Research on Spatial and Temporal Divergence and Influencing Factors of the Coal Industry Transformation and Development Under Energy Security and Dual-Carbon Target

Abstract

To achieve the “dual-carbon” target and ensure energy security, there is an urgent need to promote the transformation of the energy system, of which the coal industry is the main battlefield. In order to study the spatial and temporal characteristics and influencing factors of the coal industry transformation and development (CITD), this article establishes an evaluation index system for the transformation and development of the coal industry, including 17 indicators in six dimensions. The projection pursuit (PP) model, which relies on the Real Coded Accelerating Genetic Algorithm (RAGA), is applied to assess the CITD index in 23 Chinese provinces between 2011 and 2021. The findings indicate that (1) the CITD index in China as a whole shows an upward trend, and the regional differences are more obvious, in the following order: eastern, central, and western. (2) There is striking spatial autocorrelation in the CITD in China, and the CITD in this region has a striking spatial spillover effect on the neighboring regions. (3) Human capital, foreign direct investment level, and employment density are positively correlated with CITD, while industrial development level and government intervention extent are negatively correlated with it. Policymakers should incorporate the findings of the study and formulate targeted policies to provide ideas for fueling the transformational development of the coal industry.

1. Introduction

High dependence on fossil fuels and rising greenhouse gas emissions threaten the global climate [1]. There is an urgent need to advance a systemic transition, an inclusive and just transition, in order to combat greenhouse gas emissions-related climate change as a group and keep temperatures rising to only 1.5 °C or 2 °C above pre-industrial levels globally [2]. China, the biggest developing nation in the world and a contributor to greenhouse gas emissions, has taken action actively [3,4]. China presented the “dual-carbon” target, which demands reaching the carbon peak by 2030 and carbon neutrality by 2060 [5]. At the Climate Ambition Summit, it was proposed that by 2030, the carbon dioxide emissions per unit of GDP in China would be reduced by more than 65 percent compared with 2005, the share of non-fossil fuels in primary energy consumption would be increased to about 25 percent, forest storage would be increased by 6 billion cubic meters compared with 2005, and the total installed capacity of wind and solar energy would be more than 1.2 billion kilowatts. Energy security is a fundamental guarantee for the successful achievement of the “dual-carbon” target. However, China is facing an imbalance in energy structure due to the constraints and limitations imposed by a number of factors, including natural resource endowments, technological levels, and the basis for economic development, which has put enormous pressure on energy security. China has vast coal reserves but limited oil and gas reserves. Coal has traditionally offered a solid assurance of steady social and economic advancement [6] and for decades to come, coal will continue to be the primary energy source [7]. Like the construction industry, as the main demand side of coal consumption, the decarbonization of commercial buildings has not yet been significant despite the increase in electrification levels globally [8], and there are still many challenges to achieving the goal of carbon neutrality in commercial buildings by the mid-century [9]. Changes in the demand side of coal are also driving the transformation and upgrading of the coal industry in reverse. Achieving the “dual-carbon” target requires promoting energy transformation on the premise of ensuring energy security, and the coal industry is a key area. The transformational development of the coal industry is an important path for achieving the “dual-carbon” target and alleviating the pressure on energy security. Therefore, it is of great practical significance to study the spatial and temporal divergence and influencing factors of the transformation and development of the coal industry.

In order to better safeguard energy supply security and finish the energy structure change smoothly, it is necessary to establish a comprehensive index system for the coal industry [10,11], which can be used to accurately depict the level and direction of transformation and development so as to achieve the “dual-carbon” target on schedule. At present, the establishment of various comprehensive indicators has gradually become a research hotspot [12,13,14,15]. The sustainable development of the coal industry is represented in four essential facets: society, economy, environment, and resources [16]. Twenty-eight secondary indicators covering six dimensions are part of the coal industry development index system that Zhao et al. devised [10]. Ren et al. [17] selected capital, resources, labor, technology, and the energy system as the factor-driven measures to assess the growth of the coal sector. An assessment index system with 23 indicators covering five dimensions—innovation-driven, safety and health, intelligent and efficient, diverse economy, and green and low-carbon—was constructed by Kang et al. [18]. In the existing literature, the indicator system is not comprehensive, making it challenging to gauge the comprehensive level of CITD. The coal sector in China, being a traditional industry, has restricted future potential due to energy security and carbon limits. Therefore, steps must be adopted to support the transformation in order to achieve clean and efficient usage of coal.

In terms of evaluation methodology, the ranking of various programs can be impacted by different index weights, hence it is crucial to choose a reasonable method of determining weights [19]. At present, the weights of indicators can be determined in a variety of ways, both at home and abroad, mainly by the EW method [20], TOPSIS method [21], DEA method [11], AHP method [22], and so on. The combination weighing approach was created to compensate for the inadequacies of the subjective and objective methodologies. To establish weights, for instance, the most often used entropy-weight TOPSIS [23,24] blends subjectivity and objectivity. Spanidis Philip-Mark et al. [25] used a SWOT-AHP combination approach, based on circular economy practice and methodology, to assess the sustainable transformation strategy of the surface coal mines. Zhao et al. [10] employed the minimum deviation combination weighting method to ascertain the indices weights and constructed a thorough assessment model based on grey theory and TOPSIS. Lian et al. [26] assessed the comprehensive development of renewable energy using the AHP-EM comprehensive evaluation model. It has been found that the projection pursuit model is rarely applied in transformation evaluation. The method is capable of downscaling non-linear high-dimensional data, which allows for a more accurate evaluation of sample data. The genetic algorithm (GA) [27] and improved algorithms [28] have been used to compute the ideal projection direction selection of the PP model. Meanwhile, due to wide geographical distribution in China, provinces differ greatly from one another in terms of their respective levels of economic development, resource endowment, and environmental quality [29]. Coal resources in China have a large total amount but uneven distribution characteristics, showing a development landscape of more in the north and west and less in the south and east [30]. Therefore, it is important to properly take into account regional differences when supporting the coal industry transformation, which has been less addressed in previous studies.

Analyzing impact factors is a fundamental and important element in the study of transformative development. Scholars have analyzed the factors influencing transformational development from different perspectives. Du et al. [31] show that institutional openness policies (e.g., pilot free trade zones) significantly enhance regional energy efficiency through trade liberalization and spatial spillovers, with mechanisms covering industrial agglomeration optimization, technological co-innovation, and foreign trade. Han et al. [32] studied the path of development and the affected variables of chemical upgrading and transformation in Shandong Province, and concluded it must follow the route of green development by technological innovation so as to carry out transformation and upgrading. Wang et al. [33] proposed that more funding should be allocated to scientific research to better assist the adoption and promotion of green technology and the green transition of pollution-intensive industries in China. Environmental regulation can guide industrial green transformation through technological innovation [34]. The implementation of environmental regulatory policies and the search for low-cost, cleaner production technologies will help optimize the capability in the coal industry for production and are an unavoidable decision to support the transformation and upgrading of the coal industry [35]. Yuan et al. [36] found that foreign direct investment, government intervention capacity, and human capital levels are all conducive to promoting industrial green transformation. Liu et al. [37] concluded that industrial value added and urbanization level have a negative effect on energy green consumption transition, and government administrative capacity has a positive effect on it. In addition, the DEMATEL methodology is able to identify the factors in complex systems that influence a carbon-neutral transition in the energy sector by constructing a causal network of factors, and the results of the study suggest that carbon trading, innovation, and digitization are effective pathways for a carbon-neutral transition in the energy sector [38]. The coal-to-green, clean, efficient, low-carbon transition has become an inevitable tendency, and the coal industry must also change production capacity and technological innovation to help realize the “dual-carbon” target [39]. It can clearly be seen that the CITD is impacted by a multitude of factors, including available technology, human resources, available capital, and national energy systems, among others [40,41]. Most of the existing studies are focused within the fields of industry and chemical industry, while there is a lack of research on the factors affecting the CITD. The limitations of the study lead to unscientific and unreasonable problems in the formulation of policies for the CITD. Therefore, this paper needs to study the influencing factors of the transformation and development of the coal industry.

