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Abstract

This paper evaluates China’s oil security from 2010 to 2019 under the “double carbon” constraint using an entropy-based multi-indicator system. The system encompasses four dimensions: supply security, demand security, market security, and the “double carbon” constraint, with 16 secondary indicators. The results show that China’s oil security experienced three stages: a peak followed by a decline and then a low-level fluctuation. Supply security deteriorated due to tightening domestic oil production, while demand security remained low despite improvements in consumption structure. Market security was negatively impacted by rising import dependence and declining foreign exchange reserves. However, the “dual carbon” constraint has led to an annual increase in China’s carbon emission intensity score and a continuous rise in the proportion of clean energy consumption, effectively alleviating the pressure on oil security. The findings highlight the need for China to increase oil supply investment, ensure smooth market operations, and promote low-carbon development to enhance its oil security. The TOPSIS method is employed to validate the robustness of the results. Future research should combine subjective and objective methods, comprehensively utilize the advantages of various methods, and improve the accuracy and effectiveness of analysis.

Keywords

oil security supply and demand double carbon constraint entropy method

Introduction

Since energy is an indispensable support for social and economic development in the modern world, the security of the energy sector is an essential component of national security (Demski et al., 2018; Lee & Wang, 2022; Xu, 2006). Fossil energy, especially oil, has always been an indispensable core energy resource in China’s economic and social development (Bambawale & Sovacool, 2011; Leung, 2011; Odgaard & Delman, 2014). From 1990 to 2019, China’s oil consumption increased from 114.86 to 645.07 million tons, with an average annual growth rate of 6.13% (NBSC, 2021). Meanwhile, due to the slow growth in Chinese oil production, China is increasingly dependent on international oil markets (Demski et al., 2018; Wang et al., 2018; Yang et al., 2022). China’s oil self-sufficiency declined from 120.95% in 1990 to 28.55% in 2019 (NBSC, 2021).

However, recent major emergencies have heightened China’s concerns about its oil security. First, as a result of the spread of COVID-19 worldwide since January 2020, international oil demand and prices have dropped significantly (Cheng et al., 2021; Li et al., 2022a; Mensi et al., 2021; Narayan, 2022; Yousaf, 2021; Yıldırım et al., 2022). Oil prices for West Texas Intermediate (WTI) declined from approximately $60 per barrel to below $30 per barrel by the beginning of March 2020 and then dropped to $−37 per barrel by the end of April 2020. Second, following the outbreak of the Russian-Ukrainian conflict in March 2022, the WTI oil price skyrocketed to more than $130 per barrel. The repeated occurrence of major emergency situations has led to an imbalance between supply and demand in both the domestic and international oil markets, as well as sharp fluctuations in international oil prices. As a result, China’s oil supply and demand have become more uncertain. Therefore, how to accurately measure China’s oil supply and demand security and propose countermeasures have become real problems that require study and resolution.

In addition, the implementation of carbon peak and carbon neutrality (referred to as “double carbon”) has resulted in higher production requirements for existing oil production facilities in China. To effectively address climate change and achieve sustainable development, China announced that it would strive to reach a carbon peak by 2030 and carbon neutrality by 2060 at the 75th United Nations General Assembly in September 2020 (Jiang et al., 2022; Ke et al., 2022; Normile, 2020). During the 26th United Nations Climate Change Conference (COP26) in November 2021, the Chinese government released its “1 + N” policy framework to guide the fight against climate change and achieve “double carbon” (UNDP, 2021). The establishment of “double carbon” has set new constraints for China’s economic and social development in the future. Since China should gradually reduce its dependence on traditional energy sources to achieve sustainable development, the “double carbon” factor must be considered to accurately evaluate and estimate the level of energy security in the future.

Oil security has become the focus of recent studies due to its importance as a strategic resource. Based on the operating logic of the oil industry and the importance of oil to countries or regions, numerous studies have been conducted to assess the security of oil supply and demand. Among the various analysis systems constructed, supply security, demand security, and market security are considered to be the most important factors (Brown, 2018; Brown & Huntington, 2017; Ji et al., 2019; Kraidi et al., 2021; Su et al., 2017; Yuan et al., 2020; Zhang et al., 2013; Zhao & Chen, 2014). In recent years, with changes in the global political and economic situation and the development of the oil industry, some new influencing factors have been incorporated into the oil supply and demand security system. For example, petroleum ecological factors (Gong et al., 2022), environmental factors (Wang et al., 2020), industrial development factors (Wang et al., 2021), and policy factors (K. Wang et al., 2022). The addition of the above factors makes the analysis of the oil supply and demand system more comprehensive and systematic.

