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47 results for “climate change mitigation”
Resource heterogeneity leads to unjust effort distribution in climate change mitigation
<p>Climate change mitigation is a shared global challenge that involves the collective action of a set of individuals with different tendencies to cooperation. However, we lack an understanding of the effect of resource inequality when diverse actors interact together toward a common goal. Here, we report the results of a collective-risk dilemma experiment in which groups of individuals were initially given either equal or unequal endowments. We found that the effort distribution was highly inequitable, with participants with fewer resources contributing significantly more to the public goods than the richer - sometimes twice as much. An unsupervised learning algorithm classified the subjects according to their individual behavior, finding the poorest participants within two "generous clusters'" and the richest into a "greedy cluster''. Our results suggest that policies would benefit from educating about fairness and reinforcing climate justice actions addressed to vulnerable people instead of focusing on understanding generic or global climate consequences.</p> <p>Vicens J, Bueno-Guerra N, Gutiérrez-Roig M, Gracia-Lázaro C, Gómez-Gardeñes J, Perelló J, et al. (2018) Resource heterogeneity leads to unjust effort distribution in climate change mitigation. PLoS ONE 13(10): e0204369. https://doi.org/10.1371/journal.pone.0204369</p>
Climate change impact and mitigation cost data - The economically optimal warming limit of the planet
<p>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:</p> <p>Ueckerdt F, Frieler K, Lange S, Wenz L, Luderer G, Levermann A (2018) The economically optimal warming limit of the planet. Earth System Dynamics. <a href="https://doi.org/10.5194/esd-10-741-2019">https://doi.org/10.5194/esd-10-741-2019</a></p> <p>Below the individual file contents are explained. For further questions feel free to write to Falko Ueckerdt (ueckerdt@pik-potsdam.de).</p> <p> </p> <p><strong>Climate change impact data</strong></p> <p>File 1: Data_rel-GDPpercapita-changes_withCC_per-country_all-RCP_all-SSP_4GCM.csv</p> <p>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.</p> <p> </p> <p>File 2: Data_rel-GDPpercapita-changes_withCC_per-country_all-SSP_4GCM_interpolated-for-REMIND-scenarios.csv</p> <p>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).</p> <p> </p> <p>File 3: Data_rel-GDPpercapita-changes_withCC_per-country_SSP2_12GCM_interpolated-for-REMIND-scenarios.csv</p> <p>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).</p> <p><br> 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, <a href="https://tntcat.iiasa.ac.at/SspDb/">https://tntcat.iiasa.ac.at/SspDb/</a>, last accessed 15Nov 2019).</p> <p> </p> <p><strong>Climate change mitigation cost data</strong></p> <p>The scenario design and runs used in this paper have first been conducted in [1] and later also used in [2].</p> <p>File 4: REMIND_scenario_results_economic_data.csv</p> <p>File 5: REMIND_scenarios_climate_data.csv</p> <p>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.</p> <p>In the scenario description “FFrunxxx” (column 2), the code “xxx” specifies the scenario as follows. See [1] for a detailed discussion of the scenarios.</p> <p>The first dimension specifies the climate policy regime (delayed action, baseline scenarios):</p> <p>1xx: climate action from 2010<br> 5xx: climate action from 2015<br> 2xx climate action from 2020 (used in this study)<br> 3xx climate action from 2030<br> 4x1 weak policy baseline (before Paris agreement)</p> <p>The second dimension specifies the technology portfolio and assumptions:</p> <p>x1x Full technology portfolio (used in this study)<br> x2x noCCS: unavailability of CCS<br> x3x lowEI: lower energy intensity, with final energy demand per economic output decreasing faster than historically observed<br> x4x NucPO: phase out of investments into nuclear energy<br> x5x Limited SW: penetration of solar and wind power limited<br> x6x Limited Bio: reduced bioenergy potential p.a. (100 EJ compared to 300 EJ in all other cases)<br> x6x noBECCS: unavailability of CCS in combination with bioenergy</p> <p>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.).</p> <p>xx1 0$/tCO2 (baseline)<br> xx2 10$/tCO2<br> xx3 30$/tCO2<br> xx4 50$/tCO2 <br> xx5 100$/tCO2<br> xx6 200$/tCO2<br> xx7 500$/tCO2<br> xx8 40$/tCO2<br> xx9 20$/tCO2<br> xx0 5$/tCO2</p> <p>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).</p> <p> </p> <p>[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.</p> <p>[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.</p>
Database and model code for "Material efficiency and climate change mitigation of passenger vehicles"
