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Methodology

Ambitious domestic action and equitable distribution of mitigation effort

In 2015, countries adopted the Paris Agreement, and agreed to “[…] strengthen the global response to the threat of climate change[…], including by holding the increase in the global average temperature to well below 2°C […] and pursuing efforts to limit the temperature increase to 1.5°C above pre-industrial levels” (UNFCCC 1992).

Article 4.1 of the Paris Agreement outlines key operational steps to be taken to enable the achievement of the long-term temperature goal:

  • To reach “global peaking of greenhouse gas emissions as soon as possible”
  • “To undertake rapid reductions thereafter in accordance with best available science”
  • “To achieve a balance between anthropogenic emissions by sources and removals by sinks in the second half of this century”

This establishes a mandatory requirement for all parties to take domestic action to reduce emissions in their countries. The Agreement further affirms that action taken for implementation should “reflect equity and the principle of common but differentiated responsibilities and respective capabilities (CBDR)”.

This implies that in order to make a fair contribution to meeting the Paris Agreement’s goals, developed countries need to both take domestic emissions reduction action and assist developing countries to reduce their emissions. This means that a developed country’s total NDC “fair share” action range is the total sum of domestic reductions plus support for emission reductions action overseas which can be in the form of climate finance, or other support for mitigation consistent with the Paris agreement (Climate Action Tracker 2018).

For developing countries, the 1.5°C compatible pathways are not their own domestic emission reduction targets to be achieved without support. The fair share and equity considerations embedded in the Paris Agreement imply that without support a developing country would only reduce its emissions to its “fair share” range, and the gap between this fair share range and the 1.5°C compatible domestic pathway could only likely be bridged with support from developed countries in one form or another. If a developing country’s current policy pathway lies above its fair share range then it should take further action domestically to bring its emissions to at least this range.

While there are several equity principles, pathways considered here are not aligned with a given equity principle, but are however aligned with the notion of “highest plausible ambition”: pathways that are technically and economically feasible. These pathways take into account present day characteristics, such as the current infrastructure (e.g., emissions intensity of the economy), of individual countries.

How are global Paris Agreement-compatible pathways defined?

Paris Agreement compatible pathways used in the analysis are defined in the IPCC Special Report on 1.5°C (IPCC SR1.5) as those that limit warming to 1.5°C with no or limited overshoot (<0.1°C). In these pathways, the increase of global average temperature above its pre-industrial level is limited to below 1.6°C for the whole twenty-first century and below 1.5°C by 2100 (typically 1.3°C) (IPCC 2018a).

Each country is provided with a set of illustrative pathways as well as a 1.5°C compatible range for total greenhouse gases (GHG) and CO₂ emissions, excluding LULUCF.

While any one global pathway as defined above is internally consistent with the 1.5°C limit, an assessment of multiple pathways is not. As an example, consider the archetypal pathways described above. Following a constructed emissions scenario at the higher end of this range, i.e., resulting from the delayed pathway in the near term and the immediate pathway in the long term, would not necessarily limit warming to 1.5°C as either individual pathway achieved. Therefore, from the full set of 1.5°C pathways, we highlight a number of ‘illustrative’ pathways per country to further interrogate technological and sectoral specific transformations that enable reaching this climate goal.

National emissions pathways are based on global least-costs pathways assessed by the IPCC Special Report 1.5°C and selected based on criteria as described in the following section.

Underlying global pathways used in the analysis

Sustainability criteria

All modelled pathways that limit warming to 1.5 °C rely on CDR either to offset so-called “hard to abate” sectors where abatement is in an earlier phase than in other sectors (such as agriculture, industrial processes or aviation) or, if warming exceeds 1.5 °C, to bring global temperature back down to a safer level.

However, the amount of CDR required will depend on the pace of global progress in reducing emissions; early action to rapidly decarbonise and reduce the overall need for CDR will be essential. While measures to reduce emissions often come with co-benefits for society (for example, improved energy access, lower costs, cleaner air), the same is not true for many CDR options. If deployed at a larger scale, CDR technologies would entail negative side-effects across different dimensions of sustainable development objectives. Their technological and economic viability have not been proven yet and limited progress has been observed in planning and deploying them at national levels (Fyson et al. 2020).

The IPCC SR1.5 finds limits for a sustainable use of both carbon dioxide removal (CDR) options globally by 2050 to be below 5 GtCO₂ p.a. for bioenergy with carbon capture and sequestration (BECCS) and below 3.6 GtCO₂ p.a. for sequestration through afforestation and reforestation (AR) while noting uncertainty in the assessment of sustainable use and economic and technical potential in the latter half of the century (Fuss et al. 2018; IPCC 2018b).

