A Global Roll-out of Nationally Relevant Policies Bridges the Emissions Gap

doi:10.21203/rs.3.rs-126777/v1

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A Global Roll-out of Nationally Relevant Policies Bridges the Emissions Gap

https://doi.org/10.21203/rs.3.rs-126777/v1

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Closing the remaining emissions gap between Nationally Determined Contributions (NDCs) and the global emissions levels needed to achieve the Paris Agreement’s climate goals will likely require a comprehensive package of policy measures. National and sectoral policies can help fill the gap, but success stories in one country cannot be automatically replicated in other countries, but need to be adapted to the local context. Here, we develop a new Bridge scenario based on nationally relevant measures informed by interactions with country experts. We implement this scenario with an ensemble of global integrated assessment models (IAMs). We show that a global roll-out of these good practice policies closes the emissions gap between current NDCs and a cost-optimal well below 2 °C scenario by two thirds by 2030 and more than fully by 2050, while being less disruptive than a scenario that delays cost-optimal mitigation to 2030. The Bridge scenario leads to a scale-up of renewable energy (reaching 50%-85% of global electricity supply by 2050), electrification of end-uses, efficiency improvements in energy demand sectors, and enhanced afforestation and reforestation. Our analysis suggests that early action via good-practice policies is less costly than a delay in global climate cooperation.

Climatology

Environmental Policy

Paris Agreement

emissions gap

bridge scenario

good practice policies

climate policy

Integrated Assessment Models

In the Paris Agreement, countries agreed to limit global warming to well below 2 °C, and if possible to 1.5 °C¹. For implementation, the Paris Agreement relies on mitigation action at the national level. These actions are communicated via nationally determined contributions (NDCs) and long-term strategies, containing each country’s pledged contribution to global mitigation. A key question is whether the collective action of all countries leads to the implementation of the Paris Agreement’s climate goals^2,3. For this, countries agreed on a global stocktake process to periodically review collective progress and to adapt efforts in order to meet the Paris Agreement’s global climate mitigation goals.

Several publications have already shown that the aggregated impact of NDCs is insufficient^4,5. In addition, global emissions implied by nationally implemented policies are, collectively, even exceeding the global emissions levels projected under current NDCs⁴. This means that current NDCs and policies need to be strengthened. The cost-optimal scenarios from global integrated assessment models (IAMs) can provide guidance on how to do this. In reality, however, it is not always possible to implement these pathways⁶. For instance, influential societal actors might be able to prevent certain measures if they go against their interests. Market distortions can also make certain measures unattractive. Other solutions might lack societal support and also the rate at which a transition can be implemented may be slowed down, such as in the case of closing coal mines and coal-fired power plants (given the impact on coal miners and coal-dependent regions and communities). At the same time, however, there is also evidence of effective implementation of climate policies⁷. Here, good practice policies are defined as successfully implemented policies in one or more countries with a noticeable impact on greenhouse gas (GHG) emissions. In some cases, these policies are not even part of the cost-optimal mix suggested by models but could be easier to implement. It has been suggested that scaling up these good practice policies to other parts of the world might in the short-term be a more feasible and convincing strategy^8-12. Fekete et al.⁷, Roelfsema et al.⁸ and Kriegler et al⁹ investigated the impact of replicating such good practice policies in other parts of the world by focusing on global GHG emissions and sector-level indicators related to feasibility. Although helpful as first step, this earlier work is limited by 1) the formulation of good practice policies at the global scale, and 2) being based on a single model.

Better information on such good practice policies is needed to support the UNFCCC global stocktake in 2023. Here, we build on the earlier work^7-9, also going beyond relatively abstract cost-optimal pathways as guidance for policy-making by focusing on concrete policy measures that can be implemented to close the emission gap. We do this for the first time using multiple models (both global and national) to assess a common set of reduction measures. These measures have been defined in consultation with national experts, making the scenarios more relevant. The key scenario is referred to as the Bridge scenario, as it aims to bridge the gap between the ambition levels set out by countries by 2030 and those consistent with the Paris Agreement. This scenario includes a set of well-defined measures that can be implemented in the 2020-2030 period and go beyond the ambition of the NDCs, and that would still allow reaching the Paris climate goals in the long-term by transitioning to a cost-optimal path towards 2 °C after 2030. The Bridge scenario was developed in multiple rounds. First, national modelling teams[1] responded to the proposed good practice policies (based on literature), considering whether these policies could be realistically implemented in their countries and, if not, what other target levels or years would be feasible. Second, the policy list was adjusted to further differentiate country groups, in terms of timing and stringency of the targets. Third, some national models ran the refined scenarios and provided feedback, upon which the list was further refined. As such, we eventually defined three country groups, which were found to offer enough differentiation to be nationally relevant, while still adhering to a common set of policy measures. Finally, all national and global model teams ran the agreed set of scenarios. In the context of the global stocktake, here we focus on the results at the global level and a number of large countries, while more detailed national-level results by national models can be found elsewhere¹¹.

