Impact of Natural Gas Peak Shaving on High-Quality Economic Development

Abstract

As energy demand continues to grow, the enhancement of natural gas storage and peaking capacity has become an important measure to ensure national energy security and to achieve the goals of carbon peaking and carbon neutrality. Gas storage and peaking have mature development models in the international arena, and China is making every effort to develop this system. This study reveals the impact and promotion of natural gas storage and peaking technology on high-quality economic growth in different regional economic environments through sample data from 30 provinces in China, from 2006 to 2022. The results show that natural gas storage and peaking directly promote high-quality economic development and have a positive spatial spillover effect on high-quality development in neighboring regions, a finding verified by the robustness test and endogeneity test. A heterogeneity analysis further revealed that there are significant differences among eastern, central, and western regions in terms of natural gas storage and peaking capacity and quality of economic development. The eastern region has a stronger gas storage and peaking capacity, while the central and western regions have a weaker capacity. Mechanism analysis shows that R and D technology (RDT) efficiency and green finance have a positive moderating effect on the benchmark effect. This has significant implications for policymakers and business leaders, suggesting that peak gas storage and peaking can drive improvements in broader regional corporate sustainability practices and increase regional levels of high-quality development.

1. Introduction

Energy is not only the cornerstone of the progress of human civilization but also a core element of national security and people’s livelihood. Currently, the world is undergoing an unprecedented transformation, which is being driven by a new round of revolutions in science technology and industry, as well as new trends in global climate governance. The close integration of new energy and information technology is driving the transformation of living and production methods to low-carbon and intelligent, marking China’s gradual entry into a new era dominated by non-fossil energy. Therefore, building a modern energy system and ensuring national energy security is crucial to achieving the goals of carbon peaking and carbon neutrality, as well as key pillars for promoting high-quality economic and social development.

The issue of energy not only affects the quality of life of every citizen but also involves many aspects of economic construction and has far-reaching political, practical, and strategic significance. At the United Nations General Assembly in September 2020, China pledged to strive to peak carbon dioxide emissions by 2030 and to achieve carbon neutrality by 2060. As a key signatory to the Paris Agreement, China has an important responsibility to achieve these climate goals. With the strategic adjustment of the national energy structure and the strengthening of air pollution control, as well as the implementation of the “coal-to-gas” policy and the increase in market demand, China is facing the challenge of insufficient supply of natural gas and the imbalance between supply and demand, which has led to increasing pressure on energy security. At present, the world is facing a critical period of energy transition. As the problem of climate change becomes increasingly serious, countries are seeking ways to reduce carbon emissions and improve energy efficiency. The role of natural gas as a transitional energy source is becoming increasingly prominent. China is a country with a vast territory and uneven economic development. There are significant differences between different regions in terms of their level of economic development, industrial structure, and technological innovation capacity. These differences not only affect the structure and scale of natural gas demand in each region but also put forward different requirements and challenges for the construction and operation of natural gas storage and peaking capacity. In the winter of 2017, the large-scale adoption of natural gas heating in northern China largely alleviated the problem of high pollution but triggered a serious “gas shortage”, completely exposing the inadequacy of China’s natural gas storage and peaking capacity, proving the importance of optimizing and upgrading natural gas storage and peaking system. Therefore, an in-depth study of the relationship between natural gas storage and peaking capacity and the high-quality economic development of each region is of great significance to the formulation of regionalized and differentiated energy policies.

In the optimization research and the measurement literature on natural gas storage and peaking, foreign scholars have previously carried out relevant research [1]. A study of gas storage and peaking facilities located in Buenos Aires, Argentina, found that all the liquefied LNG peaking storage facilities and peaking facilities provide great convenience to peaking, compared with the use of pipeline network direct peaking, as it is more efficient and practical. Subsequent studies have focused on the construction of natural gas storage and peaking systems in developed countries. In China, Guo et al. (2012) studied the optimization of the natural gas storage and peaking system in a natural gas reserve-rich and relatively developed region [2]. Li et al. (2021) proposed a multi-scale approach to quantify the optimal size of China’s natural gas reserves, using three sub-models matching the temporal and spatial resolutions of daily and monthly peaking reserves and strategic reserves, to calculate the optimal size of natural gas reserves [3]. On a technical level, Li et al. (2016) proposed a multi-intelligence simulation model for analyzing the peaking efficiency of time-of-day natural gas pricing for residential consumers, which mainly includes governmental intelligence, the gas operator’s intelligence, and seven intelligences of residential users with different income levels [4].

Natural gas storage and peaking technologies have been widely used in many countries and regions. Schultz and David (2020) found that the United States has accumulated extensive experience in the field of gas storage and peaking and has constructed large-scale gas storage reservoirs [5]. Sharples (2016) found that natural gas storage facilities play an important role in meeting fluctuating natural gas demand, helping the EU to stabilize energy supplies, and play an important role in providing heat and power to European energy consumers [6]. However, natural gas storage and peaking face a number of challenges. Firstly, gas storage and peaking systems are expensive to build and operate [7]. The construction of gas storage requires substantial investment and technical support, while factors such as safety, environmental impacts, and maintenance costs need to be considered during operation [8]. These factors may limit the scale and popularity of gas storage and peaking systems. Second, the operation and management of gas storage and peaking systems face certain technical challenges [9]. The operation of gas storage requires precise monitoring and control to ensure the safe and stable operation of the storage system [10]. In this regard, Al-Shafi et al. (2023) proposed that buffer gases are required during the operation of underground gas storage to maintain optimal subsurface conditions for efficient gas storage [11]. CO₂ captured from CCS is an excellent candidate for use as buffer gas in UGS or UHS systems. Finally, natural gas storage and peaking also require consideration of environmental impacts and sustainable development [12]. Patil et al. (2010) argued that the construction of gas storage is an important project [13], because of the adverse impacts on agroecosystems in case of leakage of CO₂ gases from the storage site to the surface and that adequate environmental assessment and protective measures are required [14]. In addition, in recent years, with the proliferation of applications of technologies, such as big data and machine learning, there has been an increasing number of studies on hierarchical forecasting and spatial and economic analogies predicting natural gas consumption [15,16].