In this context, we assess that a selection of 11 scenarios from the IPCC SR1.5 are in line with sustainability limits as described above. We apply the BECCS limitation in 2050 and the AR limit as an average over the second half of the century, noting that forestry-related sequestration can exhibit interannual variability. These will be the underlying models used for the construction of the 1.5°C compatible range.

Model Scenario SSP Source
IMAGE_3.0.1 SSP1-19 SSP1 IMAGE 3.0
MESSAGE-GLOBIOM_1.0 SSP1-19 SSP1 MESSAGE-GLOBIOM
AIM_CGE_2.0 SSP2-19 SSP2 AIM Hub
AIM_CGE_2.1 TERL_15D_LowCarbonTransportPolicy SSP2 AIM Hub
C-ROADS-5.005 Ratchet-1.5-noCDR SSP2 C-Roads
C-ROADS-5.005 Ratchet-1.5-noCDR-noOS SSP2 C-Roads
IMAGE_3.0.1 IMA15-LiStCh SSP2 IMAGE 3.0
MESSAGE-GLOBIOM_1.0 SSP2-19 SSP2 MESSAGE-GLOBIOM
REMIND_1.7 CEMICS-1.5-CDR8 SSP2 REMIND
WITCH-GLOBIOM_4.4 CD-LINKS_NPi2020_400 SSP2 WITCH

Selecting and defining the illustrative pathways

Out of the subset of 1.5°C pathways we highlight a number of ‘illustrative’ pathways per country to further interrogate technological and sector-specific transformations that enable reaching this climate goal. The provision of these pathways, in addition to the broader 1.5°C compatible range, is essential for three reasons.

First, the selection of illustrative pathways guarantee compatibility with the Paris Agreement when assuming that all countries follow a specific pathway – that is, each scenario presents an internally consistent trajectory to arrive at 1.5°C. Integrated assessment models allocate the global emissions reductions across regions differently, and as a consequence, the scenario which has the highest emission budget varies per country. If all countries would follow the scenario with their highest emission budget, this would lead in total to an exceedance of the 1.5°C temperature limit. Only a single, illustrative, up-to-date global pathway is guaranteed to provide a consistent estimate of all national emissions levels in line with the Paris Agreement. Any national level of emissions above/below this pathway (within the “IPCC range”) will imply other countries must do more/less than their respective illustrative levels.

Providing a deep dive into what it means to be Paris Agreement-compatible is the second motivation behind providing illustrative pathways. These pathways are assessed in detail, investigating not only the aspect of nation-wide emissions but also unravelling sectoral emissions and providing energy consumption on the primary, secondary and final energy level.

Lastly, some of the Paris Agreement-compatible pathways rely heavily on CDR or fossil carbon capture and storage (CCS), especially late in the century. Providing illustrative pathways allows for identifying sustainable emissions reduction pathways, contrasting them to other pathways that are more dependent on long-term carbon removals.

Selection criteria

From the global Paris Agreement-compatible pathways, as described in section 2, illustrative pathways are highlighted throughout the analysis with regards to emissions pathways. In order to complement the analysis with additional lines of evidence, additional pathways are considered for the energy fuel compositions. These pathways are selected based on the following criteria:

  • Minimising global warming throughout the 21st century, including both: temperature reached by the end of the century and the number of warming years exceeding 1.5°C.
  • Minimising the reliance on CDR such as either as large-scale AR, the use of BECCS or direct air capture combined with CCS (DACCS). This criterion is covered in the first selection process described in section 3.2.
  • Minimising the reliance on fossil fuels combined with CCS while these technologies have lower emissions than unabated fossil fuels, they do not allow a full decarbonisation of the plant. Additionally, they require high investments upfront and the risk of locking the countries into stranded assets, making it harder to decarbonise in the long term. Moreover, studies show that forecast costs of fossil with CCS are not competitive with renewable energy costs. So far, although there are considerable investments by some countries in power plants with CCS, they have not delivered any significant results (Climate Action Tracker 2020b; Sgouridis et al. 2019).
  • Immediate action in line with ‘highest plausible ambition’ countries have a limited carbon budget to reach the temperature limit compatible with the Paris Agreement, so the sooner they take action, the lower the risk of running out of time to implement action and remaining within that budget. It allows countries to take into account the needed investments in long-lasting energy infrastructure and the required time to transform specific end-use sectors such as transport (fleet turn-over).
  • Data availability not all pathways are provided with a granularity and availability of data suitable to downscale their energy system from regional to national level. Thus, this criterion is key in the selection of the pathways as well.

