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[Display omitted] • Measure carbon emission inequalities (CEIs) of urban agglomerations under rapid urbanization. • Identify the key factors determining CEIs within the three urban agglomerations. • Account for the differences between adjacent or distant urban agglomerations. • The declining CEI is mainly due to changes in industrial structure and economic development. • CEI in the upper reaches of the YREB increases due to the changes in energy intensity. Increasing countries are articulating ambitious goals of carbon neutrality. However, large inequalities in regional emissions within a country may hinder progress toward a carbon–neutral future, as the unequal distribution of reduction responsibilities among regions could impair just transition and exacerbate uneven development, which necessitates an in-depth understanding of the mechanism of multi-scale carbon inequalities within country, region, and city. Yet, the evolution of carbon inequalities within urban agglomerations and the differences between adjacent or distant urban agglomerations have not been well understood, especially in countries undergoing rapid urbanization. Using the data of 89 cities in China's Yangtze River Economic Belt (YREB) during 2006–2021, this paper quantifies carbon emissions inequality (CEI) at different scales in a systematic regional-urban agglomeration-city hierarchical structure. Then, under the integrated mean logarithmic deviation-logarithmic mean Divisia index (MLD-LMDI) decomposition framework, multi-scale CEIs are perfectly decomposed into six interrelated drivers, i.e., industrial emission structure, energy emission intensity, industrial energy mix, energy intensity, industrial structure, and economic development. The results show that economic development, energy intensity, and industrial energy mix disparities are the main determinants accounting for CEIs at different scales. The decreasing CEI in YREB is mainly due to the changes in industrial structure and economic development, while the energy intensity effect partially hinders the mitigation of CEI. In the upper reaches of the YREB, the energy intensity effect accounts for over 94% growth of CEI during 2006–2021, while the decline in CEIs in middle and lower reaches is primarily caused by the effects of industrial energy mix and industrial structure, respectively. Further spatial decomposition analysis reveals more refined city-level heterogeneous effects and emphasizes the prioritized emission reduction direction for each city. This paper offers implications for reducing carbon inequality and insights into coordinated carbon emissions mitigation at the regional level for a carbon–neutral future. [ABSTRACT FROM AUTHOR]

• Net carbon sink efficiency is used to measure carbon neutral performance. • A spatial spillover effect existed between urbanization and net carbon sink efficiency across counties. • Being surrounded by highly urbanized units resulted in positive effects on a unit's net carbon sink efficiency. • The impact of socioeconomic factors on net carbon sink efficiency is heterogeneous. Reducing carbon emissions and improving vegetation carbon sequestration (VCS) have long been recognized as the key ways to address the aim of carbon neutrality, requiring global effort and typical regional experiences. This paper develops an assessment framework for measuring the net carbon sink efficiency (NCSE) based on the concept of net-zero carbon sink to depict both carbon emission and VCS simultaneously. This paper primarily runs a spatial autocorrelation analysis to explore spatial characteristics of urbanization and NCSE of Yangtze River Delta urban agglomeration (YRD) in China from 2000 to 2020, followed by employs a spatial econometric model to investigate the spatial spillover effect of urbanization on NCSE. The results show that NCSE in YRD exhibits clear spatial disparities, descending from the southern part dominated by mountainous hills to the flatter northern region. The spatial evolutionary trend of urbanization and NCSE has a significant positive correlation, manifested as H-H and L-L agglomeration, covering most counties in southeast coastal areas and northern plains, respectively. The NCSE can improve even during the continued urbanization in YRD. Specifically, for every 1% increase in urbanization, NCSE will increase by 0.164%, of which 0.118% in direct and 0.046% in indirect growth, respectively. In addition, GDP per capita, industrial structure, technological progress, road area and direct foreign investment are benefit for NCSE. In contrast, population density, environmental regulation, and built-up area tend to decline NCSE. These findings may help to formulate the urban sustainable development planning and differentiated carbon neutrality policies. [ABSTRACT FROM AUTHOR]

