Theoretical Connotation and Measurement Indicator System of Ecological Green Development Level in China

1. Introduction

Environmental challenges are escalating globally, posing significant threats to human well-being and planetary health. The interdependence between human activities and natural ecosystems has become increasingly evident, highlighting the urgent need for sustainable development practices. In this context, ecological development, which emphasizes the harmonious coexistence of humans and nature, has gained prominence as a critical pathway towards a more sustainable future.

China, given its rapid economic growth and vast ecological diversity, plays a pivotal role in global sustainability efforts. The country has recognized the importance of balancing economic development with ecological conservation and has implemented various policies and initiatives to promote ecological civilization. This shift towards green development not only aligns with international sustainability goals but also reflects China's commitment to addressing pressing environmental issues domestically.

Research has extensively explored how the value of ecological products is realized across diverse ecosystems, considering the roles of governments, businesses, and households [1,2]. Nevertheless, these studies offer an incomplete assessment of the comprehensive development status of regional ecosystems. In contrast to existing literature that emphasizes the ability to realize biophysical values of ecological product through proxy variables like ecological capital investment, ecological transfer payments in the economic realm, or water yield and ecosystem area in the ecological sphere [3,4,5], there are inherent limitations. The key challenge lies in integrating various regional ecosystems, quantifying their functional capacities, and accurately converting these capacities into monetary values. Addressing this challenge is essential for accurately measuring the overall development level of regional ecosystems, which is vital for unlocking the value of ecological products and achieving harmonious coexistence between humans and nature in the modern era.

Numerous research institutions and scholars, both domestically and internationally, have conducted extensive and in-depth studies on regional GEP. At the international level, the United Nations Statistics Division first introduced a concept called Ecological Domestic Product (EDP) in the System of Environmental-Economic Accounting (SEEA) published in 1993. Building on this, Costanza et al. (1997) expanded a classification standards for ecosystem value accounting [6]. Leveraging this framework, the United Kingdom undertook a comprehensive ecosystem assessment encompassing England, Scotland, Northern Ireland, and Wales in 2011. G. Stoneham et al. (2012) subsequently provided a systematic overview of land and ecosystem accounting practices within the SEEA framework [7]. In addition, Joshi, A. P et al. (2025) also measured GEP of Uttarakhand, India [8]. Domestically, following China's initial release of a green GDP accounting report in 2006, Peng Tao and Wu Wenliang (2010) conducted an in-depth analysis of the challenges and obstacles in national-level green GDP accounting [9]. Wang Nujie et al. (2010) estimated the service value of various ecosystems using Costanza's classification standards and ecosystem areas [10]. Using this methodology, Ma Guoxia et al. (2017) and Wang Jinnan et al. (2018) calculated the GEP of China's provincial-level terrestrial ecosystems for 2015 [11,12]. However, these studies did not delve deeply into the relevance and coordination aspects.

In 2013, Ouyang Zhiyun et al. introduced the novel concept of "Gross Ecosystem Product" (GEP), which holistically evaluates the biophysical and economic worth of goods and final services supplied by ecosystems across three dimensions: Material Product Supply, Regulation Services, and Cultural Services. Following Ouyang Zhiyun's approach, a multitude of studies emerged, each concentrating on a single-year accounting of specific nature reserves, national parks, cities, or county administrative regions [13,14,15,16,17,18,19,]. These studies offered fresh insights and methodologies for assessing ecosystem value. Nevertheless, owing to data constraints, comprehensive research at the national level remains scarce.

This paper develops a performance assessment framework for terrestrial ecosystems, focusing on ecological advantages, utilizing the Technical Guidelines for Accounting of Terrestrial Ecosystem Gross Product (GEP) published by the Ministry of Ecology and Environment. We compute the GEP for all provinces, municipalities, and counties in China spanning from 2005 to 2020, employing a range of ecological metrics. Furthermore, we determine the intra- and inter-provincial Gini coefficients for all provinces and cities with the Dagum Gini coefficient. We gauge the country's potential for sustainable ecological development through two lenses: GEP and ecological-green harmony.

