A sustainable development pathway for climate action within the UN 2030 Agenda – Nature


Overview and SDG indicator typology

Our modelling ecosystem is built around the integrated assessment modelling framework REMIND–MAgPIE. Both through extensions of the core framework and through the inclusion of additional downstream models, we substantially extend the coverage of the SDG space, leading to a total of 56 SDG indicators or proxies across all 17 SDGs. Broadly, the representation of these indicators in our modelling framework can be classified into four groups (Supplementary Table 1):

  1. 1.

    Exogenous scenario assumptions: our input data for population, labour productivity growth and educational attainment in the SDP scenario are taken from SSP1 (refs. 60,61). The same holds true for the scenarios for the Gini coefficient62, which are used in the downstream model for inequality and poverty.

  2. 2.

    Demand projections: energy and food demand projections are derived with dedicated models and used as inputs in REMIND–MAgPIE. The demand projections for the SDP scenario are constructed to enable rapid progress towards SDG 2 and SDG 7 by assuming sufficient nutrition and faster growth of per-capita energy demands in regions with currently low values (details below).

  3. 3.

    Endogenous results of REMIND–MAgPIE: GHG emissions, energy system characteristics and land-use patterns are direct results of the REMIND–MAgPIE optimization. Policy measures that enable or enhance progress towards the SDGs are implemented as parameter settings or constraints in the model (Supplementary Table 3). For example, we implement an additional coal phase-out policy that limits residual coal use in the SDP to values similar to the SSP1-1.5C scenario despite the lower carbon price.

  4. 4.

    Results from additional downstream models: climate and development finance is calculated as a postprocessing of the scenario results. The indicators for ocean, political institutions and conflict, inequality and poverty and air pollution are computed with dedicated models that take the scenario quantification by REMIND–MAgPIE as an input (details below).

For each SDG, we select one headline indicator (two for SDGs 13, 15 and 16) to be shown in the main figures. Headline indicators are selected with the aim to be representative of key aspects of the SDG, quantifiable in our modelling framework and with a quantitative target (note the exception for SDG 17). In many cases, our choice follows van Vuuren et al.32; see also Supplementary Table 1. Results for the full set of indicators are shown in the Supplementary Information.

Scenario setup

Including our main SDP scenario we model four main scenarios that are chosen such that their comparison illustrates the effects of different interventions on SDG and climate outcomes.

  • SSP2-NDC: socioeconomic development continues along a ‘middle-of-the-road’ pathway similar to recent historical trends. Energy, resource and food demands are largely determined by the growth of per-capita income levels, with no substantial break compared to historical trends. There is only weak climate policy according to the current NDC pledges until 2030 and with a corresponding level of regional ambition thereafter (Supplementary Information section 6.2 and Supplementary Fig. 23).

  • SSP1-NDC: socioeconomic development follows a more optimistic pathway with higher GDP and lower population growth, also as a consequence of policy interventions in the areas of education and gender equality (intervention A). There is a general trend towards higher resource efficiency and environmentally more conscious lifestyles, which reduces overall energy and material demands (intervention B). Note, however, that interventions A and B are not resolved via explicit policy measures—instead we capture them through adapting model inputs appropriately33,37 (reflecting the outcome of the policy measures). Climate policy follows the NDCs and their extrapolation (as in SSP2-NDC).

  • SSP1-1.5C: the socioeconomic trends of the SSP1-NDC scenario are supplemented with an ambitious climate policy consistent with the 1.5 °C target from the Paris Agreement (intervention C).

  • SDP-1.5C: for our SDP scenario, which represents the main innovation of this study, additional sustainable development policies in the area of global cooperation, national redistribution, healthy and sustainable nutrition, energy access, as well as further sustainability policies for the energy and land-use systems are added (interventions D–F). Baseline GDP and population are identical to the SSP1-based scenarios, energy and food demands are projected separately (details below). On the supply side of intervention D (both land and energy), several of the sustainability policies follow Bertram et al.26. Additional policies introduced in this study include a coal phase-out policy (differentiated by income level), as well as a protection of biodiversity hotspots. A detailed comparison of the modelling assumptions for the different scenarios is given in Supplementary Tables 2 and 3.

Furthermore, we use the following auxiliary scenarios as reference cases or for additional analysis:

  • SSP2-NPi: this ‘national policies implemented’ scenario uses the same baseline assumptions as the SSP2-NDC scenario but only includes already implemented climate policies (as opposed to intended future policies)63. We use it as a reference case for calculating policy costs (for example, GDP loss due to mitigation policies) for the SSP2-based scenarios.

  • SSP1-NPi: same as SSP2-NPi but for SSP1-based scenarios.

  • SDP-NPi: same as SSP2-NPi but for SDP scenario.

  • SSP2-1.5C: this scenario starts from the same baseline as the SSP2-NDC scenario and implements only ambitious climate policies without any extra sustainability policies. It is not used in the main scenario cascade shown in this study but only for additional analysis and visualizations (Supplementary Information).

  • SSP1/SDP ‘hybrid’ scenarios: for an additional decomposition analysis (Extended Data Fig. 3), we have simulated scenarios with an SDP parameterization on the energy (REMIND) and SSP1 parameterization on the land (MAgPIE) side and vice versa. Further details are given in the Supplementary Information (section 3).

Climate policy

We implement ambitious climate policies as a not-to-exceed (peak) budget64 for CO2 emissions consistent with the 1.5 °C target. Using a peak budget instead of the end-of-century budgets often used in previous integrated assessment model (IAM) scenario studies allows for a more direct link between CO2 budget and temperature at peak warming and limits the possibility to compensate for continued high emissions in the near-term with large amounts of CO2 removal later.

For the SSP1-1.5C and SSP2-1.5C scenarios, we use a peak budget of 900 GtCO2 (counting from 2011 onwards; that is, around 610 GtCO2 from 2018 onwards), consistent with limiting warming to 1.5 °C with low overshoot (<0.1 °C; ref. 3) at median warming response65. However,…


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