As the amount of research on oil security continues to increase, the methods involved in oil security evaluation are becoming increasingly diversified. According to the number of evaluation indicators, oil security evaluation methods can be divided into single-indicator and multi-indicator evaluation methods. The single-indicator evaluation method typically focuses on a particular aspect of the research problem and uses a single evaluation indicator to conduct the corresponding analysis and research. For instance, in some studies, single indicators such as oil imports, oil prices, or oil consumption are specifically analyzed and evaluated (Chen et al., 2022; Li et al., 2022b; Nazlioglu et al., 2022; Raza & Lin, 2021; Ren et al., 2023; Siddiqui et al., 2022). Since the multi-indicator evaluation method involves a broader range of evaluations and is more inclusive, it has gradually become more popular over time. Yang et al. (2022) constructed an energy security evaluation system based on the DPSIR model and evaluated China’s energy security from five dimensions: energy security driving forces, energy security pressures, energy security state, energy security impacts, and energy security responses. Liu et al. (2020) constructed a hybrid assessment framework that integrates traditional indicators and complex network indicators of global oil trade to assess oil security at the national level. Krane and Medlock (2018) analyzed three perspectives of diversification, self-sufficiency, and environmental pressure and concluded that US oil security would benefit from these three factors. An energy security assessment framework has been developed to evaluate the energy security of oil-importing small island developing states from seven perspectives: import dependency, energy prices, climate change and resilience, governance, infrastructure, equity, and energy efficiency (Raghoo et al., 2018). Overall, the existing research on energy security provides effective references for this study, from evaluation methods to the establishment of indicator systems.

From the above analysis, it can be concluded that China’s oil security is currently facing a severe situation. Under the “dual carbon” goal, a timetable has been set for the green transformation of traditional energy in China, and the reality that over 70% of China’s oil demand relies on imports cannot be changed. Therefore, the low-carbon transformation of energy and international political and economic uncertainty have put dual pressure on China’s oil security. Therefore, it is necessary to conduct a precise assessment of China’s oil supply and demand security under the constraint of “dual carbon.” This paper aims to examine China’s oil security under the “double carbon” constraint based on a multi-indicator evaluation method, covering the period between 2010 and 2019. This paper contributes to the literature in two ways. First, in reviewing recent studies, some have taken environmental or ecological factors into account when discussing oil security. However, despite the rapid development of China’s carbon emission reduction in recent years, few studies have incorporated the “double carbon” constraint into energy security analyses. Thus, this paper adds the “double carbon” constraint factor to the traditional supply and demand factors for analysis, which is a further improvement of the analysis framework of the oil supply and demand security evaluation under the new situation, and the results obtained are a valuable addition to existing research in related fields. Second, the geopolitical situation in the world has become increasingly tense since 2010, and the international oil market has experienced significant uncertainty, which has placed tremendous pressure on the energy security of major oil-consuming countries. This paper has a high level of timeliness and provides an accurate assessment of China’s current situation in terms of oil security.

Materials and Methods

To conduct a systematic evaluation of China’s oil supply and demand security, it is necessary to establish an indicator analysis system. After reading the literature, select the entropy method for indicator analysis. This is because the entropy method is an objective weighting method, and its core idea is to determine the weights of each indicator through the optimization of information entropy, thereby avoiding the problem of subjective weighting. It is precisely based on this advantage that the entropy method has been applied in the research of measuring and evaluating oil market indicators (Luo & Yang, 2023; Mensi et al., 2014). To further strengthen the results obtained by the entropy method, the TOPSIS method was used for robustness testing of the results. This is because the TOPSIS method can fully utilize the information of raw data and reduce information loss. Meanwhile, this method does not require an objective function, thus avoiding the subjectivity of the data. Therefore, this method is commonly used to handle multi criteria evaluation problems (Rehman & Ali, 2021; Zhang et al., 2024).

In this paper, the analysis idea is as follows. First, the indicators that affect China’s oil supply and demand are selected, and a system for comprehensive evaluation is constructed. Second, to determine the weight value of each evaluation indicator, an entropy method is used, and the comprehensive score of each evaluation indicator is calculated. Third, to check the stability of the evaluation results, a TOPSIS method was used to calculate the proximity value of the evaluation indicator. Finally, the comprehensive score and proximity value of China’s oil supply and demand security are compared and analyzed, and a conclusion is drawn. The framework and flowchart of this paper are shown in Figure 1.

Figure 1.

The framework and flowchart of this paper.

System Construction

Considering the existing research, the basic operating mechanism of the oil industry, and the current targets for “double carbon,” this paper recommends screening the factors affecting China’s oil supply and demand from four dimensions: supply, demand, market, and “double carbon” constraints (Lu et al., 2014; Gholz et al., 2017; He & Guo, 2021; Pan et al., 2020). Based on the above four dimensions, a total of 16 secondary indicators were selected to construct an indicator system, as shown in Table 1.

Table 1.

Oil Supply and Demand Security Evaluation System.