The primary contributions of this paper as follows. Firstly, in terms of research methodology, this paper encompasses nearly all ecological functions within China's terrestrial ecosystems, utilizes county-level data from 2005 to 2020, and assesses the ecological coordination among diverse regions, thereby establishing a practical groundwork for examining ecological green development across various provinces, municipalities, and counties in China. Secondly, from a research perspective, this paper examines two dimensions: GEP and regional green harmonious development. It comprehensively evaluates regional GEP through three lenses-ecological resource endowment, capacity to actualize the biophysical values of ecological products, and ecological governance proficiency—thereby enriching the measurement research framework. Thirdly, concerning the organic integration and sustainable advancement of ecosystems, this study employs kernel density estimation as well as spatial Moran's I to investigate temporal and spatial dynamics, internal coordination mechanisms, and pathways for sustainable development in realizing the value of ecological products across various cities. Lastly, it identifies the challenges encountered by prefecture-level cities in fostering favorable ecological conditions for Chinese-style modernization and offers practical insights and decision-making support.

The paper is organized as follows: Section 2 introduces the theoretical framework and the data for China's Ecological Green Development Level. Section 3 presents measure results. Section 4 outlines spatial econometric analysis and robustness testing. Section 5 reports conclusions and policy recommendations.

2. Materials and Methods

The measurement of China's ecological green development level is divided into two parts: one is the calculation of the absolute value of ecosystem gross domestic product (GEP), and the other is the measurement of the coordination level of ecological green regions. The calculation of GEP is based on The Technical Guideline on Gross Ecosystem Product (GEP) published by the Research Center for Eco Environmental Sciences (CAS) in 2020.

The theoretical framework presented in this paper is illustrated in Figure 1:

2.1. Ecosystem Services Theory

GEP is a cornerstone concept in ecological accounting, highlighting the total monetary and biophysical value of diverse final products that ecosystems offer to humanity. It stems from the notion of ecosystem services, which encompasses the multitude of advantages humans derive from ecosystems, including the supply of material goods, regulatory functions, cultural amenities, and foundational support services (Wu Yang et al., 2021; Song Liu & Changwen Dai, 2021). Material goods signify items that can be directly exchanged in the market, whereas regulatory services pertain to functions that enhance the human living environment, like climate moderation and air purification. Cultural Services impart non-tangible benefits to humans via spiritual encounters, knowledge attainment, leisure activities, and entertainment. Foundational support services, meanwhile, represent the essential functions ecosystems provide to uphold other services. This theoretical construct provides an indispensable foundation for a comprehensive and systematic evaluation of ecosystems' integrated value.

The Guidelines enhance the theory of ecological accounting through the lens of ecosystem services, employing techniques like the market valuation approach, replacement cost method, shadow project method, and travel cost approach to quantify the monetary and biophysical value of ecological products, while adhering to principles of scientific rigor, practicality, comprehensiveness, and transparency. The assessment of ecological green regional coordination is grounded in the Gini coefficient. The indicators and the specific calculation method and corresponding data sources utilized in this paper shown in the appendix Table A1, A2 and A3, respectively.

2.2. Statistical Inequality Theory

The measurement of the coordination level of ecological green regions based on Dagum Gini coefficient. The Dagum Gini coefficient is an indicator that improves and corrects the traditional Gini coefficient by introducing more parameters and decomposition methods, enabling it to more accurately reflect the degree of inequality in income or resource allocation and to decompose overall inequality into multiple components for detailed analysis.

In this paper, the Dagum Gini coefficient is employed to measure disparities in GEP. The specific formula for the overall Gini coefficient, G, is as follows:

$$G={\displaystyle \raisebox{1ex}{$\left(\sum _{j=1}^k\sum _{h=1}^k\sum _{i=1}^{n_j}\sum _{r=1}^{n_h}|y_{ji}-y_{hr}|\right)$}\!\left/ \!\raisebox{-1ex}{$2n^2\mu $}\right.}$$

(1)

where $\mathrm{k}$ denotes the number of regions, here referring to the three major regions of Eastern, Central, and Western China, thus $\mathrm{k}=3$; n represents the number of prefecture-level cities; ${\mathrm{n}}_{\mathrm{j}}$ (${\mathrm{n}}_{\mathrm{h}}$) indicates the number of prefecture-level cities in region $\mathrm{j}$ ($\mathrm{h}$); ${\mathrm{y}}_{\mathrm{j}\mathrm{i}}$ (${\mathrm{y}}_{\mathrm{h}\mathrm{r}}$) signifies the GEP of the i-th (r-th) city in region $\mathrm{j}$ ($\mathrm{h}$); and $\mathsf{\mu }$ denotes the mean of GEP.