In summary, this paper aims to address the improvement and dynamic assessment of the evaluation index system for the CITD, as well as the status quo of insufficient identification of spatial relevance and key influencing factors of transformation and development. Currently, although research on the CITD has been carried out from social, economic, technological, environmental, and other dimensions, the evaluation models focus on static assessment, which is difficult to comprehensively reflect the new requirements under the goals of energy security and “dual-carbon”. For this reason, based on energy security and the realization of the “dual-carbon” goal, this paper constructs a transformational development index for the coal industry that includes six dimensions: economic support, safety and security, environmental protection, technological innovation, industrial transfer, and resource utilization, and adopts the projection tracing model to conduct a comprehensive evaluation, in order to make up for the deficiencies of the static assessment. In addition, in response to the fact that existing studies mainly focus on the country as a whole or individual provinces, with less research on the differences between provinces and regions, this paper argues that the CITD has spatial relevance, and its spatial distribution characteristics need to be further explored. Meanwhile, the existing literature is deficient in the identification of key influencing factors, making it difficult to clarify the direction of future transformation. Therefore, on the basis of spatial correlation analysis, this paper will use spatial econometric modeling to conduct an in-depth discussion on the influencing factors of the CITD, with a view to providing scientific basis and practical guidance for the CITD.

The primary contributions of this thesis include the following: (1) A multi-dimensional evaluation index system was constructed, laying the groundwork for precisely assessing the level of transformational development of the coal industry. The RAGA-PP model is also used to dynamically assess CITD, making the evaluation results more objective and accurate. (2) In view of the uneven distribution of Chinese coal resources, spatial differences should be fully considered. Spatial correlation analyses are needed for CITD. (3) Based on the spatial econometric model, the major CITD affecting elements are studied and analyzed to fulfill the coal industry low-carbon transformation.

The rest of the paper is structured as follows: the research methodologies and data sources are presented in Section 2; the results of the evaluation analysis, spatial correlation analysis, and analysis of influencing factors for CITD are derived in Section 3; the research findings are discussed in Section 4; and the conclusions and policy recommendations are summarized in Section 5.

2. Methods and Materials

2.1. Method

The aim of this research is to investigate the coal industry transformation and development in China under the “dual-carbon” target and energy security. Firstly, the index system of CITD is constructed, and the RAGA-PP model is used to measure CITD. Secondly, regional differences in CITD are considered. Then, the influencing factors of CITD are further discussed. Finally, the above research conclusions are further summarized to comprehensively give policy recommendations to propel the coal industry transformation and development. Figure 1 demonstrates the research framework.

2.1.1. Evaluation Model for CITD

(1)

Evaluation model

The rationale of the PP model is to be able to project nonlinear high-dimensional data into the low-dimensional subspace in certain combinations [42,43], so that the projection values of low-dimensional vectors embody the characteristics or structure of the sample data [44]. The orientation of the projection can reveal the features or structure of different high-dimensional datasets [45]. Although the RAGA-PP model pass optimizes the efficiency of the projection direction search, there are still limitations. One is parameter sensitivity. Hyperparameters such as population size, crossover probability, and mutation probability of the genetic algorithm need to be set in advance. The projection-seeking model based on an accelerated genetic algorithm needs to be adjusted to the optimal state by adjusting to the optimal hyperparameter, which is adjusted to the optimal state when the hyperparameter value increases further and the final output objective function value does not change much, i.e., when the convergence state is reached [46]. Second are the limitations of high-dimensional data adaptability. When the dimensionality of the indicators exceeds 20, the problem of “dimensional catastrophe” in projection tracing may be aggravated, leading to a decrease in the interpretability of the projection direction. The indicator system of this study contains 17 indicators, which overcomes this limitation to a certain extent. The following are the primary steps [47,48]:

Step 1 Data pre-processing

Given the differences in the constructed evaluation indicators in terms of scale, standardization of the raw data is required.

Positive indicator normalization processing formula:

$$x\left(i,j\right)={\displaystyle \frac{x^{*}\left(i,j\right)-x_{min}\left(j\right)}{x_{max}\left(j\right)-x_{min}\left(j\right)}}$$

(1)

Reverse indicator normalization processing formula:

$$x\left(i,j\right)={\displaystyle \frac{x_{max}\left(j\right)-x^{*}\left(i,j\right)}{x_{max}\left(j\right)-x_{min}\left(j\right)}}$$

(2)

where $x_{max}\left(j\right){, x}_{min}\left(j\right)$ are the jth indicator maximum and minimum values, respectively, after normalization.

Step 2 Building the projection metric function

With the PP model, the P-dimensional data $\left\{x\left(i,j\right)|j=\mathrm{1,2},\cdots ,p\right\}$ is projected, the optimal projection direction $a=\left\{a\left(1\right),a\left(2\right),a\left(3\right),\cdots ,a\left(p\right)\right\}$ is formed, and the projection value $z\left(i\right)$ is derived.

$$z\left(i\right)={\sum }_{j=1}^{p}a\left(j\right)x\left(i,j\right),i=\mathrm{1,2},\cdots ,n$$

(3)

$a$ is a vector with unit length. The expression for the projection indicator function is as follows:

$$Q\left(a\right)=S_z{D}_z$$

(4)

where the local density of the projected values $Z\left(i\right)$ is $D_z$ and the standard deviation is $S_z$, i.e.,

$$S_z=\sqrt{{\displaystyle \frac{{\sum }_{i=1}^n{\left(Z\left(i\right)-E\left(z\right)\right)}^2}{n-1}}}$$

(5)

$$D_z={\sum }_{i=1}^n{\sum }_{j=1}^n\left(R-r\left(i,j\right)\right)\times u\left(R-r\left(i,j\right)\right)$$

(6)

$u\left(t\right)$ is the unit step function, $u\left(t\right)=\left\{\begin{array}{c}1,t\ge 0\\ 0,t\le 0\end{array}\right.; r\left(i,j\right)$ is the separation between samples, $r\left(i,j\right)=\left|Z\left(i\right)-Z\left(j\right)\right|;$R is the radius of local density; the general value is $r_{max}+{\displaystyle \frac{p}{2}}\le R\le 2p$.

Step 3 Optimizing the projection index function

The projection goal function is maximized in accordance with the established restrictions in order to identify the ideal projection direction vector.

Maximizing the target function:

$$MaxQ\left(a\right)=S_z{D}_z$$

(7)

Restrictive condition:

$$s.t.{\sum }_{i=1}^p{a}^2\left(i\right)=1$$

(8)

Step 4 Computing the projection value

Building on the third step, the vector of the ideal projection direction $a^{*}$ is multiplied by the standardized indicator value $\mathrm{x}\left(i,j\right)$, and then the product is accumulated to arrive at the optimal projection value $Z_i^{*}$ of each sample, i.e., the CITD index. The larger projection value indicates a higher CITD index. An intricate nonlinear optimization problem must be solved in order to determine the best projection direction [49]. In this research, the problem is solved using RAGA, which has strong optimization performance to achieve high-dimensional global optimization [46]. Consequently, the RAGA-PP model is constructed in this paper, and the flow chart is demonstrated in Figure 2.

$$Z_i^{*}={\sum }_{j=1}^n{a}_j^{*}\times x\left(i,j\right)$$

(9)

(2)

Evaluation indicators

Energy security is an overall and strategic issue related to national economic and social development. Coal is an important guarantee of energy security and an important raw material for industrial production, and its main position as energy “ballast” in China will not change. Although the global energy consumption structure is gradually transforming to clean energy, coal consumption is still an important part of energy consumption in China. At present, the status quo of the lack of domestic coal supply is becoming more and more serious; the gap in coal supply needs to rely on imports to meet energy security issues that have begun to be highlighted. Energy security is a complex systemic issue. In 2007, the Asia Pacific Energy Research Centre (APERC) proposed a more comprehensive concept of energy security 4A, namely availability, accessibility, acceptability, and affordability [50]. Energy security has been enriched in terms of security of use, price security, and ecological security.

