Search Results (7)

The Multiscale Geographically and Temporally Weighted Regression model overcomes the limitation of estimating spatiotemporal variation characteristics of regression coefficients for different variables under a single scale, making it a powerful tool for exploring the spatiotemporal scale characteristics of regression relationships. Currently, the most widely used estimation method for multiscale spatiotemporal geographically weighted models is the backfitting-based iterative approach. However, the iterative process of this method leads to a substantial computational burden and the accumulation of errors during iteration. This paper proposes a non-iterative estimation method for the MGTWR model, combining local linear fitting and two-step weighted least squares estimation techniques. Initially, a reduced bandwidth is used to fit a local linear GTWR model to obtain the initial estimates. Then, for each covariate, the optimal bandwidth and regression coefficients are estimated by substituting the initial estimates into a localized least squares problem. Simulation experiments are conducted to evaluate the performance of the proposed non-iterative method compared to traditional methods and the backfitting-based approach in terms of coefficient estimation accuracy and computational efficiency. The results demonstrate that the non-iterative estimation method for MGTWR significantly enhances computational efficiency while effectively capturing the scale effects of spatiotemporal variation in the regression coefficient functions for each predictor. Full article

Under the rapid urbanization process, PM_2.5 pollution has become an increasingly critical issue. Changes in land-use types will inevitably affect PM_2.5 concentration. Meanwhile, the problem of imbalance and inadequacy of regional development remains prominent. This study took the Huaihe River Ecological Economic Belt as the research object, integrating the spatial econometric model with the Geographically and Temporally Weighted Regression (GTWR) and Multiscale Geographically Weighted Regression (MGWR) models, to investigate the spatiotemporal heterogeneity and spillover effect of the association between PM_2.5 concentration and land use from 1998 to 2021. The main findings are as follows: (1) PM_2.5 concentration in the study area from 1998 to 2021 showed an upward and then a downward trend, taking 2013 as a turning point, with respective magnitudes of 50.4% and 42.1%; (2) land use exerts a significant spillover effect on PM_2.5 pollution. Except for grassland and cropland, the direct effect of each land type on PM_2.5 pollution exceeds its indirect effect; (3) the influence of land use on PM_2.5 pollution exhibits significant spatiotemporal variations. The impact coefficient of forests remains relatively consistent across the entire region, whereas that of cropland, water bodies, and impervious surfaces varies markedly across different regions, particularly in the northeastern and southern cities of the study area. The results of this study may give new ideas for collective governance and joint environmental remediation in different cities and probably provide some basis for the formulation of air pollution control policies and urban land planning. Full article

Rapid economic growth in China has brought about a significant challenge: the widening gap in regional development. Addressing this disparity is crucial for ensuring sustainable development. However, existing studies have largely overlooked the intrinsic spatial and temporal dynamics of regional disparities on various levels. This study thus employed five advanced multiscale geographically and temporally weighted regression models—GWR, MGWR, GTWR, MGTWR, and STWR—to analyze the spatio-temporal relationships between ten key conventional socio-economic indicators and per capita GDP across different administrative levels in China from 2000 to 2019. The findings highlight a consistent increase in regional disparities, with secondary industry emerging as a dominant driver of long-term economic inequality among the indicators analyzed. While a clear inland-to-coastal gradient underscores the persistence of regional disparity determinants, areas with greater economic disparities exhibit pronounced spatio-temporal heterogeneity. Among the models, STWR outperforms others in capturing and interpreting local variations in spatio-temporal disparities, demonstrating its utility in understanding complex regional dynamics. This study provides novel insights into the spatio-temporal determinants of regional economic disparities, offering a robust analytical framework for policymakers to address region-specific variables driving inequality over time and space. These insights contribute to the development of targeted and dynamic policies for promoting balanced and sustainable regional growth. Full article

Urbanization is growing at a rapid pace, and this is being reflected in the rising energy consumption from fossil fuels, which is contributing significantly to greenhouse gas impacts and carbon emissions (CE). Aiming at the problems of the time delay, inconsistency, uneven spatial coverage scale, and low precision of the current regional carbon emissions from energy consumption accounting statistics, this study builds a precise model for estimating the carbon emissions from regional energy consumption and analyzes the spatio-temporal characteristics. Firstly, in order to estimate the carbon emissions resulting from energy consumption, a fixed effects model was built using data on province energy consumption and NPP-VIIRS-like nighttime lighting data. Secondly, the PRD urban agglomeration was selected as the case study area to estimate the carbon emissions from 2012 to 2020 and predict the carbon emissions from 2021 to 2023. Then, their multi-scale spatial and temporal distribution characteristics were analyzed through trends and hotspots. Lastly, the influence factors of CE from 2012 to 2020 were examined with the OLS, GWR, GTWR, and MGWR models, as well as a ridge regression to enhance the MGWR model. The findings indicate that, from 2012 to 2020, the carbon emissions in the PRD urban agglomeration were characterized by the non-equilibrium feature of “high in the middle and low at both ends”; from 2021 to 2023, the central and eastern regions saw the majority of its high carbon emission areas, the east saw the region with the highest rate of growth, the east and the periphery of the high value area were home to the area of medium values, while the southern, central, and northern regions were home to the low value areas; carbon emissions were positively impacted by population, economics, land area, and energy, and they were negatively impacted by science, technology, and environmental factors. This study could provide technical support for the long-term time-series monitoring and remote sensing inversion of the carbon emissions from energy consumption in large-scale, complex urban agglomerations. Full article

