Preamble

Measurement and Spatial Convergence of Green Development Levels in Prefecture-level Cities of the Yellow River Basin

Ren Shiqi, Wang Yongyu (School of Statistics and Data Science, Lanzhou University of Finance and Economics, Lanzhou, Gansu)

Green development is a critical measure for promoting ecological protection and high-quality development in the Yellow River Basin. Taking 81 prefecture-level cities in the Yellow River Basin from 2011 to 2021 as the research subjects, this study constructs a comprehensive evaluation index system based on the logic and internal mechanisms of green development. We employ the entropy weight method, kernel density estimation, and $\beta$ convergence models to explore the green development levels, distributional characteristics, and spatial convergence of these cities. The results indicate that: 1) The green development level of prefecture-level cities in the Yellow River Basin shows an upward trend, with a development pattern of "downstream > upstream > midstream" forming across the reaches, and significant differences persist between cities. 2) The peaks of the kernel density curves are unevenly distributed, the widths are narrowing, and a right-tail phenomenon is present. This suggests an imbalance in green development levels, where certain cities exhibit high levels of development and a substantial gap exists between them and others. 3) Significant absolute $\beta$ convergence and conditional $\beta$ convergence exist across the entire basin and its three major reaches, with the convergence speed following an increasing trend of "upstream > midstream > downstream."

Furthermore, there are differentiated spatial spillover effects in the convergence process of green development levels among the prefecture-level cities in the three major reaches. The conclusions of this study hold significant practical importance for improving the green development levels of cities in the Yellow River Basin and promoting coordinated regional development across the entire basin.

关键词

Green development level; Statistical measurement; Distribution characteristics; Spatial convergence; Yellow River Basin. Article ID: Since the reform and opening up, the Yellow River Basin has achieved significant economic growth by leveraging its natural advantages of abundant energy resources. However, this progress has inevitably exerted unprecedented pressure on the local ecological environment. The Yellow River Basin is characterized by complex topography, significant variations in altitude, and spans multiple climatic zones from east to west, resulting in an extremely sensitive ecosystem that is prone to degradation under external interference. With rapid socio-economic development, environmental issues such as water pollution, land degradation, and loss of biodiversity have become increasingly prominent. The interplay between natural and anthropogenic factors poses severe challenges to the high-quality development of the Yellow River Basin. Managing the Yellow River has historically been a vital task for national stability and prosperity, receiving high priority from the Party and the State. In September 2019, General Secretary Xi Jinping chaired a symposium in Zhengzhou on the ecological protection and high-quality development of the Yellow River Basin, officially elevating this goal to a major national strategy. In September 2024, at a symposium in Lanzhou focused on comprehensively promoting these goals, General Secretary Xi Jinping emphasized the need to prioritize ecology and green development. High-quality development in the Yellow River Basin requires a green development strategy to establish a solid ecological foundation, thereby achieving the goal of harmonious coexistence between humanity and nature and ensuring the long-term peace and stability of the Yellow River. In 1989, British environmental economist Pierce first proposed the concept of "green development" in his book Blueprint for a Green Economy, emphasizing that economic development should not come at the cost of environmental pollution. The Organisation for Economic Co-operation and Development (OECD) defines green development as fostering economic growth and development while ensuring that natural assets continue to provide the resources and environmental services essential to human well-being. The World Bank views green development as an environmentally friendly and socially inclusive mode of development. As our understanding of the relationship between economic activity and the resource environment deepens, green development has been endowed with new connotations: pursuing high efficiency, low pollution, and low energy consumption while maintaining economic growth and minimizing negative environmental impacts. It is a form of sustainable development that also accounts for social equity. Regarding the measurement of green development, scholars both domestically and abroad differ in their research methods, indicator selection, and research scope. In terms of methodology, some scholars measure green development efficiency by constructing models such as SBM, which account for inputs, expected outputs, and undesirable outputs. Other scholars focus on constructing comprehensive evaluation index systems to measure green development levels, such as those based on the conceptual connotations of green development. In terms of indicator selection, primary focus is placed on economic factors.

Aspects such as cleaner production and green innovation are utilized to conduct a comprehensive assessment of the current state of green development. Regarding the scope of research, while attention to the urban level is increasing, studies have traditionally concentrated on the national or industrial levels. Specifically concerning the Yellow River Basin, existing research has examined green development at the provincial level. Results generally indicate that while overall green development efficiency is not high, it is on an upward trend, though regional differences remain significant. Some scholars have analyzed green development levels at the provincial level by constructing indicator systems. Their findings suggest that the overall level of green development is continuously improving, exhibiting a spatial pattern of being higher in the east and lower in the west. Scholars have conducted in-depth explorations of green development issues, laying a solid theoretical foundation for subsequent research. The main contributions of this study are reflected in the following aspects: first, it clarifies the logic and internal mechanisms of green development within the Yellow River Basin.

结果

Measurement of Green Development Levels in the Yellow River Basin

This study constructs a comprehensive evaluation index system based on a process-oriented framework to measure the green development levels across the Yellow River Basin. Existing research has paid relatively little attention to green development at the municipal level. As hubs for transportation and infrastructure, cities often face increasingly severe resource and environmental challenges alongside their economic growth. Therefore, conducting research at the city level is of significant practical importance.

Taking prefecture-level cities as the primary unit of analysis, current findings indicate that the green development level of the Yellow River Basin is on an upward trajectory. However, there are significant disparities between the upper, middle, and lower reaches, and the rate of improvement varies across these regions. Few scholars have focused on the evolution of the gap in green development levels, an issue that is crucial for promoting coordinated development throughout the basin.

To address this gap, this paper employs convergence models to test whether a convergence effect exists in the green development levels of prefecture-level cities within the Yellow River Basin. The objective is to provide a scientific basis for the formulation of relevant regional policies.

1 数据与方法

Study Area Overview

The Yellow River originates from the northern foothills of the Bayan Har Mountains on the Qinghai-Tibet Plateau and eventually flows into the Bohai Sea. Since the river systems in Sichuan Province primarily consist of the Yangtze River and its tributaries, the Yellow River only flows through the Aba Prefecture. To maintain the integrity of geographical units and administrative regions, this study selects specific prefectural-level cities as research objects. Considering the natural boundaries of the Yellow River Basin, we have categorized the prefectural-level cities included in each reach of the river.

