A Study on the Decoupling Effect Between Economic Development Level and Carbon Dioxide Emissions: An Empirical Analysis Based on Mineral Resource-Based Cities in Southwest China

Abstract

Mineral resource-based cities (MRBCs) refer to cities with mining and processing of mineral resources as the main industry, so there is a close relationship between their economic development and resource consumption. However, this relationship often hinders its rapid transition towards economic diversification and low-carbon models. Based on quantifying the economic index level of 18 MRBCs in southwest China, this paper has employed the Tapio elasticity coefficient method (Tapio model) and Environmental Kuznets Curve (EKC curve) to analyze the decoupling effect between the economic index and carbon dioxide. After the deep research of the “decoupling” phenomenon and its dynamic changes between economic development and carbon emissions, this paper has aimed to explore a low-carbon transformation path suitable for each city. The research results have indicated that: (1) The overall trend of carbon dioxide emissions is increasing, but the growth rate is gradually slowing down, effectively controlling the situation of carbon dioxide emissions. (2) The economic index level shows an upward trend, and the growth rate gradually increases, which signifies a positive trend in economic development. (3) The decoupling effect began in MRBCs in southwest China in 2013, and the decoupling effect was achieved in 2019.

1. Introduction

1.1. Research Objective

With the clear establishment of global temperature control targets in the Paris Agreement, which aims to keep the global average temperature rise within 1.5 °C to 2 °C and the goal of achieving net zero greenhouse gas emissions worldwide by 2050 and 2070 [1], low-carbon development has become a global consensus. In this context, various regions, especially MRBCs, are facing a profound contradiction between rapid economic growth and sustainable development goals in the process of achieving the “dual carbon” goal [2]. These types of cities often rely on mineral resource extraction and processing industries with high carbon emissions, so their economic models are significantly characterized by extensive development paths of high input, high energy consumption, high emissions, and low efficiency, which have gradually become an important factor restricting China from achieving comprehensive carbon peak [3,4]. Therefore, the in-depth exploration of how MRBCs can achieve decoupling between carbon dioxide emissions and economic growth has become a focus of attention for both academia and policymakers [5,6]. By using the EWM-TOPSIS model (EWM: Entropy weight method; TOPSIS: Technique for Order Preference by Similarity to Ideal Solution) to quantify the economic indices of 18 MRBCs in southwest China, this paper has conducted research on velocity decoupling and quantity decoupling based on Tapio model and EKC curve, respectively. To provide reference and inspiration for MRBCs on the path of low-carbon transformation and to provide a basis and reference for the formulation of government energy-saving and emission-reduction policies.

1.2. Research Background

The core of low carbon has lied in reducing greenhouse gas emissions, especially carbon dioxide, aimed at mitigating the negative impacts of global climate change [7,8]. On the one hand, low carbon development has meant a key pillar of sustainable development strategy. On the other hand, it has been closely linked to decoupling effects. The two have promoted each other [9]. The decoupling effect refers to the phenomenon where the rate of energy consumption is slower than the economic growth during a specific period, which has been used to evaluate the relationship between energy consumption, environmental emissions, and economic growth [10]. Low carbon development is characterized by low energy consumption, low pollution, and low emissions, which matter for the sustainable development of the economy and society [11].

The measurement methods of decoupling analysis mainly cover two dimensions: speed decoupling and quantity decoupling [12]. The decoupling of speed has focused on examining the deviation between the growth rate of environmental pollution and economic growth [13]; that is, when the growth rate of environmental pollution was lower than the economic growth rate, the traditional connection between environmental pressure and economic performance was broken, achieving decoupling at the speed level [14]. Tapio’s elasticity coefficient has become the preferred tool due to its intuitiveness, sensitivity, and wide adaptability [15]. This coefficient has accurately characterized the dynamic balance between carbon emissions and economic growth in a proportional form, making the changing trends of decoupling effects in different periods clear at a glance [16]. In addition, Tapio’s elasticity coefficient, with a high degree of sensitivity, can capture the slightest changes between carbon emissions and economic growth, thus detecting potential signs of decoupling effects in a timely manner [17].

Quantity decoupling has concentrated on the downward trend of the total amount of environmental pollutants in economic growth. The EKC curve has been employed most commonly [18]. By revealing the complex relationship between economic development and environmental pollution in a graphical manner [19], the EKC curve can clearly demonstrate how environmental pollution has gradually declined after reaching its peak of economic growth. By analyzing the trend of the EKC curve in-depth, policymakers can accurately determine the environmental protection stage of economic development and then formulate more precise and effective environmental protection strategies, which is conducive to accelerating the realization of decoupling effects [20].

Although mature research results have been accumulated in the field of regional decoupling effects, most of these achievements focus on the analysis of a single relationship between carbon emissions and regional GDP. However, in actual urban development, the regional economic situation is far from being fully covered by GDP, so this limitation may lead to bias in the analysis of decoupling effects. Therefore, in order to improve the accuracy and comprehensiveness of the analysis, this paper must take into account the multiple economic indicators comprehensively when exploring the decoupling effect, with the aim of revealing the true level of regional economic development more fully. From a multi-dimensional perspective, this paper has not only considered the financial revenue and expenditure capacity of cities but also explored factors such as their innovation environment, development vitality, economic stability, industrial structure diversity, and degree of openness to comprehensively construct a framework for measuring regional economic level. And it has also helped in obtaining a deeper understanding of the complex interactive relationship between regional economy and carbon emissions.

In addition, there are limitations in the selection of research objects for decoupling effects. The current research results are often simply divided based on geographical location but without clear representativeness. Considering the significant characteristics of MRBCs (research object) in terms of environmental pollution, high carbon emissions, and energy consumption, this paper has analyzed the relationship between carbon emissions and the regional economy. Conducting in-depth research on the decoupling effect of such cities can not only reveal the relationship between their unique economic level and carbon emissions but also provide a reference for other cities on the path of low-carbon transformation.

