Distributional impact of carbon pricing in Chinese provinces

• Published in: Energy economics 2019-06, Vol.81, p.327-340
• Main Authors: Wang, Qian, Hubacek, Klaus, Feng, Kuishuang, Guo, Lin, Zhang, Kun, Xue, Jinjun, Liang, Qiao-Mei
• Format: Article
• Language: English
• Subjects: Carbon; Carbon pricing; Carbon tax; Climate change; Coal; Electricity; Electricity pricing; Energy consumption; Energy economics; Expenditures; Fuels; Households; Income distribution; Inequality; Input-output analysis; Provinces; Regression analysis; Residential energy; Rural areas; Rural communities; Transportation; Urban areas
• ISSN: 0140-9883; 1873-6181
• DOI: 10.1016/j.eneco.2019.04.003

Based on a Multi-Regional Input-Output (MRIO) model, and combined with the 2012 MRIO table for 30 Chinese provinces, this paper analyzes the distributional impacts of carbon pricing on households within and across Chinese provinces. The results show regressive distributional effects of carbon pricing across provinces, i.e. poor provinces are affected more by the price. Carbon pricing also shows rural-urban regressivity (i.e. rural households are impacted more heavily than urban households) in more than half of the provinces. Within each selected province, carbon pricing has mostly regressive effects, i.e. poorer urban households are more affected than richer urban households in all provinces and poorer rural households more than richer rural households in one third of the provinces. When looking more specifically at direct energy consumption, we find that the carbon pricing on domestic fuels generally shows regressivity, while pricing carbon on transport fuels shows progressivity. In addition, the impact of carbon pricing on residential direct expenditures (mainly on electricity and coal) is the most important contributor to the regional regressivity across provinces. •This study analyzes the distributional impact of carbon pricing on the household groups across Chinese province.•Households in poor region may face higher per capita burden rate from carbon pricing.•Rural household is likely to bear with higher cost rate of carbon pricing to their total consumption.•However, pricing carbon on transport fuels shows progressivity across income groups in both urban and rural areas.•Policy maker may consider recycling the carbon revenue to support vulnerable low income household.