Ellipse Model Research Articles

Investigating spatio-temporal variations and contributing factors of land use-related carbon emissions in the Beijing-Tianjin-Hebei Region, China

The land use change is the primary factor in influencing the regional carbon emissions. Studying the effects of land use change on carbon emissions can provide supports for the development policies of carbon emission. Using land use and energy consumption data, this study measures carbon emissions from land use dynamics in the Beijing-Tianjin-Hebei region from 2000 to 2020. The standard deviation ellipse model is employed to investigate the distribution characteristics of the spatial patterns of carbon emissions, while the Geographically and Temporally Weighted Regression (GTWR) model is used to examine the contributing factors of carbon emissions and their spatial and temporal heterogeneity. Results indicate a consistently increasing trend in carbon emissions from land use in the Beijing-Tianjin-Hebei region from 2000 to 2020. Construction land is characterized with both the primary source and an increasing intensity of carbon emissions. Besides, the spatial distribution of carbon emissions from land use in the Beijing-Tianjin-Hebei region demonstrates an aggregation pattern from in the northeast-southwest direction towards the center, with a greater aggregation trend in the east–west direction compared to that in the south-north direction. During the study period, a positive correlation was documented between carbon emissions and factors including total population, economic development level, land use degree, and landscape patterns. This correlation showed a decreasing trend and reached a stable level at the end of the study period. Moreover, the analysis showed a negative correlation between industrial structure and carbon emissions, which showed an increasing trend and reached a relatively high level at the end of the study period.

Post-new-horizons Orbits and Masses for the Satellites of Pluto

We report on ephemerides for the satellites of Pluto based on the large set of astrometric measurements. Our orbit fit yielded the following masses with 1σ uncertainties: GM Pluto = 869.3 ± 0.4 km3 s−2, GM Charon = 106.1 ± 0.3 km3 s−2, GM Nix = 1.50 ± 0.52 × 10−3 km3 s−2, GM Hydra = 2.01 ± 0.27 × 10−3 km3 s−2, corresponding to the densities of ρ Pluto = 1.853 ± 0.004 g cm−3, ρ Charon = 1.705 ± 0.006 g cm−3, ρ Nix = 0.88 ± 0.31 g cm−3, and ρ Hydra = 1.21 ± 0.19 g cm−3. Masses of Kerberos and Styx remain unconstrained, and it is unlikely that we will be able to measure them even if we obtain abundant 1 mas precision astrometry during the next 20 yr. We summarize the results of orbit integration in terms of osculating and precessing ellipse model mean elements. All satellites reside in near-circular orbits, and Kerberos and Styx have 0.4 deg and 0.3 deg inclinations with respect to Charon’s orbit plane. The nodal regression periods for Kerberos and Hydra are ∼9 and ∼14 yr respectively. We found that Charon’s orbit pole can be approximated as: R.A. = 133.0073 + 0.0036T deg, and decl. = –6.2449 + 2.5 × 10−5 T deg, where T is Julian centuries from the epoch J2000, based on 5000 yr of orbit integration.

Spatiotemporal Evolution, Spatial Agglomeration and Convergence of Environmental Governance in China—A Comparative Analysis Based on a Basin Perspective

Scientifically measuring the level of environmental governance (EGL) and understanding its spatial convergence has important reference value for ecological governance. In this paper, the global entropy method is applied to measure the EGL of 284 prefecture-level cities in China from 2007 to 2019, which are divided into three major river basins, including the Yellow River, Yangtze River, and Pearl River, to observe the spatial–temporal evolutionary characterization through a standard deviation ellipse model. The coefficient of variation and the spatial econometric model are the tools used to conduct the spatial convergence test. The results are as follows: (1) China’s EGL is low overall, though it is fluctuating upward at low magnitude, and the three major river basins follow the ranking: The Pearl River Basin > The Yangtze River Basin > The Yellow River Basin. (2) Spatially, the distribution pattern of China’s EGL changes from “scattered and sporadic” to “multipolar core”. (3) The center of China’s environmental governance was concentrated in the east from 2007 to 2019, and the EGL in the midstream and downstream regions of the three major river basins increased rapidly. (4) Environmental governance in China has significant absolute and conditional β-convergence characteristics, as do the three major basins, while the ranking of convergence speed remains “Yangtze River Basin > Yellow River Basin > Pearl River Basin”. Of these, economic development accelerated the convergence rate of environmental governance in China and its three major river basins; financial pressure significantly inhibited the convergence of the EGL of the Yellow River Basin. The improvement of the EGL in the Pearl River Basin was also negatively influenced by the industrial structure.