1.3. Chapter Arrangement

The organization of this paper is as follows: the first part describes the purpose and background of the study. The second part is a literature review. The third part briefly introduces the research framework, research methods, and data sources of this paper. The fourth part calculates the economic index level of 18 MRBCs and analyzes the carbon dioxide level of 18 cities; it analyzes the characteristics of speed decoupling and quantity decoupling in various cities. The fifth part is the discussion. The sixth part is the conclusion.

2. Literature Review

2.1. Decoupling Effect Analysis

The decoupling effect was first proposed in the 1960s [21], which means that in the process of industrial development, the total amount of material energy consumption increases with the growth of economic aggregate at the beginning of industrial development, but it will change in reverse after a certain stage, so as to achieve economic growth and reduce material energy consumption [22]. The Organization for Economic Cooperation and Development (OECD) has gradually introduced the concept of decoupling theory into the research of agricultural policy development and gradually expanded to the fields of environmental economy [23]. Since then, with the increasingly prominent environmental problems, the decoupling effect has gradually become an important indicator to evaluate the relationship between economic growth and resource and environmental consumption [24]. The decoupling effect has a wide range of research fields involving environmental economics, energy policy, transportation carbon emissions, and other aspects [25]. Decoupling types are mainly divided into relative decoupling and absolute decoupling [26]. Relative decoupling refers to the growth of resource utilization and environmental pressure at a relatively low rate during economic development; that is, the rapid economic development is accompanied by a relatively small increase in resource utilization and environmental pressure [27]. The absolute decoupling is that the growth rate of resource utilization and environmental pressure is decreasing in the process of economic development, although the total amount of resource utilization is becoming larger and larger. Relative decoupling occurs first and will eventually turn into absolute decoupling under artificial control [28]. In the research process of the decoupling effect, scholars gradually realized that the decoupling state can be finally realized, but it needs a long process, and it is a process of repeated development. In some cases, the pressure of the environment will rise again after a period of decline; that is, there is a “double hook” state [29]. Therefore, the realization of absolute decoupling requires long-term efforts and sustained policy support.

There are various research methods and models for the decoupling effect, including the decoupling elasticity coefficient method, LMDI factor decomposition method, structural equation model, etc., in addition to the Tapio model and EKC curve [14,30,31]. The decoupling elasticity coefficient method distinguishes the different decoupling states by calculating the decoupling elasticity coefficient, which can reflect the sensitivity of carbon emissions to economic development [29]. However, it has great limitations and often needs to be quantified in combination with other methods. The LMDI method can achieve complete decomposition with flexible and diverse methods and is widely used in the decomposition of carbon decoupling indicators [30]. However, it is highly dependent on the quality and integrity of data, which directly affects the analysis results and is not applicable to backward cities. The structural equation model uses the theory of planned behavior to build a model for econometric analysis and studies the influence path and influence coefficient between potential variables [31]. However, it can only predict the data and cannot mine specific data information.

2.2. Economic Level Analysis

As an important tool for measuring the level of economic activity, the economic index is of great significance for understanding economic operations, predicting economic trends, and formulating investment strategies [32]. The economic index is a digital indicator used to reflect the level of economic activity, which is usually calculated by collecting and analyzing data within a certain range [33]. It can reflect the economic development of a country, region, or industry and is an important tool for economic analysis. The development and evolution of economic indexes has experienced a process from simple to complex, from single to multiple [34]. Early economic indexes mainly focused on a few key indicators, such as GDP and CPI. Although these indicators can reflect the overall operation of the economy, they are difficult to fully reveal in terms of the internal structure and dynamic changes of the economy [35]. With the continuous development of economic theory and statistical methods, economic indexes have been gradually enriched and improved, forming a diversified index system, including commodity indexes and stock market indexes [36]. These indexes not only cover all areas and levels of the economy but also can more accurately reflect the actual situation and trends of the economy.

At present, the calculation methods of economic index mainly include the weighted average method, the entropy weight method, etc. [37,38]. The weighted average method can give different weights according to the importance of each index and then calculate the weighted average as the value of the economic index. This method can comprehensively consider the impact of various indicators on the economy, but the determination of weight has a certain subjectivity [37]. The entropy weight rule can solve the subjective weighting in the weighted average method, but it has high requirements for the data quality of economic indexes [38]. However, for the relatively backward cities, the data quality of the relevant indicators of the economic index is poor. Therefore, only the entropy weight method can not measure the level of the city’s economic index well.

Summarizing the current research results, although there are many studies on economic level analysis and decoupling analysis. However, it is basically concentrated on the impact of a single economic index on carbon emissions [14]. And the research on decoupling is also basically concentrated in regions with relatively developed economies [39]. However, due to the relatively high level of economic development in economically developed regions, there are certain advantages in completing decoupling and achieving low-carbon development. However, cities with slow economic development have a low economic index, which is not conducive to decoupling.

3. Materials and Methods

With MRBCs in southwest China as the research object, after collecting the relevant indicators of carbon dioxide and economic index from 2000 to 2020, the paper has made analyses of the economic index of 18 MRBCs in each year, as well as the decoupling of speed and quantity in each city. The specific path of low-carbon transformation in MRBCs has been explored (Figure 1). As can be seen from Figure 1, it is not comprehensive to measure the economic index level only by a single indicator. Based on a large number of documents, this paper summarizes that the level of economic index mainly includes six aspects: income and expenditure capacity, innovation environment, development vitality, stability, diversity, and openness. In the quantitative analysis of the economic index level, the EWM algorithm is used to determine the index weight. After eliminating the subjective judgment of the weight, the TOPSIS model is used to determine the comprehensive score of the economic index level. On this basis, the Tapio model and EKC curve are used to simulate the decoupling of MRBCs, and finally, the specific path to achieve low-carbon development is obtained. The detailed steps are as follows:

Step 1: Collecting relevant economic indicators and carbon dioxide emissions of 18 MRBCs in southwest China from 2000 to 2020;

Step 2: Building an index system for evaluating the economic index of MRBCs in southwest China and using the EWM-TOPSIS model to calculate the economic index level of each city;

Step 3: Analyzing the spatiotemporal characteristics of carbon dioxide emissions from MRBCs in southwest China;

Step 4: Conducting a study on the decoupling of speed and quantity in 18 MRBCs in southwest China by virtue of the Tapio model and EKC curve;

Step 5: Based on the analysis results of decoupling speed and quantity, exploring the specific path of low-carbon development in each city.

