• Energy Research
• 11. Sustainability close
• Applied Energy close

Abstract Understanding the drivers of past and present energy consumption trends is important for a range of stakeholders, including governments, businesses and international development organizations, in order to prepare for impacts on global supply chains caused by changes in future energy price or availability shocks. In this paper we use environmentally-extended input–output tables to: (a) quantify the long-term drivers that have led to diversified energy footprint profiles of 186 countries around the world from 1990 to 2010; (b) identify which countries and sectors recorded an increase or decrease in energy footprints during this time period; (c) highlight the effect of international outsourcing of energy-intensive production processes by decomposing the structural and spatial change in energy footprints; and (d) discuss the implications for national economic policy for the identified drivers. To this end, we use a detailed Multi-Regional Input–Output database and three prevalent structural decomposition analysis methods. To reduce biases in the results due to time lapse and currency variations, we convert input–output tables to common US$ and 1990-constant prices. This study provides a broad overview of the magnitude and distribution of the drivers for energy footprints across countries. The results of this study demonstrate that for almost all countries affluence and population growth are driving energy footprints worldwide, which is in part counteracted by the retarding effect of industrial energy intensity. In particular, this study demonstrates that with increasing per-capita GDP, the total energy footprint of a country is increasingly concentrated on imports or consumption.

The technical, economical and environmental performances of combined cooling, heating and power (CCHP) system are closely dependent on its design and operation strategy. This paper analyzes the energy flow of CCHP system and deduces the primary energy consumption following the thermal demand of building. Three criteria, primary energy saving (PES), annual total cost saving (ATCS), and carbon dioxide emission reduction (CDER) are selected to evaluate the performance of CCHP system. Based on the energy flow of CCHP system, the capacity and operation of CCHP system are optimized by genetic algorithm (GA) so as to maximize the technical, economical and environmental benefits achieved by CCHP system in comparison to separation production system. A numerical example of gas CCHP system for a hotel building in Beijing is given to ascertain the effectiveness of the optimal method. Furthermore, a sensitivity analysis is presented in order to show how the optimal operation strategy would vary due to the changes of electricity price and gas price.

Abstract Impact of climate change on energy use in office buildings in a city within each of the five major architectural climates across China – Harbin (severe cold), Beijing (cold), Shanghai (hot summer and cold winter), Kunming (mild) and Hong Kong (hot summer and warm winter) – was investigated for two emissions scenarios. For low forcing, the estimated increase in cooling energy use was 18.5% in Harbin, 20.4% in Beijing, 11.4% in Shanghai, 24.2% in Kunming and 14.1% in Hong Kong; and the reduction in heating 22.3% in Harbin, 26.6% in Beijing, 55.7% in Shanghai, 13.8% in Kunming and 23.6% in Hong Kong. Space heating is usually provided by oil- or gas-fired boiler plants, whereas space cooling mainly relies on electricity. There would certainly be a shift towards electrical power demand. More energy use in buildings would lead to larger emissions, which in turn would exacerbate climate change and global warming. Energy conservation measures were considered to mitigate the impact of climate change on building energy use. These included building envelope, indoor condition, lighting load density and chiller coefficient of performance. It was found that raising the summer indoor design condition by 1–2 °C could result in significant energy savings and have great mitigation potential.

Abstract The prediction of building energy consumption plays a crucial role in building energy management and conservation because it contributes to effective building operation, energy efficiency evaluation, fault detection and diagnosis, and demand side management. Although a large number of energy prediction methods have been proposed, each method has its pros and cons and still exhibits the potential to be improved. This study proposes an evolutionary double attention-based long short- term memory model and introduces binary features by using feature combination. The proposed model is adopted to analyse the building energy consumption data of a green building in Shenzhen, China. The prediction performance of the proposed hybrid model measured via root-mean-square-error and mean absolute error are 4.02 and 2.87 respectively, which are evidently better than those of the base models. Results also show that an attention mechanism can improve the efficiency of the long short-term memory algorithm with which the model uses the input time series data. Meanwhile, binary features exert a significant effect on energy consumption. The proposed model is valuable to researchers and practitioners. It helps researchers apply artificial intelligence-based methods to building energy prediction from the perspective of paying selective attention to input data. Practitioners will benefit from developing accurate diagnosis of building energy efficiency and decision support for building retrofitting.

