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This folder contains datasets (in dta format) and the STATA do-file that replicate results contained in: Bozzola, M., Lamonaca, E., & Santeramo, F. G. (2023). Impacts of climate change on global agri-food trade. Ecological Indicators, 154, 110680. https://doi.org/10.1016/j.ecolind.2023.110680 Lamonaca, E., Bozzola, M., & Santeramo, F. G. (2024). Climate distance and bilateral trade. Economics Letters, 237, 111624. https://doi.org/10.1016/j.econlet.2024.111624 The datasets contains: (i) time-varying measures of long-run climate conditions of countries (ii) the difference in long-run climate conditions between country-pairs, here defined as Climate Distance. The sample includes twenty economies accounting for two-third of global agri-food exports and representatives of different prevailing climates, observed over the period 1996-2015. Researchers, practitioners and policy makers interested in the evaluation of the economic impacts of climate changes may use climate distances for further analyses.

MedSens data is a dataset including the abundance of selected Mediterranean marine species, collected by trained volunteers (EcoDivers, i.e. scuba divers, free divers and snorkelers) according to the Reef Check Mediterranean Underwater Coastal Environment Monitoring (RCMed U-CEM) protocol (Cerrano et al., 2017), and maintained by the non-profit association Reef Check Italia. This dataset is a subset of 25 selected species from the Reef Check Med - key Mediterranean marine species dataset, and it is specifically intended to calculate the MedSens index developed by Eva Turicchia, Carlo Cerrano, Matteo Ghetta, Marco Abbiati and Massimo Ponti (Turicchia et al., 2021). MedSens data is provided as ESRI shapefiles in WGS84 geographic coordinates (EPSG:4326). MedSens abstract Citizen science (CS) projects may provide community-based ecosystem monitoring, expanding our ability to collect data across space and time. However, the data from CS are often not effectively integrated into institutional monitoring programs and decision-making processes, especially in marine conservation. This limitation is partially due to difficulties in accessing the data and the lack of tools and indices for proper management at intended spatial and temporal scales. MedSens is a biotic index specifically developed to provide information on the environmental status of subtidal rocky coastal habitats, filling a gap between marine CS and coastal management in the Mediterranean Sea. The MedSens index is based on 25 selected species, incorporating their sensitivities to the pressures indicated by the European Union’s Marine Strategy Framework Directive (MSDF) and open data on their distributions and abundances, collected by trained volunteers (mainly scuba divers, but also free divers and snorkelers) using the Reef Check Mediterranean Underwater Coastal Environment Monitoring (RCMed U-CEM) protocol. The species sensitivities were assessed relative to their resistance and resilience against physical, chemical, and biological pressures, according to benchmark levels and a literature review. The MedSens index was calibrated on a dataset of 33,021 observations from 569 volunteers (2001 to 2019), along six countries’ coasts. A free and user-friendly QGIS plugin allows easy index calculation for areas and time frames of interest. The MedSens index was applied to Mediterranean marine protected areas (MPAs) and the management and monitoring zones within Italian MPAs. In the studied cases, the MedSens index responds well to the local pressures documented by previous investigations. MedSens converts the data collected by trained volunteers into an effective monitoring tool for the Mediterranean subtidal rocky coastal habitats. MedSens can help conservationists and decision-makers identify the main pressures acting in these habitats, as required by the MSFD, supporting them in the implementation of appropriate marine biodiversity conservation measures and better communicate the results of their actions. By directly involving stakeholders, this approach increases public awareness and the acceptability of management decisions, enabling more participatory conservation tactics. References Cerrano C, Milanese M, Ponti M (2017) Diving for science - science for diving: Volunteer scuba divers support science and conservation in the Mediterranean Sea. Aquat Conserv 27:303–323 https://doi.org/10.1002/aqc.2663 Turicchia E, Cerrano C, Ghetta M, Abbiati M, Ponti M (2021) MedSens index: The bridge between marine citizen science and coastal management. Ecol Indic 122:107296 https://doi.org/10.1016/j.ecolind.2020.107296 {"references": ["Cerrano C, Milanese M, Ponti M (2017) Diving for science - science for diving: Volunteer scuba divers support science and conservation in the Mediterranean Sea. Aquat Conserv 27:303\u2013323 http://dx.doi.org/10.1002/aqc.2663", "Turicchia E, Cerrano C, Ghetta M, Abbiati M, Ponti M (2021) MedSens index: The bridge between marine citizen science and coastal management. Ecol Indic 122:107296 https://doi.org/10.1016/j.ecolind.2020.107296"]}

