Search Results (65,043)

This study investigates European citizens’ perspectives on farmers’ roles, highlighting gender, age, education, political orientation, community size, social class, and attitudes towards the EU. This study was developed using 21,002 interviews with European Citizens from all 27 EU countries. A quantitative data analysis methodology was used from the European Eurobarometer 97.1 survey. Seven models were formulated and tested. It is shown that men prioritise economic growth and food stability, while women emphasise environmental protection and animal welfare. Younger individuals focus on rural job creation, whereas older citizens value food security. Higher education levels correlate with environmental and animal welfare concerns. Right-leaning citizens favour economic development, whereas left-leaning individuals prioritise ecological issues. Larger communities emphasise economic growth, while smaller ones focus on environmental preservation. Social class influences priorities, with higher classes concerned about sustainability and lower classes about job creation. Pessimistic views about the EU correlate with food safety concerns, while optimistic views align with environmental and animal welfare priorities. These findings suggest that aligning agricultural and food policies with citizens’ diverse needs can foster a more sustainable and resilient European food system. Full article

As an important part of green manufacturing, remanufacturing has important practical significance for alleviating resource shortage and waste, developing circular economy and promoting sustainable development. In recent years, remanufacturing scheduling (RS), which can achieve high efficiency and green remanufacturing through the reasonable allocation of resources, has become a research hotspot in the field of remanufacturing. To offer a comprehensive evaluation of the research dynamics and development trends of RS, this paper systematically reviews the publications from 2010 to 2025 via Scopus, Web of Science, and the IEEE Xplore database. Firstly, the research background of RS, related remanufacturing policies and the generalized connotation of remanufacturing are introduced. Then, selected and valid publications are analyzed from time aspect, country aspect, and keyword aspect through Citespace software. In addition, based on remanufacturing level, modeling idea, optimization objectives, solution method, production scenarios and practical application, publications are further grouped and reviewed. In addition, according to the research gap existing in recent studies, some future development trends are accordingly pointed out, aiming to provide valuable insights for research related to RS. Finally, meaningful conclusions are drawn and the importance of RS is emphasized once again. Full article

As the world’s largest carbon emitter and one of the countries facing severe air pollution challenges, China is under growing pressure to promote coordinated carbon reduction and air pollution control in support of sustainable development. From the perspective of interprovincial trade-embedded emissions, this study examines the spatiotemporal evolution, regional heterogeneity, and driving mechanisms of the synergy between embodied carbon emissions and air pollution emissions across 30 provincial-level regions in China in the 2012–2017 period. The multi-regional input–output (MRIO) model and coupling coordination degree (CCD) model are used to measure embodied emissions and the synergy effect, while the stochastic impacts by regression on population, affluence, and technology (STIRPAT) and geographically and temporally weighted regression (GTWR) models are employed to identify the main driving factors and their spatiotemporal heterogeneity. The results show that the overall synergy index of embodied carbon and air pollution emissions in China showed an increasing trend, and provinces with high-quality coordination shifted southward. Low-carbon policy and technology development mainly acted as positive drivers, whereas air pollution reduction policy and energy intensity tended to exert inhibitory effects; the role of energy consumption was more conditional and stage-specific. These findings provide useful evidence for differentiated governance, coordinated air pollution and carbon reduction, and the green and low-carbon transition. Full article

Financial development is widely regarded as an important factor influencing renewable energy consumption. Nevertheless, empirical studies conducted by various scholars have revealed that the effect of financial development on renewable energy consumption remains controversial. Based on this backdrop, this paper endeavors to analyze the nonlinear influence of financial development on renewable energy consumption from the perspective of moderating effects. First of all, this paper theoretically analyzes the potential moderating effects of financial development itself, urbanization, and environmental regulation on the impact of financial development on renewable energy consumption. Subsequently, leveraging the Panel Smooth Transition Regression (PSTR) model and the global panel data of 143 countries from 1996 to 2020, the empirical tests are conducted to verify these moderating effects. The results indicate that the variations in moderating variables can lead to disparities in the influence of financial development on renewable energy consumption. Specifically, with the increase in financial development level, urbanization rate, and environmental regulation intensity, the promoting effect of financial development on renewable energy consumption gradually strengthens. Finally, based on the aforementioned research findings, this paper proposes corresponding policy recommendations from the perspectives of these moderating factors. Full article

