Methodology

Ambitious domestic action and equitable distribution of mitigation effort

In 2015, countries adopted the Paris Agreement. Article 2 specifies that the long term temperature goal in the Paris Agreement is to hold 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 2015). In shorthand terms this has become known as the Paris Agreement’s 1.5°C degree temperature limit, and this warming limit provides a basis for evaluating global and national action to meet emission pathways consistent with this goal.

The Agreement further affirms that actions taken for implementation should “reflect equity and the principle of common but differentiated responsibilities and respective capabilities (CBDR).”

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”

It is critical to note that equity and fairness issues are embedded in this article which operationalises the Paris Agreement’s long-term temperature goal, specifying that developing countries will take longer to peak emissions and that the modality of getting to net zero needs to be done on the “basis of equity, and in the context of sustainable development and efforts to eradicate poverty.”

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).

The IPCC AR6 also followed this approach to defining 1.5°C alignment. In the 1.5°C no and limited overshoot pathways assessed in the AR6, the increase of global average temperature above the pre-industrial level is also limited to below 1.6°C during the twenty-first century and below 1.5°C by 2100. Some of these pathways do not achieve net zero greenhouse gases emissions in the second half of the century, thus not consistent with Article 4.1 of the Paris Agreement. The IPCC AR6 Working Group III has established a subcategory C1a, where all pathways achieve net zero greenhouse gas emission around 2070-2075 (Ganti et al. 2022), to be consistent with Article 4.1 of the Paris Agreement. These pathways also reach net zero CO₂ emissions around 2050, in line with the findings of the IPCC’s Special Report on 1.5°C.

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-cost 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.

It is worth noting that an increase in the Earth’s climate sensitivity beyond what is currently assumed could increase the need for CO₂ removal from the atmosphere, as could the crossing of tipping points that lead to significant release of greenhouse gases from natural systems affected by global warming, such as forest dieback, Amazon destabilisation or release of CO₂ or methane from peatlands and tundra.

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, many CDR technologies would entail negative side-effects across different dimensions of sustainable development objectives. Their technological and economic viability at scale 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.

Selecting and defining the illustrative pathways

Out of the subset of 1.5°C pathways, we highlight several ‘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. 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 21^st 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 previously in this section.

• 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 plants. 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. Although there are considerable investments by some countries in power plants with CCS, they have not delivered any significant results (Climate Action Tracker 2020c; 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 act, the lower the risk of running out of time to implement action and remaining within that budget. It allows countries to consider 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 must 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 be directly 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.

Other pathways used in select country profiles include

• Australia: emissions and energy pathways from the Climate Action Tracker’s “Scaling Up Climate Action – Australia” report (Climate Action Tracker 2020b), developed using an adapted Open Source Energy Modelling System (OSeMOSYS) for Australia
• Europe, France, Germany, Italy, Poland, Spain: emissions pathways from the Paris Agreement Compatible Scenarios for Energy Infrastructure (PAC 2020)

Discussion and limitations on global-least costs pathways

Global least-cost pathways provide important guidance, with the caveats below, on where and when emission reductions can cost-effectively occur, but no guidance on who should pay for them. Under the fair share and equity framing of the Paris Agreement it is clear that developed countries need to be reaching at least the level of least-cost domestic emission pathways, and further should be providing support to developing countries such that they can also meet the respective level of emissions implied in the range of least cost 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, 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 they underestimate the feasible space for 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; 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 summarised 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 2022).

It should be noted that in the IEA database, secondary energy consumption is assigned to both electricity and heat generation. For downscaling purposes, we assume that the 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 into 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 emissions 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 annual GDP by the given scenario, this interpolation defines how the emissions of the macro-region are shared amongst the countries.

Economy wide GHG and CO₂ 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, 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 ‘From global to national pathways,’ 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 ‘Underlying global pathways used in the analysis’ (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 ‘Energy – CO₂ emissions’ for the illustrative pathways. As in section ‘Agriculture sector’ 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 ‘Industrial processes, waste and energy non-CO₂ emissions,’ 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 energy sector CO₂ 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 (50^th 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 5^th percentile while we highlight the median of this range, the 25^th 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 decarbonisation benchmarks

Economy-wide GHG and CO₂ emissions benchmarks

As referred in section ‘How are global Paris Agreement-compatible pathways defined?’, 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 5^th percentile, forming the higher and lower bound of the 1.5°C 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 which is the middle of the range i.e. the 25^th 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 includes 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, through their structure, they include 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 trigger is 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 ‘Discussions on, and limitations of global-least costs pathways,’ 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.

End-use sectors benchmarks (transport, buildings and industry)

The headline numbers and the ranges for the indicators provided for the end-use sectors are derived in the same manner as described for the power sector in section ‘Carbon intensities and fuel shares.’

Land-Use and Forestry sector benchmarks

Deriving downscaled pathways for the land-use and forestry sector requires a different approach to other sectors because what is possible in each country depends on its geography and existing land-uses. Sustainability concerns are particularly relevant for this sector – demand for land for growing food crops, water availability, land tenure issues and the need to protect biodiversity, all needs to be considered.

For these reasons, our method uses spatially resolved land-use change patterns from a scenario that is designed to meet sustainability criteria in the land sector, including those related to water availability and protected areas, as an input. The scenario we have chosen for this analysis was co-developed for this project by PBL using the IMAGE model, and was designed to reduce demand for agricultural land through lifestyle changes and limitations on the use of dedicated bioenergy crops.

The IMAGE Land and Climate module simulates land allocation for different land use types, including forest and agricultural land, for 26 macro-regions. The macro-regions consist of countries with similar economic systems. The land use allocation simulation is then used as an input to a global vegetation model to calculate an emissions trajectory for land use and forestry sector for each macro-region (PBL, 2021).

Emissions in land use and forestry sectors are mainly driven by deforestation, land use change from one type to another, and soil management in agricultural land such as drainage of peatland. On the other hand, removals in the sector are mainly driven by afforestation and reforestation, and the rate of removals depends on the rate at which trees are planted, the age of the forest at each point in time, and the biome and forest type.

To develop a simple method for downscaling land use and forestry emissions, we have assumed that the majority of modelled emissions and removals in the scenario relate to changes in forest land area. This assumption will not hold for all countries, but forest-related land management activities are the strongest drivers of land-related emission and removals in the countries we have assessed to-date.

