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Cost-effectiveness calculations for GHG emission abatement measures in Cyprus

In this work is analyzed the Greenhouse Gases production (GHG) by combustion of binary and ternary mixtures, in a 5500 W portable engine generator of alternating current ; connecting to the generator three electric charges as a dynamic brake. It were measured the decrements in the production of CO, HC and NOx of up to 99%, 93% and 67%, respectively, as well as an increment in CO2 up to 35%; in addition, reduction in uptake fuel of more than 36%. Therefore, the reduction of GHG emissions will improve the quality of air in the cities, at the same time the quality of people’s health.

OECD DAC data on climate change-related development assistance from 11 donor countries that has been collapsed to a donor-year level.

This data supports the paper "Onshore wind and the likelihood of planning acceptance: learning from a Great Britain context"The full paper is available at Energy Policy https://doi.org/10.1016/j.enpol.2019.01.002

Lithosphere thermal thickness in the European region. Digital data for Fig. 7a in:Artemieva I.M. "Lithosphere structure in Europe from thermal isostasy", Earth Science Reviews, 2018.Please cite this article when using the data.

1. Temperature of every channel (including 3-stage water in troughs, circular outer surface, circulating water and ambient); 2. Yield rate; 3. Volume rate of circulating water; 4. Electric conductivity of raw water, etc.

Below is replication data and code, supporting the manuscript title “Uncovering the Intersections of Women’s Empowerment and Gender Equality in Climate Resilience Capacities within Climate Hotspots for Women in Zambia.” The data was collected in Luapula and North-Western provinces in Zambia. The selection of these provinces was based on identifying subnational climate hotspot areas for women in agrifood systems. Luapula province was identified as a hotspot for perennial crops, while the North-Western province was identified as a hotspot for mixed farming and vegetables, as well as for cereals and perennials. The data collection took place in the Zambezi district within the North-Western province and the Kawambwa district in Luapula province. These districts are rural areas where the majority of households depend on agrifood systems for their livelihoods. To obtain comprehensive insights, we interviewed 199 households, conducting separate interviews with both male and female co-heads. Among these households, 82 were located in the Zambezi district in the North-Western province, while 117 were situated in the Kawambwa district in Luapula province. The data presents information on various important aspects, including women's empowerment, access to and knowledge of climate-smart agriculture (CSA) and climate information services (CIS), perceptions and experiences related to climate change, as well as socio-economic and demographic characteristics of the households. The replication data is part of the AICCRA Zambia Baseline Survey 2022 (Zambezi district, North-western province and Kawambwa district, Luapula province) (Gbegbelegbe et al. 2022. AICCRA Zambia Baseline Survey 2022, Harvard Dataverse, V1, https://doi.org/10.7910/DVN/NLWWKM). We acknowledge and are thankful for the collaboration with IITA, in particular, Sika Gbegbelegbe, Steve Cole and David Chikoye, for the development of research tools and designing and conducting the data collection in the study areas in Luapula and North-western provinces, Zambia.

This dataset contains physiological (skin conductance) and behavioral (choice) data for 14 audio-visual stimuli. Each stimuli is composed of the variable landscape type (LANDSCAPE) with seven levels, and the variable amount of renewable energy infrastructure (SCENARIO) with two levels. The audio-visual stimuli are provided as high resolution videos. In addition, the statistical analysis is also published here as R-code.

Fig.7 shows the influence of the mass ratio on the thermodynamic efficiency of different TSHS cycles. Fig.8 depicts the influence of the mass ratio on the thermal energy storage density of different TSHS cycles. Fig.9 indicates the influence of the conversion rate on the thermodynamic efficiency of different TSHS cycles. Fig.10 represents the influence of the conversion rate on the thermal energy storage density of different TSHS cycles. The data files correspond to abovementioned figures.