• An evaluation system has been developed to assess the carbon neutrality potential. • A rising curve and function model of carbon neutrality potential are established. • The upgrade law of urban agglomeration's carbon neutrality potential has been verified. • Carbon neutrality potential of Chinese urban agglomeration is wavy upward. The formation and development of urban agglomerations are frequently accompanied by the enhancement of regional carbon neutrality potential. The specific performance demonstrated that the carbon neutrality potential of urban agglomerations exhibited a wavy upward tendency over time due to the agglomeration of production elements, industrial transformation, and the evolution of a joint game among cities. To begin with, this paper analyzes and models the fundamental principles of the evolution law of carbon neutrality potential of urban agglomerations, as well as the geometric expression of their evolution. Afterward, an evaluation index system for urban agglomeration carbon neutrality potential is constructed in this paper, and the entropy weight method is employed to calculate the carbon neutrality potential of each city within the agglomeration as well as Chinese urban agglomerations as a whole. Following this, a global spatial analysis of the carbon neutrality potential of various urban agglomerations and cities within agglomerations is conducted using Arc GIS10.8, and a method for calculating the joint intensity and threshold value of the carbon neutrality potential of urban agglomerations is developed. As a last stage, the MATLAB simulation verification approach is utilized to verify the evolution law of carbon neutrality potential and to explore the sustainable development path of carbon neutrality for Chinese urban agglomerations. The conclusions of this research indicate: (1) The spatial and temporal pattern of carbon neutrality potential of Chinese urban agglomerations is unbalanced; (2) By constructing a joint strength model, this study calculates the joint intensity and joint threshold value of the carbon neutral potential of Chinese urban agglomerations;(3) The evolution curve of the carbon neutrality potential of Chinese urban agglomerations essentially exhibits a wavy upward pattern that varies with time and urban joint development. [ABSTRACT FROM AUTHOR]

• Propose a methodology for measuring of regional synergistic emission reduction. • Regional synergistic emission reduction networks have a multilevel nested structure. • Explained the evolution characteristics of regional synergistic emission reduction. • Identified driving factors of regional synergistic emission reduction networks. Synergistic regional emission reduction (SRER) is an important part of the strategy to achieve carbon neutrality. In this paper, a system efficiency value-added model is proposed to measure the strength of SRER network relationships for 40,186 sets of city pairs and the SRER levels of 284 cities in China from 2011 to 2021. Subsequently, the evolution characteristics of the SRER network, the spatial and temporal characteristics of urban SRER levels, and the driving factors of the SRER network evolution are further analyzed. The results are as follows: SRER network is shifting from a fan-shaped network pattern of "three-region clustering and sporadic coverage" to a shuttle-shaped nested network pattern of "east-west linkage and multi-region support". Time series analysis found that urban SRER level in China showed fluctuating growth characteristics during the study period. Spatial analysis reveals that the spatial autocorrelation of urban SRER level is decreasing, and the spatial distribution range is expanding towards the southwest. Emission reduction comparative advantages (CAER), green innovation division of labor (GIDL) and urban economic linkages (LCEL) are driver factors of SRER network evolution. This study proposes an evaluation method for the urban SRER level from network relationships perspective. Subsequently, network evolution characterization, spatial and temporal characterization, and driving factor analysis were conducted. The results of the study provide important theoretical guidance and inspiration for grasping the current status and enhancement paths of China's urban SRER level. [ABSTRACT FROM AUTHOR]

• A composite index named GLCDES is developed to dynamically assess the level of Green Low-carbon Circular Development. • The combined-assignment VIKOR model based on game theory is developed to thoroughly estimate the construction level of GLCDES. • Southwest China's Green Low-carbon Circular Development Economic System construction level is examined from 2012 to 2021, showing how the long-side principle governs each region's value. • Selecting and strengthening the long side in combination with its reality is an effective way to improve the construction level of GLCDES. Emission peak and carbon neutrality are critical components of China's overall ecological civilization, and the establishment of a sound Green Low-carbon Circular Development Economic System (GLCDES) is a fundamental countermeasure for solving China's resource, environmental, and ecological problems. This paper used the new development concept as entry points to establish a comprehensive evaluation index system encompassing six subsystems: development dynamics, production and living systems, green benefits, low-carbon benefits, circularity benefits and socio-economic benefits. The combined-assignment VIKOR model, based on game theory, was used to thoroughly estimate the construction level of GLCDES by combining panel data from four southwestern provinces, including Chongqing, Sichuan, Guizhou and Yunnan, from 2012 to 2021. The study discovered that: the construction level of GLCDES in all four southwestern provinces increased over the past ten years, but the increase was not significant, the four development effectiveness indices, including green, low-carbon, circularity and socio-economic, were significantly improved, the development dynamics index was not significantly enhanced, and the index of production and living systems declined in fluctuation from year to year. The construction level of GLCDES between regions was very uneven and can be divided into two echelons, with the gap between Chongqing and Guizhou being particularly obvious. Regions can be categorized into three major categories based on the number of dominant subsystems. According to the above results, the total value of the dynamic comprehensive evaluation of each region is governed by the principle of the long side, which is an effective way to improve the construction level of GLCDES by selecting and strengthening the long side in combination with its reality. [ABSTRACT FROM AUTHOR]