At present, natural gas storage and peaking play an important role in guaranteeing energy security, coping with energy consumption peaks, and achieving energy structure optimization. With the continuous development of China’s economy and the growth of energy consumption, the requirements for natural gas storage and peaking capacity are also increasing. Therefore, it is of great significance to construct a natural gas storage and peaking evaluation index system, to study how natural gas storage and peaking capacity affects high-quality economic development, and the mechanisms and paths through which natural gas storage and peaking capacity affects high-quality economic development, in order to improve the overall efficiency and security of China’s energy industry.

Compared with the previous literature, the marginal contributions of this paper are mainly in the following three aspects. Firstly, this paper provides a valuable addition to existing research through the impact of natural gas storage and peaking on high-quality economic development. Second, the paper provides an in-depth analysis of the mechanisms and heterogeneity of economic geography on the benchmarking effect. Thirdly, further discussion reveals that the efficiency of science and technology innovation and green finance can positively modulate the promotional effect between natural gas storage and peaking on high-quality economic development. All the results suggest that natural gas storage and peaking help to promote high-quality economic development.

The remainder of the paper is organized as follows. Section 2 presents the theory introduction and hypothesis development. Section 3, goes over the research methodology. Section 4 presents the empirical analysis of this study. Lastly, Section 5 concludes the study and gives policy implications.

2. Theory and Hypotheses

2.1. Natural Gas Peak Shaving and High-Quality Economic Development

On the one hand, natural gas storage and peaking technology play a crucial role in promoting high-quality economic development and play a significant role in the field of technological innovation. Advances in this technological field have not only significantly improved efficiency and safety standards in the energy sector but have also had a profound impact on the structural transformation of the economy as a whole. Firstly, innovations in gas storage technology have played a central role in improving the efficiency and safety of energy storage [17]. The diversification of natural gas storage methods, such as underground storage (including in the form of salt caverns and depletion fields) and liquefied natural gas (LNG) storage, among others, has not only increased the flexibility to adapt to different geographic and economic conditions but has also significantly improved storage efficiency. Underground reservoirs use specific geological structures to store natural gas, while LNG storage technology, which liquefies natural gas, greatly optimizes the process of transporting and storing natural gas by reducing its volume to facilitate long-distance transport and storage. Secondly, there has been innovation in safety technology. Advanced monitoring equipment and real-time surveillance systems, such as sensors, surveillance cameras, and remote-control technologies, have greatly improved the safety and operational efficiency of gas storage facilities. The application of big data and artificial intelligence has become particularly important in predicting and managing potential risks, improving the ability to respond quickly to market changes and risk management [18]. Being able to analyze the supply and demand dynamics of the natural gas market in real time helps the gas production sector to more accurately forecast and adjust storage capacity to fluctuations in market demand.

On the other hand, natural gas storage and peaking are conducive to improving the quality of China’s livelihood. Firstly, the development of natural gas storage and peaking technology helps to narrow the consumption gap between urban and rural areas. In traditional energy supply, rural areas often face energy shortages, limiting consumption and economic development [19]. Improvements in natural gas storage and peaking have ensured that rural areas enjoy a stable energy supply even during periods of peak energy demand, which directly improves the quality of life of rural residents and promotes the development of local commerce and industry [20]. This stable energy supply provides more economic opportunities and effectively improves the overall consumption level in rural areas, helping to reduce consumption differences between urban and rural areas [21]. Secondly, natural gas storage and peaking can reduce the urban–rural income gap. The construction and operation of gas storage facilities create new employment opportunities and provide diversified sources of income for rural areas. This not only raises the income of residents but also attracts more commercial investment and enterprises to move in, enhancing the vitality of the rural economy, which in turn gradually reduces the income gap [22]. Finally, the improvement of natural gas storage and regulation technology has also played a positive role in increasing livelihood-based financial expenditures. Improving the efficiency of natural gas storage and regulation reduces the government’s fiscal burden in the energy sector, enabling the government to invest more resources in key public services such as education, healthcare, and social security [23].

In summary, we suppose that:

Hypothesis 1.

Natural gas peak shaving can significantly contribute to high-quality economic development.

2.2. The Moderating Effect of Research, Development, and Technology Efficiency (RDT)

RDT efficiency essentially reflects an economy’s ability in terms of the efficiency of technology development, application, and transformation. Efficient RDT not only promotes the development of new technologies but also accelerates the improvement and application of existing technologies, thereby optimizing overall resource allocation and use efficiency.

Firstly, it enhances the technological efficiency of natural gas storage and peaking [24]. Increased RDT efficiency means that new technologies in natural gas storage and peaking technologies can be discovered, developed, and put into use more quickly. Through efficient RDT, more advanced natural gas compression technologies, liquefied natural gas (LNG) technologies, and more efficient underground storage technologies can be developed. These technological advances can directly improve the safety and flexibility of natural gas storage, reduce energy losses, and ensure the continuity and stability of the energy supply, thereby supporting the stable operation of economic activities [25]. Second, it can optimize energy allocation and consumption [26]. In an environment of high efficiency in science and technology innovation, the application of natural gas storage and peaking technologies is not only limited to providing basic energy security but also to achieving the optimal allocation of energy consumption, through accurate data analysis and forecasting models [27]. The use of advanced forecasting tools and big data analytics can more accurately predict fluctuations in energy demand, adjust natural gas supply in real time, reduce waste, and increase the efficiency of energy use, which is especially critical for driving high-quality economic development [28]. Third, it supports the goal of environmentally sustainable development. High-quality economic development emphasizes not only the speed and efficiency of economic growth but also the sustainability and environmental impact of development. Increased efficiency in RDT can help develop and promote more environmentally friendly technologies for natural gas utilization, such as reducing GHG emissions during natural gas extraction, storage, and transportation, which directly contributes to environmental protection and sustainable development objectives [29].

Hypothesis 2.

The efficiency of RDT can positively modulate the promotional effect between natural gas storage and peaking on high-quality economic development.

