NGFS Scenarios Portal

General questions
Transition scenarios / IAMs
General questions Socio-economic assumptions Spatial and temporal aspects Economics Energy system Emissions and Climate Policies
Macro-economic impacts from climate physical risks
Chronic physical risk Acute physical risk
Macroeconomy/NiGEM

General questions

How often will the NGFS scenarios be updated?

Going forward, the long-term scenarios should be updated for latest data and development on a yearly basis. Additional models and analytical improvements however will follow a biennial cycle, hence targeting 2025 for the next enhancements. Interim updates are still possible with updated runs/models, but without affecting the overall projections.

Which updates do the NGFS scenarios contain as compared to the previous vintage?

NGFS scenarios reflect regular updates, including updated policy and baseline GDP trajectories in the short-term from the latest snapshot of the IMF World Economic Outlook 2022. Growth rates then converge to SSP2 growth rates. The resulting impacts on emissions are endogenously computed by the models. Moreover, the availability of CDR technologies has been limited further (see technical documentation, Module 1, section 2.2). In addition, the scenarios contain a few specific updates that reflect the current geopolitical landscape, namely, the energy-market disruption of the war in Ukraine (via trade limitations with Russia).

Do the NGFS scenarios reflect the impacts of the war in Ukraine on energy prices?

The scenarios include data to reflect the consequences of the war in Ukraine on energy prices, contributing to an overall increase in disorderliness.

Do the NGFS scenarios reflect the economic, energy and emission impacts of COVID-19?

The NGFS scenarios do include these effects. We updated policy and baseline GDP trajectories by using IMF WEO 2022 projections for 2025 GDP values. SSP2 growth rates are assumed for the rest of the century meaning that the new GDP projections do not catch up to the original absolute SSP2 values. Scenarios also reflect the capacity build-up until 2020 and baseline energy demand has been updated to include the effects of COVID-19. The resulting impacts on emissions are endogenously computed by the models.

Where can I find the documentation of scenarios and models available?

The technical documentation of the scenarios and models used to generate them is available at this URL, including links to further information.

How can I integrate these data into my risk assessment analysis?

The NGFS has released several documents to showcase how various actors used the NGFS Scenarios, including a public survey, and a joint report with the FSB on how NGFS and FSB members used the NGFS Scenarios.

Which scenario is the most realistic and likely to occur?

Future developments are inherently uncertain and difficult to predict. Against the backdrop of climate change, assigning probabilities to a scenario is impossible due to the multifaceted Knightian uncertainty we are facing, as regards climate change itself, its impacts, future policy commitments and implementation, geopolitical tensions and technological developments, just to name a few.

Scenario analysis is one approach to tackle this fundamental uncertainty by shedding light one multiple futures, thereby giving a range of possible outcomes using an “if – then” logic, rather than assigning probabilities, which would run the risk of conveying a false sense of certainty. Specifically, the NGFS scenarios rely on models designed to simulate the complex and non-linear dynamics of energy, economy and climate systems, accounting for various possible policy and technology paths. Therefore, they allow a detailed exploration of various plausible futures and an understanding of intertemporal policy trade-offs.

What are the differences between the IEA scenarios and the NGFS scenarios?

There are a number of features that distinguish the NGFS from the IEA scenarios.

First, there are more NGFS scenarios. There are 7 NGFS scenarios produced by 3 IAMs that span a wider range of policy and technological dimensions, including exploration of disorderly scenarios. In contrast, there are 3 main IEA scenarios in the World Energy Outlook 2020: Current Policies scenario (“a baseline picture of how global energy markets would evolve if governments make no changes to their existing policies and measures”), Stated Policies scenario (“a detailed sense of the direction in which today’s policy ambitions would take the energy sector”) and Sustainable Development Scenario (“a way to meet sustainable energy goals in full, requiring rapid and widespread changes across all parts of the energy system”). However, the IEA scenarios can be roughly mapped to the NGFS scenarios. The Current Policies scenario correspond to each other. The IEA Stated Policies scenario corresponds to the NGFS NDCs scenario. The Sustainable Development scenario is roughly in line with the orderly Below 2 °C scenario, while the NGFS Net Zero 2050 scenario corresponds to the new IEA scenarios NZE published in May 2021.

