Global Risk Analytics

The Global Risk Analytics is a multi-stakeholder project focused on promoting sustainability and resilience in communities by providing stakeholders with the information and tools they need to address global risks effectively. Global Risk Analytics aims to provide a centralized platform for stakeholders to access, analyze, and act upon the information related to global risks through partnerships with governments, private sector organizations, and non-profit organizations. The data gathered from these partnerships is analyzed and visualized on a user-friendly platform that provides various tools and resources for risk mitigation efforts.

Global Risk Analytics leverages decentralized technologies and participatory mechanisms to centralize data and provide a comprehensive view of risk data to support its goals. The platform utilizes the Internet of Things (IoT) and Low-Power Wide Area Network (LoRaWAN) communication systems to monitor and collect data on environmental conditions and provide real-time updates and alerts to stakeholders. The project also aims to promote interoperability, security and transparency through standard data indexing and analysis language.

The ultimate goal of Global Risk Analytics is to create a real-time, open data platform that stakeholders can utilize in risk mitigation efforts, promoting sustainability and resilience in communities. The platform can also support community emergency response teams (CERT) with data-driven and holistic open-source tools, helping them to make informed decisions and respond effectively to emergencies.

Motivation

Data has become an indispensable tool for decision-making, policy formulation and effective resource allocation in today’s world. The ability to gather and analyze data has become increasingly important in addressing complex global challenges such as climate change, public health, and sustainable development. To do so, it is crucial to have reliable and accurate data systems in place. However, significant challenges with existing data ecosystems limit their effectiveness and hinder decision-making. In this guide, we will explore the limitations of current data collection methods and sources, data accuracy and quality issues, and data analysis and dissemination challenges. These challenges have a profound impact on decision-makers ability to make informed choices and develop practical solutions to global problems. To address these challenges, it is essential to understand the issues and identify ways to improve the existing data ecosystems.

Data is the foundation of any decision-making process and is crucial in shaping our understanding of the world around us. However, data collection, analysis, and dissemination are not without challenges. This comprehensive guide will outline the limitations of current data collection methods and sources, data accuracy and quality issues, and data analysis and dissemination challenges.

A. Limitations of current data collection methods and sources

1. Limited scope of data collection: Most existing data collection methods are limited to certain geographic regions or specific data types, resulting in a lack of comprehensive coverage.
2. Reliance on traditional methods: A large part of the data that is collected today still relies on traditional methods such as surveys, which can be time-consuming, expensive, and prone to human error.
3. Inadequate technology: In many instances, existing data collection methods are not equipped with the latest technology, making them inefficient and less effective.

B. Issues with data accuracy and quality

1. Data bias: Data accuracy can be impacted by various preferences, such as selection bias, measurement bias, and data entry errors.
2. Quality control issues: Quality control is a critical aspect of data collection and analysis. However, many data collection methods lack proper quality control procedures, leading to errors and inconsistencies in data.
3. Lack of transparency: The lack of transparency in data collection and analysis can impact the accuracy and quality of data, making it difficult to assess the reliability of results.

C. Challenges with data analysis and dissemination

1. Data compatibility: Different data sources may use different formats, making integrating data from multiple sources challenging for analysis.
2. Limited data analysis capacity: Many organizations do not have the resources or expertise to analyze large volumes of data effectively.
3. Data dissemination: The dissemination of data is often limited to specific groups or organizations, making it difficult for stakeholders to access and utilize the data effectively.

Background

The lack of data standardization for participatory mechanisms in Responsible Research and Innovation (RRI) creates significant challenges in effectively gathering and utilizing data for sustainable development. This results in difficulties in ensuring that the data collected from public sources is accurate, consistent, and relevant to the goals of RRI. The absence of a unified data indexing standard exacerbates these issues and undermines the ability of organizations and communities to utilize data effectively in their decision-making processes.

Participatory RRI is a research approach that aims to engage stakeholders in the co-creation and co-design of research and innovation. It seeks to address societal challenges and promote sustainable development by integrating ethical, social, and environmental considerations into the research process. However, to effectively realize the goals of participatory RRI, it is crucial that data be gathered and analyzed in a standardized manner.

The lack of data standardization for participatory RRI has far-reaching impacts on sustainable development. It creates inefficiencies in data collection and analysis processes, leading to suboptimal outcomes and hindering progress toward realizing the United Nations’ Sustainable Development Goals (SDGs). Additionally, the absence of a unified data indexing standard also exacerbates existing data quality and privacy concerns, further hindering the ability of organizations and communities to utilize data effectively for sustainable development.

Given the crucial role that data plays in driving sustainable development and the impact that the lack of standardization can have, a data indexing standard must be developed to address these challenges and ensure that data collected from public sources is effectively utilized for the advancement of RRI and sustainable development.

Method

The Global Risk Analytics has emerged as a systemic solution that aims to upgrade the existing data ecosystem and improve the quality and accuracy of information with more inclusive, diverse and equitable architecture.

Global Risk Analytics takes a comprehensive approach to address the data challenges by leveraging the latest advances in technology and data collection methods. The approach of Global Risk Analytics involves upgrading data infrastructure and scaling data-gathering channels through IoT and participatory RRI programs. This approach enables Global Risk Analytics to collect more comprehensive and accurate information on global risks and their impacts on human societies and ecosystems.

Global Risk Analytics leverages the power of the Internet of Things (IoT) and participatory RRI programs to upgrade data methods and scale data-gathering channels. Using IoT devices, Global Risk Analytics can collect real-time data from many sources and across a wide range of geographies. Participatory RRI programs, on the other hand, enable individuals and communities to participate in data collection and analysis, which leads to a more comprehensive understanding of global risks and their impacts.

Global Risk Analytics relies on Low-Power Wide-Area Network (LPWAN) systems such as LoRaWAN to collect, analyze, and disseminate information. LoRaWAN is an energy-efficient and low-cost network that allows Global Risk Analytics to gather data from remote and hard-to-reach areas. By integrating LPWAN systems, Global Risk Analytics can ensure that data is collected and analyzed in real-time, which leads to a more accurate and up-to-date understanding of global risks.

Data structure

The Internet of Things (IoT) has revolutionized the way we collect and manage data, with billions of devices connected to the network and transmitting data on a daily basis. However, the sheer volume of data generated by these devices has made it increasingly challenging to manage and process them effectively. This is particularly true for data related to Responsible Research and Innovation (RRI) and Sustainable Development, which requires a high level of standardization and interoperability between different data sources and systems.

In response to this challenge, the Global Risk Analytics data indexing standard has been developed to provide a common data structure for collecting and organizing data related to RRI and sustainable development in the context of the LoRaWAN ecosystem. This whitepaper outlines the key features and benefits of the Global Risk Analytics data structure and provides guidelines for its implementation.

The Global Risk Analytics data structure is based on a relational database model, with a clear hierarchy of data types, elements, and relationships between them. This ensures that all data is organized in a standard format and can be easily processed and analyzed by different systems and tools.

Data Types

The Global Risk Analytics data structure defines a set of data types representing different aspects of RRI and sustainable development. These data types include information on stakeholders, research projects, RRI practices, and sustainability metrics.

Data Elements

Each data type is further defined by a set of elements that provide specific information about that data type. For example, data elements for the “Stakeholder” data type might include information such as name, address, role, and organization.

Data Relationships

The Global Risk Analytics data structure also defines relationships between different data types. This allows data to be linked and cross-referenced in meaningful ways, providing a more complete and accurate picture of the data. For example, relationships between the “Stakeholder” and “Research Project” data types can link stakeholders with the projects they are involved in.

Benefits

The Global Risk Analytics data structure provides several benefits for data management and analysis in the context of RRI and sustainable development. These include:

1. Standardization: The Global Risk Analytics data structure ensures that all data is organized in a standard format, making it easier to process and analyze.
2. Interoperability: The Global Risk Analytics data structure allows data from different sources to be combined and compared, providing a more comprehensive picture of RRI and sustainable development.
3. Flexibility: The Global Risk Analytics data structure can be easily extended and adapted to accommodate new data types and data elements as they become available.
4. Data Quality: The Global Risk Analytics data structure provides guidelines for data quality, ensuring that data is accurate, complete, and reliable.
5. Scalability: The Global Risk Analytics data structure can be scaled to accommodate large amounts of data, making it suitable for use in large-scale IoT systems.

