Planning and governance for green transformation of the Yellow River Basin: an analytical framework for green evaluation and governance in resource-based cities

The green transformation of national spatial planning is a critical pathway to global sustainable development. Existing research has examined sector-specific strategies, but the role of integrated spatial planning as an institutional driver of urban sustainability remains underexplored. This study focuses on resource-based cities in China’s Yellow River Basin. It develops a spatial-planning-oriented analytical framework aligned with the Sustainable Development Goals (SDGs) to assess green transformation dynamics. Using a panel dataset for 36 cities from 2006 to 2020, we use entropy weighting, coupling coordination modelling, kernel density estimation and grey relational analysis to evaluate transformation performance, spatial and temporal evolution and key influencing factors. The results show three main findings. Comprehensive green transformation levels improve markedly, and the coupling coordination degree shifts from moderate to relatively high coordination. Transformation levels display a clear spatial clustering pattern characterised by “east leading, centre rising and west catching up”. The urbanisation rate, per capita GDP and fiscal expenditure act as dominant drivers, while technological innovation has a weaker but still positive effect. The study highlights the need to embed differentiated governance strategies within the national spatial planning system. It also offers targeted recommendations on planning hierarchy, policy integration and technical standards to support a coordinated green transition in resource-intensive regions.

1 Introduction

Promoting high-quality green and low-carbon transformation in regional cities is one of the basic requirements for building a new national spatial planning system in the new era (Deng et al., 2021). The long-standing development approach that prioritises economic growth at the expense of the ecological environment needs to be transformed. Against the background of constraints on resource reserves and growing concern for ecological conservation, the green transformation of cities has become a global trend (Chen et al., 2022). Leading developed countries, led by the United States, have begun to build green alliance systems, such as the ‘Transatlantic Green Alliance’ (Yue et al., 2023) and the ‘Indo-Pacific Climate Partnership’ (Lenaerts and Tagliapietra, 2022). China faces multiple pressures. These include preventing a tragedy of the commons in domestic urban development, promoting regional sustainable development and managing public resources more effectively. In response, the Chinese government has taken a strong stance on green transformation and has made it a core task of national spatial planning. Policy documents emphasise that future high-quality development must embed the idea of green transformation in the whole process of urban and rural construction (Chen et al., 2023a). They also set out specific measures for transforming development modes, achieving carbon peaking (Liang et al., 2023), reaching carbon neutrality and advancing other aspects of urban and rural green transformation (Bharti and Singh, 2023). Recent policies show that the construction of ecological civilisation in China has moved from institutional design to practical exploration. It has gradually entered a stage of high-quality green transformation that fits China’s national conditions and reflects distinct Chinese characteristics (Breed and Mehrtens, 2022). However, China still faces many challenges, including incomplete green transformation planning, weak institutional mechanisms and other urgent tasks (Lu et al., 2020; Ju et al., 2025).

China is also a global powerhouse in resource extraction, processing and consumption (Tang et al., 2022). For many years, resource-based cities have operated with high levels of investment, consumption and emissions (Wang et al., 2024), which has supported rapid economic development (Chen et al., 2023a; Gong and Guo, 2024; Li et al., 2021). At the same time, conflicts between regional resource demand and ecological environmental protection have become increasingly intense (Fan and Zhang, 2021). The extensive development model based on a resource-dependent economy has led to growing pollution and environmental degradation in many regions (Li et al., 2021). The contradictions between socio-economic development and resource and environmental protection have become more prominent (Ruan et al., 2020; Wang et al., 2022). As basic drivers of national economic and social development (Liu et al., 2025), resource-based cities play a key role in China’s low-carbon transition (Yang et al., 2022) and green transformation (Li et al., 2021; Zhang T et al., 2022; Zhang X et al., 2022). Their transition outcomes have a strong influence on the success or failure of ecological civilisation construction at both regional and national scales (Xu et al., 2021). Green and low-carbon transformation in resource-based cities is not only a difficult issue in resolving the tragedy of the commons in these cities. It is also an important direction for innovation in urban governance (Chen et al., 2023b) and for achieving high-quality green development (Liu et al., 2022). As both a historical legacy and a requirement for sustainable development (Fan and Zhang, 2021), the green transformation of resource-based cities has become a broad consensus in the academic community. It is seen as a key way to avoid the path of ‘resource exhaustion leading to urban decline’ (Sun et al., 2023).

Existing studies on resource-based cities mainly examine several aspects of green transformation. Scholars analyse the challenges of green transformation (Li K et al., 2023), development strategies (Ding et al., 2022), development directions (Su and Fan, 2022) and influencing factors (Pan et al., 2023). They also study the spatiotemporal evolution of green production elements (Xing et al., 2023), carbon emissions (Wang et al., 2023a), urban ecological efficiency (Yang et al., 2022) and other features of resource-based cities (Wang and He, 2022). In terms of evaluation systems, most studies adopt a macro perspective (Chen and Zhang, 2021). They conduct sustainability assessments of resource-based cities by building indicator systems that include green innovation (Li Q et al., 2023), economic resilience, ecological efficiency and energy transition (Sun et al., 2023) and other factors. They also provide policy recommendations from different angles (Li Q et al., 2023).

A review of the literature shows that current research on urban and rural green transformation has three main characteristics (Zeng S. et al., 2023; Zhang J. et al., 2022). Existing studies focus on ecological restoration, pollution control (Zeno M. et al., 2023), industrial upgrading, government policy and technological innovation, but they rarely treat the institutional and governance dimensions of national spatial planning as a core analytical framework (Luo et al., 2022). Methodological approaches can be grouped into econometric methods, such as difference-in-differences and spatial regression, and composite evaluation methods, such as entropy weighting, data envelopment analysis and coupling coordination. However, many studies do not build a clear logical link between their research questions and the methods they use (Sun Y. et al., 2023; Wang Y. et al., 2022; Wang Z. et al., 2022). Much of the research is fragmented in scale and scope (Wang et al., 2025). It often focuses either on micro-level practices or on macro-level policy assessments and lacks systematic analysis of how spatial planning institutions shape the endogenous dynamics of regional urban transformation (Zeng et al., 2023). In this context, using the perspective of institutional mechanisms and models of national spatial planning is not only a new angle. It also responds to an important theoretical and practical gap (Su et al., 2023). From a theoretical point of view, spatial planning affects land allocation, industrial structure, infrastructure systems and cross-regional governance arrangements. It shapes the long-term path of urban transformation and creates strong path dependence and lock-in effects. From a practical point of view, China’s ongoing territorial spatial planning reform and its dual-carbon strategy create an urgent need for research that links planning institutions with measurable outcomes of urban transformation (Chen et al., 2023b). This perspective therefore has both explanatory power and policy relevance (Liu et al., 2023; Mi et al., 2020).

