Inclusive Green Development in China’s Petroleum and Gas Industry: Regional Disparities and Diagnosis of Drivers

Abstract

According to the “Five Development Concepts” of the new national development plan, the study of inclusive green development in the petroleum and gas sector (IGDPG) is crucial for enhancing production efficiency and safeguarding the environment and resources. This study constructs the IGDPG indicator system from industrial development, social opportunity equity, poverty and income inequality reduction, and green ecology dimensions, and the CRITIC Portfolio empowerment-TOPSIS method was used to measure the level of IGDPG in the eastern, central, and western regions using panel data. The Dagum Gini coefficient method was applied to analyze regional disparities and their causes, while the obstacle degree model and the Tobit model were used to identify internal and external factors of IGDPG. We found that IGDPG levels in the three regions showed fluctuating growth, and the eastern region (0.394) had much higher IGDPG levels than the central (0.337) and western (0.355) regions. The overall Gini coefficient for IGDPG is small, while inter-regional disparities are the primary source of overall disparities, and the intra-regional disparities of the three main areas exhibit a declining tendency. In terms of internal factors, social opportunity equity has been identified as the primary obstacle constraining IGDPG. Externally, factors such as industrial cluster, industrial upgrading, urbanization rate, and digital economy exhibit a facilitative effect on IGDPG, whereas environmental burdens exert an inhibitory influence. Moreover, all of these internal and external drivers demonstrate significant regional variations. Therefore, breaking regional restrictions and promoting the coordinated development of IGDPG so as to improve China’s IGDPG level as a whole is the forecasted trend.

1. Introduction

Since the 11th Five-Year Plan, China has made significant strides in sustainable development and environmental protection, entering a new phase focused on environmental ideas and initiatives [1]. Beyond advancing the green and low-carbon economy, the Chinese government has actively advocated for a society that is resource-efficient and ecologically friendly. The concept of “ecological civilization” was first formally introduced in the 18th CPC National Congress report in 2012, which also proposed “promoting green, recycling, and low-carbon development” and to “build a beautiful China,” thereby underscoring the green economy’s importance. The guidelines for the task of creating an ecological civilization have been further reinforced by the nineteenth CPC National Congress, emphasizing green development and accelerating ecological civilization reforms, alongside ensuring livelihood protection and enhancing well-being during development. China is currently at a crucial phase of changing its growth momentum, optimizing its economic structure, and changing its method of development. Its economic and social development has progressed from rapid growth to high-quality development. However, China’s development process is still beset by a number of unresolved issues, such as uneven and inadequate development, poor-quality and inefficient development, social inequity, widening income gaps, and the depletion of natural resources coupled with ecological environment degradation. The absence of “inclusiveness” and “greening” in the conventional growth paradigm is the primary cause of these issues [2].

The petroleum and gas industry’s greatest feature, when compared to non-resource-based industries, is its heavy reliance on natural resources. The industry’s production is directly related to ecological and environmental conditions, and it primarily depends on the exploitation and processing of natural resources. The development of the petroleum and gas business is confronted with the issue of reform and innovation due to the ongoing depletion of petroleum and gas reserves and the degradation of environmental circumstances. It is imperative that the petroleum and gas industry become greener as soon as possible. To put it another way, the sustainable growth of the petroleum and gas industry necessitates the rational and economical development and use of resources, improvements in petroleum and gas utilization efficiency, and the expansion of the sustainability of energy development. The sustainability of environmental resources should be preserved, harm to the natural environment should be kept to a minimum, and environmental and ecological resources should be successfully protected throughout the development process.

Countries mostly rely on the exploitation of non-renewable resources like petroleum and gas to provide greater economic returns and job opportunities in order to achieve sustainable and inclusive green development [3]. After more than 70 years of history, China ranked sixth in terms of petroleum production and fourth in terms of gas production in 2021 globally. However, as China’s economy continues to grow at an accelerated rate, the country’s domestic petroleum and gas supply is no longer able to keep up with the demands of economic growth. At the same time, the Russian–Ukrainian conflict and the New Crown plague made the counter-globalization trend worse [4]. Additionally, it has caused conflicts between global energy supply and demand to sharply worsen: the energy market is rife with uncertainty; the petroleum and gas supply structure has become more vulnerable, further enhancing the security of China’s petroleum and gas resources; and the imbalance and incoherence of inter-city development, including employment, infrastructure, energy supply, and other social equity issues, has emerged as one of the unresolved issues limiting China’s progress. As a result, inclusive green development—which embodies the ideas of “innovation, coordination, green, openness, and sharing” [5]—has steadily emerged as a key strategy for China’s petroleum and gas sector to shift its development mode and achieve sustainable development.

It is difficult to achieve inclusive green development through the synergistic development of the economy, society, and environment of the petroleum and gas industry because of China’s vastness, regional variations in the endowment of petroleum and gas resources, and disparities in living standards. In conclusion, what is the connotation of inclusive green development in the petroleum and gas industry (IGDPG)? What are China’s IGDPG levels and regional variations? What are the issues preventing IGDPG from developing in China? In order to investigate the extent of IGDPG and offer more specific policy recommendations for the logical organization of the petroleum and gas sector and the creation of green development strategies, it is imperative that the aforementioned issues are answered.

This paper is arranged in the following format: A review of the literature is presented in Section 2, which focuses on three main topics: inclusive growth, green growth, and inclusive green development. The theoretical framework and indicator system of IGDPG are introduced in Section 3. The research methods are introduced in Section 4, which include the CRITIC Portfolio empowerment-TOPSIS method, Dagum Gini coefficient method, obstacle degree model, and Tobit model. The empirical results of index measurement, regional gap, and internal and external influencing factors are analyzed in Section 5. This paper’s conclusions, policy recommendations, and limitations are presented in Section 6.

2. Literature Review

2.1. Green Growth

The notion of green economy was first proposed in 1989 by Pearce et al., who described it as a sustainable approach to economic development that avoids ecological harm or resource depletion as a result of economic growth under pressure that both the environment and population can tolerate. There is still disagreement about what constitutes green growth, despite the fact that studies on the green economy have been conducted since then [6]. There are now two commonly used and recognized definitions of green growth. The first is that it is a type of growth that, in addition to economic development, ensures a steady supply of resources and environmental services [7]. The second is that, from the perspectives of growth rate and resilience, it is a method of growth that can accomplish resource conservation and is clean and more resilient without slowing down the speed of economic growth [8].

Many academics have investigated the measurement and empirical analysis of green growth using techniques like econometrics or mathematical programming because the concept itself is not readily observable. Kim et al. [9] selected 12 variables using the OECD framework to compare green growth strategies across nations; Musango et al. [10] used a system dynamics approach to evaluate the green economy of South Africa in different contexts; Guo [11] used structural equation modeling to examine the connection between technical advancement, environmental regulation, and the performance of regional green growth (RGGP); Bagheri et al. [12] proposed an improved multifactorial energy input-output model to explore the green growth pathway; Luukkanen et al. [13] used the sustainability window approach to analyze the green growth–resource use productivity gap; the impact of a green growth economy on financial stability was investigated by Jadoon et al. [14] using a two-step systematic generalized method of moments (GMM); and Zhang et al. [15] used a panel QARDL model to study the short- and long-term effects of financial vulnerability, ICT capital, environmental policy austerity, and education on green growth.

