Benchmarking China’s power system transition

Benchmarking China’s power system transition | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Benchmarking China’s power system transition Jiang Lin, Liqun Peng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6314403/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract UNEP, in its latest Emissions Gap Report, highlights the significant distance between countries’ pledges and what is needed to stay on a 1.5° C pathway, and calls on all nations to raise the ambition of their nationally determined contributions, updates of which are due by February 2025. As the world’s largest carbon emitter, China’s commitment is critical to the success of any global compact addressing climate change. Power sector accounts for over 40% of China’s carbon emission, thus is a key area of both progress and concern. We argue that the share of non-fossil electricity generation is a more appropriate and robust benchmark of measuring China’s power system transition, as it would be minimally influenced by the significant uncertainty in demand growth. By contrast, capacity targets such as tripling renewable installations by 2030 are less useful, especially given the record growth of solar and wind buildout. Our analysis shows that China could reach around 80% of non-fossil generation by 2035 across a variety of demand, technology, and carbon constrain scenarios. Earth and environmental sciences/Environmental social sciences Earth and environmental sciences/Environmental social sciences/Climate change mitigation Earth and environmental sciences/Environmental social sciences/Climate change policy Earth and environmental sciences/Environmental social sciences/Environmental economics Physical sciences/Engineering Figures Figure 1 Figure 2 Figure 3 Main 1 UNEP, in its latest Emissions Gap Report, highlights the significant distance that remains between countries’ emissions reduction pledges and what is needed to stay on a 1.5° C pathway (UNEP, 2024 ). The report calls on all nations to raise the ambition of their nationally determined contributions (NDCs), updates of which are due by February 2025. Given each country’s varying circumstances, such enhanced NDCs could take different forms, including national emission targets such as those released by the United Kingdom at COP 29 (UK, 2024) and/or various sectoral policies. 2 As the world’s largest emitter of greenhouse gases (GHGs), China’s commitment is critical to the success of any global compact for avoiding the worst consequences of climate change. While there is no clear indication of how China may update its current NDC apart from including all economy-wide GHGs per its recent pledge to this effect, China’s progress toward its current NDCs may offer some insight into its priorities for enhanced action. China’s current NDCs include carbon peaking by 2030; carbon neutrality by 2060; and, by 2030, reaching 1,200 gigawatts (GW) of solar and wind capacity, supplying 25% of primary energy from non-fossil fuel sources, and improving energy intensity by 60–65% (MEE, 2022). 3 China met its solar and wind capacity goal in 2024, six years ahead of schedule (NEA, 2024a), and also appears likely to meet its carbon peaking goal early (Normile, 2024). In fact, China’s rapid progress in scaling up solar and wind power alongside electric vehicles (EVs) ranks among the more encouraging signs from the global transition to a carbon-neutral economy. Even so, fossil fuels accounted for over 60% of China’s total electricity generation in 2023 (b, 2024b) and its power sector emissions are still on the rise, in part due to rapid growth in electricity demand. 4 Scientific consensus indicates that the best strategy for achieving economy-wide decarbonization is to first decarbonize power systems and then to electrify as much of the economy as possible, with remaining emissions to be addressed through carbon removals and sinks (Steinberg et al., 2017 ; Davis et al., 2018 ; Wang et al., 2023 ). The degree to which countries have decarbonized their power systems is therefore a useful indicator of progress toward economy-wide decarbonization, and the use of an appropriate metric is of interest to both scientists and policymakers. 5 Share of non-fossil electricity generation provides the most appropriate and robust benchmark of progress toward power sector decarbonization. This metric accounts for both fossil- and non-fossil generation (i.e., from renewables, hydro, and nuclear), along with electricity demand growth. By contrast, supply-focused targets (e.g., tripling renewable installations by 2030) are less useful, because emissions could still rise if demand growth outpaces non-fossil electricity generation. Another metric, non-fossil share of primary energy use, involves a complicated conversion to primary energy (Lewis et al., 2015 ) and is rarely used outside of China. 