This study examines the effect of innovation on climate resilience in developing countries, covering annual data from 2008 to 2022, with a focus on how this relationship varies across different levels of vulnerability. The primary purpose is to understand whether innovation contributes uniformly to climate resilience or if its impact differs depending on a country’s resilience status. Addressing this question is crucial for developing evidence-based and context-specific climate policies. To capture these heterogeneous effects, this study employs a panel quantile regression approach using data from developing countries. This method allows the estimation of the influence of innovation proxied by the Global Innovation Index (GII) and the climate resilience Index. The findings show that innovation has a consistently positive and statistically strong impact on climate resilience across all quantiles, with the strongest impact at the median. The results carry important policy implications. Firstly, developing countries should prioritize innovation-driven strategies to strengthen resilience across different climate risk profiles. Secondly, policies supporting renewable energy deployment should target countries with higher emissions to maximize their impact. Thirdly, fiscal tools, such as environmentally aligned tax policies, should be emphasized particularly in more vulnerable contexts. Finally, trade policies, population dynamics and integration of climate finance variables must be integrated into climate strategies to enhance long-term sustainability. Full article

Against the backdrop of accelerating climate change, extreme weather events have increasingly caused yield losses in agricultural crops. Meanwhile, they undermine the stability of production systems, posing an increasingly severe threat to agriculture. This study draws on the “diversity–stability” hypothesis to construct a country-level measure of agricultural production resilience in China (ARES). Using output time series for multiple agricultural products, we capture the co-movements of shocks and system resilience through output stability and volatility. By combining ARES with climate exposure measures, we assemble a panel dataset covering 1343 counties over the period 2000–2023 and employ a dynamic panel threshold model to jointly account for persistence in ARES and state-dependent nonlinearities in climate impacts. The results reveal significant path dependence in ARES and pronounced threshold effects across climate dimensions. In the full sample, extreme high-temperature days become significantly detrimental after crossing the threshold, whereas extreme low-temperature days become significantly beneficial in the high-exposure regime. Extreme rainfall days and extreme drought days generally exhibit positive effects that weaken markedly beyond their respective thresholds, indicating diminishing marginal gains in ARES under severe exposure. The comprehensive climate physical risk index significantly suppresses ARES when it is below the threshold value; however, after surpassing the threshold, its marginal effect becomes significantly weaker. Heterogeneity analyses across hilly, plain, and mountainous areas, as well as nationally designated key counties for poverty alleviation and development, further show that threshold locations and regime-specific effects differ substantially by terrain and development conditions. These findings highlight the need for “threshold-based” climate adaptation governance, emphasizing targeted investments and risk-financing instruments to prevent ARES collapse under tail-risk regimes. Full article

International tourism plays an important role in the global service economy, contributing to trade, employment, and regional development. For this reason, identifying the factors that influence tourist flows is an important issue for tourism policy, market strategy, and infrastructure planning. A large body of research has applied gravity models to analyze tourism flows between countries. While this approach provides a clear economic interpretation, it is usually based on linear specifications and may therefore capture only part of the relationships present in tourism data. This study examines the economic and geographic determinants of international tourism flows to Mongolia using a framework that combines a traditional gravity model with machine learning techniques. Mongolia serves as an instructive empirical setting, a landlocked, geographically peripheral destination whose inbound demand determinants have received limited systematic empirical attention. The analysis uses panel data for 27 origin countries covering the period from 2000 to 2024. In the first stage, a gravity model is estimated to assess how tourism flows relate to economic size and geographic distance. The results show that tourism flows tend to increase with the economic size of origin and destination countries, while greater geographical distance is associated with lower tourism flows. The estimated distance elasticity ranges from approximately −1.85 to −2.10 across model specifications, which is larger in absolute terms than the values typically reported in cross-country studies. This result is consistent with the relatively high travel cost barriers associated with Mongolia’s geographic location. These findings are consistent with the distance decay relationship commonly reported in the tourism literature. In the second stage, machine learning algorithms, including Random Forest, LightGBM, and XGBoost, are used as complementary interpretive instruments rather than forecasting tools to explore possible nonlinear relationships among the explanatory variables. To make the results more interpretable, the contribution of individual variables is examined using SHAP (Shapley Additive Explanations). The machine learning results indicate that some relationships in tourism demand may be nonlinear and not fully captured by the linear gravity specification. Specifically, distance sensitivity is approximately 6.5 times greater in nearby markets than in long-haul markets, with a structural inflexion at around 5700 km. Further analysis suggests that the influence of geographical distance is not uniform across all markets. In particular, tourism flows originating from middle-income countries appear to be more sensitive to increases in travel distance than those from higher-income countries. Overall, the findings indicate that economic size and geographical distance remain key determinants of international tourism flows to Mongolia. At the same time, the use of machine learning methods provides additional insight into potential nonlinear patterns in tourism demand. By combining econometric modelling with explainable machine learning techniques, the study offers an integrated analytical perspective for examining international tourism flows at geographically peripheral destinations where standard gravity assumptions may be insufficient. Full article