3.1. Study Area

Resource-based cities are one of the most special types of cities in modern cities. The development of resource-based cities is closely related to the resource reserves within the city. In the process of development, resource-based cities can fully demonstrate their excessive dependence on resources [40]. Therefore, resource-based cities will inevitably face the dilemma of resource depletion and difficulty in urban transformation, which will, in turn, affect the low-carbon development of resource-based cities. MRBCs represent a special type of resource-based city. Compared to general resource-based cities, MRBCs usually have higher energy consumption [41], more imbalanced industrial structures [42], and higher carbon emissions [43]. Taking MRBCs as the research object for analyzing decoupling effects is more representative. This paper selects MRBCs in southwestern China as the research object. Due to the fact that Aba Tibetan and Qiang Autonomous Prefecture, Liangshan Yi Autonomous Prefecture, Qiannan Buyi and Miao Autonomous Prefecture, Qiannan Buyi and Miao Autonomous Prefecture, and Chuxiong Yi Autonomous Prefecture are minority areas with slow development and difficult data collection in Bijie City, they are not the research objects of this paper. Therefore, 18 cities in Sichuan (Dazhou, Guang’an, Guangyuan, Luzhou, Nanchong, Panzhihua, Ya’an, Zigong, Chongqing, Guizhou (Anshun, Liupanshui), Yunnan (Baoshan, Lijiang, Lincang, Puer, Qujing, Zhaotong) and Xizang (Shannan) are selected as research objects in this paper.

3.2. Data Sources

This paper collects the relevant data from 2000 to 2020 from the perspective of time series. The data on economic index levels are mainly from Statistical yearbook of Chinese cities (2000–2020), Statistical yearbook of Sichuan Province (2000–2020), Statistical yearbook of Yunnan Province (2000–2020), Statistical yearbook of Chongqing City (2000–2020), Statistical yearbook of Guizhou Province (2000–2020), Statistical yearbook of Tibet Autonomous Region (2000–2020), etc. The data on carbon dioxide comes from https://www.ipe.org.cn/MapLowCarbon/LowCarbon.html?q=5 (accessed on 4 October 2024).

3.3. Construction of Index System

Through literature review and comparison, considering the actual economic situation of mineral MRBCs in Southwest China, based on the concept and index system of the existing economic index level, combined with the connotation and economic characteristics of MRBCs, and fully considering six aspects of income and expenditure capacity, innovation environment, development vitality, stability, diversity, and openness, the index system of the economic index of MRBCs in Southwest China is constructed, as shown in

Table 1. Indicator system of economic index for MRBCs.

Decision Layer (A)	Criteria Layer (B)	Indicator Layer (C)	Reference
Economic index	Income and expenditure capacity	Per capita GDP	[44]
		Regional fiscal revenue
		Per capita disposable income of residents	[45]
	Innovation environment	Science and technology investment
		Number of patent applications	[4]
		Internal R&D expenditure
	Development vitality	Growth rate of the output value of secondary industry	[46]
		Growth rate of employees in the secondary industry
		Total retail market of consumer goods
	Stability	Basic pension insurance participation rate	[47]
		Deposit and loan ratio of financial institutions at the end of the year
	Diversity	Optimization degree of industrial structure	[48]
		Upgrading level of industrial structure
	Openness	Proportion of actual utilization of foreign capital in GDP	[49]
		Total international trade

.

3.4. Method

This paper has studied the decoupling effect of MRBCs in Southwest China, including both speed decoupling and quantity decoupling, in order to explore the specific paths for MRBCs to achieve low-carbon development. The process has mainly encompassed the following steps: (1) Using the EWM-TOPSIS model to quantify the economic indices of 18 MRBCs in southwest China; (2) using the Tapio model to analyze the decoupling effect of speed decoupling in MRBCs in southwest China; (3) using EKC curve to investigate the quantitative decoupling effect in MRBCs in southwest China. (4) Performing robustness testing on the EWM-TOPSIS model with the EIS index.

3.4.1. EWM-TOPSIS Model

The EWM-TOPSIS model refers to a comprehensive evaluation model that combines both EWM and TOPSIS methods. This model calculates the information entropy of each evaluation indicator through EWM to determine the weight, with full consideration of the uncertainty between evaluation indicators to avoid the influence of subjective factors and make weight allocation more accurate. TOPSIS can accurately reflect the differences in quality between evaluation indicators, further improving the accuracy of evaluation results [50]. The relatively intuitive results obtained by the EWM-TOPSIS model can clearly calculate the specific steps of the economic index level, as shown in Figure 2.

3.4.2. Tapio Model

The decoupling theory is a fundamental theory that describes the link between economic growth and resource consumption or environmental pollution, which has been applied to various fields such as agriculture and energy [51]. The Tapio model uses the “elasticity concept” to dynamically analyze the relationships between variables, thus improving the accuracy of decoupling analysis. The calculation formula is as follows:

$$T_{(C{O}_2,EI)}=(\Delta C{O}_2/C{O}_2)/(\Delta EI/EI)$$

(1)

Among them, $T_{(C{O}_2,EI)}$ is the elasticity coefficient, which represents the trend of carbon dioxide emissions changing with the level of economic index. It can also express the decoupling state through size. The Tapio model is divided into three states founded on the magnitude of decoupling elasticity: negative decoupling, decoupling, and connection. Next, according to the magnitude of elasticity, the three states are subdivided into eight decoupling states: weak negative decoupling, strong negative decoupling, expansion negative decoupling, decay decoupling, strong decoupling, weak decoupling, decay connection, and expansion connection, as shown in

Table 2. The Tapio model.

