Base Scenario Research Articles

Applying Topological Information for Routing Commercial Vehicles Around Traffic Congestion

The growth of urbanization, population, and economic activity has led to a substantial increase in freight transportation demand, exceeding the capacity of existing infrastructure and creating new challenges across various regions. This has resulted in significant traffic congestion, increased travel times, and higher operational costs for commercial vehicle fleets. Leveraging topological data, such as road networks and traffic patterns, can enable more efficient routing strategies to navigate around congested areas. This study presents a comprehensive approach to truck rerouting strategy by integrating spatial analysis, truck characteristics, traffic conditions, road geometry, and cost–benefit analysis to select alternative routes suitable for commercial vehicle fleets. Incorporating real-time traffic information and predictive analytics, commercial vehicle operators can optimize their routes, reduce fuel consumption, and improve overall delivery efficiency. Three case studies were presented to demonstrate the proposed diversion decision framework. Two scenarios were designed for each case study: a base scenario with no diversion and an optimized scenario with a diversion strategy. The travel times, fuel consumption, and economic impacts between the two scenarios were compared and quantified as a total annual saving of USD 52 million. This approach goes beyond selecting alternative routes and provides decision makers with measurable benefits that justify diversion strategies.

Green hydrogen as a sustainable operations strategy: a socio-economic perspective

Hydrogen is an energy carrier that can support the development of sustainable and flexible energy systems. However, decarbonization can occur when green sources are used for energy production and appropriate water use is manifested. This work aims to propose a socio-economic analysis of hydrogen production from an integrated wind and electrolysis plant in southern Italy. The estimated production amounts to about 1.8 million kg and the LCOH is calculated to be 3.60 €/kg in the base scenario. Analyses of the alternative scenarios allow us to observe that with a high probability the value ranges between 3.20-4.00 €/kg and that the capacity factor is the factor that most affects the economic results. Social analysis, conducted through an online survey, shows a strong knowledge gap as only 27.5% claim to know the difference between green and grey hydrogen. There is a slight propensity to install systems near their homes, but this tends to increase due to increased knowledge on the topic. Respondents state sustainable behaviours, and this study suggests that these aspects should also be transformed into the energy choices that are implemented every day. The study suggests information to policy-makers, businesses and citizens as it outlines that green hydrogen is an operations strategy that moves toward sustainable development.

Electric vehicles charging infrastructure framework using internet of things

Electric vehicles (EVs) sales have grown rapidly recently, and more growth is expected over the coming years. A challenging problem arises when managing different battery requirements of moving EVs through reliable Charging Stations (CSs). Current concerns for EV users are long waiting lines at CSs and dropping below a predefined battery capacity limit. For this reason, this paper proposes an Internet of Things (IoT)-based EV charging scheduling system, which with the use of IoT technologies, decides the optimal assignment between EVs and Charging Points (CPs) located at different CSs at given time t. By using cloud computing and real time data such as number of EVs, number of CSs, number of CPs at different CSs … etc; the scheduling controller uses a recursive algorithm to generate all possible scenarios, and then shares the optimal assignment (that minimizes the average waiting time and fulfill battery constraints and charging needs) with all EVs. To test the validity of the IOT based scheduling system, sensitivity analysis by running different scenarios (pertaining to different parameters) was conducted. The different scenarios were compared to a base scenario where the system was not used and real-life random assignment is considered. The different run scenarios show superiority over the base scenario in terms of average waiting time (WT) and battery capacity threshold. For example, in the base scenario, violation of battery capacity threshold occurred 9.1% of the time, making random selection an unreliable choice versus no violations when the IOT scheduling system is used. Also, all tested scenarios under the IOT scheduling system show shorter average WT compared to the base scenario. For instance, scenarios 2 and 3 show more than 35% and 55% decrease in WT compared to the base scenario.

