Search Results (1,368)

The global transition to renewable energy requires hybrid solutions that address variability while delivering tangible co-benefits and verifiable mitigation outcomes. This study evaluates a novel small hydropower–photovoltaic (SHP–PV) hybrid system in the Kyrgyz Republic that integrates flexible Bitcoin mining loads and waste-heat recovery for greenhouse heating. A techno-economic model was developed for a 10 MW configuration, allocating annual net generation of 57.34 GWh between grid export and on-site mining through a single decision parameter. Mitigation accounting applies a combined margin grid factor of 0.4–0.7 tCO₂/MWh for exported electricity and a diesel factor of 0.26–0.27 tCO₂/MWh_fuel for heat displacement, yielding Article 6–eligible reductions from both electricity and recovered heat. Waste-heat recovery from mining supplies ≈15 MWh_th/year to a 50 m² greenhouse, displacing diesel use and demonstrating visible sustainable development co-benefits. Economic analysis reproduces annual revenues of ≈$1.9 million, with a levelized cost of electricity of $48/MWh and an indicative IRR of ~6%, consistent with positive but modest returns under merchant operation and uplift potential under mixed allocations. This study concludes that componentized accounting—exported electricity credited under grid displacement and diesel displacement credited from recovered heat—ensures Article 6 integrity and positions SHP–PV hybrids as replicable, multi-service renewable models for Central Asia. Unlike prior hybrid studies that treat generation, economics, and mitigation separately, our framework integrates allocation (α), financial outcomes, and Article 6 carbon accounting within a unified structure, while explicitly modeling Bitcoin mining as an endogenous flexible load with thermal recovery—advancing methodological approaches for multi-service renewable systems in climate policy contexts. Full article

Addressing climate change and achieving carbon neutrality are urgent global responsibilities, with China’s “dual carbon” goals presenting a significant challenge and opportunity for its energy sector. Green finance, as a pivotal driver for fostering low-carbon and high-quality development in the energy industry, significantly accelerates its green transition. Employing an integrated micro-macro framework, this study first develops a tripartite evolutionary game model involving government, local energy enterprises, and external energy enterprises to analyze the micro-mechanisms of corporate low-carbon decision-making under green finance policies. Subsequently, utilizing panel data from 30 Chinese provinces (2013–2021), it empirically examines the macro impact of green finance on the industry’s low-carbon, high-quality development using a spatial Durbin model (SDM). Key findings include the following: (1) Game analysis reveals that local enterprises’ low-carbon transition propensity and emission reduction returns increase with R&D investment but are negatively moderated by the tax rate level within green finance policies. (2) Spatial econometric results demonstrate that green finance significantly facilitates local energy industry low-carbon transition via technological progress, confirming a significant negative spatial spillover effect on neighboring regions, with notable regional heterogeneity. (3) The effectiveness of green finance policy exhibits significant regional disparity, being markedly stronger in eastern China compared to central and western regions. The findings provide a theoretical and practical foundation for improving market mechanisms and regional coordination in China’s green finance policies, offering a valuable reference for the design of green finance systems in other major emerging and developing economies. Full article