2.3. The Moderating Effect of Green Finance (GF)

Green finance includes the provision of funds and financial services for environmental protection, energy efficiency, climate change mitigation, and other sustainable development projects. These financial instruments and services aim to ensure economic growth while reducing environmental impacts and promoting efficient use of resources [30].

First, financial support and incentives must be considered. Green finance provides critical financial support for the development and application of natural gas storage and peaking technologies [31]. This includes investing in more efficient gas storage technologies, improving gas transmission and distribution infrastructure, and developing new peaking methods. By providing financial incentives, such as loan concessions and tax breaks, green finance encourages firms and individuals to adopt energy-efficient and emission-reducing technologies and products, thereby improving the efficiency of energy use and the quality of economic performance throughout society [32]. These funds and incentives can help alleviate the financial shortages faced by startup environmental technologies and accelerate the marketization process of innovative technologies. Second, they facilitate policy implementation. The establishment of green financial mechanisms is usually accompanied by government policy support for environmental protection and energy efficiency. Through green financial instruments, such as green bonds and green funds, governments are able to ensure continued investment in natural gas storage and peaking projects, as well as the updating and promotion of related technologies [33]. In addition, green finance helps governments to implement price incentives, such as carbon trading and pollution rights trading. This can help optimize the market allocation and use of natural gas and other energy sources and promote the economic transition to low-carbon development. Third, they increase public and market participation. Green finance is not only the behavior of financial institutions but also includes broad public participation. Through green financial products, such as green savings accounts, green stocks, and bonds, ordinary investors can directly participate in the financial support of environmental protection projects, such as natural gas storage and peaking. Such participation not only raises public awareness of the importance of environmental protection but also helps to build a green development environment supported by the whole society [34]. This active participation of investors and consumers in turn boosts the interest of companies in investing in and developing green technologies.

Hypothesis 3.

Green finance can positively modulate the promotion effect between natural gas storage and peaking on high-quality economic development.

The Theoretical framework diagram of this paper is shown in Figure 1.

3. Data and Methodology

3.1. Date

The sample range used is from 2006 to 2022, and due to the serious lack of data in Hong Kong, Macao, Taiwan, and Tibet, the data used are from 30 provinces, municipalities, and autonomous regions in China (except Hong Kong, Macao, Taiwan, and Tibet). The data used for natural gas storage and peaking are from the China Statistical Yearbook of Urban Construction, and the data used for high-quality economic development and control variables are from the Wind database, the Cathay Pacific database, provincial statistical yearbooks, the China Financial Yearbook, the China Statistical Yearbook of Regional Economy, and the website of the National Bureau of Statistics (NBS). The data relating to the use of RDT efficiency are obtained from the China Science and Technology Statistics Yearbook, National Bureau of Statistics, and the statistical yearbooks of each province in the past years. The data relating to green finance come from the National Bureau of Statistics, provincial and municipal statistical yearbooks, environmental condition bulletins, China Science and Technology Statistical Yearbook, China Financial Yearbook, China Industrial Statistical Yearbook, China Energy Statistical Yearbook, other specialized statistical yearbooks, websites of People’s Bank of China, other authoritative institutions, official websites of listed companies, annual reports, etc.

The data processing and empirical analysis in this paper were performed using Stata 18 software.

3.2. Variable Definition

3.2.1. Explained Variable

The high quality of economic development depends on the high-quality development of the real economy, which should not only focus on the size of the real economy but also pay more attention to the improvement of economic efficiency, the adjustment of industrial structure, technological innovation, and the coordinated development of the ecological environment. In terms of indicator selection, the Fifth Plenary Session of the 18th Central Committee put forward the new development concept of “innovation, coordination, greenness, openness, and sharing” [35]. This study selected indicators of the level of high-quality development of the economy from these five aspects. The composition of the indicators is shown in

Table 1. Indicator system of explained variables.

Tier 1	Tier 2	Tier 3	Definition of Indicators
High-quality economic development	innovation	GDP (+)	Regional GDP growth rate
		R&D investment intensity (+)	R&D Expenditure of Industrial Enterprises Above Scale/GDP
		Technology transaction activity (+)	Technology transaction turnover/GDP
	coordination	Demand structure (+)	Total retail sales of consumer goods/GDP
		Urban and rural structures (+)	Urbanization rate
		Proportion of government finances (−)	Government debt balance/GDP
		industrial structure (+)	Increase in the ratio of tertiary sector output to regional GDP
	greenness	green area (+)	Area of urban green space
		urban greening (+)	Green coverage
		Harmless disposal of domestic waste (+)	Non-hazardous treatment rate of domestic waste
	openness	External trade dependence (+)	Total exports and imports/GDP
		Share of foreign investment (+)	Total exports and imports of foreign-invested enterprises/GDP
		marketability (+)	Area marketization index
	sharing	Urban-rural consumption gap (−)	Consumption expenditure per urban resident/Consumption expenditure per rural resident Disposable income per urban resident/Disposable income per rural resident
		Urban-rural income gap (−)	Consumption expenditure per urban resident/Consumption expenditure per rural resident Disposable income per urban resident/Disposable income per rural resident
		Share of fiscal expenditure on people’s livelihoods (+)	Proportion of housing security expenditure, health care expenditure, local finance expenditure on education and social security and employment expenditure in local finance budget expenditure

Note: “+” denotes a positive relationship, indicating that an increase in the indicator contributes positively to high-quality economic development. Conversely, “−” denotes a negative relationship, meaning that an increase in the indicator may hinder or reduce the quality of economic development.

.

The entropy method is formulated as follows:

$$P_{it}={\displaystyle \frac{X_{it}}{{\sum }_{t=1}^T{X}_{it}}}$$

(1)

$$D_{it}=1+\left({\displaystyle \frac{1}{\ln T}}\right)\times {\displaystyle \sum _{t=1}^T}\left(P_{it}\times \ln \left(P_{it}\right)\right)$$

(2)

$$W_{it}={\displaystyle \frac{D_{it}}{{\sum }_{i=1}^I{D}_{it}}}$$

(3)

$${EH}_{it}={\displaystyle \sum _{i=1}^I}{W}_{it}{X}_{it}$$

(4)

where $X_{it}$ is each of the three-level indicators selected for this study, $P_{it}$ is the proportion of the sample under the Xth indicator, $D_{it}$ is the information utility value of $X_{it}$, and $W_{it}$ is the weight coefficient of $X_{it}$. This is used to calculate High-quality economic development (EH).

