The use of environmental scenarios to project future health effects: a scoping review.

Environmental risks are a substantial factor in the current burden of disease, and their role is likely to increase in the future. Model-based scenario analysis is used extensively in environmental sciences to explore the potential effects of human activities on the environment. In this Review, we examine the literature on scenarios modelling environmental effects on health to identify the most relevant findings, common methods used, and important research gaps. Health outcomes and measures related to climate change (n=106) and air pollution (n=30) were most frequently studied. Studies examining future disease burden due to changes or policies related to dietary risks were much less common (n=10). Only a few studies assessed more than two environmental risks (n=3), even though risks can accumulate and interact with each other. Studies predominantly covered high-income countries and Asia. Sociodemographic, vulnerability, and health-system changes were rarely accounted for; thus, assessing the full effect of future environmental changes in an integrative way is not yet possible. We recommend that future models incorporate a broader set of determinants of health to more adequately capture their effect, as well as the effect of mitigation and adaptation efforts.

Typology of coastal urban vulnerability under rapid urbanization.

Coastal areas are urbanizing at unprecedented rates, particularly in low- and middle-income countries. Combinations of long-standing and emerging problems in these urban areas generate vulnerability for human well-being and ecosystems alike. This baseline study provides a spatially explicit global systematization of these problems into typical urban vulnerability profiles for the year 2000 using largely sub-national data. Using 11 indicator datasets for urban expansion, urban population growth, marginalization of poor populations, government effectiveness, exposures and damages to climate-related extreme events, low-lying settlement, and wetlands prevalence, a cluster analysis reveals a global typology of seven clearly distinguishable clusters, or urban profiles of vulnerability. Each profile is characterized by a specific data-value combination of indicators representing mechanisms that generate vulnerability. Using 21 studies for testing the plausibility, we identify seven key profile-based vulnerabilities for urban populations, which are relevant in the context of global urbanization, expansion, and climate change. We show which urban coasts are similar in this regard. Sensitivity and exposure to extreme climate-related events, and government effectiveness, are the most important factors for the huge asymmetries of vulnerability between profiles. Against the background of underlying global trends we propose entry points for profile-based vulnerability reduction. The study provides a baseline for further pattern analysis in the rapidly urbanizing coastal fringe as data availability increases. We propose to explore socio-ecologically similar coastal urban areas as a basis for sharing experience and vulnerability-reducing measures among them.

Afforestation for climate change mitigation: Potentials, risks and trade-offs.

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.

Data for long-term marginal abatement cost curves of non-CO2 greenhouse gases.

This dataset represents long-term marginal abatement cost (MAC) curves of all major emission sources of non-CO2 greenhouse gases (GHGs); methane (CH4), nitrous oxide (N2O) and fluorinated gases (HFCs, PFCs and SF6). The work is based on existing short-term MAC curve datasets and recent literature on individual mitigation measures. The data represent a comprehensive set of MAC curves, covering all major non-CO2 emission sources for 26 aggregated world regions. They are suitable for long-term global mitigation scenario development, as dynamical elements (technological progress, removal of implementation barriers) are included. The data is related to the research article: "Long-term marginal abatement cost curves of non-CO 2 greenhouse gases" [1].

Multi-model comparison of the economic and energy implications for China and India in an international climate regime.

This paper presents a modeling comparison on how stabilization of global climate change at about 2 °C above the pre-industrial level could affect economic and energy systems development in China and India. Seven General Equilibrium (CGE) and energy system models on either the global or national scale are soft-linked and harmonized with respect to population and economic assumptions. We simulate a climate regime, based on long-term convergence of per capita carbon dioxide (CO2) emissions, starting from the emission pledges presented in the Copenhagen Accord to the United Nations Framework Convention on Climate Change and allowing full emissions trading between countries. Under the climate regime, Indian emission allowances are allowed to grow more than the Chinese allowances, due to the per capita convergence rule and the higher population growth in India. Economic and energy implications not only differ among the two countries, but also across model types. Decreased energy intensity is the most important abatement approach in the CGE models, while decreased carbon intensity is most important in the energy system models. The reduction in carbon intensity is mostly achieved through deployment of carbon capture and storage, renewable energy sources and nuclear energy. The economic impacts are generally higher in China than in India, due to higher 2010-2050 cumulative abatement in China and the fact that India can offset more of its abatement cost though international emission trading.