Framework For Quantitative Assessment Research Articles

Assessing uncertainty in building material emissions using scenario-aware Monte Carlo simulation

Global greenhouse gas emissions from the built environment remain high, driving innovative approaches to develop and adopt building materials that can mitigate some of those emissions. However, life-cycle assessment (LCA) practices still lack standardized quantitative uncertainty assessment frameworks, which are urgently needed to robustly assess mitigation efforts. Previous works emphasize the importance of accounting for the three types of uncertainties that may exist within any quantitative assessment: parameter, scenario, and model uncertainty. Herein, we develop a quantitative uncertainty assessment framework that distinguishes between different types of uncertainties and suggest how these uncertainties could be handled systematically through a scenario-aware Monte Carlo simulation (MCS). We demonstrate the framework’s decision-informing power through a case study of two multilevel ordinary Portland cement (OPC) manufacturing scenarios. The MCS utilizes a first-principles-based OPC life-cycle inventory, which mitigates some of the model uncertainty that may exist in other empirical-based cement models. Remaining uncertainties are handled by scenario specification or sampling from developed probability distribution functions. We also suggest a standardized method for fitting distributions to parameter data by enumerating through and implementing distributions based on the Kolmogorov–Smirnov test. The level of detail brought by the high-resolution parameter breakdown of the model allows for developing emission distributions for each process of OPC manufacturing. This approach highlights how specific parameters, along with scenario framing, can impact overall OPC emissions. Another key takeaway includes relating the uncertainty of each process to its contributions to total OPC emissions, which can guide LCA modelers in allocating data collection and refinement efforts to processes with the highest contribution to cumulative uncertainty. Ultimately, the aim of this work is to provide a standardized framework that can provide robust estimates of building material emissions and be readily integrated within any uncertainty assessment.

Systematic Framework for Quantitative Assessment of Indoor Air Quality Under Future Climate Scenarios; 2100s Projection of a Belgian Case Study

Alteration of Indoor Air Quality (IAQ) levels in the context of changing climate is correlated with shifting air pollutant emissions, variations in ambient climate, and the mitigation/adaptation strategies applied in buildings to deal with increasing extreme weather events and energy demands. In this study, firstly, a systematic modeling-based framework for the quantitative assessment of the impacts of future building retrofit and climate scenarios on IAQ is presented. After describing the framework, its practical implementation in a demonstrative case study is presented. The proposed framework includes three main parts: i) IAQ measurements, ii) IAQ model design, and iii) future IAQ state evaluation. Regarding the case study, fabricated indoor monitoring devices (O3, CO, NO, NO2, PM2.5, PM10, VOCs, air temperature, relative humidity, and air pressure) based on Low-Cost Sensors were developed, and calibrated with reference analyzers. An indoor measurement campaign was conducted in a naturally ventilated residential building (+2 exhaust fans) in the Wallonia region, south of Belgium (summer of 2021). An IAQ model was designed in the multizone IAQ and ventilation software, CONTAM. The validation and calibration processes were carried out with the aid of experimental data from the indoor measurement campaign. The calibrated IAQ model showed a total conformity of +95% from the average concentration perspective. Finally, predicted future outdoor air pollution and indoor and outdoor climate data of the case study were fed to the IAQ model (basis-year 2021), and indoor contaminant levels under different climate scenarios were quantitatively assessed till 2100.

Feed-in-tariff is key to Japan’s current biomass power’s viability, even with environmental externalities

Bioenergy is increasingly recognized as an effective tool for removing carbon dioxide from the atmosphere. However, its economic feasibility remains underexplored, particularly when accounting for environmental impacts. This study proposes a quantitative assessment framework to calculate the cost-benefit ratio of biomass power generation and to assess the sustainability of its supporting policy tools, such as feed-in-tariffs (FIT). The framework accounts for benefits through electricity generation and environmental externalities, namely emissions from feedstock production and procurement, such as the transportation of biomass materials. This allows for quantification and a detailed discussion of multiple environmental burdens of biomass energy and economic costs. As a case study, this framework was applied to a hypothetical biomass plant in Japan, which has the fifth-largest biomass market globally. We prepare several scenarios to consider diverse conditions within the Japanese biomass industry, including the types of biomass materials used (pellets versus chips), their sources (domestic versus international), and the biomass technologies employed. The results show that using pellets, predominantly imported, significantly increases biomass energy costs. The increase in cost is directly proportional to the quantity of utilized pellets and their transportation distances. However, pellet production location —whether in Vietnam or Canada—doesn’t significantly change the overall cost calculations in our study. Our result is consistent across various biomass technologies, showing that the high selling price under the feed-in-tariff system, rather than material type, supply origin, or transportation mode, plays the most critical role in economic feasibility, even when accounting for environmental externalities. Thus, decision-makers must reevaluate the efficacy of FIT policies for wood biomass powers, where fuel costs share a substantial portion. We also discuss its synergies with local industries and trade-offs with other land-use objectives.

