Search Results (9,085)

Social progress and the improvement of living standards are often accompanied by the intensification of ecological crises. The long-term abuse of natural resources has led to the accumulation of ecological liabilities, which in turn seriously hinders economic development. This has prompted all sectors of society to recognize the importance of ecological carrying capacity (ECC) and ecological security (ES). Remarkable progress has been made in karst desertification control (KDC), which has helped reshape the ECC and ES pattern of forests. Currently, the research field of ECC and ES is experiencing rapid development. Further studies in these areas have immeasurable value in promoting regional sustainable development strategies and strengthening ecological civilization construction. The objective of this paper is to provide an overview of the current research status and potential challenges in the field of ECC and ES, with a view to optimizing the program of forest restoration and protection in KDC. This study systematically analyzed 350 relevant studies and found that (1) research on forest ECC and ES has shown a strong growth trend overall, especially after 2017, with a growth rate exceeding 75%; (2) the literature predominantly focuses on the assessment of forest ECC (40.58%) and the enhancement of forest ES (23.42%); and (3) geographically, research findings are heavily concentrated in Asia, representing 95.40% of the total. Notably, China emerges as the primary contributor to research in this field, accounting for a substantial 94.12%. Based on the above analysis, this review summarizes the significant advancements in forest ecosystems, ECC, and ES, while also delving into the key scientific issues that need to be addressed. Furthermore, it offers valuable insights from forest ecosystems in tackling KDC, with the goal of offering guidance and strategic recommendations for future research and practices in managing delicate ecological environments. Full article

Background: This paper investigates the impact of Ecuador’s Ley Orgánica de Educación Superior (LOES), enacted in 2010, which mandated comprehensive reforms in higher education. Objective: The objective of this study is to determine whether the implementation of the LOES has led to a significant increase in academic publications in Ecuador, thereby contributing to sustainable educational practices and aligning with the Sustainable Development Goals (SDGs). In this context, sustainability refers to the ongoing capacity of higher education institutions to generate research that contributes to the long-term development of knowledge, aligns with global sustainability goals, and ensures educational practices that are responsive to societal needs. Methods: Using a synthetic control method, this study evaluates the impact of the LOES on the number of academic publications recorded in Scopus. A synthetic control consisting of data from countries with similar educational and economic profiles serves as a counterfactual to Ecuador’s situation. Results: Before the LOES, the average annual number of publications was stable at 253.4 per year. Post-2010, this number surged, reaching an average of 3645.84 publications per year, representing a 358.41% increase. By 2023, the actual number of publications in Ecuador rose to approximately 6584, significantly surpassing the synthetic control estimate of 1431 publications. Conclusions: The findings confirm that the LOES has been effective in increasing research output and advancing educational sustainability in Ecuador. This analysis not only contributes to the literature on the impact of educational policies but also demonstrates the utility of synthetic control methods in assessing policy effectiveness, underscoring the potential of targeted interventions to promote academic excellence and sustainability in education. Full article

The paper presents a framework for assessing inequality in passenger rail services by examining connectivity and accessibility across two different travelframes: favorable (daytime travel) and unfavorable (nighttime travel). The unfavorable timeframe is often characterized by a lack of adequate first-/last-mile transport availability to train stations, impacting passenger access. The study employs a newly developed inequality-to-investment ratio to classify rail transport entities like stations, routes, or zones as either winners or losers, using a performance persistence analysis. A case study was conducted using data on the superfast express (SE) train schedule and investments from 2016 to 2020 across sixteen zones of the Indian Railway, assessing service inequalities during the hours of favorable (7 a.m. to 9 p.m.) and unfavorable (9 p.m. to 7 a.m.) timeframes. Results revealed inconsistencies in inequalities among zones. Moreover, the research demonstrates that the choice between connectivity and accessibility metrics significantly influences the identification of better or worse performing zones as winners or losers, respectively, throughout the 2016–2020 period and during both timeframes. The study underscores the importance of selecting appropriate measures and suggests re-evaluating or increasing investments in certain zones of Indian Railways based on these insights. The framework proposed in this paper can be used to assess inequalities within any transportation system receiving investments or government funds, contributing to the broader goal of ensuring equitable access to transportation, which is essential for a sustainable transportation system. Full article