[1] Covering Australia, Brazil, Canada, China, EU, India, Indonesia, Japan, Republic of Korea, Russia, United States; i.e. approximately 75% of global emissions.

In order to discuss the possible impacts of the Bridge scenario, we compare it to four other scenarios i.e. the impacts of current policies (CurPol), the conditional NDCs (NDCplus), and the models’ cost-optimal pathways towards 2 °C (starting immediately: 2Deg2020, and with a delay: 2Deg2030) (see Methods and Supplementary Information for more details). For the first two scenarios, the current policies and NDCs were extended beyond 2030 by assuming equivalent effort, i.e. by extrapolating the ‘equivalent’ carbon price in 2030, using the GDP growth rate of the different regions up to 2050 for the extrapolation (see Supplementary Information part C). For the Bridge scenario, the defined set of measures was implemented up to 2030 (Table 1) and a cost-optimal path towards 2 °C was implemented after 2030 (see Supplementary Information part C). A full description of the scenarios and additional results can be found in the Supplementary Information.

[Please see the supplementary files section to view Table 1.]

A bridge over the emissions gap

The model outcomes (Figure 1 and Supplementary Figures B 7 and B 8) show that the CurPol and NDCplus scenarios both fall considerably short of the emission reductions needed to implement the Paris Agreement (consistent with earlier work). In contrast, the good practice policies included in the Bridge scenario are able to reduce GHG emissions close to the needed levels in 2030, followed by a longer-term trajectory similar to the ambitious benchmark of 2Deg2020. The Bridge scenario has a less steep reduction than the 2Deg2030 scenario in the short term, offering a pathway that largely closes the 2030 emissions gap without adding substantial challenges in the short and medium terms. Here, the emissions gap is defined as the difference between the NDCplus scenario and the 2Deg2020 scenario. The Bridge scenario closes that global emissions gap by 71% (median, range 26%–275%) by 2030, and compensates the slower start by a slightly deeper emission reduction in 2050, 106% (85%–112%). The 2030 emissions gap is closed by 17% in the USA, 49% in India, 56% in the EU and 75% in China.

Figure 2 shows the rates of GHG emissions reductions in the Bridge scenario, compared to the CurPol, NDCplus, and cost-optimal case (immediate: 2Deg2020 and delay: 2Deg2030). In contrast to the increase in GHG emissions under current policies in some countries, emissions decline everywhere in the Bridge scenario, especially in the 2030–2050 period. In most countries, the Bridge scenario shows smaller reductions than the immediate action 2Deg2020 scenario in the short term (2030), and smaller reductions than the 2Deg2030 scenario in the longer term (2050). As such, good practice policies can constitute an alternate pathway in line with the Paris Agreement’s climate goals, without relying on carbon pricing only as in cost-optimal scenarios, while not significantly increasing the burden in the longer term.

Which measures have the largest effect on emissions?

The emissions gap between the NDCplus and 2Deg2020 scenarios amounts to approximately 12 GtCO₂e in 2030 (model median). The Bridge scenario closes this gap with 71%. The energy supply sector (through higher renewable energy share, electrification, energy efficiency improvement) is the largest contributor to emissions reductions between the NDCplus and Bridge scenarios, both in 2030 and in 2050 (Figure 3). In most models, also mitigation of non-CO₂ emissions, the transport sector (zero-carbon vehicles and efficiency improvements), and AFOLU (notably in 2030) play an important role. This indicates potential to enhance NDC ambition in specific areas.

