"The EU 2050 low-carbon strategy: which policy design?"

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Anil Markandya

Ikerbasque Professor, BC3, Spain

Honorary Professor, Bath University, UK

Issues to Discuss

We have an EU commitment to reduce GHGs by 80% by 2050.

Implications of such a commitment depend on what other countries do.

We look at these implications for a variety of possible coalitions with different commitment to reduce emissions.

A key instrument in achieving the 80% reduction will be taxes on GHGs. One question that comes up a lot is the distributional implications of these taxes.

We look at the distributional implication of carbon taxes and pollution taxes for different EU member states.

Lastly we already have a system of implicit carbon taxes but they are very inefficient. We look at the extent of this inefficiency to see what kind of changes we will need to make to get a unified carbon tax system in place.

2Date: meeting

Meeting the 2050 Goal – Different Scenarios

1. Introduction

We consider different coalitions for reducing GHGs and examine their impacts in terms of: Emissions of GHGs

Energy

Carbon leakage Industrial and Terrestrial (ICL/TCL)

Land use

Price of food

Climate system

4Date: meeting

5

2. Model:

Global Change Assessment Model (GCAM): an Integrated Assessment model that links the world’s energy, agriculture and land use systems with a climate model.

• GCAM was one of the four models chosen by IPCC to create one of the main scenario (RCP 4.5) for the IPCC’s AR5 (Thomson et al. 2011).

• GCAM model can track not only fossil fuel and industrial emissions but also terrestrial emissions associated to land use change.

• GCAM contains detailed representations of technology options for each of its economic components with technology choice determined by market probabilistic competition (Clarke and Edmonds 1993).

More info: http://wiki.umd.edu/gcam/

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3. Scenarios (1/3): different coalitions

Scenarios

Scenario Participating Regions

REF None

FR1 EU-27

FR2 EU-27 + US

FR3 All Developed

FR4 All Developed + China

FR5 All Developed + BASIC

FR6 All, except Africa, Russia, Middle East

BASIC (Brazil, South Africa, India and China) group was

formed by an agreement on 28 November 2009.

Share of the global total CO2 emissions by

scenario, 2050

7

3. Scenarios (2/3): temporal commitments

First Time Period:

• Developed countries follow EU if inside the agreement: then they reduce emissions by 80% in 2050.

• Developing countries follow China: peak emissions in 2030.

Second Time Period:

• From 2030 for developing countries and from 2050 for developed countries.

• Common but differentiated convergence approach.

• Developed and developing countries converge towards an equal per-capita emissions levels in 2100 [0.5 tons of CO2-eq to meet the 2 C target]. (Could be linked to the ambition mechanisms of Paris).

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4. Scenarios (3/3): emissions per capita (tCO2pc)

9

5. Results: energy system

The main channel for ICL is the change in fossil fuel prices, which decrease with the increasing size of coalition.

Global Fossil Fuel Price Index,2050 (2010=100)

Global Energy Mix, 2050 (EJ/y)

10

5. Results: global land-use change

There is an incentive to trade products from land with low carbon density (e.g. crop land) for products from land with high carbon density (e.g. forest) as CO2 from land has now a market price.

Evolution of Global Forest Area (Mkm2)

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5. Results: regional land-use change

As a result of the same factors there will be also an incentive for deforestation in non-participant regions.

Africa, Russia and ME

Evolution of Regional Forest Area (Mkm2 wrt to REF)

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5. Results: side-effects on the price of food

There is a important trade-off to pay from afforestation: an important increase in the price of food.

The highest increases in prices are in the price of animal products such as beef and poultry.

Global Food Price index (Base 2010=100)

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5. Results: climate system (1/3)

Price of CO2 increases with participation as global mitigation effort will also increase.

In scenarios FR4, FR5 and FR6 there will be two CO2 markets (developing and developed ) and, therefore, there will be two prices for CO2.

CO2 prices by regions ($/tCO2 US$2010)

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5. Results: climate system

Unilateral action from developing countries will have a small impact.