• The paper analyzes the impact of psychological characteristics and policy environment on public participation. • The paper expands the Theory of Planned Behavior (TPB), and adds policy variables to the TBP model. • The paper provides a research perspective of management science and psychology for ecological compensation research. • Psychological cognition can significantly improve the public's willingness to participate. • The influence of policy environment on public behavior is far greater than that on their will. Public participation in river basin ecological compensation is one of the effective means to adjust the multi stakeholder relationship and promote low-carbon development. This paper takes the upper Yellow River basin section as the research area, adds ecological compensation policy variables to the Theory of Planned Behavior (TPB). An integrated theoretical framework is established from the two levels of personal psychological cognition and policy environment, which can comprehensively consider multiple factors that affect individual behavior. This paper uses the Structural Equation Modeling (SEM) to empirically analyze the impact of psychological factors and river basin ecological compensation policy on the public's willingness and behavior to participate in river basin ecological compensation. The results showed that the three psychological cognitive factors of behavior attitude, subjective norm and perceived behavior control had a significant impact on the public's willingness to participate in ecological compensation. The ecological compensation policy also has a significant impact on the willingness and behavior of the public to participate in ecological compensation. The impact coefficient of ecological compensation policy on the participation willingness is 0.085, and the impact coefficient on the participation behavior is 0.551. The existing ecological compensation policies in carbon neutral contexts have a stronger effect on the public participation behavior, but a weaker impact on the public's willingness to participate. The existing policies in carbon neutral contexts play a small role in mobilizing the public's enthusiasm from the psychological cognitive level, but the willingness to participate has a significant direct impact on the participation behavior. The research conclusions of this paper can help to broaden the theoretical framework of public participation and provide new ideas and methods for improving the ecological compensation mechanism of the river basin. [ABSTRACT FROM AUTHOR]

• Analyze the synergy of CO 2 emission factors of energy-intensive industries via EMD. • Synergy of CO 2 emission factors: long-term trend, short-term volatility. • Elastic coefficient model is used to reveal the heterogeneity of the 10 factors. • Six industries are divided into "stable and balanced" and "sensitive and uneven". Carbon emission is a global problem that countries around the world are paying attention to and urgently need to be resolved. Under the requirements of China's high-quality economic development and the carbon peaking and carbon neutrality goals, energy-intensive industries, as a key link in the industrial chain and the main source of China's CO 2 emissions, have attracted much attention. This paper made a statistical analysis of the scale development of six energy-intensive industries, and explored the fluctuation characteristics of their carbon emissions. The empirical mode decomposition (EMD) model was further introduced to analyze the and synergistic effect among the driving factors of carbon emission, revealing the synergistic mechanism of long-term trend and short-term volatility of factors. Then, the elastic sensitivity analysis model of carbon emissions in energy-intensive industries was constructed to study the heterogeneity of driving effects of factor indicators. From the perspective of factors driving, this article reveals the heterogeneity of the driving effects of ten key factor indicators including economic level (GDP), urbanization level (UP), industrial structure (IS), technological innovation (TI), and environmental regulation (ER); from the perspective of industry development, this paper summarized the "stable and balanced" and "sensitive and uneven" trends presented by the heterogeneity of the six energy-intensive industries. Combining the characteristics of industry emission fluctuations, driving factors' synergies and heterogeneity, from the perspective of short-term and long-term development, four targeted emission reduction control strategies were proposed to provide reference for the low-carbon development of energy-intensive industries. [ABSTRACT FROM AUTHOR]