Modeling foot-and-mouth disease dissemination in Rio Grande do Sul, Brazil and evaluating the effectiveness of control measures

IntroductionFoot-and-mouth disease (FMD) affects multiple food-animal species and spreads rapidly among ungulate populations, posing significant challenges for disease control. Understanding the dynamics of FMD transmission and evaluating the effectiveness of control measures are critical for mitigating its impact. This study introduces a multiscale compartmental stochastic model to simulate FMD spread and assess countermeasures.MethodsWe developed a model that integrates population dynamics, including births, deaths, and species-specific transmission dynamics, at both the between-farm and within-farm levels. Four scenarios were created to evaluate different control strategies: the base scenario included vaccinating 20 farms and depopulating four infected farms, while alternative scenarios increased vaccination and depopulation capacities or omitted vaccination altogether.ResultsOur simulations showed that bovines were the most frequently infected species, followed by swine and small ruminants. After 10 days of initial spread, the number of infected farms ranged from 1 to 123, with 90.12% of simulations resulting in fewer than 50 infected farms. Most secondary spread occurred within a 25 km radius. An early response to control actions significantly reduced the time spent managing outbreaks, and increasing daily depopulation and vaccination capacities further enhanced control efforts.DiscussionEmergency vaccination effectively reduced the magnitude and duration of outbreaks, while increasing depopulation without vaccination also eliminated outbreaks. These findings highlight the importance of rapid response and capacity scaling in controlling FMD outbreaks, providing valuable insights for future decision-making processes in disease management.

Modeling the effect of land-use change on runoff water quality in the urban watershed: a case study of Settat city, Morocco

ABSTRACT The purpose of this study is to create a model of the quantity and quality of runoff from the urban watershed. The modeling was performed using a personal computer stormwater management model (PCSWMM) tool and was based on washoff and buildup for total suspended solids (TSS), total nitrogen (TN), and total phosphorus (TP) measured in the field. In addition, low impact development (LID) was used under four different scenarios to improve runoff control of the urban hydrologic cycle and reduce pollutant concentrations. Model performance was analyzed using the R2 and NSE goodness-of-fit indices. Thus, three typical rainfall events, as well as field measurement parameters (TSS, TN, and TP), were used for the model's calibration and validation. The base scenario quantified the runoff volume and pollutant load without any intervention in the runoff process control. Scenarios S2 (BR: bioretention) and S3 (PP: permeable pavement) contributed, separately, in reducing runoff and pollutant load but with relatively small percentages. In addition, the results show that scenario S4 (BR and PP) has the most significant impact on flow reduction, by about 10% compared with the base scenario, as well as on the pollutant load reduction, by about 31, 29, and 40% for the parameters TSS, TN, and TP, respectively.

Carbon dioxide injection into the Achimov formations using reinjection technology by the example of Ach3-4 formation in the Novo-Urengoy area of the Urengoy field

The prerequisites for the study are the calculation results for the cycling process, where carbon dioxide is proposed as the injection agent into the Achimov formations instead of dry gas, with the goal of increasing the condensate recovery factor. The work is focused on the efficiency assessment of carbon dioxide reinjection technology and reducing carbon footprint at a late stage of field development. The research object is the Аch3-4 formation within the Novo-Urengoy license area of the Urengoy field. The leading method to identify this problem is the results of the full-scale composite dynamic model in the ECLIPSE 300 format. The model takes into account the history of field development on depletion. The articles deals with two schemes for injecting carbon dioxide into the formation. In the first scheme, pure carbon dioxide is injected in a closed-loop system, but carbon neutrality through storage is not achieved. In the second scheme, carbon dioxide is injected using reinjection technology. Once injection begins, gas production stops. Only the condensate separated from the formation gas during low-temperature separation is sold and sent for further processing. After allocation of the condensate, the mixture of natural gas and carbon dioxide, in a specific proportion, is sent to the compressor station for reinjection into the formation in a gaseous state. Injecting pure carbon dioxide achieves a condensate recovery factor similar to that of gas injection with a 30 % carbon dioxide mixture. However, this option is less economically viable compared to the base and other scenarios due to high capital costs for upgrading the existing gas processing equipment (requiring the construction of an amine treatment unit). With carbon dioxide injection using reinjection technology, in addition to recovering extra condensate that had condensed during natural depletion, a reduction in the carbon footprint is also achieved. To maximize the condensate recovery factor, the optimal concentration of carbon dioxide in the injection mixture has been determined. The optimal timing for the start of injection was identified to maximize gas recovery. Economic efficiency is expected from the additional recovery of condensate trapped in the reservoir and from achieving carbon neutrality through the monetization and storage of carbon dioxide.

