Stakeholder-designed scenarios for global food security assessments

To guide policymaking, decision makers require a good understanding of the long-term drivers of food security and their interactions. Scenario analysis is widely considered as the appropriate tool to assess ‘wicked problems’, such as ensuring global food security, that are characterized by a high level of complexity and uncertainty. This paper describes the development process, storylines and drivers of four new global scenarios that are specifically designed to explore global food security up to the year 2050. To ensure the relevance, credibility and legitimacy of the scenarios, they have been developed using a participatory process, involving a diverse group of stakeholders. The scenarios consist of storylines and a scenario database that presents projections for key drivers, which can be used as an input into global simulation models

IMAGE and MESSAGE Scenarios Limiting GHG Concentration to Low Levels

This report discusses the attainability of low greenhouse gas concentrations levels based on an analysis using two integrated assessment models (MESSAGE and IMAGE). Model runs were preformed which explored the feasibility of reaching radiative forcing levels in 2100 between 2.6 to 2.9 W/m2 above pre-industrial levels. Such low targets are necessary to limit global mean temperature increase to below 2oC compared to pre-industrial levels with high probability. The analysis examines the attainability of low targets systematically with respect to key uncertainties, including alternative baseline development pathways, availability of different technologies, emissions of bio-energy, and impacts of forestry and land use assumptions. A number of sensitivity tests were carried out to test the robustness of achieving low GHG concentration targets. The results from the two models are discussed in detail comprising energy profiles and emission pathways consistent with such low stabilization targets

Greenhouse gas mitigation potentials in the livestock sector

Acknowledgements This paper constitutes an output of the Belmont Forum/FACCE-JPI funded DEVIL project (NE/M021327/1). Financial support from the CGIAR Program on Climate Change, Agriculture and Food Security (CCAFS) and the EU-FP7 AnimalChange project is also recognized. P.K.T. acknowledges the support of a CSIRO McMaster Research Fellowship.Peer reviewedPostprin

Scenarios to explore global food security up to 2050: Development process, storylines and quantification of drivers

To guide policymaking, decision makers require a good understanding of the long-term drivers of food security and their interactions. Scenario analysis is widely considered as the appropriate tool to assess complex and uncertain problems, such as food security. This paper describes the development process, storylines and drivers of four new global scenarios up to the year 2050 that are specifically designed for food security modelling. To ensure the relevance, credibility and legitimacy of the scenarios a highly participatory process is used, involving a diverse group of stakeholders. A novel approach is introduced to quantify a selection of key drivers that directly can be used as input in global integrated assessment models to assess the impact of aid, trade, agricultural and science policies on global food and nutrition security

Interactions between social learning and technological learning in electric vehicle futures

The transition to electric vehicles is an important strategy for reducing greenhouse gas emissions from passenger cars. Modelling transition pathways helps identify critical drivers and uncertainties. Global integrated assessment models (IAMs) have been used extensively to analyse climate mitigation policy. IAMs emphasise technological change processes but are largely silent on important social and behavioural dimensions to technological transitions. Here, we develop a novel conceptual framing and empirical evidence base on social learning processes relevant for vehicle adoption. We then implement this formulation of social learning in IMAGE, a widely-used global IAM. We apply this new modelling approach to analyse how technological learning and social learning interact to influence electric vehicle transition dynamics. We find that technological learning and social learning processes can be mutually reinforcing. Increased electric vehicle market shares can induce technological learning which reduces technology costs while social learning stimulates diffusion from early adopters to more risk-averse adopter groups. In this way, both types of learning process interact to stimulate each other. In the absence of social learning, however, the perceived risks of electric vehicle adoption among later adopting groups remains prohibitively high. In the absence of technological learning, electric vehicles remain relatively expensive and therefore only for early adopters an attractive choice. This first-of-its-kind model formulation of both social and technological learning is a significant contribution to improving the behavioural realism of global IAMs. Applying this new modelling approach emphasises the importance of market heterogeneity, real-world consumer decision-making, and social dynamics as well as technology parameters, to understand climate mitigation potentials

Future air pollution in the Shared Socio-economic Pathways

Emissions of air pollutants such as sulfur and nitrogen oxides and particulates have significant health impacts as well as effects on natural and anthropogenic ecosystems. These same emissions also can change atmospheric chemistry and the planetary energy balance, thereby impacting global and regional climate. Long-term scenarios for air pollutant emissions are needed as inputs to global climate and chemistry models, and for analysis linking air pollutant impacts across sectors. In this paper we present methodology and results for air pollutant emissions in Shared Socioeconomic Pathways (SSP) scenarios. We first present a set of three air pollution narratives that describe high, central, and low pollution control ambitions over the 21st century. These narratives are then translated into quantitative guidance for use in integrated assessment models. The resulting pollutant emission trajectories under the SSP scenarios cover a wider range than the scenarios used in previous international climate model comparisons. In the SSP3 and SSP4 scenarios, where economic, institutional and technological limitations slow air quality improvements, global pollutant emissions over the 21st century can be comparable to current levels. Pollutant emissions in the SSP1 scenarios fall to low levels due to the assumption of technological advances and successful global action to control emissions

