Global agenda which class is the best

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As an important next step, this would shed further light on their importance as preconditions or also bottlenecks for sustainable development. A key modelling assumption is the linking between climate policies and poverty alleviation through the national and international redistribution of carbon pricing revenues 52 , Notably, this requires strong institutional capacity at both levels, and other beneficial uses of these revenues, such as education initiatives or infrastructure development 54 , are also possible.

Furthermore, additional revenues for such development policies could be generated from other sources, such as bequest or land rent taxation. We do not attempt to quantify the adverse effects of climate impacts on SDG outcomes As such, the residual impacts below the 1. Also, the detrimental effects of the COVID pandemic 56 , 57 are not yet captured in our modelling framework; thus, the gap towards certain SDGs will probably be larger in a post-pandemic world.

Despite these limitations, this comprehensive SDP scenario represents a pathway towards a more sustainable future. It demonstrates the possibility of moving towards the socioeconomic targets of the Agenda , while at the same time respecting the Paris climate target and other key planetary boundaries.

As such, it offers a vision of how to reconcile human well-being with planetary integrity. 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 :. Exogenous scenario assumptions: our input data for population, labour productivity growth and educational attainment in the SDP scenario are taken from SSP1 refs.

The same holds true for the scenarios for the Gini coefficient 62 , which are used in the downstream model for inequality and poverty. 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 SSP 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. In many cases, our choice follows van Vuuren et al. Results for the full set of indicators are shown in the Supplementary Information. 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.

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 and with a corresponding level of regional ambition thereafter Supplementary Information section 6. 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 appropriately 33 , 37 reflecting the outcome of the policy measures.

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.

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:. We use it as a reference case for calculating policy costs for example, GDP loss due to mitigation policies for the SSP2-based scenarios.

It is not used in the main scenario cascade shown in this study but only for additional analysis and visualizations Supplementary Information.

Further details are given in the Supplementary Information section 3. We implement ambitious climate policies as a not-to-exceed peak budget 64 for CO 2 emissions consistent with the 1. 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 CO 2 budget and temperature at peak warming and limits the possibility to compensate for continued high emissions in the near-term with large amounts of CO 2 removal later.

For the SSP For our SDP scenario, the transition to healthy and sustainable diets substantially reduces land-use-related emissions of non-CO 2 GHGs such as methane and nitrous oxide.

Therefore the CO 2 peak budget compatible with the 1. For the implementation of the peak budget, we assume that CO 2 prices in high-income regions increase linearly until the budget is reached, while lower-income regions initially face substantially lower prices details below. Linearly increasing CO 2 prices, in contrast to the more common exponentially rising CO 2 prices with a growth rate equalling the social discount rate, increase the near-term ambition of climate policy but limit the price increases at a later stage.

Both the optimal peak year and the required CO 2 price in this year are determined endogenously through an iterative algorithm, thus determining the rate of increase of the CO 2 price before the peak year We further implement a regional differentiation of carbon prices until mid-century to model a period of staged accession: in high-income regions, the CO 2 price follows the trajectory described above.

Lower- and middle-income regions, on the other hand, initially face substantially lower prices, where the respective reduction factor is assigned according to their GDP PPP per capita values in We assume that the reduction factor converges to unity following a convex trajectory; from onwards a globally uniform carbon price is used. An overview of the resulting regional carbon prices for the different mitigation scenarios is given in Supplementary Fig. This level of differentiation represents an intermediate case between a globally uniform carbon price and the substantially higher degree of differentiation required to equalize mitigation costs as a fraction of GDP between countries without any international transfers The differentiated carbon prices also form one of the components of our burden-sharing scheme; see below for a description of the other building blocks.

In contrast to previous studies on sustainable pathways, we explicitly address the question of equitably sharing the mitigation burden, as well as the global effort of meeting the SDGs.

In addition to the staged accession to climate policy description above , the scheme consists of the following two components:.

International redistribution of carbon pricing revenues: one-third of the energy sector GHG pricing revenues from each region are paid into an international scheme.

Payouts from the scheme are distributed to regions proportionally to their population shares and their GDP per-capita gap to the richest region. The scheme is gradually introduced until and then phases out over time as emissions, and therefore also carbon pricing revenues, reduce to near-zero around mid-century.

