Spatial Temporal Evolution Research Articles

Coupling coordination between agricultural carbon emission efficiency and food security in China: The spatial-temporal evolution and prediction.

Addressing climate change and food insecurity is crucial for achieving the Sustainable Development Goals (SDGs), with the enhancement of Agricultural Carbon Emission Efficiency (ACEE) and the assurance of Food Security (FS) playing pivotal roles. This study conducts a comprehensive evaluation of the coordination between ACEE and FS across 31 Chinese provinces from 2007 to 2021. The methodology employs the Super Slack-Based Model (SBM) and the Entropy- Weighted Technique for Order Preference by Similarity to Ideal Solution (EW-TOPSIS) for ACEE and FS assessments, respectively, while utilizing the Coupling Coordination Degree Model (CCDM) to gauge their coordination. This research applies Global spatial autocorrelation (GA), Local Indicators of Spatial Association (LISA) clustering, and Markov chain analysis to investigate the spatial correlation characteristics and dynamic evolutionary probability of Coupling Coordination Degree (CCD). Additionally, a Combination Forecasting Model predicts CCD trends through 2030. The findings indicate positive trends in both ACEE and FS, albeit with significant regional disparities and a notable lag of FS behind ACEE improvement. The evolution of China's CCD from "barely coordinated" towards "primary coordination" is evident, with the northeastern and central regions outperforming their western and eastern counterparts. Spatial analysis reveals pronounced clustering of CCD, indicating the impact of spatial spillover effects. Markov chain analysis shows that provinces have at least a 71.4% probability of maintaining their current CCD status, revealing substantial system inertia. Projections point to increasing CCD levels across various regions, with the northeastern region expected to reach 0.827 by 2030 (a 5.72% increase from 2021), followed by the central (0.742), western (0.659), and eastern regions (0.638); however, regional imbalances persist, highlighting the need for concentrated efforts in areas experiencing CCD stagnation or decline. The study concludes with a call for region-specific development strategies, enhanced resource allocation and technological support, and a comprehensive policy framework that accounts for the long-term and spatial dimensions, aiming to facilitate equitable advancements in reducing agricultural carbon emissions and enhancing food security.

Spatial–temporal evolution of logistics parks in Yangtze River economic belt

Based on the indexes of ‘innovation driven – service quality – green efficiency – collaborative integration – regional scale’, the evaluation system of high-quality development (HQD) of logistics parks in the Yangtze River economic belt (YREB) is constructed. The temporal evolution, spatial connection and network density of the HQD level of logistics parks in the YREB are explored by entropy weight measurement, exploratory spatial data analysis and gravity model. By analysing the panel data of 11 provinces in China from 2019 to 2021, the main conclusions are drawn as follows. (a) In terms of temporal evolution, the HQD score ranking of some provinces changed slightly from 2019 to 2021. The downstream region is relatively stable, while the HQD level of parks in the middle and upper reaches is uncertain. (b) In terms of spatial evolution, it is more and more obvious that a horizontal situation of ‘high→middle→low’ is developing, and a regional aggregation state is presented in a small range. (c) In terms of network density, the spatial linkages of logistics parks are unevenly distributed and complex at various levels, and the linkages describe a multi-core development structure decreasing from downstream to upstream. Accordingly, practical suggestions are given for the HQD of YREB.

The spatial–temporal evolution characteristics and influencing factors of coordinated development in the Yellow River Basin: Based on the perspective of flood-sediment transport, eco-environmental, and socio-economic subsystems

The spatial–temporal evolution characteristics and influencing factors of coordinated development in the Yellow River Basin: Based on the perspective of flood-sediment transport, eco-environmental, and socio-economic subsystems

Interpretable machine learning for analyzing the spatial-temporal evolution and influencing factors of green economic development

Interpretable machine learning for analyzing the spatial-temporal evolution and influencing factors of green economic development

Research on Spatial–Temporal Coupling and Driving Factors of Regional Economic Resilience and Port Logistics: Empirical Evidence from Southern Guangxi, China