Data regarding green patents and environmental concern were obtained from the Chinese Research Data Services (CNRDS) platform. $PM_{2.5}$ concentration data were sourced from Dalhousie University in Canada. The administrative boundary maps used in this study were based on standard maps downloaded from the Standard Map Service website of the Ministry of Natural Resources, under the specific map approval number [FIGURE:1].

0650 号的标准地图制作

The base map boundaries remained unchanged throughout the study period. Data were primarily sourced from the China City Statistical Yearbook, the China City Construction Statistical Yearbook, and the statistical yearbooks and bulletins of individual prefecture-level cities. For missing data points in specific indicators, linear interpolation was employed to ensure data continuity. In 2019, the State Council incorporated Laiwu City into Jinan City; however, as the research interval of this paper spans from 2011 to 2021, the 2011 administrative planning map was utilized as the spatial baseline. Consequently, data for the year 2019 onwards were statistically aggregated under Jinan City to maintain longitudinal consistency.

To eliminate the influence of subjective factors, an objective weighting method was applied to the indicators. The calculation steps are as follows: first, the indicators were standardized to ensure comparability across different dimensions. Following standardization, the entropy weight method was used to determine the final weights for each metric.

X ′ i j =

X ′ i j =

In the equation: $X_{ij}$ represents the value of the $j$-th indicator for the $i$-th city, while $X'_{ij}$ denotes the standardized value.

These represent the maximum and minimum values, respectively, where $i = 1, 2, 3, \dots, n$ and $j = 1, 2, \dots, m$.

This represents the proportion of the $i$-th city within the $j$-th indicator.

Measurement and Spatial Convergence of Green Development Levels in Prefecture-Level Cities of the Yellow River Basin

P i j = X ′ i j ∑

i = 1

Information Entropy of Indicators

In the context of multi-criteria decision-making and statistical analysis, the information entropy of an indicator serves as a critical measure of the uncertainty and the amount of information provided by that specific variable. Based on the principles of information theory, the entropy value reflects the degree of dispersion within the data; a higher degree of dispersion implies a lower entropy value, which conversely indicates that the indicator provides a greater amount of information and should therefore be assigned a higher weight in the overall evaluation.

Mathematical Definition and Calculation

To determine the information entropy for a given set of indicators, the data must first be normalized to ensure comparability across different scales. For a dataset containing $n$ samples and $m$ indicators, let $x_{ij}$ represent the value of the $j$-th indicator for the $i$-th sample. The normalized value $p_{ij}$ is typically calculated as:

$$p_{ij} = \frac{x_{ij}}{\sum_{i=1}^{n} x_{ij}}$$

Once the probability distribution $p_{ij}$ is established, the information entropy $e_j$ for the $j$-th indicator is defined as:

$$e_j = -k \sum_{i=1}^{n} p_{ij} \ln(p_{ij})$$

In this expression, $k$ is a constant, usually defined as $k = 1/\ln(n)$, which ensures that the entropy value $e_j$ falls within the range $[0, 1]$. If $p_{ij} = 0$, it is conventionally assumed that $p_{ij} \ln(p_{ij}) = 0$ based on the limit properties of the function.

Significance in Weighting

The utility of information entropy lies in its ability to objectively determine weights, often referred to as the Entropy Weight Method (EWM). The information utility value $d_j$ for an indicator is calculated as $d_j = 1 - e_j$. A larger $d_j$ signifies that the indicator is more effective at discriminating between different samples. Consequently, the weight $w_j$ for each indicator is derived by normalizing these utility values:

$$w_j = \frac{d_j}{\sum_{j=1}^{m} d_j}$$

By utilizing information entropy, researchers can minimize the influence of subjective bias, ensuring that the importance assigned to each indicator is purely a

e j = - 1 l n n ∑

i = 1

Determination of Indicator Weights

In the comprehensive evaluation process, determining the weights of indicators is a critical step that directly influences the accuracy and objectivity of the final evaluation results. To ensure the scientific rigor of the weight distribution, this study adopts a combined weighting approach that integrates subjective and objective methods.

Subjective Weighting Method

Subjective weighting primarily relies on the professional knowledge and practical experience of experts in the field. The Analytic Hierarchy Process (AHP) is frequently employed to decompose complex decision-making problems into a hierarchical structure. By conducting pairwise comparisons of indicators at each level, a judgment matrix is constructed. The consistency of these judgments is then verified to calculate the relative importance (weight) of each indicator. This method effectively captures the qualitative insights of experts and ensures that the evaluation framework aligns with theoretical expectations and industry standards.

Objective Weighting Method

To mitigate the potential bias inherent in subjective judgments, objective weighting methods are utilized to extract weight information directly from the data's inherent characteristics. Common techniques include the Entropy Weight Method and the Mean Squared Error method. For instance, the Entropy Weight Method determines weights based on the degree of variation in each indicator; a higher degree of dispersion implies that the indicator provides more information and should therefore be assigned a larger weight. By utilizing the objective distribution of the data, this approach enhances the impartiality of the evaluation process.

Combined Weighting Strategy

Recognizing that subjective methods may lack empirical grounding and objective methods may ignore the practical significance of indicators, this study employs a combined weighting strategy. By calculating a weighted average or utilizing optimization models to integrate the results from both AHP and objective methods, we derive a final set of weights. This integrated approach ensures that the indicator system is both theoretically sound and empirically robust, providing a reliable foundation for subsequent analysis and decision-making.

w j =

j = 1

Calculation of Comprehensive Green Development Scores for Prefecture-level Cities

To evaluate the green development level of various prefecture-level cities, this study constructs a comprehensive evaluation index system. The calculation process follows a rigorous multi-step approach involving data normalization, weight determination, and the synthesis of final scores.

1. Construction of the Evaluation Index System

The green development level is a multi-dimensional concept that encompasses economic growth, resource efficiency, and environmental protection. Following established academic frameworks, we select indicators across three primary dimensions:
- Economic Growth Quality: Focusing on GDP growth rates, industrial structure optimization, and innovation capacity.
- Resource Utilization Efficiency: Measuring the consumption of energy, water, and land relative to economic output.
- Environmental Protection and Governance: Assessing pollution emission intensities (e.g., $SO_2$, wastewater, and dust) and the effectiveness of environmental infrastructure.

