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Conservation and Economic Data for New England Towns 1990-2015
Land protection, whether public or private, is often controversial at the local level because residents worry about lost economic activity. We used panel data and a quasi-experimental impact-evaluation approach to determine how key economic indicators were related to the percentage of land protected. Specifically, we estimated the impacts of public and private land protection based on local area employment and housing permits data from 5 periods spanning 1990-2015 for all major towns and cities in New England. To generate rigorous impact estimates, we modeled economic outcomes as a function of the percentage of land protected in the prior period, conditional on town fixed effects, metro-region trends, and controls for period and neighboring protection. Contrary to narratives that conservation depresses economic growth, land protection was associated with a modest increase in the number of people employed and in the labor force and did not affect new housing permits, population, or median income. Public and private protection led to different patterns of positive employment impacts at distances close to and far from cities, indicating the importance of investing in both types of land protection to increase local opportunities. The greatest magnitude of employment impacts were due to protection in more rural areas, where opportunities for both visitation and amenity-related economic growth may be greatest. Overall, we provide novel evidence that land protection can be compatible with local economic growth and illustrate a method that can be broadly applied to assess the net economic impacts of protection.
Incentive mechanisms and the provision of public goods: Field experiment data for testing alternative economic frameworks to supply ecosystem restoration on Virginia's Eastern Shore: 2008 data.
This dataset consists of participant responses in one of two economic experiments conducted on Virginia's Eastern Shore during 2008 and 2009 by Elizabeth C. Smith used to gauge resident preferences and willingness-to-pay for ecosystem restoration activities. This dataset was designed to be used to examine a practical method to implement an individualized pricing approach to public good provision, grounded in Lindahl's marginal benefit theory. The study's focus was on ecosystem valuation and market approaches that have potential to provide public goods, examining the potential to generate revenues for public goods from consumers. While willingness-to-pay measurement techniques have been used to assess preferences for many environmental goods, this research goes a step further to explore real money auctions that generate revenues sufficient to pay for restoration activities. The data from the field experiments conducted in coastal Virginia were used, along with laboratory experiment data, to evaluate the performance of auction mechanisms in generating revenues relative to potential (Hicksian) willingness to pay for marginal increments in public goods. The field execution of this experiment involved residents of Virginia's Eastern Shore and local public goods. This application involved half-acre increments of ecosystem restoration for sea grass habitat in coastal lagoons, plantings for migratory bird habitat, and, in some auctions, clam-based increments of water quality services, defined as delaying the harvest of clams for six months beyond normal harvest by an existing aquaculture firm. To perform these tasks, participants were provided a budget, between $90 and $150. The auctioneer described for participants the ecosystem services that may result from additional ecosystem restoration associated with each activity. The actual levels of ecosystem restoration provided were based on aggregate offers reaching a pre-determined (but unknown to the participants) provision p
Incentive mechanisms and the provision of public goods: Field experiment data for testing alternative economic frameworks to supply ecosystem restoration on Virginia's Eastern Shore: 2009 data.
This dataset consists of participant responses in one of two economic experiments conducted on Virginia's Eastern Shore during 2008 and 2009 by Elizabeth C. Smith used to gauge resident preferences and willingness-to-pay for ecosystem restoration activities. This dataset was designed to be used to examine a practical method to implement an individualized pricing approach to public good provision, grounded in Lindahl's marginal benefit theory. The study's focus was on ecosystem valuation and market approaches that have potential to provide public goods, examining the potential to generate revenues for public goods from consumers. While willingness-to-pay measurement techniques have been used to assess preferences for many environmental goods, this research goes a step further to explore real money auctions that generate revenues sufficient to pay for restoration activities. The data from the field experiments conducted in coastal Virginia were used, along with laboratory experiment data, to evaluate the performance of auction mechanisms in generating revenues relative to potential (Hicksian) willingness to pay for marginal increments in public goods. The field execution of this experiment involved residents of Virginia's Eastern Shore and local public goods. This application involved half-acre increments of ecosystem restoration for sea grass habitat in coastal lagoons, plantings for migratory bird habitat, and, in some auctions, clam-based increments of water quality services, defined as delaying the harvest of clams for six months beyond normal harvest by an existing aquaculture firm. To perform these tasks, participants were provided a budget, between $90 and $150. The auctioneer described for participants the ecosystem services that may result from additional ecosystem restoration associated with each activity. The actual levels of ecosystem restoration provided were based on aggregate offers reaching a pre-determined (but unknown to the participants) provision p
Dataset on the Index of Sustainable Economic Welfare for the EU27 and beyond.
