# An Early Warning System for Oil Security in China

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## Abstract

**:**

## 1. Introduction

## 2. Oil Early Warning Model Construction

#### 2.1. Construction of the Oil Early Warning Indicator System

#### 2.2. Model Construction Based on Factor Analysis

- (1)
- Data forward processingTransfer the negative indicators in Table 1 into positive indicators by a certain method to eliminate the weakening of the positive and negative indicators during evaluation so that the evaluation result is closer to the actual status.
- (2)
- Data standardizationIf there is a forward data matrix ${X}^{\prime}=\left({X}_{1}^{\prime},{X}_{2}^{\prime},\cdots ,{X}_{p}^{\prime}\right)={\left({X}_{ij}^{\prime}\right)}_{n\times p}$, where n is the sample size and p is the number of evaluation indicators. ${x}_{ij}^{\prime}$ is the normalized value of the i-th sample of the j-th indicator. The standard data transformation method is:$${x}_{ij}=\frac{{x}_{ij}^{\prime}-{\overline{x}}_{j}}{{S}_{j}}(i=1,2,\cdots ,n;j=1,2,\cdots ,p),$$${x}_{ij}$ is the standardized data and ${x}_{ij}^{\prime}$ is the data after the forward processing.$${\overline{x}}_{j}=\frac{1}{n}{\displaystyle \sum _{i=1}^{n}{x}_{ij}^{\prime}}=\mathrm{mean}\mathrm{of}{x}_{ij}^{\prime},$$$${S}_{j}=\sqrt{\frac{{\displaystyle \sum _{i=1}^{n}{({x}_{ij}^{\prime}-{\overline{x}}_{j})}^{2}}}{n-1}}=\mathrm{standard}\mathrm{deviation}\mathrm{of}{x}_{ij}^{\prime},$$
- (3)
- Determine the main factors① Calculate the factor correlation coefficients and establish the correlation coefficient matrixThe principal component factor is determined after processing and standardizing the sample data. The correlation coefficient matrix of the standardized sample data is:$$R=\left[\begin{array}{cccc}{r}_{11}& {r}_{21}& \cdots & {r}_{1p}\\ {r}_{21}& {r}_{22}& \cdots & {r}_{2p}\\ \cdots & \cdots & \cdots & \cdots \\ {r}_{p1}& {r}_{p1}& \cdots & {r}_{pp}\end{array}\right],$$$${r}_{ij}=\frac{1}{n-1}{\displaystyle \sum _{i=1}^{n}{x}_{ii}{x}_{ij}},$$② Correlate eigenvalues and eigenvectors of the correlation coefficient matrix RUse an iterative method to solve p non-negative eigenvalues ${\lambda}_{1}>{\lambda}_{2}>,\cdots >{\lambda}_{p}$ of eigenvalue $\left|R-\lambda I\right|=0$. There is an eigenvalue equation that can find the eigenvector ${u}_{k}$ corresponding to eigenvalue ${\lambda}_{k}$.$$U=\left({u}_{1},{u}_{2},\cdots {u}_{p}\right)=\left[\begin{array}{cccc}{u}_{11}& {u}_{12}& \cdots & {u}_{1p}\\ {u}_{21}& {u}_{22}& \cdots & {u}_{2p}\\ \cdots & \cdots & \cdots & \cdots \\ {u}_{p1}& {u}_{p2}& \cdots & {u}_{pp}\end{array}\right],$$③ Calculate the variance contribution rate, select factor mFactor analysis generally use m (m < p) main factors instead of p main factors. The value of m is based on the cumulative variance contribution rate.The variance contribution rate of the k-th indicator is ${\alpha}_{k}=\frac{{\lambda}_{k}}{{\displaystyle \sum _{i=1}^{p}{\lambda}_{i}}}$. The cumulative variance contribution to the m-th indicator is $\sum _{i=1}^{m}\frac{{\lambda}_{i}}{{\displaystyle \sum _{i=1}^{p}{\lambda}_{j}}}$.When the cumulative variance contribution rate ≥ 75%, the number of indicators is the value of the main factor m.Take the first m eigenvalues and the corresponding eigenvectors to find the main factor load matrix:$$A=\left[\begin{array}{cccc}{a}_{11}& {a}_{21}& \cdots & {a}_{1m}\\ {a}_{21}& {a}_{22}& \cdots & {a}_{2m}\\ \cdots & \cdots & \cdots & \cdots \\ {a}_{p1}& {a}_{p2}& \cdots & {a}_{pm}\end{array}\right]=\left[\begin{array}{cccc}{u}_{11}\sqrt{{\lambda}_{1}}& {u}_{21}\sqrt{{\lambda}_{2}}& \cdots & {u}_{1m}\sqrt{{\lambda}_{m}}\\ {u}_{21}\sqrt{{\lambda}_{1}}& {u}_{22}\sqrt{{\lambda}_{2}}& \cdots & {u}_{2m}\sqrt{{\lambda}_{m}}\\ \cdots & \cdots & \cdots & \cdots \\ {u}_{p1}\sqrt{{\lambda}_{1}}& {u}_{p2}\sqrt{{\lambda}_{2}}& \cdots & {u}_{pm}\sqrt{{\lambda}_{m}}\end{array}\right],$$④ Implement maximum variance orthogonal rotation on A.The purpose of rotating the factor load matrix is to simplify the factor load matrix. Thus the coefficients diverge between the poles zero and one to explain the main factor. There are many ways to rotate the factor load matrix. In this study, the orthogonal rotation method with the largest variance is chosen.⑤ Calculate the score of each factorAccording to the factor scores coefficient matrix and standardized data determined by step ④, the main factors can be expressed as a linear combination of indicator variables:$${F}_{i}={\beta}_{i1}{x}_{1}+{\beta}_{i2}{x}_{2}+{\beta}_{i3}{x}_{3}+\cdots +{\beta}_{ip}{x}_{p},$$

