Search Results (451)

Air pollution remains a critical environmental and public health issue, requiring diverse research perspectives, including those related to energy production and consumption. This study examines the relationship between Poland’s energy mix and air pollution trends by integrating national statistical data on primary energy consumption and renewable energy sources over the past 15 years with air pollution measurements from the last eight years. The air pollution data, obtained from reference-grade monitoring stations, focus on particulate matter (PM). To address discrepancies in temporal resolution between daily PM measurements and annual energy sector reports, a bootstrapping method was applied within a regression framework to assess the overall impact of individual energy components on national air pollution levels. Seasonal decomposition techniques were employed to analyze the temporal dynamics of specific energy sources and their contributions to pollution variability. A key aspect of this research is the role of renewable energy sources in air quality trends. This study also investigates regional variations in pollution levels by analyzing correlations between geographic location, industrialization intensity, and the proportion of green areas across Poland’s administrative regions (Voivodeships). This spatially explicit approach provides deeper insights into the linkages between energy production and pollution distribution at a national scale. Poland presents a unique case due to its distinct energy mix, which differs significantly from the EU average, its persistently high air pollution levels, and recent regulatory changes. These factors create an ideal setting to assess the impact of energy sector transitions on environmental quality. By employing high-resolution spatiotemporal big data analysis, this study leverages measurements from over 100 monitoring stations and applies advanced statistical methodologies to integrate multi-scale energy and pollution datasets. From a PM perspective, the regression analysis showed that High-Methane Gas had a neutral impact on PM concentrations, making it a suitable transition energy source, while renewables exhibited negative regression coefficients and coal-based sources showed positive coefficients. The findings offer new perspectives on the long-term environmental effects of shifts in national energy policies. Full article

Territorial spatial conflicts (TSCs) refer to a contradiction of utilization resulting from the inconsistency of the needs and objectives of different subjects of interest for spatial resources in planning, utilization, and management. This research aimed to unveil the TSCs, construction land expansion (CLE), and their relationship in Guizhou Province from 1980 to 2020, both temporally and spatially. This paper established indicators to assess CLE, including construction land expansion velocity, construction land expansion intensity, and construction land expansion pattern to analyze the expansion characteristics of construction land in Guizhou Province. At the same time, the territorial spatial conflict indicator (SCII) was constructed to study the TSCs in Guizhou Province, and its evolution pattern was explored through the cold hotspot analysis. On this basis, it investigated the relationship and linkage between TSCs and CLE through the ordinary least squares (OLS) regression model and geographically weighted (GWR) regression model. Furthermore, this paper also constructed an economic elasticity coefficient and a population elasticity coefficient to analyze the collaborative relationship between TSCs and GDP along with population volume. The research revealed that while the velocity and intensity of CLE in Guizhou Province have escalated over time, this expansion displayed considerable geographical variation across various locations. Simultaneously, the TSCs intensified, demonstrating a slight positive correlation with the expansion. The study of the spatial and temporal evolution characteristics and response relationship between the TSCs and CLE provided a reference for the optimization of regional territorial space. It is highly valuable and significant in fostering efficient utilization of land resources, adjusting to economic and social transformations, and improving the scientific rigor of spatial planning. Full article

Urban functional zones (UFZs) are related to people’s daily activities. Accurate recognition of UFZs is of great significance for an in-depth understanding of the complex urban system and optimizing the urban spatial structure. Emerging geospatial big data provide new ideas for humans to recognize urban functional zones. Point-of-interest (POI) data have achieved good results in the recognition of UFZs. However, since humans are the actual users of urban functions, and POI data only reflect static socioeconomic characteristics without considering the semantic and temporal features of dynamic human activities, it leads to an incomplete and insufficient representation of complex UFZs. To solve these problems, we proposed a data-synthesis-driven approach to quantify and analyze the distribution and mixing of urban functional zones. Firstly, representation learning is used to mine the spatial semantic features, activity temporal features, and activity semantic features that are embedded in POI data and social media check-in data from spatial, temporal, and semantic aspects. Secondly, a weighted Stacking ensemble model is used to fully integrate the advantages between different features and classifiers to infer the proportions of urban functions and dominant functions of each urban functional zone. A case study within the 5th Ring Road of Beijing, China, is used to evaluate the proposed method. The results show that the approach combining dynamic and static features of POI data and social media data effectively represents the semantic information of UFZs, thereby further improving the accuracy of UFZ recognition. This work can provide a reference for uncovering the hidden linkages between human activity characteristics and urban functions. Full article