State		$\Delta \mathit{C}{\mathit{O}}_2/\mathit{C}{\mathit{O}}_2$	$\Delta \mathit{E}\mathit{I}/\mathit{E}\mathit{I}$	${\mathit{T}}_{(\mathit{C}{\mathit{O}}_2,\mathit{E}\mathit{I})}$
Negative decoupling	Weak negative decoupling	$<0$	$<0$	$0\le t<0.8$
	Strong negative decoupling	$>0$	$<0$	$t<0$
	Expansion negative decoupling	$>0$	$>0$	$t>1.2$
	Decay decoupling	$<0$	$<0$	$t>1.2$
Decoupling	Strong decoupling	$<0$	$>0$	$t<0$
	Weak decoupling	$>0$	$>0$	$0\le t<0.8$
Connection	Decay connection	$<0$	$<0$	$0.8\le t<1.2$
	Expansion connection	$>0$	$>0$	$0.8\le t<1.2$

.

3.4.3. EKC Curve Model

Based on the traditional EKC hypothesis, the EKC curve model has reflected the relationship between environmental pollution and economic growth [52]. By taking the economic growth level as the horizontal axis and the environmental pollution level as the vertical axis, the classic EKC curve has presented an inverted U-shaped pattern between the two [53]. With the continuous deepening of research, scholars at home and abroad have found that there are various relationships between the two, including U-shaped, monotonically increasing linear, monotonically decreasing linear, N-type, and inverted N-type [54]. According to the current research results, the EKC curve is basically simulated as per the following model:

$$Y_C={\beta }_0+{\beta }_1{r}_G+{\beta }_2{(r_G)}^2+{\beta }_3{(r_G)}^3+{\epsilon }_C$$

(2)

Among them, $Y_C$ is the amount of carbon dioxide emissions, $r_G$ is the economic index, ${\beta }_0$ is the intercept, ${\beta }_1$, ${\beta }_2$, and ${\beta }_3$ are estimated parameters, ${\epsilon }_C$ is a random error, and the curve shape and parameter values are shown in

Table 3. Relationship between parameters and curves of the EKC model.

Parameter Values	The Relationship Between Economic Indices and Carbon Dioxide	The Shape of the Curve
${\beta }_1={\beta }_2={\beta }_3=0$	No association	Straight line
${\beta }_1>,{\beta }_2={\beta }_3=0$	A linear relationship, $Y_C$ and increases with the increase of $r_G$	Monotonically increasing straight line
${\beta }_1<0,{\beta }_2={\beta }_3=0$	A linear relationship, $Y_C$ and decreases with the decrease of $r_G$	Monotonically decreasing straight line
${\beta }_1<0,{\beta }_2>0,{\beta }_3=0$	$Y_C$ decreases first and then increases with the decrease of $r_G$	U-shaped
${\beta }_1>0,{\beta }_2<0,{\beta }_3=0$	$Y_C$ increases first and then decreases as $r_G$ decreases	Inverted U-shaped
${\beta }_1>0,{\beta }_2<0,{\beta }_3>0$	$Y_C$ increases first, then decreases, and then increases again with the increase of $r_G$	N-type
${\beta }_1<0,{\beta }_2>0,{\beta }_3<0$	$Y_C$ decreases first, then increases, and then decreases again with the increase of $r_G$	Inverted N-shaped

.

3.4.4. Robust Test

The robustness test aims to verify the stability and consistency of evaluation methods and indicators in the face of parameter changes, that is, to evaluate whether these methods and indicators can continuously provide reliable and stable explanatory results under different conditions [55]. In order to verify the robustness of the EWM-TOPSIS model, this paper has compared it with the ESI index calculation results. Through this comparison, this paper has not only confirmed that the EWM-TOPSIS model can maintain the stability of its results but also further proven that the model can exhibit relative objectivity and reliability in the evaluation process. In this study, the comprehensive warning index ESI was calculated based on EWM.

$$ESI={\displaystyle \sum _{i=1}^{10}{\omega }_i\times C_i}$$

(3)

Among them, ${\omega }_i$ represents the weight of each indicator, $i$ is the $i$th indicator, and $C_i$ is the indicator value of the indicator layer.

4. Results

4.1. Current Status of Carbon Dioxide Emissions in MRBCs

The carbon dioxide data of 18 MRBCs in southwest China from 2000 to 2020 are shown in Figure 3. Figure 4 shows the overall carbon emission growth rate of MRBCs in Southwest China.

From Figure 3 and Figure 4, it can be seen that (1) in terms of emissions, the carbon dioxide emissions of MRBCs in southwest China have increased from 146.025 million tons to 425.995 million tons, showing a clear upward trend. This trend has reflected the region’s high dependence on fossil fuels in economic development. At the same time, it has also proven that when promoting economic development, the research and application of carbon emission control and emission reduction technologies have still been paid more attention to. MRBCs in the same province have been classified. As a municipality directly under the central government, Chongqing has the highest carbon dioxide emissions, with an average annual carbon dioxide emissions of 125.51 million tons, which has been due to its strong industrial foundation and high energy demand. Sichuan, Guizhou, and Yunnan have also exhibited high emissions, which is closely related to their abundant mineral resources and frequent development activities. In contrast, Xizang’s emissions are low, mainly because its smaller economic volume is related to the lower level of industrialization. (2) In terms of month-on-month growth rate: Although the overall growth rate is still positive, the month-on-month growth rate is gradually decreasing, indicating that MRBCs in southwest China have begun to realize the unsustainability of high carbon emissions. Therefore, they are taking measures such as adjusting their development structure and promoting low-carbon technologies to reduce the growth rate of carbon emissions. In Xizang, the highest emission growth rate is 23.51%, which means it is in the initial stage of economic development, and its industrial structure and industrial base are still in the process of accelerating development. The month-on-month growth rates of Yunnan, Sichuan, Chongqing, and Guizhou are 8.23%, 6.01%, 5.16%, and 5.14%, respectively. Although the month-on-month growth rate is lower than that of Xizang, continuous efforts are still needed to achieve a deeper low-carbon transformation. Shannan has the largest month-on-month growth rate because it is located in Xizang, and its economic development is relatively backward. At present, its development model is still being improved. The smallest month-on-month growth in Liupanshui is mainly due to the city’s continuous optimization of its industrial structure and implementation of energy-saving and emission-reduction policies.

