Effect Of Spatial Correlation Research Articles

Spatial correlation effect of haze pollution in the Yangtze River Economic Belt, China.

With the rapid development of industry, haze pollution has become an urgent environmental problem. This study innovatively utilizes network-based methods to investigate the spatial correlation effects of haze pollution transmission between urban clusters in the Yangtze River Economic Belt. A spatial correlation network of haze pollution in the Yangtze River Economic Belt was constructed using 328 urban meteorological data collection points as research samples, and its structural characteristics were examined. Main findings are as follows: (1) The spatial correlation network of PM2.5 in the Yangtze River Economic Belt urban agglomeration exhibits typical network structural characteristics: obvious spatial correlation within the network. (2) Chengdu, Chongqing, Wuhan, Nanchang, Yichang, Changsha and Yueyang are located at the center of the spatial network. They have more receiving and sending relationships. (3) 36 cities can be divided into four types: bilateral overflow, net beneficiary, net overflow and broker. Each type has different functional characteristics and linkage effects in the network. (4) Haze pollution positively correlates with the city's synergistic development capacity and urbanization rate, the higher the city's development level and the higher the Urbanization rate, the stronger its haze pollution capacity. This study provides new insights into the study of the spatial correlation and impact of haze pollution.

Role of spatial correlation on the performance of colored photovoltaic modules with integrate disordered media

Colored photovoltaic (PV) modules with integrated disordered coatings exhibit attractive potential for generating renewable electricity. However, most existing studies on these modules rely on the assumption that the disordered coatings are random systems, neglecting the effects of spatial correlation. In this work, we thoroughly investigate the effects of spatial correlation on the color properties and performance of colored PV modules with integrated disordered coatings, using the full-wave electromagnetic simulation techniques. Our findings indicate that increasing the degree of spatial correlation results in a sharper and narrower reflectance peak, while having a negligible impact on the peak position. This trend suggests that the spatial correlation offers an alternative strategy for producing more vivid color, although it is less effective in expanding the color range. On the other hand, the spatial correlation has little impact on the power conversion efficiency (PCE) of PV modules. Therefore, it is feasible to produce colored PV modules with more vivid colors without significantly affecting the PCE by simply adjusting the degree of spatial correlation.

Study of spatial spillover effects and threshold characteristics of the influence of urbanization on grain green production efficiency in China under carbon constraints.

Food security is a basic guarantee for maintaining social stability, and improving green and healthy grain production is the way to promote food security. China is currently experiencing rapid urbanization that has an impact on grain production and security, with reduced arable land, environmental pollution, insufficient agricultural population, and inadequate resource allocation. How to deal with the relationship between urbanization and grain production becomes the key to solve this problem. Therefore, this study constructs an evaluation system of grain green production efficiency (GGPE) with non-expected output containing carbon emissions, and uses the super-efficient SBM model to measure the level of GGPE, and constructs a spatial econometric model to examine the spatial correlation and spillover effect of urbanization on GGPE; then constructs a panel threshold model to analyze the nonlinear threshold characteristics between urbanization and GGPE. It was found that (1) from 2000 to 2019, China's GGPE showed an overall upward trend, and the performance of GGPE varied among different provinces. (2) According to the results of the spatial econometric model, China's GGPE shows obvious spatial characteristics, and the level of urbanization not only directly affects regional GGPE but also can indirectly affect neighboring regions' GGPE through spatial spillover effects, and the indirect effects among regions are larger than the direct effects. (3) Spatial threshold effect results, there is a significant non-linear threshold characteristic of the impact of urbanization rate on GGPE. In terms of the influence effects of other variables, the influence of each variable on GGPE is roughly in the same direction as the influence under the spatial spillover effect, but the degree of influence is slightly different.

Effect of government environmental attention on green transformation: Empirical analysis from a spatiotemporal perspective in China

