Recommended citation

Article citation will be added once the article is available.

Content

• Use of the dataset and full description
• Abstract
• Support
• Files included in the dataset
• Notes
• Data format description (columns)
• Data sources
• Changelog
• References

Use of the dataset and full description

Before using the dataset, please read this document and the article describing the methodology, especially the "Discussion and limitations" section.

The article will be referenced here as soon as it is published.

Please notify us (johannes.guetschow@pik-potsdam.de) if you use the dataset so that we can keep track of how it is used and take that into consideration when updating and improving the dataset.

When using this dataset or one of its updates, please cite the DOI of the precise version of the dataset used and also the data description article which this dataset is supplement to (see above). Please consider also citing the relevant original sources when using the RCP-SSP-dwn dataset. See the full citations in the References section further below.

Support

If you encounter possible errors or other things that should be noted or need support in using the dataset or have any other questions regarding the dataset, please contact johannes.guetschow@pik-potsdam.de.

Abstract

This dataset provides country scenarios, downscaled from the RCP (Representative Concentration Pathways) and SSP (Shared Socio-Economic Pathways) scenario databases, using results from the SSP GDP (Gross Domestic Product) country model results as drivers for the downscaling process harmonized to and combined with up to date historical data.

Files included in the dataset

The repository comprises several datasets. Each dataset comes in a csv file. The file name is constructed from dataset properties as follows:

The "Source" flag indicates which input scenarios were used.

• PMRCP: RCP scenarios downscaled using the SSPs: emissions and socio-economic data; scenarios are available both harmonized to historical data and non-harmonized.
• PMSSP: Downscaled SSP IAM scenarios: emissions and socio-economic data; scenarios are available both harmonized to historical data and non-harmonized.

the "Bunkers" flag indicates if the input emissions scenarios have been corrected for emissions from international shipping and aviation (bunkers) before downscaling to country level or not. The flag is "B" for scenarios where emissions from bunkers have been removed before downscaling and "" (no flag) where they have not been removed.

The "Downscaling" flag indicates the downscaling technique used.

• IE: Convergence downscaling with exponential convergence of emissions intensities and convergence before transition to negative emissions.
• IC: Regional emission intensity growth rates for all countries.
• CS: Constant emission shares as a reference case independent of the socio-economic scenario.

All files contain data for all countries and variables. For detailed methodology descriptions we refer to the paper this dataset is a supplement to. A reference to the paper will be added as soon as it is published.

Finally the data description including detailed references is included: RCP-SSP-dwn_v1.0_data_description.pdf.

Notes

If you encounter problems with the size of the csv files please let us know, so we can find solutions for future releases of the data.

Data format description (columns)

"source"

For PMRCP files source values are

• RCPSSP
• PMRCP
• PMRCPMISC

For PMSSP files source values are

• SSPIAM
• PMSSP
• PMSSPMISC

For possible values of

"scenario"

For PMRCP files the scenarios have the format

For PMSSP files the scenarios have the format

Model codes in scenario names

• AIMCGE: AIM-CGE
• IMAGE: IMAGE
• GCAM4: GCAM
• MESGB: MESSAGE-GLOBIOM
• REMMP: REMIND-MAGPIE
• WITGB: WITCH-GLOBIOM

"country"

ISO 3166 three-letter country codes or custom codes for groups:

Additional "country" codes for country groups.

• EARTH: Aggregated emissions for all countries
• ANNEXI: Annex I Parties to the UNFCCC
• NONANNEXI: Non-Annex I Parties to the UNFCCC
• AOSIS: Alliance of Small Island States
• BASIC: BASIC countries (Brazil, South Africa, India and China)
• EU28: European Union (still including the UK)
• LDC: Least Developed Countries
• UMBRELLA: Umbrella Group

"category"

Category descriptions.

• IPCM0EL: Emissions: National Total excluding LULUCF
• ECO: Economical data
• DEMOGR: Demographical data

"entity"

Gases and gas baskets using global warming potentials (GWP) from either Second Assessment Report (SAR) or Fourth Assessment Report (AR4).

Gases / gas baskets and underlying global warming potentials

• CH4: Methane (CH₄)
• CO2: Carbon Dioxide (CO₂)
• N2O: Nitrous Oxide (N₂O)
• FGASES: Fluorinated Gases (SAR): HFCs, PFCs, SF₆, NF₃
• FGASESAR4: Fluorinated Gases (AR4): HFCs, PFCs, SF₆, NF₃
• KYOTOGHG: Kyoto greenhouse gases (SAR)
• KYOTOGHGAR4: Kyoto greenhouse gases (AR4)

"unit"

The following units are used:

• Million2011GKD: Million 2011 international dollars
• ThousandPers: Thousand persons
• kt: kilotonnes
• Mt: Megatonnes
• Gg: Gigagrams
• MtCO2eq: Megatonnes of CO₂ equivalents using the GWPs defined by "entity"
• GgCO2eq: Gigagrams of CO₂ equivalents using the GWPs defined by "entity"

Remaining columns

Years from 1850-2100.

