Scenario Analysis Research Articles

Effects of street tree configuration and placement on roadside thermal environment within a tropical urban canyon.

The Urban Heat Island (UHI) effect increases surface and air temperatures, affecting urban health and well-being. A well-known UHI mitigation measure is the increased roadside tree vegetation facilitating evapotranspiration and shade. In its implementation, the identification of thermally optimal street tree configurations and a quantitative assessment of how various street tree configurations impact the roadside thermal environment were deemed essential and were chosen as the main aims of the study. Twelve tropical urban canyons were categorized into three clusters representative of different street tree placement and configuration scenarios. A control cluster devoid of any roadside trees was also selected. The CFD-based 3-D microclimate model 'ENVI-met' was used to identify suitable roadside urban tree planting scenarios for better microclimate regulation. From a tree planting scenario analysis done as part of the study, the greening scenario of using a 'Continuous tree row (Densely foliated - high Leaf Area Density - LAD)' tree configuration was recognized with the highest ambient temperature reduction of 1.41°C. The study outcomes reveal that tree configuration of high LAD street trees placed in closer spacing contributes towards the better cooling effect of roadside environments and thus improves thermal comfort for warmer tropical climates of higher humidity levels. The study's findings offer valuable insights for urban planning professionals and policymakers involved in designing future cities and urban developments. They emphasize the importance of strategic tree-planting designs and configurations to enhance thermal comfort and livability in urban areas. This highlights the need to avoid ad-hoc procedures and instead prioritize well-planned roadside tree configurations within urban canyons.

Driving factors analysis and scenario prediction of CO2 emissions in power industries of key provinces along the Yellow River based on LMDI and BP neural network

IntroductionPower industry is one of the largest sources of CO2 emissions in China. The Yellow River Basin plays a supportive role in guaranteeing the effective supply of electricity nationwide, with numerous power generation bases. Understanding the drivers and peak of CO2 emissions of power industry in the Yellow River Basin is vital for China to fulfill its commitment to reach carbon emissions peak by 2030.MethodsThe Logarithmic Mean Divisia Index (LMDI) model was employed to explore the drivers to the change of CO2 emissions in power industries of three study areas, including Inner Mongolia Autonomous Regions, Shanxi Province, and Shandong Province in the Yellow River Basin. And Back Propagation (BP) neural network was combined with scenario analysis to empirically predict the trend of the amount of CO2 emitted by power industry (CEPI) from provincial perspective.ResultsCEPI in Inner Mongolia under the scenarios of a low degree of CO2 emissions promotion with a medium degree of CO2 emissions inhibition (LM) and a low degree of CO2 emissions promotion with a high degree of CO2 emissions inhibition (LH) scenario can reach a peak as early as 2030, with the peak value of 628.32 and 638.12 million tonnes, respectively. Moreover, in Shanxi, only CEPI under a low degree of CO2 emissions promotion scenarios (LL, LM, LH) can achieve the peak in 2025 ahead of schedule, with amounts of 319.32, 308.07, and 292.45 million tonnes. Regarding Shandong, CEPI under scenarios of a low degree of CO2 emissions promotion with a high degree of CO2 emissions inhibition (LH) and a medium degree of CO2 emissions promotion with a high degree of CO2 emissions inhibition (MH) could achieve the earliest peak time in 2025, with a peak of 434.6 and 439.36 million tonnes, respectively.DiscussionThe earliest peak time of CEPI in Shandong Province and Shanxi Province is 2025, but the peak of CEPI in Shanxi is smaller than that of Shandong. The peak time of CEPI in Inner Mongolia is relatively late, in 2030, and the peak is larger than that of the other two provinces. The per capita GDP is the most positive driving factor that contributes to the CEPI. Shandong has a strong economy, and its per capita GDP is much higher than Shanxi’s. Therefore, even under the same peak time, the CEPI in Shandong is much higher than that of Shanxi. Inner Mongolia is extensive and sparsely populated, which makes its per capita GDP rank among the top in China. In addition, Inner Mongolia’s coal-based power generation structure and high power generation also contribute to its late CO2 peak time and large CO2 peak.