These data were collected as part of a case study for the ipaast project. The aim of the survey was to produce datasets interoperable for applications in archaeology and precision agriculture. The OptRx® Crop Sensors (AgLeader Technology, Ames, IO, USA) measure the reflectance in the 630–685 nm (red), 695–750 nm (RE red edge) and 760–850 nm (NIR—Near InfraRed) wavebands. Using those wavebands, NDVI and NDRE indexes are calculated. NDVI and NDRE are vegetative indexes obtained from the red, red-edge and NIR wavebands with formulas 1 and 2: NDVI = NIR−REDNIR+RED ; NDRE= NIR−RENIR+RE The two index values range from -1 (bare ground or water) to 1 (highly vigorous vegetation). To collect data, the sensor was mounted on a ground vehicle, a Kubota B2420 tractor. The sensor was paired with a GNNS receiver, GPS 6500 from AgLeader Technology (Ames, IO, USA). The instrumentation was coupled with the hardware and the rough book (Panasonic ToughPad FG-Z1, Panasonic Core. It was possible to install the sensor facing the ground using a metal bracket positioned on the front of the tractor. The sensor was positioned 1.15 m from the ground, emitting a rectangular footprint of 1.14 m in length and 20cm in width. The data were collected every 30 cm in alternate rows. 12 rows in total were analysed, covering a surface of 1.07 ha. Data were processed on QGIS. First, the data was interpolated with the Inverse Distance Weighting (IDW) function. The function was set up with a distance coefficient P of 4, with 40 rows and 98 columns. A Gaussian filter with a standard deviation value of 2 and a range of research of 3 was subsequently applied to create a representative raster.

This repository contains the data, scripts and results for the paper "Demand-side policies for power generation in response to the energy crisis: A model analysis for Italy", https://doi.org/10.1016/j.esr.2024.101329. Results in the paper are divided into three sections, corresponding to the numbers of the folders inside this dataset. They are described as follows: 1 - EU policy impact: What is the impact on the Italian electricity of the european proposal of cutting power demand and shifting it during peak hours on gas consumption, system costs and emissions? 2 - Gas cost sensitivity: Which would be Italy’s most convenient power system considering different gas prices? 3 - DSM in mitigation: What could be the role of demand side measures in power systems with a high penetration of RES?

The dataset was collected with the the Mediterranean LEK initiative. The initiative was initially conceived by the international basin-wide monitoring program CIESM Tropical Signals (funded by the Albert II of Monaco Foundation) and subsequently adopted by the projects BALMAS (Ballast Water Management System for Adriatic Sea Protection, IPA Adriatic Cross-Border Cooperation Programme; FAO-AdriaMed and FAO-MedSudMed. This action was recently supported by the Interreg Med Programme (Grant number Pr MPA-Adapt 1MED15_3.2_M2_337) 85% co-funded by the European Regional Development Fund, which implemented the use of standard LEK protocols for Marine Protected areas and partially supported the writing of this publication. Data refers to self-reported trends of various fish species, which were regarded as increaseing by a sample of fishermen from various Mediterranean countries. Interviews were elicited from fishermen through semi-structured protocols (see Azzurro et al., 2011). {"references": ["Azzurro, E., Moschella, P., & Maynou, F. (2011). Tracking signals of change in Mediterranean fish diversity based on local ecological knowledge. PLoS One, 6(9), e24885."]} The project was co-funded by the European Union through Grant number Pr MPA-Adapt 1MED15_3.2_M2_337

