Data Sources: The temporal scope of this study encompasses the period from 2011 to 2025, with a particular emphasis on the nine prefecture-level cities within Guizhou Province. The raw data for various ecological civilization indicators are primarily sourced from the 2011-2025 Statistical Yearbooks published by the statistical bureaus of the nine prefecture-level cities in Guizhou Province, the statistical bulletins on national economic and social development issued by each prefecture-level city, and the Guizhou Province Macroeconomic Database. In this study, a set of indicators was developed to assess the environmental performance of Guizhou Province and its prefecture-level cities. The indicators included environmental monitoring indicators, such as the annual average concentration of PM2.5, which were derived from the ecological and environmental status bulletins released by the relevant bureaus. Public interest is measured using “ecological civilization” as the sole search keyword. On the Baidu Index platform, the geographic filter is set to specific prefecture-level cities based on user location, and separate indices for desktop and mobile devices are extracted for each city. The annual index is calculated as the arithmetic mean of the daily indices from January 1 to December 31 of that year. The city’s public interest for that year is then measured as the arithmetic mean of the annual desktop and mobile indices; For dates where search volume fell below the platform’s statistical threshold and was displayed as 0, these values were included in the annual average as 0, without being excluded or smoothed. Government attention was measured by collecting the full texts of the annual government work reports for the nine prefecture-level cities from 2011 to 2025 and counting the total number of times the term “ecological civilization” appeared in the full reports using it as a search term. Given that the annual government work reports of each prefecture-level city are full-text reports delivered by the mayor to the People’s Congress at the same level, their length and structural scope are essentially consistent, making word frequencies comparable across cities and years. This study comprises a total of 4,995 data points, of which 298 original observations were missing, accounting for 5.97% of the total. Linear interpolation and exponential smoothing methods were used to reasonably estimate and complete the missing data (see Appendix 1).

The descriptive statistics for the primary variables in this study are presented in Table 2. The sample consists of panel data from nine prefecture-level cities in Guizhou Province, covering the period from 2011 to 2025, with a total of 135 observations. The first-level indicators encompass four dimensions: ecological economy (A1), ecological environment (A2), ecological livability (A3), and ecological culture (A4), which collectively comprise 37 secondary indicators. These secondary indicators can be categorized into three types: benefit-based, cost-based, and fixed indicators.

Table 2. Descriptive statistics of key variables.

In terms of the ecological economy, the average per capita GDP (A1-1) was 39,394.04 yuan, with a maximum of 90,995.00 yuan and a minimum of only 4,608.00 yuan. The standard deviation was as high as 18,511.60, and the data exhibited a clear right-skewed distribution, indicating that the economic output of more than half of the prefectures and cities was below the provincial average. This finding reflects imbalances in economic development across regions. The maximum value of value-added in the tertiary sector (A1-2) was approximately 31 times the minimum value, indicating a vast disparity in the scale of the service economy across prefectures. The average share of value-added in the tertiary sector in GDP (A1-3) was 50.02%, with a difference of 34.67 percentage points between the maximum and minimum values, reflecting uneven progress in industrial structure transformation and upgrading. The average rate of decline in energy consumption per unit of GDP (A1-4) was 5.43%, with the maximum at 20.39%. The minimum is set at 0.28%. Fiscal expenditure on energy conservation and environmental protection (A1-6) exhibited a 11.5-fold variation between its maximum and minimum, reflecting substantial disparities among prefectures and cities in terms of investment in green development.

In terms of the ecological environment, the average forest coverage rate (A2-5), which reflects the ecological baseline, and the average rate of good air quality (A2-6) were 56.65% and 97.20%, respectively, indicating a generally high and relatively stable level. The coefficients of variation for fixed indicators such as average temperature, annual precipitation, and average relative humidity were all less than 0.20, suggesting limited differences in climatic background conditions across regions. However, the standard deviations for total industrial wastewater discharge (A2-9) and total sulfur dioxide emissions (A2-10) were as high as 2,718.34 and 5.21, respectively. The maximum value of the former was more than 160 times the minimum value, indicating that some industrial cities still face significant pressure to reduce emissions. The mean annual average concentration of PM2.5 (A2-7) was 27.67 micrograms per cubic meter, with a difference of 38 micrograms per cubic meter between the maximum and minimum values. This indicates that there is still room for improvement in air quality in some local areas.

