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Abstract This paper assesses the effects of a global emissions trading scheme (GETS) for international aviation and shipping as a way of reducing emissions of both greenhouse gases (GHG) and other atmospheric emissions that lead to air pollution. A prior assessment of such integration requires the coupling of energy–environment–economy (E3) global modelling of mitigation policies with the atmospheric modelling of pollution sources, mixing and deposition. We report the methodology and results of coupling of the E3MG model and the global atmospheric model, p-TOMCAT. We assess the effects of GETS on the concentrations of atmospheric gases and on the radiative forcing, comparing a GETS scenario to a reference BASE scenario with higher use of fossil fuels. The paper assesses the outcome of GETS for atmospheric composition and radiative forcing for 2050. GETS on international shipping and aviation reduces their CO 2 and non-CO 2 emissions up to 65%. As a consequence atmospheric concentrations are modified and the radiative forcing due to international transport is reduced by different amounts as a function of the pollutant studied (15% for CO 2 , 35% for methane and up to 50% for ozone).

Plug-in electric vehicles can potentially emit substantially lower CO2 emissions than internal combustion engine vehicles, and so have the potential to reduce transport emissions without curtailing personal car use. Assessing the potential uptake of these new categories of vehicles requires an understanding of likely consumer responses. Previous in-depth explorations of appraisals and evaluations of electric vehicles have tended to focus on ‘early adopters’, who may not represent mainstream consumers. This paper reports a qualitative analysis of responses to electric cars, based on semi-structured interviews conducted with 40 UK non-commercial drivers (20 males, 20 females; age 24–70 years) at the end of a seven-day period of using a battery electric car (20 participants) or a plug-in hybrid car (20 participants). Six core categories of response were identified: (1) cost minimisation; (2) vehicle confidence; (3) vehicle adaptation demands; (4) environmental beliefs; (5) impression management; and, underpinning all other categories, (6) the perception of electric cars generally as ‘work in progress’ products. Results highlight potential barriers to the uptake of current-generation (2010) plug-in electric cars by mainstream consumers. These include the prioritization of personal mobility needs over environmental benefits, concerns over the social desirability of electric vehicle use, and the expectation that rapid technological and infrastructural developments will make current models obsolete. Implications for the potential uptake of future electric vehicles are discussed.

Abstract Energy consumption in the household sector has become an important issue in China's energy consumption and an important unit of China's clean energy transformation. Currently, the potential air pollution, carbon emissions and health risks caused by energy consumption in many areas cannot be ignored, and refined and regionalized index-based research data necessary to support decision making are lacking. Based on household-level survey data collected from Qinghai Province, China, we estimated greenhouse gas (GHG) and air pollutant emissions from spatial perspectives, including household energy consumption in pastoral, agropastoral, and agricultural zones. The findings suggest that the total annual GHG and pollutant emissions per capita in the area was 2296.32 kg per year. The highest amount of pollutants was emitted from the pastoral zones, followed by the agropastoral and agricultural zones. CO2 is the primary GHG emitted by household energy consumption. Dung burning was the cause of the high PM2.5 emissions in the pastoral areas, while the use of coal was the primary cause of GHG and pollutant emissions in the agropastoral and agricultural zones. These findings highlight the need to integrate household energy policies with rural development to enable a complete transition towards cleaner fuels.

Efficient and rational implementation of building stock CO2 emission reduction strategies and policies requires the application of comprehensive building stock models that have the ability to: (a) estimate the baseline energy demand of the existing building stock, (b) explore the technical and economic effects of different CO2 emission reduction strategies over time, including the impact of new technologies, and (c) to identify the effect of emission reduction strategies on indoor environmental quality. The aims of this paper are fourfold: (a) to briefly describe bottom-up and top-down methods and overview common bottom-up modelling techniques (statistical and building physics based), (b) to critically analyse the existing bottom-up building physics based residential energy models focusing on their purposes, strengths, and shortcomings, (c) to compare five building physics based bottom-up models focusing on the same building stock – UK case study, and (d) to identify the next generation of coupled energy–health bottom-up building stock models. This paper has identified three major issues which need to be addressed: a) the lack of publicly available detailed data relating to inputs and assumptions, as well as underlying algorithms, renders any attempt to reproduce their outcomes problematic, b) lack of data on the relative importance of input parameter variations on the predicted demand outputs, and c) uncertainty as to the socio-technical drivers of energy consumption – how people use energy and how they react to changes in their home as a result of energy conservation measures.