Equal-effort burden sharing in the long term: in addition to this partial redistribution of revenues, we assume a transition to an equal-effort burden-sharing scheme Additional interregional climate and development finance transfers are calculated such that relative GDP losses calculated with respect to the respective NPi scenario are equalized between regions from onwards.

This provides additional financial inflows to developing regions also beyond the time of net carbon neutrality, to compensate for their substantially higher relative policy costs than high-income regions 68 , 70 , The scheme is gradually introduced between and , thus reaching its full effect at the same time when the convergence to a globally uniform carbon price is completed. Compared to previous burden-sharing schemes discussed in the literature for example, refs.

Instead, we combine differentiated carbon prices with international climate and development finance transfers This mixed policy approach honours the principle of common but differentiated responsibility, as well as objectives of equity and sustainable development: a key underlying principle of our approach is that climate change mitigation should not deepen existing socioeconomic inequalities but should improve the development prospects of the Global South see also the Greenhouse Development Rights framework Recognizing that meeting the SDG agenda is a global challenge, our burden-sharing scheme understands carbon pricing and an international redistribution of part of its revenues, as an important source of funding for fostering sustainable development.

Figure 5 displays a regional analysis of SDG achievements in ; here we detail the methodology of this analysis. For each indicator, we set the zero line at the worst regional value in ; note that this differs from the global gap analysis in Fig. This approach takes into account if regions already perform well for a given indicator, instead of evaluating only whether a small remaining gap is fully closed for example, reducing extreme poverty from a value marginally above zero to exactly zero in high-income countries.

For each indicator we then compute the SDG achievement score using the targets from Supplementary Table 1. Several indicators are extensive quantities; for these we perform the regional analysis on a per-capita basis. Using the example of GHG emissions, this corresponds to comparing regional per-capita emissions to the global per-capita target value.

Note that this again differs from the global analysis in Fig. As a consequence, the global average score displayed in Fig. Here, we provide brief descriptions of the individual models and their linking. For this work, a model version close-to-identical to v. REMIND regional model of investments and development models the global economy and energy system with 12 world regions, where large economies are resolved individually and smaller economies are grouped into model regions.

The macro-economy of every region is modelled using a Ramsey growth model with a production function with constant elasticity of substitution. The main production factors are capital, labour and energy, where through the last the macro-economic core is hard-linked to a detailed representation of the energy system covering all major primary energy carriers, conversion technologies and end-use sectors.

Regions are first solved individually by maximizing intertemporal regional welfare; the global solution is found by iteratively adjusting market prices for primary energy carriers and the composite good and updating the regional solutions until all markets are cleared. MAgPIE model of agricultural production and its impact on the environment describes the global land-use system using an economic partial-equilibrium approach with the same 12 model regions as REMIND. Agricultural production is subject to spatially explicit clustered from 0.

All major crop and livestock product types are represented, as well as supply chain losses and demand for non-food agricultural goods. The model simulates a detailed representation of the agricultural nitrogen cycle using mass balance approaches that estimate inorganic fertilization requirements on the basis of harvest quantities, the availability of organic fertilizers and a trajectory for nitrogen uptake efficiency 84 , Carbon stocks of vegetation and soils are estimated using the LPJmL model and are affected by land-cover changes On the basis of a representation of carbon stocks and the nitrogen cycle, the emissions associated with land use and agricultural production are calculated.

The BII accounts for net changes in the abundance of organisms in relation to human land-use and age class of natural vegetation. Changes are then expressed relative to a reference land-use class, for which primary vegetation forested or non-forested is used and are weighted by a spatially explicit range-rarity layer Primary vegetation and mature secondary vegetation have a BII of 1, while other land-cover classes, such as cropland 0.

This soft-coupled framework allows for a higher degree of process detail in the two individual models, while the solution converges to the one of a single joint optimization problem.