The proposal of a “dual-carbon” target provides a guarantee of energy security. In order to implement the new development philosophy, build the new development paradigm, and realize the “dual-carbon” target as scheduled, the “Guiding Opinions on High-quality Development of the Coal Industry in the 14th Five-Year Plan” (hereinafter referred to as the Opinions) were put forward. The Opinions point out that the key tasks are to optimize the layout of coal resource development, deepen the structural reform of the coal supply side, promote the development of coal science and technology innovation, and promote the construction of ecological civilization in mining areas. In addition, the Central Economic Work Conference held in 2021 proposed that China should be based on the basic national conditions of coal-based energy and focus on achieving safe, high-efficiency, green, and intelligent coal mining and clean, high-efficiency, low-carbon, and intensive use. Based on the above key tasks and the transformational development of objectives, and with reference to relevant literature [51,52,53], following the principles of reasonableness, independence, comparability, and feasibility, the index system of CITD is constructed. The specific content is shown in

Table 1. Coal industry transformation and development indicator system.

Level 1 Indicators	Secondary Indicators	Unit	Calculation Formula	Attributes
B1	C1	yuan	GDP/total population	+
	C2	hundred million	−	+
	C3	%	−	+
B2	C4	%	Total energy production/total energy depletion	+
	C5	%	Non-carbon energy depletion/total energy depletion	+
	C6	%	−	+
B3	C7	%	−	+
	C8	ten thousand tons	Solid waste, water waste, air waste	−
	C9	ten thousand yuan	−	+
B4	C10	quantities	−	+
	C11	%	Expenditure on scientific research/GDP	+
	C12	%	Science and technology expenditure/generic public budget expenditure	+
B5	C13	%	Ratio of the number of persons employed in the coal industry in a region to the number of persons employed in all industries in the region/ratio of the number of persons employed in the industry in the country to the number of persons employed in all industries in the country	+
	C14	%	Inward transfer from other provinces/raw coal production	+
B6	C15	Tons of standard coal/ten thousand yuan	Energy depletion/industrial value added	−
	C16	Tons of standard coal/ten thousand yuan	Total energy depletion/GDP	−
	C17	Tons of standard coal/ten thousand yuan	Total energy depletion of the coal industry/value added of the coal industry	−

Note: in the formula column (−) represents no formula; in the attribute column (+) represents a positive index and (−) represents a negative index.

.

Economic support dimension (B1): China is the greatest coal producer and consumer in the world. Coal supports economic and social advancement in China. The economic environment in which the coal industry operates will influence the direction of its transformation and development. The economy is the basis for supporting other aspects, and the dimension will be measured from three aspects: per capita GDP (C1) [54], general public budget revenue (C2), and the rate at which investments are growing in the coal mining and washing industry (C3).

Energy security dimension (B2): It will be challenging to alter the coal-dominated energy structure in China in the near future, as coal will remain the staple energy source, guaranteeing energy supply and security in China for a long time to come. The safety and security dimension refers to capacities for energy security given current coal resource circumstances in China. This not only relies on the sustainable development and efficient use of coal resources but also requires comprehensively enhancing the resilience and stability of the energy system by improving the supply chain, stabilizing the market, promoting the synergistic development of multiple energy sources, and other multifaceted measures. Specific indicators include the energy self-reliant rate (C4) [51], the share of non-carbon energy depletion in total energy (C5) [55], and the growth rate of the coal price index (C6) [52].

Environmental protection dimension (B3): China has become the top emitter of greenhouse gases in the world, with annual carbon emissions from coal usage contributing over 70% of the carbon emissions in the country yearly. The substantial quantity of greenhouse gas emissions has led to an increase in the average temperature year by year. Every industry is a primary player in environmental protection. The dimension is measured by the following three aspects: forest coverage (C7) [56], emissions of three wastes from the coal industry (C8) [57], and the amount of investment completed in the pollution control of the coal industry (C9).

Technological innovation dimension (B4): The future development trend of the coal industry is intelligent construction. Improving the technological level of the coal industry is the key to accelerating the coal industry transformation and development. By matching the four transformation directions of “safety, high efficiency, greenness, and intelligence” in the “Fourteenth Five-Year Plan” Guidelines for High-Quality Development of the Coal Industry, we filter the indicators that can directly quantify the effect of policy implementation (e.g., C10, C11, and C12 correspond to the requirements of intelligence). Therefore, the dimension includes three indicators, namely: the number of patents for clean and high-efficiency use of coal (C10), the ratio of research expenditure in GDP (C11) [58], and the ratios of science and technology in generic public budget expenditure (C12) [54].

Industrial transfer dimension (B5): China vigorously promotes mergers and acquisitions of coal enterprises to optimize resource allocation and increase the concentration of the industry, which is to facilitate the transformational development of the coal industry. Due to the limitations of mining conditions and coal resource endowment in China, the transfer of the coal industry will accelerate the coal industry transformation and development. The industry transfer dimension can be reflected by the following indicators: location quotient (C13) [59] and industry openness (C14).

Resource utilization dimension (B6): As the traditional energy industry in China, the coal industry should take the path of clean and efficient resource utilization to consistently lower levels of energy use and carbon emissions. The dimension includes three indicators: comprehensive energy depletion of 10,000 yuan added value (C15), energy depletion per unit of GDP (C16) [57], and comprehensive energy depletion of 10,000 yuan added value in the coal industry (C17). Among them, C17 focuses on responding to the core task of “safe mining and low-carbon utilization” put forward by the Central Economic Work Conference, and directly quantifies the target of resource utilization efficiency improvement.

Although the indicators in this paper have taken into account as many factors as possible, they cannot be exhaustive. Therefore, the following limitations exist: first, the data availability bias of positive indicators (e.g., C5, C10) may lead to systematically high transformation scores in the eastern technology-leading provinces; second, the overlap of some dimensional indicators (e.g., B1 and B6 both contain energy consumption indicators) may amplify the weight of specific dimensions through the projection tracing model; third, emerging indicators such as digitization level are not included (e.g., the proportion of intelligent working faces in coal mines), which may underestimate the dynamic progress of provinces with accelerated transformation in the past two years.

2.1.2. Spatial Autocorrelation Model for CITD

Using both local and global spatial autocorrelation, the spatial autocorrelation model is a methodology for spatial analysis that analyzes the spatial correlation and differences between things [60,61]. The global Moran’s I is employed to analyze the overall spatial correlation of CITD in China, which takes values between [−1, 1]. In the event that Moran’s I falls within the interval [−1, 0), it implies that CITD has a negative spatial correlation. When Moran’s I falls between (0, 1], it signifies a positive spatial correlation for CITD. It is shown that there is no spatial connection in CITD if Moran’s I equals 0. The formula is as follows:

$$I={\displaystyle \frac{{\sum }_{i=1}^n{\sum }_{j=1}^n{w}_{i,j}\left(x_i-\overline{x}\right)\left(x_j-\overline{x}\right)}{S^2{\sum }_{i=1}^n{\sum }_{j=1}^n{w}_{i,j}}}$$

(10)

where $I$ is the global Moran index, n denotes the number of the provinces, $w_{i,j}$ denotes the spatial weight, $x_i$ and $x_j$ denote the attributes of areas i and j, $\overline{x}$ is the mean value of the study subject, and $S^2$ denotes the variance of the attribute value.