Development of Interpretable Probability Ellipse in Tropical Cyclone Track Forecasts Using Multiple Operational Ensemble Prediction Systems

AbstractMost tropical cyclone (TC) forecasting centers have implemented a probabilistic circle to represent track uncertainty at a specified lead time. Recent studies suggest that probability ellipses constructed from ensemble prediction systems can convey the anisotropy of track predictability. In this study, a new probability ellipse model is developed to interpret the extent of forward speed and heading uncertainties in ensemble forecasts by selecting an equal proportion of members in the along‐ and cross‐track directions. This method is validated using the 2019–2021 western North Pacific (WNP) TC track forecasts from the ensemble predictions of the European Centre for Medium‐Range Weather Forecasts, the United States National Centers for Environmental Prediction, and the Korea Meteorological Administration. When the proportion of ensemble members in the ellipse is set to 70%, more than one‐half (50.0%–73.6%) of the forecasts, depending on the lead time, indicate reduced area compared with that of the circle. The mean areas of the probability ellipses are 4.9%, 7.0%, 10.0%, and 11.5% smaller than those of the circle in 48‐, 72‐, 96‐, and 120‐hr forecasts, respectively. The forward speed shows greater uncertainty than the heading, as evidenced by the along‐track radii being larger than the cross‐track counterpart in ∼60% of the samples, regardless of the lead time. In addition, the regional distribution of the along‐track/cross‐track ratio in the probability ellipses can explain the dominant direction of the track error in a particular location. The proposed probability ellipse shows potential for application in operational TC track predictions.

Spatial Characteristics of the Non-Grain Production Rate of Cropland and Its Driving Factors in Major Grain-Producing Area: Evidence from Shandong Province, China

The non-grain production rate (NGPR) of cropland is a grave threat to global grain and food supply, and has been a hot issue across the world. However, few scholars explored the impacts of the NGPRs of different cropland types, such as those of paddy land and irrigated land in the same region. Thus, according to the third land survey data, this research first estimated the NGPRs of cropland, paddy land, irrigated land, and dry land at different scales in Shandong Province, China in 2019. Then, their spatial characteristics at a county scale were identified by combining the standard deviation ellipse model and spatial autocorrelation analysis. Finally, the potential driving factors of the NGPR of cropland were explored with the geographical weight regression model. Results are as follows: (1) The NGPR of cropland is at relatively lower level in Shandong Province and is dominated by that of irrigated land, and the NGPR of dry land is higher than those of other cropland types; (2) Significant regional differences exist in the NGPR of cropland, with profound severity in the southeast and much lower in the northwest; (3) At the provincial scale, the total power of agricultural machinery per capita and utilization degree of cropland factors can relieve the NGPR of cropland in nearly the entire research area. The proportion of GDP of the primary industry in GDP, urban population rate, and DEM are the main obstacles for NGPR decrease. At the county scale, the influences of driving factors varied across regions. This research can provide targeted and regional differentiated references for policy improvement and NGPR management.

