Search Results (642)

Classification is an important task in the field of machine learning. Decision tree algorithms are a popular choice for handling classification tasks due to their high accuracy, simple algorithmic process, and good interpretability. Traditional decision tree algorithms, such as ID3, C4.5, and CART, differ primarily in their criteria for splitting trees. Shannon entropy, Gini index, and mean squared error are all examples of measures that can be used as splitting criteria. However, their performance varies on different datasets, making it difficult to determine the optimal splitting criterion. As a result, the algorithms lack flexibility. In this paper, we introduce the concept of generalized entropy from information theory, which unifies many splitting criteria under one free parameter, as the split criterion for decision trees. We propose a new decision tree algorithm called RSE (RS-Entropy decision tree). Additionally, we improve upon a two-term information measure method by incorporating penalty terms and coefficients into the split criterion, leading to a new decision tree algorithm called RSEIM (RS-Entropy Information Method). In theory, the improved algorithms RSE and RSEIM are more flexible due to the presence of multiple free parameters. In experiments conducted on several datasets, using genetic algorithms to optimize the parameters, our proposed RSE and RSEIM methods significantly outperform traditional decision tree methods in terms of classification accuracy without increasing the complexity of the resulting trees. Full article

As a major energy producer and consumer, China has witnessed rapid growth in carbon emissions, which are closely linked to changes in regional climate and the environment. Water, energy, and food (W-E-F) are the three most critical components of human production and daily life, and achieving the coordinated development of these three resources and connecting them with climate change through the carbon emissions generated during their utilization processes has become a key issue for realizing regional ecological sustainable development. This study constructs a dynamic two-stage network slack-based measure-data envelopment analysis (SBM-DEA) model, which integrates the water–energy–food (W-E-F) system with the climate change process to evaluate China’s comprehensive ecological efficiency from 2011 to 2022, and adopts the Dagum Gini coefficient decomposition, kernel density estimation, hierarchical clustering, and geographical detector model to analyze provincial panel data, thereby assessing efficiency patterns, regional differences, and driving mechanisms. The novelty and contributions of this study can be summarized in three aspects. First, it establishes a unified framework that incorporates the W-E-F nexus and climate change into a dynamic network SBM-DEA model, enabling a more systematic assessment of ecological efficiency. Second, it uncovers that interregional overlap effects and policy-driven factors are the dominant sources of spatial and temporal disparities in ecological efficiency. Third, it further quantifies the interactive effects among key driving factors using Geodetector, thus offering practical insights for regional coordination and policy design. The results show that China’s national ecological efficiency is at a medium level. Southern China has consistently maintained a leading position, while provinces in northwest and southwest China have remained relatively backward; the efficiency of the water–energy–food integration stage is relatively high, whereas the efficiency of the climate change stage is medium and exhibits significant temporal fluctuations. Interregional differences are the main source of efficiency gaps; ecological quality, environmental protection efforts, and population size are identified as the primary driving factors, and their interaction effects have intensified spatial heterogeneity. In addition, sub-indicator analysis reveals that the efficiency related to total wastewater, air pollutant emissions, and agricultural pollution shows good synergy, while the efficiency associated with sudden environmental change events is highly volatile and has weak correlations with other undesirable outputs. These findings deepen the understanding of the water–energy–food-climate system and provide policy implications for strengthening ecological governance and regional coordination. Full article

Urban park green space (UPGS) is a key component of urban green infrastructure, yet it faces multiple contradictions, such as insufficient quantity and uneven distribution. Taking Zhengzhou City as a case study, this research explored the impacts of temporal thresholds and the modifiable areal unit problem (MAUP) on UPGS accessibility and equity. An improved multi-modal Gaussian two-step floating catchment area (G2SFCA) method was employed to measure UPGS accessibility, while the Gini coefficient and Lorenz curve were used to analyze its equity. The results show that (1) UPGS presents a dual-core agglomeration feature, with accessibility blind spots surrounding the edge of the study area and relatively low equity in the western and southern regions; (2) changes in temporal thresholds and spatial scales have a significant impact on UPGS accessibility (p < 0.001), whereas their impact on equity is minor; and (3) UPGS distribution suffers from spatial imbalance, with a huge disparity in resource allocation. This study overcomes the limitations of traditional evaluation methods that rely on a single mode or ignore scale effects and provides a more scientific analytical framework for accurately identifying the spatial heterogeneity of UPGS accessibility and the imbalance between supply and demand. Full article

