Search Results (92)

Climate change is intensifying the frequency and severity of extreme weather events, threatening the resilience of buildings and urban infrastructure. While technical solutions for climate adaptation in buildings are well documented, their economic viability remains a critical, yet underexplored, dimension of decision-making. This novel systematic review analyzes publications with an exclusive focus on climate adaptation strategies for buildings using cost-based evaluation methods. This review categorises the literature into three methodological clusters: Cost–Benefit Analysis (CBA), Life Cycle Costing (LCC), and alternative methods including artificial intelligence, simulation, and multi-criteria approaches. CBA emerges as the most frequently used and versatile tool, often applied to evaluate micro-scale flood protection and nature-based solutions. LCC is valuable for assessing long-term investment efficiency, particularly in retrofit strategies targeting energy and thermal performance. Advanced methods, such as genetic algorithms and AI-driven models, are gaining traction but face challenges in data availability and transparency. Most studies focus on residential buildings and flood-related hazards, with a growing interest in heatwaves, wildfires, and compound risk scenarios. Despite methodological advancements, challenges persist—including uncertainties in climate projections, valuation of non-market benefits, and limited cost data. This review highlights the need for integrated frameworks that combine economic, environmental, and social metrics, and emphasises the importance of stakeholder-inclusive, context-sensitive decision-making. Ultimately, aligning building adaptation with financial feasibility and long-term sustainability is achievable through improved data quality, flexible methodologies, and supportive policy instruments. Full article

A comprehensive life cycle cost (LCC) calculation model for engineering structures is proposed, in which economic, environmental, and social cost components are systematically integrated within a unified framework. Unlike traditional LCC approaches that primarily emphasize direct economic expenditures, the model is designed to incorporate pollutant emissions and social externalities, thereby enabling a more holistic evaluation of sustainability over the entire lifespan of the structure. The effectiveness of the model has been demonstrated through application to case studies, where long-term cost implications were captured and sustainable design and maintenance decisions were supported. This integrated framework represents an advancement over existing LCC methodologies by providing a more comprehensive and practical tool for the evaluation of engineering structures. Full article

Plastic pollution is a growing global challenge, and traditional plastic waste management methods are proving inadequate in tackling the issue. Enzymatic biodegradation has emerged as a promising alternative or addition to plastic waste management due to its environmentally friendly profile. Polyethylene terephthalate (PET) is among the most widely used polymers in packaging, and recent research has identified several PET-degrading enzymes, such as TfCut2, IsPETase, and LCC, as promising candidates for biodegradation applications at the industrial level. This has led to extensive efforts to improve their catalytic efficiency, with targeted mutagenesis being the preferred method employed for their modification. To this end, molecular dynamics (MD) simulations coupled with experimental validation have provided critical atomistic-level insights into the effect of mutations on enzymatic function. The present systematic review examines the role of mutations in determining enzymatic activity and thermostability, analyzing their structural and mechanistic contributions across 20 studies. The integration of MD simulations and experimental findings allows elucidation of the mechanistic details governing polymer degradation, as well as identification of key residue and enzyme hotspots that enhance catalytic performance. The review further highlights the role of MD simulations as powerful tools in providing valuable insights to guide targeted mutations for enzyme efficiency optimization. Full article

This study investigates the deployment of VERIFY, a dynamic life cycle assessment (LCA) and life cycle costing (LCC) tool, tailored to evaluate the energy and environmental performance of building renovation strategies. The tool was applied to three diverse building renovation projects across Europe, offering insights into how life cycle-based tools can enhance decision-making by integrating operational data and modeling of energy systems. The paper highlights how VERIFY captures both embodied and operational impacts—addressing limitations of conventional energy assessments—and aligns with EU frameworks such as Level(s). Key findings from the case studies in Italy, Spain, and the Netherlands demonstrate how LCA/LCC-based approaches can support energy efficiency objectives and guide sustainability-aligned renovation investments. Across the three case studies, the tool demonstrated up to 51% reduction in primary energy demand, 66% decrease in life cycle greenhouse gas emissions, and 51% reduction in life cycle costs. These outcomes provide researchers with a validated dynamic LCA/LCC framework and offer practitioners a replicable methodology for planning and evaluating sustainability-driven renovations. Despite their advantages, the effective use of LCA tools in energy renovation faces challenges, including limited data availability, regulatory fragmentation, and methodological complexity. The paper concludes that advanced tools such as VERIFY, when harmonized with evolving EU energy performance and sustainability standards, can strengthen the evidence base for deep energy renovation and carbon reduction in the building sector. Full article

