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Methodology

China’s existing coal fleet retrofit progress is collected from the following sources:

• State Grid Corporation of China: Service report on new energy development (2020)
• National Energy Administration (NEA) press release on 2021 coal flexibility retrofit progress
• China Electric Power Planning and Engineering Institute (EPPEI): China electric power development report 2023
• Li, Z., et al. (2024) Study on China’s power system flexibility resource development strategy under the “dual carbon” targets. Engineering Sciences, 2024, 26(4): 108-120
• China Electricity Council (CEC): National electric power supply and demand situation analysis and forecast report

China’s national coal capacity factor (CF) is calculated by dividing annual coal electricity generation by the maximum possible generation, defined as mid-year coal power installed capacity multiplied by 8,760 hours per year (8,784 hours for leap years).

The change in coal capacity factor variance is decomposed as follows:

1. Monthly coal generation is expressed as the sum of monthly demand minus generation from other sources in that month.
2. Each series is expressed in equivalent coal capacity factor terms by dividing each month by the coal capacity hours in that month (Coal capacity * Days in month * 24). Because the same denominator is used across all series, the identity (coal = Demand – sum(Generation)) is preserved.
3. Each series is normalised to remove the long term growth/decline trends. We achieve this by subtracting the 12×2 centred rolling mean of the series. Because this operation is linear and applied to every series, the identity is again preserved.
4. The variance is calculated for two 36 month periods, 2016-2018 and 2023-2025. The variance of this sum is the sum of all sign-adjusted covariance terms, including each series’ own variance. Pair covariances are generally split evenly between series (for example, wind-solar covariance is assigned half to wind and half to solar). The exception is pairs involving demand, where the full pair covariance is assigned to the non-demand component. We make this choice on the basis that demand is more or less out of the control of the system planner, while the planner can actively choose what generation technologies to deploy.
5. To calculate the change over time, 2016-18 values are subtracted from 2023-25 values.

The 2025-2030 estimates for selected flexibility sources is from:

• Wind and solar installed capacity is based on China’s nationally determined contribution (NDC) of expanding wind and solar power installed capacity to 3,600 GW by 2035, and the NEA’s 2026 outlook.
• Retrofitted existing coal power capacity is based on the 2027 retrofit target in the National Development and Reform Commission (NDRC) and NEA’s guiding opinions on strengthening power grid load regulation, energy storage and smart dispatch capabilities.
• Pumped hydro installed capacity is based on China’s mid- to long-term development plan for pumped hydro (2021-2035).
• New coal power (built since 2016) is calculated based on the Ember’s Electricity Data Explorer and coal plant retirement data from Global Energy Monitor (GEM).
• Battery energy storage capacity is based on the NDRC and NEA action plan for scaling up new energy storage (2025-2027), China Energy Storage Alliance (CNESA) annual lithium-ion battery energy storage installed capacity data, 2025 lithium-ion installed capacity and 2030 outlook.

Province demand for 2025 is taken from 电查查. Hydro generation for 2025 is taken from NBS. Wind generation is based on NBS, with unreported generation estimated based on historic CEC data. Solar generation is based on NBS, with unreported generation estimated based on historic CEC data, ERA5 weather data, NEA capacity data, National Renewable Energy Consumption Monitoring and Early Warning Center data on utilisation, and Ember monthly estimates of solar generation.

Acknowledgements

Contributors

Ember: Shiyao Zhang, Matt Ewen, Jivan Zhen Thiru, Richard Black, Muyi Yang, Dinita Setyawati, Dave Jones, Duttatreya Das, Sachin Sreejith, Ardhi Arsala Rahmani, Lauren Orso

We thank our external reviewers: Xindi Li, Project Lead for Energy Transition, Senior Analyst, Institute for Global Decarbonization Progress (iGDP); Rebecca McKimm, China Programme Manager, International Energy Agency (IEA); Camille Paillard, Energy Analyst, International Energy Agency (IEA); Ivo Walinga, China Programme Officer, International Energy Agency (IEA)

Cover image

Aerial view of a coal-fired power station featuring multiple tall chimneys and cooling towers, situated adjacent to an industrial port and a city skyline in China.

Photo credit:  dongfang zhao / Getty Images Plus