Second, the NGFS offers 2 sets of scenarios that have been created using the REMIND-MAgPIE that integrates the macro-economic climate damages into the optimisation procedure.

Third, the NGFS modelling framework includes 3 models whereas the IEA uses a single model (i.e. the World Energy model). This allows for exploring the uncertainty related to model structures and techno-economic (and potentially other) assumptions.

Fourth, while the temporal resolution of both the IEA and NGFS scenarios is in the order of 5 to 10 years, there are at least 25 regions represented in the World Energy model (up to 120 regions in the oil and gas module) and 12 to 32 in the IAMs used to generate the NGFS scenarios.

Fifth, these large-scale energy-economy models represent sectors that are relevant from an energy viewpoint, i.e. coal, oil and gas, power, buildings, industry and transportation. Each sector is represented with a different level of detail depending on the model.

Sixth, most IAMs cover land use (e.g. agriculture, forest) and its related emissions. For the recent IEA NZE scenario, the IEA linked their model to the GLOBIOM land use model. However, the data is not available yet for other scenarios.

Finally, the NGFS IAMs have been linked to a macro-economic model (NiGEM) to extend the macro-economic information.

Has the NGFS nominated representative scenarios in the same way as in Phase I?

Because we had a larger number of scenarios in Phase I, we designated some of them as “representative” to help users select scenarios. From Phase II onwards, we decided to focus on the scenarios that are most relevant for central banks and supervisors and reduced the total number of scenarios. Given this focus, we no longer need to differentiate between representative and alternate scenarios.

To what extent can the NGFS scenarios be used by various non-financial sector companies to conduct their own scenario analysis?

In terms of transition risks, all sectors can use the scenarios for analyzing to which extent their own current energy consumption and inputs (both energy and land-use products) might require transformation, and how existing decarbonization or net-zero plans might have to be updated to align with NGFS climate scenarios. The Integrated Assessment Models (IAMs) used for the NGFS scenarios provide a number of results for industry sub-sectors like cement, steel and chemicals. The NGFS is currently working on improving the sectoral granularity of the scenarios. On the side of climate risk, the NGFS physical risk models allow, in this vintage, to obtain a long-term estimate of acute and chronic physical risk losses at country level different types of hazard or driver. These projections can be used by the financial sector in a number of ways, starting from assessing the risk of their portfolios and counterparts vis-a-vis specific natural disasters.

How can I find guidance on data access?: NGFS scenario data can be accessed through multiple channels. The starting point of navigation is always the NGFS Scenarios Portal. Here, users can find links to each of the data sources. Additionally, the NGFS Scenario Data User Guide, which provides guidance on the first steps of each of the data access methods, that differ in simplicity, functionality and data types. The existing toolkit has been expanded with the EnTry tool, which provides an easier and open source interface to the already existing Pyam package. EnTry is expected to provide much better data access and transparency for all NGFS users with very low entry investment (e.g. no installations nor advanced programming skills needed).

NGFS Scenario Portal

NGFS Scenario Data User Guide

Transition scenarios / IAMs

General questions

In which year are variables in the NGFS scenarios computed endogenously in the models (i.e. starting value)?: Models have, by design, different calibration years, mostly 2005 or 2015. All policy scenarios are however assumed to follow (closely) the trajectory of the Current Policies scenario until 2020 so that stringent mitigation is only happening in the first time step thereafter, i.e. 2025. More information on endogenous and exogenous variables can be found in the Technical documentation (model descriptions: Modules 2,3 and 4).
Do the models consider (irreversible) climate impacts and positive feedback loops where warming leads to more GHG emissions (e.g. methane emissions from melting of permafrost)?: The NGFS Scenarios rely on a damage function to compute GDP impacts of climate change, but do not account for tipping points or feedback effects. However, the release includes a full set of pathways in which mitigation policy today reacts endogenously to future climate impacts (i.e., incorporating the trade-off between current consumption losses and future climate damages). This is based on a special version of the REMIND-MAgPIE model that integrates future macro-economic climate damages and is available for two levels of damages.

