An input-output-based Bayesian neural network method for analyzing carbon reduction potential: A case study of Guangdong province

Economic development, population growth, industrialization and urbanization have led to large increases in anthropogenic carbon emission that has caused a variety of negative impacts on climate change and eco-environment systems. This study develops an input-output-based Bayesian neural network (IO-BNN) method for simulating the carbon emission of various economic sectors and generating the desired schemes of emission reduction. IO-BNN is applied to Guangdong province to identify its carbon emission path, carbon peak, and carbon reduction potential over a long-term planning horizon (2021-2050), in which multiple scenarios are designed to examine the effects of different environmental policies on economic and energy activities. Major findings are: (i) the key sectors and factors affecting Guangdong's carbon emission and economic development are equipment (Equ), construction (Con), transport and storage (Tra), other service (Oth), per capita energy consumption (CEC), and primary energy consumption (EC); (ii) under different environmental policies, Guangdong's carbon emission would reach the peak during 2025-2035 and then continuously decrease during 2036-2050; (iii) Guangdong's carbon emission would peak in 2025 under the optimal scenario, associated with adjustment of industrial structure (i.e. part of secondary industry would be shifted to tertiary industry) as well as reduction of primary energy consumption.

Automated summary

Economic development, population growth, industrialization, and urbanization have led to significant increases in anthropogenic carbon emissions, resulting in various negative impacts on climate change and eco-environmental systems. This study introduces the IO-BNN method, a Bayesian neural network based on input-output analysis, to simulate carbon emissions of different economic sectors and generate emission reduction strategies. The method is applied to Guangdong province, revealing the key sectors and factors influencing carbon emissions and economic development, as well as the potential for reduction. The findings suggest that under different environmental policies, Guangdong's carbon emissions will peak between 2025-2035 and then steadily decline until 2050, with the optimal scenario achieving the peak in 2025 through industrial structural adjustments and reduced primary energy consumption.