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Carbon emissions from the electricity industry (CEEI) account for a large proportion of China's total carbon emissions, and it is important to study the spatial correlation between CEEI and the influencing factors to promote cross-regional synergistic emission reduction and low-carbon development of the power system. In this paper, the quasi-input-output (QIO) model is applied to assess the transfer of carbon emissions generated by electricity trading based on the consideration of electricity carbon transfer, and the exploratory spatial data analysis (ESDA) method is applied to analyze the spatial correlation effect of carbon emissions from China's electric power sector from 2001 to 2020, analyzes its distribution pattern in both spatial and temporal dimensions, and applies the improved logarithmic mean Divisia index (LMDI) two-stage decomposition model to decompose the changes in CEEI into 11 influencing factors from the perspective of the whole industrial chain of power production, transmission, trade, and consumption. The research results show that (1) the spatial distribution of CEEI has obvious unevenness and aggregation characteristics, with high-high aggregation areas and hot spot aggregation areas generally concentrated in the North China Power Grid and the East China Power Grid, but the aggregation trend is gradually decreasing, while low-low aggregation areas and cold spot aggregation areas are concentrated in the Northwest China Power Grid and the Central China Power Grid, but the area is very limited. (2) The direction of carbon emission diffusion in China's electricity industry is gradually transitioning from southwest-northeast to northwest-southeast, and the east-west diffusion trend is stronger than the north-south diffusion trend and carbon emissions are gradually shifting to the northwest grid. (3) The total amount of electricity production is the most influential factor in the change of CEEI, driving the cumulative growth of CEEI by 4495.34 Mt, followed by GDP per capita and electricity consumption intensity. Coal consumption for power generation, the share of thermal power, and net electricity exports were the main factors inhibiting the increase in carbon emissions from the power sector, with cumulative contributions of −797.74 Mt, −619.99 Mt, and −47.76 Mt, respectively. [ABSTRACT FROM AUTHOR]

Air pollution in northern China is relatively serious during the winter heating period, which attracts the attention of the state and government, especially in Beijing–Tianjin–Hebei Region. To further explore the issue of air pollutant emission and energy utilization in Beijing–Tianjin–Hebei during the heating season, this paper establishes a panel data model which describes the long-term relationship between air pollutant emission, heating capacity, coal, and power consumption with the data from 2004 to 2017. Based on this, we draw the following conclusions: there is a positive relationship between winter heating capacity and air pollutant emissions, which indicates that the energy consumed by heating will produce atmospheric pollutants. However, the increase of electricity consumption does not necessarily reduce pollution, which means that replacing coal with electricity is not the best way to solve air pollution in heating season, but requires the coordination of multiple energy sources. In addition, there are obvious differences in the analysis results in Beijing, Tianjin, and Hebei. For example, the impact of coal and electricity consumption on pollutant emissions in Beijing is quite different from that in Tianjin and Hebei. The local economic development and energy conditions should be fully taken into account when formulating policies. [ABSTRACT FROM AUTHOR]

In consideration of the fuzziness and the uncertainty in decision information, this paper proposes a three-parameter interval grey linguistic variable decision-making method based on projection model and prospect theory. First, some properties are defined, such as the concepts and the relational calculation rules of grey additive linguistic variables. Second, considering the decision maker's subjective information and the attribute's objective information, a mathematical model is constructed to obtain the attribute weights. Third, based on the new definitions of the prospect values, the comprehensive prospect values are calculated viewing dynamic alternatives as the reference point, and a ranking of alternatives is obtained. At last, an illustrative example is taken in the present study to make the proposed method comprehensible. [ABSTRACT FROM AUTHOR]

In consideration of the interaction among attributes and the uncertainty in the decision information, this paper proposes an intuitionistic trapezoidal fuzzy random decision making method based on Mahalanobis-Taguchi Gram-Schmidt and evidence theory. Firstly, by the acquired intuitionistic trapezoidal fuzzy samples in different periods of the decision making process, the unknown parameters of entire intuitionistic trapezoidal fuzzy populations with known distribution pattern are estimated, and then an intuitionistic trapezoidal fuzzy random matrix is obtained. Secondly, an intuitionistic fuzzy decision matrix with expectation and variance is established, and then a new score function is defined to transform a normalized expectation intuitionistic fuzzy number matrix into a score function matrix. Finally, Mahalanobis-Taguchi Gram-Schmidt and improved evidence theory are combined to deal with the score function matrix, and a ranking of alternatives is obtained according to the belief function value. An illustrative example is taken in the present study to make the proposed method comprehensible. [ABSTRACT FROM AUTHOR]

Annual power load forecasting is essential for the planning, operation and maintenance of an electric power system, which can also mirror the economic development of a country to some extent. Accurate annual power load forecasting can provide valuable references for electric power system operators and economic managers. With the development of Energy Internet and further reformation of electric power market, power load forecasting has become a more difficult and challenging task. In this paper, a new hybrid annual power load forecasting model based on LSSVM (least squares support vector machine) and MFO (Moth-Flame Optimization algorithm) is proposed, which the parameters of LSSVM model are optimally determined by the latest nature-inspired metaheuristic algorithm MFO. Meanwhile, the rolling mechanism is also employed. The forecasting results of China's annual electricity consumption indicate the proposed MFO-LSSVM model shows much better forecasting performance than single LSSVM, FOA-LSSVM (LSSVM optimized by fruit fly optimization), and PSO-LSSVM (LSSVM optimized by particle swarm optimization). MFO, as a new intelligence optimization algorithm, is attractive and promising. The LSSVM model optimized by MFO can significantly improve annual power load forecasting accuracy. [ABSTRACT FROM AUTHOR]