<p>This record provides all data points and model code necessary to compute the results presented in P. Wolfram, Q. Tu, N. Heeren, S. Pauliuk, E. Hertwich (2020) "Material efficiency and climate change mitigation of passenger vehicles", published in Journal of Industrial Ecology. All data is described in section 2 of the manuscript. The code can be run in MATLAB. </p>
Forest carbon prospecting for climate change mitigation: Version 1.0
<p>This data package includes the two 1-km resolution global maps (.tif) of tropical forests between ~23.44°N and 23.44°S produced from the study: 1) investible forest carbon (in tCO<sub>2</sub>e ha<sup>-1</sup>y<sup>-1</sup>) and 2) forest carbon return-on-investment (Net Present Value in USD ha<sup>-1</sup>y<sup>-1</sup>) over a 30-year timeframe. It also includes the R script to reproduce these layers and their uncertainties. </p> <p><em><strong>Investible Forest Carbon</strong>: </em>The investible forest carbon map was produced based on the total volume of CO<sub>2</sub>e associated with the three main carbon pools in the tropics, namely aboveground carbon, belowground carbon and soil organic carbon. This is followed by the application of key Verified Carbon Standard (VCS) criteria including additionality, to determine the magnitude and areas of investible forest carbon across the tropics.</p> <p><em>Aboveground carbon.</em> A stoichiometric factor of 0.475 was applied to recent spatial data on aboveground carbon biomass to obtain carbon stock based on established carbon accounting methodologies. An uncertainty analyses was also performed to account for potential variability in stoichiometric factor. Subsequently, a conversion factor of 3.67 was applied to the carbon stock layer to obtain the volume of CO<sub>2</sub>e associated with this carbon pool.</p> <p><em>Belowground carbon</em>. Belowground carbon biomass was firstly derived by applying two allometric equations relating to root to shoot biomass to the most recent spatial dataset on aboveground carbon biomass following established carbon accounting methodologies. The two equations are:</p> <p> Belowground biomass = 0.489×aboveground biomass^0.89; and</p> <p> Belowground biomass = 0.26×aboveground biomass</p> <p>A stoichiometric factor of 0.475 was subsequently applied to the estimated belowground carbon biomass to obtain the carbon stock. An uncertainty analyses was then performed to determine the mean, minimum and maximum values for belowground carbon. Following that, a conversion factor of 3.67 was applied to the carbon stock layer to obtain the volume of CO<sub>2</sub>e associated with this carbon pool.</p> <p><em>Soil Organic Carbon</em>. Organic carbon density of the topsoil layer (0-30 cm) was obtained from the European Soil Data Centre as it represented the best data available for soil organic carbon. A conversion factor of 3.67 was subsequently applied to derive the volume of CO<sub>2</sub>e associated with this carbon pool.</p> <p><em>Applying VCS criteria</em>. The criterion of additionality is a pre-condition for carbon credits to be certified under the VCS. This implies that only the volume of forest carbon that are under imminent threat of decline or loss if left unprotected by a conservation intervention can be certified under the VCS. The volume of forest carbon under threat of loss was based on the best available data on predicted deforestation rates across the tropics (through to the year 2029), and annualized over predicted 15-year period. The estimated annual deforestation rates was then applied to the total volume of CO<sub>2</sub>e associated with tropical forests as estimated above, deriving the volume of CO<sub>2</sub>e that would be certifiable and thus investible under the VCS. In addition, a conservative 10-year decay estimate was assumed for the estimate of the belowground carbon pool, and lands that will likely not be certifiable for other reasons, including recently deforested areas (i.e. for the period of 2010-2017), a well as human settlements, were excluded. Lastly, the VCS requirement to set aside buffer credits of 20% was accounted for to consider the risk of non-permanence associated with Agriculture, Forestry and Other Land Use (AFOLU) projects.</p> <p><strong><em>Return</em>-<em>on-Investment</em></strong>. From the investible forest carbon map, the relative profitability of these areas was then modelled to produce a global forest carbon return-on-investment map based on their NPV. The NPV of returns were based on several simplifying assumptions following established values from previous studies. </p> <p><em>Cost of project establishment</em>. The cost of project establishment was estimated to be at $25 ha<sup>-1</sup>. This was based on a range of costs that are key to the development of a project, including but not limited to project design, governance and planning, enforcement, zonation, land tenure and acquisition, surveying and research. </p> <p><em>Cost for annual maintenance</em>. The cost for annual maintenance was estimated to be $10 ha<sup>-1</sup>, which included aspects such as in education and communication, monitoring, sustainable livelihoods, marketing, finance and administration.