4.2. Development Status of Economic Index Level of MRBCs

In order to provide a detailed description of the economic level of MRBCs in southwest China, this paper has comprehensively evaluated multiple economic indicators with the use of the EWM-TOPSIS model. To test the effectiveness of the EWM-TOPSIS model, the ESI index based on EWM has been introduced for robustness analysis. According to the calculation results of the ESI index based on the EWM and EWM-TOPSIS model (as shown in

Table 4. Annual average calculation results of economic index levels.

Year	ESI Index	EWM-TOPSIS
2000	0.485	0.312
2001	0.476	0.321
2002	0.471	0.305
2003	0.475	0.317
2004	0.500	0.309
2005	0.512	0.315
2006	0.528	0.314
2007	0.534	0.313
2008	0.523	0.297
2009	0.514	0.315
2010	0.571	0.316
2011	0.573	0.340
2012	0.593	0.345
2013	0.603	0.384
2014	0.581	0.409
2015	0.598	0.436
2016	0.691	0.457
2017	0.657	0.556
2018	0.687	0.549
2019	0.667	0.647
2020	0.652	0.645

), the fluctuation range of the results is about 0.17, and the fluctuation situation is basically consistent. It has been shown that the EWM-TOPSIS model used in this paper to calculate the economic index level has good applicability, the calculation results are effective, and the method is reasonable.

Further quantifying the regional economic level of 18 cities from 2000 to 2020, as shown in Figure 5. Based on the economic level of 18 cities, calculate the overall economic index of MRBCs in Southwest China (Figure 6) and analyze the year-on-year month-on-month growth rate (Figure 7).

From Figure 5, Figure 6 and Figure 7, it can be seen that (1) in terms of economic index level, the economic index of MRBCs in southwest China has increased from 0.312 to 0.645, showing a slow but stable growth trend. It has displayed that MRBCs in southwest China still maintain sustained economic development momentum. (2) In terms of differences in economic index levels between regions and cities, Sichuan has the highest annual average economic index level, reaching 0.413, followed by Guizhou, Yunnan, Chongqing, and Xizang. This means that compared to other regions, Sichuan has a relatively complete industrial system and development momentum. Guang’an, Zigong, and Luzhou have shown outstanding performance in economic indices. In contrast, the annual average economic index levels of cities such as Zhaotong, Qujing, and Shannan are relatively low, mainly because the three cities are located in relatively underdeveloped areas. (3) In terms of month-on-month growth rate: From 2000 to 2020, the economic index of MRBCs in southwest China showed an upward trend in the month-on-month growth rate, which fully demonstrates the continuous improvement of the region’s economic level and the sustained improvement of the economic situation. Xizang’s economic index grew the fastest month on month, at 0.072, because of the region’s latecomer advantage, preferential policies, and unique resource endowments. This is consistent with the relatively fast month-on-month growth rate of the Shannan Economic Index.

4.3. Analysis of the Decoupling Relationship Between Carbon Emissions and Economic Index in MRBCs

When analyzing the decoupling relationship between carbon emissions and economic indices in MRBCs in southwest China, this paper has comprehensively taught the multiple dimensions of the economy, including income and expenditure capacity, innovation environment, development vitality, stability, diversity and openness, which can enrich the research framework of regional economic level. Analyzing carbon emissions and economic indicators together can help explore ways to reduce carbon emissions while maintaining economic growth [56]. From the perspectives of decoupling speed and decoupling quantity, the correlation between carbon emissions and economic indices in MRBCs can more accurately determine the degree of coordination between economic development and environmental protection and provide a basis for relevant policy formulation and adjustment.

4.3.1. Speed Decoupling Analysis

Using the Tapio model, the velocity decoupling index of MRBCs in southwest China was calculated, as shown in Figure 8 and

Table 5. Decoupling elasticity coefficient and status of MRBCs.

Year	$(\Delta \mathit{C}{\mathit{O}}_2/\mathit{C}{\mathit{O}}_2)/(\Delta \mathit{E}\mathit{I}/\mathit{E}\mathit{I})$	State
2001	−1.411	Strong decoupling
2002	−1.893	Strong negative decoupling
2003	4.249	Expansion negative decoupling
2004	−4.262	Strong negative decoupling
2005	8.390	Expansion negative decoupling
2006	−29.013	Strong negative decoupling
2007	−15.955	Strong negative decoupling
2008	−0.941	Strong negative decoupling
2009	1.563	Expansion negative decoupling
2010	31.373	Expansion negative decoupling
2011	0.996	Expansion negative decoupling
2012	1.616	Expansion negative decoupling
2013	−0.043	Strong decoupling
2014	0.407	Weak decoupling
2015	−1.104	Strong decoupling
2016	0.593	Weak decoupling
2017	0.242	Weak decoupling
2018	−4.098	Strong negative decoupling
2019	0.267	Weak decoupling
2020	−13.618	Strong negative decoupling

. Strong decoupling and weak decoupling occurred 7 times, accounting for 35% of the total. These two states indicate that the growth rate of carbon emissions is lower than or equal to the economic growth rate, which is an ideal model for sustainable development. Although their proportion is not high, their frequency of occurrence has increased since 2013, meaning policy adjustments and increasing environmental awareness have begun to show results. In the future, these positive trends should be strengthened to promote more cities to achieve strong decoupling. The strong negative decoupling was mainly concentrated before 2008, reflecting the high dependence of economic development on mineral resources and the lagging environmental protection measures at that time. The rapid economic growth during this period was achieved at the cost of sacrificing the environment. The common expansion of negative decoupling between 2009 and 2012 showed that while the economy was growing, carbon emissions were also rapidly increasing, but the growth rate began to slow down. This reflects that policy adjustments and market mechanisms have begun to play a role. Although the extensive development model has not been completely shaken off, there are obvious signs of a transition toward green development.