Different from the global Moran index, the local Moran index mainly studies the spatial connection between regions and neighboring regions and reveals the local spatial autocorrelation characteristics of regions by comparing the differences in the eigenvalues of individual regions and neighboring regions. According to the value of the local Moran index, regions can be classified into four types: high aggregation, low aggregation, high–low anomaly, and low–high anomaly. The local Moran index is used to identify local spatial clustering and anomalies. Local spatial autocorrelation can embody the degree of similarity between a region and neighboring regions [62]. The spatial correlation and degree of difference in CITD are then further analyzed using the local spatial autocorrelation approach. The calculation formula is as follows:

$$I^{\prime }=\left[n\left(x_i-\overline{x}\right){\sum }_{j=1}^n{W}_{ij}\left(x_j-\overline{x}\right)\right]/{\sum }_{i=1}^n{\left(x_i-\overline{x}\right)}^2$$

(11)

where $I^{\prime }$ is the local Moran index, and other letters indicate the same as Formula (10).

2.1.3. Analytical Model for Influencing Factors in CITD

(1)

Measurement methods

Spatial econometric modeling is capable of quantifying complex or time-varying relationships between different spatial units in both time and space dimensions, separately or simultaneously. This feature makes spatial econometric modeling widely applicable in a variety of fields and provides a powerful tool and methodology for solving various practical problems. Spatial econometric analysis is utilized to investigate the spatial correlation between things [63]. Three categories of spatial econometric models exist: the Spatial Durbin Model (SDM), the Spatial Lag Model (SLM), and the Spatial Error Model (SEM).

SEM is mainly used to analyze the differences in CITD among provinces due to geographic location, which represents the impact of the error shock of CITD in neighboring provinces on the regional CITD. The model expression is as follows:

$$Y_{it}={\sum }_{k=1}^m{\beta }_k{{\rm X}}_{ikt}+{\sum }_{j=1}^n{\rho }_j(W_{ij}\times {\phi }_{it})+{\mu }_i+{\lambda }_i+{\epsilon }_{it}$$

(12)

where $Y_{it}$ is the CITD index of province i in year t; ${\beta }_k$ is the kth variable coefficient; ${{\rm X}}_{ikt}$ is the indicator of the kth variable affecting the CITD index of province i in year t; $W_{ij}$ is the spatial weight; ${\rho }_j$ is the spatial autoregressive error term; ${\phi }_{it}$ is the spatial autoregressive error term; ${\mu }_i$ is the spatial fixed effect; ${\lambda }_i$ is the time fixed effect; and ${\epsilon }_{it}$ is the random error.

SLM is mainly used to analyze the spillover effect of CITD in neighboring provinces on this province. That is, the CITD of a province is determined by the size and orientation of the spatial influence of the CITD in adjacent provinces. The model expression is as follows:

$$Y_{it}={\sum }_{j=1}^n{\delta }_j(W_{ij}\times Y_{jt})+{\sum }_{k=1}^m{\beta }_k{{\rm X}}_{ikt}+{\mu }_i+{\lambda }_i+{\epsilon }_{it}$$

(13)

where $Y_{jt}$ is the CITD index in year t of province j; ${\delta }_j$ is the spatial autoregressive coefficient.

SDM is mainly used to analyze the influencing elements of the CITD in the province and effects on both the CITD of the province and that of neighboring provinces. The influence of the spatial lag term is also considered, which can avoid some estimation bias and thus identify key influencing factors more accurately. The model expression is as follows:

$$Y_{it}={\sum }_{j=1}^n{\delta }_j(W_{ij}\times Y_{jt})+{\sum }_{k=1}^m{\beta }_k{{\rm X}}_{ikt}+{\sum }_{k=1}^m{\theta }_k(W_{ij}\times {{\rm X}}_{ikt})+{\mu }_i+{\lambda }_i+{\epsilon }_{it}$$

(14)

where ${\theta }_k$ is the spatially lagged explanatory variable coefficient.

For the selection of three common spatial econometric models, SEM, SLM, and SDM, the OLS residuals are generally tested by four statistics, LM-lag, LM-error, robust LM-lag, and robust LM-error, from the spatial lag and spatial error models. Firstly, SLM is selected if the LM-lag test is not rejected and SEM is selected if the LM-error test is not rejected; both rejections indicate that spatial econometric modeling is not appropriate; if neither is rejected, then the more robust LM-lag or robust LM-error tests are carried out, and SLM is selected if the robust LM-lag test is not rejected; if the robust LM-Error test is not rejected, then SEM is chosen; if both are not rejected, then SDM can be chosen, and the Wald and LR tests are used to further examine whether the SDM model degrades to SLM or SEM; if the test result is passed, SDM is the optimal model, and vice versa, SAR or SEM is chosen. Finally, the results of the Hausman test were used to discern whether the model chose fixed or random effects.

(2)

Measurement indicators

Based on the pertinent research conducted by scholars [64,65,66], the paper selects the explanatory variables urbanization level (URB), industrial development level (IND), human capital (HC), foreign direct investment level (FDI), extent of openness to the outside world (OW), degree of government intervention (GI), and employment density (ED), and the explained variable is the CITD index, as displayed in

Table 2. Explanatory variables for the CITD index.

Explanatory Variables	Enunciation	Unit
URB	Urban population/total population	%
IND	Industrial value added/GDP	%
HC	Number of students enrolled in higher education/total population	%
FDI	Foreign direct investment/GDP	%
OW	Value of imports of goods/GDP	%
GI	Fiscal expenditure/GDP	%
ED	Number of employed persons/area of administrative division	%

.

2.2. Data Source

Beijing, Tianjin, Shanghai, Chongqing, Zhejiang, Guangdong, Hainan, and Tibet Autonomous Region no longer have coal mining and washing industries. Based on the availability of data, the paper excludes data from these provinces and cities, and the 23 provincial municipalities studied are divided into three major regions, namely, the eastern area, the central area, and the western area. The specific division is displayed in

Table 3. Regional division of China’s 23 provinces and cities.

Area	Provinces and Municipalities	Area	Provinces and Municipalities
eastern region	Hebei Province	western region	Sichuan Province
	Liaoning Province		Guizhou Province
	Jiangsu Province		Yunnan Province
	Fujian Province		Shaanxi Province
	Shandong Province		Gansu Province
central region	Shanxi Province		Qinghai Province
	Anhui Province		Ningxia Hui Autonomous Region
	Jiangxi Province
	Heilongjiang Province		Xinjiang Uighur Autonomous Region
	Henan Province
	Hubei Province		Guangxi Province
	Hunan Province		Inner Mongolia Autonomous Region
	Jilin Province

.

The coal industry in the aforementioned regions is the investigation subject of the paper, and the panel data for 2011–2021 are selected to study CITD. The research data mentioned above are gathered from the statistical yearbooks of China, China Energy, China Environmental, China Population and Employment, China Industrial, China Price, China Social, China Education Expenditure, and China Science and Technology. Additionally, statistical yearbooks and statistical bulletins for provinces are also gathered. These data are relevant to the coal mining and washing industries. The Patent Information Service Platform for Key Industries provides information on the quantity of patents on clean and high-efficiency use of coal. Since some provinces only count the industrial “three wastes” emissions but not the coal industrial “three wastes” emissions, it is common for academics to use the ratio of coal industry output to industrial output value as a conversion factor and then multiply it with the industrial “three wastes” emissions to measure the “three wastes” emissions of the coal industry [67]. The same algorithm is used for the amount of investment completed in the coal industry’s pollution control. In view of some missing data, the indicator of value added of the coal industry in some provinces cannot be obtained. In the paper, the main business income is chosen to replace the value added of the coal industry for calculation [68]. Linear interpolation was used to fill in missing data due to non-publication. However, this method cannot correct measurement errors already present in the original data and relies on extrapolation assumptions for missing values at the beginning and end of the series (e.g., missing initial data for some provinces in 2011), which may cause marginal bias. Therefore, the comprehensive energy consumption of 10,000 yuan of added value in the coal industry in Shandong Province in 2011–2012 filled by the interpolation method is negative. The data in this paper for that year are filled in using the average of the last three years.