See section Use scenarios
Are the scenario data enough to carry out financial risk analysis? Should they be supplemented with results from additional models (e.g. short-term economic/financial models)? If so, which models would be better suited (e.g. agent-based models, DSGE, structural macro-models)?: For many financial applications, supplementing the scenario data with additional data and modelling may be necessary. The NGFS and the FSB released a joint report to showcase how their members carried out climate scenario analysis and identify best practices. This is a nascent field, and no recommendation can be made as of yet on the types of models. Specifically, the correct model depends on what the local context warrants (e.g. further sectoral downscaling, inclusion of additional climate shocks, etc.).

Climate Scenario Analysis by Jurisdictions: Initial findings and lessons

Socio-economic assumptions

Do the NGFS scenarios rely on or use all the SSPs or just SSP2?: All NGFS scenarios are currently based on SSP2 socio-economic assumptions, except the Low Demand scenario, which includes SSP2 and SSP1 assumptions. This is mainly to ease comparability of the scenarios. The sensitivity of the scenarios to alternative socio-economic assumptions could be explored in future work, depending on needs.
Do models include a dietary change, e.g. meat consumption per capita developments?: Most scenarios assume an evolution of dietary change in line with historic trends, so increasing meat consumption with rising affluence. The Low Demand scenario includes more dedicated lifestyle changes as a mitigation option. Results vary across models, from slower increase in livestock production to important decrease (-50% by 2050 in the REMIND-MAgPIE model).
Will the NGFS provide scenarios that cover the potential for de-growth (i.e. non-classical models) and the depletion of fossil fuels?: The NGFS scenarios reflects the need to transition to a net-zero economy fast and under the least-cost option for disrupting the current energy-economy system. While they do not take a stance on what level of growth is desirable, they shed light on the tradeoff between high growth today and climate damages in the medium- to long-term. In addition, in the new Low Demand scenario, a future with ambitious energy demand reductions, dietary changes and an increased sharing economy, reflecting strong behavioural changes that go in the direction of putting less emphasis on consuming evermore, is mapped out.
Do the NGFS scenarios also consider the social dimension of climate change, such as social polarisation or climate migration?: The NGFS scenarios currently do not capture this. Most IAMs are working on incorporating social dimensions gradually, but these efforts are at an early stage.
Why are the NGFS scenarios produced with three different integrated assessment models?: Different model outputs allow users to obtain a range of results, capturing model uncertainty to some degree, and allowing users to draw robust insights across models. Furthermore, different models have different properties which make them more suitable for different applications; for example, GCAM offers the highest regional granularity, MESSAGE and REMIND-MAgPIE are providing optimised scenarios under given constraints, GCAM offers higher detail for the transport and buildings sectors, while REMIND-MAgPIE offers higher details for industry and transport.
Will the input files for the integrated assessment models be made available so that the results can be reproduced and additional model outputs not included in the IIASA database can be accessed?: The integrated assessment model codes are openly available, so the modelling can be transparently reviewed. Fully replicating the NGFS scenarios would require the input data, part of which is not openly available due to licensing. Please contact us if you are missing specific variables; we can consider requests on a case-by-case basis.

Policy assumptions

Which policies are considered under the scenario “Current Policies”?: The Current Policies scenario includes a stylised representation of existing policies. Most of this work is based on the climate policy database established as part of the CD-Links and ENGAGE projects (McCollum et al. 2018, Roelfsema et al. 2020). The scenarios reflect the status of the database from the March 2022. Please note, however, that only a subset of policies reported in the database can be represented in the models due to limits in their sectoral granularity. Some policies are also captured via proxies, e.g. carbon prices or overall final energy reductions to represent efficiency policies.