Background: Post-COVID-19 condition (‘long COVID’) involves fluctuating symptoms across multiple organ systems and disability or functional loss, which may be episodic, continuous, or permanent. Qualitative research is essential to capture lived experiences and explain how social and health system contexts may influence improvement, recovery, and service use. We synthesised perspectives from people living with long COVID and healthcare professionals to inform service design and policy. Methods: We conducted a systematic review and qualitative meta-synthesis. MEDLINE, Embase, PsycINFO, CINAHL, Scopus, and Web of Science were searched for studies published between 1 January 2020 and 19 August 2025. Eligible studies reported qualitative data from adults with long COVID (≥12 weeks after acute infection) and/or healthcare professionals in any setting. We excluded non-qualitative, non-primary, or non-English reports. Two reviewers independently screened, extracted, and appraised studies using the Critical Appraisal Skills Programme checklist. Data were synthesised thematically. The protocol was registered with the Open Science Framework. Findings: Of 1544 records screened, 49 studies met the inclusion criteria: 41 involving patients, two involving professionals, and six involving both. Eight patient themes (including symptom burden, identity disruption and stigma) and four professional themes (including recognition, care coordination and holistic care models) were identified. Recognition emerged as a cross-cutting mechanism: validation and consistent pacing guidance facilitated engagement and safer activity, whereas invalidation and inconsistent advice were associated with distress, avoidance, and disengagement. Trajectories showed gradual expansion of multidisciplinary care models, but major capacity and equity gaps persisted. Most studies had low methodological concerns, although heterogeneity in populations and settings was substantial. Interpretation: Long COVID is a chronic, biological condition that also intersects with social and psychological dimensions, and may present with episodic, continuous, or progressive trajectories. Healthcare services must prioritise early validation, provide consistent pacing and relapse prevention guidance, expand access to multidisciplinary and peer-supported rehabilitation, integrate mental healthcare, strengthen coordinated pathways, and support graded return to work. Explicit attention to equity is required to avoid widening disparities. Full article

Renewable energy sources can be of significant help to rural communities with inadequate electricity access. This study presents a comprehensive techno-economic assessment of a 500 kWp solar Photovoltaic (PV) energy system designed for Ibadan, Nigeria. A novel hybrid modeling framework was developed in which technical performance analysis was employed using PVSyst, whereas economic and optimization analysis was carried out using HOMER. Simulation outputs from PVSyst were integrated as inputs into HOMER, enabling a more accurate and consistent cross-platform assessment. Nigeria’s enduring energy crisis, marked by persistent grid unreliability and limited electricity access, necessitates need for exploration of sustainable alternatives. Among these, solar photovoltaic (PV) technology offers significant promise given the country’s abundant solar irradiation. The proposed system was evaluated using meteorological and load demand data. PVSyst simulations projected an annual energy yield of 714,188 kWh, with a 25-year lifespan yielding a performance ratio between 77% and 78%, demonstrating high operational efficiency. Complementary HOMER Pro analysis revealed a competitive levelized cost of energy (LCOE) of USD 0.079/kWh—substantially lower than the baseline grid-only cost of USD 0.724/kWh, and a Net Present Cost (NPC) of USD 6.1 million, reflecting considerable long-term financial savings. Furthermore, the system achieved compelling environmental outcomes, including an annual reduction of approximately 160,508 kg of CO₂ emissions. Sensitivity analysis indicated that increasing the feed-in tariff (FiT) from USD 0.10 to USD 0.20/kWh improved the project’s financial viability, shortening payback periods to just 5.2 years and enhancing return on investment. Overall, the findings highlight the technical robustness, economic competitiveness, and environmental significance of deploying solar-based energy solutions, while reinforcing the urgent need for supportive energy policies to incentivize large-scale adoption. Full article

Addressing Artificial Intelligence (AI) faculty deficiencies in higher education, this paper develops the AI+ Dual-qualified Teacher Capability Ecology Model (AI-DTCEM) based on Capability Ecology Theory. The model is developed after a thorough analysis of the current state of new engineering talent cultivation in universities and the innovative practical abilities required in the AI+ environment. This paper proposes an implementation framework characterized by “three-dimensional collaboration, four-tier progression, and five-element drive.” Additionally, it uses the collaborative education project involving Hangzhou Normal University, Zhejiang University, and Hangzhou Ruishu Technology Co., Ltd. as a backdrop to introduce a deep collaborative education model, showcasing the theoretical and practical achievements of this project. Using NetLogo as the simulation platform, this paper designs a 96-month system dynamics experiment to compare and analyze the outcomes of four scenarios: the baseline experiment, the AI-enhanced experiment, the policy-driven experiment, and the comprehensive optimization experiment. This study reveals the following findings: (1) Policy-driven initiatives are crucial for the successful construction of dual-qualified teacher teams, with the policy-driven scenario achieving the highest overall skill level (9.332). (2) The application of AI technology significantly enhances teacher skill development, resulting in AI skill improvements ranging from 116.6% to 163.4%. (3) The comprehensive optimization scenario (utilizing a collaborative mechanism) achieves systemic advantages, realizing a 100% dual-qualified teacher ratio. However, this comes with diminishing marginal returns on investment. This research provides a theoretical foundation, quantitative analysis, and practical pathways for developing dual-qualified teacher teams in the AI+ era. Full article

