Mineral resource dependence and green transformation: the impact and challenges of green finance in Chinese cities

Abstract

Overcoming the resource curse while balancing environmental protection and economic growth presents significant challenges. We developed an evaluation system to measure the green economic efficiency (GEE) of 262 cities and introduced the concept of the mineral resource curse. This study investigates both linear and non-linear relationships between mineral resource dependence (MRD) and GEE, as well as the role of green finance in this context. To address the endogeneity issue arising from the bidirectional causality between MRD and GEE, we used the interaction of relief degree of land surface and time as instrumental variables (IV). The empirical results indicate that a city’s MRD significantly inhibits GEE. Specifically, an increase in MRD hinders GEE by reducing green investment and crowding out green credit. By categorizing cities according to resource cycle theory, we found that non-resource-based and regenerative cities effectively avoided the mineral resource curse. However, resource-based, growing, mature, and declining cities did not benefit from their mineral resources. This conclusion remains robust even after changing IV and using various estimation methods such as IVTOBIT. Furthermore, this study discusses the practical effects of green finance. We found that green investment has a limited impact, failing to offset the negative influence of MRD on GEE. Under the influence of green credit, the relationship between MRD and GEE exhibits an inverted U-shape. This finding highlights the limitations of green finance policies at the municipal level in China. Overall, this study provides new evidence for improving China’s green finance system and promoting green economic development. It offers a fresh perspective for China and other developing countries in their green transformation efforts and avoidance of the mineral resource curse.

Introduction

Resource-based cities have significantly contributed to rapid economic development (Yan et al. 2019). However, they face challenges such as resource non-renewability, market demand fluctuations, and lack of diversity in urban and industrial structures (Zhang 2022). These factors have led to economic growth slowdowns and severe ecological degradation. Environmental degradation and ecosystem damage cost China approximately 10% of its annual GDP (WorldBank 2023). This necessitates that China actively control its dependence on resources and adopt a national economic accounting system based on green GDP, thereby overcoming the limitations of traditional economic development models regarding resource utilization and environmental protection.

The concept of a green economy has been closely related to sustainable development since its introduction at the 2012 ‘Rio+20’ United Nations Conference on Sustainable Development (Eaton 2013). It emphasizes that economic development must occur within the Earth’s natural limits and resource constraints, especially given global climate change and environmental degradation. The importance of a green economy is increasingly prominent. City governments, as key agents in policy formulation and implementation, have promoted sustainable urban development through strategies such as low-carbon city planning and the use of renewable energy (Bulkeley and Betsill 2005). These measures effectively promote the efficient use of resources within cities, facilitating green economic growth (Rosenzweig et al. 2010).

However, the relationship between natural resources and economic development is complex and variable. Auty (1993) first highlighted the paradox where abundant natural resources might unexpectedly impede economic advancement. Dependency on resources frequently results in their overexploitation, subsequently hindering innovation and the growth of diverse industries (Haseeb et al. 2021; He and Mou 2020). Areas with high resource endowments often develop an excessive dependence on resources. While resource extraction activities can bring substantial short-term profits, they often weaken the motivation for innovation in the region, leading to the crowding out of high-quality labor and suppressing the development of other industries, resulting in a rigid industrial structure. Various studies reveal that dependency on natural resources often obstructs economic growth due to factors like the Dutch disease effect, the displacement of human capital and technological innovation, and a decline in institutional quality (Adhvaryu et al. 2021; Colagiuri and Morrice 2015; Umar et al. 2021). Tornell and Lane (1999) found that abundant oil resources in Mexico and Venezuela led to weak institutional quality, resulting in slow economic growth. Similarly, Inuwa et al. (2022) confirmed the resource curse hypothesis by showing that Nigeria’s abundant oil resources negatively impacted its economic growth. Another stream of literature suggests that a region’s or country’s natural resource abundance can positively promote economic growth (Ampofo et al. 2020; Lin and Xu 2020; Sun et al. 2017). From this analysis, it is clear that merely relying on natural resources may not drive economic growth. The classic dilemma of “environmental protection vs. economic growth” needs to be revisited. This naturally turns our attention to whether cities in China with varying degrees of resource dependence can overcome this dilemma and achieve the blessing of resources.

This paper focuses on green development, investigating the impact of mineral resource dependence on the green economic efficiency of Chinese cities. We propose the concept of the ‘mineral resource curse’ to offer more specific insights into this theory. Typically, the resource curse theory examines phenomena such as slowed economic growth, increased political corruption, and lagging social development in countries and regions dependent on natural resources (Angrist and Kugler 2008). Compared to other natural resources, mineral resources have unique characteristics; their extraction often involves large-scale capital investment, environmental degradation, and the potential for economic monoculture. Furthermore, given the growing global demand for mineral resources, especially in new energy and high-tech industries, the exploitation and use of mineral resources have significant implications for green economic growth.

Existing literature has explored the “crowding-out effect” of natural resource dependence in areas such as technological innovation (Cheng et al. 2021), human capital (Khan et al. 2023; Shao et al. 2023), and openness to foreign trade (Wang et al. 2022; Zhao et al. 2023). This has led our research to expand from the perspective of green finance, a new model within the financial system designed to protect and improve the environment, which is significant for promoting sustainable development, economic growth, and accelerating the development of financial institutions (Scholtens and Dam 2007). Green credit and green investment, as key tools of green finance, provide funding for environmentally friendly projects, stimulate green technological innovation in polluting businesses through credit constraints, and promote the development of clean technology and sustainable practices, thereby achieving green transformation in emerging economies (Hu et al. 2021a). Given that green investment and green credit have common goals in promoting sustainable development but differ in concept, operational mechanisms, and scope of impact, we analyze them separately, focusing on whether cities with different levels of MRD promote GEE through appropriate financial means.

During the research process, the studies by Guo et al. (2023) provided significant insights for this paper. However, they conflated the concepts of resource dependence and resource abundance, overlooking the endogeneity issues arising from omitted explanatory variables and bidirectional causality. Specifically, although they used urban panel data, they neglected the heterogeneity issues among non-resource-based cities, resource-based cities, and different developmental stages of resource-based cities. Additionally, we found no literature that simultaneously considers green credit and green investment to analyze their individual and combined impacts on MRD due to GEE.