The coronavirus pandemic has severely affected our daily lives, with direct consequences on passenger transport. This in turn has strongly impacted the energy demand of the transport sector and associated CO2 emissions. We analyse near real-time passenger mobility and related emission trends in Europe between 21 January and 21 September 2020. We compiled a dataset of country-, sector- and lockdown- specific values, representing daily activity changes in private, public, and active passenger transport. In the aggregate, surface passenger transport emissions fell by 11.2% corresponding to 40.3 MtCO2 in Europe. This decline was predominantly due to the reduction of private passenger transport in five European countries (France, Germany, Italy, Spain, and the UK). During the first lockdown in April 2020, CO2 emissions from surface passenger transport declined by 50% in Europe, resulting in a 7.1% reduction in total CO2 emissions. After April 2020, private passenger travel recovered rapidly, while public passenger flows remained low. Solely prompted by the private sector, a rebound in total emissions and surface passenger transport emissions of 1.5% and 10.7%, respectively, was estimated at the end of the study period. The resulting situation of increased private and decreased public passenger transport is in contradiction to major climate goals, and without reversing these trends, emission reductions, as stated in the European Green Deal are unlikely to be achieved. Our study provides an analysis based on a detailed and timely set of data of surface passenger transport and points to options to grasp the momentum for innovative changes in passenger mobility.

Abstract Urban built environment regulations can effectively mitigate traffic CO2 emissions. Thus, it is critical to quantify the elasticities of altering built environment configurations. To address this issue, we have built nationwide spatial autoregressive models to differentiate between localized and spillover effects across 325 Chinese cities in the years of 2005 and 2015. Our results indicate that a 1% increase in built-up areas’ size, compactness, and isolation is associated with increases of 0.35%, −0.14%, and 0.13%, respectively, in adjacent traffic CO2 emissions. The underlying reason is that the spatial configurations of built environment do not only systemically affect the probability, frequency, speed, and distance of intracity motorised travels, but also have impacts on the intercity transboundary mobility of motor vehicles. In addition, the built-up areas’ compactness effect has an antagonistic relation with the per capita GDP effect. Thus, our findings provide evidence that the built environment configuration-related measures can benefit traffic CO2 emission reductions in adjacent cities. It is therefore necessary for policymakers to make a traffic CO2 mitigation strategy at the city agglomeration level.

Uncertainties in building operation and external factors such as occupant behavior, climate change, policy changes etc. impact building performance, resulting in possible performance deviation during operation compared to the predicted performance in the design phase. Multiple low-energy building configurations can lead to similar optimal performance under deterministic conditions, but can have different magnitudes of performance deviation under these uncertainties. Low-energy buildings must be robust so that these uncertainties do not result in significant variations in energy use, cost and comfort. However, these uncertainties are rarely considered in the design of low-energy buildings and hence, the decision making process may result in designs that are sensitive to uncertainties and might not perform as intended. Therefore, to reduce this sensitivity, performance robustness assessment of low-energy buildings considering uncertainties should be assessed in the design phase. The probability of occurrences of these uncertainties are usually unknown and hence, scenarios are essential to assess the performance robustness of buildings. Therefore, a non-probabilistic robustness assessment methodology, based on scenario analysis, is developed to identify robust designs. Maximum performance regret calculated using the minimax regret method is used as the measure of performance robustness. In this approach, the preferred robust design is based on optimal performance and performance robustness. The proposed methodology is demonstrated using a case study with a policymaker as the decision maker. The proposed methodology can be used by designers and consultants to aid decision makers in the design phase to identify robust low-energy building designs that deliver preferred performance in the future operation.