Quantitative Assessment Framework for the Postearthquake Traffic Flow Capacity of Bridges Based on the Loss Model of Vertical Load-Carrying Capacity

Quantitative Assessment Framework for the Postearthquake Traffic Flow Capacity of Bridges Based on the Loss Model of Vertical Load-Carrying Capacity

Quantitative assessment framework for seismic resilience of petroleum depots

Petroleum depots are an important part of the national strategic reserve base and should maintain functionality during an earthquake or recover immediately after an earthquake; this is known as seismic resilience. To quantify the seismic resilience and functionality of a petroleum depot, a quantitative assessment framework is proposed. First, the influence of the components on the function and seismic resilience is quantified from the perspective of the system by establishing a component–subsystem–system model. Second, the relationship between the damage index and functional loss and the corresponding relationship between recovery time and seismic resilience level are established using functional loss and functional recovery as quantitative indicators. A petroleum depot case study is presented, in which the functional loss and recovery time are explicitly expressed to verify the applicability of the proposed resilience assessment framework. The framework is also applicable to industrial complexes such as chemical plants and refineries, and can help determine the optimal location and design, allowing decision-makers to better formulate emergency and post-earthquake recovery plans.

Multi-scale flow regimes and driving forces analysis based on different models: a case study of the Wu River basin

Abstract Quantitatively separating the influence of climate change and human activities on runoff is crucial to achieving sustainable water resource management in watersheds. This study presents a framework for quantitative assessment by integrating the indicators of hydrologic alteration, the whale optimization algorithm and random forest (WOA-RF), and the water erosion prediction (WEP-L) models. This framework quantifies the differences in hydrological conditions and their driving forces at multi-timescales (annual, seasonal, and monthly). The results indicate that the runoff of the Wu River has decreased since 2005. Climate factors were found to influence the interannual variation of runoff mainly. Meanwhile, human activities had a more significant impact in autumn, with a relative contribution rate of 59.0% (WOA-RF model) and 70.8% (WEP-L model). Monthly, the picture is more complex, with the results of the WOA-RF model indicating that climate change has a significant impact in July, August, and September (88.8%, 92.7%, and 79.3%, respectively). However, the WEP-L model results showed that the relative contribution of land use is significant in April, May, June, October, and November (51.24%, 64.23%, 63.63%, 53.16%, and 50.63%, respectively). The results of the study can be helpful for regional water allocation.

A novel quantitative assessment framework of the IAQ resilience performance of buildings: The resilience score metric

Resilience performance in building design has emerged as a critical consideration in the face of increasing uncertainties or ‘shocks’ posed by natural disasters, climate change & excessive pollution. To evaluate and optimize building design decisions, holistic performance metrics are needed. This work defines a novel quantitative assessment framework of indoor air quality (IAQ) resilience which output is the resilience score (RS) metric that integrates all resilience aspects and building-relevant pollutants. The framework was demonstrated via simulations on a validated model of a case study educational building in Belgium for 3 ventilations systems: constant air volume (CAV), demand-controlled ventilation (DCV), DCV without filters, and 3 shock types (mechanical MS, internal IS, outdoor shocks OS). Results showed that OS was the least critical shock type, followed by IS and MS. With increasing degree of shock, RSMS decreased for all 3 systems linearly by 64% until 2.8 h of power outage, beyond which the rate of decrease slowed down considerably. CAV & DCV had the same RSMS and that of DCV w/o filters was 13% lower. The RSIS for CAV & DCV deteriorated by 38.8% & 46% before plateauing at values of 0.45 & 0.36 respectively. CAV had better resilience against IS. The effect of filters did not reflect in the RSIS. The RSOS for CAV & DCV deteriorated by 51% & 26% before plateauing at values of 0.49 & 0.73 respectively. DCV had the best resilience against OS, followed by DCV w/o filters & CAV.