</p> <p><em>Carbon price</em>. A constant carbon price of $5.8 t<sup>-1</sup>CO­<sub>2</sub>e for the first five years was applied. This price was based on an average price of carbon for avoided deforestation projects reported recently by Forest Trends’ Ecosystem Marketplace (i.e. for the period 2006 – 2018). Subsequently, a 5% price appreciation was applied annually over a project timeframe of 30 years.</p> <p><em>Discount rate</em>. We calculated NPV of annual and accumulated profits over 30 years based on a 10% risk-adjusted discount rate. </p> <p>Further details for these datasets and their uncertainties are presented in Koh et. al. For questions or issues on the spatial data layers, please contact Yiwen Zeng (<a href="mailto:zengyiwen@nus.edu.sg">zengyiwen@nus.edu.sg</a>). </p>
Data from paper: Large carbon sink potential of Secondary Forests in Brazilian Amazon to mitigate climate change (public)
<p><strong>Title</strong>: Large carbon sink potential of Secondary Forests in the Brazilian Amazon to mitigate climate change</p> <p><strong>Contact:</strong> Viola Heinrich (viola.heinrich@bristol.ac.uk)</p> <p><strong>This repository contains</strong>:</p> <ol> <li>Zipped folder:<strong> Fig1_data_input.zip</strong> - all the files needed to produce Figure 1a-e of the main paper. Set the working directory to folder containing the file and use the script "Fig1a_f_plot.R" to run (see below). The folder contains the input files of the 6 driving variables used to build regrowth models seen in Figure 1 - these files are in the format "<strong><driver>_assessment_v2.csv</strong>". The columns in the files are: A: age of secondary forest; B: 50th percentile (median) of the modal Aboveground Biomass (AGB) value for the given age (note, units are in biomass not carbon: Mg/ha/yr); C: The bias-corrected AGB value, calculated by subtracting the lowest AGB value in column B such that the AGB data starts at or near 0Mg/ha/yr at age 1. D: the number of secondary forest pixels observed to have the given age, E: "Threshold" : the threshold limits of the given driver e.g. 0 Fires in fire_assessmentv2.csv implies the corresponding secondary forest pixels experienced 0 fires throughout the analysis period. The folder also contains the output regrowth models seen in Figure 1 in the format "<strong>regrowth_model_<driver_threshold>.RData" </strong>where driver_threshold refers to the driving variable name and the associated threshold limit for the given driver.</li> <li>Zipped folder:<strong> Fig2_regions_outline.zip</strong> - contains the boundaries of the 4 regions identified in Figure 2a of the main paper in a shapefile (.shp) format and the corresponding file formats needed to produce and load a shapefile. </li> <li>Zipped folder: <strong>Fig1g_2b_e_variable_importance.zip</strong> - contains the output files of the random forest analysis assessing the variable importance for the whole Amazon ("whole_Amazon" subfolder) and for the different regions identified in Figure2a. Files are given as .RDS files that can be loaded in R and the corresponding figures produced using the script "Fig1g_2b_e_plot.R". Files start with the region of interest e.g. "whole_Amazon" or "NE_sector". Middle part of the filename - importance_conditionalTrue/False - this determines whether the importance was calculated using the conditional permutation (True) or not (False). The end of the file name - seed<NUM> - denotes the number of the random seed that was set to extract the sample data. e.g. whole_Amazon_2500_cforest_important_conditionalTrue_seed200.RDS - shows the conditional permutation importance assessment using a sample size of 2500 when the setseed parameter was set to 200 to extract a random sample representing the whole Amazon. The remaining files are the random forest output - as .RDS file. Please note the code to produce the random forest model and the importance assessment has not been included here - this code takes multiple days to run, so only the input and outputs have been included here. Please contact the corresponding author (see end) for more information on this. </li> <li>Zipped folder: <strong>Fig3_data_input.zip</strong> - all the files needed to produce Figure 3a-d of the main paper. Set the working directory to folder containing the file