In terms of ecological livability, the average urban domestic sewage treatment rate (A3-8) and urban domestic waste harmless treatment rate (A3-9) were 95.21% and 95.71%, respectively. These figures indicate that Guizhou’s prefecture-level cities have achieved significant results in urban environmental governance in recent years and are generally at a high level. However, the total natural gas supply (A3-7) ranged from a maximum of 112,263.48 ten thousand cubic meters to a minimum of 0, with a standard deviation as high as 20,448.05. The distribution of data for this indicator exhibits a severe right-skew, indicative of the persistent under-supply of natural gas infrastructure, with the supply remaining at a low level in more than half of the regions. The coefficient of variation for the floor area of completed buildings (A3-1) was recorded at 1.38, and the standard deviations for municipal indicators such as comprehensive tap water production capacity and the length of drainage pipelines approached their respective means, suggesting significant disparities in the allocation of public services across regions.

In the terms of ecological culture, the mean values for public attention to ecological civilization construction (A4-1) and government attention (A4-2) were 16.81 and 4.32, respectively. Notably, the standard deviations for both were greater than the respective means. The data demonstrate a substantial right-skewed distribution, indicating that the overall social attention to ecological civilization in Guizhou remains minimal. Furthermore, the level of attention allocated to ecological civilization construction by the public and governments in select prefectures and cities is deemed to be inadequate and in urgent need of enhancement. The maximum number of graduates from conventional institutions of higher education (A4-5) was approximately 138 times the minimum, while the maximum total collection size of public libraries (A4-10) was 380 times the minimum. This underscores the pronounced regional concentration of educational and cultural resources. The mean population coverage rates for radio and television (A4-8, A4-9) were 95.02% and 97.11%, respectively, with coefficients of variation below 0.06. This indicates that fundamental cultural dissemination facilities have essentially achieved full coverage across the region.

Step 1: The original evaluation matrix constructed in this step is of order 135 × 37. This study comprises 135 evaluation subjects and 37 evaluation indicators.

Step 2: Calculation of indicator weights. In accordance with the model, entropy values, coefficients of variation and indicator weights were calculated using Equations (1) to (4); the results are shown in Table 3.

Table 3. Results of indicator weight calculations.

Step 3: Normalisation of indicators. Efficiency-oriented and cost-type indicators are normalised by range according to Equations (5) and (6) respectively, whilst fixed-type indicators are processed according to Equation (7). Since fixed-type metrics generally lack universally accepted optimal values, average values must be used as an approximation of the optimal values: mean temperature 15.90˚C, annual precipitation 1206.86 mm, mean relative humidity 79.40 per cent, and hours of sunshine 1258.76 hours.

Step 4: Normalise the matrix. Multiply the normalised matrix by the indicator weights according to Equation (8) to obtain the weighted normalised decision matrix.

Step 5: Determine the positive and negative ideal solutions. Determine the positive and negative ideal solutions using Equations (9) and (10). Since the minimum values of each indicator are 0 after range normalisation, the negative ideal solution is the zero vector; the positive ideal solution is the weight vector for each indicator, which is not shown separately here.

Step 6: Calculate the Euclidean distances D⁺and D⁻ between each observation and the positive and negative ideal solutions, respectively, using Equations (11) to (12); see Table 4 and Table 5. Then, calculate the proximity using Equation (13); the results are shown in Table 6. In this paper, the normalization of indicators, the calculation of entropy weights, and the determination of positive and negative ideal solutions are all performed uniformly based on the combined sample of all 135 “city-year” pairs. Consequently, the proximity index is a relative measure within the sample, reflecting the relative position of the evaluated entities within the sample rather than a measure of the absolute level of ecological civilization construction or the distance from an absolute ideal state.