It will be very difficult to met the 2 degree target if some countries never join to the coalition (unless negative emission technologies or Bioenergy in combination with Carbon Capture & Storage would be available)

Global temperature change, 2010-2100 (ºC)

1. Carbon Leakage

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Fragmented climate policy -> carbon leakage (Böhringer et al. 2012)

Carbon leakage: carbon regulation/price in some countries could increase the carbon emissions of non-participatory countries.

Main channels: change in the price of fossil fuels (ICL, Calvin et al. 2009) and changes in competitiveness (Rutherford et al 1995).

However, there is another channel has received little attention: the carbon leakage triggered by land use changes or “terrestrial carbon leakage” (TCL)

Literature on TCL: Wise et al. (2009), Calvin et al. (2010, 2014) and Kuik (2014)

No previous study has analyzed the “industrial” (ICL) and “terrestrial” (TCL) channel in combination (Otto et al. 2015). We explore these two forms of leakage when emissions from both sources are taxed identically.

Our analysis does not cover carbon leakage due to change in competiveness.

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5. Results: regional carbon leakage (GtCO2, wrt REF, 2010-2050)

Africa, Russia and MEAfrica, Russia and ME Africa, Russia and ME

Africa, Russia and MEAfrica, Russia and MEAfrica, Russia and ME

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5. Results: global carbon leakage (GtCO2)

The relation between leakage (in absolute terms) and the size of the coalition implementing the climate regime shows an inverted-U shape.

TCL is relevant for the period 2020-2050, when land-use change take place. However, in the longer period 2020-2100 and once the carbon storage potential of afforestation is fully exploited, the ICL effect dominates.

Global carbon leakage (GtCO2) for 2020-2050 (left) and 2020-2100 (right)

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5. Results: carbon leakage rate (%)

Carbon leakage rate: % of the reduction shifted to non-participants.

ICL is below 30%, but TCL is much larger due to the afforestation/deforestation processes.

Total carbon leakage rate decreases with the size of the coalition from 24% in FR1 to 5% in FR6.

Cumulative carbon leakage rate (%) for 2020-2050 (left) and 2020-2100 (right)

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6. Conclusions

1. A large coalition is needed to get us close to the 2°C target.

2. Fragmentation takes us for from that target.

3. Fragmentation in terms of coalitions can lead to relevant carbon leakage effects, especially if terrestrial carbon leakage is considered.

4. Fragmentation can hurt vulnerable countries (even if they do not participate in the climate coalition), because it induces deforestation in those regions and increases (remarkably) the global price of food.

5. The dominant type of carbon leakage up to 2050 is the terrestrial channel, although industrial carbon leakage takes over during the second half of the century, once the carbon storage potential of afforestation is fully exploited.

Distributional Implications of Carbon and Pollution Taxes

Who pays for mitigation policies?

Most studies find regressivity in GCCrelated taxes, but this conclusion cannot betaken as a rule because it depends on thecase study.

GCC tax

LAP Tax

Here, we conduct a distributional analysis of an LAP tax (based on the

internalization of the external costs of several pollutants) and compare it in a comprehenive way with a GCC tax

(tax on CO2)

The distributional implications of a revenue-neutral tax reform are also explored.

Environmental taxes allocated to

producers

∆ Consumer prices

Income impacts of

households

INPUT-OUTPUTMODEL

DEMAND MODEL (AIDS)

Methodology

Scenarios Description Tax Equivalent

GCC tax Tax on CO2 emissions levied on

producers.

€25/t CO2

LAP Tax Tax on NH3, NOX, SO2, NMVOC,

and PM10 emissions levied on

producers.