Climate Policy Database

Kriegler et al. (2018) “Short term policies to keep the door open for Paris climate goals”

McCollum et al. (2018) “Energy investment needs for fulfilling the Paris Agreement and achieving the Sustainable Development Goals” (In particular Supplementary Data 3)

Roelfsema et al. (2020) “Taking stock of national climate policies to evaluate implementation of the Paris Agreement”
Which policies are considered under the scenario “NDCs”?: In this scenario, countries are assumed to implement pledged policies in addition to current policies and keep their level of ambition beyond the NDC horizon constant. The cut-off date for targets being considered here is those published by the UNFCCC until end of March 2023.

Spatial and temporal aspects

How granular are modelling results in terms of regions and sectors? Where can I find the list of countries included in each region?

Regional granularity differs between the participating models. The MESSAGEix-GLOBIOM model and REMIND-MAgPIE have 12 model regions, whereas the GCAM model has 32.

Sectoral granularity also differs, but the results are reported in a harmonised way for economic and energy sectors. The NGFS is currently working on improving this sectoral representation. More details can be found in the Technical documentation (Module 1, section 3.2).

Is it possible to get a more granular breakdown of the model pathways, e.g. by country rather than by world regions?

Key economic, energy and emissions variables have been downscaled to 184 countries.

Why are data missing for several countries in the NGFS scenarios (e.g. no BRA, JPN, MEX, RUS data from MESSAGEix-GLOBIOM)?

These countries are only represented explicitly in the GCAM model (and partly in the REMIND-MAgPIE model). For these countries, users have to rely on the downscaled model data.

Is optimisation in the models performed at the global or regional level?

This differs across models.

GCAM is a dynamic recursive market equilibrium model and not an optimising model. That is, no attempt is made to optimise the aggregate economic system over time or at any moment in time. While GCAM’s economic agents are assumed to individually optimise either expected utility/profits or minimise expected costs, economic activity is set in a probabilistic context. GCAM’s economic agents undertake investments without knowledge of future outcomes and thus can, and often do, hold unfulfilled expectations about the future.

In REMIND-MAgPIE, optimisation happens at the regional level, and a Nash algorithm ensures market clearance of trade at the global level.

The MESSAGEix-GLOBIOM optimisation happens at the global level across all regions at once.

Economics

Is GDP computed endogenously or exogenously in the models?

GDP is an exogenous input assumption in the GCAM model, but a semi-endogenous output for REMIND-MAgPIE and MESSAGEix-GLOBIOM. The latter models take the SSP2 GDP trajectories (adjusted in the short-term) for calibrating assumptions on exogenous productivity improvement rates in a no-policy reference scenario. GDP trajectories in other scenarios thus reflect the general equilibrium effects of constraints and distortions by policies (so changes in capital allocation and prices, but without taking potential damages from climate impacts into account).

Considering that the models are assuming perfect foresight and optimising behaviour, do you think that in the “real world”, carbon prices have to be higher, or GDP effects would be stronger?

Not necessarily, as the models have simplifying assumptions on the functioning of economies that go both ways. Assuming perfect foresight and optimising behaviour indeed leads to a downward bias for carbon prices. On the other hand, assuming economies to be in a full equilibrium without idle capital or work-force, and with given fixed demands for energy services leads to an upward bias for carbon prices. Carbon prices are very sensitive to assumptions about the evolution of demand for food and energy (which in the real world will to some extent be sensitive to climate policies) and to technological development.

Are model prices real or nominal prices?

All model prices are real prices, so the indices are indices for real prices.

How are the particular carbon prices linked to a given carbon budget? Is it related to what the price has to be in order for alternative energy sources to be viable?

Yes, that is a viable interpretation. The temporal trajectory of carbon prices is iteratively adjusted up or downwards until the defined temperature target/carbon budget is fulfilled.

When carbon price is assumed to increase under a scenario, that should act as a (tax) wedge between demand and supply; the path for the oil price is reflective of the (gross) price from the consumer perspective or the (net) one from the producers’ angle?

The oil supply is modelled via supply-cost curves, and without explicit representation of the exploration infrastructure. This is a simplifying assumption for the modelling, but does not suggest that oil prices will indeed rise in such scenarios. A more detailed analysis of the oil sector is therefore a useful complement to the use of NGFS scenarios (using the scenario demand information).