2.2. Inclusive Growth

In 2007, the Bank of Asia introduced the inclusive growth theory, which was founded on pro-poor-growth principles [16]. The World Bank’s Commission on Growth and Development released The Growth Report: Strategies for Sustained Growth and Inclusive Development in May 2008 [17], further outlined the necessity of maintaining long-term, inclusive growth, and defended the conviction that fostering inclusivity can lead to significant outcomes. In current research, the concept of inclusive growth is interpreted from three primary theoretical approaches: The first viewpoint emphasizes the equal opportunity dimension [18], contending that it focuses on the establishment of equal development opportunities, with a particular emphasis on the capacity of marginalized groups to engage in and reap the benefits of economic development, as embodied in the “pro-poor” path of development [19]. The second theoretical viewpoint regards it as an inclusive growth paradigm that helps everyone in society [20]. According to the third viewpoint, inclusive growth is fundamentally a sustainable development paradigm, and its meaning is compatible with China’s new development concept, which must be understood entirely from the standpoint of synergistic development in terms of ecological protection, social equity, and economic efficiency. This viewpoint has grown to be a crucial theoretical framework for evaluating the economy’s high-quality development [21].

Once the meaning of inclusive growth was clarified, researchers explored how to assess it. The measurement of inclusive growth indicators is still up for debate in the literature. Some researchers have chosen to measure it using individual variables such as household income [22] and total factor productivity [23]. Nonetheless, other academics have created frameworks and indicator systems for inclusive growth based on the idea of inclusive growth [24,25,26,27].

Additionally, researchers have looked into how many factors affect inclusive growth. Ojha [28] examined secondary education spending and technology advancements using a balanced approach, which can lead to inclusive growth. According to Chen et al. [29], urbanization contributes to a reduction in the income gap between urban and rural areas, and the government’s balance in public goods also contributes to a reduction in the public goods gap between urban and rural areas. Only by reducing the gap between the two aspects can urban and rural areas grow inclusively. Hu et al. [30] used a spatial econometric model and a threshold effect model to examine the spatial effect of inclusive growth and the influence of economic openness to the outside world on the inclusiveness of growth. Fan et al. [31] investigated the mechanisms of corporate capability and cluster institutional environment on the inclusive growth of SMEs and examined the effect of cluster-shared resources on the inclusive growth of SMEs from the SMEs’ point of view. Tan et al. [32] confirmed the positive effects of the interaction between upgrading industrial structure and financial development on inclusive growth using a two-step system GMM regression model and a fixed-effects regression model. Through empirical testing, Long et al. [33] discovered that digital inclusive finance can both directly increase the level of inclusive growth and improve its quality through entrepreneurship and innovation.

2.3. Inclusive Green Development

Since the United Nations announced new Sustainable Development Goals (SDGs) in 2015, many countries have started putting new development policies into place, with the goal of achieving inclusive green growth [34,35]. Originally coined at the United Nations Rio + 20 Conference, the term aims to resolve the non-inclusive and non-green contradictions in the development process [1,36] by combining the ideas of inclusive growth and green growth to create a new model of economic development [37]. Through altering and optimizing the mode of production, inclusive green development manages the contradictions and conflicts between humans and nature, as well as between humans. It is a significant and tangible method of attaining the synergistic growth of the three main systems of the economy, society, and nature, as well as fostering high-quality development and achieving shared prosperity [38].

Much research has been performed by academics on the empirical examination of inclusive green development. Using a multi-dimensional indicator system, several researchers have assessed the degree of inclusive green development from a regional standpoint. For example, Herrero [39] and Albagoury [40] have done so at the national level, while Li et al. [41] and Zhu [42] have done so at the city–region level. However, other researchers, like Zhang [43] and Lv [44], concentrated on the company as well as the industry level.

Furthermore, researchers have discovered that there are a variety of factors influencing inclusive green development, and that the same factor may have different effects in various samples. In order to address the gap in the literature on the recycling of carbon taxes for inclusive green development, Ojha et al. [45] employ a recursive dynamic CGE model with energy sector and endogenous income distribution modules to enhance income distribution by investigating the effects of using carbon tax revenues for investment. Using panel regression modeling and mediated effects modeling, Qian et al. [46] discovered that energy-oriented technical advancement can support inclusive green growth by advancing and cleaning industrial structures, and that these two mechanisms work in conjunction. According to Zhou and Wu [2], institutional transformation contributes significantly to inclusive green development on a national level and exhibits a comparable pattern in the eastern, central, and western areas. However, only in the eastern region does technology innovation significantly contribute to inclusive green development. And the beneficial impact of urbanization level on inclusive green development is only substantial in the central region; capital stock per capita has a large positive influence both nationally and in the eastern and western regions.

According to the previously indicated literature review, in analyzing the concepts of inclusive growth and green growth, we can see that economic development often gives rise to conflicting relationships between the economy, society, and ecology. Inclusive growth focuses on choosing between growth and fairness, while green growth focuses on balancing growth and the environment. Indeed, each concept has its limitations. For instance, while the pursuit of inclusive growth may result in reduced economic efficiency and slower development rates, an overemphasis on green growth could exacerbate interregional wealth disparities. Many western regions are ecologically fragile areas, and an excessive focus on green growth could widen the gap with eastern regions. In addition, green growth requires technological support, which could exacerbate the exploitation of developing countries by developed countries and limit the economic growth of developing countries. Consequently, the notion of inclusive green development has progressively surfaced. Scholars have conducted extensive research on the definition, measurement, and influencing factors of inclusive green development, which has significantly aided in the advancement of both high-quality economic growth and regionally inclusive green development. There is still need for improvement, even if previous studies have expanded the field’s reach, and the current study contains the following drawbacks: First, in terms of the research topic, the majority of the literature is predicated on the notion of inclusive green growth as it currently exists, and examines it in various regional contexts without providing a precise definition of the term and meaning of inclusive green growth at the industrial level. Second is the problem with the approach of spatial difference analysis. The accuracy of the analysis of spatial differences in inclusive green development is decreased because, despite the introduction of techniques like the coefficient of variation and the Terrell index to analyze regional differences in relative terms, the current research is unable to precisely identify the contribution of inter-regional disparities to overall disparities. Third, with respect to the choice of influencing factors, while research has been performed on the effects of a number of factors, including institutional change, technological innovation, and human capital, on inclusive green growth, these studies have only looked at external environmental factors and have not thoroughly investigated their internal mechanisms.

Based on this, this paper’s contributions are as follows: First, it offers a theoretical foundation for the research by clarifying the meaning of IGDPG for the first time. In order to give a comprehensive analysis of IGDPG in China, the degree of IGDPG in the three main areas was measured for the first time using the CRITIC Portfolio empowerment-TOPSIS method. Second, the spatial differences of IGDPG and the sources are thoroughly examined using the Dagum Gini coefficient approach, which identifies differences both within and between regions. Finally, from the standpoint of internal and external aspects, the obstacle degree model and Tobit model are utilized to identify the driving factors of the growth of IGDPG, and reveal the root cause and action path of IGDPG-level change.

3. Conceptual Framework

3.1. Connotation of IGDPG

The sustainable development of the petroleum and gas industry means the rational exploitation and utilization of resources, improvements in petroleum and gas utilization efficiency, and the enhancement of the sustainability of resource development. Environmental and ecological resources should be effectively protected, harm to the natural environment should be minimized, and the sustainability of environmental resources should be maintained during the development process. With the aim to tackle the problem of mutual harmonization and compatibility among ecology, economy, and society during the growth of the petroleum and gas industry. This paper puts forward the new concept of “IGDPG,” taking into account the unique characteristics of the petroleum and gas industry, as well as the connotations of inclusive green development, and accurately deals with the primary role of humans in the development of the petroleum and gas industry. Some of its connotations are below:

(1)

IGDPG is a notion of development that emphasizes the changes in quality and quantity (such as total amount, scale, speed, etc.), with a greater focus on the manner and quality of progress. The ability of petroleum and gas to generate a variety of significant products through development and usage is reflected in IGDPG, in addition to the overall quantity characteristics, such as the endowment and structure of these resources. Every industry exists, emerges, and develops to satisfy the needs of people, whether in the present or future, and to satisfy their ever more complex, varied, and individualized needs. As a result, the meaning of IGDPG is also part of a dynamic evolution process with the changing needs and pursuits of human beings for a better life.