Developing an Assessment Framework for Evaluating Knowledge-Based Safety Culture in Construction Organizations

ABSTRACT Many studies on safety in construction projects have indicated that positive safety culture in the workplace environment would reduce accidents. The inherent project-based characteristic of construction organizations means knowledge and experience are generally lost when a project is completed. This transitory nature hinders an organization’s attempt to develop and maintain a knowledge-based positive safety culture. Also, this can be overcome by continuous monitoring and reviewing of the knowledge-based safety culture required to enforce safety standards and regulations required for construction organizations. In order to achieve this, a quantitative assessment framework for knowledge-based safety culture is a prerequisite. Therefore, this study aims to develop a measurable quantitative framework based on the importance of factors pertaining to psychological, behavioral, organizational, and knowledge dimensions of knowledge-based safety culture. A survey questionnaire is employed to examine the importance of these factors. The survey findings are used to formulate a framework suitable for use in the construction industry. Furthermore, this framework is validated by considering case scenarios among safety-certified and non-certified construction organizations. The proposed analytical framework can be used for assessing the knowledge-based safety culture scores of construction organizations.

A quantitative framework for sustainability assessment

The present climate change crisis forced humanity to opt for sustainable development. Sustainability assessment is vital to determine the relative superiority among alternatives, characterized by multiple sustainability indicators to ensure sustainable development. Various methods, such as the Euclidean distance method, geometric mean method, and elimination et choice translating reality, have been suggested in the literature to identify the most sustainable option among alternatives. These diverse approaches adopt different normalization and aggregation formulations (the two most significant steps of any sustainability assessment), leading to conflicting results. This paper proposes a generalized sustainability framework to quantitatively identify the most suitable alternative. The proposed framework incorporates various mathematical characteristics of normalization and aggregation processes and identifies mathematical functions that satisfy these characteristics. Based on the desired characteristics, the proposed approach identifies the min–max normalization function and a novel antinorm-based aggregation function as one of the appropriate functions for a quantitative sustainability framework. To illustrate the applicability of the proposed framework, different case studies are adopted from the literature: sustainable power plants for electricity generation in Portugal, sustainable feedstocks for the biodiesel supply chain, and sustainable negative emission technologies. The results are compared with those reported in the literature, and the efficacy of the suggested framework is demonstrated. The proposed framework may be utilized for multi-criteria decision-making.

Exploring greenhouse gas emissions pathways and stakeholder perspectives: In search of circular economy policy innovation for waste paper management and carbon neutrality in Hong Kong

Waste paper disposed in landfills notably contributes to greenhouse gas (GHG) emissions and impedes more sustainable, circular alternatives, such as recycling. In Hong Kong, this unsustainable approach is currently dominant as 68% of waste paper products are treated in landfills in 2020. To contextualize the impact of local waste paper management and explore mitigation potentials of circular alternatives, this paper develops a quantitative assessment framework around GHG emissions development trajectories. Combining guidelines of the Intergovernmental Panel on Climate Change (IPCC), national GHG inventories, and local parameters from life cycle analysis, five GHG emissions projections were simulated along the Shared Socioeconomic Pathways (SSPs) until 2060. Most recent baselines indicate that Hong Kong's current waste paper treatment generated 638,360 tons CO2-eq in 2020, comprising 1,821,040 tons CO2-eq from landfill and 671,320 tons CO2-eq from recycling, and −1,854,000 tons CO2-eq from primary material replacement. Proceeding along a Business-as-Usual scenario under SSP5, GHG emissions will dramatically increase to a net 1,072,270 tons CO2-eq by 2060, whereas a recycling-intensive scenario will lead to a net saving of −4,323,190 tons CO2-eq. To complement the quantitative evidence on the benefits of waste paper recycling, field research was conducted to explore the feasibility of circular policy innovation from the perspective of recycling stakeholders. These empirical qualitative and quantitative findings from stakeholders' business routines and material transactions provide crucial indications for policy and institutional innovation: Essentially, for Hong Kong to improve waste paper recycling capacities and facilitate a circular economy (CE), local stakeholders require support via fiscal policy measures (financial subsidies or tax reductions) and infrastructure improvements (delivery access and material storage). In sum, this study employs a novel analytical framework combining original qualitative and quantitative evidence to provide policy innovation towards circular, GHG emission-saving waste paper management.