Oil prices are reflective of the gross price paid by consumers. The price at the second energy level (i.e. Price|Secondary Energy|Oil) includes the costs of extraction, transport and transformation as well as the carbon tax. The price at the final energy level (i.e. Price|Final Energy|Transportation|Liquids) includes additional transport and distribution costs and other taxes and subsidies.

Even in the 1.5 °C scenarios, oil and gas prices seem to remain relatively high and in some cases increase over the scenario. Could you explain the intuition behind this?

Primary energy oil and gas prices reflect the dynamics of long-term oil and gas markets in equilibrium. More concretely, in a no to little climate action scenario (e.g. Current Policies, NDCs), oil and gas prices increase over time because demand for these fuels keeps rising while marginal production costs increase. Oil and gas prices are lower in 1.5°C and 2°C scenarios compared to Current Policies, but still, increase over time. In 1.5°C and 2°C scenarios, the demand for these fuels does not drop immediately because it is cost-effective to continue to use them for a while (e.g. Evs are more expensive and there is no policy push for them in those scenarios, natural gas can be cheaper than renewables in some regions and when used with CCS can act as a bridge fuel). Oil and gas are also used in the petrochemical industry. These dynamics explain why prices do not drop steeply. Both, oil and gas demand and prices would drop more steeply if the policy assumptions do not only foresee a comprehensive carbon price (see also next answer), but also explicit policies to regulate oil and gas use (efficiency standards) or push their competitors (EV subsidies, direct support for industry electrification, change in energy taxation).

Why are oil prices not the same across regions in REMIND?

Trade costs for importing/exporting oil can lead to slight differences in oil prices across regions.

Are annual investments only related to the investments needed for changing the energy system, or does it also include what is needed to uphold/maintain demand from current sources? Like new upstream oil/gas projects.

Annual investments for fossil extraction include the required investments to maintain the production observed in the respective scenario. The investments are estimated using historical investment intensities of extraction, so might be an overestimate in scenarios with declining demand.

What is the correct interpretation of relatively smooth economic growth in face of such dramatic changes in the industry after 2030?

Several aspects of Integrated Assessment Models can explain these results. First they include a simplified representation of the economy. In particular, they assume economic equilibrium and only account for a few economic frictions. Second, these models are designed to find ways to sustain economic growth. When a climate constraint is added (i.e. keeping warming below 2 °C), IAMs switch to low-carbon energy technologies.

There is a significant difference in the GDP impacts from transition risk between the IAM forecasts and the NiGEM forecast. How can we interpret these differences?

The NiGEM scenarios demonstrate that the estimated impact on GDP depends heavily on the fiscal policy assumptions. In the NiGEM Net Zero scenario, 50% of carbon revenue is assumed to be channelled back into the economy in the form of government investment. This raises potential output, offsetting more of the transition and physical costs compared to the IAM scenarios.

What assumptions are made for the role of monetary policy, including any impact on inflation expectations?

For the NiGEM scenarios, central banks target a weighted average of inflation deviations from target and output deviations from target. There is a trade-off, as inflation is generally rising and output falling in the scenarios, so the interest rate response differs depending on the scenario and also on the individual county and their sensitivity to both inflation and output. It is assumed that 50% of the carbon price is passed directly to consumer prices.

GDP losses from physical risks are estimated somewhere between -5% and -15% by 2100, which may not necessarily be considered significant. What would be the best argument to push policy makers that climate change should be taken seriously?

GDP loss estimates are highly uncertain and typically do not include acute risks and indirect damages from societal repercussions, such as conflict. They also often do not account for tipping points in the Earth system. This is the case for the estimates from Kalkuhl and Wenz (2020) used in the scenario release. Uncertainty is only covered for the climate response, not for the choice of damage estimate or covering a broader range of damages.

What do the models assume about the pass-through of carbon cost, e.g., do the demand curves for a given product assume the end user will have to pay for all (or part) of the carbon cost?

The IAMs do not represent different users explicitly, or model explicit products, only energy services (e.g. mobility by mode for transport, floor space for buildings, and certain materials for industry). The demand for energy services is assumed to be fully elastic, so energy price increases lead to reduced demand and/or technology change. The prices of these energy services therefore fully reflect the carbon cost.

