A multi-model analysis of post-Glasgow climate action and feasibility gap

doi:10.21203/rs.3.rs-2319580/v1

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A multi-model analysis of post-Glasgow climate action and feasibility gap

https://doi.org/10.21203/rs.3.rs-2319580/v1

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published 18 May, 2023

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The COP26 Glasgow process resulted in many countries strengthening their 2030 emissions reduction targets and announcing net-zero pledges for 2050–2070. We use four diverse integrated assessment models (IAMs) to assess CO₂ emission trajectories in the near- and long-term based on national policies and pledges, combined with a non-CO₂ infilling model and a simple climate model to assess the temperature implications of such trajectories. Critically, we also consider the feasibility of national long-term pledges towards net-zero, to understand where the challenges to achieving them could lie. Whilst near-term pledges alone lead to warming above 2°C, the addition of long-term pledges leads to emissions trajectories compatible with a well-below 2°C future, across all four IAMs. However, whilst IAM heterogeneity translates to diverse decarbonisation pathways towards long-term targets, all modelled pathways indicate several feasibility concerns, relating to the cost of mitigation, as well as to rates and scales of deployed technologies and measures.

Earth and environmental sciences/Climate sciences/Climate change/Climate-change mitigation

Scientific community and society/Social sciences/Climate change/Climate-change mitigation

Scientific community and society/Social sciences/Carbon and energy/Energy modelling

National long-term climate pledges keep the global peak warming below-2°C (between 1.7 and 1.8°C)
Emission intensity reduction needs to accelerate post-2030 to achieve long-term ambitions if NDCs not strengthened
Globally implemented policies are inconsistent with 2030 NDCs
Feasibility concerns of modelled long-term pledges are found for all models and regions, yet on different angles of feasibility
Dedicated policy required to overcome mapped feasibility bottlenecks for achieving long-term targets consistent with the Paris Agreement
Focus on short- and long-term implementation of announced pledges crucial to avoid exceeding 2°C of global warming.

In the Paris Agreement, adopted in 2015, countries agreed to hold global mean temperature rise to well-below-2°C while pursuing to limit it to 1.5°C. Since then, the scientific community has focussed its efforts on understanding what it would take for the world to meet this target¹. Modelling since Paris has looked at requirements to meet the most stringent 1.5°C ambition², quantification of the required transformations^3,4, and of the consequences of living in a 2°C rather than a 1.5°C world⁵. Initial climate pledges made in the context of the Paris negotiations were soon found inadequate⁶, but, despite the slower-than-necessary pace^7,8, countries have kept ramping up their climate action and ambition since. Several studies have so far quantified the actual impact of international policy efforts^9–11 and established that loosely defined business-as-usual scenarios missing that impact started making less sense^12,13, thereby increasing the pressure for a better and a more realistic analysis of where the world is headed based on current policies and pledges¹⁴. A growing body of literature^15–17, thus, shed light on what policies currently in place as well as official 2030 Nationally Determined Contributions (NDCs) would yield, establishing that they are inadequate to limit warming to 2°C^18,19.

Although the critical climate talks of the 26th Conference of the Parties (COP26)—a milestone for ratcheting ambition—were delayed by a year due to COVID-19, parties followed up on their commitments; by the time COP26 was completed in Glasgow in November 2021, more than 120 countries had upgraded their 2030 targets²⁰ and major emitters representing over 70% of the world’s CO₂ emissions had announced and/or adopted net-zero commitments²¹. A handful of studies attempted to quickly assess the outcome of these new promises^22–26, showing that—if fully implemented—global climate ambition could hold the rise in global temperatures to just-below-2°C by the end of the century. More efforts to comprehend the effect of the new generation of NDCs and long-term targets (LTTs) followed^27,28. Nonetheless, each of these studies was based on a single model and, despite stagnation of new such bold promises in 2022 largely due to the current energy crisis²⁹, a multi-model assessment of global climate pledges remains critical. Our study contributes to this research gap, aiming to enhance the robustness and confidence in our knowledge of the possible global warming outcomes, by exploiting a diverse ensemble of models.

Model diversity also implies a plurality of modelling paradigms, theories, solution mechanisms, and thus of pathways that each model yields³⁰. Given the pursuit of and political commitment to 1.5°C—which was further strengthened in the Glasgow Climate Pact¹—and the ambition reflected in announced net-zero pledges, special emphasis is increasingly placed on thresholds or boundaries that modelled pathways must stay within to be considered feasible³¹. According to the 6th Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6), feasibility refers to the potential for a mitigation (or adaptation) option to be implemented, based on a diversity of context-dependent factors³²—including institutional, financial, and political factors^33–35. Conversely, from a modelling perspective, feasibility refers to quantifiable geophysical, environmental-ecological, technological, economic, and even sociocultural factors³⁶. Feasibility assessments hitherto referred to challenges at the global level^37,38 and/or have been used to explicitly assess 1.5°C-compliant pathways³⁹, without touching upon regional feasibility concerns of decarbonisation pathways that are required to achieve announced pledges. Our study builds on and expands the multidimensional feasibility assessment framework established by Brutschin et al. (2021)³⁷, further disaggregating it at the regional level, to assess to what extent and from which perspective countries’ policy targets, NDCs, and LTTs are feasible.