Microplastic (MP) contamination in freshwater systems has emerged as a growing environmental concern. This study investigated the occurrence and seasonal variability of MPs in surface water, the water column, and sediments at selected sites in Lake Alhajuela, Panama. Lake Alhajuela is an artificial reservoir that supplies water to the Panama Canal lock system and to the cities of Panama and Colón, serving more than 50% of the country’s population. MPs were isolated using two digestion protocols followed by density separation, and fragments and films larger than 1 mm were chemically characterized using FTIR–ATR spectroscopy. Mean MP concentrations were 759 ± 536 MPs L⁻¹ in surface water, 328 ± 140 MPs L⁻¹ in the water column, and 109 ± 87 MPs g⁻¹ in sediments. Statistical analyses revealed no significant differences among sampling sites; however, significant seasonal differences were observed (p < 0.01). Smaller MPs (63–249 µm) were more abundant compared to larger MPs (>250 µm). Fragments and fibers were the most predominant type of MP reported. Our results confirm the presence of MPs in the surface and water column, as well as sediments of the Alhajuela Lake. Further studies are needed to elucidate the fate, sources, transport, and distribution of MPs across Lago Alhajuela as well as to assess the lake’s potential contribution of MPs to Gatun Lake and the Panama Canal system. Full article

Background: Although technology has improved the quality of diabetes management, it may also introduce subjective burdens and reveal barriers to its use. The primary aim of this research was to investigate the association between the use of advanced diabetes technology, such as continuous glucose monitoring, insulin pumps, mobile applications, and diabetes distress in adults with type 1 diabetes mellitus (T1DM). Methods: This multicenter, cross-sectional study conducted across Southeastern European countries included 499 adults with T1DM. All participants signed informed consent and completed the 20-item Problem Areas in Diabetes (PAID) Questionnaire. A total score of 40 or above was classified as high diabetes distress. Statistical analyses were performed using ANOVA, χ2 test, and logistic regression. Results: The mean age of participants was 49.11 ± 13.99 years, with a mean HbA1c value of 7.9 ± 1.46%. The mean PAID total score was 29.19 ± 19.51. High levels of diabetes distress were found in 28.86% of the participants. About 20% of participants used advanced diabetes technologies. Significant predictors of diabetes distress were gender, BMI, and HbA1c. After accounting for these predictors, advanced technology use was associated with a 42% lower likelihood of experiencing high levels of diabetes distress compared to those who used blood glucose meters. Conclusions: Diabetes distress remains a frequent issue among individuals with T1DM. However, patients using advanced diabetes technologies exhibited less distress. Our findings highlight the importance of a comprehensive approach to T1DM management that integrates technological advancements and psychosocial support. Full article

Background: Human cysticercosis, caused by the larval stage (cysticerci) of the pork tapeworm Taenia solium, is an important zoonotic disease. The disease is prevalent in developing countries where porcine cysticercosis is common and undercooked pork is habitually consumed. Objective: This study aimed to develop an immunochromatography-based test kit for the rapid diagnosis of human cysticercosis using low-molecular-weight antigens purified from cyst fluid of the T. solium Asian genotype to detect specific IgG antibodies in whole blood. The kit was designated as “the cysticercosis whole-blood test kit (iCys WB kit).” Methods: It was evaluated under laboratory conditions using 164 whole-blood samples, of which 21 were from confirmed cysticercosis cases. The results of the iCys WB kit, which detects anti-T. solium (cysticercus) antibodies in simulated whole blood samples, were compared with results from corresponding human serum samples. Results: When using both sample types, iCys WB kit demonstrated an accuracy of 98.8%, a sensitivity of 91.7%, a specificity of 100%, a positive likelihood ratio of 0, a negative likelihood ratio of 0.083, and an ROC area of 0.96. The agreement between results obtained from simulated whole-blood and serum samples showed perfect concordance. Conclusions: The iCys WB kit is a valuable easy-to-handle diagnostic tool and may be applicable for supporting clinical diagnosis at the point of care. Full article