Data Sources

The following data sources were used during the generation of this dataset:

Scenario data

• RCP scenarios website/data
• SSP basic elements website/data
• SSP IAM scenarios website/data
• SSP CMIP6 scenarios website/data

Historical data

• CDIAC data
• CEDS CMIP6 data paper/data
• EDGAR version 4.3.2: data, paper
• IMO GHG report report
• PRIMAP-hist v2.1 paper, <a

This paper and dataset presents the overview of the Shared Socioeconomic Pathways (SSPs) and their energy, land use, and emissions implications. The SSPs are part of a new scenario framework, established by the climate change research community in order to facilitate the integrated analysis of future climate impacts, vulnerabilities, adaptation, and mitigation. The pathways were developed over the last years as a joint community effort and describe plausible major global developments that together would lead in the future to different challenges for mitigation and adaptation to climate change. The SSPs are based on five narratives describing alternative socio-economic developments, including sustainable development, regional rivalry, inequality, fossil-fueled development, and middle-of-the-road development. The long-term demographic and economic projections of the SSPs depict a wide uncertainty range consistent with the scenario literature. A multi-model approach was used for the elaboration of the energy, land-use and the emissions trajectories of SSP-based scenarios. The baseline scenarios lead to global energy consumption of 400–1200 EJ in 2100, and feature vastly different land-use dynamics, ranging from a possible reduction in cropland area up to a massive expansion by more than 700 million hectares by 2100. The associated annual CO2 emissions of the baseline scenarios range from about 25 GtCO2 to more than 120 GtCO2 per year by 2100. With respect to mitigation, we find that associated costs strongly depend on three factors: (1) the policy assumptions, (2) the socio-economic narrative, and (3) the stringency of the target. The carbon price for reaching the target of 2.6 W/m2 that is consistent with a temperature change limit of 2 °C, differs in our analysis thus by about a factor of three across the SSP marker scenarios. Moreover, many models could not reach this target from the SSPs with high mitigation challenges. While the SSPs were designed to represent different mitigation and adaptation challenges, the resulting narratives and quantifications span a wide range of different futures broadly representative of the current literature. This allows their subsequent use and development in new assessments and research projects. Critical next steps for the community scenario process will, among others, involve regional and sectoral extensions, further elaboration of the adaptation and impacts dimension, as well as employing the SSP scenarios with the new generation of earth system models as part of the 6th climate model intercomparison project (CMIP6). Recommended article. The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview, Global Environmental Change, Volume 42, Pages 153-168, 2017, ISSN 0959-3780, DOI:110.1016/j.gloenvcha.2016.05.009.

What does the data show?

Population from the UK Climate Resilience Programme UK-SSPs project. The data is available for the end of each decade. Provided on a 2km Transverse Mercator Grid (prj4string: “+proj=tmerc +lat_0=49 +lon_0=-2 +k=0.9996012717 +x_0=400000 +y_0=-100000 +a=6377563.396 +rf=299.324975315035 +units=m +no_defs”).

The source data was originally at a 1km resolution, but for usability it has been converted to 2km resolution.

This dataset contains SSP1, SSP2, SSP3, SSP4 and SSP5. For more information see the table below.

Indicator

Population

Metric

Population

Unit

Headcount

Spatial Resolution

2km grid (sourced from 1km grid)

Temporal Resolution

Decadal

Sectoral Categories

N/A

Baseline Data Source

ONS 2019; LCM 2015, Worldpop 2020

Projection Trend Source

IIASA; UK SSP urbanisation

What are the naming conventions and how do I explore the data?

This data contains a field for each SSP scenario and the year at the end of each decade. For example, 'SSP1_2040' is the projection for 2040 in the SSP1 scenario.

There are a small number of features in this data with much higher population values than the majority of features. This can skew the styling, and so if you want to emphasise areas of high density population you may wish to adjust the style settings to account for this.

To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578

Please note, if viewing in ArcGIS Map Viewer, the map will default to ‘SSP1_2020’ values.

What are Shared Socioeconomic Pathways (SSPs)?

The global SSPs, used in Intergovernmental Panel on Climate Change (IPCC) assessments, are five different storylines of future socioeconomic circumstances, explaining how the global economy and society might evolve over the next 80 years. Crucially, the global SSPs are independent of climate change and climate change policy, i.e. they do not consider the potential impact climate change has on societal and economic choices.

Instead, they are designed to be coupled with a set of future climate scenarios, the Representative Concentration Pathways or ‘RCPs’. When combined together within climate research (in any number of ways), the SSPs and RCPs can tell us how feasible it would be to achieve different levels of climate change mitigation, and what challenges to climate change mitigation and adaptation might exist.

Until recently, UK-specific versions of the global SSPs were not available to combine with the RCP-based climate projections. The aim of the UK-SSPs project was to fill this gap by developing a set of socioeconomic scenarios for the UK that is consistent with the global SSPs used by the IPCC community, and which will provide the basis for further UK research on climate risk and resilience.

Useful links:Further information on the UK SSPs can be found on the UK SSP project site and in this storymap.Further information on RCP scenarios, SSPs and understanding climate data within the Met Office Climate Data Portal.