Building on this rationale, this study makes four contributions (Figure 1). First, it proposes an analytical framework for green transformation that links national spatial planning institutions with urban governance models and helps to fill an important gap in existing research. Second, it builds an SDG-oriented indicator system and uses a global entropy weighting method to generate a more robust assessment of green transformation levels (Du and Ren, 2023). This is combined with kernel density estimation and coupling coordination analysis to capture spatiotemporal heterogeneity. Third, it differs from earlier work that lists methods without clear justification. This study sets out the links between each research question and the methods used (Li K et al., 2023; Li L, et al., 2022; Li Q et al., 2023). Grey relational analysis is used to identify the driving factors of transformation (Zhang J et al., 2022), and Pearson correlation is used for robustness checks. This improves methodological coherence and rigour. Fourth, it develops concrete policy recommendations that follow the logic of national spatial planning. These recommendations cover institutional design, regulatory frameworks, technical standards and disciplinary system building (Song and Levine, 2025; Sun J. et al., 2023). They provide practical guidance for embedding green transformation in the governance system of resource-based cities. Taken together, these contributions offer both theoretical insight and practical value and advance the study of urban green transition from a spatial planning perspective (Wang et al., 2023; Li X. et al., 2022).

Figure 1

Figure 1. Framework of the paper.

2 Constructing a governance system for high-quality green transformation in national spatial planning

2.1 Fundamental understanding of China’s urban green transformation

There is a growing consensus that green transformation is a top priority for countries worldwide (Xu et al., 2023). The period around 2035 is a critical juncture at which both the urban population and carbon emissions are projected to peak at the same time, a “two peaks overlapping” scenario. On this basis, several points require attention. Cities are the main arena for carbon reduction. Both new construction and the retrofitting of existing infrastructure pose major challenges. Cities serve as key platforms for industry (Bianchini et al., 2023), transport, construction and energy utilisation (Luo et al., 2022). This makes the control of urban carbon emissions a key factor in China’s achievement of its “dual carbon” goals. Over the next 15 years, an additional 150 to 200 million urban residents are expected. This will create significant pressure on urban carbon reduction and on the achievement of the “dual carbon” objectives. Therefore, integrating high-quality green transformation and the “dual carbon” goals into the spatial planning system is an urgent and critical issue. The coming years represent a critical window of opportunity for green transformation. It is therefore imperative to accelerate the formation of a spatial governance system for green transformation (Wang et al., 2023a). The “dual carbon” objectives must become a central strategic choice for China’s urbanisation and for urban and rural development. This requires advancing the green transformation of cities in order to avoid a high-carbon lock-in effect (Xu et al., 2020). Given the challenge of the “two peaks overlapping” scenario for urban population and carbon emissions by 2035, it is essential to focus on efficiency improvements and technological innovation in the period up to 2035. This involves optimising urban functional structures, restoring natural ecological spaces, controlling unregulated urban sprawl and promoting urban green renewal in order to achieve a high-quality carbon peak in urban areas. It also requires establishing green systems in urban operation and management processes in order to achieve carbon neutrality (Chen et al., 2023c).

2.2 The logic and framework of national spatial planning governance in the context of green transformation

2.2.1 The planning logic and framework

At present, urban planning concepts have shifted from traditional procedural planning theories to more systemic and rational planning approaches (Chen et al., 2023c). Green and low-carbon urban planning must respond to diverse objectives and demands from social groups, economic systems, infrastructure and physical space (De Toro et al., 2023). The transformation and establishment of green national spatial planning are promoted through measures such as green planning concepts, planning evaluation systems, planning techniques and public participation in planning processes (Obaideen et al., 2023). The green transformation of national spatial planning is an inherent requirement for the coordinated development of China’s new urbanisation and ecological civilisation. It is also an important benchmark for assessing the level of urban ecological civilisation. Green transformation will shape whether China can move towards high-quality development and will influence the path of its future sustainable development. Under conditions of constrained resources and energy, China’s urbanisation faces the challenge of “three highs”. The “dual carbon” goals also face multiple pressures, including rising urbanisation levels, the need for sustainable economic development and the demands of modernisation (Wang et al., 2023b). At present, these combined challenges are evident, as shown in Figure 3. A review of past and current urban planning, construction and management shows that it is necessary to adjust the development trajectory of the current high-carbon model in order to avoid a high-carbon lock-in effect on urban spatial structure and infrastructure. At the same time, it is necessary to explore key planning techniques for green urbanisation in fields such as urban layout, transport, green buildings, infrastructure and urban operations (Sheng et al., 2023).

