Climate Risk Classification Standard for Scenario Generation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems lack a consistent and scalable approach to classify and manage climate-related risks, particularly under radical uncertainty, such as those posed by climate change, which hinders effective risk management and financial planning.
Innovation Solution
A computer system utilizing a Climate Risk Classification Standard (CRCS) hierarchy and machine learning to generate integrated climate risk data, creating a causal graph and knowledge graph that maps climate data to geographic space and time, allowing for the quantification of risk factors and generation of multifactor scenario sets for stress testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a comprehensive classification standard is implemented to classify climate-related risks, then measurement precision and reliability of risk assessment are improved, but device complexity and difficulty of operation increase
Solution Approach 1:
The classification standard divides climate-related risks into distinct categories including transition risks (policy, legal, reputational, technological) and physical risks (acute and chronic). This segmentation enables precise measurement and assessment of different risk types while maintaining system manageability through structured organization.
Solution Approach 2:
The classification standard serves multiple functions simultaneously: it classifies risks, quantifies their impacts, guides data collection, and supports scenario generation. This multi-functionality improves measurement precision across diverse risk types without requiring separate systems for each function.
2Productivity
If automated scenario generation using machine learning is implemented, then productivity and scalability of risk assessment are improved, but device complexity increases
Solution Approach 1:
The system uses machine learning algorithms to automatically generate scenarios and assess risks without requiring manual intervention for each scenario. The automated pipeline processes input data, generates multiple scenarios, and produces risk assessments efficiently, improving productivity while the standardized algorithms keep complexity manageable.
Solution Approach 2:
The system varies key parameters such as climate change scenarios, economic conditions, and time horizons to generate diverse risk scenarios automatically. This parameter-based approach enables scalable scenario generation where the same framework can produce numerous scenarios by simply changing input parameters rather than requiring complex manual modeling for each.
3Reliability
If consistent framework for scenario generation is implemented, then reliability and reproducibility of risk assessment are improved, but ease of operation decreases
Solution Approach 1:
The framework provides standardized procedures and guidelines specifically tailored for different risk types and scenarios. Each section offers localized instructions and templates appropriate to that specific risk category, making the consistent framework easier to operate by providing targeted guidance rather than requiring users to navigate a monolithic complex system.
Data Source
AI summary
Embodiments relate to computer systems and methods for computer models and scenario generation. The system involves generating integrated climate risk data using a Climate Risk Classification Standard hierarchy that maps climate data and multiple risk factors to geographic space and time. A computer model involves risk factors modeled as graphs of nodes, each node corresponding to a risk factor and connected by edges or links. The nodes of the graph create scenario paths for the model. A hardware processor populates the graphs of nodes using a machine learning, natural language processing and expert judgement systems. The system automatically generates multifactor scenario sets using the scenario paths for the climate model to compute the likelihood of different scenario paths for the computer model.