Petrochemical Risk Assessment in Coastal China and Implications for Land-Use Dynamics
Abstract
1. Introduction
2. Materials and Methods
2.1. Study Area and Data Sources
2.2. Multi-Source Data-Driven Risk Assessment Frameworks for Coastal Petrochemical Clusters
2.2.1. Petrochemical Accident Risk Assessment System
- The severity of accident risk reflects the negative impact of petrochemical accidents on the economy and society. It is directly related to economic losses, casualties, and accident types.
- The likelihood of an accident reflects the safety characteristics of the petrochemical enterprises. This refers to the likelihood of a petrochemical accident occurring in different enterprises. According to the research of Wang et al. [20], the likelihood of accidents in petrochemical enterprises is closely related to factors such as the equipment condition and management level of the enterprise. We use “enterprise age” to indirectly measure the degree of equipment aging, while “enterprise nature”, “enterprise scale” and “registration status” are mainly used to evaluate the management standards and safety culture level of the enterprise.
2.2.2. Methods and Testing
- 1.
- FCM clustering
- 2.
- T-S fuzzy model
- 3.
- Modeling steps
- (i).
- Data processing
- (ii).
- FCM processing incident sample classification
- (iii).
- Construction of T-S fuzzy model and case inference
- 4.
- Model reliability
3. Results of Analysis
3.1. Distribution Characteristics of Accidents at Various Risk Levels
3.2. Spatial Distribution of Petrochemical Risk Levels
3.3. Spatiotemporal Evolution of Petrochemical Risk-Affect Zones
3.4. Association Between Petrochemical Risk and Land-Use Dynamics
4. Discussion
4.1. Drivers of Coastal Petrochemical Risk Dynamics
4.2. Multi-Level Prevention Strategies for Coastal Potential Petrochemical Risks
4.3. BUA Expansion Drives Changes in Petrochemical Risk Zones
4.4. A New Paradigm for Petrochemical Risk Assessment from a Land-Use Perspective and Its International Applicability
4.5. Limitations and Future Work
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
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Data Type | Data Source | Data Description | Processing |
---|---|---|---|
Petrochemical accident data | China Chemical Safety Association | 2012–2022/Test | Crawler technology is used on these sources of petrochemical accident data to obtain information about the incident itself (human error or equipment malfunction and condition of the site), the emergency response, consequences of the incident (injuries and property damage), and the location of the incident. A total of 960 chemical accidents have been documented. |
Chemical Accident Information Network | |||
Ministry of Emergency Management of the People’s Republic of China | |||
Enterprise information data | National Enterprise Credit Information Publicity System | 2012–2022/Test | The enterprise name can be used as a keyword to retrieve additional information from the national enterprise credit information publicity system, including the age, registration status, size, and nature of the enterprise. |
Land-use data | China Land Cover Dataset (CLCD) | 30 m/2012–2022/Raster | This dataset classifies land cover into nine distinct classes: cropland, forest, shrubland, grassland, water, snow/ice, bare land, impervious surfaces, and wetland [42]. Spatial projection transformation, study area extraction, cloud masking, and additional preprocessing are applied. CLCD is the first annual land cover dataset in China. The overall classification accuracy is above 80%, which can well meet the requirements of this study for event continuity and regional integrity. |
Severity | Classification | Number of Fatalities (N1) | Number of Seriously Injured (N2) | Direct Economic Loss (106 RMB) (N3) |
---|---|---|---|---|
IV | General accidents | N1 < 3 | N2 < 10 | N3 < 10 |
III | Larger accidents | 3 ≤ N1 < 10 | 10 ≤ N2 < 50 | 10 ≤ N3 < 50 |
II | Major accidents | 10 ≤ N1 < 30 | 50 ≤ N2 < 100 | 50 ≤ N3 < 100 |
I | Particularly major accidents | N1 ≥ 30 | N2 ≥ 100 | N3 ≥ 100 |
Target Layer A | Criteria Layer B | No. | Indicator Layer C | Interpretation of Indicators |
---|---|---|---|---|