Defining Scenarios And Feasibility Indicators

Quantifying gaps requires quantifying where the world is headed given current levels of climate action and ambition. We use three scenarios, each corresponding to a different level of climate action or stated ambition (Fig. 1): (1) a scenario with current emission reduction policies until 2030 with a post-2030 extrapolation maintaining 2020–2030 emission intensity tendencies (EI), (2) a scenario with current NDC targets for 2030 with the same EI extrapolation, and (3) a scenario with NDC targets until 2030 followed by LTTs. All policies and targets submitted or announced by June 2022 are included in these scenarios.

Comparing the scenario outcomes allows us to subdivide the climate action gap, that is, the difference between the emission reductions and related temperature outcomes that can be expected with the current set of policies in all countries, with the announced Paris goal of pursuing to keep global temperature increase below 1.5°C. The difference in 2100 or peak temperature (depending on whether a peak is reached in the 21st century) of current policies and that of 2030 NDCs, both extended by EI trends, is termed the implementation gap. The temperature difference between the 2030 NDCs extended by EI trends, and the 2030 NDCs followed by LTTs (where stated) is termed the “long-term ratchet gap”, referring to the pace in which post-2030 action must be accelerated relative to pre-2030 action to deliver on long-term targets. The difference between the peak temperature of 2030 NDCs followed by available LTTs and the 1.5°C target is termed the “ambition gap”.

Apart from the climate action gap, this study looks into the feasibility of pathways based on country-specific policies and announced emission reduction targets. Feasibility concerns are measured by comparing specific model outcomes with threshold values found in the literature (see Methods). A total of 10 different feasibility indicators are estimated, which can be divided into three categories: (a) socioeconomic feasibility concerns related with the cost burden of mitigation policies, (b) technology scale-up feasibility concerns related with the velocity at which clean technologies can replace existing technologies in place, and (c) physical feasibility constraints related with the physical potentials for bioenergy production and carbon storage. Feasibility concerns are measured by model region and for each 10-year time-step (between 2020 and 2050) to illustrate “where” and “when” we find the largest bottlenecks to climate change mitigation.

Global Action Gap

We focus on global CO₂ emissions from energy and industrial processes to 2050 as all our IAMs represent these emissions sources as a minimum. Taking into account all relevant national and regional energy and climate policies on top of socio- and techno-economic baseline assumptions, we find that emissions will stabilise or start declining in the current decade, reaching 33–38 Gt by 2030 (Fig. 2). If policy effort is sufficiently strengthened to reach stated NDC emission targets, we find across models that emissions are reduced towards 2030, reaching 30–33 Gt. If all countries continue their declining trend in emission intensity of GDP beyond 2030, global emissions will achieve levels of around 24–30 Gt and 19–23 by 2050—for current policies and NDCs, respectively. However, if countries accelerate action post-2030 to meet their stated long-term emission targets, we find 2050 emissions in the range of 10–13 Gt.

The model spread is largest for current policies, since models run largely in forecasting mode, simulating the impact of policies relative to a model-dependent no-policy baseline. Despite many of the model input assumptions being harmonised to reduce unwanted response heterogeneity (see SI), no-policy baselines tend to differ strongly, driven by inherent model characteristics as well as remaining unharmonised inputs¹⁸. Therefore, despite the converging effect of modelling a common set of current energy and climate policies, model variation still tends to be large in such exercises^14,40 (Table 1). The model spread for emissions significantly decreases when emission targets from NDCs and LTTs are used as absolute constraints to the models. The remaining emission spread can be explained by a mix of factors, such as model regions overperforming their stated targets, differences in regional aggregation, and the CO₂ vs non-CO₂ share in emission reductions. While total emissions outcomes between models converge when applying constraints, the distribution of emissions over the different sectors diverge between the models, reflecting the heterogeneity of mitigation pathways preferred by each model (see Extended Fig. 1).

