Research on the evolution and influencing factors of carbon emission efficiency in the Yangtze River Delta urban agglomeration

Enhancing carbon emission efficiency is a critical pathway for advancing global climate governance and achieving the “Dual Carbon” goals. Precisely mapping the spatiotemporal evolution patterns of carbon emission efficiency in urban agglomerations and thoroughly analyzing the underlying driving mechanisms are of paramount importance for optimizing the nation’s overall carbon neutrality pathway. This study examines 26 cities within the Yangtze River Delta urban agglomeration from 2005 to 2023. Employing methods such as the Super-EBM model, exploratory spatiotemporal data analysis, and the Tobit model, it delves into the spatiotemporal evolution of carbon emission efficiency and its influencing factors. Key findings include: (1) Overall carbon emission efficiency remained stable at approximately 0.85 during the study period, with minor fluctuations. (2) Significant spatial disparities in carbon emission efficiency emerged, exhibiting gradually increasing convergence. (3) Local spatial structures of carbon emission efficiency were relatively dynamic yet demonstrated strong spatial dependence, while overall spatial structures remained stable with pronounced spatial cohesion. (4) Urbanization level, economic development level, population density, degree of openness, and technological innovation have a significant impact on improving carbon emission efficiencye, whereas industrial structure and environmental regulation showed no significant promotional impact.

1 Introduction

Global climate change is one of the most severe challenges facing human society today. Promoting global climate governance has become a common consensus in the international community (Tian and Pang, 2025; Wang et al., 2023). As the world’s largest carbon emitter, China is actively driving low-carbon transformation and has set the ambitious goal of “peaking carbon emissions by 2030 and achieving carbon neutrality by 2060 (Duan and Zhu, 2025; Huang et al., 2025; Li and Chen, 2025).” Moreover, at the 2025 United Nations Climate Change Summit, China further strengthened its nationally determined contributions, pledging its utmost efforts under the Paris Agreement framework and demonstrating its responsible leadership as a major global power (Wang C. et al., 2025).

Over the past few decades, China’s rapid urbanization has led to the emergence of urban agglomerations. As strategic hubs for national economic development and concentrated centers of energy consumption, these urban clusters are undoubtedly the primary battleground and key to achieving the “Dual Carbon” goals (Li et al., 2018; Lin et al., 2025). Their carbon emission efficiency directly determines the effectiveness of the nation’s emission reduction efforts. The Yangtze River Delta (YRD) stands as one of China’s most dynamic, open, and innovative economic regions (Wang et al., 2024). Its massive economic scale and energy consumption volume position it as pivotal in the national carbon reduction strategy (Wang H. et al., 2025). Therefore, accurately depicting the spatio-temporal evolution characteristics of the carbon emission efficiency (CEE) of the Yangtze River Delta urban agglomeration (YRDUA) and deeply deconstructing its internal driving factors not only plays a key supporting role in the high-quality development of the region itself, but also has significant strategic significance for optimizing the overall national carbon neutrality realization path (Guo et al., 2025; Jin et al., 2024).

A rational evaluation system helps reduce uncertainties in carbon emission efficiency assessments while enhancing the accuracy and reliability of evaluation outcomes (Tan et al., 2025). Although single-factor indicators are relatively straightforward, they overlook core input elements of production systems such as capital, labor, and energy consumption. Consequently, the total factor productivity indicator framework has emerged as the mainstream method for evaluating carbon emission efficiency by systematically incorporating multiple input and output factors (Mao et al., 2024; Zhang et al., 2025). Data Envelopment Analysis (DEA) is a multi-input, multi-output method grounded in linear programming that calculates efficiency through distance functions. DEA offers strong flexibility and adaptability as it requires neither knowledge of the decision unit’s actual production function nor prior parameter estimation (Liu et al., 2024). Consequently, this approach is now a crucial instrument for assessing efficiency. The EBM model, which is based on a hybrid distance function model, combines radial and non-radial methods. It provides more accurate and thorough efficiency assessments by precisely reflecting the improvement ratio between target and actual values of decision-making units (Tone and Tsutsui, 2010). While the EBM model partially overcomes the limitations of traditional DEA models, the efficiency values it measures cannot exceed 1, meaning efficient DMUs cannot be compared. To circumvent this limitation, the traditional EBM model was refined into the Super-EBM model, which permits optimal efficiency values exceeding 1 (Lv et al., 2025; Zheng et al., 2024). Despite overcoming this constraint, the current application of the Super-EBM model remains limited.

Regarding the spatiotemporal patterns of carbon emission efficiency, existing research primarily focuses on single dimensions such as time or space, with relatively insufficient investigation into spatiotemporal interaction effects (Ma D. et al., 2025). In analyzing spatiotemporal patterns, methods including kernel density estimation (Shi and Huang, 2024), spatial autocorrelation models (Jin et al., 2024), trend surface analysis (Ma et al., 2026), Markov chains (Dong et al., 2025), and exploratory spatial data analysis (Wang L. et al., 2025) are widely employed. Markov chains are one of these that can be used to analyze stochastic processes mathematically. This approach uses transition probability matrix analysis to describe the dynamic evolution of diverse elements within an area across time, treating the evolution of regional elements as a Markov process. Its extended model, the spatial Markov chain model, can reflect the state of various elements within a region and their dynamic transition characteristics while exploring their spatial spillover effects (Gao et al., 2023). Exploratory spatiotemporal data analysis is formed by introducing a temporal dimension based on exploratory spatial data analysis. It not only depicts the spatiotemporal co-evolution patterns of elements within local regions and reveals the spatiotemporal dynamics of spatial disparities, but also further uncovers the temporal changes in local neighborhood spatial relationships (Wang and Shao, 2024). Consequently, exploratory spatiotemporal data analysis has gained popularity. Common research methods for carbon emission efficiency determinants include geographically weighted regression models (Xu et al., 2025), econometric models (Zhu et al., 2024), STIRPAT models (Dong et al., 2025), decomposition analysis (Ma S. et al., 2025), and random forest regression (Jiang et al., 2025). Influencing factors encompass economic, social, and environmental dimensions, such as levels of economic development, urbanization, and environmental regulations. The Tobit model is an econometric model based on maximum likelihood estimation, classified as a limited dependent variable or truncated econometric model (Jin et al., 2024). Since carbon emission efficiency qualifies as a “limited dependent variable” in this study, the Tobit model was selected to investigate its influencing factors.

Although existing research has conducted numerous explorations around the assessment methods, spatiotemporal patterns, and influencing factors of carbon emission efficiency, there are still significant research gaps: Firstly, the assessment methods of carbon emission efficiency have evolved through a single indicator and the traditional DEA model. Although the Super-EBM model has achieved the precise ranking of efficient decision-making units, its application at the urban agglomeration scale is still relatively limited. Secondly, most of the existing research focuses on the analysis of a single dimension of time or space, and the exploration of spatiotemporal interaction effects is relatively weak, making it difficult to fully reveal the intrinsic correlation mechanism behind the evolution of efficiency models. Thirdly, systematic research on carbon emission efficiency at the scale of urban agglomerations is still relatively scarce, making it difficult to meet the increasingly interdependent demands of factor flow, industrial correlation and policy coordination in the process of regional integration.