Based on a comprehensive evaluation index system for regional economic resilience and port logistics development, this study employs multiple methodologies including coupling coordination degree model, kernel density estimation, gravity center model, spatial autocorrelation model, and geographic detector model to explore the spatial–temporal evolution patterns and driving factors of coupling coordination between regional economic resilience and port logistics in the Guangxi Beibu Gulf Economic Zone from 2012 to 2022. The results indicate that: (1) The coupling coordination degree between the two systems showed an upward trend during the study period, although with stage-specific bipolar differentiation that weakened in the later stages. (2) The spatial distribution pattern of coupling coordination evolved from a “single-core” driven by Nanning to a “dual-core” led by Nanning and Yulin, forming a distinct concentric layer structure; the gravity center of coupling coordination exhibited a “southeast–northwest” dynamic migration pattern. (3) Spatial autocorrelation analysis revealed significant positive spatial dependence of coupling coordination within the study area, with spatial agglomeration values showing a “core–transition–depression” differentiation pattern. (4) Information technology level emerged as the dominant driving factor, forming a “technology–finance–infrastructure” ternary collaborative driving model with financial development level and logistics infrastructure level, which became the main force promoting the coordinated development of the coupled systems.

Assessing geomorphological and hydro morphological changes of Chilika lagoon

The largest brackish water lagoon in Asia, Chilika Lagoon, is a Ramsar-designated wetland that is experiencing major hydro-meteorological and geomorphological changes as a result of both natural and man-made factors. The spatial-temporal evolution of Chilika's landscape and hydrological regime over the previous 50 years is summarised in this review, with a focus on the interactions between tidal interactions, sediment dynamics, climatic variability, and changes in land use and land cover (LULC). In connection with upstream catchment modifications and coastal processes, important geomorphological changes are analysed, such as mouth migration, deltaic progradation, and sedimentation-induced basin fragmentation. Remote sensing-based studies and multi-decadal datasets are used to critically evaluate hydro-meteorological drivers, including cyclonic disturbances, freshwater inflow, evaporation, salinity intrusion, and monsoonal variability. The review also highlights current conservation and restoration efforts, such as the opening of the new lagoon mouth in 2000, and examines the effects of these changes on local livelihoods, fisheries, and biodiversity. High-resolution hydrodynamic modelling, integrating long-term socio-ecological datasets, and scenario-based forecasting under climate change are among the research gaps that currently exist. The necessity of adaptive, multidisciplinary management strategies that balance ecological sustainability and socioeconomic resilience in the Chilika Lagoon system is highlighted by this review.

Temporal–Spatial Evolution of Regional Policy Mix Configuration Paths for the Sustainable Development of the New Energy Vehicle Industry Based on the Industrial Ecosystem

The sustainable development of the new energy vehicle (NEV) industry necessitates the dynamic optimization of policies tailored to local circumstances. However, certain regional policies may face ineffectiveness and demand urgent attention and adjustment. This study aims to promote the sustainable development of the NEV industry by dynamically optimizing and adjusting regional policies and policy environments. This research delves into the dynamic joint effect of regional policies and policy environments on sustainable development from the perspective of the industrial ecosystem. First, this study dynamically reveals China’s cross-regional policy intensity’s evolutionary differences through improved word embeddings. Secondly, it further compares dynamic differences in configuration paths of regional policy mixes and policy environments through dynamic Qualitative Comparative Analysis and Necessary Condition Analysis. Finally, it uncovers dynamic changes and differences in the necessity degree of various elements in cross-regional configuration paths. Key findings include the following: (1) The joint effect of policy mixes and policy environments can promote sustainable development of the NEV industry. (2) There exists spatial heterogeneity in each region’s configuration paths for sustainable development of the NEV industry. The Eastern region of China has rich configuration paths, while those in other regions are relatively few. (3) The configuration paths across regions change dynamically, showing four evolutionary trajectories: shifting, oscillating, emerging, and sustained.

Spatial-temporal evolution of land ecological health and influencing factors in China's ecological civilization pilot area

It is crucial to assess the land ecosystem health (LEH) in China’s national pilot zones (NPZ) for ecological conservation and identify the primary factors that impact it. This is crucial for developing China’s ecological civilization and fostering sustainable, eco-friendly economic development. The study utilizes the pressure-state-response (PSR) and environment-economy-society (EES) model, relying on the panel data of 11 cities of Jiangxi in the NPZ for ecological conservation from 2005 to 2020, measuring the LEH in the national ecological civilization pilot zone using the comprehensive index evaluation method. The main contribution of this study is to optimize the land ecological health index system, construct an improved PSR-NES theoretical model that is more suitable for NPZ, reveal the spatial and temporal evolution characteristics of LEH in NPZ from the city scale, and refine the key driving factors. Results show that (1) The LEH exhibited a general shift from a state of “less healthy” level to the “healthier” level; (2) Differences in LEH evolution in different cities. The overall trend of “north to south and east to west” is gradually improving. (3) Urbanization has a strong role in the promotion of LEH, and economic development shows a significant dampening effect.

