National / Regional Models for Europe
This diversity of tools is an asset for the PARIS REINFORCE project and, in order to make efficient use of them, we must inform on their potential uses for climate policy support. Evidently, not all questions can be equally addressed by all models, nor will all models that can address a specific question give similar answers. It should be noted that, although four models are based on detailed technological and sectoral approaches, the last one, NEMESIS, is a macroeconomic model that is a detailed sectoral model, but of which econometrics and granularity do not allow for an explicit characterisation of technologies. The policy issues to be addressed by the models are mitigation of GHG emissions and adaptation to climate change. However, the tools are better suited for studying mitigation options than they are for delving into adaptation.
This section begins with the presentation of the main drivers, or exogenous variables, such as socioeconomic assumptions or energy prices and others that are essential inputs for the modelling simulations. Once defined, the mechanisms involved in each model in the mitigation scenarios (and, for the EU-TIMES model, some adaptation measures) are defined. After considering these drivers and mechanisms, we take stock of policy instruments that can be implemented in each model either directly or after specific modelling adjustments. We also look at the capability of the model outputs to track sustainable development goals (SDGs). Thereafter, we summarise how each model calculates a mitigation pathway. Finally, potential interlinkages are discussed, towards effectively exploiting the complementarities among the documented models and respective analyses.
Before exploring the capabilities of the five models, we first present here the main drivers and exogenous assumptions necessary for the modelling simulations. All five models share a common set of drivers, namely GDP, population and fossil fuel price projections (except GDP in EU in NEMESIS, and population in ALADIN). The GDP and population projections are used to define the socioeconomic context, such as the shared socioeconomic pathways (SSP) scenarios (Riahi et al., 2017). The population projections should also consider the number of households (FORECAST, LEAP and EU-TIMES) and household size (LEAP and EU-TIMES), whereas, in the NEMESIS model, population projections should be characterised by age group and educational attainment level, as in SSP population projections (KC and Lutz, 2017). The sector-specific models require in addition more precise economic data than GDP projections. The FORECAST, EU-TIMES and LEAP models require some projections of sectoral economic activities, and particularly for the industrial and tertiary sectors in FORECAST. Furthermore, the EU-TIMES model also uses macroeconomic variables, such as private consumption to proxy the evolution of households’ disposable income that drives their energy demand.
Beside socioeconomic assumptions, technological specifications are also drivers of all five models, including for vehicle drive technologies in ALADIN (which also uses detailed datasets on drivers’ profile and transport infrastructure availability); electricity generation in NEMESIS, process heat, machine drives, steam, and all other industrial processes in FORECAST and more generally all energy-related technologies in EU-TIMES and LEAP.
This section presents the different mitigation and adaptation options included in the five models, as detailed in the following tables.
Upstream technologies like hydrogen production and synthetic fuel production are mitigation options available in the EU-TIMES model through electrolysis and biomass to liquid respectively. All models (except for ALADIN and FORECAST) cover a large set of mitigation options in the electricity and heat generation sectors, ranging from nuclear to renewables: hydro-electricity, solar photovoltaic (PV) and concentrating solar power (CSP), onshore and offshore wind turbine, biomass and geothermal, with the exception of nuclear, hydro and geothermal in NEMESIS, in which they are exogenously determined. Furthermore, carbon capture and sequestration/storage (CCS) technologies are also available mitigation measures in EU-TIMES and NEMESIS, including coal, gas and biomass CCS. For heat generation, only EU-TIMES and LEAP cover biomass and geothermal, but no CCS option is available.
In the buildings sector, the energy system models, FORECAST, EU-TIMES and LEAP, cover a large range of climate change mitigation measures. The NEMESIS model includes mitigation options in the built environment as well, but these are less detailed. Finally, the FORECAST model includes a very high degree of detail for space heating and cooling and residential and tertiary sector appliances, allowing for in-depth analysis of mitigation measures in the European built environment.