## 3. Calculation and Discussion

#### 3.1. Main Factor Analysis of Resource Security

_{1}, R

_{2}, R

_{3}, R

_{5}and R

_{8}. Among them, R

_{1}, R

_{2}and R

_{8}have a positive correlation with ${F}_{r1}$, while R

_{3}and R

_{5}have a negative correlation. It shows that oil reserve capacity is an important factor to ensure oil resource security. Thus, the first common factor ${F}_{r1}$ was named as oil reserve factor. This is in line with Su et al. [51], who argued that it was urgent to strengthen China’s strategic reserve to improve China’s oil supply security. As China is short of oil, R

_{3}and R

_{5}are lower than the world average level. This is the main objective factor restricting the development of China’s oil economy. Hence both R

_{3}and R

_{5}havenegative correlations with ${F}_{r1}$, which is also in line with the actual development of China’s oil industry. The second public factor ${F}_{r2}$ accounts for 18.109% of the total variance and it accounts for 23.594% after rotation. It is mainly related to R

_{6}and R

_{7}. The reserve replacement rate and efficiency of oil process and conversion can represent the development capability of China’s oil industry. The second common factor ${F}_{r2}$ is named the sustainable development factor. Because of the positive correlation between R

_{7}and ${F}_{r2}$, improving oil processing conversion efficiency is one of the most important ways to improve oil resource security under the premise of the lack of oil resources in China. The Chinese government has realized the importance of this aspect, so it keeps increasing the investment in oil technology and continuously plays an important role in the sustainable development of China’s oil industry. Feng and Wang [52] found that during the 11th and 12th five-year periods, the technologies emphasized and promoted by the Chinese government had yielded positive results in the energy innovation of sustainable development.

#### 3.2. Main Factor Analysis of Market Security

_{1}, M

_{3}, M

_{4}, M

_{6}, M

_{7}and M

_{8}, so it belongs to comprehensive factor. Among them, M

_{3}has a negative correlation with ${F}_{m1}$. The balance between oil supply and demand is a basic indicator for evaluating oil security level. A serious imbalance will be harmful to the sustainable development of the oil market. The balance between oil supply and demand is mainly determined by market price. Thus M

_{8}occupies the highest coefficient of 0.973. Thus the oil industry price index has the greatest impact on ${F}_{m1}$. This factor determines the level of market-oriented consumption prices and becomes another major factor affecting the market balance between supply and demand. The second common factor ${F}_{m2}$ is mainly related to M

_{2}. It is named the oil price factor. For oil importing countries, the higher the price of oil, the higher the cost of import is, and the lower the safety is.