Changes in energy and land-use systems

Figure 4 shows projected changes in energy and land-use systems under five scenarios: CurPol, NDCplus, Bridge, 2Deg2020, and 2Deg2030. The Bridge scenario significantly increases mitigation action compared to the CurPol and NDCplus scenarios. In fact, on several indicators, the prescribed policies (Table 1 and Supplementary Information part A) close the gap with the cost-optimal 2Deg2020 scenario almost completely. By 2050, the Bridge scenario is more ambitious than the 2Deg2020 scenario for many indicators, compensating for the delay with respect to the cost-optimal pathway. Figure 4 Panel a, for example, shows that the target to increase the renewable electricity share by 1.4% per year in the Bridge scenario (measure 9) leads to deployment far beyond the CurPol and NDCplus scenarios in 2050 (i.e. towards 75%, versus around 50%), but similar to the 2Deg2020 (in line with previous research¹³) and lower than the 2Deg2030. In 2030, however, the Bridge scenario is similar to 2Deg2020, so it does not increase the global trend in terms of installing renewables in the short term (it may do so regionally, however, see Baptista et al.¹¹). As a result of the assumed penetration of non-fossil fueled vehicles (measure 20), the Bridge scenario shows a significant increase in the share of electricity in transport, even more so in Bridge than in 2Deg2020 (Panel b). This starts in 2030, but manifests especially in 2050. However, in some models, the target to increase non-fossil fueled vehicles actually leads to an increase of biofuel powered engines (Supplementary Figure B 1) rather than electrification (explaining the relatively large range), but less so than the 2Deg2030 scenario in 2050. Following CCS (measure 13), efficiency improvement (measure 14), and F-gas emission reduction (measure 10) targets in industry, industrial emissions (expressed as CO₂ emissions from industrial processes as well as F-gases, panel c), are projected to decrease, in Bridge slightly more so than in 2Deg2020 (by 2050). Because the measures in the buildings sector focus on energy efficiency improvements (measures 3-6), the share of electricity in buildings (panel D) is not projected to change significantly in the short term, but Bridge makes up for that by 2050. Panel E shows that the afforestation policy (measure 16) leads to slightly more afforestation in 2030, followed by a large scale-up in 2050, but not as large as in 2Deg2030. As such, CO₂ emissions from agriculture, forestry and other land-use (AFOLU) are projected to be reduced by 38% (model median) by 2030 and by 120% by 2050 in the Bridge scenario, relative to 2015 levels. Supplementary Figure B 2 shows the same indicators but for the NDCplus-convergence scenario instead of NDCplus: by 2050, the convergence scenario is closer to the Bridge scenario than NDCplus for most indicators. Figure B 3, finally, shows the projected changes in the primary energy mix. Bridge sees lower total primary energy supply mainly due to the efficiency improvement and transport electrification measures, but not as low as 2Deg2020, and a shift from fossil fuels to renewable energy sources, especially by 2050. As a result of the scale-up of renewable energy, electrification of energy demand, and efficiency improvements, CO₂ emissions from the energy sector are projected to decrease.

Costs of building the bridge

While the good practice policies may have benefits in terms of social and political acceptability, earlier work (Kriegler et al., 2018) has highlighted that a set of regulatory measures may be more costly than a comprehensive carbon pricing scheme, leading to a non-cost-optimal transition across regions and sectors. A uniform price signal ensures that mitigation happens first where costs are lowest, leading to the overall efficient outcome, in absence of other market failures. Furthermore, climate action as represented in the Bridge scenario implies a more gradual path for emission reductions in the period 2020-2030 compared to the immediate implementation of the cost-optimal policy (2Deg2020). This delay can further raise costs of the Bridge scenario, depending on the evolution of technology costs. The salience of a carbon price, however, may also raise opposition especially from low-income households facing energy poverty and food-insecurity¹⁴, carbon-intensive regions and vulnerable trade-exposed industries that may complicate or delay its implementation¹⁵. Arguably, the good practice policies included in the Bridge scenario face lower implementation barriers and could speed up climate action compared to a scenario in which cost-optimal policy measures are pursued. A fair evaluation of the costs of the Bridge scenario therefore involves two comparisons: one with the immediate and cost-optimal climate policy (2Deg2020), and one with a delayed implementation of uniform carbon pricing, starting in 2030 (2Deg2030) and therefore requiring more disruptive action to meet the 2 °C target.