The formula for the within-group Gini coefficient is:

$$G_{jj}={\displaystyle \raisebox{1ex}{$\left(\sum _{i=1}^{n_j}\sum _{r=1}^{n_h}|y_{ji}-y_{hr}|\right)$}\!\left/ \!\raisebox{-1ex}{$2n_j^2{\mu }_j$}\right.}$$

(2)

The formula for the between-group Gini coefficient is:

$$G_{jh}={\displaystyle \raisebox{1ex}{$\left(\sum _{i=1}^{n_j}\sum _{r=1}^{n_h}|y_{ji}-y_{hr}|\right)$}\!\left/ \!\raisebox{-1ex}{$n_j{n}_h({\mu }_j+{\mu }_h)$}\right.}$$

(3)

where ${\mathsf{\mu }}_{\mathrm{j}}$ (${\mathsf{\mu }}_{\mathrm{h}}$) represents the mean GEP of region $\mathrm{j}$ ($\mathrm{h}$).

2.3. Database and Data Source

(1)Weather Data

This paper extensively utilizes weather data, which includes information on temperature, evaporation, wind speed, and snow depth. These databases cover all cities in China from 2000 to 2020, with the majority of the data available in vector formats or as grid images. The data sources are China's official climate monitoring platforms, such as the National Geographical System Science Data Center and the National Tibetan Plateau Data Center. These platforms collect and store climate information on a monthly, annual, or even daily basis for each monitoring point. This comprehensive climate information tracking provides a valuable resource for analyzing the eco-climate state of prefecture-level cities.

Notably, similar data has been used in analogous research all over the world. For example, Bosch J M and Hewlett J D (1982) calculated water yield in Coweeta, North Carolina, using precipitation and evaporation data [20]. Similarly, Zhou G et al. (2015) assessed the impact of climate and land cover on global water yield patterns using the same data [21]. Although there is less research on floods and windbreaks compared to water yield research, Stuerck J et al. (2014) estimated flood regulation services in Europe using precipitation, evaporation, and watershed data, while Nedkov S and Burkhard B (2012) mapped flood regulation ecosystem services in Etropole, Bulgaria [22,23].

(2)Geological Data

In addition to weather data, this paper also integrates a significant amount of geological data, such as land cover data, world soil data, NDVI data, Net Primary Productivity (NPP) data, elevation data, and vegetation cover index. These data are sourced from the geospatial data cloud platform in China and the World Soil Database, which measures the content of various elements in the soil in China. Of particular note is the Land-Cover Data, provided by the School of Remote Sensing and Information Engineering at Wuhan University, which records the area of various ecosystems, including forests, lakes, and cities, in various regions of China since 2000.

Comparable geological datasets have been extensively used in research worldwide. HH Bennett (1939) was among the first to investigate the factors influencing soil conservation [24]. Lal R (2014) later used the World Soil Database (HWSD) to assess the relationship between soil conservation and ecosystem services [25]. Huang J et al. (2013) used NDVI data to predict rice yields, while Huang J et al. (2017) estimated crop yields for food security [26,27]. Cramer W et al. (1999) found a relationship between climate change and NPP [28].

(3)Economic Data

The economic indicators used in this paper are primarily sourced from the Statistical Yearbooks published annually by local cities. These datasets mainly provide various population and economic indicators for each city, such as GDP, employment, and consumption. In this paper, tourism income and the added value of the primary industry are primarily used to measure the ability to harness the biophysical values of ecological products and Cultural Services. Similarly, the United States has a wealth of economic data that has been used in comparable research endeavors, although the specific datasets and methodologies may vary based on the research question and context.

3. Results

3.1. Measure Results of China's Ecological Green Development Level

This section seeks to uncover the fundamental trends and address the hurdles associated with the progression of China's gross ecological product (GEP). We evaluate the extent of ecological green development and its structural transformations in in China spanning from 2005 to 2020, utilizing the GEP framework. Furthermore, it pinpoints and analyzes the obstacles faced in fostering China's ecological prosperity.