Digital transformation is reshaping societies by promoting the adoption of advanced technologies. Moreover, the digitization of public services has become an important focus for governments. In this paper, digital government performance indicators are analyzed to improve the efficiency of digitizing public services. Based on this awareness, the seven main criteria and twenty-one sub-criteria are determined. Then, a fuzzy decision-making framework is proposed to evaluate digital government performance across 165 countries as alternatives. To the best of our knowledge, limited studies have investigated an integrated clustering-based fuzzy decision-making framework for evaluating digital government performance. The intuitionistic trapezoidal fuzzy number-based analytical hierarchy process (ITFNAHP), a part of the introduced framework, is developed to find the weights of the main criteria and sub-criteria. Digital technologies, innovation, and the economy are the most significant criteria for digital government operations. The k-means clustering method is then employed to group the alternatives. The four clusters are obtained from the clustering technique. Next, the technique of order preference similarity to ideal solution (TOPSIS) is introduced to rank the digital governments of each cluster. Switzerland, Rwanda, North Macedonia, and Eswatini are the top choices among others in each cluster, respectively. Additionally, a sensitivity analysis is conducted considering the ten different situations. In addition, the managerial and policy implications are discussed, including the achievement of Sustainable Development Goals (SDGs). Full article

The energy sector and its technological landscape are rapidly changing, driven by the global need to minimize the reliance on non-renewable resources. The energy transformation over the past five years has resulted in sustainable energy initiatives involving innovative adaptations of energy technologies by regional local authorities. In this context, and as local action will eventually decide global sustainability, this article explores the ways that sustainable strategies and energy actions were comprehended and adopted by regional public authorities. The focus area is the island of Crete in Greece. Owing to its geographical position and the nearly autonomous institutional structure of the broader state apparatus, it serves as a microcosm of a state, rendering it an effective imitation of the Greek state. The methodology of this study is derived from both the relevant literature outcomes and the national legislative framework. A document review served as a preliminary tool to investigate national and regional policy frameworks. This was followed by in-depth interviews with regional stakeholders to collect primary data on implementation. This study’s originality derives from addressing the gap between the proposed measures imposed by the state, along with various sustainable activities from a holistic perspective, and their actual uptake in Crete. The analysis of the results provides insights regarding their effectiveness based on the regional authorities’ approach in a developed South Mediterranean country. The article confirms that municipalities’ heterogeneity and structural differentiation are critical for sustainable energy transition and concludes with future research directions worthy of thorough examination, towards energy transition maturity of an insular region. Full article

This research delves into the interconnectedness of economic growth, environmental sustainability, and social development within the six Gulf countries (GCC): Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates. Employing ARDL and VECM methodologies, the study examines the short- and long-run dynamics between these variables from 2010 to 2023. Key findings reveal that while digital initiatives, foreign direct investment (FDI), trade openness, and political stability positively influence economic growth and social development, investments in renewable energy and environmental sustainability may initially impose short-term economic costs. However, these investments are crucial for long-term sustainability and contribute to enhanced social well-being. To foster a more sustainable and equitable future, the study recommends that GCC policymakers prioritize balanced strategies that promote economic growth, environmental sustainability, and social development simultaneously. This study details targeted investments in digital infrastructure, fostering innovation, encouraging sustainable practices, and implementing effective policies to mitigate the short-term costs of transitioning to a more sustainable economy. By adopting such a holistic approach, the GCC states can navigate the challenges and opportunities of sustainable development and ensure a prosperous future for their populations. Full article

This paper presents a hybrid econometric and machine-learning framework for forecasting GDP that bridges long-run structure with short-run regime dynamics. Using annual World Bank data spanning 1960 to 2024, the framework combines three complementary components: an ARIMA baseline that captures persistence, a three-state Hidden Markov Model (HMM) that provides probabilistic regime identification, and an LSTM-based extension that learns nonlinear patterns associated with regime transitions. Detailed out-of-sample forecasting evidence is reported for five representative countries (the United States, China, Germany, India, and Greece), chosen to illustrate performance across different volatility profiles and economic environments. Across these case studies, the integrated HMM–LSTM approach often delivers lower forecast errors than the benchmark alternatives, although the magnitude of the gains is not uniform across countries. Beyond point forecasting performance, the regime layer yields an interpretable probabilistic representation of business cycle conditions that can support real-time monitoring and early-warning assessment. By combining transparency with adaptability, the proposed framework contributes to the forecasting literature and provides a practical decision-support tool under heightened macroeconomic uncertainty. Full article