Uncertainty assessment of machine tool squareness error identification using on-machine measurement

Identification and compensation of geometric errors in the machine tool are widely performed to increase machining accuracy. Periodic verification of the geometric errors, as introduced in ISO 230-2, helps monitor the machine tool accuracy during continued manufacturing operation and detect accuracy degradation early on. While interferometry-based error identification techniques are commonly applied, on-machine measurement (OMM), with its increasing availability to machine tools, can be used to identify geometric errors as an alternative to traditional interferometry-based techniques. As geometric errors also contribute to OMM error, assessment of the uncertainty of error identification is essential to ensure the reliability of the identification. This work presents a method to evaluate the uncertainty in the OMM-based geometric error identification process by Monte Carlo simulation. The error ellipse model, which represents OMM errors with better accuracy, is utilized to improve the identification uncertainty. The squareness errors, as position-independent geometric errors that contribute to machining inaccuracy, are taken as an example to demonstrate the presented method. The influence of the artifact setup errors on the error identification is also investigated. A series of experiments are conducted to evaluate the uncertainty of the OMM-based identification method. The geometric error obtained with the presented method is found to deviate by less than 10% from that obtained with an interferometry-based commercial instrument. The uncertainty obtained from the proposed Monte Carlo simulation method matches well with the uncertainty results obtained by the Guide to the Expression of Uncertainty in Measurement method and repeated measurements.

Spatiotemporal characteristic analysis of PM2.5 in central China and modeling of driving factors based on MGWR: a case study of Henan Province.

Since the start of the twenty-first century, China's economy has grown at a high or moderate rate, and air pollution has become increasingly severe. The study was conducted using data from remote sensing observations between 1998 and 2019, employing the standard deviation ellipse model and spatial autocorrelation analysis, to explore the spatiotemporal distribution characteristics of PM2.5 in Henan Province. Additionally, a multiscale geographically weighted regression model (MGWR) was applied to explore the impact of 12 driving factors (e.g., mean surface temperature and CO2 emissions) on PM2.5 concentration. The research revealed that (1) Over a period of 22 years, the yearly mean PM2.5 concentrations in Henan Province demonstrated a trend resembling the shape of the letter "M", and the general trend observed in Henan Province demonstrated that the spatial center of gravity of PM2.5 concentrations shifted toward the north. (2) Distinct spatial clustering patterns of PM2.5 were observed in Henan Province, with the northern region showing a primary concentration of spatial hot spots, while the western and southern areas were predominantly characterized as cold spots. (3) MGWR is more effective than GWR in unveiling the spatial heterogeneity of influencing factors at various scales, thereby making it a more appropriate approach for investigating the driving mechanisms behind PM2.5 concentration. (4) The results acquired from the MGWR model indicate that there are varying degrees of spatial heterogeneity in the effects of various factors on PM2.5 concentration. To summarize the above conclusions, the management of the atmospheric environment in Henan Province still has a long way to go, and the formulation of relevant policies should be adapted to local conditions, taking into account the spatial scale effect of the impact of different influencing factors on PM2.5.

Similarity-Based Remaining Useful Lifetime Prediction Method Considering Epistemic Uncertainty

Measuring the similarity between two trajectories is fundamental and essential for the similarity-based remaining useful life (RUL) prediction. Most previous methods do not adequately account for the epistemic uncertainty caused by asynchronous sampling, while others have strong assumption constraints, such as limiting the positional deviation of sampling points to a fixed threshold, which biases the results considerably. To address the issue, an uncertain ellipse model based on the uncertain theory is proposed to model the location of sampling points as an observation drawn from an uncertain distribution. Based on this, we propose a novel and effective similarity measure metric for any two degradation trajectories. Then, the Stacked Denoising Autoencoder (SDA) model is proposed for RUL prediction, in which the models can be first trained on the most similar degradation data and then fine-tuned by the target dataset. Experimental results show that the predictive performance of the new method is superior to prior methods based on edit distance on real sequence (EDR), longest common subsequence (LCSS), or dynamic time warping (DTW) and is more robust at different sampling rates.

