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What is mitigation potential? 

The concept of “mitigation potential” has been developed to assess the quantity of net greenhouse gas emission reductions that can be achieved by a given mitigation option relative to specified emission baselines.1 For the purpose of the GMPA pilot, we apply the definitions provided in the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (see also figure below for illustrating estimated potentials at global scale). 

Biogeophysical potential 

The mitigation potential constrained by biological, geophysical and 

geochemical limits and thermodynamics, without taking into account 

technical, social, economic and/or environmental considerations. 

Technical potential 

The mitigation potential constrained by biogeophysical limits as well as 

availability of technologies and practices. Quantification of technical 

potentials takes into account primarily technical considerations, 

but social, economic and/or environmental considerations are 

occasionally also included, if these represent strong barriers for the 

deployment of an option. 

Economic potential 

The portion of the technical potential for which the social benefits 

exceed the social costs, taking into account a social discount rate and 

the value of externalities. 

Data collection and modeling in the pilot phase focused on defining Technical potentials at specific cost levels, accounting for capital and operational & maintenance cost. Biogeophysical potentials were collected and calculated first and provide input to the modeling of Technical potentials. Economic potentials follow from the full scope of Technical potentials by identifying the point where benefits balance costs. While this project focuses on national potentials and potentials from collaboration between nations, the IPCC figure below illustrates at a global scale that for Wind and Solar energy in particular, large emissions reductions can be achieved at net zero cost. 


Figure 1: Overview of mitigation options and their estimated ranges of costs and potentials in 2030 at the global scale.  

Figure SPM.7 from IPCC (2022) "Mitigation of Climate Change" Working Group III Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change.

To visualize the mitigation potential at different price points, and the benefits of international collaboration, GMPA uses a new type of diagram called the “Dynamic Pareto Abatement Cost Curve”, or the D-PAC Curve. The D-PAC curve is very similar to the traditional Marginal Abatement Cost (MAC) Curve, but it has a few changes. The MAC curve allows users to select an amount of total abatement, and find the marginal abatement cost and technology mix. The MAC curve similarly allows users to select a marginal abatement cost, and find the total abatement and technology mix. However, the MAC curve assumes that all measures are independent, and does not model any interplay. 

The D-PAC curve also accounts for interplay, but looks at the cumulative costs and emissions instead of the marginal costs and cumulative emissions like the MAC curve. Figure 2 shows a schematic of a D-PAC Curve. The outline of the D-PAC curve is the pareto frontier and represents the trade-off between total system emissions and total system costs. Users can either start with a “carbon budget” and find the corresponding minimum system cost to meet that carbon budget as in Figure 2. The vertical slice is the technology mix of the optimal system that meets that emissions budget. 


Figure 2 

Similarly, users can select the “cost budget” for the total system costs and read the minimum system emissions to meet the cost budget; this is shown in Figure 3. With this users can see how certain technologies get used more or less in the optimal energy mix as the emissions or costs of a system are changed. 

Figure 3 

With the D-PAC curve, users can also add a baseline point. In GMPA, the baseline point used is the Current Energy Mix projected to future energy demand. Users can then plot a line from the baseline current energy mix point to a specific point on the graph. The mitigation potential is difference in emissions between the baseline and the new point. The additional cost is the cost difference between the new point and the baseline system. The additional average abatement cost is the ratio of the additional cost and the mitigation potential. This is shown in Figure 4. If the gradient of the line is zero, then the additional cost is zero, and the mitigation potential is the mitigation potential at zero additional cost.  

Figure 4 

Note that the additional average abatement cost is different to the marginal abatement cost. To get the marginal abatement cost at a point, users can find the gradient of the graph at a point. Note also that the maximum mitigation potential is thus the difference between the no-action total system emissions and the minimum emissions possible as in Figure 2. 

National and international modelling was performed using STEVFNs energy system model-generator. Modelling was performed by setting annual emissions limits and finding the cost-optimal technology mix that meets all hourly demands with the emissions constraints.  