(2)

The goal of the IGDPG is to fairly distribute petroleum and gas resources among regions and two generations. Analyzed from a temporal viewpoint, it includes the state and structure of petroleum and gas resources needed for life and production of different people in the same generation and between two generations; analyzed from the perspective of space dimension, it includes the level, capacity, and trend of IGDPG in different regions in the whole process of petroleum and gas resources development, utilization, and conservation.

IGDPG is a system coupling and coordination concept. In order for the system to reach a state of harmonious high-quality development, it refers to the coordinated development, symbiosis, and inclusion of petroleum and gas resources, economy, society, environment, and other subsystems within the IGDPG system, showing the multi-dimensional system coupling effect of time and space, as well as quantity and quality.

3.2. Establishment of Index System

In order to create the IGDPG indicator system, as illustrated in Figure 1, this study breaks down the intricate system of IGDPG into four main dimensions: industrial development, social opportunity equity, poverty and income inequality reduction, and green ecology. We did this by combining the findings of earlier studies with the concepts and features of the petroleum and gas industry. The petroleum and gas industry studied in this paper refers to the exploitation and development of petroleum and gas resources in a narrow sense, which primarily includes the petroleum and gas extraction and petrochemical and petroleum processing industries. From 2012 to 2021, this article covers 30 provinces (municipalities and autonomous territories) in mainland China, excluding Tibet, Hong Kong, Macao, and Taiwan. The missing data are supplemented by linear interpolation. The sample data were obtained from the National Bureau of Statistics, provincial statistical yearbooks, and various statistical yearbooks (including energy, industry, science and technology, urban, fixed assets, and other statistical yearbooks).

3.3. Driving Factors

It is clear from the aforementioned research that IGDPG shows notable regional disparities, a feature brought about by the combined influence of several causes. The driving factors of IGDPG are identified from an external environment perspective in this paper in terms of the domestic and international literature on inclusive green growth. Finally, five factors—industrial clusters (IC), industrial upgrading (IU), urbanization rate (UR), digital economy (DE), and environmental burden (EB)—are chosen for regression analysis.

(1)

IC: IC helps promote diversified division of labor and cooperation, and, through externalities, facilitates the dissemination and spillover of knowledge and technology [47], accelerates scientific and technological innovation, and promotes the transformation and application of scientific and technological innovation achievements, thereby driving the sustainable development of the petroleum and gas industry.

$$IC={\displaystyle \frac{C_{it}/GD{P}_{it}}{{\displaystyle \sum _{i=1}^n{C}_{it}/{\displaystyle \sum _{i=1}^{n}GD{P}_{it}}}}}$$

(1)

In the formula, C_it represents the output value of the petroleum and gas industry in province i during period t; GDP_it represents the regional gross domestic product of province i during period t. If IC > 1, it indicates that the petroleum and gas industry exhibits a clustering phenomenon within the region; if IC < 1, it suggests that the petroleum and gas industry is relatively dispersed within the region.

(2)

IU: The number of tertiary industries, such as modern services and emerging technology industries, rises with the degree of industrial structure diversification, and the associated economic, social, and ecological effects intensify [48]. This makes the petroleum and gas industry more suited to green development and transformation.

IU = value added from the tertiary industry/value added from the secondary industry.

(3)

UR: More people from rural areas have moved to cities as a result of urbanization, choosing to work in secondary and tertiary industries other than agriculture. The economic gap between rural and urban areas has decreased, and rural inhabitants’ incomes have improved as a result. However, because petroleum and gas resources are essential to people’s everyday lives, the increase in urbanization also suggests a rise in the consumption of these resources. As a result, there will be an increase in demand for petroleum and gas resources and their products, which will propel the growth of the petroleum and gas sector and draw in further capital investment. More material assistance is subsequently made available for the industry’s technological innovation, equipment upgrading, and infrastructural improvements, all of which contribute to the petroleum and gas sector’s sustained growth. More material assistance is subsequently made available for the industry’s technological innovation, equipment upgrading, and infrastructural improvements, all of which contribute to the petroleum and gas sector’s sustained growth.

UR = urban population/total population.

(4)

DE: Under traditional circumstances, low technological efficiency and economies of scale result from people’s mobility. Technical and R&D staff in the petroleum and gas resource sector will be pushed to progress into new digital domains as a result of the technological advancements brought about by the digital economy, which will replace conventional low-efficiency, low-quality production activities [49]. This will lay the foundation for petroleum and gas resource-based enterprises to make breakthroughs in green production and pollution control, ultimately achieving the goal of green economic development.

(5)

EB: Due to discrepancies in infrastructure, social security, and industrial economic development chances, the intensification of EB may make regional inequities worse, which could result in unjust social development. Additionally, it might restrict the widespread distribution of industrial earnings, thereby increasing the wealth divide. Furthermore, environmental pollution will be dispersed unevenly among regions as EB grows, leading to differing levels of environmental governance pressure. This will negatively impact the petroleum and gas industry’s ability to achieve inclusive green development.

$$E{B}_{ijt}={\displaystyle \frac{P_{ijt}}{P_{jt}}}/{\displaystyle \frac{GD{P}_{it}}{GD{P}_t}}={\displaystyle \frac{{\displaystyle \sum _{j=1}{P}_{ijt}}}{{\displaystyle \sum _{i=1}{\displaystyle \sum _{j=1}{P}_{ijt}}}}}/{\displaystyle \frac{GD{P}_{it}}{{\displaystyle \sum _{i=1}GD{P}_{it}}}}$$

(2)

In this context, P_ijt and P_jt represent the environmental pollution emissions in region i and nationwide at time t, respectively. This paper focuses on industrial waste gas pollution, including three pollutants: sulfur dioxide, nitrogen oxides, and smoke (dust) emissions. In addition, industrial wastewater pollution is also considered, involving three pollutants: chemical oxygen demand, ammonia nitrogen, and petroleum.

4. Method

4.1. CRITIC Portfolio Empowerment-TOPSIS Method

The determination of indicator weights is crucial for the assessment of IGDPG level. The weights of the indicators can currently be determined using a variety of approaches in academia, which are typically separated into two groups: subjective and objective weighting methods [50]. A type of objective weighing technique called Criteria Importance Though Intercrieria Correlation (CRITIC) considers the degree of information that the indicators indicate, as well as the correlation between each indicator, to determine the importance of the variables in these two aspects. This work integrates the two weighting methods, since it has been shown that the equalization method, a type of subjective weighting method, may reflect the connotative qualities of the assessment object when combined with the research of other researchers. In order to determine the comprehensive ranking of the evaluation object, TOPSIS, also known as “the sequencing method of approximating ideal solutions,” first determines the distance between the evaluation object and the corresponding positive and negative ideal targets, and then it determines the nearness degree of each object to the ideal evaluation object. The TOPSIS evaluation method’s drawbacks, such as its inability to offer quantitative results of indicator weights and its inability to reflect the importance of each indicator, can be compensated for by incorporating the CRITIC method. Thus, as illustrated in Figure 2, this work uses the CRITIC Portfolio empowerment-TOPSIS method to assess the IGDPG level.