Table 1

Model key characteristics
	GCAM-PR	GEMINI-E3	MUSE	TIAM-Grantham
Model type	Partial equilibrium	General equilibrium	Agent-based energy-system	Partial equilibrium
Solution dynamic	Recursive-dynamic	Recursive-dynamic	Recursive-dynamic	Inter-temporal optimisation (perfect foresight)
Technology choice	Logit choice	Nested CES function	xx	Winner takes all
GHG emission coverage (reported)	Fossil CO₂, CH₄, N₂O, Land-use CO₂, F-gases,	Fossil CO₂, CH₄, N₂O, F-gases	Fossil CO₂	Fossil CO₂
Model regions	32	11	20	15
Time horizon	2100	2050	2100	2100
Unconstrained baseline CO₂ ¹	High	High	Low	Medium
Impact of current policies ¹	Medium	High	Low	High
Major feasibility concerns with LTTs²:
Timing and indicator	Long-term bioenergy and carbon storage	Long-term energy demand reduction	Near and long-term carbon pricing	Near term technology scale-up
Region(s)	India, Japan	USA	EU, Japan	EU, India, Japan
Explanation based on model structure	Due to the endogenous representation of the land sector, no hard limits are set to bioenergy supply. High energy prices stimulate bio-energy output from land beyond sustainable limits.	As a general equilibrium model, the entire economy is simulated, including economic feedbacks to end-use sectors. High energy prices therefore lead to relatively high demand reduction	Due to high inertia by modelled agents and technology stickiness, high carbon prices are required to switch to low carbon technologies and/or reduce demand.	As an inter-temporal perfect foresight model, agents have perfect foresight towards the future, driving the near-term investment in renewable technologies.
¹ For baseline CO₂, High > 40 GtCO₂, low < 30 GtCO₂, Medium = 30–40 GtCO₂ by 2050. For impact from current policies, High > 6 GtCO₂, Low < 3 GtCO₂, Medium = 3–6 GtCO₂ reduction to baseline in 2030. ² Major feasibility concerns are identified separately per model, and not by comparing feasibility concerns between the different models.

After harmonising the emission data, infilling missing species, and running the emission outcomes through the simple climate model FaIR (see Methods), we find that current ambition levels signalled through implemented energy and climate policies will increase global temperatures to 2.1–2.4°C above pre-industrial levels by 2100, depending on the model (1.9–2.7°C when including climate uncertainty at the 25–75% interval), while ambitions levels stated in present NDCs slightly limit this temperature increase to 2.0-2.2°C (1.7–2.5 for 25–75% interval). In both cases, warming will continue after 2100, as global CO₂ emissions have not yet reached net-zero levels. If countries also comply with their stated LTTs after meeting their current NDC pledges in 2030, temperature increase will be further limited and stabilise around 1.7–1.8°C (1.5–2.0°C for 25–75% interval), which is arguably in line with a “well-below 2°C” future⁴¹. Depending on the model applied, this translates to an implementation gap leading to 0.1–0.4°C additional warming on the one hand, and a long-term ratchet gap equivalent to another 0.2–0.5°C of warming on the other. The remaining global ambition gap compared to the Paris target of keeping global temperature increase to 1.5°Cwould be around 0.2–0.3°C for all models. For 3 out of 4 models (GEMINI, MUSE and TIAM), the long-term ratchet gap contributes most to the entire climate action gap. This confirms previous assessments showing that mitigation in current NDCs is not aligned with long-term targets for most countries^24,27. For GCAM, the implementation gap contributes most to the entire gap, instead.

Disaggregating the emission results for the six largest emitters (Fig. 4) shows where the different gaps are more relevant. The implementation gap is measured to be largest (in relative terms) for the USA and Japan, which have relatively ambitious NDCs but their policies are lagging behind. For countries with relatively less ambitious NDCs, like China, India, and Russia, the implementation gap is smaller or non-existent, as existing policies overachieve NDC targets in several cases. In the EU, the implementation gap is relatively small due to ambitious policies. The long-term ratchet gap is significant for all cases, meaning that, with current NDC targets, all six countries require a significant boost in post-2030 climate action for their net-zero targets to be achievable. However, differences between models are non-negligible, as GCAM results show an implementation gap in all countries except Russia, while GEMINI results show an implementation gap only for the USA. Driven largely by announced emissions targets, pathways towards long-term targets are relatively similar between the models, except India and Russia, due to different modelled or assumed contributions from non-CO₂ emissions and natural sinks towards net-zero targets. We have not assessed the ambition gap at the country level, as that would require an assessment of fairness and equity⁴².

Scenario Feasibility

The feasibility of the modelled scenarios based on national policies and targets is measured by comparing specific scenario outcomes with pre-determined thresholds. Surpassing a threshold indicates that the feasibility of achievement in that dimension might become concerning. The global results show that feasibility concerns vary strongly between models, between scenarios, and over the different time periods (Fig. 3a). Logically, deeper mitigation efforts imply larger feasibility concerns, as they drive models further away from their emissions-unconstrained baselines. Achieving stated long-term targets among all countries, which is the only option of keeping temperature increase well-below-2°C (Fig. 2), implies that regional feasibility thresholds must be passed three to six times (global weighted average and with median thresholds), depending on the model and aggregated over the different feasibility dimensions. The feasibility metrics are only based on mitigation, and do not consider the adaptation challenges that are driven by a lack of mitigation. In fact, the feasibility of adequate adaptation to make up for a lack of mitigation may be significantly more concerning—i.e. in terms of costs, pace of investment scale-up, and land and freshwater availability^43,44—but this is outside the scope of this study. These feasibility concerns can therefore best be understood as key aspects that require attention for successful implementation of the ambitious mitigation policies.