Reliably and accurately estimate energy in super-capacitor via a model of cyclic voltammetry

As effective energy storage device super-capacitors have been widely applied in energy storage field. Cyclic voltammetry (CV) test is utilized to characterize the electrochemical performance of super-capacitors. Even if there are basic formulas to estimate specific capacitance by integral of CV, the integrable model of CV was not given in these literatures. Meanwhile, storage energy during one CV has not been known up to now. Researchers are keen to be able to calculate energy from CV. However, most current studies have not already provided integrable model of CV which is in any shape. The establishment of the accurate CV model is an important basis to realize the reliable estimation of energy density in super-capacitors via CV measurement, and also a necessary work to develop the super-capacitors management system. To fill this gap of knowledge, correct basic formula for calculation energy (density) is provided in this work for the first time in this work. To reliably and accurately calculate energy (density), a novel equivalent circuit model as dynamic model in explicit functional form of quasi ellipse shape of CV is established in this work. This quasi ellipse shape model of CV was validated on a set of thirty-five experimental CV curves, the results calculated from quasi ellipse model agree well with the experimental data because the essential novelty of the proposed model is that it is from Kirchhoff's current law and this law tells us how to calculate the total current density of a parallel circuit. Because this quasi ellipse shape model of CV is confirmed through experimental results and the integral of the model is also in explicit functional form, reliable and accurate energy (density) of each CV curve was also obtained. As another essential innovation of the proposed methodology is that quantitative relationship between the scan rate, potential window, intrinsic performance parameters of super-capacitor, measurement temperature, bending angle and energy (density) is quantitatively searched, this work makes a significant practical contribution and it makes impact on the research work on the research community. Hence, more scientific and practical significance of CV can be gained from the passage of this article.

Trajectory optimization method based on ellipse model for dynamic motion control of piezoelectric transducer in an optical resonance cavity

External-cavity diode laser (ECDL) can continuously change the output wavelength by scanning its optical resonance cavity (ORC), which is a piezoelectric transducer (PZT) actuated mechanical system. However, because of the inherent hysteresis characteristic of PZT and limited system bandwidth, the dynamic response of the ORC system exhibits both the nonlinearity and vibration while high-frequency triangular wave scanning, thereby resulting in the degradation of the output wavelength performance of the ECDL. As a result, a trajectory optimization method is presented to improve the scanning characteristics of the ORC system. Firstly, the hysteresis behavior of PZT is described by the elliptic model to generate a compensation voltage for the PZT. Secondly, the cusp region of the compensation voltage is optimized by a frequency domain objective function to greatly reduce the high-order harmonic components of the compensation voltage. Finally, an optimal voltage trajectory for driving the ORC system is obtained. The experimental results demonstrate the validity of our proposed trajectory optimization method for simultaneously suppressing the nonlinearity and vibration of the ORC system within the scanning frequency of 100 Hz.

Crop Mapping and Spatio–Temporal Analysis in Valley Areas Using Object-Oriented Machine Learning Methods Combined with Feature Optimization

Timely and accurate acquisition of crop planting areas and spatial distribution are deemed essential for grasping food configurations and guiding agricultural production. Despite the increasing research on crop mapping and changes with the development of remote sensing technology, most studies have focused on large-scale regions, with limited research being conducted in fragmented and ecologically vulnerable valley areas. To this end, this study utilized Landsat ETM+/OLI images as the data source to extract additional features, including vegetation index, terrain, and texture. We employed the Random Forest Recursive Feature Elimination (RF_RFE) algorithm for feature selection and evaluated the effectiveness of three machine learning algorithms—Support Vector Machine (SVM), Random Forest (RF), and Rotation Forest (ROF)—for crop extraction. Then, based on the optimal classifiers, the main crops in the Huangshui basin for the years of 2002, 2014, and 2022 were extracted. Finally, the transfer matrix, the gravity center model, and the Standard Deviation Ellipse (SDE) model were used to analyze the spatio—temporal changes of crops over the past 20 years in the Huangshui basin. The results showed that the spectral, vegetation index, and terrain features played a crucial role in crop extraction. Comparing the performance of the classifiers, the ROF algorithm displayed superior effectiveness in crop identification. The overall accuracy of crop extraction was above 86.97%, and the kappa coefficient was above 0.824. Notably, between 2002 and 2022, significant shifts in crop distribution within the Huangshui basin were observed. The highland barley experienced a net increase in planting area at a rate of 8.34 km2/year, while the spring wheat and oilseed rape demonstrated net decreases at rates of 16.02 km2/year and 14.28 km2/year, respectively. Furthermore, the study revealed that highland barley exhibited the most substantial movement, primarily expanding towards the southeast direction.

