Several methods of processing wood into strong, durable products for the construction industry provide transformative opportunities to substitute for less sustainable building materials. Carbon storage is a further advantage, with the added possibility of combustion for bioenergy at end of life. Intense research activity in this area is expected to open up new markets for wood fiber during the lifetime of trees now being planted. Here, wood-derived materials are classified according to the particle size, from metres to nanometres, into which the wood is fragmented before reconstitution. Materials made by densifying or chemically modifying solid wood with no fragmentation are already in production for exterior doors, window frames and cladding, with improved uniformity and stability compared with unmodified wood. Pre-commercial developments promise further gains in durability and strength. Emerging developments extend these process technologies to wood that has been chipped or stranded or pulped, retaining the above advantages over raw timber for weather-facing applications and adding processability by moulding or extrusion. Crucially, the raw material does not then need to be sawn timber but can be bioenergy-grade wood biomass. This will facilitate afforestation strategies that combine the aims of carbon sequestration and biodiversity.

High-rise buildings represent technological, urban and life-style trends of the modern urban landscape, yet there are limited data regarding their embodied carbon and environmental impacts, particularly when compared to low- or mid-rise buildings. Given that the projected growth of the global urban population by 2050 requires cities with higher density and potentially greater number of high-rise buildings, it is crucial to develop a clear understanding of the embodied carbon and environmental impacts of high-rise buildings. This article utilizes the building in-formation modelling (BIM) based life cycle assessment (LCA) in Revit and Tally to examine the embodied carbon and environmental impacts of an actual high-rise building structure case-study in Chicago that uses hybrid concrete steel structure. The results show that the embodied carbon and environmental impacts of the high-rise building structure are dominated by the impacts of the product stage in the building life cycle, and concrete as the main structural material. Specifically, this study reveals that concrete constitutes a substantial 91% share of the total mass of the building structure, with 74% contribution to life cycle global warming potential, 53% to acidification po-tential, 74% to eutrophication potential, 74% to smog formation potential, and 68% to non-renewable energy. On the other hand, steel accounts for 9% of the building structure mass, estimated to constitute 26% of the global warming potential, 47% to acidification potential, 26% to eutrophication potential, 26% to smog formation potential, and 32% to non-renewable energy.

As the largest contributor of carbon emissions in China, the building sector currently relies mostly on enterprises’ own effort to report carbon emissions, which usually results in challenges related to information transparency and workload for regulatory bodies, who play an otherwise vital role in controlling the building sector’s carbon footprint. In this study, we established a novel regulatory model known as QCEPM (Quantitative Carbon Emission Prediction Model) by conducting multiple linear regression analysis using the quantities of concrete, rebar, and masonry structures as independent variables, and the embodied carbon emissions of a building as the dependent variable. We processed the data in the detailed quantity list of 20 multi-story frame-structure buildings and fed them to QCEPM for solution. Comparison of the QCEPM-calculated results against the time-consuming and error-prone manual calculation results suggested a mean absolute percentage error (MAPE) of 2.26%. Using this simplified model, regulatory bodies can efficiently supervise the embodied carbon emissions in multi-storey frame structures by setting up carbon quota for a project in its approval stage, allowing the construction enterprise to carry out dynamic control over the audited three most important building materials throughout a project’s planning and implementation phase.

Abstract: Embodied pedagogy maintains that teaching and learning abstract concepts can benefit significantly from integrating bodily movements in the process. Here we explored the involvement of eye movements in the process of collective embodied learning of a concept in physics - angular velocity. Embodied learning was accomplished by the subjects forming a line that rotated around a central object, in this case, a bottle. We tracked the gaze resulting from eye and head movements in 12 subjects, who both actively participated in the collective embodied exercise and passively watched it. The tracking data of 7 of these 12 subjects passed our tracking reliability criteria in all trials and are reported here. During active learning, the learners tended to look ahead of the rotating line (by 35.18±14.82 degrees). In contrast, while passively watching others performing the task, the learners tended to look directly at the line. Interestingly, while the learners were performing the collective exercise they were unaware of looking ahead of the rotating line. We discuss possible differences between active and passive embodied learning that might be indicated by the observed differences in gaze control.