Table 4. Distance D⁺ between each city and the positive ideal solution, 2011-2025.

Table 5. Distance D⁻ between each city and the positive ideal solution, 2011-2025.

Table 6. Proximity to the level of ecological civilization construction and rankings of prefecture-level cities in Guizhou, 2011-2025.

4.4.1. Analysis of Indicator Weights

As demonstrated in Table 3, the entropy-weighted distribution of the 37 secondary indicators manifests distinct imbalances. The weights for the six indicators—total natural gas supply (A3-7), public interest in ecological civilization construction (A4-1), number of graduates from regular institutions of higher education (A4-5), completed floor area of residential buildings (A3-1), total book holdings in public libraries (A4-10), and government attention to ecological civilization construction (A4-2)—account for a combined weight of 0.5379. The six indicators in question are responsible for the transmission of more than half of the information present within the indicator system. They are the primary drivers of the variation in the level of ecological civilization construction among Guizhou’s prefecture-level cities. These indicators are the group with the highest degree of dispersion in the descriptive statistics, indicating that the existing disparities among prefecture-level cities in terms of municipal infrastructure, public attention, and science, education, and cultural resources are the key factors driving the variation in the level of ecological civilization construction. From a dimensional summary perspective, the combined weights for ecological culture, ecological livability, ecological environment, and ecological economy are 0.3967, 0.3821, 0.1316, and 0.0896, respectively. The ecological culture and ecological livability dimensions together account for nearly 80 percent. Within the ecological environment dimension, the weights for total industrial wastewater discharge (A2-9) and total sulfur dioxide emissions (A2-10) are significantly higher than those of other environmental indicators. Pollution reduction is the core factor distinguishing the development levels of various cities and prefectures within this dimension. It is important to acknowledge that the entropy-weighted method is an objective weighting approach. The magnitude of the weights reflects the information content and discriminative power of the indicators within the sample, rather than their importance in a policy context. The weights for indicators such as average temperature (A2-1), average relative humidity (A2-3), the rate of good air quality (A2-6), radio (A4-8), and television population coverage (A4-9) are all less than 0.001 because the variations within the sample are extremely small, not because the indicators themselves are insignificant.

4.4.2. Analysis of Changes in Distance from the Ideal Solution

As demonstrated in Table 5and Table 6, the alterations in the distance between each prefecture-level city and the positive and negative ideal solutions during the study period manifest two distinctive trajectories. One type of trajectory that can be identified is a trajectory of continuous improvement. For Guiyang City, the distance D⁺ from the positive ideal solution decreased from 0.1816 in 2011 to 0.1349 in 2025, showing a 25.72% decline. Concurrently, the distance D⁻ from the negative ideal solution increased from 0.0996 to 0.1612, indicating an increase of 61.85%. The relative distance to the exact solution continues to decrease. Zunyi City demonstrated an even more significant improvement. The D⁺ index exhibited a decline from 0.2303 to 0.1641, while the D⁻ index demonstrated an increase from 0.0530 to 0.1556, marking a 193.58% rise. This development signifies that the region in question possesses the most pronounced propensity to make up for lost ground among the nine prefecture-level cities. Another category exhibits a stagnant improvement trajectory: Liupanshui City’s D⁺ registered a slight decline, from 0.2419 to 0.2232. Conversely, D⁻ exhibited an increase from 0.0391 to 0.0668, though its absolute level remains the lowest in the province. Anshun City’s D⁺ registered a 5.54% decrease, while Qianxinan Prefecture’s D⁺ remained virtually unchanged during the observation period. However, its D⁻ dropped from 0.0610 to 0.0584, indicating a decline rather than an increase. This suggests that the relative gap between these cities and prefectures and the ideal state has not effectively narrowed, and their development momentum remains insufficient.