Scientific evaluation of cultivated land quality (CLQ) is necessary for promoting rational utilization of cultivated land and achieving one of the Sustainable Development Goals (SDGs): Zero Hunger. However, the CLQ evaluation system proposed in previous studies was diversified, and the methods were inefficient. In this study, based on China’s first national standard “Cultivated Land Quality Grade” (GB/T 33469-2016), we constructed a unified county-level CLQ evaluation system by selecting 15 indicators from five aspects—site condition, environmental condition, physicochemical property, nutrient status and field management—and used the Delphi method to calculate the membership degree of the indicators. Taking Jimo district of Shandong Province, China, as a case study, we compared the performance of three machine learning models, including random forest, AdaBoost, and support vector regression, to evaluate CLQ using multi-temporal remote sensing data. The comprehensive index method was used to reveal the spatial distribution of CLQ. The results showed that the CLQ evaluation based on multi-temporal remote sensing data and machine learning model was efficient and reliable, and the evaluation results had a significant positive correlation with crop yield (r was 0.44, p < 0.001). The proportions of cultivated land of high-, medium- and poor-quality were 27.43%, 59.37% and 13.20%, respectively. The CLQ in the western part of the study area was better, while it was worse in the eastern and central parts. The main limiting factors include irrigation capacity and texture configuration. Accordingly, a series of targeted measures and policies were suggested, such as strengthening the construction of farmland water conservancy facilities, deep tillage of soil and continuing to construct well-facilitated farmland. This study proposed a fast and reliable method for evaluating CLQ, and the results are helpful to promote the protection of cultivated land and ensure food security. Full article

As Sustainable Development Goals (SDGs) gain traction in Chinese society, fostering sustainable entrepreneurship among university students has emerged as a key priority for universities and governments. Methods for increasing students’ sustainable entrepreneurship skills and knowledge for the creation of sustainable startups have attracted substantial attention. This study constructs a moderated mediation model based on entrepreneurial cognition theory to investigate the mediating roles of opportunity identification and attitude in the relationship between sustainable entrepreneurship education and sustainable entrepreneurial intention among university students, in addition to the moderating effect of empathy. The study surveyed 307 students from universities in the Yangtze River Delta region and employed hierarchical regression analysis to test the hypotheses. The results indicate that sustainable entrepreneurship education enhances students’ sustainable entrepreneurial intention by fostering their opportunity identification and attitude, and this enhancement effect is stronger when their level of empathy is higher. These findings enrich entrepreneurial cognition and empathy theories within the context of sustainable entrepreneurship and offer valuable insights for universities and policymakers in developing strategies to support sustainable entrepreneurship among university students. Full article

Fine three-dimensional (3D) reconstruction of real forest scenes can provide a reference for forestry digitization and forestry resource management applications. Airborne LiDAR technology can provide valuable data for large-area forest scene reconstruction. This paper proposes a 3D reconstruction method for complex forest scenes with trees, shrubs, and grass, based on airborne LiDAR point clouds. First, forest vertical distribution characteristics are used to segment tree, shrub, and ground–grass points from an airborne LiDAR point cloud. For ground–grass points, a ground–grass grid model is constructed. For tree points, a method based on hierarchical canopy point fitting is proposed to construct a trunk model, and a crown model is constructed with the 3D α-shape algorithm. For shrub points, a shrub model is directly constructed based on the 3D α-shape algorithm. Finally, tree, shrub, and ground–grass models are spatially combined to achieve the reconstruction of real forest scenes. Taking six forest plots located in Hebei, Yunnan, and Guangxi provinces in China and Baden-Württemberg in Germany as study areas, experimental results show that the accuracy of individual tree segmentation reaches 87.32%, the accuracy of shrub segmentation reaches 60.00%, the height accuracy of the grass model is evaluated with an RMSE < 0.15 m, the volume accuracy of shrub and tree models is assessed with R² > 0.848 and R² > 0.904, respectively. Furthermore, we compared the model constructed in this study with simplified point cloud and voxel models. The results demonstrate that the proposed modeling approach can meet the demand for the high-accuracy and lightweight modeling of large-area forest scenes. Full article