#### 3.3. Main Factor Analysis of Consumption Security

_{3}, C

_{4}and C

_{5}. Among them, C

_{3}and C

_{4}have a positive correlation with ${F}_{c1}$, while C

_{5}has a negative correlation. C

_{5}is the key indicator affecting ${F}_{c1}$. All these three indicators reflect the comparison between the current year’s oil consumption and the previous year’s. Hence the first common factor ${F}_{c1}$ is named the consumption growth factor. At present, there are two main ways for China to control C

_{5}. One is to increase the efficiency of oil usage; another one is to use oil substitutes to reduce consumption. The oil substitutes mainly include the following three aspects, i.e., firstly, automotive gas, methanol gasoline, ethanol gasoline, biodiesel and other alternative vehicle gasoline and diesel fuel; secondly, pure electric vehicles, hybrid vehicles and other energy-saving and new energy vehicles; thirdly, petrochemical products replaced by coal-oil and coal polyethylene. Li et al. [7] found that with the improvement of China’s new energy technology and alternative fuels, the intensity of oil consumption was gradually decreased. The data from the China Statistical Yearbook 2016 [8] also confirm Li’s conclusion. The proportion of oil in primary energy has shown a declined trend since 2000. The second common factor ${F}_{c2}$ is mainly related to C

_{1}, C

_{2}and C

_{7}, so ${F}_{c2}$ is named the consumption weight factor. It is closely related to GDP (Gross Domestic Product) and the growth rate of GDP [7]. In 2016, China’s annual GDP was 74,412.7 billion RMB (Chinese currency) and its GDP growth rate was 6.7%. However, energy consumption structure has been further optimized [56]. The energy consumption per unit of GDP decreased by 5% on a year-on-year basis, and its non-fossil energy consumption reached 13.3%, increased by 1.3%. Wang et al. believed that as a result of urbanization, the transformation of the energy consumption structure was inevitable [57,58]. The energy structure should be continuously optimized by reducing consumption intensity and developing new renewable energy [59]. China is in the rapid urbanization stage. An important way to improve oil system security is by developing new energy and renewable energy to reduce the oil consumption intensity.

#### 3.4. Comprehensive Evaluation of the China Oil Early Warning System

_{1}= 0.097r

_{1}+ 0.105r

_{2}− 0.043r

_{3}+⋯− 0.004c

_{5}− 0.028c

_{6}+ 0.104c

_{7},

_{2}= −0.026r

_{1}− 0.065r

_{2}− 0.122r

_{3}+⋯+ 0.015c

_{5}+ 0.144c

_{6}− 0.085c

_{7},

_{3}= −0.038r

_{1}− 0.038r

_{2}+ 0.022r

_{3}+⋯− 0.242c

_{5}− 0.073c

_{6}+ 0.042c

_{7},

_{1}+ 0.185 × F

_{2}+ 0.170 × F

_{3},

_{1}accounts for 49.641% of the total variance. After rotation, it accounts for 45.485%. F

_{1}is related to most of the indicators, so it is named a comprehensive factor. In order to improve oil system security, it should give priority to improving F

_{1}. The second common factor F

_{2}accounts for 17.745% of the total variance and it accounts for 18.521% after rotation. F

_{2}is mainly related to R

_{7}and M

_{2}. Among them, M

_{2}has a positive correlation with F

_{2}, while R

_{7}has a negative correlation. Wang and Kong [61] believed that the oil processing conversion efficiency in China had a great impact on economic development. There was a strong linear relationship between them. Thus, the second principal component factor F

_{2}can be named the economic price factor. The third common factor accounts for 13.652% of the total variance and after rotation, it accounts for 17.032%. F

_{3}is mainly related to C

_{3}, C

_{4}and C

_{5}. Among them, C

_{3}and C

_{4}have a positive correlation with F

_{3}, while C

_{5}has a negative correlation. So F

_{3}can be named as oil consumption factor. The three main factors above are similar to the factor analysis results in Section 3.1, Section 3.2 and Section 3.3. It shows that except for China’s own conditions for oil storage, all factors are closely related to government policy guidance. The studies of Yao and Chang [62,63] came to the same conclusion that China’s macroeconomic policy would affect China’s energy security evolution. The government should step up the implementation of policies and ensure the safety and stability of the entire oil market through macro control. On 13 May 2017, the “Opinions on Deepening the Reform of the Oil and Gas System” issued by the State Council [64] clearly pointed out that it was necessary to promote the sustainable and healthy development of the oil and gas industry through reform. The government is supposed to increase the proved reserves of resource and continuously improve the resource allocation efficiency to ensure supply safety of oil and gas resources.

