompanied by pronounced threshold effects and spatial heterogeneity. (4) By integrating the analysis results of the dynamics mode and decoupling effect, this study constructs a 4 × 3 systematic decision-making toolkit. It proposes differentiated and adaptive planning strategies for 12 zoning categories, providing a scientific basis and decision-making references for refined water resource governance in similar areas worldwide. The innovation of this study lies in establishing a nonlinear analytical framework that spans the entire process of “identification—diagnosis—attribution—planning”, advancing the research paradigm in this field from linear to nonlinear approaches. Full article

Understanding how information flows across financial segments during global crises is crucial for analyzing complex and highly interconnected markets. This study investigated the dynamic information flow between cryptocurrencies, commodities, stock market indices of G10 countries, five-year sovereign CDS spreads, ten-year government bond yields, foreign exchange market variables, and technology company stocks using daily return data spanning from 1 January 2018 to 24 March 2026. Transfer Entropy is estimated using two alternative approaches: directly from the original variables and from independent components obtained via Independent Component Analysis (ICA), which reduces noise and uncovers latent relationships. A sliding-window framework is employed to capture time-varying directional information flow and to assess changes across major global events, including the COVID-19 pandemic, the Russia–Ukraine conflict, and the Middle East tensions. The results indicate that the magnitude and direction of information flow change significantly during crisis periods, revealing an event-sensitive and dynamically evolving connectivity structure between financial segments. Overall, the integration of ICA and Transfer Entropy provides a clearer and more reliable representation of directional interactions in multidimensional financial systems under the conditions of heightened uncertainty. Full article

Background/Objectives: The COVID-19 pandemic imposed logistical challenges on health systems, particularly for mass vaccination campaigns under emergency conditions. In decentralized health systems, the absence of a structured preparedness phase may compromise coordination, allocation, and operational performance. This study analyzes the vaccination campaign in a municipality in southern Brazil, examining how the overlap of the preparedness and response phases affected outcomes and how alternative logistical scenarios could have altered campaign performance. Methods: An empirical analysis was conducted using scenario-based simulation with stock and flow structures. The model represents vaccine procurement, distribution across national, state, regional, and municipal levels, and municipal vaccination capacity. Real data from the 2021 vaccination campaign in the municipality were used to build a Business-as-Usual scenario, compared with alternative scenarios involving changes in procurement predictability, allocation rules, and operational capacity. Results: Vaccination outcomes were strongly conditioned by upstream allocation decisions, particularly at the national state level. Isolated adjustments at intermediate supply chain levels produced limited improvements when upstream constraints persisted. Scenarios combining improved alignment between forecasted and acquired doses with operational capacity showed higher vaccination potential, revealing a gap between observed performance and system capacity. Conclusions: The findings reinforce that preparedness is a critical determinant of vaccination performance and must precede response in emergency contexts. Supply predictability alone is insufficient without coordinated allocation mechanisms and operational readiness across governance levels. This study provides empirical evidence on how preparation-related decisions shape vaccination outcomes in decentralized health systems and inform logistical coordination in future emergencies. Full article

This study assesses the elasticity between integrated media performance and corporate reputation by examining the relationship between Media Output Score (MOS) and RepScore™ in the Portuguese telecommunications sector (Altice/MEO, NOS, and Vodafone) between 2021 and 2023. Adopting a longitudinal observational design, the analysis compares inter-annual variation in communication output with corresponding changes in stakeholder-based reputation. Media performance is operationalized through MOS as a composite indicator of visibility, favorability, readership, targeting, and social amplification, while corporate reputation is measured using third-party RepScore™ data. The findings indicate directional alignment between media output and corporate reputation; however, the magnitude of reputational adjustment appears substantially lower than the amplitude of media volatility. Across heterogeneous crisis contexts, including cybersecurity incidents and governance-related events, reputational scores exhibit incremental and comparatively stable evolution despite pronounced fluctuations in media performance. These results suggest that the relationship between media output and corporate reputation is characterized by constrained responsiveness at the annual level, consistent with a stock–flow interpretation in which communication signals operate as high-variance flows and reputation evolves as a path-dependent stock. By empirically illustrating this asymmetry, the study contributes to media influence research by identifying a structural boundary condition in the translation of media exposure into stakeholder evaluation. The findings further clarify the analytical distinction between output-level communication metrics and outcome-level reputational constructs in digital media environments. Full article