Abstract The conventional approach of selecting HVAC systems is based on a designer's knowledge and experience, and this may lead to flawed decisions. The ever-growing accumulation of building performance data makes the machine learning algorithm based HVAC system selection possible. This study presents an innovative approach wherein the Bayesian Network technique is applied to select the most energy efficient primary HVAC systems. The database upon which the approach is developed is the 2012 Commercial Building Energy Consumption Survey (CBECS). The first step of this research involves clustering a group of similar buildings for the target building. Euclidean distance is adopted to calculate the similarity of a building with the target building. In the meantime, a survey is carried out to investigate the major factors that designers considered when selecting the primary HVAC system. The survey results show that climate zone, design cooling/heating load, and principal activity type are three main factors considered by designers and only 13% designers value the building energy consumption. In this study, all factors that could potentially influence the HVAC system's energy consumption are used to construct the directed acyclic graph of the proposed Bayesian Network Classifier. This classifier is trained with high energy efficient buildings data after the filtering out of irrational outliers. Then, the trained classifier is applied to select the primary cooling systems for three case buildings. The results indicate that the selected systems coincide with common HVAC design logic. The proposed method provides designers with an innovative approach to select energy efficient HVAC systems by using the data of hundreds of high energy efficient buildings. This study demonstrates the feasibility and capability of data-driven building design.

Abstract This paper critically reviews the growing literature optimising hydrogen infrastructure. We examine studies across spatial scales: national scale studies using energy system models; regional scale studies optimising spatially disaggregated hydrogen infrastructure; and local scale studies optimising the siting of filling stations. For the latter two types of study, we critically assess the assumptions made around hydrogen demand, a key exogenous input into these studies. We identify knowledge gaps and issues that have not been sufficiently addressed in the literature, and we suggest areas for further work.

Abstract Municipal Solid Waste in general and its organic fraction in particular is a potential renewable and non-seasonal resource. In this work, a life cycle assessment has been performed to evaluate the environmental impacts of two future scenarios using biogas produced from the organic fraction of municipal solid waste (OFMSW) to supply energy to a group of dwellings, respectively comprising distributed generation using solid oxide fuel cell (SOFC) micro-CHP systems and condensing boilers. The London Borough of Greenwich is taken as the reference case study. The system is designed to assess how much energy demand can be met and what is the best way to use the digestible waste for distributed energy purposes. The system is compared with two alternative scenarios fuelled by natural gas: a reference scenario, where the electricity is supplied by the grid and the heat is supplied from condensing boilers, and a fuel cell micro-CHP system. The results show that, although OFMSW alone can only supply between 1% and 4% of the total energy demand of the Borough, a saving of ∼130 tonnes of CO 2 eq per year per dwelling equipped with micro-CHP is still achievable compared with the reference scenario. This is primarily due to the surplus electricity produced by the fuel cell when the micro-CHP unit is operated to meet the heat demand. Use of biogas to produce heat only is therefore a less desirable option compared with combined heat and power production. Further investigation is required to identify locally available feedstock other than OFMSW in order to increase the proportion of energy demand that can be met in this way.

The objective of this study is to quantify the extent to which variation in heating season indoor temperatures is explained by dwelling and household characteristics and increased by energy efficiency improvements in low income households. A survey of dwellings in the Warm Front home energy efficiency scheme was carried out in five urban areas of England. Half-hourly living room and main bedroom temperatures were recorded for 2-4 weeks over two winters. For each dwelling, regression of indoor on outdoor temperature was used to obtain estimates of daytime living room and night time bedroom temperatures under standardized conditions (outdoor temperature of 5 degrees C. The results indicate that the median standardized daytime living room temperature was 19.1 degrees C and the median standardized night time bedroom temperature 17.1 degrees C. Temperatures were influenced by property characteristics, including its age, construction and thermal efficiency and also by the household number of people and the age of the head of household. Dwellings that received both heating and insulation measures through the Warm Front scheme had daytime living room temperatures 1.6 degrees C higher than pre-intervention dwellings, night time bedroom temperatures were 2.8 degrees C higher. Warm Front energy efficiency improvements lead to substantial improvements of both living room and bedroom temperatures which are likely to have benefits in terms of thermal comfort and well-being. (C) 2005 Elsevier B.V. All rights reserved.

Abstract A lack of empirical data for residential indoor temperature has important implications for policymakers in terms of energy performance objectives and the use of energy demand models for the building stock. This study investigates winter indoor temperatures, relative humidity and vapour pressure excess in 2009–2010 across various types and ages of buildings using half-hourly monitoring of 96 dwellings representative of residential buildings in Belgrade, including those with district heating (DH) with no direct occupant control in which heating is charged on a floor area basis. The average daily living room temperature of 22.8 °C (95%CI: 21.9–22.7) in DH dwellings was 2.3 °C higher than those with other heating types, including individual central heating (ICH) and non-central heating (non-CH), daily bedroom temperature of 22.3 °C (95%CI: 21.9–22.7) was 3.0 °C higher. Evening living room and night bedroom temperatures in ICH dwellings were 22% and 37% of the time respectively below 18 °C, and 10% and 27% of the time respectively in non-CH dwellings. The high degree of overheating in DH dwellings indicates the considerable potential to reduce energy consumption, if user controls and heating bills reflected household consumption were introduced.