<div>This dataset contains data about two ISEWs for the EU27, its individual Member States (MS), the UK and the US. Following Van der Slycken and Bleys (2023) (1), two variants of the ISEW are presented in this dataset: the ISEW_BCE accounts for the benefits and costs of the present and pasts activities experienced in the present and within a specific country (Benefits and Costs Experienced); the ISEW_BCPA accounts for the benefits and costs of present activities experienced in the present and in the future, both domestically and internationally (Benefits and Costs of Present economic Activities).</div> <div> </div> <div>This document contains different datasets. Two datasets contain a summary of the values of the ISEWs and their components in ‘per capita’ terms. One summary presents the results for the EU27 (and MS) and the other one presents the results for the UK and the US (Non-EU countries). Additionally, each component is presented in some details in different pages, allowing to see the value of the different subcomponents included in each component (and even the value of some items with subcomponents for some components).</div> <div> </div> <div>The period covered by this dataset is 1995-2020.</div> <div> </div> <div>All the components are described in the accompanying table and in the report.</div> <div> </div> <div> </div> <div>(1) Van der Slycken, J. and Bleys, B. (2023). Towards ISEW and GPI 2.0: Dealing with Cross-Time and Cross-Boundary Issues in a Case Study for Belgium. <em>Social Indicators Research</em>, 168(1):557-583.</div>
Data for: Techno-economic analysis of a novel laccase production process utilizing perennial biomass and the aqueous phase of bio-oil, Iowa, USA 2023-2025
This dataset contains the experimental design, measurements, and derived variables used to parameterize a techno‑economic model of laccase production via two‑stage solid‑state fermentation of prairie biomass with bio‑oil aqueous phase induction. It includes nutrient screening data for Pleurotus ostreatus growth on prairie biomass with alternative nitrogen sources and a corn‑steep solids dose series; factorial/response‑surface experiments varying substrate bed depth, substrate‑to‑inoculum (S:I) ratio, and pre‑induction growth time; and time‑resolved induction measurements. For each run and replicate, the data record the full set of spectrophotometric absorbances at 0–210 s, fitted slopes and r-square values, dilution and volume factors, and laccase activities normalized per mL and per gram of biomass, alongside the exact culture timings and environmental conditions used in the ABTS assay at 420 nm. Results tables provide the fitted central‑composite design model terms (coefficients, F‑statistics, and p‑values) used directly as inputs to the minimum laccase selling price (MLSP) calculations, together with the underlying per‑condition raw results.
Data for: Techno-economic and environmental assessment of converting mixed prairie to renewable natural gas with co-product hydroxycinnamic acid, Iowa, USA, 2022-2023.
This dataset compiles model outputs, parameter sets, and documentation supporting a techno‑economic analysis (TEA) and life‑cycle assessment (LCA) of co‑digesting beef cattle manure with pretreated mixed prairie biomass to produce renewable natural gas (RNG), with hydroxycinnamic acids (HCA) and digestate‑derived biochar co‑products. It accompanies the study by Katherine Wild, Elmin Rahic, Lisa A Schulte Moore, and Mark Mba Wright "Techno-economic and environmental assessment of converting mixed prairie to renewable natural gas with co-product hydroxycinnamic acid," in Biofuels, Bioproducts, & Biorefining, 2024 (https://doi.org/10.1002/bbb.2710). The integrated simulation and assessment framework quantifies process performance, economics, and greenhouse‑gas intensity across five scenarios representing combinations of alkaline‑ethanol pretreatment for HCA extraction, liquid recirculation fractions, and biochar addition. This data collection includes: stream‑level mass flow/composition tables for each scenario; RNG, biochar, and HCA annual production summaries; literature‑based methane/biogas yield benchmarks; equipment‑level capital costs; TEA assumptions; emission‑factor inventories and displacement credits; and full sensitivity/uncertainty matrices for MFSP and GWP.