We use the external

cost of CASES project

but only internalize

47.2% of external

costs

Revenue-Recycling Reduction in social security

contributions paid by employers

7.5% reduction in SS

contributions

Scenarios

0,00 2,50 5,00 7,50 10,00

Electricity, water and gas production

Food Sector

Energy sector

Industries

Mining and quarrying

Sanitary and vetinary activities; social services

Real estate activities and entrepreneurial services

Education

Financial intermediation

Homes that employ domestic staff

top

5B

ott

om

5

GCC tax LAP tax

Change (%) in production prices. Top and bottom sectors

0,00%

0,20%

0,40%

0,60%

0,80%

1,00%

1,20%

1,40%

1 2 3 4 5 6 7 8 9 10

LAP tax GCC tax

Cost impacts change (EV, %) by expenditure deciles

The importance of consumption pattern

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1 2 3 4 5 6 7 8 9 10

Other services

Restaurants and hotels

Education

Leisure and culture

Communication

Transport

Health

Home furnishing and home maintenance

House

Clothing and footwear

Alcoholic beverages, tobacco and narcotic

Food

Second exercise:Revenue recycling

-1,5 0,0 1,5 3,0 4,5 6,0 7,5 9,0

Electricity, water and gas production

Food Sector

Energy sector

Industries

Mining and quarrying

Sanitary and vetinary activities; social services

Real estate activities and entrepreneurial services

Education

Financial intermediation

Homes that employ domestic staff

top

5B

ott

om

5

GCC tax LAP tax

The impact of revenue recycling on price change

0,00%

0,10%

0,20%

0,30%

0,40%

0,50%

0,60%

0,70%

0,80%

1 2 3 4 5 6 7 8 9 10

LAP tax GCC tax

Cost impacts after recycling per expenditure group

Conclusion

•LAP taxes are more regressive than GCC taxes. LAP is more linked togoods that are consumed by low incomes groups than GCC taxes, whereits potential regressive effect is compensated by the higher consumptionof transport and energy of the higher income groups.

•The result does not improve with the revenue-recycling effect because,again, the beneficiaries of this policy are labour-intensive and non-polluting goods that are consumed proportionally more by high incomegroups.

•Although these results are of course an empirical matter, they can beextrapolated to countries with similar production and consumptionprofiles.

•Although it was thought that LAP taxes might be easier to implementbecause their effects (mainly on health) are felt more immediately bycitizens and by low-income households than those of GCC taxes, this maynot be the case if the distributional issue is factored into the policymaker’s equation

•If it is wished to correct the distributional effect of this type of tax reformthe standard approach, i.e. reducing taxes on labor, may not improve thedistributional effect. However given that the overall regressivity of thesetaxes is low, various specific combinations of policies could be design tocompensate the households or groups that are most affected.

Policy implications:

Inefficiencies in the Taxation of Carbon

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How do we measure static efficiency?

Ideally, the static efficiency is assessed in terms of how successful the current policy mix is in equalising the marginal abatement cost across sectors and across emitters.

This measure is typically approximated through the carbon price: the policy mix is statically efficient if it succeeds in generating a uniform carbon price across sectors and emitters.

Carbon prices can be explicit, such as the carbon price of the EU ETS, or implicit.

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Implicit carbon price: financial support by technology divided by the amount of CO2 emissions avoided.

Example: average support is 50 €/MWh and emissions account for 0.5 tCO2/MWh. This implies an implicit carbon price of 100 €/tCO2.

Average financial support by technology is obtained from CEER database.

The amount of CO2 avoided:

National mix (excluding renewables)

EU mix (excluding renewables)

Natural gas

RES support mechanisms in the electricity sector

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Hydro Wind Biomass Biogas PVGeo-

thermalWaste

Czech Republic 83.2 21.1 59.3 166.2 790.4 :: ::

France 133.2 385.2 536.8 420.7 5381.0 :: ::

Germany 67.4 77.6 228.6 :: 733.8 294.5 ::

Italy 149.9 142.1 224.8 :: 759.5 153.8 ::

Netherlands 224.9 185.4 171.0 :: 890.2 :: 111.3

Spain 124.8 129.2 219.8 :: 1134.3 :: 84.5

United Kingdom 131.0 145.4 129.5 127.6 416.7 :: ::

RES-E support mechanisms: Implicit carbon price (€/tCO2) (2010). National Mix

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Hydro Wind Biomass Biogas PVGeo-

thermalWaste

Czech Republic 97.5 24.7 69.5 194.8 926.2 :: ::

France 22.9 66.3 92.3 72.4 925.7 :: ::

Germany 66.5 76.6 225.6 :: 723.9 290.5 ::

Italy 149.9 142.0 224.7 :: 759.2 153.8 ::