Primary indicators	Secondary indicators	Unit	Indicator explanation	Trend
1. Supply security	1.1 Recoverable reserves	billion tons	Oil reserves that can be extracted from underground reservoirs in the country	+
	1.2 Production	million tons	National annual oil production	+
	1.3 Reserve production ratio	%	Ratio of the remaining recoverable reserves at the end of the year to the current year’s output	+
	1.4 Strategic reserves	million tons	National strategic oil reserve at the end of the year (calculated according to the data published on the official website of PetroChina)	+
2. Demand security	2.1 Consumption proportion	%	Ratio of total national oil consumption to total national energy consumption	−
	2.2 Consumption	million tons	Total national oil consumption	−
	2.3 Consumption intensity	ton/million RMB	Ratio of total national oil consumption to total national GDP	−
	2.4 Consumption elasticity	1	Ratio of national oil consumption growth rate to national GDP growth rate	−
3. Market security	3.1 Import percentages	%	Ratio of national net oil imports to total national energy consumption	−
	3.2 Market concentration	%	Proportion of oil import volume of the three largest countries or regions in the total import volume	−
	3.3 Foreign exchange reserves	million US dollars	Total national foreign exchange reserves at the end of the year	+
	3.4 International price volatility	1	(highest monthly closing price of WTI crude oil in the current year’s lowest monthly closing price of WTI crude oil in the current year) / average monthly closing price of WTI crude oil in the current year	−
4. “Double carbon” constraint	4.1 Carbon emissions	ton	National carbon dioxide emissions	−
	4.2 Carbon emission intensity	ton/million RMB	Ratio of national carbon dioxide emissions to total national GDP	−
	4.3 Environmental governance investment	million RMB	The sum of the investment in urban environmental infrastructure construction and industrial pollution source control in China	+
	4.4 The proportion of clean energy consumption	%	Proportion of clean energy consumption such as natural gas, wind energy and solar energy in the total energy consumption of the country	+

Note. Since the vast majority of domestic oil is exploited by PetroChina, Sinopec, and CNOOC, the average value of the reserve production ratio announced by the above three companies is used as the domestic oil reserve production ratio data for follow-up research.

Methodology

This paper uses the entropy method to determine the weights of the evaluation indicators and calculate the comprehensive evaluation score. The entropy method is an objective weighting analysis method that measures uncertainty (He et al., 2018; Sun, 2021; Yu et al., 2021). Generally, a greater amount of information will result in a smaller uncertainty and a smaller entropy, whereas a smaller amount of information will result in greater uncertainty and greater entropy. Therefore, entropy values can be used to calculate equal weights for multiple indicators based on their degree of variation, thus providing the basis for comprehensive evaluations of multiple indicators.

Data Standardization

Before determining the weights, it is necessary to standardize the data. This is because the calculation of information entropy requires data to be non negative, and negative numbers or zero have no clear explanation in information entropy. If the data contains numbers or zero, it may lead to inaccurate calculation results of information entropy. To avoid inconsistent data directions and to eliminate the influence of negative numbers and zero on subsequent analyses, the data must be reversed or forwarded before determining the weights.

For positive indicators, the formula is as follows:

y_{ij} = \frac{x_{i, \max} - x_{ij}}{x_{i, \max} - x_{i, \min}} + 0.0001

(1)

For negative indicators, the formula is as follows:

y_{ij} = \frac{x_{i, \max} - x_{ij}}{x_{i, \max} - x_{i, \min}} + 0.0001

(2)

where $x_{ij}$ represents the indicator value of the $i$ th ( $i = 1, 2, \dots, m$ ) item of the $j$ th ( $j = 1, 2, \dots, n$ ) indicator before processing, and $y_{ij}$ represents the indicator value of the $j$ th indicator in the $i$ th item after normalization processing. The reason for adding 0.0001 to the right side of formulas (1) and (2) is to ensure that the value of information entropy is non-negative to avoid calculation problems.

Determine the Comprehensive Evaluation Score

First, calculate the weight of each indicator. Then calculate its entropy value. Furthermore, the difference coefficient of the indicators is calculated. The larger the difference coefficient, the greater the weight corresponding to the information, and vice versa. Finally, calculate the weight corresponding to each piece of information to provide a basis for comprehensive evaluation.

Step 1: Calculate the proportion of the jth indicator in the ith project to this indicator:

P_{ij} = \frac{C_{ij}}{Σ_{j = 1}^{n} C_{ij}}

(3)

Step 2: Calculate the entropy value of the jth indicator:

e_{j} = - k \times Σ_{i = 1}^{m} P_{ijln} P_{ij}

(4)

where $k > 0$ ; $\ln$ is the natural logarithm; $e_{j} \geq 0$ . In Equation 4, the constant $k$ is related to the number of samples $m$ . Generally, let $k = 1 / lnm$ , then $0 \leq e \leq 1$ .

Step 3: Calculate the difference coefficient of the $j$ th indicator,

g_{j} = 1 - e_{j}

(5)

For the $j$ th indicator, the greater the difference in indicator value $C_{ij}$ , the greater its impact on the evaluation of the plan. That is, the larger $g_{j}$ is, the more important the indicator is. Therefore, the weights can be calculated by:

W_{j} = \frac{g_{j}}{Σ_{j = 1}^{n} g_{j}}

(6)

Step 4: Calculate the comprehensive evaluation score:

S_{i} = σ_{j = 1}^{n} W_{j} y_{ij}

(7)

where $S_{i}$ is the comprehensive evaluation score, and $W_{j}$ is the obtained indicator weight.