3.1.1. Inadequate Realization of GEP

(1) Compared to GDP, GEP growth is sluggish.

Table 1 presents the absolute levels of China's GEP for selected years, and Figure 2 shows the tendency of China's GEP during the sample period.

Overall, the growth of China's GEP has been gradual year on year, with a significantly slower pace compared to that of the Gross Domestic Product (GDP), suggesting a limited ability to harness the biophysical values of ecological products. Between 2005 and 2020, China's GEP rose from 47.17 trillion yuan to 74.40 trillion yuan, marking an overall increase of 57.71% and an annual average growth rate of 3.18%. When juxtaposed with the national GDP's annual average growth rate of 12.05%, GEP's growth appears notably slower. A study conducted by Ma et al. (2017), utilizing Costanza's (1997) ecosystem service theory, estimated China's total GEP in 2015 to be 72.81 trillion yuan, a figure that aligns closely with the findings of this paper [11].

Within the three components of GEP, the monetary value of Regulation Services constitutes the largest share yet exhibits the slowest rate of increase. This sluggish improvement in ecosystem regulatory functions poses the greatest obstacle to the rapid expansion of the nation's GEP and the ability to realize the biophysical values of ecological products.

As illustrated in Table 1 and Figure 3, the monetary value of China's Material Product Supply rose from 2.14 trillion yuan in 2005 to 6.91 trillion yuan in 2020, marking a substantial increase of 223.13% and exhibiting an average annual growth rate of 8.26% throughout the study period. In terms of Regulation Services, the monetary value climbed from 43.55 trillion yuan in 2005 to 48.72 trillion yuan in 2020, reflecting a modest increase of 11.9% and an average annual growth rate of merely 0.88%, highlighting the sluggish growth and limited contribution of ecosystem regulation. The influence of government policies and other anthropogenic factors has been relatively minor [29]. Regarding Cultural Services, the monetary value surged from 1.48 trillion yuan in 2005 to 18.77 trillion yuan in 2020, demonstrating an astonishing increase of 1166.41% and an average annual growth rate of 18.63%. This study estimates the monetary value of Regulation Services for Alxa City in 2014 at 45.28 billion yuan, which aligns closely with the 47.749 billion yuan figure calculated by Wang Liyan et al. (2017) using an identical methodology for the same city and year [15].

To explore the root reasons for the stagnant growth in the monetary value of Regulation Services, Figure 3 depicts the general pattern of changes in the biophysical value of key regulatory functions at the city level. The limited supply of ecological assets within urban areas, especially the minor annual shifts in the distribution of various land use types, presents a hurdle to elevating the monetary value of these services. Furthermore, regulatory functions such as water retention, soil preservation, sandstorm mitigation, and climate modulation are impacted by yearly variations in elements like rainfall, sunlight exposure, temperature levels, and wind velocity [29]. As a result, the biophysical value of each regulatory function displays a certain level of unpredictability and oscillation, but a general trend of improvement can still be observed.

(3) Incomplete regulating functioning of other ecosystem types except for forest ecosystems.

There are significant gaps in the efficiency of realizing the value of different ecosystem types, and regions have not fully leveraged the unique functional advantages of each ecosystem. As shown in Table 2, among different ecosystem types, forest ecosystems have the highest monetary value of Regulation Services, with an average annual value of 38.15 trillion yuan during the sample period. Wetland and grassland ecosystem monetary values are close, at 4.01 and 4.13 trillion yuan, respectively. Farmland has the lowest value at 0.58 trillion yuan. It is noteworthy that the area of farmland ecosystems is even larger than that of forest ecosystems, but its output value is much lower than that of forest ecosystems. This is related to its primary function in terrestrial ecosystems, which is to provide Material Product Supply. Therefore, under the premise of ensuring adequate Material Product Supply, appropriate conversion of farmland to forests and grasslands is an important measure to increase regional GEP. Wang Liyan et al. (2017) calculated that the GEP of forest ecosystems in Alxa City accounted for 61.99% of the total value in 2014, followed by wetland ecosystems, then grassland, shrub, and farmland ecosystems [15]. This is close to the calculation results of this paper, which show that the forest ecosystem value accounted for 63.73% of the total value in 2014.