Lenvatinib plus pembrolizumab in the patients with advanced previously treated endometrial cancer: A cost-effectiveness analysis in the United States and in China.

To investigate the cost-effectiveness of lenvatinib plus pembrolizumab (LP) compared to chemotherapy as a second-line treatment for advanced endometrial cancer (EC) from the United States and Chinese payers' perspective. In this economic evaluation, a partitioned survival model was constructed from the perspective of the United States and Chinese payers. The survival data were derived from the clinical trial (309-KEYNOTE-775), while costs and utility values were sourced from databases and published literature. Total costs, quality-adjusted life years (QALYs) and incremental cost-effectiveness ratio (ICER) were estimated. The robustness of the model was evaluated through sensitivity analyses, and price adjustment scenario analyses was also performed. Base-case analysis indicated that LP wouldn't be cost-effective in the United States at the WTP threshold of $200 000, with improved effectiveness of 0.75 QALYs and an additional cost of $398596.81 (ICER $531392.20). While LP was cost-effective in China, with improved effectiveness of 0.75 QALYs and an increased overall cost of $62270.44 (ICER $83016.29). Sensitivity analyses revealed that the above results were stable. The scenario analyses results indicated that LP was cost-effective in the United States when the prices of lenvatinib and pembrolizumab were simultaneously reduced by 61.95% ($26.5361/mg for lenvatinib and $19.1532/mg for pembrolizumab). LP isn't cost-effective in the patients with advanced previously treated endometrial cancer in the United States, whereas it is cost-effective in China. The evidence-based pricing strategy provided by this study could benefit decision-makers in making optimal decisions and clinicians in general clinical practice. More evidence about budget impact and affordability for patients is needed.

CAR T-cell Therapy for Diffuse Large B-cell Lymphoma in Canada: A Cost-Utility Analysis.

Chimeric antigen receptor (CAR) T-cell therapy is a novel cell therapy for treating non-Hodgkin lymphoma. The development of CAR T-cell therapy has transformed oncology treatment by offering a potential cure. However, due to the high cost of these therapies, and the large number of eligible patients, decision makers are faced with difficult funding decisions. Our objective was to assess the cost-effectiveness of tisagenlecleucel for adults with relapsed/refractory diffuse large B-cell lymphoma in Canada using updated survival data from the recent JULIET trial. We developed an individual-simulated discrete event simulation model to assess the costs and quality-adjusted life-years (QALY) of tisagenlecleucel compared with salvage chemotherapy. Survival estimates were obtained from a published clinical trial and retrospective analysis. If patients remained progression free for 5 y, they were assumed to be in long-term remission. Costing and utility data were obtained from reports and published sources. A Canadian health care payer perspective was used, and outcomes were modeled over a lifetime horizon. Costs and outcomes were discounted at 1.5% annually, with costs reported in 2021 Canadian dollars. A probabilistic analysis was used, and model parameters were varied in 1-way sensitivity analyses and scenario analyses. After we incorporated the latest clinical evidence, tisagenlecleucel led to an additional cost of $503,417 and additional effectiveness of 2.48 QALYs, with an incremental cost-effectiveness ratio of $202,991 compared with salvage chemotherapy. At a willingness-to-pay threshold of $100,000/QALY, tisagenlecleucel had a 0% likelihood of being cost-effective. At the current drug price, tisagenlecleucel was not found to be a cost-effective option. These results heavily depend on assumptions regarding long-term survival and the price of CAR T. Real-world evidence is needed to reduce uncertainty. For patients with diffuse large B-cell lymphoma who failed 2 or more lines of systemic therapy, CAR T was not found to be a cost-effective treatment option at a willingness-to-pay threshold of $100,000.These results heavily depend on the expected long-term survival. The uncertainty in the model may be improved using real-world evidence reported in the future.