We use forest cover loss within each country as a proxy for emissions, and we assume that sequestration from forest cover gain over time is a proxy for removals. Forest sequestration is estimated based on the evolution of age structure of growing forests, using a simple forest stock model. We use carbon sequestration potential estimates from Teske, et al., 2019, and follow a similar approach to this work to calculate forest stock changes over time. We also check that forest cover gains do not exceed the potentials assessed in Griscom, et al., 2017, which were evaluated to meet food, fibre, and biodiversity needs. For this sector we focus on those countries for which the land and forest sector plays a particularly significant role in their current and/or future emissions.

When interpreting our results, it is important to note that there is a discrepancy between nationally reported and modelled land-use and forestry emissions and removals, which is described by Grassi, et al., 2021. This is because of differences in how anthropogenic emissions and removals are defined and estimated, and results in an offset for many countries between historical and projected data. When countries set emissions targets both for this sector and for the whole economy, this discrepancy needs to be taken into account.

Investments requirements in the power sector

As the current version of SIAMESE does not explicitly provide investments requirements, we calculate the energy investments requirements ex-post.

Figure 1 shows the methodology flowchart for WP4. We compute the capacity factor of different power plant technologies at the macro-region level for each scenario by dividing annual electricity generation by the installed capacity for the corresponding scenario at the macro-region level. We then calculate the corresponding power generation capacities for each country using downscaled generation data developed in WP2 for the country and applying power plant capacity factors calculated for the corresponding macro-region.

The retirement schedule of existing power plants is another key input parameter; we obtain information on existing power generation capacities for each country from the WEPP Platts, GGPT and GCPT databases. For generation capacities, technical information for existing power plants is given in detail and at a unit level in Platts database. We aggregate existing generation units according to the modelled power plant technologies in WP2. Based on the information provided by Platts for the commissioning year, status (operating, retired, cancelled, etc.) and taking the technical lifetime of power plants into account, we derive the retirement schedule of existing power plants and calculate how the capacity of different power plant technologies decline over time for each country.

The capital cost of power plant technologies is another input parameter to our calculations. We use recent capital costs data from IRENA 2019. Here, we consider non-biomass renewables as an aggregated technology. Therefore, we derived the capital cost of non-biomass renewables by using a weighted average for each technology, applying the technology costs data given by IRENA (2019). Weights are derived based on the share of each technology in total renewable power generation from IAM scenario data.

We then calculate investment requirements by employing capital accumulation equations, in line with the approach used in IAMs (Bosetti et al 2007; Sferra et al., 2019). Here, a distinction is made between already installed capacities which will remain until end of their lifetime (KWold) and new power plants installed over the modelled time horizon (KWnew) based on SIAMESE results, which are subject to a depreciation rate (see Formula in Figure 1). The annual depreciation rate is calculated according to power plant technology-specific lifetimes.

Figure 1: Methodology flow-chart for investment requirements in the power sector.:

Assessing co-benefits of replacing coal with renewables in the power sector

Overview

Decarbonisation of the power sector is not only important for climate change mitigation, but also critical in supporting the achievement of the Sustainable Development Goals (SDGs). A shift from coal-fired power generation to renewable energy sources has the potential to generate co-benefits such as employment opportunities and reduce air pollution from burning fossil fuels. To illustrate the potential for such co-benefits, we have developed an “SDG tool” analysis framework that estimates i) the employment potentials (contributing to SDG8) and ii) the air pollution reduction and health benefits potential (contributing to SDG3) of an accelerated phase-out of coal-fired power generation and replacement with solar and wind energy. The co-benefits analysis applied focuses on the electricity sector and specifically on benefits from phasing out coal that can positively impact development.

We have applied our SDG tool to two test case countries, Poland and Pakistan, both shown in the National Pathway Explorer. This section summarises the general methodology and key underlying assumptions. More details and country-specific information for Pakistan and Poland can be found in the Technical Background document for the co-benefits results.

Scenarios and approach used for the co-benefits analysis

We compare two different scenarios for the co-benefits analysis. One scenario illustrates the planned development of coal-fired power generation capacities for the respective country that follows current coal power generation plans. This is contrasted with a scenario that assumes an accelerated phase out of coal from the power system and replaces coal by renewable energy sources. Both scenarios as well as the underlying approach are described in more detail below. The scenarios are then used as inputs to estimate i) employment impacts (see section ‘Assessing employment impacts from replacing coal power generation with renewable energy’) and ii) air pollution and health benefits (see section ‘Assessing air pollution reduction and health benefits from replacing coal power generation with renewable energy’).

Note that the scenarios used for the co-benefits analysis are different from other scenarios used in the National Pathway Explorer as they specifically focus on replacing coal power generation with wind and solar.

Planned Coal Developments Scenario (“BAU”)

This scenario illustrates a likely “Business-as-Usual (BAU)” scenario of expected coal developments for the respective country. The scenario assumes that existing and planned coal power capacities for the respective country are installed as planned and remain active until either a planned phase-out date (if already defined) or a retirement date according to lifetime assumptions. The information on existing and planned coal power generation capacities and unit retirement years is based on unit-specific data from the Global Coal Plant Tracker (GCPT) which was checked and updated based on relevant local data sources and based on consultation with local experts.¹

For Poland, information from the INSTRAT database has been used, which compiles unit-level specific information on all Polish coal power plants bringing together information from the Europe Beyond Coal database, the JRC Open Power Plants database, the ARE power plant catalogue, and the generators list of the database of the Polish Transmission System Operator PSE.² This has been used to update the GCPT unit-level information on operating and planned coal generation capacities as well as for assigning unit-specific capacity factor information. Where no planned retirement dates for coal generation units could be identified based on these sources, we have assumed a maximum lifetime of 70 years.³ To ensure that the assumptions on future coal capacity developments and policy phase out assumptions of our scenario are in line with existing local “Current Policy” Scenarios, we have compared our scenario with the European Commission’s EU POLES “Reference Scenario” capacity projections for “solid fossil fuel” power generation capacities for Poland.

For Pakistan, we have updated the unit-level information on operating and planned coal generation capacities based on details from the Pakistani “Indicative Generation Capacity Expansion Plan IGCEP 2021-30”, a key power system planning document from the National Transmission and Despatch Company in Pakistan, as well as information from the National Electric Power Regulatory Authority’s (NEPRA) State of the Industry Report from August 2021. The IGCEP includes detailed information on the year when new coal power plants are planned to go online, the capacity information and even plant-specific capacity factors, which we matched to the GCPT data derive power generation for Pakistan. The IGCEP also contains projected capacities until 2030 for Pakistan. As the Pakistani coal fleet has mostly been built very recently, the time horizon of our analysis (until 2035) is not affected by lifetime assumptions. Find the information on the resulting BAU scenario in the” Technical Background document”:/media/final_draft_co-benefitstechnicalbackgrounddoc_short.pdf for the co-benefits results.