This study investigates the dynamic relationship between key macroeconomic indicators, specifically inflation (CPI), the Federal Funds Rate, GDP growth, unemployment, and money supply, and U.S. stock market returns, represented by the S&P 500 index, over the period January 2015 to June 2025. The objective is to understand how inflation and monetary policy affect market performance in both the short and long run. Using an Autoregressive Distributed Lag (ARDL) modeling framework and Error Correction Model (ECM), the study examines monthly S&P 500 returns alongside macroeconomic variables, accounting for lagged effects and potential cointegration. The model captures both immediate and delayed impacts, employing the Bounds Testing approach to confirm long-run equilibrium relationships. Results show significant mean-reversion in stock returns, a delayed negative impact of inflation and interest rate increases, and a positive contemporaneous response to GDP growth. Unemployment exhibits a counterintuitive positive effect on returns, suggesting forward-looking investor expectations. The money supply also positively influences equity prices, supporting liquidity-based asset pricing theories. This paper provides updated empirical evidence on macro-finance linkages and highlights the complex interplay of monetary policy, inflation, and market expectations in shaping U.S. equity returns. Full article

Thailand generated 27.2 million tons of municipal solid waste in 2024, of which 12% was plastic waste, predominantly single-use plastics. The mismanagement of plastic waste can lead to significant long-term environmental issues, including the release of toxic chemicals through open burning and air pollution, posing risks to human health. Effective and efficient plastic waste collection and recycling are therefore essential to address the reduction and management of plastic waste, as well as to support a low-carbon energy transition. This study assessed three community-driven initiatives by conducting a comparative sustainability assessment of plastic bag recycling under real-life conditions in Thailand using a multi-criteria decision-making framework. The results of the assessment in three municipalities showed that the actual collection rates in all initiatives remained extremely low (0.0014–0.1555%). The highest rankings were observed with recycling initiatives driven by superior collection rates and favorable economic returns. The hindrances to promoting sustainability are found to be due to policy inconsistency, ineffective leadership, and behavioral barriers. The practical collection rates should increase to at least 25% to be more sustainable in terms of economic, social, and environmental aspects compared to those without the recycling initiative. These findings, thus, provide specific targets for improving plastic waste separation and management strategies in all regions facing similar challenges. Full article

Background: Tourists returning home and visitors from abroad often carry antimicrobial-resistant (AMR) bacteria. Many of these resistant bacteria are acquired from, or were spread via, the environment (especially water). Understanding the impact from acquiring resistant bacteria via tourism upon global antimicrobial resistance is limited. Methods: Traveller transmission of AMR bacteria can be estimated from combining the numbers of travellers with AMR bacteria rates in different regions and the prevalence of communicable diseases. We used resistance data (WHO and contemporary publications) to measure the prevalence of E.coli resistance to third-generation cephalosporins. The study uses data from 2019, the year with the most complete dataset that also predates disruptions to travel caused by the COVID-19 pandemic. We then used the global burden of disease study and travel data from the World Travel and Tourism to create regional and country level indices measuring the impact of AMR bacteria for 241 countries. Estimates of global travel patterns were obtained using a gravity-style trip distribution model. Findings: Regions with the highest impact of AMR bacteria from returning travellers were Northern Europe and Western Europe. The region with the highest impact of AMR bacteria from visiting travellers was the Caribbean where small island countries receive large numbers of visitors. For countries/administrative regions with populations greater than 5 million, the AMR bacterial travel impacts measured in decreasing risk order from the highest were Hong Kong, Denmark, New Zealand, Hungary, Norway and Sweden. Interpretation: For some countries the incidence of AMR infection among both visitors and returning travellers is much higher than in the domestic population. This impact and how these bacteria are acquired from the environment, must be factored into public health policies for containing global spread of AMR bacteria and as part of a One Health approach. Full article

