• Energy Research
• 2021-2025close
• 7. Clean energy close
• 11. Sustainability close
• 1. No poverty close
• 3. Good health close

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.DKRZ.MPI-ESM1-2-HR.ssp126' 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 MPI-ESM1.2-HR climate model, released in 2017, includes the following components: aerosol: none, prescribed MACv2-SP, atmos: ECHAM6.3 (spectral T127; 384 x 192 longitude/latitude; 95 levels; top level 0.01 hPa), land: JSBACH3.20, landIce: none/prescribed, ocean: MPIOM1.63 (tripolar TP04, approximately 0.4deg; 802 x 404 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the Deutsches Klimarechenzentrum, Hamburg 20146, Germany (DKRZ) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, landIce: none, ocean: 50 km, ocnBgchem: 50 km, seaIce: 50 km.

Cities are progressively heightening their climate aspirations to curtail urban carbon emis- sions and establish a future where economies and communities can flourish within the Earth’s eco- logical limits. Consequently, numerous climate initiatives are being launched to control urban car- bon emissions, targeting various sectors, including transport, residential, agricultural, and energy. However, recent scientific literature underscores the disproportionate distribution of climate poli- cies. While cities in the Global North have witnessed several initiatives to combat climate change, cities in the Global South remain uncovered and highly vulnerable to climate hazards. To address this disparity, we employed the Balanced Iterative Reducing and Clustering using the Hierarchies (BRICH) algorithm to cluster cities from diverse geographical areas that exhibit comparable socio- economic profiles. This clustering strives to foster enhanced cooperation and collaboration among cities globally, with the goal of addressing climate change in a comprehensive manner. In summary, we identified similarities, pa􏰀erns, and clusters among peer cities, enabling mutual and generaliza- ble learning among worldwide peer-cities regarding urban climate policy exchange. This exchange occurs through three approaches: (i) inner-mutual learning, (ii) cross-mutual learning, and (iii) outer-mutual learning. Our findings mark a pivotal stride towards a􏰀aining worldwide climate ob- jectives through a shared responsibility approach. Furthermore, they provide preliminary insights into the implementation of “urban climate policy exchange” among peer cities on a global scale.

Building energy simulation is an analytical process to help building owners and designers evaluate the energy performance of the building. Uncertainty in the building energy modelling influences the building renovation from two perspectives: 1) calculating as-built energy consumption, 2) analysing the energy performance of renovation alternatives. Energy models can enhance by incorporating contextual and surrounding data. To this aim, we conducted a systematic study to investigate the effect of surrounding buildings in different distances, heights, and directions in studying the as-built energy consumption of an example building. The research also investigates the impact of a specific surrounding building on the energy performance of three different renovation alternatives, namely the modification of windows, external walls, and roofs. The results demonstrate that a higher height to distance ratio of the surrounding buildings often causes a decrease in energy consumption. In addition, a surrounding building located in the south direction causes more effect on the energy result than other directions when the building is in the northern hemisphere. For renovation scenarios, if there is a specific building in the south of the building under renovation, the window modification leads to less energy consumption than other renovation scenarios. The paper discusses that for renovation projects, an initial examination of surrounding buildings before selecting the renovation alternative is crucial; since different placements of surrounding buildings can affect the performance of renovation scenarios differently, which can cause a variation in the cost of renovation.

The data from a set of experiments investigating the effects of acute LSD administration on binge-like ethanol drinking, binge-like sucrose consumption, homeostatic eating and drinking, and discrete-trial current-intensity intracranial self-stimulation in male C57Bl/6J mice. The data have been published as an article in Journal of Psychopharmacology in June 2022: doi.org/10.1177/02698811221104641

This dataset, in the context of the MultiPACK Project, describes the development of a CO2 air/water reversible heat pump, specifically investigating the domestic hot water (DHW) production operating mode. A dynamic model of the heat pump is developed with the software Simcenter Amesim. After validation against experimental data, the numerical model is utilized to predict the performance of the heat pump to varying hot water demand, evaporator air inlet conditions and high-pressure value, leading to the discussion of the optimal control strategy. A paper, based on this dataset, "Experimental and numerical investigation of a transcritical CO2 air/water reversible heat pump: analysis of domestic hot water production (14th Gustav Lorentzen Conference, Kyoto, Japan, 6th- 9th December 2020, DOI:10.18462/iir.gl.2020.1160).