This modelling assumes a greenfield model built in 2050 to meet fixed electricity and high-temperature heating demand, minimizing the net present value of a 30-year project, i.e. from 2050-2080.  

First, a baseline case study for no action was set up for the Pilot countries with their current energy mix, determining the no-action emissions on its own (autarky). From this baseline, reductions towards zero emissions were determined that would constrain the scenarios. A total of eleven scenarios with emissions constraints ranging from those obtained for no action to zero were run to build the cost-optimal technology mixes shown in the Dynamic Pareto Abatement Cost Curve (D-PACC). 

After determining the total mitigation potential in autarky for each country, two additional case studies were created:  

  1. A combination of countries in autarky, with maximum total emissions defined as the sum of their individual autarky emissions   

  1. A combination of countries collaborating (adding electricity and ammonia transport between them), with maximum total emissions defined as the total emissions as one collaboration.  

Then, the methodology implementing emissions constraints in eleven scenarios with respect to the sum of the set of countries no action emissions were set up. Results from these then build the D-PACC for collaboration and for the set of countries without collaboration. In some cases, the set will not be able to reach zero emissions in the modelled sectors when modelled independently, as their individual reductions make the problem infeasible at some level of constraint for total emissions. These, however, may reach zero (or at least higher emissions reduction) when energy trade is enabled through green electricity and ammonia trade. 

Electricity and high-temperature heating demand projections were obtained from 7th edition APEC Energy Outlook reports combined with IEA data as used in the OSeMOSYS CCG starter data kits. Technology capital and operational costs were translated from detailed OSeMOSYS CCG starter data kits where available. Data for other technologies were estimated based on literature figures for technologies, including high-voltage direct current (HVDC) submarine cables. Please note that costs refer to the capital cost and the operational and maintenance cost. It does not include carbon taxes/ subsidies, or non-economic costs such as damage to flora/fauna and human health, cost of environmental damage. 

In the pilot, additional “detailed national modelling” was performed using OSeMOSYS energy system model-generator. These are supposed to emulate potentially different energy system models currently used by different countries. In future phases of GMPA, countries will be encouraged to share their national models and data. These will be translated to the generalized STEVFNs system-of-systems model-generator.   

This is done for the following benefits:   

  1. Reduce barrier to entry for countries to engage with GMPA.   

  1. Leverage on national modeling efforts around the world instead of repeating modelling efforts.   

  1. Have a scalable, distributed method of managing and updating national modelling data.   

  1. Ensure the input assumptions on GMPA are “owned” by countries so that they are comfortable enough with the results for the results to be useful as a basis to bring parties onto the negotiation table.   

In the pilot, a detailed national modelling was performed for each country in the Pilot by building a “0th order” OSeMOSYS “starter data kits” using the methodology developed by Climate for Compatible Growth (CCG) that is applied to more than 60 countries around the world. The model determines the least cost optimal technology mix pathway from 2015-2070 to meet all end-use energy demands given some emissions constraints. The sectors included are power, transport, industry, household, and commercial sectors.   

The D-PAC Curves for detailed national modelling were developed by setting annual emissions constraints for 2050. The annual emissions from 2025 to 2050 were constrained to reduce linearly to the 2050 values. The emissions were assumed to be constant from 2050-2070. The value on the x-axes is the annual emissions constraint at 2050. The value on the y-axes of the stacked charts and the bar charts is the net present value of the money spent on each technology from 2015-2070. Note that this means that there will be some investments in fossil fuel technologies even when the pathway leads to net zero by 2050. While this may seem less intuitive than the results of the greenfield models, they are more representative of the difference in cumulative pathway costs rather than the costs for a specific year.   

The results on the D-PACC for the “detailed national modelling” are slightly different from the data presented on the D-PAC Curves for the rest of the modelling. This is done in the pilot to demonstrate that different information can be presented using the same visualization. The aims of presenting different types of results with slightly different interpretations are as follows:   

  1. Get feedback from users to understand which kind of model results/ assumptions are more useful for planning and negotiations.   

  1. Provide alternative methods of looking at the problem to check if both methods give the same policy recommendation and insights. This is inspired by similar methods used by physicists to interpret results for “first picture of a black hole”.   

