Search Results (937)

Evaluation of the effectiveness of state policy instruments is a permanent element of economic science. This paper addresses the issue of investment support under the Common Agricultural Policy (CAP). Using data on Polish farms from 2015–2023, a Propensity Score Matching–Difference in Differences (PSM-DiD) analysis was conducted to assess changes in the economic results of agricultural producers that invest using this support. The comparison of the economic results achieved by supported investors with both non-investing agricultural producers and unsupported investors is a distinguishing element of this study. The relatively rarely used Competitivness Index (CI), which measures the ratio of earned income to the sum of the alternative use of the owned means of production, was used. The positive change in the CI during the analyzed period was 0.14 higher for supported investors than non-investors. No statistically significant change was found were compared to unsupported investors. A clear increase in income, total fixed assets, liabilities, and the level of production in the population of producers using support in relation to non-investors and investing without CAP support was also observed. However, in relationships with investors using their own funds, these differences were mainly due to the difference in the level of investments and were not statistically significant when introducing a correction regarding the scale of the investment. The obtained results remain in line with the results of research shown by a significant part of economists undertaking a similar issue. Full article

This study assesses the projected impacts of climate change on sediment retention and soil loss in Savannakhet Province, Lao PDR, through the application of the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Sediment Delivery Ratio (SDR) model. Using climate projections under SSP2-4.5 and SSP5-8.5 scenarios for the mid- and late-21st century (2050 and 2080), compared against a 2015 baseline, the analysis quantifies changes in sediment dynamics and ecosystem service provision. Results reveal a substantial increase in sediment retention, particularly in forested and flooded vegetation areas, under moderate and high-emission pathways. However, an overall rise in soil loss is observed across croplands and urbanized zones, driven by intensified high-risk areas, which requires conservative management. This study advocates for ecosystem-based adaptation (EbA) strategies—including afforestation, intercropping, and riparian restoration—to enhance watershed resilience. These nature-based solutions align with national adaptation goals and offer co-benefits for biodiversity, climate regulation, and rural livelihoods. Full article

This study investigates the predictive performance of financial indicators in forecasting stock prices within the automotive sector using an adaptive neuro-fuzzy inference system (ANFIS). In light of the growing complexity of global financial markets and the increasing demand for automated, data-driven forecasting models, this research aims to identify those financial ratios that most accurately reflect price dynamics in this specific industry. The model incorporates four widely used financial indicators, return on assets (ROA), return on equity (ROE), earnings per share (EPS), and profit margin (PM), as inputs. The analysis is based on real financial and market data from automotive companies, and model performance was assessed using RMSE, nRMSE, and confidence intervals. The results indicate that the full model, including all four indicators, achieved the highest accuracy and prediction stability, while the exclusion of ROA or ROE significantly deteriorated model performance. These findings challenge the weak-form efficiency hypothesis and underscore the relevance of firm-level fundamentals in stock price formation. This study’s sector-specific approach highlights the importance of tailoring predictive models to industry characteristics, offering implications for both financial modeling and investment strategies. Future research directions include expanding the indicator set, increasing the sample size, and testing the model across additional industry domains. Full article

This study evaluates the risk-adjusted performance of Hospitality REITs using multi-factor asset pricing models and downside risk measures with the aim of assessing their diversification potential and crisis sensitivity. Unlike prior studies that examine REITs in aggregate, this study isolates Hospitality REITs to explore their unique cyclical and macroeconomic sensitivities. This study looks at the risk-adjusted performance of Hospitality Real Estate Investment Trusts (REITs) in relation to more general REIT indexes and the S&P 500 Index. The study reveals that monthly returns of Hospitality REITs increasingly move in tandem with the stock markets during financial crises, which reduces their historical function as portfolio diversifiers. Investing in Hospitality REITs exposes one to the hospitality sector; however, these investments carry notable risks and provide little protection, particularly during economic upheavals. Furthermore, the study reveals that Hospitality REITs underperform on a risk-adjusted basis relative to benchmark indexes. The monthly returns of REITs show significant volatility during the post-COVID-19 era, which causes return-to-risk ratios to be below those of benchmark indexes. Estimates from multi-factor models indicate negative alpha values across conditional models, indicating that macroeconomic variables cause unremunerated risks. This industry shows great sensitivity to market beta and size and value determinants. Hospitality REITs’ susceptibility comes from their showing the most possibility for exceptional losses across asset classes under Value at Risk (VaR) and Conditional Value at Risk (CvaR) downside risk assessments. The findings have implications for investors and portfolio managers, suggesting that Hospitality REITs may not offer consistent diversification benefits during downturns but can serve a tactical role in procyclical investment strategies. Full article