_{1}are basically the same. It shows that F

_{1}is the key factor that affects F with the highest weight. Curve F

_{2}shows a continuously declining trend in general. This is an important issue for the Chinese government, namely how to reduce the influence of economic prices on the oil security system effectively. Although the curve F

_{3}is declining overall, it shows a gradual upward trend during 2008–2015. It shows that the impact of this factor on the overall oil system security has been effectively controlled through the efforts of the Chinese government. The “12th Five-Year Period” (2011–2015) is a crucial period for the Chinese government to readjust its economic structure and transform its economic development pattern. During this period, the overall safety level of the oil system has been on an upward trend, because the indicator M

_{6}with load value of −0.875 in the first main factor decreases year by year. This reflects that the pressure on oil imports has been reduced and the domestic oil system has been safeguarded. Also, it is closely linked with the active development of new and renewable energy, the improvement of oil extraction and utilization technologies and encouragement for using oil substitutes. In addition, Zhao et al. [65] believed that the current Chinese traditional energy policies, i.e., improving energy production efficiency and improving production technology, were still important measures to reduce energy consumption. Consumption policies should focus on adjusting the consumption structure according to the proportion of different products in final demand. China should continue to step up the implementation of relevant traditional policies, i.e., the “13th Five-Year Plan for Comprehensive Energy Conservation and Emission Reduction”, [66] based on its own economic development rules. In the meantime, the government should continue to issue a series of policies to encourage the use of oil substitutes in consumption terminals.

## 4. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

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**Figure 1.**The trend of China oil system security evaluation from 2001 to 2015. (

**a**) A line chart according to composite factor scores which shows annual changes in security level; (

**b**) Fitting curves according to annual scores which show the trend China oil security level.

Element | Indicator | Abbr. | Unit | Meaning | Indicator Source | Data Source |
---|---|---|---|---|---|---|

Resource security | Proportion of oil reserves in the world total oil reserves | R_{1} | % | Domestic oil recoverable reserves/world oil recoverable reserves | [34] | [1] |

Reserve-production ratio | R_{2} | % | Domestic oil remaining recoverable reserves/domestic oil production | [14,35,36] | [1] | |

China’s oil reserves per capita | R_{3} | t/person | Domestic oil reserves/domestic population | [14] | [8] | |

Oil production growth rate | R_{4} | % | (Annual oil production − the production of the previous year increments)/the previous year oil production | [14] | [1] | |

Proportion of oil production in the world total oil reserves | R_{5} | % | Domestic oil production/world oil production | [34] | [1] | |

Reserve replacement rate | R_{6} | % | Newly verified oil recoverable reserves/current annual consumption of oil reserves | [19,34] | [37] | |

Efficiency of oil process and conversion | R_{7} | % | Oil processing conversion output/oil processing conversion input | [36] | [8] | |

Proportion of oil production | R_{8} | % | Oil production/China’s total energy production | [19] | [8] | |

Market security | International oil price | M_{1} | USD/barrel | Current price of oil | [38,39] | [1] |

International oil price volatility rate | M_{2} | % | (Current price of oil − base period oil price)/base period oil price | [40,41] | [1] | |

Supply and demand balance ratio | M_{3} | % | China’s total oil supply/China’s total oil consumption | [35,39] | [8] | |

Import dependence rate | M_{4} | % | (Domestic oil imports − domestic oil exports)/domestic oil consumption | [14,19,41] | [8] | |

Import source concentration rate | M_{5} | % | Sum of top 5 countries or regions oil imports/total imports | [14,34] | [42] | |

Consumption of oil imports to GDP(gross domestic product) | M_{6} | % | GDP consumed by domestic oil imports/current GDP | [19,43] | [8] | |

Oil import share | M_{7} | % | Oil imports/international market oil trade | [14,41] | [1,8] | |

Oil industry price index | M_{8} | Indicators for measuring changes in ex-factory prices and changes in the prices of industrial products | [14] | [8] | ||

Consumption security | Proportion of consumption | C_{1} | % | Oil consumption/total energy consumption | [19,35] | [8] |

Oil consumption intensity | C_{2} | t/RMB | Domestic oil consumption/domestic GDP | [19,38] | [8] | |

Oil consumption elasticity coefficient | C_{3} | % | Oil consumption growth rate/GDP growth rate | [44] | [8] | |

Oil consumption growth rate | C_{4} | % | Current year’s oil consumption growth/last year’s oil consumption × 100% − 1 | [35,39] | [8] | |

Oil saving rate | C_{5} | % | (1 − current annual oil consumption per unit of GDP/the previous year’s oil consumption per unit of GDP) × 100% | [45] | [8] | |