This research aims to enhance comprehension of the MRD and GEE, offering a new perspective on the financial drivers behind green economic growth. Our main contributions are: (1) Micro-level study: Unlike previous studies that mainly focused on national or provincial macro-level research, we chose a micro perspective, using 262 prefectural-level cities in China from 2006 to 2017 as our sample. We employed a non-angular, non-radial super-efficiency SBM model under dual constraints of energy and environment to recalculate the GEE indicators, encompassing aspects of economic growth, resource conservation, and environmental protection. (2) Green finance indicators: We subdivided green finance indicators into green credit and green investment to explore their financial transmission mechanisms in the impact of MRD on GEE. (3) Addressing endogeneity: To address the endogeneity issues arising from bidirectional causality, we identified instrumental variables (IV) from a geographical perspective, reducing errors in our empirical analysis results. (4) Subdivision of resource city types: Based on the classification method of the “Sustainable Development Planning for Resource-Based Cities (2013–2020)”, we conducted contrast regressions for resource-based and non-resource-based cities. We further categorized resource-based cities into growing, mature, declining, and regenerative types according to resource cycle theory, examining the differential impacts of MRD and GEE in various city types. (5) We employed the dynamic threshold method to further examine the role and differences of green investment and credit in the influence of MRD on GEE, providing new insights into understanding the complex relationship between the two. Our findings aim to contribute policy guidance for developing nations in fostering green growth and offer empirical backing for associated theoretical frameworks.

The remaining structure of this paper is organized as follows. The next section describes the literature review and research hypotheses. Section 4 provides empirical evidence, and the final section concludes. A detailed overview of the entire analysis process is illustrated in Supplementary Appendix A, which outlines each step and its respective components.

Literature review and theoretical research hypotheses

Definition and measurement of GEE

Green economic efficiency represents a crucial measure of the balanced progress between economic and ecological aspects within a region (Shuai and Fan 2020). Methods for measuring green economic growth mainly fall into three categories: Firstly, early scholars often used the neoclassical economic growth accounting model, which, however, overlooks the issue of undesired outputs. Secondly, stochastic frontier analysis is another common method. Lastly, Data Envelopment Analysis (DEA) is also widely used for measuring green economic growth. Nonetheless, traditional DEA methods have certain limitations, such as not considering the impact of slack variables and the potential overestimation of production efficiency. To overcome these issues, we draw on the study by Fukuyama and Weber (2009) and adopt a non-radial, non-oriented distance function model based on slack variables, constructing a more accurate green economic growth measurement model.

The “Resource Curse” hypothesis

While natural resources are essential for economic development (Bergougui 2024; Ling et al. 2022; Tang et al. 2022), economic growth rates in some resource-dependent areas lag significantly. This suggests that resource abundance is not a necessary condition for economic development. Since Auty (1993) proposed the resource curse hypothesis, numerous scholars have studied the relationship between natural resources (especially non-renewable energy resources) and economic growth, repeatedly validating the existence of this hypothesis (Ahmad et al. 2021; Cheng et al. 2020; Mueller 2022; Qian et al. 2021; Tiba and Frikha 2019; Wang et al. 2023). With the increasing concerns about global warming and environmental pollution, scholars have recently extended their research to the field of green economy. Studies using cross-national and Chinese samples by Bergougui and Murshed (2021), Brooks and Kurtz (2022) and Biresselioglu et al. (2019) have investigated the relationship between energy resource richness (focusing on oil, natural gas, and coal) and green development. Their conclusions also confirm the existence of the resource curse. Bergougui and Murshed (2023) used a sample of 107 developing countries to verify that natural resources can inhibit sustainable development, particularly in countries with high resource endowments. Belaid et al. (2021) examined the relationship between oil revenues and economic growth in the Middle East and North Africa, highlighting the existence of the resource curse. Additionally, (Tang et al. 2022) explored the impact of natural resource abundance on financial development in ASEAN countries, demonstrating that favorable business regulations can mitigate the financial resource curse.

In conclusion, excessive dependence on resources evidently hampers green development. But does this phenomenon also exist at the city level? To explore this question, we analyzed data from 262 Chinese cities. Our preliminary findings (Fig. 1) indicate that the relationship between GEE and MRD in these cities aligns with the conclusions drawn from existing research. Based on this, we propose our first research hypothesis:

Fig. 1 [Images not available. See PDF.]

Scatter plots of the GEE and MRD.

H1: MRD in Chinese cities exhibits a “Mineral resource curse,” suppressing GEE.

Resource dependence and green investment

Excessive reliance on natural resources can impede green economic growth, notably by crowding out green investments. Traditional investment models focused on economic gains often neglect environmental impacts, while green investments are vital for clean energy and efficiency. Sachs et al. (2019) emphasize the importance of green investments for Sustainable Development Goals (SDGs), advocating for tools like green bonds and carbon markets. Studies by (Gu et al. 2021) show that green investments enhance long-term performance through improved environmental practices. However, resource-based cities’ dependency on a single industry challenges economic diversification and green investment. This dependency may skew consumption towards non-renewable energy and obstruct low-carbon investment (Pigato et al. 2020; Ulucak and Baloch 2023), thus hindering green growth. Based on this, we propose the second research hypothesis:

H2: MRD inhibits green investment, thereby negatively impacting GEE.

Resource dependence and implementation of green credit

Green credit, as an important green financial tool endorsed by governments, encourages financial institutions to direct funds towards clean production sectors. Research by (Hu et al. 2021a) demonstrates that green credit can stimulate green technological innovation in heavily polluting companies through credit constraints. Lv et al. (2023) found that the implementation of green credit policies significantly boosts enterprises’ green total factor productivity, particularly for those with smaller commercial credit scales and lower efficiency in fund utilization, through financing scale and cost effects. Zhu (2022) notes that green credit facilitates industrial upgrading through technological innovation, capital, and financing channels. However, in countries and regions dependent on natural resources, a substantial portion of capital and resources may be preferentially allocated to resource extraction industries, crowding out funds available for green investments. This crowding-out effect could pose a challenge to the development of green credit. Considering this, we propose the hypothesis:

H3: An increase in MRD crowds out green credit, thereby inhibiting GEE.