3. Results

3.1. Analysis of the Evaluation Findings for CITD

In the article, RAGA, which is described in Section 2, is applied to solve the PP model. MATLAB 2021 programming is used to tackle the data, population size $\mathrm{N}=400$, crossover likelihood ${\mathrm{P}}_{\mathrm{c}}=0.8$, mutation probability ${\mathrm{P}}_{\mathrm{m}}=0.2$, optimization variable dimension $\mathrm{n}=17$, number of random numbers required in the direction of mutation (maximum number of iterations) $\mathrm{M}=10$, and the number of accelerations is 20. One can derive the optimal projection directions for each index $a^{*}$= (0.2515, 0.3602, 0.1000, 0.0620, 0.1512, 0.2502, 0.1674, 0.3477, 0.0343, 0.2766, 0.2262, 0.2106, 0.0515, 0.2322, 0.3566, 0.4159, 0.1699), which represents the weight of each index. The significance of the evaluation indexes is represented by the following, in descending order, as illustrated in Figure 3: C16, C2, C15, C8, C10, C1, C6, C14, C11, C12, C17, C7, C5, C3, C4, C13, and C9.

3.1.1. Characterization of the Temporal Evolution

According to the principle of the PP model, the calculation can obtain the CITD index of 23 provinces from 2011 to 2021, as shown in

Table 4. The CITD index.

Sequence	Provinces	Year											Averages
		2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
1	Hebei	1.316	1.332	1.360	1.422	1.412	1.485	1.605	1.545	1.536	1.546	1.671	1.476
2	Shanxi	1.093	1.056	0.913	0.903	0.832	0.794	1.027	1.031	1.006	1.037	1.284	0.998
3	Nei Monggol	1.187	1.186	1.243	1.305	1.269	1.274	1.206	1.139	1.108	1.113	1.286	1.211
4	Liaoning	1.546	1.576	1.616	1.639	1.568	1.459	1.541	1.575	1.540	1.527	1.680	1.570
5	Jilin	1.490	1.505	1.521	1.497	1.521	1.545	1.627	1.561	1.531	1.529	1.590	1.538
6	Heilongjiang	1.430	1.452	1.494	1.466	1.448	1.460	1.543	1.530	1.487	1.493	1.588	1.490
7	Jiangsu	1.841	1.916	1.962	2.033	2.062	2.256	2.320	2.329	2.356	2.352	2.729	2.196
8	Anhui	1.529	1.546	1.541	1.577	1.600	1.803	1.888	1.868	1.916	1.908	2.045	1.747
9	Fujian	1.690	1.685	1.715	1.730	1.756	1.790	1.892	1.939	1.917	1.938	2.061	1.828
10	Jiangxi	1.537	1.535	1.556	1.595	1.599	1.670	1.806	1.761	1.770	1.851	1.990	1.697
11	Shandong	1.540	1.574	1.666	1.662	1.633	1.624	1.876	1.804	1.858	1.908	2.048	1.745
12	Henan	1.447	1.447	1.502	1.531	1.533	1.612	1.768	1.738	1.770	1.789	1.934	1.643
13	Hubei	1.663	1.653	1.611	1.681	1.721	1.759	1.899	1.934	1.989	1.935	2.086	1.812
14	Hunan	1.641	1.651	1.640	1.691	1.694	1.700	1.788	1.809	1.827	1.872	1.985	1.754
15	Guangxi	1.564	1.548	1.542	1.568	1.609	1.580	1.649	1.588	1.599	1.584	1.684	1.592
16	Sichuan	1.508	1.531	1.530	1.580	1.596	1.637	1.756	1.743	1.745	1.762	1.908	1.663
17	Guizhou	1.202	1.233	1.281	1.367	1.393	1.435	1.573	1.593	1.604	1.598	1.635	1.447
18	Yunnan	1.536	1.544	1.553	1.570	1.589	1.572	1.640	1.638	1.671	1.673	1.746	1.612
19	Shaanxi	1.602	1.597	1.578	1.566	1.528	1.596	1.731	1.733	1.762	1.884	2.246	1.711
20	Gansu	1.243	1.230	1.197	1.204	1.173	1.183	1.264	1.299	1.301	1.301	1.387	1.253
21	Qinghai	1.156	1.131	1.101	1.162	1.135	1.158	1.286	1.207	1.148	1.289	1.366	1.194
22	Ningxia	0.823	0.916	0.926	0.958	0.926	0.992	1.158	1.068	1.029	1.006	1.198	1.000
23	Xinjiang	1.346	1.338	1.324	1.338	1.271	1.263	1.363	1.377	1.394	1.355	1.456	1.348
	averages	1.432	1.443	1.451	1.480	1.473	1.506	1.618	1.600	1.603	1.620	1.765	1.545

, and the trend graph is shown in Figure 4. According to Table 4 and Figure 4, evidently, the CITD index average across 23 provinces is 1.545 in 2011–2021. The CITD index grows from 1.432 in 2011 to 1.765 in 2021, with a growth rate of about 2.1 percent on average per year. Although the overall CITD index is not high, the trend is gradually rising. Probably because the increased capacity of coal supply ensured energy security, the effective promotion of green mining technology has led to an increase in the level of clean production and efficient use of coal. The ability to drive development through technology in the coal industry has been strengthened, and the innovation system has been made sound and perfected. The CITD index continued to rise in most provinces in 2016 and 2017. The CITD index average value grew from 1.473 in 2015 to 1.618 in 2017, with a growth rate of about 7.3 percent, which is mainly brought about by increased security and safety. Since the “13th Five-Year Plan”, the “coal to gas” policy has improved the energy structure, leading to a significant improvement in energy security in 2017. In addition, the coal industry accelerated the release of high-quality production capacity in 2017, and raw coal production resumed growth, which is the first positive growth since 2014, and the CITD index also rose rapidly as a result.

3.1.2. Characterization of Spatial Differentiation

By using ArcGIS 10.7 [69], CITD from 2011 to 2021 is classified into five grades: worse, bad, medium, good, and better. Four time points—2011, 2014, 2017, and 2021—are selected for spatial visualization (Figure 5). The average CITD index values for the east, center, and west for the study period are 1.763, 1.585, and 1.403, respectively. As demonstrated in Figure 5, a general pattern of distribution is “lower in the west and higher in the east and center”. Accordingly, the CITD index in the central region is ranked second, the CITD index in the eastern area is the best, and the CITD index in the western area is the worst. The provinces with a good CITD index in 2011 are Jiangsu, Fujian, Hubei, and Hunan. However, in 2014, 2017, and 2021, only Jiangsu province has a good CITD index.

At the provincial level, the first is the eastern region. From 2011 to 2021, the CITD index in Jiangsu and Fujian shows an upward trend that is higher than the average value of 23 provinces, ranking first and second, respectively, maybe due to them being in the eastern region, the swift progress in the economy, and their stronger technological capabilities. Nevertheless, the CITD index values of Hebei, Liaoning, and Shandong provinces do not rank in the top few, maybe because Hebei Province has a weak foundation for the transformation of the coal industry and faces pressure to restructure, adding to the burden of transformation. Liaoning Province, as a major industrial province, is biased towards coal for energy consumption. There is a serious shortage of coal production capacity in the province, and the dependence of coal consumption on the outside of the province is up to more than 80 percent. As a major province in energy consumption and carbon emissions, total coal consumption and carbon emissions in Shandong rank third and first in the country, respectively, and the green and low-carbon transition faces challenges. The second is the central region.