The energy demands for the industry, transport and residential and commercial sectors in REMIND are determined endogenously. The model can respond to climate policies with a demand reduction by switching to more efficient technologies for example, from internal combustion engines to battery electric vehicles. However, the relation between energy demands and economic output is inferred from a calibration phase The input trajectories for this calibration, representing the energy demands in the absence of climate policies details in Supplementary Information , are calculated with the EDGE Energy Demand Generator model suite based on GDP per capita and cost trends and additional scenario assumptions 66 , At the same time, we include ambitious reductions of energy demands in high-income countries, which are driven by a shift towards less energy-intensive lifestyles as well as increases in energy efficiency Extended Data Fig.

For the industry sector, we start from the lower value of the existing SSP1 and SSP2 trajectories but apply an additional GDP-per-capita-dependent factor to the rate of change of energy intensity. Parameter values are chosen to allow for an increase of final energy FE demands in lower-income regions to reflect the additional energy demand for infrastructure buildup In middle- and higher-income regions, demands are reduced substantially Supplementary Fig.

Besides improvements in energy efficiency, this also requires substantial reductions in material demands and recycling of energy-intensive materials such as steel In the transport sector, the guiding principle is a gradual convergence to a provision of an equal amount of useful that is, motive energy per capita across regions. The resulting trajectories are presented in the right panel of Supplementary Fig.

Energy demands for residential and commercial buildings are derived using the EDGE-Buildings model 90 , These assumptions are applied to the SSP1 socioeconomic dynamics but are augmented by an even faster transition to modern energy carriers in developing regions than in the SSP1 scenario. We note that, in particular, the replacement of traditional biomass as a cooking fuel with modern appliances for example, electricity and liquefied petroleum gas can lead to a temporary reduction of cooking final energy demand.

At the same time, UE continues to increase, as modern technologies have vastly superior FE-to-UE conversion efficiencies. Food demand scenarios for the SSP1 and SSP2 scenarios are based on a food demand model with population growth, change of demographic structure and per-capita income as main drivers The model combines anthropometric and econometric approaches to estimate the distribution of underweight, overweight and obesity, as well as body height by country, age-cohort and sex.

It furthermore estimates food intake and food waste, as well as the dietary composition between four major food items: animal-source calories, empty calories, calories from fruits, vegetables and nuts, as well as staple calories. All elasticity parameters within the model are estimated on the basis of past observed data. To account for less material-intensive consumption patterns in the SSP1 storyline, food waste and dietary composition patterns are estimated on the basis of different functional forms than in the SSP2 scenario, assuming lower food waste, animal calories and processed foods and higher consumption of fruits, vegetables, nuts and staples.

For the SDP scenario, we assume a gradual transition to the dietary patterns proposed by the EAT—Lancet Commission 36 by that is, to both healthy and sustainable diets with low food waste. Total food intake is still estimated on the basis of the anthropometric equations of the food demand model but taking into account the assumption of a healthy body weight.

Our food demand model accounts for the reduction of real per-capita income due to rising food prices and for a reduction of intake and a change of food composition when real income falls note, however, that distributional aspects are not included yet. Under the food price effects of climate policies, we find only a small impact on the prevalence of underweight, even in the absence of additional sustainability policies Fig. The reason for this is that our empirically estimated income-elasticities of underweight and food intake 95 are rather low compared to other models that often work with food expenditure elasticities Moreover, we only consider the income effect and not the substitution effect of the price shock.

The income effect should, however, be the dominant effect for low-income households given that food is an existential need. We calculate projections for the income inequality and poverty indicators at the country level following the approach of Soergel et al. Starting from a baseline income distribution with a level of inequality determined by the Gini projections for the SSPs 62 , changes to the distribution due to climate policy are determined by the aggregate GDP loss, increased energy and food expenditures and the recycling of carbon pricing revenues.

Importantly, this captures the potentially regressive effects of food and energy price increases, as well as the progressive effect of revenue recycling policies. For the SDP scenario we assume that revenues including net transfer revenues are redistributed on an equal-per-capita basis.

While more targeted redistribution schemes are conceivable, they also face a number of difficulties in practice 97 and therefore we do not implement them here see also the discussion in Soergel et al. For the other mitigation scenarios, revenues are recycled without progressive redistribution policies that is, without changing the level of inequality. Note that the inequality and poverty indicators are calculated in postprocessing that is, we do not feed the results back into the models for energy or food demand.