As low-cost carriers (LCCs) continue expanding their networks and enhancing profitability through connecting services, passenger demand has become a critical factor in flight connection planning. However, demand is inherently uncertain due to economic cycles, seasonal fluctuations, and external disruptions, creating challenges for network design. This study proposes a flight connection planning model tailored to LCC operations that explicitly accounts for demand uncertainty. The model determines the optimal set of connecting itineraries to introduce over the existing network of flights, identifies promising transfer airports, and provides passenger allocation strategies across flights. We apply the model to Spring Airlines’ real-world network to evaluate its effectiveness. Results show that the proposed model outperforms the deterministic benchmark in feasibility and stability under varying demand scenarios. Specifically, under the same constraint of selecting up to 10 transfer airports, our model increases the number of connecting itineraries by 59.5% compared to the deterministic model and achieves a more balanced passenger distribution. Across 10 representative demand scenarios, the average standard deviation of load factors is reduced by 26.1% compared to the deterministic benchmark. Moreover, the deterministic solution yields a 22.9% failure rate for planned connections, while our model maintains 100% feasibility. These findings highlight the model’s value as a resilient, practical decision-support tool for airline planners. Full article

Green finance—including bilateral and multilateral development aid and concessional loans—has emerged as a critical tool in supporting the transition to a low-carbon economy, particularly in emerging economies. Türkiye, since the early 2000s, has increasingly relied on climate-related official development flows in alignment with its sustainability and emissions reduction targets. This study examines the impact of green finance and financial globalization on environmental sustainability in Türkiye over the period 2001–2021. It specifically tests the load capacity curve (LCC) hypothesis, which posits a non-linear (U-shaped) relationship between financial drivers and ecological outcomes. The study employs the load capacity factor (LCF) as an environmental pressure indicator and uses ARDL, FMOLS, DOLS, and CCR estimation methods to ensure robustness. The results indicate that green finance has a long-term positive effect on Türkiye’s environmental sustainability, whereas financial globalization shows mixed effects. The findings confirm the presence of a U-shaped relationship between green finance and environmental pressure, supporting the LCC hypothesis. These results contribute to the limited empirical literature on green finance in emerging economies and suggest that policy frameworks should emphasize the sequencing and institutional alignment of green financial flows. Policymakers in Türkiye and similar economies may benefit from integrating green finance strategies with targeted regulatory reforms to maximize ecological benefits. Full article

The building sector significantly contributes to global resource depletion and greenhouse gas emissions, necessitating integrated approaches to evaluate both environmental and economic performance. This study developed a sustainability-oriented assessment framework—applied in a Chinese context—that integrates life cycle assessment (LCA), life cycle costing (LCC), and carbon financial optimization to evaluate the life cycle performance of prefabricated steel buildings. Using publicly available databases (CEADs, Ecoinvent, and the Chinese Life Cycle Database), the framework quantified cradle-to-grave environmental impacts across raw material extraction, prefabrication, transport, on-site assembly, operation, and end-of-life stages. Emissions were monetized using standardized emission factors and official cost coefficients, enabling environmental costs to be expressed in financial terms. A dynamic financial simulation module was incorporated to assess the effects of carbon price fluctuations and quota allocation schemes. Sensitivity analyses were performed to examine the influence of key variables such as retrofit investment costs, emission reduction efficiency, and carbon policy scenarios on financial returns. The results show that material production and operational energy use dominate life cycle carbon emissions, jointly contributing more than 90% of the total impacts. Moderate decarbonization investments—such as HVAC upgrades and improved insulation—can achieve positive net economic returns under baseline carbon pricing. This integrated, data-driven framework serves as a practical decision-support tool for policymakers and industry stakeholders. It is adaptable across different regions and material systems, supporting the global transition toward low-carbon and financially viable construction practices. Full article

Spatial disparities in rangeland conditions across Kazakhstan complicate field-based assessments of livestock-carrying capacity (LCC), a critical metric for the country’s food security and economic planning. This study developed a geospatial livestock-carrying capacity (GLCC) modeling framework to quantify LCC spatio-temporal dynamics at the Oblast level, by integrating satellite-derived data on vegetation, water resources, and terrain with in situ measurements. By providing ground-truth observations and contextual detail, field-based measurements complement remote sensing data and help to validate estimates and improve the reliability of the GLCC model. The modeling framework was successfully applied and validated in a case study in the Akmola Oblast, Kazakhstan, to specifically map the spatial and temporal distributions of LCC, using publicly available MODIS NPP data and in situ data from 51 field sites. The modeling results showed distinct spatial patterns of LCC across the Oblast, reflecting variability in rangeland productivity with higher values concentrated in southern and southeastern regions (up to 0.5 animals/ha). The results also depicted significant interannual LCC fluctuations (ranging from 0.099 to 0.17 animals/ha) possibly due to rainfall variability, and thus an indicator of climate-related risks for livestock management. Although there is still room for further improvement, particularly in model parameterization to account for grazing pressures, forage quality, and livestock species, the GLCC modeling framework represents a simple modeling tool to map livestock-carrying capacity, a more meaningful indicator to rangeland managers. Further, this work underscores the value of integrating remote sensing with field-based observations to support data-driven rangeland management planning and resilient investment strategies. Full article