The Global Risk Analytics data indexing standard provides a common data structure for organizing and managing data related to RRI and sustainable development in the context of the LoRaWAN ecosystem. By providing a standard format for data and defining relationships between different data types, the Global Risk Analytics data structure makes it easier to process and analyze data. It provides a more complete and accurate picture of RRI and sustainable development.

Data Standards

The rise of the Internet of Things (IoT) and the increasing use of data for various purposes has brought about the need for harmonized data standards. The Global Risk Analytics is a significant initiative that requires a standardized method of establishing data standards to achieve sustainable development. The following highlight the importance of harmonizing open data standards in the LoRaWAN ecosystem and its impact on developing a Global Risk Analytics method.

Data standards play a crucial role in ensuring data consistency, accuracy, and interoperability. They provide a common language for data exchange, essential for data sharing, privacy, and security, which are critical for sustainable development. Harmonizing data standards makes it possible to use data effectively across different platforms, systems, and technologies.

Several data standards are commonly used in the context of open data platforms, LoRaWAN, and data commons. Some of these include:

1. ISO/IEC 27001: This standard provides guidelines for information security management, ensuring data is stored and processed securely.
2. IEEE Standards Association: This standards organization provides data privacy, security, and management guidelines.
3. Open Data Protocol (OData): This protocol supports data exchange between open data platforms and ensures that data is queried and updated in a standardized way.
4. General Data Protection Regulation (GDPR): This regulation protects personal data in the European Union, including data stored on open data platforms and in data commons.
5. LoRa Alliance: This organization provides specifications and guidelines for implementing LoRaWAN networks and using Low Range (LoRa) wireless communication technology.
6. W3C Linked Data Platform (LDP): This set of specifications supports the creation and management of linked data, a key element of data commons.

Harmonizing open data standards involves adopting existing standards and protocols to ensure data consistency, reliability, and accuracy. It also requires taking into account the specific requirements of the Global Risk Analytics, such as real-time data transmission, data privacy, and data security. By harmonizing open data standards, the LoRaWAN ecosystem can support the data exchange necessary for sustainable development.

Harmonizing data standards is crucial to establishing a Global Risk Analytics method for sustainable development. By adopting existing data standards and protocols and considering the specific requirements of the Global Risk Analytics in the LoRaWAN ecosystem, stakeholders can ensure data consistency, accuracy, and reliability. This will provide a strong foundation for the effective use of IoT through participatory mechanisms in pursuing collective intelligence for sustainable development.

Privacy and Security

Implementing robust data privacy and security policies is essential to achieve responsible and sustainable development through open data gathered by IoT devices in the LoRaWAN ecosystem. The Global Risk Analytics approach emphasizes the importance of ensuring data protection and responsible use, particularly in light of the sensitive nature of the information that IoT devices can collect in the LoRaWAN network.

These policies must outline the steps to secure the data from unauthorized access, misuse, and tampering, such as implementing encryption and access control mechanisms. Furthermore, the policies must ensure the privacy of the individuals who are the source of the data is protected by limiting the use of the data to its intended purpose, preventing unauthorized disclosure, and ensuring compliance with relevant laws and regulations.

It is imperative that the Global Risk Analytics approach takes into account the privacy and security implications of collecting open data through IoT devices in the LoRaWAN ecosystem. By doing so, it can ensure the responsible and sustainable use of this data in the development of the Global Risk Analytics.

Privacy

Data privacy and security are becoming increasingly essential concerns in today’s world, especially with the rapid advancement of technology and the rise of the Internet of Things (IoT) devices. The LoRaWAN ecosystem, which utilizes IoT devices to gather open data, is no exception. It is crucial to ensure that the data collected is protected from unauthorized access and misuse and that the privacy of individuals is respected. This is where data privacy and security policies come into play. Implementing best practices in the IoT ecosystem allows for safeguarding personal information and ensuring that the data collected is used responsibly.

The following principles must be applied to ensure privacy when gathering open data through IoT devices in the LoRaWAN ecosystem for Global Risk Analytics:

1. Data De-identification: Before making the open data publicly available, it should undergo a de-identification process to remove any personal information that could lead to the identification of individuals. Techniques such as masking, anonymization, or pseudonymization can be used for this purpose.
2. Controlled Access to Personal Data: Only authorized personnel should be granted access to personal data collected by IoT devices through the implementation of access control mechanisms such as password protection, biometric authentication, or multi-factor authentication.
3. Secure Data Storage: All collected data should be securely stored using encryption technologies such as AES or RSA and secure storage solutions such as cloud storage, encrypted hard drives, or encrypted flash drives.
4. Data Retention and Disposal Policy: There should be clear policies for data retention and disposal, ensuring that data is not kept for longer than necessary and is disposed of securely when it is no longer needed.
5. Data Access and Use Policy: Policies should outline who has access to the data and how it can be used to ensure that data is only used for the purpose for which it was collected and not disclosed to unauthorized parties.
6. Compliance with Laws and Regulations: All data privacy and security measures should comply with relevant laws and regulations, including local, national, and international laws and regulations, as well as industry-specific regulations.
7. Regular Monitoring and Assessment: Regular monitoring and assessment of data privacy and security measures are crucial to ensure their effectiveness and promptly identify and address potential vulnerabilities.

By following these best practices, organizations can ensure that individuals have control over their data and that it is protected from unauthorized access and use in the IoT ecosystem, in line with the Global Risk Analytics approach to privacy.

Security

Data security is crucial in collecting and managing data through the Internet of Things (IoT) and open data ecosystems. Data protection from unauthorized access and use is essential to ensuring data confidentiality, integrity, and availability, which are the critical components of data security. In the context of the LoRaWAN ecosystem, the Global Risk Analytics approach to data security strongly emphasizes implementing best practices to ensure that data is protected from unauthorized access and use. This includes measures such as data encryption, secure data storage, and access controls, among others. This introduction will provide a comprehensive overview of the Global Risk Analytics approach to data security in the context of IoT and open data ecosystems.

The following principles must be applied to ensure security when gathering open data through IoT devices in the LoRaWAN ecosystem for Global Risk Analytics:

1. Encryption of Data: Data collected through IoT devices should be encrypted to protect it from unauthorized access.
2. Secure Storage of Data: Data collected through IoT devices should be stored securely to prevent unauthorized access or theft.
3. Access Controls: Only authorized personnel should have access to the data collected through IoT devices. Access control mechanisms such as password protection, biometric authentication, or multi-factor authentication should be implemented.
4. Data Retention and Disposal: Policies should outline how long data will be kept and how it will be disposed of when it is no longer needed.
5. Data Access and Use: Clear policies should outline who has access to the data and how it can be used to ensure it is only used for the purpose it was collected and not disclosed to unauthorized parties.
6. Compliance with Laws and Regulations: All data security measures should comply with relevant laws and regulations, including local, national, and international laws and regulations, as well as industry-specific regulations.
7. Regular Monitoring and Assessment: Regular monitoring and assessment of data security measures will help to ensure they are effective and potential vulnerabilities are identified and addressed promptly.

By following these best practices, organizations can ensure control over their data and that it is protected from unauthorized access and use in the IoT ecosystem, in line with the Global Risk Analytics approach to security.

Data Transmission

Data transmission is a critical component of the Internet of Things (IoT) and open data ecosystems. It involves the transfer of data from various sources to the LoRaWAN network. In the context of the Global Risk Analytics, it is essential to develop a data transmission protocol that can efficiently and securely transmit data while ensuring that it is transmitted promptly. Global Risk Analytics data transmission protocol protects data confidentiality, integrity, and availability while optimizing data transmission.

Developing a robust and efficient data transmission protocol is critical to the success of any project that involves gathering and managing data over a communication network. The LoRaWAN ecosystem, a low-power, a wide-area network designed for IoT devices, requires a data transmission protocol that meets its specific requirements.

The Global Risk Analytics data transmission protocol is a comprehensive solution designed to meet these requirements while ensuring secure and efficient data transmission from IoT devices to the network. This protocol is based on fundamental principles such as efficiency, security, compatibility, reliability, and scalability and is designed to address the unique challenges posed by the LoRaWAN ecosystem.

