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AbstractThe co‐occurrence of chronic pain and alcohol use disorders (AUDs) involves complex interactions between genetic and neurophysiological aspects, and the research has reported mixed findings when they both co‐occur. There is also an indication of a gender‐dependent effect; males are more likely to use alcohol to cope with chronic pain problems than females. Recently, a new conceptualization has emerged, proposing that the negative affective component of pain drives and maintains alcohol‐related behaviors. We studied in a longitudinal fashion alterations in alcohol drinking patterns and pain thresholds in a mouse model of chronic neuropathic pain in a sex‐dependent manner. Following partial denervation (spared nerve injury [SNI]), stimulus‐evoked pain responses were measured before chronic alcohol consumption, during drinking, during a deprivation phase, and following an episode of excessive drinking. During the course of alcohol drinking, we observed pronounced sex differences in pain thresholds. Male mice showed a strong increase in pain thresholds, suggesting an analgesic effect induced by alcohol over time, an effect that was not observed in female mice. SNI mice did not differ from sham‐operated controls in baseline alcohol consumption. However, following a deprivation phase and the reintroduction of ethanol, male SNI mice but not female mice showed more pronounced excessive drinking than controls. Finally, we observed decreased central ethanol sensitivity in male SNI mice but not in females. Together with our finding, that ethanol is able to decrease a pain‐induced negative affective memory we come to following conclusion. We propose that a lower sensitivity to the intoxicating effects of alcohol together with the ability of alcohol to reduce the negative affective component of pain may explain the higher co‐occurrence of AUD in male chronic pain patients.

Nearly three billion people in low- and middle-income countries (LMICs) rely on polluting fuels, resulting in millions of avoidable deaths annually. Polluting fuels also emit short-lived climate forcers and greenhouse gases (GHGs). Liquefied petroleum gas (LPG) and grid-based electricity are scalable alternatives to polluting fuels but have raised climate and health concerns. Here, we compare emissions and climate impacts of a business-as-usual household cooking fuel trajectory to four large-scale transitions to gas and/or grid electricity in 77 LMICs. We account for upstream and end-use emissions from gas and electric cooking, assuming electrical grids evolve according to the 2022 World Energy Outlook’s “Stated Policies” Scenario. We input the emissions into a reduced-complexity climate model to estimate radiative forcing and temperature changes associated with each scenario. We find full transitions to LPG and/or electricity decrease emissions from both well-mixed GHG and short-lived climate forcers, resulting in a roughly 5 millikelvin global temperature reduction by 2040. Transitions to LPG and/or electricity also reduce annual emissions of PM2.5 by over 6 Mt (99%) by 2040, which would substantially lower health risks from Household Air Pollution. Primary input data was collected from the following sources: Baseline household fuel choices - WHO household energy database (https://www.nature.com/articles/s41467-021-26036-x) End-use emissions - US EPA lifecycle assessment of household fuels (https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=339679&Lab=NRMRL&simplesearch=0&showcriteria=2&sortby=pubDate&timstype=Published+Report&datebeginpublishedpresented) Upstream emissions - Argonne National Labs GREET Model (https://greet.es.anl.gov/index.php) Current and future population estimates - UNECA (http://data.un.org/Explorer.aspx?d=EDATA) Input data was processed by defining household fuel choice scenarios, estimating national household fuel consumption based on these scenarios, and applying fuel-specific emission factors to create country-specific emission pathways. These emission pathways were input into the FaIR model (https://zenodo.org/record/5513022#.Yt_jfHbMLb0) which generated additional data for each scenario including time series of pollution concentrations, radiative forcing, and temperature changes. All data is provided in CSV format. Nothing proprietary is required. 

