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Comprehensive and reliable information on anthropogenic sources of greenhouse gas emissions is required to track progress towards keeping warming well below 2°C as agreed upon in the Paris Agreement. Here we provide a dataset on anthropogenic GHG emissions 1970-2019 with a broad country and sector coverage. We build the dataset from recent releases from the “Emissions Database for Global Atmospheric Research” (EDGAR) for CO2 emissions from fossil fuel combustion and industry (FFI), CH4 emissions, N2O emissions, and fluorinated gases and use a well-established fast-track method to extend this dataset from 2018 to 2019. We complement this with information on net CO2 emissions from land use, land-use change and forestry (LULUCF) from three available bookkeeping models.

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.CMIP.CMCC.CMCC-CM2-SR5.historical' 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.

The aim of this study was to identify the attitudes of the public in the United States and in 12 European countries towards foreign policy issues and transatlantic issues. The survey concentrated on issues such as: United States and European Union (EU) leadership and relations, favorability towards certain countries, institutions and people, security, cooperation and the perception of threat including issues of concern with Afghanistan, Iran, and Russia, energy dependence, economic downturn, and global warming, Turkey and Turkish accession to the EU, promotion of democracy in other countries, and the importance of economic versus military power. Several questions asked of respondents pertained to voting and politics including whether they discussed political matters with friends and whether they attempted to persuade others close to them to share their views on politics which they held strong opinions about, vote intention, their assessment of the current United States President and upcoming presidential election, political party attachment, and left-right political self-placement. Demographic and other background information includes age, gender, race, ethnicity, religious affiliation and participation, age when stopped full-time education and stage at which full-time education completed, occupation, number of people aged 18 years and older living in the household, type of locality, region of residence, prior travel to the United States or Europe, and language of interview. computer-assisted personal interview (CAPI); computer-assisted telephone interview (CATI); paper and pencil interview (PAPI)The original data collection was carried out by TNS, Fait et Opinion -- Brussels on request of the German Marshall Fund of the United States.The codebook and setup files for this collection contain characters with diacritical marks used in many European languages.A split ballot was used for one or more questions in this survey. The variable SPLIT defines the separate groups.For data collection, the computer-assisted face-to-face interview was used in Poland, the paper and pencil interview was used in Bulgaria, Romania, Slovakia and Turkey, and the computer-assisted telephone interview was used in all other countries.Additional information on the Transatlantic Trends Survey is provided on the Transatlantic Trends Web site. (1) Multistage random sampling was implemented in the countries using face-to-face interviewing. Sampling points were selected according to region, and then random routes were conducted within these sampling points. Four callbacks were used for each address. The birthday rule was used to randomly select respondents within a household. (2) Random Digit Dialing was implemented in the countries using telephone interviewing. Eight callbacks were used for each telephone number. The birthday rule was used to randomly select respondents within a household. The adult population aged 18 years and over in 13 countries: Bulgaria, France, Germany, Italy, the Netherlands, Poland, Portugal, Romania, Slovakia, Spain, Turkey, the United Kingdom, and the United States. Smallest Geographic Unit: country Response Rates: The total response rate for all countries surveyed is 23 percent. Please refer to the "Technical Note" in the ICPSR codebook for additional information about response rate. Please refer to the "Technical Note" in the ICPSR codebook for further information about weighting. Datasets: DS1: Transatlantic Trends Survey, 2008

This dataset contains the underlying data for the following publication: Li, R., Perdana, S., Vielle, M. (2021), Potential integration of Chinese and European emissions trading market: welfare distribution analysis, Mitigation and Adaptation Strategies for Global Change, 26:22 https://doi.org/10.1007/s11027-021-09960-7.

The datasets present collected data through resident surveys on the perceptions on tourism development and the state of cultural heritage, as part of the Horizon 2020 funded project SmartCulTour (www.smartcultour.eu). The data is collected on individual respondent level for Local Administrative Units (LAUs) for the following municipalities/cities: Spain: Huesca, Graus, Benasque, Barbastro, Ainsa, Jaca, Sariñena the Netherlands: Rotterdam, Dordrecht, Molenlanden, Ridderkerk, Zwijndrecht, Barendrecht, Delft Belgium: Dendermonde, Puurs-Sint-Amands, Bornem, Berlare, Aalst, Denderleeuw, Willebroek Croatia: Split, Trogir, Kaštela, Solin, Sinj, Dugopolje, Klis Finland: Utsjoki Italy: Vicenza, Caldogno, Grumolo, Pojana Maggiore, Lonigo, Montagnana The data is presented as cross-sectional data and available for the following year: 2020. Please consult the metadata on each dataset for an overview of collected indicators and units of measurement.

