Two-stage multilateral trade-based prediction model for freight transport carbon emission of Belt and Road countries along Eurasian Landbridges

Abstract

Global freight distribution patterns have been affected by trading policies and the pandemic outbreak. The Belt and Road Initiative, trade conflicts, and the COVID-19 pandemic have changed the global logistics flow, shifting cargos from maritime and air transport to railway transport along the countries in the Eurasian Landbridge. Though railway freight emits less carbon than road truck transportation, the increased use of railway freight brings in a higher volume of carbon emissions to cities located along the landbridges. Achieving net zero carbon emission is becoming more important, but there is a lack of literature in assessing the environmental impact of cross-border railway logistics transportation among Belt and Road countries. A novel two-stage multilateral trade-based prediction model is developed, integrating a modified gravity model and nonlinear autoregressive neural network for trade and emission forecasting. The model evaluates railway freight along the landbridge over ten years and forecasts the impact of carbon emissions from trading and logistics along the corridor in the subsequent five years. It further analyses the emissions impact of the proposed Third Eurasian Landbridge and the extended Second Eurasian Landbridge. The findings provide insights for the development of railway freight transport, considering trade and logistics flow, carbon emission mitigation strategies, and sustainability impact between China and other Belt and Road countries. While countries such as India and Kazakhstan were forecast to have significant amounts of carbon emissions in the projected period, the rapid growths in locations with smaller emission amounts such as Kunming and Georgia should draw attention and require continuous monitoring.