Based on this, this study takes 26 cities in YRDUA as the research objects. Based on empirical data from 2005 to 2023, it aims to systematically answer two core scientific questions: First, what subtle and profound new characteristics does the spatio-temporal evolution of carbon emission efficiency in YRDUA present? What are the core influencing factors that drive the formation and evolution of these characteristics? This study will comprehensively apply methods such as the Super-EBM model, kernel density analysis, standard deviation ellipse, exploratory spatiotemporal data analysis, and Tobit model to reveal the intrinsic driving logic of the evolution of regional CEE, with the aim of filling the research gap in the spatiotemporal dynamic assessment of carbon emission efficiency in previous literature. To provide solid scientific basis and decision-making reference for optimizing the collaborative governance mechanism in the YRD region and promoting the realization of the country’s “Dual Carbon” goals.

2 Materials and methods

2.1 Research area

The YRDUA is located along China’s eastern coast. According to the Outline of the Yangtze River Delta Regional Integration Development Plan, the YRDUA encompasses Shanghai and parts of Jiangsu, Zhejiang, and Anhui provinces, comprising 26 cities as shown in Figure 1. Covering only 2.2% of China’s land area, the YRD contributes over one-fifth of the nation’s GDP. Its rapid development has resulted in substantial energy consumption and carbon emissions. In recent years, the region has implemented a series of low-carbon development strategies (Gao et al., 2024). The effectiveness of its emission reduction efforts offers valuable insights for low-carbon development in other developed regions worldwide.

Figure 1

Figure 1. The study area map of this research, (b) is a partial enlarged view of (a).

2.2 Methods

2.2.1 Super-EBM model

The EBM model innovatively combines the strengths of radial and non-radial DEA models, employing a hybrid distance function model at its core. This enables precise reflection of the improvement ratio between the target value and actual value of Decision-Making Unit (DMU), thereby providing more accurate and comprehensive efficiency assessments. However, the EBM model faces the limitation that measured efficiency values cannot exceed 1, meaning equally efficient decision units cannot be compared. To address the issue of unrankable efficient units with identical efficiency scores of 1, the Super-EBM model is proposed. The Super-EBM model improves efficiency assessment’s accuracy and applicability while maintaining the benefits of the EBM model (Ma D. et al., 2025). Therefore, this study employs the Super-EBM model to calculate carbon emission efficiency. In this study, the model is set as output-oriented, with the weights of input indicators and output indicators each accounting for half. The model is expressed in Equations 1–5.

${\rho }^{*}=\mathit{min}\frac{\theta -{\epsilon }_x{\displaystyle {\sum }_{i=1}^m}\frac{w_i^{-}{s}_i^{-}}{x_{i0}}}{\zeta +{\epsilon }_y{\displaystyle {\sum }_{r=1}^s}\frac{w_r^{+}{s}_r^{+}}{y_{r0}}+{\epsilon }_b{\sum }_{p=1}^q\frac{w_p^{b-}{s}_p^{b-}}{b_{p0}}}(1)$

${\displaystyle \sum _{j=1}^n}{\lambda }_j{x}_{ij}+s_i^{-}=x_{i0}\theta ,i=1,2,3\dots ,m(2)$

${\displaystyle \sum _{j=1}^n}{\lambda }_j{y}_{rj}-s_r^{+}=y_{r0}\zeta ,r=1,2,3\dots ,s(3)$

${\displaystyle \sum _{j=1}^n}{\lambda }_j{b}_{pj}+s_p^{-}=b_{p0}\zeta ,p=1,2,3\dots ,q(4)$

${\lambda }_j\ge 0,s_i^{-}\ge 0,s_r^{+}\ge 0,s_p^{b-}\ge 0(5)$

Where, ${\rho }^{*}$ represents the measured carbon emission efficiency value; ${\lambda }_j$ represents the linear combination coefficient of the decision-making unit. ${\mathrm{x}}_{\text{ij}}$, ${\mathrm{y}}_{\text{rj}}$, ${\mathrm{b}}_{\text{pj}}$, represent inputs, expected outputs, and expected outputs, respectively. ${\mathrm{s}}_{\mathrm{i}}^{-}$, ${\mathrm{s}}_{\mathrm{r}}^{+}$, ${\mathrm{s}}_{\mathrm{p}}^{-}$, represent the relaxation variables of the input, expected output, and unexpected output variables, respectively. $w_i^{-}$, $w_r^{+}$, $w_p^{b-}$, represent the weights of input, expected output and unexpected output, respectively.

2.2.2 Kernel density estimation

Kernel density estimation is a nonparametric statistical method used to estimate an unknown probability density function given a dataset (Shi and Huang, 2024). Its core principle involves taking a known density function as a “kernel” and averaging it over the positions of data points to generate a smooth estimated curve that illustrates the distribution of a random variable’s probability density. The higher the peak of the kernel estimation curve, the more densely concentrated the data is at that location. An increase in peak height indicates a more concentrated data distribution, with differences gradually diminishing. An increase in the number of peaks suggests a two-tiered or multi-tiered differentiation in the data distribution. The specific form of the density function can be expressed as Equation 6.

$f\left(y\right)=\frac{1}{nh}{\displaystyle \sum _{i=1}^n}K\left(\frac{y_i-\overline{y}}{h}\right)(6)$

Where, $n$ represents the number of samples, $y_i$ represents the carbon emission efficiency value of the $i$ sample, $\overline{y}$ represents the average carbon emission efficiency of all samples, $K$ and $h$ represent the kernel density function and bandwidth respectively.

2.2.3 Standard deviation ellipse

The standard deviation ellipse is a spatial statistical technique that reveals an element’s movement trajectory and spatial distribution features by combining the center of gravity with basic parameters (Yu and Li, 2025). It aids in studying the geometric center position of the element, as well as its direction of movement and overall trend. The size of the ellipse directly reflects the concentration of the data; when the ellipse’s area is small, the data is more concentrated. The major axis indicates the primary direction of data distribution, pointing toward the most densely populated side. The minor axis reflects the breadth of data dispersion; a longer minor axis signifies greater data dispersion in that direction. The center of gravity represents the geometric center of the entire dataset. The specific calculation formulas are given in Equations 7–10.