Data Explanation

In this paper, data from 2010 to 2019 were evaluated and analyzed comprehensively. Among these data, the international oil price volatility data are monthly, while the rest are annual. All raw data come from the China Statistical Yearbook, China Energy Statistical Yearbook, China Environmental Statistical Yearbook, China Mineral Resources Report, the official website of the New York Mercantile Exchange, the official website of the State Administration of Foreign Exchange of the People’s Republic of China, the official website of the Ministry of Housing and Urban Rural Development, the official website of the Ministry of Ecology and Environment and the official websites of the three China’s major oil companies (i.e., PetroChina, Sinopec, and CNOOC).

Results

According to the above research methods and data, each indicator in the oil supply and demand security system needs to be weighted (Table 2). Then the evaluation indicator score and the comprehensive score must be calculated (Table 3), followed by a ranking comparison (Table 4 and Figure 2).

Table 2.

Weight Calculation Results of Each Indicator in China’s Oil Supply and Demand Security Evaluation System.

Primary indicators	Secondary indicators	Entropy value	Difference coefficient	Weights of secondary indicators	Weights of primary indicators
Supply security	Recoverable reserves	0.9162	0.0838	0.0486	0.2608
	Production	0.8708	0.1292	0.0749
	Reserve production ratio	0.8731	0.1269	0.0736
	Strategic reserves	0.8526	0.1474	0.0855
Demand security	Consumption proportion	0.8861	0.1139	0.0661	0.2239
	Consumption	0.9319	0.0681	0.0395
	Consumption intensity	0.9022	0.0978	0.0567
	Consumption elasticity	0.9022	0.0978	0.0567
Market security	Import percentages	0.8769	0.1231	0.0714	0.2404
	Market concentration	0.8337	0.1663	0.0965
	Foreign exchange reserves	0.8903	0.1097	0.0636
	International price volatility	0.9434	0.0566	0.0328
“Double carbon” constraint	Carbon emissions	0.8759	0.1241	0.0720	0.2750
	Carbon emission intensity	0.9053	0.0947	0.0549
	Environmental governance investment	0.8926	0.1074	0.0623
	The proportion of clean energy consumption	0.9227	0.0773	0.0448

Table 3.

Each Indicator Score and Comprehensive Score of China’s oil supply and Demand Security Evaluation System.

Secondary indicators	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019
Recoverable reserves	0.0000	0.0084	0.0193	0.0241	0.0314	0.0398	0.0399	0.0449	0.0486	0.0464
Production	0.0406	0.0403	0.0539	0.0612	0.0656	0.0749	0.0308	0.0065	0.0000	0.0050
Reserve production ratio	0.0682	0.0736	0.0677	0.0679	0.0575	0.0157	0.0000	0.0154	0.0192	0.0137
Strategic reserves	0.0000	0.0071	0.0141	0.0212	0.0283	0.0353	0.0424	0.0495	0.0565	0.0636
Consumption proportion	0.0662	0.0855	0.0772	0.0745	0.0635	0.0276	0.0166	0.0083	0.0083	0.0000
Consumption	0.0661	0.0611	0.0541	0.0471	0.0409	0.0277	0.0221	0.0133	0.0073	0.0000
Consumption intensity	0.0000	0.0127	0.0172	0.0214	0.0249	0.0243	0.0280	0.0324	0.0370	0.0395
Consumption elasticity	0.0124	0.0328	0.0233	0.0236	0.0239	0.0000	0.0264	0.0251	0.0292	0.0230
Import percentages	0.0567	0.0495	0.0497	0.0514	0.0487	0.0434	0.0273	0.0169	0.0055	0.0000
Market concentration	0.0114	0.0282	0.0115	0.0243	0.0342	0.0477	0.0483	0.0520	0.0567	0.0000
Foreign exchange reserves	0.0000	0.0241	0.0336	0.0704	0.0720	0.0349	0.0118	0.0211	0.0163	0.0188
International price volatility	0.0517	0.0286	0.0455	0.0549	0.0000	0.0131	0.0156	0.0413	0.0173	0.0533
Carbon emissions	0.0714	0.0412	0.0204	0.0103	0.0150	0.0235	0.0297	0.0234	0.0129	0.0000
Carbon emission intensity	0.0000	0.0090	0.0134	0.0210	0.0307	0.0393	0.0477	0.0546	0.0596	0.0623
Environmental governance investment	0.0000	0.0077	0.0231	0.0302	0.0384	0.0228	0.0336	0.0448	0.0395	0.0372
The proportion of clean energy consumption	0.0000	0.0000	0.0116	0.0219	0.0334	0.0412	0.0527	0.0682	0.0862	0.0965
Comprehensive score	0.4447	0.5100	0.5357	0.6253	0.6083	0.5113	0.4727	0.5178	0.5002	0.4594

Table 4.

Evaluation Results of China’s Oil Supply and Demand Security.