Results: Predicted cooling effect, deaths prevented and associated economic value from public green spaces in Paris V2
<p>This dataset represents results predicting the cooling effect, deaths prevented and associated economic value for public green spaces in Paris for 40 hot days above the minimum mortality threshold in 2019. </p> <p>This is version 2. The value of a statistical life (VSL) has been corrcted and all values adjusted. </p> <p>The data format is a shapefile with coordinate reference system RGF93 v1 / Lambert-93 (EPSG:2154).</p> <p>Please see the Variable_name csv file for description of the variable names. </p> <p>The (non-reproducible) code is available at https://github.com/j-k-garrett/REGREEN_Paris_heat</p> <p>These results are from the submitted (September 2025) paper entitled:</p> <p><strong><span>Nature-Based Solutions for Urban Heat: Health and Economic Value of Paris’s Public Green Spaces</span></strong></p> <p>Authored by:</p> <p>Joanne K. Garrett<sup>1</sup>, David Neil Bird<sup>2</sup>, Timothy J. Taylor<sup>1</sup>, Elizabeth McCarthy<sup>3</sup>, David H. Fletcher<sup>4</sup>, Benedict W. Wheeler<sup>1</sup>, Marianne Zandersen<sup>5</sup>, Laurence Jones<sup>3</sup></p> <p><sup>1</sup>European Centre for Environment and Human Health, University of Exeter, Penryn, Cornwall, UK</p> <p><sup>2 </sup>Institute for Climate, Energy and Society, JOANNEUM RESEARCH, Graz, Austria</p> <p><sup>3</sup> Department of Environmental Studies, Schiller Institute for Integrated Science and Society, Boston College, USA</p> <p><sup>4</sup> UK Centre for Ecology & Hydrology, Environment Centre Wales, Bangor, Gwynedd, Wales, UK</p> <p><sup>5 </sup>Department of Environmental Science, iClimate Interdisciplinary Centre for Climate Change, Aarhus University, Denmark</p> <p> </p>
Database of water, agriculture and economic development in Huang-Huai-ai region of China
<p>The database of water, agriculture and economic development contains 61 prefecture-level cities in the Huang-Huai-Hai region from 2010 to 2019.</p> <p>Firstly, we summarize the city-level agricultural dataset from the Provincial Bureau of Statistics, which contains the annual agricultural output, total planting area, labor, fertilizer, and machinery of each prefecture-level city. </p> <p>Secondly, we collect agricultural output (total land value per hectare) as the output and four main types of inputs: labor, fertilizer, machinery, and agricultural water consumption.</p> <p>Thirdly, we also collect city-level unbalanced panel data from the Water Resources Bulletin database, which contains annual data on agricultural water consumption, groundwater supply, precipitation, and groundwater resources.</p>
Economical routes to size-specific assembly of self-closing structures
<p>This data contains images related to a publication on the self-assembly of DNA origami particles (<a href="https://www.science.org/doi/10.1126/sciadv.ado5979">https://www.science.org/doi/10.1126/sciadv.ado5979</a>). In this work, we conduct self-assembly experiments with various unique subunit types that target two different diameters of tubule structures.</p> <p>We provide image data of tubules that are associated with the probability distributions reported across several figures in the main text. Images of tubules are in the ZIP archives and show the section of tubules we analyzed to produce the probability distributions in the manuscript. Each folder of images has an associated CSV file that relates an image name to the type of tubule that the image was identified as. Tubule types have "m" and "n" values.</p> <p>We provide full tomogram reconstruction data for the multicomponent tubules that are shown in Figure 2 of the main text. In the ZIP archive, each tubule image has two files associated with it: a REC file that contains the tomogram reconstruction data and an MDOC file that contains imaging metadata. REC files can be opened with the open-source software IMOD.</p> <p>We provide raw image data of pitch- and width-controlled tubules that have been labeled with gold nanoparticles. These accompany the representative images in Figure 4 in the main text. (Pitch Controlled 4-color with GNPs.zip, Width Controlled 4-color with GNPs.zip).</p> <p>We provide raw image data of length-controlled tubules. These images accompany Figure 5 in the main text. (Length Controlled Tubule Images.zip)</p> <p><strong>Associated publication citation:</strong></p> <div> <p><span>Thomas E. Videbæk <em>et al., </em></span><span>Economical routes to size-specific assembly of self-closing structures. </span><span><em>Sci. Adv. </em></span><span><strong>10</strong>, </span><span>eado5979 </span><span>(2024). </span><span>DOI:<a href="https://doi.org/10.1126/sciadv.ado5979">10.1126/sciadv.ado5979</a></span></p> </div>