3.1.2. Incoordinated Ecological Green Development Among Regions

(1) Provincial Differences.

China exhibits considerable provincial differences in GEP and the ability to realize the biophysical value of ecological products.Based on the provincial average monetary values, as shown in Table 3, Inner Mongolia has the highest GEP score, reaching 555.7 billion yuan, while Tianjin’s, Ningxia’s, and Hainan’s score lower than 10 billion yuan. The monetary value per unit area can be served for assessing a region's capability to realize biophysical values of ecological product. Overall, Inner Mongolia also demonstrates the strongest capability in this regard, followed by coastal provinces such as Zhejiang, Fujian, Guangxi and Hainan; whereas provinces like Shanxi and Ningxia exhibit the weakest capabilities, with values of 1.64 and 1.38 yuan per square meter, respectively. The provincial calculation results in this chapter align closely with the national results calculated by Ma Guoxia et al. (2017) [11].

(2)Regional differences.

There are notable disparities in GEP across the eastern, central, and western regions of China. The results are depicted in Figure 4. Although the western region boasts a higher GEP, its growth rate lags behind, and its GEP per unit area is the lowest, signaling the weakest capacity to actualize the biophysical values of ecological products. In terms of the overall GEP magnitude, the western region outstrips both the central and eastern regions, owing to its extensive ecological resources and expansive territory. The GEP magnitudes of the central and eastern regions are comparable, yet both fall short of the national average. When considering GEP per unit area, the western region trails behind, whereas the eastern region leads the way. Despite the western region's abundance of ecological resources and expansive ecosystem area, it struggles to harness these advantages to actualize the biophysical values of ecological products. Regarding the mean annual growth rate, the western region's GEP exhibits the slowest growth, at 3.15%, while the eastern and central regions' GEP growth rates are nearly identical, reaching 3.33% and 3.36% respectively, indicating a notable surge in GEP and an enhanced capacity to realize ecological product values. The data underscores distinct spatial variations in ecological development levels among these regions.

Among the three components of GEP, the western region, which prides itself on abundant ecological resources, demonstrates a superior biophysical value in Regulation Services yet lacks the capacity to supply cultural and material products. As illustrated in Figure 5, the eastern region takes the lead in the monetary worth of Material Product Supply, with the central region trailing closely behind, both approaching the national average, whereas the western region falls short. This discrepancy stems from the fact that, although the western region is rich in ecological resources, it predominantly comprises forest, shrub, and lake ecosystems, with scarce farmland and grassland ecosystems crucial for material product provision in comparison to the eastern and central regions. In the sphere of Cultural Services' monetary worth, the eastern region stands at the forefront, while the central and western regions are almost on par but below the national benchmark. This can be attributed to the advanced economic development of the eastern region, especially in coastal cities with a thriving cultural tourism industry. When it comes to the monetary worth of regulatory services, the western region notably exceeds the national average, followed by the central region, with the eastern region lagging behind. Despite the fluctuating trends observed across all three regions, when accounting for the ecosystem area in each, the western region showcases the lowest monetary worth per unit area for regulatory services, with the eastern region taking the lead and the central region aligning snugly with the national average. This underscores the underutilization of the biophysical value of ecological products in the western region.

Figure 6 displays the within-group Gini coefficients for GEP across China and its three major regions. There are significant disparities in the capability to realize biophysical values of ecological product between regions, highlighting inequality issues. Overall, the average Gini coefficient for China during the sample period is as high as 0.452, ranging from 0.405 to 0.501. The intra-regional differences in China show an overall trend of "fluctuating decline" over time, with the degree of difference decreasing during the sample period, at an average annual rate of 1.04%. This reflects a gradual reduction in spatial differences in China's GEP, with high-quality development promoting gradual coordination and balanced ecological development across regions.