This article aims to assess the benefits of floor-slab insulation measures using extruded polystyrene (XPS) and polyisocyanurate (polyiso) insulation materials at various levels of insulation thicknesses for a detached residential building. The EnergyPlus simulation analysis was carried out within the seven energy zones (represented by eight locations) of South Africa in accordance with the South African national code for building energy efficiency (SANS10400-XA). The energy savings and payback periods due to use of the insulation over a lifecycle period of 50 years were assessed. Cape Town (zone 4) behaved differently from other locations and hardly benefitted from the application of floor-slab insulation measures. Generally, polyiso insulation performed better than XPS, for vertical gap insulation. For vertical gap insulation, lower insulation thicknesses required higher insulation depths to maximize energy savings. Similarly, lower insulation thicknesses required higher perimeter insulation widths to maximize energy savings, for the horizontal perimeter insulation method. The locations that benefitted most from vertical gap floor-slab insulation were Pretoria (zone2), Kimberley (zone6), Nelspruit (zone3), Fraserburg (zone7), Welkom (zone1), Mthatha (zone5), Ixopo (zone5H) and Cape Town (zone4) in that order. This order was almost similar with those for the horizontal perimeter floor-slab insulation and horizontal full floor-slab insulation methods.

We develop new theory from a broad case-study approach to explore a better understanding of what constitutes knowledge and its value as identifiable to human-machine teams. From past research, this trail of exploration will lead to these initial questions: What is the value of debate in the furtherance of knowledge? Will machines with AI be able to contribute to a debate if we humans cannot define it or determine its value sufficiently for a machine’s understanding, contribution, exploration and identification? Like humans, as teammates, machines must be able to determine with AI what constitutes the usable knowledge that contributes to a team’s success in the field (e.g., testing "knowledge" in the field, identifying new knowledge, using knowledge to develop innovation) or its failure (viz., trouble shooting; identifying weaknesses; discovering vulnerabilities, hiding by deception). It matters not whether a debate is public, private or unexpressed by an individual human or machine agent alone; we speculate in this exploration that the process advances the science of autonomous human-machine teams and assists in interpretable machine learning. We conclude with questions and a speculation: How does a human become aware or express its awareness of knowledge? Can a machine be as expressive as its human teammates? And how does a human-machine teammate become aware that its teammates possess sufficient knowledge to perform a task? We speculate that the structure of “knowledge," once found, is resistant to alternatives (i.e., it is ordered); that its functional utility is generalizable; and that its applications are multifaceted (akin to maximum entropy production). The complexity of the team is taken into consideration in our search for knowledge, which can also be used as an information metric.

The link between language processing and motor systems has been the focus of increasing interest to Cognitive Neuroscience. Some classical papers studying Event Related Potentials (ERPs) induced by linguistic stimuli have found differences in electrophysiological activity when comparing action and non-action words; more specifically, a bigger p200 for action words. On the other hand, a series of studies have validated the use of a grip force sensor (GFS) to measure language-induced motor activity during both isolated words and sentence listening, finding that action words induce an augmentation in the grip force around 250-300 ms after the onset of the stimulus. The purpose of the present study is to combine both techniques to assess if the p200 is related to the augmentation of the grip force measured by a GFS. We measured ERP and GFS changes elicited by listening to action and non-action words while maintaining an active grasping task in 10 healthy subjects. Our results show that the amplitude of the p200 in central electrodes is correlated to the augmentation in the GFS around 300 ms induced by linguistic stimuli. To our knowledge, this is the first study where the electrophysiological activity and the changes in the grip force induced by auditory language processing are put together, opening new venues of interpretation for the sensorimotor interaction in language processing.

