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Jan H van Heerden Heinrich Bohlmann. T owards finding the tax incidence of carbon taxes in south africa. OUTLINE OF THE PAPER. The Problem Possible Solutions Previous Study The Data Adjusting the Model. Policy Simulations Results Conclusion Further work. THE PROBLEM. - PowerPoint PPT Presentation
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TOWARDS FINDING THE TAX INCIDENCE OF CARBON TAXES IN SOUTH AFRICA
Jan H van Heerden
Heinrich Bohlmann
OUTLINE OF THE PAPER
• The Problem• Possible Solutions• Previous Study• The Data• Adjusting the Model
• Policy Simulations• Results• Conclusion• Further work
THE PROBLEM• South Africa ranks amongst the first world countries in
the world in CO2 pollution, and its footprint looks bad
CO2 per capita: 1999
0
5
10
15
20
25
India
Mex
ico
Portu
gal
Slovak
Rep
ublic
Ita
ly
Poland
South
Afric
a
Korea
United
King
dom
Czech
Rep
ublic
Austra
lia
United
Sta
tes
Ton CO2 per capita
CO2/95 pppUS$ GDP: 1999
0
0,2
0,4
0,6
0,8
1
1,2
kg CO2/95 pppUS$ GDP
Emissions intensity
Source: International Environmental Agency (IEA). 2001. Key world energy statistics. Paris: IEA. (www.iea.org/statist/key2001/keyworld-2001.pdf)
GHG Emissions M-Tons - 2009
Rank Country M - ton %
1 China 7 711 25.40%
2 United States 5 425 17.80%
3 India 1 602 5.30%
4 Russia 1 572 5.20%
5 Japan 1 098 3.60%
6 Germany 766 2.50%
7 Canada 541 1.80%
8 Korea, South 528 1.70%
9 Iran 527 1.70%
10 United Kingdom 520 1.70%
11 Saudi Arabia 470 1.50%
12 South Africa 450 1.50%
13 Mexico 444 1.50%
14 Brazil 420 1.40%
15 Australia 418 1.40%
16 Indonesia 413 1.40%
17 Italy 408 1.30%
18 France 397 1.30%
19 Spain 330 1.10%
20 Taiwan 291 1.00%
21 Poland 286 0.90%
POSSIBLE SOLUTIONS
1. Carbon Emissions Tax
Actual measured emissions; or
2. Proxy tax bases:
A. Fossil Fuel Input (Upstream): where fuels enter the economy based on the carbon content of the fuel.
B. Output Tax (Downstream): (i) At point where fuel is combusted.
(ii) May be based on average emissions of production processes.
Previously
• In 2004/5 the Dutch government funded a project (PREM) to search for double dividends in the environment and economy of South Africa.
• We used a static CGE model to simulate the effects of carbon, fuel and energy taxes in the country.
• We found triple dividends with some tax and recycling combinations (environment, economy and poverty)
• Van Heerden, et al., Searching for Triple Dividends in South Africa: Fighting CO2 pollution and poverty while promoting growth, The Energy Journal, 2006
This paper
• Gives preliminary results of a World Bank project to search for double dividends in the environment and economy of South Africa.
• We use a dynamic CGE model to • expand the electricity industry from being a single
producer and distributor of electricity to a few generators and one distributor, and
• simulate the effects of a fuel input tax in the country.
THE DATA (1)
• Updated 2011 database of South Africa
• Core data taken from the 2011 SU tables (StatsSA)
• Database aggregated to 45 sectors, with the electricity
sector then split between 8 generators and 1
transmitter/distributor based on available data.
THE DATA (2)
Electricity Supply, R
Leontief
up to Electricity Other costs Primary factors
CES
Good 1 (not electricity)
Imported Good 1
Domestic Good 1
CES
Good N (not electricity)
Imported Good N
Domestic Good N
CES
Land Labour Capital
CES
Labour type 1
Labour type O
up to
CES
Good 1 from region 1
Good 1 from region 2
Good 1 from region R
up to Labour type 2
NEM
CES
Generation 1, NEM region 1
Generation M, NEM region 1
Generation M, NEM region N up to
Source: MMRF document from http://www.copsmodels.com/archivep.htm#tppa0080
Database split of the electricity sector
• We used the procedure followed by the MMRF model of CoPS:
• Database split.docx
THE MODEL (1)
• Change in revenue dR= T.dX + X.dT • T is rate and X is base
• But % change in X is x = 100*dX/X
• Therefore dR = TxX/100 + X.dT• = Rx/100 + X.dT• dR affects government revenue and dT all prices
THE MODEL (2)• ! Leontief demand for inputs !
• Equation E_x1_sa # Demands for commodity composites # (all,c,COM)(all,i,IND52) x1_s(c,i) - [a1_s(c,i) + a1tot(i)] = z(i);
• Equation E_x1_sb # Demands for commodity composites #(all,c,COM45) x1_s(c,"ElecSup") - [a1_s(c,"ElecSup") + a1tot("ElecSup")] = z("ElecSup"); ! CES demand for inputs !
• Equation E_x1_sc(all,c,GEN) x1_s(c,"ElecSup") - a1_s(c,"ElecSup")
• = z("ElecSup") - SIGMAGEN(c)*[p1_s(c,"ElecSup") + a1_s(c,"ElecSup") - p1_gen];
POLICY SIMULATIONS
• The modelling exercises focus on two pieces of government policy in South Africa
• Integrated Resource Plan (IRP) for Electricity (2010-2030)• http://www.doe-irp.co.za/content/IRP2010_updatea.pdf
• Carbon tax of R120/ton CO2e from 2016• http://www.thedti.gov.za/parliament/Reducing_greenhouse_gas.pdf
Baseline forecast (1)
Baseline forecast
RESULTS: Carbon tax/no recycling
CONCLUSIONS
• Implementing a CES demand function for generated electricity by the supplying industry causes a switch to green electricity but not nearly enough. Currently the supplier merely uses coal generated power much more efficiently and not enough substitution takes place.
• The carbon tax by itself – especially with all the exemptions for the first five years – is not enough. Regulation of coal generated power, as well as pro-active stimulation of green generation together with the tax will be necessary to reach the targets.