Through a detailed analysis of the development trajectories of 18 MRBCs from 2000 to 2020, there are mainly eight categories: strong decoupling, weak decoupling, decline decoupling, weak negative decoupling, expansion negative decoupling, strong negative decoupling, expansion connection, and decline connection. For a more intuitive analysis, this paper has set numerical labels for these eight decoupling states, decreasing from 8 to 1. Among them, the strong decoupling of 8 indicates that while the economy is growing steadily, carbon emissions or resource consumption have achieved negative growth, which is the ideal state in the decoupling analysis. The recession link of 1 reflects that while the economy is declining, the rate of reduction in carbon emissions or resource consumption has not exceeded or matched that of economic recession, thus making it the most pessimistic scenario in decoupling analysis. Figure 9 visually displays the decoupling status calculation results of these 18 cities during the investigation period.

From Figure 9, it can be seen that the decoupling effect of speed in 18 MRBCs, from high to low, is as follows: Panzhihua, Baoshan, Chongqing, Nanchong, Shannan, Guang’an, Lincang, Guangyuan, Luzhou, Zigong, Liupanshui, Qujing, Dazhou, Pu’er, Ya’an, Zhaotong, Anshun, and Lijiang. Panzhihua City has shown an extremely strong decoupling trend from 2000 to 2020, achieving nine strong and weak decoupling states. This indicates that the city has attached great importance to environmental protection in the process of economic development and has taken effective measures to reduce carbon emissions and resource consumption. Its declining connectivity state only appeared in the early stages, and then it quickly shifted towards a more active decoupling direction, which further proves that Panzhihua City has put emphasis on low-carbon and sustainable development. In sharp contrast to Panzhihua, Lijiang has achieved strong and weak decoupling five times but has experienced negative decoupling eight times, indicating its prominent contradiction between economic development and environmental protection. In addition, the expansion of connectivity status has also occurred three times, indicating that Lijiang still needs to optimize its industrial structure further and improve resource utilization efficiency in economic development with the aim of reducing its negative impact on the environment. The decoupling effect between Zigong and Baoshan is showing a downward trend. Although Zigong is mainly in a weak decoupling state, with a total of 8 occurrences, the overall downward trend means that its efforts in environmental management have been weakened or faced new challenges. Baoshan, on the other hand, exhibited a more unstable decoupling state, with strong negative decoupling occurring 10 times. This signifies that Baoshan neglects environmental protection due to its excessive pursuit of economic growth. Luckily, it can adjust in a timely manner to return to a decoupling state. This volatility has reflected Baoshan’s continuous exploration and adjustment on the path of sustainable development. Around 2012, as a key node in the changes of decoupling effects in various cities, it coincided with the timing of China’s 12th Five Year Plan. This plan, with emphasis on the importance of ecological civilization construction and green development, has provided policy guidance and support for MRBCs. Therefore, it can be considered that the progress of these cities in decoupling is closely related to the promotion of national policies.

4.3.2. Quantity Decoupling Analysis

The level of decoupling between carbon emissions and economic indices in MRBCs in southwest China needs to be further analyzed. With carbon emissions as the vertical axis and economic index as the horizontal axis of the fitting curve, this paper has selected six curve models, including linear curve, quadratic curve, cubic curve, logarithmic curve, composite curve, and growth curve, to simulate the EKC curve. In order to evaluate whether the correlation between the fitted curve and the data is strong enough and whether the correlation has statistical significance, this paper has tested the significance of six curve models, whose results are shown in

Table 6. Fitting effect of the EKC curve for MRBCs.

	${\mathit{\beta }}_0$	${\mathit{\beta }}_1$	${\mathit{\beta }}_2$	${\mathit{\beta }}_3$	R2
Linear curve	67.521	579.95 ****	0	0	0.533 ****
Quadratic curve	−415.87 *	−2867.68 **	−2484.92 *	0	0.607 ***
Cubic curve	−2571.84	17,808.38	−35,630.42 **	23,576.31 **	0.654 ***
Logarithmic curve	551.57 ****	264.13 ****	0	0	0.570 ***
Composite curve	6.73 ****	−0.40 ****	0	0	0.496 ***
Growth curve	4.83 ****	2.04 ***	0	0	0.430 ***

* p < 0.1, ** p < 0.05, *** p < 0.01, **** p < 0.001.

.

From Table 5, it can be seen that the quadratic curve has the best-fitting effect, with a large R² value and significant coefficients. Therefore, the EKC curve formula for fitting the economic indicators of MRBCs in southwest China to carbon dioxide emissions is as follows:

$$Y_C=-415.87+2867.68r_G-2484.92{(r_G)}^2$$

According to Table 3, the EKC curve fitted in this paper presents an inverted U-shape. When the EKC curve reaches its extremum, the economic index will be at 0.58, which will occur in 2019. When the economic index of MRBCs in southwest China is greater than 0.58, the carbon dioxide emissions of MRBCs in southwest China show a downward trend as the economic index increases. From the perspective of changes in economic indices, the economic indices of MRBCs in southwest China are showing an upward trend. Therefore, in the current situation, these cities have basically entered a stage where carbon dioxide emissions decrease with the increase of economic indices, which means that energy conservation and emission reduction are relatively effective.

Further analysis of the specific situation of 18 MRBCs in southwest China is shown in

Table 7. EKC fitting effect and parameters for each city.

	${\mathit{\beta }}_0$	${\mathit{\beta }}_1$	${\mathit{\beta }}_2$	${\mathit{\beta }}_3$	R2	Shape
Dazhou	−8.91	81.20	−66.43	0	0.50	Inverted U-shaped
Guang’an	−22.73	106.69	−86.45	0	0.60	Inverted U-shaped
Guangyuan	−26.67	136.88	−117.83	0	0.60	Inverted U-shaped
Luzhou	−26.67	176.05	−302.02	177.32	0.70	N-type
Nanchong	1.49	28.21	−6.65	0	0.30	Inverted U-shaped
Panzhihua	−108.60	794.22	−1524.35	939.11	0.70	N-type
Ya’an	17.81	−115.55	291.46	−211.50	0.30	Inverted N-shaped
Zigong	−8.38	48.46	−40.39	0	0.90	Inverted U-shaped
Shannan	−3.24	31.13	−82.16	68.04	0.60	N-type
Chongqing	−49.75	836.71	−769.71	0	0.80	Inverted U-shaped
Anshun	13.50	−40.98	97.14	0	0.50	U-shaped
Liupanshui	−255.49	1825.62	−3764.31	2553.26	0.90	N-type
Baoshan	88.05	−516.13	1036.48	−637.13	0.50	Inverted N-shaped
Lijiang	−21.19	95.87	−74.09	0	0.90	Inverted U-shaped
Lincang	12.18	−74.89	202.47	−149.18	0.60	Inverted N-shaped
Pu’er	−3.27	29.64	−20.27	0	0.50	Inverted U-shaped
Qujing	37.17	−204.27	648.81	−521.85	0.40	Inverted N-shaped
Zhaotong	−1.88	25.29	−16.82	0	0.50	Inverted U-shaped

.