In the road transport sector, the ALADIN and EU-TIMES models cover almost all mitigation measures, with a significant degree of detail in ALADIN in particular. LEAP and NEMESIS also cover numerous options (gas vehicles, electric vehicles or biofuels) except for hydrogen fuel cell vehicles in both models and hybrid electric vehicles in LEAP. The technological options for GHG emission reductions in aviation and shipping are relatively limited in all five models. The NEMESIS, LEAP and EU-TIMES models allow biofuel substitution whereas electric engines are possible only in LEAP and EU-TIMES. More precisely, biofuels, hydrogen, electricity and gas (for shipping only) are only available in the EU-TIMES model, with a few modifications. Railways electrification is also available in EU-TIMES, LEAP and NEMESIS. Finally, modal shift can be used to favour low-carbon transports, through exogenous assumptions, in EU-TIMES, whereas in NEMESIS, the granularity of the model only allows modal shifts for households (distinguishing households’ demand for road, rail, air and other transport services).
For the manufacturing sectors, the FORECAST model includes more than 80 processes and 200 different saving options allowing for an in-depth analysis of mitigation measures and pathways. EU-TIMES and LEAP cover almost all mitigation measures listed in the table below, except for hydrogen in process heat for the EU-TIMES model and options for machine drives in LEAP. NEMESIS also covers different options but due to non-explicit technological specification, these are more limited.
To mitigate GHG emissions from agriculture, NEMESIS and LEAP cover emission reductions from energy use whereas, the EU-TIMES model directly incorporates behavioural changes.
Finally, in all models and for all sectors, efficiency measures can be used as GHG mitigation options with a different degree of detail and mostly as exogenous assumptions in the NEMESIS model.
|Mitigation Measures||Upstream Technologies||Synthetic fuel production||x|
|Electricity and Heat Generation Technologies||Electricity generation||CCS||x||x|
|Nuclear fission||x||x||x( 5 )|
|Hydro||x||x||x( 5 )|
|Geothermal||x||x||x( 5 )|
|Solar PV & CSP||x||x||x|
|Wind onshore & offshore||x||x||x|
|Buildings||Heating||Gas replacing oil / coal||x||x||x||x( 2 )|
|Electricity||x||x||x||x( 2 )|
|Solar thermal||x||x||x||x( 2 )|
|Building shell efficiency||x||x||x|
|Other||High Degree of Detail|
|Building shell efficiency||x||x|
|Behaviour change (less energy service demand)||x||x( 5 )||x|
(1): Can be implemented by fuel prices; (2): Technology not explicitly modelled; (3): Can be added with some changes; (4): Only for households; (5): Exogenously determined; (6): In some sub-sectors (e.g. cement) (7): FORECAST also includes Ambient heat, (8): FORECAST also includes biomass.
|Mitigation Measures||Transport||Road||Gas (LNG / CNG) vehicles||x||x||x||x( 2 )|
|Hybrid electric vehicles||x||x||x( 2 )|
|Fully electric vehicles||x||x||x||x( 2 )|
|Hydrogen fuel cell vehicles||x||x|
|Biofuels in fuel mix||x( 1 )||x||x||x( 2 )|
|Efficiency||x||x||x||x( 5 )|
|Other||Overhead catenary for heavy duty vehicles|
|Rail||Electric rail||x||x||x( 2 )|
|Efficiency||x||x||x( 5 )|
|Aviation||Biofuels in fuel mix||x||x||x( 2 )|
|Hydrogen planes||x( 3 )|
|Electric planes||x( 3 )||x|
|Efficiency||x||x||x( 5 )|
|Shipping||Gas (LNG / CNG)||x( 3 )||x|
|Hydrogen||x( 3 )|
|Biofuels in fuel mix||x||x||x( 2 )|
|Electric||x( 3 )||x|
|Efficiency||x||x||x( 5 )|
|Modal shifts||x( 5 )||x( 4 )|
|Other behaviour changes (e.g. travelling less)||x||x|
(1): Can be implemented by fuel prices; (2): Technology not explicitly modelled; (3): Can be added with some changes; (4): Only for households; (5): Exogenously determined; (6): In some sub-sectors (e.g. cement), (7): FORECAST also includes Ambient heat, (8): FORECAST also includes biomass
|Mitigation Measures||Industry||Process heat( 7 )||Gas replacing oil / coal||x||x||x||x( 2 )|
|Biomass||x||x||x||x( 2 )|
|Electricity||x||x||x||x( 2 )|
|Machine drives||Gas replacing oil / coal||x||x||x( 2 )|
|Electricity||x||x||x( 2 )>|
|Steam( 8 )||Gas replacing oil / coal||x||x||x||x( 2 )|
|Electricity||x||x||x||x( 2 )|
|CHP||Gas replacing oil / coal||x||x||x|
|Overall industry||CCS||x||x( 5 )||x|
|Other||More than 200 different saving options plus innovative process technologies|
|Behaviour changes (lower mat. consumption)||x||x||x|
|Agriculture & LULUCF||Energy use||Gas replacing oil / coal||x||x( 2 )|
|Biomass||x||x( 2 )|
|Electricity||x||x( 2 )|
|Behaviour changes (less product demand)||x|
(1): Can be implemented by fuel prices; (2): Technology not explicitly modelled; (3): Can be added with some changes; (4): Only for households; (5): Exogenously determined; (6): In some sub-sectors (e.g. cement), (7): FORECAST also includes Ambient heat, (8): FORECAST also includes biomass.
