Balancing Economic Growth and Environmental Conservation: Assessing Supportive Policies in Resources-Based Cities in China

Abstract

This study investigates the impact of comprehensive reforms on the economic development and environmental protection of resource-based cities in China within the context of the ‘National Sustainable Development Plan for Resource-based Cities (2013–2020)’. Employing a difference-in-differences methodology, we find that these reforms not only bolster economic stability but also significantly enhance urban ecological environment, resulting in a win–win outcome for economic prosperity and environmental preservation. Our analysis reveals that the reforms drive sustained economic growth and ecological enhancement by downsizing industries, adopting green technologies, and optimizing industrial composition. Moreover, we identify that these impacts are more pronounced in mature and declining cities and in regions with robust official promotion strategies and stringent environmental regulations. These findings provide valuable insights for addressing the “resource curse” within social systems and for formulating policies that balance stable economic growth and environmental protection in resource-based cities.

1. Introduction

Advancing green, low-carbon economic and social progress is fundamental to attaining high-quality development. In 2020, at the 75th United Nations General Assembly, China committed to peak carbon emissions by 2030 and to reaching carbon neutrality by 2060. With the escalating impacts of global climate change and environmental challenges, the significance of environmental protection has garnered universal attention. Nonetheless, economic development necessitates a continuous increase in energy supply, rendering resource-based cities critical for maintaining economic and social stability. The growth of these cities has a substantial impact on the overall quality of China’s economic progress. For resource-based cities, the pursuit of a balance between economic advancement and environmental stewardship, overcoming the ‘resource curse’, and fostering sustainable green development, remain formidable challenges. Establishing robust, long-term frameworks to facilitate the sustainable growth of these cities is imperative, though the necessary reforms are extensive. In response, the State Council introduced the “National Sustainable Development Plan of Resource-Based Cities (2013–2020)” (abbreviated as ‘NSDPRBC’), offering essential guidance to support the sustainable transformation of resource-based cities.

With the advancing implementation of the dual-carbon strategy, sustainable development in resource-based cities has drawn significant attention from scholars worldwide. This paper, through a review of relevant studies, identifies that existing research on $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$, largely concentrating on assessing urban transformation effectiveness and analyzing the economic implications of $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$. To evaluate resource-based city transformation, various models are utilized, such as green transformation performance metrics [1], the Technique for Order Preference by Similarity to an Ideal Solution ($\mathrm{T}\mathrm{O}\mathrm{P}\mathrm{S}\mathrm{I}\mathrm{S}$), and Exploratory Spatial Data Analysis ($\mathrm{E}\mathrm{S}\mathrm{D}\mathrm{A}$) models [2], alongside the Data Envelopment Analysis ($\mathrm{D}\mathrm{E}\mathrm{A}$) model [3]. Additionally, some scholars view urban resilience—defined as a city’s ability to withstand, adapt to, and recover from risk impacts [4]—as a crucial metric of transformation in resource-based cities [5]. Concerning $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$’s impact on economic variables, certain researchers approach the reform as a quasi-natural experiment, positing that both $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ and the Resource-Exhausted City Program ($\mathrm{R}\mathrm{E}\mathrm{C}\mathrm{P}$) play roles in improving firms’ sustainable development outcomes [6,7] and fostering green technological advancements in high-pollution sectors [8]. Other research highlights that the reform significantly curtails the share of secondary industry in the Gross Domestic Product ($\mathrm{G}\mathrm{D}\mathrm{P}$) of resource-based cities and reduces pollution levels [9].

In the process of transforming and developing resource-based cities, although $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ has achieved considerable overall success, its implementation has also produced certain negative outcomes. Studies show that $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ has not successfully lowered carbon emissions in expanding resource-based cities, revealing limitations in the policy’s adaptability to the distinct characteristics of cities across various development stages [10]. Additionally, the policy has been ineffective in promoting labor and capital investment, thus dampening economic growth and constraining the economic vitality and sustainability of resource-based cities [11]. The policy has further impacted institutional quality negatively, especially in public services and human resources, thereby undermining these cities’ foundational development capacity [12]. Interestingly, the implementation of $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ has unexpectedly led to a rise in carbon dioxide intensity within resource-based cities, likely due to the policy’s restrictive effects on innovation incentives and environmental regulations [13].

Most current research, both domestically and internationally, predominantly examines the transformation efficiency of resource-based cities and the isolated impact of these transformations on either economic growth or environmental outcomes. Yet, with the accelerating pace of globalization and industrialization, the conflict between economic progress and environmental protection has become increasingly pronounced. This issue is especially critical for resource-based cities that depend on extracting specific natural resources. For these cities, a key challenge is how to maintain steady economic growth while fostering an environmentally sustainable society. As sustainable development becomes a long-term strategic directive for reform policies, an essential question arises: Has the reform truly met its intended objectives? To date, existing studies have not offered a definitive answer.

This study aims to provide an in-depth evaluation of the $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$’s impact on economic stability and environmental quality in China’s resource-based cities. It specifically investigates how this policy creates a sustainable framework to balance economic and environmental goals in these cities. Beyond assessing the $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$’s effectiveness in China, this research aims to extract insights with potential global applicability, especially under the growing challenges of climate change and environmental stress. China’s approach to promoting sustainable development in resource-based cities offers valuable lessons, particularly for other developing countries and regions reliant on natural resources, guiding them toward feasible policy solutions. Using panel data from 284 Chinese prefecture-level cities spanning 2008 to 2020, this study applies a Difference-in-Differences ($\mathrm{D}\mathrm{I}\mathrm{D}$) model to empirically investigate the $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$’s effects on economic stability and environmental quality, alongside the mechanisms through which these effects are realized.

While both domestic and international scholars have thoroughly investigated the impact of $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ from multiple perspectives, this paper offers additional contributions in three main areas: (1) Distinct from existing studies that analyze $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ from a single perspective [9,13], this study integrates stable economic growth and ecological environmental quality into the analytical framework, examining $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$’s dual impact on these aspects of resource-based cities through both theoretical and empirical lenses. This approach not only bolsters theoretical support for sustainable development in resource-based cities but also complements the assessment of $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$’s policy effects. (2) Most existing studies use GDP growth rates [11] and green total factor productivity [14] to gauge urban economic growth and environmental protection. In contrast, this paper measures stable economic growth by evaluating whether resource-based cities can move beyond stagnation points and introduces gridded prefecture-level data for more precise assessments of ecological environmental quality. Additionally, by leveraging micro-level land use data, this paper assesses industrial scale and structure, offering a more comprehensive view of urban industrial patterns. (3) Existing literature typically emphasizes $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$’s effects on emissions reduction or transformation efficiency [1,10], often neglecting the spatial spillover effects of policy. This paper, by constructing a Spatial Difference in Difference ($\mathrm{S}\mathrm{D}\mathrm{I}\mathrm{D}$) model, provides a comprehensive examination of $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$’s impacts on neighboring regions, supporting coordinated regional development from both theoretical and empirical perspectives.

Figure 1 provides a flowchart of this study.

2. Institutional Background and Theoretical Analysis

2.1. Institutional Context

In recent decades, numerous countries have grappled with the challenges of transforming resource-dependent cities. The experiences of the United States and Germany serve as instructive examples. During the New Deal era, the United States promoted industrial and technological innovation to diversify cities reliant on coal, while Germany’s Ruhr region, in response to resource depletion, transitioned from a resource-based economy to an innovation-driven one by developing high-tech industries and implementing environmental measures. These cases offer valuable insights for other resource-dependent cities seeking sustainable transformation.

