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With the promotion of environmental protection in the construction industry, the mission to achieve more sustainable use of resources during the production process of concrete is also becoming important. This study was conducted to assess the environmental sustainability of concrete slurry waste (CSW) management by life cycle assessment (LCA) techniques, with the aim of identifying a resource-efficient solution for utilisation of CSW in the production of partition wall blocks. CSW is the dewatered solid residues deposited in the sedimentation tank after washing out over-ordered/rejected fresh concrete and concrete trucks in concrete batching plants. The reuse of CSW as recycled aggregates or a cementitious binder for producing partition wall blocks, and the life cycle environmental impact of the blocks were assessed and compared with the conventional one designed with natural materials. The LCA results showed that the partition wall blocks prepared with fresh CSW and recycled concrete aggregates achieved higher sustainability as it consumed 59% lower energy, emitted 66% lower greenhouse gases, and produced lesser amount of other environmental impacts than that of the conventional one. When the mineral carbonation technology was further adopted for blocks curing using CO 2 , the global warming potential of the corresponding blocks production process was negligible, and hence the carbonated blocks may be considered as carbon neutral eco-product. [ABSTRACT FROM AUTHOR]

Sustainable building retrofit can provide a great opportunity to reduce the energy consumption of existing buildings to commensurate the climate policies and enhance the building energy performance. Given it strategic importance, there is no shortage of research studies on sustainable building retrofit globally in particular the retrofit measures and technologies for different types of buildings. Despite that, sustainable building retrofit is climate and location specific, and decision-makers are still facing challenges to identify an optimum set of retrofit measures for different climatic conditions according to the trade-off between energy saving and retrofit cost. One reason for that is because of the lack of available information especially those related to the cost of sustainable building retrofit measures. This study aims to develop an integrated energy-cost model for selecting the optimal retrofit solutions for existing high-rise residential buildings in various climatic zones in China. Based on in-depth literature review and analysis of published reports, different alternative retrofit measures for wall, window, shading system, heating and cooling systems, and renewable energy technologies for buildings in different climate zones of China are identified. The retrofit costs related to the identified sustainable building retrofit measures are analyzed by referring to the local renovation techniques and cost in each zone through developing a set of equations. A total 28 practical sustainable building retrofit measures along with a set of retrofit options are identified. A deterministic decision framework is developed to facilitate decision-makers achieving a specific energy saving target and evaluating the associated costs based on the identified retrofit measures for high-rise residential buildings in the studied climatic zones. The practical implications of the deterministic decision framework are examined through the prototype buildings in climatic zones being studies. The findings not only actively support the selection of cost-effective sustainable retrofit measures for high-rise residential buildings in China, but the methodological framework and its implications are also equally applicable to other countries with diverse climatic characteristics and standards of living. • Developed integrated energy-cost model for finding optimal retrofit solutions. • Identified sustainable retrofit measures based on climates, building features and retrofit cost. • Developed 28 retrofit measures for wall, window, shading, HVAC, and renewable energy systems. • Optimum retrofit solutions can be adopted from identified set of retrofit options. [ABSTRACT FROM AUTHOR]

