20253 research outputs found
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Gradual and abrupt vegetation changes between 2017 and 2024 around the Neusiedl Lake, Austria
No full textMany consequences of land use and land cover changes (LULCC) can be captured by remote sensing imagery, and here in particular by estimates of photosynthetic activity. For example, the harvesting of trees or reed will cause an abrupt drop in vegetation cover, while planting crops and fertilization and watering will result in gradual increase in vegetation cover over time. Vegetation can be captured by the Normalized Difference Vegetation Index (NDVI), a widely used remote sensing metric that quantifies the health and density of vegetation.
Provided here are two intermediate analysis results showing abrupt and gradual vegetation change in the study region of Neusiedl in the INSPIRE project (https://www.inspire-biodiversa.com/). Vegetation cover was estimated as monthly NDVI Sentinel 2 Level-2A data. Input satellite imagery were pre-processed by removing clouds and gaps and constructing atmospherically corrected surface reflectance estimates at monthly time steps (arithmetric average aggregation)
A climate impact taxonomy operationalizing IPCC physical driver and risk concepts
No full textThis taxonomy was developed by the International Institute for Applied Systems Analysis (IIASA) as part of the Scoping the Climate Impact Landscape (SCIL) project, funded by ClimateWorks. It is designed to capture the breadth of climate impacts and to map them onto different risk dimensions, while pointing to adaptation and mitigation linkages. The taxonomy uses the IPCC AR6 Working Group I and II assessments as main references and applies the IPCC concepts of Climatic Impact-Drivers (CIDs) and Representative Key Risks (RKRs) as guiding taxonomy categories. The taxonomy does NOT provide a comprehensive or exhaustive analysis of all climate impacts, but is intended to help the user improve their understanding of the diverse climate impact landscape and to facilitate a more targeted exploration of research engagement opportunities.
The taxonomy is targeted at users from philanthropies engaging in the climate space and any non-experts interested in learning more about the climate impact landscape. For each RKR-specific ensemble of CIDs, the taxonomy provides more specific climate impact information grouped in five broad categories: Climate Impact Characteristics; Climate Impact Assessment; Adaptation Linkages; Mitigation Linkages, IPCC AR6 & AR7 Chapter References.
An online version of the climate impact taxonomy published here can be interactively explored in the Climate Impact Taxonomy app. An integrated feedback mechanism encourages community engagement and supports continual refinement to increase the value of the taxonomy as a dynamic and easily accessible resource
Dataset for the analysis of Tree-Quest measurements collected in Laxenburg Park and Stadtpark
No full textThis repository contains data necessary to reproduce the results published in the manuscript that is currently under review with a title: "Tree-Quest: A Citizen Science App for Collecting Single-Tree Information
LAMASUS - Response curves for climate impacts of grassland and cropland management across the EU
No full textFrom simulations with EPIC-IIASA, we derived gridded predictions of average annual changes in topsoil organic carbon storage (kg C m-2yr-1) for croplands and grassland under different types of management. Predictions were made for present-day (2015-2024) and for different future periods (2041-2060, 2061-2080, 2081-2100) following high mitigation (RCP-SSP 126) and low mitigation (RCP-SSP 585) scenarios. Further, we provide future projections based on two global circulation models: of the MPI in Germany and the IPSL in France. We further provide maps of present-day topsoil (0-15 cm) organic carbon stocks (kg C m-2) under current crop- and grassland management as reference, and simulations of climate change impacts under business as usual scenarios
Scripts and data for the analysis and figures for RIME paper
No full textRepository for reproducing the analysis in the first paper for the Rapid Impact Model Emulator (RIME)
Global land cover data set at 10m for 2015 (Geo- Wiki)
No full textThis data set was created to support the C-GLOPS (Copernicus Global Land Operational Services) and WorldCover projects during the time period 2017-2022
Scientific literature on carbon dioxide removal revealed as much larger through AI-enhanced systematic mapping
Get PDFCarbon dioxide removal plays an important role in any strategy to limit global warming to well below 2 °C. Keeping abreast with the scientific evidence using rigorous evidence synthesis methods is an important prerequisite for sustainably scaling these methods. Here, we use artificial intelligence to provide a comprehensive systematic map of carbon dioxide removal research. We find a total of 28,976 studies on carbon dioxide removal—3–4 times more than previously suggested. Growth in research is faster than for the field of climate change research as a whole, but very concentrated in specific areas—such as biochar, certain research methods like lab and field experiments, and particular regions like China. Patterns of carbon dioxide removal research contrast with trends in patenting and deployment, highlighting the differing development stages of these technologies. As carbon dioxide removal gains importance for the Paris climate goals, our systematic map can support rigorous evidence synthesis for the IPCC and other assessments
The role of green credit in energy resilience: A quasi-natural experiment from China
No full textGreen credit policy is an important practice of financial regulation, and its possible effect on energy resilience cannot be ignored. This article adopts theoretical derivation and empirical strategies to study the effect and mechanism of green credit on energy resilience. The promulgation of “green credit guidelines” is used as a quasinatural experiment, and the findings indicate that green credit can significantly improve energy resilience. These conclusions remain valid after excluding other policy interferences during the study period, using instrumental variable estimation, replacing variables, and conducting placebo tests. Furthermore, the improvement effect of green credit on energy resilience is primarily observed in locations with a better financial environment, longer tenure of officials, and a higher degree of marketization. Mechanism analysis reveals that green credit can affect energy resilience by mitigating resource misallocation, accelerating energy technology innovation, and promoting industrial structure rationalization. This paper provides valuable policy insights to improve green credit policy and prevent systematic energy risks
Identifying and Mitigating Gender Bias in Social Media Sentiment Analysis: A Post-Training Approach on Example of the 2023 Morocco Earthquake
Get PDFSentiment analysis is a cornerstone in many contextual data analyses, from opinion mining to public discussion analysis. Gender bias is one of the well-known issues in sentiment analysis models, which can produce different results for the same text depending on the gender it refers to. This gender bias leads to further bias in other text analyses that use such sentiment analysis models. This study reviews existing solutions to reduce gender bias in sentiment analysis and proposes a new method to address this issue. The proposed method offers more practical flexibility as it focuses on sentiment estimation rather than model training. Furthermore, it provides a quantitative measure to investigate the gender bias in sentiment analysis results. The performance of the proposed method across five sentiment analysis models is presented using texts containing gender-specific words. The proposed method is applied to a set of social media posts related to Morocco’s 2023 earthquake to estimate the gender-unbiased sentiment of the posts and evaluate the gender-unbiasedness of five different sentiment analysis models in this context. The result shows that, although the sentiments estimated with different models are very different, the gender bias in none of the models is drastically large