[Display omitted] • Mangrove species of multiple natural reserves was implemented fine classifications. • Two adaptive stacking algorithms achieve classification accuracies of 90.4%-96.2%. • ACE and AMO stackings outperform traditional stacking in classifying mangrove species. • MPViT has excellent mangrove species classification performance. • Interpretation of the sensitivity of remote sensing features to identify mangrove species. Mangrove is the highest productive ecosystems in the global coastal zone, which has high blue carbon sink function and carbon neutrality potential. Fine species classification is essential for mangrove conservation and sustainable development, and has attracted much attention in recent years using ensemble learning and multi-dimensional data. However, the current mangrove species classification based on traditional stacking ensemble learning still faces challenges due to the correlation between base classifiers, differences in meta -classifier capabilities, the subjectivity of parameter tuning, and data redundancy. To address these issues, this paper utilized unmanned aerial vehicle (UAV) multispectral images of three mangrove nature reserves in Beibu Gulf, south China, to examine the classification and generalization ability of our proposed Adaptive Stacking Ensemble Learning (ASEL) algorithm for different mangrove species. We also aim to verify the feasibility of the Multi-Path Vision Transformer for Dense Prediction (MPViT) algorithm for mangrove species mapping, and compare its performance with the ASEL algorithm for mangrove species classification. Finally, we used the SHapley Additive Explanations (SHAP) method to measure the contribution of feature variables to the model, exploring the sensitivity of different image features to mangrove species mapping. This study highlights that: (1) The two ASEL algorithms achieved high accuracy classification of mangrove species with the overall classification accuracy ranging from 79.8% to 96.2%. The ACE-Stacking and AOM-Stacking algorithms performed better classification ability than the traditional stacking algorithm, with the mean overall classification accuracy increasing from 0.9% to 3.3%. The McNemar test further indicated that the differences in classification results derived from three algorithms were significant at the 95% confidence interval, demonstrating the better classification and generalization ability of the ASEL algorithm. (2) The MPViT algorithm achieved better classification accuracy in the three reserves, with an overall accuracy of 95.5%-97.3%, which was 0.6%-5.4% higher than the two ASEL algorithms. The average accuracy of identifying mangrove species was over 95.3% in all reserves, demonstrating the desirable mangrove classification performance of MPViT algorithm. (3) The identification accuracies (F1 scores) of mangrove species in different reserves were ranged from 0.848 to 0.984. Cyperus malaccensi had the highest identification accuracy. (4) The SHAP method interpreted the great contribution of digital surface model and variable atmospherically resistant index features on mangrove species classification. The red band and ratio vegetation index was sensitive to Aegiceras corniculatum and to Cyperus malaccensi. [ABSTRACT FROM AUTHOR]

• The carbon emission coefficients method and net ecosystem productivity (NEP) were used to evaluate urban carbon emissions and green space carbon sinks, respectively. • A multi-objective programming model (MOP) was constructed to derive the optimal solution of the amount of urban green space based on the carbon balance theory. • Combined with ventilation resistance and species migration resistance assessment, priority distribution areas of green space in 2035 were identified. • The PLUS model was introduced to optimize the spatial pattern of urban green space. • Optimized green space network stability and carbon sinks will be enhanced in 2035. Cities experience the most intensive level of human activity. As a result, more than 60 % of global CO 2 emissions come from urban areas. Urban green space has the dual ecological benefits of increasing carbon sinks and reducing carbon emissions. Creating green space is essential to promoting the development of a low-carbon cycle in a city. Therefore, exploring the quantitative structure and spatial pattern optimization of urban green space from the perspective of carbon balance can effectively improve the total carbon sink of a city. Based on the carbon balance theory, this paper first evaluates the carbon offsetting capability (COC) of urban green space in Beijing in 2020. Then, CO 2 emissions is predicted, COC improvement targets are established, and the quantity of standard green space is calculated under these targets in 2035. A multi-objective programming model (MOP) is constructed to derive the optimal solution to determine the amount of standard green space needed to meet the constraints of urban development planning and maximize the carbon sink. A circuit model is used to identify the priority distribution area of green space in 2035, and the Patch-generating Land Use Simulation (PLUS) model is used to simulate the spatial pattern optimization results. The results show that: (1) CO 2 emissions in Beijing caused by human activities in 2020 totaled about 240.12 million tons, the net absorption of CO 2 of green space was about 8.99 million tons, and the COC was about 3.74 %; (2) in 2035, Beijing's CO 2 emissions will be about 265.40 million tons. Under 5 %, 10 %, 15 %, 20 %, and 25 % COC improvement gradients, the demand for standard green space will be 12,204.80 km2, 12,763.80 km2, 13,353.85 km2, 13,943.90 km2, and 14,533.96 km2, respectively; (3) the results of the Multi-Objective Programming (MOP) model show that the optimal COC in 2035 is 4.19 %, and the standard green space quantity is 13,012.24 km2; (4) In 2035, Beijing's urban green space will be characterized by a network structure of circular radiation. The optimized COC is 4.37 %, and the standard green space quantity is 13,577.86 km2, which is largely consistent with the MOP model's predicted result. This study can provide theoretical and methodological support for urban green space planning, and expand nature-based solutions for the low-carbon development in cities. [ABSTRACT FROM AUTHOR]