Statistical downscaling of GCMs wind speed data for trend analysis of future scenarios: a case study in the Lombardy region

AbstractNear-surface wind speed is a key climatic variable, affecting many sectors, such as energy production, air pollution, and natural hazard. Lombardy region of Italy is among the European areas with lowest average wind speed, leading generally to low air quality and wind energy potential. However, it is also one of the most affected area by tornadoes in Italy. Here we investigate possible changes in wind circulation as due to prospective global warming. We analysed wind speed WS under future scenarios (SSP1-2.6 and SSP5-8.5) from six Global Climate Models (GCMs) until 2100, tuned against observed WS data. We employed a statistical downscaling method, namely Stochastic Time Random Cascade (STRC) to correct locally GCMs outputs. Three statistical tests, i.e. Linear Regression, Mann Kendall, Moving Window Average, were carried out to analyse future trends of: annual WS averages, 95th quantile (as an indicator of large WS), and the number of days of calm wind per year (NWC). The proposed STRC algorithm can successfully adjust the mean, standard deviation, and autocorrelation structure of the GCM outputs. No strong trends are found for the future. The chosen variables would all display non-stationarity, and the 95th percentile display a positive trend for most of the stations. Concerning NWC, notable discrepancies among GCMs are seen. The STRC algorithm can be used to successfully adjust GCMs outputs to reflect locally observed data and to then generate credible long-term scenarios for WSs as a tool for decision-making.

Acetylcholine triggered enzymatic cascade reaction based on Fe7S8 nanoflakes catalysis for organophosphorus pesticides visual detection

BackgroundOrganophosphorus pesticides (OPs) play important roles in the natural environment, agricultural fields, and biological prevention. The development of OPs detection has gradually become an effective strategy to avoid the dangers of pesticides abuse and solve the severe environmental and health problems in humans. Although conventional assays for OPs analysis such as the bulky instrument required analytical methods have been well-developed, it still remains the limitation of inconvenient, inefficient and lab-dependence analysis in real samples. Hence, there is an urgent demand to develop efficient detection methods for OPs analysis in real scenarios. ResultsHere, by virtue of the highly efficient catalytic performance in Fe7S8 nanoflakes (Fe7S8 NFs), we propose an OPs detection method that rationally integrated Fe7S8 NFs into the acetylcholine (ACh) triggered enzymatic cascade reaction (ATECR) for proceeding better detection performances. In this method, OPs serve as the enzyme inhibitors for inhibiting ATECR among ACh, acetylcholinesterase (AChE), and choline oxidase (CHO), then reduce the generation of H2O2 to suppress the oxidation of 3,3′,5,5′-tetramethylbenzidine (TMB) that catalyzed by Fe7S8 NFs. Benefiting from the integration of Fe7S8 NFs and ATECR, it enables a sensitive detection for OPs (e.g. dimethoate). The proposed method has presented good linear ranges of OPs detection ranging from 0.1 to 10 μg mL−1. Compared to the other methods, the comparable limits of detection (LOD) of OPs are as low as 0.05 μg mL−1. SignificanceFurthermore, the proposed method has also achieved a favorable visual detection performance of revealing OPs analysis in real samples. The visual signals of OPs can be transformed into RGB values and gathered by using smartphones, indicating the great potential in simple, sensitive, instrument-free and on-site analysis of pesticide residues in environmental monitoring and biosecurity research.

Life cycle thinking for the assessment of waste and circular economy policy: status and perspectives from the EU example

Life cycle thinking methods such as life cycle assessment (LCA) and costing (LCC) were originally developed to assess the performance of products and services (business-making decisions). However, they are increasingly deployed to support policy-making along the entire policy cycle, including via impact assessment (IA) of different policy options. These applications are associated with a number of challenges, mainly related to the dynamic and prospective nature of policy IA, typically forward-looking into 10–20 years ahead. This requires the application of prospective scenario analyses to develop the baseline scenario, reflecting the ‘do nothing’ into the future (business-as-usual), and the alternative scenarios, reflecting the consequences incurred by the implementation of the policy options under study. Such prospective and broadened boundary nature of policy IA challenges traditional LCA/LCC practices mostly based on retrospective, static scenarios and datasets. The present study provides an overview of recent IA studies supporting waste policy to show the state-of-the-art and the main challenges associated with the application of life cycle methods in IA, focusing on the most recent EU waste and circular economy policies. Moreover, since specific and transparent guidance on how to implement consistently these methods in policy IA is lacking, the study provides an initial guidance for application of life cycle thinking in IA drawing upon the knowledge obtained conducting waste policy IA studies. Key challenges in the field are still to be addressed, and might inspire further research to improve the application of life cycle thinking to policy assessment.