The risk of indoor undercooling during winter in rural southern China poses a significant challenge to health and equity, with substantial spatial disparities driven by climatic variation and the absence of heating infrastructure. This study quantifies undercooling risk and spatial inequity across 78 rural regions using Typical Meteorological Year (TMY), simulation-based analyses, with the Indoor Undercooling Hour (IUH), Indoor Undercooling Degree (IUD) and the Gini coefficient as key indicators. Results show that indoor undercooling in self-built rural dwellings is widespread, with the lower and middle reaches of the Yangtze River, the Yangtze River Delta, and high-altitude south-western regions being particularly affected. Marked inequities are observed, reflected by Gini values for IUH and IUD of 0.46 and 0.58, respectively. Pronounced disparities exist across regions in both undercooling risk and socio-economic and demographic conditions, with south-western regions experiencing heavier health inequities due to smaller populations and weaker economies. Passive retrofit strategies can substantially reduce undercooling; however, exclusive reliance on them may exacerbate inequity among regions. Accordingly, active measures, such as centralized heating, are recommended in high-risk areas to promote health and equity. Full article

Understanding the coupled and coordinated development of China’s urban and rural basic education systems is crucial for fostering their interaction and synergistic growth. Using China’s provincial panel data from 2011 to 2023, this study measures the coupled and coordinated development level of urban–rural basic education (CCD-URBE) via the entropy weight method, G1-method and coupling coordination degree model. On this basis, the Dagum Gini coefficient decomposition method, traditional and spatial Markov chain models, as well as convergence test models are employed for empirical research. The results show that: (1) During the study period, the CCD-URBE across the nation and the four major regions improves significantly. Both intra-regional and inter-regional disparities show a consistent downward trend. Inter-regional disparities are the main source of the overall disparities, and the contribution rate of transvariation density to the overall disparities exhibits the most significant increase. (2) The CCD-URBE demonstrates strong stability, as most regions tend to maintain their original CCD-URBE grades. Meanwhile, neighborhood grades moderate the local transition probability significantly. Neighborhoods with high CCD-URBE promote the upward improvement of the local CCD-URBE, while those with low CCD-URBE inhibit it. (3) The CCD-URBE across the nation and the four major regions shows obvious trends of σ-convergence, absolute β-convergence, and conditional β-convergence. The central region, which has lower CCD-URBE, exhibits higher convergence speed. Based on these findings, targeted policy implications are derived. Full article

In recent years, the rapid expansion of urban rail transit has significantly improved travel efficiency, yet it has also exacerbated spatial inequality in service coverage. Accessibility, as a fundamental metric for evaluating the equity of service distribution, remains limited by three major shortcomings in current assessment methods: the neglect of actual road network characteristics, reliance on a single static scale, and the absence of quantitative mechanisms to assess accessibility equity. These deficiencies hinder a comprehensive understanding of how equity evolves with the spatiotemporal dynamics of rail systems. To address the aforementioned issues, this study proposes an innovative spatiotemporally dynamic and multi-scale analytical framework for evaluating urban rail accessibility and its equity implications. Specifically, we develop a network-based buffer decay model to refine service population estimation by incorporating realistic walking paths, capturing both distance decay and road network constraints. The framework integrates multiple spatial analytical techniques, including the Gini coefficient, Lorenz curve, global and local spatial autocorrelation, center-of-gravity shift, and standard deviation ellipse, to quantitatively assess the equity and evolutionary patterns of accessibility across multiple spatial scales. Taking the central urban area of Hangzhou as a case study, this research investigates the spatiotemporal patterns and equity changes in metro station accessibility in 2019 and 2023. The results indicate that the expansion of the metro network has partially improved overall accessibility equity: the Gini coefficient at the TAZ (Traffic Analysis Zone) scale decreased from 0.56 to 0.425. Nevertheless, significant inequality remains at finer spatial resolutions (grid-level Gini coefficient = 0.404). In terms of spatial pattern, the core area (e.g., Wulin Square) forms a ‘high-high’ accessibility agglomeration area, while the urban fringe area (e.g., northern Yuhang) presents a ‘low-low’ agglomeration, and the problem of local ‘accessibility depression’ still exists. Additionally, the accessibility centroid has consistently shifted northwestward, and the long axis of the standard deviation ellipse has rotated from an east–west to a northwest-southeast orientation, indicating a growing spatial polarization between core and peripheral zones. The findings suggest that improving equity in urban rail accessibility cannot rely solely on expanding network size; rather, it requires coordinated strategies involving network structure optimization, branch line development, multimodal integration, and the construction of efficient transfer systems to promote more balanced and equitable spatial distribution of rail transit resources citywide. Full article