• Environmental regulation (ER) directly promotes China's GTFP growth. • Both Industrial structure rationalization and advancement have mediating effects. • Green and production technological innovation mediate between ER and GTFP growth. Green total factor productivity (GTFP) growth is an essential route to realize the target of carbon peak and carbon neutrality. Chinese government has implemented several environmental regulations (ER) to accelerate GTFP growth. However, the influencing path of ER on China's GTFP growth is still puzzled. This paper based on Porter's and the Compliant Hypotheses, employs super-efficiency Epsilon-based measure (EBM) model and Malmquist-Lunberger (ML) index under meta -frontier to estimate the provincial GTFP growth from 1998 to 2018. A Systematic Generalized Method of Moments (SYS-GMM) model was applied to probe the nexus between ER and GTFP growth from the perspective of industrial structure upgrading and technological innovation. The results reveal that the average ML index is 0.955, while efficiency change (EC) and technology change (TC) index are 1.003 and 0.953. The Porter's hypothesis was confirmed refuting the Compliance Cost Hypothesis. Environmental regulation directly promotes China's GTFP growth. ER can also facilitate industrial structure rationalization (ISR) and industrial structure advancement (ISA), which further promote China's GTFP growth. Green technological innovation (GTI) and production technological innovation (PTI) mediates between ER and GTFP growth in China. Hence, the Chinese government should strengthen the ER for industrial structure upgrading, technological innovation, and GTFP growth in China. [ABSTRACT FROM AUTHOR]

[Display omitted] • Visualization and quantitative analysis of the global ecological carbon sinks and sources. • Enhance the prediction of carbon sequestration dynamics under climate change. • Climate change potentially affecting the carbon-climate feedback. • Research content is becoming clearer and more systematic. • Keyword clustering the classification of the research into six relatively independent subtopics. Climate change is extensively affecting the global ecosystem, especially the ecosystem carbon sequestration and sequestration potential, which are issues of global concern. The proposed concept of "carbon neutrality" in 2020 has brought ecological carbon sequestration to the forefront. Therefore, the research on climate change and ecosystem carbon sequestration need to be systematically reviewed, summarized, and examined. Based on the Web of Science database, this paper analyzed 4005 articles (1992–2022 year) after rigorous screening via a bibliometrics analysis and then presented the research trends and future research focus of ecological carbon sequestration. The following conclusions are drawn: (1) the research over the last 30 years has steadily improved, and the annual number of publications has increased in a cubic polynomial fashion (R 2 = 0.9937), with 87.57 % of the total publications appearing after 2009. Global Change Biology and Science of the Total Environment are the most influential journals in the field; (2) participation in such research is becoming increasingly common with expanding research areas covering Eurasia, America, Oceania, and Africa. The United States and China are the most productive and influential countries; (3) the diversity of this research is increasing, and the research content is becoming more explicit and systematic. Most research focuses on climate change, carbon sequestration, management, land-use changes, and nitrogen and soil organic carbon; (4) although traditional ecological evaluation techniques were essential early on, remote sensing and modeling have become the primary methods for assessment; (5) keyword clustering allows the classification of the research into six relatively independent subtopics: climate change and terrestrial ecosystems, sequestration and organic-carbon, ecosystem services, nitrogen and carbon cycling, biomass and forest, and blue carbon, which provides a reference for further research on ecological carbon sequestration and carbon neutrality. [ABSTRACT FROM AUTHOR]

