DOI: 10.5281/zenodo.17481633
title: EcoChain

description: CarbonLedger: A Cross-Border Carbon Accounting and Privacy-Aware Scheduling Framework

📘 Overview

CarbonLedger is a computational framework designed to enhance low-carbon governance in the digital economy.The system integrates cross-border data scheduling, privacy-preserving mechanisms, and carbon-aware optimization to establish a secure and efficient global carbon accounting model.

At its core, CarbonLedger introduces the Carbon-Aware Data Scheduling Model (CADSM), which minimizes carbon emissions while ensuring privacy protection and compliance with international data regulations such as the GDPR.



🌍 Key Features





Cross-Border Data Scheduling:Efficiently manages carbon-related data transfer between international nodes while minimizing energy consumption and transmission latency.




Privacy-Preserving Optimization:Integrates privacy constraints into scheduling decisions, ensuring secure data handling and compliance with regional privacy laws.




Low-Carbon Governance Intelligence:Employs data-driven decision algorithms to support sustainable policy-making and emission monitoring.




Adaptive Carbon-Aware Model:Dynamically balances carbon reduction targets, computation cost, and privacy protection in real time.




Scalability and Reliability:Supports large-scale, multi-country carbon data systems across digital platforms and cloud environments.





🧠 Methodology

CarbonLedger consists of several key modules that collaboratively achieve energy-efficient and privacy-aware carbon data processing:





Carbon-Aware Data Scheduling Model (CADSM):Optimizes cross-border data transmission based on multi-objective functions combining energy efficiency, emission targets, and privacy compliance.




Data Privacy Strategy (DPS):Introduces an Innovative Data Privacy Strategy (IDPS) using deep encryption and differential privacy for secure cross-border data exchange.




Multimodal Encoder Architecture (MEA):Encodes and processes carbon data from heterogeneous sources, integrating both structured and unstructured data streams.




Graphical Propagation Layer (GPL):Models inter-regional relationships among data nodes, supporting distributed optimization and adaptive decision-making.




Low-Carbon Decision Engine (LCDE):Provides policy recommendations and carbon accounting results to support regulatory reporting and enterprise sustainability management.





⚙️ Architecture

The diagram on pages 8–11 of the paper illustrates the system’s architecture:



The Carbon-Aware Data Scheduling Model (Figure 1) controls global data transfer paths.

The Innovative Data Privacy Strategy (Figure 2) ensures privacy constraints at every stage.

The Multimodal Encoder (Figure 4) fuses diverse data sources for carbon tracking.


Together, these modules form a closed-loop optimization system that supports both decarbonization and digital governance.



📊 Experimental Setup



Dataset: Cross-Border Carbon Emissions Records (He et al., 2025)

Platform: Python 3.10, PyTorch 2.1, and SciPy Optimization Toolkit

Hardware: NVIDIA Tesla V100 GPU (16GB)

Metrics: Carbon Reduction Efficiency (CRE), Privacy Compliance Rate (PCR), and Scheduling Cost (SC)




📈 Results

CarbonLedger achieved:



+11.2% improvement in carbon reduction efficiency

-17.5% decrease in total energy consumption

+9.8% improvement in privacy compliance over baseline scheduling methods


Ablation studies (see Tables 1–4, pages 13–16) confirm the effectiveness of the CADSM and IDPS modules across multiple test environments.



🧩 Repository Structure



graphql





 



CarbonLedger/
│
├── src/
│   ├── data.py                # Dataset loading and preprocessing
│   ├── model.py               # CADSM and MEA model definitions
│   ├── train.py               # Training and optimization script
│   ├── privacy_utils.py       # Privacy-preserving computation methods
│   ├── scheduler.py           # Cross-border data scheduling logic
│   └── utils.py               # Helper utilities
│
├── configs/
│   └── cadsm_config.yaml      # Model and scheduling configuration
│
├── data/
│   └── sample_dataset.csv     # Example input data
│
├── results/
│   └── logs/                  # Training logs and result outputs
│
├── requirements.txt
└── README.md





🛠️ Installation



bash





 



# Clone the repository
git clone https://github.com/yourusername/CarbonLedger.git
cd CarbonLedger

# Create a virtual environment
python -m venv venv
source venv/bin/activate     # Linux/Mac
venv\Scripts\activate        # Windows

# Install dependencies
pip install -r requirements.txt





▶️ Usage Example



bash





 



# Run the main carbon scheduling experiment
python src/train.py --config configs/cadsm_config.yaml



To evaluate the framework with your own dataset:



bash





 



python src/train.py --data_path data/your_dataset.csv --mode eval





📚 Citation

If you use or reference this project, please cite:



css





 



Liu, R. (2025).
A Carbon Accounting System Integrating Cross-Border Data Scheduling and Privacy Protection for Low-Carbon Governance in the Digital Economy.
Frontiers in Environmental Science.





🏷️ License

This project is released under the MIT License.You are free to use, modify, and distribute it with attribution.



👩‍💻 Author

Rui LiuDepartment of Accounting,Wuxi Taihu University, Jiangsu, China📧 Contact: liurui@wxu.edu.cn