|Adaptation Measures||Land||Water use restrictions||x|
|Urban||Additional cooling of buildings||x|
|Building material choices||x|
As reported in the following table, four families of policy instruments (emissions mitigation, energy, land, and trade policy instruments) are covered by the different models.
|Emissions mitigation policy instruments||Tax||1||1||1||3||1|
|Emissions target / quota (annual)||2||3||1||1||1|
|Emissions target / quota (cumulative)||2||2||1||1||2|
|Regulations (emissions standards, etc…)||2||1||1||1||2|
|Financial supports (e.g. negative emissions, Green Climate Fund)||2||1||1||1||2|
|Energy policies instruments||Tax||1||1||1||1||1|
|Energy mix target||3||2||1||3||2|
|Regulations (thermal regulation in buildings, bans on diesel cars, etc.)||2||1||1||1||2|
|Land policies instruments||Protected lands||3||3||2||3||3|
|Trade policies instruments||Carbon border tax on imports||3||3||1||3||2|
|Carbon border supports on exports||3||3||1||3||2|
|Regulation policies (certifications, best-available technologies, etc.)||2||3||2||3||2|
|2 Feasible with modifications|
|3 Not feasible|
Among the five emissions mitigation policy instruments listed in the table above, all models can deal with carbon tax or carbon price (except for LEAP), with annual emissions targets or quota (except for FORECAST and with some adaptations for ALADIN), with multi-annual emissions targets or quota, regulations and financial support (with some modifications for ALADIN and NEMESIS).
The models also cover five energy policy instruments. All can implement energy taxation and/or energy subsidies and can fix an energy mix target (except for ALADIN and with some modifications for FORECAST and NEMESIS). Similarly, it is possible to fix efficiency targets in all models (but it will imply modifications in ALADIN, FORECAST and NEMESIS). All models (NEMESIS with some modifications) can use regulation instruments (e.g. norms), such as, thermal regulations in buildings or bans on diesel cars in urban areas. Two land policy instruments can be activated in the EU-TIMES model if some modifications are done: a carbon sink pricing or land use change emissions tax and an afforestation target.
Finally, the EU-TIMES and NEMESIS models can implement both carbon border tax on imports and carbon border support on exports (with some modifications for NEMESIS); while the implementation of trade regulation policies—based for instance on certifications, best available technologies or standards—can be incorporated in ALADIN and EU-TIMES (assuming some modifications).
The following table details fifteen of the seventeen SDGs set by the United Nations in 2015 for the year 2030. SDG13 on climate action, already investigated in the previous sections, and SDG17 on revitalising global partnership for sustainable development are excluded, as out of the scope of the featured modelling tools. Among these fifteen SDGs, the five models can deliver indicators to track directly or indirectly eight of the SDGs.
|§1. No Poverty|
|§2. Zero hunger|
|§4. Quality education|
|§5. Gender equality|
|§6. Clean water and sanitation||x||x|
|§7. Affordable and clean energy||x||x||x||x|
|§8. Decent work & economic growth||x||x|
|§9. Industry, innovation & infrastructure||x||x||x||x|
|§10: Reduced inequalities||x|
|§11: Sustainable Cities & Communities||x|
|§12: Responsible production & consumption||x||x|
|§13: Climate action|
|§14: Life below water|
|§15: Life on land|
|§16: Peace, Justice and institutions|
The bottom-up models, ALADIN and FORECAST, are able to provide indirect measures related to SDG7, SDG9 and SDG12. ALADIN considers innovation in technology and infrastructure that can be useful for SDG9 (industry, innovation & infrastructure). More specifically, the FORECAST model can consider the detailed impact of innovation (superior to technology readiness level 5 - TRL5) on CO2 emissions and energy demand for the industrial sectors, and can calculate the differential of investment between scenarios. FORECAST can deliver insights on SDG7 on affordable and clean energy, to the extent of considering potential use of renewables on the demand side. FORECAST can also give useful information for sustainable cities and communities (SDG11) by considering the impact of increased secondary production on CO2 emissions and energy demand.