Agrisolar Food, Energy, and Water and economic Lifecycle Scenario (FEWLS) Tool Data
<p>Input data and baseline outputs for the Agrisolar Food, Energy, Water, and economic Lifecycle Scenario (FEWLS) Tool. Note that corresponding code is linked in the attached Github doi (https://doi.org/10.5281/zenodo.10023281). </p> <p>The FEWLS tool was developed and used in the recently submitted research article, <em>Food-energy-water and economic outcomes of agrisolar co-location in irrigated regions</em>. In general, this code takes in a ground-mounted solar PV shape file (with some auxiliary information) and generates user set lifespan predictions for food (Calorie), energy (GWh), water (m3), and economic (USD) effects due to offsetting agricultural land with solar PV energy generation. </p>
Decarbonizing primary steel production : Techno-economic assessment of green steel production in Norway
<p>Python codes for the modelling of a grid connected Hydrogen direct reduced plant combined with an electrical arc furnace for steel production. </p>
Process modeling, environmental and economic sustainability of the valorization of whey and eucalyptus residues for resveratrol biosynthesis
<p>Tables included in the article "Process modeling, environmental and economic sustainability of the valorization of whey and eucalyptus residues for resveratrol biosynthesis"</p>
Socio - Economic Survey on Green Transition in Albania - Households
<p>Socio-Economic Survey on Green Transition in Albania - Households</p> <p>The file contains the dataset (cleaned), the questionnaire in Albanian, the coding used for data processing in SPSS, and the detailed results for each question in the questionnaire (organised in sections).</p> <p> </p>
Socio-Economic Survey on Green Transition in Albania - Businesses
<p>Socio-Economic Survey on Green Transition in Albania - Businesses</p> <p>The file contains the dataset (cleaned), the questionnaire in Albanian, the coding used for data processing in SPSS, and the detailed results for each question in the questionnaire (organised in sections).</p> <p> </p>
Datset of automated economic reasoning problems for QE / SMT
<p>This dataset is generated by 45 economics theorems "A implies H" where A are assumptions and H a hypothesis. These are taken from textbooks and papers and chosen for their suitability for automatic solution with Quantifier Elimination (QE) or Satisfiability Modulo Theory (SMT) technology. </p> <p>For each theorem three problems are generated: checking the compatibility of the assumptions; checking for the existence of an example of the theorem; and checking for the existence of a counterexample. </p> <p>There are three files:</p> <p>1. EconomicReasoningBenchmarks-Apr18-SMT2.zip</p> <p>This zip file will uncompress into a directory with 45 files, one for each theorem stating the three existence checks within the SMT2 format. Thus these files are suitable for use with any SMT solver supporting the theory.</p> <p> </p> <p>2. EconomicReasoningBenchmarks-Apr20-Redlog.txt</p> <p>This plain text file can be run with the Redlog Package for the Computer Algebra System Reduce. It contains definitions and calls to Redlog's QE command to check for a counterexample for all 45 theorems.</p> <p> </p> <p>3. EconomicReasoningBenchmarks-Apr23-Maple.txt</p> <p>This plain text file is for use with the Maple Computer Algebra System. For each theorem it provides the polynomials used in the Tarski formula to check for a counterexample. The polynomials are given as a list of lists with the outer list representing logical OR between entries and each inner list logical AND. </p> <p> </p>
Local Governance in Ukraine during the full-scale Russian invasion. – Merged data from online surveys of local self-government authorities by the Congress of Local and Regional Authorities of the Council of Europe in 2022 and Kyiv School of Economics in 2024.
The dataset includes responses from two waves of online surveys targeting local self-government representatives in Ukraine, with a focus on crisis governance during the ongoing Russian war. The first wave was conducted from August 30 to September 20, 2022, by the Congress of Local and Regional Authorities of the Council of Europe, yielding 241 responses (16% of all Ukrainian local communities). The second wave was conducted by Kyiv School of Economics from January 1 to March 12, 2024, with 181 responses (14% of government-controlled municipalities). Data formats include CSV and SAV files, along with an XSL codebook for both waves. The merged dataset comprises 442 responses from small, medium, and large municipalities under varied security conditions, with a total file size of approximately 4 MB.