4.2. Dagum Gini Coefficient Method

Although the traditional Gini coefficient can show the general inequality of economic variables, it is unable to identify the causes of these general inequalities. The Dagum Gini coefficient, a novel approach put forth by DAGUM, breaks down the Gini coefficient into three parts: ultra-high density, intra-group disparity, and inter-group disparity. This approach, which is currently in widespread usage, not only precisely identifies the specific source of spatial differences in economic variables, but also makes it possible to see the dynamic process of the contribution rate of these three categories of differences [51].

The fundamental equation of Dagum Gini coefficient is as follows:

$$G={\displaystyle \sum _{j=1}^k{\displaystyle \sum _{h=1}^k{\displaystyle \sum _{i=1}^{n_j}{\displaystyle \sum _{r=1}^{n_h}| y_{ji}-y_{hr} |}}}}/2n^2\overline{y}$$

(3)

where k is the number of regions divided, n is the number of municipalities, and G is the overall Gini coefficient, which is the average value of IGDPG in each province. The country is separated into three main regions, east, central, and west, in accordance with the National Bureau of Statistics’ (NBS) division standard, k = 3. The number of provinces in the region of j(h) is indicated by n_j (n_h), while the level of IGDPG of any province or municipality in the region of j(h) is indicated by y_ji (y_hr).

G is the sum of the contributions from intra-group disparity (G_w), inter-group disparity (G_nb), and ultra-high density (G_t). D_jh is the mutual influence of the IGDPG between areas j and h, G_jj is the Gini coefficient of area j, and G_jh is the Gini coefficient between areas j and h.

$$G_{jj}={\displaystyle \frac{\frac{1}{2\overline{Y_j}}{\displaystyle \sum _{i=1}^{n_j}{\displaystyle \sum _{r=1}^{n_j}| y_{ji}-y_{jr} |}}}{n_j^2}}$$

(4)

$$G_{jh}={\displaystyle \frac{{\displaystyle \sum _{i=1}^{n_j}{\displaystyle \sum _{r=1}^{n_h}| y_{ji}-y_{hr} |}}}{n_j{n}_h\overline{(Y_j+Y_h)}}}$$

(5)

$$G_w={\displaystyle \sum _{j=1}^k{G}_{jj}{p}_j{s}_j}$$

(6)

$$G_{nb}={\displaystyle \sum _{j=2}^k{\displaystyle \sum _{h=2}^{j=1}{G}_{jh}(p_j{s}_h+p_h{s}_j)D_{jh}}}$$

(7)

$$G_t={\displaystyle \sum _{j=2}^k{\displaystyle \sum _{h=1}^{j-1}{G}_{jh}(p_j{s}_h+p_h{s}_j)(1-D_{jh})}}$$

(8)

$$D_{jh}={\displaystyle \frac{d_{jh}-p_{jh}}{d_{jh}+p_{jh}}}$$

(9)

$$d_{jh}={\displaystyle {\int }_0^{\infty }d{F}_j(y){\displaystyle {\int }_0^y(y-x)d{F}_h(x)}}$$

(10)

$$d_{jh}={\displaystyle {\int }_0^{\infty }d{F}_j(y){\displaystyle {\int }_0^y(y-x)d{F}_h(x)}}$$

(11)

Among them, $p_j=n_j/n$, $s_j={\displaystyle \frac{n_j\overline{Y_j}}{n\mu }}$, the function F is the cumulative probability density function of the IGDPG of the region, d_jh denotes the difference of the IGDPG between the regions, i.e., the mathematical expectation of the sum of all sample values of y_ij − y_hr > 0 in regions j and h, and p_jh is the hypervariable first-order moment, which denotes the mathematical expectation of the sum of all sample values of y_ij − y_hr < 0 in regions j and h.

4.3. Obstacle Degree Model

The degree to which various factors have obstructed China’s high and low levels of IGDPG over time was determined in this study using the obstacle degree model. The obstacle degree model was used to diagnose and assess the components, and three indicators—factor contribution (M_ij), indicator deviation (S_ijt), and obstacle degree (N_ijt)—were used to quantify and rank the amount of the barrier degree [52]. The following is the precise formula:

$$M_{ij} = w_i \times w_{ij}$$

(12)

$$S_{ijt} = 1 - Y_{ijt}$$

(13)

$$N_{\mathit{ijt}}={\displaystyle \frac{S_{\mathit{ijt}}\times M_{ij}}{{\displaystyle \sum _{j=1}(M_{ij}\times S_{\mathit{ijt}})}}}$$

(14)

where w_i is the weight of the i-th classification index, w_ij is the weight of the jth individual indicator data under the ith categorical indicator, and N_ijt is the obstacle degree of the jth individual indicator under the ith categorical indicator in year t. W_i and w_ij are the weights of the indicators in the IGDPG indicator system.

4.4. Tobit Model

James Tobin first presented the Tobit model in 1958, and it makes use of maximum likelihood estimation. The Tobit model’s benefit is its ability to successfully address the issue of biased parameter estimation in ordinary least squares, which arises from the dependent variable’s truncation [53]. The IGDPG index values obtained from the TOPSIS method in the preceding section are between 0 and 1 and are correlated with the independent variables, which is consistent with the applicability of the Tobit model. The specific model is as follows:

$${\mathit{IGDPG}}_{\mathit{it}} = \left\{\begin{array}{c}{\alpha }_0+\beta X_{it}+u_{it}\\ 0\end{array}\right.$$

(15)

Among them, α₀ + βX_it + >0, α₀ is the constant term, IGDPG_it represents the IGDPG index of city i in year t, β is the regression parameter vector, and $u_{it}$ is the residual term. This paper constructs the Tobit regression model as follows:

$${\mathit{IGDPG}}_{it} = {\beta }_0 + {\beta }_1\mathit{IC} + {\beta }_2\mathit{IU} + {\beta }_3\mathit{UR} + {\beta }_4\mathit{DE} + {\beta }_5\mathit{EB}$$

(16)

5. Results and Discussion

5.1. Index Measurement

Based on the CRITIC Portfolio empowerment-TOPSIS method evaluation model, the IGDPG index results were obtained (

Table 1. Evaluation results of IGDPG in different regions.

Region	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	Mean Value
East	0.383	0.390	0.376	0.396	0.396	0.402	0.395	0.383	0.408	0.406	0.394
Central	0.323	0.338	0.326	0.341	0.342	0.347	0.339	0.299	0.357	0.357	0.337
West	0.347	0.354	0.348	0.365	0.359	0.362	0.360	0.313	0.369	0.374	0.355
Nation	0.354	0.363	0.352	0.370	0.368	0.372	0.367	0.335	0.380	0.381	0.364

). The IGDPG indexes of the eastern, western, and central regions are 0.394, 0.355, and 0.337. The eastern region is clearly ahead of the other regions, and this trend indicates a stable state. The IGDPG levels of the three regions exhibit a stepwise distribution, with the eastern region > western region > central region. Accordingly, the central and western regions are comparatively backward, while the eastern region has the highest IGDPG index, with more pronounced regional differences. This is also consistent with the measurement results of Xu et al. [54] on inclusive green growth performance, which show that the development of the petroleum and gas sector is consistent with the degree of national development. This might be because the eastern region clearly outperforms the central and western regions in terms of infrastructure, labor resources, economic base, population consumption, and investment in science and technology innovation. These factors will all help to raise the IGDPG level in the eastern region [55].