Selected pathways

As explained in section 4, global pathways are provided at macro-regional level. Depending on how well the above criteria is met in the global pathways for different macro-regions, the optimal choice may depend on the country in question. Keeping this in mind, we assessed global pathways from the IPCC SR1.5 database and selected illustrative pathways to provide the 1.5°C compatible emissions pathways at national level based on their performance at regional level.

These illustrative pathways are then downscaled to the national level as described in section 4, and are renamed for readability based on their global behaviour. Out of the subset of eleven of models derived from the first selection process, three models are retained for sectoral analysis:

  • [SSP1] [High CDR reliance] > IMAGE SSP1-19: follows a narrative based on a world of sustainability-focused growth and equality (SSP1). Technical development in the energy sector is enabled, among others, through a globally high reliance on CDR technologies and a comparatively low CO₂ price (Van Vuuren et al. 2018).
  • [SSP1] [Low CDR reliance] > AIM SSP1-19: follows a narrative based on a world of sustainability-focused growth and equality (SSP1). Technical development in the energy sector is enabled through a globally low reliance on CDR technologies and a comparatively high CO₂ price (Fujimori et al. 2017; Rogelj et al. 2018). Note: the model AIM SSP1-19 is located at the border of the sustainability criteria described above and allows a better performance for the Asian region. We have thus decided to include it in our illustrative pathways.
  • [High energy demand] [Low CDR reliance] > REMIND_1.7: is an energy-economy general equilibrium model linking a macro-economic growth model with a bottom-up engineering-based energy system model. (Luderer et al. 2013).

While assessments using integrated assessment models (IAMs) provide a consistent estimation of the minimum necessary ambition for Paris Agreement compatibility of each benchmark, additional lines of evidence are needed to fully explore the landscape of pathways to meet the Paris Agreement, including literature using bottom-up, hybrid, and sectoral models to estimate the top-end range of plausible ambition, given the lag between recent technology trends and what is included in IAMs used here.

This includes the addition of two scenarios: the MESSAGE ‘Low Energy Demand’, relying on energy efficiencies and a high uptake of renewable energy and a scenario based on the study “Global Energy System based on 100% Renewable Energy” by the LUT University and the Energy Watch Group (Ram et al. 2019). The scenario describes a feasible global transition to 100% renewable energy in the power, heat, transport and desalination sector before 2050. The scenario data is available for download in aggregated form for nine macro-regions for the years 2015 to 2050 in five-year steps.

It has to be noted that the scenario does not cover the full global primary energy consumption as it does not consider consumptions for industrial processes. As such, the primary energy consumption indicated in the scenario cannot directly be compared to the full primary energy consumptions given by the integrated assessment models. Nevertheless, the scenario clearly indicates that the primary energy consumption in all sectors considered can be fully decarbonised by renewables (Ram et al. 2019). Thus, these additional illustrative pathways are selected for the energy composition analysis:

  • Global: Low Energy Demand > MESSAGE LED: relies on a pervasive transformation of the demand side of resource systems including food, energy, land, and water exploring the impacts of digitalisation, sharing economy and behavioural change and does not build on CCS technologies (Grubler et al. 2018).
  • Global: 100%RE > EWG_LUT_Global100RE: cost-effective, cross-sectoral, global 100% renewable energy system that does not build on negative CO₂ emission technologies. Cost-optimal mix of technologies is based on local available renewables (Ram et al. 2019).

While five illustrative pathways have been selected for the full assessment of countries, their performance might vary depending on the region covered and the sector covered. Thus, while they are used for the full selection of countries, they are selected on an ad-hoc basis at country level based on their modelling performance.

Model Scenario Analysis coverage
AIM_CGE_2.0 SSP1-19 Full coverage (economy-wide + power, emissions + energy mix)
IMAGE_3.0.1 SSP1-19 Full coverage (economy-wide + power, emissions + energy mix)
MESSAGE-GLOBIOM_1.0 Low Energy Demand Energy mix only (power + primary energy)
EWG_LUT Global 100RE Full power sector + primary energy mix (not including non-energy fossil fuel demand)
REMIND_1.7 CD-LINKS_NPi2020_400 Full coverage (economy-wide + power, emissions + energy mix)

Discussions on, and limitations of global-least costs pathways

The scenarios considered were generated by Integrated Assessment Models, which are part of the IPCC special report on 1.5°C, published in 2018. As there is a delay in the publication of emission and energy consumption data, often two to three years, the majority of the historical scenario data only goes up until 2015. Scenario data thus may differ from more recent historical data in the period between 2015 and the present. We address this issue separately for emissions and consumption data as follows

  1. emission data is treated with data harmonisation routines to match historical data and
  2. for the consumption data, only projected years after 2020 are considered.