China’s resource-based cities, traditionally reliant on resources such as coal, oil, and metals, have experienced rapid economic growth. However, continued resource extraction has increasingly intensified depletion, posing severe challenges to economic stability and social development. Many cities, constrained by a single-industry structure and lacking diversified economic support, are experiencing economic decline. Furthermore, extraction-related issues, such as land degradation, water contamination, and air pollution, have worsened living conditions and hindered sustainable development, amplifying risks of unemployment, poverty, and social instability.

To tackle these challenges, the Chinese government has continuously refined its strategy. Early policies emphasized regulatory actions and environmental protection. In 2007, however, the State Council released the “Guiding Opinions on Promoting the Sustainable Development of Resource-Based Cities”, promoting a shift from resource dependency to economic diversification. This approach was further reinforced by the “National Plan for Sustainable Development of Resource-Based Cities (2013–2020)”, which provided structured guidance for 262 cities, covering 126 prefecture-level and 62 county-level cities. These cities are classified into 31 growing, 141 mature, 67 declining, and 23 regenerating cities. The plan emphasizes a transition toward innovation-driven growth, enhancing social equity and strengthening ecological protections to support sustainable urban development.

2.2. Theoretical Analysis and Research Hypotheses

2.2.1. The Impact of $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ on Economic Stable Growth and Ecological Environmental Quality in Resource-Based Cities

Amid the “dual carbon” goals, resource-based cities, heavily reliant on a single resource, face twin challenges of economic and ecological transformation due to insufficient internal motivation for change. No organization can flourish in a context of resource scarcity; reform policies can introduce unique resources and informational capital, thereby strengthening these cities’ strategic development capacities. Consequently, $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ is anticipated to offer clear guidance for achieving steady economic growth and enhancing ecological environmental quality.

$\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ exerts a dual influence on the stable economic growth of resource-based cities. Positively, $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ injects new momentum into economic expansion. Firstly, ongoing adjustments in industry support mechanisms enable resource-based cities to explore unique development models adapted to local conditions, with structural shifts offering economic growth benefits to some degree [15]. Although such cities face new economic and environmental challenges due to reforms, traditional resource industries may encounter difficulties, while emerging industries are expected to rise, injecting new momentum into the economy and cultivating new growth points [16]. Secondly, constrained by resource scarcity, resource-based cities will pursue higher production efficiency, thereby enhancing their long-term competitiveness. Furthermore, $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ emphasizes environmental governance and protection by strengthening environmental management and formulating environmental regulations and policies, which promotes the effective implementation of environmental governance and protection efforts. This not only effectively improves the ecological environment and enhances residents’ quality of life but also creates favorable conditions for the thriving of green industries. Finally, the benefit-sharing mechanism of the reform policy adheres to a people-centered approach, optimizing the distribution of resource revenues to achieve synchronized growth in residents’ incomes and economic development.

On the negative side, $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ also presents some economic challenges for resource-based cities. Firstly, with the exit or downsizing of certain resource-based industries, these cities may face unemployment issues and a slowdown in economic growth. Secondly, stricter environmental policies may lead to rising production costs, restricting certain high-polluting industries, which may experience dual pressures of production and employment in the short term, dampening economic momentum [17]. Lastly, during the reform process, resource-based cities may face uncertainties regarding external investment. When investors observe that the dominant industries in the city are in decline, they may adopt a cautious attitude toward the region’s economic prospects and reduce investment.

$\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ offers policy support to enhance ecological environmental quality. Firstly, as reforms deepen, both the government and private sector are expected to become more active in green investments. Specifically, in critical areas like clean energy and the circular economy, the government is anticipated to implement various incentives to advance green development in resource-based cities [18]. Secondly, the development order constraint mechanism of the reform policy establishes a coordinated evaluation system for resource development and urban sustainable development, facilitating alignment between resource extraction and urban growth. At the same time, this policy emphasizes the comprehensive and cyclic utilization of resources, which is likely to enhance urban energy efficiency. Finally, to meet ecological and environmental standards, the resource development compensation mechanism of $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ requires resource-based cities to rehabilitate ecological damage caused by past extraction activities to achieve ecological balance. Under the guidance of comprehensive reforms, industrial enterprises within resource-based cities will place greater emphasis on environmental considerations in production processes, thereby improving ecological environmental quality. Based on these points, we propose Hypothesis 1.

H1a. 

$NSDPRBC$ can facilitate the coordinated advancement of stable economic growth and the improvement of ecological environmental quality in resource-based cities.

H1b. 

$NSDPRBC$ can promote the improvement of ecological environmental quality in resource-based cities.

H1c. 

$NSDPRBC$ constrains stable economic growth in resource-based cities.

2.2.2. Analysis of the Mechanisms Through Which $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ Affects Economic Stable Growth and Ecological Environmental Quality in Resource-Based Cities

Drawing from the theoretical framework of Grossman and Krueger (1995) [19], this study examines how $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ affects stable economic growth and ecological quality in resource-dependent cities through scale, technological, and structural effects.

From a scale effects perspective, reform policies mandate the suppression of high-energy-consuming industries and the gradual phasing out of outdated capacities. To align with these policies, enterprises will downscale high-pollution sectors and shift to modern manufacturing [20], thereby reducing environmental burdens. Second, reform policies have tightened environmental entry and emission standards for key industries, capping total pollutant emissions. Enhanced environmental standards compel firms to adopt cleaner technologies and pollution controls, effectively lowering emissions from industrial production [21]. Additionally, in resource-dependent cities, industrial production scale dictates product supply; reform policies can drive large-scale adoption of energy-saving technologies to better match market needs. Comprehensive reforms may steer the industrial strategies of resource-dependent cities toward sustainable development, though the cost–price gap in green products may hinder market acceptance. Reform policies’ pricing mechanisms can flexibly reflect market conditions, and scale effects allow fixed costs, such as management fees and Research and Development ($\mathrm{R}\&\mathrm{D}$) investments, to be distributed across a larger product base, reducing unit costs. This aids in achieving stable economic growth and enhanced ecological quality.

From a technological effects perspective, $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ mandates the integration of green mining concepts throughout resource development and utilization, focusing on eco-friendly production processes and sustainable mining practices. Reform policies prioritize innovation as the main driver, advancing urban sustainable development through green innovation. This includes creating environmentally friendly products and technologies to boost resource efficiency and reduce pollution [22]. It supports clean production [23], and cities leading in green innovation often exhibit stronger environmental performance [24]. Second, facing both economic and environmental pressures, many cities invest in technological innovation to achieve cleaner, more efficient production. Green innovation raises variable costs in resource-dependent sectors and involves high-risk investments that may struggle with financing. To address this, reform policies facilitate targeted loan financing to these cities, easing financial pressures and ensuring stable operations. Lastly, as sustainable development gains traction globally, green technological innovation drives resource-dependent cities toward efficient and eco-friendly production methods [25]. Through technological advancements, effective resource use and recycling become possible, enabling these cities to achieve sustainability across economic, environmental, and social fronts.

From the perspective of structural effects, first, $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ creates strong environmental pressure within resource-dependent cities, encouraging the flow of production factors towards more efficient sectors, thereby enhancing overall organizational efficiency [26]. This, in turn, allows $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ to optimize resource allocation and achieve sustainable development in resource-dependent cities while establishing a stable mechanism for fiscal investment growth, guiding and encouraging the aggregation of various production factors towards alternative industries. Second, the fundamental principles of $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ emphasize a people-centered approach and harmonious development; its sustainable development concept promotes environmental protection in resource-dependent cities by enhancing public awareness and participation. The shift in environmental protection consciousness can facilitate the proliferation of green consumption, recycling, and sustainable lifestyles, creating a favorable environment for emerging green markets and industries. Furthermore, $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ encourages a transition from a traditional resource-dependent industry structure to an environmentally friendly one, leveraging emerging industries and technologies to enhance the value of the industrial chain, mitigate resource consumption and environmental pollution, and promote the growth of green industries while improving resource efficiency and alleviating environmental pressures to achieve environmental protection goals in resource-dependent cities. Additionally, $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ can strengthen urban environmental constraints, enhance industrial ecological efficiency [27], and facilitate adjustments in energy consumption structures, promoting sustainable energy development and reducing pollution emissions [28]. Lastly, when cities demonstrate a commitment to green and sustainable development, they may attract domestic and international investors who resonate with environmental protection ideals. Structural effects can also promote economic balance between regions. Regions traditionally reliant on resource industries may face risks of resource depletion or market volatility, while economic diversification helps mitigate these risks, ensuring a win–win situation for stable economic growth and ecological quality. Based on these points, this paper proposes Hypothesis 2.