As the primary policymaker and implementer, the government plays an essential role in driving regional green transformation development (GTD). The study of the impact mechanisms of government environmental attention (GEA) on GTD can offer a scientific basis and reference for policy-making and regional governance. However, the varying focus of local governments on environmental issues and the externalities of environmental governance underscore the complexities in the influence of GEA on GTD. This paper proposes a method that integrates multi-source data to explore the impact of GEA on GTD from a spatiotemporal perspective. A text mining framework is first proposed to provide a more accurate measurement of the GEA, and the geographically and temporally weighted regression model is used to analyze the spatiotemporal characteristic and mechanism of GEA's impact. Additionally, the spatial correlation effect is explored based on the gravity model. The results indicate that the level of GTD in China demonstrates an increasing trend with an average annual growth rate (AAGR) of 3.22%, while the variance between provinces is also increasing. GEA has a significant positive effect on GTD (R2 = 0.866), with an average growth rate of 18.1% following the implementation of major environmental policies. The spatial distribution pattern of GEA's impact shows the characteristics of “central > eastern > western > northeastern”. GEA promotes GTD by increasing environmental governance expenditure and enhancing public environmental services, while the transmission pathway of promoting green innovation has not yet worked. The spatial correlation and spillover effects of GEA have shown an overall increasing trend, with the AAGR of 8.92% in the gravity value. Inter-provincial cooperation can promote GTD, and certain collaborative clusters among provinces have been formed. This paper provides novel insights into the theoretical connection between GEA and GTD. The research findings can serve as a reference for the government to propose targeted policies for promoting regional green transformation.

Investigating Flow-Induced Vibration by Regarding the Alignment of Flow Modes and Structural Modes

In this work, a modal matching approach is developed to investigate the impact of the spatial correlation of wall pressure fluctuations on structural vibration and its role in suppressing flow-induced vibration in aircraft. The modal matching method defines the wavelength ratio between the convection wave and the flexural wave to capture the spatial characteristics of both the flow mode and the structural mode. In comparison to conventional methods such as the finite element method and analytical method, the proposed modal matching approach allows for obtaining the vibration response in the spatial–wavenumber domain. To validate the spatial correlation effect on structural vibrations obtained with the modal matching approach, a wind tunnel test was conducted to measure flow-induced vibrations. The calculated spatial–wavenumber data for flow-induced vibrations reveal that the crests and troughs of the vibration velocity alternate periodically as the wavelength of the wall pressure fluctuations changes. At the resonance frequency, a vibration velocity trough can be observed when the spatial correlation of wall pressure fluctuations aligns with the structural mode. Additionally, the concept of resonance independence of flow-induced vibrations is introduced to describe the phenomenon where the most severe structural vibration, determined by the matching value between the aerodynamic mode and the structural mode, does not always occur at the resonance frequency. The modal matching approach effectively suppresses structural vibration by utilizing the spatial correlation of wall pressure fluctuations, offering a new perspective on controlling flow-induced vibrations.

Spatial differentiation and driving factors of scientific and technological innovation efficiency under the background of the digital economy

Scientific and technological innovation (STI) activities are facilitated under the influence of the digital economy and depend on digital elements to support innovation activities. This study measures digital element inputs and outputs using data related to the digital economy and estimates STI efficiency in 29 provincial regions in mainland China from 2010 to 2021 employing the global super-efficiency slacks-based measure (SBM) model. Then, spatial characteristics and driving forces of STI efficiency are quantified using spatial autocorrelation and geographic detectors. The results reveal that STI efficiency exhibits a fluctuating, W-shaped pattern, and the spatial correlation effect shows a gradually declining trend from 2010 to 2021. The findings reveal that the core drivers of STI efficiency are digital financial development, e-commerce development, digital access level, and related factors. Therefore, strengthening inter-regional innovation links, promoting the construction of 5 G mobile internet and accelerating the integration of digital industries into traditional industries are recommended.

Probabilistic assessment of bearing capacity of skirted foundation under combined loadings with a rigid base

Skirted foundations are critical components in offshore applications where combined loads are common in deep‐water environments. Their ultimate capacity under VH (vertical‐horizontal) combined loading is traditionally determined using VH failure envelopes, which are primarily constructed using numerical methods. These methodologies, however, frequently ignore the spatial variability inherent in seabed soils due to geological formations. This paper investigates the effect of spatial variability of undrained shear strength and embedment ratio impact on the capacity of skirted foundations subjected to VH combined loading. For this, OptumG2 software is used to perform Monte Carlo simulation combined with random finite element limit analysis. This paper investigates the stochastic analysis of bearing capacity and failure envelopes, with a particular emphasis on understanding the effect of spatial correlation on undrained shear strength. The study focuses on the horizontal scale of fluctuation and the soil strength heterogeneity index, shedding insight on previously undiscovered areas. Novel findings highlight how a rigid base affects VH failure envelopes and offer insights into evaluating the vertical bearing capacity of skirted foundations. [Correction added on 8 July 2024, after first online publication: The above statement has been updated in this version.]