This dataset contains the data displayed in the figures or the article "High-resolution projections of ambient heat for major European cities using different heat metrics". The different files contain: Data_Fig1_DeltaTXx_EURO-CORDEX_1981-2010_to_3K-European-warming_RCP85.nc: Change of yearly maximum temperature in Europe between 1981-2010 and 3 °C European warming relative to 1981-2010. Data_Fig2_timeseries-GSAT-ESAT_EURO-CORDEX_CMIP5_CMIP6_1971-2100_RCP85_SSP585.xlsx: Time series of global mean surface air temperature (GSAT) for CMIP5 and CMIP6 models, and for European mean surface air temperature (ESAT) for EURO-CORDEX, CMIP5, and CMIP6 models for the period 1971-2100. Data_Fig3_TX-distribution_distance-from-city-centre_E-OBS_1981-2010.xlsx: Distribution of average daily maximum temperature in summer (June, July, August) in 1981-2010 for E-OBS for all investigated cities. Temperature data are indicated as a function of the distance to the city centre. Data_Fig3_TX-distribution_distance-from-city-centre_ERA5-Land_1981-2010.xlsx: Distribution of average daily maximum temperature in summer (June, July, August) in 1981-2010 for ERA5-Land for all investigated cities. Temperature data are indicated as a function of the distance to the city centre. Data_Fig3_TX-distribution_distance-from-city-centre_EURO-CORDEX_1981-2010.xlsx: Distribution of average daily maximum temperature in summer (June, July, August) in 1981-2010 for the EURO-CORDEX models for all investigated cities. Temperature data are indicated as a function of the distance to the city centre. Data_Fig3_TX-distribution_distance-from-city-centre_weather-stations_1981-2010.xlsx: Distribution of average daily maximum temperature in summer (June, July, August) in 1981-2010 for GSOD and ECA&D stations for all investigated cities. Temperature data are indicated as a function of the distance to the city centre. Data_Fig4_TX-ambient-heat_EURO-CORDEX_3K-European-warming.xlsx: Daytime heat metrics for the investigated cities: HWMId-TX at 3 °C European warming relative to 1981-2010, TX exceedances above 30 °C at 3 °C European warming relative to 1981-2010, and TXx change between 1981-2010 and 3 °C European warming relative to 1981-2010 for EURO-CORDEX models. Data_Fig5_Contribution-of-explanatory-variables-to-total-explained-variance.xlsx: Contribution of different explanatory variables (climate and location factors) to the total explained variance of spatial patterns of heat metrics. Data_Fig6_TN-ambient-heat_EURO-CORDEX_3K-European-warming.xlsx: Nighttime heat metrics for the investigated cities: HWMId-TN at 3 °C European warming relative to 1981-2010, TN exceedances above 20 °C at 3 °C European warming relative to 1981-2010, and TNx change between 1981-2010 and 3 °C European warming relative to 1981-2010 for EURO-CORDEX models. Data_Fig7_TX-ambient-heat_CMIP5_3K-European-warming.xlsx: Daytime heat metrics for the investigated cities: HWMId-TX at 3 °C European warming relative to 1981-2010, TX exceedances above 30 °C at 3 °C European warming relative to 1981-2010, and TXx change between 1981-2010 and 3 °C European warming relative to 1981-2010 for CMIP5 models. Data_Fig7_TX-ambient-heat_CMIP6_3K-European-warming.xlsx: Daytime heat metrics for the investigated cities: HWMId-TX at 3 °C European warming relative to 1981-2010, TX exceedances above 30 °C at 3 °C European warming relative to 1981-2010, and TXx change between 1981-2010 and 3 °C European warming relative to 1981-2010 for CMIP6 models. Data_Fig8_GCM-RCM-matrix_ambient-heat_3K-European-warming.xlsx: GCM-RCM matrices for the three heat metrics.

Abstract The metropolitan area of La Plata, Argentina, represents 1.6% of households in Argentina. Based on the analysis of the energy retrofit of a house, we provide indicators in order to propose a methodology that facilitates the extrapolation, at regional level, of the impact of a general energy retrofit in homes. The results obtained are presented from micro to macro level, in context of energy shortage and recurrent crisis in the supply of fuels and electricity in Argentina. The improved thermal resistance of walls and ceilings throughout the residential park of La Plata lead to a reduction of 12% of the energy consumed in heating, and a saving of 30,000 TEP / year. We conclude that the incidence of additional insulation on the cost of building justifies its use, partially solving the lack of fuel in our country, reducing fixed costs in housing, providing thermal comfort to users and generating economic reactivation of the construction sector.

