REMIND model
The REMIND-EU7 model was used to create the 36 scenarios evaluated in this paper. REMIND-EU preserves the global coverage of the REMIND10,25 model - an open source integrated assessment model widely used in climate assessment literature11–13 -, and extends its spatial representation by introducing eleven additional European regions to better represent national policies and energy systems.
REMIND is an energy–economy–climate multi-regional intertemporal welfare-optimisation model. It solves for an intertemporal Pareto optimum in economic and energy investments by hard-coupling a Ramsey-type macroeconomic growth model with a technology-detailed energy model, combining the strengths of bottom-up and top-down approaches. It covers all relevant greenhouse gas emitting sectors, as well as options for carbon dioxide removal26, thus allowing for an integrated assessment of pathways towards climate neutrality. Land-use, agricultural emissions, bioenergy supply and other land-based mitigation options are represented through reduced-form emulators derived from the detailed land-use and agricultural model MAgPIE (Model of Agricultural Production and its Impact on the Environment)27,28.
REMIND features a substantially high level of detail in the representation of energy-system technologies, trade, and global capital markets. For the scenarios presented here, the model optimises regions individually and uses an iterative adjustment mechanism to clear international markets for (primary) energy carriers and non-energy goods29.
A short overview of the key components of the model is given in the following paragraphs. The model code is available open source at https://github.com/remindmodel and further documented at https://rse.pik-potsdam.de/doc/remind/3.2.0/.
Energy System Modelling
The energy supply system in REMIND represents the conversion of primary energy carriers into secondary energy carriers and their transport and distribution to end-use sectors. The energy system further accounts for system inertias and path dependencies induced by ageing capital stocks, e.g. in power-plant infrastructure and endogenous learning-by-doing. Additionally, REMIND accounts for challenges related to rapid upscaling of new technologies via cost-markups that are assumed to increase with the square of year-to-year capacity additions30. The REMIND model represents the endowments of exhaustible primary energy resources31 as well as renewable energy potentials based on bottom-up estimates32,33. REMIND accounts for cost reductions in solar photovoltaics, concentrating solar power, wind energy and battery storage endogenously via learning-by-doing. Technological progress for all other technologies is parameterized via exogenous assumptions.
The REMIND model captures the challenges and options related to the temporal and spatial variability of wind and solar power32. In addition to flexible demand response, also inter-regional pooling as well as short-term storage (diurnal time-scales, mostly via batteries) and long-term storage (up to seasonal time-scales) play a key role for facilitating VRE integration. REMIND parameterizes corresponding technology and region-specific VRE storage and grid expansion requirements34 as well as curtailment rates (i.e., unused surplus share of VRE electricity generation), which are derived with the help of two detailed electricity production cost models34,35.
Energy End Uses
An important feature of this study is the representation of demand sectors and cross-sectoral mitigation strategies. In the industry sector, REMIND represents four subsectors: steel, cement, chemicals and other manufacturing. Both primary (virgin) steel production from iron ore and secondary steel production from scrap are represented via a simplified stock-flow-model based on Pauliuk et al.36. Energy demand in these subsectors is broken down into heat demands, mechanical work and feedstocks. Mechanical work is already electrified, or can be readily electrified in the future. We further consider indirect electrification of the high-temperature heat inputs for primary steel, cement production and chemical industry via hydrogen. The substitution of heat supply carriers in other manufacturing is represented via a constant elasticity of substitution production function. Feedstocks in the chemical industry must be supplied as hydrocarbon fuels.
Concerning the transport sector, for this study we adopt the coupled system REMIND/EDGE-T37 to analyse the carriers and transport modes transition towards climate neutrality. Mobility is divided into passenger and freight demands, each broken down by trip length into long-distance and short-medium distance components. The market for each transport demand category is split across different transport modes and vehicle types. Multiple technology options are available for each vehicle type: electricity can be consumed directly in battery electric cars, buses and trucks, and electric trains. Indirect electrification via hydrogen is available for all road transport options. For passenger cars, mode choice accounts for the value of time of alternative modes. In addition, the technology choice module accounts for dispreferences, e.g., due to range anxiety or low model availability.
