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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]

Under the background of the gradual scarcity of terrestrial resources and the increasingly fierce competition for global marine resources, the task of green development of marine economy and green transformation of marine industry is imminent. Carbon finance is a product of low-carbon economic development and a derivative of green finance. It is an effective way to alleviate the global climate crisis and promote the transformation of the global economy to a low-carbon economy to achieve sustainable development. Studying the mechanism of carbon finance on the development of marine economy will help to grasp the influencing factors of the green transformation of the marine industry in an all-round way, better promote the sustainable development of China's marine economy, and realize the strategy of marine power at an early date. Based on this, this paper selects China's data from 2006 to 2020, and uses the AHP-entropy method to select 26 indicators from five levels: economic growth, energy conservation and emission reduction, marine ecology, structural adjustment and scientific and technological innovation to construct a comprehensive evaluation index system for the green transformation of China's marine industry. Based on the PLSR model, the impact of carbon finance on the green transformation of China's marine industry is empirically analyzed, and the results show that: (1) The comprehensive score of China's marine industry green transformation from 2006 to 2020 fluctuates and rises: from 2006 to 2009, the comprehensive score of China's marine industry green transformation is low, and the transformation effect is not obvious. From 2009 to 2012, the comprehensive score of green transformation of the marine industry improved significantly. From 2012 to 2018, the comprehensive score of China's marine industry green transformation fluctuated little and showed a slow growth trend, while China's marine economic transformation grew steadily at a low speed. In 2019–2020, due to the impact of the epidemic, the speed of transformation has decreased again. (2) The carbon ratio and the green credit balance of commercial banks have a role in promoting the green transformation of China's marine industry, while the carbon trading volume and carbon intensity have a restraining effect on the green transformation of China's marine industry. From the perspective of effect, carbon intensity has the strongest effect, followed by carbon ratio, carbon trading volume is slightly weaker, and the role of green credit balance of commercial banks is the weakest. Based on the research conclusions, relevant policy recommendations were put forward. [ABSTRACT FROM AUTHOR]