Accelerated Coal Phase-out Scenario (“Acc”)

This scenario assumes an accelerated coal phase-out in line with an emission pathway for coal-related emissions derived from a 1.5°C scenario from the REMIND model, downscaled to the respective country level using the SIAMESE model. The SIAMESE-downscaled emissions pathway was harmonised to match 2022 coal emissions of the current coal fleet. Based on this Paris Agreement-compatible emissions constraint for coal generation emissions, we derive a unit-level phase-out schedule for coal power generation capacities prioritising the shut-down of coal generation units with the highest carbon intensity, assigning accelerated retirement years to all coal generation units in the country.

We then estimate the resulting coal-fired electricity generation over time based on the newly assigned accelerated phase-out dates and the (unit- or plant-specific) capacity factor information and compare this to the coal-fired electricity generation estimates of the BAU (Planned Coal Development Scenario) phase-out dates and generation estimates. This yields a “coal generation gap” to be “filled” by renewable energy, assuming that the same electricity generation level as in the Planned Coal Development Scenario needs to be achieved.

To assess the renewable energy capacities to be installed to fill the “coal generation gap,” we use the following approach: we estimate the technical potential for solar PV rooftop, PV open field (mainly utility scale PV) and wind offshore and wind onshore by applying the spatially and temporally re-solved simulation models of the open-source Python-based packages GLAES (Geospatial Land Eligibility for Energy Systems) and RESKit (Renewable Energy Simulation Toolkit).^4,5 The renewable modelling framework make use of global data sets for land eligibility and weather data depending on a technology selection, maximum renewable energy potentials (onshore/offshore wind; rooftop/open-field PV), generation time series for each renewable group, and depending on cost projections a cost assessment (e.g. LCOE).⁶ This yields a list of potential installation sites for each renewable energy technology (solar PV rooftop, PV open field, wind offshore and wind onshore) with estimates on capacities and electricity generation potentials as well as costs for the respective country. We then sort the potential installation sites for each RE sub-technology by their LCOE estimates from cheapest installations to most investment intensive sites.

Based on local information and input from local experts, we then derived shares for each RE technology type by defining how much the respective RE technology is planned to contribute to filling the “coal generation gap”. For Poland, these shares have been derived based on scenario data from the Polish energy think tank “Forum Energii.” For Pakistan, these technology-specific shares have been derived based on the “Indicative Generation Capacity Expansion Plan IGCEP 2021-30” from the National Transmission and Despatch Company as well as based on a recent World Bank study assessing RE jobs in Pakistan.

Applying the derived contribution shares for each RE sub-technology to the overall “coal generation gap”, we calculate the amount of electricity generation to be covered by solar PV rooftop, PV utility scale and wind offshore and onshore, respectively, to replace the coal generation that is phased out earlier than currently planned. We then identify the least expensive sites and related generation and capacity estimates for each RE sub-technology based on the list for the technical potentials explained above and assign an installation year for those sites that are needed to fill the technology-specific contribution to the “coal generation gap”. This yields our scenario data for each year and sub-technology on i) newly added capacities, ii) installed capacities for a given year and iii) related electricity generation estimates for a given year. This scenario data on RE-needs for filling the “coal generation gap” is then combined with the information of coal capacities (operating as well as retired) and electricity generation estimates based on the accelerated phase out years at unit-level.

Find the information on the resulting BAU scenario in the Technical Background document for the co-benefits results.

Note on the interpretation of the scenarios and caveats of the approach

For the co-benefits analysis, we apply a simplified approach specifically focusing on coal-fired power generation and replacing coal with renewable energy (wind and solar). Note that this approach does not cover the entire power system and does not apply complex energy system modelling. The presented scenarios and results do not take other potentially existing power generation capacities into account, such as natural gas or biomass or hydro capacities, which may exist or be planned in the country. Likewise, the Accelerated phase-out scenario focuses on RE capacities that are added specifically for replacing coal, and other already existing solar or wind capacities are not taken into account for the co-benefits analysis.

The estimation of electricity generation is simplified, using capacity factor information to estimate electricity generation. While capacity factors are plant- or even unit-specific for coal and gric-cell-specific for RE technologies, we assume that capacity factors and related electricity generation estimates remain constant over time. This means that changes in load, which typically happen e.g. for coal power plants when the overall composition of the power system changes, are not taken into account. This is a necessary simplification, as accounting for these load factor changes would require detailed bottom-up energy system modelling which was beyond the scope of this work. Our co-benefits analysis does not make specific assumptions about changes in power demand over time. We assume that the estimated generation of the operating coal power plants in the BAU scenario needs to be covered in any given year by the power generation of installed wind and solar replacing phased-out coal plant generation in the Accelerated coal phase-out scenario.

Assessing employment impacts from replacing coal power generation with renewable energy

Approach for estimating the employment impacts from replacing coal with RE

We apply an employment-factor-based approach to estimate the job creation potential of replacing coal with solar and wind. This approach has been applied in multiple studies, such as Ram et al. (2020), Ram et al. (2022) and Rutovitz et al. (2015) as well as a recent study by the World Bank for Pakistan.

The approach estimates direct jobs associated with electricity generation and includes jobs in manufacturing of technology parts (within the country), construction & installation of new capacities, and operations & maintenance of installed capacities.⁷ Applying the scenario data described in section ‘Scenarios and approach used for the co-benefit analysis’ for the respective countries, employment estimates are derived as follows: newly installed capacity for electricity generation in a given year creates local jobs in manufacturing of technology parts (accounting for the share that these are produced within the country) and jobs in construction and installation of this added capacity over the construction period. The total capacity that is in place and operating in a given year contributes to jobs in operation and maintenance over the lifetime of the respective installation. The calculation is applied for each relevant technology for electricity generation with technology-specific employment factors and assumptions on lifetime and construction duration. To account for regional differences in labour productivity, regional adjustment factors can be applied to adjust the ‘base’ employment factors for each technology and job activity.