Multi-Component Resilience Assessment Framework for a Supply Chain System

The goal of this paper is to develop a quantitative resilience assessment framework for a supply chain system exposed to multiple risk factors. Most existing studies on supply chain resilience have primarily focused on assessing the system’s ability to withstand and recover from disruptions caused by a single type of hazard. However, a supply chain system is exposed to multiple exogenous and endogenous events and conditions over a planning horizon, and a comprehensive assessment of resilience should take into account multiple risk factors. Moreover, contrary to the conventional resilience assessment methods focusing on the short duration during which the system is impacted by a disaster event, the proposed framework measures the resilience capacities of the system over a long-term horizon through multi-risk assessment and multi-component resilience assessment. Specifically, a new multi-component resilience index is proposed to measure (a) hazard-induced cumulative loss of functionality, (b) opportunity-induced cumulative gain of functionality, and (c) non-hazard-induced cumulative loss of functionality. The case study results indicate that all three types of risk factors contribute to the overall resilience index significantly and ignoring any one of them may result in inaccurate supply chain performance and resilience assessment.

Developing a multidimensional assessment framework for clean technology transfer potential and its application on the Belt and Road Initiative countries

The transfer of industrial clean technology can contribute to the transformation of industry sustainable development to address the dual challenges of climate change and environmental pollution. Although its importance is widely acknowledged, there is still a lack of scientific and quantitative assessment framework to precisely and effectively identify high-potential cooperation targets in the context of countries with diverse backgrounds. To fill this gap, this study proposed a multidimensional assessment framework for industrial clean technology transfer potential with application in 58 BRI developing countries. This study reviewed existing literature on the main barriers and drivers of clean technology transfer and extracted four assessment dimensions as primary indicators, namely the recipient country's macro environment, industry development, clean technology demand, and clean technology transfer guarantee, with 13 secondary indicators, 52 tertiary indicators based on the open-source database and common assessment index. The established framework was then used to assess the potential for China's industrial clean technology transfer to 58 developing countries under the Belt and Road Initiative, with the weights of indicators at all levels determined by the analytic hierarchy process method based on expert questionnaires and equal weighting method. The findings demonstrate that there are significant differences in clean technology transfer potential among studied countries. Qatar, the United Arab Emirates, and Malaysia are the best options for China, while Bolivia, Cameroon, and Algeria have the lowest technology transfer potential. Our work can not only assist technology providers with overall country selection in industrial cleaner technology transfer cooperation and strengthen their risk prevention capacity, but also help countries with low cooperation potential or obvious weaknesses to identify deficiencies and make targeted improvements, as well as serve as a reference for other international technology cooperation assessments.

How to Develop Sustainably after Poverty Alleviation in Poverty-Stricken Areas under Paired Assistance: A Quantitative Assessment Framework Based on System Dynamics Model

The paired assistance approach is an important measure for achieving poverty alleviation in China. The sustainable development of poverty-stricken areas after poverty alleviation has become an important task in preventing a return to poverty and promoting rural revitalization. Paired assistance is a dynamic process of balanced economic, social, and population development. This study establishes a system dynamics (SD) model for developing poverty-stricken regions under paired assistance. This study takes Lvchun County in Yunnan Province as the research object, designates six scenarios, and simulates the development of Lvchun County from 2019 to 2035 to evaluate its sustainable development after poverty alleviation. The results show that the cessation of poverty alleviation funding will not increase the incidence of poverty under paired assistance but will lead to an unbalanced scenario of “poor people and a rich government”. In the multi-scenario simulation, only by continuing the cooperation foundation formed by paired assistance and paying attention to the resource endowment of poverty-stricken areas can collaborative development be adopted to achieve sustainable development by 2030. This result may be a valuable resource for the world’s underdeveloped regions to reference as they seek to eliminate poverty, reduce the risk of returning to poverty, and promote sustainable development.

Urban resilience under the COVID-19 pandemic: A quantitative assessment framework based on system dynamics

The COVID-19 pandemic, which lasted for three years, has had a great impact on the public health system, society and economy of cities, revealing the insufficiency of urban resilience under large-scale public health events (PHEs). Given that a city is a networked and multidimensional system with complex interactions, it is helpful to improve urban resilience under PHEs based on system thinking. Therefore, this paper proposes a dynamic and systematic urban resilience framework that incorporates four subsystems (governance, infrastructures, socioeconomy and energy-material flows). The composite index, system dynamics and epidemic simulation model are integrated into the framework to show the nonlinear relationships in the urban system and reflect the changing trend of urban resilience under PHEs. Then, urban resilience under different epidemic scenarios and response policy scenarios is calculated and discussed to provide some suggestions for decision-makers when faced with the trade-off between the control of PHEs and the maintenance of city operation. The paper concludes that control policies could be adjusted according to the characteristics of PHEs; strict control policies under a severe epidemic could lead to a significant decrease in urban resilience, while a more flexible control strategy can be adopted under a mild epidemic scenario to ensure the normal operation of urban functions. Moreover, the critical functions and impact factors of each subsystem are identified.