The protocol incorporates the latest technologies and best practices in data transmission and is designed to be compatible with the LoRaWAN network. This ensures that data can be transmitted over the web in a timely and secure manner without any loss or corruption of data.

In addition, the Global Risk Analytics data transmission protocol is designed to be scalable, allowing for future growth in the number of IoT devices and data sources. This scalability, combined with its efficiency and security features, makes the Global Risk Analytics data transmission protocol a critical component in gathering open data sets through IoT devices in the LoRaWAN ecosystem.

LoRaWAN Protocols

In the LoRaWAN ecosystem, some standard data transmission protocols include:

1. LoRaWAN: A long-range wireless network protocol that operates in the unlicensed spectrum and is specifically designed for IoT devices.
2. MQTT: A lightweight publish/subscribe messaging protocol widely used in IoT systems.
3. CoAP: The Constrained Application Protocol is a specialized web transfer protocol for use with constrained nodes and networks in the IoT.
4. LwM2M: A lightweight machine-to-machine communication protocol optimized for IoT devices with limited resources.
5. 6LowPAN: A low-power wireless networking protocol designed to work with low-power IoT devices and networks.
6. Zigbee: A low-power wireless network protocol widely used in IoT devices and networks.

It’s important to note that the choice of a data transmission protocol depends on the specific requirements and constraints of the IoT application and the LoRaWAN network.

Data Protocols

Many data transmission protocols are widely used in various industries and applications. Some of the most commonly used protocols are:

1. Transmission Control Protocol (TCP): a reliable, connection-oriented protocol that provides error-free data transfer.
2. User Datagram Protocol (UDP): a connectionless protocol that provides fast data transfer but does not guarantee reliability.
3. File Transfer Protocol (FTP): a protocol used for transferring files over a network.
4. Simple Mail Transfer Protocol (SMTP): a protocol used for transmitting email messages.
5. Hypertext Transfer Protocol (HTTP): a protocol used for transmitting web pages and other content over the internet.
6. Hypertext Transfer Protocol Secure (HTTPS): a secure version of HTTP, which uses encryption to protect data during transmission.
7. Remote Procedure Call (RPC): a protocol for executing procedures on remote systems.
8. Internet Protocol (IP): a protocol for transmitting data over the internet.
9. Internet Control Message Protocol (ICMP): a protocol for transmitting control messages over the internet.
10. Post Office Protocol (POP): a protocol for retrieving email messages from a server.
11. Internet Message Access Protocol (IMAP): a protocol for accessing and manipulating email messages on a server.
12. Simple Network Management Protocol (SNMP): a protocol for managing and monitoring network devices.

These are just a few examples of data transmission protocols. The choice of protocol depends on the application’s specific requirements, such as reliability, security, speed, and compatibility with other systems.

Data Validation

The Global Risk Analytics approach to data validation is vital to collecting open data through the Internet of Things (IoT) devices in the LoRaWAN ecosystem. This approach aims to increase the precision and consistency of the data being gathered, processed, and stored. The utilization of Artificial Intelligence (AI) and Machine Learning (ML) techniques in the data validation process is crucial in automating the process and enhancing the data quality. This leads to improved reliability and credibility of the data.

The integration of AI/ML in open data has a profound effect on sustainable development. It enhances the accuracy and reliability of data, making it more useful for decision-making and analysis. In the context of the LoRaWAN ecosystem, implementing AI/ML in the data validation process is essential as it ensures that the data collected is high quality and suitable for various applications and use cases.

The Global Risk Analytics data validation process incorporates the latest AI/ML techniques, such as machine learning algorithms and deep learning models. These techniques automate the process and increase its accuracy. The process is designed to identify and correct any data errors and ensure that the data is consistent and precise. The data validation process is flexible and adaptable, allowing it to update or modify to suit evolving requirements easily.

AI/ML

The Global Risk Analytics approach to data validation utilizes a combination of AI/ML algorithms to ensure the accuracy and consistency of the data being collected through IoT devices. Some of these approaches include:

1. Artificial Neural Networks (ANNs): ANNs can detect patterns and anomalies in the data and classify and validate them based on learned relationships between inputs and outputs.
2. Decision Trees: Decision trees are algorithms that can classify data based on certain attributes and validate it using specific rules. They provide a clear visual representation of the decisions being made, which can help identify errors in the data.
3. Random Forest: Random Forest is an ensemble method that combines multiple decision trees to increase the accuracy and robustness of the classification and validation of data.
4. Naive Bayes: Naive Bayes is a probabilistic algorithm that can classify and validate data based on features and probabilities. It is simple to implement and is suitable for large datasets with many features.
5. Clustering Algorithms: Clustering algorithms, such as K-means and hierarchical clustering, group similar data points based on their proximity to each other and can be used to validate data based on their relationship to different points in the same group.
6. Anomaly Detection: Anomaly detection algorithms, such as Isolation Forest and One-Class SVM, identify and validate unusual data points or deviate from normal behaviour. These algorithms are particularly useful for detecting errors or outliers in the data.

These AI/ML approaches are just a few examples of the techniques used for data validation in the Global Risk Analytics approach. The specific approach used will depend on the type and characteristics of the data being validated. The Global Risk Analytics approach is designed to be flexible and adaptable to changing requirements, making it easier to implement the most appropriate approach for each scenario.

Data Management

Data management is a critical component of any organization’s success, as it enables the efficient and effective use of data to inform decision-making and drive business objectives. In today’s complex and rapidly changing business landscape, organizations must be able to access and analyze data from a wide range of sources, including both internal and external data sources, to remain competitive and achieve their goals.

The Global Risk Analytics is a powerful data management solution that provides organizations with fast and efficient access to critical data, specifically in the context of global risks. Global Risk Analytics operates in a secure, zero-trust environment and leverages AI to provide organizations with quantitative analytical products, including climate risk and disaster prevention, preparedness, and response.

Data Structure

Global Risk Analytics’s data structure is designed to provide organizations with a robust and efficient means of accessing and analyzing data. With its ability to combine multiple data sources, Global Risk Analytics enables organizations to view their operations and make informed decisions comprehensively. The platform is optimized explicitly for global risks and operates in a secure, zero-trust environment, providing organizations with the peace of mind they need to make essential business decisions.

Data Analytics

One of the critical advantages of Global Risk Analytics is its use of AI to provide organizations with quantitative analytical products. The platform leverages AI to allow organizations to assess real-time data and take proactive measures to mitigate risks. This includes the ability to analyze data related to climate risk and disaster prevention, preparedness, and response, which is critical in today’s rapidly changing business landscape.

Data Products

Global Risk Analytics offers a suite of products built on its open-source stack that enables organizations to reduce risk and improve operational efficiency. Whether organizations are looking to identify areas for improvement or gain a comprehensive view of their operations, Global Risk Analytics provides the tools they need to make informed decisions. With its ability to acquire data from internal and external sources, Global Risk Analytics delivers the data organizations need, when needed, and in the format they require.

UNISDR

The United Nations Office for Disaster Risk Reduction (UNISDR) Risk and Vulnerability Assessment is a highly regarded framework used to evaluate disaster risks. The framework takes a holistic approach to assessing disaster risks and considers four key dimensions: physical, social, economic, and environmental risks. This approach ensures that all aspects of disaster risk are evaluated, including the direct and indirect impacts of a disaster.

The physical dimension evaluates the impact of natural hazards such as earthquakes, hurricanes, and tsunamis and technological hazards such as chemical spills and nuclear accidents. The social dimension assesses the vulnerability of communities, particularly in terms of their ability to cope with and recover from disasters. The economic dimension evaluates the impact of disasters on the local, national, and global economies, including the cost of relief, reconstruction, and loss of income. The environmental dimension evaluates the impact of disasters on the natural environment, including damage to ecosystems, wildlife, and land.

Considering all four dimensions of risk, the UNISDR Risk and Vulnerability Assessment provides a comprehensive approach to disaster risk reduction. Organizations, governments, and individuals widely use the framework to evaluate disaster risks and prioritize risk reduction measures. The framework can also be used to track progress and evaluate the effectiveness of disaster risk reduction initiatives. The UNISDR Risk and Vulnerability Assessment is essential in developing effective disaster risk reduction strategies and programs.