Towards sustainability: The spatiotemporal patterns and influence mechanism of urban sprawl intensity in the Yangtze River Delta urban agglomeration

Urban sprawl has been a great challenge to current sustainable development goals, especially in urban agglomeration areas. Based on the nighttime light and WorldPop population grid data from 2011 to 2020, this study established a multidimensional evaluation system of urban sprawl intensity in the Yangtze River Delta Urban Agglomeration (YRDUA) with a "scale-density-structure" framework. The Dagum Gini coefficient, spatial Markov chain, and econometric analysis were applied to analyze urban sprawl intensity. The results suggested that: (1) the urban sprawl intensities of the cities in the YRDUA became more imbalanced over the decade; (2) the mild sprawl cities were dominant in the study area, but the moderate and severe sprawl cities were mostly located in the west and the only inland province--Anhui; (3) the spatial distribution of urban sprawl intensity indicated "club convergence" and strong spatial correlation effect, but poor stability; and (4) land resource mismatch and population urbanization were the main explanatory variables for urban sprawl intensity, with large cities more sensitive to the former, and medium cities to the latter. These findings can provide important policy implications to sustainable development and compact growth.

Impact of Component Correlation on Probabilistic Seismic Responses of Steel Frames by Modifying Non-Positive Definite Correlation Matrices

ABSTRACT This paper studies the effects of spatial correlation between the structural components on probabilistic seismic performance of steel moment resisting frames, using extended incremental dynamic analysis (IDA) method to consider the effects of aleatoric uncertainties caused by ground motion records. In this probabilistic assessment, a combination of Latin hypercube sampling (LHS) and Cholesky decomposition (CD) methods are utilized to take the epistemic uncertainties of the numerical model into account. In case the target correlation matrix is not positive definite, two powerful optimization algorithms are employed to find the closest positive definite correlation matrix.

Accommodating spatio-temporal dependency in airline demand modeling

The objective of the current study is to examine monthly air passenger departures at the airport level considering spatial interactions between airports. In this study, we develop a novel spatial grouped generalized ordered probit (SGGOP) model system of monthly air passenger departures at the airport level. Specifically, we estimate two variants of spatial models including spatial lag model and spatial error model. In the presence of repeated demand measures for the airports, we also consider temporal variations of spatial correlation effects among proximally located airports by employing space and time-based weight matrix. The proposed model is estimated using monthly air passenger departures for five years for 369 airports across the US. The proposed spatial model is implemented using composite marginal likelihood (CML) approach that offers a computationally feasible framework. From the estimation results, it is evident that air passenger departures at the airport level are influenced by different factors including MSA specific demographic characteristics, built environment characteristics, airport specific factors, spatial factors, and temporal factors. Moreover, spatial autocorrelation parameter is found to be significant validating our hypothesis of the presence of common unobserved factors associated with the spatial unit of analysis. In this study, we also perform a validation analysis to examine the predictive performance of the proposed spatial models. The results highlight the superiority of spatial error model compared to spatial lag model and the independent model that ignores the spatial interactions. Finally, we undertake an elasticity analysis to quantify the impact of the independent variables.

Fiscal expenditure efficiency and green economic efficiency: evidence from Chinese cities

This study develops indicators for evaluating Green Economy Efficiency (GEE) and Fiscal Expenditure Efficiency (FEE) at the prefecture level and above within Chinese cities. Utilizing the super-efficiency SBM model and a three-stage DEA model, we empirically examine the influence of FEE on GEE. To address the issue of endogeneity arising from reverse causality between FEE and GEE, this research employs fiscal autonomy and officials’ tenure as instrumental variables for FEE. The key findings are summarized as follows: (1) GEE exhibits a fluctuating upward trend from 2006 to 2020, while FEE remains relatively stable overall. Notably, the GEE and FEE in the eastern region surpass those in the central and western regions. (2) A one percentage point increase in FEE corresponds to a substantial 7.597% rise in GEE, indicating a significant promotional effect, a result consistently upheld in robustness tests. Furthermore, factors such as urbanization level, economic development, education level, and industrial structure also contribute to urban green economic growth. (3) FEE primarily contributes to GEE through local economic growth, urban infrastructure development, and regional innovation levels. (4) The impact of FEE on GEE is more pronounced in developed cities, larger cities, those with high fiscal freedom, and highly urbanized cities. Based on these conclusions, policy recommendations are proposed: (1) contingent upon local circumstances, each municipal government should optimize its fiscal expenditure structure and enhance FEE based on its unique endowments; (2) make full use of spatial correlation and knowledge spillover effect to promote the coordinated growth of FEE and GEE; (3) improve urban infrastructure construction and encourage regional technological innovation.