Environmental and economic potential of decentralised electrocatalytic ammonia synthesis powered by solar energy
<p>Dataset associated with the publication "Environmental and economic potential of decentralised electrocatalytic ammonia synthesis powered by solar energy" by Sebastiano C. D'Angelo, Antonio J. Martín, Selene Cobo, Diego Freire-Ordóñez, Gonzalo Guillén-Gosálbez, and Javier Pérez-Ramírez, available at <a href="https://doi.org/10.1039/D2EE02683J">https://doi.org/10.1039/D2EE02683J</a>. The dataset includes the numeric data required to plot all the figures embedded in the main manuscript and in the Electronic Supplementary Information (ESI).</p> <p>The structure of the dataset is here elucidated sheet by sheet:</p> <ul> <li><strong>GeneralParameters</strong>: numerical values for the scaled functional unit used in the study, the world population value adopted, and the three voltage efficiencies assumed in different parts of the study.</li> <li><strong>AL_BaseCase_SensECE</strong>: numerical values associated with the results for the ammonia leaf scenarios adopting a voltage efficiency of 63% (base case) and a Faradaic efficiency varying from 1% to 100%; highest, average, and lowest capacity factors for the solar power production were here used. The ammonia leaf configuration here assessed is the one including solar panels, electrolyzer, and fuel cell as key components. The results report all the ReCiPe 2016 (hierarchical approach) midpoints and endpoints and the values for the assessed planetary boundaries; the levelised cost of ammonia (LCOA) is reported, as well.</li> <li><strong>AL_EtaV75_SensECE</strong>: this sheet has the structure as the previous one, but includes the results for the ammonia leaf scenario using 75% voltage efficiency, instead of 63%. The remaining assumptions do not deviate from the base case.</li> <li><strong>AL_Eta100_SensECE</strong>: this sheet has the structure as the previous one, but includes the results for the ammonia leaf scenario using 100% voltage efficiency, instead of 63%. The remaining assumptions do not deviate from the base case.</li> <li><strong>AL_NoFC_H2Vented_SensECE</strong>: this sheet has the same structure as the sheet "AL_BaseCase_SensECE", but includes the ammonia leaf scenario using a configuration with no fuel cell. The hydrogen by-product was here considered vented to the air. The remaining assumptions do not deviate from the base case.</li> <li><strong>AL_NoFC_H2Subst_SensECE</strong>: this sheet has the same structure as the sheet "AL_BaseCase_SensECE", but includes the ammonia leaf scenario using a configuration with no fuel cell. The hydrogen by-product was here considered substituting the production of an equivalent quantity from a water electrolyzer deployed in the same location as the ammonia leaf. The remaining assumptions do not deviate from the base case.</li> <li><strong>AL_BaseCase_SpatAnal_BreakFEff</strong>: numerical results for the ammonia leaf base case scenario stemming from the spatial analysis performed on a global grid of 1140 points. The yearly average capacity factors for the solar panels at each location are included, and the results portraying the breakeven Faradaic efficiency for the indicators climate change - CO<sub>2</sub> concentration, global warming, human health, and levelised cost of ammonia were included. The assumptions for the voltage efficiency and the other parameters correspond to the base case.</li> <li><strong>AL_BaseCase_SpatAnal_AbsValues</strong>: numerical results for the ammonia leaf scenarios using the base case state-of-the-art (34%) and 100% Faradaic efficiency, as well as the base case voltage efficiency of 63%. The same metrics as the previous sheet are reported. The structure of the sheet is the same as the previous one.</li> <li><strong>AL_BaseCase_Breakdowns</strong>: breakdown of the same four indicators as the previous sheet for the best and worst combination of Faradaic efficiency and solar panels capacity factors, i.e., 34% Faradaic efficiency and 6% capacity factor on one side and 100% Faradaic efficiency and 26% capacity factor on the other side. The breakdown is divided into solar panels, electrolyser, fuel cell, and other elements. A further breakdown of the levelised cost of ammonia (LCOA) into capital expenditure (CAPEX) and operating expenditure (OPEX) is provided, as well. The voltage efficiency is the same as the base case, as well as the other parameters.