The CITD index in the central region, such as Hunan, Hubei, Jiangxi, and Anhui, is in good shape, maybe due to the increased capacity of coal supply security and the continued acceleration of the clean and low-carbon process. The last is the western region. Gansu, Inner Mongolia, Qinghai, and Ningxia have low CITD index values of 1.253, 1.211, 1.194, and 1.000, correspondingly, which are never higher than the mean. The state of CITD is worrying, maybe due to the lack of talents related to clean and efficient coal utilization technology. The accurate surveying ability of coal resources is weak, and the degree of informatization and intelligence on coal reserves is low. There are more deficiencies in the accurate regulation and efficient guarantee of coal supply. The coal sector in Shaanxi, Sichuan, and Yunnan provinces in the western region is transforming well. As a major coal province, Shaanxi Province produces the third-most coal in the nation. Shaanxi Province not only continues to facilitate the clean, efficient, and intensive utilization of coal, but the level of coal chemical processes is also in the leading position in the country. Sichuan Province is continuously optimizing the coal production structure and increasing the process of getting rid of outdated production capacity. In Yunnan Province, the concentration on the coal industry is increasing, and the industrial structure has been optimized. Furthermore, it has continued to increase the exploration of coal deposits and enhanced the ability to guarantee a supply of coal. The CITD index is closely correlated with the degree of economic growth in the area and the endowment of energy resources. The overall CITD index of coal-resource-rich regions is lower. Shanxi, Inner Mongolia, and Xinjiang rank last in the national CITD index. The CITD in areas with abundant renewable energy resources is developing well. In the ranking of the national CITD index, Sichuan, Hubei, and Yunnan enter the top 12, with Hubei ranking third with a score of 1.812.

3.2. Analysis of the Spatial Correlation Findings for CITD

In order to further investigate the evolution pattern of the spatial characteristics of the CITD index in China, the study measures the Moran index for 23 provinces in China between 2011 and 2021. As illustrated in

Table 5. Moran index results and tests.

Year	Moran’s I	Z-Statistic	p-Value
2011	0.220	1.906	0.028
2012	0.244	2.049	0.020
2013	0.260	2.176	0.015
2014	0.282	2.361	0.009
2015	0.327	2.676	0.004
2016	0.355	2.913	0.002
2017	0.469	3.652	0.000
2018	0.437	3.423	0.000
2019	0.460	3.573	0.000
2020	0.461	3.565	0.000
2021	0.398	3.172	0.001

, Moran’s I varies within the interval of (0.220, 0.469), which is via the significance test at 5 percent from 2011 to 2021. The CITD in China exhibits significant spatial autocorrelation. There are spatial spillover effects of the CITD in this region on nearby regions. In terms of the evolutionary pattern, the Moran’s I demonstrates an increasing trend, rising from 0.220 in 2011 to 0.469 in 2017, which indicates a significant increase in the spatial correlation effect. Since 2017, the Moran’s I has shown a fluctuating downward trend, decreasing to 0.398 in 2021.

From Figure 6, the slopes of the regression curves of the Moran scatterplot in 2011, 2014, 2017, and 2021 are all positive. The Moran scatterplot in the research period mostly demonstrates the distributional features of the L-L type and H-H type. The results demonstrate that the spatial agglomeration trend of the CITD is significantly positive. The central and eastern regions of China generally show a “high-high” aggregation status, while most provinces in the western region show a “low-low” aggregation status. Comparing the results of the four years, the type of CITD in most provinces of China has not changed. The relatively leading provinces, such as Jiangsu, Fujian, Shandong, Hubei, and Hunan, have been in the H-H type of agglomeration. Most of the western regions, such as Ningxia, Inner Mongolia, and Qinghai, have been in the L-L type. The spatial correlation of the CITD index is relatively stable. The number of provinces falling into the H-L type agglomeration area has decreased. Since 2017, only two provinces, Sichuan and Shaanxi, are H-L type. Jilin and Liaoning changed from H-L in 2011 to L-L in 2021. The number of provinces in the L-H type aggregation area increases slightly in 2021, but the only one that is stable is Guizhou Province. Guangxi, Shanxi, and Yunnan fall into the L-H type aggregation area in 2021. The CITD index in these provinces did not increase significantly, but to a certain extent, it led to the coal industry transformation and development in the neighboring provinces.

3.3. Analysis of the Influencing Factor Results for CITD

The outcomes of the LM test and the robust-LM test are shown in

Table 6. Test results.

Model	SEM		SLM
	Value of the LM Statistic	p-Value	Value of the LM Statistic	p-Value
LM test	148.416	0.000	127.607	0.000
Robust-LM test	27.225	0.000	6.416	0.011
LR test	104.570	0.000	105.110	0.000
Wald test	130.010	0.000	179.010	0.000

. The 1% significance level test is passed by the LM test and the robust-LM test for SEM. Both the robust-LM test and the LM test for SLM pass at the 5% and 1% significance levels, respectively. The tests for SEM and SLM are significant, and SDM needs to be considered. Further judgment is needed to determine whether SDM degenerates into SEM and SLM. Then the LR test and the Wald test are used. The findings demonstrate that the LR test value for SEM is 104.570, and the Wald test value is 130.010, both of which are obtained via the 1% level of significance test. The LR test value and Wald test value for SLM are 105.110 and 179.010, respectively, which are also obtained via the 1% level of significance test. Therefore, SDM does not degenerate into SLM and SEM, as demonstrated in Table 6.

The value of the Hausman test statistic for the spatial panel model is 65.62, with a p-value of 0.000 and a significance level of 1%. As a result, the original hypothesis that the panel model is a random effects model is disproved, and a spatial panel model with fixed effects is taken into consideration. Three models make up the panel regression model with fixed effects: spatio-temporal, spatial, and temporal fixed effects. As demonstrated in

Table 7. Outcomes of the Spatial Durbin Model.

Variable	Temporal Fixed Effects Model	Spatial Fixed Effects Model	Spatio-Temporal Fixed Effects Model
URB	−0.136	3.205 ***	2.687 ***
IND	−0.432 **	0.015	−0.155
HC	28.002 ***	−21.310 ***	−22.123 ***
FDI	5.886 ***	1.064	0.818
OW	0.187	−0.208	−0.157
GI	−0.616 ***	−0.869 ***	−0.795 ***
ED	13.368 ***	−4.032	0.452
W_URB	−2.652 ***	−1.099	−4.650 ***
W_IND	−1.475 ***	0.635 ***	−0.402
W_HC	−16.863	7.289	2.929
W_FDI	10.348 ***	−0.422	2.505
W_OW	2.256 ***	−0.340	−0.375
W_GI	0.959 ***	−0.566 *	−0.756 *
W_ED	−13.465 ***	−52.020 ***	−35.108 ***
ρ	0.157 *	0.410 ***	0.223 ***
R2	0.431	0.239	0.217
sigma2	0.0165 ***	0.003 ***	0.003 ***

Note: *, **, *** indicate significance at 10 percent, 5 percent, and 1 percent significance levels, respectively.

, comparing the goodness of fit of these three effects models, the greatest R-square, 0.4308, is found in the temporal fixed effects model, which is chosen.

The analysis is carried out on each independent variable regression coefficient. The coefficients of HC, FDI, and ED are positive and have a high significance level of 1%. Human capital inputs, the increase in foreign direct investment levels, and the concentration of employment density can significantly promote the regional development in the coal industry. Talent constitutes the core body of green technology innovation. The state of human capital plays a crucial role in influencing green technology innovation. Human capital theory also suggests that higher levels of education and technical expertise provide viability for the development and application of green technologies. Therefore, the increase in the level of human capital provides high-quality labor for various industries, promotes the advancement of environmental protection technology, and develops intelligent resource extraction, so that the efficiency of energy use can be enhanced and thus promotes the transformation of the coal industry. FDI can achieve the technological progress of regions and enterprises through technological spillovers. As regions promote the construction of an ecological civilization, foreign investment has paid more attention to the coal industry, which is developing efficiently, greenly, and innovatively. Advanced management, models, and technologies are brought, which have facilitated the transformational development of the coal industry. Higher employment density can create a concentration trend within a specific range, which facilitates the centralized management of energy-intensive industries. Additionally, highly skilled personnel can be drawn, which helps to increase energy and environmental protection efficiency and foster the transformational development in the coal industry.