6 For these reasons, share of non-fossil electricity generation provides both a robust metric for setting policy targets and a transparent benchmark for international comparison. In the U.S., for example, the share of non-fossil generation was about 40% in 2023 (EIA, 2024) versus about 36% in China in 2022, the most recent year for which data are available (CEC, 2024c). The European Union leads the world with a 67% non-fossil electricity share, while Japan lags other developed economies at 31% (Ember, 2024 ). 7 Here, we analyze multiple scenarios using this metric to evaluate possible pathways for China’s power system transition. Our results indicate that China’s share of non-fossil generation could reach 67–73% by 2030 and 78–84% by 2035 under a variety of scenarios for electricity demand, technology costs, and carbon constraints. (See Methods section for detailed scenario assumptions.) Notably, shares of non-fossil generation largely remain consistent across scenarios (Fig. 1 ) and align with other recent studies (Abhyankar et al., 2022 ; He et al., 2020 ; Kahrl et al., 2021 ; Peng et al., 2024 ; Zhang et al., 2024 ). 8 That the share of non-fossil generation stays fairly consistent across scenarios offers policymakers some measure of certainty regarding policy outcomes. That said, carbon constraints appear to have the largest influence on non-fossil share of generation, followed by technology costs and demand growth. For example, under the high demand scenario, where electricity demand is more than 25% higher than under the base scenario, the share of non-fossil generation increases by only 1–2 percentage points. By contrast, the difference in share of non-fossil generation among carbon constraint scenarios and technology cost scenarios ranges from 1–4 percentage points and 1–3 percentage points, respectively. 9 While electricity demand appears to have little effect on non-fossil share of generation, it does significantly affect both solar and wind installations and total generation. In other words, the higher the demand, the more solar and wind capacity is installed and the more electricity generation is needed. Across all scenarios, total solar and wind installations range from 2,045 to 3,120 GW in 2030 and 2,723 to 4,399 GW in 2035. This translates to average annual additions of 138 to 291 GW of solar and wind capacity between 2024 and 2035, a difference of more than 100%. In 2023, China installed over 300 GW of solar and wind and is on pace to match that figure in 2024 (CEC, 2024a). This suggests China is well-equipped to install 291 GW of solar and wind per year, enabling 65% non-fossil generation by 2030 and around 80% by 2035, even under the high demand scenario. 10 Setting ambitious long-term targets has been integral to China’s clean energy and climate strategy, and its success in building a strong clean energy economy demonstrates the effectiveness of this approach. With the feasibility of the 1.5° C pathway now in doubt, it behooves China to raise its ambitions both domestically, such as in its upcoming 15th Five-Year Plan (FYP) for 2025–2030, and internationally in its forthcoming NDC update. Choosing an appropriate benchmark, such as share of non-fossil electricity generation, would make this effort both more transparent and robust, as it would be minimally influenced by the significant growth that may occur in China’s electricity demand due to electrification and emerging new end-uses such as data centers. 11 To support such an ambitious target, China must accelerate power sector reforms. This would involve developing and issuing new guidelines for system planning, grid operation, and electricity markets. For example, renewables and storage should be allowed to participate in China’s wholesale electricity market. Regional market integration should also be strengthened to allow greater sharing of renewables and other resources. Such sharing across a large balancing area would support renewable integration, enhance grid flexibility and stability, and reduce cost and emissions (Lin et al., 2022 ; Abhyankar et al., 2022 ). 12 In terms of system planning, more systematic resource adequacy processes are likely needed to ensure the long-term reliability of China’s electricity system. Such processes would involve specifying levels of firm capacity needed to maintain a reliability target; mechanisms to encourage adequate firm capacity investment; better accounting for the firm capacity value of renewable generation and storage; improved scarcity pricing; and incorporating more demand response resources (Abhyankar et al., 2022 ). Fortunately, China is moving forward on many fronts in reforming its electricity sector, including the recent launching of an inter-provincial wholesale electricity market (NEA, 2024b). Setting ambitious targets with a transparent, robust benchmark such as non-fossil share of electricity generation would give China’s clean energy industry and the global community a clear signal of China’s determination to accelerate