The more complex impact of green investment and green credit

The development of green investment not only optimizes fund allocation (Gao et al. 2023) but also aids in the research and development of clean production and pollution control technologies. These investments, by facilitating the transfer of production factors such as talent, technology, knowledge among industries, optimize resource allocation efficiency, support environmentally friendly investment projects, and thus achieve industrial upgrading and green economic growth (Zhang et al. 2021). Green credit, by providing funds to polluting enterprises, stimulates their innovation and R&D activities, thereby improving production technologies, reducing pollutant emissions, and enhancing green production efficiency (Ren et al. 2022) However, the impact of these investments and credits may be limited in the initial stages of market development. Over time and as the market matures, their positive impacts will gradually become apparent and accelerate.

Although green investments and green finance are widely considered key tools for promoting green economic growth in theory, their actual effectiveness may be influenced by various factors, showing complexity and conditional limitations. For instance, according to Smith and Stirling (2010), the maturity of the market significantly affects the effectiveness of green investments and financial products. The speed of technological innovation and market adaptability may affect the effectiveness and attractiveness of green investments (Acemoglu et al., 2012). Moreover, when evaluating the efficacy of green investments and financial instruments, it’s crucial to consider factors like China’s distinct resource ownership and operational rights framework, the setup of administrative jurisdictions, and the government’s role in market regulation(Bergougui and Murshed 2021). For example, government support may promote the development of certain green projects but may also lead to market failure, hindering more efficient resource allocation (Zhang et al. 2016). Based on this, we propose hypothesis H4.

H4: Under the influence of green credit and green investment, the relationship MRD and GEE may exhibit dynamic non-linear characteristics.

Methods and variables

Econometric model

To explore the impact of MRD on urban GEE, we constructed the following panel data regression model, referencing the research on the resource curse hypothesis by Shao and Yang (2014):

1

$${\mathit{GEE}}_{\mathit{it}}={\beta }_0+{\beta }_1{\mathit{MRD}}_{\mathit{it}}+{\beta }_2{X}_{\mathit{it}}+{\mu }_i+{\gamma }_t+{\epsilon }_{\mathit{it}}$$Here, i represents the city, and t represents the year. $\mathit{GEE}$ is green economic efficiency. MRD is the degree of mineral resource dependence. $X_{\mathit{it}}$ represents a series of control variables. ${\mu }_i$ and ${\gamma }_t$ are city fixed effects and time fixed effects, respectively. ${\epsilon }_{\mathit{it}}$ is the random error term. ${\beta }_0-{\beta }_2$ represent coefficients to be estimated. For the following equations, the symbols retain their meanings as defined previously and will not be reintroduced for brevity.

Furthermore, to delve deeper into how MRD influences GEE, we constructed the following mediation effect model:

2

$$M_{\mathit{it}}={\gamma }_0+{\gamma }_1{\mathit{MRD}}_{\mathit{it}}+{\gamma }_2{X}_{\mathit{it}}+{\mu }_i+{\gamma }_t+{\epsilon }_{\mathit{it}}$$

3

$${GEE}_{it}={\theta }_0+{\theta }_1{MRD}_{it}+{\theta }_2{M}_{it}+{\theta }_3{X}_{it}+{\mu }_i+{\gamma }_t+{\epsilon }_{it}$$Here, M represents the mediating variables, measuring green investment (GI) and green credit (GC). ${\gamma }_0-{\gamma }_2$ and${\theta }_0-{\theta }_3$ are coefficients to be estimated.

Finally, to verify the potential non-linear relationship and dynamic effects between MRD and GEE, we introduced the dynamic threshold panel regression model. Considering the persistence of the GEE variable and the bias in the estimation results of static thresholds and the arising endogeneity issues, we followed the approach of Seo et al. (2019) and used green credit and green investment as threshold variables to construct the following dynamic threshold panel regression model:

4

$$\begin{array}{ccc}{GEE}_{it}\hfill & =\hfill & {\pi }_0+{\pi }_1{GEE}_{it-1}+{\pi }_2{MRD}_{it}\cdot I\left(qit\le \mathrm{\theta }\right)+{\pi }_3{MRD}_{it}\cdot I\left(qit>\mathrm{\theta }\right)\hfill \\ \hfill & \hfill & +{\pi }_4{X}_{it}+{\mu }_i+{\gamma }_t+{\epsilon }_{it}\hfill \end{array}$$Here, ${\mathit{GEE}}_{\mathit{it}-1}$ represents the first lag of green economic efficiency; $\mathit{qit}$ is the threshold variable; I(·) is an indicator function, equal to 1 when the condition in the parenthesis is met and 0 otherwise; θ is the specific threshold value.

Data

Dependent variable: green economic growth (GEE)

This study selects green economic efficiency as the core indicator to measure green economic growth in Chinese cities. To use the non-radial directional distance function, we constructed the environmental technology index following the approach of Zhou and Ang (2008) and Färe and Grosskopf (2004). The specific calculation is provided in Supplementary Appendix B. To further decompose the efficiency of capital (K) and labor (L), each city was considered as a separate decision-making unit. Following the method of Wang et al. (2016), we set the weights of these two factors to zero. The input variable considered is energy input (E), with expected output as GDP. Undesired outputs include industrial sulfur dioxide (SO2), industrial waste gas (WG), and industrial waste water (WW). Using the super-efficiency DEA model, we calculated the proportion of increase or decrease for the above five variables, ultimately determining the GEE of each city in different years as the dependent variable for the study.

5

$$\begin{array}{ccc}{GEE}_{it}\hfill & =\hfill & \frac{1}{2}\left(\frac{\left(E_{it}-{\beta }_{E,it}^{*}{E}_{it}\right)/\left(G_{it}+{\beta }_{G,it}^{*}{G}_{it}\right)}{E_{it}/G_{it}}\right)\hfill \\ \hfill & \hfill & +\frac{1}{2}\left(\frac{1}{3}\sum _{U=SO2,WG,WW}\frac{\left(U_{it}-{\beta }_{U,it}^{*}{U}_{it}\right)/\left(G_{it}+{\beta }_{G,it}^{*}{G}_{it}\right)}{U_{it}/G_{it}}\right)\hfill \\ \hfill & =\hfill & \frac{\frac{1}{2}\left(1-{\beta }_{E,it}^{*}\right)+\frac{1}{2}\left(\frac{1}{3}\left(1-{\beta }_{SO2,it}^{*}\right)+\frac{1}{3}\left(1-{\beta }_{WG,it}^{*}\right)+\frac{1}{3}\left(1-{\beta }_{WW,it}^{*}\right)\right)}{1+{\beta }_{G,it}^{*}}\hfill \end{array}$$

In the model, ${\beta }_E^{*},{\beta }_G^{*},{\beta }_{\mathit{SO}2}^{*},{\beta }_{\mathit{WG}}^{*}$, and ${\beta }_{\mathit{WW}}^{*}$ are the optimal solutions of the super-efficiency DEA model. The construction system of the GEE index is specifically shown in Table 1.

