Institution
International Institute for Applied Systems Analysis
Nonprofit•Laxenburg, Austria•
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.
Papers published on a yearly basis
Papers
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TL;DR: According to the latest Global Tracking Framework (2015), 18% of the global and 57% of African population live without access to electricity services, a key impediment towards social and economic development as discussed by the authors.
109 citations
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TL;DR: In this article, the authors present an overview of observed and potential future climate change impacts on those forests with an emphasis on their aggregate carbon balance and processes driving changes therein, concluding that the impacts of climate change on Russia's boreal forest are often superimposed by other environmental and societal changes in a complex way, and the interaction of these developments could exacerbate both existing and projected future challenges.
108 citations
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TL;DR: In this paper, the authors compare five different emissions inventories estimating emissions of carbon dioxide (CO2), carbon monoxide (CO), nitrogen oxides (NOx), sulfur dioxide (SO2), and particulate matter with an aerodynamic diameter of 10 µm or less (PM10) from China.
Abstract: . Anthropogenic air pollutant emissions have been increasing rapidly in China, leading to worsening air quality. Modelers use emissions inventories to represent the temporal and spatial distribution of these emissions needed to estimate their impacts on regional and global air quality. However, large uncertainties exist in emissions estimates. Thus, assessing differences in these inventories is essential for the better understanding of air pollution over China. We compare five different emissions inventories estimating emissions of carbon dioxide (CO2), carbon monoxide (CO), nitrogen oxides (NOx), sulfur dioxide (SO2), and particulate matter with an aerodynamic diameter of 10 µm or less (PM10) from China. The emissions inventories analyzed in this paper include the Regional Emission inventory in ASia v2.1 (REAS), the Multi-resolution Emission Inventory for China (MEIC), the Emission Database for Global Atmospheric Research v4.2 (EDGAR), the inventory by Yu Zhao (ZHAO), and the Greenhouse Gas and Air Pollution Interactions and Synergies (GAINS). We focus on the period between 2000 and 2008, during which Chinese economic activities more than doubled. In addition to national totals, we also analyzed emissions from four source sectors (industry, transport, power, and residential) and within seven regions in China (East, North, Northeast, Central, Southwest, Northwest, and South) and found that large disagreements exist among the five inventories at disaggregated levels. These disagreements lead to differences of 67 µg m−3, 15 ppbv, and 470 ppbv for monthly mean PM10, O3, and CO, respectively, in modeled regional concentrations in China. We also find that all the inventory emissions estimates create a volatile organic compound (VOC)-limited environment and MEIC emissions lead to much lower O3 mixing ratio in East and Central China compared to the simulations using REAS and EDGAR estimates, due to their low VOC emissions. Our results illustrate that a better understanding of Chinese emissions at more disaggregated levels is essential for finding effective mitigation measures for reducing national and regional air pollution in China.
108 citations
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TL;DR: In this paper, a Bayesian Model Averaging method was proposed to perform inference under model uncertainty in the presence of potential spatial autocorrelation, using spatial filtering in order to account for uncertainty in spatial linkages.
Abstract: In this paper we put forward a Bayesian Model Averaging method aimed at performing
inference under model uncertainty in the presence of potential spatial autocorrelation
The method uses spatial filtering in order to account for uncertainty in
spatial linkages Our procedure is applied to a dataset of income per capita growth and
50 potential determinants for 255 NUTS-2 European regions We show that ignoring
uncertainty in the type of spatial weight matrix can have an important effect on the
estimates of the parameters attached to the model covariates After integrating out
the uncertainty implied by the choice of regressors and spatial links, human capital
investments and transitional dynamics related to income convergence appear as the
most robust determinants of growth at the regional level in Europe Our results imply
that a quantitatively important part of the income convergence process in Europe is
influenced by spatially correlated growth spillovers
108 citations
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TL;DR: Li et al. as mentioned in this paper used multiple altimetric missions and Landsat satellite data to create high-temporal-resolution lake water level and storage change time series at weekly to monthly timescales for 52 large lakes (50 lakes larger than 150 km 2 and 2 lakes smaller than 100 km 2 ) on the Tibetan Plateau (TP).
Abstract: . The Tibetan Plateau (TP), known as Asia's water tower, is
quite sensitive to climate change, which is reflected by changes in hydrologic state
variables such as lake water storage. Given the extremely limited ground
observations on the TP due to the harsh environment and complex terrain, we
exploited multiple altimetric missions and Landsat satellite data to create
high-temporal-resolution lake water level and storage change time series at
weekly to monthly timescales for 52 large lakes (50 lakes larger than 150 km 2 and 2 lakes larger than 100 km 2 ) on the TP during 2000–2017. The data sets are available online at https://doi.org/10.1594/PANGAEA.898411 (Li et al., 2019). With
Landsat archives and altimetry data, we developed water levels from lake
shoreline positions (i.e., Landsat-derived water levels) that cover the
study period and serve as an ideal reference for merging multisource lake
water levels with systematic biases being removed. To validate the
Landsat-derived water levels, field experiments were carried out in two
typical lakes, and theoretical uncertainty analysis was performed based on
high-resolution optical images (0.8 m) as well. The RMSE of the
Landsat-derived water levels is 0.11 m compared with the in situ
measurements, consistent with the magnitude from theoretical analysis
(0.1–0.2 m). The accuracy of the Landsat-derived water levels that can be
derived in relatively small lakes is comparable with most altimetry data.
The resulting merged Landsat-derived and altimetric lake water levels can
provide accurate information on multiyear and short-term monitoring of lake
water levels and storage changes on the TP, and critical information on lake overflow flood monitoring and prediction as the expansion of some TP lakes
becomes a serious threat to surrounding residents and infrastructure.
108 citations
Authors
Showing all 1418 results
Name | H-index | Papers | Citations |
---|---|---|---|
Martin A. Nowak | 148 | 591 | 94394 |
Paul J. Crutzen | 130 | 461 | 80651 |
Andreas Richter | 110 | 769 | 48262 |
David G. Streets | 106 | 364 | 42154 |
Drew Shindell | 102 | 340 | 49481 |
Wei Liu | 102 | 2927 | 65228 |
Jean-Francois Lamarque | 100 | 385 | 55326 |
Frank Dentener | 97 | 220 | 58666 |
James W. Vaupel | 89 | 434 | 34286 |
Keywan Riahi | 87 | 318 | 58030 |
Larry W. Horowitz | 85 | 253 | 28706 |
Robert J. Scholes | 84 | 253 | 37019 |
Mark A. Sutton | 83 | 423 | 30716 |
Brian Walsh | 82 | 233 | 29589 |
Börje Johansson | 82 | 871 | 30985 |