Ratio of oil production growth rate to consumption demand growth rate | C_{6} | % | Oil production growth rate/consumption demand growth rate | [46] | [8] | |

Oil share of primary energy consumption | C_{7} | % | Annual oil consumption/total annual primary energy consumption | [19,34] | [8] |

Indicator | 2001 | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

R_{1} | 0.023 | 0.017 | 0.021 | 0.014 | 0.013 | 0.013 | 0.013 | 0.012 | 0.011 | 0.011 | 0.009 | 0.010 | 0.011 | 0.011 | 0.011 |

R_{2} | 19.90 | 14.80 | 19.10 | 13.40 | 12.10 | 12.10 | 11.30 | 11.10 | 10.70 | 9.90 | 9.90 | 11.40 | 11.90 | 11.90 | 11.70 |

R_{3} | 1.880 | 1.888 | 1.882 | 1.916 | 1.904 | 2.099 | 2.144 | 2.176 | 2.210 | 2.367 | 2.404 | 2.461 | 2.475 | 2.510 | 2.543 |

R_{4} | 0.017 | 0.012 | 0.016 | 0.024 | 0.046 | 0.019 | 0.008 | 0.019 | −0.002 | 0.071 | −0.001 | 0.020 | 0.015 | 0.007 | 0.015 |

R_{5} | 0.046 | 0.048 | 0.048 | 0.045 | 0.046 | 0.047 | 0.048 | 0.048 | 0.049 | 0.052 | 0.051 | 0.050 | 0.050 | 0.050 | 0.049 |

R_{6} | 3.176 | 4.248 | 2.946 | 3.464 | 3.649 | 2.807 | 3.303 | 3.592 | 2.918 | 2.494 | 3.019 | 3.180 | 2.161 | 2.007 | 2.067 |

R_{7} | 0.976 | 0.967 | 0.964 | 0.965 | 0.969 | 0.969 | 0.972 | 0.962 | 0.967 | 0.970 | 0.974 | 0.971 | 0.977 | 0.975 | 0.976 |

R_{8} | 15.9 | 15.3 | 13.6 | 12.2 | 11.3 | 10.8 | 10.1 | 9.8 | 9.4 | 9.3 | 8.5 | 8.5 | 8.4 | 8.4 | 8.5 |

M_{1} | 25.93 | 26.16 | 31.07 | 41.49 | 56.59 | 66.02 | 72.20 | 100.06 | 61.92 | 79.45 | 95.04 | 94.13 | 97.99 | 93.28 | 48.71 |

M_{2} | −0.146 | 0.009 | 0.188 | 0.335 | 0.364 | 0.167 | 0.094 | 0.386 | −0.381 | 0.283 | 0.196 | −0.010 | 0.041 | −0.048 | −0.478 |

M_{3} | 1.014 | 1.005 | 1.015 | 1.013 | 1.000 | 1.002 | 1.000 | 1.000 | 1.002 | 1.002 | 1.006 | 1.001 | 1.000 | 1.001 | 1.001 |

M_{4} | 0.309 | 0.328 | 0.393 | 0.475 | 0.439 | 0.482 | 0.504 | 0.538 | 0.566 | 0.575 | 0.605 | 0.611 | 0.602 | 0.617 | 0.625 |

M_{5} | 0.606 | 0.608 | 0.592 | 0.601 | 0.611 | 0.643 | 0.613 | 0.636 | 0.612 | 0.571 | 0.580 | 0.590 | 0.600 | 0.600 | 0.600 |

M_{6} | 0.010 | 0.010 | 0.013 | 0.020 | 0.024 | 0.028 | 0.028 | 0.033 | 0.019 | 0.026 | 0.031 | 0.028 | 0.025 | 0.024 | 0.023 |

M_{7} | 0.054 | 0.062 | 0.075 | 0.093 | 0.091 | 0.101 | 0.107 | 0.117 | 0.135 | 0.157 | 0.167 | 0.172 | 0.182 | 0.190 | 0.196 |

M_{8} | 100 | 94.6 | 112.7 | 134.8 | 175.0 | 213.6 | 217.8 | 266.0 | 175.5 | 241.9 | 301.1 | 299.3 | 286.8 | 277.0 | 173.7 |