National spatial planning for high-quality green transformation is a new spatial planning system that places high quality at its core. Its goal is to guide China onto a modernisation path that is green and low carbon and to achieve sustainable development characterised by innovation, coordination, greenness, openness and shared benefits. High-quality green development seeks to build a new pattern of harmonious coexistence between humans and nature. It represents a deep integration of green development and high-quality development and is characterised by sustainability, efficiency, coordination, stability and strategic significance (Sun et al., 2023). On this basis, and supported by planning technology systems, compilation and approval systems, legal and regulatory systems and supervision and implementation systems, a governance framework for national spatial planning in the context of green transformation is established, as shown in Figure 2. Innovation, coordination, openness, sharing and green development form an integrated whole. They are closely connected and reinforce each other, supporting the realisation of high-quality green transformation in national spatial planning. Innovation is a key factor in the strategic transformation of economic structures, because it can enhance resource utilisation efficiency and total factor productivity. It is a fundamental driver of industrial greening (Mertens et al., 2022). Coordinated development is a basic principle that guides economic and social development. It can help to maintain sustainable and stable economic growth, ensure social stability and promote more balanced regional development and welfare. Openness reflects the growing interconnection between domestic and external factors. It can facilitate the flow of capital and provide an important platform for innovative development. Shared development is an inherent requirement for inclusive growth, because it can help to ensure that the benefits of development reach the wider population and reduce income disparities between urban and rural areas, regions and individuals. The high-quality green transformation of national spatial planning takes place under the joint interaction of the economic, natural and social systems. It is driven by green wealth (Zeng et al., 2023), green growth (Abubakar and Alshammari, 2023) and green welfare and aims to transform development power, efficiency and quality. Policies such as new urbanisation provide practical pathways for this transformation (Li et al., 2022). Depending on the characteristics of the object being evaluated, the 17 goals are used as a reference framework (Cai et al., 2023). Linking the evaluation of green transformation in national spatial planning with the Sustainable Development Goals helps to strengthen the scientific and international grounding of the evaluation indicators and contributes to the fulfilment of China’s commitments (Zhang T et al., 2022).

Figure 2

Figure 2. Territorial spatial planning system for high-quality green and low-carbon transformation.

2.3 Territorial governance in the context of high-quality green transformation

The green transformation of territorial space is the result of the combined action of several forces, such as policy incentives, industrial transformation and technological progress (Sharifi et al., 2023). These forces are embedded in the organisational system of territorial governance and, through interactions among organisational elements, drive the shift towards green governance. In the context of high-quality green transformation, the territorial governance system can be understood as comprising three core elements: governance subjects, governance objects and governance processes. These elements follow different development paths during the transformation process. Governance subjects are shifting from government-led management to multi-stakeholder co-governance (Sun et al., 2023). Embedding the idea of green development in social, economic, ecological and governmental activities can help to address key problems in territorial governance. The green transformation of territorial space depends on active participation by enterprises, social organisations and citizens. This participation supports the emergence of a multi-stakeholder co-governance framework in territorial space (Zeng et al., 2023).

The objects of governance are also shifting from a narrow focus on ecological governance to more coordinated control. As a practical pathway for ecological civilisation construction, green transformation extends beyond traditional fields of ecological governance, such as resource conservation, ecological restoration and pollution prevention. It places greater emphasis on comprehensive regulation and coordination of territorial space to guide ecological construction, economic development and social development towards green transformation (Zhao et al., 2023). This approach supports closer integration of ecological, economic and social benefits and helps to promote more sustainable patterns of production, everyday life and ecological protection. At the same time, the governance process is shifting from passive responses to more proactive reform. The green transformation of territorial space is driven by pressures such as local resource depletion, economic decline, environmental degradation and rising social tensions. These pressures compel territorial space governance to adapt in economic, social and ecological domains. Sustaining this process requires stakeholders to maintain a high level of commitment and willingness to advance green governance. Green governance transformation requires fundamental adjustments to existing governance models, development patterns and lifestyles (Sharifi et al., 2023). Such changes are likely to raise production and living costs for governments, enterprises and residents and to reshape social power structures and the distribution of interests. They can provoke strong opposition from some stakeholders, particularly vested interests. In the early stages, green governance transformation therefore tends to take the form of passive adaptation to economic and ecological pressures. Only when green transformation can deliver substantial economic, social and ecological benefits will stakeholders be more willing to promote green governance actively (Zhang T et al., 2022).

3 Methods

3.1 Study area and data sources

As the primary production area for China’s resource service function and the foundational area for national strategic resource security, resource-based cities represent the core regions where China’s ecosystem is most vulnerable and where the assessment of green transformation is highly sensitive. The level of green transformation in resource-based cities is crucial in showcasing the modernization of the country’s governance capacity. How to improve the regional level of green transformation and promote high-quality development with a green orientation has become a critical issue that needs to be addressed urgently in China’s current economic and ecological development for high-quality development. The level of green transformation in resource-based cities in the Yellow River Basin, which is the most complex region in terms of human-environmental interactions, with the most prominent human-environmental conflicts and rapid changes in the economic-social-environmental system, is crucial for the success or failure of high-quality development in northern China and even the entire country (Figure 3).

Figure 3

Figure 3. Spatial distribution of resource-based cities.

This study uses several types of data, depending on data availability and quality. Statistical data are drawn mainly from the China Urban Statistical Yearbook, China Regional Statistical Yearbook, China Science and Technology Statistical Yearbook, China Industrial Statistical Yearbook, China Economic Yearbook and China Energy Yearbook for the years 2006–2020, as well as from local statistical yearbooks. Data on green innovation are taken from the China National Intellectual Property Database. Data on the green economy and green investment are taken from the People’s Bank of China and the National Bureau of Statistics. Missing values are supplemented using a linear interpolation method. We use remote sensing data, including digital elevation models (DEM) (Zhang X et al., 2022). These data are obtained from remote sensing data providers at a spatial resolution of 30 m. They are calibrated, projected and extracted using ArcGIS and ENVI.

3.2 Model setting

In the context of China’s rapid urbanization and resource-dependent economic model, resource-based cities face a dual challenge of sustaining economic growth while achieving ecological transformation. To address this complex issue, this study adopts a methodological framework that integrates evaluation, spatial analysis, coupling measurement, and driving-factor identification. Specifically, a global entropy weighting method is employed to construct an objective and comparable assessment of green transformation levels across cities and years; kernel density estimation and spatial autocorrelation methods are used to capture the spatiotemporal distribution and clustering characteristics of transformation levels; the coupling coordination degree model is introduced to reveal the interactive relationships and synergistic dynamics among socio-economic, industrial, technological, and environmental subsystems; grey relational analysis, complemented by Pearson correlation tests, is applied to identify the main driving factors and ensure robustness (Zhang X et al., 2022).