Agricultural development is vital for East Africa’s economic growth, yet the region faces significant disparities and systemic barriers. A critical problem exists due to the lack of an integrated quantitative framework to systematically comparing agricultural capacities and facilitate optimal resource allocation, as existing studies often overlook combined internal and external factors. This study proposes a comprehensive multi-criteria decision-making (MCDM) model to assess, categorize, and strategically profile the agricultural development capacity of 18 East African countries. The method employed is an integrated Entropy-MAIRCA model, which objectively weighs six criteria (the food production index, arable land, production fluctuation, food export/import ratios, and the political stability index) and ranks countries by their distance from an ideal development state. The experiment applied this framework to 18 East African nations using official data. The results revealed significant differences, forming four distinct strategic groups: frontier, emerging, trade-dependent, and high risk. The food export index (C4) and production volatility (C3) were identified as the most influential criteria. This model’s contribution is providing a science-based, transparent decision support tool for designing sustainable agricultural policies, aiding investment planning, and promoting regional cooperation, while emphasizing the crucial role of institutional factors. Full article

Heuristic optimization is used to find sustainable cogeneration steam power plants with steam reheat and supercritical main steam parameters. Design solutions are analyzed for steam consumer (SC) pressures of 3.6 and 40 bar and a heat flow rate of 40% of the fuel heat flow rate. The objective functions consisted in simultaneous maximization of global and exergetic efficiencies, power-to-heat ratio in full cogeneration mode, and specific investment minimization. For 3.6 bar, the indicators improve with the increase in the ratio between reheating and main steam pressure. The increase in SC pressure worsens the performance indicators. For an SC steam pressure of 40 bar and 9 feed water preheaters, the ratio between reheating and main steam pressure should be over 0.186 for maximum exergetic efficiency and between 0.10 and 0.16 for maximizing both global efficiency and power-to-heat ratio in full cogeneration mode. The average global efficiency for an SC requiring steam at 3.6 bar is 4.4 percentage points higher than in the case with 40 bar, the average specific investment being 10% lower. The Pareto solutions found in this study are useful in the design of sustainable cogeneration supercritical power plants. Full article

Accurate forecasting of household waste generation is essential for sustainable urban planning and the development of data-driven environmental policies. Conventional statistical models, while simple and interpretable, often fail to capture the nonlinear and multidimensional relationships inherent in waste production patterns. This study proposes a machine learning-based regression framework utilizing Random Forest and XGBoost algorithms to predict annual household waste generation across four metropolitan regions in South Korea Seoul, Gyeonggi, Incheon, and Jeju over the period from 2000 to 2023. Independent variables include demographic indicators (total population, working-age population, elderly population), economic indicators (Gross Regional Domestic Product), and regional identifiers encoded using One-Hot Encoding. A derived feature, elderly ratio, was introduced to reflect population aging. Model performance was evaluated using R², RMSE, and MAE, with artificial noise added to simulate uncertainty. Random Forest demonstrated superior generalization and robustness to data irregularities, especially in data-scarce regions like Jeju. SHAP-based interpretability analysis revealed total population and GRDP as the most influential features. The findings underscore the importance of incorporating economic indicators in waste forecasting models, as demographic variables alone were insufficient for explaining waste dynamics. This approach provides valuable insights for policymakers and supports the development of adaptive, region-specific strategies for waste reduction and infrastructure investment. Full article