Work in progress includes collaborations with Integrated Assessment Model teams to provide scenarios which limit global warming to 1.5°C under the consideration of latest available historical data.

Beyond possible discrepancies with recent historical data, care should be taken in the interpretation of global model results. While global cost-effective pathways assessed by the IPCC Special Report 1.5°C provide useful guidance for an upper-limit of emissions trajectories for developed countries, they underestimate the feasible space for such countries to reach net zero earlier. The current generation of models tend to depend strongly on land-use sinks outside of currently developed countries and include fossil fuel use well beyond the time at which these could be phased out, compared to what is understood from bottom-up approaches.

The scientific teams which provide these global pathways constantly improve the technologies represented in their models – and novel CDR technologies are now being included in new studies focused on deep mitigation scenarios meeting the Paris Agreement. A wide assessment database of these new scenarios is not yet available; thus, we rely on available scenarios which focus particularly on BECCS as a net-negative emission technology.

Accordingly, we do not yet consider land-sector emissions (LULUCF) and other CDR approaches which developed countries will need to implement in order to counterbalance their remaining emissions and reach net zero GHG.

From global to national pathways

Illustrative pathways

The scenario data underlying 1.5°C compatible pathways specifies how future energy consumption and emissions should be composed in different regions of the world. Typically, this data is only available for regional aggregates called macro regions. For example, in the integrated assessment model IMAGE, the North Africa macro region “NAF” comprises the countries/subregions Algeria, Egypt, Libya, Morocco, Tunisia and Western Sahara. This again means that in order to determine national energy consumption and emission pathways, the data of the macro-regions needs to be downscaled to the national level.

The downscaling process itself can be broken down into several sub-steps:

  1. Defining the macro-region(s) in which the country of interest is located. The European Union is a special case, since as an agglomeration it can span over several macro-regions.
  2. Country’s historical emissions and energy consumption are determined for all countries in the macro-region(s).
  3. Future emissions and energy consumption are obtained from the scenario data underlying the to be downscaled 1.5°C compatible pathway.
  4. The macro-region’s scenario data is adapted to match the country’s historical data in a base year. This process is called harmonisation and it is required to update the pathways to the latest available historical data.
  5. The macro-region’s emissions and energy consumption are downscaled to its countries. They are distributed to the countries in an internally consistent way, which preserves total values and matches the historical value of each country.

Different methods are deployed for the downscaling process: For most sectors we employ an intensity convergence method, however, other approaches are utilised where best suited depending on the sector/emissions to be downscaled. A description of how the different sectoral emissions were downscaled, and harmonised, for the analysis performed here is given in the following.

Energy – CO₂ emissions

Energy consumptions in the energy sector in general and the power sector specifically are downscaled with the Simplified Integrated Assessment Model with Energy System Emulator (SIAMESE) method as described in Sferra et al. (Sferra et al. 2019). Through this method, energy emissions pathways are derived directly from the downscaled energy consumption at national levels brought back to emissions by applying emissions factors, allowing to obtain national emissions pathways directly consistent from the related energy system (Sferra, Schaeffer, and Torres 2018).

The method is wrapped in the general downscaling process as follows:

Downscaling energy consumption:

  • Historical primary and secondary energy consumption are obtained from the International Energy Agency (IEA) World Energy Balances (WEB) (IEA 2019b).
Consumption by fuel Determination method
Non-biomass renewable the sum over the consumption of wind, solar, geothermal and hydro energy
Biomass the difference between renewable energy consumption and the aforementioned non-bio renewable energy consumption
Coal the sum over peat and coal
Oil the sum over oil, oil products and natural gas liquids
Gas and Nuclear directly obtained from the database

It has to be noted that in the IEA database, secondary energy consumption is assigned to both electricity and heat generation. For downscaling purposes, we assumption that a majority of the consumption is dedicated to the power sector with heat generation being a by-product of centralised electricity generation.

  • The consumption projections for the macro-region(s) are obtained from the integrated assessment models IMAGE, AIM and MESSAGE as well as a 100% renewable scenario dataset (EWG LUT), see section above. For the integrated assessment models, the primary energy consumption and secondary energy consumption (electricity) are directly obtained from the available data set. For the EWG LUT scenario, the data is processed and aggregated to the consumption classes.
  • During the harmonisation process, the historical energy consumption in the macro-region(s) is set equal to the determined historical energy consumption in the subregions. The energy consumption projections are not altered.
  • The energy consumption is downscaled from macro-region(s) to countries by applying the method SIAMESE described by Sferra et al (Sferra et al. 2019). Conceptually, the model allocates energy consumption (and emissions) to the country level by maximising welfare in all countries belonging to the same macro-region under a common set of assumptions (e.g. technological availability, expected GDP and population growth at the country level, common SSP storylines).
  • To reduce the computational load of the method, the countries are aggregated into two regions before the downscaling. The first region holds the country of interest and the second region holds all other countries.