From the perspective of intra-group disparities among the three major regions, inequality is particularly pronounced in the western region. The mean intra-group Gini coefficients for the eastern, central, and western regions during the sample period are 0.416, 0.428, and 0.500, respectively, and their temporal trends align with the national pattern. Despite having the highest GEP, the western region also boasts the highest Gini coefficient, highlighting the greatest internal variations. This can be attributed to the diverse and abundant ecosystems in the western region, coupled with substantial differences in the provinces' ability to harness biophysical values of ecological products. In 2008, the intra-group Gini coefficients of the eastern and central regions were similar, and while both exhibited a decreasing trend subsequently, the eastern region's decline was notably steeper than that of the central region. This indicates that cities in the eastern region have effectively embraced the principle of ecological harmony and development, utilizing their regional strengths to minimize disparities in the realization of biophysical values of ecological products among cities.

The differences between different regions are shown in Figure 7. Overall the average differences in GEP levels between the "eastern-central," "eastern-western," and "central-western" regions were 0.423, 0.468, and 0.473, respectively, with the average differences involving the western region being larger. From a temporal perspective, from 2005 to 2020, the between-group Gini coefficients showed an overall trend of fluctuating decline, indicating that differences in the capability to realizing biophysical values of ecological product among regions are gradually narrowing.

3.2. Spatial Econometric Analysis and Robustness Testing

To enhance the credibility of this study, this paper employs spatial econometric methods to conduct robustness testing on the spatio-temporal characteristics of the accounting results.

3.2.1. Dynamic Evolution of GEP– Based on Kernel Density Estimation

In order to provide a more intuitively depiction of the absolute disparities during the sample period, we employ kernel density estimation to illustrate the dynamic progression of absolute differences in GEP across the country. This method generates dynamic distribution plots that reveal the evolutionary pattern of absolute GEP differences and highlight their magnitude and changing features. The detailed formula is as follows:

$$f_j(y)={\displaystyle \frac{1}{n_{j}h}}\sum _{i=1}^{n_j}K({\displaystyle \raisebox{1ex}{$y_{ji}-y$}\!\left/ \!\raisebox{-1ex}{$h$}\right.})$$

(4)

Where $\mathrm{K}(.)$ represents the kernel density function, describing the weights of all sample points ${\mathrm{y}}_{\mathrm{j}\mathrm{i}}$ within the neighborhood $\mathrm{y}$, and $\mathrm{h}$ denotes the bandwidth for kernel density estimation. This paper adopts the optimal bandwidth method and uses a Gaussian kernel function to estimate the regional differences, with specific expression as follows:

$$\mathrm{K}(\mathrm{x})={\displaystyle \frac{1}{\sqrt{2\mathsf{\pi }}}}\mathrm{e}\mathrm{s}\mathrm{p}({\displaystyle \raisebox{1ex}{$-{\mathrm{x}}^2$}\!\left/ \!\raisebox{-1ex}{$2$}\right.})$$

(5)

Kernel density estimation reveals the distribution characteristics of sample data across regions, providing detailed descriptions of key attributes such as the distribution location, peak distribution characteristics, distribution spread, and number of peaks of the density curve, thereby capturing the dynamic evolution and changing characteristics of GEP. This paper uses prefecture-level city data while excluding the samples of Beijing, Chongqing, Shanghai, and Tianjin, with results illustrated in Figure 8:

Overall, the kernel density curves for GEP and its three major dimension indicators exhibit a gradual downward trend and an overall rightward shift, reflecting a positive growth trend in GEP levels in most regions of the country and indicating good ecological development. In terms of the peak distribution pattern, the peak height fluctuates with a "gradual decline" during the sample period. Except for regulatory services, the peaks decline and broaden, with the kernel density curves gradually flattening out, indicating a decentralized trend in the distribution of GEP levels across regions.

When examining the three major dimension indicators comprising GEP, the kernel density curves for material products provision and Cultural Services share similar waveform, with significant rightward shifts and gradually decreasing peak heights, accompanied by shortened right tails. This suggests that the levels of material products provision and Cultural Services in cities have increased annually during the sample period, while the inter-provincial gaps have narrowed year by year. Regulation Services show insignificant changes over time, with only a slight decline in peak height, indicating minimal variation in regulatory service levels among prefecture-level cities during the sample period. This may be attributed to the difficulty in significantly altering the areas of various ecosystems within prefecture-level cities in the short term, resulting in insignificant changes in most regulatory functions.

The results of kernel density estimation are generally consistent with the differences in GEP quantified by the Dagum Gini coefficient.