5.2. Regional Disparities and Sources

As the previous section shows, China’s IGDPG level exhibits spatial imbalance characteristics. Therefore, the regional disparities and their sources in accordance with the spatial scale division standards of the three main regions were identified using the Dagum Gini coefficient and its decomposition method (

Table 2. Dagum Gini coefficient and its decomposition of IGDPG in China from 2012 to 2021.

Year	G	Intra-Region Disparity			Inter-Region Disparity			Contribution Rate
		East	Central	West	East- Central	East- West	Central-West	Gw	Gnb	Gt
2012	0.0918	0.0775	0.0566	0.0973	0.1039	0.1002	0.0861	30.27%	40.15%	29.58%
2013	0.0937	0.0789	0.0662	0.0999	0.1006	0.1038	0.0884	30.83%	34.38%	34.79%
2014	0.0893	0.0755	0.0612	0.0962	0.0986	0.0941	0.0883	30.93%	34.58%	34.49%
2015	0.0806	0.0682	0.0537	0.0825	0.0942	0.0849	0.0776	30.12%	39.99%	29.89%
2016	0.0790	0.0776	0.0456	0.0780	0.0886	0.0885	0.0685	30.96%	41.31%	27.74%
2017	0.0774	0.0746	0.0502	0.0728	0.0888	0.0869	0.0666	30.55%	42.32%	27.13%
2018	0.0742	0.0651	0.0458	0.0734	0.0885	0.0805	0.0674	29.77%	44.80%	25.43%
2019	0.1013	0.1211	0.0453	0.0639	0.1333	0.1231	0.0607	29.16%	56.18%	14.67%
2020	0.0698	0.0665	0.0531	0.0601	0.0844	0.0761	0.0605	30.07%	43.35%	26.58%
2021	0.0706	0.0628	0.0562	0.0636	0.0860	0.0721	0.0680	29.95%	40.62%	29.43%

).

(1)

Overall spatial imbalance

With the exception of modest increases in 2013 and 2021, the overall Gini coefficient increased more significantly in 2019 to reach the study period’s highest point (0.1013) and decreased in 2020 to reach the study period’s lowest point (0.0698) over the 2012–2021 period. The reason for this is that China’s petroleum and gas sector made several advances in 2019. Examples include the reversal of years of decline in crude petroleum production, the continued increase in natural gas production, the 50 million cubic meter increase in daily gas supply capacity, the historic breakthrough in overseas petroleum and gas equity production equivalence, and several new developments in petroleum and gas pipeline construction, exploration, and exploitation technologies. However, the tendency of the gap between IGDPG in different provinces being wider is evident because of the significant variations in the development of the petroleum and gas industry in different provinces with regard to capital availability, technology level, and geographic position. In 2020, China’s three major energy companies announced that they have entered a new stage of green transformation and development. PetroChina has proposed to actively promote the comprehensive and integrated development of fossil and new energy sources and is committed to building a low-carbon energy ecosystem. This is the first time that “green and low-carbon” has been incorporated into a corporate strategy. Sinopec has simultaneously established a goal to become the world’s foremost clean energy chemical corporation. The objective of achieving the shift from conventional petroleum and gas to new energy was also explicitly stated by China National Offshore Oil Corporation. Furthermore, amid the 2020 pandemic, petroleum and gas companies not only fulfilled their production and operational tasks, but also mobilized resources to benefit the public, establishing an impregnable anti-epidemic “energy shield.” Simultaneously, they provided robust energy support for China’s efforts to build a moderately prosperous society in all respects and advance its modernized economic system. Therefore, there is a noticeable tendency for the IGDPG gap between provinces to close under the combined influence of many causes and backgrounds.

(2)

Intra-regional imbalance

From the internal situation of the region, with a mean value of 0.0788, the western region’s Gini coefficient has been at a higher level for a long time, which indicates that the western region’s IGDPG has the strongest intra-regional imbalance and the largest intra-regional gap. The eastern region comes next, with a mean value of 0.0768 for the Gini coefficient, which is not much different from that of the western region. The central region has the lowest Gini coefficient, with a mean value of 0.0534, indicating that the degree of intra-regional disparities in the central region is relatively small. Of them, the western region’s overall Gini coefficient shows a pattern of gradual decline over the study period, falling from 0.0973 in 2012 to 0.0636 in 2021—a 34.64% drop. Similarly to China’s overall trend of change, the eastern region’s Gini coefficient shows a fluctuating downward trend, a notable increase in 2019, and the largest decrease in 2020, when it dropped from 0.0775 in 2012 to 0.0628 in 2021—an 18.97% decrease. There is a fluctuating state of change in the central region’s Gini coefficient, which decreased from 0.0566 in 2012 to 0.0562 in 2021—a decrease of 0.7%. The rate of decrease indicates that the western region is the highest, the eastern region the second highest, and the central region the lowest. This outcome is in line with earlier research by Ren et al. [56]. On the one hand, the country has implemented policies like Western Development, which have greatly aided in the building of infrastructure and promoted the manufacturing sector [57]. And the petroleum and gas sector is a significant part of the manufacturing sector, which greatly aids in the development of the IGDPG and causes the gap within it to gradually close. On the other hand, a more inclusive economic transformation and industrial upgrades have been made possible by China’s growing openness to the outside world, especially through policies like the Belt and Road Initiative, which have promoted capital inflows as well as the sharing and exchange of technological resources and knowledge [58]. On the whole, the phenomenon of intra-regional imbalance is steadily waning over time, and the differences within China’s three main regions are all exhibiting a tendency of lessening.

(3)

Inter-regional imbalance

Regional disparities in IGDPG levels exist not only within regions, but also between regions. Table 2 shows that the inter-regional disparities between the east–central, east–west, and central–west regions did not exhibit the same pattern of change during the period of 2012–2021. In 2019, inter-regional disparities of the east–central and east–west regions all showed a significant increase, and in 2020 there was a clear decline. From 2012 to 2018, the east–central region shows a flat decline, while the east–west region shows a fluctuating downward trend, with decreases of 17.23% and 28.04%, respectively, from 0.1039 and 0.1002 in 2012 to 0.086 and 0.0721 in 2021. In contrast, the central–west region exhibits a slight fluctuating reduction over the course of the study, falling 21.03% from 0.08612 in 2012 to 0.06801 in 2021. The inter-regional disparities in the east–west region for the 2012–2021 period have been in the middle of the range, with the exception of 2013, when they were marginally higher than those of the east–central and central–west regions. Throughout the study period, the inter-regional disparities in the central–west region have been in the lowest position, indicating that the central–west region has the smallest gap. As stated by Li et al. [59], the focus of promoting the coordinated domestic development of IGDPG is to make up for the shortcomings of petroleum and gas industry expansion in the west.

(4)

Sources and contributions of regional disparities

As can be seen from Table 2, the average contribution rate of intra-regional disparities, inter-regional disparities, and ultra-high density to the Gini coefficient is 30.26%, 41.77%, and 27.97%, which suggests that intra-regional disparities, inter-regional disparities, and ultra-high density can explain the regional disparities of IGDPG in China. Specifically, inter-regional disparities have the highest contribution rate, and they are the primary causative factor influencing the IGDPG in China, confirming the empirical findings of Qin et al. [60]. This could be because the spatial arrangement of the petroleum and gas industry’s development has not changed significantly, which exacerbates the inter-regional differences. Therefore, the goal should be to reduce the inter-regional disparities in order to achieve a balanced development of the level of IGDPG. The contribution rate of intra-regional disparities is the second largest and remains stable throughout the study period, indicating that intra-regional disparities is a significant factor influencing the regional disparities in China’s IGDPG; the contribution rate of ultra-high density is the lowest, suggesting that some of the cross-over problems in the process of IGDPG are significant factors contributing to the overall disparity in China’s IGDPG. Although the average contribution rate of inter-regional and intra-regional disparities to the Gini coefficient is large, the contribution rate of inter-regional disparities has shown a fluctuating pattern of change during the period 2012–2021, while the contribution rate of intra-regional disparities has been in a stable state, essentially staying at 30% throughout the study.