Sustainable building retrofitting has attracted serious research attention in recent years as it can significantly reduce energy consumption and thus help achieve energy reduction targets set by governments around the world. In this paper, a sustainable retrofit decision-making model is developed to uncover the optimal set of retrofit solutions according to local climatic conditions, building features, and retrofit costs. Net present value (NPV), a common method for analyzing the feasibility of a building retrofit solution, is used to determine the optimal retrofit solution of existing high-rise residential buildings in a temperate zone and hot summer-cold winter zone, involving prototypical 1990s apartments of 116 and 53 m2 floor areas respectively. This reveals that the lighting system, wall insulation, and upgraded window glazing are essential optimal retrofit measures for the temperate zone, whereas the heating system and shading devices are also essential for the hot summer-cold winter zone. The results indicate that the optimal NPV can be obtained by pursuing a 40% energy saving, as it can result in energy saving of up to 50 kWh/m2/year and 95 kWh/m2/year at an average retrofit cost of approximately USD 1.30 and 3.20 m2/year in the temperate and hot summer-cold winter zones respectively. The sensitivities associated with the most influential optimal NPV and energy savings input parameters are critically analyzed; these can be used by decision-makers to determine the risks and uncertainties related to various cost-effective retrofit measures. The model can help in devising the most suitable sustainable retrofit measures for existing buildings commensurate with the energy reduction targets of policy and decision-makers in the two climate zones. It is also amenable to being adapted for other climatic zones to identify the most appropriate sustainable building retrofit for particular local climatic conditions and building characteristics. [ABSTRACT FROM AUTHOR]

Ability to forecast the waste electrical and electronic equipment (WEEE) generation can help formulate a robust future WEEE management system. Previous studies applied forecasting models, such as the grey model and artificial neural networks, to predict WEEE generation from a country perspective, leading to less accurate forecasts due to huge socio-economic differences in rural and urban areas. Additionally, there has been the incompatibility of a single forecasting model for all WEEE types, and this remained a research gap. Taking advantage of respective forecasting models, this study presents a hybrid model, Grey Artificial Neural Network, to forecast the WEEE generation of 31 province-level regions in China while evaluating the socio-economic analysis of seven WEEE types via Pearson correlation analysis. More than 70% of WEEE from province-level regions strongly correlates (R > ±0.8) with the gross domestic product and the population, whereas some top WEEE-generating province-level regions (i.e., Tianjin and Shanghai) correlate weakly to moderately. The root mean square error and mean absolute error of the developed Grey Artificial Neural Network hybrid model are the lowest at 8.29 and 6.48, compared to the grey model (13.53 and 11.13) and back-propagation neural network (9.21 and 7.22). Though the Grey Artificial Neural Network hybrid model has the lowest error, posterior mean square deviation ratio analysis indicates that this hybrid model is only suitable for washing machines, refrigerators, color televisions, and personal computers (urban area), while the back-propagation neural network is suitable for monochrome televisions, air conditioners, and personal computers (rural area). Compared to 2019, it is projected that an additional 32.92 M units of WEEE will be generated by 2025, suggesting that China should build at least 15 extra recycling centers (14% more based on 2016) to handle the increased WEEE generation. This study provides policy implications for effective WEEE monitoring and collection systems to build resilient WEEE management. [Display omitted] • Grey artificial neural network hybrid model with higher accuracy is developed. • Province's socio-economic correlation and e-waste prediction are conducted. • Top e-waste-generating province-level regions exhibit different socio-economic correlation. • China's e-waste will reach 286 M units in 2025, 13% more than in 2019. • Policy and strategy are recommended for the increased e-waste generation. [ABSTRACT FROM AUTHOR]

Energy simulation and prediction plays a vital role in energy policy and decision making. This study has been conducted to predict the future energy demand in the urban residential buildings of Chongqing a city in south west China. The comparative study adopts and compares the results of different demand models to improve estimation efficiency for future projections. A structured questionnaire survey was undertaken to collect primary household energy consumption data for inclusion in the annual energy consumption simulation model. An ANN model, two Grey models, a Regression model, a Polynomial model and a Polynomial regression model were used to forecast and compare demand. The precision of the models have been used statistical methods. The predicted results show that the total residential building energy and electricity consumption in urban areas of Chongqing is increasing rapidly. Based on MRPE (%) and the statistical tests, the study concluded that an ANN model is the most acceptable forecasting method of the six models. Hence, based on ANN model, urban residential building energy consumption will be at 1005 × 10 4 SCE and electricity consumption will be at 264.81 × 10 8 kW h in 2025 which is about three times and four times higher than that of the 2012, respectively. [ABSTRACT FROM AUTHOR]