2. Data Preprocessing and Normalization

Given that the indicators have different units and scales, we apply the Min-Max normalization method to ensure comparability. For positive indicators (where higher values represent better performance), we use:

$$x'{ij} = \frac{x$$} - \min(x_j)}{\max(x_j) - \min(x_j)

For negative indicators (where lower values represent better performance, such as pollution levels), we use:

$$x'{ij} = \frac{\max(x_j) - x$$}}{\max(x_j) - \min(x_j)

where $x_{ij}$ represents the original value of indicator $j$ for city $i$, and $x'_{ij}$ represents the normalized value.

3. Weight Determination using the Entropy Method

To minimize subjective bias, this study employs the Entropy Weight Method to determine the importance of each indicator. This objective weighting method calculates weights based on the information provided by the variance of each indicator.

First, we calculate the proportion of the $i$-th city for the $j$-th indicator:
$$p_{ij} = \frac{x'{ij}}{\sum$$}^{n} x'_{ij}

Next, we calculate the entropy value $e_j$ for each indicator:
$$e_j = -k \sum_{i=1}^{n} p_{ij} \ln(p_{ij})$$
where $k = 1/\

j = 1

Kernel Density Estimation

To understand the distributional characteristics of green development levels across prefecture-level cities in the Yellow River Basin, this study employs Kernel Density Estimation (KDE) for characterization.

Kernel Density Estimation is a non-parametric method used to estimate the probability density function of a random variable. Unlike parametric methods, KDE does not assume a specific functional form for the underlying distribution, allowing for a more flexible and accurate representation of the data's actual distribution. By smoothing the data points using a kernel function, KDE provides a continuous visualization of the distribution, which is particularly useful for identifying multi-modality, skewness, and the evolution of spatial disparities over time.

For a set of independent and identically distributed observations $x_1, x_2, \dots, x_n$ representing the green development levels of cities, the kernel density estimator is defined as:

$$f(x) = \frac{1}{nh} \sum_{i=1}^{n} K\left(\frac{x - x_i}{h}\right)$$

In this expression, $n$ represents the number of prefecture-level cities, $h$ denotes the bandwidth (smoothing parameter), and $K(\cdot)$ is the kernel function. The choice of bandwidth $h$ is critical, as it determines the degree of smoothing; a smaller bandwidth reveals more local structure but may introduce noise, while a larger bandwidth provides a smoother estimate at the risk of obscuring important distributional features.

By applying KDE to the green development indices of the Yellow River Basin, we can observe the dynamic evolution of development levels. Specifically, this method allows us to analyze the shift in the distribution's center (indicating overall progress), the change in the height and width of the peaks (reflecting regional convergence or divergence), and the presence of "tails" or multiple peaks (suggesting polarization or the formation of distinct development clusters). This approach provides a robust empirical basis for understanding the spatial-temporal patterns of green development in the region.

f ( ) x = 1 n h ∑

i = 1

In this study, $f(\cdot)$ represents the probability density function, $n$ denotes the total number of prefectural-level cities, and $y_i$ signifies the observed sample values. To investigate whether the green development of cities in the Yellow River Basin exhibits a trend of narrowing development gaps, we employ absolute and conditional $\beta$-convergence models for empirical testing.

Absolute $\beta$-convergence emphasizes an unconditional "catch-up effect." This approach does not account for external socioeconomic factors, focusing instead on whether cities with initially lower levels of green development can grow at a faster rate to eventually converge toward a common steady state. In contrast, conditional $\beta$-convergence accounts for the inherent social and economic disparities between different prefectural-level cities, examining the presence of a catch-up effect under specific conditional constraints.

= α + β l n G D L i , t + ρ W i j l n æ

= α + β l n G D L i , t + ρ W i j l n æ

The green development level in year $t$ and the growth of the green development level in year $t$ are analyzed using the convergence coefficient $\beta$. When $\beta$ is significantly negative, it indicates the existence of a convergence trend. The term $\rho$ represents the spatial autocorrelation coefficient, which reflects the spatial interaction of green development levels among prefecture-level cities. $W$ denotes the spatial weight matrix, while the coefficient for the city's green development level is represented accordingly. The coefficients for control variables are denoted by $\gamma$, where $X$ represents the set of control variables. The indices $i$ and $t$ refer to the city and year, respectively, and $\varepsilon$ represents the random disturbance term.

Construction of the Indicator System

At the urban level, green development lies in coordinating the intrinsic relationship between environmental protection and economic development. It aims to promote urban growth with the minimum possible consumption of resources and environmental impact, emphasizing the construction of ecological civilization and the creation of livable cities.

Green development is a dynamic and interactive process. Solid foundational conditions not only provide the necessary prerequisites for green development but also stimulate further momentum, ensuring the feasibility of green initiatives. A robust power engine helps drive the implementation of green practices and policies, leading to superior development outcomes. These development results, in turn, reinforce the foundational conditions and the driving forces, forming a positive feedback loop. To effectively implement green development, it is essential to consider the interrelationships and synergies between foundations, drivers, and results. This approach forms a coordinated development path, promoting the long-term effectiveness and sustainability of green development strategies.

结果

A procedural framework for the logic and internal mechanisms of green development at the city level is essential. Regarding the foundations of green development, implementing green development concepts requires cities to possess robust basic conditions for production. Green development should simultaneously account for production, daily life, and ecology—specifically economic development, green lifestyles, and the resource base. This development emphasizes the coordination and efficiency of economic scale and structure.

The economic foundation of green development is measured by the proportion of the tertiary industry. The prevalence of public transportation not only reflects basic livelihood standards but also encourages residents to practice green living concepts. Accordingly, water supply coverage and the number of buses per 10,000 people are selected to measure the living standards associated with green development. Since water resources and urban space are critical constraints on development, total water supply and comprehensive urban water production capacity are selected to measure the resource base. Regarding the drivers of green development, high-quality growth and the support of power engines are emphasized. Environmental protection facilities can both improve urban environmental cleanliness and promote environmental concepts, serving as the fundamental impetus for green development. Consequently, the number of public toilets, sewage treatment plants, and specialized sanitation vehicles are selected to measure the prevalence of environmental infrastructure. Innovation possesses the dual characteristics of being "green" and "innovative," serving as a direct driver; thus, green invention patents and green utility model patents are selected to measure green innovation levels from both absolute and relative dimensions. Furthermore, government investment in science and education, along with high-level human capital, creates a leverage effect that serves as the endogenous power for green development. This is measured by the proportion of scientific and technological expenditure in budgetary spending, the proportion of education expenditure in budgetary spending, and the number of college students per 10,000 people. Regarding the outcomes of green development, the goal is harmony between humanity and nature. Green development in the Yellow River Basin emphasizes the implementation of ecological priority and intensive conservation concepts, primarily manifested in resource conservation and clean production. Resource conservation emphasizes reducing the consumption of resources such as construction land during economic development; therefore, the area of construction land reflects the degree of resource conservation. Clean production aims to reduce waste generated by production activities. As the industrial system of the Yellow River Basin is dominated by industry, pollutants mainly consist of wastewater and dust; thus, the emission intensities of industrial wastewater and dust are selected to reflect the degree of clean production. Ecological livability is reflected in the reduction of haze and the improvement of the urban environment. The green coverage rate of built-up areas, the household waste treatment rate, and the sewage treatment rate reflect the degree of ecological livability. Adhering to the principles of scientific rigor, accuracy, and data availability, and drawing on existing research, a comprehensive evaluation index system for green development levels has been constructed.