Spatial–temporal trends in global childhood overweight and obesity from 1975 to 2030: a weight mean center and projection analysis of 191 countries

BackgroundThe geographic information science-based interactive map provided good prospects for the public health to study disease prevalence. The purpose of this study is to understand global spatial–temporal trends of childhood overweight and obesity and underlying causes help formulating intervention strategies.MethodsThis multiple cross-sectional study included data on childhood overweight and obesity prevalence, gross national income per capita, and urbanization rate for 191 countries from 1975–2016. Autoregressive integrated moving average model, standard deviational ellipse model and mixed-effects models were used to explore spatial–temporal trends of childhood overweight and obesity and associations with gross national income per capita and urbanization rate.ResultsGlobally, childhood overweight and obesity rate would reach 30.0% in 2030 (boys: 34.2%, girls: 27.4%). By 2030, it would reach 58.3% in middle- and high-income countries and 68.1% in Western Pacific region. Spatial–temporal trendline for childhood overweight and obesity in 1975–2030 exhibited a “C” shape, migrating from 1975 (15.6。E, 24.6。N) to 2005 (10.6。E, 21.7。N), then to 2030 (14.8。E, 17.4。N). The trendline for urbanization rate was also an irregular "C", and the turning point appeared five years earlier than childhood overweight and obesity.ConclusionsGlobally, childhood overweight and obesity prevalence will continue to increase. Its weight mean center migrated from western countries to Asia and Africa following economic development.

Analysis of the Magnetization-Induced Field Error in a Superconducting Bending Magnet for a Compact, Rapid-Cycling Heavy-Ion Synchrotron

An ongoing design study of a 90-degree superconducting bending magnet (SCBM) is being conducted for a compact, rapid-cycling heavy-ion synchrotron at the National Institutes for Quantum Science and Technology (QST). The SCBM is designed to generate a 3.5 T dipole field and a 1.5 T <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\cdot$</tex-math></inline-formula> m <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{-1}$</tex-math></inline-formula> quadrupole field for bending and horizontally defocusing heavy-ion beams. Since a heavy-ion beam is injected into the synchrotron at the field of 0.3 T, and accelerated to 3.5 T in 5 s, and then it is extracted at the field range from 1.1 T to 3.5 T, field errors derived from the magnetization at such a ramp rate will cause instability of the beam during the acceleration. In this paper, the field errors derived from both the DC and the AC effects are analyzed with numerical models in two dimensions (2D). The DC effects from the screening current in the superconducting coil and iron hysteresis behavior are estimated by the nested ellipse model and the finite element method (FEM) coupled with an inverse Jiles-Atherton model. The AC effects from the eddy current generated in the thermal shield, the beam pipe and the thermal path are estimated by a transient FEM analysis and analytical method. The change of the field error due to the magnetization is estimated to be about 1 unit at the time of beam injection.

Spatio-temporal evolution of coupling coordination between new infrastructure and regional sustainability in China.

To achieve high-quality development, it is crucial to encourage the coordinated development of new infrastructure (NI) and regional sustainability (RS). Based on the coupling mechanism of NI and RS, this study develops a comprehensive evaluation index system, coupling coordination degree model (CCDM), spatial autocorrelation model, and standard deviation ellipse model used to estimate the coupling coordination degree (CCD) of NI-RS in China mainland between 2013 and 2020 and analyze the temporal and spatial evolution characteristic. The results showed the following: (1) The NI index fluctuated and increased, while the RS index increased year by year, showing a gradient-decreasing distribution pattern from east to west; (2) CCD increases year by year, but the spatial distribution is unbalanced, and CCD in eastern coastal areas is significantly higher than that in western areas. (3) The CCD of NI and RS showed a significant spatial positive correlation, showing that the high-value cluster in the east and the low-value cluster in the west. (4) The standard deviation ellipse of the national CCD showed a northeast-southwest distribution, the center of gravity migrated to the southwest, and the spatial clustering characteristics were significant. (5) Finally, the GM (1,1) model indicates a steady increase in CCD from 2021 to 2025. Based on the location distribution and evolution of the CCD between NI and RS in China, corresponding policy suggestions are put forward.