Despite the known differences in consumption patterns between rich and poor households, we do not expect the changes in poverty rates to affect the environmental pressures in a substantial way see also Hubacek et al. These factors have not been modelled by earlier IAM-based scenario analyses, leaving it largely unclear which implicit trajectories are consistent with or even required by such scenarios.

More generally, this reflects a lack of integration of governance and conflict research and IAM-based scenario studies. This quantifies the trajectories which are implied by the exogenous scenario assumptions education, population and GDP.

We include the endogenous effects of mitigation costs and international transfers on GDP per capita details below , thus extending earlier work on governance and conflict likelihood in the SSP baselines 99 , We estimate both models using country—year data for all relevant indicators from to The institution model estimates the yearly change in the strength and quality of rule of law and civil liberties. The model takes as predictors the quality of rule of law and civil liberties and change in the quality of rule of law and civil liberties in the previous year, GDP per-capita growth, the share of men without primary education, the gender gap in primary education and the population growth.

The conflict model estimates the change in fatalities in a country and is based on the following predictors: conflict fatalities and the change of conflict fatalities in the previous year, population growth, GDP per-capita growth and the number of men without primary education.

Earlier models on economic development and governance assumed that unobserved differences between countries partially converge , To account for different scenario-specific global convergence, we follow this practice in both models. We note that SDP and SSP1 projections are very similar because they share several identical drivers education and population. While GDP per capita slightly varies between these scenarios due to mitigation costs and international transfers, this does not substantially change the institution and conflict outcomes given the estimated regression coefficient for GDP per capita.

Furthermore, explicitly modelling feedback loops to other goals is beyond the scope of this analysis but is an important avenue for future research. We model the whole source—receptor relationship of air pollution-induced health impacts; see Rauner et al.

The model chain starts with aggregated emission factors, capturing pollution control policies as well as technology research, development, deployment and diffusion derived from the GAINS GHG—air pollution interactions and synergies model Using spatially explicit data on demographics and urbanization allows the calculation of exposure level and disease-specific disability adjusted life years lost. The urban air pollution concentration is calculated by an urban-population-weighted average of the concentration in each spatial grid cell 0.

The statistical—dynamical atmosphere model almost realistically reproduces the large-scale features of patterns of wind, precipitation and temperature. The two-dimensional dynamic—thermodynamic sea-ice model is based on the theory of the elasto-viscous-plastic rheology. A fully three-dimensional coarse resolution ocean general circulation model—an improved version of MOM3 refs. From to , the model is forced by historical CO 2 emissions, subsequently continuing until by using the model output from the corresponding REMIND scenarios.

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Rogelj, J. Zhenmin, L. Tackling climate change to accelerate sustainable development. Change 9 , — Article Google Scholar. Sustainable development through climate action. Change 9 , Pradhan, P. A systematic study of sustainable development goal SDG interactions. Earths Future 5 , — McCollum, D. Connecting the Sustainable Development Goals by their energy inter-linkages. Sachs, J. Six transformations to achieve the Sustainable Development Goals.

Analysing interactions among Sustainable Development Goals with integrated assessment models. Breuer, A. Translating sustainable development goal SDG interdependencies into policy advice. Sustainability 11 , Achievements and needs for the climate change scenario framework. Change 10 , — Moyer, J. Alternative pathways to human development: assessing trade-offs and synergies in achieving the Sustainable Development Goals.

Futures , — Integrating global climate change mitigation goals with other sustainability objectives: a synthesis—supplement. Jakob, M. Implications of climate change mitigation for sustainable development. Energy investment needs for fulfilling the Paris Agreement and achieving the Sustainable Development Goals. Energy 3 , — Iyer, G. Implications of sustainable development considerations for comparability across nationally determined contributions. Change 8 , — Fujimori, S. Measuring the sustainable development implications of climate change mitigation.

Fuso Nerini, F. Connecting climate action with other Sustainable Development Goals. Percentage of Accredited Health Facilities. Average Healthy Life Expectancy. Download report. Our Vision. Competitive Knowledge Economy. Safe Public and Fair Judiciary. Cohesive Society and Preserved Identity.