and use the script "Fig3_plot.R" to run (see below). The folder contains the input files of the 6 driving variables used to build regrowth models seen in Figure 3 - these files are in the format "<strong><REGION>-Group.csv</strong>". See bullet point 1 for explanations for the columns in the file. Again column E -"threshold" denotes the code used to identify the the 4 subclasses of regrowth seen in the Figure. Where 11 = No disturbance; 12 = Only burning; 21 = Only (multiple) deforestations; 22 = Both burning and multiple deforestations as disturbance. The code takes data in AGB and converts to AGC. The folder also contains the output regrowth models seen in Figure 3 in the format <strong>"regrowth_model_<region_disturbance_type>.RData" </strong>where region_disturbance refers to the region and the type of disturbance experienced. </li> <li> Zipped folder: <strong>Fig4_5_carbon_sink_2017.zip </strong>- Contains two subfolders: a) <strong>Map_aggre_0.1deg</strong> -this folder contains .tiff files (and associated files) of the losses, gains and net change in AGC between 2016 - 2017 in secondary forests in Amazonia - this has been aggregated to 0.1 degree grid cells so each cell contains the total sum of the losses/gains experienced by secondary forests in that 0.1degree grid cell. b) <strong>secondary_forest_by_region_and_disturbance </strong>- this folder contains .tiff files (and associated files) of the secondary forest data at the original resolution (30m) for 2016 and 2017 split up according to the regions identified in Figure 2, and the type of disturbance (if any). The associated files include a .dbf file which includes additional data [read "README.txt" file in folder] - upon loading the data in a GIS software - the age of the secondary forest pixel will be displayed - open the attribute table to see more data associated with that given pixel e.g. modelled associated AGB for a given pixel. Files in this folder can be used to make Figure 4d and Figure 5 - see script "Fig4_Fig5_plot.R" in the code repository (see below). </li> </ol> <p><strong>Code: </strong>The corresponding code mentioned here can be access here: <a href="https://github.com/heinrichTrees/secondary-forest-regrowth-amazon-public">heinrichTrees/secondary-forest-regrowth-amazon-public (github.com)</a></p> <p><strong>Data usage: </strong>When using any code or data in this repository or another related to this study please cite Heinrich et al.2021 and the original paper as well as the DOI of this repository. </p> <p>If you need anything else, please contact the corresponding author: Viola Heinrich (viola.heinrich@bristol.ac.uk)</p>
Data: Managing European Alpine forests with close-to-nature forestry to improve climate change mitigation and multifunctionality
<p><strong>The repository contains the data supporting the findings of the study: <em>Managing European Alpine forests with close-to-nature forestry to improve climate change mitigation and multifunctionality</em></strong></p> <p><strong>Abstract:</strong></p> <p>Close-to-nature forestry (CNF) has a long tradition in European Alpine forest management, playing a crucial role in ensuring the continuous provision of biodiversity and forest ecosystem services, including protection against natural hazards. However, climate change is causing huge uncertainties about the future applicability of CNF in the Alpine region. The question arises as to whether current CNF practices are still suitable for adapting forests to climate change impacts while also meeting the increasing societal demands regarding Alpine forests, including their potential contribution to climate change mitigation.</p> <p>To answer this question, we simulated forest development using the ForClim forest model at two Alpine study sites, together representing a large biogeographic gradient from high-elevation inner Alpine forests (Switzerland) to lower-elevation south-eastern Alpine forests (Slovenia). The simulations considered three climate scenarios (historical climate, SSP2‑4.5 and SSP5-8.5) and six alternative management strategies, including both current CNF management practices and climate-adapted versions. Using a multi-criteria decision analysis framework, we assessed the joint impacts of climate and management on biodiversity and key ecosystem services of the investigated regions, including carbon sequestration (CS) inside and outside the forest ecosystem boundary. </p> <p>The joint effects of climate change and CNF varied, both among and within the study sites along the biogeographical gradient. While CS was more resistant to climate change under current CNF at the south-eastern Alpine site, it was more sensitive at the inner Alpine site, where CS potentials decreased at lower elevations. This adverse