Redistributive schemes in the case of Carbon Border tax

France - May 02 2019
  • #carbon
  • #esg

Redistributive schemes in the case of Carbon Border tax

A few weeks ago, we illustrated the impact of a carbon border tax (CBT) on French citizens (link). We showed that while rich people are the main emitters thus contributing the most in absolute terms, poor people lost the most in terms of disposable income (about 1% per year against 0.3% for the top-income decile). This gap is one of the reasons of the current yellow vest movement in France, were the most vulnerable households feel that they would be more penalized in the case of a carbon tax.

One way to reduce this gap is to implement redistribution schemes: the income of the CBT is totally or partially redistributed to the consumers. The reallocation could take different forms, for example, energy vouchers or subsidies favouring the transition to a low carbon pathway. It can also be a lump-sum allocation (everyone receives the same amount) or a degressive allocation, meaning that the received amount is inversely proportional to the level of revenue. Unsurprisingly, this second form is more efficient in reducing inequalities, as is shown in the following figure, were we present the impact of a degressive redistribution scheme following a 20€ CBT.

Net impact of a degressive redistributive scheme after a 20€/tCO2e carbon tax at the EU border

In the first decile, the proportion of net contributors is reduced to less than 10%, meaning that most people with a low income could benefit from a CBT. Conversely, people with high income would mostly lose in the case of a degressive redistribution. This highlights the fact that a smart redistribution scheme could significantly reduce the inequalities created by a border carbon tax.

This assessment remains however highly theoretical. Also, one should bear in mind that the success of such a measure lies not only in its monetary acceptability but also in the creation of alternatives to a carbon intensive lifestyle.

Ruben Haalebos, Analyst, Data Science Dpt.
Source: Beyond Ratings

 

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