In Europe, the recent application of regulations oriented to zero-energy buildings and climate neutrality in 2050 has led to a reduction in energy consumption for heating and cooling in the construction sector. The thermal insulation of the building envelope plays a key role in this process and the requirements about the maximum allowable thermal transmittance are defined by country-specific guidelines. Typically, high insulation values provide low energy consumption for heating, however, they may paradoxically imply the risk of overheating in summer period and thus negatively affect the overall performance of the building. In addition, the embodied energy and related emissions caused by the manufacturing and transportation processes of thermal insulation cannot be further neglected in the evaluation of the best optimal solution. Therefore, this paper aims to evaluate the influence in terms of embodied and operational energy of various walls’ thermal insulation thicknesses on residential buildings in Europe. To this end, the EnergyPlus engine was used for the energy simulation within Ladybug & Honeybee tools, by parametrically conducting multiple iterations; 53 variations of external wall U-value, considering high and low thermal mass scenarios, were simulated for 100 reference cities of the European context, using a representative multifamily building as a reference. The results demonstrate that massive walls generally perform better than lightweight structures and, of course, the best solution in terms of energy varies according to each climate. The optimal values are graphically reported on the map of Europe according to specific climatic features, providing a guidance for new constructions and building retrofit.

In this review, our goal is to design and test quantum-like algorithms for Artificial Intelligence (AI) in open systems to structure a human-machine team to be able to reach its maximum performance. Unlike the laboratory, in open systems, teams face complexity, uncertainty and conflict. All task domains have complexity levels, some have low, and others high levels. Complexity in this new domain is affected by the environment and the task, which are both affected by uncertainty and conflict. We contrast the individual and interdependence approaches to teams. The traditional and individual approach focuses on building teams and systems by aggregating the best available information for individuals, their thoughts, behaviors and skills. Its concepts are characterized chiefly by one-to-one relations between mind and body, a summation of disembodied individual mental and physical attributes, and degrees of freedom corresponding to the number of members in a team; however, this approach is characterized by the many researchers who have invested in it for almost a century with few effects that can be generalized to human-machine interactions; by the replication crisis of today (e.g., the invalid scales for self-esteem, implicit racism, and honesty); and by its many disembodied concepts. In contrast, our approach is based on the quantum-like nature of interdependence. It allows us to theorize about the bistability of mind and body, but it proposes a measurement problem and a non-factorable nature. Bistability addresses team structure and performance; the measurement problem solves the replication crisis; and the non-factorable aspect of teams reduces the degrees of freedom and the information derivable from teammates to match findings by the National Academies of Science. We begin with a review of the science of teams and a focus on human-machine team research in the laboratory versus the open; justifications for rejecting traditional social science while supporting our approach; a full understand of the complexity of the domain, tasks in the domain, and how teams address both; the mathematics involved; a review of results from our model versus the open field; a discussion of the results; conclusions; and the path forward to successfully advance the science of interdependence and autonomy.

Background and objectives: The language-induced grip force modulation (GFM) can be used to better understand the link between the language and motor functions as an expression of the embodied language. However, the contribution of each brain hemisphere to the language-induced GFM is still unclear. Using six different action verbs as stimuli, this study evaluated the GFM of the left hand in unimanual task to characterize the left- and right-hemisphere contributions. Materials and Methods: The left-hand GFM of 20 healthy consistent right-handers subjects was evaluated using the verbs “to write”, “to hold”, “to pull”(left-lateralized central processing actions), “to draw”, “to tie”, and “to drive” (bi-hemispheric central processing actions) as linguistic stimuli. The time between the word onset and the first interval of statistical significance regarding the baseline (RT) was also measured. Results: The six verbs produced language-induced GFM. The modulation intensity was similar for the six verbs, but the RT was variable. The verbs “to draw”, “to tie”, and “to drive”, whose central processing of the described action is bihemispheric showed a longer Rt compared to the other verbs. Conclusions: The possibility that an action is performed by the left-hand does not interfere with the occurrence of GFM when this action verb is employed as linguistic stimulus. Therefore, the language-induced GFM seems mainly rely on the left hemisphere, and the engagement of the right hemisphere seems to slow down the increase in the GFM intensity.