Exploring the impacts of woodland management on ecosystem services – a deliberative method

ABSTRACT There is a need for operational decision-making methodologies applicable at local management scales which are inclusive and enable the integration of plural values, knowledges and perspectives of co-produced ecosystem services. Here we describe a deliberative scenario analysis method using ‘management intervention bundles’ as distinct scenarios to assess the perceived impact of changing management on woodland ecosystem services. We used three hypothetical future management scenarios, Biodiversity Conservation, People Engagement and Austerity, alongside scenarios of the Past, Present and an existing Management Plan. We assessed the perceived impacts of these scenarios on 11 ecosystem services using local expert workshops in six sites across Scotland. The experts were chosen to represent a range of different perspectives, from biodiversity to the local economy, community concerns and recreation. Overall, Management Plan, Biodiversity Conservation and People Engagement scenarios performed significantly better than Past, Present and Austerity scenarios. Further quantitative and in-depth qualitative analysis revealed trade-offs and noteworthy patterns. We explore some of these key trade-offs and patterns and argue that our methodology has potential to be an effective tool for local managers to support local decision-making at management scales for co-produced ecosystem services. Our methodology enabled a diverse group of local experts to express and deliberate a range of values, experiences and viewpoints. This knowledge sharing and collective learning allowed the development of shared values and perspectives, which are thought to be critical in more equitable and inclusive decision-making.

Research on driving mechanism and prediction of electric power carbon emission in Gansu Province under dual-carbon target

The electric power industry is a key industry for the country to achieve the double carbon target. Its low carbon development has a double effect on this industry and helps other industries to achieve the carbon peak target. This paper firstly uses the IPCC inventory method to calculate carbon emissions in the production phase of the power industry in Gansu Province from 2000 to 2019, followed by the ridge regression method and the STIRPAT model to analyse the quantitative impact of six major drivers on carbon emissions, and finally, the scenario analysis method is used to forecast carbon emissions in this phase. The results show that the carbon emissions of Gansu Province show a trend of rising and then falling, and reached a peak of 65.66 million tons in 2013. For every 1% increase in population effect, urbanisation level, affluence, clean energy generation share, technology level and industrial structure, carbon emissions will grow by 4.939%, 0.625%, 0.224%, − 0.259%, 0.063% and 0.022% respectively. Because of the clean energy advantage in Gansu Province, the low-carbon development scenario will continue to have low carbon emissions during the scenario cycle, which can be reduced to 53.454 million tons in 2030; the baseline scenario will achieve a carbon peak in 2025, with a peak of 62.627 million tons; the economic development scenario has not achieved carbon peak during the scenario cycle, and carbon emissions will increase to 73.223 million tons in 2030.

Decision Support System Driven by Thermo-Complexity: Scenario Analysis and Data Visualization