Online car-hailing is gradually shifting towards a predominant use of electric vehicles, a change that is advantageous for developing a sustainable society. Understanding the patterns of changes in online car-hailing travel can assist transportation authorities in optimizing vehicle dispatching, reducing idle rates, and minimizing resource wastage. The built environment influences the demand for online car-hailing travel. Previous studies have commonly employed the geographically weighted regression (GWR) model and the geographically and temporally weighted regression (GTWR) model to examine the relationship between the demand for online car-hailing trips and the built environment. However, these studies have ignored that the impact range of the built environment also varies with time and space. To fully consider the variations in the impact range of the built environment, this study established multi-scale geographically and temporally weighted regression (MGTWR) to examine the spatiotemporal impacts of urban built environments on the demand for online car-hailing travel. An empirical study was conducted to assess the effectiveness of the MGTWR model using point of interest (POI) data and online car-hailing order data from Haikou. The evaluation indicators showed that the MGTWR model has higher fitting accuracy than the GTWR model. Moreover, the impact of each type of POI on the demand for online car-hailing travel was analyzed by examining the temporal and spatial distribution of the regression coefficients. Additionally, we observed that transport facility POIs and healthcare service POIs exerted the most pronounced influence on the demand for online car-hailing. In contrast, the impact of shopping service POIs and catering service POIs was relatively weaker. Full article

Promoting technological innovation in the natural gas industry is a feasible means of achieving energy transition. Guided by the geographic innovation theory, this article carries out research on the scale, technical fields, capabilities, and influencing factors of technological innovation in the natural gas industry of 312 Chinese prefecture-level cities, making use of the cusp catastrophe model, the center of gravity and standard deviational ellipse, exploratory spatial data analysis, and geographically and temporally weighted regression (GTWR). The research shows the following: (1) Technological innovation in China’s natural gas industry has continuously expanded in terms of scale, with the number of participating cities increasing, showing a spatially uneven pattern of local agglomeration and national diffusion. (2) There have been significant innovation achievements in natural gas equipment and engineering, but natural gas utilization is lagging in comparison, with drilling, new materials, environmental protection, pipe network engineering, and digital services becoming frontier fields, and collaborative innovation with the thermoelectric, metalworking, automotive, and other related industries having been initially established. (3) The unevenness of technological innovation capabilities is obvious, with the core advantages of Beijing–Tianjin being continuously strengthened and Sichuan–Chongqing, the Yangtze River Delta, the Pearl River Delta, Shandong Peninsula, and Liaodong Peninsula forming high-level technological innovation capability agglomerations. (4) The spatiotemporal pattern of technological innovation capability is the result of multiple factors, with northeastern cities mainly being affected by natural gas demands, northwestern cities being highly sensitive to capital strength, eastern cities mostly relying on urban development, and cities in North China mainly being bolstered by the strength of talent. (5) It is necessary to carry out further multi-agent and multi-scale future research on technological innovation in the natural gas industry and its relationship with the energy transition and to explore the interactivity of the influencing factors. This study may provide strategies for technological innovation in the natural gas industry from the perspective of the energy transition. Full article

Background and Objectives: It is important to understand the temporal and spatial distributions of standing wood carbon storage in forests to maintain ecological balance and forest dynamics. Such information can provide technical and data support for promoting ecological construction, formulating different afforestation policies, and implementing forest management strategies. Long-term series of Landsat 5 (Thematic Mapper, TM) and Landsat 8 (Operational Land Imager, OLI) remote sensing images and digital elevation models (DEM), as well as multiphase survey data, provide new opportunities for research on the temporal and spatial distributions of standing wood carbon storage in forests. Methods: The extracted remote sensing factors, terrain factors, and forest stand factors were analyzed with stepwise regression in relation to standing wood carbon storage to identify significant influential factors, build a global ordinary least squares (OLS) model and a linear mixed model (LMM), and construct a local geographically weighted regression (GWR), multiscale geographically weighted regression model (MGWR), temporally weighted regression (TWR), and geographically and temporally weighted regression (GTWR). Model evaluation indicators were used to calculate residual Moran’s I values, and the optimal model was selected to explore the spatiotemporal dynamics of standing wood carbon storage in the Liangshui Nature Reserve. Results: Remote sensing factors, topographic factors (Slope), and stand factors (Age and DBH) were significantly correlated with standing wood carbon storage, and the constructed global models exhibited fitting effects inferior to those of the established local models. LMM is also used as a global model to add random effects on the basis of OLS, and R² is increased to 0.52 compared with OLS. The local models based on geographically weighted regression, namely, GWR, MGWR, TWR, and GTWR, all have good performance. Compared with OLS, the R² is increased to 0.572, 0.589, 0.643, and 0.734, and the fitting effect of GTWR is the best. GTWR can overcome spatial autocorrelation and temporal autocorrelation problems, with a higher R² (0.734) and a more ideal model residual than other models. This study develops a model for carbon storage (CS) considering various influential factors in the Liangshui area and provides a possible solution for the estimation of long-term carbon storage distribution. Full article