3.2.1 Calculation of theil index

The Theil Index is used to measure socioeconomic disparities and income differentiation within resource-based cities. Its advantage lies in its ability to decompose inter- and intra-regional differences, thereby revealing imbalances in population and social development. This provides quantitative support for assessing the social foundations of green transformation. Using the Theil index to characterize the spatiotemporal evolution characteristics of resource-based urban regional economies in the Yellow River Basin, the formula is as follows (Wang et al., 2022):

$Thei{l}_t={\displaystyle \sum _{i=1}^2}\left[\frac{I_{lt}}{I_t}\right]\mathit{ln}\frac{I_{lt}/P_{lt}}{I_t/P_t}=\left[\frac{I_{lt}}{I_t}\right]\ln \frac{I_{it}/P_{lt}}{I_t/P_t}+\left[\frac{I_{2t}}{I_t}\right]\ln \frac{I_{2t}/P_{2t}}{I_t/P_t}(1)$

In the equation, Theil_t represents the urban-rural income gap in resource-based cities in the Yellow River Basin, $I_{lt}$ and $I_{2t}$ respectively denote the total income of urban and rural residents at stage t; $I_t$ represents the total income at a specific stage; $P_{1t}$ and $P_{2t}$ represent the total population of urban and rural areas at stage t, and $P_t$ represents the total population at stage t.

3.2.2 Global entropy method

The global entropy method integrates multidimensional indicators using objective weights, avoiding subjective weighting bias. A unified evaluation matrix across the entire sample time series ensures comparability across periods and regions, providing a stable foundation for subsequent spatial analysis. This method’s innovation lies in its introduction of the concept of “global spatiotemporal entropy,” enabling dynamic and objective measurement of the level of green transformation (Xu et al., 2022). In this study, the global entropy method is employed to assess the level of green transformation in resource-based cities in the Yellow River Basin from 2006 to 2020. For the comprehensive evaluation of M indicators over T years in N regions, historical cross-sectional data is organized to create cross-sectional tables for each year, denoted as X^t=(X^t_ij). These T cross-sectional data tables are then arranged to form the NT × M global entropy evaluation matrix X.

$X^t={\left(X^1,X^2,X^3\dots \dots X^t\right)}_{\text{NT}\times \mathrm{M}}={\left(X_{ij}^t\right)}_{NT\times M}(2)$

In the equation, X^t_ij represents the value of the j indicator for the i region in the year t, where 1 < t ≤ T, 1 < i ≤ N, and 1 < j ≤ M.

Min-max normalization is employed to standardize the evaluation indicator data. The formulas for range normalization are as follows (Zhao et al., 2023):

$N_{i}j=\frac{N_{ij}-\min \left(N_{ij}\right)}{\max \left(N_{ij}\right)-\min \left(N_{ij}\right)}\times \mathrm{a}+\left(1-\mathrm{a}\right)\text{\hspace{0.17em}Positive\hspace{0.17em}indicators}(3)$

$N_{i}j=\frac{\max \left(N_{ij}\right){-N}_{ij}}{\max \left(N_{ij}\right)-\min \left(N_{ij}\right)}\times \mathrm{a}+\left(1-\mathrm{a}\right)\text{\hspace{0.17em}Negative\hspace{0.17em}indicators}(4)$

In the equation, N_ij represents the original value of the j indicator for system i. max (N_ij) and min (N_ij) are respectively the maximum and minimum values of the j indicator for system i. a is the linear transformation parameter, and 1-a is the shift range. To avoid zero values after standardization and reduce the impact of data shift on weights and entropy values, a is set to 0.9999.

Calculate the weight of each indicator (Wang et al., 2022):

$y_{ij}^t=x_{ij}^t/{\displaystyle \sum _{i=1}^T}{\displaystyle \sum _{i=1}^N}{x}_{ij}^t(5)$

The j indicator:

$e_j=-k{\displaystyle \sum _{t=1}^T}{\displaystyle \sum _{i=1}^N}{y}_{ij}^t\times {\ln }_{y_{ij}^t}(6)$

When there are n samples in a completely unordered distribution, where y^t_ij = 1/n, the orderliness is 0, and in this case, the entropy value is maximum, e = 1. For a specific indicator j, the entropy value ej can be calculated as follows: e_j = $-k{\sum }_{t=1}^T{\displaystyle {\sum }_{i=1}^N}\left(\frac{1}{n}\times \text{In}\frac{1}{n}\right)=K\ln \left(NT\right)=1$, and K = $\frac{1}{\ln \left(NT\right)}$. The resulting e_j values will fall in the range 0 ≤ e_j ≤ 1,

Calculate Information Utility Value (Wang et al., 2023):

$d_j=1–e_j(7)$

Calculate Indicator Weights:

$w_j=d_j/{\displaystyle \sum _{j=1}^m}{d}_j(8)$

$F={\displaystyle \sum _{j=1}^m}{w}_j{f}_{ij}(9)$

3.2.3 Calculation of coupling and coordination degree

The coupled coordination model captures the interactions and joint performance of the five systems of society, economy, industry, technology and environment. It can reveal structural constraints and development imbalances among these systems and is a core tool for analysing the synergistic mechanisms of multi-dimensional green transformation (Luo et al., 2022). In this study, the traditional dual-system framework is extended to a five-system coupled framework, which better reflects the governance logic of multi-dimensional coordination in national spatial planning. The Yellow River Basin is a complex multi-dimensional mega-system, characterised by diverse coupling relationships among population, economy, industry, technology and environment (Abubakar and Alshammari, 2023).

$H=\sqrt[5]{\frac{U_1\times U_2\times U_3\times U_4\times U_5}{{\left(\frac{U_1+U_2+U_3+U_4+U_5}{5}\right)}^5}}(10)$

U1 to U5 represent the scores of five primary indicators, and H is within the range [0, 1].

Next, calculate the Comprehensive Coordination Index (K) (Wang et al., 2023):

$K=p_1{U}_1+p_2{U}_2+p_3{U}_3+p_4{U}_4+p_5{U}_5(11)$

Because the five primary indicators are equally important and satisfy the condition p1 + p2 + p3 + p4 + p5 = 1, and K∈ [0, 1].

