DigitalTwinSDGs: Using Large Language Models to Achieve Intelligent Mapping Between Digital Twins and Sustainable Development Goals

This article introduces the DigitalTwinSDGs project, which aims to establish an intelligent connection between digital twin systems and the United Nations Sustainable Development Goals (SDGs) using large language model technology, promoting the deep integration of smart cities and sustainable development. The core of the project is to use the semantic capabilities of LLMs to address the mapping challenges between the two, providing data-driven decision support for urban managers. The following will discuss aspects such as background, technical architecture, and application scenarios.

Digital twin technology is reshaping fields such as urban planning and infrastructure management, while the 17 UN SDGs point the way for global development. How to combine the two to make digital twins better serve sustainable development is a major issue in smart city construction. The DigitalTwinSDGs project uses the semantic understanding capabilities of LLMs to automatically establish intelligent mappings between digital twins and SDG sub-goals, providing decision support for urban managers, which is an innovative exploration in this interdisciplinary field.

Core Concepts:

• Digital Twin: A real-time mirror of physical entities built in virtual space, covering urban infrastructure, dynamic data streams, simulation, and interactive interfaces.
• SDGs: 17 goals of the UN 2030 Agenda, divided into social, economic, environmental, and governance dimensions, with each goal containing quantifiable sub-goals. Mapping Challenges:

1. Semantic Gap: Digital twins focus on technical parameters, while SDGs use social development language, leading to large differences in conceptual systems;
2. Many-to-Many Relationships: A single digital twin indicator may affect multiple SDGs, and vice versa;
3. Context Dependency: The contribution of the same technical indicator to SDGs varies across different cities.

Role of LLMs: Semantic understanding (parsing technical and policy language), knowledge integration (fusing world knowledge to understand causal chains), context reasoning (adjusting mapping weights), natural language generation (converting results into reports). Mapping Framework:

1. Indicator Parsing: Convert raw digital twin indicators into structured semantic representations;
2. Goal Decomposition: Break down SDG sub-goals into actionable evaluation dimensions;
3. Semantic Matching: Calculate semantic similarity between indicators and SDG dimensions;
4. Relationship Validation: Filter mismatches using domain knowledge bases and expert rules;
5. Impact Modeling: Quantify the impact of indicator changes on SDG achievement and build prediction models.

Urban Planning and Decision-Making: Evaluate the contribution of plans to SDGs, identify goal conflicts, and optimize resource allocation; Real-Time Monitoring and Early Warning: Track SDG progress, detect anomalies, and push early warnings; Policy Effect Evaluation: Attribute policy impacts, quantify effects, and promote successful experiences; Multi-Stakeholder Collaboration Platform: Promote data sharing among government departments, incentivize government-enterprise collaboration, and enhance transparency of public participation.

Implementation Points:

• Data Integration: Integrate digital twin data (BIM, GIS, IoT), SDG benchmark data, and domain knowledge bases;
• Model Selection: Embedding models (semantic similarity), generative models (reports), reasoning models (causal analysis), requiring domain adaptation;
• Interpretability: Mapping visualization, reasoning traceability, confidence annotation. Challenge Responses:
• Data Quality: Progressive deployment, starting from fields/regions with good data foundations;
• Model Bias: Introduce diversity assessment, regular audits, and manual review;
• Dynamic Adaptability: Continuous learning mechanism, regular updates of mapping models.

Future Directions: Multimodal fusion (incorporating 3D visualization data), cross-city knowledge transfer, real-time simulation optimization (closed-loop decision-making), and citizen science integration (crowdsourced data). Summary: The DigitalTwinSDGs project connects technology-oriented digital twins with humanistic-oriented SDGs through LLMs, providing an innovative tool for the sustainable transformation of smart cities. Under the dual agenda of digital transformation and sustainable development, this project demonstrates the potential of AI to solve complex social problems and provides a practical example for interdisciplinary collaborative innovation.