The critical components of The UNISDR’s Risk and Vulnerability Assessment are:

1. Hazard assessment: Identification and analysis of the natural, technological and human-induced hazards that pose a threat to a particular area.
2. Vulnerability assessment: Analysis of the characteristics and conditions of a community, infrastructure, or environment that increase its susceptibility to the impacts of a hazard.
3. Capacity assessment: Analysis of the existing capacities of a community, infrastructure, or environment to withstand, cope with and recover from the impacts of a hazard.
4. Risk assessment: The integration of hazard, vulnerability, and capacity assessments to determine the overall level of risk and its potential impacts on a community, infrastructure, or environment.
5. Risk management: The development and implementation of strategies, policies and measures to reduce risk and build resilience to disasters.
6. Monitoring and evaluation: Ongoing monitoring and evaluation of the implementation of risk management strategies and policies to assess their effectiveness and make necessary modifications.
7. Communication and knowledge management: Effective communication and sharing of knowledge and information to facilitate risk assessment, management and decision-making.

Global Risk Analytics + UNISDR

Hazard Assessment: In hazard assessment, Global Risk Analytics plays a crucial role by leveraging AI to provide organizations with a comprehensive understanding of the natural, technological and human-induced hazards that pose a threat to a particular area. The platform offers real-time data and analysis, allowing organizations to identify potential risks and develop proactive measures to mitigate them.

Vulnerability Assessment: Global Risk Analytics’s data indexing solution helps organizations in vulnerability assessment by providing real-time data on the characteristics and conditions of a community, infrastructure, or environment that increase its susceptibility to the impacts of a hazard. The platform’s ability to acquire data from both internal and external sources ensures that organizations have a complete understanding of their risk profile and can take necessary steps to reduce their vulnerability.

Capacity Assessment: Global Risk Analytics’s data management solution allows organizations to assess their existing capacities to withstand, cope with, and recover from the impacts of a hazard. The platform’s ability to provide real-time data and analysis, along with its robust data structure, enables organizations to quickly access the data they need to assess their capacities and develop strategies to improve their resilience.

Risk Assessment: Global Risk Analytics integrates hazard, vulnerability, and capacity assessments to determine the overall level of risk and its potential impacts on a community, infrastructure, or environment. The platform’s secure, zero-trust environment and ability to provide real-time data and analysis make it an ideal solution for risk assessment. With Global Risk Analytics, organizations can quickly access the data they need to make informed decisions and develop strategies to reduce risk and improve their resilience.

Risk Management: Global Risk Analytics helps organizations in risk management by providing real-time data and analysis that support the development and implementation of strategies, policies and measures to reduce risk and build resilience to disasters. The platform’s ability to acquire data from both internal and external sources ensures that organizations have a complete understanding of their risk profile and can take necessary steps to mitigate risk.

Monitoring and Evaluation: Global Risk Analytics supports ongoing monitoring and evaluation of the implementation of risk management strategies and policies by providing real-time data and analysis. The platform’s secure, zero-trust environment and its ability to provide real-time data ensure that organizations can assess the effectiveness of their risk management strategies and make necessary modifications.

Communication and Knowledge Management: Global Risk Analytics plays a crucial role in communication and knowledge management by providing a secure, zero-trust environment for data exchange and analysis. The platform’s ability to acquire data from both internal and external sources ensures that organizations have a complete understanding of their risk profile and can share information and knowledge effectively. Global Risk Analytics’s secure data exchange and analysis environment help organizations facilitate risk assessment, management, and decision-making.

IPCC

The Intergovernmental Panel on Climate Change (IPCC) Assessment Reports is a crucial framework that provides a comprehensive overview of the state of knowledge on climate change and its impacts. The reports are widely recognized as the authoritative source of information on climate change and provide a robust foundation for policy decisions at the national and international levels.

International experts produce the IPCC Assessment Reports based on extensive scientific and technical assessments of the latest climate change research. The reports cover the full spectrum of climate change impacts, including risk’s physical, biological, and socio-economic dimensions. This provides decision-makers with a comprehensive view of climate change’s complex and interconnected nature, causes, and potential impacts.

The IPCC Assessment Reports have been instrumental in raising awareness about the urgent need for action on climate change. The reports provide a comprehensive and robust scientific basis for policy decisions and highlight the need for a coordinated and comprehensive approach to reducing the risks associated with climate change. The reports are a valuable tool for governments, organizations, and individuals looking to understand the latest research on climate change and the risks it poses to human societies and the natural world.

The IPCC Assessment Reports are a crucial component of the global response to climate change and provide a robust foundation for informed decision-making on this critical issue. Global Risk Analytics, with its focus on ESG risk and its robust data management and analysis capabilities, is an ideal platform to support the implementation of the recommendations from the IPCC Assessment Reports and help organizations take proactive measures to reduce the risks associated with climate change.

The Intergovernmental Panel on Climate Change (IPCC) Assessment Reports are comprehensive assessments of the state of knowledge on climate change and its impacts. The key components of the IPCC Assessment Reports include:

1. Physical Science Basis: An analysis of the latest scientific data on the physical and chemical changes in the Earth’s climate system, including greenhouse gas emissions, temperature records, and atmospheric chemistry.
2. Impacts, Vulnerability, and Adaptation: An assessment of the impacts of climate change on various sectors, including ecosystems, human health, water resources, food security, and coastal zones. It also includes an analysis of the vulnerability and exposure of different populations to the impacts of climate change and the potential for adaptation.
3. Mitigation of Climate Change: An assessment of the options and strategies for reducing greenhouse gas emissions and limiting the magnitude and impacts of climate change.
4. Synthesis Report: A summary of the findings from the three previous reports, synthesizing the latest state of knowledge on climate change and its impacts.
5. Technical Summary: A condensed summary of the Synthesis Report, providing key messages and conclusions in a specialized format.
6. Summary for Policymakers: A summary of the Synthesis Report and Technical Summary tailored for policymakers and decision-makers.

These components are regularly updated and reviewed by the IPCC, taking into account new scientific evidence and advances in knowledge. The IPCC Assessment Reports provide a comprehensive and authoritative basis for policymaking on climate change and its impacts.

The IPCC’s current data ecosystem faces several challenges including limited data sources, potential inaccuracies in data, lack of transparency and trust in data, and limitations in data analysis methods. These challenges limit the IPCC’s ability to achieve its mission of providing accurate and comprehensive assessments of the state of the climate and its impacts.

Global Risk Analytics can be a complementary component to the IPCC’s data ecosystem to address these challenges. Global Risk Analytics upgrades data methods and massively scales data-gathering channels through IoT and participatory RRI programs. By relying on LPWAN systems such as LoRaWAN, Global Risk Analytics can help the IPCC achieve its objectives and improve the quality and accuracy of its data.

Global Risk Analytics’s role in helping IPCC:

1. Data Gathering: Global Risk Analytics leverages IoT technologies to collect large amounts of data from a diverse range of sources, including remote areas and underrepresented communities. This can give the IPCC a more comprehensive understanding of the state of the climate and its impacts.
2. Data Accuracy: Using LPWAN systems, Global Risk Analytics can ensure that data is accurately and reliably transmitted to central repositories. This can reduce the potential for errors and inaccuracies in data, improving the overall quality of data used by the IPCC.
3. Data Analysis: Global Risk Analytics uses advanced data analysis techniques to make sense of the large amounts of data it collects. This can help the IPCC to have a more complete and accurate understanding of the state of the climate and its impacts.
4. Data Transparency and Trust: Global Risk Analytics uses blockchain technology to ensure data transparency and build trust in the data used by the IPCC. This can help to increase the credibility and reliability of the IPCC’s assessments and conclusions.

By leveraging IoT and participatory RRI programs, Global Risk Analytics can help the IPCC overcome the challenges in its current data ecosystem and achieve its mission of providing accurate and comprehensive assessments of the state of the climate and its impacts.

WMO

The World Meteorological Organization (WMO) is a specialized agency of the United Nations that focuses on weather, climate, and water issues. The organization’s main objective is to provide information and tools to support effective decision-making, especially regarding managing weather and climate change risks. The WMO operates a comprehensive data ecosystem that encompasses data collection, management, analysis, and dissemination to achieve this.

