The food demand scenarios for PSS1 and PSS2 scenarios are based on a food demand model with population growth, changing demographic structure and head income as the main driver95. The model combines anthropometric and econometric approaches to estimate the distribution of underweight, overweight and obesity, as well as body size by country, age cohort and sex. In addition, it estimates food intake and food waste, as well as the nutritional composition between four main foods: animal calories, empty calories, calories from fruits, vegetables and nuts, and basic dietary calories. All elasticity parameters of the model are estimated based on data observed in the past. To account for less material-intensive consumption patterns in the SSP1 scenario, food waste and food composition patterns are estimated based on different functional forms than in the PSS2 scenario, assuming less food waste, animal calories and processed foods, and higher fruit consumption, vegetables, nuts and basic products. For the SDP scenario, we foresee a gradual transition to the eating habits proposed by the EAT`Lancet36 Commission by 2050 (i.e. to a healthy and sustainable diet with low food waste). Total food intake is always estimated based on the anthropometric equations of the food demand model, but taking into account the assumption of a healthy body weight. Moyer, J. D. & Bohl, D.
K. Alternative pathways to human development: assessment of trade-offs and synergies in achieving the Sustainable Development Goals. Futures 105, 199â210 (2019). Managing this ambitious transformation towards sustainability requires peace, strong national and global institutions, and global global cooperation.45,46,47 SDGs 16 and 17 are therefore goals, but also prerequisites for sustainable development27. The optimistic assumptions of the socio-economic scenario for the SSP1 and SDP scenarios therefore imply strong institutions and peace (Intervention A; Extended data Fig. 1). To quantify these two factors, we model for each country the quality of the rule of law and individual freedoms48 and the number of deaths in armed conflict49,50. Overall, the SDP and SSP1 scenarios describe a convergence of institutional quality, but significant regional differences remain. The population-weighted global average of the institutional quality indicator (range 0-1) improved moderately from 0.61 in 2015 to 0.76 in 2050, but is below the target of 0.9 (ref.
32). All scenarios predict a further decline in the number of deaths in armed conflict (after a recent peak of 140,000 deaths > in 2014); but at first only at a slow pace. By 2050, only the SDP and SSP1 scenarios predict a significant probability that the number of armed deaths will be reduced to 20,000 < (the most recent minimum in 2005). However, these projections are associated with considerable uncertainty, as our models include only institutional quality and peace structural covariates for which long-term scenario projections currently exist. Results from other downstream models: Climate and development finance is calculated as post-processing of scenario outcomes. Indicators of ocean, political institutions and conflicts, inequality and poverty, and air pollution are calculated using dedicated models that use the quantification of scenarios by REMINDâMAgPIE as input (details below). A value of zero represents the value of the indicator in 2015, while 100% indicates that the goal is completely met or even exceeded. The SSP2-NDC and SDP-1.5C scenarios are represented as bars, the "intermediate" scenarios SSP1-NDC and SSP1-1.5C with symbols. The left side shows the results for 2030, while the right side is for 2050.
An overview of the objectives used is given in Supplementary Table 1. In some cases, the 2050 targets are more ambitious than the 2030 targets. Negative values represent a worsening of the situation. We have lowered the scale to 30%, but note that the indicators “agricultural water consumption”, “food waste”, “IBI” and “nitrogen fixation” in the SSP2-NDC scenario deteriorate well beyond this value (the absolute values compared to the targets are shown in Figure 1). With the exception of “energy- and mobility-efficient buildings”, which shows the average value of low-income regions, all indicators are sums or overall averages. Aragonite saturation (SDG14) was excluded because the current values are so close to the target value that the deviation indicator is not significant. In MAgPIE, the impact of land-use change on terrestrial biodiversity is assessed via the BII (Ref. 87,88). The IBI takes into account net changes in the abundance of organisms relative to human land use and the age group of natural vegetation. Changes are then expressed relative to a reference land use class for which primary vegetation (wooded or non-wooded) is used and weighted with a spatially explicit area scarcity layer89. Primary vegetation and mature secondary vegetation have a BII of 1, while other land cover classes such as arable land (0.5 to 0.7) have lower BII values.
Pradhan, P., Costa, L., Rybski, D., Lucht, W. & Kropp, J. P. A systematic study of the interactions between the Sustainable Development Goals (SDGs). Earths Future 5, 1169â1179 (2017). The REMINDâMAgPIE37,74,75 framework consists of a multi-regional energy-economy-climate model (REMIND; Ref. 66.76), associated with a spatially explicit Earth system model (MAgPIE; Ref. 77). The framework integrates the magicc simple climate model (Ref. 65) and takes biophysical information from the LPJmL plant and hydrological model (Ref.
78) (details below). BOTH REMIND and MAgPIE are open source with full documentation (see references in the next sentence). For this work, a model version was used that is almost identical to v.2.1.3 (REMIND)79.80 and the v.4.2.1 model (MAgPIE)81.82 (code availability). The transition to healthy and sustainable food avoids an increase in food prices through climate policy. This leads to a decrease in food expenditure, particularly in sub-Saharan Africa (SSA), and at the same time to an increase in the availability of food (Fig. 4a). Agricultural emissions of N2O and CH4 will be significantly reduced in all regions, greatly facilitating the achievement of ambitious climate targets. Similarly, the shift away from energy-intensive lifestyles in industrialized countries (Fig.
4b) facilitates the decarbonization of energy supply. Overall, this puts the 1.5°C target within reach with significantly lower carbon prices: we expect about $150 per tonne of CO2 for high-income regions and $25 per tonne of CO2 for sub-Saharan Africa in 2030, which is about half the values of the SSP1-1.5C scenario. These lower prices reduce the risk of negative political side effects and facilitate the implementation of a comprehensive carbon pricing system from a political economy perspective. Transparent wide bars represent 2030 values, massive thin bars are values for 2050, 2015 values are indicated by the dotted vertical line. We show the scenarios SSP2-NDC (red, high), SSP1-1.5C (green, center) and SDP-1.5C (blue, bottom); the SSP1-NDC scenario is omitted for visual clarity. a, Sectoral final energy demand per capita, b, Share of electricity and hydrogen in final energy, by sector c, Total final energy demand relative to the 2050 values of Grubler et al.34 and Millwards-Hopkins et al.43 and the values of the IEA`s “Sustainable Development” scenario for 203013. . .