effect could be partly mitigated by fostering the use of climate-adapted tree species. However, current CNF and adaptations of it did not meet multiple management objectives equally well: while protection from gravitation hazards and timber production also benefited from this silvicultural practice, biodiversity benefited from CNF variants with low-intensity or no management. </p> <p>In conclusion, CNF has a high potential to continue fulfilling its crucial role in European Alpine forests. A differentiated approach will be needed in the future, however, to identify forest stands where adaptive measures are required, especially at sites particularly vulnerable to climate change. In combination with less intensively managed or unmanaged areas, CNF provides a management portfolio that will help European Alpine forests to meet the demands of future society.</p> <p><strong>Data:</strong></p> <p>There is one folder for each case study, including: </p> <ul> <li>simulated biodiverstiy and ecosystem service indicators</li> <li>forest stand metadata</li> <li>normlized utility values for indicators</li> <li>partial utility values for biodiversity and ecosystem service groups</li> </ul> <p>This study was conducted as part of the <strong>ONEforest project</strong>, which received funding from the <strong>European Union's Horizon 2020</strong> research and innovation programme under the <strong>grant agreement Nº 101000406</strong>.</p>
Model output data and figures' code for Fujimori & Wu et al., Land-based climate change mitigation measures can affect agricultural markets and food security
<p>Model output data and figures' code for "Fujimori & Wu et al., Land-based climate change mitigation measures can affect agricultural markets and food security" in Nature Food (DOI: 10.1038/s43016-022-00464-4)</p>
Dataset: How do news about a heatwave affect public prioritization of climate change adaptation and mitigation behaviors?
<p><span>These datasets contain survey data that was used to evaluate the effect of the exposure to heatwave news texts on people’s preference for climate mitigation and adaptation actions, as presented in the manuscript titled “<em>How do news about a heatwave affect public prioritization of climate change adaptation and mitigation behaviors?</em>”. Three versions of the dataset are available:</span></p> <ol> <li><strong>Original dataset</strong>: This version contains choice text as data points and includes all finished survey responses that passed the attention check questions (n=1209).</li> <li><strong>Original recoded dataset</strong>: This version was generated by recoding choice text into numerical values. The 'Income' variable, representing household income levels for both Canadian and US residents, was added by converting reported income ranges to a unified scale based on exchange rate equivalencies. The "Income_Canadians" and "Income_US" columns were subsequently removed to avoid repetitions. </li> <li><strong>Final dataset</strong>: This version excludes observations from participants who completed the survey in under four minutes and those who selected the same response for every item within each matrix-style question (also known as straight-lining). Additionally, responses with missing values in questions regarding political views, gender, and household income, as well as responses where participants identified as non-binary or indicated that their gender was not listed, were omitted (see “Methods” for more details). Dependent variables have been added based on the original responses, including personal-level mitigation and adaptation likelihoods, personal-level mitigation preference, and both non-weighted and weighted collective-level mitigation preference. Furthermore, the dataset includes a 'Climate Change Concern' variable, derived through principal component analysis of thirteen variables expressing participants’ climate change attitudes and efficacy beliefs concerning climate actions. Variables not used in the subsequent data analysis were removed. Age, political views, education, and income columns were standardized. The final dataset was used for the data analysis presented in the manuscript.