The WMO operates a global network of meteorological and hydrological observing stations, which collect and provide high-quality, timely, reliable data and information on weather, climate, and water. The organization also provides guidance, standards and technical support for data collection and management to its Member States and other relevant stakeholders. The WMO’s data management system includes a wide range of data products, including climate data, weather forecasts, and climate outlooks, as well as the World Climate Data and Monitoring Programme, which provides a long-term perspective on climate variability and change.

The WMO also plays a key role in coordinating international efforts to monitor and assess climate change. The organization collaborates with a wide range of other organizations, including the United Nations Framework Convention on Climate Change (UNFCCC), the Intergovernmental Panel on Climate Change (IPCC), and the Global Climate Observing System (GCOS), to ensure that the data and information provided by its data ecosystem are of the highest quality and are used to support effective action on climate change.

The organization faces several data collection and management challenges despite WMO’s crucial role in providing information to support global risk management. These challenges, such as limited data sources, a lack of participatory RRI programs, and outdated data methods, impact the quality and accuracy of the data that WMO provides.

To address these challenges, Global Risk Analytics can be a complementary component to WMO’s data ecosystem and accelerate diversity, inclusivity and equity in the global data ecosystem. Global Risk Analytics upgrades data methods and scales data-gathering channels through participatory RRI and collective intelligence powered by decentralized IoT ecosystems. By relying on LPWAN systems such as LoRaWAN, Global Risk Analytics can help WMO achieve its objectives and improve the quality and accuracy of its data.

Global Risk Analytics utilizes IoT devices, such as sensors and gateways, to gather and transmit data in real-time. The deployment of these devices at the local level, combined with participatory RRI programs, enables Global Risk Analytics to standardize and transmit data from a much more comprehensive range of sources, including communities most impacted by weather, climate, and water-related risks. This increased data-gathering capability can enable WMO to make more informed decisions and provide more accurate information to support global risk management.

Global Risk Analytics implements advanced data analysis techniques in RRI’s lab environments to comprehensively understand weather, climate, and water-related risks. These techniques build collective intelligence that can enable WMO to identify trends, patterns, and potential risk scenarios that would otherwise be missed. The ability to analyze data in real-time, combined with the increased data gathering capabilities of Global Risk Analytics, can enable WMO to make faster, more informed decisions and to provide more accurate information to support global risk management.

Global Risk Analytics relies on LPWAN systems, which are ideal for collecting and transmitting data from remote areas over long distances. LPWANs are also designed to support large numbers of devices, enabling Global Risk Analytics to collect data from many sources in the LoRaWAN network. This increased data-gathering capability enables a more inclusive, diverse and equitable architecture supporting WMO to make more informed decisions and provide more accurate information to support global risk management.

UNEP

The United Nations Environment Programme (UNEP) is a global organization that addresses environmental risks and their impacts on human societies and ecosystems. The organization’s main aim is to provide relevant data and information to support decision-making at the global level.

UNEP operates in many areas, including climate change, disaster risk reduction, resource efficiency, and environmental governance. In these areas, UNEP gathers and analyzes data to better understand the environment’s state and the risks it poses to human societies and ecosystems. The organization also provides policy advice, support, and technical assistance to governments, civil society organizations, and private sector partners to help them develop and implement effective responses to these environmental risks.

One of the critical challenges UNEP faces is the limited availability of quality data and information on environmental risks, particularly in developing countries. Additionally, the organization faces difficulties ensuring that the data it collects and uses is relevant, up-to-date, and accessible to all stakeholders.

To address these challenges, UNEP has implemented various data management and information systems to improve its data and information quality and accuracy. The organization also works closely with other international and national organizations, such as the World Meteorological Organization (WMO) and the Intergovernmental Panel on Climate Change (IPCC), to ensure that the data and information it provides is comprehensive and consistent.

UNEP is crucial in providing environmental information and data to support decision-making and policy development at the global, regional and national levels. UNEP’s work focuses on the following areas:

1. Environmental Assessment and Monitoring: UNEP regularly assesses the state of the environment and provides information on environmental trends and trends related to sustainable development.
2. Environmental Risk Assessment and Management: UNEP provides data and information to support risk assessment and management of environmental risks, including climate change, land degradation, and water scarcity.
3. Environmental Policy Development: UNEP provides technical and policy support to Member States and the wider international community in developing and implementing environmental policies and strategies.

Despite the critical role that UNEP plays in providing environmental information and data, the organization faces several challenges in data management, including:

1. Data Quality and Accuracy: Ensuring the quality and accuracy of environmental data is essential for effective decision-making and policy development. However, UNEP often relies on data from a limited number of sources, which may not always be accurate or representative.
2. Data Accessibility and Integration: Making environmental data accessible and integrating it into decision-making processes is a challenge for UNEP, as data is often held by multiple organizations and stakeholders and may be difficult to access or use.
3. Data Management and Preservation: Ensuring environmental data’s long-term preservation and management is critical for effective decision-making and policy development. However, this can be a challenge for UNEP, as data may be held in multiple formats and require significant resources to manage and preserve.

Global Risk Analytics can play a complementary role in supporting UNEP’s work by addressing the challenges faced by the organization in data management. Global Risk Analytics upgrades data methods and massively scales data-gathering channels through IoT and participatory RRI programs. By relying on LPWAN systems such as LoRaWAN, Global Risk Analytics can help UNEP achieve its objectives and improve the quality and accuracy of its data.

1. Improved Data Quality and Accuracy: By leveraging IoT and participatory RRI programs, Global Risk Analytics can provide UNEP with access to high-quality and accurate environmental data from a wide range of sources. This will enable the organization to make more informed decisions and develop more effective policies and strategies.
2. Improved Data Accessibility and Integration: Global Risk Analytics can help UNEP to integrate environmental data into decision-making processes by providing a centralized repository for data that is easily accessible and usable. This will help to ensure that data is used more effectively to support decision-making and policy development.
3. Improved Data Management and Preservation: Global Risk Analytics can help UNEP to manage and preserve environmental data over the long term by providing a secure and reliable data repository that is easily accessible and usable. This will help to ensure that data is used effectively in decision-making and policy development for generations to come.

Limitations

The Global Risk Analytics is an innovative solution to organizations’ challenges in addressing global risks. By incorporating the latest advances in IoT and participatory RRI programs, Global Risk Analytics aims to provide organizations with more accurate and up-to-date data on global risks. This can help organizations make better-informed decisions and take more effective actions to mitigate these risks.

However, it is essential to carefully consider the potential benefits and limitations of Global Risk Analytics to ensure its effective deployment and use. One potential limitation is the cost of deploying and maintaining IoT and RRI systems, which may require significant resources and expertise. Additionally, the reliability of LPWAN systems may also be a concern, as these systems may not always provide the level of accuracy and quality required by organizations.

Furthermore, effective data management and analysis systems are also critical to the success of Global Risk Analytics. Without proper data management, organizations may be overwhelmed by the large amounts of data generated by IoT and RRI systems, making it difficult to extract valuable insights and make informed decisions.

Despite these limitations, Global Risk Analytics has the potential to significantly enhance the work of key organizations such as the United Nations Environment Programme (UNEP), the World Meteorological Organization (WMO), and the Intergovernmental Panel on Climate Change (IPCC). Global Risk Analytics can help organizations make better-informed decisions and take more effective actions to mitigate these risks by providing organizations with more accurate and up-to-date data on global risks.

Architecture

The Global Risk Analytics is a cutting-edge framework that provides organizations with a comprehensive understanding of global risks and their potential impacts. With its user-driven and data-driven approach, Global Risk Analytics is designed to support organizations in making informed decisions and taking effective action in the face of global risks.

Global Risk Analytics has three main dimensions: risks, impacts, and analytics. These dimensions are interrelated and work together to provide organizations with a complete picture of global risks.

Risks

The Risks dimension of the Global Risk Analytics is an integral component of the comprehensive framework for risk management. This dimension helps organizations to understand the different types of global risks that they face. The risks dimension is broken down into eight sub-index categories, which are: environmental risks, catastrophic risks, financial risks, health risks, technology risks, systemic risks, political risks, and public risks.