Study PopulationThe target population of the study were women aged 18 years to 69 years from households in Mwea East sub County that have experienced climate change events. As shown in table 3.1 below, the total population of female in Mwea East sub County in this age category was estimated at 38,734 (Kenya National Bureau of Statistics (KNBS)Volume III, table 2.5, (2019).Sample SizeA sample size of 449 respondents was determined as adequate for statistical analysis for the study using an online sample size calculator (calculator.net, 2021). 95% confidence level and 4.6% margin of error was used to calculate the sample size of 449 respondents determining the level of accuracy of the sample from the total estimated population of 38,734 women aged 18-69 years in Mwea East sub County.Data CollectionThe administration of the questionnaire was done by the Principal Investigator (PI) along with the KIIs, which were conducted after the questionnaire had been administered. The questionnaires were administered by 11 data collection assistants who were trained by the researcher. One of the 11 data collectors was the team leader. The researcher collected data in 5 of the households to demonstrate and practice the data collection process. Data AnalysisQuantitative and qualitative data were analyzed and triangulated to validate the findings. The quantitative data was analyzed using a combination of the IBM SPSS techniques including frequencies, cross-tabulations, bivariate statistics, means, correlations and descriptive ratio statistics. Qualitative data from both respondents and key informants’ interviews were documented using filed notes and thematically analyzed. The analysis from both sets of data was then merged to present the results. Study PopulationThe target population of the study were women aged 18 years to 69 years from households in Mwea East sub County that have experienced climate change events. As shown in table 3.1 below, the total population of female in Mwea East sub County in this age category was estimated at 38,734 (Kenya National Bureau of Statistics (KNBS)Volume III, table 2.5, (2019).Sample SizeA sample size of 449 respondents was determined as adequate for statistical analysis for the study using an online sample size calculator (calculator.net, 2021). 95% confidence level and 4.6% margin of error was used to calculate the sample size of 449 respondents determining the level of accuracy of the sample from the total estimated population of 38,734 women aged 18-69 years in Mwea East sub County.Data CollectionThe administration of the questionnaire was done by the Principal Investigator (PI) along with the KIIs, which were conducted after the questionnaire had been administered. The questionnaires were administered by 11 data collection assistants who were trained by the researcher. One of the 11 data collectors was the team leader. The researcher collected data in 5 of the households to demonstrate and practice the data collection process. Data AnalysisQuantitative and qualitative data were analyzed and triangulated to validate the findings. The quantitative data was analyzed using a combination of the IBM SPSS techniques including frequencies, cross-tabulations, bivariate statistics, means, correlations and descriptive ratio statistics. Qualitative data from both respondents and key informants’ interviews were documented using filed notes and thematically analyzed. The analysis from both sets of data was then merged to present the results.

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.

This dataset contains terrestrial fluxes of nitrous oxide (N2O), methane (CH4) and ecosystem respiration (carbon dioxide (CO2)) calculated from static chamber measurements in riparian buffers of oil palm plantations on mineral soil, in Riau, Sumatra, Indonesia. Measurements were made monthly, from January 2019 until September 2021, with a break from April 2019 to October 2019 to allow for felling and replanting, and another break from January 2021 to June 2021 due to Covid-19 restrictions. To help to reduce the environmental impact of oil palm plantations, riparian buffers are now required by regulations in many Southeast Asian countries. The experiments were conducted to investigate the impact of greenhouse gas emissions from the riparian buffers. Research was funded through NERC grant NE/R000131/1 Sustainable Use of Natural Resources to Improve Human Health and Support Economic Development (SUNRISE) Greenhouse gas concentrations were measured using static chambers, enclosed for 45 minutes. Multiple regressions (including linear and hierarchical multiple regression) were fitted to calculate the best fit flux, using the RCflux R package, written by Dr Peter Levy (UKCEH).

We provide a dataset from a household survey in Mpanda region in Western Tanzania (N = 137) that was conducted in 2011. Household heads (or replacements) were interviewed. The topics addressed covered a broad range of socio-economic data and including, among others, household information (number of household members, age, sex, religion etc.), agricultural production (e.g. crops produced and livestock owned) including number and size of plots, income generation, energy access and owned assets.

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).

This dataset underpins the study "Synergies and conflicts of energy development and water security in Africa". The study provides insights into energy supply and demand, power generation, investments and total system costs, water consumption and withdrawal as well as carbon dioxide emissions for the African continent. We developed a model to evaluate energy supply and water requirements to cover the energy needs of the African continent during the period 2015-2065. The model was developed using the open-source modeling system for long-term energy planning OSeMOSYS. The objective function is to minimise total energy system costs, rather than, for example, co-optimise the energy and water sectors. Other energy resources were also included in the model except for adding the water analysis, and the dataset was updated based on the latest available information. The OSeMOSYS model developed to conduct the study “Energy projections for African countries”, itself extended from the Electricity Model Base for Africa (TEMBA), was further extended, included exports for all fuels, water loss due to evaporation in hydropower plants and more scenarios examined. Furthermore, the latest available data on the energy system of Africa was also updated. The TEMBA model produces aggregate energy, and detailed power system results in each country in the African continent. The power sector results are also reported with power pool aggregation. The OSeMOSYS model and input data used to produce these results can be found at KTH-dESA/jrc_temba: TEMBA 2.1 (Version v2.1) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4889373 (Authors: Ioannis Pappis, Vignesh Sridharan, Will Usher, & Mark Howells. (2021). The initial study was funded by the Joint Research Centre of the European Commission (contract number C936531 - JRC/PTT/2018/C.7/0038/NC).