Fuzzy Interval Ranking for Personal Carbon Emission Estimation
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Solution Overview
Problem
There is a lack of effective methods for accurately estimating and ranking personal carbon emissions in daily life, considering the uncertainties and variations in behaviors such as food, clothing, housing, and transportation, which hinders the development of low-carbon living habits.
Innovation Solution
A method using fuzzy interval theory to collect and process data on personal daily activities, calculating carbon emissions, and ranking individuals within a group by summing fuzzy interval values and applying credibility measures to compare emissions, providing a scalable and adaptable approach for estimating and ranking carbon footprints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional exact calculation methods are used for carbon emissions, then calculation precision is improved, but the method becomes unrealistic and difficult to implement due to uncertainty in daily life behaviors
Solution Approach 1:
The patent transforms the calculation approach from exact point values to fuzzy interval parameters. Instead of calculating precise carbon emissions, the method uses interval numbers [a, b] to represent ranges of possible emissions, changing the parameter type from scalar to interval. This resolves the contradiction by maintaining calculation feasibility while accommodating behavioral uncertainties through parameter transformation.
Solution Approach 2:
The patent employs a simplified estimation model that accepts lower precision in exchange for practical implementability. By using fuzzy interval theory with basic addition and comparison operations, the method creates a lightweight calculation system that is easy to implement but still provides useful ranking information, effectively trading some precision for operational ease.
2Loss of information
If detailed data collection on all daily activities is performed, then measurement completeness is improved, but the complexity of the system increases significantly
Solution Approach 1:
The patent segments the carbon emission calculation into four distinct life domains: food, clothing, housing, and transportation. Each domain is handled independently with its own data collection and calculation processes. This segmentation reduces overall system complexity by breaking down the comprehensive data collection task into manageable, modular components that can be implemented and processed separately.
Solution Approach 2:
The patent collects data on key behavioral categories (food, clothing, housing, transportation) rather than attempting to measure every single activity. This partial action approach captures the essential sources of carbon emissions without requiring complete monitoring of all daily behaviors, thereby reducing data collection complexity while maintaining sufficient measurement completeness for effective estimation and ranking.
3Adaptability or versatility
If fuzzy interval theory is applied to handle behavioral uncertainties, then adaptability to real-life variations is improved, but calculation and comparison complexity increases
Solution Approach 1:
The patent transforms uncertain behavioral data into fuzzy interval parameters [a, b], where a and b represent lower and upper bounds of possible emissions. This parameter transformation allows the system to handle uncertainties systematically using interval arithmetic operations (addition, comparison) rather than requiring complex probabilistic models, thus improving adaptability while controlling calculation complexity through parameter simplification.
Data Source
AI summary
An estimation and ranking method for carbon emission of individual life, which includes the following steps: collecting and acquiring personal daily life clothing, food, housing, and transportation data; performing fuzzy interval processing on the collected data, and calculating carbon emissions corresponding to each behavior expressed by the fuzzy interval number according to the carbon emission coefficient of each behavior; using fuzzy interval number addition, the carbon emissions corresponding to each individual behavior are added according to different time scales to obtain the fuzzy interval value of the total carbon emissions of the individual on different time scales; use the fuzzy interval number comparison method to obtain the ranking of the carbon emissions of individuals in a specific group on the same time scale. The present invention performs effective size comparison and ranking of personal carbon emissions within a certain group range, and the analysis result is reasonable and accurate.