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Survey of Timisoara City residents conducted by the West University of Timisoara for the SMARTEES project between March and August 2020 (n=439). The survey was aimed at (1) understanding individual behaviours related to the environment and energy in general, and (2) assessing how people make decisions about energy efficiency measures in particular (i.e., perceptions about existing regional or national programmes aiming to improve the energy efficiency of homes through upgrades to the building fabric with a neighbourhood-scale heat network retrofit). It includes data about citizens' attitudes, behaviours and social networks. Files include the dataset in two formats: .csv and .sav. The questionnaire, a data dictionary and background and sampling details are also included.

This dataset contains key characteristics about the data described in the Data Descriptor Global offshore wind turbine dataset. Contents: 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format

[EN] Buildings have become a key source of greenhouse gas (GHG) emissions due to the consumption of primary energy, especially when used to achieve thermal comfort conditions. In addition, buildings play a key role for adapting societies to climate change by achieving more energy efficiency. Therefore, buildings have become a key sector to tackle climate change at the local level. However, public decision-makers do not have tools with enough spatial resolution to prioritise and focus the available resources and efforts in an efficient manner. The objective of the research is to develop an innovative methodology based on a geographic information system (GIS) for mapping primary energy consumption and GHG emissions in buildings in cities according to energy efficiency certificates

I am an applied physics scientist and an elctric engineer, focusing on solar photovoltaic science and engineerings, in particular PV/storage smart powering system.

This dataset consists of hourly internal and daily external temperature data from 82 certified Passivhaus dwellings in the UK. The data can be used for calculating overheating risk and guaging how comfortable a home would be in the summer. This data come from 16 different sites and includes houses and flats. Some of the data is from the living room only, for other dwellings there were sensors in muitple rooms and these are indicated. As this data was compared to CIBSE TM59 "Design methodology for the assessment of overheating risk in homes", there is a calculation of the running mean temperature and maximum temperature. The variables are Timestamp = time and date SiteID = Site number (1-16) DWType = dwelling type (House or Flat) HouseID = unique reference number for each dwelling in dataset Room = room type LR = living room , BR= bedroom, KI= Kitchen, BT= bathroom T.int = internal temperature (mean hourly) T.ext.daily = external temperature (mean daily) T.rm = running mean temperature calculated using the method described in CIBSE TM59 T.max = maximum daily intenral temperature calculated using the method described in CIBSE TM59 This data was provided by the Technology Stratergy Board Building Performance Evaluation Program, and is available from the digital catapault. Other data was provided by WARM low energy Consultancy and indidiual home owners. All data has been anonymised

In recent years, graphene has found its use in numerous industrial applications due to its unique properties. While its impermeable and conductive nature can replace currently used anticorrosive toxic pigments in coating systems, due to its large strength to weight ratio, graphene can be an important component as a next-generation additive for automotive, aerospace and construction applications. The current bottlenecks in using graphene and graphene oxide and other two-dimensional materials are the availability of cost-effective, high-quality materials and their effective incorporation (functionalization and dispersion) into the product matrices. On overcoming these factors, graphene may attract significant demands in terms of volume consumption. Graphene can be produced on industrial scales and through cost-effective top-down routes such as chemical, electrochemical and/or high-pressure mechanical exfoliation. Graphene, depending on end applications, can be chemically tuned and modified via functionalization so that easy incorporation into product matrices is possible. This paper discusses different production methods and their impact on the quality of graphene produced in terms of energy input. Graphene with an average thickness below five layers was produced by both methods with varied defects. However, a higher yield of graphene with a lower number of layers was produced via the high-pressure exfoliation route.This article is part of a discussion meeting issue ‘A cracking approach to inventing new tough materials: fracture stranger than friction’.

Comparison of MY2000-2017 passenger vehicle emissions for baseline and MY1996 Tech Change scenarios.

These datasets support the findings of the study titled “A spatial cost-benefit-externality modelling framework for siting of variable renewable energy farms: A case in Bohol, Philippines.” This study introduces a four-stage energy modelling framework for siting variable renewable energy farms using Geographic Information System and Cost-Benefit Analysis. There are only two files in this repository. The data with the filename “Cost-Benefit-Externality Model” holds the implementation of the model in assessing the viability of VRE in the province of Bohol. Input parameters of the model can be varied in this file. The data with the filename “Sensitivity Analysis” contains the sensitivity analysis results we conducted for the economical parameters of the model.