Land use is at the core of various sustainable development goals. Long-term climate foresight studies have structured their recent analyses around five socio-economic pathways (SSPs), with consistent ...storylines of future macroeconomic and societal developments; however, model quantification of these scenarios shows substantial heterogeneity in land-use projections. Here we build on a recently developed sensitivity approach to identify how future land use depends on six distinct socio-economic drivers (population, wealth, consumption preferences, agricultural productivity, land-use regulation, and trade) and their interactions. Spread across models arises mostly from diverging sensitivities to long-term drivers and from various representations of land-use regulation and trade, calling for reconciliation efforts and more empirical research. Most influential determinants for future cropland and pasture extent are population and agricultural efficiency. Furthermore, land-use regulation and consumption changes can play a key role in reducing both land use and food-security risks, and need to be central elements in sustainable development strategies.
Afforestation is considered a cost‐effective and readily available climate change mitigation option. In recent studies afforestation is presented as a major solution to limit climate change. However, ...estimates of afforestation potential vary widely. Moreover, the risks in global mitigation policy and the negative trade‐offs with food security are often not considered. Here we present a new approach to assess the economic potential of afforestation with the IMAGE 3.0 integrated assessment model framework. In addition, we discuss the role of afforestation in mitigation pathways and the effects of afforestation on the food system under increasingly ambitious climate targets. We show that afforestation has a mitigation potential of 4.9 GtCO2/year at 200 US$/tCO2 in 2050 leading to large‐scale application in an SSP2 scenario aiming for 2°C (410 GtCO2 cumulative up to 2100). Afforestation reduces the overall costs of mitigation policy. However, it may lead to lower mitigation ambition and lock‐in situations in other sectors. Moreover, it bears risks to implementation and permanence as the negative emissions are increasingly located in regions with high investment risks and weak governance, for example in Sub‐Saharan Africa. Afforestation also requires large amounts of land (up to 1,100 Mha) leading to large reductions in agricultural land. The increased competition for land could lead to higher food prices and an increased population at risk of hunger. Our results confirm that afforestation has substantial potential for mitigation. At the same time, we highlight that major risks and trade‐offs are involved. Pathways aiming to limit climate change to 2°C or even 1.5°C need to minimize these risks and trade‐offs in order to achieve mitigation sustainably.
Afforestation is often presented as a key climate change mitigation option, but potential risks and trade‐offs are generally disregarded. In this study we show afforestation has large potential for mitigation, which could reduce overall costs. However, we also show there could be major risks regarding implementation and permanence as well as large trade‐offs with food security. Pathways aiming to limit climate change to 2°C or even 1.5°C need to minimize these risks and trade‐offs in order to achieve mitigation sustainably.
The EU’s 2018 Bioeconomy Strategy Update and the European Green Deal recently confirmed that the bioeconomy is high on the political agenda in Europe. Here, we propose a conceptual analysis framework ...for quantifying and analyzing the development of the EU bioeconomy. The bioeconomy has several related concepts (e.g., bio-based economy, green economy, and circular economy) and there are clear synergies between these concepts, especially between the bioeconomy and circular economy concepts. Analyzing the driving factors provides important information for monitoring activities. We first derive the scope of the bioeconomy framework in terms of bioeconomy sectors and products to be involved, the needed geographical coverage and resolution, and time period. Furthermore, we outline a set of indicators linked to the objectives of the EU’s bioeconomy strategy. In our framework, measuring developments will, in particular, focus on the bio-based sectors within the bioeconomy as biomass and food production is already monitored. The selected indicators commit to the EU Bioeconomy Strategy objectives and conform with findings from previous studies and stakeholder consultation. Additionally, several new indicators have been suggested and they are related to measuring the impact of changes in supply, demand drivers, resource availability, and policies on sustainability goals.
•Greenhouse gas and air pollutant emissions and global land use could develop in very different directions based on societal trends in the energy system and agriculture.•A combination of resource ...efficiency, preferences for sustainable production methods and investment in human development could lead to lower anthropogenic greenhouse gas emissions and land-use in 2100 than in 2010.•The SSP1 storyline could be a basis for further discussions on how climate policy can be combined with achieving other societal goals.