3.2.2. Explanatory Variable

Existing studies on the evaluation of natural gas storage and peaking are mostly categorized into two types. One is to construct an evaluation model for the coordinated development of the integration of production, transportation, and marketing of natural gas development enterprises, which mostly adopts the entropy method, the hierarchical analysis method (AHP), and the scoring method to determine the weights of the indicators at each level and calculate the scores of the three levels of indicators. The main indicators include those for production, transportation, and marketing, as well as coordination and management indicators between these stages [36]. The other method revolves around the comprehensive evaluation and path selection of investment in the unconventional natural gas industry, which is quantitatively analyzed in terms of resource potential, techno-economics, industrial development, and ecological environment. This evaluation model was constructed using the TOPSIS model and gray correlation evaluation [37]. Since the TOPSIS model and gray correlation usually need to rely on expert scoring or other subjective methods in determining the weights, which have more subjective factors, this study adopted the first approach to calculate the weights by means of the entropy weighting method.

In this study, considering the development and construction of natural gas storage and peaking, the indicators were selected from the four aspects of production, storage, supply, and marketing. Among them, selected as tertiary indicators for natural gas production capacity were natural gas production, the number of legal entities in the electricity, heat day gas, and water production and supply industry, the number of employed persons in urban units in the electricity, heat, gas, and water production and supply industry, and the investment in fixed assets in the state-owned economy oil and natural gas extraction industry. Gas storage capacity was selected as a tertiary indicator in terms of natural gas storage. In terms of natural gas supply, the length of the gas pipeline, the total amount of gas supply, the number of households supplied with gas, and the population using gas were selected as tertiary indicators. Finally, in terms of natural gas sales, the volume of gas sold and the volume of gas lost were selected as tertiary indicators. The composition of the indicators is shown in

Table 2. Indicator system of explanatory variable.

Tier 1	Tier 2	Tier 3
Natural gas peak shaving	Production	Natural gas production
		Number of legal entities in the electricity, heat, gas, and water production and supply industry
		Persons employed in urban units in the electricity, heat, gas, and water production and supply industry
		Investment in fixed assets in the oil and gas extraction sector of the state economy
	Storage	gas storage capacity
	Supply	Length of gas supply pipeline
		Total gas supply
		Number of households supplied with gas
		Population using gas
	Marketing	Sales of gas
		Gas losses

.

3.2.3. Moderator Variable

In terms of the selection of Research, Development, and Technology Efficiency indicators (RDT), drawing on the research of Chen et al. (2021), with the help of the SBM-DEA super-efficiency model, the full-time equivalents of R and D personnel of industrial enterprises categorized as above large scale and the funds for the development of new products of industrial enterprises categorized as above large scale were selected as input indicators [38]. The number of effective invention patents of industrial enterprises categorized as above large scale and the sales revenues of new products of industrial enterprises categorized as above large scale were selected as output indicators. The variable selection is shown in

Table 3. Selection of Research, Development, and Technology Efficiency indicators.

	Input Indicators	Output Indicators
Research, Development, and Technology Efficiency	Full-time equivalent of research and development personnel in industrial enterprises above designated size	Number of valid invention patents in industrial enterprises above designated size
	Expenditure on new product development in industrial enterprises above designated size	Sales revenue of new products in industrial enterprises above designated size

.

In the selection of green financial indicators, drawing on the research of Li et al. (2023), green finance is divided into four secondary indicators, namely green credit, green securities, green insurance, and green investment [39]. The green financial index is fitted with the help of the entropy value method. Among them, the green credit index is measured by the percentage of interest expense of the high energy consumption industry and the percentage of new bank loans of environmental protection enterprises listed in A-share companies. Green securities indicators are measured by the ratio of the market value of A-share listed environmental protection enterprises and the ratio of A-share listed high energy consumption enterprises to A-share market value. Green insurance is measured by the ratio of environmental pollution insurance compensation and the scale of environmental pollution insurance. Green investment is measured by the ratio of investment in environmental pollution control and the ratio of fiscal environmental protection expenditure. The variable selection is shown in

Table 4. Selection of green finance indicators.

	Secondary Indicators	Tertiary Indicators	Indicator Description
Green Finance	Green Credit	High energy-consuming industries interest expense ratio	Interest expenses of six major high energy-consuming industries/Total industrial interest
		Proportion of new bank loans to A-share listed environmental protection companies	New bank loans to A-share listed environmental protection companies/Bank loans to A-share listed companies
	Green Securities	Proportion of market value of A-share listed environmental protection companies	Market value of A-share listed environmental protection companies/Total market value of A-share listed companies
		Proportion of market value of A-share listed high energy-consuming companies	Market value of A-share listed high energy-consuming companies/Total market value of A-share listed companies
	Green Insurance	Proportion of environmental pollution insurance compensation	Agricultural insurance expenses/Agricultural insurance revenue
		Scale of environmental pollution insurance	Agricultural insurance revenue/Property insurance revenue
	Green Investment	Proportion of environmental pollution control investment	Environmental pollution control investment/GDP
		Proportion of fiscal environmental protection expenditure	Fiscal environmental protection expenditure/Total fiscal expenditure

.

3.2.4. Control Variables

In terms of the selection of control variables, this study selected six indicators, such as human capital investment in advanced manufacturing industry, entropy of advanced manufacturing industry location, scale of electric power consumption, construction of public cultural facilities, financial support for local education, and scale of school-age labor force population. Indicator selection and descriptive statistics are displayed in

Table 5. Variable definitions.