Finally, calculate the coupling coordination degree (J):

$J=\sqrt{H\times K}(12)$

Based on existing research, the comprehensive evaluation index and coupling coordination degree are divided into five levels (Table 1).

Table 1

Table 1. Division criteria.

3.2.4 Kernel density estimation

Kernel density estimation is used to depict the dynamic distribution and evolution of green transformation levels and to reveal polarisation and convergence trends among different city groups. This method helps to identify structural differentiation and temporal evolution in the transformation process. It also provides an empirical basis for optimising spatial governance. The specific formulae for the kernel density function are as follows (Wang et al., 2023):

$\left.F\left(x\right)=\frac{1}{\text{Nh}}{\displaystyle \sum _{i=1}^N}\mathrm{K}\left(\frac{\left.X_i-\mathrm{x}\right)}{\mathrm{h}}\right.\right)(13)$

$\left.K\left(x\right)=\frac{1}{\sqrt{2\mathrm{\pi }}}\exp \left(-\frac{\left.{\mathrm{x}}^2\right)}{2}\right.\right)(14)$

In the equation, K represents the Kernel density, x is the random variable, N is the number of samples, X_i represents the composite index of the i sample, x represents the mean of the composite index, and h represents the bandwidth, which determines the accuracy of the kernel density estimation.

3.2.5 Spatial autocorrelation model

A spatial autocorrelation model can quantitatively identify the spatial agglomeration effects and local spillovers of green transformation levels among cities, revealing the spatial pattern of “strong in the east, weak in the west” and its diffusion pathways. The introduction of this model enables research to move beyond single-point comparisons and enter the stage of spatial dependency analysis, embodying the concept of regional coordination within the context of national spatial planning. Moran’s I index and LISA maps are used for characterization. The formulas for Moran’s I index are as follows (Wang et al., 2023):

$I=\frac{N\sum i\sum j{Y}_{ij}\left(X_i-\overline{X}\right)\left(X_j-\overline{X}\right)}{\left(\sum i{\sum }_j{Y}_{ij}\right){\displaystyle {\sum }_{i=1}^n}{\left(X_j-\overline{X}\right)}^2}(15)$

$I=\frac{X_j-\overline{X}}{S_x^2}{\sum }_j\left[Y_{ij}{\left(X_j-\overline{X}\right)}^2\right](16)$

$S_x^2=\frac{{\sum }_j{Y}_{ij}{\left(X_j-\overline{X}\right)}^2}{n}(17)$

3.2.6 Grey relational analysis model

A grey relational model combined with a Pearson correlation test is used to identify the strength of correlations between the driving factors (fiscal expenditure, GDP, energy consumption and other factors) and the level of green transformation. This combination serves as a sensitivity analysis to validate the robustness of the model results (Zhao et al., 2023). In this study, the subsequence is composed of factors such as fiscal expenditure, per capita GDP and number of patent authorisations. The coupling coordination degree is used as the parent sequence. After both the subsequence and the parent sequence are standardised, the grey relational coefficient is calculated using the following formulas (Li et al., 2022):

$\mathrm{Y}=\left[\mathrm{Y}\left(1\right),\mathrm{Y}\left(2\right)\dots \dots ,\mathrm{Y}\left(\mathrm{n}\right)\right];{\mathrm{X}}_{\mathrm{i}}=\left[\mathrm{X}\left(1\right),\mathrm{X}\left(2\right)\dots \dots ,\mathrm{X}\left(\mathrm{n}\right)\right](18)$

The difference sequence is:

$△\mathrm{i}\left(\mathrm{k}\right)=\left|\mathrm{Y}\left(\mathrm{k}\right)-{\mathrm{X}}_{\mathrm{i}}\left(\mathrm{k}\right)\right|,\mathrm{k}=1,2\dots \dots \mathrm{n}(19)$

${\mathrm{\xi }}_{\mathrm{i}}=\frac{{{\min }_{\mathrm{i}}\min }_{\mathrm{k}}{{△\mathrm{i}\left(\mathrm{k}\right)+\rho \min }_{\mathrm{i}}\min }_{\mathrm{k}}△\mathrm{i}\left(\mathrm{k}\right)}{△\mathrm{i}\left(\mathrm{k}\right){{+\rho \min }_{\mathrm{i}}\min }_{\mathrm{k}}△\mathrm{i}\left(\mathrm{k}\right)}(20)$

${\mathrm{L}}_{\mathrm{i}}=\frac{{\sum }_{\mathrm{k}=1}^{\mathrm{n}}\mathrm{\xi }{}_{\mathrm{i}\left(\mathrm{k}\right)}}{\mathrm{n}}(21)$

In the equation, ξi(k) represents the correlation coefficient. The parameter ξ usually takes a value in the range (0, 1) and is often set to 0.5. To test the robustness of the model results, a sensitivity analysis is carried out by combining grey relational analysis with Pearson correlation testing. GRA provides the relational degree between each driving factor and the overall green transformation index, while Pearson correlation coefficients are used to check the statistical consistency and stability of these relationships (Wang et al., 2022b). By comparing the direction and significance of the two sets of results, the analysis tests whether the identified drivers remain robust under alternative statistical perspectives.

3.3 Construction of green transformation level evaluation indicators

Drawing on a high-quality national spatial planning framework for green transformation and benchmarking against the United Nations Sustainable Development Goals (SDGs), this study constructs a five-dimensional indicator system covering population, economy, industry, technology and environment (Table 2). In resource-based cities, green transformation of the population refers to steady improvements in population quality and living standards. Following relevant research, this dimension is measured using the Theil index based on indicators such as population size and disposable income of urban and rural residents. Education expenditure and educational attainment are also included. The economic dimension reflects how the concept of green transformation drives green expenditure, green investment and green credit in the region. By reshaping the allocation of economic resources, it affects the green transformation level of resource-based cities. It is measured using the shares of environmental projects, conservation expenditure and total bond issuance accounted for by resource-based cities. The industrial dimension of green transformation is characterised by the industrial sophistication index and the level of industrialisation, which together reflect the efficiency of resource conversion. The technological dimension is characterised mainly by green innovation and technological support. For resource-based cities in the Yellow River Basin, it is measured using the share of green invention patents, the share of green utility model patent applications and the amount of science and technology expenditure. The environmental dimension is characterised mainly by environmental pollution and greening. The environmental pollution index is calculated using entropy weighting based on three types of emissions: industrial wastewater, exhaust gas and solid waste (Jian et al., 2023).

