Climate policy/Policy issues

Component is implemented in:

FAIR model (version Fair-SimCap 3.0) Components: Climate policy

Related IMAGE components

Atmospheric composition and climate Carbon, vegetation, agriculture and water Drivers Emissions Energy conversion Energy supply Energy supply and demand

Models/Databases

AD RICE model IIASA database MAGICC model TIMER model

Key publications

Den Elzen et al., 2016 Hof et al., 2017 Van den Berg et al., 2020

References

Admiraal et al., 2016 Den Elzen and Höhne, 2010 Den Elzen and van Vuuren, 2007 Den Elzen et al., 2007 Den Elzen et al., 2011b Den Elzen et al., 2012a Den Elzen et al., 2012b Den Elzen et al., 2012c Den Elzen et al., 2015a European Commission, 2010 Harmsen et al., 2016 Hof et al., 2008 Hof et al., 2010 Hof et al., 2011 Hof et al., 2012 Hof et al., 2013 Hof et al., 2016 Höhne et al., 2012 Kuramochi et al., 2016 Mendoza Beltrán et al., 2011 Roelfsema et al., 2013 Roelfsema et al., 2014 Rogelj et al., 2016 UNEP (2016) UNEP, 2012 Van Vliet et al., 2012 Van Vuuren et al., 2011a Van Vuuren et al., 2012 Van den Berg et al., 2015

Flowchart Climate policy. See also the Input/Output Table on the introduction page.

Baseline developments

FAIR can be used to analyse baseline developments, such as expected climate change damage. However, more often baseline developments are explored using the larger IMAGE framework, and the FAIR model receives this information as input for policy analysis.

As part of the IMAGE framework, FAIR can be used to evaluate a range of policies and strategies, including:

• Long-term mitigation strategies such as emission reductions over time (Den Elzen et al., 2007; Den Elzen and van Vuuren, 2007; Van Vliet et al., 2009; Van Vuuren et al., 2011b; 2012);

• Evaluation of current reduction proposals by countries and policy options until 2030 (Den Elzen et al., 2011a; 2011b; 2012b; Hof et al., 2013; UNEP (2016); Den Elzen et al., 2016);

• Evaluation of domestic climate and energy policies for the next 10 to 20 years (Höhne et al., 2012; Roelfsema et al., 2013; 2014; Den Elzen et al., 2015a; Kuramochi et al., 2016);

• Evaluation of burden sharing or effort sharing regimes (Den Elzen and Höhne, 2010; Den Elzen et al., 2012a; Hof et al., 2012; Hof et al., 2016);

• Analysis of regional abatement costs and emission trading (Den Elzen et al., 2008; Den Elzen et al., 2011a; Mendoza Beltrán et al., 2011);

• Evaluation of proposals for financing climate policies (Hof et al., 2009; 2011);

• Evaluation of trade-offs between mitigation costs, adaptation costs and the benefits of reduced climate damage (Hof et al., 2008; 2009; 2010); Admiraal et al., 2016);

• Assessment of the policy costs, emission profiles and climate effects resulting from the use of different climate metrics such as the GWP (Van den Berg et al., 2015; Harmsen et al. (2016));

The FAIR Policy Evaluation module has been used in determining emission reductions resulting from pledges made for 2020 (Den Elzen et al., 2012c). In 2011, Brazil presented a new, higher estimate for national business-as-usual (BAU) emissions, against which a 36 to 39% reduction pledge was made. The total pledge for all greenhouse gas emissions including emissions from deforestation was a reduction of 20 to 24% compared to the PBL/IIASA BAU emission projections. This reduction is substantially lower than pledged by Brazil from national BAU projections .

As shown in the figure below, all reductions result from reduced emissions from deforestation (REDD). The contributions from REDD projects (about 560 MtCO2) are expected to exceed or match the required total reduction in all greenhouse gas emissions of 470 and 570 Mt CO2eq for the 36% and 39% reduction pledge scenarios.

