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Abstract An attempt has been made to evaluate the total primary energy inputs to major building materials. These are then followed through to their usage in various types of housing construction to obtain an estimate of the total energy requirement for housebuilding. Typical two storey houses are found to require between 95 and 180 GJ of primary energy input (for major building components only). For large blocks of flats, constructed in reinforced concrete, the energy requirement is about 230–265 GJ per flat. Comparisons have been made between different types of construction, but the large regional variations make the comparisons very dependent on location.

Rapid industrialisation and urbanisation are contributing to the entry of emerging contaminants into the environment, posing a significant threat to soil health and quality. Therefore, several remediation technologies have been investigated and tested at a field scale to address the issue. However, these remediation technologies face challenges related to cost-effectiveness, environmental concerns, secondary pollution due to the generation of by-products, long-term pollution leaching risks, and social acceptance. Overcoming these constraints necessitates the implementation of sustainable remediation methodologies that prioritise approaches with minimal environmental ramifications and the most substantial net social and economic advantages. Hence, this review delves into diverse contaminants that threaten soil health and quality. Moreover, it outlines the research imperatives for advancing innovative remediation techniques and effective management strategies to tackle this concern. The review discusses a remediation treatment train approach that encourages resource recovery, strengthens the circular economy, and employs a Life Cycle Assessment (LCA) framework to assess the environmental impacts of different remediation strategies. Additionally, the study explores mechanisms to integrate sustainability principles into soil remediation practices. It underscores the necessity for a comprehensive and systematic approach that takes into account the economic, social, and environmental consequences of remediation methodologies in the development of sustainable solutions.

The overarching goal of this work is to develop and demonstrate methods that support effective agro-pastoral risk management in a changing climate. Disaster mitigation strategies, such as the Sendai Framework for Disaster Risk Reduction (SFDRR), emphasize the need to address underlying causes of disaster risk and to prevent the emergence of new risks. Such assessments can be difficult, because they require transforming changes in meteorological outcomes into sector-specific impact. While it is common to examine trends in seasonal precipitation and precipitation extremes, it is much less common to study how these trends interact with crop and pasture water needs. Here, we show that the Water Requirement (WR) component of the widely used Water Requirement Satisfaction Index (WRSI) can be used to enhance the interpretation of precipitation changes. The WR helps answer a key question: was the amount of rainfall received in a given season enough to satisfy a crop or pasture's water needs? Our first results section focuses on analyzing spatial patterns of climate change. We show how WR values can be used to translate east African rainfall declines into estimates of crop and rangeland water deficits. We also show that increases in WR, during recent droughts, has intensified aridity in arid regions. In addition, using the PWB, we also show that precipitation increases in humid areas of western east Africa have been producing increasingly frequent excessive rainfall seasons. The second portion of our paper focuses on assessing temporal outcomes for a fixed location (Kenya) to support drought-management scenario development. Kenyan rainfall is decreasing and population is increasing. How can we translate this data into actionable information? The United Nations and World Meteorological Organization advise nations to proactively plan for agro-hydrologic shocks by setting aside sufficient grain and financial resources to help buffer inevitable low-crop production years. We show how precipitation, WR, crop statistics, and population data can be used to help guide 1-in-10 and 1-in-25-year low crop yield scenarios, which could be used to guide Kenya's drought management planning and development. The first and second research components share a common objective: using the PWB to translate rainfall data into more actionable information that can inform disaster risk management and development planning.

Accurate building construction cost prediction is critical, especially for sustainable projects (i.e., green buildings). Green building construction contracts are relatively new to the construction industry, where stakeholders have limited experience in contract cost estimation. Unlike conventional building construction, green buildings are designed to utilize new technologies to reduce their operations’ environmental and societal impacts. Consequently, green buildings’ construction bidding and awarding processes have become more complicated due to difficulties forecasting the initial construction costs and setting integrated selection criteria for the winning bidders. Thus, robust green building cost prediction modeling is essential to provide stakeholders with an initial construction cost benchmark to enhance decision-making. The current study presents machine learning-based algorithms, including extreme gradient boosting (XGBOOST), deep neural network (DNN), and random forest (RF), to predict green building costs. The proposed models are designed to consider the influence of soft and hard cost-related attributes. Evaluation metrics (i.e., MAE, MSE, MAPE, and R2) are applied to evaluate and compare the developed algorithms’ accuracy. XGBOOST provided the highest accuracy of 0.96 compared to 0.91 for the DNN, followed by RF with an accuracy of 0.87. The proposed machine learning models can be utilized as a decision support tool for construction project managers and practitioners to advance automation as a coherent field of research within the green construction industry.