This study analyzes the relationship between stock market returns and investment flows in investment funds in Spain. Through a quantitative analysis covering the period from December 2001 to June 2025, it examines not only the existence of a correlation but also its temporal structure, functional form, and heterogeneity across different geographical areas (U.S., Europe, Japan, and Spain). Using monthly data on net flows from INVERCO and market indices, the study employs Ordinary Least Squares (OLS) regression models, segmented regressions, and fixed-effects panel models to obtain robust estimates. The results confirm a positive and statistically significant relationship between past returns and subsequent investment flows, with a temporal lag ranging from one to three months. This delay varies notably by geographical region, suggesting the existence of different investor profiles and information channels. The study also finds evidence of a convex relationship, indicating that investors react asymmetrically, aggressively pursuing high returns more than penalizing low ones. These findings, interpreted through the lens of behavioral finance, point to pro-cyclical and reactive behavior of Spanish investors, driven by biases such as loss aversion, trend-following, and delays in information processing. The study contributes to the academic literature by providing updated and methodologically robust evidence on Spain, a market that has traditionally been underexplored, and offers practical implications for investors, fund managers, and regulators in terms of financial education and risk management. Full article

Pre-fattening (also referred to as nursery culture) of Manila clam is a priority for this sector of aquaculture, as it allows hatchery-produced seed (1–3 mg) to reach sowable juvenile sizes of 30–100 mg and reduces reliance on natural juvenile recruitment. This study evaluated the efficiency of two early pre-fattening systems, both in economic terms and in product quality: conventional upwelling units (a high-density system) and flat-bottom tanks (a mid-density system), the latter tested with and without a sand layer. The 51-day trial was conducted under autumn environmental conditions (temperature 13–25.8 °C; salinity 25–28 ppt; chlorophyll-a 3–24 µg/L), starting with 1.34 mg seed maintained under a water flow rate ≥ 15–20 mL/min/g. In upwelling units, the initial density was ~216 ind./cm². Four grading events produced four size classes, with total mean weights ranging from 6.4 mg in the smallest (tails) to 46.3 mg in the largest (heads). The overall population mean size was 19.0 mg, with a specific growth rate (SGR) of 5.2%/day and mortality of 17.6%. Flat-bottom tanks, stocked at ~30 ind./cm², achieved higher growth (overall weighted mean: 28.0 mg; SGR ~6%/day), but exhibited higher mortality (26.0% on average), with no significant effect from the presence of bottom sand. Overall, flat-bottom systems showed promising growth performance with reduced labor requirements, suggesting that this system could represent a viable alternative to upwelling. However, the associated rearing protocol could still be improved by optimizing stocking density and water exchange rates. Full article

With the rapid growth of passenger demand, the imbalance between transport capacity and passenger flow has become increasingly severe. Existing studies seldom consider the impacts induced by passenger demand uncertainty under a flexible train composition mode. To address this issue, this study investigates the integrated optimization of train timetabling and rolling stock circulation planning under a flexible train composition mode. The objective is to minimize the number of stranded passengers and operational costs. A scenario-based robust optimization framework is introduced, and a mean risk objective is formulated by combining the expected objective value with the expected absolute deviation of each scenario’s objective value from the expectation. By using linearization techniques, the model is transformed into a mixed integer programming (MIP) problem, which balances the operating cost and robustness while satisfying safety and service level requirements. The model is validated through a case study of Shanghai Metro Line 16. Numerical experimental results indicate that, in a single scenario, compared with the fixed train composition scheme, the proposed scheme reduces the objective function value by 28.3%. Simultaneously, it can enhance the robustness of the train timetable and rolling stock circulation plan under the condition of uncertain passenger demands. The related findings provide decision support for the design of urban rail transit operating plans. Full article