Year	Comprehensive score		Supply security		Demand security		Market security		“Double carbon” constraint
Year	Score	Rank	Score	Rank	Score	Rank	Score	Rank	Score	Rank
2010	0.4447	10	0.1089	10	0.1447	5	0.1198	7	0.0714	8
2011	0.5100	6	0.1294	5	0.1922	1	0.1305	6	0.0579	10
2012	0.5357	3	0.1550	4	0.1718	2	0.1402	3	0.0686	9
2013	0.6253	1	0.1744	2	0.1666	3	0.2010	1	0.0834	7
2014	0.6083	2	0.1828	1	0.1532	4	0.1548	2	0.1175	6
2015	0.5113	5	0.1658	3	0.0795	8	0.1392	4	0.1268	5
2016	0.4727	8	0.1131	9	0.0930	6	0.1029	8	0.1636	4
2017	0.5178	4	0.1162	8	0.0791	9	0.1314	5	0.1911	3
2018	0.5002	7	0.1244	7	0.0818	7	0.0958	9	0.1982	1
2019	0.4594	9	0.1288	6	0.0625	10	0.0722	10	0.1959	2

Figure 2.

The comprehensive scores and the primary indicator scores of China’s oil supply and demand security evaluation.

Overall Analysis of China’s Oil Supply and Demand Security Level

Table 2 shows the entropy value, difference coefficient, and weight for each indicator in China’s oil supply and demand security evaluation system. For the primary indicators, it can be seen that the weights from largest to smallest are the “double carbon” constraint, supply security, market security, and demand security. For the secondary indicators, except for market concentration, which accounts for 9.65% of the total weight, all other indicators have a weight below 9%, while both oil consumption and international oil price volatility have weights below 4%.

It is important to note that the level of security in China’s oil supply and demand is determined by the combined effects of four factors, that is, supply security, demand security, market security, and “double carbon” constraints. As shown in Table 4 and Figure 2, China’s oil supply and demand security has gone through three different stages over the past few years.

The first stage is from 2010 to 2013. At this stage, China’s oil supply and demand security level is in a state of continuous improvement. The comprehensive score of oil supply and demand security rose from 0.4447 in 2010 to 0.6253 in 2013, ranking first. During this period, China’s oil supply and demand security state reached its lowest point in 2010. In addition to ranking fifth in oil demand security, China’s supply security score, market security score, and “double carbon” constraint score ranked 10th, 7th, and 8th, respectively, making the comprehensive score of China’s oil supply and demand security shallow that year. The peak appeared in 2013. In that year, except for the “double carbon” constraint score, which ranked seventh, all three supply security scores, demand security scores, and market security scores increased significantly compared with 2010, placing them second, third, and first, respectively. As a result, China’s oil supply and demand security were excellent in 2013.

The second stage is from 2013 to 2016. There has been a decline in China’s oil supply and demand security at this stage, and its comprehensive score has fallen from 0.6253 in 2013, ranked first, to 0.4727 in 2016, ranked eighth, a drop of nearly 25%. As seen, while the “double carbon” constraint policy provided some support to China’s overall security level of oil supply and demand, other factors had a negative impact during this stage. The reasons are as follows: first, the oil supply security score dropped from 0.1744 in 2013, ranking second, to 0.1131 in 2016, ranking ninth. Second, the oil demand security score dropped from 0.1666 in 2013, ranking third, to 0.0930 in 2016, ranking sixth. Third, the oil market security score dropped from 0.2010 in 2013, ranking first, to 0.1029 in 2016, ranking eighth.

The third stage is from 2016 to 2019. At this stage, the security level of China’s oil supply and demand is in a low range, and its comprehensive score hovers between 0.4594 and 0.5178. Specifically, in this stage, the supply security score increased slightly, from 0.1131 in 2016, ranking ninth, to 0.1288 in 2019, ranking sixth. The “double carbon” constraint score increased from 0.1636 in 2016, ranking fourth, to 0.1959 in 2019, ranking second. However, the demand security score dropped from 0.0930 in 2016 to 0.0625 in 2019, ranking sixth. The market security score also declined, from 0.1029 in 2016, ranking eighth, to 0.0722 in 2019, ranking 10th.

Comparative Analysis of the Primary Indicators of China’s Oil Supply and Demand Security Level

Oil supply security is determined by combining oil recoverable reserves, production, reserve production ratios, and strategic reserves. The weights of the four indicators are 0.0486, 0.0749, 0.0736, and 0.0855. As shown in Table 4 and Figure 2, the supply security score has increased year by year since 2010, reaching a high point of 0.1828 in 2014. Following that, the supply security score began to decline gradually, reaching a low point of 0.131 in 2016, ranking ninth; however, it gradually rose by 2019 to 0.1288, ranking sixth. Table 3 shows that, except for 2019, the two indicator values of oil recoverable reserves and strategic reserves have grown steadily for a decade, which has effectively supported China’s oil supply. However, after oil production reached a decade high of 0.0749 in 2015, it fell rapidly to 0.0308 in 2016 and has declined since then. The oil reserve production ratio dropped rapidly from 0.0575 in 2014 to 0.0157 in 2015 and continued to remain low. Thus, although oil strategic reserves and recoverable reserves serve as support, the shortage of oil production and reserve production ratio since 2014 indicates a tight state on the oil supply side, and the security situation of China’s oil supply is becoming increasingly severe.

