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Downscaled land cover for SSP IAM "marker" scenarios, 2010-2100
No full textThis dataset is comprised of data from the Shared Socio-economic Pathways (SSP) database, downscaled using the Downscalr package (available here). The data comprises of global projections for land use modelled by GLOBIOM, covering the period from 2000 to 2100. The projections are based on combinations of three different scenario models – Representative Concentration Pathways (RCP), Shared climate Policy Assumptions (SPA), and Shared Socio-economic Pathways (SSP).
The dataset consists of 18 NetCDF files, each representing a different combination of RCP, SPA, and SSP pathways. Each file contains projected data for the entire 100-year period.
Each file has the following structure:
1. One raster file containing data on land use types (GlOBIOM land use projections)
2. One raster file containing information on pixel size (redundant) (pixel_area). This raster file is redundant for analysis purposes
NGFS Phase 5 Short Term Scenarios
No full textPlease do not request a data download here.
Rather, the data is available for download at the NGFS Scenario Explorer under this download link: https://data.ece.iiasa.ac.at/ngfs-phase-5-short-term/#/downloads. In order to download click on Guest login. You will be forwarded to the downloads page. At the bottom of the downloads page you can download the data.
The license permits use of the scenario ensemble for scientific research and commercial use, but restricts redistribution of substantial parts of the data. Please refer to the FAQ and legal code for more information.
Version 1.1 Release Notes
Changes made to data
Price|Level Annualised YoY Growth variable added – this is the CPI inflation rate annualised, comparing the average of the four quarters price level indexes of one year to the average of the four quarters in the year prior.
Policy Rate growth variables removed.
For some green energy PDs in the Highway to Paris scenario, the summation of the pd_adjusted and the baseline_pd variables results in a negative value. These have now been bounded to fix this issue.
About the data set
The NGFS short-term scenarios represent the first publicly available tool to provide a structured analysis of the immediate effects of climate policies and climate change on financial stability and economic resilience. The short-term scenarios complement the existing NGFS toolkit for assessing climate risk, including the NGFS long-term scenarios, which have become a well-established resource for financial actors to assess how economies might evolve over the coming decades. By bridging the gap between understanding the long-term risks arising from climate change and the benefits and costs of the green transition, the short-term scenarios address immediate policy needs and enhance our ability to respond effectively to climate-related challenges.
About NGFS
The Network for Greening the Financial System (NGFS) is a group of 127 central banks and supervisors and 20 observers committed to sharing best practices, contributing to the development of climate– and environment–related risk management in the financial sector and mobilising mainstream finance to support the transition toward a sustainable economy.
This Scenario Explorer is a web-based user interface for NGFS Scenarios. This provides intuitive visualizations & display of time series data and download of the data in multiple formats
Input data for Cooling China without warming the planet: climate and co-benefits of HFC phase-down
No full textThis data is for the paper Cooling China without warming the planet: climate and co-benefits of HFC phase-down
EU Regional Policy Payments 2007-2013
No full textThis dataset contains aggregated, temporally and spatially explicit data compiled from the various national and subnational databases, covering the period from 2007-2013. The project-level data is presented in an aggregate form. The dataset consists of six files, each corresponding to a different level of NUTS coding (NUTS 1-3) according to the 2016 NUTS specification.
For each file, the following columns are included the following:
Identifiers:
NUTS Code: The unique identifier for the NUTS (2016) region
Year: The starting year of the projects considered for aggregation.
Category: Broad intervention field (category) of policy payment; 5 categories.
Variables:
Total eligible expenditure: The (imputed) monetary amount of funding that could be granted. The total eligible expenditure is usually larger than the realized policy payments. It can be interpreted as an upper bound. All values are expressed in Euro at current prices.
The temporal dimension is yearly, ranging from 2007-2013. There are some observations with a later date due to the N+3 rule. The spatial dimension is identified by NUTS codes (2016), with granularity ranging from level 1 to level 3.
Each project can fall into one or more categories (BIF):
R&D, innovation (rd_innov)
ICT infrastructure (ict)
Productive investment and business development (prod_business)
Energy infrastructure (energy)
Environmental infrastructure and environment (environ)
Culture, heritage, and tourism (cultur_heritage_tour)
Urban and territorial dimension (urban_territor)
Transport infrastructure (transport)
Quality employment and labour mobility (lab_market)
Social inclusion
Social, health and education infrastructure (soc_infra)
Education and training (educ_train)
Technical assistance and institutional capacity (ta_inst_cap_other)
The projects are categorized into 13 categories of intervention based on the available data post 2013 where this categorization is present. The main features for prediction are derived from the project descriptions which were translated into English using machine translation. Firstly, the words contained in the text documents are tokenized and stemmed. Next, stop words, numbers, punctuation, separators, and hyphens are removed as well as tokens occurring less than 50 times within and across all documents or less than 40 times across all documents, leaving roughly 4 000 tokens to be used as document features. In addition to the document features, three other variables which are readily available in both data sets are utilized: The amount of Euros allocated to the project (adjusted for differences over time), the type of the fund (ESF, ERDF, …) and the country of implementation.