Table 2 illustrates the internal factors that produce the regional gap in China’s IGDPG have been evolving over time. 2015 is a watershed year, according to the trend of change; prior to 2015, inter-regional disparities dominated regional disparities, followed by ultra-high density and intra-regional disparities; after 2015, inter-regional disparities dominated regional disparities, followed by intra-regional disparities and ultra-high density.

The contribution rate of inter-regional disparities and ultra-high density exhibit opposing tendencies from 2012 to 2021, suggesting that the contribution rate of ultra-high density inhibits the expansion of the contribution rate of inter-regional disparities. The alternating relationship between the effects of the contribution rate of inter-regional disparities and the contribution rate of ultra-high density on IGDPG may be caused by a number of factors, including the unbalanced economic development of petroleum and gas industry, the difference of resource allocation, and the change of policy orientation among different regions [61]. Furthermore, the contribution rate of ultra-high density and the overall Gini coefficient exhibit opposing patterns from 2012 to 2018, suggesting that the former hinders the latter’s growth throughout this time frame. Because the ultra-high density is primarily used to identify the cross-over phenomenon between regions, in other words, the contribution rate of ultra-high density characterizes the contribution of the interaction between intra-regional disparities and inter-regional disparities of the IGDPG among the three major regions to the overall regional disparity. Therefore, the fluctuation of the contribution rate of ultra-high density indicates that the issue of cross-over of China’s petroleum and gas industry is more prominent and has always existed. The current study offers fresh evidence that regional disparities and internal imbalances in IGDPG are diminishing in comparison to earlier findings [62]. This evidence reflects both the level of IGDPG in various regions and the sources of the disparities.

5.3. Driver Analysis

(1)

Internal drivers

After assessing the level of IGDPG, clarifying the differences in the degree of obstacles of internal factors in its development process is essential to guide policy formulation and promote IGDPG. Therefore, by establishing an obstacle degree model to calculate the obstacle degree size of IGDPG, 2012, 2015, 2018, and 2021 were selected as representative years to rank the obstacle degrees of various factors in the indicator layer, and the top eight obstacle factors were analyzed (

Table 3. Obstacle degree of the main factors of the IGDPG indicators (%).

Region	Year	Sequencing
		1	2	3	4	5	6	7	8
East	2012	C2 (6.01)	A1 (4.66)	C3 (4.43)	A2 (4.08)	B3 (3.98)	C4 (3.8)	D9 (3.71)	B5 (3.65)
	2015	C1 (6.5)	C4 (5.96)	C2 (4.45)	B9 (3.99)	A1 (3.63)	B3 (3.59)	A5 (3.49)	A11 (3.41)
	2018	C1 (8.5)	C3 (7.93)	C4 (7.26)	A1 (5.95)	B6 (4.9)	C2 (4.46)	A3 (4.3)	D2 (4.29)
	2021	C1 (13.28)	C3 (11.76)	B5 (9.23)	B6 (8.71)	D2 (8.51)	A8 (7.38)	B10 (6.63)	A15 (6.34)
Central	2012	C2 (6.16)	C3 (5.27)	A2 (4.18)	B5 (4.05)	D9 (3.8)	B9 (3.71)	B7 (3.59)	B1 (3.51)
	2015	C1 (5.44)	B3 (5.27)	C2 (5.24)	D9 (4.22)	D3 (3.74)	B9 (3.7)	C4 (3.61)	A9 (3.55)
	2018	C1 (7.38)	A1 (6.79)	B6 (5.12)	C3 (4.91)	C2 (4.89)	D2 (4.86)	C4 (4.33)	A5 (4.3)
	2021	C1 (13.84)	C3 (11.71)	A1 (10.33)	B6 (8.27)	D2 (8.08)	B5 (8.02)	A11 (6.74)	A3 (6.71)
West	2012	C2 (6.05)	A1 (4.69)	C3 (4.51)	A2 (4.1)	D9 (3.73)	B9 (3.64)	B7 (3.53)	C1 (3.53)
	2015	C1 (6.83)	C2 (5.31)	D3 (4.65)	D9 (3.46)	A7 (3.46)	B9 (3.43)	D2 (3.36)	A9 (3.33)
	2018	C4 (8.91)	C1 (8.85)	C3 (6.02)	B6 (5.55)	D2 (5.51)	A1 (5.38)	C2 (4.74)	A15 (4.03)
	2021	C1 (13.24)	C3 (10.45)	B3 (9.22)	B5 (8.64)	B6 (8.15)	D2 (7.56)	C4 (7.12)	A8 (6.91)
Nation	2012	A2 (6.81)	D6 (4.62)	A3 (4.28)	B6 (4.2)	C9 (4.1)	C5 (3.97)	C6 (3.88)	B7 (3.84)
	2015	A1 (6.83)	A2 (5.44)	C7 (4.35)	D6 (3.86)	B7 (3.83)	C9 (3.63)	A4 (3.61)	D12 (3.44)
	2018	A1 (8.52)	A4 (8.11)	A3 (7.35)	D5 (6.78)	C2 (5.75)	C3 (4.99)	B8 (4.31)	D12 (3.76)
	2021	A1 (13.13)	C2 (8.85)	C3 (8.83)	B8 (8.63)	D11 (7.49)	D15 (7.2)	D7 (7.17)	C10 (6.72)

).

From 4.29% in 2012 to 4.38% in 2015, 5.95% in 2018, and 8.98% in 2021, the average values of the top eight indicator layer factor hurdles in the eastern area have increased, as indicated in Table 3. Furthermore, five factors have appeared three times among the top eight obstacle factors over the last four years, indicating the rather good consistency of the factors that considerably limit the IGDPG in the eastern region. When examining particular indicators, the largest barrier to IGDPG in the eastern region in 2012 was C2, suggesting that, during this time, promoting equality in income distribution, reducing the urban-rural wealth gap, and raising awareness of the urban–rural per capita income gap had a major impact on IGDPG in the eastern region. The second and third obstacles A1 and C3 are also above average, indicating that they are also key factors restricting IGDPG in the eastern region. C1, C4, and C2 were the three obstacles that were above the average level in 2015. Then, as the C4 obstacle steadily decreased, the C3 obstacle increased to 7.93% in 2018 and 11.76% in 2021, making it the second largest factor limiting the IGDPG of the eastern region.

The indicator layer obstacle factors of central region show comparatively poor stability. Only two of the top eight factors occurred three times throughout the four-year period, suggesting that the factors restricting IGDPG in central region fluctuate greatly. C2 and C3 were the main barriers to the IGDPG of the central area in 2012, as evidenced by the fact that they were greater than the average of the top eight obstacle factors (4.28%). Three components (C1, B3, and C2), listed from highest to lowest, exceeded the average value of the top eight obstacle factors (4.35%) in 2015. The major factors changed to C1 and A1 in 2018, and, by 2021, C1 and A1 were still in the top three, although C3 returned to the key factors list and came in second.