H2. 

$NSDPRBC$ can influence the stable economic growth and ecological quality of resource-based cities through scale effects, technological effects, and structural effects.

3. Research Design

3.1. Model Specification

The $\mathrm{D}\mathrm{I}\mathrm{D}$ model is a widely utilized tool for causal inference in policy evaluation, primarily by comparing the performance differences of a “treated group” (policy-affected group) and a “control group” (policy-unaffected group) before and after policy implementation to infer causal effects. Compared with other estimation techniques, $\mathrm{D}\mathrm{I}\mathrm{D}$ offers distinct advantages in mitigating time-invariant biases within the sample, such as historical or geographic factors influencing the variables. This approach enables more precise identification of a policy’s net effect, supporting stronger causal inference. This method is particularly suited to evaluating policies like $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ that are applied across diverse regions and periods. Therefore, this paper utilizes a DID model with data from 284 prefecture-level cities in China to examine the impact of $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ policy implementation on stable urban economic growth and environmental quality. The model is structured as follows:

$$\begin{array}{c}{Seg}_{it}={\alpha }_0+{\alpha }_1\times {RBplan}_{it}+{\alpha }_2\times {Control}_{it}+{\delta }_i+{\eta }_t+{\mu }_{it}\end{array}$$

(1)

$$\begin{array}{c}{Green}_{it}={\alpha }_0+{\alpha }_1\times {RBplan}_{it}+{\alpha }_2\times {Control}_{it}+{\delta }_i+{\eta }_t+{\mu }_{it}\end{array}$$

(2)

In the equation, i represents the prefecture-level city (municipality, autonomous prefecture, or league), and t represents the year. The explained variables $Se{g}_{it}$ indicate the economic stable growth of the city, while $Gree{n}_{it}$ represents the ecological environmental quality of the city. $RBpla{n}_{it}$ indicates whether city i was included as a resource-based city and initiated the $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ in year t. $Contro{l}_{it}$ represents control variables affecting the economic stable growth and ecological environmental quality of the city to ensure the randomness of the experimental grouping and the timing of policy implementation. ${\delta }_i$ and ${\eta }_t$ represent regional fixed effects and year fixed effects, respectively, and ${\mu }_{it}$ is the random disturbance term, following a standard normal distribution.

3.2. Variable Selection

3.2.1. Explained Variables

Economic stability ($Se{g}_{it}$): This study draws on the method of Eichengreen et al. (2012) [29] to assess the stability of economic growth by examining whether growth can cross the turning point when it is at a deceleration inflection point. The following conditions must be met:

$$\begin{array}{cc}\hfill & g_{t,t+n}⩾\Gamma \hfill \\ \hfill & \Delta g=g_{t,t+n}-g_{t-n,t}⩾\Lambda \hfill \\ \hfill & y_{t,ϵ}\in \left[y_1^{*},y_2^{*}\right]\hfill \end{array}$$

(3)

In this context, $g_t$ represents the GDP growth rate, calculated with 2007 as the base year, while $g_{t-n,t}$ and $g_{t,t+n}$ denote the average annual GDP growth rates for the periods from $t-n$ to t and from t to $t+n$, respectively. Given the rapid pace of current economic development and the significant fluctuations in growth rates, this paper sets $n=3$ and $\Gamma =3.5\%$, with $\Lambda =2\%$, using binary discrete values to represent economic stable growth. Economic stable growth implies that the economy can accelerate recovery during periods of slowdown; that is, if the region meets the constraint equation in Equation 3, it is assigned a value of 1; otherwise, it is assigned a value of 0.

Ecological Environment Quality ($Gree{n}_{it}$): This study adopts the ecological environment condition index to represent the quality of urban ecological environments. The ecological environment condition index is derived from the China High-resolution Ecological Environment Quality dataset ($\mathrm{C}\mathrm{H}\mathrm{E}\mathrm{Q}$), calculated using a remote sensing-based assessment model [30]. The original data comprise a comprehensive evaluation index stored as geographic raster data with a spatial resolution of 500m, ranging from 0 to 1. Additionally, this study utilizes the zonal statistics function in $\mathrm{A}\mathrm{r}\mathrm{c}\mathrm{G}\mathrm{I}\mathrm{S}$ to calculate the annual mean ecological environment condition index for each prefecture-level city [31], thereby generating yearly ecological environment quality values for each city.

3.2.2. Core Explanatory Variables

The core explanatory variable $RBpla{n}_{it}$ is the interaction term between the variables $trea{t}_i$ and $pos{t}_t$. $trea{t}_i=1$ indicates that region i is a resource-based city mentioned in the policy, while $trea{t}_i=0$ indicates that the region is part of the control group. With 2013 as the policy implementation reference point, $pos{t}_t=1$ indicates that the $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ was implemented in period t, and $pos{t}_t=0$ indicates that it was not implemented during period t.

3.2.3. Control Variables

In addition to the core variables, this study includes city-level control variables as follows: (1) fiscal decentralization ($Fis$), defined as the ratio of general government expenditures to fiscal revenues; (2) population size ($Pop$), represented by the logarithm of the total year-end population; (3) level of technological investment ($St$), calculated as the logarithm of scientific and technological expenditures; (4) foreign direct investment level ($Fdi$), indicating the proportion of foreign investment in the city relative to its GDP; (5) industrial structure ($Ind$), defined as the share of value added by secondary industries in the city’s GDP; and (6) infrastructure development level ($Lnroad$), measured by the logarithm of per capita road area.

3.3. Data Description

The policy data in this study are obtained from the “National Sustainable Development Plan of Resource-Based Cities (2013–2020)”, issued by the State Council of China. The ecological environment quality data originate from the China Historical High-resolution Ecological Environment Quality ($\mathrm{C}\mathrm{H}\mathrm{E}\mathrm{Q}$) dataset, provided by the National Earth System Science Data Center. The city-level data come from the EPS database, with minor missing values filled via interpolation. The land transfer data are from the China Land Market website. Given that 2008 represents the onset of a global post-crisis recovery, a phase of economic transition in China, and the initiation of environmental policies, and considering the $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$’s target year of 2020, this study sets the sample period as 2008–2020. This period enables a thorough assessment of the policy effects. Additionally, main continuous variables were winsorized at the 1% level on both tails, and nominal variables were deflated using 2008 as the base year.

4. Empirical Analysis

4.1. Descriptive Statistics of Variables

The descriptive statistics for the variables used in this study are shown in

Table 1. Descriptive statistics of main variables.