4.4.3. Comprehensive Evaluation of Proximity

As demonstrated in Table 7, from a province-wide perspective, the average proximity index of the nine prefecture-level cities increased from 0.2094 in 2011 to 0.3209 in 2025, indicating a cumulative increase of 53.25% and an average annual growth rate of 3.09%. Following a surge to 0.3345 in 2022, the figure underwent a modest decline. A general upward trend in the level of ecological civilization construction in Guizhou has been observed. However, the mean values are only at a moderate level within the sample, and the relative distance from the ideal solution remains significant. At the prefecture-level city level, the average proximity scores exhibited a discernible tiered differentiation during the study period. Guiyang City maintained its leading position with an average of 0.5630, being the sole region with an average exceeding 0.50. The proximity score of Zunyi City peaked at 0.6733 in 2021 before experiencing a slight decline, this may be due to a slight decline in some of Guiyang’s own indicators, while other prefecture-level cities have been steadily improving, gradually narrowing the gap between Guiyang and other cities. From 2021 to 2025, public attention toward ecological civilization construction in Guiyang City fell from 102 instances to 17, government attention dropped from 10 instances to 1, the floor area of completed residential buildings decreased from 20,075,400 square meters to 11,586,300 square meters, and the total number of books in public libraries declined from 7,599,200 to 4,260,700. The contribution rates of these four indicators to the widening gap between the city’s performance and the ideal solution were 72.4%, 26.4%, 19.3%, and 10.5%, respectively; The average proximity score of Zunyi City was 0.3388, marking an upward trend from seventh place in 2011 to second place in 2025. Its annual average growth rate in proximity reached 7.06%, the highest in the province, and together with Guiyang City, it forms the Central Guizhou growth pole. Qiannan Prefecture, Southeast Guizhou Prefecture, Tongren City, Anshun City, Bijie City, and Southwest Guizhou Prefecture all had average values ranging from 0.20 to 0.27, forming the middle tier. Among them, Bijie City ascended from eighth to fifth place, manifesting clear characteristics of a latecomer rapidly gaining ground. Conversely, Qianxinan Prefecture experienced a decline, dropping from fifth to ninth place. Its 2025 proximity index was lower than that of 2011, suggesting a regression rather than a progression. Liupanshui City exhibited an average of 0.1915, placing it at the bottom of the rankings. However, its proximity index exhibited a steady upward trend, increasing from 0.1391 to 0.2303, with no interruption in this positive progression. In summary, during the review period, the level of ecological civilization construction in Guizhou’s prefecture-level cities exhibited an overall enhancement, accompanied by a discernible differentiation among tiers. The provincial capital demonstrated a prominent leading role; however, the alignment index for most prefecture-level cities and prefectures remained below 0.30, and the relative distance from the ideal solution within the sample is still quite large, Moreover, cities and prefectures at the lower end of the spectrum exhibit inadequate momentum for enhancement. In the future, targeted measures should be implemented for the middle tier and lagging regions, with a focus on addressing shortcomings in municipal infrastructure and ecological culture.

The proximity scores and their rankings depict the static distribution of the evaluation results, but they do not adequately reveal the dynamic evolution and spatial differentiation patterns of ecological civilization development. Drawing on the spatiotemporal analysis framework developed by Li, X. H. et al. (2024) [34], this section conducts a further analysis of the evaluation results from the dual perspectives of temporal evolution and spatial differentiation.