${\overline{X}}_w=\frac{{\displaystyle {\sum }_{i=1}^n}{w}_i{x}_i}{{\displaystyle {\sum }_{i=1}^n}{w}_i}(7)$

${\overline{Y}}_w=\frac{{\displaystyle {\sum }_{i=1}^n}{w}_i{y}_i}{{\displaystyle {\sum }_{i=1}^n}{w}_i}(8)$

${\sigma }_x=\frac{\sqrt{{\displaystyle {\sum }_{i=1}^n}\left(w_i{\overline{x}}_i\mathit{cos}\theta -w_i{\overline{y}}_i\mathit{sin}\theta \right)}}{{\displaystyle {\sum }_{i=1}^n}{w}_i^2}(9)$

${\sigma }_y=\frac{\sqrt{{\displaystyle {\sum }_{i=1}^n}\left(w_i{\overline{x}}_i\mathit{sin}\theta -w_i{\overline{y}}_i\mathit{cos}\theta \right)}}{{\displaystyle {\sum }_{i=1}^n}{w}_i^2}(10)$

In the formula, $\left({\overline{X}}_w,{\overline{Y}}_w\right)$ represents the center of the ellipse, ${\sigma }_x$ and ${\sigma }_y$ are the standard deviations of the two axes respectively, $\left(x_i,y_i\right)$ is the spatial distribution of carbon emissions, and $w_i$ is the weight. $x$ and $y$ are the relative coordinates of each point at the center of the ellipse, and $\theta$ is the Angle between the true north direction of the ellipse and its major axis.

2.2.4 Exploratory spatiotemporal data analysis

Exploratory Spatiotemporal Data Analysis (ESTDA) aims to identify hidden spatiotemporal trends, patterns, and aberrations within datasets. ESTDA rigorously studies dynamic evolution processes throughout the temporal dimension in addition to looking at the spatial characteristics of data (Wang and Shao, 2024). This approach successfully reveals the heterogeneity, interdependence, and evolutionary mechanisms of spatiotemporal data by offering a more thorough characterisation of its structural characteristics and evolutionary patterns. It provides essential methodological support for improving our comprehension of intricate spatiotemporal events. This study uses ESTDA as the primary analytical technique to thoroughly examine the spatiotemporal evolution features and inherent patterns of the research topic due to its advantages in clarifying the multidimensional aspects of spatiotemporal data (Liu et al., 2023). LISA spatiotemporal transation and LISA time paths constitute the main components of this framework.

LISA time path: By displaying the coupled motion of spatial unit observations over time and related spatial lags, this continuous version of the Markov transition matrix exposes the degree and pattern of spatiotemporal impacts between spatial units. Its geometric features include relative length (${\mathrm{\Gamma }}_i$), tortuosity (${\epsilon }_i$), and movement direction (${\theta }_i$). The specific formulas are provided in Equation 11 through Equation 13 below.

${\mathrm{\Gamma }}_i=\frac{N{\displaystyle {\sum }_{t=1}^{T-1}}d\left(L_{i,t},L_{i,t+1}\right)}{{\displaystyle {\sum }_{i=1}^N}{\displaystyle {\sum }_{t=1}^{T-1}}d\left(L_{i,t},L_{i,t+1}\right)}(11)$

${\epsilon }_i=\frac{{\displaystyle {\sum }_{t=1}^{T-1}}d\left(L_{i,t},L_{i,t+1}\right)}{d\left(L_{i,1},L_{i,T}\right)}(12)$

${\theta }_i=\mathit{arctan}\frac{{\displaystyle {\sum }_j}\mathit{sin}{\theta }_j}{{\displaystyle {\sum }_j}\mathit{cos}{\theta }_j}(13)$

Where $\mathrm{T}$ is the annual time interval and $\mathrm{N}$ is the entire sample. The LISA coordinate for sample $\mathrm{i}$ in year $\mathrm{t}$ is ${\mathrm{L}}_{\mathrm{i},\mathrm{t}}$, $\mathrm{d}\left({\mathrm{L}}_{\mathrm{i},\mathrm{t}},{\mathrm{L}}_{\mathrm{i},\mathrm{t}+1}\right)$ is the distance that city $\mathrm{i}$ moves from year $\mathrm{t}$ to year $\mathrm{t}+1$, denotes the distance traveled by province $\mathrm{i}$ from the first year to the last year. ${\mathrm{\Gamma }}_{\mathrm{i}}$ >1 signifies that the average LISA moving distance across all cities is less than the relative moving distance of city $\mathrm{i}$. The local space is more active the larger ${\mathrm{\Gamma }}_{\mathrm{i}}$ is. ${\mathrm{\epsilon }}_{\mathrm{i}}$ >1 represents that the relative movement of LISA in city $\mathrm{i}$ is greater than the average movement. As ${\mathrm{\epsilon }}_{\mathrm{i}}$ increases, the local spatial dependency tendency and the local spatial structural fluctuations become more curved and dynamic. The direction of travel in the city is indicated by ${\theta }_i$. The synergistic growth of the metropolis and the surrounding cities is positive between 0° and 90°. 90°–180° indicates the slow growth of a city and the quick growth of neighboring cities. 180°–270° illustrates the combined negative growth of the city and its surrounding areas. A range of 270°–360° denotes rapid city expansion and slow city growth for surrounding cities.

LISA spatiotemporal transition: Spatiotemporal transitions are categorized into four types. Among these, the ratio of a particular jump variable to the overall jump variable throughout the observed time period is represented by Spacetime Flow ($SF$) and Spacetime Convergence ($SC$). The specific formulas are given in Equations 14, 15, with detailed descriptions of each jump type provided in Table 1.

$SF=\frac{Type\mathit{II}+Type\mathit{III}}{m}(14)$

$SC=\frac{TypeI+Type\mathit{IV}a}{m}(15)$

Table 1

Table 1. The major categories of LISA spatiotemporal transition.

2.2.5 Tobit model

In this study, carbon emission efficiency is classified as a “bounded dependent variable.” To address the issue of truncated independent variables in the model, Tobin (1958) proposed the maximum likelihood Tobit model, also known as the truncated regression model or constrained dependent variable model. The Tobit model based on maximum likelihood estimation specifically addresses situations where the dependent variable is truncated or extreme (Zhang et al., 2023). The specific model equations are given in Equations 16, 17.

$Y_{it}^{*}=\alpha +{\beta }_i{X}_{it}+{\epsilon }_{it}(16)$

$\left\{\begin{array}{l}{Y}_{it}=Y_{it}^{*},Y_{it}^{*}>0\\ Y_{it}=0,Y_{it}^{*}\le 0\end{array}\right.(17)$

Among them, $i$ and $t$ represent the sample and the year respectively. $Y_{it}$ is the observed variable, and $Y_{it}^{*}$ is the latent variable in the model, representing the carbon emission efficiency of the $i$ city in the $t$ year. $X_{it}$ is the set of all independent variables in the model. ${\beta }_i$ and ${\epsilon }_{it}$ respectively represent the estimated coefficients of the model and the random error perturbation term.

2.3 Indicator selection and data sources

Establishing a sound evaluation system for the total factor carbon emission efficiency (CEE) enhances the accuracy and reliability of efficiency outcomes. Input indicators include employment in primary, secondary, and tertiary industries; fixed capital stock; and total energy consumption. GDP serves as the expected output, while CO₂ emissions represent the unintended output. Table 2 below provides a detailed explanation of input and output indicators for each CEE calculation.

Table 2

Table 2. A comprehensive indicator system for carbon emission efficiency.