The four different shapes of EKC curves (U-shaped, inverted U-shaped, N-type, and inverted N-type) shown in Table 7 reveal the complexity of the relationship between economic development and carbon emissions in each city. (1) The inverted U-shaped curve appears in nine cities (Dazhou, Guang’an, Guangyuan, Nanchong, Zigong, Chongqing, Lijiang, Pu’er, and Zhaotong), which are basically in the middle to late stage of economic development, with per capita income constantly increasing and industrial structures gradually leaning towards the tertiary industry. Therefore, as the economy develops to a certain extent, with the optimization and upgrading of industrial structures and the application of environmental protection technology, the environmental quality gradually improves. The various timing of decoupling among cities reflect the differences in the effectiveness of emission reduction measures and the speed of economic restructuring. This indicates that in order to promote emission reduction, a variety of policies need to be formulated based on the specific situations of different cities. (2) The inverted N-shaped curves of four cities, Ya’an, Baoshan, Lincang, and Qujing, display that carbon emissions have experienced two fluctuations of increase and decrease during the process of economic development. The failure to decouple the years between extreme points means that these cities have encountered significant challenges and fluctuations in the process of emission reduction. These four cities are all developing from a single traditional industry to diversified industries, and their transformation and adjustment of industrial structure are relatively long. In this process, there is repeated environmental pollution. Therefore, it is necessary to strengthen environmental protection policies and technological innovation to achieve stable decoupling. (3) The N-type curves of four cities, Luzhou, Panzhihua, Shannan, and Liupanshui, show that these cities have achieved a brief decoupling during a specific period of time, but their carbon emissions have increased again, which is mainly because all four cities have a relatively strong industrial foundation and currently their pillar industries are still with serious pollution emissions. But, with the rising global demand for environmental protection, the four cities are also actively completing urban transformation. They have achieved certain results in emissions reduction, but they still need to consolidate and expand the decoupling effect. Among them, Luzhou, presenting an N-type curve without extreme points, indicates its decoupling effect is not ideal, which requires more comprehensive environmental protection strategies to promote emission reduction. (4) As the only city to exhibit a U-shaped curve, Anshun’s rising EKC curve throughout the study period signifies that the relationship between its economic development and carbon emissions has not been effectively controlled. Anshun is currently accelerating the construction of multiple key industrial projects, but there has been no increase in investment in environmental protection. Therefore, with more attention to environmental protection, Anshan has still continued emission reduction efforts to avoid further deterioration of environmental problems.

5. Discussion

5.1. Main Discussion

MRBCs are very important in the process of regional economic development. Due to their fragile environment and uneven economic development, MRBCs in southwest China have weak capabilities in achieving decoupling. Therefore, founded on the deep analyses of the economic development status of MRBCs in southwest China, this paper has quantified the economic level using the EWM-TOPSIS model and explored the decoupling state between the economy and the environment from two dimensions of growth rate and total scale, by virtue of Tapio model and EKC curve respectively. In addition, the results of two studies have been comprehensively studied, and the specific path of low-carbon development in MRBCs has been examined. The specific conclusion is as follows:

(1)

The overall carbon dioxide emissions of MRBCs in southwest China are on the rise, reflecting the increase in energy consumption during industrialization and urbanization processes, especially in industrial activities that rely on mineral resources. However, the gradual weakening of the month-on-month growth rate indicates that these cities that have begun to realize the importance of environmental protection are actively taking measures to transition towards low-carbon. As a municipality directly under the central government, Chongqing has the highest level of economic development and industrialization, resulting in the highest carbon emissions. Xizang has the smallest carbon emissions, which is related to its low level of industrialization and special geographical environment. Among the 18 cities, except for Chongqing, Liupanshui has the highest carbon emissions, with an average annual emission of 30.32 million tons, but the growth rate is the smallest, at 0.042. This reveals its industrial structure with high-energy consumption and excessive dependence on mineral resources but also proves that its urban development is in a mature stage. Shannan has the smallest carbon emissions, with an average annual emission of 0.36 million tons. Notwithstanding, its highest growth rate is just 0.235, mainly due to its relatively small economic scale and rapidly developing economic model.

(2)

Using the EWM-TOPSIS model to quantify economic levels, it has studied the economic indices of 18 MRBCs in southwest China. The overall economic level of MRBCs in southwest China shows a slow upward trend from 0.312 to 0.645. This indicates that although these cities face multiple challenges, such as resource dependence and environmental pressure, they still maintain stability in economic development. The economic index level of MRBCs in Sichuan Province is the highest, with an annual average of 0.413. It is mainly because these cities have a relatively mature industrial foundation, like a complete industrial chain and a relatively developed economic system. The economic index growth rates of all cities are positive, indicating that their economic activities are still actively expanding in the macroeconomic environment. Xizang has the largest month-on-month growth rate of 0.072, mainly led by national policy support, accelerated infrastructure construction, and the rapid development of tourism and other characteristic industries. Among the 18 cities, Shannan’s economic index has risen the fastest, mainly due to the driving effect of vital investment and the favorable policy environment. And Anshun has the slowest growth rate, largely caused by its single industrial structure and the difficulty of transformation and upgrading.