Variables	Control Group			Treatment Group			Full Sample
	Obs	Mean	Sd	Obs	Mean	Sd	Obs	Mean	Sd
$Seg$	2780	0.169	0.375	912	0.292	0.455	3692	0.200	0.400
$Green$	2780	0.492	0.124	912	0.492	0.144	3692	0.492	0.129
$Fis$	2780	2.794	1.863	912	3.278	1.750	3692	2.913	1.847
$Pop$	2780	5.950	0.647	912	5.663	0.689	3692	5.879	0.669
$St$	2780	10.252	1.511	912	9.910	1.052	3692	10.168	1.420
$Fdi$	2780	0.018	0.017	912	0.014	0.016	3692	0.017	0.017
$Ind$	2780	0.472	0.106	912	0.461	0.114	3692	0.469	0.108
$Lnroad$	2780	3.487	0.529	912	3.719	0.465	3692	3.544	0.523

. After policy implementation, the mean values of $Seg$ and $Green$ in the treatment group were marginally higher than those in the control group. This suggests that, compared to non-resource-based cities, $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ may support stable economic growth and environmental improvements in resource-based cities.

4.2. Correlation Analysis of Variables

The correlation analysis of the variables is presented in

Table 2. Correlation analysis of variables.

Variables	Seg	Green	RBplan	Fis	Pop	St	Fdi	Ind	Lnroad
$Seg$	1.000
$Green$	−0.043 ***	1.000
$RBplan$	0.132 **	0.00004	1.000
$Fis$	0.098 **	0.122 **	0.113 ***	1.000
$Pop$	−0.110 ***	0.108 **	−0.185 **	−0.141 ***	1.000
$St$	−0.097 ***	-0.010	−0.104 ***	−0.501 ***	0.490 ***	1.000
$Fdi$	−0.057 ***	0.003	−0.114 **	−0.397 ***	0.123 ***	0.397 ***	1.000
$Ind$	−0.163 ***	−0.162 ***	−0.044 ***	−0.425 ***	−0.148 ***	−0.011	0.128 ***	1.000
$Lnroad$	0.067 ***	0.056 ***	0.192 **	0.427 ***	−0.366 ***	−0.442 ***	−0.262 ***	−0.038 **	1.000

Note: ** p < 0.05, *** p < 0.01.

. Prior to the influence of $RBplan$, $Seg$ and $Green$ were unable to achieve a coordinated win–win outcome. A significant positive correlation is observed between $RBplan$ and $Seg$. However, due to omitted variables and other confounding factors, the positive relationship between $RBplan$ and Green does not reach statistical significance without the inclusion of fixed effects. All correlation coefficients are below 0.8, suggesting, based on empirical judgment, that there is no multicollinearity among the variables used in this study.

4.3. Benchmark Regression Results

To ensure the validity and robustness of the model specification, we conducted Hausman and Variance Inflation Factor ($V\mathrm{I}F$) tests before the baseline regression analysis. The Hausman test results strongly rejected the assumptions of both pooled Ordinary Least Squares ($\mathrm{O}L\mathrm{S}$) and random effects models, favoring a two-way fixed effects model. This suggests that the fixed effects model better addresses potential endogeneity, reducing estimation bias from omitted variables. Additionally, the VIF test results showed low collinearity among explanatory variables ($V\mathrm{I}F$ values well below 10), confirming robustness and indicating that multicollinearity does not significantly impact the estimation results. Thus, the two-way fixed effects model aligns with theoretical expectations and allows for more accurate estimation of $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$’s effects on $\mathrm{S}eg$ and $Green$, thereby enhancing the credibility and interpretability of the findings.

Table 3 presents the empirical results based on Models (1) and (2). Regardless of the inclusion of control variables, $RBplan$ shows a significant positive effect on both $\mathrm{S}eg$ and $Green$. This indicates that the implementation of $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ has substantially contributed to economic stability and environmental improvements in resource-based cities. The mechanism behind this is that $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ encourages these cities to diversify their industrial structures and reduce dependence on single-resource industries, alleviating the “resource curse” [6] and enabling a shift toward a more sustainable economic model. The policy’s positive impact on environmental quality aligns with its goals, promoting cleaner production, pollution reduction, and ecological protection [32]. This supports Hypothesis 1a, confirming that $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ has effectively enhanced both economic and environmental outcomes in resource-based cities.

The regression results in Table 3 show that $Fis$ has a significant negative effect on stable economic growth in Model (1) but a significant positive effect on environmental quality in Model (2). This phenomenon can largely be attributed to the dependency of local governments in resource-based cities on revenue from resource extraction and heavy industry, leading them to prioritize short-term economic returns over long-term economic stability and sustainable growth. Although fiscal decentralization grants local governments greater autonomy, when governance capacity is limited, it may result in inefficient or even wasteful resource allocation, thereby hindering stable economic growth. Furthermore, competition among local governments for tax revenue, along with subsidies to high-pollution industries, can yield immediate economic benefits but intensifies reliance on low-value-added industries [33] and overlooks the need for industrial upgrading, ultimately restraining sustained economic growth. However, when environmental protection policies are emphasized at the national level, fiscal decentralization can incentivize local governments to invest more actively in environmental management, leading to improvements in ecological quality. Under such conditions, local governments leverage their fiscal autonomy to allocate more resources toward environmental goals, effectively enhancing environmental quality when environmental policy is prioritized [10].

Table 3. Benchmark regression results.

Variables	(1)	(2)	(3)	(4)
	Seg		Green
$RBplan$	0.072 ***	0.056 ***	0.014 ***	0.010 ***
	(0.017)	(0.018)	(0.004)	(0.004)
$Fis$		−0.011 *		0.011 ***
		(0.006)		(0.001)
$Pop$		−0.031 **		−0.003
		(0.014)		(0.003)
$St$		−0.034 ***		0.005 ***
		(0.008)		(0.002)
$Fdi$		0.526		−0.476 ***
		(0.486)		(0.098)
$Ind$		−0.438 ***		−0.104 ***
		(0.088)		(0.020)
$Lnroad$		−0.004		0.045 ***
		(0.017)		(0.005)
$Cons$	0.182 ***	0.960 ***	0.489 ***	0.314 ***
	(0.007)	(0.141)	(0.002)	(0.033)
$Region/Year$	Yes	Yes	Yes	Yes
N	3692	3692	3692	3692
$R^2$	0.182	0.195	0.609	0.669

Note: * p < 0.10, ** p < 0.05, *** p < 0.01. The values in parentheses represent the standard errors.

Table 3. Benchmark regression results.

Variables	(1)	(2)	(3)	(4)
	Seg		Green
$RBplan$	0.072 ***	0.056 ***	0.014 ***	0.010 ***
	(0.017)	(0.018)	(0.004)	(0.004)
$Fis$		−0.011 *		0.011 ***
		(0.006)		(0.001)
$Pop$		−0.031 **		−0.003
		(0.014)		(0.003)
$St$		−0.034 ***		0.005 ***
		(0.008)		(0.002)
$Fdi$		0.526		−0.476 ***
		(0.486)		(0.098)
$Ind$		−0.438 ***		−0.104 ***
		(0.088)		(0.020)
$Lnroad$		−0.004		0.045 ***
		(0.017)		(0.005)
$Cons$	0.182 ***	0.960 ***	0.489 ***	0.314 ***
	(0.007)	(0.141)	(0.002)	(0.033)
$Region/Year$	Yes	Yes	Yes	Yes
N	3692	3692	3692	3692
$R^2$	0.182	0.195	0.609	0.669

Note: * p < 0.10, ** p < 0.05, *** p < 0.01. The values in parentheses represent the standard errors.