2 结果与分析

Measurement Results of Green Development Levels

Due to space constraints, this paper reports only the measurement results for the green development level and its constituent indicators. These indicators include the proportion of the tertiary industry, the coverage rate of water supply, and the coverage rate of gas supply. Urban infrastructure and public service capacity are represented by the number of public buses per 10,000 people, the number of public toilets, the number of sewage treatment plants, and the number of specialized environmental sanitation vehicles.

Innovation and technological progress are assessed through several metrics: the number of green invention patents granted, the number of green utility model patents granted, the proportion of green inventions relative to total inventions, and the proportion of green utility models relative to total utility models. Furthermore, the regional commitment to human capital and innovation is measured by the proportion of scientific and technological expenditure in the total budget, the proportion of educational expenditure in the total budget, and the number of enrolled university students per 10,000 people.

结果

Construction land area, industrial wastewater discharge intensity, dust emission intensity, urban green coverage rate, domestic waste treatment rate, and sewage treatment rate. The symbol (+) indicates a positive indicator, while (-) indicates a negative indicator.

Analysis of Green Development Levels in the Yellow River Basin

An analysis of the green development levels across 100 prefecture-level cities in the Yellow River Basin from 2010 to 2019 reveals that the overall average green development level for the entire basin is 0.356, with an average annual growth rate of 1.42%. These figures suggest that the green development level of prefecture-level cities in the Yellow River Basin remains relatively low overall. However, the steady, albeit slow, upward trend in these levels indicates that the Yellow River Basin has made consistent progress in its green development initiatives over the study period.

Regional Analysis by River Reach

An analysis of different segments of the river reveals significant spatial disparities. The downstream region maintains a leading position, with an average green development level of 0.412 and an average annual growth rate of 1.65%. This indicates that the downstream areas are not only performing at a higher baseline but are also improving at a faster pace compared to other regions within the basin.

Measurement and Spatial Convergence of Green Development Levels in Prefectural Cities of the Yellow River Basin

Abstract

This study evaluates the green development levels of prefectural-level cities in the Yellow River Basin across four key temporal snapshots: 2006, 2011, 2016, and 2021. By constructing a comprehensive evaluation index system, the research analyzes the spatial distribution characteristics and evolutionary trends of green development in major cities, including Hohhot, Ordos, Hulunbuir, Bayannur, and Ulanqab. Furthermore, the study employs spatial econometric models to examine the spatial convergence of green development, providing empirical evidence for regional coordinated development and ecological protection strategies in the Yellow River Basin.

1. Introduction

The Yellow River Basin serves as a vital ecological barrier and economic zone in China. Achieving high-quality green development in this region is essential for national ecological security and sustainable economic growth. As the primary administrative units for policy implementation, prefectural-level cities play a crucial role in balancing industrial transformation with environmental preservation. This paper focuses on the measurement of green development levels and explores whether regional disparities are narrowing over time through spatial convergence analysis.

2. Methodology and Data Sources

2.1 Evaluation Index System

To accurately measure the green development level, this study constructs a multi-dimensional indicator system encompassing economic growth quality, resource utilization efficiency, and environmental protection capacity. The entropy weight method is utilized to assign weights to these indicators, ensuring an objective assessment of the green development score for each city.

2.2 Spatial Convergence Model

To analyze the dynamic evolution of green development, we employ $\sigma$-convergence and $\beta$-convergence models. The $\sigma$-convergence reflects the trend of the standard deviation of green development levels over time, while the spatial $\beta$-convergence model accounts for geographic spillover effects, expressed as:

$$ \ln\left(\frac{y_{i,t+T}}{y_{i,t}}\right) = \alpha + \beta \ln(y_{i,t}) + \rho W \ln\left(\frac{y_{i,t+T}}{y_{i,t}}\right) + \varepsilon_{i,t} $$

where $y_{i,t}$ represents the green development level of city $i$ at time $t$, $W$ is the spatial weight matrix, and $\rho$ is the spatial autore

Mean values for the upstream region from 2006 to 2011

is 0.418, with a growth rate of 1.66%, establishing a pattern of "downstream > midstream > upstream."

The regional development pattern of green development levels is closely related to factors such as the natural environment and infrastructure. Regions with convenient transportation and higher levels of economic development possess more resources available for green development. Between 2016 and 2021, these regions achieved significant progress in industrial structural adjustment, with high-tech industries and service sectors developing rapidly, thereby facilitating green and sustainable development.

The middle reaches are currently in a transition phase from traditional industry to green industry. On one hand, efforts are focused on the transformation of heavy industries such as iron and steel; on the other hand, there is a continuous increase in investment and support for emerging green industries.

Measurement and Spatial Convergence of Green Development Levels in Cities of the Yellow River Basin

Analysis of Regional Green Development Characteristics

The cultivation of emerging green industries has facilitated a strategic transformation in the middle reaches of the Yellow River, leading to a gradual improvement in green development levels. However, as this region is currently in a transitional phase, its overall green development performance remains inferior to that of the downstream areas. In contrast, the upstream regions consist largely of plateau areas characterized by fragile ecological environments and relatively limited economic development. These areas rely heavily on resource-based industries and traditional agriculture and animal husbandry. The extraction and processing stages of resource-based industries exert significant environmental pressure, while traditional agricultural practices suffer from extensive production methods. Consequently, the green development level in the upstream region is low and its growth rate remains sluggish.