Urban stormwater management systems are increasingly strained by rapid urbanization and climate change, yet existing planning approaches often lack holistic optimization frameworks that account for both green and grey infrastructure (GREI) under uncertain future conditions. This study introduces a multi–objective optimization framework for Grey–Green Infrastructure (GGI), which integrates green infrastructure (GI) with GREI to enhance urban flood resilience, cost efficiency, and adaptability. The framework addresses life cycle cost (LCC), technological resilience (Tech-R), and operational resilience (Oper-R), offering a comprehensive approach to navigating the complexities of urban stormwater management. Key findings reveal that: (1) GGI systems optimized for resilience achieve a 33% improvement in Oper-R, with only a marginal increase in LCC of less than 9%, highlighting their robustness under GREI failure scenarios; (2) the integration of bioretention cells (BCs) and porous pavements (PPs) into GGI increases Tech-R by 7.1%, enhancing soil water retention and permeability, particularly in densely urbanized contexts; and (3) decentralized GGI systems exhibit superior adaptability to extreme weather events, with Design D reducing LCC to USD 53.9 M while maintaining no overflow under a 5–year rainfall event. The framework was validated in Zhujiang New Town, Guangzhou, where optimized GGI designs reduced average pipe diameters and manhole depths by 0.2–0.3 m compared to GREI–only systems, demonstrating both cost and resilience advantages. These findings provide decision–makers with a robust tool for evaluating trade–offs in stormwater infrastructure planning, advancing sustainable urban water management. Full article

Background/Objectives: With advancements in Large Language Models (LLMs), counseling chatbots are becoming essential tools for delivering scalable and accessible mental health support. Traditional evaluation scales, however, fail to adequately capture the sophisticated capabilities of these systems, such as personalized interactions, empathetic responses, and memory retention. This study aims to design a robust and comprehensive evaluation scale, the Comprehensive Evaluation Scale for LLM-Powered Counseling Chatbots (CES-LCC), using the eDelphi method to address this gap. Methods: A panel of 16 experts in psychology, artificial intelligence, human-computer interaction, and digital therapeutics participated in two iterative eDelphi rounds. The process focused on refining dimensions and items based on qualitative and quantitative feedback. Initial validation, conducted after assembling the final version of the scale, involved 49 participants using the CES-LCC to evaluate an LLM-powered chatbot delivering Self-Help Plus (SH+), an Acceptance and Commitment Therapy-based intervention for stress management. Results: The final version of the CES-LCC features 27 items grouped into nine dimensions: Understanding Requests, Providing Helpful Information, Clarity and Relevance of Responses, Language Quality, Trust, Emotional Support, Guidance and Direction, Memory, and Overall Satisfaction. Initial real-world validation revealed high internal consistency (Cronbach’s alpha = 0.94), although minor adjustments are required for specific dimensions, such as Clarity and Relevance of Responses. Conclusions: The CES-LCC fills a critical gap in the evaluation of LLM-powered counseling chatbots, offering a standardized tool for assessing their multifaceted capabilities. While preliminary results are promising, further research is needed to validate the scale across diverse populations and settings. Full article

Sustainability is a key challenge today. The high emission impact of the mobility sector requires a shift from internal combustion engines to electric traction motors. In order to improve sustainability holistically, the entire lifecycle from raw materials, manufacturing, use and end-of-life must be considered during development. Although a lot can be carried out to influence sustainability during the development phase, knowledge about the product is still very limited. Considering the main lifecycle stages already during the development phase requires a systematic development approach. Furthermore, integrating data from previous product generations is required. Generating a digital twin which collects data over the lifecycle is a useful tool which enables the prediction of evaluation criteria for the lifecycle stages. However, when using the digital twin, a suitable description model needs to be generated. A cross-lifecycle evaluation model based on Life Cycle Costing (LCC) and Life Cycle Assessment (LCA) was used to evaluate sustainability throughout the whole lifecycle. Information for evaluation was generated using a cross-lifecycle modeling approach, which enabled the combination of different lifecycle perspectives during development. To show the potential of evaluating different solutions from different perspectives, the methodology was demonstrated with a lightweight rotor of an electric traction motor. The great potential of the process model is shown. Full article