Analysis on Spatial-temporal Evolution and Driving Forces of Ecological Environment Quality in Different Frozen Ground Regions of the Three-River Headwater Region

Under the trend of frozen ground degradation caused by global warming， the study on the spatial-temporal evolution and driving forces of ecological environment quality in different frozen ground regions of the Three-River Headwater Region （TRHR） can provide a theoretical basis for regional ecological environment governance and ecological civilization construction. The remote sensing ecological index （RSEI） was constructed based on the MODIS data set of google earth engine from 2000 to 2022， and the spatial-temporal evolution of ecological environment quality in different frozen ground regions of the TRHR was explored by coefficient of variation， migration of gravity center， Theil-Sen slope estimation， Mann-Kendall test， and Hurst index. The driving force of natural and human factors on the spatial differentiation of ecological environment quality was compared and analyzed by Geodetector. The results showed that： ① From 2000 to 2022， the ecological environment quality of the TRHR was at a medium level as a whole， showing a spatial distribution pattern of "low in the northwest and high in the southeast" . Among them， the mean value of RSEI in different frozen ground regions were： predominantly continuous and island permafrost region （0.630） > mountain permafrost region （0.624） > middle-thick seasonally frozen ground region （0.587） > predominantly continuous permafrost region （0.429）. ② In the past 23 years， the ecological environment quality of the TRHR was improved as a whole， the rising rate of RSEI was 0.001 5 a-1， and the center of gravity of each grade of RSEI migrated to the interior of predominantly continuous permafrost region. More than 57.00% of the frozen ground regions showed an improvement trend， and the middle-thick seasonally frozen ground region reached 73.37%. ③ In the future， the ecological environment quality of the TRHR will be generally anti-sustained in the future， and the frozen ground regions will be dominated by a slight deterioration trend. The area of deterioration trend in the middle-thick seasonally frozen ground region will be the largest （61.95%）. ④ Precipitation was the dominant factor for the spatial differentiation of ecological environment quality in different frozen ground regions of the TRHR， and the influence of human factors was not significant. The driving force of the multi-factor synergy was greater than that of the single factor， and the interaction between precipitation and temperature factors had the strongest explanatory power. The driving factors of each frozen ground region were different， so ecological management policies should be tailored to local conditions in different frozen ground regions.

Spatial-temporal Evolution and Prediction of Carbon Reserves in Wanjiang River Basin with the InVEST-PLUS Model

Exploring the impact of space-time-evolution of land use on ecosystem carbon reserves has positive feedback significance for the global emission reduction and foreign exchange increase target. Based on three periods of land use data from 2000 to 2020， combined with the InVEST model， the spatiotemporal changes of carbon storage in Wanjiang River Basin were analyzed. The PLUS model was used to predict the four scenarios of natural development， urban protection， farmland protection， and ecological protection in Wanjiang River Basin in 2040， and the carbon storage of the ecosystem in different scenarios was estimated. The results show that： ① Land use change directly affected the carbon storage of the study area. Between 2000 and 2020， the area of cultivated land， grassland， and unused land decreased by 1 096.73 km2， 5.92 km2， and 4.55 km2， respectively， while the area of forest land， water bodies， and construction land increased by 48.9 km2， 69.68 km2， and 988.6 km2， respectively. The carbon storage decreased by 2.84×106 t， and the distribution of carbon storage was consistent with the spatial distribution of land use. Areas with higher carbon storage had less construction land and more concentrated and distributed ecological land. ②The multi-scenario simulation for 2040 showed that the overall land use pattern was consistent， with significant local changes. In the scenarios of natural development， urban development， and ecological protection， carbon storage decreased by 2.485×106 t， 2.513×106 t， and 0.749×106 t， respectively. Among them， in the scenario of farmland protection， carbon storage decreased by at least 0.029×106 t. ③ The spatial differentiation of carbon storage was mainly influenced by climate factors. The geographic detector indicated that the annual average precipitation （0.139） had the highest explanatory power and was the main driving factor for the spatial differentiation of carbon storage in the Wanjiang River Basin. The synergistic impact type （0.382） between vegetation net primary productivity and annual average precipitation was the strongest. The reduction of carbon reserves can be controlled by adopting the policy of farmland protection and ecological protection. In future planning， ecological land should be protected， and the expansion of construction land should be controlled to improve the level of carbon reserves.

