†Equal Contribution, *Corresponding Author
As the world's second-largest economy, China has experienced rapid industrialization and urbanization, resulting in high energy consumption and significant carbon emissions. This development has intensified conflicts between human-land relations and environmental conservation, contributing to global warming and urban air pollution, both of which pose serious health risks. This study uses nighttime light (NTL) data from 2005 to 2019, along with scaling techniques and statistical analysis, to estimate city-scale energy carbon emissions over a 15-year period. Focusing on Northeast China, a traditional industrial region comprising 36 cities across three provinces, we examine spatial patterns of energy carbon emissions and assess spatiotemporal evolution through spatial autocorrelation and dynamic changes. These changes are further evaluated using standard deviation ellipse (SDE) parameters and SLOPE values. Additionally, the Tapio decoupling index is applied to explore the relationship between city-scale emissions and economic growth. Our findings for the 36 cities over 15 years are: (1) Heilongjiang shows low, declining emissions; Jilin improves; Liaoning has high, steadily increasing emissions. (2) The global spatial autocorrelation of energy carbon emissions is significant, with a positive Moran's I, while significant local Moran's I clusters are concentrated in Heilongjiang and Liaoning. (3) The greatest emission changes occurred in 2015, followed by 2019, 2005, and 2010. (4) Emission growth is fastest in Heilongjiang, followed by Liaoning and Jilin. (5) Tapio analysis shows positive decoupling in Heilongjiang, declining decoupling in Jilin, and no change in Liaoning. This study provides a quantitative basis for dual carbon goals and offers emission reduction strategies for government, industry, and residents, supporting energy transition and sustainable urban planning.
energy carbon emissions; nighttime light (NTL) data; spatiotemporal evolution; Tapio decoupling analysis; Northeast China
Integrated “mountains–rivers–forests–farmland–lakes–grasslands” protection and restoration projects play a critical role in improving regional surface water quality. However, the spatiotemporal mechanisms through which different ecological restoration measures influence surface water quality remain insufficiently understood. Taking the Yimeng Mountains region as a case study, we sampled and assessed 21 physico-chemical water quality indicators at 49 sites across distinct hydrological periods. On this basis, we developed the regional Water Quality Index (WQI) evaluation framework, using a least absolute shrinkage and selection operator (LASSO) regression model to identify key water quality variables, and combined two-way analysis of variance and the coefficient of variation method to elucidate the spatiotemporal effects and dominant controls of different ecological restoration projects on regional water quality. The results show that ① water quality was poorest during the normal-flow period and best during the high-flow period, with a spatial pattern characterized by a gradient from relatively good conditions upstream to degraded conditions downstream. This pattern was closely related to pollutant accumulation during the normal-flow period and dilution effects during high flows. ②Total phosphorus (TP), total nitrogen (TN), and 5-day biochemical oxygen demand (BOD₅) were the dominant controlling factors. The WQI–LASSO model exhibited high goodness of fit (R² = 0.99), and adjusting the weight of TP to 4 substantially enhanced the regional applicability of the model. ③ Permanganate index (CODMn) and ammonia nitrogen (NH₃-N) were strongly influenced by hydrological period, whereas BOD₅ and chemical oxygen demand (COD) were more sensitive to restoration measures.Those indicated the effectiveness of organic pollution control interventions.④ Among the eight restoration units, the forest quality improvement and water-source protection units showed markedly higher water-quality stability than the other units (coefficient of variation, CV < 2% vs. regional mean CV = 6.3%), and their ecological buffering capacity index was 4.8 times higher than that of the mining restoration unit, demonstrating the advantage of vegetation cover in regulating hydrological fluctuations. These findings provide an important reference for water pollution control and integrated water-quality management in the Yimeng Mountains, particularly by offering quantitative evidence to support differentiated management strategies across multiple hydrological periods.
water quality index; water quality stability; ecological restoration; multiple hydrological periods; LASSO regression
Abstract: Rapid urbanization promotes socio-economic development but also poses challenges for urban management, particularly in achieving a balanced job-housing relationship. Such imbalances can aggravate traffic congestion, increase energy consumption, and reduce commuting efficiency. Addressing these urban issues requires accurate job-housing space identification (JHSI). The emergence of spatiotemporal big data in geography has popularized location-based service (LBS) data, especially mobile signaling data, for JHSI applications. However, employing mobile signaling data for JHSI presents challenges stemming from both dataset limitations and methodological complexities, including data accessibility constraints due to privacy concerns. This study develops an optimized JHSI approach using a novel LBS dataset and changing the identification lens into base stations. The newly adopted dynamic population data features simplified, privacy-sensitive fields. By establishing time thresholds for working and living hours based on local daily routines and applying straightforward statistical processing to these defined base station fields, we can derive estimated job-housing spaces. This approach not only achieves concise, high-precision identification with readily available data but also enables lightweight dataset applications with enhanced feasibility and broader applicability. We implemented this optimized approach in Haidian District, Beijing, using five days of 2023 data to evaluate method’s applicability and quantify job-housing imbalances at subdistrict and town scales. Results demonstrate the approach’s accuracy and multi-scale utility in assessing job-housing relationships. We contend that this optimized method advances JHSI-related perspectives in macro-level daily research, facilitates further LBS-driven urban applications, and contributes to improving human livability and quality of life in urban areas.