Table 1. Construction of the GEE index system.

Main index	First index	Second Index	Indicator measurement
GEE	Expected output	Economic growth	Real GDP
	Non-expected outputs	Environmental contamination	Industrial SO2
			Industrial waste gas
			Industrial waste water
	Input indicators	Capital Inputs	Capital stock
		Labor Inputs	Annual job numbers
		Energy Inputs	Energy usage

Explanatory variable: mineral resource dependence

Mineral resource dependence, the primary variable of focus in our study, indicates the extent to which a city’s economic expansion is reliant on mineral resources and related industries. Utilizing Li et al. (2023) approach, we assess MRD through the percentage of employment in the mining sector relative to the city’s total employment.

Mediating and threshold variables: green investment, green credit

To explore the influence of MRD on GEE, our research incorporates a green finance lens, identifying green credit and green investment as intermediary and threshold factors, respectively. Following Shen et al. (2021) methodology, we quantify a city’s green investment by its environmental pollution control investment as a share of GDP. Similarly, inspired by Lv et al. (2023) approach, we determine green credit by the proportion of a city’s environmental protection project credit to the total provincial credit.

Control variables

To minimize the potential estimation error from omitted variables, this paper controls for several factors that may affect GEE: (1) GDP per capita (pgdp): This represents the urban economic development level of each prefecture-level city in China. (2) Urbanization rate (nrl): This is the ratio of urban permanent residents to the total permanent population, reflecting the degree of population concentration in cities. Urbanization affects both the economy and the environment (Shuai and Fan 2020). (3) Industrial structure (IS): This is measured by comparing the value added by the secondary industry to that of the tertiary industry. The secondary industry is typically resource-intensive and highly polluting, while the tertiary industry is relatively cleaner and less dependent on resources. These industries are important indicators of an economy’s resource efficiency and environmental performance (Wang et al. 2019b). (4) Foreign Direct Investment (FDI): There are two opposing views on FDI’s impact on the local environment. One view suggests that foreign enterprises bring advanced knowledge, management experience, and technology, improving local energy efficiency and environmental quality (Hatzipanayotou et al. 2002). The other view supports the pollution haven hypothesis, which posits that strict environmental regulations in developed countries push polluting industries to developing countries, damaging their ecological environment (Copeland and Taylor 2000). Following Jiang et al. (2021), we measure FDI as the proportion of actual foreign direct investment in GDP. (5) Government intervention (gov): Theoretically, government intervention can adjust resource allocation, mitigate monopolies and information asymmetry caused by market failures, and enhance GEE. However, excessive intervention can disrupt market order and hinder GEE improvement. We measure the degree of government intervention by the proportion of local fiscal general budget expenditure to GDP Sha et al. (2021).

Addressing endogeneity issues

Endogeneity is often a concern when studying the impact of MRD on GEE. To mitigate endogeneity, this study employs the instrumental variable (IV) method for parameter estimation. Searching for IVs typically starts from a geographical or historical perspective, as certain geographical indicators are naturally formed and historical variables are far removed from the current economic system of interest, satisfying the condition of exogeneity. Conversely, some geographical indicators may correlate with explanatory variables (such as crime rates, institutions, etc.), meeting the condition of relevance. Based on Chen et al. (2023), we select the relief degree of land surface as our IV. This metric, determined by a region’s highest and lowest altitudes, area of flat land, and total area, is a naturally occurring and objective geographical characteristic. Consequently, it is presumed that this indicator does not directly influence GEE. However, the relief degree might be significantly linked to a region’s mineral resource richness, as areas with more complex terrains tend to have greater mineral wealth. Hence, utilizing the relief degree (Relief) as an IV is deemed appropriate. We crafted an interaction term between the IV and the time variable, denoted as Relief*t.

Data sources

This study selects panel data from 262 prefecture-level cities in China for the years 2006–2017. In calculating GEE, the expected output GDP is deflated using constant 2003 prices. The amount of FDI is converted according to the annual exchange rate of US dollars to RMB and deflated using the corresponding index published by the National Bureau of Statistics, based on 2003 prices. All data primarily come from the “China Statistical Yearbook” and the “China City Statistical Yearbook.” More detailed data characteristics are shown in Table 2.

Table 2. Summary of statistical descriptions.

Variable	N	Mean	SD	Max	Min
GEE	3144	0.306	0.115	0.098	1.177
MRD	3144	5.595	9.334	0.000	57.820
pgdp	3144	10.37	0.699	4.595	13.060
nrl	3144	0.506	0.163	0.115	1.000
IS	3144	1.035	0.363	0.103	3.104
FDI	3127	0.020	0.019	0.000	0.132
gov	3142	0.163	0.078	0.043	1.485
GI	3144	0.014	0.028	0.001	0.361
GC	3144	0.057	0.111	0.006	1.420

Empirical analysis

Multicollinearity test

To address potential multicollinearity and its impact on regression accuracy, we present our test results in Table 3. The average VIF (Variance Inflation Factor) is 2.46, well below the threshold of 5, indicating no significant multicollinearity among our chosen variables.

Table 3. Analysis of multicollinearity diagnostics.