Netherlands 183.7 151.5 139.7 :: 727.2 :: 90.9

Spain 82.1 85.0 144.6 :: 746.3 :: 55.6

United Kingdom 117.1 129.9 115.8 114.0 372.5 :: ::

RES-E support mechanisms: Implicit carbon price (€/tCO2) (2010). EU Mix

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Hydro Wind Biomass Biogas PVGeo-

thermalWaste

Czech Republic 143.2 36.3 102.0 285.9 1359.8 :: ::

France 33.6 97.3 135.6 106.2 1359.0 :: ::

Germany 97.7 112.5 331.2 :: 1062.8 426.5 ::

Italy 220.0 208.5 329.9 :: 1114.5 225.8 ::

Netherlands 269.8 222.4 205.0 :: 1067.6 :: 133.5

Spain 120.6 124.8 212.4 :: 1095.7 :: 81.7

United Kingdom 172.0 190.8 170.0 167.4 546.9 :: ::

RES-E support mechanisms: Implicit carbon price (€/tCO2) (2010). Natural Gas

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Implicit carbon price: excise tax per unit of energy product divided by the CO2-eq emissions per unit.

Example: excise tax is 0.5 €/litre and emissions account for 2 kgCO2/litre. This implies an implicit carbon price of 250 €/tCO2.

Taxes are obtained from the IEA and emission factors from the IPCC.

Energy taxes

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Energy taxes: Implicit carbon price (€/tCO2) (2012).

Electricity Natural gas Diesel Unleaded

gasoline

Light

fuel oilLPG

Industry Households Industry Households

Czech Republic 1.91 2.03 6.03 0.00 163.61 222.40 9.86 53.10

France 212.41 299.49 7.23 5.50 151.10 248.71 20.86 37.09

Germany 72.67 160.52 19.95 27.23 175.71 270.36 22.94 56.87

Italy 196.11 152.04 21.79 ::: 230.74 316.01 151.28 90.87

Netherlands 30.84 18.77 13.37 84.42 162.02 318.20 ::: 58.11

Poland 6.12 6.12 0.00 0.00 129.19 175.54 20.73 68.25

Spain 21.85 36.76 0.00 0.00 133.26 191.20 31.20 19.78

United Kingdom 7.55 0.00 4.40 0.00 263.54 312.09 50.62 :::

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What happens if we include other externalities?

We include data from the IMF on air pollution, accidents…

Excise tax (€/litre)

local air pollution (€/litre)

congestion(€/litre)

Accidents (€/litre)

road damage (€/litre)

Implicit Carbon Price

(€/tCO2)

Czech

Republic 0.4366 0.139 0.178 0.103 0.041 -9.670

France 0.43 0.128 0.391 0.101 0.039 -84.400

Germany 0.47 0.154 0.302 0.077 0.026 -33.069

Italy 0.606 0.146 0.217 0.116 0.026 37.942

Netherlands 0.44028 0.121 0.409 0.068 0.010 60.408

Poland 0.35461 0.098 0.107 0.164 0.012 -13.215

Spain 0.361 0.164 0.343 0.085 0.030 -94.172

United

Kingdom 0.67415 0.091 0.356 0.050 0.026 72.019

DIESEL

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Excise tax (€/litre)

local air pollution

congestion accidentsImplicit

Carbon Price (€/tCO2)

Czech

Republic 0.511 0.008 0.189 0.151 70.973

France 0.604 0.008 0.373 0.112 47.289

Germany 0.655 0.008 0.287 0.085 113.385

Italy 0.717 0.007 0.207 0.129 163.110

Netherlands 0.736 0.007 0.390 0.076 113.887

Poland 0.398 0.009 0.114 0.239 15.657

Spain 0.457 0.015 0.326 0.094 8.866

United

Kingdom 0.715 0.004 0.339 0.056 137.781

GASOLINE

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Conclusions

There is little harmonisation in the promotion of renewables and energy taxes at the EU level.

The implicit carbon price set by the different policies, vary widely.

In the long-term, in order to maximize the static efficiency of the EU climate instrument mix, the implicit carbon price set by the different policies should convergence with the carbon price of the EU ETS.

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