A Taguchi-STIRPAT input–output model for unveiling the pathways of reducing household carbon emissions under dual-carbon target—A case study of Fujian province Journal Articles uri icon

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abstract

  • This study develops a novel Taguchi-STIRPAT input-output (TSIO) model for exploring pathways to reduce carbon emission from the perspective of household consumption, through incorporating input-output model (IOM), Taguchi design (TD), and STIRPAT model. TSIO can not only identify the main factors (carbon emission intensity, consumption structure, per capita consumption, and population) and evaluate their effects on indirect household carbon emissions (IHC), but also predict IHC from a long-term perspective to achieve the dual-carbon target. TSIO is then applied to Fujian province (China), where multiple scenarios related to multiple factors with multiple levels are examined. Results reveal that (i) among all sectors, the highest contributors to IHC are residence (RES), followed by food, cigarettes, and drinks (FCD), and transport and communication (TC); it is suggested that the government can consider market mechanism to control these high-carbon emission consumption behaviors; (ii) the decline in the share of RES consumption has the largest effect on rural and urban IHC; the share of RES consumption is considered to be a key factor in predicting carbon emissions; (iii) under the optimal scenario, IHC would peak in 2025 and decrease to 10.07 × 106 ton in 2060; this scenario can effectively mitigate household carbon emissions by reducing carbon emission intensity and the share of RES consumption; at the same time, it can ensure a sustained increase in per capita consumption. The results unveil the pathways of household carbon reduction under the dual-carbon target in Fujian province and suggest the local government should adopt policies (such as taxation and financial incentives) to limit residential consumptions with high carbon emission intensity.

authors

  • Cai, Tianchao
  • Li, Yongping
  • Wang, Panpan
  • Huang, Gordon
  • Liu, Jing

publication date

  • February 2024