• An LCA procedure at corporate level which incorporated the absorption of CO 2 by trees. • A joint LCA and time-series DEA methodology to calculate eco-efficiency score. • An input–output indicator system constructed to assess the eco-efficiency. • Environmental impact, wood fiber and energy are key factors to enhance eco-efficiency. Complex forestry enterprises have the potential to lead the current industrial upgrade under the strategy of carbon neutrality and carbon peaking. This paper aims to investigate the inputs and outputs influencing the eco-efficiency of complex forestry enterprises through a case study. A hybrid ensemble approach of life cycle assessment (LCA) and time-series data envelopment analysis (time-series DEA) was proposed to evaluate the case enterprise's eco-efficiency. It is found that, from the perspective of the average intensity of key input and output factors, environmental impact in efficient years is 45.25% of that in inefficient ones, and when it comes to the fiber consumption and the energy consumption, this percentage turns into 65.53% and 77.66%. With the findings, we provide guidelines for complex forestry enterprises to enhance the eco-efficiency in improving environmental impact and in giving full play to the advantages of forestry industrial chain resources. Our findings also contribute to the existent literature by providing a systematic study based on a corporate time-series dataset. [ABSTRACT FROM AUTHOR]

• Impact-based water footprint of rolled steel production is first performed. • Fossil energy footprint and carbon footprint are evaluated by using LCA. • Maritime transport, iron power and direct emissions are major contributors. • Adjustment of industrial structure is essential for carbon mitigation of steel industry. The Iron and Steel Industry (ISI) is a difficult-to-mitigate sector that has considerable contributions to energy consumption, water pollution, and CO 2 emissions. Despite broad interest in energy consumption and CO 2 emissions generated by steel production, there are few impact-oriented Water Footprint (WF) assessments of steel production. This study evaluates the water, carbon and fossil energy footprints generated from rolled steel production on the basis of life cycle assessment. Key contributors attributing to those environmental impacts are also identified so as to explore their potential mitigation. Using the Monte Carlo model, an uncertainty analysis is conducted with the aim of providing reliable results for decision-making. Results show that the impact per ton China's rolled steel production has on global warming, human health, ecosystem quality, and resources is 4.40 × 103 kg CO 2 eq (GSD2 = 1.24), 4.15 × 10-3 Disability Adjusted of Life Years (DALY) (GSD2 = 1.31), 7.08 × 10-5 Species·yr (GSD2 = 2.04), and 4.39 × 104 MJ (GSD2 = 1.28), respectively. Global warming and freshwater ecotoxicity is the dominant contributor to the damage impact on human health and ecosystem quality, respectively. Results also show that the transport (freight ship) of raw materials and the production of iron powder are the two main contributors to the overall impact on the environment that rolled steel production has. Several policy recommendations are proposed in this paper, which focus on adjusting the steel industry's industrial structure, promoting the national-level carbon emissions trading in the ISI, promoting international cooperation, and improving public awareness of green and low-carbon development. Such policy recommendations provide decision-makers with valuable insights, which thus allow them to prepare the appropriate mitigation policies. [ABSTRACT FROM AUTHOR]

[Display omitted] • China's agricultural carbon emission performance (ACEP) has greatly improved. • Technological progress promotes ACEP, but efficiency changes does little. • China's ACEP has an aggregation effect, provinces with lower ACEP are catching up. • China's ACEP is converging and growing at the same time. • China's ACEP will increase further in terms of spatial scope and temporal trends. The spatial characteristics of carbon emissions form the basis for exploring carbon neutrality measures for different regions. As the second largest carbon emission source globally, agricultural carbon emissions should not be ignored. This paper used panel data from 2009 to 2019 to calculate the agricultural carbon emissions of 30 provinces in China, and used the ML index, spatial Morans'I index and three convergence indices to analyze the dynamic changes and spatial pattern of China's agricultural carbon emission performance (ACEP). The results showed that: (i), China's ACEP has increased by 1.5% from 2009 to 2019, following a spatial pattern of eastern (1.041) > western (1.020) > central (0.974);(ii) the contribution of technological progress to China's ACEP was 1.035, exceeding 1, and thus indicating that technological progress can improve ACEP, while the contribution of efficiency changes was only 0.991; (iii) China's ACEP has exhibited a spatial aggregation effect over the last 10 years (P < 0.05), but the value of Moran's I index dropped from 0.265 in 2009 to 0.202 in 2019, which indicates that the spatial aggregation of ACEP is weakening; and (iv) the ML index for 30 provinces in China showed σ convergence, which indicates that the ACEP is improving, while there is absolute β convergence and conditional β convergence, which indicates a "catch-up effect" among regions. The results indicate that China's ACEP is likely to increase further in terms of spatial scope and temporal trends. [ABSTRACT FROM AUTHOR]