Flood frequency analysis is essential for designing resilient hydraulic infrastructure, but traditional stationary models fail to capture the influence of climate variability and land-use change. This study applies a bivariate logistic model with non-stationary marginals to eight gauging stations in Sinaloa, Mexico, each with over 30 years of maximum discharge records. We compared stationary and non-stationary Gumbel and Generalized Extreme Value (GEV) distributions, along with their bivariate combinations. Results show that the non-stationary bivariate GEV–Gumbel distribution provided the best overall performance according to AIC. Importantly, GEV and Gumbel marginals captured site-specific differences: GEV was most suitable for sites with highly variable extremes, while Gumbel offered a robust fit for more regular records. At station 10086, where a significant increasing trend was detected by the Mann–Kendall and Spearman tests, the stationary GEV estimated a 50-year return flow of 772.66 m³/s, while the non-stationary model projected 861.00 m³/s for 2075. Under stationary assumptions, this discharge would be underestimated, occurring every ~30 years by 2075. These findings demonstrate that ignoring non-stationarity leads to systematic underestimation of design floods, while non-stationary bivariate models provide more reliable, policy-relevant estimates for climate adaptation and infrastructure safety. Full article

Water scarcity, climate change, population growth, and rising water demand highlight the urgency of adopting effective water conservation measures. The transition from traditional irrigation systems, such as flood irrigation, to modern ones, like drip irrigation, is often seen as a panacea to improve irrigation efficiency and address water shortages. Despite the flourishing literature on the efficiency gains achieved by shifting to drip irrigation, trade-offs associated with replacing traditional irrigation systems with modern irrigation technologies remain unexplored. Building on this gap, this paper provides a systematic literature review to analyze the current state of knowledge and research on the trade-offs associated with this transition. The review analyses not only the possible effects on agricultural productivity and irrigation efficiency at the farm scale, but also the environmental implications and socio–economic consequences that may emerge at a larger scale. We found that while studies conducted at the field-level emphasize clear benefits associated with the adoption of drip irrigation, including higher crop yields and improved water use efficiency, basin-scale analyses reveal drawbacks, including increased consumptive use, reduced return flows for ecosystem processes, and more generally limited real water savings. Overall, our findings stress the need for more holistic, multi-scale, and interdisciplinary approaches to assess the impact of irrigation modernization, along with the need for policy frameworks that balance agricultural productivity gains with sustainable water management. Full article

Shimer documents that the search-and-matching model driven by productivity shocks explains only a small share of the observed volatility of unemployment and vacancies, which is known as the Shimer puzzle. We revisit this evidence by replacing the representative firm’s optimization with a deep reinforcement learning (DRL) agent that learns its vacancy-posting policy through interaction in a Diamond–Mortensen–Pissarides (DMP) model. Comparing the learning economy with a conventional log-linearized DSGE solution under the same parameters, we find that while both frameworks preserve a downward-sloping Beveridge curve, learning-based economy produces much higher volatility in key labor market variables and returns to a steady state more slowly after shocks. These results point to bounded rationality and endogenous learning as mechanisms for labor market fluctuations and suggest that reinforcement learning can serve as a useful complement to standard macroeconomic analysis. Full article

Tourism performance often depends on the joint provision of built (“hard”) and environmental (“green”) infrastructure, yet their combined effects are not well established. Using official data for Saudi Arabia’s 13 regions (2023–2024), this study constructs composite hard and green indices, estimates ordinary least squares models with heteroskedasticity-consistent inference, and probes spatial heterogeneity using geographically weighted regression (exploratory) alongside k-means/hierarchical clustering. Hard infrastructure is the strongest and most consistent correlate of overnight visitors and spending, whereas green infrastructure exhibits non-positive marginal effects over the observed range of hard capacity; a negative, statistically significant Hard × Green interaction indicates diminishing returns to greening as built capacity increases. Clustering differentiates metropolitan hubs from nature-oriented regions, underscoring place-specific policy needs. Practically, results support sequencing prioritizing foundational access and basic accommodation in under-served regions, quality upgrades and public-realm enhancement in mature centers, and targeted green interventions where marginal gains are greatest. Key limitations (cross-sectional design; coarse green metrics) motivate richer environmental indicators and longitudinal data to clarify dynamics and thresholds over time. Full article

