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GCRI · 系统性风险管理

识别与风险分类体系

Risks that are not named are not managed. Identification finds the hazards, threats, vulnerabilities and consequences that matter, and a taxonomy gives them shared names and classes so that registers can be compared, aggregated and linked. This page sets out how identification works, where taxonomies help and where they hide interconnection, and the reference vocabularies that make one register readable beside another.

01画布

工作如何开展。

系统 · 围绕本主题示意

风险登记册

Identification and risk taxonomy · Enterprise A · Review cycle Q3

三项高于风险偏好的风险GCRI · 系统性风险管理

剩余风险

可能性 × 影响,每个单元格的风险数

每个单元格的风险数量。底色按风险偏好区间显示。

超出风险偏好

供应商集中度采购高▲ 1
数据中心受淹运营高▲ 0
定价中的模型误差金融中▲ 0
关键人员流失人口中▼ 0

依据国际标准的风险管理流程

  1. 范围、背景、准则设置
  2. 标识分类体系
  3. 分析可能性 · 影响
  4. 评估相对风险偏好
  5. 处置计划
  6. 监测与审查KRI
  7. 记录与报告理事会

处置行动

  1. T-11双源关键部件负责人:采购 · 已逾期4 天逾期
  2. T-14将备份站点迁至洪水水位之上负责人:运营due 28 天后到期
  3. T-17独立模型验证负责人:财务due 27 天后到期
Risk register: the screen’s structure, terms and states for Identification and risk taxonomy.示例名称和示意数值;是设计方案,并非记录或实时系统。

图风险登记册:示例名称和示意数值,并非记录或实时系统。

02问题

Identification and risk taxonomy: how the risk develops.

风险识别 beyond registers. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.

机制

Identification follows experience

Workshops and registers list the risks people have seen before. Novel, slow or cross-sector risks are under-identified unless horizon scanning, scenario work and external sources are used deliberately.

机制

Categories set what is seen

A taxonomy that sorts risks into separate boxes, such as operational, financial and strategic, can hide the links between them. Risks that span categories are split or counted twice.

机制

Names differ between registers

Two organisations describe the same hazard in different terms and at different levels of detail. Without a shared reference vocabulary, their registers cannot be combined into a picture of shared exposure.

机制

Registers go stale

A register reviewed once a year misses emerging risks and keeps retired ones. Without triggers for review, the list drifts away from the organisation’s real exposure.

03地理

亚洲的危害与暴露。

Population density, flood water and active fires over Asia, from NASA GIBS. Each layer carries its own date.

交互式地图需要JavaScript;图层列表和来源见下文。

图层 3

本页面的危害与暴露

关于本图层
证据
估计值
来源
世界网格化人口 v4.11(NASA SEDAC(哥伦比亚大学 CIESIN))
提供方
NASA SEDAC(哥伦比亚大学 CIESIN)
许可协议
CC BY 4.0
年份版本
null;基于人口普查的估算,静态(2020)
处理结果
约 1 公里网格
不确定性
基于人口普查的 2020 年每平方公里人口估算,作为风险暴露背景展示;不是受任何危害影响的人数。
经由以下途径到达
gibs.earthdata.nasa.gov,仅在您允许实时地图后访问
关于本图层
证据
观测(云层造成缺口)
来源
VIIRS 全球洪水产品(NASA LANCE)
提供方
NASA LANCE
许可协议
NASA 开放数据
年份版本
每日;卫星探测;云层造成缺口
构建时的最新内容
2026-10-14
处理结果
约 250 米网格
不确定性
卫星探测;云层造成缺口。卫星或模型栅格数据,按提供方发布的原样呈现。云层、条带缺口和反演误差会造成缺口和噪声;数值是指标,而非某地的测量值。
经由以下途径到达
gibs.earthdata.nasa.gov,仅在您允许实时地图后访问
关于本图层
证据
观测(云层和过境时间造成缺口)
来源
VIIRS NOAA-20 375 米(FIRMS)(NASA LANCE FIRMS)
提供方
NASA LANCE FIRMS
许可协议
NASA 开放数据
年份版本
每日;卫星探测;云层和过境时间造成缺口
构建时的最新内容
2026-10-13
处理结果
约 500 米网格
不确定性
所示日期的卫星热点探测结果,包括天然气火炬和工业热源;并非已确认的火情、事件报告或预警。
经由以下途径到达
gibs.earthdata.nasa.gov,仅在您允许实时地图后访问
关于本图层
证据
观测
来源
VIIRS NOAA-20 校正反射率(NASA LANCE)
提供方
NASA LANCE
许可协议
NASA 开放数据
年份版本
每日;卫星图像
构建时的最新内容
2026-10-14
处理结果
约 250 米网格
不确定性
卫星图像。卫星或模型栅格数据,按提供方发布的原样呈现。云层、条带缺口和反演误差会造成缺口和噪声;数值是指标,而非某地的测量值。
经由以下途径到达
gibs.earthdata.nasa.gov,仅在您允许实时地图后访问
关于本图层
证据
模型生成(GIBS 发布的最新年份)
来源
MODIS Terra 和 Aqua 土地覆盖(MCD12Q1)(NASA LP DAAC)
提供方
NASA LP DAAC
许可协议
NASA 开放数据
年份版本
每年;卫星分类;GIBS 发布的最新年份
构建时的最新内容
2024-01-01
处理结果
约 500 米网格
不确定性
所示年份的卫星土地覆盖类别,含农田;该分类存在已知误差,并非农场登记册。
经由以下途径到达
gibs.earthdata.nasa.gov,仅在您允许实时地图后访问
关于本图层
证据
观测(持续云层仍会造成缺口)
来源
VIIRS NOAA-20 Level 3,8 天(NASA LANCE)
提供方
NASA LANCE
许可协议
NASA 开放数据
年份版本
8 天;卫星 8 天合成;持续云层仍会造成缺口
构建时的最新内容
2026-09-14
处理结果
约 1 公里网格
不确定性
截至所示日期的 8 天内地表的卫星皮肤温度,是高温风险暴露指标;不是气温、热指数,也不是高温预警。
经由以下途径到达
gibs.earthdata.nasa.gov,仅在您允许实时地图后访问

