Determinants investigation and peak prediction of CO2 emissions in China's transport sector utilizing bio-inspired extreme learning machine.

The transport sector is recognized as one of the largest carbon emitters. To achieve China's carbon peak commitment in the Paris Agreement on schedule, it is indispensable to explore the peak carbon emissions and mitigation strategies in the transport sector. Many researches in the past have contextualized in China's total emissions peak, while the study about forecasting China's transport CO2 emissions peak seldom appeared, especially the application of intelligent prediction model. To further investigate the determinants and forecast the peak of transport CO2 emissions in China accurately, a novel bio-inspired prediction model is proposed in this paper, namely, the extreme learning machine (ELM) optimized by manta rays foraging optimization (MRFO), hereafter referred as MRFO-ELM. Adhering to this hybrid model, the mean impact value (MIV) method is then employed to evaluate and differentiate the importance of thirteen influencing factors. Additionally, three scenarios are set to conduct prediction of China's transport CO2 emissions. The empirical results indicate that the proposed MRFO-ELM has excellent performance in terms of the optimization searching velocity and prediction accuracy. Simultaneously the level of vehicle electrification is verified to be one of the emerging major factors affecting China's transport CO2 emissions. The transport CO2 emissions in China would peak in 2039 under the baseline model scenario, while the plateau would occur in 2035 or 2043 under sustainable development mode and high growth mode, respectively. The peak years imply much pressure on China's transport carbon emissions abatement currently, whereas active policy adjustments can effectively urge the earlier occurrence of the emission peak. These new findings suggest that it is essential for China to improve the energy mix and encourage the electric energy replacement in line with urbanization pace, so as to achieve CO2 emissions mitigation in the transport industry.

DNA metabarcoding assays reveal a diverse prey assemblage for Mobula rays in the Bohol Sea, Philippines.

Diet studies provide base understanding of trophic structure and are a valuable initial step for many fields of marine ecology, including conservation and fisheries biology. Considerable complexity in marine trophic structure can exist due to the presence of highly mobile species with long life spans. Mobula rays are highly mobile, large, planktivorous elasmobranchs that are frequently caught either directly or as bycatch in fisheries, which, combined with their conservative life history strategy, makes their populations susceptible to decline in intensely fished regions. Effective management of these iconic and vulnerable species requires an understanding of the diets that sustain them, which can be difficult to determine using conventional sampling methods. We use three DNA metabarcode assays to identify 44 distinct taxa from the stomachs (n = 101) of four sympatric Mobula ray species (Mobula birostris, Mobula tarapacana, Mobula japanica, and Mobula thurstoni) caught over 3 years (2013-2015) in a direct fishery off Bohol in the Philippines. The diversity and incidence of bony fishes observed in ray diets were unprecedented. Nevertheless, rays showed dietary overlap, with krill (Euphausia) dominating their diet. Our results provide a more detailed assessment of sympatric ray diets than was previously described and reveal the complexity that can exist in food webs at critical foraging habitats.