Bioenergy potential from agricultural by-product in 2030: An AI-based spatial analysis and climate change scenarios in a Chinese region

• DOI: 10.1016/j.jclepro.2024.140621
• Publication Date: 2024-01-05T04:52:33Z
• Authors: Zhan Shi; Giovanni Ferrari; Ping Ai; Francesco Marinello; Andrea Pezzuolo

Addressing the pressing need for sustainable bioenergy sources amid growing climate crisis, this study investigates potential crop output in Jianghan Plain, Hubei, China. The research leverages Backpropagation Artificial Neural Network models to predict accumulated net primary productivity (NPP) of various crops, i.e., single rice, double maize, and wheat, Plain. highlight a strong predictive performance, with SE model achieving an R2 0.83 wheat 0.90, implying these can be utilized reliably future NPP forecasting. Our evaluation past values reveals negative correlation between increase observed temperature trends. This emphasizes significant impact on potential. For predictions, scenario (S3a) under Temperature Control strategies at medium-effort mitigation yields favourable outcome all particularly SE. Additionally, results suggest that aggressive decarbonization global warming are vital enhancing values. However, also highlights trade-offs stresses necessity region-specific reconciliation strategies; instance, midland counties benefit from control, whereas sideline favours CO2 mitigation. further spatial distribution bioenergy, southwestern part plain holding more potential, providing essential reference energy planning. By 2030, when fully integrated reasonable responses, there could substantial residues reaching (1.2–1.9)×109 MJ, which is 11% less than current.