最新可用

NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap贡献者 (ODbL) · Natural Earth

正在加载最新的NASA影像。

拖动以移动。按住 Ctrl 或 ⌘ 并滚动、双指缩放或使用按钮缩放;悬停可查看指针下的数值;重置可返回该区域。

危害与风险暴露,以亚洲为取景。NASA 发布的卫星和模型产品,各带自己的日期。它们是指标,而非对任何地点的评估。

数据:NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap 贡献者 · Natural Earth · NASA EONET · 美国地质调查局 · NASA JPL OPERA DIST-ALERT来源与许可

  • We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA 开放数据
  • We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA 开放数据
  • Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). 采用 CC BY 4.0 许可。颜色依据已发布的色标重绘;显示处的数值已按六边形网格平均(经修改)。 doi:10.7927/H49C6VHW. CC BY 4.0
  • VIIRS 近实时产品(洪水、地表温度、积雪覆盖、校正反射率),NASA LANCE;影像来自 NASA GIBS。颜色依据已发布的色标重绘;显示处的数值已按六边形网格平均(经修改)。 NASA 开放数据
  • NASA 资源管理火灾信息系统(FIRMS),VIIRS 375 m 活跃火点产品,NASA LANCE;矢量瓦片来自 NASA GIBS。颜色依据已发布的色标重绘;显示处的数值已按六边形网格平均(经修改)。 NASA 开放数据
  • MODIS 陆地产品(植被指数、总初级生产力、土地覆盖),NASA LP DAAC 和 LANCE;影像来自 NASA GIBS。颜色依据已发布的色标重绘;显示处的数值已按六边形网格平均(经修改)。 NASA 开放数据
  • ASTER GDEM 是 METI 和 NASA 的产品(ASTER Global Digital Elevation Model V003,NASA LP DAAC);影像来自 NASA GIBS。颜色依据已发布的色标重绘;显示处的数值已按六边形网格平均(经修改)。引用格式:NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team (2019). ASTER Global Digital Elevation Model V003. doi:10.5067/ASTER/ASTGTM.003. NASA 开放数据
  • 使用 Natural Earth 制作。免费矢量和栅格地图数据,见 naturalearthdata.com。 公有领域
  • © OpenStreetMap 贡献者(ODbL),经由 NASA GIBS(海岸线、边界、道路和地名)。颜色依据已发布的色标重绘;显示处的数值已按六边形网格平均(经修改)。 ODbL
  • Blue Marble,NASA Earth Observatory;影像来自 NASA GIBS。颜色依据已发布的色标重绘;显示处的数值已按六边形网格平均(经修改)。 NASA 开放数据
  • 地球仪和平面地图上的背景信息:海岸线、边界、道路和地名来自 NASA GIBS 参考图层(OpenStreetMap 数据,ODbL,© OpenStreetMap contributors);Blue Marble 影像可作为可选底图;3D 地形和高程读数来自 ASTER GDEM 高程色彩指数(5 m 至 200 m 分级,为便于辨识而作了夸张处理)。边界按已发布的样式绘制,不代表对任何领土的立场。
  • 实时事件:NASA Earth Observatory Natural Event Tracker(EONET);事件仅供参考,并保留其原始来源链接。
  • 实时:NASA EONET。事件仅供参考,不是主管机构的预警。
  • 实时:美国地质调查局。事件仅供参考,不是主管机构的预警。
  • Observed (EO): NASA JPL OPERA DIST-ALERT (contains modified Copernicus Sentinel data 2026). We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
  • NASA GIBS: We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
  • 河流流量预报,30天及区间(GloFAS,经Open-Meteo):包含经修改的Copernicus Emergency Management Service 2026年信息;非洪水警报。
  • 天气数据来自 Open-Meteo.com (https://open-meteo.com/), 依据 CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). 授权。
  • 可选图层(图层列表中有 11 个来源:实时、预报、观测和背景)开启时会在地图上和此处显示其提供方署名;预报是模型输出,派生指数是估计值。