General Information

The Pop-AUT database was developed for the DISCC-AT project, which required subnational population projections for Austria consistent with the updated Shared Socio-Economic Pathways (SSPs). For this database, the most recent version of the nationwide SSP population projections (IIASA-WiC POP 2023) are spatially downscaled, offering a detailed perspective at the subnational level in Austria. Recognizing the relevance of this information for a wider audience, the data has been made publicly accessible through an interactive dashboard. There, users are invited to explore how the Austrian population is projected to evolve under different SSP scenarios until the end of this century.

Methodology

The downscaling process of the nationwide Shared Socioeconomic Pathways (SSP) population projections is a four-step procedure developed to obtain subnational demographic projections for Austria. In the first step, population potential surfaces for Austria are derived. These indicate the attractiveness of a location in terms of habitability and are obtained using machine learning techniques, specifically random forest models, along with geospatial information such as land use, roads, elevation, distance to cities, and elevation (see, e.g., Wang et al. 2023).

The population potential surfaces play a crucial role in distributing the Austrian population effectively across the country. Calculations are based on the 1×1 km spatial resolution database provided by Wang et al. (2023), covering all SSPs in 5-year intervals from 2020 to 2100.

Moving to the second step, the updated nationwide SSP population projections for Austria (IIASA-WiC POP 2023) are distributed to all 1×1 km grid cells within the country. This distribution is guided by the previously computed grid cell-level population potential surfaces, ensuring a more granular representation of demographic trends.

The base year for all scenarios is 2015, obtained by downscaling the UN World Population Prospects 2015 count for Austria using the WorldPop (2015) 1×1 km population count raster.

In the third step, the 1×1 km population projections are temporally interpolated to obtain yearly projections for all SSP scenarios spanning the period from 2015 to 2100.

The final step involves the spatial aggregation of the gridded SSP-consistent population projections to the administrative levels of provinces (Bundesländer), districts (Bezirke), and municipalities (Gemeinden).

Dashboard

The data can be explored interactively through a dashboard.

Data Inputs

Updated nationwide SSP population projections: IIASA-WiC POP (2023) (https://zenodo.org/records/7921989)

Population potential surfaces: Wang, X., Meng, X., & Long, Y. (2022). Projecting 1 km-grid population distributions from 2020 to 2100 globally under shared socioeconomic pathways. Scientific Data, 9(1), 563.

Shapefiles: data.gv.at

WorldPop 2015: WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00647

Version

This is version 1.0, built upon the Review-Phase 2 version of the updated nationwide SSP population projections (IIASA-WiC POP 2023). Once these projections are revised, this dataset will be accordingly updated.

File Organization

The SSP-consistent population projections for Austria are accessible in two formats: .csv files for administrative units (provinces = Bundesländer, districts = Politische Bezirke, municipalities = Gemeinden) and 1×1 km raster files in GeoTIFF and NetCDF formats. All files encompass annual population counts spanning from 2015 to 2100.

What does the data show?

The data shows projections of population age structure (thousands of people per age class) from the UK Climate Resilience Programme UK-SSPs project. The data is available for each Office for National Statistics Local Authority District (ONS LAD) shape simplified to a 10m resolution.

The age structure is split into 19 age classes e.g. 10-14 and is available for the end of each decade. For more information see the table below.

This dataset contains only SSP2, the 'Middle of the Road' scenario.

Indicator

Demography

Metric

Age Structure

Unit

Thousands per age class

Spatial Resolution

LAD

Temporal Resolution

Decadal

Sectoral Categories

19 age classes

Baseline Data Source

ONS 2019

Projection Trend Source

IIASA

What are the naming conventions and how do I explore the data?

This data contains a field for the year at the end of each decade. A separate field for 'Age Class' allow the data to be filtered e.g. by age class '10-14'.

To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578

Please note, if viewing in ArcGIS Map Viewer, the map will default to 2020 values.

What are Shared Socioeconomic Pathways (SSPs)?

The global SSPs, used in Intergovernmental Panel on Climate Change (IPCC) assessments, are five different storylines of future socioeconomic circumstances, explaining how the global economy and society might evolve over the next 80 years. Crucially, the global SSPs are independent of climate change and climate change policy, i.e. they do not consider the potential impact climate change has on societal and economic choices.

Instead, they are designed to be coupled with a set of future climate scenarios, the Representative Concentration Pathways or ‘RCPs’. When combined together within climate research (in any number of ways), the SSPs and RCPs can tell us how feasible it would be to achieve different levels of climate change mitigation, and what challenges to climate change mitigation and adaptation might exist.

Until recently, UK-specific versions of the global SSPs were not available to combine with the RCP-based climate projections. The aim of the UK-SSPs project was to fill this gap by developing a set of socioeconomic scenarios for the UK that is consistent with the global SSPs used by the IPCC community, and which will provide the basis for further UK research on climate risk and resilience.

Useful links:

Further information on the UK SSPs can be found on the UK SSP project site and in this storymap. Further information on RCP scenarios, SSPs and understanding climate data within the Met Office Climate Data Portal.

