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Promoting low-carbon development in the iron and steel industry (ISI) is essential for China to achieve the carbon neutrality targets. This paper separately adopts the Logarithmic Mean Divisia Index (LMDI) technique and the Mean Impact Value (MIV) method to analyze the impact of driving factors on the CO 2 emissions of ISI in the historical and future dimensions. Furthermore, this paper establishes the extreme learning machine model optimized by the bat algorithm (BA-BP) to explore the carbon emission reduction pathways of ISI in the business-as-usual (BAU) scenario, the low-speed, medium-speed and high-speed development scenarios considering the constraints of carbon neutrality targets. The results reveal that: (1) Production capacity and energy efficiency are important drivers of CO 2 emissions in ISI; (2) The emission reduction situation is not optimistic under the BAU scenario, and it is difficult to accomplish the carbon neutrality goals by 2060; (3) Under the most ideal emission reduction pathway (corresponding to the high-speed development scenario), ISI will reach its peak in 2022 with the peak value of 2143.42 MtCO 2. Compared to the peak year, the CO 2 emissions will be reduced by 654.69 MtCO 2 and 1558.61 MtCO 2 in 2030 and 2050, respectively. Moreover, the achievement of short-term and long-term emission reduction targets depends on production capacity decline and technological progress, respectively. The optimal emission reduction pathway provides a reference for ISI to formulate periodic emission reduction targets. • The improved extreme learning machine is employed to predict the carbon emissions. • The emission reduction pathways are explored under the carbon neutrality target. • Controlling production capacity is a crucial reduction strategy. • The energy rebound effect is supposed to be weakened. • The application of advanced technologies will greatly promote emission reduction. [ABSTRACT FROM AUTHOR]

Abstract With the frequent occurrence of extreme weather, power transmission line icing, a general phenomenon during winter which may bring serious economic losses to the electric power system, has attracted increasing attention. However, owing to the complexity nature of wire ice covering, it is essential to establish an icing thickness forecasting model with high-accuracy for guaranteeing the security and stability of the power grid. Hence, this paper proposes a hybrid model that combines wavelet transform (WT) with extreme learning machine optimized by bat algorithm (BA-ELM). The original icing data containing icing thickness and meteorological factors are first denoised by WT and then divided into several stages based on the characteristics of icing period. Bivariate correlation analysis and partial auto correlation function (PACF) are used to select the inputs of different stages. Subsequently, ELM whose input weights and bias threshold were optimized BA is built to forecast icing thickness. To verify the developed model, icing data from two power transmission lines located in Hunan province are applied for experiments. The simulation results demonstrate that not only the proposed model shows a better performance but also the staged modeling can highly improve icing thickness prediction accuracy. [ABSTRACT FROM AUTHOR]