Capital structure decisions are among the most critical financial choices for firms, as they directly influence them. There is an ongoing debate among researchers regarding the optimal capital structure, motivating this study to examine the impact of various factors on firms’ capital structure, while also considering the COVID-19 pandemic as an influential external factor. This study investigates the financial behavior of 147 non-financial firms listed on the Egyptian Stock Exchange over the period of 2011–2022, using firm year observations. Firms were classified into 14 sectors, excluding banking and non-banking financial services, due to their unique regulatory environments. Data were collected from multiple reliable sources, including financial statements, corporate reports, and EGX databases. Advanced econometric techniques, including the Panel Generalized Method of Moments (GMM), the Arellano–Bond test, and Johansen cointegration analysis, were employed to address endogeneity and explore long-run relationships. The results show that leverage is persistent over time, is positively associated with firm size, tangible assets, and growth opportunities, and is negatively related to profitability, cash flow, and liquidity. The COVID-19 pandemic had a small but significant positive effect, and sectoral differences were also observed. The findings provide robust insights into corporate financing behavior in emerging markets, highlighting the interplay between firm characteristics, external shocks, and financing decisions. Full article

Rapid digitalization and artificial intelligence are restructuring land systems by altering the functional relationship between built environments, socio-ecological processes, and territorial capital accumulation. This paper provides a conceptual and literature-based analysis of how hybrid physical–digital infrastructures are reshaping urban–rural interactions, land-use intensity, and long-term sustainability conditions. Rather than developing a fully operational measurement model, the study critically examines the limitations of aggregate productivity indicators and existing evaluation frameworks in capturing spatial reorganization processes, capital durability, and long-term dynamics. Building on insights from sustainability economics and socio-ecological systems research, the paper proposes a stock–flow interpretative perspective to better understand the interaction between physical, natural, and intangible capital within evolving land systems. The analysis focuses on three structural drivers of land system transformation: (i) the virtualization of services and the expansion of cyberspace-based infrastructures; (ii) demographic contraction and aging processes affecting land demand and settlement structures; and (iii) capital deepening in energy-intensive digital networks with implications for land–climate interactions. Within this context, particular attention is given to infrastructure life-cycle dynamics, entropy-related capital decay, and the role of artificial intelligence in reshaping labor–land relationships. The paper highlights the need for new evaluation approaches capable of distinguishing between value generated through material land transformation and value emerging from intangible and digital layers. In this sense, it aims to contribute to ongoing debates on land management and spatial planning by outlining a research agenda for the development of spatially grounded, stock–flow-based sustainability metrics. The findings suggest that future land governance and urban development strategies will need to explicitly account for hybrid spatial architectures and their long-term resource and climate implications in order to preserve territorial resilience and intergenerational equity. Full article

Freight transportation is a significant contributor to greenhouse gas (GHG) emissions in the US. As an emerging technology, truck platooning leverages vehicle-to-vehicle communications to enable trucks to travel in convoys with close proximity, which reduces air drag and consequently truck fuel use and GHG emissions. However, uncertainties remain about how this emerging technology may be adopted and its climate impacts. To this end, this paper investigates the role of truck platooning adoption in mitigating the climate impact of trucking from a system perspective. Considering the dynamic nature of truck platooning adoption, system dynamics (SD) models based on stock and flow diagrams are developed to estimate the potential reduction in fuel use and CO₂ emissions in the US trucking sector when truck platooning technology becomes available. The results show that adopting platooning could save 292 million metric tons of CO₂ emissions in 180 months after the initial introduction of the technology in the US truck sector. The analysis provides insights for accelerating truck platooning adoption while enhancing its environmental impact. Full article