</li> <li><strong>AL_BaseCase_CAPEXSens</strong>: numerical results for the levelised cost of ammonia (LCOA) in dependence of the sensitivity on the capital expenditure (CAPEX) for the ammonia leaf configuration assessed in the base case. Two cases assuming state-of-the-art (34%) and 100% Faradaic efficiency were assumed, and lowest, average, and highest capacity factor are included. The remaining parameters do not deviate from the base case configuration.</li> <li><strong>AL_gHB_BestMap</strong>: numerical results to produce the map showing the best technology between ammonia leaf (AL) and green Haber-Bosch (gHB) in the category climate change - CO<sub>2</sub> concentration for all the assessed locations. column D shows the share of safe operating space (%SOS) for each location, while column E shows which technology was selected, where 1 is ammonia leaf and 2 is green HB.</li> <li><strong>AL_BaseCase_Sensitivity</strong>: percentual variation of the results obtained assuming the base configuration ammonia leaf for a state-of-the-art Faradaic efficiency and an average capacity factor for the solar panels. The varied parameters include the voltage efficiency (columns C-D-E), the levelised cost of electricity (columns G-H-I), the electrolyser cost (columns K-L-M), the fuel cell cost (columns O-P-Q), the electrolyser environmental impact (columns S-T-U), and the fuel cell environmental impact (columns W-X-Y).</li> <li><strong>CompTech_BaseCase</strong>: environmental and economic metrics characterizing the assessed Haber-Bosch scenarios (business as usual, BAU; blue Haber-Bosch; green Haber-Bosch for lowest, average, and highest solar panels capacity factor; BAU assuming natural gas spot prices in Europe in August 2022). The reported metrics are the ReCiPe 2016 (hierarchical approach) midpoints and endpoints, the planetary boundaries, and the levelised cost of ammonia (LCOA).</li> <li><strong>CompTech_EtaV75</strong>: this sheet has the same structure as the previous one, but the hydrogen electrolyser used for the green Haber-Bosch scenarios was assumed to have a 10% stack efficiency improvement. The remaining parameters are the same.</li> <li><strong>CompTech_EtaV100</strong>: this sheet has the same structure as the previous one, but the hydrogen electrolyser used for the green Haber-Bosch scenarios was assumed to have a 100% stack efficiency. The remaining parameters are the same.</li> <li><strong>CompValues_Fig1</strong>: numerical values for yearly global warming impacts of a selection of countries, as well as for the yearly human health impacts of selected diseases and catastrophic events.</li> </ul> <p> </p>
Dataset for Hydropower Expansion in Eco-Sensitive River Basins under Global Energy-Economic Change
<p>The data presented in this repository can be fed into the codes provided in <a href="https://github.com/kamal0013/chowdhury-etal_2023_hydropower">this GitHub repository</a> to reproduce the results of the following paper:</p> <p> </p> <p>Chowdhury, A.F.M.K., Wild, T., Zhang, Y. <em>et al.</em> Hydropower expansion in eco-sensitive river basins under global energy-economic change. <em>Nat Sustain</em> <strong>7</strong>, 213–222 (2024). <a href="https://doi.org/10.1038/s41893-023-01260-z">https://doi.org/10.1038/s41893-023-01260-z</a></p> <p> </p> <p><strong>Summary</strong></p> <p>In this study, we investigate how rapid economic growth and transition to low-carbon energy may impact hydropower development, with potential countervailing effects of increasingly cost-competitive variable renewable energy (VRE). We explore the effects of these forces on hydropower expansion in the world's 20 most eco-sensitive river basins, that have substantial untapped hydropower potential and ecological richness. Our investigation is based on the Global Change Analysis Model (GCAM), an integrated model of global energy-water-economy dynamics. The GCAM outputs and other data provided in this repository, in combination with the Jupyter Notebooks provided in <a href="https://github.com/kamal0013/chowdhury-etal_2023_hydropower">this GitHub repository</a>, can be used to conduct our key analysis, and reproduce the relevant results.</p>
Country resolved combined emission and socio-economic pathways based on the RCP and SSP scenarios