Table 2

Table 2. Comprehensive evaluation index system of green transition level of resource cities in the Yellow River.

To ensure the scientific validity and rationality of the weights at each level in the evaluation index system, this study uses the analytic hierarchy process and its consistency ratio to test the consistency of the weights. The main steps are as follows. Judgement matrices are constructed for the control layer, primary indicators and secondary indicators. The maximum eigenvalue of each judgement matrix is calculated. The consistency index is derived using the following formula (Xie et al., 2020):

$CI\frac{{\mathrm{\lambda }}_{\max }-\mathrm{n}}{\mathrm{n}-1}CI=n-1{\mathrm{\lambda }}_{\max }-\mathrm{n}(22)$

When CI < 0.1, the consistency of the matrix is acceptable and the weight distribution is reasonable. If CI ≥ 0.1, the judgment matrix needs to be adjusted and the weights recalculated. In this study, the CI values of all judgment matrices at each level are less than 0.1, indicating that the weight system has good consistency and interpretability.

4 Results

4.1 The overall pattern of green transformation level

Overall, from 2006 to 2020, the green transformation level of resource-based cities in the Yellow River Basin exhibited a fluctuating growth trend (Figure 4). During this period, the comprehensive evaluation index increased from 0.3226 in 2006 (poor level) to 0.5128 in 2020 (satisfactory level), marking a significant rise of 58.94%. The average annual growth rate was 4.21%.The comprehensive evaluation index from 2006 to 2010 falls within the range of [0.3, 0.4), indicating that during this period, the overall green transformation level of resource-based cities in the Yellow River Basin was relatively low. After 2010, the comprehensive evaluation index increased to [0.4, 0.6), indicating an overall trend towards a satisfactory level of green transformation. The coupling coordination degree increased from 0.5296 (moderate coordination level) in 2006 to 0.6768 (satisfactory level) in 2020, marking a significant rise of 27.80%. The average annual growth rate was 1.99%.From 2006 to 2011, the coupling coordination degree was within the range of [0.5, 0.6), and from 2012 to 2020, the coupling coordination degree increased to [0.6, 0.7), indicating an overall shift from a moderate coordination level to a relatively high coordination level. This suggests that the subsystems of society, economy, industry, technology, and the environment in resource-based cities in the Yellow River Basin are currently in a stage of coordinated development, with significant room for further improvement.

Figure 4

Figure 4. Green transformation levels in different life cycles of resource-based cities in the Yellow River Basin. (a) Growing city. (b) Declining city. (c) Regenerative city. (d) Mature city. (e) Growing city. (f) Dcclining city. (g) Regenerative city. (h) Mature city.

Across the development stages in the urban life cycle, the overall green transformation level of the four types of resource-based cities shows an upward but fluctuating trend. Growth-stage cities are in the early phase of resource industry development and have a relatively large share of resource industries in their economic structure. Their green transformation level fluctuates markedly over time. In 2006, the green transformation level was at its lowest, at 0.3091, and it reached a peak of 0.5169 in 2018. Overall, the level increased by 63.55% between 2006 and 2020. Mature-stage cities, driven by high resource productivity and profitability, push regional economies to their highest development stage. At the same time, in order to ease the growing tension between economic development and the ecological environment, they place greater emphasis on regional green transformation. From 2006 to 2018, the green transformation level of mature cities showed a stable average annual increase of 0.027. Overall, the level increased by 44.94% from 2006 to 2020.

Declining-stage cities lie in the late development stage of resource-based cities. In these cities, the resource dividend has largely disappeared and the “resource curse” is severe. They are in a transition period in which they eliminate outdated production capacity and foster emerging industries, so they attach strong importance to green transformation. A cross-sectional comparison of the four types of resource-based cities over the same period shows that, apart from 2008 to 2017, the green transformation level of declining-stage cities remains consistently high in all years. It is clearly higher than the average evaluation level of the four types of resource-based cities. The declines in these 2 years are closely linked to specific macroeconomic contexts. In 2008, the global financial crisis broke out and international market demand fell sharply. Export prices in resource-based industries, especially energy and raw materials, dropped, which reduced urban fiscal revenue and cut green investment expenditure. At the same time, firms’investment in environmental governance and green innovation decreased, directly affecting the level of green transformation. In 2017, China pushed forward supply-side structural reform, and one of the key tasks was to cut overcapacity, especially in resource-based industries such as coal and steel. Although this reform supports green development in the long term, in the short term it suppressed industrial output and reduced the scale of some green investments. As a result, the green transformation level of some cities declined during the adjustment period. Renewable-stage cities are in a phase of development that follows resource depletion and involves a shift away from natural resources. The directions of urban transformation become more diverse, and their green transformation level fluctuates markedly over time. In 2006, the green transformation level was at its lowest, at 0.2863, and it reached its highest value of 0.5065 in 2020. Overall, the level increased by 76.91% from 2006 to 2020.

The overall green transformation level exhibits a fluctuating growth pattern characterized by ‘low level - satisfactory level - good level’. Create spatial evolution maps of green transformation levels Basin at four time points: 2006, 2010, 2015, and 2020, it is observed that the overall pattern exhibits a fluctuating growth characteristic of ‘low level - satisfactory level - good level’. Specifically, in 2006, the overall green transformation level of the 36 resource-based cities in the Yellow River Basin was relatively poor. Only Tongchuan City achieved a satisfactory level within the range [0.4, 0.5), while the remaining 35 resource-based cities were in the poor level within the range [0.2, 0.4). Starting from 2006, explicitly emphasized China’s commitment to a scientific and green development path. Over the course of 15 years, the number of poorly evaluated resource-based cities in the Yellow River Basin decreased significantly from 35 in 2006 to just 1 in 2020. At the same time, the number of resource-based cities achieving a satisfactory level significantly increased, growing from 1 in 2006 to 35 in 2020. This indicates that the overall green transformation level in the basin has experienced steady improvement. During the period of ecological civilization construction in the new era from 2015 to 2020, the number of cities with a poor level of green transformation further decreased, declining from 4 in 2015 to 1 in 2020. The number of cities achieving a satisfactory level continued to increase, growing from 27 in 2015 to 35 in 2017. At the same time, the number of cities evaluated as having a good level increased from 2 in 2015 to 6 in 2020.