Transformation pathways for buildings energy demand are derived from the EDGE-Buildings model. The EDGE-Buildings model projects energy service demands for the subsectors (i) space heating, (ii) water heating, (iii) cooking, (iv) space cooling, and (v) appliances and lighting, based on exogenous socio-economic and climate pathways. The REMIND buildings module is then calibrated to meet these energy service trajectories, and represents technology choice to meet these service demands.
Beyond energy-related CO2, REMIND further represents a wide spectrum of greenhouse gas emissions. CH4 emissions from fossil resource extraction are represented by source. REMIND is coupled to the MAgPIE land use model27 to derive CO2 emissions from land use, land use change and forestry, as well as CH4 and N2O emissions from agricultural activities. Abatement options for CH4 and N2O emissions from energy supply, agriculture, waste and wastewater are based on marginal abatement cost curves from Harmsen et al.38. Emissions from fluorinated gases are represented exogenously from Van Vuuren et al.39. Emissions from aerosols and short-lived trace gases are based on the GAINS model40. Our modelling framework employs the MAGICC41 reduced complexity climate model to evaluate the resulting changes in global climate variables from the emerging emission scenarios. The impacts of climate change on energy systems and the economy are not considered in the modelling.
Scenario design
To derive cost-efficient transition pathways, climate policy is represented by assuming an economy-wide CO2-price increasing linearly from today's levels firstly to the levels required to reach the EU green deal 2030 emissions target and later to reach greenhouse gas neutrality in 2050. We further assume that the CO2 price is equal across sectors. European green deal 2030 policies underpinned by concrete measures and firm governance are embedded in the scenarios definition, and additional measures are assumed to overcome key market failures, such as a phase-out of internal combustion engine vehicle sales in the transport sector as implemented in the Regulation (EU) 2023/85142.
Four different dimensions are considered for the sensitivity analysis used to identify robust features of the energy transition to climate neutrality as well as key uncertainties: (1) realised short-term emissions reductions, (2) evolution of final energy demand, (3) availability of sustainable bioenergy, and (4) availability of CCS.
There is some ambiguity in the interpretation of EU 2030 emissions targets, and corresponding uncertainty about GHG emission reduction levels that will be achieved. According to the weakest interpretation, under the Green Deal climate law the EU reduces GHG emissions including LULUCF and intra-EU aviation by at least 55% by 2030. However, EU policymakers have provisionally agreed to increase the 2030 nominal reduction target to 57% as part of an agreement to boost natural carbon sinks. The 59% reduction case is motivated by the possibility that 2030 emissions are further reduced in anticipation of increasingly stringent post-2030 emission constraints, e.g. via banking of permits in the EU-ETS, and/or additional efforts promoted by countries with more stringent 2030 targets, e.g. Germany 65% reduction target.
Energy efficiency improvements greatly reduce the transformation requirements on the supply side. However, its effectiveness is highly dependent on implementation and success of energy efficiency programs, increased demand side action and cross-sectoral decision making - much of which is under the regulatory authority of the member states. We examine three final energy evolution scenarios to represent the uncertainty realisation of potential efficiency measures in the demand sectors (see Table 1).
The amount of sustainable bioenergy available for the EU is highly uncertain. We consider a scenario with bioenergy potential limited to 7.5 EJ/yr, corresponding to a scenario with strongest sustainability constraints11, as well as a bioenergy limit of 12 EJ, corresponding to sustainable domestic bioenergy potentials for the EU under more optimistic assumptions, also based on Ref. 11.
Finally, the capacity for geological carbon storage is highly dependable on the creation of a robust regulatory framework that covers permits, monitoring, cross-border collaboration, local storage acceptance, remuneration and long-term liability, across different European countries of varying legal systems. These regulatory challenges, public acceptance issues as well as technology and cost development give rise to substantial uncertainty. We therefore consider an optimistic reference case with long-term geological injection rates capped at 556 MtCO2/yr, and a limCCS case with long-term yearly geological injection rates capped at 278 MtCO2/yr.
The reference policy scenario assumes a continuation of energy and climate policies currently implemented with 57% emissions reduction by 2030, final energy projections following current observed trends (reference option), biomass availability limited to 7.5 EJ/yr, and long-term maximal geological injection rate limited to 556 Mt CO2/y (default CCS option).