Traditional intensive agricultural system impedes ecological functions, such as nutrient cycling and biodiversity conservation, resulting in excessive nitrogen discharge, CH₄ emission, and ecosystem service losses. To enhance critical ecosystem services and mitigate environmental externalities in paddy fields, we developed a multi-objective agricultural system (MIA system), which combines two eco-functional units: paddy wetlands and Beitang (irrigation water collection pond). Pilot study results demonstrated that the MIA system enhanced biodiversity and inhibited pest outbreak, with only a marginal reduction in rice production compared with the control. Additionally, the paddy wetland effectively removed nitrogen, with removal rates of total nitrogen and dissolved inorganic nitrogen ranging from 0.06 to 0.65 g N m⁻² d⁻¹ and from 0.02 to 0.22 g N m⁻² d⁻¹, respectively. Continuous water flow in the paddy wetland reduced the CH₄ emission by 84.4% compared with the static water conditions. Furthermore, a simulation experiment indicated that tide flow was more effective in mitigating CH₄ emission, with a 68.3% reduction compared with the drying–wetting cycle treatment. The emergy evaluation demonstrated that the MIA system outperformed the ordinary paddy field when considering both critical ecosystem services and environmental externalities. The MIA system exhibited higher emergy self-sufficiency ratio, emergy yield ratio, and emergy sustainable index, along with a lower environmental load ratio. Additionally, the system required minimal transformation, thus a modest investment. By presenting the case of the MIA system, we provide a theoretical foundation for comprehensive management and assessment of agricultural ecosystems, highlighting its significant potential for widespread application. Full article

In order to reduce gas consumption and increase the renewable energy proportion, this paper proposes a poly-generation system that couples geothermal, solar, and liquid natural gas (LNG) cold energy to produce steam, gaseous natural gas, and low-temperature nitrogen. The high-temperature flue gas is used to heat LNG; low-temperature flue gas, mainly nitrogen, can be used for cold storage cooling, enabling the staged utilization of the energy. Solar shortwave is used for power generation, and longwave is used to heat the working medium, which realizes the full spectrum utilization of solar energy. The influence of different equipment and operating parameters on the performance of a steam generation system is studied, and the multi-objective model of the multi-generation system is established and optimized. The results show that for every 100 W/m² increase in solar radiation, the renewable energy ratio of the system increases by 1.5%. For every 10% increase in partial load rate of gas boiler, the proportion of renewable energy decreases by 1.27%. The system’s energy efficiency, cooling output, and the LNG vaporization flow rate are negatively correlated with the scale of solar energy utilization equipment. The decision variables determined by the TOPSIS (technique for order of preference by similarity to ideal solution) method have better economic performance. Its investment cost is 18.14 × 10 CNY, which is 7.83% lower than that of the LINMAP (linear programming technique for multidimensional analysis of preference). Meanwhile, the proportion of renewable energy is only 0.29% lower than that of LINMAP. Full article

The widespread adoption of cloud computing has amplified the demand for electric power. It is strategically important to address the limitations of reliable sources and sustainability of power. Research and investment in data centres and power infrastructure are therefore critically important for our digital economy. A novel heuristic for the minimisation of energy consumption in cloud computing is presented. It draws similarities to the concept of “network slices”, in which an orchestrator enables multiplexing to reduce the network “churn” often associated with significant losses of energy consumption. The novel network slicing–ring fencing ratio is a heuristic calculated through an iterative procedure for the reduction in cloud energy consumption. Simulation results show how the non-convex equation optimises power by reducing energy from 10,680 kJ to 912 kJ, which is a 91.46% efficiency gain. In comparison, the Heuristic AUGMENT Non-Convex algorithm (HA-NC, by Hossain and Ansari) reported a 312.74% increase in energy consumption from 2464 kJ to 10,168 kJ, while the Priority Selection Offloading algorithm (PSO, by Anajemba et al.) also reported a 150% increase in energy consumption, from 10,738 kJ to 26,845 kJ. The proposed network slicing–ring fencing ratio is seen to successfully balance energy consumption and computing performance. We therefore think the novel approach could be of interest to network architects and cloud operators. Full article