Recommended citation Article citation will be added once the article is available. Content Use of the dataset and full description Abstract Support Files included in the dataset Notes Data format description (columns) Data sources Changelog References Use of the dataset and full description Before using the dataset, please read this document and the article describing the methodology, especially the "Discussion and limitations" section. The article will be referenced here as soon as it is published. Please notify us (johannes.guetschow@pik-potsdam.de) if you use the dataset so that we can keep track of how it is used and take that into consideration when updating and improving the dataset. When using this dataset or one of its updates, please cite the DOI of the precise version of the dataset used and also the data description article which this dataset is supplement to (see above). Please consider also citing the relevant original sources when using the RCP-SSP-dwn dataset. See the full citations in the References section further below. Support If you encounter possible errors or other things that should be noted or need support in using the dataset or have any other questions regarding the dataset, please contact johannes.guetschow@pik-potsdam.de. Abstract This dataset provides country scenarios, downscaled from the RCP (Representative Concentration Pathways) and SSP (Shared Socio-Economic Pathways) scenario databases, using results from the SSP GDP (Gross Domestic Product) country model results as drivers for the downscaling process harmonized to and combined with up to date historical data. Files included in the dataset The repository comprises several datasets. Each dataset comes in a csv file. The file name is constructed from dataset properties as follows:

SummaryThis metadata record provides details of the data supporting the claims of the related manuscript: “Projecting future populations of urban agglomerations: around the world and through the 21st century ”.The data consist of HTML files with interactive maps for future populations projections of urban agglomerations, and HTML file displaying figures for postdictions of urban agglomerations, as well as 5 .csv files containing the raw data.The related study estimated population trends throughout the 21st century for approximately 20,000 urban agglomerations in 151 countries by working within the Shared Socioeconomic Pathways (SSPs) and using a simple urban growth model.Data accessThe following resources, which were among the sources of the data analyzed in the related study, are available from the links below.- Postdiction results for 1794 urban agglomerations http://stwww.eng.kagawa-u.ac.jp/~kii/Research/UPP_2020/UPP_2020.html#postdiction-for-1794-agglomerations-link- Temporal evolution from 2010 to 2100 of the geographical distribution of urban agglomerations, arranged by population scale, as predicted within the various SSP scenarios http://stwww.eng.kagawa-u.ac.jp/~kii/Research/UPP_2020/UPP_2020.htmlThese data are also available in raw .csv form via the 'Raw data' link on the same page, and also in the 5 files included as part of this data record.- Available urban-population data include the UN’s World Urbanization Prospects 2018 (https://population.un.org/wup/) and Gridded Population of the World, v4 (https://doi.org/10.7927/H4BC3WMT). Available settlement-point data include, in addition to the above urban population sources, World Gazetteer (https://www.arcgis.com/home/item.html?id=346ce13fa2d4468a9049f71bcc250f37) and GeoNames (https://www.geonames.org/). GDP per capita data is available from OECD.stat (https://stats.oecd.org/), Global Metro Monitor (https://www.brookings.edu/research/global-metro-monitor/), and World Development Indicators (http://datatopics.worldbank.org/world-development-indicators/). OpenStreetMap is available at https://www.openstreetmap.org/. Scenario data for SSPs are available at the IIASA-SSP database (https://doi.org/10.1016/j.gloenvcha.2016.05.009). CodeCode used for the analysis can be downloaded from the author's lab's website: http://stwww.eng.kagawa-u.ac.jp/~kii/Research/UPP_2020/UPP_2020.html#codes. These are written in R. They are provided only for the purpose of tracing the analytical procedure. They are not executable without appropriate datasets.

This is a repository of global and regional human population data collected from: the databases of scenarios assessed by the Intergovernmental Panel on Climate Change (Sixth Assessment Report, Special Report on 1.5 C; Fifth Assessment Report), multi-national databases of population projections (World Bank, International Database, United Nation population projections), and other very long-term population projections (Resources for the Future).

More specifically, it contains:

• in other_pop_data folder files from World Bank, the International Database from the US Census, and from IHME

• in the SSP folder, the Shared Socioeconomic Pathways, as in the version 2.0 downloaded from IIASA and as in the version 3.0 downloaded from IIASA workspace

• in the UN folder, the demographic projections from UN

• IAMstat.xlsx, an overview file of the metadata accompanying the scenarios present in the IPCC databases

• RFF.csv, an overview file containing the population projections obtained by Resources For the Future

'- the remaining .csv files with names AR6#, AR5#, IAMC15# contain the IPCC scenarios assessed by the IPCC for preparing the IPCC assessment reports. They can be downloaded from AR5, SR 1.5, and AR6

This data in intended to be downloaded for use together with the package downloadable here.

The dataset was used as a supporting material for the paper "Underestimating demographic uncertainties in the synthesis process of the IPCC" accepted on npj Climate Action (DOI : 10.1038/s44168-024-00152-y).

The dataset provides annual water withdrawal and consumption estimates for Europe at a spatial resolution of 5 arcminutes, covering the period from 2020 to 2100 for four SSPs (1, 2, 3, and 5). Below, we outline the procedure used to downscale the population projections to a 5-arcminute resolution and describe the main equations applied to project water withdrawal and consumption under different SSPs.

The development of the high-resolution (5 arcminute) projected water withdrawal and consumption for Europe follows the methodology outlined by Wada et al. (2011a, 2011b). This new release incorporates new projections for population, GDP per capita, and urbanization patterns from the latest SSP database (v3.0.1; available at https://data.ece.iiasa.ac.at/ssp/). Since this update is still in progress as of August 25^th, 2024, some necessary input data are sourced from an earlier version of the SSP data (SSP 2013, see Table 1). All data and methods used to generate the results provided in this dataset are described in the Readme - Data and Methods file.