The power plant sector is adopting the co-firing of biomass and solid recovered fuel (SRF) with coal in an effort to reduce its environmental impact and costs. Whereas this intervention contributes to reducing carbon emissions and those of other pollutants related with the burning of fossil fuel, it may also result in hidden impacts that are often overlooked. When co-firing, the physical and chemical properties of the mixed fuels and the subsequent technical implications on the process performance and by-products are significant. Interconnections between multiple values nested within four domains of value, i.e. environmental, economic, technical and social, mean that changes in the one domain (in the co-firing case, the technical one) can have considerable implications in the other domains as well. In this study, using a systematic and flexible approach to conceptualising multi-dimensional aspects associated with the co-firing of biomass and SRF with coal, we unveil examples of such interconnections and implications on overall value delivered through the use and recovery of waste resources. Such an analysis could underpin the selection of useful metrics (quantitative or semi-quantitative descriptors) for enabling a systemic multi-dimensional value assessment, and value's distribution amongst interconnected parts of resource recovery systems; key in enabling sound analysis and decision-making.

Abstract Energy modelling is the process of using mathematical models to develop abstractions and then seek insights into future energy systems. It can be an abstract academic activity. Or, it can insert threads that influence our development. We argue therefore, that energy modelling that provides policy support (EMoPS) should not only be grounded in rigorous analytics, but also in good governance principles. As, together with other policy actions, it should be accountable. Almost all aspects of society and much of its impact on the environment are influenced by our use of energy. In this context, EMoPS can inspire, motivate, calibrate, and ‘post assess’ energy policy. But, such modeling is often undertaken by too few analysts under time and resource pressure. Building on the advances of ‘class leaders’, we propose that EMoPS should reach for practical goals — including engagement and accountability with the communities it involves, and those it will later affect. (We use the term Ubuntu, meaning ‘I am because you are’ to capture this interdependency). We argue that Ubuntu, together with retrievability, repeatability, reconstructability, interoperability and auditability (U4RIA) of EMoPS should be used to signal the beginnings of a new default practice. We demonstrate how the U4RIA principles can contribute in practice using recent modelling of aspirational energy futures by Costa Rica as a case study. This modelling effort includes community involvement and interfaces and integrates stakeholder involvement. It leaves a trail that allows for its auditing and accountability, while building capacity and sustainable institutional memory.

Abstract The optimized green-grey infrastructures are promising solutions to combat the urban flood and water quality problems which have been severe owe to the increasing urbanization and climate change. However, the focusses in existing researches have been either on finding the best strategy by scenario analysis method or optimal design of LID practices under the hypothesis of unchanged grey infrastructures. Little is known regarding the synergistic effect of synchronous optimization design of both green and grey infrastructures. In the study, we conduct green-grey infrastructures synchronous optimization by modifying the decision variables of traditional simulation-optimization frameworks and investigate how external uncertainties will affect their performance. The methodology was applied to a case study in Suzhou, China. The results showed that although the cost of green-grey synchronous optimized scenarios is lower than that of green optimized only scenarios by 1.69–4.19 thousand USD per km2, the runoff/pollutants reductions of green-grey synchronous optimized scenarios are 0.11%–5.24% higher than that of green optimized only scenarios. In the green-grey synchronous optimized scenarios, green infrastructures can contribute to runoff/pollutants control by 50%–63%/62%–70%, while grey infrastructures can contribute to the remaining part by 37%–50%/30%–38%.

The authors compare the energy consumption and CO2 emissions from vehicles using internal combustion engines (ICE), battery electric vehicles (BEV), fuel cell electric vehicles (FCEV), and two types of hybrid vehicles, BEV-ICE hybrid and BEV-FCEV hybrid. This paper considers several scenarios for four countries’ electricity production from primary energy sources to estimate total CO2 release. Energy consumption of the vehicle per 100 km, emissions during manufacturing, battery production, and lifecycle of the vehicle are considered in the total amount evaluation of CO2 released. The results show that with current technologies for battery manufacturing, and a significant proportion of national grid electricity delivered by fossil fuels, BEV is the best choice to reduce carbon emissions for shorter driving ranges. In the case of electricity generation mainly by low-carbon sources, FCEV and BEV-FCEV hybrid vehicles end up with lower carbon dioxide emissions. In contrast, with electricity mainly generated from fossil fuels, electric vehicles do not reduce CO2 emissions compared to combustion cars.