C_{1} | 21.2 | 21 | 20.1 | 19.9 | 17.8 | 17.5 | 17 | 16.7 | 16.4 | 17.4 | 16.8 | 17 | 17.1 | 17.4 | 18.1 |

C_{2} | 2.06 × 10^{−5} | 2.05 × 10^{−5} | 2.04 × 10^{−5} | 2.16 × 10^{−5} | 1.99 × 10^{−5} | 1.90 × 10^{−5} | 1.75 × 10^{−5} | 1.62 × 10^{−5} | 1.52 × 10^{−5} | 1.58 × 10^{−5} | 1.49 × 10^{−5} | 1.45 × 10^{−5} | 1.41 × 10^{−5} | 1.36 × 10^{−5} | 1.35 × 10^{−5} |

C_{3} | 0.210 | 0.913 | 0.943 | 1.670 | 0.234 | 0.563 | 0.360 | 0.181 | 0.308 | 1.405 | 0.305 | 0.675 | 0.583 | 0.505 | 0.936 |

C_{4} | 0.017 | 0.083 | 0.094 | 0.169 | 0.027 | 0.072 | 0.051 | 0.018 | 0.029 | 0.149 | 0.029 | 0.053 | 0.045 | 0.037 | 0.065 |

C_{5} | 0.061 | 0.007 | 0.005 | −0.061 | 0.078 | 0.049 | 0.080 | 0.072 | 0.059 | −0.039 | 0.060 | 0.024 | 0.030 | 0.034 | 0.004 |

C_{6} | 0.952 | 0.146 | 0.174 | 0.139 | 1.732 | 0.265 | 0.163 | 1.095 | −0.081 | 0.480 | −0.025 | 0.373 | 0.323 | 0.193 | 0.230 |

C_{7} | 0.231 | 0.229 | 0.217 | 0.215 | 0.192 | 0.189 | 0.184 | 0.182 | 0.179 | 0.192 | 0.183 | 0.188 | 0.190 | 0.196 | 0.208 |

Factor | The Initial Eigenvalues | Extracting Square Loaded | Rotating Square Loaded | ||||||
---|---|---|---|---|---|---|---|---|---|

Totals | Variance Contribution Rate (%) | Cumulative Variance Contribution Rate (%) | Totals | Variance Contribution Rate (%) | Cumulative Variance Contribution Rate (%) | Total | Variance Contribution Rate (%) | Cumulative Variance Contribution Rate (%) | |

1 | 4.603 | 57.542 | 57.542 | 4.603 | 57.542 | 57.542 | 4.165 | 52.057 | 52.057 |

2 | 1.449 | 18.109 | 75.651 | 1.449 | 18.109 | 75.651 | 1.888 | 23.594 | 75.651 |

3 | 0.978 | 12.222 | 87.873 | ||||||

4 | 0.469 | 5.863 | 93.736 | ||||||

5 | 0.391 | 4.892 | 98.628 | ||||||

6 | 0.066 | 0.828 | 99.456 | ||||||

7 | 0.032 | 0.395 | 99.851 | ||||||

8 | 0.012 | 0.149 | 100.000 |

Indicators | Factor | |
---|---|---|

1 | 2 | |

R_{1} | 0.969 | 0.042 |

R_{2} | 0.946 | 0.189 |

R_{3} | −0.827 | 0.522 |

R_{4} | −0.134 | −0.381 |

R_{5} | −0.712 | 0.356 |

R_{6} | 0.442 | −0.744 |

R_{7} | −0.185 | 0.843 |

R_{8} | 0.945 | −0.203 |

Factor | The Initial Eigenvalues | Extracting Square Loaded | Rotating Square Loaded | ||||||
---|---|---|---|---|---|---|---|---|---|

Totals | Variance Contribution Rate (%) | Cumulative Variance Contribution Rate (%) | Totals | Variance Contribution Rate (%) | Cumulative Variance Contribution Rate (%) | Total | Variance Contribution Rate (%) | Cumulative Variance Contribution Rate (%) | |

1 | 4.661 | 58.260 | 58.260 | 4.661 | 58.260 | 58.260 | 4.643 | 58.037 | 58.037 |

2 | 1.679 | 20.984 | 79.244 | 1.679 | 20.984 | 79.244 | 1.697 | 21.207 | 79.244 |