Environmental Risks

Environmental risks pose a significant threat to the health of our planet and the well-being of its inhabitants. They are the result of human activities, natural events, and other factors that have a negative impact on the environment. These risks can take many forms, including climate change, deforestation, loss of biodiversity, pollution, and water scarcity. It is important for organizations to understand and manage these environmental risks in order to reduce their negative impacts and contribute to sustainable development.

1. Climate change: Risks from climate change can include sea level rise, increased frequency and intensity of extreme weather events, and harm to biodiversity, which can lead to impacts on food security, human health, and infrastructure.
2. Loss of biodiversity: This can result in a disruption of the ecological balance and reduction of ecosystem services, such as water regulation, pollination, and pest control, leading to negative impacts on food production, human health, and other industries that rely on these services.
3. Pollution: Environmental pollution can lead to adverse health effects, harm wildlife and aquatic life, and damage ecosystems.
4. Deforestation: Deforestation can result in loss of habitat for wildlife, reduction in carbon sequestration, and disruption of the water cycle, leading to negative impacts on biodiversity, food security, and regional climate.
5. Water scarcity: This can result in decreased access to fresh water for human consumption, agriculture, and industry, leading to potential conflicts over water resources, harm to biodiversity, and reduced food production.

These environmental risks can have significant and long-lasting impacts on communities, ecosystems, and the environment. It is important for organizations to be aware of and manage these risks in order to reduce their impact and contribute to sustainable development.

Catastrophic Risks

Catastrophic risks are a growing concern in today’s rapidly changing world, with the potential to cause widespread destruction and have long-lasting impacts on individuals, organizations, communities, and nations. These risks, which can include natural disasters, pandemics, cyber attacks, and nuclear incidents, are characterized by their high likelihood of occurrence, severe consequences, and difficulty in predicting and mitigating their effects. Understanding and preparing for these risks is crucial for organizations to be able to minimize their impact and reduce their overall risk profile. This guide will provide a comprehensive overview of catastrophic risks, their significance and impact, and the steps organizations can take to mitigate and respond to them.

• Natural disasters: Such as hurricanes, earthquakes, and tsunamis, these risks can cause significant damage to infrastructure and disrupt the lives of millions of people.
• Pandemics: Outbreaks of disease that can spread rapidly and have a major impact on global health, the economy, and critical services.
• Cyber attacks: Cybersecurity incidents that can disrupt critical systems, steal sensitive information, and cause widespread panic.
• Nuclear incidents: Incidents involving nuclear weapons or nuclear power plants that can cause significant harm to people and the environment.

The consequences of catastrophic risks can be severe and long-lasting, affecting individuals, organizations, communities, and nations. These risks can disrupt supply chains, impact infrastructure, cause widespread panic, and harm people and the environment.

Given the potential consequences of catastrophic risks, it is important for organizations to understand and prepare for these risks. This includes conducting risk assessments, developing contingency plans, and investing in risk mitigation measures. It is also crucial for organizations to collaborate with other stakeholders and have open and transparent communication during and after a catastrophic event.

While catastrophic risks can be challenging to predict and mitigate, organizations must be prepared to respond to these events to reduce their impacts and contribute to sustainable development. Advanced analytics tools such as Global Risk Analytics can help organizations understand their exposure to catastrophic risks and make informed decisions on how to mitigate and respond to these risks.

Financial Risks

Financial risks refer to the potential negative consequences and impacts that can result from financial events, activities, and conditions. These risks arise from various sources, including market conditions, creditworthiness of borrowers, operational efficiency, and liquidity of financial instruments, among others.

The significance of financial risks lies in their ability to cause significant financial losses and harm to organizations, financial markets, and even entire economies. For instance, market risk can result in significant losses due to sudden and unpredictable changes in market conditions, impacting the value of an organization’s investments and financial instruments. Credit risk can limit an organization’s access to funding and stability of financial markets by increasing the risk of default by borrowers. Operational risk can harm an organization’s reputation and bottom line by resulting in losses due to internal processes, systems, or human errors.

Financial risks can have far-reaching and long-lasting impacts on organizations, financial markets, and economies. Therefore, it is crucial for organizations to understand and manage these risks to maintain their financial stability and mitigate potential losses. This can be achieved through various risk management strategies, including diversification of investments, proper due diligence of borrowers, implementation of effective internal processes and systems, and adequate liquidity management.

Understanding and managing financial risks is critical for organizations to maintain financial stability and support sustainable growth and development. Some of the common financial risks include:

1. Market risk: refers to the risk of losses due to changes in market conditions, such as changes in interest rates, currency exchange rates, and stock prices.
2. Credit risk: refers to the risk of default by a borrower, which can impact an organization’s ability to access funding and the stability of financial markets.
3. Operational risk: refers to the risk of loss due to inadequate or failed internal processes, systems, or human error, which can impact an organization’s reputation, customer satisfaction, and bottom line.
4. Liquidity risk: refers to the risk of an organization’s inability to meet its financial obligations as they come due, which can impact its ability to continue operations and pay its debts.
5. Regulatory risk: refers to the risk of loss due to changes in laws and regulations, which can impact an organization’s ability to operate in certain markets or comply with legal requirements.

Health Risks

Health risks refer to the potential negative consequences and impacts that can arise from various factors that can affect human health and well-being, such as diseases, exposure to hazardous substances, and unhealthy living conditions. These risks can take many forms, including communicable diseases, chronic illnesses, environmental pollution, and lack of access to safe water and sanitation, among others.

Health risks are significant because they can have wide-ranging and long-term impacts on individual and population health, as well as the overall functioning and sustainability of ecosystems. For example, communicable diseases can spread quickly and cause widespread illness and death, while chronic illnesses can result in reduced quality of life, decreased productivity, and increased healthcare costs. Environmental pollution can impact the health of both human populations and wildlife, as well as damage ecosystems and harm biodiversity. Lack of access to safe water and sanitation can result in waterborne diseases and other health problems, particularly in low-income communities.

To address and mitigate the impact of health risks, it is important for individuals, communities, and governments to prioritize public health measures and initiatives, such as improving access to healthcare, promoting healthy living conditions, and reducing exposure to hazardous substances. It is also important to invest in research and development to better understand the causes and impacts of health risks, and to develop new and more effective treatments and prevention methods.

Health risks are a major challenge for individuals, communities, and ecosystems, and addressing these risks requires a multi-faceted and proactive approach that considers the root causes and potential impacts of these risks. By investing in public health initiatives and scientific research, we can work towards a healthier and more sustainable future for all. Some of the most common health risks include:

1. Infectious diseases (e.g. COVID-19, HIV/AIDS, tuberculosis, etc.)
2. Non-communicable diseases (e.g. heart disease, cancer, diabetes, etc.)
3. Environmental health risks (e.g. air pollution, water pollution, exposure to toxic chemicals, etc.)
4. Lifestyle-related health risks (e.g. tobacco use, physical inactivity, unhealthy diet, etc.)
5. Mental health risks (e.g. depression, anxiety, stress, etc.)
6. Occupational health risks (e.g. exposure to hazardous substances, work-related stress, etc.)
7. Natural disasters and humanitarian crises (e.g. earthquakes, hurricanes, floods, etc.)
8. Health-related conflicts (e.g. war, displacement, etc.)
9. Health system-related risks (e.g. inadequate access to healthcare, lack of resources, etc.)
10. Emerging health risks (e.g. new infectious diseases, emerging technology, etc.)

The significance of these health risks lies in their potential to impact the physical, mental, and social well-being of individuals and communities, as well as their ability to cause widespread illness and death. In addition, the impacts of health risks can have far-reaching consequences for economies, societies, and the environment, by affecting workforce productivity, healthcare costs, and the sustainability of ecosystems.

Technology Risks

Technology risks refer to the potential negative consequences and impacts that can arise from the use, development, and implementation of technology. These risks can take many forms, including cyber security risks, data privacy risks, reputational risks, and operational risks, among others.

The significance of technology risks lies in their ability to impact individuals, organizations, communities, and even entire ecosystems. For example, cyber security risks, such as hacking and malware, can compromise sensitive information, disrupt critical systems, and cause significant financial losses. Data privacy risks, such as data breaches, can compromise personal information and result in damage to an individual’s reputation and financial stability. Reputational risks, such as negative publicity or loss of customer trust, can impact an organization’s bottom line and its ability to attract and retain customers.