The research article of Ram et al. (2020) and Ram et al. (2022) provides employment factors for a list of different electricity generation technologies, including coal, solar PV rooftop, PV utility scale, wind onshore and wind offshore; as well as regional adjustment factors and decline factors. We have researched or derived our own country-specific employment factors wherever data was available for Poland or Pakistan. If no country-specific employment factors could be researched or derived due to data limitations, we have applied the employment factors from Ram et al. (2022). The local share assumptions for local manufacturing of technology parts have been derived based on country-specific literature and internet research and input from local experts.

You can find more information on the employment analysis in the Technical Background document specifically for the co-benefits results.

Note on the interpretation of the employment estimates and caveats

The employment analysis focuses on the employment implications with a focus on direct jobs in the electricity sector related to replacing coal-fired power generation capacity with renewable energy capacity of solar and wind.

As mentioned in the scenario section (see section ‘Note on the interpretation of the scenarios and caveats of the approach’), this does not take into account the whole energy system, meaning that, other power generation capacities and related employment are not included. Likewise, potentially pre-existing RE installations and related jobs are not taken into account either. Also, storage capacities and related jobs are neglected in this analysis as this would require more complex energy system modelling. A build-out of RE jobs would typically be related to building out storage capacities, which can be expected to create additional jobs in the Accelerated coal phase out scenario.

Employment related to fuel supply (e.g. coal mining, fuel transport) and transmission and distribution are not taken into account, nor are jobs in decommissioning.

The employment factor approach used here focuses on direct employment and does not quantify indirect employment further down the supply chain nor employment induced by the spending of wages throughout the economy. Still, a comparison of jobs for the different technologies over time can yield an indicative picture of the overall developments and employment effects for the analysed scenarios. However, the estimates should not be interpreted as a projection of net employment effects.

Assessing air pollution reduction and health benefits from replacing coal power generation with renewable energy

Approach for estimating the air pollution and health benefits from replacing coal with RE

The analysis focuses on air pollution and related health impacts from coal-fired power generation and benefits of avoided impacts by an accelerated coal phase out.

The assessment of reduced air pollution and related health benefits from replacing coal power generation with solar and wind has been implemented following the methodology suggested by Parry et al 2014. It consists of different steps, which can broadly be divided into the i) exposure analysis and ii) the health impact analysis.

For the exposure analysis, we first apply pollutant-specific emission factors from the GAINS model to estimate the annual emissions for fine particulate matter (PM2.5), Sulphur dioxide (SO₂) and Nitrogen Oxides (NO_x) based on generation information for the operating coal power plant units in each scenario and year.⁸ This gives us an estimate of the amounts of emitted pollutants for each coal unit and year as well as the avoided air pollution emissions for the accelerated coal phase-out. Second, we approximate the dispersion of the emitted pollutants by distance circles around the power plants to estimate changes in the respective concentration levels.⁹ For this, we make use of GPS information of the power plant locations from the Global Coal Plant Tracker. We then identify the population living within these distance bands based on gridded population data and projected population developments. Estimates from the literature (Zhou et al. 2006) of intake fractions (i.e. grams of air pollutants inhaled by exposed population per emitted tonne) per distance band are then applied to calculate the change in concentration levels for each air pollutant applying an annual breathing rate. These estimated changes in concentration levels per distance band and pollutant are then used as an input for the health impact analysis.

The health impact analysis uses so-called concentration-response functions recommended by the World Health Organization(WHO) to compute the relative health risk of an increase of one tonne for different diseases by multiplying the estimated change in the concentration level from the exposure analysis for each pollutant and distance band with the disease specific concentration-response function. To compute the health impacts, we need to first compute the number of so-called “base cases” (i.e. the baseline mortality rate of the country) using age-weighted mortality rates for each disease and the information on the exposed population. Then, we calculate the number of premature deaths per tonne of pollutant for each cause of death using the relative health risk and base cases above. We then multiply the number of premature deaths per tonne of pollutant for each cause of death with the estimated pollutant emissions to obtain the total premature deaths per pollutant and cause for each power plant. We consider the following types of diseases contributing to overall premature deaths from coal-power generation: chronic obstructive pulmonary disease (COPD), lung cancer, ischemic heart disease and stroke.

Note on the interpretation of the air pollution and health estimates and caveats

The analysis of air pollution impacts and health benefits related to an accelerated coal phase-out is based on a simplified approach using distance bands and does not apply complex air quality modelling. The parameters to translate emitted air pollution into inhaled pollution and concentration level changes have been estimated by Zhou et al. (2006) using data for China. While this study has been applied in other approached as well, it remains as a simplifying assumption that relationships empirically estimated for China are also applicable for other countries with different geographical and methodological conditions. However, complex air quality models are not openly available for many countries, requiring long run times and computational power as well as specific expertise and programming skills.

The concentration-response functions that have been applied to translate exposure to concentration level changes into health impacts assume a linear relationship (i.e. that the health impact is the same independent on the pre-existing air pollution concentration level). This is a common simplifying assumption made in the literature and it is commonly argued that the linear relationship holds roughly true for most relevant concentration levels.

¹ GCPT Version February 2022, cross-checked with version from July 2022 (link to GCPT website).

² Link to the INSTRAT database and the underlying unit-specific information.

³ While 70 years lifetime for coal power plants is higher than the standard lifetime assumption, several currently still operating coal power plants are already above 65 years old and this lifetime assumption has brought us closest to matching the projected capacities of the EU POLES Reference scenario over time.

⁴ Find more information on github.com/FZJ-IEK3-VSA/glaes

⁵ Find more information on github.com/FZJ-IEK3-VSA/RESKit

⁶ First, the land eligibility analysis evaluates the amount and distribution of land which is eligible for renewable energy sources based on a comprehensive set of exclusion factors and constraints informed from the land eligibility literature review. These reflect the most common (socio-political, physical, conservation pseudo-economic) constraints for placement of wind turbines and PV modules commonly considered in renewable potential studies. Once the distinction is made between available and excluded areas, the placement algorithm identifies individual turbine/PV module locations within the eligible areas followed by hourly simulation of generation profiles.

⁷ Moreover, jobs in fuel supply, transmission and decommissioning can be assessed as well if the relevant scenario information is available. For our analysis we however decided to not include these.

⁸ Greenhouse Gas and Air Pollution Interactions and Synergies (GAINS) model developed by the International Institute for Applied Systems Analysis (IIASA)

⁹ Estimating the exposed population living within four distance bands from each power plant: 0–100 km; 100–500 km; 500–1,000 km.

