Since 2005, the Chinese government has engaged in an ambitious effort to move China's energy system away from coal and towards more environmentally friendly sources of energy. However, China's investment in coal power has accelerated sharply in recent years, raising concerns of massive overcapacity and undermining the central policy goal of promoting cleaner energy. In this paper, we ask why China engaged in such a pronounced investment boom in coal power in the mid-2010s. We find the protective rules under which China's coal power industry has historically operated have made excessive investment extremely likely unless the central government serves as a "gatekeeper," slowing and limiting investment in the face of incentives for socially excessive entry. When coal-power project approval authority was decentralized from the central government to local governments at the end of 2014, the gate was lifted and approval time considerably shortened, allowing investment to flood into the market. We construct a simple economic model that elucidates the effects of key policies on coal power investment, and examine the model's predictions using coal-power project approval records from 2013 to 2016. We find the approval rate of coal power is about 3 times higher when the approval authority is decentralized, and provinces with larger coal industries tend to approve more coal power. We estimate that local coal production accounts for an additional 54GW of approved coal power in 2015 (other things equal), which is about 1/4 of total approved capacity in that year.
The emergence of climate change (CC) is affecting and changing the development of the natural environment, biological species, and human society. In order to better understand the influence of climate change and provide convincing evidence, the need to quantify the impact of climate change is urgent. In this paper, a climate change model is constructed by using a radial basis function (RBF) neural network. To verify the relevance between climate change and extreme weather (EW), the EW model was built using a support vector machine. In the case study of Canada, its level of climate change was calculated as being 0.2241 (“normal”), and it was found that the factors of CO2 emission, average temperature, and sea surface temperature are significant to Canada’s climate change. In 2025, the climate level of Canada will become “a little bad” based on the prediction results. Then, the Pearson correlation value is calculated as being 0.571, which confirmed the moderate positive correlation between climate change and extreme weather. This paper provides a strong reference for comprehensively understanding the influences brought about by climate change.
Since 2005, the Chinese government has engaged in an ambitious effort to move China's energy system away from coal and towards more environmentally friendly sources of energy. However, China's investment in coal power has accelerated sharply in recent years, raising concerns of massive overcapacity and undermining the central policy goal of promoting cleaner energy. In this paper, we ask why China engaged in such a pronounced investment boom in coal power in the mid-2010s. We find the protective rules under which China's coal power industry has historically operated have made excessive investment extremely likely unless the central government serves as a "gatekeeper," slowing and limiting investment in the face of incentives for socially excessive entry. When coal-power project approval authority was decentralized from the central government to local governments at the end of 2014, the gate was lifted and approval time considerably shortened, allowing investment to flood into the market. We construct a simple economic model that elucidates the effects of key policies on coal power investment, and examine the model's predictions using coal-power project approval records from 2013 to 2016. We find the approval rate of coal power is about 3 times higher when the approval authority is decentralized, and provinces with larger coal industries tend to approve more coal power. We estimate that local coal production accounts for an additional 54GW of approved coal power in 2015 (other things equal), which is about 1/4 of total approved capacity in that year.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.