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Energy Policy
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Energy Policy
Article . 2018 . Peer-reviewed
License: Elsevier TDM
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Estimating Peak uranium production in China – Based on a Stella model

Authors: Chi Keung Marco Lau; Jianchun Fang; Wanshan Wu; Zhou Lu;

Estimating Peak uranium production in China – Based on a Stella model

Abstract

Abstract This paper uses the Logistic Curve and the STELLA model to simulate the Hubbert Peak uranium production in China. We used three scenarios to estimate China's Peak uranium. And the results are quite robust. According to Scenario 3, the Hubbert Peak uranium production is expected to be reached in 2065 with 4605 t per year. Before the peak, China's uranium demand will grow at the rate of about 7.69% per year, which is about three times the growth rate of production. China's uranium import dependence is estimated to continue to increase. In order to improve uranium resources security, the Chinese government needs to increase investment in uranium ore exploration, encourage private sector's investment in uranium production to promote competition, and improve cooperation with non-uranium mining enterprises for more efficient use of resources. To enhance the country's uranium security, China should establish development funds, accelerate acquisition of uranium enterprises abroad, increase R&D in the unconventional uranium resources such as phosphate mine, and take advantage of price downturn in uranium market to increase strategic reserves.

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citations
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!
24
Top 10%
Top 10%
Top 10%
hybrid