Efforts to quantify and reduce greenhouse gas (GHG) emissions of the built environment often neglect embodied emissions, instead focusing on reducing emissions from building operations. Utilizing sustainably sourced mass timber offers low embodied carbon alternatives to traditional concrete and steel structural systems, however the variability in embodied carbon for different mass timber approaches remains understudied. In this study, we used life cycle assessment (LCA) to compare the whole building embodied carbon of nine mass timber design options and two typical concrete and steel reference cases for an eight-story mixed-use building, ensuring structural, acoustic, thermal, programmatic, and fire-rating equivalence between the designs. The study found that the mass timber designs vary significantly, ranging between a 14-52% reduction in whole building embodied carbon from the most impactful reference case, and a 31-73% reduction when considering the structural systems alone. This study demonstrates the value that whole building LCA (WBLCA) provides as a primary driver for low-carbon structural system design and architectural development of mass timber buildings, beyond that of single material comparisons using environmental product declarations (EPDs).

Objective: To evaluate the effects of left intraparietal sulcus (IPS) inhibition by repetitive transcranial magnetic stimulation (rTMS) on grip force modulation (GFM) for both hands during a unimanual task. Methods: GFM induced by manual action-verb listening was evaluated for each hand in a unimanual task, and the motor-evoked potentials (MEP) were recorded for both left and right hemispheres prior to and following the left IPS inhibition. Left IPS inhibition was obtained by rTMS (5 min of 1.0 Hz, 60% of maximal stimulator output) of the international 10–20 system P3 point. Seven healthy right-handed subjects were evaluated. Results: One-way repeated measures ANOVA found that MEP amplitude and duration increased following IPS inhibition in the left hemisphere and did not change in the right hemisphere. Language-induced modulation did not change in the left hemisphere, while it was significantly attenuated in the right hemisphere. Since IPS inhibition increased the left primary motor cortex (M1) excitability, the maintenance of language-induced modulation intensity suggests it was also attenuated. Conclusion: Left IPS inhibition increased left M1 excitability without changing right M1 excitability, while attenuating the language-induced GFM for both the left and right hands.

Global teacher shortage is a serious concern with grave implications for the future of education. This calls for novel ways of addressing teacher roles. The economic benefits of tireless labour inspires the need for teachers who are unlimited by natural human demands, highlighting consideration for the affordances of robotics and Artificial Intelligence in Education (AIED) as currently obtainable in other areas of human life. This however demands designing robotic personalities that can take on independent teacher roles despite strong opinions that robots will not be able to fully replace humans in the classroom of the future. In this paper, we argue for a future classroom with independent robot teachers, highlighting the minimum capabilities required of such personalities in terms of personality, instructional delivery, social interaction and affect. We describe our project on the design of a robot teacher based on these. Possible directions for future system development and studies are highlighted.

Children’s beneficial relationships with animals are well known. Companion animals, particularly dogs have become an integral part of family life and children’s material culture. Aside the proven physiological benefits there is little research about what children say about their relationships with animals and how they describe them. In this paper we bring together both horse-human and dog-human interactions, finding common ground for understanding the complexity of human development, well-being and flourishing. Dogs in schools are fast becoming a trend in helping support and enhance children’s learning as well as their social and emotional well-being. Studies have shown that the very presence of a dog can increase children’s concentration, executive function and behavior. Also, equine therapy is gaining momentum and empirical studies are showing noteworthy benefits to children and young people. However, the lack of children’s voices means that the mechanisms for these benefits are somewhat unknown and unclear. In seeking to explore this, the authors utilize a visual, sensory and diffractive ethnographic approach to illuminate and illustrate, experiment and re-enact, how the children relate, share spaces and multiple subjectivities with their classroom canine, “Ted” and companion horse “Henry”. ”Henry” is part of a programme in which youngsters care for and engage in activities with horses.