Green hydrogen is becoming increasingly popular, with academics, institutions, and governments concentrating on its development, efficiency improvement, and cost reduction. The objective of the Ministry of Petroleum, Mines, and Energy is to achieve a 35% proportion of renewable energy in the overall energy composition by the year 2030, followed by a 50% commitment by 2050. This goal will be achieved through the implementation of feed-in tariffs and the integration of independent power generators. The present study focused on the economic feasibility of green hydrogen and its production process utilizing renewable energy resources on the northern coast of Mauritania. The current investigation also explored the wind potential along the northern coast of Mauritania, spanning over 600 km between Nouakchott and Nouadhibou. Wind data from masts, Lidar stations, and satellites at 10 and 80 m heights from 2022 to 2023 were used to assess wind characteristics and evaluate five turbine types for local conditions. A comprehensive techno-economic analysis was carried out at five specific sites, encompassing the measures of levelized cost of electricity (LCOE) and levelized cost of green hydrogen (LCOGH), as well as sensitivity analysis and economic performance indicators. The results showed an annual average wind speed of 7.6 m/s in Nouakchott to 9.8 m/s in Nouadhibou at 80 m. The GOLDWIND 3.0 MW model showed the highest capacity factor of 50.81% due to its low cut-in speed of 2.5 m/s and its rated wind speed of 10.5 to 11 m/s. The NORDEX 4 MW model forecasted an annual production of 21.97 GWh in Nouadhibou and 19.23 GWh in Boulanoir, with the LCOE ranging from USD 5.69 to 6.51 cents/kWh, below the local electricity tariff, and an LCOGH of USD 1.85 to 2.11 US/kg H₂. Multiple economic indicators confirmed the feasibility of wind energy and green hydrogen projects in assessed sites. These results boosted the confidence of the techno-economic model, highlighting the resilience of future investments in these sustainable energy infrastructures. Mauritania’s north coast has potential for wind energy, aiding green hydrogen production for energy goals. Full article

Due to industrial growth and its impact on the environment, the increasing amount of industrial waste requires a comprehensive approach aligned with the principles of sustainable development. The main goals are not only to preserve natural resources but also to encourage innovation in the reuse of waste materials. In an attempt to reduce the problems regarding waste disposal and wastewater treatment in the textile industry, fibrous textile waste was used as a starting material to obtain carbon adsorbents for the removal of pollutants from wastewater. Waste cotton and mixed yarns, mainly consisting of polysaccharide cellulose, were hydrothermally carbonized and activated with KOH to convert them into efficient carbon adsorbents for heavy metal removal from water. Characterization of carbonized material showed that after activation, an increase in specific surface area (up to 872 m²/g) and content of surface oxygen groups (6.04 mmol/g) leads to a higher affinity towards heavy metal ions, especially lead ions, and high adsorption capacity of 19.98 mg/g obtained for activated cotton yarns. The results of this research represent a contribution to the reduction of waste materials by modifying them into adsorbents, while the regeneration of adsorbents is an example of the practical application of polysaccharide-based materials in the purification of wastewater containing various heavy metal ions. Full article

This study introduces a novel recommender system that integrates academic performance and socio-demographic variables to provide personalised and contextually relevant recommendations for university degree selection. The system aims to optimise the alignment between students’ profiles and potential academic programmes by utilising advanced machine learning models, including XGBoost, Random Forest, GLMNET, and KNN. The research addresses a critical gap identified in the literature, where most existing systems rely solely on academic data, neglecting the significant impact of socioeconomic factors on educational decision-making. The proposed system demonstrates superior predictive accuracy through rigorous cross-validation and hyperparameter tuning compared to simpler models, such as linear regression. The results show that integrating socio-demographic data enhances the relevance of the recommendations, supporting students in making more informed choices. This approach contributes to educational equity by ensuring that guidance is tailored to each student’s unique circumstances, aligning with the sustainable development goal of quality education. The findings highlight the value of incorporating a comprehensive data-driven approach to improve educational outcomes and support more equitable decision-making processes. Full article