Type	Variable	Concrete Meaning	Calculation
Dependent variable	EH	High-quality economic development	Entropy method
Independent variable	NGP	Natural gas storage and peaking	Entropy method
Mediating variable	RDT	Research, Development, and Technology Efficiency	SBM-DEA
	GF	Green finance	Entropy method
Control variabes	MP	Human capital investment in advanced manufacturing	Natural logarithm of the size of the workforce in advanced manufacturing
	MS	Advanced Manufacturing Location Entropy	Percentage of employees in advanced manufacturing industry in the region/Percentage of employees in advanced manufacturing industry in the country
	ELE	Scale of electricity consumption	Natural logarithm of electricity consumption
	CUL	Construction of public cultural facilities	Natural logarithm of the number of museums in each region
	EDU	Local financial support for education	Ratio of local fiscal expenditure on education to total fiscal expenditure
	Peo	Size of the labor force population of working age	Natural logarithm of the number of persons aged 14 to 65 years

and

Table 6. Descriptive statistics.

VarName	Obs	Mean	SD	Min	Median	Max
EH	510	0.240	0.102	0.102	0.202	0.560
NGP	510	0.126	0.092	0.018	0.096	0.528
MP	510	4.682	1.195	1.755	4.662	7.033
MS	510	0.779	0.600	0.080	0.593	2.924
ELE	510	7.250	0.761	4.582	7.230	8.971
CUL	510	4.508	0.851	1.792	4.595	6.501
EDU	510	0.164	0.026	0.097	0.166	0.222
Peo	510	7.601	0.768	5.545	7.658	8.864

, respectively.

3.3. Model Setting

The Spatial Autoregressive Model (SAR), a spatial econometric model specialized in analyzing spatial dependencies or spatial interactions between different geographical locations, has been widely used in many fields, including economics, geography, and regional science. This model is especially suitable for analyzing those situations where there are obvious interactions between neighboring regions, such as economic activities, disease transmission, environmental impacts, etc. The core of the SAR model is the accurate estimation of the spatial autoregressive coefficient. If the coefficient is significantly non-zero, it indicates that the dependent variable (e.g., economic growth rate) of a region is not only affected by its own characteristics but also by the neighboring regions. One of the keys to SAR modeling is the proper definition and construction of spatial weighting matrices, which is usually based on specific research questions and data characteristics. Commonly used construction methods include distance-based weights and neighborhood weights. In this way, the SAR model can effectively reveal the complex relationships in spatial data and become an indispensable and important tool for analyzing spatial data.

In this paper, the SAR model was set up as follows:

$${EH}_{it}={\theta }_0+{\theta }_1{W}_3{EH}_{it}+{\theta }_2{NGP}_{it}+{\theta }_{\mathrm{i}}{control}_{it}+\mu$$

(5)

where $i$ and $t$ stand for province and time, respectively. ${EH}_{it}$ stands for economic high-quality development, ${NGP}_{it}$ stands for natural gas storage and peaking, and ${control}_{it}$ stands for control variables. $W_3$ is the spatial nested matrix of spatial and economic distances. $\mu$ stands for the random error term.

4. Empirical Results and Discussion

4.1. The Main Empirical Results

In the baseline regression results in

Table 7. Baseline results.

VARIABLES	EH
	(1)	(2)	(3)
NGP	0.309 ***	0.295 ***	0.309 ***
	(7.665)	(7.956)	(7.442)
MP	0.022 **	0.014 *	0.023 **
	(2.421)	(1.697)	(2.446)
MS	0.096 ***	0.090 ***	0.096 ***
	(9.677)	(9.706)	(9.488)
ELE	−0.018 **	−0.018 ***	−0.019 **
	(−2.357)	(−2.620)	(−2.443)
CUL	−0.023 ***	−0.025 ***	−0.023 ***
	(−3.282)	(−3.774)	(−3.248)
EDU	−0.401 ***	−0.545 ***	−0.400 ***
	(−2.953)	(−4.284)	(−2.894)
Peo	−0.013	0.001	−0.013
	(−1.132)	(0.073)	(−1.094)
ρ	0.424 ***	0.344 ***	0.300 ***
	(3.971)	(9.239)	(2.747)
σ	0.004 ***	0.003 ***	0.004 ***
	(15.819)	(15.488)	(15.839)
Observations	510	510	510
R2	0.588	0.603	0.586

Note: Robust standard errors are reported in parenthesis. *, **, and *** denote 10%, 5%, and 1% significance levels, respectively.

, this study used a geographic distance matrix, economic distance matrix, and economic geographic distance nested matrix for separate regressions. From the regression results, the regression coefficients of natural gas storage and peaking on high-quality development of the economy are 0.309, 0.295, and 0.309, respectively. They are also significant at the 1% level, which indicates that the development of natural gas storage and peaking can significantly promote economic high-quality development. Research hypothesis H1 was verified.

4.2. Spatial Effects Analysis

In using the Spatial Autoregressive Model (SAR) to study the impact of natural gas storage and peaking on the high-quality development of the economy (

Table 8. Spatial effects analysis.