The severe degradation of thin-layer black soil in the Southern Songnen Plain threatens both regional agricultural sustainability and national food security. While various fertile topsoil restoration technologies have been proposed, a systematic evaluation of their comprehensive benefits is lacking, hindering effective policy and technology promotion. This study addresses this gap by employing an entropy weight–fuzzy comprehensive evaluation method to assess the economic, social, and ecological performance of four predominant restoration models—no-tillage, strip-tillage, deep-tillage, and indirect return—using survey data from 263 farmers. Results identify strip-tillage as the optimal model, achieving the highest integrated benefit score (8.153) by successfully balancing superior economic profitability and social acceptance with robust ecological performance. Although no-tillage excels in ecological benefits like moisture conservation (8.901) and pesticide reduction (8.524), its economic potential is constrained by higher management costs. Deep-tillage rapidly enhances soil fertility (8.628) but is limited by high operational costs, whereas the indirect model, despite high ecological sustainability (7.781), faces adoption barriers due to technical complexity and cost. The findings underscore the necessity of moving beyond one-size-fits-all approaches. We propose a targeted promotion system based on “categorized guidance and precision adaptation”, offering a practical framework for optimizing technology deployment to support both black soil conservation and sustainable agricultural development. Full article

Amid global economic transformation, technological innovation is widely recognized as a pivotal driver for the sustainable upgrading of the automotive industry. This is particularly critical for China, the world’s largest automotive market, which faces persistent challenges, including technological bottlenecks in core components and confinement to low-value segments within global value chain. This study introduces novelty by systematically integrating and empirically testing the mediating role of Global Value Chain (GVC) and the moderating effect of industrial agglomeration within a unified framework—a focus that remains underexplored in the sector. Using panel data from 28 Chinese provinces (2000–2020), we measure industrial upgrading using the DEA-Malmquist index to capture total factor productivity changes and employ a system GMM approach. The results indicate that technological innovation directly fosters industrial upgrading and indirectly facilitates it by improving the industry’s GVC position. Moreover, industrial agglomeration positively moderates this relationship, thereby amplifying the returns to innovation within geographic clusters. These findings highlight the necessity for integrated policies that simultaneously promote technological innovation, strategic GVC positioning, and synergistic industrial agglomeration to achieve sustainable upgrading. As a paradigmatic emerging economy, China’s experience offers valuable insights for other latecomer economies pursuing industrial upgrading. Full article

Order-picking is one of the most labor- and cost-intensive operations in warehouses, especially under the pressures of e-commerce growth and supply chain disruptions. Globally, order-picking accounts for 50–75% of total warehouse operating costs and nearly 55% of labor time, making it a dominant factor in logistics performance. Improving picking efficiency is therefore essential not only for reducing operational costs but also for enhancing resilience and sustainability in logistics. This study investigates the combined impact of storage space allocation and picker routing strategies on performance in a real-world edible oil factory warehouse with a three-block U-shaped layout. Three allocation policies (dedicated, turnover-based class storage, and family-based class storage) and three routing methods (S-shape, return, and midpoint) were tested in nine combinations over a five-week period. Results show that storage allocation has a stronger influence on picking efficiency than routing decisions. The family-based (Class 2) allocation with return routing achieved the lowest weekly picking time, reducing retrieval effort by concentrating items in low-level storage locations. Beyond efficiency gains, the findings highlight how simple, low-cost adjustments to storage policies can reduce picker travel, lower energy use, and support sustainable warehouse operations. This case study provides practical guidance for managers of small and medium-sized warehouses and offers baseline insights for the development of digital twin models and smart warehousing solutions in Industry 4.0. Full article