Decentralised by-product valorisation in the dairy value chain: An opportunity for sustainable intensification

Increasing the sustainability of the livestock sector is a vital goal in lowering the environmental burden of food production globally. The dairy sector is of particular interest in this context since it is responsible for a significant share of this burden. In the past, research has focused on approaches to lower the impacts at the farming stage of production, while fewer efforts have been directed at the later stages of the value chain. By-product valorisation is a tool within the wider framework of Circular Economy that allows to increase the efficiency of resource use by increasing the product output at high quality, potentially leading to a sustainable intensification. The present study aims to test this hypothesis. For this purpose, data was collected from a cheese production case study and pilot-scale experiments on whey valorisation, to model several scenarios of by-product valorisation in the dairy value chain. This includes a decentralised valorisation scenario modelled for the purpose of supporting a local Circular Economy and three scenarios representing common valorisation approaches. The impacts of the entire value chain are then assessed based on an attributional LCA considering three different functional units in order to examine the potential for a relative reduction of impacts per unit of product output. The results suggest that while there is no clear favourite among the investigated valorisation scenarios, by-product valorisation in general can lower the relative impacts of the value chain significantly as compared to the base scenario without valorisation. Depending on the valorisation scenario and impact category, impact reductions of up to 14%, 20% and 32% can be achieved for the three functional units, respectively. Hence, by-product valorisation allows for a sustainable intensification. While centralised lower quality valorisation is favourable in terms of product mass and overall process efficiency, decentralised and high-quality valorisation performs better when the economic value of the products is taken into consideration. Thus, the choice for the most suitable approach for a production site will depend on the context of the production and the intentions of the stakeholders.

Life cycle assessment (LCA) and economic analysis of two-stage anaerobic process of co-digesting palm oil mill effluent (POME) with concentrated latex wastewater (CLW) for biogas production

This study outlines the results of a process-based life cycle assessment (LCA) of the environmental performance of biogas production from co-digestion of palm oil mill effluent (POME) and concentrated latex wastewater (CLW) in the two-stage anaerobic digestion (AD) process. Biogas is utilized global across several sectors, including electricity generation, heating, and transportation. The Open LCA 2.0.2 software was used to evaluate the LCA at the midpoint level by using 18 different impact categories. This study reveals the three main contributions of the AD process, which were global warming (8.65 × 102 kg CO2eq), water consumption (5.32 m3), and land use change (3.39 × 102 m2 a crop eq), this result corresponded with the previous study in Malaysia that examined biogas production from the AD of POME In addition, alternative scenarios, in which a single substrate, have been established to quantify the impacts of potential improvements of biogas production to compare with base scenario of co-digestion. The results show that the environmental impacts of the base scenario released were lower than those of the single POME substrate of alternative scenario 1 in most impact categories. Furthermore, economic analysis is a crucial factor in the practical implementation of the AD process. Linking LCA with economic analysis offers a comprehensive perspective on sustainability, balancing environmental impacts with financial performance to optimize the potential of the AD co-digestion process. This is the first study to apply LCA and economic analysis to this specific co-digestion process in a two-stage AD system. The economic analysis of the two-stage AD of co-digesting POME and CLW was 1200 m3/d production of wastewater (70 % of POME and 30 % of CLW) based on the factories' data. The result indicates net present value, internal rate of return, and payback period of 515,813.94 USD, 9.13 %, and 8.87 years, respectively. This work proposed the potential approach for implementing the two-stage AD co-digestion of POME and CLW while concurrently producing valuable bioenergy carriers.

