Hong Chang

The extended Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model has been applied to analyzing the relationship between CO₂ emissions from power industry and the influential factors for the period from 1997 to 2020. The two groups found through partial least square (PLS) regularity test show two important areas for CO₂ emissions reduction from the power industry: economic activity and low-carbon electric technology. Moreover, considering seven influential factors (economic activity, population, urbanization level, industrial structure, electricity intensity, generation structure, and energy intensity) that affect the power CO₂ emissions and the practical situation in the power sector, possible development scenarios for the 13th Five-Year Plan period were designed, and the corresponding CO₂ emissions from the power sector for different scenarios were estimated. Through scenario analysis, the potential mitigation of emissions from power industry can be determined. Moreover, the CO₂ emissions reduction rates in the different scenarios indicate the possible low-carbon development directions and policies for the power industry during the period of the 13th Five Year Plan.

Accurate forecasting of fossil fuel energy consumption for power generation is important and fundamental for rational power energy planning in the electricity industry. The least squares support vector machine (LSSVM) is a powerful methodology for solving nonlinear forecasting issues with small samples. The key point is how to determine the appropriate parameters which have great effect on the performance of LSSVM model. In this paper, a novel hybrid quantum harmony search algorithm-based LSSVM (QHSA-LSSVM) energy forecasting model is proposed. The QHSA which combines the quantum computation theory and harmony search algorithm is applied to searching the optimal values of and C in LSSVM model to enhance the learning and generalization ability. The case study on annual fossil fuel energy consumption for power generation in China shows that the proposed model outperforms other four comparative models, namely regression, grey model (1, 1) (GM (1, 1)), back propagation (BP) and LSSVM, in terms of prediction accuracy and forecasting risk.

He accurate forecasting of carbon dioxide (CO₂) emissions from fossil fuel energy consumption is a key requirement for making energy policy and environmental strategy. In this paper, a novel quantum harmony search (QHS) algorithm-based discounted mean square forecast error (DMSFE) combination model is proposed. In the DMSFE combination forecasting model, almost all investigations assign the discounting factor (β) arbitrarily since β varies between 0 and 1 and adopt one value for all individual models and forecasting periods. The original method doesn’t consider the influences of the individual model and the forecasting period. This work contributes by changing β from one value to a matrix taking the different model and the forecasting period into consideration and presenting a way of searching for the optimal β values by using the QHS algorithm through optimizing the mean absolute percent error (MAPE) objective function. The QHS algorithm-based optimization DMSFE combination forecasting model is established and tested by forecasting CO₂ emission of the World top‒5 CO₂ emitters. The evaluation indexes such as MAPE, root mean squared error (RMSE) and mean absolute error (MAE) are employed to test the performance of the presented approach. The empirical analyses confirm the validity of the presented method and the forecasting accuracy can be increased in a certain degree.