Spatial analysis on carbon emission abatement capacity at provincial level in China from 1997 to 2014: An empirical study based on SDM model

2019 ◽  
Vol 10 (1) ◽  
pp. 97-104 ◽  
Author(s):  
Yan Wang ◽  
Tao Zhao ◽  
Juan Wang ◽  
Fang Guo ◽  
Xu Kan ◽  
...  
2013 ◽  
Vol 12 (22) ◽  
pp. 6974-6979
Author(s):  
Wu Ming-Li ◽  
Tong Feng-Ming ◽  
Wang Fei ◽  
Zhang Hua ◽  
Li Song-Hua

2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Bin Chen ◽  
Man Yu

In an uncertainty market, social learning plays a significant role in obtaining information to make better decisions. Under cap-and-trade regulation, this paper aims to investigate firms’ pricing and carbon emission abatement issues considering the impact of social learning. This paper establishes a two-period model in a market consisting of a manufacturer and heterogeneous consumers. The manufacturer produces two alternatives (ordinary product and low-carbon product) and makes decisions on sales prices and carbon emission abatement levels. Consumers make decisions on whether and which product to buy. Consumers are not sure about their valuations of products and have the opportunity to discover their true valuation by social learning. The results show that the emission abatement level on ordinary product is affected by the pricing strategy for both types of products. However, the emission abatement level on low-carbon product is only affected by its own pricing strategy. It also shows that social learning lowers the emission abatement level on ordinary product, whereas it improves the emission abatement level on low-carbon product when charging a high price for low-carbon product. Moreover, the price of ordinary product in period 1 is no less than that in period 2. In contrast, the price of low-carbon product in period 2 is higher than that in period 1.


Energies ◽  
2016 ◽  
Vol 9 (5) ◽  
pp. 329 ◽  
Author(s):  
Shihong Zeng ◽  
Yan Xu ◽  
Liming Wang ◽  
Jiuying Chen ◽  
Qirong Li

Energy Policy ◽  
2012 ◽  
Vol 41 ◽  
pp. 871-877 ◽  
Author(s):  
Mingxi Wang ◽  
Mingrong Wang ◽  
Shouyang Wang

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