</li> </ol> <p>The following variables/columns can be found across the three versions of the dataset:</p> <ul> <li>Dependent variables: <ul> <li>Starting with “<em>Personal_Mitigation</em>”: participant’s self-reported likelihood of taking selected personal-level climate change mitigation actions</li> <li>Starting with “<em>Personal_Adaptation</em>”: participant’s self-reported likelihood of taking selected personal-level climate change adaptation actions</li> <li>Starting with “<em>Collective_Mitigation</em>”: participant’s ranking of the collective-level climate change mitigation initiatives</li> <li>Starting with “<em>Collective_Adaptation</em>”: participant’s ranking of the collective-level climate change adaptation initiatives</li> <li><em>Personal_Mitigation_Likelihood</em>: personal-level mitigation likelihood (present only in the final dataset)</li> <li><em>Personal_Adaptation_Likelihood</em>: personal-level adaptation likelihood (present only in the final dataset)</li> <li><em>Personal_Preference</em>: personal-level mitigation preference (present only in the final dataset)</li> <li><em>Collective_Preference_Unweighted</em>: non-weighted collective-level mitigation preference (present only in the final dataset)</li> <li><em>Collective_Preference_Weighted</em>: weighted collective-level mitigation preference (present only in the final dataset)</li> </ul> </li> <li>Independent variables: <ul> <li><em>Group</em>: group that the participant was assigned to as part of the experimental intervention</li> <li><em>Distance</em>: indicates whether the participant was assigned to read about a heatwave occurring in their community or a city 6,000 km away (for experimental groups only)</li> <li><em>Severity</em>: indicates whether the participant was prompted to read about a heatwave without or with the mention of associated causalities (for experimental groups only)</li> </ul> </li> <li>Covariates and supporting variables: <ul> <li><em>Gender</em>: gender identity</li> <li><em>Identity</em>: ethnic and/or racial identity</li> <li><em>Age</em>: age</li> <li><em>Political_Views</em>: position on the liberal-conservative continuum</li> <li><em>Education</em>: highest level of education</li> <li><em>Country</em>: country of residence</li> <li><em>Canada_Province</em>: province or territory of residence (for Canadian participants only)</li> <li><em>US_State</em>: state of residence (for US participants only)</li> <li><em>Duration_Residence</em>: duration of residence in the current community</li> <li><em>Income_Canadians</em>: annual household income in Canadian dollars (for Canadian participants only)</li> <li><em>Income_US</em>: annual household income in US dollars (for US participants only)</li> <li><em>Income</em>: annual household income for both Canadian and US residents derived by converting reported income ranges to a unified scale based on exchange rate equivalencies</li> <li><em>Efficacy_Mitigation_Personal</em>: belief regarding the response efficacy of personal-level climate change mitigation actions</li> <li><em>Efficacy_Mitigation_Collective</em>: belief regarding the response efficacy of collective-level climate change mitigation actions</li> <li><em>Efficacy_Adaptation_Personal</em>: belief regarding the response efficacy of personal-level climate change adaptation actions</li> <li><em>Efficacy_Adaptation_Collective</em>: belief regarding the response efficacy of collective-level climate change adaptation</li> <li><em>Climate_Change_Importance:</em> perception of climate change as a personally important issue</li> <li>Climate_Change_Worry: level of worry about climate change</li> <li>Starting with “<em>Climate_Risk</em>”: beliefs regarding the degree of harm that climate change will cause to plants and animal species (Climate_Risk_Animals_Plants), future generations of people (Climate_Risk_Future_Generations), people in developing countries (Climate_Risk_Developing_Countries), people in participant’s country (Climate_Risk_Country), people in participant’s community (Climate_Risk_Community), and the participant personally (Climate_Risk_Personal)</li> <li>Climate_Change_Onset_Time: belief regarding when climate change will start harming people in their community</li> <li><em>Six_Americas_Segment</em>: the Global Warming's Six Americas segment participant aligns with derived based on the Six Americas Short SurveY (SASSY) Group Scoring Tool</li> <li><em>Climate_Change_Concern</em>: variable derived through PCA of thirteen variables expressing participants' climate change attitudes and efficacy beliefs pertaining to climate actions (present only in the final dataset)</li> <li><em>Survey_Duration_Seconds</em>: The amount of time it took the respondent to complete the survey</li> </ul> </li> </ul>
Data repository - The role of peatland degradation, protection and restoration for climate change mitigation in the SSP scenarios
<p>This datasets provides regional and spatial-explicit gridded data for the analysis presented in the manuscrip "The role of peatland degradation, protection and restoration for climate change mitigation in the SSP scenarios" under review in "Environmental Research: Climate" with reference "ERCL-100126"</p>
Figure 7.2 of IPCC-IPBES report - The effects of actions to mitigate climate changes on action to mitigate biodiversity and of actions to mitigate biodiversity loss on actions to mitigate climate change