The present modelling aims to construct a computational information representation system useful for decision support system (DSS) solutions in the realization of intelligent systems or complex systems analysis solutions. Starting from an n-dimensional space (with n ≥ 7) represented by problem variables (referred to as CSF—Critical Success Factors), a dimensional embedding procedure is used to transition to a two-dimensional space. In the two-dimensional space, thanks to new lattice motion algorithms, the decision support system can determine the optimal solution with a lower computational cost based on the decision-maker’s preferences. Finally, thanks to an algorithm that takes into account the hierarchical order of importance of the seven CSFs as per the expert’s liking or according to his optimization logics, a return is made to the n-dimensional space and the final solution in the original space. As we will see, the starting and ending states in the n-dimensional space (referred to as micro-states) when projected into the two-dimensional space generate states (referred to as macro-states) which are degenerate. In other words, the correspondence between micro-states and macro-states is not one-to-one, as multiple micro-states correspond to one macro-state. Therefore, in relation to the decision-maker’s preferences, it will be the responsibility of the decision support system to provide the decision-maker with the micro-state of interest in the n-dimensional space (dimensional emergence procedure), starting from the obtained optimal macro-state. This result can be achieved starting from a flat chain of sensors capable of measuring/emulating certain specific parameters of interest. As we will see, it emerges that by considering random–exhaustive rolling value paths in order to track and potentially intervene to rebalance a dynamic system representing the state of stress/sensing of a system of interest, we are using the most general and, therefore, complex hypotheses of ergodic theory. In this work, we will focus on the representation of information in n-dimensional and two-dimensional spaces, as well as construct evaluation scenarios. We will also show the results of the decision support system in some cases of specific interest, thanks to a specific lattice motion algorithm of the realized decision-making environment.

Cost effectiveness of pembrolizumab plus lenvatinib compared with chemotherapy for treating previously treated advanced endometrial cancer in Sweden

Objective Pembrolizumab plus lenvatinib was recently approved for the treatment of advanced or recurrent endometrial carcinoma in women with disease progression on or following prior treatment with a platinum‑containing therapy in any setting, and who are not candidates for curative surgery or radiation (KEYNOTE-775/Study-309; NCT03517449). The objective was to assess the cost effectiveness of pembrolizumab plus lenvatinib compared with chemotherapy from a Swedish healthcare perspective. Materials and methods A lifetime partitioned-survival model with three health states (progression free, progressed disease, death) was constructed. Chemotherapy was represented by paclitaxel or doxorubicin. Overall survival, progression-free survival, time on treatment, and utility data were obtained from KEYNOTE-775 (database lock: March 1, 2022). Costs (in 2020 Swedish Krona [SEK]) included drug acquisition and administration, health state, end of life, adverse event management, subsequent treatment, and societal (scenario analysis). Outcomes were calculated as quality-adjusted life-years (QALY) and life-years. Model results were presented as incremental cost-effectiveness ratios for all-comers, patients with proficient mismatch repair tumors, and deficient mismatch repair tumors. Deterministic and probabilistic sensitivity analyses were conducted. Results Pembrolizumab plus lenvatinib is a cost-effective treatment when compared with chemotherapy, with estimated deterministic and probabilistic incremental cost-effectiveness ratios of SEK 795,712 and 819,757 per QALY gained. Pembrolizumab plus lenvatinib was associated with a large incremental QALY and life-year gain per person versus chemotherapy over the model time horizon (1.49 and 1.76). Limitations Time-to-event data were incomplete and semiparametric and parametric curves were utilized for lifetime extrapolation. Willingness-to-pay thresholds, costs, and utility weights vary by country, which would vary the treatment’s cost effectiveness in different countries. Conclusions This partitioned survival analysis suggests that pembrolizumab plus lenvatinib is cost effective compared with chemotherapy in Sweden for women with advanced or recurrent endometrial carcinoma following previous systemic therapy. Results were robust to mismatch repair status and to changes in parameters/assumptions.

Enlarged carbon footprint inequality considering household time use pattern

Examining household carbon emissions through a supply chain perspective reveals the climate impact of consumption behaviors and variations across societal structures and features. Despite the evident and expanding environmental inequality, strategies for its mitigation and prioritization remain debated. This study investigates the origins of carbon emission inequalities from household consumption, using Japan as a case study, a society characterized by aging demographics and comparatively equal income redistribution. By quantitatively analyzing household carbon footprints, we observe a U-shaped distribution in emissions as income levels increase. Notably, the carbon footprint sizes are strikingly similar between the lowest and highest income groups, yet inequalities emerge in education and investments in future generations. Integrating these findings, this study further conducts a scenario analysis to project shifts in future low-carbon lifestyles, indicating that middle-income groups are more prone to achieving personal-level decarbonization. This projection is vital in understanding how to effectively address carbon footprint inequalities, especially considering the entrenched preferences in wealthier demographics for investing in future generations.