Once the consumptions are downscaled, the emissions can be determined:

  • Historical emissions are obtained from the Climate Action Tracker (CAT) as a primary source where available. For countries not covered by the CAT, the PRIMAP dataset is used for energy-related sectors and from an IEA dataset for the power sector, again with the caveat of including combustion related CO₂ emissions from both electricity and heat.
  • Emissions intensities are derived from the macro-region(s) model’s emissions datasets and their respective energy consumptions. A calibration is run to match national emission projection to the downscaled energy consumptions
  • For the energy and power sectors, emissions are obtained by applying respective emissions intensities to the downscaled energy consumptions.

It has to be noted that for the emissions in the energy sector, this process is done with the full primary energy consumption set instead of using the consumption allocated to only the energy sector.

Agriculture sector

The agriculture sector emissions on the macro-region level are collected from variables in the scenario data and harmonised to historical data. The emissions for individual subregions are determined by assuming their shares in the base year are constant over the whole scenario period, a simple downscaling methodology called base-year pattern.

Industrial processes, waste and energy non-CO₂ emissions

The macro-region emissions from industrial processes, waste and non-CO₂ emissions in the energy sector are taken from variables in the IAM scenario data and harmonised to historical data in a base year, as for the agricultural sectors described in the previous section. To perform the downscaling from macro-regions to the subregions a methodology based on intensity convergence is used; more specifically the Impact, Population, Affluence, and Technology (IPAT) method as developed by Van Vuuren et al. (2007) (van Vuuren, Lucas, and Hilderink 2007) and extended by Gidden et al (2019)(Gidden et al. 2019). It assumes that emission intensities (i.e. the ratio of emissions to GDP) will converge from their values in the historical base year to the macro region intensity in the last year of the scenario data, in the year 2100. This is made possible by an exponential interpolation of emission intensities from the base-year to the convergence year. Together with the yearly GDP by the given scenario, this interpolation defines how the emissions of the macro region are shared amongst the countries.

Economy wide GHG emissions pathways

After the emissions projections of the illustrative pathways in each sector have been downscaled to the individual countries with the methodologies described in the previous sections 5.1.1-5.1.3, they are aggregated to consistent economy-wide emissions pathways. For each country, we show the total GHG emissions in CO₂e emissions based on 100-year global warming potentials from the Fourth Assessment Report of the IPCC, as well as the CO₂ only emissions (IPCC 2007). Total CO₂ emissions cover energy CO₂ emissions and non-energy CO₂ emissions.

The 1.5°C compatible range

Each country is provided with illustrative pathways as well as a 1.5°C compatible range (blue shaded area). While the illustrative pathways are emissions pathways consistent with a downscaled energy mix from regional to national as described in section 5, the 1.5°C compatible range is derived from eleven global models of the IPCC special report on 1.5°C selected on the basis of sustainability criteria as described in section 3 (Huppmann et al. 2019).

For each of the global least-cost emission pathways, sectoral emissions are harmonised to historical emissions in the base year and downscaled from the macro region to subregions similarly to the downscaling methodology described in section 5.1 for the illustrative pathways. As in 5.1.2 the Agriculture sector emissions are distributed to the subregions based on their historical share in the base-year (the base-year pattern method) and we apply the emissions intensity convergence method to downscale the emissions from industrial processes, waste and energy non-CO₂ sectors. As described with more detail in Section 5.1.3 the latter method assumes emissions intensity in each country converges from its present-day value to the regional value for each given Integrated Assessment Models (IAM) pathway by the end of the modelled time horizon (i.e., by 2100).

In contrast to the illustrative pathways, for the pathways in the 1.5°C compatible range, the CO₂ energy sector emissions are also downscaled using the emissions intensity convergence downscaling method. In most pathways, these emissions become negative long before 2100. We apply an affine transformation to the exponential convergence model to account for the shift from positive to negative emissions in the downscaling routine.

We then assess the full distribution of downscaled outcomes to find the median (50th percentile) of country-level emissions pathway in order to form an upper-bound for Paris Agreement compatibility for each country. The lower bound is defined by the 5th percentile while we highlight the median of this range, the 25th percentile. While each illustrative pathway follows a single consistent pathway, the boundaries of the 1.5°C compatible range are based on a statistical distribution, so that the emissions of illustrative pathways may appear outside of the range.