Since the different models significantly differ in structure resulting in a wide variety of mitigation pathways, feasibility concerns also differ between the models (Table 1), with GEMINI showing the lowest and MUSE the highest concerns. However, the distribution of feasibility concerns over the different feasibility dimensions and over time are crucial for the interpretation. For example, the high overall concern for MUSE is largely driven by high carbon prices and demand reduction pathways. In contrast, these dimensions are hardly of concern for GCAM and TIAM, where the pace of technology deployment and—in the case of GCAM—reliance on bioenergy and CCS are the main sources of feasibility concern. When evaluating feasibility concerns over time, all three scenarios from TIAM stand out for showing most concerns in the near term (2030), predominantly related to the high pace of wind and solar energy deployment to deliver on 2030 targets. In GCAM, feasibility concerns are relatively small in the run-up to 2030, but arise in later periods, largely driven by an increasing reliance on bioenergy with carbon capture and storage (BECCS). The latter is concerning from three different feasibility perspectives: the pace of technology deployment, the availability of sustainable bioenergy resources, and geological carbon storage capacity.

Since mitigation scenarios in this study are entirely driven by country-specific climate policies and ambitions, we also specify an overview of measured feasibility concerns for the six largest emitters, averaged over the 2020–2050 period (Fig. 4). While again large differences between models exist, overall, we see relatively low concerns in China and Russia, and high concerns in the USA, the EU, and Japan. These differences are likely driven by more ambitious near- and long-term targets in the latter group, which—despite being already in an emission reduction phase for at least a decade—still confront high feasibility constraints to meet their near- and longer-term targets. Specific feasibility issues that stand out include energy demand reductions in the USA (GEMINI), carbon pricing in the EU (MUSE), and bioenergy and carbon storage potentials for Japan (GCAM). Feasibility concerns about the pace of technology deployment play a relatively small role in China, the USA, and the EU, but a significant role in India, Russia, and Japan.

The interpretation of the measured feasibility concerns can be subjective. The pre-determined thresholds are not set in stone, and often large ranges for such thresholds exist in literature^37,38, while threshold levels strongly affect measured feasibility concerns (Fig. 3b). Experts in different fields may have very different views on what is feasible or not. Also, country-specific features, such as the country size, stage of development, and economic structure, as well as access to international financial markets, will influence the threshold level. While some of these features are weighted in the feasibility assessment (e.g., the thresholds for carbon pricing depend on GDP per capita levels), not all country-specific features can be taken into account (e.g., economic structure). Also, some historical cases prove that the chosen thresholds can be overcome. An example is the surge of gas-fired power in the Netherlands and nuclear power in France, which respectively surged from 5–80% and 25–75% of the power mix in one decade, surpassing the applied feasibility threshold in this study over 10-fold. Since such historical examples of fast transitions are typically driven by public policy and support⁴⁵, the feasibility analysis can also be interpreted as a mapping exercise of where policy support is strongly needed to overcome existing constraints, which is crucial for achieving stated climate targets as all models and scenarios analysed in this study surpass several feasibility thresholds. Nevertheless, as the results show, this mapping strongly depends on the applied model: deep structural differences between models lead to a wide variety of pathways reaching the same climate targets and, hence, different policy interventions are necessary from different modelling perspectives to make these pathways feasible.

The results in this study suggest that if announced national near- (2030) and longer-term (2050–2070) emission reduction ambitions throughout the world are achieved, the global peak temperature increase will stay below 2°C with ~ 75% certainty. However, if climate action is not strengthened after 2030, long-term ambitions will not be achievable, and global temperature increase will be around or above 2°C by the end of this century. All applied models agree that—with the current pace of policy implementation—, temperature increase will not exceed 2.5°C over the course of the century but will still have a rising trend thereafter. These results clearly show that climate action and ambitions have notably improved since 2020, when the same set of models and scenario structure projected 2030 emissions to be on average 3 Gt CO₂ higher (see Extended Fig. 2) and mean global temperature to be 0.2–0.3°C warmer by 2100¹⁸. This improvement, alongside our finding that global emissions are set to peak in the current decade with current NDCs, is in line with earlier post-COP26, single-model assessments^{22–24, 27,28}. The latter, however, also found increasing emissions for current policies scenarios throughout and after the 2020s^22,27,28, whereas all models in our study find either a reduction or stabilisation of emissions with current policies within this decade.

Accounting for post-2030 extrapolations of current policies and NDCs, this study shows significantly lower emissions and end-of-century temperatures than other studies; additionally, our multi-model LTT projections are slightly more optimistic (by around 0.1°C). An important reason for this difference likely lies in the applied extrapolation method: in contrast to a continued trend in emission intensities used in this study, other studies apply a carbon price equivalent to 2030 action and increasing over time with GDP levels^23,27,28, a method leading to more conservative emission reductions in most models¹⁸. There is no straightforward answer on which policy extrapolation method is better: while continuing a trend in emission reductions may falsely bank on an emission reduction trend that might not be equally attainable in the future, relying on extrapolated carbon prices may put too much faith in highly uncertain future model assumptions, especially considering that, e.g., the decline in costs of low-carbon technologies have traditionally been underestimated in such assumptions¹². Another important difference with several other model studies in the literature is that current policies are modelled (a) explicitly (i.e., not proxied via carbon prices until 2030) and (b) on top of an up-to-date and harmonised set of socio- and techno-economic assumptions. This contributes to the relatively optimistic assessment of 2030 emissions in the study, as it endogenously assumes ratcheting of emission reduction targets (by overperforming on these targets) where these are not ambitious.