Both energy system models, LEAP and EU-TIMES, are able to provide information for SDG6 on clean water and sanitation, by quantifying water consumption (and withdrawal for EU-TIMES). Both are also well-designed to deliver several indicators related to SDG7 and particularly concerning renewable and clean energy sources. Furthermore, EU-TIMES can mobilise a macroeconomic module (requiring some modelling adjustments), and provide GDP deviation between scenarios, which is useful for SDG8 (decent work and economic growth). EU-TIMES quantifies system (energy-related) costs and investment needs that can be used to track SDG9, while it can also implicitly provide indicators related to the SDG11, considering the building stock and subsets of retrofit measures.
Finally, NEMESIS, as a macroeconomic model, is well-designed to provide indicators related to SDG8 on, for example, annual growth rate of real GDP per capita; annual growth rate of real GDP per employed person; average hourly earnings of employees or unemployment rate by educational level. NEMESIS also delivers indicators for SDG7, such as renewable energy share in gross inland energy consumption; energy intensity in terms of primary energy and GDP. As granularity of industrial economic activities is relatively large in NEMESIS, the model can give insights into SDG9, through indicators on manufacturing value added, manufacturing employment or CO2 emissions per unit of value added. Finally, it can compute the labour share of GDP, a useful indicator for SDG10 on inequalities.
Beyond the capability of the five models to provide indicators for the SDGs, some are indirectly considered in the model through scenario assumptions or drivers. For example, NEMESIS takes into account, as drivers, the level of education attainment of the population, and therefore considers some aspects of SDG4 (on education) in its scenario design. Similarly, the EU-TIMES model considers afforestation and exploitation patterns as input for renewable potentials, which are indirect elements of SDG15 (life on land).
Since all five models are significantly different, they do not operate homogeneously to calculate climate change mitigation pathways. Furthermore, they are all limited by their geographical coverage (EU and EU-national) and some by a sectoral focus; therefore, they cannot directly deal with global climate change mitigation issues, such as limiting global warming to a target level.
The five models calculate mitigation pathway as follows:
ALADIN projects the stock and total energy consumption and CO2 emissions of road vehicles (passenger cars, as well as light- to heavy-duty vehicles) in scenarios. Thus, CO2 mitigations can be calculated by comparison of scenarios with different policies.
FORESCAST is a bottom-up simulation tool that calculates long-term scenarios for future energy demand and CO2 emissions of individual countries until 2050 within a single model run. In the first step of the scenario process, an ambition level is determined qualitatively and quantitatively, which is then translated into important general and sectoral model parameters (e.g. CO2 price, energy carrier prices, renovation rates, financial incentives for RES, etc.). After this first model setting, a scenario run is started. This is an explorative simulation approach considering the dynamics of technologies and socioeconomic drivers.
LEAP is a scenario-based modelling tool, the climate module of which calculates changes in the atmospheric concentration of all implicated GHG emissions between a reference scenario (which depicts the current condition of a system in terms of energy—demand and supply—and demographics—population, income, etc.) and a number of GHG mitigation-oriented scenarios based on current and future limitations, aligned with the ongoing global treaties and agreements (such as the goals set by the Paris Agreement or by increasing ambition).
EU-TIMES is a scenario-based tool, which produces dynamic least-cost pathways subject to a number of environmental and technical constraints. The model allows the exploration of several mitigation policies, including targets (e.g. annual GHG emissions binding targets, cumulative carbon budgets), and sectoral/technology-specific policies (e.g. standards, subsides and taxes). Results provides country-specific implications for (i) the economy (including energy prices, investments in the energy system, marginal CO2 abatement costs, etc.), (ii) the energy mix (fuels and technologies) and energy dependence, and (iii) the environment (in particular GHG emissions).
NEMESIS uses a recursive-dynamic principle and is solved annually. Thus, the model can implement climate change mitigation policies on the basis of either annual GHG emissions constraints or a predetermined level of a climate policy instrument. In the first case, the model adjusts the level of the policy instrument, mainly a carbon tax, to reach the emissions target and, in the second case, the level of the policy instrument is predefined and the model calculates the related GHG emissions. In the case of an annual GHG emissions binding target, several modelling simulations can be done considering different pathways (under carbon budget constraint) and ranked according to the selected criterion.