Together with the document features they are used as inputs for a random forest trained on a random subsample of 50 000 observation of the available data post 2013. Overall accuracy for the final model out of sample is 56 percent, with the largest category making up around 28 percent of observations, resulting in a Kappa value of 0.44. Due to the very imbalanced distribution of categories (some categories make up less than 1 percent of all observations) the sensitivities and specificities regarding the categories vary widely and categorization into smaller categories is often highly insensitive. Also, since the text descriptions in the data before 2013 are of poorer quality, one must expect the model to perform worse in practice. Some additional checks were performed comparing the results to the distribution of counts in categories post 2013 and the distribution of project sums before 2013. Model scores are tweaked to overweight smaller categories to achieve a better resemblance of the two distributions. The underlying project-level data on EU regional funds contains variables on the project itself (title, description, location, and project end and/or start date), the project’s beneficiary (name, location), the policy area to which the policy area contributes, and monetary information (type of fund used, co-financing rate, paid sums, eligible costs, etc.). Substantial effort was made to manually check
The dataset contains observations for all EU member states, and in the case of Interreg projects, on some EU neighboring states. Please note that this dataset is intended for research and analysis in the fields of climatology, environmental science, and related disciplines. Users are encouraged to cite this dataset appropriately if utilized in academic or scientific publications
Historical Agronomic Dataset - Insights from Mexico (2012 - 2022). Expanded and harmonized version.
No full textGLOBIOM output data for the aquaculture and aquafeed supply chains in CLEVER D7.2 deliverable
No full textSupplement for CLEVER D7.2 deliverable including output data for the aquaculture & aquafeed supply chain results. It analyzes future projections to 2050 for the blue food and agricultural sectors, including aquaculture and fish-based vs crop-based aquafeed requirements and related impacts on terrestrial biodiversity through land use. The scenarios include a business-as-usual future to 2050, as well as sensitivity scenarios around non-fed aquaculture capacity developement and changes in feeding practices, and a counterfactual scenario in which the blue food sector remains constant to 2020.
The record contains a readme file (readme_D72_AquafeedSupplyChains.docx) and a model output reporting csv file (GLOBIOM_outputs_D72_AquafeedSupplyChains_Global.csv)
Getting into the doughnut: A framework for assessing systemic resilience in the global food system
No full textThe global food system’s recent disruptions reveal its vulnerability to cascading failures, highlighting the urgent need to strengthen its systemic resilience, a vital precondition for global food security. Though modeling
is key to comprehending its complex behavior and informing policy and decisions, the conceptualization, assessment, and modeling of systemic resilience are still in their infancy, raising questions about the suitability of existing models for evaluating resilience-building solutions. Utilizing insights from complexity theory and systems thinking, this paper proposes a holistic framework of seven criteria to evaluate modeling approaches and policies for systemic resilience. An assessment of five existing modeling approaches and associated examples of existing models reveals important gaps in current methodologies, especially regarding the transmission and amplification of impacts on the macro scale. Hence, we call for enhancing the analytical preparedness capability through the development of new models and clear communication of current shortfalls to stakeholders for improved governance
Deliverable D4.3 – Guidelines for transferability of successful instruments from other policy areas to climate change adaptation
No full textThe deliverable D4.3 identifies participatory elements in digitalization policies and evaluates their applicability to climate change adaptation, offering guidelines for transferring these tools to climate adaptation policies. It highlights digital participation, discussing its advantages and limitations to justify the focus on digitalization policies aimed at fostering citizen engagement
Bayesian methodology on reconstructing education- and age-specific fertility rates
No full textEducation-specific fertility rates are useful for tracking the impacts of education policies on fertility rates. These estimates also serve as foundations for designing and developing population projections. We estimate education- and age-specific fertility rates for 79 countries within the Demographic and Health Surveys (DHS) database from 1970 to 2020 in five-year intervals. Using a Bayesian approach, we combine data from multiple sources to estimate UN-consistent education- and age-specific fertility rates
Subnational variations in the quality of household survey data in sub-Saharan Africa
Get PDFNationally representative household surveys collect geocoded data that are vital to tackling health and other development challenges in sub-Saharan Africa. Scholars and practitioners generally assume uniform data quality but subnational variation of errors in household data has never been investigated at high spatial resolution. Here, we explore within-country variation in the quality of most recent household surveys for 35 African countries at 5 × 5 km resolution and district levels. Findings show a striking heterogeneity in the subnational distribution of sampling and measurement errors. Data quality degrades with greater distance from settlements, and missing data as well as imprecision of estimates add to quality problems that can result in vulnerable remote populations receiving less than optimal services and needed resources. Our easy-to-access geospatial estimates of survey data quality highlight the need to invest in better targeting of household surveys in remote areas