Building decarbonization is a major challenge for cities. Deciding which buildings to retrofit buildings, and when and how, is difficult, given the complex interaction between energy costs and investment requirements. Several tools have been developed in recent years to help public and private stakeholders with these decisions, but none cover aspects the authors think are fundamental. For this reason, an urban buildings retrofit tool was developed and is presented in this article. This new tool is based on a bottom-up approach, with all buildings simulated individually, considering aspects such as shading and adjacencies. As a second step, three scenarios with different levels of ambition were implemented in the tool, and the energy demand and emissions resulting from these scenarios were calculated. As a third step, the retrofitting scenarios’ initial investment and operational costs were implemented using a detailed Life Cycle Costing (LCC) approach. A robust and scalable structure was developed and applied to calculate the LCC of various retrofitting scenarios in Montréal, which will be described in detail. Full article

Gait analysis is essential for evaluating walking patterns and identifying functional limitations. Traditional marker-based motion capture tools are costly, time-consuming, and require skilled operators. This study evaluated a 3D Marker-less Motion Capture (3D MMC) system using pose and depth estimations with the gold-standard Motion Capture (MOCAP) system for measuring hip and knee joint angles during gait at three speeds (0.7, 1.0, 1.3 m/s). Fifteen healthy participants performed gait tasks which were captured by both systems. The 3D MMC system demonstrated good accuracy (LCC > 0.96) and excellent inter-session reliability (RMSE < 3°). However, moderate-to-high accuracy with constant biases was observed during specific gait events, due to differences in sample rates and kinematic methods. Limitations include the use of only healthy participants and limited key points in the pose estimation model. The 3D MMC system shows potential as a reliable tool for gait analysis, offering enhanced usability for clinical and research applications. Full article

This study evaluated the possibility of using two complementary electronic sensors (rising plate meter (RPM) and active optical sensor (AOS)) to obtain a global indicator, pasture crude protein (CP) in kg ha⁻¹. This parameter simultaneously integrates two essential dimensions: pasture dry matter availability (dry matter (DM) in kg ha⁻¹) measured by RPM, and pasture quality (measured by AOS), and supports management decisions, particularly those related to the stocking rates, supplementation, or rotation of animals between grazing parks. The experimental work was carried out on a dryland biodiverse and representative pasture, and consisted of sensor measurements, followed by the collection of a total of 144 pasture samples, distributed between three dates of the pasture vegetative cycle of 2023/2024 (Autumn—December 2023; Winter—February 2024; and Spring—May 2024). These samples were subjected to laboratory reference analysis to determine DM and CP. Sensor measurements (compressed height (H_RPM) in the case of RPM, and normalized difference vegetation index (NDVI) in the case of AOS) and the results of reference laboratory analysis were used to develop prediction models. The best correlations between CP (kg ha⁻¹) and “H_RPM × NDVI” were obtained in the initial and intermediate phases of the cycle (autumn: R² = 0.86 and LCC = 0.80; and Winter; R² = 0.74 and LCC = 0.81). In the later phase of the cycle (spring), the accuracy of the forecasting model decreased dramatically (R² = 0.28 and LCC = 0.42), a trend that accompanies the decrease in the pasture moisture content (PMC) and CP. The results of this study show not only the importance of extending the database to other pasture types in order to enhance the process of feed supplement determination, but also the potential for the research and development of proximal and remote sensing tools to support pasture monitoring and animal production management. Full article

Wood is considered a promising raw material for the circular bioeconomy and has the ability to store biogenic carbon, and this is one reason why we want to extend the service life of the wood. In order to consider the influence of durability in our study, we used two wood species with different lifespans. Beech (Fagus sylvatica L.) belongs to the group of very sensitive wood species, as the durability of the untreated wood is estimated to be around 5 years; meanwhile, pine (Pinus sylvestris L.) belongs to the group of moderately resistant wood species, where the durability of the untreated wood is estimated to be up to 15 years. While toxic chemicals are often used for wood preservation, hydroxyapatite offers an environmentally friendly solution for wood mineralization. This study presents life cycle assessment (LCA) and life cycle cost (LCC) analyses comparing a novel hydroxyapatite (HAp) mineralization method with a service life of 50 years to a non-mineralized reference alternative. LCA was based on EN ISO 14040 and EN ISO 14044, while LCC was adapted from the European Commission’s LCC tool for public procurement. The results of the LCA show that mineralized wood has a lower overall impact on the environment than surface-treated beech wood but a higher impact than surface-treated pine wood. Most impact categories were determined by electricity consumption with the exception of stratospheric ozone depletion, water consumption, and land use. Water consumption proved to be the category where the mineralization process was problematic due to water consumption during the leaching process. The LCC showed that mineralized wood is the most cost-effective solution for the exterior façade, as all costs, but especially investment costs, were lower. The differences in the LCA and LCC results are mainly due to the different lifetimes of the two alternatives. It can be concluded that if energy-intensive processes and chemicals are used in the production of the material, the extended lifetime must be sufficient to account for the additional impacts that occur during the production phase. Full article