Variable	VIF	1/VIF
MRD	1.03	0.971
pgdp	3.84	0.260
nrl	2.30	0.434
IS	2.24	0.447
FDI	1.19	0.838
gov	1.54	0.650
GI	3.60	0.278
GC	3.96	0.253
Mean VIF	2.46

Impact of MRD on GEE

Firstly, we empirically tested Eq. (1) using the IV method. To ensure the robustness of the estimation results, we gradually added control variables. As shown in the first column of Table 4, in the absence of any control variables, the coefficient of MRD is significantly negative, suggesting that the level of MRK is closely related to the reduction in urban GEE. This indicates that, overall, the GEE of cities at the prefectural level in China has not received the blessing of resources. Despite the introduction of control variables, this inverse relationship remains significant, as evident in columns (2) through (6). Particularly in column (6), the regression coefficient of MRD is −0.014, implying that for every 1% increase in MRD, the city’s GEE decreases by 0.014%, thereby preliminarily validating our H1 hypothesis. Additionally, the coefficient of MRD fluctuates between −0.014 and −0.021, demonstrating the robustness of the estimation results. This finding is consistent with the mineral resource curse proposed in this paper, where high dependence on resources hinders green economic development (Sachs and Warner 1995; Xu 2022).

Table 4. Baseline regression analysis results.

Variables		GEE
	(1)	(2)	(3)	(4)	(5)	(6)
MRD	−0.012**	−0.018**	−0.018**	−0.021***	−0.020***	−0.014**
	(0.005)	(0.007)	(0.007)	(0.008)	(0.007)	(0.006)
pgdp		0.066***	0.066***	0.027**	0.027**	0.013
		(0.012)	(0.012)	(0.013)	(0.013)	(0.011)
nrl			−0.076***	−0.061**	−0.067***	−0.073***
			(0.026)	(0.027)	(0.026)	(0.023)
IS				0.076***	0.082***	0.084***
				(0.009)	(0.009)	(0.008)
FDI					−0.701***	−0.502***
					(0.160)	(0.135)
gov						−0.263***
						(0.046)
N	3144	3144	3144	3144	3144	3144
Year	YES	YES	YES	YES	YES	YES
City	YES	YES	YES	YES	YES	YES
Kleibergen–Paap rk LM statistic	11.573	10.189	10.280	10.686	11.385	13.112
P-value	0.001	0.001	0.001	0.001	0.000	0.000
First stage regression
Relief*t	−0.381***	−0.365***	−0.367***	−0.373***	−0.392***	−0.426***
	(0.108)	(0.110)	(0.110)	(0.110)	(0.113)	(0.114)
_cons	YES	YES	YES	YES	YES	YES
F test	12.45	11.00	11.13	11.58	12.05	13.96
P-value	0.000	0.000	0.000	0.000	0.000	0.000

***p < 0.01; **p < 0.05; *p < 0.1; robust standard errors in parentheses.

Table 4 shows that the Kleibergen–Paap rk LM statistics significantly reject the under-identification null hypothesis, indicating a strong correlation between the instrumental and endogenous variables. Additionally, first stage regression F statistics exceeding 10 further reject the weak IV hypothesis. Therefore, the IV in this study is valid. The discussion of the validity of the IV in the following sections follows the same logic as presented here.

Validation of the green finance pathway

To explore the role of green finance in the influence of MRD on GEE, we selected green investment (GI) and green credit (GC) as mediating variables. In validating H2, we first used green investment (GI) as a mediating variable and performed regression analysis on Eqs. (2) and (3). As shown in column (2) of Table 5, MRD has a significant negative crowding-out effect on GI. In column (3), the negative effect of MRD continues, while GI has a significant positive impact on GEE. This suggests that an increase in MRD inhibits the improvement of GEE by crowding out green investments, thereby validating H2. According to H3, we incorporated green credit (GC) as a mediating variable. Results in columns (4) and (5) of Table 5 indicate that MRD has a significant negative effect on GC, and GC positively affects GEE. This implies that an increase in MRD negatively impacts GEE by inhibiting green credit, thus confirming H3. The beneficial influence of green credit and investment on GEE aligns with Muganyi et al. (2021).

Table 5. Mediation effect regression.

Variables	GEE	GI	GEE	GC	GEE
	(1)	(2)	(3)	(4)	(5)
MRD	−0.014**	−0.091**	−0.014**	−0.035**	−0.014**
	(0.006)	(0.048)	(0.006)	(0.017)	(0.006)
GI			0.703***
			(0.220)
GC					0.181***
					(0.065)
pgdp	0.013	0.006	0.012	0.004	0.013
	(0.011)	(0.053)	(0.013)	(0.020)	(0.013)
nrl	−0.073***	−0.029	−0.078***	−0.010	−0.078***
	(0.023)	(0.018)	(0.027)	(0.007)	(0.027)
IS	0.084***	0.121**	0.085***	−0.037*	0.085***
	(0.008)	(0.051)	(0.011)	(0.019)	(0.010)
FDI	−0.502***	−0.614	−0.535***	−0.286**	−0.534***
	(0.135)	(0.128)	(0.153)	(0.467)	(0.153)
gov	−0.263***	−0.413*	−0.153**	−0.130*	−0.153***
	(0.046)	(0.022)	(0.060)	(0.093)	(0.059)
N	3144	3144	3144	3144	3144
Year	YES	YES	YES	YES	YES
City	YES	YES	YES	YES	YES
Kleibergen–Paap rk LM statistic	13.112	13.891	14.109	13.891	14.161
P-value	0.000	0.000	0.000	0.000	0.000

***p < 0.01; **p < 0.05; *p < 0.1.

In summary, the positive effects of green investment and green credit on urban green development are hindered by the crowding-out effect under mineral resource dependence. These results are consistent with the theoretical frameworks proposed Hu et al. (2021b) and Zhang et al. (2021), which suggest that green finance can stimulate green technological innovation and support sustainable industrial transformation. This finding emphasizes the need for city governments to strengthen investments in clean energy, renewable energy, and environmental industries, and to promote green finance development to ensure coordinated economic growth and environmental protection.

Robustness tests

Replacement of dependent variable

In the baseline regression model, we gradually added control variables, initially confirming the robustness of the estimation results. To further verify this, we remeasured green economic efficiency (GEE_sc) using the Super-CCR method. The estimation results are shown in column (1) of Table 6. Results indicate that even after replacing the dependent variable, the negative impact of MRD on GEE remains significant, further supporting the validity of the baseline regression results.

Table 6. Replacement of key variable indicators and estimation methods.

