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The Intergovernmental Panel on Climate Change (IPCC) report that to limit warming to 1.5 °C, Bioenergy with Carbon Capture and Storage (BECCS) is required. Integrated assessment models (IAMS) predict that a land area between the size of Argentina and Australia is required for bioenergy crops, a 3–7 time increase in the current bioenergy planting area globally. The authors pose the question of whether vertical farming (VF) technology can enable BECCS deployment, either via land sparing or supply. VF involves indoor controlled environment cultivation, and can increase productivity per unit land area by 5–10 times. VF is predominantly being used to grow small, high value leafy greens with rapid growth cycles. Capital expenditure, operational expenditure, and sustainability are challenges in current VF industries, and will affect the ability to utilise this technology for other crops. The authors argue that, whilst challenging, VF could help reach wider climate goals. Application of VF for bioenergy crops could be a game changer in delivering BECCS technologies and may reduce the land footprint required as well as the subsequent associated negative environmental impacts. VF bioenergy could allow us to cultivate the future demand for bioenergy for BECCS on the same, or less, land area than is currently used globally.

Climate change, environmental degradation, and limited resources are motivations for sustainable forest management. Forests, the most abundant renewable resource on earth, used to make a wide variety of forest-based products for human consumption. To provide a scientific measure of a product’s sustainability and environmental performance, the life cycle assessment (LCA) method is used. This article provides a comprehensive review of environmental performances of forest-based products including traditional building products, emerging (mass-timber) building products and nanomaterials using attributional LCA. Across the supply chain, the product manufacturing life-cycle stage tends to have the largest environmental impacts. However, forest management activities and logistics tend to have the greatest economic impact. In addition, environmental trade-offs exist when regulating emissions as indicated by the latest traditional wood building product LCAs. Interpretation of these LCA results can guide new product development using biomaterials, future (mass) building systems and policy-making on mitigating climate change. Key challenges include handling of uncertainties in the supply chain and complex interactions of environment, material conversion, resource use for product production and quantifying the emissions released.

Recently, robots have been widely adopted in the hospitality and tourism industry. Efficient robots can help hoteliers and tourism suppliers with their repetitive or manual labor. Due to the coronavirus disease (COVID-19) pandemic, there is an increasing number of publications on robotic applications in hospitality and tourism. However, a comprehensive literature review of this realm remains lacking. Therefore, to provide a holistic view of the existing literature on robotic applications in hospitality and tourism, this study reviewed 86 extant robotic application-related articles by conducting descriptive analysis and content analysis. The findings of this study showed that most of the existing relevant studies were conducted from the perspective of consumers in the hospitality context. Potential future research directions for academics are identified herein. Practical implications on robotic adoption are also provided for industry practitioners.

A variety of pipelines are distributed under urban roads. The upgrading of pipelines is bound to occupy certain road resources, compress the driving space of motor vehicles for a long time, aggravate the traffic congestion in the construction section, and then affect the traffic operation of the whole region. A reasonable layout of traffic signs for inducement to guide the traffic flow in the area where the construction section is located is conducive to promoting a balanced distribution of traffic flow in the regional road network, so as to achieve the reduction of automobile exhaust emissions and the sustainable development of traffic. In this paper, the layout optimization method of regional traffic signs for inducement is proposed. Taking the maximum amount of guidance information that the regional traffic signs can provide as the objective function, and taking the traffic volume, the characteristics of intersection nodes and the standard deviation of road saturation as the independent variables, the layout optimization model of guidance facilities is constructed, which can optimize the layout of traffic guidance signs in the area affected by the construction section, and achieve the goal that the minimum number of facilities can provide the maximum amount of guidance information. The results of the case study show that among the 64 alternative locations where traffic guidance signs can be set in the study area, eight optimal locations are finally determined as the setting points of guidance facilities through this model, and the effective increment of guidance information is the largest at this time. The model proposed in this paper can be used for reference to promote the sustainable development of traffic in the area where the construction section is located.