Within NIGEM is there any consideration of additional investment needed by firms and consumers in an economic transition (replacing machinery, retrofitting buildings...), and the opportunity costs for consumption from this extra depreciation?

In the Net Zero 2050 and Below 2°C scenarios, we have assumed that carbon taxes generate fiscal revenues which are then partially reinvested into the economy (50% of revenues is channelled into government investment). This crowds in further private investment, resulting in a moderate investment boom. We have not made additional assumptions regarding the depreciation rate or opportunity costs, but there may be some crowding out of private consumption compared to a scenario where carbon revenue is channelled into personal sector transfers or tax relief. In other scenarios, investment is determined endogenously within NiGEM without additional assumptions.

To what extent do the NGFS scenarios incorporate the impact of the pandemic on countries' capacities to finance the transition to a low-carbon emissions economy? Certain countries that are key to reduce emissions were heavily hit.

In order to account for the COVID-19 pandemic and its impact on economic systems and growth, the near-term GDP and final energy demand trajectories have been adjusted to incorporate the impact of the COVID-19 shock. In some cases, this has a permanent impact on the level of potential output. The short term (up to 2023) base files used by NiGEM include the COVID affected numbers from the NIESR forecast of 2023, while the IAM GDP projections include the IMF COVID adjusted GDP and final energy data. The baseline assumptions for the IAM’s and NiGEM are matched by 2025. The baseline values determine the potential for carbon revenue to be raised and channelled into investment, so scenarios are consistent with a post-COVID world.

Energy system

To what extent the models cover the limitations in the roll-out of (new) technologies? E.g. availability of lithium & other minerals for car/home batteries, solar PV?: The models do not endogenously track flows and stocks of minerals and raw materials, but assumptions on overall potentials are informed by studies (including from the life-cycle assessment, LCA community) that have analysed these requirements. Therefore, temporary price spikes (similar to the 2008 polysilicon price spike) of these materials are not included. Their impact likely also is of minor importance on the 5-year time step perspective.
Are efficiency improvements of solar PV, wave energy, etc. considered or is the roll-out of renewable energy based on current efficiencies?: Continued improvement of technologies in terms of decreasing capital costs is included, which is the biggest effect of efficiency improvements.
Are there any new energy generation technologies that are not included in the current versions of the models but are to be considered for updates?: Yes, the models only take proven technologies into account, and thus do not consider technologies like fusion reactors, small modular reactors, or electric planes. As soon as these technologies are successfully demonstrated, they will be included in the models.
What variables does the “buildings” sector entail? Are there any particular real estate variables in the NGFS scenarios?: The variables included on sectoral level are, final energy demand, energy efficiency, and energy prices. The models do not consider real estate sector specific variables. The IAMs are primarily designed to represent the dynamics of energy systems. Therefore, only energy-related variables are included on sectoral level. Moreover, note that the “buildings” sector does not fully correspond to the traditional real estate sector, as it is not represented as such within the IAMs. The buildings sector does, however, entail all residential and commercial buildings, and could, therefore, be used as a proxy.
Why is nuclear investment lower in Net Zero 2050 than in Current Policies?: This is an intertemporal and international effect. In the Net Zero scenario, alternative technologies (solar, wind and storage) experience higher learning in and become more needed in the long term. Therefore, they become more competitive than nuclear in some countries. In the Current Policies scenario, on the other hand, nuclear remains more competitive.

Emissions and Climate Policies

How large are cumulative carbon emissions across scenarios?

Cumulative total emissions of CO₂ differ across models, as scenarios were harmonised to arrive at comparable warming levels which depends on all greenhouse gases. Carbon budgets can be calculated from the provided Emissions|CO2 variable in the database.

Why do global net CO₂ emissions in the Net Zero 2050 scenarios not cross the zero line exactly in 2050?