3 | 1.148 | 14.345 | 93.589 | ||||||

4 | 0.290 | 3.624 | 97.213 | ||||||

5 | 0.138 | 1.726 | 98.939 | ||||||

6 | 0.069 | 0.858 | 99.797 | ||||||

7 | 0.015 | 0.192 | 99.989 | ||||||

8 | 0.001 | 0.011 | 100.000 |

Indicators | Component | |
---|---|---|

1 | 2 | |

M_{1} | 0.972 | 0.094 |

M_{2} | 0.137 | 0.836 |

M_{3} | −0.739 | 0.022 |

M_{4} | 0.889 | −0.383 |

M_{5} | −0.087 | 0.550 |

M_{6} | 0.858 | 0.471 |

M_{7} | 0.807 | −0.563 |

M_{8} | 0.973 | 0.019 |

Factor | The Initial Eigenvalues | Extracting Square Loaded | Rotating Square Loaded | ||||||
---|---|---|---|---|---|---|---|---|---|

Totals | Variance Contribution Rate (%) | Cumulative Variance Contribution Rate (%) | Totals | Variance Contribution Rate (%) | Cumulative Variance Contribution Rate (%) | Total | Variance Contribution Rate (%) | Cumulative Variance Contribution Rate (%) | |

1 | 3.783 | 54.047 | 54.047 | 3.783 | 54.047 | 54.047 | 3.057 | 43.669 | 43.669 |

2 | 2.023 | 28.901 | 82.948 | 2.023 | 28.901 | 82.048 | 2.750 | 39.279 | 82.948 |

3 | 0.760 | 10.860 | 93.808 | ||||||

4 | 0.409 | 5.840 | 99.648 | ||||||

5 | 0.017 | 0.249 | 99.896 | ||||||

6 | 0.006 | 0.087 | 99.984 | ||||||

7 | 0.001 | 0.016 | 100.000 |

Indicators | Factor | |
---|---|---|

1 | 2 | |

C_{1} | 0.177 | 0.949 |

C_{2} | 0.004 | 0.874 |

C_{3} | 0.951 | 0.242 |

C_{4} | 0.911 | 0.259 |

C_{5} | −0.940 | −0.240 |

C_{6} | −0.599 | 0.324 |

C_{7} | 0.221 | 0.893 |

Factor | The Initial Eigenvalues | Extracting Square Loaded | Rotating Square Loaded | ||||||
---|---|---|---|---|---|---|---|---|---|

Totals | Variance Contribution Rate (%) | Cumulative Variance Contribution Rate (%) | Totals | Variance Contribution Rate (%) | Cumulative Variance Contribution Rate (%) | Total | Variance Contribution Rate (%) | Cumulative Variance Contribution Rate (%) | |

1 | 11.417 | 49.641 | 49.641 | 11.417 | 49.641 | 49.641 | 10.461 | 45.485 | 45.485 |

2 | 4.081 | 17.745 | 67.386 | 4.081 | 17.745 | 67.386 | 4.260 | 18.521 | 64.006 |

3 | 3.140 | 13.652 | 81.038 | 3.140 | 13.652 | 81.038 | 3.917 | 17.032 | 81.038 |

4 | 1.433 | 6.230 | 87.267 | ||||||

5 | 1.008 | 4.381 | 91.649 | ||||||

6 | 0.641 | 2.789 | 94.437 | ||||||

7 | 0.438 | 1.903 | 96.340 | ||||||

8 | 0.337 | 1.466 | 97.806 | ||||||

9 | 0.316 | 1.375 | 99.181 | ||||||

10 | 0.087 | 0.378 | 99.559 | ||||||

11 | 0.053 | 0.232 | 99.792 | ||||||

12 | 0.027 | 0.116 | 99.908 | ||||||

13 | 0.016 | 0.071 | 99.979 | ||||||

14 | 0.005 | 0.021 | 100.000 |

Indicators | Factor | Indicators | Factor | Indicators | Factor | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|