Geographical named entity matching, a crucial step in address encoding, aims to enhance address resolution accuracy through the precise identification and linkage of geographical named entity data. However, existing approaches tend to ignore the spatial information of entities, leading to misclassification. Drawing on the human process of searching for addresses, this study proposes a multi-objective learning model named GNEMM that integrates the semantic and spatial information of geographical named entities. To further mimic the human cognitive process during address search, it incorporates the Retrieval-Augmented Generation (RAG) technique. By integrating newly added external address data with an advanced large language model (LLM) like GPT-4, it achieves precise address evaluation and recommendation. The model was tested using a standard geographical named entity dataset from Shandong Province, focusing on three sub-tasks: element segmentation, matching, and spatial similarity score prediction. The experimental results indicate that the method achieves a geographical named entity matching accuracy of up to 99%, with improvements of 10% and 5% in the segmentation and prediction sub-tasks. GNEMM performs best in address-matching tasks of various scales, and the vectors extracted by GNEMM perform best in the downstream retrieval and matching of various address types, which verifies its applicability in geographical named entity recommendation applications. Full article

Interior permanent magnet synchronous motors (IPMSMs) exhibit significant nonlinear electromagnetic behaviour due to the effects of saturation, cross-coupling, spatial harmonics, temperature, and iron losses. In order to effectively capture the actual electromagnetic behaviour of IPMSMs, this paper proposes an improved IPMSM nonlinear model. The proposed model is based on the nonlinear flux-linkage model and progressively incorporates the effects of spatial harmonics, temperature, and iron losses. In this paper, the discrete-time form of the improved nonlinear model is established directly. It is suitable not only for embedding into the Matlab/Simulink environment as an alternative to field circuit coupling simulation but also for deployment into field programmable gate arrays (FPGA) as the model basis for hardware-in-the-loop testing. The effectiveness and feasibility of the improved model are verified by experimental results. Full article

The direction of the relationship between economic growth and income (social) inequality remains an open area of research, with theoretical models suggesting the possibility of positive or negative covariations. This study contributes to the debate by examining the case of Mexico, a country characterized by significant income disparities. Our analysis introduces several innovations. First, we adopt a regional approach with data at the level of states, which provides a more suitable framework for comparison in regard to cross-country studies. Second, we employ three distinct measures of income inequality—the Palma ratio (P90/40), the P90/50 ratio, and the Gini index—offering a more comprehensive perspective in terms of income distribution deciles. Additionally, we incorporate a panel data approach that accounts for spatial neighborhood effects in inequality influencing growth. Our findings reveal a strong significant positive covariation between inequality and growth: periods of rising inequality coincide with accelerated economic growth, whereas periods of declining inequality align with growth slowdowns. Interestingly, the model is able to capture both positive and negative covariations for groups of states along the period of analysis, 2005–2019, highlighting the importance of considering regional heterogeneity when running national-level investigations. The effects of spatial inequality clusters on growth seem to be important too, affecting both northern and southern states. These results suggest that Mexico’s growth model appears structurally unequal, which can help to explain the persistent inequality situation shown by the country in the last decades. Full article

Industrial linkages play a crucial role in sustaining industrial agglomerations, driving economic growth, and shaping the spatial architecture of economic systems. This study explores the complexity of causal networks within the industrial ecosystems of China and the United States, using high-frequency economic data to compare the interdependencies and causal structures across key sectors. By employing the partial cross mapping (PCM) technique, we capture the dynamic interactions and intricate linkages among industries, providing a detailed analysis of inter-industry causality. Utilizing data from 32 Chinese industries and 11 United States industries spanning 2015 to 2023, our findings reveal that the United States, as a global leader in technology and finance, exhibits a diversified and service-oriented industrial structure, where financial and technology sectors are pivotal to economic propagation. In contrast, China’s industrial network shows higher centrality in heavy industries and manufacturing sectors, underscoring its focus on industrial output and export-led growth. A comparative analysis of the network topology and resilience highlights that China’s industrial structure enhances network stability and interconnectivity, fostering robust inter-industry linkages, whereas the limited nodal points in the United States network constrain its resilience. These insights into causal network complexity offer a comprehensive perspective on the structural dynamics and resilience of the economic systems in both countries. Full article