Spontaneous, unwilled subjective imagery and symbols often emerge in psychotherapy that can appear baffling and confound interpretation. Moreover, early psychoanalytic theories seemed to diverge as often as they agreed on the meaning of such content. Nevertheless, reviewing key findings in the empirical science of spontaneous thought, as well as insights gleaned from neurosciences and especially embodied cognition, it is now possible to construct a more coherent theory of interpretation that is clinically useful. Given that thought is so thoroughly embodied, it is possible to demonstrate that universalities in human physiology yield universalities in thought. Such universalities can then be demonstrated to form a kind of biologically directed universal “code” for understanding spontaneous symbolic expressions that emerge in psychotherapy. An example is given that illustrates how this can be applied to clinical encounters.

Every country has a different philosophy of education, which in contemporary China manifests itself in ideological guidance at all levels and in all types of schools. China's education has entered a new era, but ideological and political education is still needed in schools and colleges, and its core interest is to cultivate newcomers of the times who can take on the important task of national rejuvenation; in other words, the education sector has to ensure the sustainability of the cultivation of talents as well as the sustainability of socialist development. In this context, ideological and political education has initiated a triple spatial shift, i.e., from classroom space to cyberspace, from physical space to virtual space, and from campus space to social space. The triple spatial transfer has a profound historical logic, which is the inevitable process of the continuous development of ideological and political education itself, and the historical convergence of the Party's proposition, people's expectation and social development in this era. The requirements of the external situation, the needs of internal development, and the needs of the political task constitute the direct reality of the triple spatial transformation. The future direction of the spatial transformation of ideological and political education will be reflected in the intersection of subject and space, the collision of individuality and commonality, and the fusion of unity and diversity.

A capacity to reflect on experience and learn through it (reflective and reflexive practice) is deemed an essential part of ethical practice and research. Challenging traditional research approaches this arts-based, inter-disciplinary, Ph.D. project explores artistic practice as a research methodology, and how learning through experiences of ‘making’ things may enhance and amplify our understanding of the more affective aspects of human situations and experience. Grounded in the author’s artistic practice and learning through experiences of ‘making’, the author assembles and adapts frames from psychoanalysis, art (psycho)therapy, and the arts with/through which to observe and document their experience of an organisational situation. Describing the development of their method in three phases, analysis takes place through returning to revisit and rework artistic material produced, and engaging in conversation with the emergent material and others in response. Results take the form of artworks and artistic projects, including documentation of process. Emergent threads draw attention to the speculative, entangled, and affective nature of the research process, the reflexivity generated through moving and handling material, and the reflexive work of undergoing, foregrounding an ethics of responsibility, attention, and care for/of the body. As a space for imaginative encounter and performative enactment, and a site for reflexivity through which one may be pressed to notice and feel more acutely, the author argues that the research value resides in the capacity of this method to embrace complex relationalities, and engage our affective, ethical sensibilities in ways that may not emerge through more traditional approaches to reflective/reflexive practice(s). This has implications for both art therapy practice and research, amplifying the learning opportunities afforded by moving, modifying, and assembling things differently, the embodied ‘work’ of art as a method of enquiry, the cultural sensitivity of documentary fragments captured in/through various media and voices, and the value of collaborative endeavours where meaning is co-constructed in conversation.