All urban carbon emission data are sourced from the Emissions Database for Global Atmospheric Research (EDGAR). GDP and employment data for each city are derived from the China Urban Statistical Yearbook, with missing values supplemented by respective city statistical yearbooks. City energy consumption data were derived by establishing a linear simulation model for provincial energy consumption without an intercept term. This model was developed through regression analysis based on the linear correlation between provincial energy consumption and total nighttime light intensity across China. Using ArcGIS software, specific correlation coefficients between these variables were calculated annually and substituted at the city level.

To delve into the core factors shaping the spatiotemporal characteristics of carbon emission efficiency, this study considers seven influencing factors based on existing research and data availability: urbanization level, economic development level, industrial structure, population density, degree of openness to the outside world, environmental regulations, and technological innovation. Detailed descriptions of each factor are presented in the Table 3. All factor data are sourced from the Statistical Yearbook of Chinese Cities, the Statistical Yearbook of Science and Technology in China, and respective city statistical yearbooks.

Table 3

Table 3. Explanation of the indicators influencing carbon emission efficiency.

3 Results

3.1 Temporal and spatial patterns of carbon emission efficiency

Utilizing box plots and kernel density analysis, this study investigates the distribution patterns and evolution of CEE within YRDUA from 2005 to 2023. As shown in Figure 2, the overall CEE of YRDUA exhibits a stable trend with weak fluctuations, maintaining around 0.85 over the long term: it bottomed out at 0.825 in 2009 and peaked at 0.868 in 2014.

Figure 2

Figure 2. The temporal evolution trend of carbon emission efficiency, (a) and (b) are the box plot and kernel density plot of carbon emission efficiency changes, respectively.

Additionally, the clustering degree of CEE exhibited a “decline followed by increase” pattern, reaching its highest level in 2016. The concentration trend of CEE showed characteristics of “initial fluctuation followed by stabilization.” The kernel density estimation results clearly show that the distribution of CEE has not significantly changed, maintaining a unimodal distribution overall: the main peak of the kernel density curve exhibits a pattern of “initial rise followed by decline,” with the curve width continuously increasing and the peak showing a slight rightward shift. This characteristic indicates a mild increase in the concentration trend of the CEE distribution.

As shown in Figure 3, the CEE of YRDUA still holds significant improvement potential, with pronounced spatial differentiation. Cities in the eastern region demonstrate overall better performance than those in the western region. In 2005, six cities achieved CEE values exceeding 1. Shaoxing City recorded the highest value at 1.09, while Chizhou City had the lowest at just 0.65. Ten cities had efficiency values below 0.8. By 2014, the number of cities exceeding 1 remained at six, with Shaoxing still leading at 1.06, though this value had declined. Most cities achieved efficiency values around 0.7, with only Zhoushan City at 0.67. However, by 2023, while the number of cities with efficiency values exceeding 1 remained unchanged, the number of cities with efficiency values below 0.7 reached five, with Tongling City having the lowest at 0.56.

Figure 3

Figure 3. The spatial distribution characteristics of carbon emission efficiency, (a), (b), and (c) respectively show the spatial distribution of carbon emission efficiency for the years 2005, 2014, and 2023, with all three figures using a unified classification threshold.

As shown in Figure 4, the standard deviation ellipse of CEE in the YRDUA from 2005 to 2023 exhibits a “northwest-southeast” orientation, with a slight shift in its center of gravity. The center of gravity of the standard deviation ellipse was located in Huzhou City in 2005, migrated to Xuancheng City in 2014, and returned to Huzhou within the region by 2023. The trajectory of this movement followed a “southwest-northeast” path, maintaining an overall northeastward trend. The rate of movement showed minimal variation, indicating steady development. From 2005 to 2014, the standard deviation ellipse’s perimeter decreased by 1.11% and its area by 1.91%. This trend accelerated from 2014 to 2023, with the perimeter decreasing by 1.26% and the area by 2.94%. This dual reduction in both perimeter and area reflects the increasingly pronounced convergence in carbon emission efficiency within the YRD region in recent years.

Figure 4

Figure 4. Variation in carbon emission efficiency distribution dispersion and pathways of center shift.

3.2 Spatiotemporal patterns of carbon emission efficiency

3.2.1 LISA time path

A relative length less than 1 indicates relatively stable local structures, while a relative length greater than 1 signifies relatively more dynamic local structures. As show in Figure 5, overall, the local structures within the YRDUA are relatively dynamic. Between 2005 and 2023, 11 cities had relative lengths exceeding 1, accounting for 42.31% of the total. From 2005 to 2014, 13 cities (50%) had relative lengths exceeding 1, while this number decreased to 11 cities during 2014–2023. This indicates that the local spatial structure of CEE within the YRDUA is stabilizing. Anqing City exhibited the highest relative length value throughout the study period, reaching 1.8. Notably, its relative length surged to 2.58 during 2014–2023, indicating Anqing effectively enhanced carbon emission efficiency by advancing industrial restructuring. From 2005 to 2014, cities with relatively low relative lengths included Shaoxing and Suzhou, both below 0.5. From 2014 to 2023, cities with relative lengths below 0.5 included Jiaxing, Shaoxing, Shanghai, and Wuhu, with Jiaxing’s relative length value being only 0.39. Overall, from 2005 to 2023, only Shaoxing City had a relative length below 0.5 (0.44).

Figure 5

Figure 5. The geometric features of LISA’s time path. The classification thresholds in the figure captions are consistent across the following groups: (a–c), (d–f), and (g–i).

The curvature of all cities exceeded 1 during the study period, indicating a strong spatial dependency in CEE across the YRDUA. Spatial dependency in CEE within the cluster decreased during the study period. From 2005 to 2014, the average curvature of the YRDUA was 5.0, with all 18 cities exhibiting curvature below this mean. Ma’anshan City recorded the highest curvature at 14.47. From 2014 to 2023, only Changzhou City showed weaker spatial dependence, with a curvature of 0.99. The average curvature of the YRDUA decreased to 4.76. Ma’anshan City remained the city with the highest curvature, reaching 13.9. However, overall, Chuzhou City exhibited the highest curvature at 42.37, followed by Huzhou City and Xuancheng City at 19.58 and 13.52, respectively.

CEE in the YRDUA primarily exhibits positive synergistic growth characteristics. From 2005 to 2023, nine cities showed turning angles between 0° and 90°, predominantly located in the northern region, while only three cities demonstrated negative synergistic growth. From 2005 to 2014, positive synergistic growth was predominant, involving 10 cities including Changzhou and Zhenjiang, primarily in the central and southern regions. Negative synergistic growth was least common, observed in only 3 cities. However, from 2014 to 2023, the YRDUA predominantly exhibited negative synergistic growth, with 10 cities showing turning angles between 180° and 270°. Cities exhibiting positive synergistic growth shifted toward the northeast. Additionally, the number of cities showing either high growth coupled with low growth in neighboring cities or low growth coupled with high growth in neighboring cities both decreased to four compared to the 2005-2014 period. This indicates that synergistic effects enhancing carbon emission efficiency within the YRDUA have strengthened.