Improved spatial speckle contrast model for tissue blood flow imaging: effects of spatial correlation among neighboring camera pixels.

Speckle contrast analysis is the basis of laser speckle imaging (LSI), a simple, inexpensive, noninvasive technique used in various fields of medicine and engineering. A common application of LSI is the measurement of tissue blood flow. Accurate measurement of speckle contrast is essential to correctly measure blood flow. Variables, such as speckle grain size and camera pixel size, affect the speckle pattern and thus the speckle contrast. We studied the effects of spatial correlation among adjacent camera pixels on the resulting speckle contrast values. We derived a model that accounts for the potential correlation of intensity values in the common experimental situation where the speckle grain size is larger than the camera pixel size. In vitro phantom experiments were performed to test the model. Our spatial correlation model predicts that speckle contrast first increases, then decreases as the speckle grain size increases relative to the pixel size. This decreasing trend opposes what is observed with a standard speckle contrast model that does not consider spatial correlation. Experimental data are in good agreement with the predictions of our spatial correlation model. We present a spatial correlation model that provides a more accurate measurement of speckle contrast, which should lead to improved accuracy in tissue blood flow measurements. The associated correlation factors only need to be calculated once, and open-source software is provided to assist with the calculation.

A Generalized Accelerated Failure Time Model to Predict Restoration Time from Power Outages

Major disasters such as wildfire, tornado, hurricane, tropical storm, and flooding cause disruptions in infrastructure systems such as power and water supply, wastewater management, telecommunication, and transportation facilities. Disruptions in electricity infrastructure have negative impacts on sectors throughout a region, including education, medical services, financial services, and recreation. In this study, we introduced a novel approach to investigate the factors that can be associated with longer restoration time of power service after a hurricane. Considering restoration time as the dependent variable and using a comprehensive set of county-level data, we estimated a generalized accelerated failure time (GAFT) model that accounts for spatial dependence among observations for time to event data. The model fit improved by 12% after considering the effects of spatial correlation in time to event data. Using the GAFT model and Hurricane Irma’s impact on Florida as a case study, we examined: (1) differences in electric power outages and restoration rates among different types of power companies—investor-owned power companies, rural and municipal cooperatives; (2) the relationship between the duration of power outage and power system variables; and (3) the relationship between the duration of power outage and socioeconomic attributes. The findings of this study indicate that counties with a higher percentage of customers served by investor-owned electric companies and lower median household income faced power outage for a longer time. This study identified the key factors to predict restoration time of hurricane-induced power outages, allowing disaster management agencies to adopt strategies required for restoration process.

The Spatial Correlation Network of China’s High-Quality Development and Its Driving Factors

The spatial correlation of high-quality development has emerged as a crucial approach to tackling economic polarization. This study assesses the level of high-quality development in China from 2006 to 2020, utilizing the new development concept. Social network analysis is employed to explore the spatial correlation mechanism of high-quality development. The quadratic assignment procedure (QAP) is used to analyze the driving factors that impact spatial correlation systems from both internal and external perspectives. The research indicates that China’s level of high-quality development has consistently improved between 2006 and 2020. The spatial correlation network has increasingly become more connected, while the eastern provinces, specifically Shanghai and Beijing, have a significant influence in the spatial correlation of high-quality development. However, the central and western provinces, such as Xinjiang and Inner Mongolia, occupy peripheral positions in this network. Furthermore, clear spatial correlation and spillover effects are apparent among the dimensions. From an internal standpoint, innovation, greenness, openness, and sharing are the key factors that contribute to establishing a high-quality development network. In addition, external elements such as financial development, social consumption, and convenient transportation are interconnected in space, which promote the creation of a high-quality development network. Local protectionism and population concentration impede the establishment of a high-quality development network.