Manipulating cloth-like deformable objects (CDOs) is a long-standing problem in the robotics community. CDOs are flexible (non-rigid) objects that do not show a detectable level of com- pression strength while two points on the article are pushed towards each otherand include objects such as ropes (1D), fabrics (2D) and bags (3D). In general, CDOs’ many degrees of freedom (DoF) introduce severe self-occlusion and complex state-action dynamics as significant obstacles for perception and manipulation systems. These challenges exacerbate existing issues of modern robotic control methods such as imitation learning (IL) and reinforcement learning (RL). This review focuses on the application details of data-driven control methods on four major task families in this domain: cloth-shaping, rope manipulation, dressing and bag manip- ulation. Furthermore, we identify specific inductive biases in these four domains that present challenges for more general IL and RL algorithms, and summarise the future direction for the development of the field.

This study presents a literature review focused on nonverbal communication in human-robot interaction (HRI) that involves service robots with social capabilities. We aim to list the types of robots used and nonverbal communication cues examined in the reviewed studies; and the main research objectives, participant characteristics, data collection methods, and primary findings of these studies. To achieve this, we conducted a literature review of 39 relevant open access academic papers published from 2006 to 2023 that examined the utilization of nonverbal cues by both humans and robots during HRI. The results suggest that enhancing the quality of communication between humans and service robots must be improved, while there are several aspects that require more thorough exploring, needed to strengthen robot self-efficacy, trust and trustworthiness in HRI or overcome cultural differences. The results emphasize the importance of nonverbal communication in shaping the dynamics of interactions between humans and service robots.

Microbes are the most prevalent and widely-distributed life form on planet Earth. They are also cognitive organisms with memory and superior problem-solving skills that use quantum phase transitions during their remarkably-effective gathering and dissemination of information. This narrative review explores the biological ramifications of human holobionts physically embodying the human microbiome and connecting to the Internet of Microbes (IOM) beyond our body. Within the review, we consider: 1) the ways in which our microbial co-partners exerted control over our ancestors and continue to influence our current human generation, 2) how they mold virtually every key aspect of our life and even remain with our body in death and 3) how inward-looking contemplative tools such as meditation and embodied cognition provide an ideal opportunity to connect to the microbial information super highway and tap the full range of human holobiont capacities.

In this paper we present a review of recent developments in AI towards the possibility of an artificial intelligence equals that of human intelligence. AI technology has always shown a stepwise increase in its capacity and complexity. The last step took place several years ago, with the increased progress in deep neural network technology. Each such step goes hand in hand with our understanding of ourselves and our understanding of human cognition. Indeed, AI was always about the question of understanding human nature. AI percolates into our lives, changing our environment. We believe that the next few steps in AI technology, and in our understanding of human behavior, will bring about much more powerful machines, flexible enough to resemble human behavior. In this context, there are two research fields: Artificial Social Intelligence (ASI) and General Artificial Intelligence (AGI). The authors also allude to one of the main challenges for AI, embodied cognition, and explain how it can viewed as an opportunity for further progress in AI research.

This study explores the limitations of current AI systems, which predominantly function in the digital realm, and investigates the transformative potential of embodied AI. Embodied AI involves AI agents equipped with physical bodies, enabling them to interact directly with the physical world. This research focuses on how embodied AI can bridge the "embodiment gap" and enhance human-machine collaboration and learning in dynamic environments. The research examines the fundamental differences between traditional AI systems and embodied AI, emphasizing the importance of physical interaction for contextual understanding, adaptive learning, and intuitive human-machine collaboration. It explores various applications of embodied AI, including robotics, autonomous vehicles, and assistive technologies, demonstrating how physical embodiment can improve performance, safety, and user experience. Through experimental studies and real-world case analyses, the study highlights the advantages of embodied AI in tasks requiring situational awareness, dexterity, and real-time decision-making. It also addresses the challenges associated with developing and deploying embodied AI systems, such as sensor integration, real-time processing, and human-machine interface design. Findings indicate that embodied AI can significantly improve the efficacy of AI systems in dynamic and unpredictable environments. By leveraging physical embodiment, AI agents can better understand and respond to their surroundings, facilitating more natural and effective interactions with humans. The research concludes with recommendations for advancing embodied AI, including interdisciplinary collaboration, investment in sensor and actuator technologies, and the development of standardized frameworks for embodied intelligence.

