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As a major energy consumption province, the issue about the carbon emissions in Hebei Province, China has been concerned by the government. The carbon emissions can be effectively reduced due to a more rational energy consumption structure. Thus, in this paper the constraint of low carbon emissions is considered as a foundation and four energies--coal, petroleum, natural gas and electricity including wind power, nuclear power and hydro-power etc are selected as the main analysis objects of the adjustment of energy structure. This paper takes energy cost minimum and carbon trading cost minimum as the objective functions based on the economic growth, energy saving and emission reduction targets and constructs an optimization model of energy consumption structure. And empirical research about energy consumption structure optimization in 2015 and 2020 is carried out based on the energy consumption data in Hebei Province, China during the period 1995-2013, which indicates that the energy consumption in Hebei dominated by coal cannot be replaced in the next seven years, from 2014 to 2020, when the coal consumption proportion is still up to 85.93%. Finally, the corresponding policy suggestions are put forward, according to the results of the energy structure optimization in Hebei Province. [ABSTRACT FROM AUTHOR]

With the deterioration of the global greenhouse effect, the study of carbon dioxide emissions has received more and more international attention and accurate prediction of carbon dioxide emissions is also important for the formulation of reasonable energy-saving emission reduction measures. In this paper, the genetic algorithm is used to optimize the initial connection weights and thresholds of the traditional back propagation neural network (BPNN) which can give full play to the advantages of the genetic algorithm's global search capacity and BPNN's local search. The data of Hebei province in China during the period 1978-2012 are selected to carry out the carbon dioxide emissions prediction with the established model. In the view of the choice of input variables, the coal consumption, gross domestic product, total population, and urbanization level are examined by Pearson coefficient test. Auto correlation and partial correlation are applied to analyze the inner relationships between the historic carbon dioxide emissions, thus to select the input variables of BPNN. Besides, in order to verify the validity of the built model, the residual auto correlation and partial correlation are done upon the training set. The prediction results suggest the proposed model outperforms the compared models. [ABSTRACT FROM AUTHOR]