Analysis of City-Level Disparities

While green development levels have improved across all prefecture-level cities, inter-city disparities remain pronounced. Tongchuan, for instance, lags behind with slow growth, whereas Jinan and Qingdao have consistently maintained leading positions with rapid growth rates. Xi'an has emerged as a "latecomer," significantly improving its standing over time. The polarization of green development levels among cities is evident. A potential reason for this divergence is that cities in the early stages of industrialization tend to prioritize traditional growth, while more developed cities place a higher premium on green sustainability.

Cities like Tongchuan face inherent constraints such as water scarcity and an industrial structure heavily dependent on resource-intensive or high-energy-consuming sectors, which severely limits the pace of green growth. Conversely, economically developed cities like Jinan and Qingdao possess greater resources to invest in green initiatives and have achieved significant results in ecological civilization construction. Qingdao, in particular, has been recognized as one of China's most ecologically competitive cities. These leading cities attach great importance to green development, not only by issuing policy frameworks to clarify environmental responsibilities across departments but also by vigorously developing the new energy vehicle industry and promoting industrial green transformation, resulting in a rapid rise in their green development levels.

Distributional Characteristics of Green Development

[FIGURE:1]

The Kernel Density Estimation (KDE) curves for the entire basin and its three major reaches characterize the distribution of green development levels over the study period.

1. Basin-wide Distribution Analysis

• Distribution Location: The center of the curve shifts to the right year by year, indicating a general upward trend in the green development levels of prefecture-level cities across the entire basin.
• Distribution Shape: The peak value of the main crest fluctuates between high and low, while the width of the peak remains relatively stable. This suggests that while the concentration of green development levels among cities fluctuates, the overall distribution range is stable.
• Distribution Extensibility: The curve exhibits a right-hand tail, indicating that green development levels in certain cities are significantly higher than in others. Over time, this right tail has extended and widened, suggesting that the gap between high-performing cities and the rest of the basin is continuously expanding.

2. Regional Distribution Analysis (Upstream, Midstream, and Downstream)

• Distribution Location: The center positions of the curves for all three regions show a clear rightward shift, signifying significant improvements in green development levels across the upstream, midstream, and downstream areas during the research period.
• Distribution Shape: In these sub-regions, the peak value of the main crest continues to rise, implying an increasing concentration of green development levels. Simultaneously, the width of the peaks has narrowed significantly, indicating a reduction in the degree of dispersion. The combined changes in peak height and width suggest that cities with initially lower green development levels are catching up through rapid development.

[TABLE:1]

Highly developed cities are also consolidating and improving their status. In the upstream and downstream regions, the peak value first decreases and then increases, indicating that the distribution of green development levels has undergone a process of shifting from dispersion to concentration. This suggests that the degree of dispersion in green development levels is decreasing. Conversely, the combined changes in peak height and peak width indicate that the gap in green development levels among prefecture-level cities has widened, with development levels being relatively scattered. The subsequent rise in peak values suggests that green development has begun to cluster around a specific level.

Distribution extensibility analysis reveals that the middle and downstream regions exhibit a distinct right-tail characteristic. This indicates that while some cities, such as Qingdao and Jinan, have achieved high levels of green development, the majority of cities remain relatively concentrated at lower levels. The right-tail shows a trend of lengthening year by year, suggesting that the gap between a few high-level green development cities and others is gradually increasing. However, this also reflects the potential and room for further green development. Policy efforts should prioritize addressing the non-equilibrium characteristics of green development across different reaches of the Yellow River Basin, promoting coordinated green development among prefecture-level cities in a targeted manner.

Spatial Convergence Analysis of Green Development Levels

To analyze the spatial convergence of green development, this study constructs a geographic adjacency weight matrix and a geographic distance weight matrix. Based on the Hausman test, a spatial Durbin model (SDM) with both spatial and temporal fixed effects was selected for the convergence analysis. Following existing research, environmental protection concern and industrialization level are included as control variables in the convergence model.

The results of the $\beta$ convergence analysis—including both absolute and conditional convergence—show that the coefficients for the entire basin and each reach are negative at a significant confidence level. This implies that, regardless of whether inter-city differences are considered, cities with lower levels of green development are growing faster than those with higher levels. Consequently, they are converging toward a similar level, demonstrating a clear "catch-up" or convergence effect.

The variation in convergence speeds may be attributed to the weak initial green development foundation in the upstream regions. In the following tables, the geographic adjacency weight matrix and geographic distance weight matrix are utilized to verify these findings.

[TABLE:1]

In the results, $\beta$ represents the convergence coefficient, and $\rho$ represents the spatial autocorrelation coefficient. The significance levels are indicated, with $t$-values or standard errors provided in parentheses.

Measurement and Spatial Convergence of Green Development Levels in Prefectural Cities of the Yellow River Basin

The potential for spatial convergence is significant; given identical policy frameworks and resource investments, regions with lower initial development exhibit faster improvements in green development levels. Analysis of spatial spillover effects reveals distinct regional dynamics. For the upstream region, the spatial autocorrelation coefficient indicates that the ability of upstream prefectural cities to reach their respective steady states depends solely on their internal development factors.

In the midstream region, the spatial spillover effect is non-significant under the geographic adjacency weight matrix, yet it is significantly negative at the confidence level when utilizing the geographic distance weight matrix. Given that different types of weight matrices characterize spatial relationships with varying degrees of precision, the spatial spillovers in the midstream region should be interpreted with caution. This discrepancy further underscores the scientific necessity of employing multiple matrix types for robustness testing.

For the downstream region, the coefficients are significantly negative across all weight matrices at the confidence level. This suggests that improvements in the green development level of a specific prefectural city may hinder the progress of neighboring cities, indicating a negative spatial spillover effect. Such a phenomenon implies a competitive relationship between cities, where the advancement of green development in certain areas may exert pressure on surrounding cities through resource competition, subsequently leading to a decline in their relative green development levels.

These negative spillover effects demonstrate that effective coordination in green development among prefectural cities in the Yellow River Basin remains insufficient, and the level of collaborative progress is relatively low. Consequently, there is an urgent need to further strengthen complementary cooperation and construct a win-win development model for the region.

结论

Analysis of Green Development in the Yellow River Basin

1. Current Status and Distribution Characteristics

From the perspective of green development levels, the prefectural-level cities in the Yellow River Basin exhibit a growing trend, although the overall level remains relatively low. A distinct spatial pattern has emerged across the three major regions (upper, middle, and lower reaches), characterized by significant disparities and a clear polarization between cities. Jinan and Qingdao have consistently maintained leading positions with rapid growth rates. In contrast, cities such as Wuwei and Tongchuan lag behind with slower growth. Notably, Yan'an has emerged as a "latecomer," showing a rapid increase in its green development level.