4.5.1. Temporal Analysis

Figure 1 shows the results of the measurement of the degree of alignment with ecological civilization construction in Guizhou’s nine prefecture-level cities. From a province-wide perspective, the average degree of alignment across all prefecture-level cities generally followed a fluctuating upward trend, rising from 0.2094 in 2011 to 0.3209 in 2025, with an average annual growth rate of 3.09%. In terms of the evolution over time, the average proximity index rose steadily from 2011 to 2022, reaching a peak of 0.3345 in 2022 during the study period, before declining slightly to 0.3209 in 2025. This indicates that the construction of ecological civilization in Guizhou has progressed steadily overall and has entered a phase of consolidation at a high level in recent years. At the beginning of the study period, the proximity indices of all cities were generally low, with only Guiyang City reaching 0.3542—significantly higher than the other prefecture-level cities; the proximity indices of Bijie, Liupanshui, and Zunyi were below 0.2000, revealing marked disparities in the foundational conditions for development across the region. By the middle of the study period, Guiyang City’s proximity index had risen to 0.5966, further widening its lead. As the provincial capital, Guiyang possesses strong economic agglomeration and the ability to attract production factors. Investments in ecological governance, infrastructure improvements, and the provision of public services have advanced in tandem, providing robust support for its ecological civilization development. During the same period, Zunyi’s proximity index rose to 0.3312, while Liupanshui’s stood at only 0.1815, ranking last among the nine cities.By the conclusion of the study period, the proximity indices of each city had undergone changes to varying degrees compared to the beginning of the period. As a relative measure, Guiyang’s proximity index reached its apex at 0.6733 in 2021, subsequently exhibiting a modest decline to 0.5444 in 2025. This does not represent an absolute decline in development levels, but rather reflects accelerated growth in areas such as Zunyi City and Qiannan Prefecture, leading to a gradual narrowing of regional disparities. During the study period, Zunyi City’s annual average growth rate in convergence reached 7.06%, ranking first in the province; Bijie City and Liupanshui City recorded annual average growth rates of 4.65% and 3.67%, respectively; Qianxinan Prefecture’s annual average growth rate declined by 0.24%, with its convergence index falling slightly from 0.2085 in 2011 to 0.2015 in 2025, indicating relatively lagging progress. In summary, during the study period, the level of ecological civilization construction in Guizhou’s prefecture-level cities exhibited an overall enhancement. However, the proximity index for the majority of cities and prefectures remained below 0.30, indicating that the level of ecological civilization construction still requires further enhancement.

Figure 1.Proximity index for ecological civilization development in Guizhou’s Prefecture-level Cities.

4.5.2. Spatial Analysis

The proximity index is a continuous evaluation value ranging from 0 to 1. To characterize the relative levels of ecological civilization development across cities, drawing on the approach used by Peng, K. J. et al. (2025) and Li, W. et al. (2026)—who employed heatmaps to visualize spatiotemporal changes in regional evaluation levels—the proximity index is categorized into the four levels shown in Table 7 [35][36]. However, a universally accepted classification standard for the proximity index is currently lacking. The values for the 135 “city-year” observations range from 0.1101 to 0.6733. The application of the breakpoints of 0.3, 0.5, and 0.7, which are commonly used in the literature on coupling coordination, would result in no observations falling into the highest category. Consequently, the classification would become meaningless. This paper determines the cutoff points based on the actual distribution of the sample. The sample is comprised of 20, 85, 18, and 12 observations, respectively, in each level. The classification results are used solely to characterize structural differences in relative levels and do not alter the conclusions regarding the ranking of proximity.

Table 7. Classification criteria for the level of ecological civilization development in prefecture-level cities in Guizhou.