China’s oil demand security depends on the combination of the oil consumption proportion, consumption, consumption intensity, and consumption elasticity. The weights of the four indicators are 0.0661, 0.0395, 0.0567, and 0.0567. Figure 2 and Table 4 show that China’s oil demand security score was relatively high from 2010 to 2014, with a score of 0.1922 reaching its highest point in 2011. From 2014 to 2019, China’s oil demand security score hovered in the low range. According to Table 3, the change in oil consumption elasticity over the past 10 years has been relatively stable. The proportion of oil consumption fluctuated sharply from 2010 to 2014 and then stabilized. For the last 10 years, the oil consumption score has been declining, indicating that China’s oil consumption has been increasing.

Meanwhile, the oil consumption intensity score has been rising for 10 years, indicating that China has been consuming less oil than it has been growing economically in the past 10 years. This means that China’s economic development has continuously improved oil resource utilization efficiency. As a whole, although China’s oil demand security tends to be reasonable in terms of the oil consumption structure, it remains at a relatively low level.

The security of China’s oil market depends on the combined effect of the proportion of oil imports, market concentration, national foreign exchange reserves, and international oil price volatility. The weights of the four indicators are 0.0714, 0.0965, 0.0636, and 0.0328. Table 4 and Figure 2 show that China’s oil market had the highest security score at 0.2010 in 2013, ranking first, and then continued to decline. In 2019, the score was only 0.0722, ranking 10th. As seen in Table 3, the proportion of domestic oil imports has been increasing since 2010, resulting in a continuous decline in the oil import proportion score. Although the market concentration score has slowly risen over the past few years, the effect has been limited, and the score dropped directly to 0.0000 in 2019. After reaching the maximum value of 0.0720 in 2014, the score of China’s foreign exchange reserves continued to decline, and the score was approximately 0.02 from 2016 to 2019. The international oil price volatility score is relatively unstable, hovering between 0.0000 and 0.0549 for 10 years. In general, China’s oil market security situation is not optimistic over time.

The effect of the “double carbon” constraint depends on the combination of carbon emissions, carbon emission intensity, environmental governance investment, and the proportion of clean energy consumption. The weights of the four indicators are 0.0720, 0.0549, 0.0623, and 0.0448. Table 4 and Figure 2 show that the overall score of the “double carbon” constraint is on the rise, ranking 10th from 0.0579 in 2011 and reaching 0.1959 in 2019, ranking second. According to Table 3, even though China’s carbon emissions are increasing year by year and the overall carbon emission scores are declining, carbon emission intensity scores are increasing. China’s environmental governance investment score has also remained relatively stable since 2015. Furthermore, China’s clean energy industry has developed rapidly in recent years, and the utilization rate of clean energy, such as natural gas and photovoltaics, has continued to rise, increasing the proportion of clean energy consumption in China each year. All these factors have contributed to the 10-year increase in the “double carbon” constraint score.

Robustness Test

Using the TOPSIS method, the results from the above studies were tested for robustness. The TOPSIS method is an analysis method often used in management research. Through the size of the data, the core idea is to find the positive ideal solution, the negative ideal solution, and the distance between each scheme and these positive and negative ideal solutions to calculate relative progress and sort the advantages and disadvantages based on their proximity value. The research method combining entropy and TOPSIS is widely used in various evaluation studies (Li et al., 2022c; Mao et al., 2016; Meng et al., 2018). In this part, TOPSIS analysis is continued on the basis of Equations 1–6.

Step 1: Normalize the original matrix to form a standardized decision matrix $Z = {z_{ij}}$ , $i = 1, 2, \dots, m$ , $j = 1, 2, \dots, n$ .

z_{ij} = \frac{x_{ij}}{\sqrt{Σ_{i = 1}^{m} x_{ij}^{2}}}

(8)

Step 2: The weights calculated in Equation 6 are multiplied by the normalized decision matrix to obtain the weighted decision evaluation matrix $V = {v_{ij}}$ .

v_{ij} = W_{j} \times z_{ij}

(9)

Step 3: Calculate the positive ideal solution and negative ideal solution of each indicator.

(1) Positive ideal solution:

V^{+} = (V_{1}^{+}, V_{2}^{+}, \dots, V_{m}^{+}) = {\max v_{ij} | j = 1, 2, \dots, n}

(10)

(2) Negative ideal solution:

V^{-} = (V_{1}^{-}, V_{2}^{-}, \dots, V_{m}^{-}) = {\min v_{ij} | j = 1, 2, \dots, n}

(11)

Step 4: Calculate the optimal distance $D^{+}$ from the evaluation indicator value to the positive ideal solution and the worst distance $D^{-}$ between the evaluation indicator value and the negative ideal solution.