Regarding distribution characteristics, the concentration of green development levels across the entire basin has fluctuated, while the overall distribution range has remained relatively stable. In the middle reaches, the concentration of green development has increased while dispersion has decreased, indicating a more balanced developmental state. In the upper and lower reaches, the distribution alternates between concentration and dispersion, with higher degrees of variance. Overall, the disparity in green development levels among prefectural cities is gradually narrowing. However, the middle and lower reaches exhibit a pronounced "right-tail" characteristic that has extended year by year, signifying that a few specific cities have achieved substantial leaps in their green development levels.

2. Spatial Convergence Analysis

Spatial convergence analysis reveals that the entire basin, as well as its three major sub-regions, exhibit significant absolute $\beta$-convergence and conditional $\beta$-convergence. This indicates a clear "catch-up" trend in regions with lower levels of green development. The convergence speeds follow a distribution pattern of [Middle Reaches > Upper Reaches > Lower Reaches].

The spatial spillover effects vary significantly across the three segments:
- Upper Reaches: No significant spatial spillover effects were observed.
- Middle Reaches: A negative spatial spillover effect exists only under the geographic distance weight matrix.
- Lower Reaches: Significant negative spatial spillover effects persist across all tested weight matrices.

These findings suggest that the level of collaborative green development among prefectural cities remains low, necessitating the implementation of differentiated strategies.

3. Policy Recommendations for Synergistic Development

To ensure sustainable progress, the Yellow River Basin must consolidate existing achievements and refine policy measures. When formulating development strategies, authorities must account for differences in industrial structures and urbanization stages to ensure local conditions are met:

• Upper Reaches: Focus should be placed on ecological protection, developing ecological agriculture and animal husbandry, and expanding the clean energy industry.
• Middle Reaches: Efforts should be directed toward the green transformation and upgrading of traditional industries to build a modern green industrial system.
• Lower Reaches: Leveraging advanced manufacturing and high urbanization, these areas should focus on improving resource efficiency, developing emerging green industries, and leading the basin's transition.

High-level cities should play a leading role in radiating growth across the basin. Given the competitive advantage of the lower reaches, exchange programs should be organized to share experiences with the middle and upper reaches, thereby narrowing regional gaps. It is crucial to develop urban clusters that foster radiation effects rather than "resource siphoning," ensuring that leading cities drive the development of surrounding areas.

Finally, when issuing policies, decision-makers should consider the convergence trends and speeds of different regions. For areas with lower green development levels, increasing policy support and resource investment can accelerate their catch-up speed. Local governments should actively promote effective regional cooperation—such as improving inter-regional ecological compensation mechanisms and unifying environmental standards—to create a healthy environment for competition and cooperation. This will mitigate negative spatial spillovers and ultimately achieve synergistic green development across the entire Yellow River Basin.

References

The following references provide expert interpretation and strategic frameworks regarding the ecological protection and high-quality development of the Yellow River Basin:

Huang Feng. Expert interpretation and seminar on ecological protection and high-quality development of the Yellow River Basin [J]. Yellow River.

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Yang Xinmei, Huang Heping, Zhou Ruihui. Evaluation and spatiotemporal evolution of urban green development level in China[J]. Acta Ecologica Sinica, 2021. Ren W, Xu Y, Xiao H. Research on the impact of marine ecological civilization demonstration zone policies on the green development level of China's marine economy: A quasi-natural experiment based on coastal cities[J]. Marine Policy, 2023. Yang T, Zhou K L, Zhang C. Spatiotemporal patterns and influencing factors of green development efficiency in China's urban agglomerations[J]. Sustainable Cities and Society, 2022. Deng Zongbing, Li Liping, Wei Jianxiong, et al. Spatio-temporal pattern and obstacle factors of China's industrial green development[J]. Science and Technology Management Research, 2016. Ren Ping, Liu Jingwei. Theoretical connotation, evaluation criteria and path to realization of high-quality green development[J]. Inner Mongolia Social Sciences (Chinese Edition), 2019. Gong Qianwen, Li Xuemin. Construction and measurement of agricultural green development index[J]. Reform, 2020. He Aiping, An Mengtian, Li Xuejiao. Research on the green development efficiency and its improvement path in the Yellow River Basin[J]. The Journal of Humanities, 2021.

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Zhe Caihong and Han Yan. Spatiotemporal differentiation and spatial drive of green development efficiency in the Yellow River Basin [J]. Statistics & Decision. Liu Shuai, Zhang Hangyu, and Cai Wenjing. Spatial pattern and dynamic evolution of agricultural green total factor productivity in the Yellow River Basin [J]. Journal of Ecology and Rural Environment. Ren Jiamin, Guo Fuyou, Zhao Hongbo, et al. Performance evaluation and spatiotemporal heterogeneity characteristics of industrial green transformation of resource-based cities in the Yellow River Basin [J]. China Population, Resources and Environment. Tian Ze, Fang Qi, Ju Yun, et al. Evaluation of efficiency of manufacturing industry transformation and influencing factors in China's three major river basins [J]. Resources and Environment in the Yangtze Basin. Qiao Rui, Dong Feng, and An Tailong. Evaluation of green development level and analysis of obstacle factors in the Yellow River Basin [J]. Statistics & Decision. Lin Jiangbiao, Wang Yajuan, and Ma Jing. Study on the spatiotemporal differentiation and influencing factors of green development level in the Yellow River Basin [J]. Statistics & Decision. Li Kuiming, Wang Xiaoyan, and Yao Luolan. Regional differences and driving factors of agricultural green development level in the Yellow River Basin [J]. Journal of Desert Research.

Research on the measurement and spatial convergence of the green development level of prefecture-level cities in the Yellow River Basin. Can the digital economy promote the resilience of the tourism economy in the Yellow River Basin? [J].

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The spatiotemporal evolution and driving forces of the coupling coordination degree of urban high-quality development systems in the Yellow River Basin [J]. Journal of Statistics and Information. Du Yaming, Bai Yongping, Liang Jianshe, et al. Comprehensive measurement and influencing factors of carbon emission efficiency of tourism in the Yellow River Basin [J]. Arid Land Geography. Cao Kaijun, Li Ruxue, Long Shunfa, et al. Spatiotemporal evolution characteristics and influencing factors of tourism-ecosystem vulnerability in the Yellow River Basin [J]. Journal of Southwest University (Natural Science Edition).