Automated Brain Tumor Classification and Grading Using Multi-scale Graph Neural Network with Spatio-Temporal Transformer Attention Through MRI Scans.

The medical field uses Magnetic Resonance Imaging (MRI) as an essential diagnostic tool which provides doctors non-invasive images of brain structures and pathological conditions. Brain tumor detection stands as a vital application that needs specific and effective approaches for both medical diagnosis and treatment procedures. The challenges from manual examination of MRI scans stem from inconsistent tumor features including heterogeneity and irregular dimensions which results in inaccurate assessments of tumor size. To address these challenges, this paper proposes an Automated Classification and Grading Diagnosis Model (ACGDM) using MRI images. Unlike conventional methods, ACGDM introduces a Multi-Scale Graph Neural Network (MSGNN), which dynamically captures hierarchical and multi-scale dependencies in MRI data, enabling more accurate feature representation and contextual analysis. Additionally, the Spatio-Temporal Transformer Attention Mechanism (STTAM) effectively models both spatial MRI patterns and temporal evolution by incorporating cross-frame dependencies, enhancing the model's sensitivity to subtle disease progression. By analyzing multi-modal MRI sequences, ACGDM dynamically adjusts its focus across spatial and temporal dimensions, enabling precise identification of salient features. Simulations are conducted using Python and standard libraries to evaluate the model on the BRATS 2018, 2019, 2020 datasets and the Br235H dataset, encompassing diverse MRI scans with expert annotations. Extensive experimentation demonstrates 99.8% accuracy in detecting various tumor types, showcasing its potential to revolutionize diagnostic practices and improve patient outcomes.

Spatial–temporal evolution trend, decoupling effect, and emission reduction pathways of tourism transportation carbon emissions in China’s provinces under the ‘dual carbon’ goal combined with big data: from a geographic spatial perspective

Spatial–temporal evolution trend, decoupling effect, and emission reduction pathways of tourism transportation carbon emissions in China’s provinces under the ‘dual carbon’ goal combined with big data: from a geographic spatial perspective

A NEW THEORETICAL ENVELOPE FRAMEWORK IN THE HAMILTONIAN THEORY OF NONLINEAR DEEP- AND FINITE-WATER SURFACE GRAVITY WAVES

An extensive body of literature has addressed the question that how we can accurately and efficiently predict the spatial-temporal evolution of surface gravity waves in the open ocean and coastal waters. The main methods for an answer to it can be partitioned into two groups: (i) theoretical frameworks which make use of Fourier transforms such as a nonlinear Schrödinger (NLS) equation-based model [1], the High-Order Spectral method (HOS) [2,3], and Zakharov integral equations[4,5], and (ii) direct numerical solvers which propose to obtain the numerical solution of the fully nonlinear potential flow equations like OceanWave3D[6], Reef3D[7], and a harmonic polynomial cell method [8]. Generally, the first group has been known to excel at numerical efficiency[9].

Analysis of the temporal and spatial evolution of China's air passenger transportation and the impact of policies on passenger flows.

The spatial-temporal evolution of air passenger flows in China's aviation sector is critical for optimizing route networks and capacity allocation, particularly in assessing the efficacy of policy interventions to inform future regulatory frameworks. This study leverages air passenger throughput data spanning prefecture-level cities (2005-2023) and employs the Standard Deviation Ellipse (SDE) method to identify spatial agglomeration patterns in 2008, 2017, 2020, and 2022. A Difference-in-Differences (DID) model is further applied to quantify the causal effects of policy interventions on air transportation dynamics, supplemented by robustness tests and heterogeneity analyses to validate the reliability of the findings. Three key conclusions emerge from the analysis. First, while the spatial distribution of air passenger traffic predominantly aligns with a northeast-southwest axis, the strengthened north-south directional influence (reflected in the increased major-to-minor axis ratio of the SDE) indicates a bidirectional expansion of air transport coverage. Second, the spatial centroid of passenger flows exhibited a directional shift from northwestern to southwestern China between 2005 and 2023, highlighting the rising strategic importance of the southwestern region. Notably, by 2023, a reversed spatial transition emerged, with the centroid gradually reverting toward the northeast, signaling a recalibration of passenger flow patterns. Third, policy impacts display marked regional and urban heterogeneity: eastern China experienced significantly higher passenger growth compared to central, western, and northeastern regions, while provincial capitals and megacities-owing to their resource concentration and policy implementation capacity-demonstrated stronger responsiveness to regulatory measures than smaller prefecture-level cities. These findings provide actionable insights for both industry and policymakers. Airlines may utilize the empirical evidence to refine route planning and capacity adjustments, whereas policymakers could prioritize spatially differentiated regulations to mitigate regional imbalances and foster integrated national aviation development.