图层与日期

  • 人口密度,2020 年:人口:哥伦比亚大学 CIESIN,世界网格化人口 v4.11(2018),NASA SEDAC, CC BY 4.0 · 静态
  • 洪水水体,3 日合成:VIIRS 全球洪水产品,NASA LANCE · 2026年10月14日
  • 活跃火点与热异常:VIIRS NOAA-20 375 m(FIRMS),NASA LANCE FIRMS · 2026年10月13日
  • 真彩色:VIIRS NOAA-20 校正反射率,NASA LANCE · 2026年10月13日
  • 土地覆盖类型(IGBP),年度:MODIS Terra 和 Aqua 土地覆盖(MCD12Q1),NASA LP DAAC · 2024
  • 地表温度,日间,8 天合成:VIIRS NOAA-20 3 级产品,8 天,NASA LANCE · 2026年9月14日

04跨领域

A taxonomy decides which cross-domain risks are seen at all.

05模拟器

存量、流量、延迟和一个临界阈值。

两个存量,即压力和 适应能力,由一个增强型增长回路、一个延迟的平衡响应以及压力超过能力时的损害连接在一起;越过阈值后,系统可能发生突变。这是带有说明性取值的教学模型,不是预测。本页地图显示亚洲;模型本身并不绑定某个地点。

控制措施

压力增长
每年 6.0%
能力在降低压力方面的效力
每年 4.0%
对感知压力的投资响应
20%
感知延迟
4.0 年
压力的临界阈值
60 项指数
越过阈值后的放大
2.0 倍
模拟年数
50 年

读数

峰值压力
113 指数
期末能力
186 指数
超过阈值的年数
15.8 年
数字、方法与来源
年份压力能力感知
020.050.020.0
423.344.921.2
827.242.224.0
1231.742.327.7
1636.845.532.2
2042.351.537.2
2447.960.342.6
2853.271.348.0
3257.884.353.1
3666.498.458.4
4082.7115.969.7
4497.8139.783.8
48109.1169.497.3

方法

  1. 系统动力学 Forrester (1961), Industrial Dynamics, MIT Press; Sterman (2000), Business Dynamics
  2. 临界转变与临界点 Scheffer and others (2009), Early-warning signals for critical transitions, Nature 461
  3. 因果回路图 Sterman (2000), Business Dynamics, McGraw-Hill
  4. 不确定性扇形图(参数蒙特卡罗) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
  5. 单因素敏感性(龙卷风图) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley

数据

No observed data enters this model: it runs on the teaching values set by the controls, to be replaced with a programme’s own evidence.

存量、流量、延迟和临界阈值。示意性教学模型:它依据控件设定的数值运行,而非实测数据。方法:System dynamics (Forrester (1961), Industrial Dynamics, MIT Press; Sterman (2000), Business Dynamics);Critical transitions and tipping points (Scheffer and others (2009), Early-warning signals for critical transitions, Nature 461);Causal loop diagram (Sterman (2000), Business Dynamics, McGraw-Hill)。
06指标

衡量什么,以及衡量得如何。

每个指标都注明其单位、频率、背后的来源类型以及数值将附带的证据标签。此处不显示数值:数值由计划依据其自身权限提供。

指标单位节奏来源类型标签
Register entries mapped to a reference taxonomypercent of entries每次审查风险登记册已报告
Emerging risks addedcount per review cycle每次审查前景扫描 记录已报告
Risks spanning more than one category计数每次审查Risk register with linked categories已报告
Time since last register review月持续Register metadata观测
Hazards covered against a reference listpercent of applicable hazard profiles年度Comparison with UNDRR-ISC Hazard Information Profiles估算
External sources consulted in identificationcount by type每次审查Identification method record已声明
07孪生与级联模拟器

Model identification and risk taxonomy as a system. Run the cascade.