  1. Give country experts access to results from models that they are familiar with so that they can compare and be comfortable with the results for STEVFNs for individual countries before looking results from international collaboration models. 

The D-PAC Curves for the detailed national modelling can be obtained by contacting the GMPA team. 

Main objective of baseline policy mapping is to incorporate information on potential levers for accelerating action and barriers that need to be overcome at the national level. This will inform what are the additional policies needed to harness full mitigation potential of various interventions and an important step to link with case study analysis.


Nascimento et al (2021)[1] presents a useful framework for mitigation policy mapping at the national level. Please find below the framework from that paper.

Figure 1: Framework for baseline policy mapping. Source Nascimento et al (2021)

Analytical approach:

1.     Identify and collect relevant climate policies.

2.     Categorise the policies according to sectors and instruments.

3.     Define a matrix of policy options as a comprehensive policy package per sector.


Policy options:

Climate change mitigation policy instruments have long been grouped into three main categories – (i) economic instruments, (ii) regulatory instruments, and (iii) other instruments (IPCC, 2022)[2].


Please find below policy options that can be presented in the policy matrix in more detail in the country context. These options can be further grouped into the sectoral policies and analyse cross-sectoral linkages.

Table 1 policy instruments database to be considered for the baseline mapping

To develop a comprehensive database of illustrative case studies, we have followed several key steps. Initially, a selection of global case studies has been identified, with a primary focus on interventions within the electricity and transport sectors during the pilot phase. These case studies should encompass diverse policies implemented in various economies to provide a well-rounded compilation that considers regional and socio-economic differences.


These case studies should be linked to additional policy needs at the national level, aligning them with the estimated mitigation potential for various mitigation options determined by the modelling results. This holistic approach ensures that the database serves as a valuable resource for informed policy decisions and effective mitigation strategies.


Selection of case studies

When selecting case studies, focus was on policies with the primary objective of climate change mitigation. This can include:

      i.         Regulatory instruments such as energy efficiency standards, emission standards, and land-use controls.

     ii.         Economic instruments such as carbon taxes, emissions trading, and subsidies.

    iii.         Education, institutional support and other instruments.


Framework for developing library of case studies

The proposed framework for developing illustrative case studies has been adopted from IPCC AR6 (IPCC, 2022)[1].


             I.         Objective of the policy: This should capture the intended (sector) outcome of the policy.

            II.         Policy package: This should identify a well-coordinated mix of policy instruments and governance actions.

          III.         Impact of the policy package: Assess the effectiveness of the policy, categorizing it as high impact, limited impact, or no impact.

          IV.         Governance context: This should highlight the importance of context-specific governance factors both as enabler and barrier. 

           V.         Recommendation: This should highlight what is needed to close the policy gap at national level.


Policy impact analysis – lock-in and path dependence

The concept of lock-in, revolves around the idea of a technological pathway or system becoming self-reinforcing, leading to increased advantages for incumbent technologies over new entrants. This phenomenon, rooted in the concept of increasing returns, eventually results in path dependency, constraining the choices of various actors, institutions, and networks across multiple domains, including technological change, economics, political science, and institutional change.[2]

While analysing the case studies we have primarily focused on three types of lock-in, technological, institutional, and behavioural, each with distinct mechanisms influencing path dependence in socio-technical transitions.[3]

Technological lock-in: Technological lock-in is driven by economies of scale, where earlier investments spread across increasing production volumes result in increasing returns. This is evident in mega-projects like electricity or transport systems.

Institutional lock-in: Institutional lock-in involves collective action mechanisms arising from consumption patterns, norms, and customs. High-density institutions and the differentiation of power contribute to institutional lock-in, with political actors playing a significant role in imposing rules.

Behavioural lock-in: Behavioural lock-in is rooted in irreversibility due to learning and habituation, where consumers or producers become "stuck" with a product or process. Cognitive costs of switching, habituation, and informational increasing returns contribute to behavioural lock-in, with consumers preferring earlier gains over future efforts.