<p><strong>Recommended citation</strong></p> <p>Article citation will be added once the article is available.</p> <p><strong>Content</strong></p> <ul> <li><a href="#use-of-the-dataset-and-full-description">Use of the dataset and full description</a></li> <li><a href="#abstract">Abstract</a></li> <li><a href="#support">Support</a></li> <li><a href="#files-included-in-the-dataset">Files included in the dataset</a></li> <li><a href="#notes">Notes</a></li> <li><a href="#data-format-description-columns">Data format description (columns)</a></li> <li><a href="#data-sources">Data sources</a></li> <li><a href="#changelog">Changelog</a></li> <li><a href="#references">References</a></li> </ul> <p><strong>Use of the dataset and full description</strong></p> <p>Before using the dataset, please read this document and the article describing the methodology, especially the "Discussion and limitations" section.</p> <p>The article will be referenced here as soon as it is published.</p> <p>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.</p> <p>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.</p> <p><strong>Support</strong></p> <p>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.</p> <p><strong>Abstract</strong></p> <p>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.</p> <p><strong>Files included in the dataset</strong></p> <p>The repository comprises several datasets. Each dataset comes in a csv file. The file name is constructed from dataset properties as follows: <Source><Bunkers><Downscaling>.csv</p> <p><em><Source></em></p> <p>The "Source" flag indicates which input scenarios were used.</p> <ul> <li><strong>PMRCP:</strong> RCP scenarios downscaled using the SSPs: emissions and socio-economic data; scenarios are available both harmonized to historical data and non-harmonized.</li> <li><strong>PMSSP:</strong> Downscaled SSP IAM scenarios: emissions and socio-economic data; scenarios are available both harmonized to historical data and non-harmonized.</li> </ul> <p><em><Bunkers></em></p> <p>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.</p> <p><em><Downscaling></em></p> <p>The "Downscaling" flag indicates the downscaling technique used.</p> <ul> <li><strong>IE:</strong> Convergence downscaling with exponential convergence of emissions intensities and convergence before transition to negative emissions.</li> <li><strong>IC:</strong> Regional emission intensity growth rates for all countries.</li> <li><strong>CS:</strong> Constant emission shares as a reference case independent of the socio-economic scenario.</li> </ul> <p>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.</p> <p>Finally the data description including detailed references is included: RCP-SSP-dwn_v1.0_data_description.pdf.</p> <p><strong>Notes</strong></p> <p>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.</p> <p><strong>Data format description (columns)</strong></p> <p><em>"source"</em></p> <p>For <em>PMRCP</em> files source values are</p> <ul> <li>RCPSSP<Bunkers><Downscaling>: unharmonized downscaled RCP SSP scenarios</li> <li>PMRCP<Bunkers><Downscaling>: downscaled RCP SSP scenarios harmonized to and combined with historical data</li> <li>PMRCPMISC<Bunkers><Downscaling>: GDP and population data harmonized to and combined with historical data</li> </ul> <p>For <em>PMSSP</em> files source values are</p> <ul> <li>SSPIAM<Bunkers><Downscaling>: unharmonized downscaled SSP IAM scenarios</li> <li>PMSSP<Bunkers><Downscaling>: downscaled SSP IAM scenarios harmonized to and combined with historical data</li> <li>PMSSPMISC<Bunkers><Downscaling>: GDP and population data harmonized to and combined with historical data</li> </ul> <p>For possible values of <Bunkers> and <Downscaling> please see section <a href="#files-included-in-the-dataset">Files included in the dataset</a> above.</p> <p><em>"scenario"</em></p> <p>For <em>PMRCP</em> files the scenarios have the format <RCP><SSP><group>, where</p> <ul> <li><RCP> denotes the RCP scenario. Values are RCP3PD, RCP45, RCP6, and RCP85.</li> <li><SSP> denotes the SSP scenario. Values are SSP1, SSP2, SSP3, SSP4, and SSP5.</li> <li><groups> denotes the SSP basic elements GDP modeling group. Values are IIASA, OECD, and PIK. Not all RCP SSP combinations exist as some SSP storylines are not compatible with all RCP emissions scenarios. For details 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.</li> </ul> <p>For <em>PMSSP</em> files the scenarios have the format <SSP><forcing><model> where</p> <ul> <li><SSP> denotes the SSP scenario. Values are SSP1, SSP2, SSP3, SSP4, and SSP5.</li> <li><forcing> denotes the radiative forcing level of the scenario. Values are 19, 26, 34, 45, 60, 85, and BL where 19 stands for 1.9W/m<sup>2</sup> etc. and BL stands for baseline.</li> <li><model> denotes the Integrated Assessment Model (IAM) used to generate the scenario. Values can be found below</li> </ul> <p>Model codes in scenario names</p> <ul> <li>AIMCGE: AIM-CGE</li> <li>IMAGE: IMAGE</li> <li>GCAM4: GCAM</li> <li>MESGB: MESSAGE-GLOBIOM</li> <li>REMMP: REMIND-MAGPIE</li> <li>WITGB: WITCH-GLOBIOM</li> </ul> <p><em>"country"</em></p> <p>ISO 3166 three-letter country codes or custom codes for groups:</p> <p>Additional "country" codes for country groups.