The applied scenario structure in this study reflects three levels of ambition, where for each separate model region emissions are equal or lower with each subsequent level of ambition. Similarly, uncertainty to reach the modelled emission reduction also increases with each subsequent level of ambition. Hence, despite aggregated national ambitions being in line with a well-below-2°C temperature future, the likelihood of all these national ambitions being achieved should not be overestimated. For each individual country that fails to meet its targets, the probability of higher temperature levels will increase. The feasibility analysis in this study shows that, for most countries, it is all but straightforward to achieve their stated near- and long-term ambitions with the anticipated future demand patterns and technologies. Depending on the decarbonisation pathways followed, multiple and geographically heterogenous feasibility challenges come up, which have to be either overcome through dedicated policy efforts or potentially circumvented through unanticipated breakthroughs in technology or consumer behaviour⁴⁶.

Our results show that aggregating all national near- and long-term emission reduction targets is still insufficient to limit global temperature increase to 1.5°C, which is the highest ambition of the Paris Agreement. Even if the most ambitious scenarios in this study are achieved, the temperature increase may still cause significantly notable and damaging climate impacts⁴⁷, and be sufficiently strong to activate several climate tipping elements, such as the collapse of the Greenland and West Antarctic ice sheet and the die-off of low-latitude coral reefs, among others^48,49. Therefore, even if ambitions have already significantly improved in the run-up to and shortly after COP26, cumulative emissions until 2050 have to be reduced significantly more to avoid overshooting the 1.5°C target, or substantive negative emissions should be achieved after reaching LTTs to reach 1.5°C by 2100 with a high overshoot (Fig. 5). Nevertheless, the results of this study show that, while the focus on further ambition ratcheting should not be lost⁸, a high focus on the short- and long-run implementation of the existing set of ambitions is significantly more important to avoid a climate disaster.

Models included

Four global integrated assessment models are included in this research: GCAM-PR (also referred to as GCAM), GEMINI-E3 (also referred to as GEMINI), MUSE, and TIAM-Grantham (also referred to as TIAM). These are selected to reflect the broad diversity of modelling theories, spanning a range from least-cost energy system optimisation to partial and general equilibrium and to agent-based modelling. Diversity of modelling structure, theory, and solution is typically sought in multi-model studies, aiming to reach robust estimates by reflecting structural uncertainty—rather than parametric uncertainty, which has been minimised to reduce unwanted response heterogeneity⁴⁰.

GCAM⁵⁰ (Global Change Analysis Model) is a partial equilibrium IAM, achieving equilibrium between energy supply and demand in each represented sector, accounting for the changes in energy prices resulting from changes in fuels and technologies used to satisfy energy-service demands in these sectors. The model operates on a ‘recursive dynamic’ cost-optimisation basis and solves for the least-cost energy system (constrained by observed technological preferences) in a given period before moving onto the next period and performing the same process.

TIAM⁵¹ (Times Integrated Assessment Model) is also a partial equilibrium IAM and achieves similar equilibrium between energy supply and demand in each sector. However, TIAM operates on a ‘perfect foresight’ welfare cost-optimisation basis, whereby all consequences of technology deployments, fuel extraction, and energy price changes over the entire time horizon are considered when minimising the cost of the energy system to provide energy-service demands within specified emissions constraints.

GEMINI-E3⁵² (General Equilibrium Model of International–National Interactions between Economy, Energy and the Environment) differs in model solution in that it is a general equilibrium IAM, featuring a more detailed, multiple-sector representation of the economy that considers how the impacts of specific policies spread across economic sectors and regions affect environmental parameters. This means that, despite also being driven by market equilibrium, this equilibrium is assumed to take place simultaneously in each market/region. Its richer representation of the economy requires calibration to data on national and international socio-accounting information and a vector of various elasticities of substitution, but it allows endogenous calculation of market prices of inputs and outputs.

Finally, MUSE⁵³ (ModUlar energy systems Simulation Environment) is an agent-based, energy system model that provides a detailed account of the energy sector to calculate least-cost GHG emissions reduction pathways—or the costs of alternative climate policies. It is bottom-up, in that assumes short-term microeconomic equilibrium on the energy system by iterating market clearance across all sector modules and interchanging price and quantity of each energy commodity in each region, but it is also agent-based, in that it tries to determine a mitigation pathway by providing an as-realistic-as-possible description of the investment and operational decision making in each geographical region within a sector.