Techno-economic and environmental analysis of biogas-based hybrid renewable energy systems: A case study for a small-scale livestock farm

Biogas technology offers a significant benefit in enabling energy generation from distributed sources of biomass through its easy scalability. This study aimed to conduct the technical, economic, and environmental analysis of biogas-based hybrid renewable energy systems in a small-scale livestock farm located in the rural area of the Central Anatolia region in Türkiye. The HOMER software was used to analyze a total of five scenarios. These scenarios are hybrid combinations of a biogas generator, PV panels and wind turbines. The hybrid scenarios were evaluated by comparing them to each other and to the base scenario, which only has a grid connection. The optimal system has been determined to be the grid-connected Biogas/PV panel hybrid configuration, with an energy cost of 0.0688 $/kWh and a net present cost of $70,777. The optimal system has a renewable fraction of 89.9 % and a CO2 emissions mitigation potential of 13,066 kg/year compared to the base scenario. Furthermore, sensitivity analysis for various input parameters such as grid sellback rate, interest rate and load demand was performed to assess the impact of changes in these parameters on the system. Sensitivity analysis results show that increased grid sellback rates can significantly enhance financial metrics and reduce the payback period from 13.13 years to 6.63 years.

Financial Feasibility Study of New Product Line Healthy Children Food Business: Case Study of Lil'bites

Based on data from the Indonesian Nutrition Status Survey (SSGI) in 2022, 21.6% of Indonesian children suffer from wasting 7.7%. A disorder known as "wasting" in children occurs when their weight falls till it is higher than what is considered normal for their age. For children's growth and well-being, they must consume enough fruits. Although fruits provide vital vitamins and minerals that boost immunity and general health, many kids dislike them because of their flavor, texture, or inexperience with new meals. Lil'Bites, a startup focusing on children's health, sees this opportunity to create kid-friendly fruit jams as a viable approach to ensuring kids consume enough fruits. This research aims to assess the financial feasibility of developing a new product line, which is a healthy jam for kids, using a quantitative approach, utilizing company data as primary data and data from similar companies as secondary data. The study will evaluate the payback period, net present value (NPV), and internal rate of return (IRR) and assess risk through sensitivity analysis. The results based on the base scenario indicate that the Lil'Bites new product line project is financially feasible, resulting in a payback period of 1.9 years, an NPV of IDR401,807,628, and an IRR of 54.94%, which is significantly higher than the WACC of 9.36%.

A bioeconomic model for a multispecies small-scale fishery system

A fishery encompasses various interconnected systems, including ecological, socioeconomic, and governing systems. Managing fisheries requires the simultaneous consideration of all these systems, making it a challenging endeavor. To address these challenges, fisheries bioeconomic models have emerged as a crucial tool. They are particularly valuable in the context of small-scale fisheries, which are often complex, overlooked and poorly understood. Thus, this paper presents a dynamic multispecies and multigear bioeconomic model that can illuminate the ecological, economic, and social dimensions of small-scale fisheries under different management scenarios. The model was applied to a small-scale fisheries system in Southern Brazil that has as a notable feature a cooperative fishing behavior between dolphins and fishers. Three scenarios were explored: the base scenario (status quo), the optimal management scenario, and the constrained optimal management scenario. The model outputs demonstrated a clear tradeoff between labour effort, species conservation, and economic rent. Shifting from the base to an optimal management scenario would result in a labour employment reduction within the system but concurrently yield higher stock levels, economic rent, and wages. These results illustrate how our model can explore critical management scenarios across the multiple dimensions of fisheries systems. In essence, this research offers a novel contribution in the form of a bioeconomic model tailored for small-scale fisheries involving multiple species.