Additionally, population growth may negatively impact stable economic growth [34]. Similar to $Fis$, $St$ exhibits a significant negative effect on stable economic growth in Model (1) but a significant positive effect on environmental quality in Model (2). Technological investments generally produce long-term returns, and in resource-based cities, the economic restructuring benefits of technological innovation may have a delayed effect [35]. In the short term, technological investments demand fiscal resources, potentially creating pressures that temporarily suppress economic stability. However, these investments often lead to advancements, especially in clean technologies [36], which positively affect environmental quality. The adverse impact of $Fdi$ on environmental quality aligns with the “pollution haven” hypothesis, as foreign investment often flows into regions with lower environmental standards, increasing pollution burdens [37]. Furthermore, $Ind$ negatively influences both economic and environmental outcomes, suggesting that a high share of secondary industries leads to a concentrated industrial structure in resource-based cities, hindering economic sustainability and raising environmental risks [38]. Finally, $Lnroad$ has a significant positive effect on environmental quality, likely because improved infrastructure enhances resource flow efficiency, reduces traffic congestion, and lowers emissions, contributing to environmental improvement [39].

4.4. Robustness Tests

4.4.1. Parallel Trend Test

This study employs a difference-in-differences model under the parallel trends assumption, which requires that the change trends of the treatment and control groups remain consistent before policy implementation. Specifically, prior to the implementation of $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$, the trends in stable economic growth and ecological quality between resource-dependent and non-resource cities should be similar. An event study framework is also used to assess the dynamic effects of the policy across all periods before and the three periods after its implementation. The specific model is as follows:

$$Se{g}_{it}=\alpha +{\beta }_n\sum _{n=-5}^{7}RBpla{n}_{jk,2013+n}+{\alpha }_2\times Contro{l}_{it}+{\delta }_i+{\eta }_t+{\mu }_{it}$$

(4)

$$Gree{n}_{it}=\alpha +{\beta }_n\sum _{n=-5}^{7}RBpla{n}_{jk,2013+n}+{\alpha }_2\times Contro{l}_{it}+{\delta }_i+{\eta }_t+{\mu }_{it}$$

(5)

In this context, $RBpla{n}_{jk}$ defines the policy implementation window, taking a value of 1 in the implementation year and 0 otherwise. The year before policy implementation is used as the base period, and a trimming method is applied to observations outside this window, examining dynamic trend changes from five years before to seven years after $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ implementation, as shown in Figure 2. First, during the policy window, all regression results for $Seg$ prior to $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ implementation are insignificant, indicating no significant differences in stable growth trends between the treatment and control groups. After $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ implementation, its positive effect on economic stability begins to appear from the second period onward. Secondly, the regression results for $Green$ are insignificant in the first five periods, but the policy starts to significantly and positively impact the ecological quality of resource-based cities in the first period, supporting the parallel trend assumption.

4.4.2. Coordination Test

The baseline regression results confirm that $RBplan$ promotes steady economic growth while enhancing ecological environment quality. Building on these findings, we further apply a coupling coordination model to assess the “win–win” synergy between economic stability and environmental quality, ensuring that economic development does not compromise environmental standards. The indicators for coupling coordination, represented by ($Synergy$), are defined as follows:

$$\begin{array}{cc}\hfill & C_{it}={\displaystyle \frac{2\sqrt{Se{g}_{it}\times Gree{n}_{it}}}{Se{g}_{it}+Gree{n}_{it}}}\hfill \\ \hfill & T_{it}=a·Se{g}_{it}+b·Gree{n}_{it}\hfill \\ \hfill & Synerg{y}_{it}=\sqrt{C_{it}\times T_{it}}\hfill \end{array}$$

(6)

where $C_{it}$ represents the coupling degree between $Se{g}_{it}$ and $Gree{n}_{it}$. Since this paper examines the role of $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ in balancing economic and environmental outcomes, $Se{g}_{it}$ and $Gree{n}_{it}$ are considered equally important in the model, with both assigned equal weights ($a=b=0.5$). From these, a composite index $T_{it}$ is derived, while $Synerg{y}_{it}$ denotes the coupling coordination degree for region i in year t, measuring the coordination between $Se{g}_{it}$ and $Gree{n}_{it}$.

As shown in the first column of

Table 4. PSM and regression results excluding concurrent policy effects.

Variables	Coordination	Nearest Neighbor		Mahalanobis Matching		Excluding Concurrent
	Test	Matching				Policy Effects
	(1)	(2)	(3)	(4)	(5)	(6)	(7)
	Synergy	Seg	Green	Seg	Green	Seg	Green
$RBplan$	0.049 ***	0.053 **	0.008 *	0.040 *	0.012 ***	0.056 ***	0.011 ***
	(0.015)	(0.021)	(0.005)	(0.020)	(0.004)	(0.018)	(0.004)
$LCtreat\ast LCpost$						0.008	0.022 ***
						(0.020)	(0.004)
$CV$	Yes	Yes	Yes	Yes	Yes	Yes	Yes
$Region/Year$	Yes	Yes	Yes	Yes	Yes	Yes	Yes
N	3692	2299	2299	2582	2582	3692	3692
$R^2$	0.189	0.203	0.670	0.207	0.668	0.195	0.672

Note: * p < 0.10, ** p < 0.05, *** p < 0.01. The values in parentheses represent the standard errors.

, the $RBplan$ coefficient is significantly positive. This suggests that comprehensive reforms enable resource-based cities to balance economic growth and environmental quality, achieving a synergistic “win–win” outcome.

4.4.3. Propensity Score Matching Estimation

Given the weak constraints of the $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ policy and the randomness in selecting resource-dependent cities, the Propensity Score Matching with Difference-in-Differences ($\mathrm{P}\mathrm{S}\mathrm{M}-\mathrm{D}\mathrm{I}\mathrm{D}$) method is used to mitigate these characteristics’ impact on research outcomes. During matching, balance tests are performed on control variables for the treatment and control groups, using caliper nearest neighbor matching (1:2) and Mahalanobis matching. Columns (2) to (5) of Table 4 display the $\mathrm{P}\mathrm{S}\mathrm{M}-\mathrm{D}\mathrm{I}\mathrm{D}$ matching results. The coefficients in these columns are all significantly positive, indicating that the $\mathrm{P}\mathrm{S}\mathrm{M}-\mathrm{D}\mathrm{I}\mathrm{D}$ test results align with previous findings, confirming the robustness of the research conclusions.

4.4.4. Excluding Simultaneous Policy Effects

Considering that other policies during the sample period may affect the stable economic growth and ecological quality of resource-dependent cities, it is crucial to exclude significant concurrent policy effects to ensure robustness. The policy most relevant to this research is the low-carbon city pilot policy, initiated by the National Development and Reform Commission in 2010. Given policy lag effects, this study follows Xiao et al. (2023) [17] by selecting the first two batches of pilot cities that implemented the policy in 2010 and 2012 as the treatment group, constructing a multi-period difference-in-differences interaction term $LCtreat*LCpost$. The results in columns (6) and (7) of Table 4 indicate that after controlling for the low-carbon city pilot policy, the reform policy still achieves stable economic growth and improved ecological quality in resource-dependent cities, confirming the robustness of the conclusions.

4.4.5. Placebo Test

This study employs a strategy of randomly generating treatment group samples for the placebo test. From the kernel density distribution of the estimated coefficients (see Figure 3), it can be observed that in the placebo experiment, the average value of the pseudo-regression coefficients approaches zero, which is significantly lower than the actual estimated values of the primary explanatory variables. Additionally, most of the p-values exceed 10%, indicating that the estimation results of this study are highly robust.

5. Further Analysis

5.1. Mechanism Testing

The previous analysis indicates that $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ can achieve a ’win–win’ situation of stable economic growth and improved ecological quality. Next, this study focuses on examining how $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ influences the stable economic growth and ecological quality of resource-dependent cities through scale effects, technological effects, and structural effects.