Achieving zero net CO₂ emissions by 2050 is often seen as synonymous with keeping warming below 1.5°C. Indeed, many governments and economic and financial actors have adopted a climate target of zero net CO₂ emissions by 2050 to meet the temperature goals of the Paris Agreement. However, in the three Net Zero 2050 scenarios, global net CO₂ emissions are not exactly at zero by 2050.

Why?
The transition scenarios developed in this project are designed to keep warming below a certain threshold (e.g. 1.5°C, 2°C), with a certain probability. All Net Zero 2050 scenarios limit warming to 1.5°C above pre-industrial levels. However, this does not necessarily mean that CO₂ emissions must reach exactly net zero by 2050. In fact, the IPCC SR1.5 report found that in “model pathways with no or limited overshoot of 1.5°C, global net anthropogenic CO₂ emissions […] reach net zero around 2050 (2045–2055 interquartile range)”.

Is it still OK to use these scenarios?
It is perfectly fine to use these scenarios as if their net CO₂ emissions reached zero in 2050. Indeed, emissions in 2050 are broadly in the range of today’s emission uncertainty levels (i.e. +/− 0.5 GtCO₂ for fossil fuel and industry emissions and +/− 2.6 GtCO₂ for land use emissions, source: Global Carbon Project). They could also be compensated by trade of carbon permits between countries. Importantly, the net zero targets announced by several countries (e.g. China, EU, Japan, USA) are specifically included in the Net Zero 2050 scenarios.

How are climate and energy policies introduced in the model? Is it always through carbon pricing?

No, policies are represented in different ways, via carbon prices but also via sectoral constraints and incentives, such as renewable energy targets under the Current Policy or NDC scenarios, or efficiency targets for homes and vehicles.

Do the NDC scenarios represent conditional or unconditional NDCs?

They represent conditional NDCs. Therefore, they provide an optimistic assumption on NDC pathways (all targets, including conditional ones, are met in 2030).

Are Net Zero emissions targets included in the NDC scenario?

No, but they are included in the Net Zero 2050 and Delayed Transition scenarios. For a complete list of the targets, see the Technical Documentation (Appendix, Table 38).

Why is the NDCs scenario developed with REMIND-MAgPIE so different than the one developed with MESSAGEix-GLOBIOM?

The long-term developments beyond 2030 are not clearly defined by the scenario narrative. This scenario explicitly specifies 2030 targets (from countries NDCs), but only defines “comparable ambition levels” for climate policy stringency after 2030. This design choice was made to avoid modelling overly optimistic (2°C compatible developments after 2030) or pessimistic (full reversion to no-policy baseline) policy stringency after 2030. This loose definition of ambition levels can result in noticeably different outcomes across models.

Macro-economic impacts from climate physical risks

Chronic physical risk

GDP losses from physical risks may not necessarily be considered as significant as expected. What would be the best argument to push policy makers into taking climate change seriously?: NGFS scenarios remain anchored to historical data and coherent modelling. GDP loss estimates are highly uncertain and typically do not include all physical perils or slow-moving changes. Indirect effects from societal repercussions, such as conflict, tipping points or macroeconomic impacts, like changes in prices and productivity, are also not explicitly captured.
Are all chronic physical effects captured through damage functions and effects on GDP?: No. The aggregate empirical damage function captures productivity effects, e.g. from impacts on labour productivity or agricultural productivity, related to temperature a variety of factors like sea-level rise, oceans acidity, other non-market and social impacts (e.g. conflict, migrations).
How are the parameters of the macro-economic damage functions calibrated?: They are based on empirical analysis of the effects of historic climate on gross regional product with data for more than 1500 regions in 77 countries (Kalkuhl & Wenz 2020). It applies an annual panel model assessing effects on productivity levels and growth for temperature and precipitation. The parameters used are those from the preferred specification of the authors, based on the robustness of the regression (specification 5 in Table 4 of the paper), using the results for temperature change and the interaction term including one lag.
Do the scenarios capture the interaction between transition and physical risks?: In Phase IV, we produced scenario variants, run with the REMIND integrated assessment model, that include internalized physical damages, so that the transition trajectory is reflecting the social cost of carbon. This variant of the scenarios can be selected separately in the IIASA NGFS Scenarios Explorer. Also see the technical documentation for more details (Module 5, section 5).