Table 1: Data availability in different versions of the SSP database as of August 25^th 2024.

The database presents the scenario results of an exploratory research, carried out at the International Institute for Applied Systems Analysis (IIASA): the Low Energy Demand (LED) study (Grubler et al. 2018). The LED scenario explored how far transformative changes that combine technological changes, end-use efficiency, and new business models for energy service provision can lead for lowering energy demand, and how these changes could drive deep decarbonisation in the long-term.

The scenario development methodology included a bottom-up analysis of how currently existing, though often embryonic, social, institutional, and technological trends could become mainstream with resulting step-changes in efficiency and resulting lowered energy demand. The bottom-up demand estimations were then further explored for their supply side and emissions and climate implications with a top-down modeling framework drawing on the Shared Socioeconomic Pathways (SSP) framework (Riahi et al. 2017).

The results show that global final energy demands can be drastically reduced in 2050, to around 245 EJ/yr, or 40% lower than today, whilst significantly expanding human welfare and reducing global development inequalities. According to the knowledge of the authors, LED is the lowest long-term global energy demand scenario ever published. The LED scenario meets the 1.5°C climate target in 2100 without overshoot and keeps the global mean temperature increase below 1.5°C with a probability of more than 60%, without requiring controversial negative emission technologies, such as bioenergy with carbon capture and storage (BECCS), that figure prominently in the emission scenario literature (Rogelj et al. 2015, Anderson and Peters 2016, Creutzig et al. 2016, Smith et al. 2016).

Furthermore, the beneficial impacts of the LED scenario on a range of other sustainable development goals are also shown, demonstrating that efficiency of energy services provision plays a critical role in reaching low-energy futures without compromising increased living standards in the Global South, while at the same time reducing adverse social and environmental impacts of climate mitigation strategies that focus predominantly on large-scale supply-side transformations.

The research is published in a peer-reviewed article in Nature Energy (Grubler et al. 2018) with ample supplementary information. Water consumption and withdrawal data are published in Parkinson et al. (2018).

The data is available for download from the LED Database.

This climate change impact data (future scenarios on temperature-induced GDP losses) and climate change mitigation cost data (REMIND model scenarios) is published under doi: 10.5281/zenodo.3541809 and used in this paper:

Ueckerdt F, Frieler K, Lange S, Wenz L, Luderer G, Levermann A (2018) The economically optimal warming limit of the planet. Earth System Dynamics. https://doi.org/10.5194/esd-10-741-2019

Below the individual file contents are explained. For further questions feel free to write to Falko Ueckerdt (ueckerdt@pik-potsdam.de).

Climate change impact data

File 1: Data_rel-GDPpercapita-changes_withCC_per-country_all-RCP_all-SSP_4GCM.csv

Content: Data of relative change in absolute GDP/CAP levels (compared to the baseline path of the respective SSP in the SSP database) for each country, RCP (and a zero-emissions scenario), SSP and 4 GCMs (spanning a broad range of climate sensitivity). Negative (positive) values indicate losses (gains) due to climate change. For figure 1a of the paper, this data was aggregated for all countries.

File 2: Data_rel-GDPpercapita-changes_withCC_per-country_all-SSP_4GCM_interpolated-for-REMIND-scenarios.csv

Content: Data of relative change in absolute GDP/CAP levels (compared to the baseline path of the respective SSP in the SSP database) for each country, SSP and 4 GCMs (spanning a broad range of climate sensitivity). The RCP (and a zero-emissions scenario) are interpolated to the temperature pathways of the ten REMIND model scenarios used for climate change mitigation costs. Hereby the set of scenarios for climate impacts and climate change mitigation are consistent and can be combined to total costs of climate change (for a broad range of mitigation action).

File 3: Data_rel-GDPpercapita-changes_withCC_per-country_SSP2_12GCM_interpolated-for-REMIND-scenarios.csv

Content: Same as file 2, but only for the SSP2 (chosen default scenario for the study) and for all 12 GCMs. Data of relative change in absolute GDP/CAP levels (compared to the baseline path of the respective SSP in the SSP database) for each country, SSP-2 and 12 GCMs (spanning a broad range of climate sensitivity). The RCP (and a zero-emissions scenario) are interpolated to the temperature pathways of the ten REMIND model scenarios used for climate change mitigation costs. Hereby the set of scenarios for climate impacts and climate change mitigation are consistent and can be combined to total costs of climate change (for a broad range of mitigation action).

In addition, reference GDP and population data (without climate change) for each country until 2100 was downloaded from the SSP database, release Version 1.0 (March 2013, https://tntcat.iiasa.ac.at/SspDb/, last accessed 15Nov 2019).

Climate change mitigation cost data

The scenario design and runs used in this paper have first been conducted in [1] and later also used in [2].

File 4: REMIND_scenario_results_economic_data.csv

File 5: REMIND_scenarios_climate_data.csv

Content: A broad range of climate change mitigation scenarios of the REMIND model. File 4 contains the economic data of e.g. GDP and macro-economic consumption for each of the countries and world regions, as well as GHG emissions from various economic sectors. File 5 contains the global climate-related data, e.g. forcing, concentration, temperature.