The diversity and heterogeneity of the five models allow for covering a large set of mitigation options as well as policy instruments as detailed below. These tools will be used individually in the PARIS REINFORCE project, providing a large scope of quantitative outputs. There will exist some overlapping in the results of the models allowing for an enrichment of the analysis by comparing modelling outputs. However, the five models can be complementary also due to their different focus: NEMESIS covers macroeconomic aspects as inter-sectoral economic exchange; LEAP and EU-TIMES cover the entire energy system allowing for balancing between supply and demand and a detailed analysis of technological options; FORECAST provides for in-depth analysis of mitigation options in the industrial, tertiary and residential sectors; and ALADIN completes the puzzle with a very detailed analysis of alternative road vehicles diffusion. Thus, it seems very relevant to establish a linkage process between these tools trying (i) to provide a harmonised background for all five tools, (ii) to ensure coherency in the implementation of the tools and (iii) to take advantage of the specific expertise of each tool.
At this step, no formal linkage has been established and the following figure only draws a first attempt on how this linkage could be implemented. Starting from scenario storylines and related quantitative drivers that feed all models in a harmonised manner, macroeconomic indicators (such as GDP, households’ disposable income, etc.) and sectoral economic activity will be calculated by the NEMESIS model and will be used as drivers in the others tools. In a first round, energy system models (EU-TIMES and LEAP) will determine demand, supply and particularly energy prices of which the latter will be used as input for sectoral “energy-demand” models (FORECAST and ALADIN). From this first iteration, new energy demands from industry and buildings (FORECAST) and new vehicle fleets (ALADIN) are estimated and then used in energy-system models (EU-TIMES and LEAP). When models’ outputs converge , economic variables (changes in investment requirements from energy supply and demand side, changes in energy expenditures, etc.) and new energy prices will be implemented in NEMESIS to assess the macro-sectoral impacts. A complete loop can be achieved when using new outputs of the NEMESIS model to feed the other modelling tools (ALADIN, FORECAST, EU-TIMES and LEAP).
|Model||Example Study(s)||Research Question/Focus||Selected Key Findings|
|ALADIN||Gnann et al. (2019)||Impact of public charging infrastructure for plug-in electric vehicles on their market diffusion||The paper demonstrates the possibility of a market diffusion in Germany without any slow public charging infrastructure until 2030. Although a charging point at home is obligatory for early adopters, the second-best option for an infrastructure set-up is at work.|
|Plötz et al. (2019)||What can be the impact of catenary trucks on the European energy system?||We find that electric trucks can reduce CO2 emissions from the transport and energy sector even when no additional renewable capacity is installed.|
|FORECAST||Heat Roadmap Europe (https://heatroadmap.eu/publications/)||What are EU heating and cooling strategies on national level for heating and cooling in Europe?||Heat savings can cost-effectively reduce the total heat demand in Europe by approximately 30-50%. Based on cost and energy considerations, district heating should increase from today’s level of 10% up to 50% by 2050. Large heat pumps and other proven technologies can provide next generation district heating with renewable heat.|
|REFLEX (publication available in 2020 - www.reflex-project.eu)||What are future flexibility potentials in a low-carbon EU energy system and how can they cope with future flexibility needs for RES integration?||Electrification and the use of H2 are promising decarbonisation options for transport and industry sectors. On the demand-side, the diffusion of decentralised batteries as part of PV systems and in electric vehicles as well as H2 production by electrolysers could provide necessary flexibility for the electricity system.|
|LEAP||Martı́nez-Jaramillo et al. (2017)||How much GHG emissions could be avoided by the implementation of planning strategies for the Medellin metropolitan area between 2010 and 2040?||The results indicate that a policy combining the promotion of mass transportation could represent 5.65 Million Tons of CO2 equivalent avoided by 2040 (a 9.4% reduction).|
|Emodi et al. (2017)||What are the future GHG emissions in Nigeria in 2040||It is observed that in the Green Optimistic scenario the emissions will be 11% lower than in the reference scenario.|
|EU-TIMES||Sgobbi et al. (2016)||What can be the role of H2 production in a low carbon energy system sustainable future power system?||The paper indicates hydrogen could become a viable option already in 2030, however, a long-term CO2 cap is needed to sustain the transition. Low-carbon hydrogen production technologies dominate, and electrolysers provide flexibility by absorbing electricity at times of high availability of intermittent sources.|