Climate Scenarios Database – Technical Documentation V2.2
In the Phase I scenarios, physical risk damages were projected using three different damage functions, while only one damage function was used for Phase II. What was the rationale behind this decision?: A practical reason for this shift was that additional damage functions would have required running the scenarios with integrated physical damages and NiGEM for each of the damage functions, thus multiplying the number of model outputs by three. For Phase II, we prioritised a streamlined set of scenarios, thus favouring using of a single damage function. The range of damages spanned with this estimation is still considerable, given the uncertainty in temperature response.

Acute physical risk

Is the modelling approach consistent across acute physical risk hazards?: Only to an extent. One of the important aspects of acute physical risk is that each hazard has different physical characteristics, dynamics, and effects on the economy. Differences are further widened by data availability differences across hazards and, within hazards, across countries.
GDP losses from acute physical risk hazard are larger than in the previous phase. Are they considered in line with expectations?: NGFS scenarios remain anchored to historical data and coherent modelling. As for chronic physical risk, only specific physical phenomena are modelled and only some of the main economic channels are accounted for. While 90th percentiles are taken to explore the negative side of the spectrum of projections, many effects still remain unaccounted for.
Why only GDP losses are made available for acute physical risk estimates?: To capture impact on a country-by-country level and isolate those impacts from effects related to trade and policy, the NiGEM macroeconomic model has been used with those components turned off. For this reason and to decrease the complexity in the first release of acute physical risk data, only GDP has been made available of the macrofinancial variables.
Why acute risk losses vary so much across regions?: The granular data and models used as well as the use of specific macroeconomic channels of transmission improved the ability to capture impacts of specific hazards on a region. As an example, heatwaves are estimated to have lower impact in the North American region because the damages are estimated via the channel of their impact on labour productivity. This results in much higher losses in densely populated region, like Mediterranean or Southeast Asia (while Canada and US East Coast is not expected to suffer strongly from heatwaves).

Macroeconomy/NiGEM

What is the NiGEM baseline scenario?

The baseline scenario is a scenario without transition or physical risk. It is therefore a theoretical scenario, which serves as a starting point to compare other scenarios, as we provide deviations of transition and physical risks compared to the baseline scenario. Some variables are expressed in percentage point difference to the baseline, whilst others are expressed in absolute difference.

There is a unique baseline for each IAM as the baseline scenario (climate neutral forecast) combines the NIESR forecast with IAM current policy values (GDP, population, energy consumption).

There is a significant difference in the GDP impacts from transition risk between the IAM forecasts and the NiGEM forecast. How can we interpret these differences?

The NiGEM scenarios demonstrate that the estimated impact on GDP depends heavily on the fiscal policy assumptions. In the NiGEM Net Zero scenario, 50% of carbon revenue is assumed to be channelled back into the economy in the form of government investment. This raises potential output, offsetting a higher proportion of the transition and physical costs compared to the IAM scenarios.

What assumptions are made for the role of monetary policy, including any impact on inflation expectations?

For the NiGEM scenarios, central banks target a weighted average of inflation deviations and output deviations from target. There is a trade-off, as in the scenarios inflation is generally rising and output falling, leading to differing interest rate responses depending on the scenario and the individual country and their sensitivity to both inflation and output. It is assumed that 50% of the carbon price is passed directly to consumer prices.

In the Net Zero 2050 and Below 2°C scenarios, we have assumed that carbon taxes generate fiscal revenues which are then partially reinvested into the economy (50% of revenues are channelled into government investment). This crowds in further private investment, resulting in a moderate investment boom. We have not made additional assumptions regarding the depreciation rate or opportunity costs, but there may be some crowding out of private consumption compared to a scenario where carbon revenue is channelled into personal sector transfers or tax relief. In other scenarios, investment is determined endogenously within NiGEM without additional assumptions.

Does the NGFS provide historical data for NiGEM variables?

The NGFS does not provide historical data for NiGEM variables. These are, however, available to NiGEM subscribers.