Spatial correlation and interaction effect intensity between territorial spatial ecological quality and new urbanization level in Nanchang metropolitan area, China

While promoting the rapid development of social economy, new urbanization has brought great pressure to the ecological environment. It is of great significance to explore the interaction mechanism between ecological environment and new urbanization for the sustainable development of metropolitan area. The metropolitan area of Nanchang in China was taken as the research area, and based on remote sensing, meteorological and social statistics data, and considering the functions, structures and effects of urban, agricultural and ecological spaces as well as the realistic demands of new urbanization construction, this research constructed “natural foundation-structure-coerce-benefit” (NSCB) and “land use-economic-population-infrastructure-public service” (LEPIP) comprehensive assessment models to quantitatively analyze the spatial and temporal heterogeneity, spatial correlation and interaction strength of ecological quality and new urbanization level in the study area from mesoscopic and microscopic perspectives by using spatial analysis software such as ENVI, ArcGIS, Fragstats, Geoda and Geodetector. The results showed that the ecological environment quality and urbanization level of the land space in the study area were improved from 2005 to 2018, and the overall gap within the region was gradually reduced. The spatial relationship between land use efficiency and ecological quality was “synergistic-trade-off-synergistic”. The economic development level and ecological quality showed a gradually enhanced synergistic relationship, and the population urbanization, infrastructure construction and ecological quality showed an “N” type synergistic relationship. The results of interaction intensity showed that the combined effects of population urbanization, public service guarantee, land use efficiency and economic development had a great impact on the spatial heterogeneity of territorial ecological quality in the study area. Finally, we suggest that the urbanization construction in the study area should strengthen the industrial agglomeration effect, improve the efficiency of land output, build a good urban ecological network, and promote the coordinated and healthy development of natural resources and urban construction. The results of this study can provide scientific basis for the precise formulation of spatial planning, ecological restoration and spatial collaborative development strategies in the study area, and provide reference experience for the coordinated development of natural environment and social economy in other metropolitan areas and urban agglomerations.

Spatiotemporal Evolution and Driving Factors of Land Development: Evidence from Shandong Province, China

As populations and economies have grown rapidly, questions of land development and use have intensified. It has become a major global concern to achieve sustainable land use practices. This study reveals evolution of the spatiotemporal pattern of land development intensity of counties in Shandong Province by introducing a land development intensity measurement model combined with three-dimensional trend surface and spatial autocorrelation analyses. Geodetector and geographically weighted regression models were employed to demonstrate the interplay and spatiotemporal heterogeneity between development intensity and drivers. The empirical results show that the value of land development intensity of counties in Shandong Province shows a general growth trend, with the number of counties with higher values gradually increasing and the number of counties with lower values gradually decreasing. We also found that the spatial heterogeneity of land development intensity across counties in Shandong Province is significant, and the spatial distribution pattern is basically consistent with the “one group, two centers and three circles” strategy proposed by the Shandong Provincial Government. There is also a positive spatial correlation and clustering effect of land development intensity of counties in Shandong Province. High (low) value clusters are concentrated in core hot (cold) counties, driving some of the surrounding counties towards radial development. The alteration in the intensity of county land development is a complex occurrence that is shaped by numerous factors. Among these, GDP per capita and population density have the primary influence on land development of counties in Shandong Province. To achieve coordinated regional social, economic, and environmental benefits, land development within the county should adhere to the principle of adapting to local conditions and implement differentiated development strategies according to different development intensities.

Study on the spatial correlation effects and influencing factors of carbon emissions from the electricity industry: a fresh evidence from China.

Carbon emissions from the electricity industry (CEEI) account for a large proportion of China's total carbon emissions, and it is important to study the spatial correlation between CEEI and the influencing factors to promote cross-regional synergistic emission reduction and low-carbon development of the power system. In this paper, the quasi-input-output (QIO) model is applied to assess the transfer of carbon emissions generated by electricity trading based on the consideration of electricity carbon transfer, and the exploratory spatial data analysis (ESDA) method is applied to analyze the spatial correlation effect of carbon emissions from China's electric power sector from 2001 to 2020, analyzes its distribution pattern in both spatial and temporal dimensions, and applies the improved logarithmic mean Divisia index (LMDI) two-stage decomposition model to decompose the changes in CEEI into 11 influencing factors from the perspective of the whole industrial chain of power production, transmission, trade, and consumption. The research results show that (1) the spatial distribution of CEEI has obvious unevenness and aggregation characteristics, with high-high aggregation areas and hot spot aggregation areas generally concentrated in the North China Power Grid and the East China Power Grid, but the aggregation trend is gradually decreasing, while low-low aggregation areas and cold spot aggregation areas are concentrated in the Northwest China Power Grid and the Central China Power Grid, but the area is very limited. (2) The direction of carbon emission diffusion in China's electricity industry is gradually transitioning from southwest-northeast to northwest-southeast, and the east-west diffusion trend is stronger than the north-south diffusion trend and carbon emissions are gradually shifting to the northwest grid. (3) The total amount of electricity production is the most influential factor in the change of CEEI, driving the cumulative growth of CEEI by 4495.34 Mt, followed by GDP per capita and electricity consumption intensity. Coal consumption for power generation, the share of thermal power, and net electricity exports were the main factors inhibiting the increase in carbon emissions from the power sector, with cumulative contributions of -797.74 Mt, -619.99 Mt, and -47.76 Mt, respectively.