Impact assessment of future fleet compositions in vehicle emissions in urban areas: A methodological framework and a case study

This study explores the impact of emerging vehicle technologies on direct urban traffic emissions. It investigates emission reduction potential from shifts in fleet compositions and modal choices, especially considering climate change. To achieve this, three key research areas are explored for historical, current, and future scenarios (up to 2050): mode choice, emission factors for different vehicle categories, and diverse vehicle propulsion technologies. The estimation of the modal split is pivotal, developing a methodology utilizing Stated Preference survey data, discrete choice modeling, Monte Carlo simulations, and macroscopic traffic simulations. Future scenarios derive from the reference year’s modal split, and emission factors and fleet compositions are predetermined via an extensive literature review, aiding the assessment of their respective emissions. A subsequent sensitivity analysis identifies the impact of specific parameters on emissions, guiding future research focus. Study results underscore differences in greenhouse gas emissions and primary air pollutants between base and future scenarios.

Evaluating the performance of implementing regionally coordinating bus priority signals under different control schemes

Bus Rapid Transit (BRT) proves its effectiveness in alleviating traffic congestion, especially in urban areas. The implementation of Transit Signal Priority (TSP) for BRT has shown a significant reduction in delays. However, in densely populated urban areas, this priority can inadvertently cause additional delays for other modes of transportation. In this paper, we propose a control strategy for Regionally Coordinating Bus Priority Signals Control (RCBPSC) at urban intersections. The aim is not only to reduce bus delays but also to consider minimizing delays for pedestrians and other vehicles. To achieve this, we modeled two consecutive intersections along the Amman BRT. Essentially, we evaluated three different control scenarios in addition to the current base scenario. These scenarios include adaptive traffic signal control, RCBPSC with no signal timing optimization, and RCBPSC with signal optimization. Simulation results indicate that the adaptive traffic signal timing has the worst operational performance in terms of average delay and Level of Service (LOS) compared to the base scenario. Additionally, the results show that BRT delays significantly decrease at both intersections when we implement RCBPSC scenarios. When implementing RCBPSC with optimization scenarios, the results show an average reduction of more than 60% in intersection delay, a decrease in emissions of more than 50%, and an improved LOS for system users compared to the base scenario. The findings of this work can help agencies improve the current operational condition of BRT when implementing RCBPSC.

A novel profit-driven framework for model evaluation in credit scoring

Credit scoring is a prominent problem in machine learning (ML). ML classifiers used to assess creditworthiness need to be evaluated using appropriate metrics. Common metrics fall into two categories: cost-sensitive and traditional cost-insensitive. Traditional cost-insensitive metrics like accuracy and area under the curve (AUC) have several drawbacks. They can be difficult for managers to understand, potentially misleading in certain scenarios, and often overlook the business realities of credit scoring. Conversely, existing cost-sensitive metrics are imperfect, leading many studies to rely on classic AUC metrics. In this study, we introduce the expected profit ratio (EPR), a novel framework for evaluating classifiers in credit scoring. EPR does not require a base scenario and accurately calculates the profit. Its flexibility makes it suitable for all types of credit-scoring problems, and its simplicity aids in comprehension and explanation. By enabling accurate profit calculation, EPR helps managers, executives, and administrators make informed decisions and investments. This metric lays the foundation for more sophisticated and precise model evaluation and comparison in credit scoring. Additionally, it can be used as a performance measure, alongside fairness and other criteria needed to achieve trustworthy artificial intelligence (AI).