Environmental dataBioclimatic data and environmental data for all 56 European peatland site (geo referenced by longitude [long], latitude [lat] and altitude [ALT]. MAT = Mean annual temperature (°C), TS = Seasonality in temperature, MAP = Mean annual precipitation (mm), PS = Seasonality in precipitation, tot_sox = Total sulphur deposition SOx (mg m-2 yr-1), tot_noy = Total oxidized nitrogen deposition (mg m-2 yr-1), tot_nhx = Total reduced nitrogen deposition (mg m-2), PT warm = Lang’s moisture index. The four bioclimatic variables (MAT, TS, MAP, PS) were extracted from the WorldClim database (Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005)), and averaged over the 2000-2009 period. Atmospheric deposition data were produced using the EMEP (European Monitoring and Evaluation Programme)-based IDEM (Integrated Deposition Model) model (Pieterse, G., Bleeker, A., Vermeulen, A. T., Wu, Y. & Erisman, J. W. High resolution modelling of atmosphere‐canopy exchange of acidifying and eutrophying components and carbon dioxide for European forests. Tellus B 59, 412–424 (2007)) and consisted of grid cell averages of total reduced (NHx) and oxidised (NOy) nitrogen and sulphur (SOx) deposition. The moisture index (PTwarm) was calculated as the ratio between mean precipitation and mean temperature in the warmest quarter (Thornwaite, C. W. & Holzman, B. Measurement of evaporation from land and water surfaces. USDA Technical Bulletin 817, 1–143 (1942))Data 1_environmental data.txtplant community dataAbundance data (% cover) for all vascular plant and bryophyte species from five randomly chosen hummocks and lawns (0.25 m2 quadrats; ten in total) across 56 European Sphagnum-dominated peatlands were collected in two consecutive summers (2010 and 2011). Vascular plants and Sphagnum mosses were identified to the species level. Non-Sphagnum bryophytes were identified to the family level. Lichens were recorded as one group.Data 2_plant community data.txttraits vascular plantsPlant functional traits used to calculate functional indices for the vascular plant communities. Traits were extracted from LEDA (Kleyer, M. et al. The LEDA Traitbase: a database of life‐history traits of the Northwest European flora. J. Ecol. 96, 1266–1274 (2008)). Only trait data available for all species our data-set were extracted.ncomms_Data 3_traits vascular plants.txttraits SphagnumTrait values (means) for Sphagnum spp. C = tissue carbon content (mg g-1), N = tissue nitrogen content (mg g-1), P = tissue phosphorus content (mg g-1), Productivity ( St.w = stem width (mm), l.h.c. = length hyaline cells (µm), w.h.c. = width hyaline cells (µm), l.s.l. = length stem leaves (mm), w.s.l. = width stem leaves. These measured traits were complemented with traits extracted from the literature. These latter traits included plant length (Hill, M. O., Preston, C. D., Bosanquet, S. & Roy, D. B. BRYOATT: attributes of British and Irish mosses, liverworts and hornworts. Centre for Ecology & Hydrology, Huntingdon, UK (2007)), spore diameter and capsule diameter (Sundberg, S., Hansson, J. & Rydin, H. Colonization of Sphagnum on land uplift islands in the Baltic Sea: time, area, distance and life history. Journal of Biogeography 33, 1479–1491 (2006)), productivity (Gunnarsson, U. Global patterns of Sphagnum productivity. J. Bryol. 27, 269–279 (2005))ncomms_Data 4_traits Sphagnum.txt In peatland ecosystems, plant communities mediate a globally significant carbon store. The effects of global environmental change on plant assemblages are expected to be a factor in determining how ecosystem functions such as carbon uptake will respond. Using vegetation data from 56 Sphagnum-dominated peat bogs across Europe, we show that in these ecosystems plant species aggregate into two major clusters that are each defined by shared response to environmental conditions. Across environmental gradients, we find significant taxonomic turnover in both clusters. However, functional identity and functional redundancy of the community as a whole remain unchanged. This strongly suggests that in peat bogs, species turnover across environmental gradients is restricted to functionally similar species. Our results demonstrate that plant taxonomic and functional turnover are decoupled, which may allow these peat bogs to maintain ecosystem functioning when subject to future environmental change.

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.CMCC.CMCC-CM2-SR5.ssp245' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The CMCC-CM2-SR5 climate model, released in 2016, includes the following components: aerosol: MAM3, atmos: CAM5.3 (1deg; 288 x 192 longitude/latitude; 30 levels; top at ~2 hPa), land: CLM4.5 (BGC mode), ocean: NEMO3.6 (ORCA1 tripolar primarly 1 deg lat/lon with meridional refinement down to 1/3 degree in the tropics; 362 x 292 longitude/latitude; 50 vertical levels; top grid cell 0-1 m), seaIce: CICE4.0. The model was run by the Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici, Lecce 73100, Italy (CMCC) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, ocean: 100 km, seaIce: 100 km.

Datasets associated with Agostini, S., Houlbreque, F., Biscéré, T., Harvey, B. P., Heitzman, J. M., Takimoto, R., et al. (2020). Greater mitochondrial energy production provides resistance to ocean acidification in ‘winning’ hermatypic corals. Front. Mar. Sci. 7. doi:10.3389/fmars.2020.600836.