Variables	GEE_sc	GEE	New IV	IvTobit Model
	(1)	(2)	(3)	(4)
MRD	−0.008*		−0.012**	−0.012**
	(0.006)		(0.005)	(0.005)
MRD_e		−0.027*
		(0.014)
pgdp	0.019	0.058*	0.220**	0.219**
	(0.142)	(0.031)	(0.087)	(0.074)
nrl	−0.075***	−0.061**	−0.096***	−0.086***
	(0.028)	(0.027)	(0.024)	(0.025)
IS	0.108***	0.059***	0.086***	0.084***
	(0.011)	(0.018)	(0.011)	(0.011)
FDI	−0.004**	−0.894***	−0.264**	−0.464**
	(0.010)	(0.328)	(0.104)	(0.145)
gov	−0.381***	−0.305***	−0.093**	−0.120**
	(0.065)	−0.027*	(0.047)	(0.051)
N	3144	3144	3144	3144
Year	YES	YES	YES	YES
City	YES	YES	YES	YES
Kleibergen–Paap rk LM statistic	13.150	15.891	12.950
P-value	0.000	0.015	0.000

***p < 0.01; **p < 0.05; *p < 0.1.

Alternative measurement of explanatory variables

We employed the ratio of mining industry employment to overall employment (MRD-e) as a substitute measure for mineral resource dependence. Table 6, column (2), reveals that MRD-e significantly and negatively affects GEE.

Substitution of the IV

We substituted the original IV with the lagged one period MRD (L.MRD) as a new IV. Since L.MRD is highly correlated with MRD and is predetermined, it can avoid reverse causality. Table 6, column (3), illustrates that the findings remain consistent after this IV substitution, verifying the robustness of the empirical results.

IvTobit model estimation

Acknowledging the dependent variable’s constraints, we re-estimated the parameters using the IvTobit model, as outlined by Wang et al. (2019a), to correct for endogeneity. The outcomes, displayed in Table 6, column (4), show a significant negative effect of MRD, aligning with previous findings and confirming the robustness of our estimates.

Exclusion of special cities

Given the distinct nature of municipalities, we recalculated after omitting data from Beijing, Tianjin, Shanghai, and Chongqing. The results, seen in Table 7, column (1), indicate that MRD’s negative impact is significant at the 5% level, further solidifying the baseline regression’s robustness.

Table 7. Eliminating specific samples and subsample analysis.

Variables	Exclude special cities	Shorten sample interval	1% tail trimming	GMM
	(1)	(2)	(3)	(4)
L.GEE				0.667**
				(0.004)
MRD	−0.014**	−0.014**	−0.014**	−0.013***
	(0.006)	(0.008)	(0.006)	(0.000)
pgdp	0.013	0.015	0.013	0.021*
	(0.013)	(0.013)	(0.011)	(0.001)
nrl	−0.074***	−0.069***	−0.073***	−0.022***
	(0.028)	(0.025)	(0.023)	(0.005)
IS	0.085***	0.086***	0.084***	0.051**
	(0.011)	(0.009)	(0.008)	(0.002)
FDI	−0.551***	−0.412**	−0.502***	−0.229***
	(0.157)	(0.146)	(0.135)	(0.019)
gov	−0.152***	−0.210**	−0.263***	−0.036***
	(0.059)	(0.052)	(0.046)	(0.032)
N	3096	2620	3144	2882
Year	YES	YES	YES	YES
City	YES	YES	YES	YES
Kleibergen–Paap rk LM statistic	13.895	8.689	13.112
P-value	0.000	0.003	0.000

***p < 0.01; **p < 0.05; *p < 0.1.

Shortening the sample interval

To address any influence from the choice of sample period, we reanalyzed using data exclusively from 2007 to 2016. The findings, presented in Table 7, column (2), demonstrate that the main explanatory variables’ coefficients align with the baseline regression, even over this reduced timeframe.

Trimming

Considering the potential interference of outliers, we re-estimated the data after 1% trimming. The outcomes, seen in Table 7, column (3), confirm that MRD’s negative influence on GEE persists significantly.

Generalized method of moments estimation

The results from the Generalized Method of Moments (GMM) estimation, which utilized lagged values of the endogenous variables, are reported in the final column of Table 7. The p-value of the Hansen test (0.128) that we conducted to verify the validity of the instrumental variables indicates that our set of instruments is valid. The p-value of the AR(2) test (0.703) indicates that there is no correlation between the lagged explained variable and the error term.

City heterogeneity analysis

To delve deeper into how MRD in urban areas affects GEE, we categorized the 262 prefecture-level cities into two groups: 100 resource-based and 162 non-resource-based cities, according to the “Sustainable Development Planning for Resource-Based Cities (2013–2020).” Analysis of Table 8 reveals that in column (1), MRD’s coefficient is significantly negative for resource-based cities, while in column (2), it is significantly positive for non-resource-based cities. This suggests that resource-based cities, due to their heavy reliance on resource industries, often develop a path-dependent approach, leading to the displacement of innovative factors and skilled talents, negatively impacting non-resource industries. Conversely, non-resource-based cities, with varied industrial sectors, are less dependent on a single natural resource. Their focus on extensive green investments and technological advancements boosts green economic efficiency.

Table 8. Heterogeneity analysis.

Variables	Resource type	Non resource type	Growing type	Mature type	Declining type	Regenerative type
	(1)	(2)	(3)	(4)	(5)	(6)
MRD	−0.239***	0.030**	−0.068***	−0.011***	−0.007*	0.013**
	(0.187)	(0.015)	(0.038)	(0.004)	(0.018)	(0.012)
pgdp	0.022*	−0.241**	−0.823***	0.002	0.086	0.484
	(0.012)	(0.013)	(0.279)	(0.019)	(0.067)	(0.013)
nrl	−0.094***	−0.069***	−0.009	−0.215***	−0.061*	−0.079**
	(0.035)	(0.025)	(0.100)	(0.066)	(0.042)	(0.035)
IS	0.087***	0.100***	0.049	0.133***	0.014	0.075***
	(0.011)	(0.011)	(0.038)	(0.017)	(0.064)	(0.020)
FDI	−0.235*	−0.119	−0.314	−0.319	0.250	−0.337**
	(0.129)	(0.130)	(1.173)	(0.290)	(0.347)	(0.170)
gov	−0.146**	−0.267***	0.014	0.148	−0.017	−0.271**
	(0.058)	(0.062)	(0.305)	(0.091)	(0.092)	(0.113)
N	1187	1940	128	646	235	156
Year	YES	YES	YES	YES	YES	YES
City	YES	YES	YES	YES	YES	YES
Kleibergen–Paap rk LM statistic	30.976	5.652	10.628	17.263	3.600	13.595
P-value	0.000	0.016	0.001	0.000	0.439	0.000

***p < 0.01; **p < 0.05; *p < 0.1.

Next, considering the potential heterogeneity due to the different stages of resource development in various resource-based cities, we further divided resource-based cities into growing, mature, declining, and regenerative types for analysis. As shown in columns (3)–(6) of Table 8, in growing, mature, and declining cities, natural resource dependence inhibits the improvement of GEE. Only in regenerative cities does MRD promote the enhancement of GEE. Currently, growing, mature, and declining cities are in the initial, stable, and exhaustion stages of resource development, respectively. The economic development of these cities has not yet escaped dependence on mineral resources. This over-reliance on resources is unfavorable for the development of non-resource industries and cannot promote the enhancement of GEE. However, the economic development of regenerative cities has largely decoupled from mineral resources.