Our results (Figure 5) indicate that although the Bridge scenario raises policy costs (as expressed by GDP cost per tonne CO₂e abated relative to the Current Policy scenario) in 2050 by more than 20% (1%–38%) compared to an immediate implementation of a cost-optimal 2 °C scenario with globally uniform carbon prices (2Deg2020), it has lower policy costs (Figure 5a) and carbon prices (Figure 5b and Supplementary Figure B 9) in the near term (2030). The Bridge scenario also outperforms a delayed 2 °C scenario (2Deg2030, see Supplementary Information part C) with costs being more than 10% (-6%–33%) lower in 2050. As such, our analysis suggests that early but non cost optimal action is preferred over climate policy delay.

Interestingly, not all models in the ensemble agree on the size and sign of the trade-off between early and cost-optimal policy implementation. Multiple and counteracting effects are at play. Generally, good practice regulatory policies would raise costs particularly when the resulting energy system deviates strongly from the cost-optimal one. If the necessary changes are obvious, or when there are low-hanging fruits for climate policy, then a similar outcome may be achieved through regulation and carbon prices. The phase-out of coal and the scale-up of renewable power generation technologies^16-18 may be an example that comes close (Supplementary Figure B 10 shows that investments in the electricity sector are projected to shift from fossil fuels to renewables). However, for other trade-offs, such as efficiency improvements versus fuel shift, or the allocation of emission reductions across sectors, a mix of regulatory measures that leads to an outcome resembling the cost-optimal one may be more difficult to achieve. Therefore, while regulatory policies can be a pragmatic entry-point for climate policy, cost-efficiency in the medium and long-term is more easily achieved via comprehensive carbon pricing schemes across all sectors and regions to avoid inter-sectoral and inter-regional leakage¹². The costs of delaying climate action, on the other hand, depend on technological progress and the availability and scalability of negative emission technologies (NETs) in the future, among others¹⁹. For three out of four models that capture economic growth endogenously, the costs of delay outweigh the additional cost of regulatory good practice policies in 2050.

An advantage of the regulatory measures as implemented in the Bridge scenario is that carbon prices remain at lower levels in the near term, which may facilitate public acceptability and implementation of carbon pricing schemes with a broad sector coverage. If political consensus in favour of a comprehensive pricing scheme is not found over time, then a further intensification of the good practice policies may serve as a practical way forward to close the emissions gap. At the same time, the advantages of good practice policies in terms of acceptability may be challenged if ambitious climate targets bring cost elements to the forefront of the political debate.

Hence, our results suggest that a global roll-out of good practice policies can be a useful approach to close the emission gap in the near term, while their role in climate policy in the longer term should be reconsidered in the context of a broader policy mix²⁰, including carbon pricing²¹.

Parties to the Paris Agreement were supposed to submit updated NDCs and communicate their long-term strategies to the UNFCCC in 2020. Due to the COVID-19 pandemic, these timelines have been delayed, but scaling up climate ambition and action remains necessary to keep the Paris Agreement goals within reach. As the emissions gap seems hard to close, we built a set of relevant scenarios that may provide a pathway based on successful examples of policies. The mitigation measures were defined in a two-way interaction with country experts and assumptions were adjusted for different regions if necessary. These scenarios, especially the good practice policies (Bridge scenario), can support the ratcheting up of mitigation ambition of NDCs.

Although the granularity of the Bridge scenario has improved in terms of country differentiation compared to earlier studies, some limitations remain. In most cases, we only distinguished high-income and low-/middle-income countries, and while the measures were assessed to be implementable, this might not always be the case when moving to the country-level. Therefore, Baptista et al.¹¹ discuss the same set of scenarios in the context of national feasibility considerations. Models implemented the set of measures in different ways. The ranges, however, do tell a robust story about the Bridge scenario in relation to the reference scenarios. Although set at a relatively low level, the carbon price measure was the single most effective policy in the short term. Removing it from the set of measures resulted in significantly higher emissions. However, as many countries or regions already have a form of carbon pricing, it deserves a spot in the selection of good practice policies, especially given the differentiated timelines and pricing levels assumed in the Bridge scenario. Finally, we have not considered the impact of the COVID-19 pandemic quantitatively, effectively assuming a full recovery without significant effect on long-term, global emissions¹⁸. The policy measures explored here, however, can inform governments that aim for ‘green’ recovery packages²², by showing potential for ratcheting up mitigation ambition with a concrete set of measures.