All four models differ in the way technologies are chosen across sectors: GCAM employs a logit technology choice mechanism, which causes gradually decreasing returns as a technology is further diffused; TIAM uses a winner-takes-all optimisation mechanism, implying that the cheapest technology can dominate all new deployment; GEMINI uses a nested constant elasticity of substitution function; while MUSE follows an agent-based approach, where agent decision goals and strategies determine technology choices in each time step.

Scenario protocol

Starting this modelling exercise, harmonised socio- and techno-economic input assumptions were applied by all models, reflecting the latest available information and avoid “noise” in the model outcomes related to unaligned assumptions. For GDP projections, the IMF WEO of April 2022⁵⁴ for GDP growth until 2027 the OECD EO-109 (2021)⁵⁵ for post-2027 growth projections, reflecting the impacts of the COVID-19 pandemic as well as initial estimated impacts related to the Ukraine conflict. On techno-economic assumptions, power generation technology costs were updated to observed 2020 values (IRENA) while maintaining the future evolution of costs as reflected in Giarola et al (2021). For hydrogen, projections of different production technologies were updated according to IEA estimates (2017)⁵⁶.

The first scenario (Current Policy extrapolated with Emission Intensity, CP_EI) is based on the current portfolio of actual emission reduction policies as well as credible policy targets until 2030 in G20 countries including the entire European Union (EU) (see SI). Post-2030 action is then modelled by measuring the average rate of change in emissions intensity of GDP from 2020 to 2030 in each region and assuming emissions intensity reduction rates will remain the same after 2030. This method is also used by Ou et al (2021)²², Sognnaes et al (2021)¹⁸ and VanDyck et al (2016)¹⁵ to assess the long-term implications of NDCs. The applied policy targets until 2030 (e.g. renewable energy mx targets, vehicle fuel standards) are maintained as minimum levels beyond 2030 to avoid backtracking of achieved policies.

The second scenario (NDCs extrapolated with Emission Intensity, NDC_EI) is based on stated 2030 emission targets captured in NDCs submitted or announced by June 2022, capturing all mitigation ambition updates related to the COP26 in Glasgow (see extended Table 1). These NDC targets are applied on top of current policies (CP) modelled in the previous scenario; in model regions where current policies overachieve on the mitigation targets in NDCs, no additional emission constraints are applied, following Sognnaes et al. (2021). Emissions reductions in NDC scenarios are therefore never less ambitious than what CP implies. The same emission intensity method is applied for post-2030 action as in CP_EI.

The third and most ambitious scenario (NDCs with Long-Term Targets, NDC_LTT) is built on the NDC_EI until 2030 but, for regions that expressed an LTT, such as net-zero commitments or other targets for 2050 or later (either in law, policy documents, or only announced) (see extended table 2), emission constraints are applied that linearly decline from 2030 emissions as in the NDC_EI scenario towards said long-term target. For regions without LTTs, post-2030 emissions follow an identical path as in the NDC_EI scenario.

Since nearly all countries have submitted at least some NDC target, defining NDC targets for aggregated model regions is relatively straightforward. However, far but all countries have submitted LTTs, hence some assumptions are required if one or more countries in an aggregated model region have LTTs. In such cases, the emissions level (E) should be calculated by applying the LTT to the estimated emissions share of that specific country (i) in the entire model region (j) according to either the 2019 emissions levels⁵⁷, or, if available, the country´s emissions share in the aggregated NDC target for 2030, and applying the EI method for the rest of the region:

$${E}_{j,2050}=E{\left(LTT\right)}_{i,2050}\times \left(\frac{{E}_{i,2019 / 2030}}{{E}_{j,2019 / 2030}}\right)+E{\left(EI\right)}_{j-i,2050}*\left(\frac{\left({E}_{j,2019 / 2030}-{E}_{i,2019 / 2030}\right)}{{E}_{j,2019 / 2030}}\right)$$

Temperature assessment

To assess the implications of the modelled scenarios for global mean temperature increase, emission outcomes from the models are harmonised with historical tendencies, infilled to include the full set of greenhouse gas emissions, and fed into a climate model for probabilistic temperature simulations.

In most instances, the first stage of the temperature assessment is to harmonise the global emissions trajectories to known values in 2015 (interpolated if not already present) using ratio-based harmonization approach⁵⁸. Since not all models report the entire set of GHGs and other pollutants required for a complete temperature assessment, unreported emissions from each model participating in this intermodal comparison exercise are infilled using Silicone v1.3.0⁵⁹, using a quantile rolling window with CO₂ emissions from energy and industrial processes as the lead emissions, and based on an infilling database comprised of the harmonised AR6 database⁶⁰ filtered to match the model philosophy. The models that are included in the AR6 database are categorized based on their model type (for example general equilibrium/ partial equilibrium) and solution type (recursive dynamics/ inter temporal). The exception to this is the F-gases (SF6, HFCs and PFCs), which are not reported by enough models in each category. For these cases (where not reported otherwise), we use the F-gas total infilled as above, then break it down into its component SF6, HFC total and PFC total using the whole harmonised AR6 database and the Silicone technique DecomposeCollectionTimeDepRatio. For the GEMINI-E3 model simulating global economy dynamics over the time horizon 2015 to 2050, we extend each of the emissions till 2100 for each scenario using the Silicone tool ExtendLatestTimeQuantile, using the whole AR6 database.

