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Description

This documentation describes GCAM-China v8, which is developed based on the core Global Change Analysis Model version 8.2 (GCAM v8.2). GCAM is a multisector model developed and maintained at the Pacific Northwest National Laboratory’s Joint Global Change Research Institute (JGCRI, 2023). Both GCAM and GCAM-China are open-source community models. The documentation of the core GCAM is available at the GCAM documentation page jgcri.github.io/gcam-doc/ , and this GCAM-China manual serves as supplementary documentation of the distinctions of GCAM-China from the core GCAM. GCAM includes representations of: economy, energy, agriculture, and water supply in 32 geopolitical regions across the globe; their GHG and air pollutant emissions and global GHG concentrations, radiative forcing, and temperature change; and the associated land allocation, water use, and agriculture production across 384 land sub-regions and 235 water basins.

Reference:

GCAM-China is a China-focused version of GCAM that disaggregates the energy-economic system of the China region into 31 province-level sub-regions and six electricity grid regions that are also embedded in the global GCAM model. Electricity generation and end-use energy demand (buildings, transportation, and industry) are modeled at the provincial level in GCAM-China, and the model allows for electricity trade within grid regions. Renewable energy resources (hydro, solar, and wind) and carbon storage resources are also provincial-specific. Primary production of fossil resources including oil, gas, and coal, as well as other energy transformation sectors (hydrogen, gas, and refined liquids production) are still modeled at the aggregate national level. Agricultural and land use activities, including the supply of biomass energy feedstocks (residues and dedicated energy crops) are modeled at the level of 12 water basins in China.

GCAM-China is jointly developed and maintained by the Center for Global Sustainability at University of Maryland; the College of Environmental Sciences and Engineering at Peking University; the Institute of Carbon Neutrality at Peking University; the Department of Earth System Science, Tsinghua University; and the Institute for Carbon Neutrality, Tsinghua University.

Features

GCAM-China has been brought up to date with core GCAM v8.2 model features, including an updated model base year, from 2015 to 2021. Provincial non-CO2 emissions are now included, as is the subnational water module. Additionally, coal power is now disaggregated by vintage (year of construction). A provincial policy module is also added, with example representation of a nuclear power incentive policy.

For more details of innovations brought in during previous rounds of updates, see the descriptions in github.com/umd-cgs/gcam-china/releases


Get Started

  1. Visit the GCAM-China repository: github.com/umd-cgs/gcam-china

  2. Download the latest release for Windows or Mac from: github.com/umd-cgs/gcam-china/releases

  3. Install the required prerequisite software (Java).

    • Windows: Java 64-bit offline. Download here

    • Mac: Download here

  4. Run the model using:

    • Windows: exe/run-gcam.bat

    • Mac: exe/run-gcam.command

  5. This runs GCAM-China using configuration.xml, which is initially a copy of configuration_china.xml.

  6. After the model finishes running, output databases will be available in: output/database_basexdb

  7. Open the model interface using:

    • Windows: ModelInterface/run-model-interface.bat

    • Mac: ModelInterface/ModelInterface.app

Province abbreviations:

Two-letter abbreviations for sub-national regions and the power grid region to which each pertains is shown here: input/gcamdata/inst/extdata/gcam-china/province_names_mappings.csv

Queries:

There is a GCAM China section at the bottom of the Main_queries.xml file, which is displayed by the model interface. This section contains some energy (primary energy, electricity, refined liquids, and aggregated final energy), water, and CO2 emissions queries which should be used instead of the corresponding queries in the standard sections (e.g. energy). However, most of the other queries should work for Chinese regions, and they should work for regions outside of China.

Additionally, in order to get results for China as a whole, select all of the provinces and power grid regions, as well as the China region when running a query, and then sum up the results. This is necessary, since the China region is often the residual of the national value compared to the sum of the provincial values.


Citation

In publications, please refer to the model as GCAM-China v(release version number) and cite as shown on the Zenodo site, accessed by clicking the DOI number button:

v8 :       DOI
v7.1 :   DOI
v7 :       DOI
v6 :       DOI


GCAM-China Publications

  • Wang, H., Liu, Y., Wu, F., Dai, H., Duan, H., Guo, F., Hultman, N., Lu, X., McJeon, H., Miller, A., Tong, D., Yu, S., Yuan, W., Zhang, D., Cui, R., Zhang, Q., Ou, Y., 2026. Bridging China’s Climate Targets and Mitigation Capacity through Sectoral Policy Implementation. Environ. Sci. Technol. https://doi.org/10.1021/acs.est.5c11232

  • Yin, Z., Lu, X., Nielsen, C. P., Cui, R. Y., Ou, Y., Han, M., … & He, K. (2026). Mitigating inequity risks in China’s net-zero energy transition via an enhanced renewable-guided industrial spatial reconfiguration. The Innovation.

  • Lou, J., S. Yu., R. Cui, A. Miller, N. Hultman. “A Provincial Analysis on Wind and Solar Investment Needs towards China’s Carbon Neutrality.” Applied Energy 378 (January 15, 2025): 124841. https://doi.org/10.1016/j.apenergy.2024.124841

  • Dong, J., Li, S., Sun, Y., Gong, W., Song, G., Ding, Y., … & Gong, W. (2024). Provincial equity and enhanced health are key drivers for China’s 2060 carbon neutrality. Journal of Cleaner Production, 473, 143531.