3.2.2 LISA time-shift matrix

Table 4 shows that throughout the study period, Type I remained the predominant transition form - the transition type along the main diagonal of the table. This type indicates that neither the city itself nor its neighboring cities underwent a leap transition, accounting for 0.65 of the total. This suggests that the spatial structure of CEE within the YRDUA remained relatively stable during the study period, with a pronounced path dependency. The probability of Type I remained at 0.54 during both the 2005–2014 and 2014–2023 phases, further underscoring the relative stability of the overall spatial structure. The overall spatiotemporal cohesion probability for the study period was 0.73. During 2005–2014, the spatiotemporal cohesion probability was 0.65, with low synergistic growth being the predominant transition type. From 2014 to 2023, the spatiotemporal clustering probability decreased to 0.58, with both positive and negative synergistic growth patterns each involving 5 cities. This indicates that the spatial distribution of CEE within the YRDUA exhibits strong spatial cohesion, albeit with a declining trend.

Table 4

Table 4. Probability matrices and spatiotemporal flows for different stages.

3.3 Factors influencing carbon emission efficiency

To prevent multicollinearity among explanatory variables, the variance inflation factor (VIF) method was employed to examine the model prior to conducting Tobit regression analysis on the data. The results are presented in Table 5, where the maximum VIF value for TI is 2.47, which is less than 3. This indicates that no multicollinearity exists among the variables.

Table 5

Table 5. The test results of the variance inflation factor of the influencing factors.

As shown in Table 6, UL exerts a significant inhibitory effect on CEE improvement at the 1% significance level, EDL, PD and DO exert a significant accelerating effect on CEE improvement at the 1% significance level, while TI exerts a positive impact on CEE at the 10% significance level. In contrast, IS and ER show a positive tendency to promote CEE improvement, though their effects are not statistically significant.

Table 6

Table 6. The regression results of the Tobit model.

At the 1% significance level, UL has a negative impact on CEE. Since the reform and opening-up, the urbanization rate of the YRDUA has increased rapidly. Rapid urbanization is often accompanied by large-scale urban infrastructure construction, real estate development, and transportation network expansion - all of which are energy-intensive and high-carbon-emitting processes. Population urbanization also often leads to a shift in consumption patterns from low-carbon to high-carbon lifestyles, thereby increasing carbon emission pressures. However, as urbanization progresses, the quality of urbanization has gradually become a key factor influencing carbon emission efficiency (Chen et al., 2023). For instance, new-type urbanization can contribute to the improvement of CEE.

At the 1% significance level, EDL exerts a significantly positive impact on CEE. An increase in economic development not only raises the public’s dual expectations for living quality and environmental quality, thereby spurring robust demand for clean energy and green living environments, but also drives the transformation of the industrial structure from energy-intensive manufacturing to high-value-added service industries and high-tech industries (Wei et al., 2025). Furthermore, with the steady improvement of residents’ income levels, governments and enterprises will be equipped with stronger resource allocation capabilities, enabling them to channel more investment into technological innovation and industrial upgrading.

At the 1% significance level, PD exerts a significantly positive impact on CEE. Population agglomeration not only markedly cuts down the costs of infrastructure construction and operation, but also facilitates the development of intensive high-end service sectors and advanced manufacturing industries (Wang N. et al., 2025). In particular, the scarcity of resources such as land forces industries to upgrade toward high-value-added and low-energy-consumption orientations. Furthermore, high population density fosters dense pools of knowledge, technology, and human capital, thereby accelerating the research and development, dissemination, and application of green low-carbon technologies and innovative models.

At the 1% significance level, DO exerts a significantly positive impact on CEE. By attracting high-quality foreign investment and engaging in international trade, DO facilitates the introduction of advanced international low-carbon technologies, management expertise, and environmental standards. This intensifies market competition to a certain extent, compelling enterprises to improve resource utilization efficiency and optimize production processes in order to sustain their market competitiveness (Jin et al., 2024). To gain favorable positions in global value chains, enterprises proactively adopt more energy-efficient and cleaner production methods, thereby reducing the carbon emission intensity per unit of output.

At the 10% significance level, TI exerts a significantly positive impact on CEE. Technological innovation can directly act on the two terminal links of production and consumption. By virtue of the R&D and application of new technologies or the iterative upgrading of manufacturing processes, it drives high-carbon industries to accelerate the optimization and upgrading of industrial structures and the green transformation of production modes, thereby curbing high-energy-consumption and high-emission production activities from the source (Zhao et al., 2023). More importantly, the successful implementation of technological innovation not only brings economic benefits and competitive advantages to market entities, but also further stimulates the enthusiasm and initiative of various market participants (including enterprises and research institutions) to conduct R&D on green technologies. This forms a virtuous cycle of innovation-emission reduction and thus facilitates the efficient achievement of regional carbon emission reduction targets.

Both IS and ER exert a positive but statistically insignificant impact on CEE. The secondary industry in YRDUA has undergone in-depth green transformation. Although the proportion of the industrial structure has changed slightly, low-energy-consumption and high-value-added industries such as intelligent manufacturing and new energy have developed rapidly. The insignificant impact of IS on CEE clearly indicates that the current carbon emission reduction tasks in YRDUA should focus on technological upgrading within industries rather than staying at the macro-level industrial structure adjustment. In terms of ER, moderate environmental regulation should effectively improve CEE, while overly strict regulatory measures may distort resource allocation and hinder the improvement of CEE. However, the significant differences in regulatory intensity and enforcement among various cities reflect that the region has not yet formed a complete, unified and efficient collaborative governance system. This not only weakens the long-term and stable incentive effect that environmental regulation should have, but also directly leads to the statistically insignificant positive impact of ER on CEE.

4 Discussion

China actively advocates for a low-carbon transition in order to improve global climate governance. It has set aggressive targets to peak carbon emissions by 2030 and reach carbon neutrality by 2060. Urban agglomerations represent critical battlegrounds for national carbon emission reduction. As China’s economic core region and hub for scientific innovation, the YRDUA’s pathways to enhancing CEE are not only pivotal to achieving its own peak emission targets but also serve as a model for emission reduction across all urban agglomerations nationwide. Tracing the roots of efficiency improvement not only advances low-carbon and green development models but also provides tailored recommendations for the precise implementation of national “Dual Carbon” policies at the urban agglomeration level.