When we have a complete set of required emissions, they are run through the simple climate model FaIR version 1.6.2, calibrated to match the AR6 Working Group 1 climate assessment⁶¹. This four-box model of the world replicates the impact of emissions on atmospheric concentrations, climate forcings and temperatures, constrained both against observations and the probability distributions of fundamental climate characteristics like TCR assessed by the IPCC.

Extended Fig. 3 shows the median temperature assessments until 2100 from FaIR, while also showing the uncertainty in this temperature assessment related to infilling the emission trajectories using Silicone.

Feasibility assessment

The feasibility analysis looks at specific variables in model outcomes and compares these with several thresholds found in literature. A total of 10 different feasibility indicators are measured, which can be divided into 3 categories: socioeconomic feasibility concerns related with the cost burden of mitigation policies, technology scale-up feasibility concerns related with the velocity at which clean technologies replace existing technologies in place, and physical feasibility constraints related with the physical potentials for bioenergy production and carbon storage. Feasibility concerns are measured by model region and 10-year period (2020–2030, 2030–2040 and 2040–2050) to illustrate “where” and “when” we find the largest bottlenecks to climate change mitigation. This section explains which thresholds are used and their sources or motivation, as well as which formulas are used to quantify the measured feasibility concerns.

Identically for all indicators, a value of the size of 1 represents that the specific threshold for that indicator is surpassed by 100% in that specific model region and period. Similarly, scores of 0.5 and 1.5 mean surpassing the thresholds by respectively 50% and 150%. That also means that, as long as the threshold is not passed, even if it comes close, the feasibility concern is measured as zero. Similarly for all indicators, the global value is built up as a weighted average of the independent values in all model regions. The weighting variable, however, varies between the different indicators.

Carbon pricing

Putting a price on CO₂ emissions is traditionally the most common way in models to achieve mitigation. While the impact of carbon pricing on households depends largely on how the revenues are recycled, carbon pricing often faces societal opposition if the costs are too high or increase too fast relative to overall costs and incomes⁶². Therefore, to reflect a feasible cost path as the threshold for this indicator, we adapted the “medium concern” level from Brutschin et al (2021)³⁷ of a global average price of 60 USD in 2020, increasing by 5% per annum. Since we perform the feasibility analysis separately for each model region in this study, we adapted the thresholds value for each model region, based on the GDP per capita level relative to the global average GDP per capita level, and multiplied this with the global threshold carbon price level over time.

Final energy consumption

Traditionally, consumers continually increase their demand for energy services due to increasing incomes and technological growth providing new products and services⁶³. While end-use efficiency of energy services also tends to increase over time, the balance in terms of final energy demand relies on how fast demand for energy services grows relative to how fast end-use efficiency improves. However, end-use efficiency is often also a factor, which by itself increases demand due to lower energy service prices, known as the Jevons paradox⁶⁴. In recent literature on climate change mitigation, the reliance on voluntary consumer demand reductions through changes in lifestyles is becoming increasingly important⁶⁵. Nevertheless, the extent to which societies can reduce demand based on voluntary lifestyle changes is not endless, and involuntary reductions in final energy demand through policies or pricing will likely face societal opposition, similarly as carbon prices⁶⁶. We, therefore, apply the same “medium concern” threshold level as in Brutschin et al (2021)³⁷ of 10% energy demand decline per decade, separately for the three different end-use sectors: industry, buildings, and transport. However, in order to isolate the effect to only end-use efficiency and reduced end-use consumption, we cancel out the efficiency effects that could be the result of fuel switching in end-use sectors by using fuel- and sector-specific multipliers for each energy carrier (see Table 4 in Bertram et al 2021⁶⁷).

Technology scale-up

Climate change mitigation typically depends largely on renewable or low-carbon technologies replacing carbon-intensive technologies throughout all sectors. However, the extent to which clean technologies can ramp up from relatively small shares of the current energy mix towards dominating the energy mix by 2050, depends largely on how fast such technologies can ramp up⁶⁸. In scenarios with increasing demand for energy, ramp up capacity even needs to be higher.

Power technology scale-up: For power technologies, we measure feasibility by the increasing share of a low-carbon technology in the entire power mix in a certain model region during one decade. To also cover the effect of scale, we then multiply the increase in a technology share by the total amount of electricity production. Hence, the required ramp-up for a technology to increase from 10 to 20% of the power mix would need to be double if electricity production also doubles in that same decade. We selected thresholds for power technology scale-up that are largely similar to the “medium concern” thresholds in Brutschin et al (2021)³⁷. These thresholds are 10% for emerging technologies such as wind, solar, hydro, and ocean, and 5% for more conventional low-carbon technologies such as biomass and nuclear, as well as for thermal technologies with CCS. This value of 5% for biomass and CCS technologies is higher than the “medium concern” value in Brutschin et al (2021)³⁷ (2%) because, in their estimate, physical limitations of biomass production and carbon storage are taken into account, while we treat these limitations separately in other feasibility indicators.