its transition to a carbon-neutral economy. Methods We use an open-source power system model, GridPath, to model transition pathways of China’s power system under a variety of electricity demand, technology cost, and carbon constraint scenarios. The optimal solution minimizes the cost of producing and delivering electricity while satisfying a set of operational constraints. In our study, we use one hour as the time step and a province as a load zone. We model the Chinese electricity grid as 32 interconnected nodes, connected by 184 interprovincial transmission corridors. To model long-term investments, we employ four levels of temporal resolution: five-year investment periods, months, days, and hours. Our study divides the time span from 2025 to 2050 into six investment periods of five years each: 2021–2025, 2026–2030, 2031–2035, 2036–2040, 2041–2045, and 2046–2050. We use 12 months to characterize each investment period, two days (a peak and a median load day) to characterize each month, and six hours to characterize each day. For each day, hourly sampling begins at midnight China Standard Time (CST) and includes the 0th, 4th, 8th, 12th, 16th, and 20th hours. This results in (6 investment periods) × (12 months/investment period) × (2 days/month) × (6 hours/day) = 864 study hours during which the system is dispatched. Scenario designs We set up 12 scenarios, including two for carbon caps (high and low), two for technology cost levels (high and low), and three for electricity demand (base, medium, and high), as shown in Table 1 . Table 1 Scenario designs. Scenarios Carbon emission caps Technology cost Electricity demand H_Carbon_H_Cost High High Base H_Carbon_L_Cost High Low Base L_Carbon_H_Cost Low High Base L_Carbon_L_Cost Low Low Base H_Carbon_H_Cost_M_Demand High High Medium H_Carbon_L_Cost_M_Demand High Low Medium L_Carbon_H_Cost_M_Demand Low High Medium L_Carbon_L_Cost_M_Demand Low Low Medium H_Carbon_H_Cost_H_Demand High High High H_Carbon_L_Cost_H_Demand High Low High L_Carbon_H_Cost_H_Demand Low High High L_Carbon_L_Cost_H_Demand Low Low High Carbon emission caps. We set up two carbon emission caps: (1) a high carbon emission cap, which assumes 90% emission reduction by 2050 compared to 2021; and (2) a low carbon emission cap, which assumes 100% emission reduction by 2045. Technology costs. We set up two technology cost scenarios based on low- and high-cost projections from BNEF (2022). Figure 3 shows the high and low capital costs of different technologies. Electricity demand. Growth in China’s electricity demand over the next 25 years is highly uncertain. To address this, we set up three electricity demand projections for 2025 through 2050, as shown in Table 2 : (1) the Base scenario, representing the mean value of electricity demand projections from several recent studies (IEA, 2020; ICCSD, 2020; CNREC, 2020; Fu, et al., 2020; Jiang et al., 2018 ; and CETO, 2023); (2) the Medium demand scenario, which makes linear forecasts based on national demand data from previous years (CEC, 2024b), reaching 12,500 TWh in 2030 and 13,500 TWh in 2035 ; and (3) the High demand scenario, which is derived from the ambitious carbon-neutral scenarios in CETO (2023). Table 2 Three scenarios of electricity demand (in TWh) from 2025–2050. Electricity demand scenarios 2025 2030 2035 2040 2045 2050 Base 9,345 11,210 12,785 13,995 15,015 16,034 Medium 10,380 12,500 13,500 14,879 16,258 17,637 High 11,200 14,000 16,100 17,700 19,200 19,800 Declarations Author Contribution JL designed the study and wrote the manuscript. LQP carried out experiment and contributed to the manuscript. References Abhyankar, N., J. Lin, F. Kahrl, S.F. Yin, U. Paliwal, X. Liu, N. Khanna, Q. Luo, D. Wooley, M. O’Boyle, O. Ashmoore, R. Orvis, M. Solomon, A. Phadke. (2022). “Achieving an 80% carbon-free electricity system in China by 2035,” iScience BloombergNEF. New Energy Outlook 2022. https://about.bnef.com/new-energy-outlook/#toc-download. Carbon Brief, 2024. What to expect in China’s climate pledge for 2035. https://www.carbonbrief.org/experts-what-to-expect-in-chinas-climate-pledge-for-2035/#:~:text=Despite%20the%20country%20already%20achieving,or%20its%20existing%20national%20goals. China Electricity Council (CEC), 2024a, National power supply and demand situation analysis and forecast report for the first three quarters of 2024, “2024年三季度全国电力供需形势分析预测报告,” https://cec.org.cn/detail/index.html?3-338048 China Electricity Council. (CEC), 2024b. National power supply and demand situation analysis and forecast report from 2023 to 2024 (2023-2024 年度全国电力供需形势分析预测报告 ) . https://www.cec.org.cn/detail/index.html?3-330280. CETO. Energy Research Institute of Chinese Academy of Macroeconomic Research. China Energy Transformation Outlook 2023. https://usercontent.one/wp/www.cet.energy/wp-content/uploads/2023/12/CET_CETO 2023_Full-report-20231203.pdf (2023). China National Renewable Energy Centre (CNREC) of Energy Research Institute. China