(3)

A decoupling analysis of MRBCs in southwest China using the Tapio model and EKC curve was conducted. The turning point of speed decoupling occurred in 2013. Therefore, since 2013, MRBCs in southwest China have consciously controlled or reduced their negative impact on the environment in the process of economic development, resulting in a lower growth rate of carbon dioxide emissions than the economic index. The turning point of quantity decoupling happened in 2019. It has signified that MRBCs have not only achieved results in slowing down the growth rate of carbon dioxide emissions but have also gradually reduced their total emissions, realizing a deeper decoupling between the economy and the environment. The lag of quantity decoupling reflects the difficult process of slowing down the growth rate to actually reduce emissions. A detailed analysis of the decoupling situation of 18 MRBCs has been conducted. Chongqing and Lincang started the decoupling effect in 2011 and completed decoupling in 2016 and 2017 respectively. Zigong, Baoshan, and Ya’an began the decoupling effect in 2012 and completed decoupling in 2016, 2019, and 2023, respectively. Guang’an and Guangyuan began the decoupling effect in 2013 and completed decoupling in 2016. Dazhou, Qujing, and Nanchong can achieve decoupling in 2013, 2020, and 2039, respectively. Pu’er and Zhaotong began the decoupling effect in 2014 and 2017, respectively, and both completed decoupling in 2020. Luzhou, Panzhihua, Shannan, Anshun, and Liupanshui have decoupled in terms of entry speed but not in terms of quantity. The turning point of quantity decoupling in Lijiang was in 2019, but its decoupling speed was not ideal. From the results of this paper, it can be seen that the growth rate of carbon dioxide emissions in Lijiang does not show a relatively regular trend with the growth rate of economic indices, and its decoupling effect is not stable.

5.2. Policy Recommendations

Based on the economic index level calculation, carbon dioxide emissions analysis, and decoupling effect analysis of MRBCs in southwest China, some policy recommendations are proposed for each city.

Regarding cities that have achieved decoupling effects, including Chongqing, Lincang, Zigong, Baoshan, Ya’an, Guang’an, Guangyuan, Dazhou, Qujing, Nanchong, Pu’er, and Zhaotong, these 12 cities, rich in mineral resources such as coal and metals, have basically entered a relatively mature stage of urban development. Therefore, these cities should continue to maintain their existing decoupling achievements, strengthen their energy policies, and develop targeted management policies for different mineral resources within the cities founded on the “Opinions on Accelerating the Revolution of Energy Production and Consumption”. The supervision should be strengthened in order to ensure the sustainability and cleanliness of the energy supply and prevent the resurgence of high-polluting and high-energy-consuming industries. In addition, the regulatory system of the Energy Conservation Law should be enhanced. And the “Green Industry Compensation Policy” should also be developed to encourage continued exploration and innovation in green technologies, green industries, and other areas. We should also fully play an active role in sharing decoupling experiences and successful cases with other cities to promote global green transformation and sustainable development.

For cities that have begun decoupling but have not fully achieved decoupling, including Luzhou, Panzhihua, Shannan, Anshun, and Liupanshui, these five cities are mainly located in the border areas of multiple provinces, with industry as the main pillar industry. Therefore, these cities should develop new industries according to their own urban characteristics based on the reference to the Catalogue for the Guidance of Industrial Restructuring. For example, Luzhou and Anshun can vigorously promote Baijiu industry and tourism. Under the guidance of the National Innovation Driven Development Strategy Outline, they should vigorously develop green industries, encompassing the promotion of green technologies and applications. Additionally, enterprises should be encouraged to adopt advanced green technologies to improve the energy efficiency and environmental protection level of the production process. “Environmental incentive policies” should be formulated to encourage enterprises and individuals to actively participate in the decoupling process and promote the development of cities towards complete decoupling.

For cities with unstable decoupling effects: Lijiang. In the development, Lijiang, rich in metallic minerals, has gradually strengthened the tourism industry in its industrial structure. Therefore, Lijiang can guide and incentivize enterprises and residents to take an active part in decoupling actions under the guidance of documents such as the “Notice on Comprehensive Demonstration of Energy Conservation and Emission Reduction Fiscal Policies”. “Green credit preferential policies” should be developed to support enterprises in carrying out energy conservation, emission reduction, and green technology innovation. Activities such as “Energy Conservation Promotion Week” and “Green Travel Day” should also be implemented to increase public awareness and participation.

6. Conclusions

In view of the important position of MRBCs in Southwest China in regional economic development, environmental vulnerability, and unbalanced economic development, this paper provides an in-depth analysis. Firstly, using the EWM-TOPSIS model, the economic indexes of 18 MRBCs in Southwest China are quantitatively analyzed from the dimensions of income and expenditure capacity, innovation environment, development vitality, stability, diversity, and openness. This not only provides a more scientific and accurate basis for the study of the urban economic development level but also improves the index system and research perspective of the urban economic level. Through the joint analysis of the Tapio model and EKC curve, this paper not only reflects the short-term dynamic changes between economic growth and environmental pollution but also reveals the long-term development trend. This analysis method can provide a more comprehensive and in-depth basis and reference for the government to formulate energy conservation and emission reduction policies.

When constructing the indicator system of the economic level index in this paper, although it has been confirmed based on existing research results, there are still a few subjective opinions on the specific selection of indicators due to the complexity of data applicability. When constructing an indicator system, there may be many difficulties in quantifying or obtaining data indicators (such as the popularization rate of environmental education), and the lack of these indicators will affect the actual operation of the evaluation system. When the indicator system is not fully considered, it may not be able to comprehensively cover all important aspects of the research object. Therefore, in the subsequent research process, social surveys and questionnaires can be encompassed to conduct relevant data tests targeted at specific groups.

When exploring the decoupling phenomenon of MRBCs in southwest China, this paper has focused on the correlation between economic indices and carbon dioxide emissions. However, the essence of decoupling theory lies in a comprehensive examination of the complex interaction between the environment and the economy. In the current analytical framework, using only carbon emissions as a single measure of environmental factors clearly fails to fully capture the diversity and complexity of environmental dimensions. In view of this, further research is urgently needed. Key air pollutants such as sulfur dioxide (SO₂) and nitrogen oxides (NO_x), as well as broader ecological environmental impact factors, should be taken into consideration to construct a more complete and three-dimensional decoupling effect analysis system. Through such expansion, the dynamic relationship between urban economic development and environmental protection can be more accurately depicted, and the decoupling analysis research system can also be improved.