Chatbots are intelligent conversational computer systems designed to mimic human conversation to enable automated online guidance and support. The increased benefits of chatbots led to their wide adoption by many industries in order to provide virtual assistance to customers. Chatbots utilise methods and algorithms from two Artificial Intelligence domains: Natural Language Processing and Machine Learning. However, there are many challenges and limitations in their application. In this survey we review recent advances on chatbots, where Artificial Intelligence and Natural Language processing are used. We highlight the main challenges and limitations of current work and make recommendations for future research investigation

In Germany, as in many developed countries, over 60% of buildings were constructed before 1978, where most are in critical condition, requiring either demolition with plans for redevelopment or renovation and rehabilitation. Given the urgency of climate action and relevant sustainable development goals set by the United Nations, more attention must be shifted toward the various sustainability aspects when deciding on the strategy for renovation or redevelopment of existing buildings. To this end, this study focused on developing a smart decision support framework for aging buildings based on lifecycle sustainability considerations. The framework integrated digital technological advancements, such as building information modeling (BIM), point clouds processing with field information modeling (FIM)®, and structural optimization, together with life-cycle assessment to evaluate and rate the environmental impact of different solutions. Three sustainability aspects, namely, cost, energy consumption, and carbon emissions, were quantitatively evaluated and compared in two scenarios, namely, renovation, and demolition or deconstruction combined with redevelopment. A real building constructed in 1961 was the subject of the experiments to validate the framework. The result outlined the limitations and advantages of each method in terms of economics and environmental impact. It was further observed that optimizing the building design with the goal to reduce embodied energy and carbon in compliance with modern energy standards, was crucial to improve overall energy performance. This work demonstrated that the developed BIM-based framework to assess the environmental impact of rehabilitation work in aging buildings can provide effective ratings to guide decision-making in real-world projects.

In this paper, I give readers an idea of what some scholars are interested in, what I found interesting, and what may be of future interest in the philosophy of emotion. I begin with a brief overview of the general topics of interests in the philosophy of emotion. I then discuss what I believe to be some of the most interesting topics in the contemporary discourse, including questions about how philosophy can inform the science of emotion, conceptions of the mind and the mind-body problem, concerns about perception, cognition, and emotion, along with questions about the place of 4E approaches and meta-semantic pluralist approaches in the embodied cognitive tradition. Finally, I discuss the emerging field of cultural evolution, the import of a dual-inheritance theory in this emerging field, and I propose a possible way to integrate the frameworks of dual-inheritance theory and meta-semantic pluralism to demonstrate at least one way in which the philosophy of emotion can contribute to the emerging field of cultural evolution.

The building sector has a big potential to reduce the material resource demand needed for building construction and therefore, greenhouse gas (GHG) emissions. Digitalisation can help to make use of this potential and improve sustainability throughout the entire building’s life cycle. One way to address this potential is through the integration of Life-Cycle Assessment (LCA) into the building process by employing Building Information Modelling (BIM). BIM can reduce the effort needed to carry out an LCA and therefore facilitate the integration into the building process. A review of current industry practice and scientific literature shows two main approaches to address BIM-LCA integration. Either the LCA is performed in a simplified way at the beginning of the building process, or it is done at the very end when all the needed information is available, but it is too late for decision-making. One reason for this is the lack of methods, workflows and tools to implement BIM-LCA integration over the entire building process. Therefore, the main objective of this study is to develop an integrated BIM-LCA workflow implemented into a method for the whole building process using an existing structure for cost estimation. A tool is created and used in a case study in Switzerland to test the developed approach. The results of this study show that LCA can be performed continuously in each building phase over the entire building process using existing BIM modelling techniques. The main benefit of this approach is that the re-work caused by the need for re-entering data and the usage of many different software tools that characterise most of the current LCA practices is minimised. Furthermore, decision-making, both at the element and building levels, is supported.