<p>Here are the underlying data and codes for Figure 7.2 of the IPCC-IPBES report (https://doi.org/10.5281/zenodo.4659158)<br> </p> <p>We decide to represent only the recognized links (positive and negative) in Figure 7.2. The table1 file represents these relationships. If you decide to represent the non-recognized interactions (gray), you should use the table2 file and re-divide it in the code.</p> <p>The code produces the Sankey diagram, which was used to produce Figure 7.2. The order of the nodes was organized manually to represent better the Chapter 7 discussion. After that, you can export the figure and work in an external program.</p> <p>The final figure was produced using PowerPoint and with labels and icons inserted manually. </p>
Data for "The neglected role of abandoned cropland in supporting both food security and climate change mitigation"
<p><strong>Data for "The neglected role of abandoned cropland in supporting both food security and climate change mitigation"</strong></p> <p><strong>All files will be made publicly accessible before publication.</strong></p> <p>Version: 2.0 (Round 2 revision)</p> <p>Content (spatial resolution, data info)</p> <ol> <li>Abandoned cropland map (10arcsec, 1 raster file)</li> <li>Suitabability of abanonded cropland for reforestation and recultivation (5arcmin, 3 raster files)</li> <li>Food production potential of global abandoned cropland (5arcmin,1 raster file)</li> <li>Climate change mitigation potential of global abandoned cropland (5arcmin, 1 raster file)</li> <li>outcomes of key scenarios <ul> <li>4 key Scenarios: Maximizing food production, Maximizing climate change mitigation, Equal Allocation, Maximizing combined potential</li> <li>Data included in the output of each scenario: <ul> <li>Land allocated for reforestation and recultivation (5arcmin/1arcdeg, 5 raster files)</li> <li>Climate change mitigation potential and food production potential (5arcmin/1arcdeg, 5 raster files)</li> <li>Emission for land clearing (5arcmin, 1 raster)</li> <li>Foregone climate change mitigation potential (5arcmin, 1 raster)</li> <li>Summary table</li> </ul> </li> <li>Summary table for 4 key scenarios</li> </ul> </li> <li>Codes for generating 4 key scenarios</li> </ol>
Evaluating the usefulness of Protection Motivation Theory for predicting climate change mitigation behavioral intentions among a US sample of climate change deniers and acknowledgers
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Data for: Economic and biophysical limits to seaweed farming for climate change mitigation
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Existing land uses constrain climate change mitigation potential of forest restoration in India
<p>The datasets were developed as part of the publication "Existing land uses constrain climate change mitigation potential of forest restoration in India". Please refer to the manuscript for processing details.<br> <br> ForestBioclimaticEnvelop_ProjectionUTM is the bioclimatic envelope of forests developed. The data is in raster format (GeoTIFF 32bit Float) where pixel values = 1 represent the bioclimatic envelope of forests and remaning pixel values are NA. The spatial resolution is 60m in WGS 84 UTM 43N projection system.</p> <p>FinalOpportunity_AfterExclusions_ProjectionUTM is the feasible area of opportunity, after all exclusions of land uses and covers that cannot naturally regenerate to forests. The data is in raster format (GeoTIFF 32bit Float) where pixel values = 1 represent the bioclimatic envelope of forests and remaning pixel values are NA. The spatial resolution is 60m in WGS 84 UTM 43N projection system.</p>
A review of existing and potential blue carbon contributions to climate change mitigation in the Anthropocene
<p><span>The atmosphere concentration of CO2 is steadily increasing and causing climate change. To achieve the Paris 1.5 or 2 oC target, negative emissions technologies must be deployed in addition to reducing carbon emissions. The ocean is a large carbon sink but the potential of marine primary producers to contribute to carbon neutrality remains unclear. </span></p> <p><span>Here we review the alterations to carbon capture and sequestration of marine primary producers (including traditional 'blue carbon' plants, microalgae, and macroalgae) in the Anthropocene, and, for the first time, assess and compare the potential of various marine primary producers to carbon neutrality and climate change mitigation via biogeoengineering approaches.