5.1.1. Scale Effects

$\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ helps mitigate environmental costs by reducing industrial scale, easing the burdens of traditional growth models, and ensuring stable economic growth. It also directly lowers environmental pollution and ecological damage, enhancing ecological quality. This study uses the logarithm of total industrial output in cities above a certain scale to represent industrial scale ($Lnti$). Additionally, the urban pollution emission index ($Pollute$) characterizes urban pollution levels. According to sustainable development theory, economic development should meet present needs without compromising future generations’ ability to meet their own. Reducing industrial land, especially for high-pollution and energy-intensive industries, helps lower pollution and ecological harm, promoting resource efficiency and sustainable environmental development. Micro-level land transfer data are used to calculate urban land allocation, including the scale of newly added industrial land ($Nail$) and total industrial land scale ($Tail$), to measure the scale effects of $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ on economic growth and ecological quality in resource-dependent cities.

Columns (1)–(4) in

Table 5. Regression results for mechanism testing.

Variables	Scale Effect				Technological Effect	Structural Effect
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	Lnti	Pollute	Nail	Tail	Tc	Rsop	Ails	Tils
$RBplan$	−0.066 ***	−0.027 ***	−0.084 ***	−0.019	0.001 **	0.058 ***	−0.009 *	−0.012 ***
	(0.023)	(0.003)	(0.031)	(0.022)	(0.001)	(0.022)	(0.005)	(0.003)
$CV$	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
$Region/Year$	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
N	3692	3692	3692	3692	3692	3692	3692	3692
$R^2$	0.876	0.457	0.640	0.829	0.219	0.515	0.243	0.344

Note: * p < 0.10, ** p < 0.05, *** p < 0.01. The values in parentheses represent the standard errors.

present the mechanism test results for scale effects. Column (1) shows that $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ reduces urban industrial scale and lowers energy consumption intensity [10], supporting a shift toward cleaner industries. Column (2) displays the results for the pollution emission index ($Pollute$), revealing that $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ effectively decreases pollution intensity. Further, Columns (3)–(4) indicate that the reform curbs industrial land expansion, positively affecting economic stability and ecological quality, thus supporting the scale effect hypothesis in Research Hypothesis 2.

5.1.2. Technological Effects

$\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ fosters new economic growth by introducing and advancing green technologies, which enhance economic efficiency and reduce production costs. Additionally, green technologies decrease dependence on natural resources and reduce pollution, promoting sustainable economic development. This study utilizes the super-efficiency Non-Radial and Directional Distance Function ($\mathrm{N}\mathrm{D}\mathrm{D}\mathrm{F}$) Malmquist index to measure the Green Total Factor Productivity ($\mathrm{G}\mathrm{T}\mathrm{F}\mathrm{P}$) of 284 prefecture-level cities in China from 2008 to 2020 and decomposes it to assess technological effects by measuring green technological progress ($Tc$) related to $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$’s impact on economic stability and ecological quality in resource-dependent cities.

Column (5) in Table 5 shows the technology effect test results. The findings indicate that $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ significantly promotes green technological progress in cities at the 5% significance level, supporting the green transformation of resource-based cities [1]. This suggests that $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ enables a win–win outcome of stable economic growth and improved ecological quality through green technology advancements, confirming the technology effect in Research Hypothesis 2.

5.1.3. Structural Effects

$\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ optimizes industrial structure by reducing reliance on traditional heavy industries and high-pollution sectors, aligning with sustainable development needs. During industrial restructuring, emphasizing environmental protection can harness the synergy between policy guidance and market mechanisms, ensuring stable economic growth and improved ecological quality. This study uses the Gini coefficient to calculate the rationalization index of industries ($Rsop$) as a representation of urban industrial structure. Shifting from resource-intensive industrial land to services, high-tech industries, and low-carbon sectors is key for economic transformation. Additionally, the structure of newly added land ($Ails$) and total land structure ($Tils$) is used to examine $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$’s structural effects on economic growth and ecological quality.

Columns (6) to (8) of Table 5 show the structural effect test results. Column (6) indicates that $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ significantly enhances the rationalization index at the 1% level, promoting rational urban industrial structure allocation and boosting environmental protection, leading to stabilized short-term economic fluctuations. The results in columns (7) and (8) show that the reform has reduced the proportion of newly added and total industrial land, optimizing urban spatial structure and enhancing carrying capacity [40], thus supporting coordinated economic and ecological advancement and confirming the structural effects in Research Hypothesis 2.

5.2. Analysis from the Microenterprise Perspective

As primary contributors to pollution emissions, enterprises are the main entities impacted by $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$. The pollution restrictions imposed by $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ significantly influence enterprise production modes. The reform policies’ effects on urban economic stability and ecological quality at the macro level translate into micro-level impacts on enterprises, affecting their economic performance and green transformation. This paper takes a micro perspective on enterprises to verify the policy’s impact and constructs the following difference-in-differences model:

$$TF{P}_{ijkt}={\alpha }_0+{\alpha }_1\times RBpla{n}_{kt}+{\alpha }_2\times Contro{l}_{ijkt}+{\delta }_i+{\eta }_j+{\lambda }_k+{\delta }_t+{\mu }_{ijkt}$$

(7)

$$G{c}_{ijkt}={\alpha }_0+{\alpha }_1\times RBpla{n}_{kt}+{\alpha }_2\times Contro{l}_{ijkt}+{\delta }_i+{\eta }_j+{\lambda }_k+{\delta }_t+{\mu }_{ijkt}$$

(8)

In this context, i, j, k, and t represent individual enterprises, industries, cities, and years, respectively. $TF{P}_{ijkt}$ denotes the enterprise’s total factor productivity, calculated using the Levinsohn–Petrin ($LP$) method based on Lu and Lian (2012) [41]. $TFP$ serves as a key metric for enterprise resource efficiency and economic performance. $G{c}_{ijkt}$ indicates the green transformation index, with 113 keywords selected from Zhou et al. (2022) [42] to gauge green transformation. The frequency count of these keywords in annual reports, logged and incremented by one, reflects the level of environmental protection. The core variable $RBpla{n}_{kt}$ signifies $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ policy impact in city k and year t. Control variables include enterprise size ($Size$), leverage ($Lev$), age ($Age$), profitability ($Roa$), Tobin’s Q ($Tobinq$), and debt structure ($Ato$). ${\delta }_i$, ${\eta }_j$, ${\lambda }_k$, and ${\delta }_t$ represent fixed effects for enterprises, industries, regions, and years, respectively; ${\mu }_{ijkt}$ is the disturbance term.

Table 6. Regression results at the enterprise level.

Variables	(1)	(2)	(3)	(4)
	TFP		Gc
$RBplan$	0.166 ***	0.073 ***	0.051 **	0.050 **
	(0.056)	(0.021)	(0.024)	(0.024)
$CV$	No	Yes	No	Yes
$Firm/Ind/Region/Year$	Yes	Yes	Yes	Yes
N	17,982	17,982	17,982	17,982
$R^2$	0.840	0.955	0.764	0.765

Note: ** p < 0.05, *** p < 0.01. The values in parentheses represent the standard errors.

shows the effects of $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ at the firm level. Columns (1)–(2) in Table 6 reveal significantly positive coefficients for $RBplan$, indicating that $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ enhances firms’ $TFP$ [43], supporting steady economic performance growth. Columns (3)–(4) similarly show positive $RBplan$ coefficients, suggesting that $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ promotes firms’ green transformation [44]. These results align with macro-level findings, confirming $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$’s dual benefits of stability and green transformation. Figure 4 examines dynamic trends in micro-firms from five years before to seven years after $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$, validating the parallel trend assumption.