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 (Climate Action Tracker 2020a). Depending on the latest update provided by the Climate Action Tracker, it can be from either the 2021 or 2022 update. See captions of graphs under each country for the final information.

Current policies projections are harmonised to the latest historical data provided (2019) and emissions targets are scaled to historical reference year when the target is an 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:

Algeria:
Algeria’s 2015 INDC sets a conditional target of 22% emissions reductions below Business-As-Usual (BAU). The BAU scenario is however not provided throughout the NDC. In order to quantify Algeria’s targeted emissions level by 2030, we estimated two different BAU scenarios: one based on the last 10 years’ emissions growth rates from the country, and one based on the RCP4.5 SSP2. We then apply the respective conditional and unconditional targets by assuming two scenarios of emissions reductions in the LULUCF sector: assuming equal emissions reduction efforts across the different sectors, including LULUCF, and assuming a third of the efforts applied to the LULUCF sector. For the latter, the NDC specifies three categories of actions: energy, waste and forestry; so, we assume 1/3 of effort goes to forestry. We take in both cases the maximum and minimum levels of the combined scenarios as the range. This leads us to emissions levels targeted by the country by 2030 of 244-315 MtCO₂e for the conditional NDC and 291-351 MtCO2e for the unconditional NDC excluding LULUCF or 3% below 2015 levels to 25% above 2015 levels and 16-39% above 2015 levels by 2030, respectively.

Bangladesh:
In 2021 Bangladesh submitted an updated NDC with an unconditional emissions reduction target of 6.73% (27.53 MtCO₂e/yr) below business as usual (BAU) levels by 2030, and a conditional target of 15% (61.9 MtCO₂e/yr) by 2030 which will lead to an increase in emissions of 126% above 2012 levels (unconditional) and 89% above 2012 levels by 2030 including LULUCF (conditional). The conditional reduction is in addition to the proposed reductions in the unconditional scenario. Compared to Bangladesh’s first NDC which only covered energy related emissions from power, transport and industry sector, the updated NDC covers additional sectors following IPCC guidelines – energy, Industrial Processes and Product Use (IPPU), agriculture, forestry and other land use (AFOLU) and waste. The broader coverage means that Bangladesh will reduce more emissions in absolute terms than what it promised under its first NDC even though the percentage change will remain almost the same. As the updated NDC does not explicitly mention the global warming potential (GWP) used, we use values as they are provided by the NDC. Our analysis is based on the GWP from the 4th Assessment Report (AR4). When excluding LULUCF emissions (forestry emissions in the NDC update), Bangladesh’s unconditional emissions reduction target leads to an increase in emissions in 2030 of 198% above 2012 levels. Under the conditional target, the country would increase its emissions by 150% above 2012 levels in 2030.

The NDC update uses 2012 as the base year following the Bangladesh’s “Third National Communication” of Bangladesh. When the sector wise inventory data is converted using GWP of AR4 there is a discrepancy between emissions from energy and IPPU sector between the NDC update and the 3^rd National Communication.

Australia:
We have derived the excl. LULUCF target from 2005 emissions level in the government’s Paris Agreement inventory and 2030 LULUCF projections in the Government projections published in 2022 of -33 MtCO2e. Both these sources use global warming potentials (GWP) from the IPCC‘s Fifth Assessment Report (AR5). As the 1.5¬∞C national pathway explorer uses GWPs from the Fourth Assessment Report (AR4), we have converted the government’s emissions data to AR4 using an average conversion factor of 0.98 (AR4=0.98*AR5). For reference, the AR5 GWP weighted 2030 emissions based on for reference, the AR5 GWP numbers based on the most recent inventory the most recent inventory is 388 MtCO2e/yr excluding LULUCF in 2030.

Cameroon:
Cameroon updated its NDC in November 2021. While the target appears to be strengthened from its previous target (32% emissions reduction below BAU vs. 35% emissions reductions below BAU by 2030 in the updated target), the estimated level of emissions reached in 2030 is higher in the updated NDC mostly due to a revised BAU. This would lead to an increase in emissions of 120% above 2010 levels and an emissions level of 77 MtCO₂e/yr by 2030. The target covers an unconditional component (-12% below BAU) and a conditional component of -23% below BAU by 2030.

While Cameroon BAU‘s is provided excluding LULUCF, emissions reductions to achieve the target include the LULUCF sector (around 46% of the total mitigation potential identified to achieve the target). We have then excluded this mitigation potential from the target as we do the assessment excluding LULUCF and to be consistent with the BAU which is excluding LULUCF. Cameroon’s NDC target is expressed in Global Warming Potentials from the 4^th Assessment report.

Finally, while it appears that there is unclarity in the NDC on how the LULUCF sector is taken in account (BAU excluding LULUCF but an emissions reduction target including LULUCF), the country makes up around 12% of the Congo Basin, the second largest forest ecosystem after the Amazon. Its tree cover ranges between 43% (FAO) of its total land area to 68% (Global Forest Watch), and the country has seen a sharp increase in deforestation since 2011 with a continuing trend leading to an annual level of positive emissions from the LULUCF sector of around 34 MtCO₂e/yr. This is thus a key sector for the country.

Democratic Republic of the Congo:
The DRC‘s NDC has been updated in December 2021. GWPs are not specified thus data used for the quantification is taken from the NDC as provided, we do not make assumptions on the GWPs used. Conditional target is based on the NDC target of 21% emissions reduction below BAU by 2030, including 19% conditional on top of 2% emissions reduction unconditional. Conditional target is based on figures 2 and 3 from the DRC‘s NDC providing total emissions after reductions. Data has been extracted graphically from the NDC using a dedicated software. LULUCF emission, provided in the NDC, have been excluded given that we assess total GHG excluding LULUCF. Historical emissions used are provided in the NDC.

Czech Republic:
In its national energy and climate plan (NECP 2019) Czechia outlines a 2030 emissions reductions target of 30% below 2005 levels by 2030. The quantification of this target is a 44 MtCO₂e reduction below 2005, which when subtracted from total GHGs emissions excluding LULUCF in 2005, works out to be a 30% reduction. We therefore assume Czechia’s target is excluding LULUCF and equates to 2030 emissions of 105 MtCO₂e. Current policies projections are taken from the “With Existing Measures” Scenario (WEM) for the upper bound and the “With additional measures” scenario provided by Czechia to the EEA and the UNFCCC.