An innovative decision matrix has been developed to guide the selection and implementation of Pneumatic Urban Solid Waste Collection Systems (PUSWCS) in smart city projects. This study comprehensively collects and analyzes data on the advantages and disadvantages of pneumatic collection systems from technical, economic, and social perspectives. A decision-making tool was created to address the complexities of evaluating the desirability of incorporating PUSWCS in municipalities or specific areas, using a holistic approach. The tool assesses the technical, economic, and social feasibility of implementing PUSWCS, aligning it with the United Nations’ Sustainable Development Goals (SDGs). Specific variables are measured to assess compliance with the SDGs, distinguishing technical aspects from economic and social aspects. The methodology includes surveys of system users and technicians, expert assessments, and the development of a decision matrix that cross-references study variables and SDGs. The matrix assigns numerical values to the Magnitude (M) and Impact (I) of each variable, enabling quantitative interpretation. This holistic approach accommodates the complexities of waste management and diverse stakeholder perspectives. The results demonstrate the matrix’s effectiveness in accurately assessing the desirability of implementing PUSWCS. This confirms the matrix’s ability to optimally integrate with innovative smart city concepts and align with long-term sustainability goals. The study concludes that the design of the decision matrix allows the collection of information from experts, users, and stakeholders about economic, social, and environmental variables and relates them to the SDGs, to obtain a numerical result that allows to decide whether in a given urban environment it is advisable to implement a PUSWCS. Full article

Effective project management is crucial for organizations to achieve strategic objectives and maintain competitiveness in today’s market. The project management office (PMO) has emerged as a key enabler in enhancing project management effectiveness through centralized oversight, support, and standardization. However, evaluating the effectiveness of PMOs and identifying areas for improvement remain challenging. This paper aims to provide a comprehensive overview of project management effectiveness and the role of PMOs in achieving organizational success by establishing a maturity rating system. The research objectives include identifying critical success factors relevant to sustainable PMO effectiveness, reviewing existing literature on project management maturity models, analyzing data through literature review and questionnaires, developing a rating system based on identified success factors, and contributing to the existing literature on PMOs. The literature review and thematic analysis identified five critical themes—organizational culture, governance, competence, project controls, and engagement—each with corresponding success factors. Questionnaires were used to assess the maturity levels and relative importance of these factors, where the AHP analysis determined the weighted importance of each success factor and category. The results highlight the critical success factors for PMOs: collaboration, effective leadership, alignment with organizational goals, knowledge management, project planning, risk management, stakeholder satisfaction, and communication. By establishing a standardized and objective approach to evaluating sustainable PMO effectiveness, organizations can enhance their sustainable project management practices, improve project success rates, and address the challenges associated with evaluating PMO performance. Ultimately, adopting a systems approach enables PMOs to align strategies with organizational goals and foster a culture of continuous improvement. Full article

This study, using Jianghua Yao Autonomous County in Hunan Province as a case, systematically analyzes the response of ecosystem service value (ESV) to land use and landscape pattern changes by employing landscape-level indices and landscape-type-level indices. The findings provide a reliable basis for scientifically formulating land use planning and ecological protection policies in ethnic regions, thereby promoting regional ecological security and sustainable development. This study reveals that (1) the land use structure in the county underwent significant changes between 2000 and 2020, with grassland and shrubland areas decreasing substantially by 71.66% and 78.41%, respectively, while urban and arable land areas increased significantly by 228.30% and 15.84%, respectively. Particularly under the scenario of prioritizing economic development, these changes led to increased landscape fragmentation and a decline in ecosystem service value (from CNY 296.571 billion in 2020 to CNY 287.959 billion in 2030). (2) In contrast, the scenarios of ecological protection and sustainable development significantly enhanced the region’s ecosystem service value by increasing forest and water area, effectively maintaining the stability of the landscape pattern. These findings provide important evidence for the formulation of scientifically sound land use planning and ecological protection policies, contributing to the dual goals of economic development, tourism growth and ecological protection in Jianghua Yao Autonomous County and similar regions. Full article

In the context of the global climate change problem intensifying due to a dramatic increase in carbon emissions, smart cities, as a topical application of digitalization and intelligence, have become a new urban governance mode for countries, which helps to achieve sustainable development. This research studies the relationship between smart city construction (SCC) and carbon dioxide emissions based on the differences-in-differences model (DID) and propensity score matching (PSM) to promote China to achieve dual carbon goals and high-quality development. The findings are as follows: (a) SCC could promote carbon emission reduction by reducing urban carbon dioxide emissions by an average of 11.4%, which also has significant long-term dynamic effects. Specifically, SCC has more obvious emission reduction effects on activities, such as industrial production and waste treatment. (b) Mechanism verification shows that green technology progress is a significant booster for the carbon reduction effect in SCC. The pilot project can increase output of green patents, which helps transfer production mode and consumption patterns in an environmentally friendly manner. SCC could increase the total factor productivity (TFP) through the rational allocation and efficient use of resources, and thus reducing carbon emissions. (c) Research on city heterogeneity shows that a high level of human capital, material, and financial resources can provide support for smart cities to better achieve the carbon reduction effect. Among them, material resources have the best carbon reduction effect in the process of SCC, which could reduce carbon dioxide emissions by about 6.6–17.7%. This study is useful for policymakers to continuously and dynamically adjust urban development strategies in the future, to achieve a balance between socioeconomic prosperity and environmental sustainability. Full article