Direct Effect
NGP	0.315 ***	0.313 ***	0.312 ***
	(7.413)	(7.743)	(7.225)
MP	0.022 **	0.015 *	0.023 **
	(2.471)	(1.719)	(2.495)
MS	0.099 ***	0.096 ***	0.098 ***
	(10.061)	(10.148)	(9.905)
ELE	−0.018 **	−0.020 ***	−0.019 ***
	(−2.531)	(−2.792)	(−2.621)
CUL	−0.023 ***	−0.026 ***	−0.023 ***
	(−3.414)	(−3.907)	(−3.384)
EDU	−0.399 ***	−0.569 ***	−0.396 ***
	(−2.904)	(−4.198)	(−2.847)
Peo	−0.013	0.001	−0.013
	(−1.142)	(0.101)	(−1.102)
Indirect Effect
NGP	0.238 **	0.138 ***	0.140 *
	(2.083)	(4.955)	(1.793)
MP	0.016 *	0.006 *	0.010
	(1.700)	(1.668)	(1.568)
MS	0.074 **	0.042 ***	0.043 *
	(2.230)	(5.674)	(1.929)
ELE	−0.014	−0.009 **	−0.009
	(−1.537)	(−2.465)	(−1.415)
CUL	−0.018 *	−0.012 ***	−0.010
	(−1.840)	(−3.216)	(−1.633)
EDU	−0.300 *	−0.252 ***	−0.176
	(−1.741)	(−3.336)	(−1.549)
Peo	−0.009	0.001	−0.005
	(−0.957)	(0.141)	(−0.870)
Total Effect
NGP	0.554 ***	0.451 ***	0.452 ***
	(3.980)	(7.118)	(4.321)
MP	0.038 **	0.021 *	0.032 **
	(2.294)	(1.718)	(2.404)
MS	0.173 ***	0.138 ***	0.141 ***
	(4.609)	(9.104)	(5.321)
ELE	−0.032 **	−0.028 ***	−0.028 **
	(−2.160)	(−2.729)	(−2.303)
CUL	−0.041 ***	−0.038 ***	−0.034 ***
	(−2.770)	(−3.758)	(−2.883)
EDU	−0.700 **	−0.820 ***	−0.571 **
	(−2.494)	(−3.999)	(−2.541)
Peo	−0.023	0.002	−0.018
	(−1.106)	(0.114)	(−1.078)

Note: Robust standard errors are reported in parenthesis. *, **, and *** denote 10%, 5%, and 1% significance levels, respectively.

), this study found that gas storage and peaking have a significant spatial spillover effect on the high-quality development of neighboring cities. This spillover effect can be analyzed in detail from three aspects: regional linkage effect, environmental spillover effect, and technology and knowledge dissemination effect.

First of all, the regional linkage effect plays a key role in the process of natural gas storage and peaking impact on the economy of neighboring regions. When a region effectively implements a natural gas storage and peaking strategy, it not only ensures the stability of the region’s energy supply but also promotes the growth of the region’s economy by improving energy efficiency and optimizing the energy structure. Second, environmental spillover effects are also an important factor in the impact of natural gas storage and peaking on the economy of neighboring regions. As a clean energy source, natural gas is widely used to help reduce environmental pollution in a region. When air quality in one region improves due to the use of natural gas, this positive environmental change may spread to neighboring regions. Finally, the technology and knowledge dissemination effect is also indispensable for the high-quality economic development of neighboring regions, as a result of natural gas storage and peaking. As natural gas storage and peaking technologies are developed and applied, successful practical experience and technical knowledge can be disseminated to neighboring regions, helping them to implement similar strategies more effectively.

4.3. Robustness Test

This study chose to use the total gas supply as a proxy variable for robustness testing, which helps to deeply understand the impact of natural gas supply on the high-quality development of the economy in different regions. The Spatial Error Model with Replacement (SEM) and Spatial Durbin Model (SDM) were also chosen as robustness tests for changing the model, aiming to confirm the comprehensiveness of the SAR model in explaining the spatial dependence and to ensure the robustness and reliability of the results. In particular, SEM considers the spatial error, and SDM considers the spatial lag, in terms of the independent and dependent variables. In

Table 9. Robustness test.

VARIABLES	(1)	(2)	(3)	(4)	(5)
	EH
	SEM	SDM	SAR	SEM	SDM
NGP	0.296 ***	0.204 ***
	(7.723)	(5.842)
Total Gas Supply			3.138 ***	1.932 ***	3.148 ***
			(13.082)	(8.135)	(12.139)
MP	0.024 ***	0.030 ***	0.021 **	0.028 ***	0.019 **
	(2.675)	(3.704)	(2.575)	(3.529)	(2.151)
MS	0.083 ***	0.081 ***	0.071 ***	0.075 ***	0.083 ***
	(8.235)	(9.737)	(7.864)	(9.226)	(8.665)
ELE	−0.013	−0.026 ***	−0.017 **	−0.028 ***	−0.021 ***
	(−1.640)	(−3.580)	(−2.382)	(−3.982)	(−2.907)
CUL	−0.010	−0.019 ***	0.001	−0.010 *	−0.011 *
	(−1.381)	(−3.139)	(0.168)	(−1.775)	(−1.728)
EDU	−0.630 ***	−0.828 ***	−0.656 ***	−0.818 ***	−0.449 ***
	(−4.274)	(−6.751)	(−5.079)	(−7.008)	(−3.537)
Peo	−0.018	−0.017	−0.021 *	−0.016	−0.013
	(−1.465)	(−1.409)	(−1.815)	(−1.401)	(−1.162)
λ	0.289 ***		0.318 ***
	(4.791)		(6.232)
ρ		0.150 ***		0.189 ***	0.300 ***
		(3.102)		(4.016)	(2.881)
σ	0.004 ***	0.003 ***	0.003 ***	0.002 ***	0.003 ***
	(15.453)	(15.834)	(15.500)	(15.685)	(15.815)
Observations	510	510	510	510	510
R2	0.589	0.407	0.656	0.470	0.666

Note: Robust standard errors are reported in parenthesis. *, **, and *** denote 10%, 5%, and 1% significance levels, respectively.

, this study verified the significant promotion effect of natural gas storage and peaking on the high-quality development of the economy by verifying the performance results of the SEM model, the SDM model, and the selected replacement variable, the total gas supply, in the SAR model, the SEM model, and the SDM model, respectively. The regression results were similar to the previous benchmark regression results, proving that the robustness test was passed.

4.4. Endogeneity Test

In spatial econometric modeling analysis, especially when studying the impact of natural gas storage and peaking on high-quality economic development, the use of Generalized Moment Estimation (GMM) as an endogeneity test shows its significant advantages. By utilizing the lagged values of endogenous variables as instrumental variables, this method effectively mitigates the endogeneity problem, improves the accuracy of model estimation, and achieves an important role in coping with the time-varying heterogeneity of data. In

Table 10. Endogeneity test.