Three factors appeared three times, and one factor appeared four times among the factors in the indicator layer of the western area that placed in the top eight during the previous four years. This suggests that C1 significantly affects the IGDPG of the western region, and that the degree of C1’s obstacle keeps increasing over time, suggesting that the western region has been disadvantaged in terms of the advantages of resource revenue management—a trend that is steadily growing.

From a national perspective, factors in relation to resource endowment, energy consumption, public goods supply, and income distribution were the top five in 2012, as shown in Table 3. By 2015, resource revenue management had joined the top five and emerged as the primary factor limiting China’s IGDPG. Regarding specific indicators, A2 was the most obstructed factor in 2012, followed by D6. However, since 2015, A1 has emerged as the most obstructed factor, with factors dropping in the ranking, like D6, related to resource endowment, and A2, related to income distribution. This demonstrates how resource revenue management has a gradually greater impact on China’s IGDPG than the factors of income distribution and resource endowment. It also shows that the obstacle degree of A1 is increasing, which suggests that this obstacle to China’s IGDPG has grown stronger. In other words, China’s various regions have not adequately used the proceeds from petroleum and gas industry revenues for the general benefit of the population, and that improving this situation should be a priority. Resource revenue management still has a lot of space for improvement, which is also a reverse confirmation of the results of Li et al. [63]. Resource revenue management plays a significant role in the growth of IGDPG and is an important part of facing the “resource curse”, as well as the modernization and development of the petroleum and gas industry.

Overall, the top eight ranked obstacle factors in 2012 (with a total obstacle value of 11.95%), three are related to social opportunity equity, two are related to poverty and income inequality reduction (with a total obstacle value of 11.09%), two are related to green ecology (with a total obstacle value of 8.04%), and one is related to industrial development (with a total obstacle value of 4.62%). The top eight factors as of 2015 are as follows: the dimension of poverty and income inequality reduction reached 16.08%, the dimension of social opportunity equity reached 7.98%, the dimension of industrial development reached 7.3%, and the dimension of green ecology reached 3.83%. By 2018, the dimension of poverty and income inequality reduction accounted for 23.98%, the dimension of social opportunity equity accounted for 10.74%, the dimension of industrial development accounted for 10.54%, and the dimension of green ecological accounted for 4.31%. By 2021, the dimension of social opportunity equity accounted for 24.4%, the dimension of industrial development accounted for 21.86%, the dimension of poverty and income inequality reduction accounted for 13.13%, and the dimension of green ecological accounted for 8.63%. In general, the obstacle factors to IGDPG have changed over time, moving from “society-equality-ecology” to “equality-society-industry” and finally to “society-industry-equality” between 2012 and 2021. This further demonstrates that social opportunity equity has been a significant constraint to the IGDPG, which corresponds to the weighting calculation results mentioned earlier. Petroleum and gas resource development activities are based on the natural conditions of petroleum and gas resources, leading to the flow of factors such as land, capital, labor, technology, and information between the petroleum and gas sector and other urban sectors. Different impacts are produced by the flow of these elements in different areas. They can link disparate economies to form an organic system, giving the economy and society resource and market advantages and ultimately leading to competitive economic advantages, as seen from the standpoint of overall social resource allocation efficiency and an increase in total social welfare. This means that the pursuit of equal development opportunities stems from the equalization of basic public services, i.e., ensuring that the equalization of basic public services can drive economic growth and redistribution effects. In order to achieve IGDPG, all economic entities must have equal opportunities to engage in society and fairly share in the benefits of economic growth in the petroleum and gas sector, which can only be achieved through the industry’s sustainable development. The obstacle degree model’s findings indicate that there is a substantial correlation between the level of IGDPG and the obstruction of social opportunity fairness. As Chen et al. [64] noted, social opportunity equity problems like a dense population, inadequate healthcare resources, and lagging public facilities continue to impede the improvement of human well-being, which in turn creates a barrier to the development of inclusive growth.

(2)

External drivers

Regression analysis in this work is performed using the Tobit model. Robust regression is used for robustness testing in order to guarantee the model’s resilience. The findings demonstrate that the regression results are essentially consistent, meaning that each driving factor’s significance and impact are consistent. This suggests that the Tobit model’s output is solid and trustworthy (

Table 4. Tobit model and robust regression coefficient results.

Tobit Regression Coefficient		Robust Regression Coefficient
Constant	0.246 **	0.241 **
EB	−0.009 **	−0.011 **
IC	0.030 **	0.032 **
DE	0.105 **	0.089 **
IU	0.014 **	0.015 **
UR	0.079 **	0.092 **

* p < 0.05 ** p < 0.01.

).

Since EB and IGDPG exhibit a negative correlation and satisfy the 5% significance test, IGDPG levels fall as EB rises. A greater EB suggests that regional development is more inclined toward extensive development, putting economic gains ahead of ecological needs and green transformation. It is not possible to advance the sustainable development and green ecological transformation of the petroleum and gas industry in such a regional development environment, nor is it helpful to accomplishing the objective of IGDPG.

IGDPG levels are considerably impacted by IC, as seen by the positive correlation between IC and IGDPG, which passed the 1% significance test. This could be as a result of IC’s ability to speed up the movement and integration of capital, information, human capital, and resources within an area. Through the establishment of upstream and downstream industrial chains and the development of productive services, IC can expand opportunities and support the growth of the petroleum and gas sector while enabling the effective utilization of the profits generated by the petroleum and gas industry, improving social inclusion and closing the gap between rich and poor.

At the 1% level, the correlation between DE and IGDPG is positive and passed the significance test. Digitalization and intelligence are slowly permeating every industry in modern society, which is defined by the internet and the Internet of Things. To meet sustainable development goals, the petroleum and gas sector must also embrace DE trend. Whether in industrial chain management or research and development, the petroleum and gas industry’s integration with DE can open up new opportunities, facilitate easier information flow, and even inspire fresh ideas for cross-border cooperation and industrial integration. This contributes significantly to the advancement of IGDPG.

IU passed the 1% significance test and exhibits a positive correlation with IGDPG. The service sector, which has lower environmental pollution, is categorized as the tertiary industry, whereas the industrial sector, which has a higher level of environmental pollution, is usually categorized as the secondary industry among the three major industries. By highlighting the “symbiotic” interaction between humans and nature and placing a higher priority on green ecological development, IU can help economic development models change from focusing on quick expansion to pursuing high-quality and sustainable development. Consequently, IU helps with environmental ecology and regional economic growth, which raises IGDPG levels in such a larger context.

UR has passed the 5% significance test and significantly improves IGDPG. The growing urban population—that is, more people relocating from rural to urban areas—is the most notable aspect of UR. This has a major positive impact on the region’s social infrastructure, environmental governance, and other areas, which helps the government can improve the utilization rate of resources and make more of the tax revenue from the petroleum and gas industry available to the public.

EB has a favorable effect in the central region when viewed through the lens of the three major regions (

Table 5. Results of regression coefficients for the three regions.

	East	Central	West
EB	−0.030 **	0.012 *	−0.027 **
IC	0.019 **	0.029 **	0.045 **
DE	0.009 *	0.074	−0.152
IU	0.020 **	−0.029 *	0.012
UR	0.216 **	0.226 **	0.095

* p < 0.05 ** p < 0.01.