(1) The optimal distance:

D^{+} = \sqrt{Σ_{i = 1}^{m} {(V^{\pm} v_{ij})}^{2}}

(12)

(2) The worst distance:

D^{-} = \sqrt{Σ_{i = 1}^{m} {(V^{-} - v_{ij})}^{2}}

(13)

Step 5: Calculate the proximity value $C_{i}$ .

C_{i} = D^{-} / (D^{+} + D^{-})

(14)

Step 6: Sort according to the proximity value. The higher the $C_{i}$ value is, the higher the evaluation value level, and vice versa.

Using the TOPSIS method to analyze the data from 2010 to 2019, the obtained evaluation results and the trend of the proximity value are shown in Table 5 and Figure 3. The absolute difference between the proximity value calculated using TOPSIS and the comprehensive score calculated directly using the entropy method is less than 0.10. Thus, the results calculated by the entropy method are robust.

Table 5.

The Optimal Distance, the Worst Distance, Proximity Value, Comprehensive Score, and the Difference Between Comprehensive Score and Proximity Value.

Calculation results	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019
The optimal distance	0.1800	0.1316	0.1364	0.1495	0.1317	0.1369	0.1579	0.1766	0.1829	0.2104
The worst distance	0.1622	0.1611	0.1577	0.1769	0.1696	0.1438	0.1308	0.1483	0.1575	0.1621
Proximity value	0.4740	0.5504	0.5362	0.5420	0.5629	0.5123	0.4529	0.4565	0.4626	0.4351
Comprehensive score	0.4447	0.5100	0.5357	0.6253	0.6083	0.5113	0.4727	0.5178	0.5002	0.4594
The difference between proximity value and comprehensive score	−0.0293	−0.0404	−0.0005	0.0833	0.0454	−0.0010	0.0198	0.0613	0.0376	0.0243

Figure 3.

Proximity value, comprehensive score, and the difference between the comprehensive score and proximity value.

A specific analysis is performed on the results calculated using the TOPSIS method. Comparing Figures 2 and 3, it can be seen that the change trends of the comprehensive score and the proximity value are similar. The proximity change of China’s oil supply and demand security can also be divided into three stages. The first stage is from 2010 to 2014. During this period, China’s oil supply and demand security closeness value increased from 0.4740 in 2010 to 0.5504 in 2011 and remained at a high level, reaching 0.5629 in 2014. The second stage is from 2014 to 2016. The proximity value of China’s oil supply and demand security plummeted from 0.5629 in 2014 to 0.4529 in 2016. The third stage is from 2016 to 2019. In this period, the proximity value of China’s oil supply and demand security hovered at a low level between 0.4351 and 0.4626.

Overall, the results obtained from the specific analysis using the TOPSIS method further confirm the robustness of the results obtained from the above analysis using entropy. Further analysis in this part indicates that China’s oil supply and demand are becoming increasingly vulnerable to security threats over time.

Discussion

This study constructs an analysis index system based on the characteristics of oil supply and demand security using the entropy method from four dimensions: supply security, demand security, market security, and “dual carbon” constraints. The results show that the security situation of China’s oil supply and demand reached its highest value in 2013 from 2010 to 2019, and has been hovering at a low level since 2016. This conclusion was further confirmed through the robustness test of TOPSIS method. This is somewhat different from the evaluation results of Gong et al. (2022) on China’s oil security. After comparative analysis, it is believed that the proportion of imports in China’s oil supply and demand structure is extremely high, and it is greatly affected by international oil prices and geopolitical changes. Therefore, it is necessary to examine the market security of China’s oil supply and demand security. At the same time, the “dual carbon” target has become an important constraint for China’s traditional energy low-carbon transformation, and specific measurement indicators must be set after systematic decomposition. Therefore, it is necessary to systematically summarize the indicators of market security and “dual carbon” constraints, in order to more accurately reflect the current reality of China’s oil supply and demand security.

In terms of data, limited by the availability of some data, this study conducted specific empirical analysis based on various data from 2010 to 2019. The increasing uncertainty factors faced globally after 2020 pose new challenges to China’s oil supply and demand security. Therefore, future related research may consider using the latest data for analysis to enhance the timeliness of the study. In terms of research methods, this study adopted the entropy method for multi index evaluation. Although it has the advantage of objectivity, it also has limitations such as insufficient information utilization, lack of flexibility in weight determination, and sensitivity to extreme values. Therefore, subsequent related research should combine other methods such as fuzzy comprehensive evaluation to preprocess the data before conducting specific analysis, in order to fully utilize the advantages of various methods and improve the accuracy and effectiveness of the analysis.

Conclusions and Policy Recommendations

Conclusions

Using the characteristics of oil supply and demand security, this study develops a system for evaluating China’s oil supply and demand security based on 16 indicators from the four dimensions of production security, demand security, market security, and the “double carbon” constraint. Based on the data from 2010 to 2019, the entropy and TOPSIS methods are used to evaluate the level of security of China’s oil supply and demand. The following conclusions are obtained: According to the comprehensive score, China’s oil supply and demand security level first reaches a peak, then declines year after year, and finally hovers at a low level. This means China’s oil supply and demand security has deteriorated in recent years.