VARIABLES	(1)	(2)	(3)	(4)	(5)	(6)
	EH
	W1	W2	W3	W1	W2	W3
NGP	0.283 ***	0.303 ***	0.283 ***
	(0.045)	(0.043)	(0.045)
Total Gas Supply				3.139 ***	3.203 ***	3.155 ***
				(0.256)	(0.259)	(0.258)
Control	YES	YES	YES	YES	YES	YES
Observations	510	510	510	510	510	510

Note: Robust standard errors are reported in parenthesis. *** denote 1% significance levels.

, (1) to (3), with different matrix weights replaced, natural gas storage and peaking still show a significant promotion effect on high-quality economic development, and in (4) to (6), the total amount of gas supply also shows a significant promotion effect on high-quality economic development, indicating that the conclusions of this study are largely unaffected by endogeneity.

4.5. Heterogeneity Analysis

Table 11. Heterogeneity analysis of the eastern region, central region, and western region.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
VARIABLES	Eastern Region			Central Region			West Region
	EH
	W1	W2	W3	W1	W2	W3	W1	W2	W3
NGP	0.638 ***	0.788 ***	0.773 ***	0.110 **	0.113 **	0.110 **	0.080 ***	0.088 ***	0.079 ***
	(9.371)	(11.360)	(10.468)	(2.261)	(2.335)	(2.247)	(5.084)	(5.194)	(5.062)
MP	0.071 ***	0.069 ***	0.082 ***	0.015	0.019 **	0.014	0.048 ***	0.051 ***	0.049 ***
	(5.235)	(4.255)	(5.572)	(1.542)	(1.990)	(1.508)	(6.587)	(6.793)	(6.759)
MS	0.006	0.008	0.005	−0.015	−0.017	−0.015	−0.034 **	−0.035 **	−0.035 **
	(0.590)	(0.664)	(0.460)	(−0.879)	(−1.042)	(−0.873)	(−2.445)	(−2.428)	(−2.528)
ELE	0.002	−0.002	−0.013	−0.034 ***	−0.032 ***	−0.034 ***	−0.036 ***	−0.037 ***	−0.036 ***
	(0.064)	(−0.064)	(−0.469)	(−5.817)	(−5.503)	(−5.828)	(−8.436)	(−8.347)	(−8.475)
CUL	−0.008	−0.015	−0.021 *	0.003	0.005	0.003	0.009 ***	0.010 ***	0.008 ***
	(−0.711)	(−1.218)	(−1.800)	(0.573)	(0.921)	(0.547)	(3.045)	(3.273)	(2.942)
EDU	−0.777 ***	−0.877 ***	−0.924 ***	−0.109	−0.138	−0.102	−0.084	−0.097	−0.088
	(−3.937)	(−3.871)	(−4.236)	(−1.013)	(−1.315)	(−0.939)	(−1.353)	(−1.499)	(−1.415)
Peo	−0.131 ***	−0.132 ***	−0.118 ***	0.011	0.006	0.011	−0.034 ***	−0.038 ***	−0.035 ***
	(−5.827)	(−5.018)	(−4.816)	(1.039)	(0.572)	(1.079)	(−4.481)	(−4.794)	(−4.540)
ρ	−0.877 ***	−0.249 ***	−0.220	−0.154	0.125	−0.181	−0.564 ***	−0.136 *	−0.381 **
	(−5.562)	(−3.187)	(−1.458)	(−0.945)	(1.638)	(−1.080)	(−2.871)	(−1.933)	(−2.008)
σ	0.002 ***	0.003 ***	0.002 ***	0.000 ***	0.000 ***	0.000 ***	0.000 ***	0.000 ***	0.000 ***
	(9.964)	(9.477)	(10.500)	(8.210)	(8.191)	(8.153)	(9.644)	(9.643)	(9.939)
Observations	187	187	187	136	136	136	187	187	187
R2	0.722	0.654	0.691	0.007	0.004	0.007	0.654		0.691

Note: Robust standard errors are reported in parenthesis. *, **, and *** denote 10%, 5%, and 1% significance levels, respectively.

shows the regression results for the eastern region, central region, and western region. From columns (1)–(3), it can be seen that the regression coefficients of natural gas storage and peaking on high-quality development of the economy in three different matrices are 0.638, 0.788, and 0.773, respectively, and all of them are significant at the 1% level.

Columns (4)–(6) show the regression results for the central region, from which it can be seen that the regression coefficients of natural gas storage and peaking on high-quality development of the economy in the three different weight matrices are 0.11, 0.113, and 0.11, respectively, and all of them are significant at the 5% level.

Columns (7)–(9) show the regression results in the western region, from which it can be seen that the regression coefficients of natural gas storage and peaking on high-quality development of the economy in the three different weight matrices are 0.08, 0.088, and 0.079, respectively, and all of them are significant at the 1% level.

Taken together, the promotion effect of natural gas storage and peaking on high-quality economic development is more obvious in the eastern region, followed by the central region, and finally the western region. According to the scale and conditions of economic development in the east, center, and west, this is in line with the general law of economic development. The scale of natural gas storage and peaking in the eastern region, and the fixed asset investment, will be larger than that in the central and western regions. From the point of view of the initial conditions of resource endowment, natural gas storage and peaking support are more needed in the eastern region. Therefore, in the process of natural gas storage and peaking affecting the high-quality development of the economy, the most important thing is not the initial problem of resource endowment, but the differentiation of economic development.

4.6. Mechanism Analysis

In

Table 12. Mechanism analysis of the RDT efficiency.