A novel unit value method for urban ecosystem services assessment.

Land use and land cover in cities experience complicated changes in response to rapid urbanization, requiring significant impact on ecosystem service value. However, traditional valuation methods tend to ignore artificial ecosystem. This study develops a novel Unit Value (UV) method to evaluate urban ecosystem service values with both natural and artificial ecosystems considered comprehensively. This method is used to reveal the spatial-temporal evolution of ESV in Wuhan from 1996 to 2018. Additionally, Pearson's correlation between ESVs and driving factors is studied by using a multiple regression model. The results show that: (1) Ecosystem service values are observed to increase by 20.94% because of land use and land cover increases in woodland ((32.21%) and man-made wetland (61.73%) in 1996-2018, respectively. (2) ESVs declined in central districts and increased in suburban districts, with a clear imbalance between "high central areas" and "low suburban areas". (3) Human activities play a more important role in urban ecosystem compared to natural environmental factors, especially agriculture, per capita GDP, and population. This study provides a distinctive method for the spatiotemporal evaluation of urban ecosystem service values, establishing an insightful basis for urban sustainable development.

Regional Differences and Spatial-Temporal Evolution Characteristics of Digital Economy Development in China

The digital economy, as an emerging productive force, has become a critical catalyst for economic sustainable progress. This study examines the regional disparities and origins of the digital economy, as well as its spatial and temporal growth patterns, which are essential for bridging the financial gap among regions and promoting synchronized economic progress. We developed a multidimensional index system to evaluate the level of digital economic development across 30 Chinese provinces, encompassing aspects such as the Internet’s evolution, digital infrastructure, capacity for digital innovation, and the application of digital technologies. The regional imbalances and origins of the digital economy were scrutinized using Dagum Gini coefficient decomposition. Furthermore, the study investigated the spatiotemporal dynamics of digital economy progression in China, employing methods like kernel density estimation, migration models of the gravity center, and an analysis via the standard deviation ellipse. The results reveal the following: (1) Digital economic development is more advanced in Eastern China compared to the western regions, with variations primarily driven by inter-regional differences. (2) The absolute gap in national digital economic development levels is widening, and relative disparities within the eastern region are increasing. Significant polarization is observed in the development of the digital economy across the central and western regions, while disparities in the northeastern region are comparatively minimal. (3) The center of gravity for China’s digital economy has overall shifted southward; the standard deviation ellipse has extended both northward and southward and become more oblate; and the dispersion within China’s digital economy has intensified.

Spatial Evolution of Grassland Ecological Carrying Capacity and Low-Carbon Development Pathways for Animal Husbandry in Inner Mongolia

Inner Mongolia’s grasslands, covering 22% of China’s total grassland area, face critical challenges in balancing livestock production with carbon sequestration under climate change pressures. This study establishes an integrated assessment framework combining remote sensing monitoring, InVEST modeling, and life cycle assessment to analyze the spatial–temporal evolution of grassland ecological carrying capacity and livestock-related carbon emissions from 2000 to 2020. Key findings reveal a 78.8% increase in actual livestock carrying capacity (from 53.09 to 94.94 million sheep units), with Tongliao experiencing 185% growth, while Alxa League showed a 229,500 sheep unit decrease. The theoretical carrying capacity grew by 50.6%, yet severe ecological pressure emerged in western regions, as evidenced by Alxa League’s grass–livestock balance index exceeding 2100%. Carbon sequestration exhibited a northeast–southwest spatial pattern, decreasing by 7.4% during 2015–2020, while greenhouse gas emissions from intensive livestock systems reached 6.40 million tons CO2-eq in Tongliao by 2020. The results demonstrate that regions combining high-intensity husbandry with low carbon storage require urgent intervention. We propose three pathways: adaptive grazing management to reduce overloading in western pastoral zones, carbon monitoring systems to enhance sequestration in vulnerable ecosystems, and emission reduction technologies for intensive farming systems. These strategies provide actionable solutions for reconciling grassland sustainability with China’s dual carbon goals, offering insights for global pastoral ecosystem management.