We have shown that good practice policies can help to reach the 2 °C target in the long-term. They ensure closing the global emissions gap between NDCs and a cost-optimal 2 °C scenario by two-thirds (model median) by 2030. After 2030, more ambitious measures are needed. Such a Bridge scenario leads to lower energy sector emissions due to scale-up of renewable energy, electrification of energy demand, and efficiency improvements, and to lower land-use emissions due to afforestation—at levels and rates of change that are somewhat less than the 2Deg2020 case and less than the 2Deg2030 case. The scenario is still in a position that allows meeting the Paris goals, and, importantly, is less disruptive than 2Deg2030. The Bridge scenario raises policy costs (as expressed by GDP loss per tonne of CO₂ abated relative to the CurPol scenario) in 2050 by approximately 20% compared to a cost-optimal 2 °C scenario (2Deg2020). When put in perspective of economic growth in the coming three decades, this 20% cost increase implies that annual economic growth rates in the Bridge would be around 0.02 percentage points below the annual GDP growth in 2Deg2020. The Bridge scenario outperforms the delayed 2 °C scenario (2Deg2030) with global economic impacts being more than 10% lower in 2050. As such, early but non-cost-optimal action is preferred over climate policy delay. In the absence of immediate, all-encompassing and ambitious climate policy measures, therefore, a global roll-out and successful implementation of good practice policies can put the world on track to a 2 °C-compatible pathway without posing large additional challenges.

In short, acting stringently on 2 °C (2Deg2020) is needed, but, collectively, we are not on track (NDCplus). If we do not strengthen collective action until 2030, the best chance at limiting global warming may be 2Deg2030. However, if we manage to accelerate action until 2030 (Bridge), major disruption can be avoided, even if we do not fully reach 2Deg2020. These results illustrate that short-term implementation of practical regulation-based policies is preferable over delayed climate action. At the same time, the institutional set-up should aim to avoid inefficient policy lock-in, as more efficient instruments may gain political and societal support over time.

Models

Both national and global model teams followed the same scenario protocol for comparability. The global models included here are: AIM/CGE²³, COPPE-COFFEE²⁴, IMAGE²⁵, MESSAGEix-GLOBIOM²⁶, POLES²⁷, PROMETHEUS²⁸, REMIND-MAgPIE²⁹, TIAM-Grantham³⁰, WITCH-GLOBIOM 5.0³¹. National-level results are presented in Baptista et al.¹¹

Scenarios

In line with the global stocktake, the ratcheting up mechanism has been applied in constructing the scenario protocol (see Supplementary Information part C for the full protocol text and Supplementary Information part A for the detailed lists of good practice policies). This means that the scenarios build upon one another in terms of ambition and modelling assumptions. The Current policies scenario is the least ambitious and the 2 °C scenario is the most ambitious.

The Current policies (CurPol) scenario incorporates middle of the road socio-economic conditions throughout the century, based on the second marker baseline scenario from the Shared Socioeconomic Pathways (SSP2)³². It also assumes that climate, energy and land use policies that are currently ratified are implemented (cut-off date 1 July 2019).

The NDC-plus scenario builds further upon the CurPol scenario and assumes that the conditional NDCs (both unconditional and conditional NDC actions) as submitted by April 2020 are implemented by 2030. After 2030, the scenario reflects continuation of effort (see below).

The Bridging (Bridge) scenario builds upon the CurPol scenario and assumes that certain good practice policies, which have shown to be effective in some countries^8-10, will be implemented globally from 2020 until 2030 (see Supplementary Information part A: Table A 1 lists the good practice policies while Table A 2 gives an overview of their implementation in models, with the implemented shares ranging from 53% to 94%). After 2030, the Bridge scenario transitions to a 2 °C scenario following a cost-effective pathway. A distinction is made between low/medium income and high income countries in terms of timing and stringency of good practice policy targets. The set of policies was defined in dialogue with national model teams, granting a more realistic scenario narrative (for more details, see the Supplementary Information). As opposed to Fekete et al.¹⁰, carbon pricing is included as good practice policy, although it may be considered as a top-down policy of different nature than the other policies. Carbon pricing and emission trading schemes have been successfully implemented in various countries. Furthermore, previous work (Kriegler et al., 2018) highlights that good practice regulatory policies should be considered as complements to pricing-based approaches. In the simulations, the carbon price applies to all gases and sectors, hence represents an idealized view of carbon pricing schemes. It does not take the highest carbon price currently observed as starting point, but rather an approach in which countries were divided in three tiers with different carbon price levels and timelines to be most relevant to the countries represented here [insert REF]. As a variant and to analyse the effect of this measure, some models ran an additional scenario excluding the carbon price measure (see SI Figure B 6).