</p> <ul> <li>EARTH: Aggregated emissions for all countries</li> <li>ANNEXI: Annex I Parties to the UNFCCC</li> <li>NONANNEXI: Non-Annex I Parties to the UNFCCC</li> <li>AOSIS: Alliance of Small Island States</li> <li>BASIC: BASIC countries (Brazil, South Africa, India and China)</li> <li>EU28: European Union (still including the UK)</li> <li>LDC: Least Developed Countries</li> <li>UMBRELLA: Umbrella Group</li> </ul> <p><em>"category"</em></p> <p>Category descriptions.</p> <ul> <li>IPCM0EL: Emissions: National Total excluding LULUCF</li> <li>ECO: Economical data</li> <li>DEMOGR: Demographical data</li> </ul> <p><em>"entity"</em></p> <p>Gases and gas baskets using global warming potentials (GWP) from either Second Assessment Report (SAR) or Fourth Assessment Report (AR4).</p> <p>Gases / gas baskets and underlying global warming potentials</p> <ul> <li>CH4: Methane (CH<sub>4</sub>)</li> <li>CO2: Carbon Dioxide (CO<sub>2</sub>)</li> <li>N2O: Nitrous Oxide (N<sub>2</sub>O)</li> <li>FGASES: Fluorinated Gases (SAR): HFCs, PFCs, SF<sub>6</sub>, NF<sub>3</sub></li> <li>FGASESAR4: Fluorinated Gases (AR4): HFCs, PFCs, SF<sub>6</sub>, NF<sub>3</sub></li> <li>KYOTOGHG: Kyoto greenhouse gases (SAR)</li> <li>KYOTOGHGAR4: Kyoto greenhouse gases (AR4)</li> </ul> <p><em>"unit"</em></p> <p>The following units are used:</p> <ul> <li>Million2011GKD: Million 2011 international dollars</li> <li>ThousandPers: Thousand persons</li> <li>kt: kilotonnes</li> <li>Mt: Megatonnes</li> <li>Gg: Gigagrams</li> <li>MtCO2eq: Megatonnes of CO<sub>2</sub> equivalents using the GWPs defined by "entity"</li> <li>GgCO2eq: Gigagrams of CO<sub>2</sub> equivalents using the GWPs defined by "entity"</li> </ul> <p><em>Remaining columns</em></p> <p>Years from 1850-2100.</p> <p><strong>Data Sources</strong></p> <p>The following data sources were used during the generation of this dataset:</p> <p><em>Scenario data</em></p> <ul> <li><strong>RCP scenarios</strong> <a href="https://tntcat.iiasa.ac.at/RcpDb/">website/data</a></li> <li><strong>SSP basic elements</strong> <a href="https://tntcat.iiasa.ac.at/SspDb">website/data</a></li> <li><strong>SSP IAM scenarios</strong> <a href="https://tntcat.iiasa.ac.at/SspDb">website/data</a></li> <li><strong>SSP CMIP6 scenarios</strong> <a href="https://tntcat.iiasa.ac.at/SspDb">website/data</a></li> </ul> <p><em>Historical data</em></p> <ul> <li><strong>CDIAC</strong> <a href="http://doi.org/10.3334/CDIAC/00001_V2017">data</a></li> <li><strong>CEDS CMIP6 data</strong> <a href="https://www.geosci-model-dev.net/11/369/2018/">paper/data</a></li> <li><strong>EDGAR version 4.3.2:</strong> <a href="http://doi.org/10.2904/JRC_DATASET_EDGAR">data</a>, <a href="https://doi.org/10.5194/essd-2017-79">paper</a></li> <li><strong>IMO GHG report</strong> <a href="http://www.imo.org/en/OurWork/Environment/PollutionPrevention/AirPollution/Documents/Third%20Greenhouse%20Gas%20Study/GHG3%20Executive%20Summary%20and%20Report.pdf">report</a></li> <li><strong>PRIMAP-hist v2.1</strong> <a href="http://www.earth-syst-sci-data.net/8/571/2016/">paper</a>, <a href="https://www.pik-potsdam.de/primap-live/primap-hist/">website</a>, <a href="https://doi.org/10.5880/PIK.2019.018">data</a></li> <li><strong>PRIMAP-hist SocioEco v2.1</strong> <a href="https://doi.org/10.5880/PIK.2019.019">data</a></li> </ul> <p><strong>Changelog</strong></p> <p>For future versions</p> <p><strong>References</strong></p> <p>For full references we refer to the pdf version of the data description available in this repository and the list of related identifiers.</p>
Carbon emissions and economic assessment of farm operations under different tillage practices in organic rainfed almond orchards under semiarid Mediterranean conditions
<p>This dataset corresponds to yield, price and fuel consumption from organic rainfed almond orchards in SE Spain under different diversification and tillage practices. The objective is to carry out an integrated environmental (focused on the CO<sub>2</sub> emissions) and economic assessment of farm operations under different diversification and tillage practices through a cradle-to-farm gate life cycle assessment (LCA) based on these data.</p> <p>These data correspond to the open-access article " Carbon emissions and economic assessment of farm operations under different tillage practices in organic rainfed almond orchards under semiarid Mediterranean conditions" published in Scientia Horticulturae. (https://doi.org/10.1016/j.scienta.2019.108978), funded by the European Commission Horizon 2020 project Diverfarming [grant agreement 728003].</p>
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