Ecuador:
Ecuador’s conditional Nationally Determined Contribution (NDC) aims to reduce emissions by 20.9% below business-as-usual (BAU) scenario and unconditional target by 9% below BAU by 2025. This is equivalent with 5% emissions increase above 2015 for the unconditional NDC target and 9% below 2015 levels for the conditional NDC target. Ecuador’s NDC provides data for 2010, 2020, 2025. A linear interpolation has been used between datapoints to obtain the full BAU projections. Values are provided in GWP SAR which have been converted to GWP AR4. We applied the respective unconditional and conditional reductions to BAU by 2025 to obtain targeted 2030 emissions levels.

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

Ghana:
Ghana submitted an updated NDC in November 2021 which targets to conditionally achieve an absolute emissions reduction of 64 MtCO₂e by 2030, including LULUCF. We assume these reductions are in relation to a business-as-usual (BAU) scenario; however, this is not specified in the updated NDC. In Annex 6.1 of the updated NDC, Ghana articulates that a base year of 2019 was used. The NDC lays out 34 mitigation measures to achieve this target, of which nine are unconditional and would lead to absolute emissions reductions of around 24 MtCO₂e below BAU by 2030.

As Ghana’s NDC does not provide indications on the assumed BAU by 2030, to quantify Ghana’s NDC, we use the 3rd biennial update report (BUR) published in August 2021. This BUR provides a BAU emissions trajectory reaching around 100 MtCO₂e/yr by 2030 including LULUCF (almost double total 2019 emissions) and 83 MtCO₂e/yr excluding LULUCF. To determine the emissions level by 2030 under the reductions targeted in the updated NDC, we have subtracted the emissions reductions targeted in the NDC from the projected 2030 emissions under the BAU scenario. Since this assessment also refers to emissions excluding LULUCF, we have removed LULUCF emissions expressed in the BUR, as well as mitigation measures targeting the LULUCF sector from our calculations. These calculations lead to the target of emissions of 42 MtCO₂e/yr by 2030 excluding LULUCF, or an increase of 32% above 2015 emissions levels.

Note that there is a deviation between historical data used in this analysis (PRIMAP-Hist 2021) and nationally reported historical data (in 2019, 36 MtCO₂e in this analysis vs. 44 MtCO₂e nationally reported, excluding LULUCF – a 22% difference).

As Ghana excludes agriculture emissions from its NDC target, we have assumed agriculture emissions would remain at the current levels under the BAU scenario in 2030.

All values are reported in global warming potentials (GWP) from the IPCC’s 4^th Assessment Report.

Israel:
In its 2021 updated NDC, Israel commits to reduce emissions 27% by 2030 and 85% by 2050 relative to 2015 levels, including LULUCF, equivalent to emission levels of 58 MtCO₂e in 2030 and 12 MtCO₂e in 2050. The 2015 reference emissions and target emissions are provided in SAR. For comparability with our analysed 1.5°C pathways, we convert the given 2015 reference emissions level to AR4 using greenhouse gas emissions shares provided in Israel’s Third National Communication. LULUCF emissions are considered negligible (<1%). Historical emissions were derived by harmonising PRIMAP data to the 2015 reference emissions provided in the 2021 updated NDC.

Italy:
Italy’s Plan for the Ecological Transition adopted in March 2022 indicates an aspirational emissions reduction target by 2030 of 256 MtCO₂e equivalent to an emissions reduction of 51% below the 1990 level. However, Italy has so far not formally adopted any 2030 economy-wide emissions reduction targets at the national level. In order to estimate what Italy’s emissions targets currently are, based on the EU emissions reduction framework, we use Italy’s integrated national energy and climate plan (NECP, 2019) which provides emissions targets for those emissions covered by the EU emissions trading system (ETS) and effort sharing regulation (ESR, or non-ETS) of 43% below 2005 levels and 33% below 2005 levels by 2030, respectively, bearing in mind that -43% is an EU wide average target under this scheme. For simplicity, if we directly apply these targets to historical ETS / ESR emissions in 2005, as provided by the European Environment Agency (EEA), this leads to an estimated current target of 37% emissions reduction by 2030 below 2005 levels excluding LULUCF and a 30% emissions reduction by 2030 below 1990 levels. These emissions reduction targets are then applied to the historical data submitted by Italy (as an EU member state) to the EEA and the UNFCCC used in the analysis. The most recent and updated available data is published in The 2021 Economic and Finance Document of the Finance Ministry, which shows an updated projection of NECP scenarios for the total emissions level expected to be reached by 2030 of 303 MtCO₂e. This is equivalent to a 49% emissions reduction below 2005 levels by 2030 and 42% emissions reduction below 1990 levels by 2030.

EU member states provide emissions projections to the EEA based on two scenarios: with existing measures scenario (WEM) and the with additional measures scenario (WAM). To estimate the current emissions trajectory of Italy under Current Policies, we use the WEM as the upper bound (lower ambition level) and the WAM as the lower bound of emissions projections.

Jamaica
Historical reported emissions for the agriculture sector are deviating significantly from the PRIMAP-Hist – Country reported 2021 dataset to other third party reported GHG – such as the CAIT Climate Watch dataset used in recent studies to inform the development of Jamaica’s Long-Term Strategy. On the total GHG emissions excluding LULUCF in 2017 for example, the difference is of around 60%.

In order to assess Jamaica’s NDC and remain consistent with historical data used in the analysis to assess the 1.5°C compatible benchmarks we have used the Primap-Hist Country Reported dataset (2021 version) leading to emissions level of around 14.0 MtCO₂e by 2030 (excluding LULUCF). For comparison, using the Climate Watch dataset, 2030 emissions levels would reach 8.3 MtCO₂e by 2030, excluding LULUCF.

As Jamaica’s updated NDC covers only the energy sector and the LULUCF sector, we have assumed an emissions growth for the IPPU and Waste sectors following the last 10 years trend, and for the agriculture sector that the share of emissions in 2030 remains the same as the current share over total GHG emissions

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 35% and the conditional portion is 10%, totalling a 45% emissions reduction by 2030 which we have used for the purpose of this analysis. 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 (incl. 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 246 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“;https://unfccc.int/documents/182748). The unconditional target is calculated to be 412 MtCO₂e and the conditional 349 MtCO₂e (excluding LULUCF).

