Renewable and Sustainable Energy Reviews

Data from biofuel sector in Colombia. Ethanol and Biodiesel

This folder contains the syntax and excel files able to reproduce our empirical analysis. Instructions can be found inside the folder.

This dataset provides data on a sample of 327 core cities within the EU-28, covered by the Cities Statistics database of the European Statistics Office (Eurostat), formerly known as “Urban Audit” (UA). It is organized in three spreadsheets containing, respectively, the following data: 1. List of the analyzed plans: Country / City, City population, Climate change mitigation strategy name (in national languages / in English), Year of adoption of the strategy/plan, Type of Mitigation Local Climate Plan (M-LCP), Integrated Mitigation and Adaptation Plan, Carbon neutrality, target year carbon neutrality, Global Covenant of Mayors for Climate and Energy (GCoM), Climate Alliance (CA), C40, CNCA (Carbon Neutral Cities Alliance) 2. GHG emission targets for UA cities with a plan, by country: Country / City with a plan, Type of M-LCP, CO2 emission target (% / baseline year / target year), GHG emission target (CO2eq) (% / baseline year / target year), Geographical location (Northern/Southern Europe) 3. Key data summary on the sample: Country, Total number (No.) of cities in the sample, M-LCPs by type, Total No. of M-LCP Cities without a plan, Cities with a plan, Integrated M&A LCPs, Total population, Population in our city sample, Population representativeness in the sample

This file includes the data corresponding to each indicator of green development level of 13 cities in Beijing-Tianjin-Hebei region in 2017

Historical and Future Weather Files in Building

China provincial economic, energy and environmental data.

Assumptions, calculations and results for the article submission titled "INTEGRATION OF GREENHOUSE AGRICULTURE TO THE ENERGY INFRASTRUCTURE AS AN ALIMENTARY SOLUTION"

This MATLAB script and input csv files contain a model to predict the performance and economics of residential photovoltaic-battery systems. A description of the model is provided in the corresponding peer-reviewed publication (currently under review and to be added here with a revision upon publication). All input files must be placed in the same working directory as the MATLAB script for the model to run. This analysis is performed for the center of population of each state in the U.S (see Related Links). The input solar data for each location was obtained from the National Solar Radiation Database (see Related Links). The input load data for each location was obtained from the U.S. Department of Energy's Building America B10 Benchmark dataset (see Related Links). Current electricity prices were obtained from the Energy Information Administration's State Electricity Profiles, and future electricity prices were forecasted based on the annual percent change in the Annual Energy Outlook report's reference case predictions for the corresponding census division (see Related Links). A disclaimer for use of the TMY3 data is included in the "TMY3 Data" folder and may also be found at: https://www.nrel.gov/disclaimer.html A disclaimer for use of the load data is included in the "Load Data" folder and may also be found at: https://openei.org/wiki/OpenEI:General_disclaimer

This study examines the siting scenarios for renewable energy infrastructure (REI) in Switzerland, incorporating the external costs of ecosystem services and, innovatively, social preferences. This approach challenges the prevalent techno-economic siting paradigm, which often overlooks these externalities. To minimize the external costs of the scenarios while maximizing energy yield, Marxan, an optimization software, was employed. MARXAN was run for 2 versions: a) without ground-mounted open space PV infrastructure (excl. OS) and b) with ground-mounted open space PV infrastructure (incl. OS). In each version optimization was done using ecological costs (ECUess) or social costs (ECUsoc) in a regular grid of 4x4km (planning unit). File: PU_data.shp: compressed Shapefile (ESRI) from the MARXAN optimization with 2216 rows (objects) and 18 columns (variables) with sf (simple feature) and data frame classes. Headers are described below: X01 = Planning_units (PUs) X02 = incl.OS. number of times pu was selected in MARXAN when optimized for ecological costs X03 = incl.OS. number of times pu was selected in MARXAN when optimized for social costs X04 = incl.OS. ecological costs (ECUess) of pu when realizing the total energy (normalized) X05 = incl.OS. social costs (ECUsoc) of pu when realizing the total energy (normalized) X06 = incl.OS. total energy (MWh/a) X07 = incl.OS. energy from roof-mounted PV infrastructure (MWh/a) X08 = incl.OS. energy from wind turbines (MWh/a) X09 = incl.OS. energy from ground-mounted PV infrastructure (MWh/a) X10 = excl.OS. number of times pu was selected in MARXAN when optimized for ecological costs X11 = excl.OS. number of times pu was selected in MARXAN when optimized for social costs X12 = excl.OS. ecological costs (ECUess) of pu when realizing the total energy (normalized) X13 = excl.OS. social costs (ECUsoc) of pu when realizing the total energy (normalized) X14 = excl.OS. total energy (MWh/a) X15 = excl.OS. energy from roof-mounted PV infrastructure (MWh/a) X16 = excl.OS. energy from wind turbines (MWh/a) X17 = excl.OS. energy from ground-mounted PV infrastructure (MWh/a) geometry = Simple feature XY geometry (SFC_POINT) each representing the center of a 4x4km planning unit (PU) in EPSG 21781 (CRS CH1903 / LV03). For MARXAN optimizations (for meaning of standard files in MARXAN see https://scholar.google.ch/scholar_url?url=https://courses.washington.edu/cfr590/software/Marxan1810/marxan_manual_1_8_2.pdf&hl=de&sa=X&ei=NAWHZI6JJ8PFmAGMo47oDQ&scisig=AGlGAw-DBrsV4kUzkR6GkN1jtG66&oi=scholarr), pu_data.shp was used to generate MARXAN files input.dat, pu.dat, puvfeat.dat, spec.dat. Coordinate Reference System: CH1903 / LV03. EPSG: 21781.

This is the additional material provided with the journal article "Techno-economic evaluation and resource assessment of hydrogen production through offshore wind farms: A European perspective" published in Renewable and Sustainable Energy Reviews (https://doi.org/10.1016/j.rser.2023.113699). Datasets are provided as NetCDF files for European maps and CSVfor Economically Attractive Resource curves. European and National plots are provided as PDF files.