Assembled from the Nexus Standards area profile 全球与系统性风险: the hazards it names, the 系统 and sectors that carry the loss, the controls that dampen it and the data that observe it. Choose the components, pick an initiating hazard and step through the cascade. The Map view places the run on an example geography with open data. Every coupling weight is a stated assumption you can change. A teaching model, not a forecast, an assessment or a certification. To model a place of your own choosing, open the full System Builder.

灾害

  • 溃坝
  • 金融传染
  • 河流洪水 (在下游引发)
  • Flash flood (set off downstream)
  • 大规模流离失所(在下游引发)
  • 大范围停电(在下游引发)
  • 供水短缺(在下游引发)
  • Sovereign debt crisis (set off downstream)
  • Currency crisis (set off downstream)

控制措施

  • NS-0 章程与原则
  • NS-3 风险记录 UNI(语法、旧编码对照、分配机构)
  • NS-9 情境 UNI(部署、实例、计划实例)
  • NS-10 政策和条款(机器可读的义务:定义、约束、评估和执行)
  • NS-11 验证与监测(证据、证明、零信任符合性观测)
  • NS-14 符合性及与外部标准的对照
  • NRS-H120 危害族:互联系统的结构性脆弱(NHS-1 sys.fragility)
  • NRS-C6 系统性风险与级联风险
  • NRS-H61 危害族:级联失效(NHS-1 sys.cascade)
  • NRS-H62 危害类别:复合与多危害事件(NHS-1 sys.compound)
  • NRS-H94 危害族:系统性网络事件(NHS-1 cyber.systemic-cyber)
  • NRS-H60 危害类别:供应链与流动中断(NHS-1 sys.supply-chain)

观测

  • INFORM Risk Index(仅列作参考)
  • World Bank Open Data 和 World Development Indicators API(仅列作参考)
  • IMF 数据服务(WEO、IFS、FSI,通过 SDMX)(仅列作参考)
  • 灾害造成的直接经济损失与全球国内生产总值之比
  • 宏观经济仪表板

示例地理范围

  • 孟加拉国:134 个六边形单元
  • 今夜夜间灯光与停电(VIIRS 昼夜波段),NASA GIBS
  • 穿透云层的地表水,30 m(OPERA DSWx-S1 雷达),NASA GIBS
  • 过去半小时降雨率(IMERG),NASA GIBS
  • 地表土壤湿度(SMAP),NASA GIBS
08来源

证据来自何处。

具名公共数据集与机构记录,各自注明许可类别。依协议获得的来源仅按其条款使用,绝不再次发布。

来源它提供什么许可类别
UNDRR-ISC 危害信息概况Standard definitions and descriptions of hazards across natural, technological, biological and societal clustersPublic, UNDRR and ISC terms of use
UNDRR terminology on disaster 风险Agreed definitions of hazard, exposure, vulnerability, capacity and related terms公开,联合国使用条款
National risk registers and national risk assessmentsGovernment-published lists of risks with their descriptions and assessment approachPublic records, licence per government (UK under Open Government Licence)
IEC 31010 风险评估 技术Descriptions of identification techniques such as checklists, HAZOP and scenario analysisLicensed standard, purchase required
IRDR Peril Classification and Hazard GlossaryA hierarchical classification of perils used in loss databasesPublic, IRDR terms of use (to check)
09方法与证据

How identification and risk taxonomy is modelled, measured and tested.

模型、其假设、数据和不确定性,以科学家或工程师会核查的形式呈现。结构遵循已发表的方法。所取数值仅为示意,不是研究发现。

模型、方程与假设

The model structure used for identification and risk taxonomy, the assumptions it makes and how each is tested.