The 2 °C (2Deg2020 and 2Deg2030) scenarios assume that an average temperature increase of 2 °C without overshooting is reached by 2100 in a cost-effective way (starting from 2020 in 2Deg2020 and from 2030 in 2Deg2030). National modelling teams used a carbon budget derived from the global carbon budget of 1,000 Gt CO₂ in the period 2011-2100 (including 2011 emissions), as done in CD-LINKS (https://www.cd-links.org/) . This global carbon budget represents a high probability (66%) of keeping global warming below 2 °C. Carbon budgets have been revised since the CD-LINKS project in such a way that 1,000 Gt is even more stringent than previously. Cumulative CO₂ emissions in the 2 °C scenarios (2Deg2020, 2Deg2030, and Bridge) are not all exactly 1000 Gt, but range from 788 Gt CO₂ to 1540 Gt CO₂ (2011-2100), which is still within the range considered to be in line with 2 °C.

For the CurPol and NDC-plus scenarios, a continuation of efforts after the target year was assumed. This was implemented by extrapolating the “equivalent” carbon price in 2030, using the GDP growth rate of the different regions up to 2050. The equivalent carbon price represents the value of carbon that would yield the same emissions reduction as the NDC policies in a region. If a region has a carbon price of zero while implementing the NDC in 2030, a minimum carbon price of 1 $/tCO₂ in 2030 was assumed. If a region has a negative carbon price in 2030, the trajectory resulting from 1 $/tCO₂ was offset to the model’s 2030 starting point. For land use, a carbon price ceiling of $200/tCO₂ was applied.

Data availability

Model results can be found in the COMMIT database: https://db1.ene.iiasa.ac.at/COMMITDB/dsd?Action=htmlpage&page=about. Policy relevant data is available in the Global Stocktake tool: https://themasites.pbl.nl/globalstocktake-indicators/.

Code availability

The models are documented on the common integrated assessment model documentation (https://www.iamcdocumentation.eu/index.php/IAMC_wiki), and several have published open source code (e.g. REMIND: https://github.com/remindmodel/remind), visualisation tools or detailed documentation (see references). The R-script that was used to generate the figures can be found on GitHub: https://github.com/Hel1vs/Bridge

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Acknowledgements

This study benefited from the financial support of the European Union via the COMMIT project (Climate pOlicy assessment and Mitigation Modeling to Integrate national and global Transition Pathways), financed by Directorate General Climate Action (DG CLIMA) and EuropeAid under grant agreement No. 21020701/2017/770447/SER/CLIMA.C.1 EuropeAid/138417/DH/SER/MulitOC (COMMIT).

Author contributions

CB and others in the REMIND(MagPIE) team developed the scenarios for the REMIND-Magpie model; RS, LBB and others in the COFFEE team developed the scenarios for the COFFEE model; PF developed the scenarios for the PROMETHEUS model; KR, BvR, GU, OF developed the scenarios for the MESSAGEix-GLOBIOM team; DSH, KO, SF developed the scenarios for the AIM team; MH, MR, DvV developed the scenarios for the IMAGE model; LD, LAR developed the scenarios for the WITCH model; JD, KK developed the scenarios for the POLES model; NG, SM, AK developed the scenarios for the TIAM-Grantham model. HvS coordinated the analysis, wrote the paper, and created the figures; TV did the analysis for the costs section; all authors contributed to the definition of the good practice policy package, the analysis, and article review.

Competing interests

There are no competing interests to declare.

Additional information

Supplementary information is available for this paper.

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AglobalrolloutofnationallyrelevantpoliciesbridgestheemissionsgapSupplementaryInformationAandBVanSoestetal.docx
A global roll-out of nationally relevant policies bridges the emissions gap - Supplementary Information parts A and B
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A global roll-out of nationally relevant policies bridges the emissions gap - Supplementary Information part C
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published 05 Nov, 2021

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A Global Roll-out of Nationally Relevant Policies Bridges the Emissions Gap

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Abstract

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Introduction

Results

A bridge over the emissions gap

Which measures have the largest effect on emissions?

Changes in energy and land-use systems

Costs of building the bridge

Discussion And Conclusions

Methods

Models

Scenarios

Data availability

Code availability

References

Declarations

Additional Declarations

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