Developing a Model for Curve-Fitting a Tree Stem’s Cross-Sectional Shape and Sapwood–Heartwood Transition in a Polar Diagram System Using Nonlinear Regression

A function from the domain (x-set) to the codomain (y-set) connects each x element to precisely one y element. Since each x-point originating from the domain corresponds to two y-points on the graph of a closed curve (i.e., circle, ellipse, superellipse, or ovoid) in a rectangular (Cartesian) diagram, it does not fulfil the function’s requirements. This non-function phenomenon obstructs the nonlinear regression application for fitting observed data resembling a closed curve; thus, it requires transforming the rectangular coordinate system into a polar coordinate system. This study discusses nonlinear regression to fit the circumference of a tree stem’s cross-section and its sapwood–heartwood transition by transforming rectangular coordinates (x, y) of the observed data points’ positions into polar coordinates (r, θ). Following a polar coordinate model, circular curve fitting fits a log’s cross-sectional shape and sapwood–heartwood transition. Ellipse models result in better goodness of fit than circular ones, while the rotated ellipse is the best-fit one. Deviation from the circular shape indicates environmental effects on vascular cambium differentiation. Foresters have good choices: (1) continuing using the circular model as the simplest one or (2) changing to the rotated ellipse model because it gives the best fit to estimate a tree stem’s cross-sectional shape; therefore, it is more reliable to determine basal area, tree volume, and tree trunk biomass. Computer modelling transforms the best-fit model’s formulas of the rotated ellipse using Python scripts provided by Wolfram engine libraries.

Estimating macroscopic conductivity of randomly elliptic inclusion model with imperfect interfaces

The paper introduces some solutions for computing the effective conductivity of randomly elliptic inclusion with imperfect interfaces in two-dimensional space. Based on the coated-ellipse inclusion model and the polarization approximation, one can determine the effective conductivity of the ellipse inclusions with the approximation for lowly conducting imperfect interface (ALI) and the approximation for highly conducting imperfect interface (AHI). Some solutions, including the equivalent inclusion approximations (ALI and AHI), differential approximations (DAs), Hamilton–Crosser approximations (HAs), and fast Fourier transformation (FFT) method will be applied to determine the effective conductivity of the randomly ellipse model with AHI and ALI in two-dimensional space. The ALI and AHI results agree well with DA, HA, and FFT results showing the effectiveness of the methods.

A 3D Fast Radon transform algorithm based on deflecting ellipse model and its application on multiple attenuation

A 3D Fast Radon transform algorithm based on deflecting ellipse model and its application on multiple attenuation

Analysis of Spatiotemporal Interaction Characteristics and Decoupling Effects of Urban Expansion in the Central Plains Urban Agglomeration