This study applies the socio-metabolic approach and relatedly the concept of planetary urbanization understanding to detect the identity of the “alternative zones” embedded in the food supply chain of cities (FSC). To achieve shortened and sustainable FSCs for cities, strong alternative food networks (AFNs) should be developed and sustained. The precious element of a strong AFN is its urban areas, which serve as niche alternative food initiatives (AFIs) for sustainability transitions in food supply chains (FSCs). To achieve shorter and more sustainable FSCs in cities, it is crucial to develop and sustain empowered alternative food networks (AFNs) by deploying their AFIs. Within this context, this study examines AFIs in 12 European FUSILLI cities to understand the potential of the intrinsic AFN to accelerate the sustainable transition in FSCs. Considering the results of AFNs in accelerating sustainability transitions in FSCs. Results through spatial analyses of food ecosystems of FUSILLI cities, although there are prominent examples with a strong short and alternative food network, it is obvious that the sustainable transition into an alternative food network has proceeded; however, the analysis of AFNs in FUSILLI cities demonstrates that sustainability transitions have advanced through vigorous AFNs. However, extended urban areas still have room to supersede their place in conventional/industrial agricultural production, which remains embedded in these spaces. The same inference applies to urban—rural linkages, which need to be strengthened to support the relocation of the food system in the development of AFNs in urban areas and to create more sustainable and shortened FSCs. Also, it is obvious that cities with greater extended AFNs, for example, Rome, due to its great number of AFIs and geographical extent of AFN covering concentrated urban areas and to strengthen the rural–urban linkage for shortened food supply chains, as well as extended urban areas, and Oslo, due to its great variety of AFIs embedded in concentrated urban areas with alternative food production areas in its (erstwhile rural areas) extended urban areas. Full article

The sustainable development of regional higher education is closely related to the level of regional economic development. There is a close interdependence between higher education and economic development. Based on data from 31 provinces in China in 2022, this study uses the entropy method to construct an evaluation index system to explore the coupling and coordination relationship between the regional economy and the development of higher education, as well as the social network effects presented by various regional cities. The results indicate the following: (1) there is a trend of a more coordinated relationship between economic development and the development of higher education in the eastern region compared to the western region, exhibiting a pattern of “higher in the east and lower in the west”; and (2) the economic development and the scale of higher education in East China and Central China are coordinated, and some provinces have played a role in bridging and internal and external linkages in the spatial network effect, economic development, and the scale of higher education in some provinces in Northwest and Southwest China to moderate the imbalance and weak internal and external linkages in the spatial network. Exploring the compatibility between the scale and structure of higher education and economic development is not only of guiding significance for promoting the regional layout and development of higher education in China, but also has an important reference value for economic structural adjustment and transformation. Full article

The positions of millisecond pulsars (MSPs) can be determined with sub-milliarcsecond (mas) accuracy using both Very Long Baseline Interferometry (VLBI) and timing, referenced to the International Celestial Reference Frame (ICRF) and planetary ephemerides frame, respectively, representing kinematic and dynamical reference frames. The two frames can be connected through observations of common celestial objects, MSPs observed with VLBI and timing. However, previous attempts to establish this connection were unreliable due to the limited number of MSPs observed by both techniques. Currently, 23 MSPs have been precisely measured using both multiple timing and VLBI networks. Among them, 17 MSPs are used to link the two reference frames, marking a significant three-fold increase in the number of common MSPs used for frame linking. Nevertheless, six MSPs located near the ecliptic plane are excluded from frame linkage due to positional differences exceeding 20 mas measured by VLBI and timing. This discrepancy is primarily attributed to errors introduced in fitting positions in timing methods. With astrometric parameters obtained via both VLBI and timing for these MSPs, the precision of linking DE436 and ICRF3 has surpassed 0.4 mas. Furthermore, thanks to the improved timing precision of MeerKAT, even with data from just 13 MSPs observed by both MeerKAT and VLBI, the precision of linking DE440 and ICRF3 can also exceed 0.4 mas. The reliability of this linkage depends on the precision of pulsar astrometric parameters, their spatial distribution, and discrepancies in pulsar positions obtained by the two techniques. Notably, proper motion differences identified by the two techniques are the most critical factors influencing the reference frame linking parameters. The core shift of the calibrators in VLBI pulsar observations is one of the factors causing proper motion discrepancies, and multi-wavelength observations are expected to solve it. With the improvement in timing accuracy and the application of new observation modes like multi-view and multi-band observations in VLBI, the linkage accuracy of the dynamical and kinematic reference frames is expected to reach 0.3 mas. Full article

This study evaluates the transmission relationships and spatial correlation network structure of water resources green efficiency among seven urban agglomerations in the Yellow River Basin from 2008 to 2022. Using the Super-SBM model, water resources green efficiency was measured. A modified gravity model was then employed to assess the spatial linkage intensity among cities. Social network analysis was applied to explore the structural characteristics and evolution patterns of the network. Results reveal a fluctuating water efficiency trend, characterized by “rising, then falling, and finally rising”, with an average efficiency of 0.561. Significant regional disparities and considerable potential for improvement persist. The water efficiency network displays an uneven structure, with intensified spatial linkages and a “dense in the east, sparse in the west” pattern. The overall network density is moderate, characterized by more benefits than spillovers. Zhengzhou, Xi’an, and Jinan emerge as key hubs, exhibiting strong external influence and rapid communication within the network. The distinct and stable core-edge structure underscores the importance of inter-regional collaboration to enhance overall water resources green efficiency. This study provides critical insights and policy recommendations for optimizing water resource allocation and promoting sustainable regional development in the Yellow River Basin. Full article