The vulnerabilities of agricultural land and food production to future water scarcity

Rapidly increasing populations coupled with increased food demand requires either an expansion of agriculturalland or sufficient production gains from current resources. However, in a changing world, reduced wateravailability might undermine improvements in crop and grass productivity and may disproportionately affectdifferent parts of the world. Using multi-model studies, the potential trends, risks and uncertainties to land useand land availability that may arise from reductions in water availability are examined here. In addition, theimpacts of different policy interventions on pressures from emerging risks are examined.Results indicate that globally, approximately 11% and 10% of current crop- and grass-lands could be vul-nerable to reduction in water availability and may lose some productive capacity, with Africa and the MiddleEast, China, Europe and Asia particularly at risk. While uncertainties remain, reduction in agricultural land areaassociated with dietary changes (reduction of food waste and decreased meat consumption) offers the greatestbuffer against land loss and food insecurity

The shared socioeconomic pathways and their energy, land use, and greenhouse gas emissions implications: An overview

This paper presents the overview of the Shared Socioeconomic Pathways (SSPs) and their energy, land use, and emissions implications. The SSPs are part of a new scenario framework, established by the climate change research community in order to facilitate the integrated analysis of future climate impacts, vulnerabilities, adaptation, and mitigation. The pathways were developed over the last years as a joint community effort and describe plausible major global developments that together would lead in the future to different challenges for mitigation and adaptation to climate change. The SSPs are based on five narratives describing alternative socio-economic developments, including sustainable development, regional rivalry, inequality, fossil-fueled development, and a middle-of-the-road development. The long-term demographic and economic projections of the SSPs depict a wide uncertainty range consistent with the scenario literature. A multi-model approach was used for the elaboration of the energy, land-use and the emissions trajectories of SSP-based scenarios. The baseline scenarios lead to global energy consumption of 500-1100 EJ in 2100, and feature vastly different land-use dynamics, ranging from a possible reduction in cropland area up to a massive expansion by more than 700 million hectares by 2100. The associated annual CO2 emissions of the baseline scenarios range from about 25 GtCO2 to more than 120 GtCO2 per year by 2100. With respect to mitigation, we find that associated costs strongly depend on three factors: 1) the policy assumptions, 2) the socio-economic narrative, and 3) the stringency of the target. The carbon price for reaching the target of 2.6 W/m2 differs in our analysis thus by about a factor of three across the SSP scenarios. Moreover, many models could not reach this target from the SSPs with high mitigation challenges. While the SSPs were designed to represent different mitigation and adaptation challenges, the resulting narratives and quantifications span a wide range of different futures broadly representative of the current literature. This allows their subsequent use and development in new assessments and research projects. Critical next steps for the community scenario process will, among others, involve regional and sectorial extensions, further elaboration of the adaptation and impacts dimension, as well as employing the SSP scenarios with the new generation of earth system models as part of the 6th climate model intercomparison project (CMIP6)

A framework for the cross-sectoral integration of multi-model impact projections: land use decisions under climate impacts uncertainties

Climate change and its impacts already pose considerable challenges for societies that will further increase with global warming (IPCC, 2014a, b). Uncertainties of the climatic response to greenhouse gas emissions include the potential passing of large-scale tipping points (e.g. Lenton et al., 2008; Levermann et al., 2012; Schellnhuber, 2010) and changes in extreme meteorological events (Field et al., 2012) with complex impacts on societies (Hallegatte et al., 2013). Thus climate change mitigation is considered a necessary societal response for avoiding uncontrollable impacts (Conference of the Parties, 2010). On the other hand, large-scale climate change mitigation itself implies fundamental changes in, for example, the global energy system. The associated challenges come on top of others that derive from equally important ethical imperatives like the fulfilment of increasing food demand that may draw on the same resources. For example, ensuring food security for a growing population may require an expansion of cropland, thereby reducing natural carbon sinks or the area available for bio-energy production. So far, available studies addressing this problem have relied on individual impact models, ignoring uncertainty in crop model and biome model projections. Here, we propose a probabilistic decision framework that allows for an evaluation of agricultural management and mitigation options in a multi-impactmodel setting. Based on simulations generated within the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP), we outline how cross-sectorally consistent multi-model impact simulations could be used to generate the information required for robust decision making. Using an illustrative future land use pattern, we discuss the trade-off between potential gains in crop production and associated losses in natural carbon sinks in the new multiple crop- and biome-model setting. In addition, crop and water model simulations are combined to explore irrigation increases as one possible measure of agricultural intensification that could limit the expansion of cropland required in response to climate change and growing food demand. This example shows that current impact model uncertainties pose an important challenge to long-term mitigation planning and must not be ignored in long-term strategic decision making