Petrochemical risk level assessment | Severity of accident | X1 | Accident level | The classification is based on the criteria for classifying workplace safety accidents, as shown in Table 1. |
X2 | Classification of accidents | Types of accidents are classified as explosions, fires, spills, asphyxiation, poisoning, and other accidents. | ||
X3 | Amount of compensation for deceased persons | Under the provisions of the Work Safety Law of the People’s Republic of China, those who die in work safety accidents are compensated with a one-time death benefit, which is calculated at 20 times the per capita disposable income of urban residents nationwide for the previous year. | ||
X4 | Death toll | Number of deaths due to accidents | ||
X5 | Seriously injured | Number of people seriously injured from accidents | ||
X6 | Minor injuries | Number of minor injuries due to accidents | ||
X7 | Total number of casualties | Total number of casualties due to accidents | ||
Likelihood of accident | X8 | Age of enterprise | Time between the establishment of the enterprise and the occurrence of the accident | |
X9 | Enterprise registration status | Includes three categories: surviving, revoked, and canceled | ||
X10 | Nature of enterprise | Including state-owned enterprises, private enterprises, foreign enterprises, joint ventures, and Hong Kong, Macao, and Taiwan investment enterprises | ||
X11 | Size of enterprise | Includes four categories: micro and small enterprises, small enterprises, medium-sized enterprises, and large enterprises |
Name of Incident | Accident Level | Meaning | Significance | Area of Influence (n)/km |
---|---|---|---|---|
Extremely low-impact accidents | VI | No casualties, low economic losses | Little to no negative impact on business, the economy and society | 0 |
Lower impact accidents | V | Death toll less than 4 and economic losses not exceeding 10 million | The degree to which negative impacts are low and do not harm the environment, population, or society, and the impacts on business are much greater than the impacts on the economy and society | 1 |
General impact accidents | IV | Death toll less than 10 (greater than 4) and economic losses not exceeding 20 million | Minor impacts on the overall environment, population, or society, with a greater impact on business than on the economy and society | 2 |
Higher impact accidents | III | Death toll less than 20 (greater than 10) and economic losses not exceeding 50 million | A greater negative impact on the environment, population, or society, with no higher or lower impact on business, economy, and society | 4 |
Serious impact accidents | II | Death toll less than 40 (greater than 20) and economic losses not exceeding 100 million | Serious negative impacts on the environment, population, or society, with greater economic and social impacts than business impacts | 6 |
Accidents with particularly serious effects | I | Death toll greater than 40 and economic losses exceeding 100 million | Very serious negative impact on the environment, population, or society, with an economic and social impact that is much greater than the impact on business | 8 |
Numbers of Clusters | 5 | 6 | 7 |
---|---|---|---|
FPC | 0.85 | 0.88 | 0.86 |
FPE | 0.05 | 0.04 | 0.04 |
XB | 0.41 | 0.28 | 1.27 |
Silhouette Coefficient | 0.67 | 0.69 | 0.70 |
Davies–Bouldin | 0.46 | 0.46 | 0.46 |
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Lin, Q.; Liang, Y.; Luo, X.; Liu, Z.; Guo, A. Petrochemical Risk Assessment in Coastal China and Implications for Land-Use Dynamics. Land 2025, 14, 1811. https://doi.org/10.3390/land14091811
Lin Q, Liang Y, Luo X, Liu Z, Guo A. Petrochemical Risk Assessment in Coastal China and Implications for Land-Use Dynamics. Land. 2025; 14(9):1811. https://doi.org/10.3390/land14091811
Chicago/Turabian StyleLin, Qiaoqiao, Yahui Liang, Xue Luo, Zun Liu, and Andong Guo. 2025. "Petrochemical Risk Assessment in Coastal China and Implications for Land-Use Dynamics" Land 14, no. 9: 1811. https://doi.org/10.3390/land14091811
APA StyleLin, Q., Liang, Y., Luo, X., Liu, Z., & Guo, A. (2025). Petrochemical Risk Assessment in Coastal China and Implications for Land-Use Dynamics. Land, 14(9), 1811. https://doi.org/10.3390/land14091811