Energy demand sector technology scale-up: For the three energy demand sectors (industry, buildings, and transport), we apply the same logic of how the thresholds affects feasibility—i.e., as the percentage increase in share of a low-carbon technology in the final energy mix of that specific sector. However, we do not take the scale effect into account, because total final energy use in many cases may drop in time due to a transition towards electricity use, which is generally more efficient than thermal technologies. However, a shift from thermal to electric technologies (e.g., from an internal combustion to an electric vehicle for passenger transport) does usually not imply a reduction in technology use (e.g., number of cars), but a reduction of energy use from the same number of end-use technologies (e.g., higher efficiency of cars). Hence, the scale effect cannot be taken into account so straightforwardly in energy demand sectors. We apply the same “medium-concern” threshold level in all sectors as used in the final energy indicators in the transport sector by Brutschin et al (2021)³⁷, which is 10% for each low-carbon end-use technology (based on electricity, hydrogen, or bioenergy).

Bioenergy supply

The global supply of bioenergy is constrained by various factors, ranging from hard limits such as land availability to soft limits such as biodiversity impacts and ecosystem functioning⁶⁹. The traditional best-practice method for bioenergy production is through waste streams and by-products from forestry and agricultural practices, as—contrary to purpose-grown bioenergy resources—using waste streams avoids the competition with other land uses as well as other sustainability aspects⁷⁰. Therefore, in this study, we applied regional physical potentials for bioenergy production under sustainable conditions, separately for forestry⁷¹ and agriculture⁷². Considering minimal use of purpose-grown biomass and ensuring biodiversity production, these studies show potential global supplies of ~ 50 EJ and ~ 105 EJ by 2050 for, respectively, forestry and agriculture. We add up these regional potentials and treat these as feasibility thresholds and assume the total threshold will increase linearly from consumed levels in 2020 to these potentials in 2050. While, in reality, the majority of bioenergy resources are consumed in the same region that are produced, some trade flows exist, and future bioenergy trade could increase with higher reliance on bioenergy⁷³. Therefore, we apply a multiplier to the regionally identified feasibility concern levels, based on global bioenergy consumption relative to the global threshold level. This multiplier reflects that, if bioenergy demand in one region exceeds its sustainable supply, but globally there is still a significant amount of untapped sustainable supply, the feasibility concern will be corrected downwards. Conversely, if global bioenergy demand also exceeds the global threshold, regional feasibility concerns are corrected upwards.

Carbon storage

Mitigation scenarios in IAMs often rely on the concept of geological carbon storage, which is a potential to store captured CO₂ emissions in geological structures for centuries or millennia in order to avoid these reaching the atmosphere and contributing to climate change⁷⁴. Apart from storage, there are also several technologies for carbon usage⁷⁵. Currently, enhanced oil recovery is the most common way of carbon use and storage⁷⁶. Since oil and gas fields are commonly included in potential storage sites for carbon capture and storage, we do not focus on carbon use technologies beyond enhanced oil recovery. While the traditionally reported potentials for carbon storage are large, the technology suffers from technical, economic, financial, political, and societal issues⁷⁷, complicating the definition of realistic regional threshold values. For the analysis to stay as scientifically sound as possible, we use estimates from a relatively recent assessment of carbon storage potentials by country, which also looks at the economics of implementation taking into account geographical differences between CO₂ sources and storage areas as well as the economics of carbon storage⁷⁶. For each region, we identify the potential storage capacity separately in oil and gas basins and saline aquifers. Given that oil and gas basins tend to be closer to emission source points and that storage is more economic due to the combination with enhanced oil recovery, we translate these complexities to a rule of thumb assuming 75% of oil and gas basins could ultimately be used, and 25% of saline aquifers. This translates in a total global capacity of 214 Gt CO₂ for cumulative carbon storage. As with the bioenergy supply feasibility assessment, we assume that this level of storage needs to build up towards 2050, and so we apply a linear path from zero in 2010 to the full potential in 2050, separately for each region. If the accumulated amount of stored carbon surpasses the assumed feasible constraint in a given region and year, this will translate into feasibility concerns. Like with bioenergy, regional feasibility concerns are multiplied with the global demand for carbon storage relative to the global feasible potential to reflect possibilities to store CO₂ in foreign storage locations nearby borders.

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A multi-model analysis of post-Glasgow climate action and feasibility gap

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Introduction

Defining Scenarios And Feasibility Indicators

Global Action Gap

Scenario Feasibility

Discussion

Methods

Models included

Scenario protocol

Temperature assessment

Feasibility assessment

Carbon pricing

Final energy consumption

Technology scale-up

Bioenergy supply

Carbon storage

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