Defining benchmarks

Economy-wide GHG and CO₂ emissions benchmarks

As referred in section 2, in order to guarantee that the derived emissions still achieve the global temperature goal when aggregated together across all assessed countries, we assess the distribution of pathways from the median until the 5th percentile, forming the higher and lower bound of the 1.5° compatible range. Delaying decarbonisation – and thus emissions reductions – comes with significant risk of carbon lock-in, high-cost, high-emission technologies as well as risk of investing in stranded assets, locking the country in a pathway harder to decarbonise in the long run. Thus, the 1.5°C compatible benchmark highlighted a central figure is the middle of the range i.e. the 25th percentile. Emission values are rounded to integers.

Defining net zero GHG and net zero CO₂ years

While global cost-effective pathways assessed by the IPCC Special Report 1.5°C provide useful guidance for an upper-limit of emissions trajectories for developed countries, they underestimate the feasible space for such countries to reach net zero earlier. The current generation of models tend to depend strongly on land-use sinks outside of currently developed countries and include fossil fuel use well beyond the time at which these could be phased out, compared to what is understood from bottom-up approaches.

The scientific teams which provide these global pathways constantly improve the technologies represented in their models – and novel CDR technologies are now being included in new studies focused on deep mitigation scenarios meeting the Paris Agreement. A wide assessment database of these new scenarios is not yet available; thus, we rely on available scenarios which focus particularly on BECCS as a net-negative emission technology. Accordingly, in the assessment of net zero GHG or net zero CO₂ years we do not yet consider land-sector emissions (LULUCF) and other CDR approaches which developed countries will need to implement in order to counterbalance their remaining emissions.

Thus, net zero GHG and net zero CO₂ years provided in this analysis are excluding LULUCF emissions (sources or sinks), including the use of BECCS as negative emissions technologies and not considering any development of novel CDR.

Net zero year values are rounded to the nearest half-decade.

Power sector benchmarks

Fossil-fuel phase-out: gas and coal

While IAMs are critical modelling frameworks for assessing economy-wide mitigation measures, they include through their structure, assumptions, and underlying methodologies built-in inertia to large system changes (as opposed to marginal changes). Such pathways in many cases can exhibit long tails (residual amounts) of technologies that are in the process of being phased out in high-mitigation scenarios, which is arguably primarily due to the frameworks used to model such pathways. Indeed, sector-based bottom-up models with explicit unit-level representation see full and complete phaseouts in high mitigation scenarios. Thus, we opt for using a definition of “phase-out” which is more in line with the characteristics of electricity systems than an “absolute zero” from models’ results.

Unabated gas and coal power plants are defined as being phased out if one of the following conditions comes into effect:

The first condition addresses numerical tails which can occur during the downscaling process of one technology relative to others. The condition states that if the share of the respective plant type on the annual power generation drops below 1% it is phased out.

The second condition addresses numerical tails which can occur during the downscaling process of one technology’s total magnitude (not relative values). This one states that the respective plant type is phased out if the annual power generation drops below a technical threshold range. This range is determined varying the size (coal: 500-1000 MW, gas: 250-500 MW) and capacity factor (20-80%). This condition triggers more likely than the first condition if the total electricity generation of the country is comparably small.

A third condition is considered for coal power plants only. Mirroring an approach from a study analysing how quickly coal-fired power generation should exit the energy mix if we are to meet the goals of the Paris Agreement, we define the coal phase-out date as the year in which the underlying pathway for coal use in electricity generation without CCS reaches reductions of 90% or more below 2010 levels (year of calibration of most models with historical data). We consider this additional criteria to determine the coal phase-out date for each country (Climate Analytics 2019).

A distribution phase-out dates is thus obtained per country, per scenario, per fuel and per criteria, out of these and as a consequence of the limitations described in section 4, we provide phased out years based on the two most ambitious scenarios.

Carbon intensities and fuel shares

While assessments using IAMs provide a consistent estimation of the minimum necessary ambition for Paris Agreement compatibility, additional lines of evidence are needed to fully explore the landscape of pathways to meet the Paris Agreement. This includes literature using bottom-up, hybrid, and sectoral models to estimate the top-end range of plausible ambition given the lag between recent technology trends and what is included in IAMs used here.

To capture the variety of these available literature sources in the benchmarks, the headline numbers and ranges for carbon intensity and fuels shares are calculated based on the most two most ambitious scenarios amongst all assessed scenarios, including both IAMs and sector-specific models.

The values provided in the table are rounded. Shares are rounded to integers and carbon intensities to multiples of 10 to reflect their higher numerical uncertainty.

Data sources and assumptions

Current policies projections

For all countries assessed by the Climate Action Tracker, current policies projections data are from the Climate Action Tracker 2020 Update (Climate Action Tracker 2020a).