1 | 2 | 3 | 1 | 2 | 3 | 1 | 2 | 3 | |||

R_{1} | 0.923 | 0.143 | −0.078 | M_{1} | −0.932 | −0.055 | −0.129 | C_{1} | 0.941 | 0.057 | 0.211 |

R_{2} | 0.909 | −0.003 | −0.063 | M_{2} | −0.188 | 0.834 | 0.234 | C_{2} | 0.753 | 0.616 | 0.092 |

R_{3} | −0.748 | −0.632 | 0.077 | M_{3} | 0.769 | 0.046 | 0.280 | C_{3} | 0.160 | 0.006 | 0.962 |

R_{4} | −0.133 | 0.443 | 0.527 | M_{4} | −0.872 | −0.417 | 0.127 | C_{4} | 0.150 | 0.185 | 0.938 |

R_{5} | −0.623 | −0.498 | 0.221 | M_{5} | 0.071 | 0.410 | −0.602 | C_{5} | −0.173 | −0.094 | −0.955 |

R_{6} | 0.372 | 0.698 | −0.162 | M_{6} | −0.875 | 0.355 | −0.102 | C_{6} | 0.026 | 0.556 | −0.329 |

R_{7} | −0.130 | −0.717 | −0.189 | M_{7} | −0.756 | −0.605 | 0.145 | C_{7} | −0.906 | −0.103 | 0.250 |

R_{8} | 0.928 | 0.311 | −0.029 | M_{8} | −0.922 | −0.135 | −0.086 |

Indicators | Factor | Indicators | Factor | Indicators | Factor | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|

1 | 2 | 3 | 1 | 2 | 3 | 1 | 2 | 3 | |||

R_{1} | 0.097 | −0.026 | −0.038 | M_{1} | −0.099 | 0.046 | −0.013 | C_{1} | 0.098 | −0.044 | 0.037 |

R_{2} | 0.105 | −0.065 | −0.038 | M_{2} | −0.084 | 0.249 | 0.085 | C_{2} | 0.041 | 0.121 | 0.021 |

R_{3} | −0.043 | −0.122 | 0.022 | M_{3} | 0.078 | −0.033 | 0.056 | C_{3} | −0.004 | 0.014 | 0.247 |

R_{4} | −0.058 | 0.145 | 0.151 | M_{4} | −0.074 | −0.052 | 0.043 | C_{4} | −0.017 | 0.064 | 0.245 |

R_{5} | −0.042 | −0.090 | 0.060 | M_{5} | −0.006 | 0.094 | −0.148 | C_{5} | −0.004 | 0.015 | −0.242 |

R_{6} | −0.002 | 0.164 | −0.034 | M_{6} | −0.122 | 0.156 | 0.002 | C_{6} | −0.028 | 0.144 | −0.073 |

R_{7} | 0.039 | −0.194 | −0.063 | M_{7} | −0.048 | −0.112 | 0.041 | C_{7} | 0.104 | −0.085 | 0.042 |

R_{8} | 0.085 | 0.022 | −0.022 | M_{8} | −0.094 | 0.024 | −0.004 |

Year | ${\mathit{F}}_{1}$ | ${\mathit{F}}_{2}$ | ${\mathit{F}}_{3}$ | $\mathit{F}$ | Rank |
---|---|---|---|---|---|

2001 | 2.147 | −0.476 | −1.111 | 0.700 | 4 |

2002 | 1.502 | 0.228 | 0.227 | 0.764 | 3 |

2003 | 1.446 | 0.107 | 0.661 | 0.790 | 2 |

2004 | 0.705 | 0.975 | 1.936 | 0.830 | 1 |

2005 | −0.117 | 1.675 | −0.848 | 0.133 | 5 |

2006 | −0.198 | 0.752 | −0.459 | −0.029 | 7 |

2007 | −0.326 | 0.314 | −0.771 | −0.211 | 8 |

2008 | −0.952 | 1.582 | −1.108 | −0.313 | 10 |

2009 | −0.178 | −0.699 | −0.819 | −0.350 | 11 |

2010 | −0.942 | 0.319 | 2.247 | 0.012 | 6 |

2011 | −0.916 | −0.514 | −0.273 | −0.558 | 15 |

2012 | −0.885 | −0.281 | 0.185 | −0.423 | 12 |

2013 | −0.748 | −0.875 | −0.078 | −0.516 | 13 |

2014 | −0.611 | −1.195 | −0.191 | −0.532 | 14 |

2015 | 0.073 | −1.912 | 0.312 | −0.268 | 9 |

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Wang, Q.; Tang, H.; Yuan, X.; Wang, M.; Xiao, H.; Ma, Z. An Early Warning System for Oil Security in China. *Sustainability* **2018**, *10*, 283.
https://doi.org/10.3390/su10010283

**AMA Style**

Wang Q, Tang H, Yuan X, Wang M, Xiao H, Ma Z. An Early Warning System for Oil Security in China. *Sustainability*. 2018; 10(1):283.
https://doi.org/10.3390/su10010283

**Chicago/Turabian Style**

Wang, Qingsong, Hongrui Tang, Xueliang Yuan, Mansen Wang, Hongkun Xiao, and Zhi Ma. 2018. "An Early Warning System for Oil Security in China" *Sustainability* 10, no. 1: 283.
https://doi.org/10.3390/su10010283