ABSTRACT Context: reflecting the call being made by the United Nations to solve our current climate challenges and reduce companies’ CO2 emissions, there is a strong need for large corporations to not only employ the terminology of sustainable transitions, but to implement strategies and select new alternative sustainable solutions. Objective: this study fills a gap in the literature by developing and validating a model that helps researchers understand the factors that enable a large corporation undergoing a sustainable transition to select its new sustainable practices. The developed model used theories of sustainability transition and institutional theory with three pillars (regulative, normative, and cognitive) in order to help understand the nature of the company’s innovation selection criteria. Method: survey-based research was carried out among an oil and gas company’s employees, and structural equation modeling was used to test the model fit, validate the survey, and test the hypotheses. Results: the results showed that normative and regulative pillars play the main role in selecting renewable energy activities as a first step toward the company’s sustainable future. Conclusion: the findings provide researchers with a valuable model for understanding the main criteria for selecting new sustainable projects in established companies.

The key datasets used and generated in the paper mentioned in the title (from now on "the paper").+++ Temperature_Profile.xlsx +++This file contains the processed surface and deep-sea water temperatures that were used as inputs for the off-design analyses of the OTEC system designs. Outliers are already removed in this data set. Outliers are data points that are 1.5 times the interquartile range away from the top or bottom of the box plot. The raw temperature data can be downloaded from the HYCOM database following the download instructions elaborated in the paper.Column A: TimeShows the timestamp of the temperature data, from 01.01.1994 00:00 until 31.12.2012 21:00 in 3-hourly time steps.Columns B-C, D-E, F-G, H-IThese pairs of columns show the surface seawater temperature at 20 m depth and deep-sea water temperature at 1,000 m depth for the four locations analysed in the paper, namely Jayapura, Tarakan, Ende, and Sabang.Columns K - OShow the main statistics of the temperature files, including minimum, median, and maximum values of the surface and deep-sea water temperatures at each of the four locations.+++ System_Designs_Ende_LC +++This file contains the data for Table 4 in the paper, showing the system designs based on nine different configurations of seawater temperatures as design parameters. See sections 2.1 and 2.2 of the paper to learn more about the methods used to deduce the nine temperature configurations. The system designs are created using the temperature profiles from Ende and low-cost assumptions (LC). Please note that we used the following sign convention:Work and heat entering the system: positiveWork and heat leaving the system: negativeRows 6 - 15: Energy balance and net thermal efficiencyShows the energy balance and net thermal efficiency of the Rankine cycle on which the OTEC plant is basedRows 6 - 14 show the heat flows to the evaporator and from the condenser, the work from the turbine and to the pumps, as well as the losses.Row 15 shows the net efficiency and is calculated as follows:Row 15 = |Row 14|/Row 6Rows 17 - 28 show the exergy analysis including exergy inflow from the warm surface seawater and the exergy destruction in the system components. Row 28: Net Exergy EfficiencyRow 28 = |Row 27|/SUM(Row 17 to 19)Rows 29 to 30 show the carnot efficiency and second law efficiency. Rows 32 to 34 show the mass flows of working fluid (here ammonia or NH3), warm water (WW) and cold water (CW).Rows 36 and to 37 show the temperature differences between heat exchanger inlet and outlet of the warm water (WW) and cold water (CW).Rows 39 to 44 show the dimensions and properties of evaporator (evap) and condenser (cond), namely the heat exchanger area A, saturation temperature T and saturation temperature p of the working fluid.Rows 46 to 49 show the inner diameter and the number of required seawater pipes. Note, that the number of outlet pipes is the same as the number of inlet pipes, so if for example the number of WW pipes is 6, there are 3 inlet pipes and 3 outlet pipes for the warm water.+++ Net_Power_Profiles.xlsx +++Shows the net power output of the turbine in [kW] for 30 years (1994 - 2023) in 3-hourly time steps at the location in Ende. The values are negative as in accordance to the sign convention described above. The file contains the data for Figure 4 in the paper. There are three sheets in the file containing the net power profiles for configuration 1, 2, and 9. Please note that the four-weeks downtime period mentioned in section 2.5 is not included here yet.Column A: TimeShows the time of the year as the x-th 3-hour interval of the year.Columns B - AEShow the annual net power profiles for the years 1994 until 2023.Column AFShows the average net power output at the x-th 3-hour interval of the year.Column AGShows the standard deviation of the net power output at the x-th 3-hour interval of the yearRow 1Shows the headers for each columnRows 2 to 2929Shows the net power output in 3-hour time steps. Note that rows 474 to 481 represent the 29th February. For leap-years, these rows are filled with data, for non-leap-years, these rows are NaN.Row 2930Shows the sum of values under each column. For the annual electricity production in [kWh], the values in this row must be multiplied by factor 3 because of the 3-hourly time interval.