Carbon Emissions Baseline Optimization for De-Carbonization Investment
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Solution Overview
Problem
Existing carbon footprint estimation methodologies fail to accurately identify and prioritize the highest contributors of carbon dioxide emissions, leading to inefficient de-carbonization strategies.
Innovation Solution
A computer-implemented method that includes determining an optimization objective, establishing a CO2 emissions baseline, and generating an optimum de-carbonization investment plan to minimize life cycle costs while meeting emission reduction targets, utilizing an optimization layer that identifies maximum CO2 emissions reduction opportunities and prioritizes investments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If high-level carbon footprint estimation methodologies are used, then the estimation process is simple and quick, but the accuracy of identifying highest CO2 contributors is insufficient
Solution Approach 1:
The patent segments the carbon footprint estimation into multiple hierarchical levels: high-level organization-wide estimation and detailed facility/equipment-level analysis. This segmentation allows quick overall assessment while enabling deep dive into specific high-emission sources when needed, resolving the contradiction between speed and accuracy.
Solution Approach 2:
The patent introduces a new dimension of analysis by integrating financial data (capital expenditures, operating costs) with emissions data. This multi-dimensional approach enables identification of highest contributors not just by emissions volume but by combination of emissions and cost impact, improving accuracy without sacrificing estimation efficiency.
2Measurement precision
If detailed carbon footprint analysis is performed to accurately identify highest CO2 contributors, then the identification accuracy improves, but the complexity and resources required increase
Solution Approach 1:
The system divides the analysis into discrete facilities, processes, and equipment components, allowing detailed tracking of CO2 sources without requiring complete system redesign. Each segment can be analyzed independently and aggregated, maintaining accuracy while managing complexity through modular structure.
Solution Approach 2:
The patent creates a universal framework that handles multiple functions: emissions tracking, financial analysis, scenario modeling, and optimization recommendations. This multi-functional system reduces overall complexity by consolidating what would otherwise require separate tools and processes into a single integrated platform.
3Object-affected harmful factors
If comprehensive de-carbonization strategies are developed to address all CO2 sources, then the emission reduction coverage is complete, but the life cycle costs increase
Solution Approach 1:
The patent applies local quality by targeting de-carbonization investments specifically at facilities and equipment identified as highest contributors to CO2 emissions and costs. Rather than uniform treatment across all sources, the system optimizes intervention points based on local emission profiles and cost structures, achieving comprehensive coverage efficiently.
Solution Approach 2:
The system enables parameter changes by allowing organizations to adjust de-carbonization targets, investment levels, and time horizons based on their specific constraints and goals. This flexibility in parameters allows optimization of the trade-off between emission reduction coverage and life cycle costs for each organization's unique situation.
4Object-affected harmful factors
If de-carbonization investments are made without optimization to meet emission targets, then the emission reduction targets are achieved, but the life cycle costs are not minimized
Solution Approach 1:
The patent performs preliminary action by conducting detailed baseline analysis and scenario modeling before implementing de-carbonization investments. This advance planning identifies the most cost-effective investment pathways and allows optimization of the portfolio of projects to minimize life cycle costs while ensuring emission targets are met.
Solution Approach 2:
The system incorporates feedback loops that continuously monitor emissions, costs, and investment performance, allowing real-time optimization of de-carbonization strategies. This feedback mechanism ensures that emission reduction targets are achieved while continuously adjusting investments to minimize life cycle costs based on actual performance data.
Data Source
AI summary
Systems and methods include a method for generating optimum de-carbonization investment plans. An optimization objective is determined for reducing carbon dioxide (CO2) emissions of an organization. A CO2 emissions baseline for the organization is determined. An optimum de-carbonization investment plan is generated for the organization using the optimization objective and the CO2 emissions baseline for the organization. The optimum de-carbonization investment plan is configured to minimize life cycle costs of the organization while meeting emission reduction targets of the organization.