Current policies projections are harmonised to the latest historical data provided (2017) and emissions targets are scaled to historical reference year, when target is emissions reduction below a reference year and are kept absolute otherwise.

NDCs and domestic targets

Unless specified differently, for all countries assessed by the Climate Action Tracker, Nationally Determined Contributions or domestic targets are from the Climate Action Tracker 2020 Update (Climate Action Tracker 2020a) or when more recent, from the Climate Action Tracker – Target Update Tracker (Climate Action Tracker 2021).

For other countries, assumptions and methodology are described below:

Bangladesh:
In its 2020 updated NDC, Bangladesh reiterates pledges stated in its prior 2015 NDC. While the update NDC does not provide information on the BAU, we assume here that BAU referenced in the updated NDC is based on the prior 2015 NDC. Stationary/mobile combustion emission targets from power, transport and industry are translated into Global Warming Potential from the 4th Assessment Report (AR4) by harmonising BAU to energy historical data from PRIMAP-Hist 2019 dataset. We assumed that emissions from agriculture and waste will grow following the annual average rate between 2012-2018. IPPU and other sectors are assumed to remain constant at average historical levels between 2012-2018. Historical data source is PRIMAP-Hist 2019 dataset. In its 2015 NDC Bangladesh states measures for the sectors of Agriculture, Waste and Buildings which we have not been able to quantify here due to limited information in the NDC. Therefore, the 2030 estimated conditional target is a conservative estimate of Bangladesh NDC.

Egypt:
Submitted NDC includes only non-quantifiable measures not allowing a quantifiable assessment.

Ghana:
Ghana conditional NDC is 45% below BAU by 2030 which translates in 108% emissions reduction above 2010, base year provided in the NDC as reference for the BAU scenario.
Ghana’s NDC covers all sectors of the economy including LULUCF. For the purpose of comparability, we assessed the NDC target excluding LULUCF emissions. Two methods are employed providing the range of the assessed NDC.
The higher bound is based on the BAU projected by 2030 excluding LULUCF from the 4th National Communication converted to Global Warming Potentials AR4 using the ratio SAR/AR4 from the PRIMAP-Hist dataset and applying the conditional emissions reduction target of -45% below BAU by 2030.
The lower bound of the NDC is based on an estimated BAU excluding LULUCF scaled to 2012 historical year excluding LULUCF used in the analysis: PRIMAP-Hist 2019 dataset and in Global Warming Potentials AR4. We apply then the conditional NDC emissions reduction target of -45%.

Malaysia:
Malaysia has an NDC target to reduce GHG emissions intensity of GDP by 45% by 2030 relative to the emissions intensity of GDP in 2005. The unconditional portion is 30% and the conditional portion is 15%. The conditional target is calculated to be the unconditional portion plus the conditional portion (totalling 45%). The NDC target has economy wide coverage, only covers CO₂, CH4 and N₂O gases. The NDC target includes LULUCF, but for the purpose of comparability, we assessed the NDC target excluding LULUCF emissions. The NDC notes emissions in 2005 were 288 MtCO₂e (excl. LULUCF). The NDC also notes GDP in the base year is RM 543.578 billion (constant price at 2005).

GWP AR4 Conversion:
The NDC uses SAR global warming potential which is converted to AR4 for comparability.

The latest government source, the Biennial Update Report 3 (BUR3) published in December 2020 provides emissions data in AR4. Emissions in 2005 (excl. LULUCF) in BUR3 were 292 MtCO₂e. Emission intensity for 2005 was calculated (emissions in 2005 / GDP in 2005). We then calculated a reduction of 35% and 45%, to provide the unconditional and conditional emissions intensity for 2030 in AR4 GWP.

We then convert the emissions intensity targets to absolute values for 2030 for comparison. We multiplied government GDP projections for 2030 with the emissions intensity for 2030. GDP projections for 2030 were calculated from the NDC 2005 GDP value with growth rates available to 2030 from Biennial Update Report 2 (BUR2). The unconditional target is calculated to be 412 MtCO₂e and the conditional 349 MtCO₂e (excluding LULUCF).

The current policy projections were based on the Asia Pacific Energy Research Centre (APERC) business-as-usual scenario (BAU), which provided data for energy related CO₂ and accounts for the current energy policies (2019 and prior). The US Environmental Protection Authority (EPA 2019) was used to represent non-CO₂ energy sector data. Non energy related emissions were taken from the BAU presented in BUR2 (excluding LULUCF). Therefore, only energy sector policy is included in the current policy projections.