Cost-effectiveness of nivolumab versus surveillance for the adjuvant treatment of patients with urothelial carcinoma who are at high risk of recurrence: a US payer perspective

ABSTRACT Aim To evaluate the cost-effectiveness of adjuvant nivolumab compared with surveillance for the treatment of patients with high-risk muscle-invasive urothelial carcinoma (MIUC) after radical resection from a US healthcare payer perspective and to investigate the impact of alternative modeling approaches on the cost-effectiveness results. Material and Methods A four-state, semi-Markov model consisting of disease free, local recurrence, distant recurrence, and death health states was developed to investigate the cost-effectiveness of nivolumab compared with surveillance over a 30-year time horizon. The model used data from the randomized CheckMate 274 study (NCT02632409) and published literature to inform transitions among health states, and inputs on cost, utility, adverse event, and disease management. Scenario analyses were conducted to investigate the impact of model structure and key assumptions on the results. One-way deterministic and probabilistic sensitivity analysis were conducted to investigate the robustness of the results. Results Total expected costs were higher with nivolumab ($162,278) compared with surveillance ($63,027). Nivolumab was associated with improved survival (1.61 life-years gained compared with surveillance) and an incremental gain of 0.98 quality-adjusted life-years (QALYs). Although total treatment costs were higher for nivolumab, cost offsets were observed because of delayed or avoided recurrences and deaths experienced with nivolumab compared with observation. The incremental cost-effectiveness and cost-utility ratios were $61,462/life-year and $100,930/QALY. Limitations At the time of analysis, CheckMate 274 had limited follow-up on disease-free survival and no overall survival data. The limited evidence necessitated assumptions on modeling survival after each type of recurrence. Conclusions Nivolumab is estimated to be a life-extending and cost-effective option for adjuvant treatment of MIUC for patients who are at high risk of recurrence after undergoing radical resection in the United States. Using a threshold of $150,000/QALY, the cost-effectiveness conclusions remained consistent across the scenario and sensitivity analyses conducted.

Low energy contact X-ray brachytherapy for treatment of rectal cancer: a health technology appraisal by Health Technology Wales.

Health Technology Wales sought to evaluate the clinical and cost-effectiveness of contact X-ray brachytherapy (CXB) for early-stage rectal cancer. Relevant studies were identified through systematic searches of MEDLINE, Embase, Cochrane Library and Scopus. A cost-utility model was developed to estimate the cost-effectiveness of CXB in National Health Service Wales, using results of the Organ Preservation in Early Rectal Adenocarcinoma (OPERA) trial. Patient perspectives were obtained through the Papillon Patient Support group and All-Wales Cancer Network. The OPERA randomized controlled trial showed that CXB improved complete response and organ preservation rates compared with external-beam boost for people with T2-3b, N0-1, M0 rectal cancer who are fit for surgery. Managing more of this population non-operatively after CXB was estimated to provide 0.2 quality-adjusted life years at an additional cost of £887 per person. CXB was cost effective compared with external-beam boost at a cost of £4463 per quality-adjusted life year gained. This conclusion did not change in scenario analysis and CXB was cost effective in 91% of probabilistic sensitivity analyses. Patients valued receiving clear information on all available options to support their individual treatment choices. The detrimental impact of a stoma on quality of life led some patients to reject the idea that surgery was their only option. This evidence review and cost-utility analysis indicates that CXB is likely to be clinically and cost effective, as part of a watch and wait strategy for adults fit for surgery. Wider access to CXB is supported by patient testimonies.