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Evolutionary dynamics, regional differences and convergence characteristics of resource mismatch in China [J]. Statistics & Decision. Cai Xueling, Pang Zhiqiang. Measurement and spatial convergence of Chinese-style rural governance modernization [J]. Chinese Journal of Agricultural Resources and Regional Planning.

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Research on China's Ecological Civilization Construction and Its Evaluation System

1. Introduction

The construction of ecological civilization is a fundamental strategy for the sustainable development of the Chinese nation. Since the 18th National Congress of the Communist Party of China, the concept of ecological civilization has been elevated to an unprecedented strategic height, forming a core component of the "Five-in-One" general layout. This paradigm shift represents a transition from traditional industrial civilization toward a new stage of human development that emphasizes the harmonious coexistence of humanity and nature. To effectively guide and monitor this transition, it is essential to establish a scientific and rigorous evaluation system that can quantify progress, identify bottlenecks, and provide a basis for policy adjustment.

2. The Concept and Evolution of Ecological Civilization

Ecological civilization is not merely an environmental issue; it is a comprehensive system involving the transformation of production modes, lifestyles, and value systems. Unlike the traditional "pollute first, treat later" model of industrialization, ecological civilization emphasizes the integration of economic development with environmental protection.

The evolution of this concept in China has progressed through several distinct stages:
- The Preliminary Stage: Focusing on basic environmental protection and pollution control.
- The Development Stage: Integrating sustainable development strategies into national economic planning.
- The Strategic Stage: Elevating ecological civilization to a national strategy, emphasizing the philosophy that "lucid waters and lush mountains are invaluable assets."

3. Construction of the Evaluation Index System

A scientific evaluation system is the "wind vane" for the construction of ecological civilization. Current research focuses on developing multidimensional indicators that cover resource utilization, environmental quality, ecological protection, and institutional development.

3.1 Framework Design

The evaluation framework generally follows a hierarchical structure, often utilizing the Pressure-State-Response (PSR) model or its derivatives. The core dimensions typically include:
- Resource Efficiency: Measuring the consumption of energy, water, and land per unit of GDP.
- Environmental Quality: Monitoring air quality (PM2.5), water quality, and soil health.
- Ecological Health: Assessing forest coverage, biodiversity, and ecosystem service functions.
- Green Development: Evaluating the share of renewable energy and the growth of the circular economy.

3.2 Mathematical Modeling and Weighting

To synthesize diverse indicators into a single index, various mathematical methods are employed. Let the evaluation index be represented by $I$. The general aggregation formula is:

str uction and its indicator system in China[J]. Acta Ecologica Sini ⁃

Spatio-temporal Evolution and Influencing Factors of Green Development Levels in Resource-based Prefectural-level Cities: A Case Study of Shanxi Province

Abstract

As China enters a new stage of high-quality development, resource-based regions face the dual challenges of economic structural transformation and ecological environmental protection. This study focuses on Shanxi Province, a typical resource-based region, to evaluate the green development levels of its prefectural-level cities. By constructing a comprehensive evaluation index system for green development, we analyze the spatio-temporal evolution characteristics from 2010 to 2022 using the entropy weight method and spatial autocorrelation models. Furthermore, we employ a geographic detector model to identify the key factors influencing these patterns. The results indicate that while the overall green development level in Shanxi has shown a steady upward trend, significant spatial disparities persist between northern and southern regions. Factors such as industrial structure optimization, technological innovation, and environmental regulation intensity are identified as primary drivers. These findings provide a theoretical basis and policy references for promoting sustainable development and green transition in resource-dependent economies.

1. Introduction

Green development is a core component of the "New Development Philosophy" and a necessary path for achieving the "Dual Carbon" goals. For resource-based regions, which have long relied on the extraction and primary processing of natural resources, the traditional extensive growth model has led to severe environmental degradation and resource exhaustion. Transitioning toward a green, low-carbon economy is not only an ecological necessity but also a prerequisite for long-term economic resilience.

Shanxi Province, as China's first comprehensive reform pilot zone for energy revolution, serves as a critical microcosm for studying the green transition of resource-based economies. Despite significant efforts in coal mine closures and industrial upgrading, the province still faces complex issues regarding spatial imbalances and varying capacities for green growth among its cities. Understanding the spatio-temporal dynamics and the underlying mechanisms of green development in this region is essential for tailoring effective regional policies.

2. Research Methodology and Data Sources

2.1 Construction of the Evaluation Index System

To objectively measure the green development level, this study establishes a multi-dimensional evaluation framework. Following the principles of scientific rigor, systematicity, and data availability, we categorize indicators into three primary dimensions: economic growth quality, environmental resource pressure, and social welfare improvement.

[TABLE:1]

2.2 Measurement Methods

We utilize the Entropy Weight Method (E

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al. Spatiotemporal evolution and influencing factors of green devel

opment in resource - intensive areas at the municipal level: A case

study of Shanxi Province [J]. Economic Geography.

Wang C H. An environmental perspective extends market orientation: Green innovation sustainability [J]. Business Strategy and the Environment.

Zou Dian. Re-recognizing the Concept of Green Development: A Discussion on Xi Jinping’s Thought on Green Development [J]. Hunan Social Sciences.

Liao Xiaoping. Re - cognition of the concept of green development:

Spatio-temporal Evolution Characteristics and Influencing Factors of Green Development Level in Restricted Development Zones of Jilin Province

1. Introduction

The concept of green development is a core component of Xi Jinping’s economic thought and serves as a fundamental guideline for China's ecological civilization construction. Restricted development zones, as defined by the National Major Function Zone Plan, are areas with weak resource and environmental carrying capacities or those that play vital roles in ecological security. In these regions, large-scale, high-intensity industrialization and urbanization are restricted to maintain ecological stability. For Jilin Province, a critical grain production base and ecological barrier in Northeast China, understanding the spatio-temporal dynamics of green development within its restricted development zones is essential for balancing economic growth with environmental preservation.

2. Methodology and Data Sources

This study evaluates the green development level of restricted development zones in Jilin Province by constructing a comprehensive evaluation index system. We utilize a combination of the entropy weight method and the TOPSIS model to quantify development levels across different dimensions.

2.1 Evaluation Index System

The index system is designed around three primary dimensions: economic growth quality, environmental resource pressure, and ecological protection efficiency. We incorporate indicators such as GDP per capita, energy consumption per unit of GDP, forest coverage rates, and pollution emission intensities to provide a holistic view of green development.

