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How to achieve the "double carbon" goal in energy systems has been the concern of governments. Integrated energy system (IES) is affected by climate change during his operation, in order to study the impact of climate change on IES and achieve the "double carbon" goal in energy systems, this paper proposes an integrated machine learning(IML) to forecast the long-term load, then investigates IES costs and carbon emissions in relation to climate, followed by the establishment of carbon peak energy system(CPES) and carbon neutral energy system(CNES), finally the honey badger algorithm is used to optimize the configuration of CPES and CNES. The results show that: IML can accurately make load forecasts. Under climate change, changes in load reduce the cost and carbon emissions of IES, and changes in equipment efficiency increase the cost and carbon emissions of IES. When both are considered, the cost and carbon emissions of IES increase by 1.18% and 0.92% per decade respectively. The costs of CPES and CNES increase by 0.93% and 1% respectively for every 10 years earlier than the year of achievement. To meet China's "double carbon" goal, CPES and CNES need to increase their costs by 1.97% and 2% respectively. • Integrated machine learning proposed for long-term load forecasting. • Studied the impact of system equipment efficiency and load with climate change. • Proposing carbon peaking energy system to achieve the carbon peak goal. • Proposed carbon-neutral energy system to achieve carbon neutrality goal. • The increased costs of achieving carbon peak and carbon neutral were studied. [ABSTRACT FROM AUTHOR]

Abstract: China is a large developing country with high carbon intensity. It is likely that China will have to face the challenge of CBTA (carbon motivated border tax adjustments) in future. Meanwhile, CBTA would create price gap between CBTA users and target countries, and change the competitiveness of different producers. Under such circumstances, this paper seeks to contribute to the debate on CBTA by focusing on the potential impacts of CBTA on different regions of China from a perspective of competitiveness. We adopt two geographical divides in China (eastern–central–western zones, high-trade-openness to low-trade-openness regions), and simulate the potential impacts across regions through a general equilibrium model. Our simulation results show that CBTA would affect competitiveness of different producers, their comparative advantages, relative trade shares, outputs and emissions. CBTA would induce structure change of the economy, and result in a shift of industrial output toward non-industrial output. There are significant differences in the effects of CBTA among different regions in China. CBTA would result in a relocation of outputs across regions in China. Therefore, CBTA would also have impact on China’s regional development policy. [Copyright &y& Elsevier]