Background: Mobility limitation is a pivotal but under-documented dimension of disability in Saudi Arabia. Leveraging the 2017 National Disability Survey, this cross-sectional study provides a population-wide profile of mobility-related physical difficulty. Objectives: Five research aims were pursued: (1) estimate national prevalence and severity by sex; (2) map regional differentials; (3) examine educational and marital correlates; (4) characterize cause, duration, and familial context among those with multiple limitations; and (5) describe patterns of assistive-aid and social-service use. Methods: Publicly available aggregate data covering 20,408,362 Saudi citizens were cleaned and analyzed across 14 mobility indicators and three baseline files. Prevalence ratios and χ² tests assessed associations. Results: Overall, 1,445,723 Saudis (7.1%) reported at least one functional difficulty; 833,136 (4.1%) had mobility difficulty, of whom 305,867 (36.7%) had mobility-only impairment. Severity was chiefly mild (35% of cases), with moderate (16%) and severe (7%) forms forming a descending pyramid. Prevalence varied more than threefold across the thirteen regions, peaking in Aseer (9.4%) and bottoming in Najran (2.9%). Mobility difficulty clustered among adults with no schooling (36.1%) and widowed status (18.5%), with sharper female disadvantage in both domains (p < 0.001). Among those with additional limitations, chronic disease dominated etiology (56.3%), and 90.1% had lived with disability for ≥25 years; women were overrepresented in the longest-duration band. Aid utilization was led by crutches (47.7%), personal assistance (25.3%), and wheelchairs (22.6%), while 83.8% accessed Ministry rehabilitation services, yet fewer than 4% used home or daycare support. Conclusions: These findings highlight sizeable, regionally concentrated, and gender-patterned mobility burdens, underscoring the need for education-sensitive prevention, chronic-care management, investment in advanced assistive technology, and distributed community services to achieve Vision 2030 inclusion goals. Full article

Melissa officinalis L. (Lamiaceae) is a perennial plant species widely used in the pharmaceutical and food industries, particularly valued for its sedative properties. This study investigates the impact of synthetic mulch film and planting density as two experimental factors on agronomic performance, raw material quality, and economic efficiency in lemon balm production. The experiment was conducted at three locations in Serbia (L1: Bačko Novo Selo, L2: Bavanište, L3: Vilandrica) from 2022 to 2024, using two planting densities on synthetic mulch film (F1: 8.3 plants m⁻²; F2: 11.4 plants m⁻²) and a control treatment without mulch (C). The synthetic mulch film used was a synthetic black polypropylene film (Agritela Black, 90 g/m²), uniformly applied in strips across the cultivation area, covering approximately 78% of the soil surface. The results showed consistent increases in morphological parameters and yield across the years. Plant height in F1 and F2 treatments ranged from 65 to 75 cm, while in the control it reached up to 50 cm (2022–2024). Fresh biomass yield varied from 13.4 g per plant (C) to 378.08 g per plant (F2), and dry biomass yield from 60.3 g (C) to 125.4 g (F2). The highest essential oil content was observed in F2 (1.2% in 2022), while the control remained at 0.8%. The F2 treatment achieved complete weed suppression throughout the experiment without the use of herbicides, demonstrating both agronomic and ecological advantages. Economic evaluation revealed that F2 generated the highest cumulative profit (€142,164.5) compared to the control (€65,555.3). Despite higher initial investment, F2 had the most favorable cost–benefit ratio in the long term. This study highlights the crucial influence of mulching and planting density on optimizing lemon balm production across diverse climatic and soil conditions, while also underscoring the importance of sustainable, non-chemical weed management strategies in lemon balm cultivation. Full article