Understanding the global trade network in the printing industry is crucial for promoting sustainable development and cultural exchange and knowledge dissemination. However, the extant literature does not reveal the contours of the global cultural printed material trade network. This paper uses a social network analysis and QAP analysis to explore the global printing industry trade network pattern. The aim of this paper is to discern the core and emerging nodes and explore the evolutional characteristics on the network spatial linkage and country role. The results show the following: ① The printing industry’s global trade network is growing increasingly intricate, with trade links between nations (regions) becoming closer, the network’s connectivity steadily improving, and the hierarchical structure becoming more apparent. ② Germany, France, and Belgium are important intermediary bridges. The “circle of friends” in the trade of cultural products has a growing effect, and China can more easily establish close ties with Southeast Asia, Northern Europe, and Central and Eastern Europe. ③ The industrial chain and geographical proximity are the primary factors in the formation of the trade network. Economic proximity and political proximity significantly and positively contribute to the formation of the trade network, while institutional stability gradually plays a weaker role. As for cultural proximity, a common language and colonial relationship will positively contribute to the formation of a network, while immigrants have no obvious impact. Digital technology is becoming an “emerging force”. Additionally, this paper extends sustainable policies and recommendations for the global cultural trade. Full article

The existing quantitative geography literature contains a dearth of articles that span spatial autocorrelation (SA), a fundamental property of georeferenced data, and spatial optimization, a popular form of geographic analysis. The well-known location–allocation problem illustrates this state of affairs, although its empirical geographic distribution of demand virtually always exhibits positive SA. This latent redundant attribute information alludes to other tools that may well help to solve such spatial optimization problems in an improved, if not better than, heuristic way. Within a proof-of-concept perspective, this paper articulates connections between extensions of the renowned Majority Theorem of the minisum problem and especially the local indices of SA (LISA). The relationship articulation outlined here extends to the p = 2 setting linkages already established for the p = 1 spatial median problem. In addition, this paper presents the foundation for a novel extremely efficient p = 2 algorithm whose formulation demonstratively exploits spatial autocorrelation. Full article

Urbanization, driven by socio-economic development, has significantly impacted river ecosystems, particularly in plain city regions, leading to disruptions in river network structure and function. These changes have exacerbated hydrological fluctuations and ecological degradation. This study focuses on the central urban area of Changzhou using a MIKE11 model to assess the effects of four hydrological connectivity strategies—water diversion scheduling, river connectivity, river dredging, and sluice connectivity—across 13 different scenarios. The results show that water diversion, river dredging, and sluice connectivity scenarios provide the greatest improvements in water environmental capacity, with maximum increases of 54.76%, 41.97%, and 25.62%, respectively. The spatial distribution of improvements reveals significant regional variation, with some areas, particularly in Tianning and Zhonglou districts, experiencing declines in environmental capacity under sluice diversion and river-connectivity scenarios. In addition, the Lao Zaogang River is identified as crucial for improving the overall water quality in the network. Based on a multi-objective evaluation, combining environmental and economic factors, the study recommends optimizing water diversion scheduling at sluices (Weicun, Zaogang, and Xiaohe) with flow rates between 20–40 m³/s, enhancing connectivity at key river hubs, and focusing management efforts on the Lao Zaogang and Xinmeng rivers to strengthen hydrological and water quality linkages within the network. Full article

Urban expansion encroaches on green spaces and weakens ecosystem services, potentially leading to a trade-off between ecological conditions and socio-economic growth. Effectively coordinating the two elements is essential for achieving sustainable development goals at the urban scale. However, few studies have measured urban–ecological linkage in terms of trade-off. In this study, we propose a framework by linking the degraded ecological conditions and urban land use efficiency from a return on investment perspective. Taking a rapidly expanding city as a case study, we comprehensively quantified urban–ecological conditions in four aspects: urban heat island, flood regulating service, habitat quality, and carbon sequestration. These conditions were assessed on 1 km² grids, along with urban land use efficiency at the same spatial scale. We employed the slack-based measure model to evaluate trade-off efficiency and applied the geo-detector method to identify its driving factors. Our findings reveal that while urban–ecological conditions in Zhengzhou’s periphery degraded over the past two decades, the inner city showed improvement in urban heat island and carbon sequestration. Trade-off efficiency exhibited an overall upward trend during 2000–2020, despite initial declines in some inner city areas. Interaction detection demonstrates significant synergistic effects between pairs of drivers, such as the Normalized Difference Vegetation Index and building height, and the number of patches of green spaces and the patch cohesion index of built-up land, with q-values of 0.298 and 0.137, respectively. In light of the spatiotemporal trend of trade-off efficiency and its drivers, we propose adaptive management strategies. The framework could serve as guidance to assist decision-makers and urban planners in monitoring urban–ecological conditions in the context of urban expansion. Full article