Assessing the efficiency of the effect of innovations on the development of grain processing enterprises

This study evaluates the impact of the introduction of deep grain processing on the development of grain processing enterprises. The main problems of processing grain crops in the Republic of Kazakhstan have been considered. The need to assess the impact of the innovative process of enterprise development was outlined. The research was carried out in the North-Kazakhstan region; the object of the study was the grain processing industries in the North-Kazakhstan region. The results show the following: 1) total cost savings when expanding the level of gluten consumption (up to 3 % by weight of the volume of flour consumed) will save USD 18.5 billion. The savings are due to the significant improvement in the baking properties of the flour when adding 1−3 % gluten to the level of the highest quality wheat flour. At the same time, due to the manufacturability of the process and the use of the lowest quality raw materials, the unit cost of gluten in terms of protein is lower than the price of high-quality wheat necessary for baking; 2) in the processing of starch and amino acids, grain starch is the main product of world trade among all goods of the grain processing industry, and gluten ranks only second in terms of world imports. The share of issued patents in the area of starch production is also leading among the sectors of deep grain processing; 3) the implementation of the developed project for deep processing of grain in Northern Kazakhstan could significantly improve the indicators of innovative development of the country: the increase in innovative products of the manufacturing industry in the country as a whole would be 19.1 %. The growth of non-resource exports would equal 1.5 %. The annual increase in the enterprise's revenue could be about USD 0.47 per USD 1 of investment costs compared to the base scenario for the sale of unprocessed wheat. The scope of practical application of the results extends to the grain industry. The project has a significantly wider range of manufactured products and is focused on the markets of neighboring countries, which could reduce export risks to a minimum and provide stable demand for the company’s products

Performance Analysis and Techno-Economic Evaluation of Solar Energy Retrofitting for Coal-Fired Power Plant in Central Kalimantan Province

The power generation sector significantly contributes to climate change. Mitigation efforts are crucial to adhering to the global commitment to limit temperature increases below 2°C. One potential solution is retrofitting existing power plants with technologies that integrate renewable energy. This study explores the integration of solar energy into the operational processes of a coal-fired power plant in Central Kalimantan, replacing the extraction steam turbine in the high-pressure feed water heater No. 7. Using STEAG EBSILON for simulation, this research evaluates the power plant's performance before and after retrofitting in both power-boost (PB) and fuel-save (FS) modes under varying load conditions. The results demonstrate that thermal efficiency in both PB and FS modes increased by up to 2% compared to the base scenario. Specific fuel consumption decreased by 15.05 g/kWh in PB mode and 15.75 g/kWh in FS mode, leading to a reduction in coal consumption and CO2 emissions by 4.69% and 4.94% respectively. Additionally, the study observed that the solar percentage and solar-to-electricity efficiency increased as the load decreased. In FS mode, the solar electricity proportions at VWO, 100%, 75%, and 50% load rates were 5.23%, 5.53%, 7.76%, and 11.92%, respectively. The levelized cost of energy (LCOE) for solar electricity was calculated to be 431.82 IDR/kWh, with an expected investment return period of 5.87 years.

Production of phosphate biofertilizers as a booster for the techno-economic and environmental performance of a first-generation sugarcane ethanol and sugar biorefinery

The intensive use of fertilizers and pesticides in agriculture has a significant economic and environmental impact worldwide. Biofertilizers (aka microbial inoculants) could be a potential alternative to decrease costs and the environmental footprint linked to the use of fertilizers while boosting productivity through biological processes. This work aimed to perform a techno-economic-environmental assessment of an industrial biofertilizer production facility integrated with a sugarcane ethanol biorefinery. To this end, systems engineering tools were employed concurrently with techno-economic-environmental analyses to assess the integration of the different processes and their feasibility. Three processes for biofertilizer production are proposed varying in terms of downstream processing and the use of single or double microorganisms. Our findings indicate that the integration of biofertilizer production can enhance the biorefinery's NPV by as much as 137% in the most favorable scenario and by a minimum of 69% in the most unfavorable scenario. Regarding environmental consequences, in general, all scenarios demonstrate an improvement over the base scenario. Global sensitivity analysis showed that the solid-state fermentation and composite formulation steps of the biofertilizer process have the most substantial influence on both economic and environmental outcomes. The uncertainty analysis further unveils that the scenarios without fungus separation exhibited greater resilience in the face of market volatility. The retro-techno-economic study defined the economically viable region. Ultimately, this study demonstrates that the integration of biofertilizers into an ethanol and sugar biorefinery is a more sustainable alternative than the isolated biorefinery regarding the environmental and techno-economic aspects of sustainability.