  • Kim, H., Y. Qiu, H. McJeon, A. Clarens, P. Javadi, C. Wang, R. Wang, et al. “Provincial-Scale Assessment of Direct Air Capture to Meet China’s Climate Neutrality Goal under Limited Bioenergy Supply.” Environmental Research Letters 19, no. 11 (October 2024): 114021. https://doi.org/10.1088/1748-9326/ad77e7

  • Sun, Y., Jiang, Y., Xing, J., Ou, Y., Wang, S., Loughlin, D. H., … & Hao, J. (2024). Air quality, health, and equity benefits of carbon neutrality and clean air pathways in China. Environmental Science & Technology, 58(34), 15027-15037.

  • Yu S., J. Behrendt, A. Miller, Y. Liu, J. Adams, R. Cui, W. Li, H. Zhang, J. Cheng, D. Tong, J. Song, Q. Zhang, N. Hultman (2023). Co-benefits between Air Quality and Climate Policies in Guangdong and Shandong Provinces in China. Center for Global Sustainability, University of Maryland & Tsinghua University. 42 pp.

  • Cheng, J., Tong, D., Liu, Y., Geng, G., Davis, S. J., He, K., & Zhang, Q. (2023). A synergistic approach to air pollution control and carbon neutrality in China can avoid millions of premature deaths annually by 2060. One Earth, 6(8), 978-989.

  • Liu, Y., Tong, D., Cheng, J., Davis, S. J., Yu, S., Yarlagadda, B., … & Zhang, Q. (2022). Role of climate goals and clean-air policies on reducing future air pollution deaths in China: a modelling study. The Lancet Planetary Health, 6(2), e92-e99.

  • Cui, R.Y., Hultman, N., Cui, D. et al. A plant-by-plant strategy for high-ambition coal power phaseout in China. Nat Commun 12, 1468 (2021). https://doi.org/10.1038/s41467-021-21786-0

  • Cheng, J., Tong, D., Zhang, Q., Liu, Y., Lei, Y., Yan, G., … & He, K. (2021). Pathways of China’s PM2. 5 air quality 2015–2060 in the context of carbon neutrality. National Science Review, 8(12), nwab078.

  • Yu, S., Yarlagadda, B., Siegel, J. E., Zhou, S. & Kim, S. The role of nuclear in China’s energy future: insights from integrated assessment. Energy Policy 139, 111344 (2020).

  • Tong, D., Cheng, J., Liu, Y., Yu, S., Yan, L., Hong, C., … & Zhang, Q. (2020). Dynamic projection of anthropogenic emissions in China: methodology and 2015–2050 emission pathways under a range of socio-economic, climate policy, and pollution control scenarios. Atmospheric Chemistry and Physics, 20(9), 5729-5757.

  • Yu, S. et al. CCUS in China’s mitigation strategy: insights from integrated assessment modeling. Int. J. Greenh. Gas. Control 84, 204–218 (2019).


Advisory Committee


Contributors

Maintainers

Profile Name Affiliation
ouyang363 Yang Ou 1
thuliuyang Yang Liu 2
andym331 Andy Miller 3
rynacui Yiyun ‘Ryna’ Cui 3


Community Contributors

Alphabetically by given name

Profile Name Affiliation
Cici6101 Can Wang 4
FanWu1020 Fan Wu 1
bitzhz Hongzhi Zhang 4
wanghuaxuan Huaxuan Wang 2
ShenJX21 Jianxiang Shen 2
jerrysong0128                         Jingyang Song 1
MengtingZhu Mengting Zhu 1
rongqizhu Rongqi Zhu 1
ruiwangrr Rui Wang 2
XSu_97 Shuling Xu 5
jiawdo Weidong Jia 6
jiangyongye Yongye Jiang 1
LYQ585 Yuqin Li 7
insomnia-hum Zeyuan Liu 8


1 Peking University
2 Tsinghua University
3 Center for Global Sustainability at University of Maryland
4 Beijing Institute of Technology
5 North China Electric Power University
6 Shandong University
7 Beijing Normal University
8 Zhejiang University


Get Involved

This model is a collaborative, open-source model where we will follow these Community guidelines: Community Guidelines-Chinese or Community Guidelines-English . Your participation is welcome through the following process: clone or fork the umd-cgs/gcam-china repository, then make a branch with the new features and initiate a pull request and submit a core model proposal as specified in the umd-cgs/gcam-china/CONTRIBUTING.md document. This document outlines the process that groups can follow to propose the addition of new features to the model. Any issues / bugs / proposed features can be discussed through the Issues tab within the repository.

Contact:


Model Use Disclaimer

GCAM-China is an open-source research model intended for academic, educational, and policy analysis purposes.

Users should: - follow accepted academic and professional standards; - cite the specific GCAM-China release used, along with relevant methodological and application papers (e.g.  Wang & Liu et al., 2026 ).

See the license here: umd-cgs/gcam-china/LICENSE.md .