Overall, the differences in policy formulation and investment decisions among different types of cities significantly impact their green development efficiency. Non-resource cities and regenerative cities, such as those in the eastern coastal regions (like the Yangtze River Delta and Pearl River Delta), usually have more developed economies and higher technological levels. They also place greater emphasis on environmental protection and sustainable development. These areas generally do not rely on mineral resources but on manufacturing, high-tech industries, and services, thus the negative impact of MRD on green development is relatively smaller. These regions are also more capable of adopting and promoting green technologies, achieving green growth. In contrast, growing, mature, and declining cities face different challenges at various stages of economic development. Mature and declining cities, like the old industrial bases in Northeast China, traditionally reliant on heavy industry and resource extraction, are currently facing the challenge of economic restructuring. Meanwhile, emerging cities and city clusters (such as the Chengdu-Chongqing economic zone) are in a rapid process of urbanization and industrialization, facing challenges of balancing environmental protection and sustainable resource use while pursuing economic growth. From the above, it can be seen that the impact of MRD on GEE exhibits considerable heterogeneity among different types of cities, necessitating that policymakers and investors employ more nuanced and context-specific strategies to foster green growth in accordance with the unique characteristics of each urban area.

Further analysis

Recognizing the significance of economic status, resource allocation, and policy context in assessing various city types, and mindful of the diversity across Chinese cities, it becomes evident that custom green development strategies are crucial. This understanding leads us to a vital transition towards a more detailed analysis. To this end, we utilize data from 252 cities between 2006 and 2017, applying a dynamic threshold model for regression. This methodology is essential to unravel the intricate effects of green finance amidst the interplay of MRD and GEE.

Table 9 presents the regression results based on the threshold values of green investment and green credit. In the regression with green investment and green credit thresholds, the relationship between MRD and GEE demonstrates significant dynamic threshold effects, with threshold values of 0.073 and 0.034, significant within the 95% confidence intervals [0.067, 0.079] and [0.030, 0.039], respectively. The P-values of the linearity test also reject the null hypothesis, further confirming the dynamic non-linear relationship between MRD and GEE. Additionally, the P-value for Kink is statistically significant at the 1% level, thus avoiding the issue of knots in linear relationships.

Table 9. Dynamic threshold effects.

Variable	Lower regime	Upper regime	Overall	Post-estimation tests
	(1)	(2)	(3)
Threshold variable: GI
L(1)GEE	0.954***	−0.471***	0.595***	Kink	1.416***
	(0.034)	(0.031)	(0.006)		(0.212)
MRD	−0.068***	−0.144***	−0.063***	Threshold indicator	0.073***
	(0.025)	(0.039)	(0.024)		(0.003)
GI	1.646***	−1.467***	1.556***	95% Conf. Interval	[0.067,0.079]
	(0.285)	(0.285)	(0.212)	AR(1) (p-value)	0.007
_cons	0.214***			AR(2) (p-value)	0.655
	(0.023)			Hansen J (p-value)	0.226
				Linearity test (p-value)	0.000
Threshold variable: GC
L(1)GEE	0.828***	−0.406***	0.668***	Kink	−1.780***
	(0.028)	(0.017)	(0.006)		(0.332)
MRD	0.105***	−0.424***	−0.065***	Threshold indicator	0.034***
	(0.036)	(0.042)	(0.020)		(0.002)
GC	1.231***	−0.815***	2.064***	95% Conf. Interval	[0.030,0.039]
	(0.380)	(0.382)	(0.332)	AR(1) (p-value)	0.006
_cons	0.172***			AR(2) (p-value)	0.672
	(0.017)			Hansen J (p-value)	0.226
				Linearity test (p-value)	0.000

***p < 0.01; **p < 0.05; *p < 0.1; standard errors in parentheses.

The results with green investment as the threshold value show that the MRD coefficient is significantly negative in both the lower and upper regime. This result indicates that even if green investment develops to a certain extent, it still cannot offset the inhibitory effect of MRD on GEE. In the initial stages, insufficient investment in green technology and sustainable development results in low-level green investments in resource-based cities, which are insufficient to drive the economy towards a more sustainable model. Even with increased green investment in later stages, resource-based cities continue to face green transformation challenges due to the inertia of economic structure and the entrenched nature of traditional industries. Furthermore, reliance on resources typically results in environmental degradation and ecological damage, necessitating sustained effort and investment for resolution. As a result, the beneficial influence of GI on Green transformation might not be instantly observable. In the lower portion of Table 9, results indicate that when GC is less than or equal to 0.034, MRD positively influences GEE. When GC exceeds 0.034, MRD hinders the improvement of GEE. These findings suggest that at low levels of green credit, the development and utilization of mineral resources initially drive economic growth and provide funds for infrastructure and development. However, with increasing levels of green credit, resource-based cities may encounter challenges such as resource depletion and environmental degradation, adversely affecting sustainable development. During this phase, MRD tends to lead to overinvestment in traditional resource extraction at the expense of green economic development.

Further discussion on the results

The further analysis aligns with the actual situation in China. For instance, some cities in the northeastern region, such as Anshan in Liaoning, Jilin City in Jilin Province, and Datong in Shanxi, traditionally depend on coal and heavy industries. These areas have attempted transformation through increased green investments but often face challenges such as insufficient capacity for technology absorption, unclear market demand, or lack of policy support, making it difficult for high-level green investments to achieve the expected impact. Resource-based city clusters in western regions, such as Xi’an in Shaanxi Province and Lanzhou in Gansu Province, despite being rich in resources, encounter significant obstacles in achieving green development. This challenge is compounded in cities heavily dependent on conventional energy and resource-intensive industries. In such cities, even increased green investments may not effectively stimulate green growth unless supported by suitable policy frameworks and market mechanisms.

