International Institute for Applied Systems Analysis

About: International Institute for Applied Systems Analysis is a nonprofit organization based out in Laxenburg, Austria. It is known for research contribution in the topics: Population & Greenhouse gas. The organization has 1369 authors who have published 5075 publications receiving 280467 citations. The organization is also known as: IIASA.

Stochastic Optimization of Insurance Portfolios for Managing Exposure to Catastrophic Risks

Abstract: A catastrophe may affect different locations and produce losses that are rare and highly correlated in space and time. It may ruin many insurers if their risk exposures are not properly diversified among locations. The multidimentional distribution of claims from different locations depends on decision variables such as the insurer's coverage at different locations, on spatial and temporal characteristics of possible catastrophes and the vulnerability of insured values. As this distribution is analytically intractable, the most promising approach for managing the exposure of insurance portfolios to catastrophic risks requires geographically explicit simulations of catastrophes. The straightforward use of so-called catastrophe modeling runs quickly into an extremely large number of “what-if” evaluations. The aim of this paper is to develop an approach that integrates catastrophe modeling with stochastic optimization techniques to support decision making on coverages of losses, profits, stability, and survival of insurers. We establish connections between ruin probability and the maximization of concave risk functions and we outline numerical experiments.

Estimating the global distribution of field size using crowdsourcing.

Abstract: There is increasing evidence that smallholder farms contribute substantially to food production globally yet spatially explicit data on agricultural field sizes are currently lacking. Automated field size delineation using remote sensing or the estimation of average farm size at subnational level using census data are two approaches that have been used but both have limitations, e.g. limited geographical coverage by remote sensing or coarse spatial resolution when using census data. This paper demonstrates another approach to quantifying and mapping field size globally using crowdsourcing. A campaign was run in June 2017 where participants were asked to visually interpret very high resolution satellite imagery from Google Maps and Bing using the Geo-Wiki application. During the campaign, participants collected field size data for 130K unique locations around the globe. Using this sample, we have produced an improved global field size map (over the previous version) and estimated the percentage of different field sizes, ranging from very small to very large, in agricultural areas at global, continental and national levels. The results show that smallholder farms occupy no more than 40% of agricultural areas, which means that, potentially, there are much more smallholder farms in comparison with the current global estimate of 12%. The global field size map and the crowdsourced data set are openly available and can be used for integrated assessment modelling, comparative studies of agricultural dynamics across different contexts and contribute to SDG 2, among many others.

Historical CO 2 emissions from land-use and land-cover change and their uncertainty

Abstract: . Emissions from land use and land cover change are a key component of the global carbon cycle. However, models are required to disentangle these emissions from the land carbon sink, as only the sum of both can be physically observed. Their assessment within the yearly community-wide effort known as the “Global Carbon Budget” remains a major difficulty, because it combines two lines of evidence that are inherently inconsistent: bookkeeping models and dynamic global vegetation models. Here, we propose a unifying approach that relies on a bookkeeping model, which embeds processes and parameters calibrated on dynamic global vegetation models, and the use of an empirical constraint. We estimate that the global CO2 emissions from land use and land cover change were 1.36±0.42 PgC yr −1 (1 σ range) on average over the 2009–2018 period and reached a cumulative total of 206±57 PgC over the 1750–2018 period. We also estimate that land cover change induced a global loss of additional sink capacity – that is, a foregone carbon removal, not part of the emissions – of 0.68±0.57 PgC yr −1 and 32±23 PgC over the same periods, respectively. Additionally, we provide a breakdown of our results' uncertainty, including aspects such as the land use and land cover change data sets used as input and the model's biogeochemical parameters. We find that the biogeochemical uncertainty dominates our global and regional estimates with the exception of tropical regions in which the input data dominates. Our analysis further identifies key sources of uncertainty and suggests ways to strengthen the robustness of future Global Carbon Budget estimates.

Understanding the origin of Paris Agreement emission uncertainties.

Abstract: The UN Paris Agreement puts in place a legally binding mechanism to increase mitigation action over time. Countries put forward pledges called nationally determined contributions (NDC) whose impact is assessed in global stocktaking exercises. Subsequently, actions can then be strengthened in light of the Paris climate objective: limiting global mean temperature increase to well below 2 °C and pursuing efforts to limit it further to 1.5 °C. However, pledged actions are currently described ambiguously and this complicates the global stocktaking exercise. Here, we systematically explore possible interpretations of NDC assumptions, and show that this results in estimated emissions for 2030 ranging from 47 to 63 GtCO2e yr-1. We show that this uncertainty has critical implications for the feasibility and cost to limit warming well below 2 °C and further to 1.5 °C. Countries are currently working towards clarifying the modalities of future NDCs. We identify salient avenues to reduce the overall uncertainty by about 10 percentage points through simple, technical clarifications regarding energy accounting rules. Remaining uncertainties depend to a large extent on politically valid choices about how NDCs are expressed, and therefore raise the importance of a thorough and robust process that keeps track of where emissions are heading over time.