Based on provincial panel data from 2014 to 2023, this study employs the entropy weight method to construct an indicator system for measuring the logistical resilience of regions along China’s Belt and Road Initiative (BRI). The Dagum Gini coefficient is used to analyze regional disparities in resilience levels. Furthermore, when geographical factors are integrated, spatial autocorrelation analysis via Moran’s I index is conducted on the measurement results to explain the spatial heterogeneity among variables. The results reveal several key findings: (1) During the implementation of the BRI, the logistical resilience of regions along the route has improved to varying degrees, indicating enhanced ability of the logistics industry to withstand external risks and recover from disruptions. (2) The level of regional logistical resilience exhibits a spatial pattern similar to that of logistics industry development, characterized by a gradual decline from the southeastern coastal areas toward the northwestern inland regions. (3) Logistical resilience within the study areas has increasingly significant spatial spillover effects; that is, regions with developed logistics industries positively impact surrounding areas, driving improvements in their resilience levels. The results of this study suggest a growing trend of spatial convergence in logistical resilience across these regions. Based on these results, corresponding policy recommendations are proposed to provide insights for enhancing regional logistical resilience. Full article

Under the context of the “dual carbon” goals, the synergistic development of green finance and low-carbon innovation plays a significant role in driving the green transformation of the economy and high-quality development. This paper, based on provincial panel data from China from 1990 to 2022, employs the coupling coordination degree model, Dagum Gini coefficient, spatial autocorrelation model, and spatial Durbin model for empirical analysis. The research findings indicate the following: (1) the level of synergistic development of green finance and low-carbon innovation shows an upward trend, with the eastern region performing well, while the western region’s development remains weak, leaving much room for improvement; (2) the spatial differences in the synergistic development of green finance and low-carbon innovation are mainly due to inter-regional differences, followed by intra-regional differences, with the impact of super-variation density being relatively small; (3) regarding spatial correlation, there is a significant spatial autocorrelation in the synergistic development of green finance and low-carbon innovation, with the eastern region showing high-level clustering, while the western region exhibits low-level clustering; (4) the positive driving factors influencing the synergistic development of green finance and low-carbon innovation are ranked as follows: Government policy support > Human capital > Economic development, with industrial structure having a significant negative impact. Based on these conclusions, recommendations are made to strengthen differentiated policy support mechanisms, build cross-regional innovation collaboration networks, systematically promote the green transformation of industrial structures, expand markets, and strengthen regional cooperation. Full article

In the context of low-growth urban development, the interaction between the supply–demand structure of community public service facilities and the housing market has increasingly become a key research concern. Yet, systematic investigations into how supply–demand dynamics influence market value remain limited. To fill this gap, this study takes Wuchang District of Wuhan as the empirical case and establishes an integrated framework of “supply–demand evaluation—value effects” to assess both the equity of facility allocation and its capitalization effects. The results indicate that: (1) all categories of public service facilities in Wuchang District have Gini coefficients above 0.6, indicating substantial imbalance. Among them, elderly care, infant care, and child recreation facilities exceed 0.7, reflecting particularly severe inequality. (2) The “accessibility–housing price” quadrant model further reveals typical mismatch patterns, with “low accessibility–high price” and “high accessibility–low price” zones together accounting for 45.08%, suggesting that mismatches are widespread in the study area. (3) MGWR results show that different facility types exert differentiated effects across locations, with some even displaying opposite positive and negative effects, underscoring significant spatial heterogeneity. Overall, this study uncovers the intrinsic links between facility supply–demand structures and market value, clarifies the differentiated roles of facility types in shaping spatial value, and provides empirical evidence to support improvements in urban public service systems. Full article