Renewable Energy Outlook 2020. https://issuu. com/sandholt/docs/creo-2019-en. (2020). Davis, S. J. et al. Net-zero emissions energy systems. Science 360, eaas9793 (2018). Energy Information Administration (EIA), https://www.eia.gov/tools/faqs/faq.php?id=427&t=3, accessed on November 23, 2024. Ember, https://ember-energy.org/countries-and-regions/#countries, accessed on November 23, 2024 Fu, S., Du, X., Clarke, L. & Yu, S. China’s New Growth Pathway: From the 14th Five Year Plan to Carbon Neutrality. https://www. efchina.org/Reports-en/report-lceg-20201210-en. He, G., Lin, J., Sifuentes, F., Liu, X., Abhyankar, N., & Phadke, A. (2020). Rapid cost decrease of renewables and storage accelerates the decarbonization of China’s power system. Nature Communications , 11 (1), 2486. https://doi.org/10.1038/s41467-020-16184-x International Energy Agency (IEA). World Energy Outlook 2020. (2020). Institute of Climate Change and Sustainable Development (ICCSD). China’s Low Carbon Development Strategy and Transition Pathways: Synthesis Report. (2020). Jiang, K., He, C., Dai, H., Liu, J. & Xu, X. Emission scenario analysis for China under the global 1.5 °C target. Carbon Management 9, 481–491 (2018). Kahrl, F, J Lin, X Liu, JF Hu, 2021, “Sunsetting coal power in China,” iScience (2021) Lewis et al. 2015, Understanding China’s non-fossil energy targets. Science, DOI: 10.1126/science.aad1084 Lin, J., Abhyankar, N., He, G., Liu, X., & Yin, S. (2022). Large balancing areas and dispersed renewable investment enhance grid flexibility in a renewable-dominant power system in China. iScience , 25 (2), 103749. https://doi.org/10.1016/j.isci.2022.103749 Ministry of Ecology and Environment of the People’s Republic of China (MEE), China’s Policies and Actions for Addressing Climate Change (2022). https://english.mee.gov.cn/Resources/Reports/reports/202211/P020221110605466439270.pdf. National Energy Administration (NEA), 2024a, “提前6年兑现承诺！我国风光发电总装机超12亿千瓦,” https://www.nea.gov.cn/2024-11/08/c_1310787160.htm National Energy Administration (NEA), 2024b, “省间现货市场正式运行——我国电力体制改革再上新台阶,” https://www.nea.gov.cn/2024-10/18/c_1310786781.htm D Normile, 2024, “Have China’s carbon emissions peaked? The answer is critical to limiting global warming,” doi: 10.1126/science.zhjouko Peng LN, J Lin, U Paliwal, G He. 2024, “ ACCELERATING OFFSHORE WIND DEVELOPMENT ENHANCES ENERGY SECURITY AND PROMOTES CARBON NEUTRALITY IN CHINA’S COASTAL REGIONS,” LBNL Report Steinberg, D. et al. Electrification & Decarbonization: Exploring U.S. Energy Use and Greenhouse Gas Emissions in Scenarios with Widespread Electrification and Power Sector Decarbonization. Renewable Energy (2017). United Kingdom, “UK shows international leadership in tackling climate crisis,” press release, November 12, 2024, www.gov.uk. UNEP, 2024, Emissions Gap Report: No more hot air…please! https://www.unep.org/resources/emissions-gap-report-2024 Wang, Y. et al. Accelerating the energy transition towards photovoltaic and wind in China. Nature 619, 761–767 (2023). Zhang, D., Zhu, Z., Chen, S., Zhang, C., Lu, X., Zhang, X., Zhang, X., & Davidson, M. R. (2024). Spatially resolved land and grid model of carbon neutrality in China. Proceedings of the National Academy of Sciences , 121 (10). https://doi.org/10.1073/pnas.2306517121 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6314403","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":442230176,"identity":"42c7b2a8-8dd0-4033-9901-b8a2d27707c3","order_by":0,"name":"Jiang Lin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAm0lEQVRIiWNgGAWjYBACxgbmhgMMFRYJfCRoYQRqOSORwEaSPQyMbaRoYW5gbDxcOE8ij42B/eJjHmIddnjmNoliNgaeYmPitfBuk0hsY+BJk5xBvJY5pGtpAGlhPybxgWgtPMeAfmHmYTYgSothA/Phzzw1Nnn87O0PHyQQpWX+AyiLmceAGA0MDPIIJvsDHGpGwSgYBaNgpAMAExstC6klOXYAAAAASUVORK5CYII=","orcid":"","institution":"University of California, Berkeley","correspondingAuthor":true,"prefix":"","firstName":"Jiang","middleName":"","lastName":"Lin","suffix":""},{"id":442230177,"identity":"3e49bb99-2093-4c59-9ea8-1de7ab986c78","order_by":1,"name":"Liqun Peng","email":"","orcid":"","institution":"Lawrence Berkeley National Laboratory","correspondingAuthor":false,"prefix":"","firstName":"Liqun","middleName":"","lastName":"Peng","suffix":""}],"badges":[],"createdAt":"2025-03-26 17:08:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6314403/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6314403/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80527870,"identity":"818c8e58-e9c9-4fb4-878e-64c16f3d047f","added_by":"auto","created_at":"2025-04-14 10:15:34","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":44267,"visible":true,"origin":"","legend":"\u003cp\u003eShare of non-fossil electricity generation in China under twelve scenarios.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6314403/v1/50c8073dc52416600e1d9d6d.jpg"},{"id":80526801,"identity":"c23d8018-e156-4a5f-9c07-422861658baa","added_by":"auto","created_at":"2025-04-14 10:07:34","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":78070,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Installed capacity and (b) Total power generation in China by technology under twelve scenarios.