</span></p> <p><span>The contributions of marine primary producers to carbon sequestration have been decreasing in the Anthropocene due to the decrease in biomass driven by direct </span><span>anthropogenic activities and climate change</span><span>. The potential of blue carbon plants (mangroves, saltmarshes, and seagrasses) is limited by the available areas for their revegetation. Microalgae appear to have a large potential due to their ubiquity but how to enhance their carbon sequestration efficiency is very complex and uncertain. On the other hand, macroalgae can play an essential role in mitigating climate change through extensive offshore cultivation due to higher carbon sequestration capacity and substantial available areas. This approach seems both technically and economically feasible due to the development of offshore aquaculture and a well-established market for macroalgal products. </span></p> <p><span><em>Synthesis and applications:</em> </span><span>This paper provides new insights and suggests promising directions for utilizing marine primary producers to achieve the Paris temperature target. We propose that macroalgae cultivation can play an essential role in attaining carbon neutrality and climate change mitigation, although its ecological impacts need to be assessed further.</span></p>
Irrigation Expansion Required to Mitigate Yield Losses Under Climate Change
<p>The "code" folder contains code and related data to (1) run regression to obtain crop yield sensitivities to temperature and precipitation, (2) conduct bootstrapping approach to resample crop data 1,000 times to derive regression coefficients, and (3) derive pixel-level crop yield changes and additional irrigation needs under 1.5°C and 3°C warming.</p> <p>The "crop_production_summary.xlsx" gives the global and country-level aggregated crop production (e.g., wheat, maize, rice and barley) (unit: 10^12 kcal) during Baseline periods (1996-2005), 1.5°C and 3°C warming above pre-industrial levels (1850-1900) using three irrigation adaptation scenarios: (i) without irrigation adaptation, which means using the historical irrigation extent around 2000 (ii) with full irrigation adaptation, which means applying 100% irrigation over all croplands and (iii) with sustainable irrigation adaptation, which selectively applies irrigation where irrigation practices do not deplete freshwater stocks and impair aquatic ecosystems.</p> <p>The "irr_need_summary.xlsx" gives the global and country-level sustainable and unsustainable irrigation area (unit: million hectares) of each crop (e.g., wheat, maize, rice and barley) in 2000 and additional irrigation area needed to offset warming-induced crop yield losses under 1.5°C and 3°C warming above pre-industrial levels (1850-1900).</p> <p>The "irrigation needed" folder contains pixel-level additional irrigation area fraction for each crop needed to offset warming-induced crop yield losses under 1.5°C and 3°C warming above pre-industrial levels (1850-1900).</p> <p>The "yield change" folder contains pixel-level crop yield change for each crop under 1.5°C and 3°C warming above pre-industrial levels (1850-1900).</p> <p>The "irrigation_sustainable" folder contains pixel-level data on irrigation water sustainability during the Baseline periods (1996-2005) and under 1.5°C and 3°C warming scenarios compared to pre-industrial levels (1850-1900). In this dataset, pixels with values <1 indicate that sustainable irrigation can be applied, while values >=1 indicate that irrigation will be unsustainable.</p> <p>For further details, please contact Liyin He (lhe@carnegiescience.edu) or Lorenzo Rosa (lrosa@carnegiescience.edu).</p>
Climate change mitigation potential of widespread cover crop adoption in U.S.
<p>This geospatial dataset represents climate change mitigation benefits from widespread cover crop adoption on U.S. cropland. We simulated changes in soil organic carbon stocks and nitrous oxide fluxes over a 20-year period for baseline cover crop adoption rates (derived from historical adoption rates) and a high cover crop adoption (80%) scenario in the continental U.S. Data were generated using the DayCent ecosystem model driven by cropping histories in the USDA National Resources Inventory (NRI) and associated agricultural management data. Here we present the mean and standard deviation of annual soil organic carbon stock changes and nitrous oxide fluxes for both baseline and high cover crop adoption scenarios on a county level.</p>
Climate change mitigation potential of widespread cover crop adoption in U.S.
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Global ecosystem restoration has unexpectedly low potential to mitigate climate change
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A review of existing and potential blue carbon contributions to climate change mitigation in the Anthropocene
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ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.