(2) The coupling coordination level shows a development trend characterized by the eastern region leading, the central region rising, the western region catching up, and overall improvement.

In 2006, among the 7 cities with a good level of coupling coordination, 6 were located in the eastern region, while cities with a satisfactory level were mainly concentrated in the central and western regions, accounting for 80.56% of the total number of cities. In 2010, cities such as Yulin, Tongchuan, and Xianyang in Shaanxi Province experienced significant growth in coupling coordination level, contributing to the further improvement of coupling coordination level in resource-based cities in the central region. From 2010 to 2015, cities in the central region experienced rapid growth in coupling coordination level. In particular, there were a total of 15 cities, with 3 in Shaanxi, 9 in Shanxi, and 3 in Henan, that transitioned from a satisfactory level to a good level. At the same time, the western region began to rapidly catch up. Over a period of 5 years, Gansu and Inner Mongolia had a total of 7 cities, with 4 in Gansu and 3 in Inner Mongolia, transitioning from a satisfactory level to a good level.

(3) Similarity in proximity’--clear spatial agglomeration characteristics of green transformation level.

Moran’s I values are all greater than zero, ranging from 0.0587 to 0.2901, and are significant at the 1% level. The normality statistics (Z-values) and significance levels of Moran’s I show an increasing trend over time (Table 3). This indicates that the green transformation level of resource-based cities in the Yellow River Basin exhibits significant positive global spatial autocorrelation.

Table 3

Table 3. Moran’s I table.

From the comprehensive evaluation index (Figure 5), the green transformation level shows marked change between 2006 and 2020 and clear spatial path dependence. In 2006, cities with satisfactory green transformation levels were mainly concentrated in Shandong Province in the eastern part of the basin, while 31 cities with poor evaluations were located in the central and western regions. In 2010, overall spatial patterns changed only slightly. Some central cities, such as Yulin in Shaanxi, Yangquan in Shanxi and Jiaozuo, were upgraded to the good category. In 2015, all cities were rated satisfactory except Zhangye and Pingliang in Gansu, Xinzhou in Shanxi and Pingdingshan in Henan, which remained poor, and Jiaozuo and Dongying, which were rated good.

Figure 5

Figure 5. Spatial evolution, spatial evolution at the level of coupling coordination and LISA significance distribution (a) The level of green transformation in 2005. (b) The level of green transformation in 2010. (c) The level of green transformation in 2015. (d) The level of green transformation in 2020. (e) The level of coupling coordination in 2005. (f) The level of coupling coordination in 2010. (g) The level of coupling coordination in 2015. (h) The level of coupling coordination in 2020. (i) The LISA significance distribution in 2005. (j) The LISA significance distribution in 2010. (k) The LISA significance distribution in 2015. (l) The LISA significance distribution in 2020.

From the perspective of coupling coordination (Figure 5), cities with satisfactory ratings were mainly concentrated in Gansu, Shanxi and Shaanxi, accounting for 83.3% of all cities. In 2020, the only city rated poor was Yuncheng in Shanxi, with a score of 3.99, just below the threshold for a satisfactory rating. Cities with satisfactory ratings were mainly located in the middle and upper reaches of the Yellow River Basin and accounted for 80.56% of all cities. Cities with good ratings, apart from Jiaozuo, were all located in Shandong Province. Overall, the green transformation level displays a clear spatial clustering pattern.

It shows an overall trend where cities closer to the lower reaches of the Yellow River Basin tend to have higher green transformation levels. There is also a local pattern of bidirectional clustering with stable high and low-value areas. Using Geoda software to represent the local Moran’s I values and the corresponding significance levels, the local LISA (Local Indicators of Spatial Association) was obtained (Figure 5).

(4) Resource-based cities at different life stages and evaluated on different dimensions exhibit significant differences in their green transformation levels.

The spatiotemporal evolution trends and differences across life-cycle stages and evaluation dimensions were analysed (Figure 6). Based on the density curves, two main features stand out. The density curves of the subsystems shift gradually to the right over time. This shows that the social, economic, industrial, technological and environmental systems in resource-based cities in the Yellow River Basin have improved overall. At the same time, the peak heights of the social, economic and technological subsystems fall slightly, and the curves show persistent secondary peaks and long right tails. This pattern suggests marked polarisation in social and technological development, with large internal gaps between cities. The density curves of the industrial and environmental systems are mainly characterised by a single main peak with a slight rightward shift. The peak heights first rise and then fall, which indicates some improvement in ecological and environmental protection in resource-based cities in the Yellow River Basin over the study period, although the degree of change remains modest. This also implies that improving overall green transformation remains a difficult task and requires sustained, coordinated planning efforts. The green transformation levels of cities at different life-cycle stages increase over time. In 2005, the green transformation indices of growing, declining, regenerating and mature cities were 0.3091, 0.3531, 0.2863 and 0.3419, respectively. By 2020, these values had risen to 0.5055, 0.5435, 0.5065 and 0.4956, corresponding to growth rates of 64%, 54%, 77% and 45%, respectively. Among these groups, cities in the declining stage have the highest average level of green transformation. In 2020, 66.67% of declining cities were in the qualified range and the remaining 33.33% were in the good range. By contrast, mature cities have the lowest average level of green transformation. In 2020, 5.3% of mature cities were still in the poor range, while 78.95% were in the qualified range.