Operating speed distributions in off-street cycling facilities by vehicle type and motorization

With the growing popularity of motorized Personal Mobility Devices (PMDs) for urban travel, there is increasing need to understand the impacts on transportation facility operation. Speed is a critical aspect of vehicle performance, and the lack of robust information on PMD operating speeds hinders facility design, regulation, and policy for safe and comfortable PMD integration. The objectives of this study are 1) to derive operating speed distributions for all Personal Mobility Devices in use on off-street “cycling” facilities in metropolitan Vancouver, Canada, and 2) to determine how PMD speeds are influenced by microenvironment factors including facility type, path grade, weather, and path traffic volume. Classified speed data for 27 PMD types were collected in 4 seasons at 12 sampling locations in Vancouver, Canada. Results from the 25,053 observations show that motorization increases speeds by 3, 10, and 13 km/hr for bicycles, skateboards, and scooters, respectively, which tends to homogenize average speeds across PMD types at around 20–24 km/hr. Motorization also decreases the effect of grade on speed by about 2/3rd. Even with motorization, bicycle and other PMD speeds rarely exceed the regulatory limit of 32 km/hr – except for sit-down electric scooters which have exceptionally high speeds. Scenario analysis indicates that even with large penetration rates of motorized PMD, a 30 km/hr design speed for off-street paths would still be appropriate, although less conservative.

Technical analysis of a novel economically mixed CO2-Water enhanced geothermal system

Geothermal energy has undoubtedly been considered a promising low-carbon resource in future energy supply responding to the rising energy demand and severe climate concerns. CO2 has been demonstrated as a comparable alternative for water in developing an Enhanced geothermal system (EGS), which simultaneously unlocks the potential to sequester CO2 against climate concerns. However, an earlier thermal breakthrough is always observed owing to higher mobility in geothermal reservoirs for CO2-EGS, and an expensive cost of CO2 consumption makes CO2-EGS challenging to achieve economic profitability. In this study, a novel CO2-water-EGS is proposed to investigate the feasibility of solving the obstacles in conventional EGSs. An integrated analysis for multiple EGS scenarios is carried out from both the technical and economic perspectives. The effects of a CO2-water ratio, an injection rate and a flow scheme are comprehended. Followed by the determination of an optimal operating strategy. In the results, by reasonably selecting a CO2 mass ratio and an injection rate, CO2-water-EGS can recover more geothermal energy and earn more profits than water-EGS and CO2-EGS. The injection rate shows positive correlations to heat mining, carbon storage, and economic performance, while the impact of a flow scheme is negligible when the total volume of injected fluids remains consistent. From the optimization study, the CO2-water-EGS with the 110 kg/s injection rate and 20%wt CO2 ratio shows the highest Net Present Value (NPV) of 42.4 M$. This study proposes theoretical guidance to operators in their decision-making processes on the development of CO2-water-EGS.

Exploring the food-energy-water nexus in coupled natural-human systems under climate change with a fully integrated agent-based modeling framework

Managing water resources to meet increasing energy and food demands while maintaining environmental sustainability under climate change is a major challenge, especially when this nexus occurred in a coupled natural–human system (CNHS), where heterogeneous human activities affect the natural hydrologic cycle and vice versa. The relevant research has been limited by the lack of models that can effectively integrate human dynamics and hydrologic conditions with spatial details to examine co-evolutionary systems. To address this challenge, this paper develops a modeling framework that integrates an agent-based model (ABM; human behavior model) into a large-scale, process-based distributed hydrologic model to simulate human decisions endogenously in the hydrologic cycle. We then apply the Decision Scaling approach, an ex-post scenario analysis method, with our integrated model to study the bidirectional feedback of the CNHS under future changing climate conditions. With the Columbia River Basin (CRB) selected as the case study area, the calibration results show that the integrated model can simultaneously capture the historical irrigated water consumption and streamflow dynamics. Modeling results show that the trade-off between irrigated water consumption, hydropower generation, and streamflow will become more pronounced under hotter and wetter climate conditions at both the entire basin and regional (states and provinces) levels. Special attention should be given to “temperature thresholds” of different regions when the trade-off pattern started. The trade-off results can potentially inform the Columbia River Treaty renegotiation and provide insights for long-term water management policies.