In the scenario description “FFrunxxx” (column 2), the code “xxx” specifies the scenario as follows. See [1] for a detailed discussion of the scenarios.

The first dimension specifies the climate policy regime (delayed action, baseline scenarios):

1xx: climate action from 2010
5xx: climate action from 2015
2xx climate action from 2020 (used in this study)
3xx climate action from 2030
4x1 weak policy baseline (before Paris agreement)

The second dimension specifies the technology portfolio and assumptions:

x1x Full technology portfolio (used in this study)
x2x noCCS: unavailability of CCS
x3x lowEI: lower energy intensity, with final energy demand per economic output decreasing faster than historically observed
x4x NucPO: phase out of investments into nuclear energy
x5x Limited SW: penetration of solar and wind power limited
x6x Limited Bio: reduced bioenergy potential p.a. (100 EJ compared to 300 EJ in all other cases)
x6x noBECCS: unavailability of CCS in combination with bioenergy

The third dimension specifies the climate change mitigation ambition level, i.e. the height of a global CO2 tax in 2020 (which increases with 5% p.a.).

xx1 0$/tCO2 (baseline)
xx2 10$/tCO2
xx3 30$/tCO2
xx4 50$/tCO2
xx5 100$/tCO2
xx6 200$/tCO2
xx7 500$/tCO2
xx8 40$/tCO2
xx9 20$/tCO2
xx0 5$/tCO2

For figure 1b of the paper, this data was aggregated for all countries and regions. Relative changes of GDP are calculated relative to the baseline (4x1 with zero carbon price).

[1] Luderer, G., Pietzcker, R. C., Bertram, C., Kriegler, E., Meinshausen, M. and Edenhofer, O.: Economic mitigation challenges: how further delay closes the door for achieving climate targets, Environmental Research Letters, 8(3), 034033, doi:10.1088/1748-9326/8/3/034033, 2013a.

[2] Rogelj, J., Luderer, G., Pietzcker, R. C., Kriegler, E., Schaeffer, M., Krey, V. and Riahi, K.: Energy system transformations for limiting end-of-century warming to below 1.5 °C, Nature Climate Change, 5(6), 519–527, doi:10.1038/nclimate2572, 2015.

This dataset comprises output from simulations used in "The Effectiveness of Agricultural Carbon Dioxide Removal using the University of Victoria Earth System Climate Model" by Rebecca C Evans and H Damon Matthews, published in Biogeosciences (2025).

The data was collected to improve our understanding of how the climate responds to carbon removal when the sequestered carbon is stored temporarily in pools that can continue to interact with the atmosphere (nature-based removal). Included are time series from 12 future climate simulations: - 3 simulations that represent the climate's response to future emissions under the SSP 1-1.9, 2-4.5, and 5-8.5 emissions scenarios. - 3 simulations that have the same SSP scenarios (1-1.9, 2-4.5, 5-8.5) with the addition of a small amount of prescribed carbon dioxide removal in agricultural areas - 3 simulations that have the same SSP scenarios (1-1.9, 2-4.5, 5-8.5) but with moderate rates of agricultural CDR - 3 simulations that have the same SSP scenarios (1-1.9, 2-4.5, 5-8.5) but with high rates of agricultural CDR.

The data provided here is the output of these simulations, and can be found in a netcdf format. The data is annual globally-averaged output of temperature, atmospheric CO2, carbon pools, etc. for the 100 years from the year 2000 to 2100.

Brief description of the methodology: (1) Take projections for future CO2 under specific SSP scenarios from https://tntcat.iiasa.ac.at/SspDb (2) Modify the code of the UVic ESCM (https://terra.seos.uvic.ca/model/2.10/) to include a prescribed atmosphere-to-soil CO2 flux in agricultural areas (3) Prescribe this CO2 flux to linearly ramp from 0 in the present day, to a maximum value in 2050 based literature estimates of CDR that will be possible by 2050 for low, moderate, and high costs (4) Hold the CO2 flux constant after 2050 (5) Compare the results to the simulations with no prescribed CDR, to elucidate the impact of CDR on the climate

A more detailed description of the methodology can be found in the published paper. Model output data to create the figures in the published manuscript

Manuscript: Evans, R. C., & Matthews, H. D. (2024). The effectiveness of agricultural carbon dioxide removal using the University of Victoria Earth System Climate Model. Biogeosciences. https://doi.org/10.5194/egusphere-2024-1810

Supplementary material to the publication: Climate impacts on human livelihoods: where uncertainty matters in projections of water availabilityT. K. Lissner, D. E. Reusser, J. Schewe, T. Lakes, and J. P. KroppEarth Syst. Dynam. Discuss., 5, 403-442, 2014

Detailed content description:

Table 1.: T1_elements_definitions.odsThe table provides background information on the identified elementsof AHEAD and the translation in a quantified representation, including thechoice of data as well as the fuzzy membership functions. Tables 2. a-f: The tables provide country specific membership values for constantvalues across timeslices, as well as projected values on the basisof the ensemble mean (mean over all impact and climate models and RCPs).Country names are represented using ISO3 codes (ISO 3166-1 alpha-3). Eachfile with secnario values contains the timeslices 2000, 2030, 2060 and 2090. a. T2a_fuzzyvalues.csv (membership values of all input variables to linguistic category: adequacy is high, constant values)b. T2b_wateravail.csv (membership values to linguistic category: adequacy of water availability is high, scenario values)c. T2c_subsistence.csv (aggregated values for the subindex Subsistence, scenario values)d. T2d_infrastructure.csv (aggregated values for the subindex Infrastructure, constant values)e. T2e_societalstructure.csv (aggregated values for the subindex Societal Structure, constant values)f. T2f_AHEAD.csv (aggregated for the AHEAD Index, scenario values) Table 3.: T3_uncertainty_classes.csvCountry-specific results for the classification of uncertainty,following the decision-tree in Figure 2 and presented in Figure 5for the four scenario timeslices. netcdf-files containa) fuzzified scenario values (degree of membership to the linguistic category "conditions are adequate") for: - water availability - Subsistence subindex - AHEAD index b) First-order indicator of available renewable freshwater resources: we calculate annual mean runoff at each grid cell, and then redistribute it within each river basin according to the spatial distribution of discharge to account for cross–boundary flows between countries (Gerten et al., 2011). The result is summed up over every country and divided by the country’s population to obtain water resources per capita per year. Country-level population data according to UNWPP estimates for the historical period, and according to the Shared Socio-economic Pathways SSP2 (ONeill et al., 2012) projection for the future, is obtained from the SSP Database at https://secure.iiasa.ac.at/web-apps/ene/SspDb and linearly interpolated to obtain annual values. For further details about the model simulations, see also Schewe et al. (2014). We calculate average per capita water availability for a baseline of 1981-2010 (2000) and calculate projected changes for the scenario period 2071-2099 (2090). Filenames indicate the impact model, climate model, RCP scenario, CO2 scenario and variable names. Data on water availability are derived from ISI-MIP simulation. File name conventions follow ISI-MIP the documentation. See also https://www.pik-potsdam.de/research/climate-impacts-and-vulnerabilities/research/rd2-cross-cutting-activities/isi-mip/for-modellers/isi-mip-fast-track/simulation-protocol/simulationprotocol-as-of-2013_04_30_version2.3-1

This dataset contains output from the 10,000 Resources for the Future Socioeconomic Projections, run with the FaIR reduced complexity climate model (v2.1.0) using an IPCC Sixth Assessment Report consistent calibration of 1,001 probabilistic ensemble members (v1.0). A total of 10,010,000 climate projections are produced. Climate projections are produced for 1750 to 2301. FaIR is run using stochastically generated internal variability.

The RFF-SPs contain CO2, CH4 and N2O emissions. The scenarios have been infilled using the Silicone package (Lamboll et al. 2020) to decompose the total CO2 into fossil and land-use components, and to infill emissions of other greenhouse gases and short-lived climate forcers, following the same strategy used to infill scenarios in the IPCC Sixth Assessment Report Working Group 3 from a large database of integrated assessment model pathways (see Kikstra et al. 2022). To extend scenarios beyond 2100 - the time horizon of IAM pathways - the approach consistent with extending the SSPs for CMIP6 is used (Meinshausen et al. 2020, sec. 2.3).

Code and instructions to reproduce the results is available at https://github.com/chrisroadmap/rff-fair2.1.

Dataset contents:

• output[0-9].zip: RFF-SP projections, in batches of 1,000 individual netCDF files (data_output/stochastic/run?????.nc) where ????? is in the range 00001 to 10000). Each file contains:
  • Global mean near-surface air temperature, rebased to 1850-1900 mean
  • Ocean heat content change since 1750
  • Effective radiative forcing with respect to pre-industrial (IPCC Sixth Assessment Report Working Group 1 convention of anthropogenic components using a 1750 baseline and natural components using a long pre-1750 mean)
  • CO2 concentrations (ppm)
  • CH4 concentrations (ppb)
  • N2O concentrations (ppb)
• infilled_extended.zip: These are the infilled emissions used to run FaIR, containing 53 emissions species (data_processed/infilled_extended/emissions?????.csv). Time period covered is 2015 to 2300. Pre-2015 emissions can be obtained from the Reduced Complexity Model Intercomparison Project, using the CMIP6 historical emissions from 1750 to 2014. Bridging from 2015 to 2020 was performed using SSP2-4.5, as in the original RFF-SPs for CO2, CH4 and N2O.
• ssp.zip: For completeness and comparison, the eight main SSP scenarios (ssp119, ssp126, ssp245, ssp370, ssp434, ssp460, ssp534-over, ssp585) are run with the same emissions scenarios and calibration dataset.
• rcp.zip: For extra completeness, the four RCP scenarios (rcp26, rcp45, rcp60 and rcp85) have been provided. These are run with the same calibration dataset and therefore are not entirely consistent with IPCC AR6 assessed ranges of climate change. They are provided for comparison only.