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6314403/v1/48622443c119764f987c3135.jpg"},{"id":80526803,"identity":"11e35d7c-9576-401b-8f3b-f5631335b469","added_by":"auto","created_at":"2025-04-14 10:07:34","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":70195,"visible":true,"origin":"","legend":"\u003cp\u003eHigh and low capital costs for different technologies.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6314403/v1/b93871f3340fe57fd315b8a7.jpg"},{"id":81354565,"identity":"e85e27f1-8b2e-4ef9-9b6b-72785032c82b","added_by":"auto","created_at":"2025-04-25 07:17:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":560163,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6314403/v1/a6822827-c9cf-4745-80ab-27573a6146a2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Benchmarking China’s power system transition","fulltext":[{"header":"Main","content":"\u003cp\u003e1 UNEP, in its latest Emissions Gap Report, highlights the significant distance that remains between countries\u0026rsquo; emissions reduction pledges and what is needed to stay on a 1.5\u0026deg; C pathway (UNEP, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The report calls on all nations to raise the ambition of their nationally determined contributions (NDCs), updates of which are due by February 2025. Given each country\u0026rsquo;s varying circumstances, such enhanced NDCs could take different forms, including national emission targets such as those released by the United Kingdom at COP 29 (UK, 2024) and/or various sectoral policies.\u003c/p\u003e \u003cp\u003e2 As the world\u0026rsquo;s largest emitter of greenhouse gases (GHGs), China\u0026rsquo;s commitment is critical to the success of any global compact for avoiding the worst consequences of climate change. While there is no clear indication of how China may update its current NDC apart from including all economy-wide GHGs per its recent pledge to this effect, China\u0026rsquo;s progress toward its current NDCs may offer some insight into its priorities for enhanced action. China\u0026rsquo;s current NDCs include carbon peaking by 2030; carbon neutrality by 2060; and, by 2030, reaching 1,200 gigawatts (GW) of solar and wind capacity, supplying 25% of primary energy from non-fossil fuel sources, and improving energy intensity by 60\u0026ndash;65% (MEE, 2022).\u003c/p\u003e \u003cp\u003e3 China met its solar and wind capacity goal in 2024, six years ahead of schedule (NEA, 2024a), and also appears likely to meet its carbon peaking goal early (Normile, 2024). In fact, China\u0026rsquo;s rapid progress in scaling up solar and wind power alongside electric vehicles (EVs) ranks among the more encouraging signs from the global transition to a carbon-neutral economy. Even so, fossil fuels accounted for over 60% of China\u0026rsquo;s total electricity generation in 2023 (b, 2024b) and its power sector emissions are still on the rise, in part due to rapid growth in electricity demand.\u003c/p\u003e \u003cp\u003e4 Scientific consensus indicates that the best strategy for achieving economy-wide decarbonization is to first decarbonize power systems and then to electrify as much of the economy as possible, with remaining emissions to be addressed through carbon removals and sinks (Steinberg et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Davis et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The degree to which countries have decarbonized their power systems is therefore a useful indicator of progress toward economy-wide decarbonization, and the use of an appropriate metric is of interest to both scientists and policymakers.\u003c/p\u003e \u003cp\u003e5 Share of non-fossil electricity generation provides the most appropriate and robust benchmark of progress toward power sector decarbonization. This metric accounts for both fossil- and non-fossil generation (i.e., from renewables, hydro, and nuclear), along with electricity demand growth. By contrast, supply-focused targets (e.g., tripling renewable installations by 2030) are less useful, because emissions could still rise if demand growth outpaces non-fossil electricity generation. Another metric, non-fossil share of primary energy use, involves a complicated conversion to primary energy (Lewis et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and is rarely used outside of China.