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Energies 2017, 10(3), 391; doi:10.3390/en10030391

Assessment Analysis and Forecasting for Security Early Warning of Energy Consumption Carbon Emissions in Hebei Province, China

School of Economics and Management, North China Electric Power University, Beijing 102206, China
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Academic Editor: S. Kent Hoekman
Received: 9 February 2017 / Revised: 9 March 2017 / Accepted: 17 March 2017 / Published: 19 March 2017
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Abstract

Against the backdrop of increasingly serious global climate change and the development of the low-carbon economy, the coordination between energy consumption carbon emissions (ECCE) and regional population, resources, environment, economy and society has become an important subject. In this paper, the research focuses on the security early warning of ECCE in Hebei Province, China. First, an assessment index system of the security early warning of ECCE is constructed based on the pressure-state-response (P-S-R) model. Then, the variance method and linearity weighted method are used to calculate the security early warning index of ECCE. From the two dimensions of time series and spatial pattern, the security early warning conditions of ECCE are analyzed in depth. Finally, with the assessment analysis of the data from 2000 to 2014, the prediction of the security early warning of carbon emissions from 2015 to 2020 is given, using a back propagation neural network based on a kidney-inspired algorithm (KA-BPNN) model. The results indicate that: (1) from 2000 to 2014, the security comprehensive index of ECCE demonstrates a fluctuating upward trend in general and the trend of the alarm level is “Severe warning”–“Moderate warning”–“Slight warning”; (2) there is a big spatial difference in the security of ECCE, with relatively high-security alarm level in the north while it is relatively low in the other areas; (3) the security index shows the trend of continuing improvement from 2015 to 2020, however the security level will remain in the state of “Semi-secure” for a long time and the corresponding alarm is still in the state of “Slight warning”, reflecting that the situation is still not optimistic. View Full-Text
Keywords: energy consumption carbon emissions; security early warning; pressure-state-response (P-S-R) model; time and space analysis; back propagation neural network based on kidney-inspired algorithm (KA-BPNN) energy consumption carbon emissions; security early warning; pressure-state-response (P-S-R) model; time and space analysis; back propagation neural network based on kidney-inspired algorithm (KA-BPNN)
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Liang, Y.; Niu, D.; Wang, H.; Chen, H. Assessment Analysis and Forecasting for Security Early Warning of Energy Consumption Carbon Emissions in Hebei Province, China. Energies 2017, 10, 391.

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