5.3. Spatial Spillover Effects of $NSDPRBC$

Cities do not exist in isolation; they form interconnected networks. While the $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ primarily targets resource-based cities, it may also have spillover effects on nearby non-resource-based cities. First, as resource-based cities undergo industrial upgrades, they may establish production and trade links with non-resource cities along the industrial chain, generating positive regional effects, especially on environmental issues. Furthermore, as these cities’ economies diversify, capital and talent can flow beyond city boundaries, fostering development in adjacent regions. Additionally, the advanced technologies and management practices accumulated in resource-based cities’ sustainable development may spread to others, facilitating high-level regional coordination. Therefore, this paper applies a spatial econometric model to analyze the spillover effects of $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$. To validate the spatial econometric model, this study uses Moran’s I index to test correlations between economic growth ($Seg$) and ecological quality ($Green$) for 284 Chinese cities.

Table 7. Moran’s I index.

Year	Seg	Green
2008	0.756 ***	0.117 ***
2009	0.778 ***	0.111 ***
2010	0.795 ***	0.137 ***
2011	0.786 ***	0.152 ***
2012	0.767 ***	0.545 ***
2013	0.802 ***	0.596 ***
2014	0.787 ***	0.606 ***
2015	0.787 ***	0.264 ***
2016	0.780 ***	0.277 ***
2017	0.798 ***	0.265 ***
2018	0.791 ***	0.265 ***
2019	0.772 ***	0.188 ***
2020	0.765 ***	0.350 ***

Note: *** p < 0.01.

shows positive Moran’s I indices for both $Seg$ and $Green$, indicating significant spatial correlations. Thus, a spatial econometric model is suitable for studying $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$’s spillover effects. To determine the specific model form, we conducted LM, Wald, Hausman, and LR tests. The LM and Wald results (significant at 1%) support the spatial Durbin model, while the Hausman and LR tests indicate that bidirectional fixed effects are unsuitable for this model. Based on these results, we chose the time fixed effects model.

In summary, to examine the spatial spillover effects of the $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ on the economic stable growth and ecological environmental quality of resource-based cities, this paper constructs the following spatial econometric model based on Models (1) and (2):

$$\begin{array}{cc}\hfill Se{g}_{it}& ={\alpha }_0+{\alpha }_1\times W\times E{g}_{it}+{\alpha }_2\times W\times RBpla{n}_{it}+{\alpha }_3\times W\times contro{l}_{it}\hfill \\ \hfill & +{\alpha }_4\times Gree{n}_{it}+{\alpha }_5\times RBpla{n}_{it}+{\alpha }_6\times Contro{l}_{it}+{\delta }_i+{\eta }_t+{\mu }_{it}\hfill \end{array}$$

(9)

$$\begin{array}{cc}\hfill Gree{n}_{it}& ={\alpha }_0+{\alpha }_1\times W\times Gree{n}_{it}+{\alpha }_2\times W\times RBpla{n}_{it}+{\alpha }_3\times W\times contro{l}_{it}\hfill \\ \hfill & +{\alpha }_4\times Gree{n}_{it}+{\alpha }_5\times RBpla{n}_{it}+{\alpha }_6\times Contro{l}_{it}+{\delta }_i+{\eta }_t+{\mu }_{it}\hfill \end{array}$$

(10)

In the equation, W represents the geographic spatial weight matrix, where $W=1$ indicates that two locations are adjacent, and $W=0$ indicates that they are not. The other variables remain the same as in Models (1) and (2).

Columns (1) and (2) of

Table 8. Regression results for spatial spillover effects.

Variables	(1)	(2)
	Seg	Green
$RBplan$	0.059 ***	0.007 ***
	(0.017)	(0.003)
$CV$	Yes	Yes
$\rho$	0.344 ***	0.898 ***
	(0.020)	(0.007)
$Region$	No	No
$Year$	Yes	Yes
N	3692	3692
$R^2$	0.043	0.030

Note: *** p < 0.01. The values in parentheses represent the standard errors.

indicate that the spillover effects of the $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ on neighboring areas are similar to its local impacts, ensuring stable economic growth and promoting improvements in ecological environmental quality. This is due to the Porter effect [45] generated by the $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ in the short term, which enhances local green technology innovation effects while also raising the level of green innovation in neighboring areas through technology diffusion effects, thereby impacting the stable economic growth and ecological environmental quality of adjacent regions.

5.4. Heterogeneity Analysis

Influenced by various factors such as the type of resource-based city, the intensity of officials’ promotion incentives, and the strength of environmental regulations, the impact of the $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ on the economic stable growth and ecological environmental quality of resource-based cities varies significantly. To address this, this paper will further illustrate the heterogeneous effects of the $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ policy through grouped regression analysis.

5.4.1. Impact of Resource-Based City Types

This paper classifies resource-based cities into four categories—growth-type cities, mature cities, declining cities, and regeneration cities—based on the comprehensive classification list in the “National Sustainable Development Plan of Resource-Based Cities (2013–2020)” to examine the policy effects.

Based on the results in

Table 9. Heterogeneity regression results for resource-based city types.

Variables	Growth Stage		Mature Stage		Decline Stage		Regeneration Stage
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	Seg	Green	Seg	Green	Seg	Green	Seg	Green
$RBplan$	0.101 **	−0.038 ***	0.050 **	0.010 **	0.071 **	0.030 ***	0.018	0.005
	(0.044)	(0.013)	(0.022)	(0.004)	(0.035)	(0.008)	(0.037)	(0.006)
$CV$	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
$Region/Year$	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
N	2392	2392	3016	3016	2509	2509	2405	2405
$R^2$	0.175	0.685	0.185	0.734	0.179	0.716	0.169	0.755

Note: ** p < 0.05, *** p < 0.01. The values in parentheses represent the standard errors.

, it is evident that economic growth across all regions remains stable; however, only mature and declining cities demonstrate improvements in ecological quality [32]. Mature cities typically have undergone some level of economic restructuring, with a more diversified industrial base. This foundation allows them to leverage reform policies effectively to foster growth in non-resource sectors, ensuring steady economic progress. Furthermore, mature cities often have access to ample capital and technological resources, which facilitate the adoption of new technologies and management practices, enhancing resource efficiency and environmental protection. In contrast, for declining resource-based cities, economic transformation has become an urgent task due to resource depletion or industry contraction. Policy reforms provide these cities with critical opportunities for industrial upgrading, helping them to identify new growth drivers. Declining cities frequently face severe environmental degradation, and the emphasis on environmental protection and restoration within reform policies aids in improving ecological conditions, thereby enhancing overall urban quality of life. Additionally, declining cities may receive greater policy support and financial assistance, accelerating their economic restructuring and environmental management. This ensures stable economic growth and incremental ecological improvements, further illustrating how reform policies can promote balanced regional development.

5.4.2. Impacts of Official Promotion Incentive Intensity

This paper draws on the research of Sun and Liu (2023) [46] and selects municipal party secretaries as representatives of urban officials. The current age of prefecture-level city officials is divided into two groups based on the age of 55 as the cutoff point, categorizing them into strong and weak promotion incentive groups. Additionally, the overall sample is divided into two groups for regression analysis based on the strength of promotion incentives for officials.

The results in columns (1) and (2) of Table 10 indicate that the $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ policy has a significant effect on stable economic growth only when the intensity of promotion incentives for officials is relatively high. A possible reason for this is that the promotion incentives for officials can indirectly drive the industrial transformation and upgrading of cities, mitigating the impact of the $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ on economic growth and ensuring its stability. Columns (3) and (4) of Table 10 present the regression results of the $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ on ecological environmental quality under different promotion incentives for officials. Clearly, in regions where officials have a strong desire for promotion, the $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ is more effective in promoting ecological environmental quality. This may be because if environmental protection indicators are included in officials’ performance evaluations, they are likely to incorporate environmental protection actions into their work objectives [47]. During the pursuit of promotion, officials may be influenced by intrinsic moral incentives, viewing environmental protection as a correct and ethical behavior. The results show that, during the sample period, stable economic growth and ecological environmental quality in resource-based cities can be coordinated and advanced under high promotion incentive intensity.