Informal Green Spaces (IGS) in towns and cities are areas of varied appearance, representing a wide range of aesthetic values. In this study, we have focused on how users perceive such space and which elements match some particular values. Based on the analysis of photographic images taken by chosen IGS visitors, the technique called Visitor-Employed Photography (VEP), we elicited three primary groups of IGS images, namely landscape, scenery, and special elements. It is possible to define several visual structures in each category, with highly preferable spatial fragments and particular attributes. They are aesthetic prototypes, centers of aesthetic preferences among users of informal green areas. Recognizing proximity to the preference core allows us to define the preferred IGS landscape aesthetics. Based on the data collected, we have concluded that IGS users prefer vast grass areas, dense groups of trees and shrubs, water, and the absence of anthropogenic elements.

The purpose of this research is to introduce a qualitative sustainability performance assessment framework for food supply networks, based on the perception of their key stakeholders’ upper management. Moreover, the paper provides industry insights by exemplifying the value of the proposed framework for the UK food industry. A critical review on the most acknowledged sustainability assessment methodologies and tools resulted in the synthesis of the proposed framework. An illustrative application follows, based on data from semi-structured interviews with C-level executives from key players of the UK poultry sector. The results demonstrate an easy-to-use approach, with a comprehensive and sharp outcome on supply chain sustainability performance assessment. Industry insights demonstrate an adequate sustainability performance with respect to the entire supply chain. A detailed view on different echelons reveals specific areas that could be improved, such as the environmental performance at both farming (production) and processing levels. This work extends the scope of current sustainability performance assessment tools by providing a tangible triple bottom-line overview, as well as echelon-specific and indicator-specific details, in a user-friendly, yet straightforward, way. UK food industry insights are valuable for practitioners and academics. The illustration is based exclusively on C-level executives’ viewpoint; thus, any generalization of the results should be considered to this effect. Supply chain stakeholders, policy-makers, and researchers could perform a quick and reliable supply network sustainability performance assessment.

Interest in urban food production is growing; recent research has highlighted its potential to increase food security and reduce the environmental impact of food production. However, resource demands of urban horticulture are poorly understood. Here, we use allotment gardens in the United Kingdom to investigate resource demands of urban horticultural production across the country. We conducted a nationwide citizen science project using year-long allotment ‘diaries’ with allotment gardeners (n = 163). We analysed a variety of resources: transportation; time; water use; inputs of compost, manure and topsoil; and inputs of fertilisers, pest control and weed control. We found that, overall, an allotment demands 87 annual visits, travelling 139 km to and from the plot; 7 fertiliser additions; 4 pest control additions; and 2 weed control additions. On average, each kilogram of food produced used 0.4 hours’ labour, 16.9 L of water, 0.2 L of topsoil, 2.2 L of manure, and 1.9 L of compost. As interest in urban horticultural production grows, and policy makers build urban horticultural spaces into future sustainable cities, it is of key importance that this is carried out in a way that minimises resource requirements, and we demonstrate here that avenues exist for the diversion of municipal compostable waste and household-level city food waste for this purpose.

This paper presents a comprehensive review of machine learning (ML) based approaches, especially artificial neural networks (ANNs) in time series data prediction problems. According to literature, around 80% of the world’s total energy demand is supplied either through fuel-based sources such as oil, gas, and coal or through nuclear-based sources. Literature also shows that a shortage of fossil fuels is inevitable and the world will face this problem sooner or later. Moreover, the remote and rural areas that suffer from not being able to reach traditional grid power electricity need alternative sources of energy. A “hybrid-renewable-energy system” (HRES) involving different renewable resources can be used to supply sustainable power in these areas. The uncertain nature of renewable energy resources and the intelligent ability of the neural network approach to process complex time series inputs have inspired the use of ANN methods in renewable energy forecasting. Thus, this study aims to study the different data driven models of ANN approaches that can provide accurate predictions of renewable energy, like solar, wind, or hydro-power generation. Various refinement architectures of neural networks, such as “multi-layer perception” (MLP), “recurrent-neural network” (RNN), and “convolutional-neural network” (CNN), as well as “long-short-term memory” (LSTM) models, have been offered in the applications of renewable energy forecasting. These models are able to perform short-term time-series prediction in renewable energy sources and to use prior information that influences its value in future prediction.