Financial Feasibility of A Hair Care-Based Business

Hair loss, or alopecia, is a global concern, with 3.8% of Southeast Asia's population affected by Alopecia Areata, an autoimmune disorder. Traditional treatments like minoxidil and finasteride, while effective, often have side effects. To address this, Sassle, a haircare startup, developed an almond-based hair serum in collaboration with ITB's School of Pharmacy, offering a natural solution without adverse effects. Sassle has completed product development and is preparing to launch its go-to-market strategy. This study evaluates the financial feasibility of Sassle's product using primary data from Sassle's records and secondary data from industry comparables. Key financial metrics such as the Payback Period, Net Present Value (NPV), and Internal Rate of Return (IRR) were analyzed, along with a scenario analysis under pessimistic, base, and optimistic conditions. The base scenario indicated a payback period of 3 years and five months, a positive NPV of Rp381,007,156, and an IRR of 49%, surpassing the Weighted Average Cost of Capital (WACC) of 5.02%. In the pessimistic scenario, with a 25% sales reduction, the payback period was four years and 11 months, with a positive NPV of Rp265,828,152 and an IRR of 32%. With a 15% sales increase, the optimistic scenario predicted a 2-year, 1-month payback period, a positive NPV of Rp622,915,633, and an IRR of 67%. The findings confirm that Sassle's business model is financially viable and capable of substantial returns, providing critical insights for strategic decisions in investment, market expansion, and operational scaling.

Agent-based simulation model of micro-mobility trips in heterogeneous and perceived unsafe road environments

Safety concerns expressed by micro-mobility users seem to practically constrain the use and flexibility of these new modes. This paper introduces a new, parametric agent-based modeling approach for simulating micro-mobility, using MATSim as a platform. Perceived safety is introduced as a factor, which affects travel behavior of micro-mobility modes in a car-dominated, heterogeneous, and perceived unsafe road network. To meet the research objectives, different scenarios are simulated, using the city of Athens, Greece as a test bed, to examine the model performance and applicability. A universal scoring function based on travel time, cost, and safety, is proposed. This study also discusses the concept of unsafe discontinuities. Simulation results show that an unsafe road segment of 500 m long is sufficient to induce evident behavioral variations and hinder the practical use of e-bikes and e-scooters in the base scenario. The establishment of a cycle-friendly road network leads to significant changes in the chosen routes and decreases the average trip distance for micro-mobility users. In this case, the simulation process is guided by perceived safety, primarily focusing on formulating safer paths for micro-mobility mode users. Nevertheless, the routing behavior of car users is not modified due to the spatial uniformity of safety perceptions in a car-dominated road environment.

Prospective life cycle approach to buildings' adaptation for future climate and decarbonization scenarios

The existing building stock is crucial for enhancing decarbonization targets and mitigating climate change. This article delves into a methodological approach that combines prospective life cycle assessment, building thermal simulation using projected future climate data, and global sensitivity analysis to pinpoint the most influential parameters under current climate conditions and future scenarios. The methodology covers plausible decarbonization pathways for the electricity mix, considering the growing utilization of renewable sources, which are influenced by the building locations. An adaptive reuse process involves converting a historic residence into an office building to validate the proposed methodology. Several retrofit strategies are assessed, such as exterior wall insulation, roof insulation, and window replacement. The findings reveal a 12% rise in average usage impacts and a 7% increase in cradle-to-use impacts from the base scenario to future climate projections. Embodied impacts surpass use-phase impacts by 23% in future climates and 33% in certain baseline scenarios. Utilizing future climate data in the life cycle analysis to estimate energy requirements can aid in forecasting building performance under climate change, especially in adapting the existing building stock for enhanced thermal comfort with minimal environmental impact.