The pandemic caused by Covid-19 has tested the resilience of public institutions, already burdened by a deep and complex crisis (political, economic, managerial). This crisis has revealed a discrepancy between the needs expressed by the community and the solutions adopted to satisfy them. This has been accompanied by a progressive worsening of decision-making efficiency and weak implementation capacity in a context of increasing environmental uncertainty. It is in local institutions, in particular, that the greatest problems are revealed, because of many endemic negative factors: political fragmentation, reduced economic resources, new forms of poverty. Against the background of this scenario, our study aims to analyze the reaction of local institutions to the pandemic crisis by looking at both welfare and communication services. The objective is to identify key features in understanding the resilience of municipalities. In other words, their ability to react and adapt to change, which is essential not only to deal with emergencies, such as the pandemic, but also to make the institution itself sustainable. Our interest is focused on a specific dimension of the resilience of the municipalities, related to collaboration with the third sector. The pandemic has shown that the continuous activism of non-profit organizations has allowed for the continuation of many so-called "ordinary" services, as well as the launch of several initiatives aimed at alleviating other social problems. The research has, first of all, an exploratory character that befits a new and still ongoing phenomenon. The basic questions concern the production of local welfare policies by municipalities. The data show different levels of "interventism" and different modes of communication. On this latter point, we observe the presence of significant attention-seeking among Mayors as community builders able, on the one hand, to reinforce the spirit of solidarity and, on the other, to uphold respect for the rules. On the services side, three main models of response to the pandemic emerge, two of which refer to the public-private relationship in local welfare policies. Findings suggest that these different reactions will have consequences in the immediate future for the management of the pandemic crisis (still ongoing). Specifically, the tendency is to employ a management of services based on partnership-model, which means that public-private collaboration is a pillar of local welfare. This seems to entail a greater legitimacy for individuals or associations to participate in the formulation and implementation of policies.

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.

Organic waste production has greatly increased following human sprawl and led to the development of landfills in recent decades. This abundant and reliable anthropogenic food source has favoured several species, some of which consequently became overabundant. Landfills present hazards to wildlife, which may suffocate on plastic materials, tangle on cords, and get exposed to pollutants and pathogens. In response to environmental and public health concerns over the maintenance of landfills, the European Commission proposed to close the landfills. Our objective was to determine the impact of the Landfill European Directive on the White Stork, Ciconia ciconia, whose population recovery and growth were linked to landfill exploitation. We implemented species distribution models to project future distribution in the absence of landfills in the Community of Madrid (Spain). Habitat suitability was estimated based on nest occurrence and we included data from land cover types, human population density and two different climate change scenarios (i.e., emissions in low and high shared socioeconomic pathways). Given that protection measures, particularly implemented in protected areas, were associated with population recovery, we also evaluated the overlapping degree between protected areas and projected distribution. Our models predicted a sharp decline in breeding population distribution with landfill closure, reaching values similar to the 1984 breeding census when the species was categorized as threatened. Our results also suggest a decrease in maximum habitat suitability. Climate change also contributed to a reduction in breeding population distribution given model predictions for the extreme emission pathway (ssp5). Measures such as gradual change in landfill management, continuous monitoring of breeding populations, and evaluation of the Stork use of natural feeding areas before and after landfill closure, should be considered.  Direct census searching for nests in the whole Community of Madrid.

This is a repository of global and regional human population data collected from: the databases of scenarios assessed by the Intergovernmental Panel on Climate Change (Sixth Assessment Report, Special Report on 1.5 C; Fifth Assessment Report), multi-national databases of population projections (World Bank, International Database, United Nation population projections), and other very long-term population projections (Resources for the Future). More specifically, it contains: - in `other_pop_data` folder files from World Bank, the International Database from the US Census, and from IHME - in the `SSP` folder, the Shared Socioeconomic Pathways, as in the version 2.0 downloaded from IIASA and as in the version 3.0 downloaded from IIASA workspace - in the `UN` folder, the demographic projections from UN - `IAMstat.xlsx`, an overview file of the metadata accompanying the scenarios present in the IPCC databases - `RFF.csv`, an overview file containing the population projections obtained by Resources For the Future '- the remaining `.csv` files with names `AR6#`, `AR5#`, `IAMC15#` contain the IPCC scenarios assessed by the IPCC for preparing the IPCC assessment reports. They can be downloaded from AR5, SR 1.5, and AR6 This data in intended to be downloaded for use together with the package downloadable here. The dataset was used as a supporting material for the paper "Underestimating demographic uncertainties in the synthesis process of the IPCC" accepted on npj Climate Action (DOI : 10.1038/s44168-024-00152-y).