CEE within the YRDUA remains subject to further improvement. During the study period, disparities in CEE among cities gradually narrowed, with low-efficiency cities demonstrating a clear trend of catching up (Li and Chen, 2025). This indicates that emission reduction technologies and green development models are spreading and spilling over within the region (Pang et al., 2026). Changzhou, for instance, achieved a remarkable transformation from low efficiency to high carbon emission efficiency by actively transitioning its manufacturing sector toward new energy, securing substantial policy and financial support. Increased openness to the outside world is also a key factor in achieving efficiency gains. Tongling, as a representative resource-depleted city, has attracted some high-energy-consuming industries through the transfer of eastern industries. However, it is precisely this “latecomer advantage” that allows them to directly apply more advanced green technologies and models, avoiding the old path of “pollute first, then clean up,” thereby achieving rapid efficiency improvements. Yet resource-based cities like Ma’anshan still face immense transformation pressures, making them key targets for regional carbon reduction. Moreover, cities at the efficiency frontier—such as Suzhou and Wuxi—have seen slight declines in carbon emission efficiency. This indicates that traditional marginal improvement-based reduction processes have reached a bottleneck, with systemic restructuring gradually becoming the primary focus for efficiency-leading cities. Traditional urban sprawl is not conducive to the improvement of CEE. By advancing high-quality urban development, coordinated improvement of ecological, productive, and living spaces can be achieved, thereby effectively promoting the enhancement of CEE. This partly explains why industrial structure has not significantly improved CEE, while also validating the effectiveness of the national policy of “industrial upgrading.” Moreover, the overall clustering characteristics indicate the deepening of regional integration (Chen et al., 2023). The fragmented environmental management within the YRDUA directly hampers the effective implementation of the national “regional coordinated emission reduction” policy. Establishing new paradigms for regional collaborative governance-such as a unified carbon emissions accounting system and carbon market mechanisms-can more equitably allocate and share emission reduction responsibilities, thereby preventing regional efficiency losses.

Although the YRDUA primarily exhibits positive synergistic growth in carbon emission efficiency with strengthened collaborative effects, the persistent path dependency indicates that the regional coordination model remains focused on industrial synergy rather than functional coupling (Tong et al., 2025). Achieving functional coupling within the region can foster deep integration of industrial chains and innovation, further enhance carbon emission efficiency, and better address systemic risks. Advancing such functional coupling requires breaking down industrial homogeneity and rigid division of labor within the region while establishing risk-sharing mechanisms. The Chengdu-Chongqing urban cluster exemplifies a significant attempt at functional coupling (Ren et al., 2025). Moreover, the weakening of spatial cohesion may signal shifts in traditional efficiency growth models, particularly when local spatial structures are dynamic while the overall spatial structure remains stable. Regional growth diversification and parallel innovation models represent inevitable trends in urban efficiency growth (Naeem et al., 2025). Both Changzhou and Hefei have emerged as “parallel innovation hubs” for new technological paradigms in the new energy and photovoltaic industries, effectively reducing absolute dependence on traditional core sectors. Under a stable overall coordination framework, deepening collaborative innovation among local cities not only enhances the region’s potential to achieve “Dual Carbon” goals but also fosters greater resilience and broader efficiency improvement opportunities.

5 Policy recommendations

Establish a coordinated governance system across the entire region. Building upon unified regional emission standards and harmonized environmental regulations, create a joint regional regulatory body to centrally operate a unified carbon emissions accounting and monitoring platform. Leverage technologies such as the Internet of Things and big data to enable real-time data collection and sharing across the entire region. On this foundation, strengthen cross-city enforcement mechanisms by conducting coordinated inspections through the joint regulatory body to ensure standards are effectively implemented. Simultaneously, refine the regional integrated carbon market. Through differentiated quota allocation and innovative trading mechanisms, guide resources toward sectors with the lowest emission reduction costs. Permit trading in projects such as forest carbon sinks to flexibly coordinate regional development within strict oversight, ultimately achieving a systemic integration of environmental regulation and market incentives.

Promote deep integration of industrial functions. First, develop complementary industrial plans based on each city’s unique strengths, reinforcing differentiated positioning such as Shanghai’s R&D, Suzhou’s manufacturing, Hefei’s science and technology innovation, and Changzhou’s new energy sector. This will address industrial homogeneity at its source and foster a regional development pattern characterized by complementary specializations. Second, establish cross-regional industrial innovation alliances. Focusing on key sectors like photovoltaics and new energy, encourage enterprises, universities, and research institutions to form innovation consortia. Through intellectual property sharing and risk-sharing mechanisms, achieve collaborative breakthroughs in critical low-carbon technologies.

Implement a targeted classification guidance strategy. Acknowledge the significant disparities in urban development stages within the region and tailor pathways for cities with different endowments. For efficiency frontier cities like Suzhou and Wuxi, encourage systemic restructuring and support demonstration projects in cutting-edge technologies such as comprehensive energy internet, near-zero carbon industrial parks, and carbon capture utilization, driving fundamental transformations in energy and industrial structures. For high-efficiency catch-up cities like Changzhou and Tongling, establish technology transfer and transformation centers to accelerate the diffusion and application of advanced, mature low-carbon technologies. For cities facing greater transformation pressures, such as Ma’anshan, leverage dedicated funds and technical assistance to prioritize the cultivation of circular economy initiatives and successor industries, facilitating a smooth transition.

Cultivate multiple parallel innovation hubs. Transform the one-way radiation model overly reliant on core cities, breaking the spatial monopoly of innovation resources to stimulate overall regional vitality. By systematically evaluating each city’s industrial foundation and scientific research potential, identify and cultivate potential innovation poles. Develop these into national-level technology hubs and industrial transformation centers, forming multiple robust regional innovation growth poles. On this basis, establish regional innovation collaboration and co-construction/sharing mechanisms to promote deep linkage among these poles, ultimately creating a virtuous cycle of efficient, collaborative regional innovation ecosystems.

6 Conclusion

This study utilizes panel data from the YRDUA spanning 2005 to 2023. By employing Super-EBM model, exploratory spatiotemporal data analysis, and Tobit model, it reveals the spatiotemporal evolution characteristics of CEE within the YRDUA and identifies the core factors shaping these characteristics. The innovation lies in achieving more precise measurements of efficient units on CEE frontier while effectively capturing the spatiotemporal interactive effects of CEE. The findings reveal: (1) The overall CEE of YRDUA exhibits stable fluctuations, remaining largely consistent at approximately 0.85. (2) Significant spatial disparities exist in CEE, demonstrating a gradually intensifying convergence trend. (3) The local spatial structure of CEE is relatively dynamic and exhibits strong spatial dependencies. (4) The overall spatial structure of CEE is relatively stable with pronounced path-dependent characteristics. Although it exhibits strong spatial cohesion, this cohesion shows a weakening trend. (5) EDL, PD, DO, and TI have significant positive effects on enhancing CEE. These findings of this study provide robust scientific evidence and decision-making references for optimizing regional collaborative governance mechanisms in YRD and advancing the national “Dual Carbon” goals. However, the accuracy of urban energy consumption estimates based on nighttime light indices may be affected by differences in energy consumption structures across cities and the collection precision of nighttime light data. Future research could address the limitations of nighttime light data through multi-source data fusion.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

ZY: Conceptualization, Formal Analysis, Resources, Software, Writing – original draft, Writing – review and editing. CH: Data curation, Investigation, Methodology, Supervision, Validation, Visualization, Writing – original draft.

