SDG Interaction Analysis for Sustainable Transport
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Solution Overview
Problem
Current methodologies lack a comprehensive and accurate method to analyze interactions between Sustainable Development Goals (SDGs) in the transportation sector, particularly in sustainable transport systems, which hinders the implementation of synergistic development and maximization of synergies, leading to inefficiencies and trade-offs.
Innovation Solution
A method combining qualitative and quantitative analyses using a Delphi method, cross-impact matrix, and panel vector autoregressive models to extract knowledge and determine leverage points for maximum synergies, incorporating expert knowledge and data-driven approaches to analyze first-order and second-order interactions between SDGs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple sustainable development goals are implemented simultaneously in transportation systems, then synergistic development is promoted, but analysis accuracy and reliability deteriorate due to lack of comprehensive interaction analysis methods
Solution Approach 1:
The patent segments the complex multi-goal interaction analysis into distinct levels: first-order interactions (direct effects between goal pairs) and second-order interactions (indirect effects through intermediate goals). This segmentation allows systematic analysis of each interaction type using appropriate methodologies, thereby improving analysis accuracy while maintaining comprehensive synergistic development coverage.
Solution Approach 2:
The patent introduces cross-impact matrices as intermediary tools that quantify interaction strengths between SDGs. These matrices serve as mediators that transform qualitative expert knowledge into quantitative metrics, enabling precise measurement of interaction effects while preserving the comprehensive multi-goal analysis framework.
2Reliability
If expert knowledge and data-driven approaches are combined to analyze SDG interactions, then analysis reliability is improved, but methodological complexity increases
Solution Approach 1:
The patent merges two distinct approaches—Delphi method (qualitative expert knowledge) and panel vector autoregressive models (quantitative data-driven analysis)—into a unified hybrid framework. This combination leverages the strengths of both methods to improve reliability while the systematic integration process manages complexity through structured procedural steps.
Solution Approach 2:
The patent creates a universal analytical framework that can handle multiple types of SDG interactions (first-order and second-order) using a consistent methodological structure. This multi-functional framework applies the same core procedures across different interaction scenarios, improving reliability through consistency while avoiding the need for separate complex methodologies for each case type.
3Productivity
If first-order and second-order interactions are both analyzed, then synergistic benefits are maximized, but computational requirements and analysis time increase
Solution Approach 1:
The patent performs preliminary analysis of first-order interactions before proceeding to second-order interactions. This sequential approach allows the identification of significant direct effects first, which then inform the subsequent analysis of indirect effects. By preparing and structuring data in advance, the methodology reduces overall analysis time while comprehensively capturing both first-order and second-order synergistic benefits.
Solution Approach 2:
The patent employs dynamic analytical procedures that adapt the depth of second-order interaction analysis based on findings from first-order analysis. When direct interactions are strong and clear, extensive second-order analysis may be reduced, optimizing the balance between comprehensive synergistic benefit identification and analysis time investment.
Data Source
AI summary
The present disclosure provides a method for interactions between traffic and transportation systems based on a sustainable development goal SDG framework. The method includes: step 1: extending and perfecting, through a Delphi method, sustainable transport indicators under an SDG framework; step 2: drawing first-order gaming and synergistic effect between traffic SDGs and synergy through a typological cross-impact matrix and in combination with a panel vector autoregressive model; step 3: parsing a multi-goal second-order interaction mechanism after probability coefficients are introduced, to determine a leverage point of a multi-goal system with maximum synergistic effect; and step 4: determining stability of the leverage point of the multi-goal system based on sensitivity analysis and sectoral analysis. According to the present disclosure, a qualitative framework and a cross-impact matrix are used to meticulously extract knowledge, and a data-driven method is used to explore second-order action between goals and search the leverage point. In addition, a Delphi method is used to form a management method for sustainable development of transport.