Figure 6

Figure 6. Three-dimensional distribution of Gaussian kernel density. (a) The development level of the society system. (b) The development level of the economy system. (c) The development level of the industry system. (d) The development level of the technology system. (e) The development level of the environment system. (f) Overall comparison.

4.2 Research on driving factors

To explore the main factors that influence the green transformation level of resource-based cities in the Yellow River Basin, this study uses fiscal expenditure (X1), per capita GDP (X2), number of patent authorisations (X3), urbanisation rate (X4), population density (X5), built-up area (X6) and total energy consumption (X7) as core driving factors. Using a log-linear and stochastic regression form to analyse the effects of population, economic affluence and technological factors on environmental stress. Building on this model, we add dimensions that are specific to resource-based cities, such as energy consumption and spatial development intensity, in order to construct a more complete set of driving factors for green transformation. Grey relational analysis is used to calculate the average correlation coefficients between each factor and the green transformation level. Pearson correlation coefficients are then used to test the statistical significance of these relationships.

The correlation coefficients for the selected indicators in different years range from 0.6404 to 0.9935 (Table 4). This indicates strong associations between the chosen factors and the coupling coordination degree. In terms of average correlation, the factors are related to the coupling coordination degree in the following order: urbanisation rate (0.9798), per capita GDP (0.9654), fiscal expenditure (0.9280), built-up area (0.9250), total energy consumption (0.9025), population density (0.8587) and number of patent authorisations (0.7004). The influence of these driving factors also shows marked spatiotemporal heterogeneity. Over time, apart from the decline in the correlation of patent authorisations, all other factors show a steady upward trend. Fiscal expenditure, per capita GDP, urbanisation rate, population density, built-up area and total energy consumption all become more strongly associated with green transformation. This suggests that energy consumption and urban development exert growing pressure on the green transformation of resource-based cities, as continued urban expansion places considerable stress on urban green transition. Therefore, cities must balance economic development with ecological and environmental constraints while continuing urban construction and resource extraction. They should give priority to restoring and managing regional ecosystems in order to raise the overall level of green transformation.

Table 4

Table 4. Gray correlation and correlation coefficient.

The sensitivity analysis confirms the robustness of the model. The Pearson correlation coefficients for the key variables, urbanisation rate (0.46), per capita GDP (0.52) and fiscal expenditure (0.49), are all positive and significant (p < 0.05). This pattern is consistent with the ranking of the grey relational degrees (0.9798, 0.9654 and 0.9280, respectively). The agreement between these two independent approaches indicates that the identified dominant drivers are stable and not sensitive to model specification. Overall, the analytical framework appears robust and internally consistent.

5 Discussions

The transition toward urban sustainability represents one of the most complex challenges of our time, particularly for resource-dependent cities historically locked into carbon-intensive development pathways. These cities face the dual imperative of maintaining socioeconomic stability while fundamentally restructuring their economic foundations and environmental relationships. Their successful transformation is critical not only for regional development but also for achieving global climate goals and the broader aims of the Sustainable Development Goals (SDGs). This study examined the green transformation process of 36 resource-based cities in China’s Yellow River Basin from 2006 to 2020. We developed a multi-system evaluation framework aligned with the SDGs and implemented a sequential analytical approach that incorporated kernel density estimation, coupling coordination modelling and grey relational analysis. This approach enabled a comprehensive assessment of spatiotemporal dynamics, subsystem synergies and key driving factors. The analysis revealed substantial yet uneven progress in green transformation across the study period. The comprehensive evaluation index increased by 58.94%, and the coupling coordination degree improved from 0.5296 to 0.6768, which indicates a shift from moderate to relatively high coordination among subsystems. Spatially, transformation levels exhibited a clear “east-leading, centre-rising, west-catching-up” pattern with marked clustering effects. Cities in declining stages achieved higher transformation levels than those in mature stages, which highlights the impact of structural inertia. Economic and demographic factors, specifically the urbanisation rate, per capita GDP and fiscal expenditure, emerged as the most influential drivers, while technological innovation showed a positive but comparatively weaker effect. These findings have important implications for sustainability governance. The spatial patterns observed are consistent with established regional development theories (Delavaux et al., 2023; Xu et al., 2023), while the lifecycle differentiation challenges linear transition models. The dominant role of economic factors supports the environmental transition hypothesis, whereas the relatively weaker correlation for technological innovation suggests that it requires complementary institutional conditions to realise its full potential (Yang et al., 2019). Our analysis indicates that spatial planning systems function as key institutional mechanisms that shape transformation pathways (Bianchini et al., 2023; Wang et al., 2023a). The marked spatial heterogeneity calls for differentiated governance strategies across regions. It also underscores the importance of integrating multi-level coordination within planning systems to bridge implementation gaps (Chen et al., 2023c).

This study has several limitations that should be acknowledged. The exclusive focus on resource-based cities in the Yellow River Basin may affect the generalisability of the findings to other regional contexts with different institutional arrangements. Although our selection of drivers was theoretically informed, the quantitative framework may not fully capture key institutional, political or cultural dimensions that shape green transformation processes. Future research should extend this multi-system analytical framework to other resource-intensive regions in order to test its applicability across different contexts. Subsequent studies could also strengthen causal inference by incorporating dynamic panel models or machine learning approaches. In addition, qualitative investigations would provide valuable insights into contextual governance mechanisms and implementation barriers that quantitative analyses cannot fully capture. They would support a more nuanced understanding of sustainability transitions.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author contributions

HZ: Writing – original draft, Formal Analysis, Resources, Data curation, Methodology, Writing – review and editing, Funding acquisition, Conceptualization. XC: Project administration, Methodology, Investigation, Writing – review and editing.

Funding

The authors declare that financial support was received for the research and/or publication of this article. Study on Cultural Cognition, Consumption Characteristics, and Optimization Measures of Home Stay Facility Tourism in Gansu Province from Department of Culture and Tourism of Gansu Province (No. WLTKT2022A-04), and Promoting the High-Quality Development of the Cultural Tourism Industry from Department of Culture and Tourism of Gansu Province (No. WLTKT2021B-01).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The authors declare that no Generative AI was used in the creation of this manuscript.

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