Optimization of Personalized Mass Customization Building Portfolio by Integrating BIM and NSGA Algorithms

The objective of this study is to optimize the personalized mass customization building portfolio by using BIM and NSGA algorithms to achieve the desired balance between cost and efficiency. The study uses BIM software for cost simulation, and NSGA-II algorithm for multi-objective optimization. The mathematical model of building cost and duration is constructed and the multi-objective optimization algorithm is applied to achieve optimization under multiple objectives. The results of the study show that after 20,000 generations of iterations, 191 Pareto solutions are determined to be valid, and the stability and uniform distribution of the optimization objectives are verified, with the percentage of valid solutions reaching 92\%. In the conducted VIKOR scenario analysis, three different cost-effective optimization schemes are proposed depending on user preferences. The method of integrating BIM and NSGA algorithm can effectively improve the cost-effectiveness of building design, and provides a feasible and efficient optimization path for personalized mass-customized buildings.

Hourly Long-Term Traffic Volume Prediction with Meteorological Information Using Graph Convolutional Networks

Hourly traffic volume prediction is now emerging to mitigate and respond to hourly-level traffic congestion augmented by deep learning techniques. Incorporating meteorological data into the forecasting of hourly traffic volumes substantively improves the precision of long-term traffic forecasts. Nonetheless, integrating weather data into traffic prediction models is challenging due to the complex interplay between traffic flow, time-based patterns, and meteorological conditions. This paper proposes a graph convolutional network to predict long-term traffic volume with meteorological information. This study utilized a four-year traffic volume and meteorological information dataset in Chung-ju si to train and validate the models. The proposed model performed better than the other baseline scenarios with conventional and state-of-the-art deep learning techniques. Furthermore, the counterfactual scenarios analysis revealed the potential negative impacts of meteorological conditions on traffic volume. These findings will enable transportation planners predict hourly traffic volumes for different scenarios, such as harsh weather conditions or holidays. Furthermore, predicting the microscopic traffic simulation for different scenarios of weather conditions or holidays is useful.

Necessary CO2 emissions improvement based on rural residential coal/stove-specific coal combustion and prefectural-differentiation synergistic effect of pollutants and carbon emission reduction

Rural residential coal combustion (RRCC) is an important CO2 emission source in rural areas and these emissions vary greatly by coal and stove types. The lack of CO2 emission estimations for different coal-stove combinations restricts refinement and region-differentiated synergistic reduction of pollution and CO2 emissions from RRCC. This study established the coal and stove specific CO2 emissions inventory of RRCC based on investigated coal combustion of various coal and stove types, taking the region of Hebei in China as an example. The synergistic reduction effect of CO2 and pollutants was evaluated using 4 emission reduction scenarios. CO2 emission was 67.4% higher than estimates of RRCC based on the Statistical Yearbook and IPCC emission factors. The combination of bituminous coal and an advanced coal stove was the greatest contributor (62.1%) to CO2 emissions. Bituminous coal burnt in home Arcola has the highest per capita hourly and per unit calorific value CO2 emission, 2.3 and 2.4 times higher than the combination of anthracite briquette coal and pit coal stove and the combination of bituminous coal and an advanced coal stove, respectively. Except for NOx and CO, the coal-stove combination with the lowest per capita hourly and per unit calorific value emissions is anthracite briquette coal and an advanced coal stove. Scenario analysis shows that updated coal stoves feature the highest pollutant emission reduction, but greater CO2 and NOX emissions. For individual cities, synergistic pollution and carbon reduction is seen in Tangshan and Qinhuangdao, and organic carbon, CO, and SO2 have better synergistic positive reduction with CO2, while NOX has shown a negative effect with CO2 in other cities with the best coal and stove update scenario. Compared with the coal and stove update scenario, emission reduction effects with clean fuel substitution varies across cities. This study is helpful to understand the possible non-synergy of pollutant and CO2 reduction in the process of RRCC management. Attention should be given to issues related to the synergistic control plan of pollution and CO2 reduction for RRCC with local adaptation.