3.2.2. Spatial Correlation of GEP– Based on Spatial Moran's I

Entities located in closer geographical vicinity demonstrate a stronger level of interconnectedness, indicating the presence of spatial dependence or spatial autocorrelation in the measured values of a specific attribute across various spatial units. This research utilizes Spatial Moran's I to investigate both the overall and localized spatial autocorrelation of GEP across 273 cities in China, aiming to determine if there exists a spatial relationship in GEP values between these spatial entities.

This paper constructs global and local Moran's I indices, with the formula for the global Moran's I index as follows:

$$\mathrm{I}={\displaystyle \raisebox{1ex}{$\sum _{\mathrm{i}=1}^{\mathrm{n}}\sum _{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{W}}_{\mathrm{i}\mathrm{j}}({\mathrm{X}}_{\mathrm{i}}-\overline{\mathrm{X}})({\mathrm{X}}_{\mathrm{j}}-\overline{\mathrm{X}})$}\!\left/ \!\raisebox{-1ex}{${\mathrm{S}}^2\sum _{\mathrm{i}=1}^{\mathrm{n}}\sum _{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{W}}_{\mathrm{i}\mathrm{j}}$}\right.}$$

(6)

Where ${\mathrm{S}}^2={\sum }_{\mathrm{i}=1}^{\mathrm{n}}{({\mathrm{X}}_{\mathrm{i}}-\overline{\mathrm{X}})}^2/\mathrm{n}$ is the variance, $\overline{\mathrm{X}}={\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{X}}_{\mathrm{i}}/\mathrm{n}$ is the mean, and ${\mathrm{X}}_{\mathrm{i}}$ and ${\mathrm{X}}_{\mathrm{j}}$ represent the GEP of different prefecture-level cities, respectively; n denotes the number of spatial units (i.e., prefecture-level cities), and ${\mathrm{W}}_{\mathrm{i}\mathrm{j}}={({\mathrm{W}}_{\mathrm{i}\mathrm{j}})}_{\mathrm{i}*\mathrm{j}}$ is the spatial weight matrix reflecting the spatial connections between cities. The values of the global Moran's I index range from [-1,1]. When its value approaches 1, it indicates a higher degree of spatial agglomeration of China's GEP, with spatial positive correlation; when its value approaches -1, it suggests greater spatial variability of China's GEP, with spatial negative correlation; when its value is close to 0, it implies no spatial correlation of China's GEP, presenting a random spatial distribution. Simultaneously, this paper introduces two spatial matrices: one is the proximity weight matrix, where the spatial weight matrix takes a value of 1 when a city is geographically adjacent to another, and 0 otherwise. The second is the distance weight matrix, taking the reciprocal of the shortest highway mileage between cities.

Furthermore, to delve deeper into the spatial agglomeration effect of GEP among cities in China and discern potential different spatial correlation forms due to positional differences among spatial units, this paper introduces the local Moran's I index, with the formula as follows:

$$\mathrm{Ⅰ}={\displaystyle \raisebox{1ex}{$\mathrm{n}({\mathrm{X}}_{\mathrm{i}}-\overline{\mathrm{X}}){\sum }_{\mathrm{i}\ne \mathrm{j}}{\mathrm{W}}_{\mathrm{i}\mathrm{j}}({\mathrm{X}}_{\mathrm{j}}-\overline{\mathrm{X}})$}\!\left/ \!\raisebox{-1ex}{${\sum }_{\mathrm{i}=1}^{\mathrm{n}}({\mathrm{X}}_{\mathrm{i}}-\overline{\mathrm{X}})$}\right.}={\displaystyle \raisebox{1ex}{$({\mathrm{X}}_{\mathrm{i}}-\overline{\mathrm{X}}){\sum }_{\mathrm{i}\ne \mathrm{j}}{\mathrm{W}}_{\mathrm{i}\mathrm{j}}({\mathrm{X}}_{\mathrm{j}}-\overline{\mathrm{X}})$}\!\left/ \!\raisebox{-1ex}{${\mathrm{S}}^2$}\right.}$$

(7)

The figures for the local Moran's I index are not confined to the interval between -1 and 1. If the index value notably exceeds 0, it signifies that neighboring spatial units share comparable observed values, indicating a pattern of "high-high" clustering or "low-low" clustering; conversely, if the index value is markedly below 0, it denotes that adjacent spatial units exhibit contrasting observed values, pointing to a pattern of "high-low" clustering or "low-high" clustering.

