📊 Chinese National Climate Targets Visualization (Shiny App)

An interactive Shiny for Python web app for visualizing China's national climate policy targets — developed by the Macro and Green Finance Lab at Peking University (MGF@PKU).

This tool is designed as a template for research, presentation, and educational purposes. It is not intended for local hosting by the general public — instead, it showcases how one might integrate real-time GitHub-hosted datasets into a Shiny app.

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🔍 Overview

This app visualizes a curated dataset of China's national climate policy targets across sectors such as GHG Emissions, Energy/Power, Buildings, Transport, LULUCF, Circular Economy, Pollution, and Finance. Features include:

• 📅 Bar chart showing climate target announcements by year
• 🏷️ Bar chart showing climate targets by category

Powered by:

• shiny for interactivity
• plotly for interactive charts
• polars for fast data wrangling
• GitHub API for real-time data retrieval

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📦 Features

• Two interactive Plotly bar charts (by announcement year and by target category)
• Integrated GitHub data sync (fetches latest release from MGFPKU/target_dataset)
• Bilingual UI (Chinese / English, controlled by LANGUAGE env var)

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🧪 Setup (For Contributors Only)

This app uses GitHub as a remote data backend. If you're customizing or extending the app:

1. Clone the repo

git clone https://github.com/MGFPKU/target_visualization.git
cd target_visualization

2. Install dependencies

We use uv for fast dependency resolution:

uv sync

3. Set up environment variables

⚠️ We do not publish a GitHub token in this repo. If you need access for development or testing, contact the maintainers for a personal access token (PAT) with read-only access to the dataset repository.

You'll need to set the token in a .env file like:

GITHUB_TOKEN=ghp_...
LANGUAGE=EN

Below is what each variable does:

• GITHUB_TOKEN: a GitHub Personal Access Token (PAT) with read access to the MGFPKU/target_dataset repository. The app uses this token to download dataset.xlsx from the latest release. Keep this token private (do not commit it).
• LANGUAGE: set the UI language for the app. Use EN for English or CN for Chinese. The value controls which translations are displayed in the interface.

4. Run the app

python -m shiny run --reload app.py

Then open your browser to:

http://localhost:8000

Or more conveniently, install the shiny extension to Positron/VS Code and press the Run button.

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📁 Project structure

The repository layout and purpose of key files:

.env                  # Environment variables (not committed, create locally following setup instructions)
app.py                # Main Shiny app (UI + server, renders Plotly charts)
data.py               # Data fetching and processing logic
i18n.py               # Translation helper; reads LANGUAGE to switch UI
translation.json      # Translation strings used by `i18n.py`
sheets.json           # Sheet names and source sheet config for dataset.xlsx
pyproject.toml        # Project metadata / build config (managed by uv)
.python-version       # Python version pinning for uv
requirements.txt      # Dependency pins (optional with pyproject)
LICENSE               # License
README.md             # Project documentation (this file)
.gitignore            # Git ignore rules

Notes:

• Edit app.py to change high-level UI or chart logic.
• data.py handles fetching and parsing dataset.xlsx from the latest GitHub release.

📚 Citation

If you use the MGF dataset in your research or writing, please cite us as follows:

Macro and Green Finance Lab, Peking University (MGF@PKU)
"China's National Climate Targets Database"
https://mgflab.nsd.pku.edu.cn/MGFsjk/zczz/index.htm, 2025.

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📁 Data Source

The dataset is hosted in a private GitHub repository (MGFPKU/target_dataset) along with our data collection scripts. We apologize for the inconvenience. But the dataset provided through the app is updated in real-time and contains most information available on the original source.

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🏛️ Credits

Developed by Dianyi Yang (@kv9898) for the Macro and Green Finance Lab at Peking University (MGF@PKU).

If you use or adapt this project for academic or institutional purposes, please cite the dataset accordingly and feel free to reach out for collaboration or inquiries.