In recent years, the rapid improvement in the urbanization level of the Central Plains urban agglomeration is bound to bring about significant changes in urban land expansion and economic development. However, at present, there is little attention paid to the research on the spatiotemporal interaction characteristics of urban expansion and the interaction between urban expansion and economic development in this region, and existing research lacks a geographical analysis perspective. This study uses spatial autocorrelation, hot spot analysis, LISA time path, and standard deviation ellipse models to analyze the spatiotemporal interaction characteristics of urban expansion in the Central Plains urban agglomeration from 1990 to 2020, and it uses bilateral spatial autocorrelation and decoupling models to analyze the spatial correlation and decoupling effects of urban expansion and economic development. The results show that (1) the urban built-up area of the Central Plains urban agglomeration as a whole is growing in a “J” shape, and the expansion rate has increased rapidly in the past 10 years. (2) The spatial expansion of the city is mainly in the direction of “northwest–southeast”; the directionality has been gradually strengthened in the past 10 years, mainly in the direction of several prefecture-level cities under the jurisdiction of Anhui Province, and the spatial center of gravity of the city has shifted significantly to the south. (3) The spatial agglomeration characteristics of urban expansion in the Central Plains urban agglomeration are not obvious; local hot spots are concentrated in Jiaozuo and its surrounding areas, and urban expansion has local spatial structural instability. (4) During the 2005–2020 period, the risk of uncoordinated urban expansion and economic growth in the Central Plains urban agglomeration increased. This study is of great significance for the rational control of regional development, providing empirical reference for the formulation of the development planning of the Central Plains urban agglomeration, as well as providing a reference for research ideas and methods related to urbanization.

Coupling Evaluation and Spatial-Temporal Evolution of Land Ecosystem Services and Economic-Social Development in a City Group: The Case Study of the Chengdu-Chongqing City Group.

The Chengdu-Chongqing city group (CCCG) is one of the regions with acute human-land conflicts in China at present. The current rapid development of CCCG has caused a large negative impact on regional land ecosystem services. Land ecosystem is the basis of economic development. Reasonable economic development is also the inherent requirement of land ecosystem and an important support for land ecosystem protection. Promoting the coordinated development of the economy and land ecosystems is a prerequisite for this city group to achieve ecological protection and high-quality development. Taking CCCG as an example, this paper constructs a coupling evaluation model of economic-social development and land ecosystem service by using the entropy weight method, coupling coordination degree model, gravity center model, and standard deviation ellipse model, and explores the coupling coordination degree and spatial-temporal evolution rule of the above two systems. The study found that, from 2005 to 2020, the overall economic-social development of the CCCG showed an upward trend with a regular pattern of a 'High in the east and west, low in the central', and the 'dual-core' spatial structure with Chengdu and Chongqing as the core ; the overall performance of land ecosystem services had a gentle slope downward trend with a 'U'-shaped spatial pattern of "high around and low in the middle". The results also show that the coupling coordination degree of economic-social development and land ecosystem services in CCCG continues to rise steadily. Overall, the level of coupling coordination is low, and the type of coupling coordination has gradually evolved from severe and moderate imbalance to moderate coordination and mild imbalance. Therefore, the CCCG should make full use of the advantages of the "dual-core" cities to improve the level of peripheral economic linkage, increase investment in science and technology to enhance the endogenous power of economic development, establish cooperation mechanisms to narrow the urban gap, and integrate ecological resources to promote ecological industrialization to better realize the synergistic promotion of land ecological protection and high-quality economic development.

Uncovering the spatiotemporal impacts of built environment on traffic carbon emissions using multi-source big data

Understanding and predicting urban traffic carbon emissions constitute an urgent agenda in research and policy decision-making. Since the exhausted emissions vary in time and different urban settings, assessing the spatiotemporal distribution of carbon emissions is fundamentally important for land use planning. This paper attempts to identify the spatial and temporal heterogeneity of the impacts of land use and built environment on urban traffic carbon emissions. A spatial standard deviational ellipse (SDE) model and a geographically and temporally weighted regression (GTWR) model were developed to explore the spatiotemporal dependency of traffic carbon emissions on land use and built environment factors and applied to the core urban zones of Dalian, China. Results show the center of gravity of traffic carbon emissions have a footprint characterized by a shift to the southeast first and then to the northwest, with weekday and weekend performance being consistent. Compared to other periods, emissions are spatially agglomerated during internal hours (9:00–15:59), especially during weekdays. Land use and built environment factors affect carbon emissions differently across space and time whereas the effects of residential population density, employment density, medical, road network density on weekdays are larger than that on weekends. Furthermore, we found that increasing land use mix leads to a greater negative impact on weekday emissions. This supplements the important role of mixed land use planning in decarbonization. Based on the findings, we propose various policy interventions to support the development of carbon neutral cities.