This paper describes the possible developments in global energy use and production, land use, emissions and climate changes following the SSP1 storyline, a development consistent with the green growth (or sustainable development) paradigm (a more inclusive development respecting environmental boundaries). The results are based on the implementation using the IMAGE 3.0 integrated assessment model and are compared with a) other IMAGE implementations of the SSPs (SSP2 and SSP3) and b) the SSP1 implementation of other integrated assessment models. The results show that a combination of resource efficiency, preferences for sustainable production methods and investment in human development could lead to a strong transition towards a more renewable energy supply, less land use and lower anthropogenic greenhouse gas emissions in 2100 than in 2010, even in the absence of explicit climate policies. At the same time, climate policy would still be needed to reduce emissions further, in order to reduce the projected increase of global mean temperature from 3°C (SSP1 reference scenario) to 2 or 1.5°C (in line with current policy targets). The SSP1 storyline could be a basis for further discussions on how climate policy can be combined with achieving other societal goals.
•The SSP scenarios show a wide spread in agricultural land-use change in 2010-2050: from a decrease of 305 Mha in SSP1 to an increase of 826 Mha in SSP3.•Key drivers are population, agricultural ...efficiency, consumption, land availability, food losses, and dietary preferences.•Most land-use change is projected in Sub-Saharan Africa up to 424 Mha in the period 2010–2100.•Negative emissions from bioenergy (with CCS) and reforestation are crucial to limit temperature increases to 1.5oC in 2100.•REDD (avoided deforestation) in Sub-Saharan Africa could have substantial trade-offs negatively affecting food security.
Projected increases in population, income and consumption rates are expected to lead to rising pressure on the land system. Ambitions to limit global warming to 2 °C or even 1.5 °C could also lead to additional pressures from land-based mitigation measures such as bioenergy production and afforestation. To investigate these dynamics, this paper describes five elaborations of the Shared Socio-economic Pathways (SSP) using the IMAGE 3.0 integrated assessment model framework to produce regional and gridded scenarios up to the year 2100. Additionally, land-based climate change mitigation is modelled aiming for long-term mitigation targets including 1.5 °C. Results show diverging global trends in agricultural land in the baseline scenarios ranging from an expansion of nearly 826 Mha in SSP3 to a decrease of more than 305 Mha in SSP1 for the period 2010–2050. Key drivers are population growth, changes in food consumption, and agricultural efficiency. The largest changes take place in Sub-Saharan Africa in SSP3 and SSP4, predominantly due to high population growth. With low increases in agricultural efficiency this leads to expansion of agricultural land and reduced food security. Land use also plays a crucial role in ambitious mitigation scenarios. First, agricultural emissions could form a substantial component of emissions that cannot be fully mitigated. Second, bioenergy and reforestation are crucial to create net negative emissions reducing emissions in SSP2 in 2050 by 8.7 Gt CO2/yr and 1.9 Gt CO2/yr, respectively (1.5 °C scenario compared to baseline). This is achieved by expansion of bioenergy area (516 Mha in 2050) and reforestation. Expansion of agriculture for food production is reduced due to REDD policy (290 Mha in 2050) affecting food security especially in Sub-Saharan Africa indicating an important trade-off of land-based mitigation. This set of SSP land-use scenarios provides a comprehensive quantification of interacting trends in the land system, both socio-economic and biophysical. By providing high resolution data, the scenario output can improve interactions between climate research and impact studies.
Model‐based global projections of future land‐use and land‐cover (LULC) change are frequently used in environmental assessments to study the impact of LULC change on environmental services and to ...provide decision support for policy. These projections are characterized by a high uncertainty in terms of quantity and allocation of projected changes, which can severely impact the results of environmental assessments. In this study, we identify hotspots of uncertainty, based on 43 simulations from 11 global‐scale LULC change models representing a wide range of assumptions of future biophysical and socioeconomic conditions. We attribute components of uncertainty to input data, model structure, scenario storyline and a residual term, based on a regression analysis and analysis of variance. From this diverse set of models and scenarios, we find that the uncertainty varies, depending on the region and the LULC type under consideration. Hotspots of uncertainty appear mainly at the edges of globally important biomes (e.g., boreal and tropical forests). Our results indicate that an important source of uncertainty in forest and pasture areas originates from different input data applied in the models. Cropland, in contrast, is more consistent among the starting conditions, while variation in the projections gradually increases over time due to diverse scenario assumptions and different modeling approaches. Comparisons at the grid cell level indicate that disagreement is mainly related to LULC type definitions and the individual model allocation schemes. We conclude that improving the quality and consistency of observational data utilized in the modeling process and improving the allocation mechanisms of LULC change models remain important challenges. Current LULC representation in environmental assessments might miss the uncertainty arising from the diversity of LULC change modeling approaches, and many studies ignore the uncertainty in LULC projections in assessments of LULC change impacts on climate, water resources or biodiversity.