Figure 2 illustrates the spatial distribution of ecological civilization development levels in Guizhou’s prefecture-level cities at the beginning and end of the study period. The division of the three major regions—Eastern, Central, and Western—in this paper is based on geographic location and follows the commonly used classification method for Guizhou Province’s regional economic development pattern. The Western region includes Qianxinan Prefecture, Liupanshui City, and Bijie City. The Central region includes Anshun City, Guiyang City, and Zunyi City. The Eastern region includes Qiannan Prefecture, Qiandongnan Prefecture, and Tongren City. As demonstrated in Figure 2, the degree of ecological civilization construction in Guizhou during the specified study period manifested a distribution pattern that can be characterized as “the central region leading, with the eastern and western regions following”. In terms of typological distribution, in 2011, prefectures and cities with higher proximity scores were scattered around the central region. Guiyang City stood out among the nine cities with a proximity score of 0.3542, while Bijie City and Liupanshui City in the western region both had scores below 0.1500. By 2025, the level of ecological civilization construction had improved across all prefectures and cities. However, the gradient pattern of “medium-to-high in the center, average in the east, and low in the west” remained pronounced. The range between Guiyang City, which exhibited the highest proximity index, and Qianxinan Prefecture, which exhibited the lowest, widened from 0.2152 to 0.3429. An analysis of the growth rates across the three primary regions reveals that the average increase in the Central Region’s alignment index was 73.56 percent, which is 1.98 times that of the Eastern Region. Meanwhile, the Western Region experienced a 43.50 percent increase, indicating a widening gap between the Central Region and both the Eastern and Western Regions. This phenomenon can be attributed to the presence of two major growth poles—Guiyang and Zunyi—in the central region. These cities possess a robust economic foundation and significant advantages in factor aggregation. Furthermore, investments in ecological governance and the development of green industries have progressed concurrently. Conversely, the western and eastern regions exhibit comparatively weaker economic foundations and constrained capacity for factor aggregation, leading to a comparatively languid pace of development.

Figure 2. Heatmap of the grade advancement in ecological civilization construction levels across prefecture-level cities in Guizhou.

To more objectively characterize the evolutionary patterns of regional coordination in Guizhou’s ecological civilization development and to analyze the underlying mechanisms behind regional disparities, this paper further introduces the Dagum Gini coefficient and its decomposition method to conduct a quantitative analysis of regional disparities [30][34], as shown in Table 8. The province’s overall Gini coefficient has shown a fluctuating upward trend, rising from 0.1352 in 2011 to 0.1758 in 2025, representing a total increase of 30.01%. Regional disparities in Guizhou’s ecological civilization development are widening, and regional coordination needs to be strengthened; During this period, the overall Gini coefficient peaked at 0.2005 in 2023 before declining slightly, and regional disparities have shown signs of convergence in recent years. Analysis of the sources of variation reveals that regional imbalances in Guizhou’s ecological civilization development primarily stem from disparities among the three major regions, which account for an average annual contribution of 74.44% to the overall disparity. Intra-regional disparities and hyper-variability density contribute 20.44% and 5.12% on average, respectively. The key to addressing the uneven development of Guizhou’s ecological civilization lies in the reasonable and effective implementation of policies to promote coordinated improvement across regions.

At the regional level, it can be observed that the disparities in the level of ecological civilization development among regions primarily stem from the differences between the Central Region and the other two regions. The average proximity differences between the Central Region and the Western Region, and between the Central Region and the Eastern Region, were 0.2948 and 0.2135, respectively—both higher than the average inter-regional difference of 0.2066. From a developmental perspective, during the study period, the Gini coefficients for the Central Region relative to the Western Region and the Eastern Region both showed a fluctuating upward trend, rising from 0.2157 and 0.1302 in 2011 to 0.2964 and 0.2187 in 2025, representing increases of 37.40% and 68.06%, respectively; the Gini coefficient between the western and eastern regions, however, showed a fluctuating downward trend, falling from 0.1332 to 0.1132, a decrease of 14.98%. The level of ecological civilization development in Guizhou exhibits a distribution pattern characterized by “the central region leading, with the eastern and western regions following”, and regional disparities have widened. The central region holds an absolute lead, and the pattern of “a strong central region and weak eastern and western regions” has become somewhat entrenched. Narrowing the gap between the central region and the other two major regions is key to addressing the imbalance in the development of ecological civilization in Guizhou.

Table 8. Gini coefficients for the proximity of ecological civilization development in Guizhou’s Prefecture-Level Cities.

Note: The nine prefecture-level cities are divided into three major regions based on geographical location: the West (Southwest Guizhou Prefecture, Liupanshui City, Bijie City), the Centre (Anshun City, Guiyang City, Zunyi City) and the East (South Guizhou Prefecture, Southeast Guizhou Prefecture, Tongren City); G_w, G_nb and G_t represent intra-regional disparities, inter-regional net value disparities and hyper-variation density, respectively.