要素 i 的风险Ri = f(Hi, Ei, Vi, Ci)危害、风险暴露、脆弱性和应对能力
预期年度损失EAL = ∑k pk · Lk对年概率为 pk
耦合系统xt+1 = A xt + B ut + εtA carries the couplings, u the shock, ε the error
假设为何制作如何检验
在一次事件中,危害与暴露相互独立使损失积分保持可计算与基于联合记录拟合的 copula 进行比较
耦合在工作点附近是线性的让冲击通过 A 传播在线性范围之外施加扰动并比较
脆弱性曲线可以在不同地点之间迁移使用很少有本地曲线可用在有观测损失的地方据此进行验证
输入
带溯源信息的版本化数据集
成果
分布,绝不是单一数字
代码
连同模型卡和测试一起发布
复现
相同的数据、代码、环境和随机种子
标准的风险与状态空间表述;所列假设是该方法的典型假设,供审查之用,并非结果。
FAIR 指导原则 · GO FAIR 和 FORCE11W3C PROV · 万维网联盟关于一致处理不确定性的指南 · IPCCCEOS 分析就绪数据 · 地球观测卫星委员会
10决定

由谁决定,以及证据服务于什么。

系统性风险 Management prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.

谁来决定问题本页面服务的证据
Chief risk officers and risk teamsWhich taxonomy the register should follow, and how emerging risks enter itCrosswalks; register health checks; identification protocols
国家 风险评估 teamsWhich hazards and threats belong in the national registerCoverage against reference hazard profiles; peer register comparison
行业监管机构Which risk classes regulated entities must report in a common formatTaxonomy options with their mapping to existing reporting

该主题产生的产出

  • Crosswalks between organisational registers and reference hazard profiles
  • Identification protocols that combine workshops, horizon scanning and external data
  • Linked taxonomies that record dependencies between risk classes
  • Register health checks covering completeness, ownership and review dates
11核心问题

What identification experts ask.

  • What risks do we face that do not fit neatly into any category of our taxonomy?
  • Which risks share causes or pathways, even though they sit in different parts of the register?
  • What has changed in our environment, technology or dependencies since the register was last rebuilt?
  • Whose knowledge is missing: front-line staff, suppliers, affected communities?
12机制

Why risks are missed.

Identification fails less from lack of effort than from the way organisations are built. Registers mirror the organisation chart, so each unit reports the risks it owns and few report the risks that arise between units or outside the organisation altogether.

Silos

Categories hide links

A taxonomy that separates operational, financial and strategic risks can hide a single cause behind all three.

Inertia

Registers age

Registers are updated incrementally and drift away from how the organisation and its environment actually work.

Bias

Familiarity and optimism

People overweight recent and vivid events and underweight unfamiliar ones.

13方法与局限

How risks are found and organised.

  1. 01

    Risk ontology

    A formal structure of concepts and relationships, such as cause, event, consequence and control, that lets risks be linked rather than only listed. Building one takes sustained effort and agreement on terms.

  2. 02

    Causal mapping

    Bow-tie diagrams, fault trees and causal loop diagrams expose shared causes and pathways. They are only as complete as the knowledge of those who build them.

  3. 03

    前景扫描

    Structured search for weak signals of 新兴风险. It produces many false alarms and needs disciplined follow-up.

  4. 04

    Structured workshops

    Pre-mortems, red teams and cross-functional reviews widen perspective. Group dynamics can still suppress dissent.

14方法

如何研究,以及方法无法说明的内容。

  1. 不确定性下的决策分析多准则处置选择 · 用于应急计划的决策树在此应用
  2. 网络科学与传染依赖网络映射 · 中心性与集中度 · 失效传播模拟在此应用
  3. 不确定性量化与模型验证输入不确定性的传播 · 区分不确定性的类型 · 深度不确定性框架在此应用
15Where this page’s references

Where this page’s references lead.

阅读本页时所对照的机构和文件。状态和版本会变化;每个链接都指向发布机构。

16问题

问题与解答。

What is a risk taxonomy?
A structured set of risk names and categories, often hierarchical, used so that risks are described consistently across an organisation or between organisations.
Why use the UNDRR-ISC Hazard Information Profiles?
They give agreed definitions for hundreds of hazards, which lets national and organisational registers describe the same hazard in the same terms and supports comparison and data exchange.
How often should a risk register be reviewed?
On a fixed cycle set by the organisation and also when triggers occur, such as a major incident, a new activity, a change in a dependency or new external evidence.
What is the difference between a taxonomy and an ontology?
A taxonomy sorts risks into categories. An ontology also records relationships, such as which events share a cause or which controls address several risks, so that links can be analysed.
How often should a register be rebuilt rather than updated?
There is no fixed rule. A rebuild is warranted when the organisation, its dependencies or its environment change substantially.
Can the platform review my register?
No. It publishes methods that organisations can apply themselves.
报告贡献

从本页贡献证据。

报告 is the Consortium’s quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.

GCRI系统性风险管理情景与压力测试方法与技术组合与准备度
系统性风险管理

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