In contrast, emerging city clusters like the Chengdu-Chongqing region, although undergoing rapid urbanization and industrialization, are also striving for greener and more sustainable development. However, these cities face difficulties in translating green investments into actual green growth. For example, cities like Suzhou in Jiangsu Province and Ningbo in Zhejiang Province, despite rapid economic development, are also confronted with industrial pollution and overuse of resources. In these cities, even with increased green credit investment, the negative impact of MRD on GEE persists due to limitations in industrial structure and technological capacity. Additionally, the marginal effects of green finance decrease beyond the threshold value, meaning that each additional amount of green investment may contribute less to promoting green growth, especially in areas heavily dependent on mineral resources.

It is noteworthy that the Chinese government has implemented a series of important policies in the green finance sector, such as the carbon trading market launched in 2021, the establishment of green finance pilot zones, and the update of green bond standards. Since most of these policies were implemented after 2017, their impacts are not yet reflected in our dataset. The current analysis may not fully capture the potential effects of recent policy dynamics on green economic growth. Therefore, the rapid changes in the green finance policy environment need to be considered when interpreting our study results.

Conclusions and policy implications

Conclusion

Facing the global challenges of natural resource depletion and extreme climate changes, fostering green economic development emerges as a key strategy to address the dual issues of economic growth and environmental pollution. This study, utilizing panel data from 262 Chinese cities from 2006 to 2017 and employing the IV analysis method, explores the impact of MRD on urban GEE. It integrates the theory of the “mineral resource curse” with the current state of cities with varying levels of resource dependence in China, highlighting the mediating role and threshold effects of green financial variables.

The findings reveal that, firstly, considering the environmental impacts of economic activities, there is a noticeable “mineral resource curse” phenomenon among Chinese cities, which significantly inhibits green economic growth and hinders urban green transformation. Specifically, higher MRD inhibits the development of the green finance industry, negatively impacting GEE by crowding out green investments and credits. Secondly, after detailed categorization of cities, we see that China’s non-resource and regenerative cities have successfully escaped the “mineral resource curse” due to green economic transformation. However, the analysis of other types of cities emphasizes the necessity of context-specific policy management. Lastly, the regression results from constructing dynamic panel threshold models with green investment and green credit as threshold values show that, apart from the initial positive effect of green credit, the role of green investment and green credit is limited. Before and after the threshold value, mineral resource dependence consistently inhibits green economic efficiency, and even higher financial investments, due to government policies and urban internal regional development differences, exacerbate the negative impact of mineral resource dependence on green economic growth.

Policy implications

The findings from our study offer critical insights for overcoming the mineral resource curse, boosting green economic efficiency, and fostering green transitions in resource-based cities. Based on these insights, we recommend the following tailored policy implications:

1. Promoting economic diversification and emerging industries: Resource-dependent cities should reduce their reliance on a single resource and enhance economic resilience. Specific measures include encouraging the development of diversified economic structures and supporting emerging industries; increasing investment in clean energy, energy-saving technologies, and sustainable production methods to promote industrial upgrading and green technological innovation.

2. Developing green finance: Green finance is a crucial tool for advancing the green economy. The government can improve green finance policies by providing tax incentives and subsidies to attract private capital into the green economy; establishing green bond markets to provide more financing channels for green projects, ensuring sufficient funding support; encouraging financial institutions to innovate green financial products and enhance credit support for green projects.

3. Reducing administrative intervention and enhancing market reforms: To improve economic efficiency and service quality, the government should reduce administrative intervention in resource sectors. Specific measures include enhancing the level of market reforms and leveraging financial tools in market allocation; making government roles more transparent, simplifying approval processes, and implementing market-based resource pricing; developing financial products such as carbon emission trading and promoting green finance to encourage capital flow into sustainable projects.

4. Implementing differentiated policies: Different types of cities should formulate differentiated policies based on their characteristics. Resource-dependent cities should gradually reduce reliance on mineral resources and develop diversified economies through policy support; growing and mature cities should accelerate industrial upgrading, focusing on developing green industries and high-value-added industries to speed up and enhance the quality of the green economic transition; declining cities should use policy guidance to help adjust economic structures and revive economic vitality; non-resource-dependent and regenerating cities should continue to support their green development paths and consolidate and expand existing green economic achievements.

Future directions

In addition to the current focus, our study opens several avenues for future research. One potential direction is to extend the time span of the data to include more recent years. This extension would allow for the observation of long-term trends in the impact of green economic development policies and technological advancements on mineral resource dependence. Another promising area for future research is to include more countries and regions, providing a broader perspective. The resource curse phenomenon has been observed in countries such as Brazil, Indonesia, and Venezuela, which face challenges similar to those of China, including economic dependence on natural resources, environmental degradation, and efforts to diversify their economies and promote sustainable development (Lawson-Remer 2012; Tang et al. 2022). Additionally, exploring the spatial effects of the relationships between variables could provide deeper insights into the dynamics of green economic efficiency and resource dependence. Lastly, while our study focuses on the role of green finance, green economic efficiency is also closely related to technological absorption capacity and government policies. Future research should consider a more comprehensive and systematic examination of these factors, such as the impact of government attention to green development.

Author contributions

Yinhui Wang was responsible for conceptualization, methodology, investigation, data curation, and original draft writing. Xiaodan Gao handled review and editing of the manuscript, supervision, and project administration.

Data availability

The data that support the findings of this study are primarily derived from the China Statistical Yearbook and the China City Statistical Yearbook. Additionally, several variables were constructed by the authors, and the detailed construction methods are provided in the supplementary materials. The data are available from the corresponding author upon reasonable request.

Competing interests

The authors declare no competing interests.

Ethical approval

Ethical approval was not required as the study did not involve human participants.

Informed consent

This article does not contain any studies with human participants performed by any of the authors.

Supplementary information

The online version contains supplementary material available at https://doi.org/10.1057/s41599-024-03950-1.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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