). This is because the larger the EB, the faster the extensive industrial development. Since the western area still mostly depends on this development model, therefore, the improvement in the economic development of the petroleum and gas industry far outweighs its impact on green ecology [65], resulting in a positive impact. However, only in the eastern area DE has a notable effect. This is a result of the central and western regions’ comparatively weak economic foundation and practical difficulties in fostering the growth of DE, including a lack of skilled labor, comparatively outdated infrastructure and essential technologies, and generally low levels of digitalization [66]. Consequently, the successful release of the advantages of DE is hampered by the relatively shallow level of integration between digital technologies and traditional industries. From the standpoint of IU levels, IU has a certain inhibitory influence on IGDPG rather than significantly promoting the development of the central area. The exploitation and use of oil and gas resources are essential to the growth of the oil and gas sector. The expansion of resource-based industries will be exacerbated by the region’s production factors flowing preferentially to resource-based sectors due to the influence of high resource revenues. A “sticky effect” will result from this phenomenon, trapping the area in a resource advantage trap and creating a self-reinforcing mechanism for the resource-based economy [67]. The region’s green ecosystem will suffer significant harm if this industrial development model persists, which will impede the region’s IGDPG. In contrast, the IGDPG in the western region has not been significantly impacted by IU. The western region is still in a period of extensive industrial transformation. Simply emphasizing a reduction in industrial scale and excessive “deindustrialization” is not conducive to IGDPG and fails to leverage the advantages of IU in reducing environmental pollution, promoting employment, and increasing residents’ income. The UR has not significantly promoted the IGDPG in the western region, according to the UR level. This is due to the fact that, despite the western region’s consistent improvements in local economic development and living standards brought about by initiatives like the Belt and Road Initiative, urbanization represented by the urban population is relatively extensive and cannot adequately address the problems of resource waste and the urban–rural divide. Additionally, it has not been able to use its special advantages to advance raising the IGDPG level. New human-centered urbanization should be actively encouraged in the western region.

6. Conclusions, Policy Implications, and Limitations

6.1. Conclusions

China’s IGDPG is the subject of this study, which also explains the theoretical connotation of the term, measures China’s IGDPG level using panel data from 30 provinces between 2012 and 2021, examines the features of regional variations, identifies the underlying causes of changes in China’s IGDPG level from both internal and external perspectives, and comes to the following conclusions:

(1)

Between 2012 and 2021, the IGDPG level in the eastern, central, and western regions showed a fluctuating growth. The IGDPG index for the eastern, western, and central regions was 0.394, 0.355, and 0.337, respectively. The eastern region is significantly ahead of the rest, and this trend is stable. The IGDPG level of the three regions is distributed in a stepwise manner, with the eastern region coming in first, followed by the western region and, finally, the center region. In other words, there are clear regional variances, with the IGDPG index in eastern China being the highest and the index in central and western China being comparatively backward.

(2)

Regarding regional disparities, the overall disparity of China’s IGDPG is small, showing a fluctuating trend of “three rises and two declines,” with the western region having the largest intra-regional gap and the central region having the smallest; differentiation within the three major regions has also shown a trend of narrowing, with the phenomenon of intra-regional imbalance decreasing over time. The east–west regional disparity was consistently in the middle during the study period, while the central–west regional disparity was the smallest, with the exception of 2013, when it was marginally larger than the east–central and central–west regional disparity. Of the regional disparities that contributed to the IGDPG in China, the contribution rate of inter-regional disparities is the highest, followed by that of intra-regional disparities, and the contribution rate of ultra-high density is the lowest.

(3)

There are significant regional differences in the drivers. From the internal elements, the social opportunity equity dimension has been a significant barrier to the growth of IGDPG from 2012 to 2021. With the evolution of time, the obstacles in the indicators constraining IGDPG have shown a transition process from the “social-equity-ecology” dimension to the “equality-social- industry” dimension, and then to the “social-industry-equality” dimension. While the central region has relatively high swings in obstacle factors, the eastern region exhibits rather steady obstacle factors. In terms of resource revenue management, the western region has always been at a disadvantage, and this trend is progressively getting worse. From the standpoint of external elements, EB has an inhibiting influence on IGDPG, whereas IC, IU, UR, and DE have a boosting effect. The eastern region’s results align with those of the entire country. IU demonstrated an inhibiting impact, EB demonstrated a stimulating effect, and DE did not have a significant effect in the central region. DE, IU, and UR did not exhibit a substantial effect in the western region.

6.2. Policy Implications

First, China is a large nation with resources of gas and petroleum that are dispersed unevenly among its many areas. Consequently, during the creation of the IGDPG, a two-tiered divergence among areas formed. The data presented in this paper indicates that there are significant regional differences in the degree of IGDPG. Compared to the central and western regions, the eastern region has a notably greater degree of IGDPG. Overall, there is considerable room for improvement in the level of IGDPG. At the moment, China’s provincial petroleum and gas resource development is largely independent, with little cooperation and minimal radiating and driving influence from high-level core regions. Thus, the forecasted trend is to break down regional boundaries and encourage coordinated IGDPG development in order to fully improve China’s IGDPG standards. The eastern provinces should make the most of their central role as IGDPG leaders. Provinces in the central and western regions should concentrate on resolving the drawbacks and weaknesses in the development of IGDPG, learning from the successful experiences of IGDPG development, and adhering to the features of oil and gas resource endowments in order to build regional expertise.

Second, IGDPG has always been hampered by social opportunity equity, which ranks highly among the three main regions. A people-centered development philosophy serves as the foundation for the idea and meaning of IGDPG, which seeks to achieve shared economic benefits from the petroleum and gas industry. Therefore, to continuously improve management and service standards, China’s petroleum and gas resource endowments must be combined with its social reality. In the process of improving China’s IGDPG level by effectively promoting the increase in the petroleum and gas industry’s economic benefits, boosting the government’s financial self-sufficiency, and enhancing the income levels of the populace, we will progressively advance income distribution reforms. In order to eradicate wage growth disparities within industries and encourage the development of a more equitable “olive-shaped” income distribution structure within industries, emphasis will be placed on creating and enhancing wage growth mechanisms and payment security mechanisms for low-income workers in low-to-medium technology industries. This would guarantee that everyone benefits from industrial economic development, combine the requirements for the petroleum and gas sector to attain inclusive green growth, and foster peaceful coexistence and interdependence between humans and nature.

Third, the three main regions ought to use their resources and special growth traits to create their own development trajectories. The eastern area should, for example, emphasize the increased material security brought about by UR, further improve DE, progress IU processes, plan IC layout logically, and continue to promote green ecological protection. The central region should continue to focus on the effectiveness of environmental regulations and other measures, further improve the systematic nature of relevant environmental systems and policies, and enhance their execution and continuity. In addition, the central and western regions should establish a performance evaluation mechanism in areas such as IU, UR, and DE based on the actual results of their petroleum and gas industry transformation and development so as to encourage all relevant management departments to consciously implement the requirements of inclusive green growth, strive to improve the quality of economic growth in the region’s petroleum and gas industry, and thereby achieve the goal of transforming China’s petroleum and gas industry’s economic growth model towards inclusiveness and green growth.

6.3. Limitations

This study further enriches the research system of inclusive green development, but there are some deficiencies and limitations. In future research, multi-perspective and multi-scale explorations should be conducted. First, the macro-level data used in this study might not adequately represent the micro-level complexity of IGDPG. To better understand the factors influencing IGDPG, future research could include more specific data, such as firm-level environmental performance metrics. Second, this study examines the obstacles to IGDPG using static models. Future studies could investigate how these aspects interact and change over time using dynamic models, such as system dynamics or agent-based modeling, to obtain a more thorough grasp of the opportunities and difficulties of reaching IGDPG. Third, the results of this study cannot be applied to other nations because it was conducted in China. To find common obstacles to the global deployment of IGDPG, future studies could compare trials conducted in various nations.