The coefficients of IND and GI are negative, with the former at a significance level of 5% and the latter at a 1% level. Industrial development and government intervention have a negative correlation with the transformational development in the coal industry. Industrial development is generally crude, and high-consumption, high-pollution industrial development results in a significant loss of environmental efficiency. At the present stage, advancement in renewable energy in China is lagging behind, and industrial production and consumption are still dominated by coal. Therefore, a large number of pollutants, such as nitrogen oxides and sulfur dioxide, are generated in the utilization process, which has resulted in the coal industry transformation being more inhibited by industrial development. As local governments have not completely gotten rid of the crude development model in the pursuit of political interests and economic development, this is bound to be unfavorable to the transformation of the coal industry. Moreover, the impact of government intervention is related to the degree and direction of the intervention and other factors. In recent years, government expenditure on ecological civilization construction and other areas has increased. Although such expenditures cannot be reflected in the near future to facilitate the coal industry transition, in the long term, such expenditures will contribute to the transformational development in the coal industry. URB has a negative and insignificant coefficient. Although positive, the coefficient of OW is statistically insignificant, which demonstrates that OW and CITD have a positive correlation, but the strength is weak yet.

Analysis of the direct effect, indirect effect, and total effect in the temporal fixed effect model is required in order to further study how the explanatory variables affect CITD in this region as well as how the explanatory variables and transformation development in the neighboring regions affect CITD in this region.

Table 8. Decomposition of spatial spillovers in the SDM.

Variable	Direct Impact	Indirect Impact	Total Impact
URB	−0.234	−3.086 ***	−3.320 ***
IND	−0.500 **	−1.755 ***	−2.255 ***
HC	27.909 ***	−13.799	14.110
FDI	6.305 ***	12.739 ***	19.044 ***
OW	0.278 *	2.657 ***	2.934 ***
GI	−0.568 ***	0.993 ***	0.425
ED	12.990 ***	−13.158 ***	−0.168

Note: *, **, *** indicate significance at 10 percent, 5 percent, and 1 percent significance levels, respectively.

displays the analysis findings. Regarding direct effects, the direct effects of HC, FDI, OW, as well as ED are all strikingly positive. The transformation of the regional coal industry has benefited from the influence of these factors in the region itself. OW is an important pillar in promoting stable and rapid economic development. The slower economic development trend, coupled with the effects of the coronavirus disease 2019 (COVID-19), has thus affected the transformational development in the coal industry. The direct effect of IND and GI is significantly negative. Industrial development and government intervention slow down the transformational development of the coal industry to a certain extent.

In terms of indirect effects, FDI, OW, and GI are all significantly positive. These influencing factors in neighboring regions have a vital role in encouraging the transformational development of the coal industry in the area, as well as showing that a clear spatial spillover effect exists. These positive spatial spillovers can be attributed to the “competition effect” and the “demonstration effect” [70]. One is the competition effect. With the introduction of the “dual-carbon” goal, the transformation and development of the coal industry has become one of the development goals of local governments at all levels in China. Therefore, local governments have formed a benign competitive relationship in the process of coal industry transformation and development. The second is the demonstration effect. The successful experience of the transformation of the coal industry in some regions can be demonstrated to the neighboring regions through information exchange and technology spillover, prompting the backward regions to learn from it and accelerate the transformation. To sum up, the competition effect and demonstration effect work together to make the transformation of the coal industry show the spatial spillover effect of “both honor and glory”. URB, IND, and ED are all significantly negative, which indicates that these influences in neighboring regions hinder the transformational development of the coal industry in the area. There is a “siphoning effect” between geographically close regions [71]. As the level of urbanization, industrial development, and employment density in a region continues to increase, it forms a potential difference with the surrounding areas, resulting in the priority flow of resources, technology, and labor from the surrounding areas to the region, which inhibits the transformation of the coal industry in the surrounding areas. Although the indirect impact of human capital is negative, this hindering effect is not significant.

Combined direct and indirect impacts on CITD are known as the total effect. Firstly, URB and IND are significantly negative for CITD, which indicates that the level of urbanization and industrialization is likely to inhibit the transformational development of the coal industry. Secondly, OW and GI are significantly positively affecting the CITD. Finally, the combined effect of HC, GI, and ED is not significant.

4. Discussion

Achieving the “dual-carbon” target and guaranteeing energy security require fostering the transformational development in the coal industry. In this paper, the RAGA-PP model, the spatial autocorrelation analysis model, and the spatial measurement model are used to study CITD and provide some references for the purpose. The results of the study show an overall upward trend in CITD from 2011 to 2021, which is comparable to the study results of Yang et al. [72]. In terms of energy security, the study found that the measurement results are significantly better in the two years of 2016 and 2017, comparable to the conclusions of Gong et al. [51]. Energy conservation, abatement, and coal production capacity-reduction measures in the 13th Five-Year Plan period have increased the efficiency of coal production, thus greatly guaranteeing energy security. Although the transformational development in the coal industry has achieved good results, there is still a great deal of space for development due to the slow pace of the transformational development in the coal industry caused by issues such as efficient green production, intelligent mining technology, and energy security. Therefore, the relevant departments should further enhance the degree of coal production mechanization and intelligence, facilitate the intelligent and green development of the coal industry, and provide a guarantee for energy security.

From the characterization of spatial differentiation, there is a distribution paradigm of “east to be high and west to be low” for CITD overall, and there are more obvious spatial differences, which is similar to the results of the study by Sun Fei and other scholars [73]. Jiangsu, Fujian, and Hubei have been in the top three in terms of CITD over the years. These regions have strong economic foundations, high investment in science and technology research and development, significant resource advantages, and a focus on ecological and environmental protection. The CITD in Shaanxi and Sichuan provinces is prominent in the western region. The R&D capability in these regions is outstanding, and the influx of talents, technology, and capital supports the transformational development of the coal industry. The CITD in Hebei, Shanxi, Ningxia, and Inner Mongolia is deplorable, which is comparable to the conclusions of Li Changsheng et al. [74]. Inner Mongolia and Shanxi have abundant coal resources, which means that there are high carbon emissions and energy usage. It is challenging to change the energy structure that is dominated by coal quickly, resulting in the still arduous task of coal industry transformation. Hence, the western region demands to increase response to the national supply-side reform, resolutely eliminate heavy polluting enterprises, accelerate the elimination of excess capacity, and facilitate the transformational development in the coal industry.

From the perspective of spatial correlation, the overall Moran index shows an inverted “U” trend, and there is a notable positive spatial agglomeration in the CITD. The eastern and central areas are home to the majority of the provinces in the first quadrant, while the western area is home to the majority of the provinces in the third. The inter-provincial CITD index in China shows different degrees of polarization patterns. The results are comparable to the conclusions of Gao et al. [75]. The agglomeration level of the CITD index is higher in the eastern and central regions, with small differences and strong spatial links. The western region, however, is affected by coal resource endowment, economic development, and insufficient technological innovation, which makes the CITD index relatively low. It can be concluded that the CITD in each province of China is not isolated from the others but will be influenced by the neighboring provinces. The reasons for the phenomenon may be the cooperation and exchange between neighboring regions and the implementation of relevant policies. Therefore, the coal industry transition path must be elucidated by taking into account the disparities in development across different regions and complementing interregional disadvantages with policy orientation. Neighboring regions should strengthen economic, technological, and human resource ties and absorb advantageous resources and investments.