\u003c/p\u003e \u003cp\u003e6 For these reasons, share of non-fossil electricity generation provides both a robust metric for setting policy targets and a transparent benchmark for international comparison. In the U.S., for example, the share of non-fossil generation was about 40% in 2023 (EIA, 2024) versus about 36% in China in 2022, the most recent year for which data are available (CEC, 2024c). The European Union leads the world with a 67% non-fossil electricity share, while Japan lags other developed economies at 31% (Ember, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e7 Here, we analyze multiple scenarios using this metric to evaluate possible pathways for China\u0026rsquo;s power system transition. Our results indicate that China\u0026rsquo;s share of non-fossil generation could reach 67\u0026ndash;73% by 2030 and 78\u0026ndash;84% by 2035 under a variety of scenarios for electricity demand, technology costs, and carbon constraints. (See Methods section for detailed scenario assumptions.) Notably, shares of non-fossil generation largely remain consistent across scenarios (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and align with other recent studies (Abhyankar et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; He et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kahrl et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Peng et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e8 That the share of non-fossil generation stays fairly consistent across scenarios offers policymakers some measure of certainty regarding policy outcomes. That said, carbon constraints appear to have the largest influence on non-fossil share of generation, followed by technology costs and demand growth. For example, under the high demand scenario, where electricity demand is more than 25% higher than under the base scenario, the share of non-fossil generation increases by only 1\u0026ndash;2 percentage points. By contrast, the difference in share of non-fossil generation among carbon constraint scenarios and technology cost scenarios ranges from 1\u0026ndash;4 percentage points and 1\u0026ndash;3 percentage points, respectively.\u003c/p\u003e \u003cp\u003e9 While electricity demand appears to have little effect on non-fossil share of generation, it does significantly affect both solar and wind installations and total generation. In other words, the higher the demand, the more solar and wind capacity is installed and the more electricity generation is needed. Across all scenarios, total solar and wind installations range from 2,045 to 3,120 GW in 2030 and 2,723 to 4,399 GW in 2035. This translates to average annual additions of 138 to 291 GW of solar and wind capacity between 2024 and 2035, a difference of more than 100%. In 2023, China installed over 300 GW of solar and wind and is on pace to match that figure in 2024 (CEC, 2024a). This suggests China is well-equipped to install 291 GW of solar and wind per year, enabling 65% non-fossil generation by 2030 and around 80% by 2035, even under the high demand scenario.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e10 Setting ambitious long-term targets has been integral to China\u0026rsquo;s clean energy and climate strategy, and its success in building a strong clean energy economy demonstrates the effectiveness of this approach. With the feasibility of the 1.5\u0026deg; C pathway now in doubt, it behooves China to raise its ambitions both domestically, such as in its upcoming 15th Five-Year Plan (FYP) for 2025\u0026ndash;2030, and internationally in its forthcoming NDC update. Choosing an appropriate benchmark, such as share of non-fossil electricity generation, would make this effort both more transparent and robust, as it would be minimally influenced by the significant growth that may occur in China\u0026rsquo;s electricity demand due to electrification and emerging new end-uses such as data centers.\u003c/p\u003e \u003cp\u003e11 To support such an ambitious target, China must accelerate power sector reforms. This would involve developing and issuing new guidelines for system planning, grid operation, and electricity markets. For example, renewables and storage should be allowed to participate in China\u0026rsquo;s wholesale electricity market. Regional market integration should also be strengthened to allow greater sharing of renewables and other resources. Such sharing across a large balancing area would support renewable integration, enhance grid flexibility and stability, and reduce cost and emissions (Lin et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Abhyankar et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e12 In terms of system planning, more systematic resource adequacy processes are likely needed to ensure the long-term reliability of China\u0026rsquo;s electricity system. Such processes would involve specifying levels of firm capacity needed to maintain a reliability target; mechanisms to encourage adequate firm capacity investment; better accounting for the firm capacity value of renewable generation and storage; improved scarcity pricing; and incorporating more demand response resources (Abhyankar et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Fortunately, China is moving forward on many fronts in reforming its electricity sector, including the recent launching of an inter-provincial wholesale electricity market (NEA, 2024b). Setting ambitious targets with a transparent, robust benchmark such as non-fossil share of electricity generation would give China\u0026rsquo;s clean energy industry and the global community a clear signal of China\u0026rsquo;s determination to accelerate its transition to a carbon-neutral economy.