From the perspective of supply security, affected by the continuous tightening of the domestic oil supply side in recent years, China’s oil supply security score has declined year by year after reaching the highest point in 2014, and the oil supply security situation has become increasingly severe.

From the perspective of demand security, although China’s oil consumption structure tends to be reasonable, the oil demand security score is still low.

From the perspective of market security, the proportion of oil imports has been rising year after year, while the national foreign exchange reserves have been declining, constantly lowering the security score of China’s oil market. Security conditions in the oil market are not optimistic.

From the perspective of the “double carbon” constraint, with the continuous decline in carbon emission intensity and the continuous improvement in the utilization rate of clean energy, the oil “double carbon” constraint score has risen steadily over the past 10 years, effectively alleviating the security pressure of oil supply and demand in China.

Policy Recommendations

Oil plays a key role in ensuring the manufacturing industry’s smooth operation and promoting high-quality and sustainable economic growth. The international community has devoted increasing attention to low-carbon development in recent years. At the same time, China’s oil supply and demand security are under unprecedented pressure due to major emergencies such as COVID-19 and the conflict between Russia and Ukraine. To ensure the security of China’s oil supply and demand, the following suggestions are put forward:

First, increase investment to ensure an adequate supply of oil resources. First, investment in technology research and development should continue to increase. Oil exploration and development capabilities should be continuously improved, with special emphasis on the exploration capabilities of onshore conventional resources in mature basins. Due to the high degree of discovery of oil and gas resources in this field, frontier exploration expenditures can be appropriately increased, and a long-term risk exploration investment mechanism can be established, thereby improving the success rate of oil resource exploration. Second, investment in offshore oil exploration and development should be increased. With the limited amount of onshore oil resources and the increasing difficulty of development, it is necessary to seek new breakthroughs in the growth of offshore oil reserves and production. China has rich offshore oil reserves, but the exploration and development level is low, while the exploration and development of offshore oil requires higher technology and innovation. Therefore, it is necessary to continuously increase the proportion of investment in the exploration and development of offshore oil resources to obtain a new growth point of oil production. For example, in recent years, CNOOC’s investment in exploration and development has maintained a trend of continuous growth, laying a solid foundation for ensuring sufficient supply of oil resources.

Second, smooth oil market operation is ensured by taking multiple measures. First, the construction of strategic oil reserves should be further strengthened. It is necessary to strengthen China’s strategic oil reserve capacity to ensure the safe and stable operation of the oil market, thus enhancing the uninterrupted supply of oil and smoothing out possible abnormal fluctuations in its prices. Second, the proportion of long-term agreements should be increased. China imports a significant amount of oil to meet its enormous oil needs. It is necessary to sign long-term oil import agreements as much as possible to ensure a stable oil import volume and effectively lock in the risk of oil price fluctuations. Third, the diversification strategy of oil imports should be accelerated. As oil imports become more concentrated, the uncertainty risk on the market increases. Thus, reducing oil import concentration, increasing diversification, and strengthening oil market security are necessary. For instance, since the COVID-19, in the face of complex geopolitical changes, China has actively expanded oil import channels to ensure that the oil import volume and import concentration have been relatively low.

Third, comprehensively promote and accelerate the low-carbon development of energy companies. First, further promote the green and low-carbon formation of traditional energy companies. To achieve the green development goal of reducing carbon emissions or even “zero carbon” in the medium and long term, traditional energy companies must upgrade their low-carbon business to a strategic position as important as their traditional oil and gas business, increase their R&D investment, tilt their scientific research and capital advantage to low-carbon technology R&D, and accelerate the replacement of equipment with high energy consumption and high pollution. Second, support for new energy companies should be increased. In the future, we need to strengthen R&D and investment in new energy technologies, improve the utilization rate and penetration rate of new energy, and realize the effective substitution of new energy for traditional energy such as oil, enabling China to secure its oil supply and demand practically. Third, strengthen international technological cooperation and exchanges. The development of new energy technologies provides alternative solutions for global low-carbon transformation. China should actively seek international cooperation opportunities in fields such as photovoltaics and wind energy, accelerate the development of the new energy industry, and alleviate the pressure on the low-carbon transformation of traditional energy.

Footnotes

ORCID iD

Shengnan Huang

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by 2021 Philosophy and Social Science Research General Projects at Colleges and Universities in Jiangsu Province, Grant Number 2021SJA1219.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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China’s Oil Security Under the “Double Carbon” Constraint: An Entropy Method-Based Analysis

Abstract

Keywords

Introduction

Materials and Methods

System Construction

Methodology

Data Standardization

Determine the Comprehensive Evaluation Score

Data Explanation

Results

Overall Analysis of China’s Oil Supply and Demand Security Level

Comparative Analysis of the Primary Indicators of China’s Oil Supply and Demand Security Level

Robustness Test

Discussion

Conclusions and Policy Recommendations

Conclusions

Policy Recommendations

Footnotes

ORCID iD

Funding

Declaration of Conflicting Interests

Data Availability Statement

References