VARIABLES	(1)	(2)	(3)
	EH	EH	EH
	W1	W2	W3
NGP	0.311 ***	0.297 ***	0.311 ***
	(7.736)	(8.028)	(7.520)
NGP × RDT	0.209 *	0.194 *	0.212 *
	(1.781)	(1.771)	(1.771)
RDT	0.014	0.014	0.014
	(1.002)	(1.107)	(0.996)
MP	0.022 **	0.015 *	0.023 **
	(2.459)	(1.734)	(2.480)
MS	0.096 ***	0.090 ***	0.096 ***
	(9.715)	(9.741)	(9.530)
ELE	−0.018 **	−0.019 ***	−0.019 **
	(−2.452)	(−2.714)	(−2.538)
CUL	−0.024 ***	−0.025 ***	−0.024 ***
	(−3.346)	(−3.836)	(−3.313)
EDU	−0.404 ***	−0.546 ***	−0.404 ***
	(−2.980)	(−4.298)	(−2.924)
Peo	−0.013	0.001	−0.013
	(−1.080)	(0.126)	(−1.040)
ρ	0.420 ***	0.342 ***	0.300 ***
	(3.921)	(9.191)	(2.750)
σ	0.004 ***	0.003 ***	0.004 ***
	(15.813)	(15.455)	(15.844)
N	510	510	510
R2	0.588	0.604	0.587

Note: Robust standard errors are reported in parenthesis. *, **, and *** denote 10%, 5%, and 1% significance levels, respectively.

there are the regression results of RDT efficiency, regulating the impact of natural gas storage and peaking on high-quality economic development. In the presentation of the regression results, this study used the geographic distance matrix, the economic distance matrix, and the economic and geographic distance nested matrix for the regression. From the regression results, natural gas storage and peaking on economic high-quality development still show a significant promotion effect. The regression coefficients of the cross-multiplication terms of RDT efficiency and natural gas storage and peaking are 0.209, 0.194, and 0.212, under the three matrices, respectively. They are significant at the 10% level at the same time, indicating that with the improvement of RDT efficiency, the facilitation effect of natural gas storage and peaking on the high-quality development of the economy can be further improved. These results suggest that as the efficiency of RDT improves, the effectiveness of natural gas storage and peaking in promoting high-quality economic development will further improve. These results suggest that the contribution of natural gas storage and peaking to high-quality economic development will further increase with the improvement of RDT efficiency, and Hypothesis 2 was verified.

In order to explore the regulating effect of green finance on natural gas storage and peaking on the high-quality development of the economy, this study analyzed the regulating effect of green finance in the less developed western region and the more developed eastern region. As can be seen from

Table 13. Mechanism analysis of green finance.

VARIABLES	(1)	(2)
	EH	EH
	WEST	EAST
NGP	0.028 *	0.874 ***
	(1.672)	(10.174)
NGP × GF	0.385 ***	−1.391 ***
	(6.288)	(−5.279)
GF	−0.032	0.122 *
	(−1.233)	(1.667)
MP	0.053 ***	0.077 ***
	(8.104)	(5.832)
MS	−0.037 ***	0.001
	(−2.899)	(0.083)
ELE	−0.036 ***	−0.001
	(−9.328)	(−0.053)
CUL	0.012 ***	−0.015
	(4.612)	(−1.417)
EDU	−0.087	−1.028 ***
	(−1.560)	(−3.832)
Peo	−0.040 ***	−0.125 ***
	(−5.792)	(−5.397)
ρ	−0.418 **	−0.523 ***
	(−2.307)	(−3.712)
σ	0.000 ***	0.002 ***
	(10.191)	(11.797)
Observations	187	187
R2	0.067	0.669

Note: Robust standard errors are reported in parenthesis. *, **, and *** denote 10%, 5%, and 1% significance levels, respectively.

, green finance in the eastern region samples shows a negative regulatory effect, and in the western region samples, shows a positive regulatory effect. The positive moderating effect of green finance in the western region containing natural gas resources is based on an in-depth consideration of region-specific resource conditions and their interactions with green finance. This study further suggests that green finance may have a positive impact on natural gas storage and peaking activities in these resource-rich regions. Hypothesis 3 was verified.

5. Conclusions and Recommendations

In this paper, the role of natural gas peak shaving in high-quality economic development was analyzed theoretically and empirically. The main conclusions are as follows: first, natural gas peak shaving has a significant role in promoting high-quality economic development, and this conclusion is verified by the robustness test and endogeneity test. Second, natural gas peak shaving has a positive spatial spillover effect on the high-quality economic development of surrounding areas. Third, heterogeneity analysis further reveals that the promotion effects of natural gas peak shaving on high-quality economic development in the eastern, central, and western regions are significantly different. The facilitating effect is stronger in the eastern region, while it is weaker in the central and western regions. Fourth, the mechanism analysis shows that R and D technical efficiency and green finance have a positive moderating effect on the benchmark effect.

Based on the above findings, this paper makes the following policy recommendations: first, to vigorously promote natural gas storage and peak shaving projects to promote high-quality economic development. The government should strengthen support in the peak shaving construction and operation process through administrative regulations, financial support, tax incentives, and other behaviors. They should establish a special regulatory system department, centralize the industry management power, and be able to exercise the regulatory obligations at the national level to maintain the marketability of peak shaving construction and operation, prevent monopoly, and attract capital to continue to concentrate on the peak shaving business. At the same time, a series of laws and regulations with stronger legal effect and more stable and targeted should be formulated to limit the entire peak shaving system, so that the peak shaving construction has laws to rely on.

Second, they should continue to promote technological progress and make full use of the positive regulatory effect of research and development efficiency on natural gas storage peak shaving, to promote high-quality economic development. Therefore, on the basis of increasing R and D investment, enterprises should attach importance to R and D output, formulate an assessment system based on R and D efficiency, and establish an R and D culture and management system suitable for the characteristics of enterprises, so as to continuously improve R and D efficiency. The government should continuously improve the R and D evaluation system based on R and D results, evaluate the R and D results fairly and openly, and encourage enterprises and individuals to actively invest in R and D. They should actively promote industry-university-research cooperation, promote the deep integration between universities, scientific research institutions, and enterprises, and realize resource sharing and complementary advantages.

Third, they should establish a multi-level green finance market system to make good use of the positive regulatory role of green finance. On the one hand, we should vigorously carry out the pilot work of green finance reform, increase the cooperation between the government and social capital, and guide more social capital to participate in green and low-carbon development, so as to fully optimize the allocation of funds in the capital market and provide sufficient funding sources for low-carbon transformation. On the other hand, we will continue to increase the innovation of green financial products, enrich the types of green financial products, actively open green funds, green trusts, green insurance, green bonds, and carbon finance products, and gradually build diversified and multi-level green financial products and market system.