|Paardekooper et al. (2018)||How effectively support the decarbonisation of the heating and cooling sector in Europe and democratise the debate about the sector?||The report indicates that the European Union should focus on implementing change and enabling markets for existing technologies and infrastructures in order to take advantage of the benefits of energy efficiency in a broader sense and for the heating and cooling sector specifically.|
|NEMESIS||Muller et al., (Forthcoming)||How much can carbon border adjustments reduce the costs for EU of a unilateral climate change mitigation policy?||The implementation of a tax on the carbon content of EU importations can reduce the negative impacts of stringent GHG mitigation policies, within the EU, on competitiveness and furthermore with more positive effects when incomes from this tax are redistributed.|
|France Stratégie (2019)||What is the social value of carbon in France?||The report recommends, for 2030, to put forward a shadow price of €250/tCO2. By 2050 it is expected to align with the estimated costs of the enabling technologies required for decarbonisation —therefore a cautious range of €600 to €900/tCO2.|
Regarding the use of national and regional models for Europe, like all climate-economy models, a legitimate question has been raised, both in the literature and in the policy world, around the levels of trust that people (whether scientists, policymakers, or other stakeholders) should have in these models and their outputs (Doukas and Nikas, 2020). That is, especially, considering the underlying assumptions driving them (Kelly and Kolstad, 1999) and uncertainty ranges (Doukas et al., 2018), as well as the extent to which these are communicated alongside the results.
It is unavoidable that models, such as those documented in I2AM PARIS, cannot provide a complete representation of the world, owing to the fact that in many ways the future is unknown, and furthermore there is incomplete knowledge of past dynamics governing energy, agricultural, land and environmental systems that are represented by these models.
Despite this challenge, these models are intended to be trusted, and seen as useful and valid, by both the scientific community and—equally if not more importantly—stakeholders such as policy- and decision-makers, who will plan low-carbon strategies on their basis. Here we briefly detail the steps that have been applied in developing and using the models, as well those that will be applied in the context of the PARIS REINFORCE project, in order that such trust and validation is achieved.
The workflow is illustrated in the following figure, based on the relevant literature on evaluation and validation of integrated assessment models and drawing from the primary elements of (Schwanitz, 2013):
Model validation process of models in PARIS REINFORCE
In particular, very early in the project the consortium has put significant effort in documenting each of the employed models’ capabilities, in terms of geographic disaggregation, sectoral representation, types of greenhouse gas and pollutant emissions accounted for, technological detail, policy representation, socioeconomic inputs and outputs, and representation of metrics relevant to non-climate SDG indicators. It has done so for every national/regional model for Europe in the consortium, (a) in detailed and technical format for experts’ consideration (see here), (b) in a dynamic interface and easy-to-digest language for non-experts to comprehend and map their requirements onto (see here), and (c) in a comparative setting, allowing all stakeholders to understand which models should be employed for which policy questions (as in this page). As part of this step, the project released and shared with stakeholders a policy brief on ‘what can our models do?’ (link).
Second, as with global models and models for countries/regions outside the EU, the EU regional/national modelling approach is in line with the “prolonged nature of model validation” (Barlas, 1996). This approach integrates opinions from the scientific community with the perspectives of stakeholders and external experts that will ultimately use the model results. PARIS REINFORCE will, in this respect, undertake active engagement with national and EU stakeholders aimed at communicating what the models are, as well as what they can do, including a presentation of the modelling approach, preliminary results, and a discussion of the types of inputs and outputs the models produce and how they do this. This interaction will therefore provide an opportunity to place our models’ results in the context of previous results from other EU-wide and national low-carbon pathways modelling activities. They will also allow the project, not only to clearly communicate the modelling capabilities, features, and questions they have been asked to address in the past, but also to co-create the most pertinent questions stakeholders would like the models to address in the context of PARIS REINFORCE, in light of this well-informed stakeholder perspective. The project workflow involves first undertaking global, regionally-disaggregated modelling to explore the regional dynamics of emissions and energy/agricultural and land system transitions globally, before discussing the realism, feasibility and validity of such results with both global and regional stakeholders. In this way, global models can undertake scenarios informed by national stakeholders in Member States and various regions around the world, improving the real-world relevance of their outputs.