The impact of technology risks is not limited to just the financial or reputational effects, as technology has become an integral part of our daily lives and ecosystems. Inefficient or unreliable technology systems can disrupt essential services, such as healthcare, transportation, and communication, leading to negative impacts on public health, safety, and well-being. Therefore, it is crucial for organizations and individuals to understand the technology risks they face and take proactive measures to mitigate and respond to them.

Global Risk Analytics aims to provide comprehensive coverage of technology risks, their significance and impact, and the steps organizations and individuals can take to manage and mitigate them. Some common examples of technology risks include:

1. Cybersecurity risks: the risk of unauthorized access, theft, or damage to sensitive information and systems through cyber attacks, hacking, or other means.
2. Data privacy risks: the risk of personal or sensitive information being disclosed, misused, or lost.
3. Technical failure risks: the risk of technology systems, devices, or applications failing to perform as expected, resulting in lost time, data, or revenue.
4. Human error risks: the risk of human error or misjudgment leading to accidents, incidents, or data breaches.
5. Reputation risks: the risk of damage to an organization’s reputation due to technological failures, data breaches, or other incidents.
6. Regulatory risks: the risk of non-compliance with laws, regulations, or standards related to technology and data privacy.
7. Market risks: the risk of market changes, competition, or new entrants affecting the demand for or value of technology products or services.
8. Supply chain risks: the risk of disruptions or failures in the supply chain, affecting the availability or quality of technology components or services.

The significance and impact of technology risks are widespread and far-reaching, affecting individuals, organizations, communities, and nations. Technology risks can impact privacy and security, cause financial loss, and disrupt critical services and operations. In addition, technology risks can have long-lasting effects, such as damage to an organization’s reputation or loss of customer trust. Effective risk management and mitigation strategies are essential to minimize the impact of technology risks and ensure the continued development and growth of technology.

Systemic Risks

Systemic risks refer to the potential negative consequences and impacts that can arise from the interconnections and interactions among various systems, including financial, economic, technological, and ecological systems. These risks are characterized by their high likelihood of occurrence, severe consequences, and the potential for widespread and far-reaching impacts.

Systemic risks are the risks that are inherent to a system, as opposed to risks that are specific to a particular component of the system. These risks arise from the interactions and interconnections among different components of the system, leading to the potential for widespread impacts. Characteristics of systemic risks include:

• Interconnectedness: Systemic risks arise from the interactions and interconnections among different components of a system.
• Complexity: Systemic risks are often complex and difficult to understand, making it challenging to predict and mitigate their impacts.
• Contagion: Systemic risks have the potential to spread and impact multiple components of a system, leading to wider impacts.
• Non-linearity: Systemic risks can result in disproportionate impacts that are much greater than the sum of their individual components.
• Emergence: Systemic risks can emerge from unexpected and unanticipated events, making it difficult to prepare for and respond to them.

Examples of systemic risks:

• Financial Systemic Risks: These risks arise from the interconnectedness of financial systems, including the risk of widespread defaults, market crashes, and financial instability.
• Economic Systemic Risks: These risks arise from the interactions among different economic systems, including the risk of global economic recession, supply chain disruptions, and inflation.
• Technological Systemic Risks: These risks arise from the use and dependence on technology, including the risk of cyberattacks, data breaches, and technology failures.
• Ecological Systemic Risks: These risks arise from the interactions between human activities and the natural environment, including the risk of climate change, loss of biodiversity, and resource depletion.

Systemic risks have the potential to cause widespread and far-reaching impacts, including financial losses, economic instability, loss of life, and environmental degradation. These risks can significantly impact individuals, organizations, communities, and nations, including loss of jobs, reduced economic activity, and decreased quality of life. Below are some factors contributing to systemic risks:

• Interconnectedness: The increasing interconnectedness of systems, such as financial and economic systems, increases the likelihood and severity of systemic risks.
• Complexity: The increasing complexity of systems, including technological and ecological systems, makes it more challenging to understand and mitigate systemic risks.
• Unintended Consequences: Human activities and decisions can result in unintended consequences that contribute to the emergence and propagation of systemic risks.
• Feedback Loops: Feedback loops can amplify the impacts of systemic risks, leading to more widespread and severe consequences.

Challenges in identifying, assessing, and mitigating systemic risks:

• Data and Information: Lack of data and information can make it difficult to identify, assess, and mitigate systemic risks.
• Complexity: The complexity of systemic risks makes it challenging to predict and understand their impacts.
• Interdisciplinary Approach: Addressing systemic risks often requires an interdisciplinary approach, involving experts from multiple fields, including finance, economics, technology, and ecology.
• Lack of Awareness: Lack of awareness and understanding of systemic risks can result in insufficient resources and attention being dedicated to their identification, assessment, and mitigation.

Organizations and governments can employ various strategies to manage and prepare for systemic risks, including risk assessment and management, crisis management and contingency planning, collaboration and information sharing, and investment in risk reduction and resilience. Regular monitoring and assessment of systemic risks can help identify emerging risks and develop proactive measures to prevent or mitigate their impacts. In addition, organizations should invest in improving their risk management processes, as well as in training and educating their employees about systemic risks and how to respond to them. Finally, governments and organizations should promote cross-sector collaboration and information sharing to enhance the resilience of their systems and respond more effectively to systemic risks as they arise.

Political Risks

Political risks refer to the potential adverse effects and uncertainties that can arise from the actions and decisions of governments and political entities. These risks can take many forms, including policy changes, regulatory changes, political instability, and conflict, among others.

The significance of political risks lies in their potential to disrupt business operations, alter market conditions, and impact economic development. For example, policy changes can impact trade, investment, and immigration, leading to changes in market conditions and affecting the operations of businesses. Regulatory changes can impact the way in which businesses operate, leading to changes in costs, compliance, and liability. Political instability and conflict can result in disruptions to the supply chain, damage to infrastructure and facilities, and loss of human life.

The impact of political risks can be far-reaching, affecting individuals, organizations, communities, and nations. It is therefore critical for organizations to understand and prepare for these risks, through scenario planning, risk management, and engagement with stakeholders. Governments can play a role in reducing political risks by promoting stability, transparency, and predictability in their policies and regulations. It is also important for organizations to stay informed about the political environment in which they operate and to develop contingency plans for potential risks.

1. Political instability – The risk of instability, civil unrest, or conflict due to political factors such as corruption, electoral disputes, or social or ethnic tensions. Political instability can result in violence, disruptions to the economy, and harm to individuals, organizations, and communities.
2. Government intervention – The risk of government intervention in the economy, financial markets, or private businesses, including changes in regulations, taxes, or subsidies. Government intervention can impact the competitiveness of an industry, alter the business environment, and limit the ability of private organizations to operate freely.
3. Nationalization – The risk of the government taking over private businesses, industries, or assets. Nationalization can result in the loss of ownership and control for private organizations, decreased investment, and a decrease in economic activity.
4. Expropriation – The risk of the government confiscating private property, either for the public good or for political reasons. Expropriation can result in the loss of property rights, decreased investment, and harm to the rule of law.
5. Regulatory risk – The risk of changes in laws, regulations, or policies that can impact businesses or industries. Regulatory risk can impact the competitiveness of an industry, alter the business environment, and limit the ability of private organizations to operate freely.
6. Geopolitical risk – The risk of political or military events that can impact the stability or security of a region or country. Significance and impact: Geopolitical risk can result in violence, disruptions to the economy, and harm to individuals, organizations, and communities.
7. Political violence – The risk of violence or terrorism due to political factors such as ethnic tensions, religious tensions, or separatist movements. Political violence can result in harm to individuals, organizations, and communities, as well as disruptions to the economy and to security.
8. Corruption – The risk of bribery, fraud, or other forms of unethical behavior by public officials or political leaders. Corruption can result in decreased trust in government, decreased investment, and harm to the rule of law.

Public Risks

Public risks refer to the potential negative consequences and impacts that can arise from actions taken by governments, public authorities, or other entities that have a direct impact on the public. These risks can take many forms, including policy risks, regulatory risks, and legal risks, among others.

