Can China realize its carbon emission reduction goal in 2020: From the perspective of thermal power development
By Liwei Liu, Haijing Zong, E. Zhao, Chuxiang Chen, Jianzhou Wang. 2014
159 citations recorded by the source corpus.
This paper answers the question of whether China can realize its target to reduce the intensity of carbon dioxide (CO2) emissions per unit of GDP that was announced at the Copenhagen Conference by forecasting CO2 emission intensity in 2020 from the perspective of China’s coal-fired thermal power development. We construct a combined forecasting model with a grey model (GM(1,1)), an autoregressive integrated moving average model (ARIMA) and a second order polynomial regression model (SOPR) and improve forecast accuracy by optimizing three coefficients of the individual aforementioned models with Particle Swarm Optimization (PSO). The results show that by 2020 thermal power generation will reach 7258.83billion kWh, CO2 emissions will reach 17379.90million tons, and CO2 emission intensity will be 0.21kilogram per Yuan, which is almost twice as 40–45% of the 2005 level. It is warned that situation of meeting targets over the timescale by Chinese government is extremely serious if China’s coal-fired thermal power continues expanding at its current rate. The thermal power generation of 2020 that satisfies the emission reduction target should be controlled in an interval between 3801.45 and 4492.62billion kWh. The regional forecasting results of coal-fired thermal power generation based on administrative region and economic belt demonstrate power output from coal-fired thermal power of East China and Eastern Economic Belt respectively account for 30.3% and 45.65% that of China by the end of 2020. Shanghai, Jiangsu, Zhejiang, Shandong and Fujian are thermal power generation concentrated areas with arduous task of carbon emission reduction by 2020.
Subfields: Electrical and Electronic Engineering, Environmental Engineering, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/a201bbc470acfc82657f06364526c842be295906
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Citations in the corpus
- Short-term CO2 emissions forecasting based on decomposition approaches and its impact on electricity market scheduling (2020, methodology): “[22] employed a hybrid model based on a practical Swarm optimization, gray model, an auto-regressive integrated moving average model (ARIMA) and a second-order polynomial regression model (SOPR) to forecast Chinas CO2 intensity based on Chinas thermal power development.”
- A multi-region optimization planning model for China's power sector (2015, background): “demonstrated the difficulty in realizing the CO2 emission intensity target in 2020 if the current state of thermal power continues [5].”