Funding

The author(s) declared that financial support was not received for this work and/or its publication.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

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References

Chen, W., and Yao, L. (2024). The impact of digital economy on carbon total factor productivity: a spatial analysis of major urban agglomerations in China. J. Environ. Manag. 351, 119765. doi:10.1016/j.jenvman.2023.119765

PubMed Abstract | CrossRef Full Text | Google Scholar

Chen, W., Wang, G., Xu, N., Ji, M., and Zeng, J. (2023). Promoting or inhibiting? new-Type urbanization and urban carbon emissions efficiency in China. Cities 140, 104429. doi:10.1016/j.cities.2023.104429

CrossRef Full Text | Google Scholar

Dong, F., Wang, P., and Li, W. (2025). Study on the efficiency evolution of carbon emissions and factors affecting them in 143 countries worldwide. Urban Clim. 59, 102265. doi:10.1016/j.uclim.2024.102265

CrossRef Full Text | Google Scholar

Duan, Z., and Zhu, X. (2025). The carbon paradox in urban China: high-Quality development under the dual-control policy. Sustain. Cities Soc. 130, 106585. doi:10.1016/j.scs.2025.106585

CrossRef Full Text | Google Scholar

Gao, Z., Li, L., and Hao, Y. (2023). Dynamic evolution and driving forces of carbon emission efficiency in China: new evidence based on the RBM-ML model. Gondwana Res. 116, 25–39. doi:10.1016/j.gr.2022.12.013

CrossRef Full Text | Google Scholar

Gao, J., Zhao, S., Wang, L., and Wang, X. (2024). Spatiotemporal analysis of carbon emissions in the yangtze river Delta urban agglomeration: insights from nighttime light data (1992–2019). Ecol. Inf. 83, 102831. doi:10.1016/j.ecoinf.2024.102831

CrossRef Full Text | Google Scholar

Guo, Y., Tong, Z., Wang, Z., Xu, Z., and Yao, Y. (2025). Evolutionary characteristics and influencing mechanisms of green development efficiency in Chinese urban agglomerations: analysis of the yangtze river Delta urban agglomeration. J. Environ. Manag. 375, 124236. doi:10.1016/j.jenvman.2025.124236

PubMed Abstract | CrossRef Full Text | Google Scholar

Huang, H., Ma, J., Liu, H., and Hong, J. (2025). Peaking in pieces? Decoding the carbon peak dynamics of Chinese cities. Sustain. Prod. Consum. 59, 359–374. doi:10.1016/j.spc.2025.09.001

CrossRef Full Text | Google Scholar

Jiang, H., Hu, W., Hou, Y., Jiang, P., and Lu, N. (2025). Green fiscal policies, economic growth target constraints and carbon emission efficiency: evidence from DID and double machine learning. Energy 339, 138961. doi:10.1016/j.energy.2025.138961

CrossRef Full Text | Google Scholar

Jin, Y., Zhang, K., Li, D., Wang, S., and Liu, W. (2024). Analysis of the spatial–temporal evolution and driving factors of carbon emission efficiency in the yangtze river economic belt. Ecol. Indic. 165, 112092. doi:10.1016/j.ecolind.2024.112092

CrossRef Full Text | Google Scholar

Li, Q., and Chen, J. (2025). Temporal–spatial characteristics of carbon emissions and low-carbon efficiency of regional energy consumption: a case study of beijing-tianjin-hebei, yangtze river Delta and sichuan-chongqing regions. Ecol. Indic. 179, 114285. doi:10.1016/j.ecolind.2025.114285

CrossRef Full Text | Google Scholar

Li, J., Huang, X., Kwan, M.-P., Yang, H., and Chuai, X. (2018). The effect of urbanization on carbon dioxide emissions efficiency in the yangtze river Delta, China. J. Clean. Prod. 188, 38–48. doi:10.1016/j.jclepro.2018.03.198

PubMed Abstract | CrossRef Full Text | Google Scholar

Lin, Y., Xu, S., Xu, S., Zhu, W., and Xiong, L. (2025). Spatiotemporal evolution, driving factors and prediction of carbon emission in yangtze river Delta urban agglomeration: a comprehensive and improved application framework. Sustain. Energy Technol. Assessments 83, 104657. doi:10.1016/j.seta.2025.104657

CrossRef Full Text | Google Scholar

Liu, X., Jin, X., Luo, X., and Zhou, Y. (2023). Quantifying the spatiotemporal dynamics and impact factors of China’s county-level carbon emissions using ESTDA and spatial econometric models. J. Clean. Prod. 410, 137203. doi:10.1016/j.jclepro.2023.137203

CrossRef Full Text | Google Scholar

Liu, B., Wang, J., Feng, Y., Yang, M., and Mu, J. (2024). Mitigating the disparities in carbon emission efficiency enhancement in China’s coal resource-based cities. Energy 307, 132599. doi:10.1016/j.energy.2024.132599

CrossRef Full Text | Google Scholar

Lv, Y., Yan, S., Lai, X., and Luo, S. (2025). Unveiling the water-energy-food nexus efficiency and influencing factors in China: an integrated EBM and tobit model analysis. Ecol. Indic. 173, 113357. doi:10.1016/j.ecolind.2025.113357

CrossRef Full Text | Google Scholar

Ma, D., Hu, C., Yan, Y., Zhang, F., Zhang, J., Deng, P., et al. (2025). A comprehensive analysis of the spatiotemporal dynamics and determinants of carbon emission efficiency in China’s manufacturing sector. J. Environ. Manag. 381, 125269. doi:10.1016/j.jenvman.2025.125269

PubMed Abstract | CrossRef Full Text | Google Scholar

Ma, D., Deng, P., Yan, Y., Zhang, J., Guo, Z., Hu, C., et al. (2026). How does carbon emission efficiency vary in Chinese cities? taking 108 cities along the yangtze river economic belt as an example. Renew. Energy 256, 124208. doi:10.1016/j.renene.2025.124208

CrossRef Full Text | Google Scholar

Ma, S., Hua, X., and Liu, J. (2025). Decarbonizing Mongolia: an integrated structural path and decomposition analysis of carbon emissions (2015–2021). Resour. Conservation Recycl. 222, 108422. doi:10.1016/j.resconrec.2025.108422

CrossRef Full Text | Google Scholar

Mao, Y., Li, X., Jiao, D., and Zhao, X. (2024). Characterizing the spatial correlation network structure and impact mechanism of carbon emission efficiency: evidence from China’s transportation sector. Energy 313, 133886. doi:10.1016/j.energy.2024.133886

CrossRef Full Text | Google Scholar

Naeem, M. A., Hoque, M. E., Billah, M., and Shaik, M. (2025). Quantifying the hedge and diversification potential of green markets against climate risk. Energy Strategy Rev. 62, 101929. doi:10.1016/j.esr.2025.101929

CrossRef Full Text | Google Scholar

Pang, Q., Zhai, H., Zhao, T., Zhang, L., and Chiu, Y. (2026). How industrial agglomeration and technological innovation affect carbon emission efficiency: evidence from China. Geoenergy Sci. Eng. 257, 214256. doi:10.1016/j.geoen.2025.214256

CrossRef Full Text | Google Scholar

Ren, Y., Shao, Y., Xia, J., Liu, A., and Lei, S. (2025). Synergistic optimization between green and low-carbon energy transition and reduction of pollution and carbon emissions in the chengdu-chongqing economic circle: based on spatiotemporal evolution and spatial heterogeneity analysis. Energy 339, 139005. doi:10.1016/j.energy.2025.139005