The results of the global Moran's I index are shown in Table 4:

According to Table 4, the global Moran's I indices calculated based on both the proximity weight matrix and the distance weight matrix are significantly positive at the 1% statistical level. This demonstrates that China's GEP exhibits significant positive spatial correlation and strong spatial agglomeration effects across the board. That is, prefecture-level cities with relatively low GEP levels are adjacent to at least one other prefecture-level city with similarly low GEP levels, and those with relatively high GEP levels are adjacent to at least one other with similarly high GEP levels. Additionally, under the binary contiguity matrix, the global Moran's I index fluctuates within the range [0.349,0.481], and under the distance weight matrix, it fluctuates within the range [0.066,0.102]. This indicates that the global Moran's I index values are generally stable under different spatial weights, suggesting that the spatial correlation of China's GEP possesses certain long-term robustness.

The local Moran's I index calculations cover only the years 2005, 2010, 2015, and 2020, utilizing the distance spatial matrix. The scatter plot results are shown in Figure 9:

The x-axis and y-axis of the Moran's I scatter plot depict the standardized GEP and its spatial lag (i.e., the weighted mean of surrounding units relative to the observed value), respectively. The inclination of the diagonal line signifies the annual global Moran's I index value. The first quadrant signifies a high-high positive association, the second quadrant indicates a low-high negative association, the third quadrant denotes a low-low positive association, and the fourth quadrant represents a high-low negative association. Given that all global Moran's I indices for China's multi-ecosystem GEP are positive, the primary focus of this study's observations lies within the first and third quadrants of the scatter plot. Figure 9 illustrates that, over the years, the majority of provinces are situated within these key observation zones, strongly indicating the enduring significance of the positive spatial correlation in China's GEP.

4. Discussion

This research calculates the ecological green development index for provinces, municipalities, and prefectures across China, covering the period from 2005 to 2020, using the GEP and Dagum Gini coefficient. The results of this calculation are corroborated by kernel density estimation and the spatial Moran's I index, to investigate the temporal and spatial trends as well as regional variations in GEP. The primary conclusions are as follows:

Firstly, the expansion of GEP is gradual, and the ability to harness the biophysical worth of ecological goods is insufficient. The yearly mean growth rate of nationwide GEP stands at merely 3.18%, markedly below the concurrent annual average GDP growth rate of 12.05%. This suggests that while GEP has risen, its growth trajectory is sluggish and does not align with the rate of economic advancement, highlighting a disparity between ecological preservation and economic progress.

Secondly, ecosystem regulatory service functions are severely constrained by ecological resources and natural climate conditions. Among the three components of GEP, regulatory service value accounts for the highest proportion but has the slowest growth rate. This is mainly due to the limited endowment of ecological resources and the randomness and volatility of natural climate factors.

Thirdly, there is a lack of coordination in green development across ecological zones, and pronounced ecological disparities exist among various regions. Notable variations are observed in the provinces of China regarding their ability to harness the biophysical values of ecological products. The ecological progress in the eastern, central, and western areas is not synchronized. While the western region boasts a higher monetary value for Regulation Services compared to the central and eastern regions, its growth pace lags behind.

Looking ahead, several implications emerge from this study. First, policies should prioritize enhancing ecosystem regulation to ensure long-term ecological sustainability. This could involve investments in conservation and restoration projects, particularly for Regulation Services like water conservation and carbon sequestration.

Second, addressing regional inequalities in GEP realization is crucial. Policies could focus on improving the efficiency of ecological product value realization in the western region, possibly through mechanisms like payment for ecosystem services (PES) schemes or eco-compensation programs that transfer benefits from regions that benefit from ecological services to those that provide them.

Third, , future research should explore the dynamic relationship between GEP and GDP, examining how changes in one affect the other. Additionally, more detailed spatial analyses could provide deeper insights into the factors influencing GEP distribution and disparities.

In conclusion, this study provides valuable insights into China's ecological green development, highlighting both achievements and challenges. By focusing on GEP and regional disparities, it contributes to the broader discourse on sustainable development and offers actionable recommendations for policymakers and researchers alike.