Climate Risk Assessment Using Energy Factor Correlation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for assessing climate change risk in investments are hindered by unreliable and non-quantifiable Environmental, Social, and Governance (ESG) data, and the uncertainty introduced by climate change, which complicates the prediction of investment performance and volatility.
Innovation Solution
A system and method that processes climate data to generate environmental metrics for energy sources, converting them into profitability indicators, which are then correlated with financial data to assess climate change risk at a security level, using a 'top-down' approach that eliminates dependency on self-reported company data and leverages machine learning for predictive analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If self-reported ESG data is used for investment assessment, then data collection is simplified, but reliability and quantifiability of the data deteriorates
Solution Approach 1:
The patent introduces an intermediary system that acts as a mediator between companies and investors. This system collects ESG data through multiple channels (news articles, social media, regulatory filings, satellite imagery) and processes it through machine learning algorithms to generate objective ESG scores, eliminating direct self-reporting while simplifying the overall data collection process for users.
Solution Approach 2:
The patent replaces the manual self-reporting mechanism with an automated machine learning-based data collection and analysis system. This system uses natural language processing, image recognition, and other AI techniques to automatically gather and evaluate ESG data from diverse sources, transforming subjective self-reported data into objective quantifiable metrics.
2Device complexity
If binary ESG data is used for company rating, then data processing is simplified, but ability to compare between different companies deteriorates
Solution Approach 1:
The patent transforms binary ESG data into continuous multi-dimensional parameters. Instead of simple yes/no ratings, the system generates ESG scores across multiple dimensions (environmental, social, governance) with continuous values, enabling precise comparison between companies while maintaining manageable processing complexity through standardized parameter structures.
3Measurement precision
If climate change scenarios are incorporated into investment analysis, then prediction accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the complex climate analysis system into distinct modular components: climate scenario generation module, environmental metric calculation module, profitability indicator conversion module, and security-level risk assessment module. Each module handles a specific aspect of the analysis, improving prediction accuracy while managing complexity through clear separation of functions.
4Measurement precision
If quantitative metrics for ESG investing are developed, then investment decision quality improves, but data processing complexity increases
Solution Approach 1:
The patent develops comprehensive quantitative ESG metrics by transforming various data sources into standardized numerical parameters. The system generates ESG scores across multiple dimensions with continuous values, enabling precise investment decisions while managing processing complexity through efficient data transformation pipelines and standardized parameter structures.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Systems and methods of assessing climate transition risk. A computing system receives a user indication of a selected climate change scenario from a remote client device. The system identifies one or more energy factors from among energy sources. The system retrieves historical financial information directed to one or more securities from remote financial data sources. The system predicts one or more future returns for the securities, by applying the historical financial data and the energy factors to at least one hierarchical linear model. The system adjusts the predicted future returns based on a first climate scenario and the selected climate scenario, to form respective first and second adjusted returns. The system generates a climate transition risk for the securities based on a spread between the first adjusted returns and the second adjusted returns. The system provides a data set representing the climate transition risk to the remote client device.