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eWe use an open-source power system model, GridPath, to model transition pathways of China\u0026rsquo;s power system under a variety of electricity demand, technology cost, and carbon constraint scenarios. The optimal solution minimizes the cost of producing and delivering electricity while satisfying a set of operational constraints. In our study, we use one hour as the time step and a province as a load zone. We model the Chinese electricity grid as 32 interconnected nodes, connected by 184 interprovincial transmission corridors.\u003c/p\u003e \u003cp\u003eTo model long-term investments, we employ four levels of temporal resolution: five-year investment periods, months, days, and hours. Our study divides the time span from 2025 to 2050 into six investment periods of five years each: 2021\u0026ndash;2025, 2026\u0026ndash;2030, 2031\u0026ndash;2035, 2036\u0026ndash;2040, 2041\u0026ndash;2045, and 2046\u0026ndash;2050. We use 12 months to characterize each investment period, two days (a peak and a median load day) to characterize each month, and six hours to characterize each day. For each day, hourly sampling begins at midnight China Standard Time (CST) and includes the 0th, 4th, 8th, 12th, 16th, and 20th hours. This results in (6 investment periods) \u0026times; (12 months/investment period) \u0026times; (2 days/month) \u0026times; (6 hours/day)\u0026thinsp;=\u0026thinsp;864 study hours during which the system is dispatched.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eScenario designs\u003c/h2\u003e \u003cp\u003eWe set up 12 scenarios, including two for carbon caps (high and low), two for technology cost levels (high and low), and three for electricity demand (base, medium, and high), as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eScenario designs.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScenarios\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCarbon emission caps\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTechnology cost\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eElectricity demand\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH_Carbon_H_Cost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBase\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH_Carbon_L_Cost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBase\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL_Carbon_H_Cost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBase\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL_Carbon_L_Cost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBase\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH_Carbon_H_Cost_M_Demand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH_Carbon_L_Cost_M_Demand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL_Carbon_H_Cost_M_Demand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL_Carbon_L_Cost_M_Demand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH_Carbon_H_Cost_H_Demand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH_Carbon_L_Cost_H_Demand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL_Carbon_H_Cost_H_Demand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL_Carbon_L_Cost_H_Demand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eCarbon emission caps.\u003c/b\u003e We set up two carbon emission caps: (1) a high carbon emission cap, which assumes 90% emission reduction by 2050 compared to 2021; and (2) a low carbon emission cap, which assumes 100% emission reduction by 2045.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTechnology costs.\u003c/b\u003e We set up two technology cost scenarios based on low- and high-cost projections from BNEF (2022). Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the high and low capital costs of different technologies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eElectricity demand.\u003c/b\u003e Growth in China\u0026rsquo;s electricity demand over the next 25 years is highly uncertain. To address this, we set up three electricity demand projections for 2025 through 2050, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e: (1) the Base scenario, representing the mean value of electricity demand projections from several recent studies (IEA, 2020; ICCSD, 2020; CNREC, 2020; Fu, et al., 2020; Jiang et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; and CETO, 2023); (2) the Medium demand scenario, which makes linear forecasts based on national demand data from previous years (CEC, 2024b), reaching 12,500 TWh in 2030 and 13,500 TWh in 2035 ; and (3) the High demand scenario, which is derived from the ambitious carbon-neutral scenarios in CETO (2023).