Mozambique:
Mozambique updated NDC values are reported in AR5. Emissions target for 2025 was converted to AR4 assuming the gas share for CO₂, CH₄ and N₂O to be constant from 2019, as reported by PRIMAP 2021. While Mozambique indicates REDD+ is a key means of implementation for mitigation ambitions, LULUCF is assumed to be excluded from annual reductions based on their exclusion from the BAU and Mozambique’s stated decision not to include afforestation removals and emissions. As Mozambique only provides a numerical value for cumulative emission reductions for 2020-2025, annual emission reductions for 2025 are taken as provided in Figure 2 using plot digitisation software. Note that annual reductions illustrated in Figure 2 from the NDC document do not add up to the stated cumulative emissions for 2020-2025 (40 MtCO₂e).

Myanmar:
Myanmar’s NDC targets are expressed as a cumulative reduction from a BAU scenario over the period 2021-2030, translating to a unconditional emissions reduction target of 244 MtCO₂e, and a conditional reduction target of 415 MtCO₂e, including LULUCF. If LULUCF reductions were removed, the cumulative reductions would be 121 MtCO₂e or 22% above 2015 levels (unconditional) and 158 MtCO₂e or 13% above 2015 levels (conditional) against a BAU which would lead to emissions increase 58% above 2015 levels. The NDC does not provide clarity on the global warming potentials used, we then assume these to be done following the 4^th assessment report (AR4 GWP).

In order to quantify Myanmar’s NDC and compare it with the 1.5°C compatible emissions pathways, we calculate the required cumulative emissions reductions over the period 2021-2030 with the following assumptions:

• We use a simple linear function to interpolate between 1.5°C compatible emissions levels given for years 2020, 2025, and 2030 for the 50^th, 25^th, and 5th quartiles.
• We use a linear forecast function to extrapolate from historical data to calculate baseline emissions between 2020-2030:
  • We regress sectoral emissions (energy, industrial processes, agriculture, waste, and other) on GDP per capita.
  • We assume a 7% GDP growth starting in 2016 and out to 2030. This is in line with the 7.1% GDP growth rate assumed under the “medium” scenario of Myanmar’s 2015 Energy Master Plan. An average GDP growth of 6.54% was observed in 2015-2019 before effects from COVID caused a downturn.
  • World Bank estimates are used for population forecast out to 2030.
  • We apply the historical regression coefficient to the projected per capita GDP to calculate annual sectoral baseline emissions in 2021-2030. These are then summed to arrive at annual totals.
• Annual total emissions (excluding LULUCF) for Myanmar’s conditional and unconditional NDC scenarios are assumed to follow the same trajectory as the emissions from power generation trajectories given for these scenarios (Tables 5 and 7 in the NDC). For the unconditional NDC scenario, 4% of the total expected reduction of 121 MtCO₂e from BAU are taken to occur in 2021, growing to 18% by 2030. A similar trajectory is assumed for the conditional NDC scenario.
• We calculate the difference between baseline emissions (as derived in previous step) and the other scenarios (three 1.5°C and two NDC scenarios).

The 1.5°C compatible cumulative reductions below BAU amount to 292-385 MtCO₂e (28-37% below 2015 levels) over the period 2021-2030 (in AR4 GWP). These are compared with those given in the NDC for non-LULUCF emissions.

Namibia:
NDC updated in July 2021. GWPs not specified, we assume to be SAR because historical emissions are consistent with NC4 (March 2020), expressed in SAR. Based on NC4 inventory data, historical emissions provided are GROSS emissions (including LULUCF sources but not sinks- i.e., sources from biomass burning, grasslands and settlements). BAU also assumed to be provided in gross emissions, as NC4 net BAU is estimated to be an 87 MtCO₂e sink. However, most of emissions reductions are planned on using LULUCF sinks (essentially tree planting for AFOLU). Further, total reductions from AFOLU (~19) are greater than estimated AFOLU emissions in BAU (~17). To estimate BAU excluding LULUCF source, we apply NC4 BAU sector shares to NDC BAU. To disaggregate AFOLU, agriculture was estimated using BAU parameters listed in NC4 (pg. 200) with sub-sector emissions. Converted to AR4 using 2015 sector gas ratios (except for Agriculture, which used the estimated BAU gas shares). For converting reductions, percentages were applied to AR4 values.

Pakistan:
Pakistan’s NDC, including both conditional and unconditional commitments, is 50% below business as usual (BAU) levels by 2030 (including LULUCF). The NDC includes a GHG inventory showing that 2018 emissions stood at 490 MtCO₂e. Pakistan estimates that 2030 emissions under BAU will be 1603 MtCO₂e/yr including LULUCF, or a 227% increase from 2018 levels. Their overall Nationally Determined Contribution (NDC) translates to an emissions level of 802 MtCO₂e/yr, including LULUCF, or 64% above 2018 levels. The country’s unconditional target (15%) alone would translate to 2030 emissions levels of 1363 MtCO₂e/yr, including LULUCF (178% above 2018 levels). This may be compared to the previously submitted INDC conditional 20% target which translated to 2030 emissions levels 1282 MtCO₂e/yr including LULUCF (162% above 2018 levels).

Pakistan’s baseline emissions projections in their updated NDC appear to be unchanged from that given in the INDC: 1603 MtCO₂e/yr including LULUCF. While the updated NDC only provides the aggregated emissions value, the INDC gives a sectoral breakdown. We assume here that, like the aggregate, the sectoral composition of the baseline emissions has remained the same for the updated NDC. Importantly, we assume that baseline projections for 2030 LULUCF sector are unchanged.

We estimate emissions levels under a BAU that excludes LULUCF to be 1574 MtCO₂e/yr by 2030. Pakistan’s NDC does not specify the Global Warming Potential (GWP) used, however, “an additional study”;https://www.sciencedirect.com/science/article/pii/S1674927820300204 indicates that it uses GWPs from the IPCC Second Assessment Report (SAR). The analysis provided here is based on GWPs from the 4^th assessment report (AR4), thus for comparability and consistency purposes, we provide a range in estimating Pakistan’s 2030 unconditional and conditional updated NDC targets.

The lower bound is based on applying a 50% reduction (or 15% and 35% for unconditional and conditional targets respectively) to the stated 2030 BAU level excluding LULUCF, leading to emissions levels of 787 MtCO₂e/yr (or 1338 and 1023 MtCO₂e/yr for unconditional and conditional targets respectively) excluding LULUCF, by 2030. The NDC states that 2018 LULUCF emissions were 24.86 MtCO₂e. Thus, total emissions excluding LULUCF in 2018 were 465 MtCO₂e. Thus, in relative terms, the NDC reduction targets, excluding LULUCF, would be 69% increase over 2018 levels overall, and 188% and 120% increases from 2018 level for unconditional and conditional targets respectively.