The integration of financial technology expenditures and green total factor productivity (GTFP) constitutes a critical impetus for sustainable economic advancement. This study employs provincial panel data from China (2012–2020) and uses the SBM model with undesirable outputs, the PVAR model, moderation effect analysis, and threshold regression to investigate the underlying mechanisms and threshold effects of financial technology expenditure on GTFP. The results show that (1) financial technology expenditure has a significant promoting effect on the growth of GTFP, with a coefficient of 0.614 (p < 0.05), indicating the need for further increases in fiscal investment in science and technology; (2) the effect of financial technology expenditure on GTFP varies across the eastern, central, and western regions of China, with stronger effects observed in the eastern region, suggesting that the government should formulate differentiated financial technology expenditure policies on the basis of local conditions; and (3) that educational investment and industrial upgrading play strong moderating roles in the impact of financial technology expenditure on GTFP, with interaction term coefficients of 0.059 (p < 0.05) and 0.206 (p < 0.1), respectively. Threshold analysis further reveals that the positive effect strengthens significantly once educational investment surpasses a log value of 9.3674 and industrial upgrading exceeds a ratio of 0.0814. However, currently, China’s education investment and industrial structure upgrading are still insufficient, necessitating further increases in education investment and promoting the transformation and upgrading of the industrial structure. Full article

Agrivoltaic (Agri-PV) systems face the critical challenge of balancing photovoltaic energy generation with crop productivity, yet systematic approaches to quantifying the trade-offs between these objectives remain scarce. In this study, we identify nine essential design indicators: panel tilt angle, elevation, photovoltaic coverage ratio, shading factor, land equivalent ratio, photosynthetically active radiation (PAR) utilization, crop yield stability index, water use efficiency, and return on investment. We introduce a novel dual matrix Analytic Hierarchy Process (AHP) to evaluate their relative significance. An international panel of eighteen Agri-PV experts, encompassing academia, industry, and policy, provided pairwise comparisons of these indicators under two objectives: maximizing annual energy yield and sustaining crop output. The high consistency observed in expert responses allowed for the derivation of normalized weight vectors, which form the basis of two Weighted Influence Matrices. Analysis of Total Weighted Influence scores from these matrices reveal distinct priority sets: panel tilt, coverage ratio, and elevation are most influential for energy optimization, while PAR utilization, yield stability, and elevation are prioritized for crop productivity. This methodology translates qualitative expert knowledge into quantitative, actionable guidance, clearly delineating both synergies, such as the mutual benefit of increased elevation for energy and crop outcomes, and trade-offs, exemplified by the negative impact of high photovoltaic coverage on crop yield despite gains in energy output. By offering a transparent, expert-driven decision-support tool, this framework enables practitioners to customize Agri-PV system configurations according to local climatic, agronomic, and economic contexts. Ultimately, this approach advances the optimization of the food energy nexus and supports integrated sustainability outcomes in Agri-PV deployment. Full article

Housing prices have recently risen sharply in many countries, primarily linked to the global credit cycle. Although various factors play a role, the ability of developing countries to navigate this cycle and maintain autonomous monetary policies is crucial. This paper introduces a dynamic macroeconomic model featuring a housing production sector within an imperfect banking framework. It captures key housing and economic dynamics in advanced and emerging economies. The analysis shows domestic liquidity policies, such as bank capital requirements, reserve ratios, and currency devaluation, can stabilize investment and production. However, their effectiveness depends on foreign interest rates and liquidity. Stabilizing housing prices and risk-free bonds is more effective in high-interest environments, while foreign liquidity shocks have asymmetric impacts. They can boost or lower the effectiveness of domestic policy, depending on the country’s level of financial development. These findings have several policy implications. For example, foreign capital controls would be adequate in the short term but not in the long term. Instead, governments would try to promote the development of local financial markets. Controlling debt should be a target for macroprudential policy as well as promoting saving instruments other than real estate, especially during low interest rates. Full article