The significance of public risks lies in their potential to impact the lives and well-being of individuals and communities, as well as the functioning of organizations and the economy as a whole. For example, policy risks, which refer to the risk of changes in government policies that can impact an organization’s operations and financial performance, can disrupt business operations, reduce investor confidence, and impact the overall economy. Regulatory risks, which refer to the risk of changes in regulations or enforcement that can impact an organization’s operations, can increase costs, reduce efficiency, and impact an organization’s competitiveness. Legal risks, which refer to the risk of legal action or penalties arising from an organization’s actions, can impact an organization’s reputation, financial performance, and ability to operate.

Organizations and individuals need to understand and prepare for public risks, including monitoring developments in public policies and regulations, conducting regular risk assessments, and implementing strategies to mitigate their impacts. Governments and public authorities can also play a role in reducing public risks by promoting transparency and stability in their policies and regulations, and working to address the underlying social, economic, and environmental factors that contribute to their emergence.

Public risks can take many forms, including:

1. Natural disasters – such as hurricanes, earthquakes, and floods – can cause widespread destruction and loss of life, disrupt essential services, and impact economic activity.
2. Disease outbreaks – such as pandemics – can spread quickly and impact the health and well-being of individuals, as well as the functioning of healthcare systems and economies.
3. Terrorism – can impact individuals and communities by causing harm, spreading fear, and disrupting the normal functioning of societies.
4. Crime – can impact individuals and communities by causing harm, spreading fear, and disrupting the normal functioning of societies.
5. Cyber threats – can impact individuals, organizations, and communities by compromising data security and disrupting critical infrastructure and services.

The significance of public safety risks lies in their potential to cause harm to individuals, disrupt communities, and impact the functioning of organizations and the economy as a whole.

It is important for governments, organizations, and individuals to understand and prepare for public safety risks by implementing strategies to reduce their likelihood, and minimize their impacts. This can include investing in disaster preparedness, improving emergency response systems, developing public health programs, enhancing cybersecurity, and increasing law enforcement and intelligence capabilities. Additionally, promoting transparency, trust, and cooperation among stakeholders, and addressing underlying social, economic, and environmental factors that contribute to public safety risks, can help to build resilience and reduce their impact over time.

These categories allow organizations to have a more nuanced and detailed understanding of the risks they face. By categorizing risks in this way, organizations are able to prioritize their risk management efforts and make informed decisions. Each of the eight sub-index categories operates as a sub-Decentralized Autonomous Organization (DAO) within the Global Risk Analytics network, with dedicated platforms for interdisciplinary and cross-functional teams to collaborate and co-create.

This approach to categorizing and prioritizing risks is unique to Global Risk Analytics and provides organizations with a comprehensive view of their risks. The categorization of risks in this manner helps organizations to understand their risks better, allowing them to make informed decisions and take necessary actions to mitigate those risks.

Impacts

The Impacts dimension of the Global Risk Analytics is critical in helping organizations assess and understand the potential consequences of global risks. This dimension goes beyond just identifying the risks, it takes into consideration the potential impacts of these risks on organizations, societies, and ecosystems. The impact dimension provides a comprehensive view of the potential impacts of risks, including financial, social, health, governance, and environmental impacts.

By using the integrated assessments provided by Global Risk Analytics, organizations can get a comprehensive view of the potential impacts of global risks across their entire value chain. This information is crucial for Environmental, Social, and Governance (ESG) compliance and monitoring, as it helps organizations to understand the potential consequences of their actions on different stakeholders and the environment.

Global Risk Analytics’s impact factors provide a comprehensive and in-depth analysis of the potential impacts of risks. This information helps organizations to better understand the consequences of their actions and make informed decisions on how to mitigate or respond to these risks. For example, a company that is operating in a region that is prone to natural disasters, such as hurricanes or earthquakes, can use Global Risk Analytics to assess the potential financial, social, health, and environmental impacts of such events. This information can then be used to inform their disaster preparedness and response plans, and to identify ways to mitigate the impacts of such events.

Analytics

One of the critical components of the framework is the analytics dimension, which provides organizations with a data-driven understanding of the risks they face. The analytics dimension of Global Risk Analytics is built on a foundation of a comprehensive and up-to-date data set that is generated by integrating data from a wide range of sources, including the web and IoT sensors.

This real-time and accurate data set enables organizations to stay ahead of emerging risks and threats and to make informed decisions. The data is analyzed using advanced analytics techniques, such as machine learning, statistical analysis, and network analysis, to identify patterns and relationships within the data. The result is actionable insights that allow organizations to prioritize their risk management efforts and make informed decisions.

The analytics dimension of Global Risk Analytics also enables organizations to improve their ESG performance, increase transparency, and enhance their overall risk management capabilities. By providing organizations with a data-driven understanding of global risks, the analytics dimension of Global Risk Analytics helps organizations to better manage their risks, protect their assets, employees, customers, and stakeholders, and increase their resilience against global risks.

Hazard and exposure, vulnerability, and coping capacity are important concepts in the context of the United Nations Office for Disaster Risk Reduction (UNISDR).

Hazard refers to the likelihood of a particular natural or man-made disaster occurring, such as an earthquake, hurricane, or cyber attack. Exposure refers to the presence of people, infrastructure, or assets in a location that could be affected by the disaster.

Vulnerability refers to the degree to which people, communities, or systems are at risk to the impacts of a hazard. It is a measure of their susceptibility to harm or damage due to factors such as their physical, economic, or social conditions.

Coping capacity refers to a community’s or organization’s ability to prepare for and respond to a hazard, including their capacity to absorb the impacts of the hazard, recover from it, and reduce the likelihood of future hazards.

Global Risk Analytics’s analytics dimension can help provide a comprehensive view of hazard and exposure, vulnerability, and coping capacity by integrating data from a variety of sources, including the web and IoT sensors. This integration of data allows Global Risk Analytics to create a real-time and up-to-date view of global risks, providing organizations with the information they need to better understand the potential consequences of risks and make informed decisions on how to respond.

Using advanced analytics techniques, Global Risk Analytics can provide organizations with data-driven insights into the various aspects of global risks, including the likelihood of a particular hazard occurring, the presence of assets and people in a location that could be affected by the disaster, the degree to which communities or systems are at risk to the impacts of a hazard, and the ability of communities or organizations to prepare for and respond to the hazard.

By providing organizations with a data-driven understanding of hazard and exposure, vulnerability, and coping capacity, Global Risk Analytics enables organizations to prioritize their risk management efforts, allocate resources effectively, and make informed decisions, ultimately helping them to manage their risks better and protect their assets, employees, customers, and stakeholders.

Coverage

The Global Risk Analytics Subnational Risk Index is a powerful tool that provides a granular view of risk and its various components within a specific geographic area, such as a region or country. Unlike the global Global Risk Analytics Index, which focuses on assessing risk on a global scale, the Subnational Risk Index offers a more localized and nuanced perspective of risk tailored to the unique circumstances of a particular area. This approach is achieved through a consistent methodology, which ensures that risk assessments are standardized across different regions and countries.

The development of a Global Risk Analytics Subnational model is a locally-focused and managed process backed by the support of the Global Risk Alliance. The national working group is responsible for establishing and running the Subnational Risk Index, acting as a sub-decentralized autonomous organization (sub-DAO) with its own governance structure and decision-making authority.

The working group plays a crucial role in managing the digital twins of the risk assessment process. Digital twins are virtual models that mimic the physical systems being evaluated, and they provide real-time insights into risk factors and help to identify potential areas for improvement. This enables the working group to respond swiftly and effectively to emerging risk factors, mitigating their potential impact.

In addition to digital twins, the working group may also deploy competence cells, which serve as Community Emergency Response Teams (CERT), to address risk factors as they arise. This capability allows for rapid response to emerging risks, and helps to ensure that risks are addressed in a timely and effective manner.

The significance of the Global Risk Analytics Subnational Risk Index cannot be overstated. By providing a more nuanced and localized view of risk, the Index helps organizations and governments to understand and prepare for potential risks, improving their overall preparedness and resilience. The use of digital twins and the deployment of CERTs further enhance the ability of the working group to respond to emerging risks, reducing their impact and helping to create a safer and more secure environment for individuals and communities.