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Accelerating the energy transition towards photovoltaic and wind in China

Authors: Yijing Wang; Rong Wang; Katsumasa Tanaka; Philippe Ciais; Josep Penuelas; Yves Balkanski; Jordi Sardans; +12 Authors

Accelerating the energy transition towards photovoltaic and wind in China

Abstract

AbstractChina’s goal to achieve carbon (C) neutrality by 2060 requires scaling up photovoltaic (PV) and wind power from 1 to 10–15 PWh year−1(refs. 1–5). Following the historical rates of renewable installation1, a recent high-resolution energy-system model6and forecasts based on China’s 14th Five-year Energy Development (CFED)7, however, only indicate that the capacity will reach 5–9.5 PWh year−1by 2060. Here we show that, by individually optimizing the deployment of 3,844 new utility-scale PV and wind power plants coordinated with ultra-high-voltage (UHV) transmission and energy storage and accounting for power-load flexibility and learning dynamics, the capacity of PV and wind power can be increased from 9 PWh year−1(corresponding to the CFED path) to 15 PWh year−1, accompanied by a reduction in the average abatement cost from US$97 to US$6 per tonne of carbon dioxide (tCO2). To achieve this, annualized investment in PV and wind power should ramp up from US$77 billion in 2020 (current level) to US$127 billion in the 2020s and further to US$426 billion year−1in the 2050s. The large-scale deployment of PV and wind power increases income for residents in the poorest regions as co-benefits. Our results highlight the importance of upgrading power systems by building energy storage, expanding transmission capacity and adjusting power load at the demand side to reduce the economic cost of deploying PV and wind power to achieve carbon neutrality in China.

Countries
France, France, Spain, France
Keywords

[SDU] Sciences of the Universe [physics], Environmental impact, Developing world, Governance, PV, Wind, Optimization model, 330, [SDU]Sciences of the Universe [physics], Climate-change mitigation, Article

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
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544
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Top 1%
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674
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