CrossRef Full Text | Google Scholar

Shi, P., and Huang, Q. (2024). Assessing the evolution and convergence of energy-related carbon emission efficiency in the yangtze River Economic Belt. Process Saf. Environ. Prot. 191, 1684–1695. doi:10.1016/j.psep.2024.09.058

CrossRef Full Text | Google Scholar

Tan, J., Liang, Y., Yu, S., He, J., and Liu, J. (2025). Assessment of carbon emission efficiency in the Pearl River-xijiang economic belt over the past 15 years and exploration of improvement opportunities. Environ. Sustain. Indic. 28, 100883. doi:10.1016/j.indic.2025.100883

CrossRef Full Text | Google Scholar

Tian, Y., and Pang, J. (2025). Revealing the coupling coordination mechanisms between carbon efficiency and carbon equity: the evidence in china’s interprovincial trade. Environ. Sci. Technol. 59, 13705–13718. doi:10.1021/acs.est.4c13889

PubMed Abstract | CrossRef Full Text | Google Scholar

Tobin, J. (1958). Estimation of relationships for limited dependent variables. Econometrica 26, 24–36. doi:10.2307/1907382

CrossRef Full Text | Google Scholar

Tone, K., and Tsutsui, M. (2010). An epsilon-based measure of efficiency in DEA – a third pole of technical efficiency. Eur. J. Operational Res. 207, 1554–1563. doi:10.1016/j.ejor.2010.07.014

CrossRef Full Text | Google Scholar

Tong, N., Frazier, A. E., Tong, L., and Hu, S. (2025). Synergistic pathways between urbanization and low-carbon development: evidence from the yangtze river economic belt. J. Environ. Manag. 392, 126740. doi:10.1016/j.jenvman.2025.126740

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, Z., and Shao, H. (2024). Spatiotemporal interactions and influencing factors for carbon emission efficiency of cities in the yangtze river economic belt, China. Sustain. Cities Soc. 103, 105248. doi:10.1016/j.scs.2024.105248

CrossRef Full Text | Google Scholar

Wang, Y., Wang, R., Tanaka, K., Ciais, P., Penuelas, J., Balkanski, Y., et al. (2023). Accelerating the energy transition towards photovoltaic and wind in China. Nature 619, 761–767. doi:10.1038/s41586-023-06180-8

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, C., Zhang, Y., Chen, J., Li, D., Zhu, M., and Gan, Z. (2024). The impact of urban polycentricity on carbon emissions: a case study of the yangtze river Delta region in China. J. Clean. Prod. 442, 141127. doi:10.1016/j.jclepro.2024.141127

CrossRef Full Text | Google Scholar

Wang, C., Chen, J., Wang, Q., Li, D., Zhu, M., Gan, Z., et al. (2025a). Investigation of the influence and mechanism of urban population agglomeration on carbon emission efficiency. J. Urban Manag. 14, 1269–1290. doi:10.1016/j.jum.2025.05.006

CrossRef Full Text | Google Scholar

Wang, H., Feng, T., Kong, J., Cui, M., and Zha, D. (2025b). China’s provincial net-zero emissions effort: high-quality choices behind up-bottom quota schemes. Resour. Conservation Recycl. 215, 108118. doi:10.1016/j.resconrec.2024.108118

CrossRef Full Text | Google Scholar

Wang, L., Zhang, M., Chen, S., and Wang, W. (2025c). Spatial connection network of the co-benefits of reducing pollutants and carbon emissions in the Yangtze river Delta urban cluster. Urban Clim. 61, 102457. doi:10.1016/j.uclim.2025.102457

CrossRef Full Text | Google Scholar

Wang, N., Qu, Z., Li, J., Zhang, Y., Wang, H., Xi, H., et al. (2025d). Spatial-temporal patterns and influencing factors of carbon emissions in different regions of China. Environ. Res. 276, 121447. doi:10.1016/j.envres.2025.121447

PubMed Abstract | CrossRef Full Text | Google Scholar

Wei, Q., Xue, L., Zhang, H., Chen, P., Yang, J., and Niu, B. (2025). Spatiotemporal analysis of carbon emission efficiency across economic development stages and synergistic emission reduction in the beijing-tianjin-hebei region. J. Environ. Manag. 377, 124609. doi:10.1016/j.jenvman.2025.124609

PubMed Abstract | CrossRef Full Text | Google Scholar

Xu, Y., Xu, F., Chi, G., and Gong, Z. (2025). Carbon emissions and government interventions in urban agglomerations of China: an integrated GWR and neural network approach. Appl. Geogr. 179, 103645. doi:10.1016/j.apgeog.2025.103645

CrossRef Full Text | Google Scholar

Yao, L., and Chen, W. (2025). Temporal and spatial evolution of low-carbon transportation efficiency and its influencing factors in China. Energy 315, 134357. doi:10.1016/j.energy.2024.134357

CrossRef Full Text | Google Scholar

Yu, K., and Li, Z. (2025). Multi-scenario analysis of green water resource efficiency under carbon emission constraints in the chengdu-chongqing urban agglomeration, China: a system dynamics approach. Ecol. Indic. 171, 113139. doi:10.1016/j.ecolind.2025.113139

CrossRef Full Text | Google Scholar

Zhang, S., Yu, R., Wen, Z., Xu, J., Liu, P., Zhou, Y., et al. (2023). Impact of labor and energy allocation imbalance on carbon emission efficiency in China’s industrial sectors. Renew. Sustain. Energy Rev. 184, 113586. doi:10.1016/j.rser.2023.113586

CrossRef Full Text | Google Scholar

Zhang, L., Wang, H., Guo, B., Liu, X., Deng, C., Zhao, Z., et al. (2025). Characteristics and formation mechanism of carbon emission efficiency spatial correlation network: perspective from Shandong Province. Ecol. Indic. 170, 112996. doi:10.1016/j.ecolind.2024.112996

CrossRef Full Text | Google Scholar

Zhao, X., Long, L., Yin, S., and Zhou, Y. (2023). How technological innovation influences carbon emission efficiency for sustainable development? Evidence from China. Resour. Environ. Sustain. 14, 100135. doi:10.1016/j.resenv.2023.100135

CrossRef Full Text | Google Scholar

Zheng, H., Wu, Y., He, H., Delang, C. O., Lu, J., Yao, Z., et al. (2024). Urbanization and urban energy eco-efficiency: a meta-frontier super EBM analysis based on 271 cities of China. Sustain. Cities Soc. 101, 105089. doi:10.1016/j.scs.2023.105089

CrossRef Full Text | Google Scholar

Zhu, C., Su, Y., Fan, R., Qin, M., and Fu, H. (2024). Exploring provincial carbon-pollutant emission efficiency in China: an integrated approach with social network analysis and spatial econometrics. Ecol. Indic. 159, 111662. doi:10.1016/j.ecolind.2024.111662

CrossRef Full Text | Google Scholar