Spatial temporal evolution characteristics of defensive traditional community in Southwest China with a case study of Chengzi village in Yunnan

Strong village cohesion exhibits a positive dialectical relationship with external threats, particularly in frontier regions. Chengzi Village, as a typical defensive village, was a crucial area for the administrative governance of the southwestern region by the ancient central dynasties. The broad temporal span of construction reflects the integration of Han culture into the development trajectory of Yi ethnic villages in southwest China. Based on the context of ethnic integration and the Gemeinschaft theory, the study adopts the framework of “macro-meso-micro” to explore the defensive traits of five communities formed across different historical periods within the village. It finds that dynamic integration by opening a coexistent “passage” between internal ethnicities plays a key role in the sustainable survival of a Gemeinschaft. It provides concrete examples beyond theoretical discussions for understanding the manifestation of the Gemeinschaft in the empirical world, and offers references for the conservation and revitalization of defensive village heritage.

Spatial-Temporal Evolution Characteristics of Population Aging and its Influencing Factors in Zhongyuan Urban Agglomeration

Taking the aging population in the Zhongyuan urban agglomeration as the research subject, using the proportion of the elderly population aged 65 and above as the primary measurement index. Methods such as standard deviational ellipse,spatial autocorrelation analysis, and the optimal parameters-based geographical detector are employed to analyze the spatial-temporal evolution and influencing factors of aging at the county scale.The results indicate that:①From 2000 to 2020, population aging in the Zhongyuan urban agglomeration intensified from northwest to southeast. The region shifted from an adult-dominated to an elderly-dominated demographic. Among them, the districts and counties under the jurisdiction of Xinyang City,Henan Province, are the most obvious. ②Population aging in the Zhongyuan urban agglomeration exhibits a strong global spatial positive correlation, with distinct local spatial clustering. The high-high cluster and low-low cluster show a trend of increasing first and then decreasing, indicating that the spatial difference of population aging has narrowed.③Factors such as fertility levels, the 2010 aging ratio, birth rates, and migration rates are the primary determinants of population aging distribution. The impact of greening and healthcare levels follows, with the influence of population, natural, and economic factors diminishing progressively.④The interaction of fertility level∩2010 aging ratio and fertility level∩greening level has a strong impact on population aging, indicating that fertility level plays a major role in the process of population aging. The research can provide an important basis for the subsequent population structure adjustment.

Temporal-spatial evolution analysis of carbon emission efficiency in the logistics industry of coastal provinces in China based on the super-efficiency SBM model

BackgroundThe logistics industry is a pillar industry of China’s national economic development, and coastal provinces, as the core of China’s economic development, have highly developed logistics industry. However, the rapid development of the logistics industry in China’s coastal provinces is usually accompanied by high carbon emissions. Therefore, improving the carbon emission efficiency of the logistics industry (LCEE) in China’s coastal provinces is one of the main contents to achieve "China’s dual carbon goals". Existing research indicates that LCEE is closely related to the efficiency levels of neighboring regions, and its temporal and spatial evolution characteristics are also influenced by the change of neighborhood efficiency. However, less attention has been given to the role of geographic proximity in analyzing the temporal and spatial evolution characteristics. Thus, this paper introduces the spatial lag factor into the Markov chain (MC) to obtain the spatial Markov chain (SMC), examining the influence of neighboring provinces’ LCEE on the spatial evolution of the local LCEE in China’s coastal provinces.ResultsThe results show that: For most years between 2007 and 2022, in China’s eleven coastal provinces, the LCEE values were less than one. These low LCEE values indicated that the potential for emission reduction had not been fully tapped, and low-carbon development faced significant challenges. The primary obstacle to improving LCEE during the study period was low technical efficiency, and the development of the technology level was crucial for enhancing LCEE. In 2007–2011 and 2015, the spatial distribution of LCEE exhibited significant spatial clustering features. The primary type of spatial clustering was high-high clustering, which indicated there was an obvious trend of regional coordinated development. The LCEE of neighboring provinces influenced the state transition probabilities of their own states, and spatial spillover effects in these provinces were very evident.ConclusionsThis study conducted an in-depth analysis of the temporal-spatial evolution characteristics of LCEE in China’s coastal provinces. There are significant differences in LCEE among these provinces. Each province needs to reduce the carbon dioxide emissions of the logistics industry and improve the LCEE through regional cooperation, technological investment, and targeted policies, so as to promote the sustainable development of the logistics industry in China’s coastal provinces.