2.2 Spatial Autocorrelation Analysis

To explore the spatial distribution characteristics, we employ the Global Moran’s $I$ index to detect spatial clustering. The formula for the Global Moran’s $I$ is given by:

$$I = \frac{n \sum_{i=1}^{n} \sum_{j=1}^{n} w_{ij} (x_i - \bar{x})(x_j - \bar{x})}{\sum_{i=1}^{n} \sum_{j=1}^{n} w_{ij} \sum_{i=1}^{n} (x_i - \bar{x})^2}$$

where $n$ represents the number of spatial units, $x_i$ and $x_j$ are the observed values of the green development level for units $i$ and $j$, and $w_{ij}$ is the spatial weight matrix.

[TABLE:1]

3. Spatio-temporal Evolution Characteristics

The analysis reveals that the green development level in Jilin Province’s restricted development zones has

Journal of Agricultural Resources and Regional Planning, 2020 , 41

Economic Policy Choices for Smog Pollution Control in China: A Perspective Based on Spatial Spillover Effects

Abstract

Smog pollution has become a critical environmental issue hindering the sustainable development of China's economy. This study investigates the impact of various economic policies on smog pollution control by considering spatial spillover effects. Using spatial econometric models and panel data from Chinese provinces, we analyze the effectiveness of different policy instruments. The results indicate that smog pollution exhibits significant spatial correlation and spillover effects, where local pollution levels are influenced by neighboring regions. Furthermore, we find that market-based instruments and technological innovation play a crucial role in reducing smog concentrations. The study suggests that a coordinated regional approach, combined with optimized economic policy designs, is essential for effective smog governance in China.

1. Introduction

Since the reform and opening-up, China has achieved remarkable economic growth. However, this rapid development has been accompanied by severe environmental degradation, particularly the frequent occurrence of large-scale smog episodes. Smog pollution, characterized by high concentrations of $PM_{2.5}$ and $PM_{10}$, not only poses a significant threat to public health but also imposes substantial economic costs. Consequently, identifying effective economic policies to mitigate smog pollution has become a top priority for both the Chinese government and the academic community.

Traditional environmental economics often assumes that pollution is a localized issue. However, smog is inherently mobile, and atmospheric conditions can transport pollutants across administrative boundaries. This spatial interdependence implies that smog control in one region may be affected by the policies and emissions of neighboring regions. Therefore, analyzing smog governance through the lens of spatial spillover effects is vital for designing effective policy interventions.

[FIGURE:1]

2. Theoretical Framework and Research Hypotheses

The impact of economic policies on environmental quality can be understood through several mechanisms: the scale effect, the structural effect, and the technique effect. While economic growth may initially increase pollution (scale effect), shifts toward cleaner industries (structural effect) and the adoption of green technologies (technique effect) can eventually lead to environmental improvements.

2.1 Spatial Correlation of Smog Pollution

Smog pollutants are not confined by geographic borders. Due to natural factors such as wind speed, humidity, and topography, as well as socio-economic linkages like regional trade and industrial transfers, smog pollution in one province is likely to be positively correlated with that of its neighbors.

Hypothesis 1: Smog pollution in China exhibits significant positive spatial autocorrelation and spatial

choices for governing smog pollution based on spatial spillover ef ⁃

fects[J]. Economic Research Journal, 2023. Sun Hui, Zhang Ruowei, Zhang Zhaodi. Environmental effects of the construction of a unified national market: Based on the spatial spillover perspective[J]. China Business and Market, 2023. Han Jing, Chen Xi. Spatial-temporal evolution of urban green development level along the Yangtze River Economic Management[J]. East China Economic Management, 2021. Liu K, Xue Y, Chen Z, et al. The spatiotemporal evolution and influencing factors of urban green innovation in China[J]. Science of the Total Environment, 2023, doi: 10.1016/j.scitotenv.

Dong Zhiqing, Wang Hui. Validation of market-based environmental policies: Empirical evidence from the perspective of carbon emission trading policies[J]. Statistical Research, 2019.

Li Hongbing, Zheng Qingbiao, Li Zhen, et al. Industrial intelligence and urban air pollution control in China: Empirical evidence from the application of industrial robots[J]. Management Review, 2022. Liu Hongqin, Geng Shu. Are local governments myopic? Fiscal federalism, promotional tournament and public expenditure in China's prefecture-level governments[J]. Sociological Review of China, 2022.

Yellow River Basin. Chen Wenmei, Li Chungen. The division of social assistance expenditure responsibility in China: Theoretical basis, practical problems and optimization path[J]. Social Security Studies, 2021. Hu Wei, Ke Xinli. Regional differences and evolution paths of the coordinated development of urbanization and industrialization in China[J]. 2021.

ment of urbanization and industrialization in China[J]. Urban Prob ⁃

lems, Measurement and spatial convergence of green development level of prefecture-level cities in the Yellow River Basin REN Shiqi, WANG Yongyu (School of Statistics and Data Science, Lanzhou University of Finance and Economics, Lanzhou , Gansu, China) development in the Yellow River Basin. This study examines green development patterns across prefecture- level cities in the Yellow River Basin from . Based on the logical framework and internal mecha nisms of green development, we constructed a comprehensive evaluation index system and employed the entropy weight method, kernel density estimation, and convergence models to analyze development levels, distribution characteristics, and spatial convergence. Our analysis revealed three key findings: ( ) Temporal and spatial pat terns: Green development levels across prefecture-level cities in the Yellow River Basin demonstrated consistent improvement throughout the study period. A clear spatial gradient emerged with downstream regions outperform ing midstream areas, which in turn surpassed upstream regions. Significant differences persist between individual prefecture-level cities. ( ) Distribution characteristics: Kernel density analysis showed uneven peak distribution with a narrow width and pronounced right tail extension, indicating a substantial imbalance in green development levels. Some cities have achieved notably high green development levels, creating significant gaps compared to other urban cities in the basin. ( ) Convergence dynamics: Both absolute convergence and conditional conver gence were statistically significant across the entire basin and within the three major river sections. Convergence rates followed an upstream>midstream>downstream progression. Additionally, differentiated spatial spillover effects were observed in green development levels across the three major convergence processes. These findings over valuable insights for enhancing green development levels in prefecture-level cities throughout the Yellow River Basin and promoting coordinated regional development strategies that address the identified spatial dispari