Background/Objectives: This study aimed to assess whether income was associated with dental caries experience and dental care levels among 12-year-old children in two contrasting economic contexts, Sri Lanka (lower-middle-income) and Wales (high-income), regardless of national income status. Methods: An ecological study design was used with published data. For Sri Lanka, the income parameters used included household income, mean per capita income, and the Gini coefficient. For Wales, the Welsh Index of Multiple Deprivation was used. Dental caries was assessed using the DMFT index and its components (DT, MT, FT), while dental care was assessed using the care index, restorative index, and treatment index. Pearson correlations were used to explore associations between income measures and both caries experience and dental care indices across districts in Sri Lanka and unitary authorities in Wales. Results: The mean DMFT for 12-year-old children was 0.6 in both Sri Lanka and Wales, with caries prevalence of 30.4% and 29.6%, respectively. In Sri Lanka, both mean household income and per capita income showed moderate, statistically significant positive correlations with DMFT (r = 0.47, p = 0.01). Income inequality, measured by the Gini coefficient, was positively associated with caries experience (r = 0.42, p = 0.03). In Wales, higher deprivation (lower income) was moderately associated with increased DMFT, in areas within the 20% (r = 0.54, p < 0.01), 30% (r = 0.53, p < 0.01), and 50% (r = 0.61, p < 0.01) deprived quintiles. The dental care indices showed no clear association with income in either of the countries. Conclusions: Income-related disparities in dental caries were evident in both countries. Prevention strategies should focus on higher-income groups in Sri Lanka and on deprived populations in Wales. However, as this was an ecological study, the results are subject to ecological fallacy and should therefore be interpreted with caution. Full article

Food and nutrition production play a pivotal role in China’s transition toward a nutrition-sensitive food system. Alongside rapid urbanization and dietary shifts, substantial transformations have occurred in food production patterns. This study investigates inequality in China’s food and nutrition sectors from 1991 to 2021 by employing the Theil index and Gini coefficient, analyzing its drivers from both regional and categorical perspectives. The findings reveal significant disparities in food production concentration across different categories, with notable shifts over the study period. Land-intensive agricultural products—including cereals, oil crops, sugar crops, pulses, roots, tubers, and livestock—exhibited increasing inequality, as indicated by rising Gini coefficients and Theil indices, suggesting greater spatial concentration. In contrast, labor-intensive categories such as fruits and aquatic products showed declining inequality, reflecting broader distribution. Notably, inequality within specific food types (e.g., wheat, beet, and rapeseed production) exceeded disparities among broader food categories. Nutrition inequality, measured by both indices, also increased between 1991 and 2021. However, variations across different nutrients were relatively minor, as diversified nutrition sources mitigated inequality within food categories. Geospatial analysis further highlighted distinct patterns: cereals were the primary contributors to disparities in energy, protein, and mineral supply; oil crops and livestock products drove fat inequality; while vegetables and fruits predominantly influenced vitamin inequality. These findings offer critical insights for optimizing China’s food and nutrition distribution strategies, supporting more equitable and sustainable food system development. Full article

Rural tourism agglomerations are increasingly viewed as catalysts for diversified regional growth, integrated rural revitalization, and improved farmer prosperity. However, most studies focus on urban and developed regions, leaving spatial patterns and evolutionary mechanisms in underdeveloped rural areas poorly understood. This study takes Jilin Province, an economically lagging region, as an example, measuring rural tourism agglomeration using spatial analysis methods including the Gini coefficient, nearest-neighbor index, Ripley’s K function, kernel density, and buffer analysis. Results show that agglomeration is significant and strengthening over time, with clear regional variations. All types of rural tourism products exhibit an “increase followed by decrease” pattern across spatial scales, evolving from isolated “nodes” to continuous “areas”. Agglomeration is subject to triple constraints from natural, economic, and social dimensions. This study suggests that high-quality rural tourism development should leverage point–axis spillover from flagship scenic areas, promote surface expansion of characteristic villages and towns, and strengthen network connectivity through roads and talent-information channels. Full article

According to the “Five Development Concepts” of the new national development plan, the study of inclusive green development in the petroleum and gas sector (IGDPG) is crucial for enhancing production efficiency and safeguarding the environment and resources. This study constructs the IGDPG indicator system from industrial development, social opportunity equity, poverty and income inequality reduction, and green ecology dimensions, and the CRITIC Portfolio empowerment-TOPSIS method was used to measure the level of IGDPG in the eastern, central, and western regions using panel data. The Dagum Gini coefficient method was applied to analyze regional disparities and their causes, while the obstacle degree model and the Tobit model were used to identify internal and external factors of IGDPG. We found that IGDPG levels in the three regions showed fluctuating growth, and the eastern region (0.394) had much higher IGDPG levels than the central (0.337) and western (0.355) regions. The overall Gini coefficient for IGDPG is small, while inter-regional disparities are the primary source of overall disparities, and the intra-regional disparities of the three main areas exhibit a declining tendency. In terms of internal factors, social opportunity equity has been identified as the primary obstacle constraining IGDPG. Externally, factors such as industrial cluster, industrial upgrading, urbanization rate, and digital economy exhibit a facilitative effect on IGDPG, whereas environmental burdens exert an inhibitory influence. Moreover, all of these internal and external drivers demonstrate significant regional variations. Therefore, breaking regional restrictions and promoting the coordinated development of IGDPG so as to improve China’s IGDPG level as a whole is the forecasted trend. Full article