Third, a central aspect of achieving real-world relevance is to undertake basic harmonisation/benchmarking of the models, via targeted validity checks. This includes ensuring that base-year emissions, socio-economic assumptions, policies, and energy/agricultural/land system representations are in line with the most up-to-date verified information, and that such inputs are to the extent possible harmonised across the models used in a multi-model analysis; this is not to strip models of their invaluable diversity in the way they behave in response to specific stimuli as well as the theoretical foundations underpinning them, but quite the contrary to allow the consortium to later map the resulting ranges onto this diversity rather than uncertainties associated with ad hoc inputs assumed for each model. This also includes technology costs and performance variables. The underlying detailed protocol for achieving this has been documented in Giarola et al.
Fourth, “diagnostic” tests are and will be run for each model, to check that its responses to key input variable changes, such as stringency of climate policy (as represented by emissions targets, carbon prices, or combinations thereof), are in line with common expectations and compared to other results and models covering the same/similar regions and/or a priori defined stylised behaviours. For models covering the EU and its countries, this includes (a) comparing the resulting ranges of the multi-model exercises to those of similar studies looking at the EU, for example (Capros et al., 2014) for similar multi-model analyses or (Simoes et al., 2017) for focused single-model EU-regional analyses; (b) comparing the resulting ranges of the multi-model exercises to those of the global model inter-comparisons carried out with appropriate disaggregation at the European region, e.g. (Sognnaes et al.); and (c) carrying out index decomposition analysis of sectoral modelling results against the global IAM results (e.g., Wachsmuth and Duscha, 2019). These checks will build on the normal standard practice undertaken by each modelling group to regularly check the model code and ensure that errors/bugs are identified and eliminated, and to report the model’s performance alongside its results.
Finally, this evaluation process will be carried out in an iterative process throughout the project. It will be documented to ensure that models perform without unexplained dynamics in both reference and mitigation cases. A key element of this will be in taking documented modelling results back to stakeholders in the second half of the project, when they will be able to understand the behaviours of the models under increasingly stringent mitigation scenarios, and to ask why the models respond in the way that they do.
In combination, these evaluation steps cover the primary elements of the workflow suggested by Schwanitz (2013) on model validation. The models’ conceptual framework has to a large extent already been evaluated (and will be clearly communicated with stakeholders) as following from a principle of identifying least-cost pathways to low-carbon futures given the technological other input assumptions (like technology constraints, socio-economic dynamics, and representations of policies). The I2AM PARIS platform is intended to represent a major advance in communicating in a clear, accessible and attractive way the features, objectives, coverage, capabilities and limitations of models. Such documentation has been and will be accompanying our stakeholder interactions. Model structure and responses are being tested through both diagnostics as well as comparisons with other published low-carbon and reference case pathways. Whilst most modelling groups explicitly draw on a vast range of literature and comparative studies to understand the extent to which their results are similar to or different from others, and if so why, in PARIS REINFORCE we will explicitly undertake and present such comparisons throughout our modelling, to help better build trust in the results and the models themselves.
- Barlas, Y. (1996). Formal aspects of model validity and validation in system dynamics. System Dynamics Review: The Journal of the System Dynamics Society, 12(3), 183-210.
- Capros, P., Paroussos, L., Fragkos, P., Tsani, S., Boitier, B.,... & Bollen, J. (2014). European decarbonisation pathways under alternative technological and policy choices: A multi-model analysis. Energy Strategy Reviews, 2(3-4), 231-245.
- Doukas, H., & Nikas, A. (2020). Decision support models in climate policy. European Journal of Operational Research, 280(1), 1-24.
- Doukas, H., Nikas, A., González-Eguino, M., Arto, I., & Anger-Kraavi, A. (2018). From integrated to integrative: Delivering on the Paris Agreement. Sustainability, 10(7), 2299.
- Giarola, S., Mittal, S., Vielle, M., Perdana, S., Campagnolo, L., Delpiazzo, E., Bui, H., Anger-Kraavi, A., Kolpakov, A., Sognnaes, I., Peters, G.P., Hawkes, A., Koberle, A., Grant, N., Gambhir, A., Nikas, A., Doukas, H., Moreno, J., & Van de Ven, D.J. (Reviewed & Revised). Challenges in the harmonisation of global integrated assessment models: a comprehensive methodology to reduce model response heterogeneity. Science of the Total Environment.
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