The national projection is from the National Decree No. 7390, and the WEO 2010 projection is from the World Energy Outlook (2010) of International Energy Agency.

Policy interventions

The Global Pathfinder module FAIR-SiMCaP was used to determine what the pledges for 2020 and the INDC targets (mainly for 2030) imply for global emission pathways consistent with meeting the 2 °C target (Van Vliet et al., 2012, UNEP (2016), Den Elzen et al., 2016;). The main findings were as follows (see also the figure below):

• The global 2020 emission level resulting from implementation of the Copenhagen Accord pledges exceeds those of least-cost pathways that achieve a 2 °C target;
• Slightly postponing mitigation action compared to the least-cost scenario seems technically feasible but at higher cumulative discounted mitigation costs;
• Despite the reductions from the INDCs, the global and G20 emission level is still projected to be higher in 2030 than it was in 2010.
• The 2030 emissions are expected to reach 54 to 56 gigatonnes of carbon dioxide equivalent – far above the level of 42 needed to have a chance of limiting global warming to 2 degrees Celsius this century.

• For an even longer delay (the current Copenhagen scenario), the FAIR-SiMCaP model cannot fully compensate the higher emission level in the short term;
• A delay in emission reductions limits the flexibility in the portfolio of emission reduction options. Such delayed scenarios rely more on the use of bioenergy with carbon capture and storage (BECCS), an option with uncertain prospects for large-scale implementation.

Scenario results describing emission pathways representing optimal and delayed policy action (Copenhagen pledges) in 2020, in terms of CO₂ emission (including land use), associated radiative forcing (including all gases and aerosol forcing), and global mitigation costs (as percentage of GDP).

Effects of policy interventions on this component

Policy intervention	Description	Effect
Carbon tax (*)	A tax on carbon leads to higher prices for carbon intensive fuels (such as fossil fuels), making low-carbon alternatives more attractive.	In the climate policy component, the carbon tax leads to emission reductions via Marginal Abatement Cost (MAC) curves derived from the TIMER model.
Climate change adaptation (*)	Adaptation to climate change reduces climate damage. The model can optimally calculate the optimal adaptation level based on marginal adaptation costs and marginal avoided damage, but an alternative adaptation level can be used as well. (Reference:: Hof et al., 2009, Hof et al., 2010)	More adaptation increases adaptation costs, but reduces the damage resulting from climate change. (Reference:: De Bruin et al., 2009)
Effort- or burden-sharing of emission reductions (*)	Evaluation of burden-sharing or effort-sharing regimes. Which regions or countries should contribute, when and by how much to reduce global greenhouse gas emissions? (Reference:: Hof et al., 2012, Den Elzen et al., 2012b, Den Elzen and Höhne, 2010)	The burden-sharing regime affects regional mitigation costs, as different burden-sharing rules lead to different emission reduction targets.
Emission trading policy (*)	Analysis of the effect of rules for trading emission credits on regional abatement costs. (Reference:: Den Elzen et al., 2011a, Den Elzen et al., 2008, Mendoza Beltrán et al., 2011)	Different rules for trading emission credits lead to different regional mitigation costs. Without any restrictions on emission trading, emission reductions take place wherever it is cheapest to do so and the differences between regional targets and actual domestic emissions is traded among regions.
Financing climate policy (*)	Developed countries could provide financial resources to assist developing countries by implementation of mitigation and adaptation policies. To mobilise these funds, several mechanisms exist, of which the effect can be analysed (Reference:: Hof et al., 2009, Hof et al., 2011)	Implementation of climate financing proposals affect regional mitigation costs, as regions have to contribute to financing. The funds mobilised can be used to finance climate policies of the least developed regions.
Reduction proposals (pledges) (*)	Evaluation of current reduction proposals by countries and policy options (for the next 10-20 years). (Reference:: UNEP (2016), Den Elzen et al., 2012a, Den Elzen et al., 2011a, Den Elzen et al., 2011b, Hof et al., 2013)	The pledges affect near-term regional emission reductions, thereby influencing mitigation costs.

(*) Implemented in this component.