APERC assumptions include

  • 11th Malaysia Plan targets achieved
  • National Renewable Energy Policy renewables targets achieved
  • Additionally, 20% renewables by 2025
  • Preliminary studies for demand-side management will continue.
  • Minimum Energy Performance Standards (MEPS) and labelling programs are expanded
  • Implementation of efficient management of electrical energy regulations.
  • Support for hybrid cars and EVs.
  • National Automotive Policy (support for efficient engine vehicle deployment).
  • New rail projects, including Klang Valley Mass Rapid Transit and High-Speed Rail.
  • Support for green buildings.
  • Special industry tariff abolished and Enhanced Time of Use

Pakistan:
Pakistan’s conditional NDC is 20% below BAU by 2030 (including LULUCF). As Pakistan estimates that 2030 emissions under BAU are 1603 MtCO₂e including LULUCF, the conditional NDC translates to an emissions level of 1282 MtCO₂e including LULUCF. Excluding projected LULUCF emissions under BAU as provided in the country’s NDC, we estimate emissions levels under BAU excluding LULUCF to be 1574 MtCO₂e by 2030. It is unclear in the provided NDC which Global Warming Potentials (GWPs) are used. The analysis provided here is based on GWPs from the 4th assessment report (AR4), thus for comparability and consistency purposes, we provided a range to reflect the uncertainty in estimating Pakistan 2030 conditional NDC target. The higher bound is based on the BAU excluding LULUCF and applying a 20% reduction consistently with its conditional NDC leading to emissions levels of 1259 MtCO₂e excluding LULUCF by 2030. We do not make any assumptions at this stage on the GWPs used in the NDC. For the higher bound, we assumed that NDC targets are provided in GWPs from the second assessment report (SAR). We thus converted the conditional NDC target by using the ratio between GWPs from SAR and AR4 as provided in the PRIMAP-Hist 2019 dataset.

Senegal:
Senegal conditional NDC is 29.5% below BAU by 2030 which translates in 59% emissions reduction above 2010, base year provided in the NDC as reference for the BAU scenario. Baseline year referenced in the NDC for the BAU scenario and subsequent NDC targets are excluding forestry contributions and biomass and total aggregated contribution to the NDC suggested that LULUCF emissions are excluded. We therefore assume that the NDC targets are excluding LULUCF. Historical base year 2010 provided in the NDC differs significantly from the PRIMAP-Hist historical source used in this analysis (around 28%), we there provide a range for the NDC. The upper bound of the NDC is based on an estimated BAU excluding LULUCF scaled to historical dataset excluding LULUCF used in the analysis: PRIMAP-Hist 2019 and in Global Warming Potentials AR4. We apply then the conditional NDC emissions reduction target of -29.5%. The lower bound of the NDC is based on the provided NDC target converted to global warming potentials AR4 using the ratio SAR/AR4 from the PRIMAP-Hist 2019 dataset.

Historical emissions

For countries assessed by the Climate Action Tracker, historical emissions are taken from the latest available update from the Climate Action Tracker and extended using growth rates from the dataset ‘PRIMAP-Hist 2019’ when data years are missing. For the other countries, historical emissions are taken from the dataset ‘PRIMAP-Hist’ providing sectoral emissions time series until 2017 per gases based on country reported emissions (Gütschow et al. 2016).

In the current situation section, in order to give a snapshot of the country emissions, with the following decision tree applied in the selection of the underlying data:

  • For Annex I countries, we use the dataset provided by the country reported data to the UNFCCC (UNFCCC Secretariat 2019) which allow for more granularity in the breakdown of emissions per sub-sector.
  • For non-Annex I countries, we use PRIMAP-Hist 2019 dataset for the non-LULUCF emissions. Combustion emissions are then broken down in sub-sectors using the IEA CO₂ fuel 2019 dataset. LULUCF emissions are provided from the Climate Action Tracker, when available, and from country reported sources when available. See below country specific data sources.
  • When available, for some countries, country specific data is used see section below providing reference data for these.

Historical primary energy and energy consumption in power

Historical primary energy and historical energy consumption in power (both in absolute and shares) are taken from the International Energy Agency (IEA) World Energy Balances 2019 (IEA 2019b).

Historical power carbon intensity

Emissions from power are based on the IEA CO₂ emissions from fossil fuels 2019 and the IEA World Energy Balances 2019 (IEA 2019b, 2019a).

Projected emissions and energy consumption

Emissions projections from illustrative pathways are based on the downscaling methodology as described in section 4, the emissions projections from the 1.5° compatible range are based on the full set of IPCC 1.5°C compatible scenarios downscaled as described on section 3.2 and energy mix projections are based on the downscaling methodology as described in section 4.

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Footnotes