Agriculture is fundamental to food security and environmental sustainability. Advancing its holistic ecological transformation can stimulate socioeconomic progress while fostering human–nature harmony. Utilizing provincial data from mainland China (2013–2022), this research establishes a multidimensional evaluation framework across four pillars: agricultural ecology, low-carbon practices, modernization, and productivity enhancement. Through comprehensive assessment, we quantify China’s low-carbon green agriculture (LGA) development trajectory and conduct comparative regional analysis across eastern, central, and western zones. As for methods, this study employs multiple econometric approaches: LGA was quantified using the TOPSIS entropy weight method at the first step. Moreover, multidimensional spatial–temporal patterns were characterized through ArcGIS spatial analysis, Dagum Gini coefficient decomposition, Kernel density estimation, and Markov chain techniques, revealing regional disparities, evolutionary trajectories, and state transition dynamics. Last but not least, Tobit regression modeling identified driving mechanisms, informing improvement strategies derived from empirical evidence. The key findings reveal the following: 1. From 2013 to 2022, LGA in China fluctuated significantly. However, the current growth rate is basically maintained between 0% and 10%. Meanwhile, LGA in the vast majority of provinces exceeds 0.3705, indicating that LGA in China is currently in a stable growth period. 2. After 2016, the growth momentum in the central and western regions continued. The growth rate peaked in 2020, with some provinces having a growth rate exceeding 20%. Then the growth rate slowed down, and the intra-regional differences in all regions remained stable at around 0.11. 3. Inter-regional differences are the main factor causing the differences in national LGA, with contribution rates ranging from 67.14% to 74.86%. 4. LGA has the characteristic of polarization. Some regions have developed rapidly, while others have lagged behind. At the end of our ten-year study period, LGA in Yunnan, Guizhou and Shanxi was still below 0.2430, remaining in the low-level range. 5. In the long term, the possibility of improvement in LGA in various regions of China is relatively high, but there is a possibility of maintaining the status quo or “deteriorating”. Even provinces with a high level of LGA may be downgraded, with possibilities ranging from 1.69% to 4.55%. 6. The analysis of driving factors indicates that the level of economic development has a significant positive impact on the level of urban development, while the influences of urbanization, agricultural scale operation, technological input, and industrialization level on the level of urban development show significant regional heterogeneity. In summary, during the period from 2013 to 2022, although China’s LGA showed polarization and experienced ups and downs, it generally entered a period of stable growth. Among them, the inter-regional differences were the main cause of the unbalanced development across the country, but there was also a risk of stagnation and decline. Economic development was the general driving force, while other driving factors showed significant regional heterogeneity. Finally, suggestions such as differentiated development strategies, regional cooperation and resource sharing, and coordinated policy allocation were put forward for the development of LGA. This research is conducive to providing references for future LGA, offering policy inspirations for LGA in other countries and regions, and also providing new empirical results for the academic community. Full article

Food insecurity remains a persistent global challenge, despite significant advancements in agricultural production and technology. The main objective of this study is to identify and quantitatively assess some of the structural determinants influencing country-level food insecurity and provide an empirical background for policy-making aimed at achieving the Sustainable Development Goal of Zero Hunger (SDG 2). This study employs a beta regression model in order to study moderate or severe food insecurity across 153 countries, using a cross-sectional dataset that integrates economic, agricultural, political, and demographic independent variables. The analysis identifies low household per capita final consumption expenditure (β = −9 × 10⁻⁵, p < 0.001), high income inequality expressed as a high GINI coefficient (β = 0.047, p < 0.001), high long-term inflation (β = 0.0176, p = 0.003), and low economic globalization (β = −0.021, p = 0.001) as the most significant predictors of food insecurity. Agricultural variables such as land area (β = −1 × 10⁻⁵, p = 0.02) and productivity per hectare (β = −9 × 10⁻⁵, p = 0.09) showed limited but statistically significant inverse effects (lowering food insecurity), while factors like unemployment, political stability, and conflict were not significant in the model. The findings suggest that increased economic capacity, inequality reduction, inflation control, and global trade integration are critical pathways for reducing food insecurity. Future research could employ beta regression in time-series and panel analyses or spatial models like geographically weighted regression to capture geographic differences in food insecurity determinants. Full article