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The Yellow River Basin (YRB) is China's substantial energy consumption base. The issue of carbon emission efficiency directly affects the ecological protection and high-quality development of the YRB. It is the key to achieving carbon peak in 2030 and carbon neutralization in 2060 ("30.60") double carbon emission reduction targets. Therefore, taking YRB as the research object, this paper first calculates the carbon emission and the decoupling state in the YRB. Secondly, the super-efficiency slacks-based measurement (SE-SBM) model is combined with the Malmquist index to analyze the temporal and spatial evolution characteristics of YRB's carbon emission efficiency from static and dynamic perspectives. Thirdly, the dynamic evolution characteristics of carbon emission efficiency are analyzed with the help of the Kernel density function. Finally, the Tobit model analyzes the influencing factors of YRB's and China's carbon emission efficiency. The results show that: (1) Among the nine provinces of YRB, the decoupling state between carbon emissions and economic growth in most provinces changes from weak decoupling to strong decoupling, and the decoupling elasticity index shows a fluctuating downward trend. (2) There are significant differences in carbon emission efficiency among provinces, but on the whole, it shows a stable growth trend. The high-value area of carbon emission efficiency is increasing, and the phenomenon of two-level differentiation is improving. The decline of the technological progress index causes the Malmquist index in Qinghai and Ningxia. On the contrary, the rise of the Malmquist index in the other seven provinces is caused by improving the technical efficiency index. (3) Industrial structure, economic development, and industrialization are the main positive factors affecting YRB's carbon emission efficiency. Urbanization level, green development level, and energy consumption level are the leading negative indicators hindering YRB's improvement of carbon emission efficiency. Therefore, targeted emission reduction suggestions should be formulated according to YRB's resource endowment and development stage characteristics. [ABSTRACT FROM AUTHOR]

To reflect the future electricity demand variations in the industrial sector of western China under the "carbon peaking" and "carbon neutral" strategies, that the traditional methods of electricity consumption forecasting are no longer effective, this paper proposes a new hierarchy of electricity demand influencing factors based on the transition path of low-carbon and CO 2 emission intensity constraints of the industrial sector. Then, the feedback equations of the influencing factors based on CO 2 emission intensity is constructed, and a long-term electricity forecast model based on system dynamics is established. Finally, taking Ningxia Province as an example, the electricity demand and carbon emissions of the industrial sector are predicted under different policy constraint scenarios. The results show: (1) In the baseline scenario, electricity demand will tend to saturate with about 140.1TWh in 2030, and the CO 2 emission level of electricity demand will peak at 72.52 million tons in 2029. (2) Compared with the system dynamics model without considering CO 2 emission intensity, the mean absolute percentage error is reduced about 3.16 % by inputting the influence factors and regression coefficients designed in this paper. (3) Compared with other forecasting methods, the model proposed in this paper has the lowest mean absolute percentage error of 2.75 %. The model improves the accuracy and provides a reference for planning departments to develop energy transition plans, where the prediction results reflect the reality of the industrial sector in western China. Correspondingly, this model has reference value for relevant institutions in forecasting the electricity demand under the low-carbon development path in industrial sectors of different regions. • Influencing factors are screened according to the low-carbon transition paths. • The distributed power installed capacity and load interaction coefficient are considered. • Multiple regression equations are developed between CO 2 emission targets and influencing factors. • A long-term electricity demand forecast model driven by CO 2 constraints is constructed. • The multi-scenario electricity demand in western China is forecasted in response to carbon peaking and carbon neutral targets. [ABSTRACT FROM AUTHOR]

The mismatch between carbon transfer and economic benefits embodied in inter-provincial trade generates carbon inequality among provinces. Exacerbating carbon inequality would diminish the effectiveness of carbon policy, thereby challenging the realization of carbon neutrality target in China. Combining the network analysis and multi-regional input-output (MRIO) analysis, this paper depicted the unbalanced relationship between carbon transfer and value-added flow embodied in inter-provincial trade of China during 2012–2017 to shed light on the evolution features of trade-attributed carbon inequality from a multi-dimensional perspective. The research findings reveal that the carbon inequality embodied in inter-provincial trade of China has increased significantly during the study period, as the coefficient of structural equivalence between carbon transfer network and value-added transfer network decreased from 0.772 in 2012 to 0.634 in 2017. Different environmental regulation intensities and economic development levels among provinces are the main factors triggering the trade-attributed carbon inequality. During the surveyed years, some provinces such as Beijing, Tianjin, and Jiangsu have changed their roles from the drivers of carbon inequality to the leaders of green trade. Whilst some provinces located in the Northwest China which include Ningxia, Gansu, and Inner Mongolia are still placed in inferior positions in trade, and remain as the victims suffering from carbon inequality. The detected provincial communities present geographic adhesiveness and they are basically consistent with national urban agglomeration development planning. The research findings urge the need to shift from local carbon reduction to collective governance of carbon reduction, and provide supportive references for Chinese governments to develop integrated solutions and tailor-made policies towards equitable and sustainable development. • Network analysis is applied to unfold the evolution of trade-attributed carbon inequality. • The trade-attributed carbon inequality of China has increased significantly during 2012–2017. • Environmental regulation and economic development are the main factors of the carbon inequality. • Some provinces (Beijing, etc.) changed roles from drivers of carbon inequality to leaders of green trade. • Some provinces (Ningxia, etc.) remain as the victims suffering from carbon inequality. [ABSTRACT FROM AUTHOR]

• Statistical analysis of hourly wind speed series. • Finding out the optimal distribution to characterize the hourly wind speed series. • Guiding wind farm operation with the hourly expected wind power. Accurate wind speed characterization is important for wind farms to efficiently utilize wind energy which plays a significant role in reducing the dependence on fossil fuels and promoting global carbon peak and carbon neutrality. Since hourly wind speed density exhibit a variety of shapes, their characteristic features change substantially within a day, influenced by meteorological factors, therefore, the hourly wind speed analysis is considered to improve the accuracy of wind speed characterization. In this paper, 4 commonly used wind speed probability distributions (Weibull, Rayleigh, Gamma, Lognormal distribution) are applied for hourly wind speed analysis, and the best fitting distribution of each hourly wind speed series is identified by the goodness-of-fit test. Then the hourly wind power distribution of wind farms is determined by the corresponding optimal hourly wind speed distribution and the relation between wind power and wind speed. Finally, the hourly expected wind power calculated by the hourly wind power distribution is used to guide the hourly operation of the wind farm. The measured wind speed and wind power data of Dashuikeng wind farm in the Ningxia Autonomous Region of China is taken as an example to illustrate the results, 1) the hourly wind speed analysis performs a more accurate characterization of wind speed than annual wind speed series analysis. 2) The range of the average value of hourly wind speed series is 5.7 m/s–6.9 m/s, and the average wind speed, overall, during the daytime is smaller than that during the nighttime. 3) Gamma distribution shows a better performance than the other three distributions in characterizing the hourly wind speed series. 4) Wind energy resources are not fully consumed during the daytime, especially the period of 11:00–18:00. The proposed method improves the characterization of wind speed and guidance of the hourly operation for the wind farm, which has an important significance in raising the efficiency of wind energy utilization. [ABSTRACT FROM AUTHOR]