An issue of concern in landscape and urban planning, articulated in the United Nation’s (UN’s) Sustainable Development Goals (SDGs), is the increase in urban land consumption over time. Indicator 11.3.1 of the SDGs is dedicated to measuring it, underlining the importance of decreasing urban land consumption per person, a strategy that is understood to contribute positively to climate mitigation and to a host of other social, economic, and environmental objectives. This article aims to explore the practical implications of the official methods for measuring Indicator 11.3.1, as well as two alternatives, and to calculate and compare the global and regional trends of these indicators for the 2000–2020 period for a universe of 3470 cities and metropolitan areas that had 100,000 people or more in the year 2020. Built-up area and population data for this universe were obtained from the Global Human Settlements Layer (GHS-BUILT-S and GHS-POP) published by the European Commission. We applied methods adapted from New York University’s Atlas of Urban Expansion to map the urban extents of all cities in 2000 and 2020, and then we used these urban extents, the built-up areas, and population estimates within them to calculate values for Indicator 11.3.1 and for two alternative indicators for the 2000–2020 period. We found that the current definition of Indicator 11.3.1 of the SDGs—“Ratio of land consumption rate to population growth rate”—has significant limitations in conveying meaningful information and interpretability for practical applications. We suggest two alternative indicators that address these shortcomings: the rate of change of land consumption per person and the rate of density change. Our analysis found that, for the world at large, urban densities declined at an annual rate of 0.5–0.7% between 2000 and 2020, with significant variation in the direction and magnitude of density trends by world region. Additionally, we found density declines to be faster in smaller cities than in larger ones and faster in cities with slower population growth or population declines compared to those with more rapid population growth. Full article

Background: This study contributes to expanding the existing literature on learning technical skills in youth soccer by comparing the effectiveness of different training approaches in the development of passing skills. The ecological-dynamic approach, which emphasizes the continuous and adaptive interaction between the athlete and the environment, is analyzed in comparison to the traditional prescriptive approach, which relies on predefined techniques and exercises. The aim of the study is to determine which of the two approaches is more effective in improving the performance of young soccer players. Methods: Thirty players (age 12 ± 1.2 years) were randomly assigned to two groups: the ecological-dynamic group (ECG) and a control group (CON). Both groups underwent an eight-week training program with equal sessions. The ECG group’s training focused on adjusting constraints like the learning environment, game rules, and visual restrictions to boost adaptability and problem-solving skills. The CON group followed a traditional prescriptive approach with specific instructions, goal setting, immediate feedback, and structured exercise progression. Passing abilities were evaluated before and after the program using the Loughborough Soccer Passing Test, with a retention test administered five weeks later. Descriptive statistics, including mean values and percentage improvements, were used. A repeated measures ANOVA compared differences between the groups. Results: The analysis revealed a significant Occasion × Group interaction for all performance variables, indicating that the ECG group experienced greater improvements than the CON group. Specifically, the ECG group showed significant reductions in Trial Time (p = 0.001, η_p² = 0.6), Penalty Time (p = 0.016, η_p² = 0.4), and Overall Performance (p = 0.011, η_p² = 0.8) from pre-test to post-test. However, these improvements did not persist into the retention test (p = 0.131, p = 0.792, and p = 0.192, respectively). The CON group also improved significantly in Trial Time (p = 0.003), Penalty Time (p = 0.002), and Overall Performance (p = 0.001) from pre-test to post-test, but with smaller effect sizes and no sustained gains at retention. Conclusions: The ecological-dynamic approach (EDG) has proven to be more effective in enhancing passing skills compared to the traditional prescriptive approach (CON). Although both methods led to performance improvements, the EDG group achieved more significant progress. Full article