Symbiotic Backscatter Communication Underlying a Cell-Free Massive MIMO System

In ambient backscatter communications, backscatter devices (BDs) utilize ambient “legacy” radio signals as both a harvested energy source and a carrier on which to modulate data. Symbiotic radio, a subtype in which the legacy system assists both its own users and underlaid BDs, has attracted much research interest recently. Furthermore, cell-free massive multiple-input multiple-output (CF mMIMO) systems are promising for beyond-5G networks from both spectral and energy efficiency perspectives. To reap the benefits of both, we consider a primary CF mMIMO system where the access points (APs) aid an underlaid BD layer to both harvest energy and reflect information toward the primary receivers (which receive data from both layers). To acquire separate channel state information (CSI) of the direct and backscattered channels at the APs, a two-phase uplink pilot training method is proposed, with the effects of pilot contamination and spatial correlation between antennas accounted for. However, the receivers are assumed to only have partial CSI (statistical knowledge plus instantaneous phase information for partially-coherent reception). Assuming uplink/downlink radio channel reciprocity, the CSI is used to design downlink precoding vectors for the APs such that channel hardening is enhanced and both primary receivers and BDs benefit. We derive expressions for the average signal-to-interference-plus-noise ratios of both primary and backscatter signals, accounting for the effects of imperfect CSI, spatial correlation, pilot contamination, and channel hardening. Furthermore, the average power harvested in the BDs is derived. Simulation results demonstrate that the performance of the proposed scheme is much more uniform across all devices with CF mMIMO than with conventional co-located mMIMO. The use of CF mMIMO also largely removes the need for special consideration of the BD layer in the symbiotic system, which is not the case with co-located mMIMO.

Spatial Distribution and Influencing Factors of High-Level Tourist Attractions in China: A Case Study of 9296 A-Level Tourist Attractions

The distribution pattern of high-level tourist attractions is crucial for the sustainable development of the tourism industry. However, few studies have explored the spatial distribution and dominant influencing factors of tourist attractions of different levels from a macro perspective in China. This study, which was based on large-scale multi-source data, involved the use of kernel density analysis, local spatial autocorrelation, and geographical detector analysis to explore the spatial distribution, spatial correlation, and dominant influencing factors of high-level tourist attractions in China. The study’s results show that the spatial distribution of tourist attractions of different levels is polarized and regionally clustered, and there exist some spatial correlation effects among attractions of the same level. Additionally, different influencing factors play a different role in determining the spatial distribution of attractions of different levels. Based on market demand and tourism resources, it is necessary to regulate attractions of different levels to promote the sustainable development of high-level tourist attractions and provide a reference for the development of China’s tourism industry.

Spatio-temporal evolution of total factor structural efficiency in China based on the multiple demands of high-quality economic development

ABSTRACT Optimizing factor allocation structure and improving total factor efficiency is crucial for narrowing regional development disparities and promoting high-quality economic development. Therefore, this paper considers the multiple demands of high-quality economic development and incorporates the dual constraints of input structure optimization and non-decreasing environmental welfare into the research framework of total factor structure efficiency (TFSE). It establishes a progressive research path from total factor efficiencies to TFSE, conducts a dynamic measurement from total factor efficiencies to TFSE, and investigates their spatio-temporal evolution. The main conclusions are: China’s TFSE exhibits a stable fluctuating trend with a decreasing regional distribution from the Southeast to the Northwest under the ‘Hu Huanyong Line’. The total factor efficiencies exhibit a decreasing trend of ‘clean energy – capital – environmental construction – labour - non-clean energy’. Significant spatial correlation effects exist for TFSE and total factor efficiencies in China, with a slight decline over time. TFSE exhibits significant conditional convergence, with a convergence speed of 2.03%. The convergence speeds of total factor efficiencies decrease in the following order: ‘non-clean energy – environmental construction – labour - capital – clean energy’. Moreover, regional TFSE and total factor efficiencies exhibit club convergence with positive spatial spillover effects.