Notes about the data

• All variables in each file have dimension (timebounds, ensemble member) except for N2O which is unaffected by climate in this calibration of FaIR, having dimensions of (timebounds).
• Scenario 00001 runs from 1750 to 2301. Other scenarios run from 2020 to 2301, as the historical period is the same for every scenario. This approximately halves the size of the data output. RCPs run from 1765 to 2301.
• Note that all variables in the output files are on timebounds. 2020 corresponds to 2020-01-01, 2021 to 2021-01-01, and so on. To approximate midyear values, take the mean of consecutive years. Infilled emissions are on timepoints, i.e. are representative of midyear values. See here for an explanation.

Im Rahmen einer vom nova-Institut durchgeführten Studie modelliert das Thünen-Institut für Waldwirtschaft als Unterauftragnehmer die Auswirkungen verschiedener Politiken und gesellschaftlicher Trends auf den Holzmarkt. Die Studie ist von der Renewable Carbon Initiative (RCI) und des Bio-based Industries Consortium (BIC) beauftragt. Der zentrale Forschungsauftrag der Konsortien lautet: Unter der Prämisse, dass der chemische Industriesektor eine vollständige Defossilisierung anstrebt, soll quantifiziert werden, welcher Anteil des Kohlenstoffinputs, den die chemische Industrien als Input benötigen, im Jahr 2050 nachhaltig aus agrarischer und forstlicher Biomasse bereitgestellt werden kann. Für diesen Zweck werden ein Referenzszenario und zwei alternative Szenarien entworfen, die verschiedene zukünftige Entwicklungspfade beschreiben. Die Szenarien betreffen sowohl den landwirtschaftlichen als auch den forstwirtschaftlichen Sektor. Die hier dargestellten Forstsektoranalysen untersuchen mögliche Entwicklungen zur Verfügbarkeit forstbasierter Biomassepotentiale. Die Entwicklung der globalen Waldfläche in den Szenarien wird normativ angepasst, sodass beispielweise die Entwaldung abnimmt. Das Thünen-Institut für Waldwirtschaft nutzt zur Modellierung das Timber market Model for policy-Based Analysis (TiMBA), wobei angebots- und nachfrageseitig 19 Holz und holzbasierte Produkte in 180 Ländern bis zum Jahr 2050 simuliert werden. In enger Zusammenarbeit mit dem nova-Institut kalibriert das Thünen-Institut für Waldwirtschaft das Model. Im Anschluss werden mit TiMBA verschiedene Szenarien simuliert, wobei die Eingangsdaten und Parameter von FAOstat[1], World Bank[2], FRA[3], IIASA-SSP[4], WTO[5], the fibre yearbook 2023[6] und Skoczinski et al. 2024[7] zum Einsatz kommen. Die Modellierungsergebnisse zeigen eine Erhöhung der Rohholzlieferung zwischen 2020 und 2050 um bis zu 38%. Die steigende Nachfrage nach holzbasierten Produkten, vom Schnittholz (bis zu +36%) bis zur Textilfaser (bis zu +447%), kann dadurch gedeckt werden. Trotz dieser starken, relativen Produktionssteigerungen bleiben holzbasierte Fasern und chemische Derivate, gemessen am Gesamtvolumen aller holzbasierten Sektoren, weiterhin Nischenprodukte. Steigende Bedarfe, insbesondere in sich neu bildendenden Energiemärkten, führen zu einem Nachfrageüberhang nach Holznebenprodukten, welche heute vor allem Einsatz in der Plattenindustrie finden. Neue Nachfragemärkte umschließen beispielsweise die Pelletindustrie, Hersteller von grünem Treibstoff für die Luftfahrt sowie Bioraffinerien. Dies führt erwartungsgemäß zu steigenden Ressourcenkonkurrenzen. In den hier dargestellten Dokumenten sind Eingangsdaten, Datenkalibrierungen, Modelinputdaten, Ergebnisse der Modellierung der drei Szenarien und Hintergrundinformationen abgelegt. Die Dokumente sind auf Anfrage verfügbar. [1] FAOStat, 2023. Forestry Production and Trade, Statistical Database. www.fao. org/FAOSTAT/en/#data/FO (Accessed on November 2023). [2] World Bank, 2023: World Development Indicators: https://datatopics.worldbank.org/world-development-indicators/. [3] Forest Resource Assessment, 2020: 2020: Main Report. 2020. Rome: FAO. https://doi.org/10.4060/ca9825en. [4] International Institute for Applied Systems Analyses, 2018. Shared Socioeconomic Pathways Scenario Database (SSP). https://iiasa.ac.at/models-tools-data/ssp (Accessed on November 2023) [5] World Trade Organization, 2024. Integrated Database (IDB). http://tariffdata.wto.org/default.aspx (Accessed on August 2024) [6] The fibre year consulting, 2023. Selected data provided by the nova institute. (E-Mail 17.11.2023) [7] Pia Skoczinski, Michael Carus, Gillian Tweddle, Pauline Ruiz, Nicolas Hark, Ann Zhang, Doris de Guzman, Jan Ravenstijn, Harald Käb and Achim Raschka. 2024: Bio-based Building Blocks and Polymers – Global Capacities, Production and Trends 2023–2028. nova-Institut GmbH (Ed.),Hürth, Germany, 2024-03. https://doi.org/10.52548/VXTH2416