For the higher bound, we converted the 2030 baseline projection, excluding LULUCF, by scaling according to the ratio between SAR and AR4 historical emissions as provided in the PRIMAP-Hist dataset. This conversion factor is consistently around 0.94 SAR/AR4 in the years covered by the PRIMAP dataset. We thus arrive at a 2030 overall NDC target emissions level of 838 MtCO₂e/yr excluding LULUCF (or 1425 and 1089 MtCO₂e/yr for unconditional and conditional targets respectively). Using AR4 GWP, 2018 emissions levels, excluding LULUCF were 513 MtCO₂e. Thus, in relative terms, the NDC target, excluding LULUCF, would constitute a 63% increase over 2018 levels (178% and 112% for unconditional and conditional targets respectively)

Qatar:
Qatar updated its Nationally Determined Contribution (NDC) in August 2021. While the updated NDC is an improvement compared to the previous one, as it includes an emissions reduction target, the government provides no information on the “business as usual” (BAU) scenario used as a reference for the target. The government aims to reduce emissions by 25% below BAU by 2030 covering the sectors of energy, including transport and downstream industries, buildings and construction industry, water management, waste and infrastructure. We have assumed a BAU range based on (1) the ten-year trend of emissions from 2009–2019 based on PRIMAP data for the upper range and (2) a downscaled RCP4.5 SSP2 pathway from Gütschow et. al, 2020. As it is unclear whether the target covers industrial process emissions, we have produced a target range of emissions in 2030 including and excluding industrial process emissions. We assume the target does not cover agriculture and “other emissions” in the upper range. However, as these emissions represented less than 1% of the total emissions in 2019, and sectoral data is not available for the lower BAU range, they are considered negligible and not excluded.

Romania:
The NECP (2019) provides ETS and ESR (non-ETS) emissions targets of respectively 43.9% below 2005 levels and 2% below 2005 levels by 2030. Romania estimates in its NECP a level of emissions reductions of around 50% below 1990 levels by 2030 including LULUCF. We apply this estimated target to historical emissions from 1990 including LULUCF provided by Romania to the UNFCCC and the EEA in 2021, and exclude LULUCF in 2030 based on the “With Existing Measures” LULUCF projections, which leads to an emissions reduction of 37% below 1990 levels excluding LULUCF, or 7% above 2005 levels. It is important to note that under the proposal of the Fit for 55 Package, the respective ESR and ETS targets are being revised (to -12.7% by 2030 below 2005 levels) as is the scope of the ESR and ETS sectors. The overall emissions reductions target for Romania is therefore subject to change after approval of the Fit for 55 Package. Current policy projections are taken from the “With Existing Measures” Scenario (WEM) for the upper bound and the “With additional measures” Scenario provided by Romania to the EEA and the UNFCCC.

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.

Serbia:
Serbia’s updated NDC targets a 13.2% emissions reduction by 2030 compared to 2010 levels. Although the NDC is described as an ‘economy-wide’ target, sectors covered listed exclude the LULUCF sector. The Draft Low Carbon Development Strategy with Action plan 2019 from Serbia provides more details on the mitigation scenario leading to the 13.2% emissions reduction target by 2030 below 2010, which exclude LULUCF emissions. We therefore assumed that the NDC target is excluding LULUCF. Third National Communication and Second Biennial Update Report, based upon which the NDC emissions reductions have been calculated, are as of yet unavailable to the public, as confirmed in exchange with the Serbian Environmental Protection Agency. To quantify 2030 emissions targeted in the NDC, we used emissions data from Draft Climate Strategy and Action Plan, which have been provided by the Serbian Environmental Protection Agency, the same agency providing emissions figures for the National Communications and Biennial Update Reports. We applied to these estimates the 13.2% reduction to the 2010 base year. We then add back the projected LULUCF emissions by 2030 to estimate the absolute emissions level including LULUCF. Historical data provided by the Draft Climate Strategy and Action Plan are up to 2015, with data years provided of 2005 and 2010. We interpolate in between based on PRIMAP 2022 dataset growth rate, and extrapolate post-2015 based on the same dataset.

Tanzania:
Tanzania targets an emissions reduction of 30-35% below BAU by 2030, whereby 138-153 MtCO₂e gross emissions is expected to be reduced (including LULUCF). This would correspond to 0-7% below 2014 levels. We assume the absolute emission reductions and % reductions listed in the target formulation are including LULUCF. The NDC includes different contradictory levels of BAU by 2030, we assume the BAU is as given in Figure 1.

Tanzania’s updated NDC and original NDC do not provide information on what Global Warming Potential (GWPs) are used. Further, the source provided for the BAU estimate, “Tanzania GHGs Inventory Report and MRV System”, is not available online. We assume AR4 GWPs are used as the historical value for 2014 is less than 1 MtCO₂e different from PRIMAP‘s AR4 estimate for 2014.

As reductions from LULUCF are not available, we use a range of assumptions to exclude LULUCF from the target value. For the upper bound, we assume LULUCF is reduced proportional to the other sectors (i.e., 30% and 35%) and for the lower bound, we assume no reductions from LULUCF. While this is unlikely as the updated NDC indicates Tanzania intends to use some forestry measures to meet their target, this gives the most conservative estimate.

Zimbabwe:
Zimbabwe’s NDC provides a target by 2030 in emissions per capita. The updated NDC includes BAU, mitigation reductions, and target emissions in absolute levels which we use for the quantification. To exclude LULUCF, we use the emissions graph on page 20 that depicts the sectoral breakdown of remaining emissions in 2030 under the mitigation scenario. A plot digitiser was used to extract the data. The updated NDC is reported in SAR values (see page 40 of the update). Emissions are converted to AR4 using the ratio of AR4:SAR from PRIMAP-Hist for base year 2017.

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 gas, based on country reported emissions (Gütschow et al. 2016).

In the current situation section, to give a snapshot of the country emissions, the following decision tree is applied for 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.

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 ‘Discussion and limitations on global-least costs pathways,’ the emissions projections from the 1.5°C compatible range are based on the full set of IPCC 1.5°C compatible scenarios downscaled as described on section ‘Selecting and defining the illustrative pathways,’ and energy mix projections are based on the downscaling methodology as described in section ‘Discussion and limitations on global-least costs pathways.’