Central-government climate targets play a directive role in China, shaping how ministries and other public bodies translate national climate ambitions into concrete actions. To support evidence-based analysis of this evolving target landscape, the China Climate Target Tracker compiles quantified climate and climate-relevant targets since 2004, systematically extracted from official policy documents and government website entries. The dataset adopts a broad definition of “climate targets”: quantified commitments that directly reduce GHG emissions, contribute indirectly to emissions reduction, or significantly influence emissions pathways. The dataset is updated on a monthly basis. The downloadable dataset includes detailed source references, allowing users to identify the policy documents from which each target is derived.
|
Carbon peaking |
Carbon neutrality |
Reduce economy-wide net GHG emissions by 7–10% from peak levels, striving to do better |
|
Before 2030 |
Before 2060 |
By 2035 |
|
CO₂ emissions from fuel combustion, industrial processes and product use |
economy-wide all-GHGs |
economy-wide all-GHGs |
Note: (1) The scope of the carbon peaking target is specified in The First Biennial Transparency Report on Climate Change of the People's Republic of China (2024).
(2) The scope of the carbon neutrality target is not explicitly specified in China’s official policy documents; however, it was clarified in a 2021 speech by Xie Zhenhua, China’s then Special Envoy for Climate Change (See https://www.ncsc.org.cn/xwdt/gnxw/202107/t20210727_851433.shtml).
(3) The scope of the 2035 target is specified in China’s 2035 Nationally Determined Contributions document (2025).
Methodology
To extract quantified targets at scale, we applied a text-mining pipeline to a corpus of national-level, climate-related strategic policy documents issued by China’s central government since 2004. The corpus comprised PDF and HTML documents in both Chinese and English.
Each document was converted to plain text and cleaned to standardise spacing and sentence boundaries. The text was then segmented into sentences, and rule-based pattern matching was used to identify sentences containing target-relevant temporal expressions (e.g., “到2025年”, “与2010年相比”, “2020-2030年”), leveraging the standardised structure and recurring target phrasing typical of Chinese policy documents. The code used for target extraction has been uploaded to GitHub.
Extracted sentences were stored with metadata linking them to their source document, translated into English where required, and further processed using rule-based filters and NLP methods to disaggregate target elements.
Should you have any questions about the data and the underlying methodology, please get in touch with 705404@soas.ac.uk.
Disclaimer
While effort has been made to ensure the accuracy of the data, errors may remain. We welcome feedback – please contact us (mgf@nsd.pku.edu.cn) if you identify any inaccuracies.