Urban areas; Population Dynamic Data; Job-Housing Space Identification (JHSI); Location-Based Service (LBS) Data; Haidian District of Beijing
Regions represent complex landscapes where production, living, and ecological functions intersect, yet rapid urbanization has exacerbated conflicts within the human-land-environment system, resulting in the disordered construction of functional spaces. To address these challenges and optimize territorial spatial planning, China introduced the concept of production-living-ecological spaces (PLEs) in 2012. The evolution of PLEs strongly reflects the land-human activity interactions driven by human needs amid urbanization, yet few studies have quantitatively characterized and compared the dynamics of urbanization through the lens of PLE functional spaces. Adopting a land use-to-functional space perspective, this study examines the Beijing-Tianjin-Hebei (BTH) region, which is a key area for coordinated development since 2015. By integrating POI and land use data, 5 km grid-based PLEs mapping was conducted for 2016, 2020, and 2024. Findings show BTH’s urbanization is defined by mutual conversion of built-up land and farmland, plus coexisting ecological restoration and degradation. PLEs exhibit clear spatial differentiation: high-intensity living-production overlap in Beijing-Tianjin; fragmented production space expansion in their southern peripheries; and expanded but fragmented ecological spaces in Zhangjiakou-Chengde. These changes tie closely to regional coordination policies. This study offers a novel perspective and methodology for urbanization research, providing insights for balanced functional space construction and sustainable development. It also supports BTH’s 2030 integration goals aligned with China’s 15th Five-Year Plan.
urbanization characterization; functional space patterns; POI data; land use change; production-living-ecological space mapping
Accurate differentiation and mapping of Production-Living-Ecological space (PLES) functions are pivotal to unraveling the laws governing regional coordinated development. However, existing research falls short in four key aspects: integrating the correlations between socio-economic and natural systems, identifying functional stacking scenarios, accounting for the functional heterogeneity of built-up areas, and refining mapping methodologies. To address these limitations, this study integrates multi-source geospatial data with Point of Interest (POI) data as the primary source and land use data as a supplement, adopts grid units as the analytical scale, and develops a comprehensive mapping method that synergizes PLES function differentiation, quantitative intensity measurement, and qualitative cartography. This method comprises four sequential steps: First, data preprocessing is carried out. A PLES function classification standard for POI data is established from the perspective of socio-economic and urbanization drivers in accordance with the National Economic Industry Classification (GB/T 4754-2017), and land use data is incorporated for integration. Second, the functional attributes of POI points are matched at the grid scale; for low human activity areas without POI coverage, land use data is directly used to supplement their functional attributes. Third, the global relative density of grid functional attributes is calculated to generate intensity distribution maps. Fourth, utilizing the optimized soil tripartite method, role weights are determined based on the functional proportion within each grid, and coding is conducted by combining the uppercase and lowercase English initials and sequential order of functional attributes to produce a global PLES function qualitative map. Taking the Beijing-Tianjin-Hebei (BTH) region as the application area, this study implements the method at a 5 km grid scale using Amap POI data from 2016 to 2024 and near-real-time 10 m resolution land use data derived from Sentinel-2 in Dynamic World. Results indicate that the grid intensity, agglomeration characteristics, and temporal expansion patterns of PLES functions in 2016, 2020, and 2024 are highly consistent with the phased progress and target trajectories of BTH integrated development, thus verifying the method’s effectiveness. Additionally, the 42 map types identified across 15,088 grid units in the entire study area intuitively demonstrate the method’s potential for precise regional pattern optimization. In conclusion, this study aims to provide theoretical and methodological references for big data-enabled smart city construction that was emphasized at the 2025 Central Urban Work Conference, as well as for territorial space optimization during the 15th Five-Year Plan period. Ultimately, it seeks to support regional sustainable development initiatives.
production-living-ecological spaces; POI data; functional discrimination; cartographic methods; Beijing-Tianjin-Hebei region
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