5.4.3. Impacts of Environmental Regulation Intensity

This paper adopts the methodology of Zhang and Chen (2021) [48] by first calculating the proportion of industrial added value to GDP for each prefecture-level city. Subsequently, this proportion is multiplied by the frequency of “environmental protection”-related terms appearing in provincial government work reports to construct an indicator of environmental regulation intensity for prefecture-level cities. Additionally, the overall sample is divided into two groups based on the median of environmental regulation intensity for grouped regression analysis.

Table 10 displays the effects of the $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ on stable economic growth and ecological environmental quality under different levels of environmental regulation intensity. The results in columns (5) and (6) indicate that the $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ promotes stable economic growth, regardless of the level of environmental regulation intensity. Further inter-group difference tests reveal no significant differences between the two groups. This may be because, in the short term, strengthened environmental regulations compel industrial enterprises in resource-based cities to seek transformation and upgrading. While this process may somewhat restrict rapid economic expansion, the innovation compensation effects generated by environmental regulations can still ensure stable economic growth. From columns (7) and (8), it is clear that in cities with high environmental regulation intensity, the $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ significantly enhances ecological environmental quality. Conversely, the impact of $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ on ecological quality is minimal in cities with low regulation intensity. This implies that reducing environmental regulation may increase pollution emissions [13], hindering improvements in ecological quality.

Table 10. Heterogeneity regression results for official promotion incentive intensity and environmental regulation intensity.

Variables	Official Promotion Incentive Intensity				Environmental Regulation Intensity
	High	Low	High	Low	High	Low	High	Low
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	Seg	Seg	Green	Green	Seg	Seg	Green	Green
$RBplan$	0.053 **	0.044	0.015 ***	−0.000	0.054 **	0.065 **	0.010 **	0.008
	(0.022)	(0.030)	(0.005)	(0.006)	(0.023)	(0.029)	(0.005)	(0.007)
$CV$	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
$Region/Year$	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
N	2465	1227	2465	1227	2012	1680	2012	1680
$R^2$	0.182	0.261	0.656	0.724	0.202	0.221	0.721	0.605
Difference	0.009		0.015 **		−0.012		0.002

Note: ** p < 0.05, *** p < 0.01. The values in parentheses represent the standard errors.

Table 10. Heterogeneity regression results for official promotion incentive intensity and environmental regulation intensity.

Variables	Official Promotion Incentive Intensity				Environmental Regulation Intensity
	High	Low	High	Low	High	Low	High	Low
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	Seg	Seg	Green	Green	Seg	Seg	Green	Green
$RBplan$	0.053 **	0.044	0.015 ***	−0.000	0.054 **	0.065 **	0.010 **	0.008
	(0.022)	(0.030)	(0.005)	(0.006)	(0.023)	(0.029)	(0.005)	(0.007)
$CV$	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
$Region/Year$	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
N	2465	1227	2465	1227	2012	1680	2012	1680
$R^2$	0.182	0.261	0.656	0.724	0.202	0.221	0.721	0.605
Difference	0.009		0.015 **		−0.012		0.002

Note: ** p < 0.05, *** p < 0.01. The values in parentheses represent the standard errors.

6. Conclusions and Recommendations

6.1. Conclusions

With the intensification of environmental challenges in resource-based cities, implementing the dual-carbon policy and achieving sustainable economic development have become increasingly difficult. This study utilizes the “Comprehensive Sustainable Development Plan for Resource-Based Cities (2013–2020)” as an institutional framework, analyzing panel data from prefecture-level cities from 2008 to 2020 to examine the impacts and mechanisms of the $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ on stable economic growth and ecological environmental quality in these cities, along with its effects under varying conditions. This study aims to offer theoretical and empirical insights to support a win–win outcome for economic stability and environmental quality in resource-based cities. The findings reveal that: (1) During the sample period, $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ contributed to improved ecological quality while supporting stable economic growth. These results hold firm across multiple robustness tests. (2) The $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ mainly impacted stable economic growth and ecological environmental quality in resource-based cities through scale effects, technological effects, and structural effects. (3) Analysis from a micro perspective indicates that the $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ is beneficial for improving enterprise performance and incentivizing green transformation among enterprises. (4) The influence of the $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ on resource-based cities has positive spillover effects on neighboring non-resource-based cities. (5) In mature and declining cities, areas with stronger promotion incentives for officials, and regions with higher environmental regulation intensity, the policy effects of the $\mathrm{N}\mathrm{S}\mathrm{D}\mathrm{P}\mathrm{R}\mathrm{B}\mathrm{C}$ on economic stable growth and ecological environmental quality in resource-based cities are more effective. Resource-based cities face multiple pressures and challenges in their development, necessitating comprehensive strategies and precise actions to achieve coordinated and sustainable development in economic, social, and environmental dimensions.

This study offers significant theoretical insights for addressing the “resource curse” in developing countries, particularly in nations that are rich in natural resources yet face challenges in achieving sustained economic growth. By analyzing the impact of supportive policies on resource-dependent cities, this research provides a novel perspective for disrupting the cycle of dependency that hinders development in these areas. Within the framework of socio-technical and social systems, this study underscores the necessity of tailored strategies for developing countries, thereby extending theoretical discussions on policy design. This theoretical approach lays a valuable foundation for future research on policy-driven development models that aim to balance economic growth, social welfare, and environmental protection in resource-rich developing economies.

6.2. Recommendations

Based on the conclusions, this paper proposes the following policy recommendations:

(1) Integrating economic growth and environmental protection for green transition: The green development strategy for resource-dependent cities has broad global applicability. Governments worldwide can embed resource conservation and environmental protection into policy as core production principles, aligning economic growth with environmental goals within the social system. This strategy involves promoting renewable energy, enhancing resource use efficiency, and minimizing waste emissions while embedding green supply chain and circular economy concepts deeply into production and consumption chains. By maximizing resource allocation efficiency, resource-dependent countries can simultaneously achieve economic and environmental goals, reducing ecological stress and unlocking new growth potential.

(2) Promoting industrial diversification through technological innovation and digital transformation: Reducing dependence on a single resource and promoting economic diversification are essential to addressing the “resource curse”. Drawing from China’s experience, other nations can enact policies that encourage innovation in green and digital technologies, optimizing industrial structures and enhancing economic resilience. Digital transformation serves as a foundation for industrial upgrading, especially in emerging fields such as information technology, advanced manufacturing, and modern services. By investing in and supporting these sectors, resource-dependent cities can accelerate their shift from resource-intensive to innovation-driven economies. This diversified economic model not only enhances resource utilization efficiency within the social system but also strengthens resilience against global market fluctuations.

(3) Implementing tailored policy support for balanced development of resource-dependent cities: China’s experience demonstrates that resource-dependent cities at different stages of development require customized policy support to ensure balanced economic and environmental growth. Differentiated support policies tailored to growing, mature, declining, and regenerating cities can facilitate more balanced progress across resource-dependent economies on a national scale. By designing targeted policies, countries can provide appropriate support based on each city’s unique conditions, ensuring balanced progress in economic, social, and environmental goals, reducing regional disparities, and creating a more stable national economic structure.

(4) Fostering regional cooperation to build cross-border economic and environmental coordination mechanisms. The transformation of resource-dependent cities generates regional spillover effects, making it crucial for countries to establish cooperative frameworks that involve public, private, and non-governmental organizations. Such frameworks facilitate information sharing and collaborative environmental action, supporting resource-dependent and adjacent non-resource-dependent cities in sharing economic resources, technology, and talent. Furthermore, governments can develop long-term visions and cooperative mechanisms for regional collaboration, ensuring that regional cooperation plays a pivotal role in achieving future global sustainable development goals.