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The Annals of Regional Science
Published in:

16-04-2024 | Special Issue Paper

Effects on sectors and regions of a carbon tax increase in Sweden: analysis with an SCGE model

Authors: Peter Almström, Christer Anderstig, Marcus Sundberg

Published in: The Annals of Regional Science | Issue 2/2024

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Abstract

This paper sheds some light on the consequences of a carbon tax increase to achieve the national goal for carbon emissions. By use of a spatial computable general equilibrium model, STRAGO, the main purpose of the paper is to illustrate the effects on sectors in the economy and regions in Sweden from a climate scenario, by estimating expected substitution effects from a predetermined reduction in emissions. The climate scenario implies a gradual increase in the current carbon tax until the goal for the reduction in emissions has been reached. Doubling the carbon tax rate is expected to reduce the total consumption of fossil fuels by 18%, and in terms of reduced emissions and total welfare, the results are of the same order of magnitude as in previous Swedish studies. At the regional level, the effect on production is largest in Region West, as the production of petroleum and a large part of manufacturing is concentrated to this region. In terms of regional welfare, the northern regions, Upper-north in particular, lose more than central regions, which reflects differences in both industrial structure and carbon intensity, due to different transportation expenditures. The impact on welfare in northern regions is however mitigated by the substitution from fossil fuels to biofuels, as production of biofuels is concentrated to these regions.
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Appendix
Available only for authorised users
Footnotes
1
The goal is with respect to the sectors that will be covered by the EU regulation on the division of responsibilities.
 
2
Fit for 22 refers to the EU’s target of reducing net greenhouse gas emissions by at least 55% by 2030.
 
3
Hassler (2023) presents a broad set of proposals concerning the adaptation of the Swedish climate policy to Fit for 55.
 
4
However, taking the monetary value of reduced emissions into account total welfare might even be unchanged.
 
5
Constant Elasticity of Substitution.
 
6
Raps = Regional Analysis and Prognosis System.
 
7
MiR = Miljöräkenskaper (System of Environmental and Economic Accounts at Statistics Sweden).
 
8
This value is in line with the result from calculations based on data from Swedish IO table 2008–2014. According to these calculations the elasticity of substitution between fossil fuels and products defining biofuels is in the range 1.0–2.5.
 
9
The estimated relationship is = 1−(1−)/0.4.
 
10
In this context we use indexes on the form p(I,r)/p(i)p(r) applied to production values. p(I,r) reflects the proportion of total national production which is related to product i in region r, p(i) is the proportion of product I relative to national production, and p(r) is the proportion of national production carried out in region r.
 
11
The absolute effects on annual production are: Stockholm − 17 570 MSEK, East-middle − 6 841, Småland & islands − 5 569, South − 10 331, West − 23 960, North-middle − 6 271, Mid-north − 3 072, Upper-north − 4 096, Örebro & Västmanland − 3 951.
 
12
In the model, aggregate consumption (c) is purchased at price (p) and household expenditure equals income (m), i.e., pc = m. Denote variables before the tax increase with index 1 and after the increase with index 2, then EV is given by p1c2 = m1 + EV, and thus REV is given by REV = EV/m1 = (p1c2−m1)/m1 = (p1c2−p1c1)/(p1c1) = (c2−c1)/c1.
 
13
ASEK is an expert panel making recommendations for calculation values to be used in cost–benefit analyses for the transport sector in Sweden. The Swedish Transport Administration is responsible to develop methods and calculation values, published ASEK reports.
 
14
As mentioned above, EMEC-TIMES has been used for analyzing the effects of a 50% increase in the carbon tax, and nearly a doubling of the EU ETS price. As a result, CO2 emissions in Sweden are calculated to decrease with 13%.
 
15
The higher factor needed in EMEC than in STRAGO can be explained by the lower substitution elasticities in the EMEC model compared to the inter-fuel substitution elasticity of 2.0 applied in STRAGO, see Östblom and Berg (2006).
 
16
Combination of 8 groups by income (octiles) and 3 groups by geography (larger cities, smaller cities and countryside).
 
17
The variation of estimated welfare loss by geography seems relatively consistent with the corresponding variation between regions in our study.
 
18
Here, it could be noted that Swärdh et al (2023) make the observation that the relative welfare loss for households in octile 1 (with lowest income) is higher than in most other octiles. It is explained by car ownership and car use being surprisingly high in octile 1, which is a hint that the actual expenditures for a lot of persons in octile 1 are higher than what their registered disposable income implies.
 
19
The analysis in NIER (2017) is different, concluding that a tax shift between carbon tax and pay-roll tax is relatively more favorable (less unfavorable) for the low-income group.
 
20
MiR = Miljöräkenskaper (System of Environmental and Economic Accounts at Statistics Sweden).
 
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Metadata
Title
Effects on sectors and regions of a carbon tax increase in Sweden: analysis with an SCGE model
Authors
Peter Almström
Christer Anderstig
Marcus Sundberg
Publication date
16-04-2024
Publisher
Springer Berlin Heidelberg
Published in
The Annals of Regional Science / Issue 2/2024
Print ISSN: 0570-1864
Electronic ISSN: 1432-0592
DOI
https://doi.org/10.1007/s00168-024-01269-y

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