Europe’s rural areas are expected to witness massive and rapid changes in land use due to changes in demography, global trade, technology and enlargement of the European Union. Changes in demand for ...agricultural products and agrarian production structure are likely to have a large impact on landscape quality and the value of natural areas. Most studies address these changes either from a macro-economic perspective focusing on changes in the agricultural sector or from a local perspective by analyzing recent changes in landscapes for small case studies. This paper describes a methodology in which a series of models has been used to link global level developments influencing land use to local level impacts. It is argued that such an approach is needed to properly address the processes at different scales that give rise to the land use dynamics in Europe. An extended version of the global economic model (GTAP) and an integrated assessment model (IMAGE) are used to calculate changes in demand for agricultural areas at the country level while a spatially explicit land use change model (CLUE-s) was used to translate these demands to land use patterns at 1 km
2
resolution. The global economic model ensures an appropriate treatment of macro-economic, demographic and technology developments and changes in agricultural and trade policies influencing the demand and supply for land use related products while the integrated assessment model accounts for changes in productivity as result of climate change and global land allocation. The land use change simulations at a high spatial resolution make use of country specific driving factors that influence the spatial patterns of land use, accounting for the spatial variation in the biophysical and socio-economic environment. Results indicate the large impact abandonment of agricultural land and urbanization may have on future European landscapes. Such results have the potential to support discussions on the future of the rural area and identify hot-spots of landscape change that need specific consideration. The high spatial and thematic resolution of the results allows the assessment of impacts of these changes on different environmental indicators, such as carbon sequestration and biodiversity. The global assessment allows, at the same time, to account for the tradeoffs between impacts in Europe and effects outside Europe.
Understanding uncertainties in land cover projections is critical to investigating land‐based climate mitigation policies, assessing the potential of climate adaptation strategies and quantifying the ...impacts of land cover change on the climate system. Here, we identify and quantify uncertainties in global and European land cover projections over a diverse range of model types and scenarios, extending the analysis beyond the agro‐economic models included in previous comparisons. The results from 75 simulations over 18 models are analysed and show a large range in land cover area projections, with the highest variability occurring in future cropland areas. We demonstrate systematic differences in land cover areas associated with the characteristics of the modelling approach, which is at least as great as the differences attributed to the scenario variations. The results lead us to conclude that a higher degree of uncertainty exists in land use projections than currently included in climate or earth system projections. To account for land use uncertainty, it is recommended to use a diverse set of models and approaches when assessing the potential impacts of land cover change on future climate. Additionally, further work is needed to better understand the assumptions driving land use model results and reveal the causes of uncertainty in more depth, to help reduce model uncertainty and improve the projections of land cover.
Changes in agricultural land use have important implications for environmental services. Previous studies of agricultural land-use futures have been published indicating large uncertainty due to ...different model assumptions and methodologies. In this article we present a first comprehensive comparison of global agro-economic models that have harmonized drivers of population, GDP, and biophysical yields. The comparison allows us to ask two research questions: (1) How much cropland will be used under different socioeconomic and climate change scenarios? (2) How can differences in model results be explained? The comparison includes four partial and six general equilibrium models that differ in how they model land supply and amount of potentially available land.We analyze results of two different socioeconomic scenarios and three climate scenarios (one with constant climate). Most models (7 out of 10) project an increase of cropland of 10–25% by 2050 compared to 2005 (under constant climate), but one model projects a decrease. Pasture land expands in some models, which increase the treat on natural vegetation further. Across all models most of the cropland expansion takes place in South America and sub-Saharan Africa. In general, the strongest differences in model results are related to differences in the costs of land expansion, the endogenous productivity responses, and the assumptions about potential cropland.
PDF