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThree scenarios of electricity demand (in TWh) from 2025\u0026ndash;2050.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElectricity demand scenarios\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2025\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2030\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2035\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2040\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2045\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2050\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9,345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11,210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12,785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13,995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15,015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16,034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12,500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13,500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14,879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16,258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17,637\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11,200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16,100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17,700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19,200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e19,800\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJL designed the study and wrote the manuscript. LQP carried out experiment and contributed to the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbhyankar, N., J. Lin, F. Kahrl, S.F. Yin, U. Paliwal, X. Liu, N. Khanna, Q. Luo, D. Wooley, M. O\u0026rsquo;Boyle, O. Ashmoore, R. Orvis, M. Solomon, A. Phadke. (2022). \u0026ldquo;Achieving an 80% carbon-free electricity system in China by 2035,\u0026rdquo; \u003cem\u003eiScience\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eBloombergNEF. New Energy Outlook 2022. https://about.bnef.com/new-energy-outlook/#toc-download.\u003c/li\u003e\n\u003cli\u003eCarbon Brief, 2024. 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Spatially resolved land and grid model of carbon neutrality in China. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e, \u003cem\u003e121\u003c/em\u003e(10). https://doi.org/10.1073/pnas.2306517121\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6314403/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6314403/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUNEP, in its latest Emissions Gap Report, highlights the significant distance between countries\u0026rsquo; pledges and what is needed to stay on a 1.5\u0026deg; C pathway, and calls on all nations to raise the ambition of their nationally determined contributions, updates of which are due by February 2025. As the world\u0026rsquo;s largest carbon emitter, China\u0026rsquo;s commitment is critical to the success of any global compact addressing climate change. Power sector accounts for over 40% of China\u0026rsquo;s carbon emission, thus is a key area of both progress and concern. We argue that the share of non-fossil electricity generation is a more appropriate and robust benchmark of measuring China\u0026rsquo;s power system transition, as it would be minimally influenced by the significant uncertainty in demand growth. By contrast, capacity targets such as tripling renewable installations by 2030 are less useful, especially given the record growth of solar and wind buildout. Our analysis shows that China could reach around 80% of non-fossil generation by 2035 across a variety of demand, technology, and carbon constrain scenarios.\u003c/p\u003e","manuscriptTitle":"Benchmarking China’s power system transition","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-14 10:07:30","doi":"10.21203/rs.3.rs-6314403/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d4fe6051-538b-42e7-b899-c77ecdc87cee","owner":[],"postedDate":"April 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":47082829,"name":"Earth and environmental sciences/Environmental social sciences"},{"id":47082830,"name":"Earth and environmental sciences/Environmental social sciences/Climate change mitigation"},{"id":47082831,"name":"Earth and environmental sciences/Environmental social sciences/Climate change policy"},{"id":47082832,"name":"Earth and environmental sciences/Environmental social sciences/Environmental economics"},{"id":47082833,"name":"Physical sciences/Engineering"}],"tags":[],"updatedAt":"2025-04-25T07:08:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-14 10:07:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6314403","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6314403","identity":"rs-6314403","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}