According to the study findings of the negative influencing factor, CITD is negatively impacted by the degree of urbanization, which is comparable to the study by Kong et al. [76]. The “rapid” and “rough” modes of urbanization have led to problems such as resource and environmental damage and hindered the transformational development of the coal industry. The effect of GI on CITD is significantly negative. Too much government intervention will be unfavorable to the transformational development in the coal industry, which is comparable to the conclusions of Deng Feng et al. [77]. Local governments make “self-interested” and “short-term” decisions based on local competition and the need for performance assessment [78]. In addition, the implementation of some policies lacks longevity. For example, although the “276 working days” production restriction policy implemented in China in 2016 stabilized coal prices in the short term, it distorted supply and demand, triggered hoarding by downstream enterprises, and exacerbated market volatility [79]. There is also the coal pricing policy [80]. The government intervention behavior is obviously biased towards export response. Therefore, GI needs to shift towards adaptive governance, balancing the efficiency and equity of transition through dynamic policy frameworks, market incentives, and social inclusion mechanisms. The level of industrial development has a significantly negative impact on the transformation of the coal industry. The technological systems, infrastructure, and supply chains of industrially developed regions have often developed a path dependency on coal. This path dependence can make the transition extremely costly [81], which leads to it posing a significant constraint on the transition. This dependence makes it necessary for the industry to invest significant resources and costs in making changes. Therefore, the government should increase investment in green technologies, guide the coal industry to adopt advanced digital technologies, and encourage green innovation in the coal industry in order to promote sustainable development.

According to the study findings of the positive influencing factor, HC is a major factor for the transformational development in the coal industry. The effect of HC on CITD is significantly positive, indicating that the transformative development of the coal industry is positively impacted by the enhancement of human capital, which is comparable to the conclusions of Dong et al. [82]. The knowledge, technology, and capabilities embedded in high-level human capital play a crucial role in the transformation process of the coal industry. In addition, human capital has a spillover effect, i.e., regions with higher-than-average levels of human capital tend to generate more knowledge spillovers, thus making it easier to acquire new knowledge [83], and enterprises with high levels of human capital are more likely to practice environmental standards and increase environmental protection [84], which promotes the development of green technological innovation and helps to reduce energy consumption, thus facilitating the transformation of the coal industry. Therefore, the government needs to formulate relevant preferential policies to attract and retain excellent green technology innovation talents and then enhance the green technology innovation strength of the coal industry. The effect of FDI on CITD is significantly positive, which suggests that the transformational development of the coal industry is facilitated by the rise in foreign direct investment, which is comparable to the conclusions of Deng Feng et al. [77]. FDI will make the advanced green technology of some foreign enterprises flow into the domestic market, which will promote the domestic industrial enterprises to carry out green technological innovation [85], thus promoting the transformational development of the coal industry. Therefore, it is necessary to promote the technological upgrading of enterprises and energy conservation and emission reduction through the introduction of advanced product technology, process technology, and management technology.

5. Conclusions and Policy Suggestions

Based on the “dual-carbon” target, energy security is integrated into the evaluation index system of CITD in this research. CITD in 23 Chinese provinces is assessed using a RAGA-PP model from 2011 to 2021. The differences in time and space, as well as the elements that influence CITD, are further studied.

(1)

From 2011 to 2021, in general, the CITD level in China shows an overall upward trend, with the average value of the transformation index at 1.545. There is a big difference in the CITD index in each province, and the space shows the distribution pattern of “lower in the western part and higher in the central and eastern part”. Jiangsu Province has the highest index of 2.196, while Shanxi Province has the lowest index of 0.998.

(2)

The CITD in China shows a substantial positive spatial correlation and is characterized by spatial aggregation. Provinces with a higher transition development index in the coal industry cluster with provinces with a higher transition development index. Provinces with a lower transition development index cluster with provinces with a lower transition development index.

(3)

Human capital, foreign direct investment level, and employment density show a significant positive impact on CITD, while government interference level and industrial development level have a negative impact.

In light of the research and analysis presented in this article, the following policy recommendations will be made:

(1)

The integrated development of coal and electricity provides a guarantee for energy security. Coal and electricity are interrelated. A reasonable industrial chain is formed through a coal-power alliance to achieve resource sharing and technical cooperation, thus facilitating the synergistic development of the coal industry. Coal and electricity continue to be crucial in ensuring a steady and secure supply of electricity. Efforts should be made to develop carbon capture, utilization, and sequestration technologies and high-value-added products to facilitate the joint healthy and sustainable development of coal and power. Meanwhile, the local industrial distribution, natural conditions, and innovation capacity should be taken into account to propose the most suitable route for the local coal industry transformation and development. Beyond that, the government may enact pertinent policies to encourage the strengthening of coal-power synergistic cooperation. Financial and technical support can also be provided to further promote coal-power synergistic development and provide security for the transition of the coal industry.

(2)

Coordinated growth of coal and the contemporary coal chemical industry, as well as expansion of the coal industry chain. Vigorously develop the contemporary coal chemical sector and extend the coal industry chain through diversification and strengthening industry chain integration. The middle and western regions must take advantage of the chance to collaborate with the eastern regions. Endeavour to accelerate the breakthrough of core technological barriers. Developing modern coal chemical technology and promoting the transition of coal from fuel to raw material actively. Meanwhile, the coal industry should actively facilitate the coupled development of coal and clean energy, helping reduce carbon emissions brought on by burning coal, and facilitate green and high-quality development.

(3)

Clean production and ecological restoration are moving forward together to promote green development. Supported by technological innovation, the development of a circular economy for coal is aimed at clean production and recycling. Achieving clean production by extending the resource utilization chain, increasing the output rate of coal and derived resources, and reducing waste emissions. Informatization, digitalization, and intelligent mining are used as means to deeply integrate new-generation information technology with coal development and utilization technology. The control of the entire process of coal mining, production, processing, transformation, and utilization will be strengthened to improve efficiency and control pollutant emissions. At the same time, it focuses on ecological environment management and restoration and practices green, high-efficiency, intelligent, and safe transformation and development.

(4)

Build a regional synergistic governance system of “leading in the east, reconstructing in the center, and building a foundation in the west”. The eastern region should focus on technology overflow and value chain upgrading, build a national coal transformation innovation center, and make targeted breakthroughs in new coal-based materials, ultra-low-emission combustion technology, CCUS, and other disruptive technologies. It also needs to develop a high-end energy service industry and encourage eastern enterprises to export intelligent mine turnkey services to central and western China so as to transform technological advantages into cross-regional benefits. The central region should break the path of dependence and industrial synergy, focusing on supporting ‘coal-hydrogen storage’ integration projects. Relying on the resources of universities in Wuhan and Zhengzhou, a “coal equipment manufacturing base” should be built. The western region should make up for the capacity gap and infrastructure upgrades and establish an integrated base of wind, fire, and storage. The construction of Xinjiang National Laboratory of Coal Clean Utilization, focusing on breakthroughs in lignite refining, coal to olefin, and other technologies. Regional differentiation policy needs to break through traditional thinking. Through institutional innovation to activate factor flows, technology diffusion to reconstruct industrial ecology, and precise policies to hedge regional shortcomings, we can achieve a shift from regional imbalance to synergy.

There are still shortcomings in this paper. Firstly, there are spatial differences in CITD, while the influence factors of CITD are analyzed without considering the regional heterogeneity of each province. In the future, regional heterogeneity could be analyzed from the east, center, and west in order to better understand the differences between regions and thus make more targeted recommendations. Secondly, the research scope of this paper is large. The analysis of small areas is not deep enough, and CITD is not analyzed from the perspectives of economy, safety, environment, and industry. In the future, the dimensions can be analyzed for coupled coherence and diagnosis of obstacle factors. Thirdly, although the SWOT-AHP method was not adopted in this study, as shown in Spanidis Philip-Mark et al. [25], this method can play a unique advantage in multi-objective strategic prioritization by integrating the qualitative SWOT analysis with the quantitative weight calculation of the hierarchical analysis method (AHP). It can quantitatively assess the relative importance of contradictory factors in the transition path and provide decision tree support for regional differentiated policy design under the ”dual-carbon” goal; furthermore, by constructing a multi-body judgement matrix, it can identify the acceptance thresholds of the key stakeholders for the transition initiatives such as intelligent mining and clean use and make up for the lack of analysis of the interaction mechanism of the main bodies in the current study. Future research can apply it to complex decision-making scenarios such as mine regeneration planning and cross-regional industrial synergy to enhance the dynamic adaptability of strategic pathways.