A quantitative approach on environment-food nexus: integrated modeling and indices for cumulative impact assessment of farm management practices.

Best management practices (BMPs) are promising solutions that can partially control pollution discharged from farmlands. These strategies, like fertilizer reduction and using filter strips, mainly control nutrient (N and P) pollution loads in basins. However, they have secondary impacts on nutrition production and ecosystem. This study develops a method to evaluate the cumulative environmental impacts of BMPs. It also introduces and calculates food's environmental footprint (FEF) for accounting the total environmental damages per nutrition production. This study combines the soil and water assessment tool (SWAT) for basin simulation with the indices of ReCiPe, a life cycle impact assessment (LCIA) method. By these means, the effectiveness of BMPs on pollution loads, production yields, and water footprints (WFs) are evaluated and converted as equivalent environmental damages. This method was verified in Zrebar Lake, western Iran. Here, water consumption, as WFs, and eutrophication are the main indices that are converted into equivalent health and ecological impairments. Two methods, entropy and environmental performance index (EPI), are used for weighting normalized endpoints in last step. Results showed that using 25-50% less fertilizer and water for irrigation combined with vegetated filter strips reduce N and P pollution about 34-60% and 8-21%, respectively. These can decrease ecosystem damages by 5-9% and health risks by 7-14%. Here, freshwater eutrophication is a more critical damage in ecosystem. However, using less fertilizer adversely reduces total nutrition production by 1.7-3.7%. It means that BMPs can decline total ecological damages and health risks, which threatens nutrition production. FEF presents a tool to solve this dilemma about the sustainability of BMPs. In the study area, a 4-9% decrease in FEF means that BMPs are more environmental friendly than nutrition menacing. Finally, this study concludes that SWAT-ReCiPe with FEF provides a quantitative framework for environment-food nexus assessment. However, due to the uncertainties, this method is recommended as a tool for comparing management strategies instead of reporting certain values.

Quantitative Assessment of Aortic Hemodynamics for Varying Left Ventricular Assist Device Outflow Graft Angles and Flow Pulsation.

Left ventricular assist devices (LVADs) comprise a primary treatment choice for advanced heart failure patients. Treatment with LVAD is commonly associated with complications like stroke and gastro-intestinal (GI) bleeding, which adversely impacts treatment outcomes, and causes fatalities. The etiology and mechanisms of these complications can be linked to the fact that LVAD outflow jet leads to an altered state of hemodynamics in the aorta as compared to baseline flow driven by aortic jet during ventricular systole. Here, we present a framework for quantitative assessment of aortic hemodynamics in LVAD flows realistic human vasculature, with a focus on quantifying the differences between flow driven by LVAD jet and the physiological aortic jet when no LVAD is present. We model hemodynamics in the aortic arch proximal to the LVAD outflow graft, as well as in the abdominal aorta away from the LVAD region. We characterize hemodynamics using quantitative descriptors of flow velocity, stasis, helicity, vorticity and mixing, and wall shear stress. These are used on a set of 27 LVAD scenarios obtained by parametrically varying LVAD outflow graft anastomosis angles, and LVAD flow pulse modulation. Computed descriptors for each of these scenarios are compared against the baseline flow, and a detailed quantitative characterization of the altered state of hemodynamics due to LVAD operation (when compared to baseline aortic flow) is compiled. These are interpreted using a conceptual model for LVAD flow that distinguishes between flow originating from the LVAD outflow jet (and its impingement on the aorta wall), and flow originating from aortic jet during aortic valve opening in normal physiological state.

Security of Mineral Resources: a New Framework for Quantitative Assessment of Criticality

Security of Mineral Resources: a New Framework for Quantitative Assessment of Criticality

Quantitative Assessment Framework for Non-Structural Bird’s Nest Risk Information of Transmission Tower in High-Resolution UAV Images

As the main cause of foreign body intrusion, bird’s nests pose a significant potential risk to high-voltage transmission towers. Currently, most research uses convolutional neural networks (CNNs) to detect partial images of towers captured by manually operated unmanned aerial vehicles (UAVs). However, these methods have difficulties in learning the locations and potential risk information of bird’s nests and generating structured data results. To address these challenges, this paper presents a quantitative assessment framework for non-structural bird’s nest risk information of transmission towers using high-resolution UAV panoramic images. The integrity of the tower’s structure information is maintained by using panoramic UAV images. The proposed method involves estimating the tower’s pose and determining whether each key point of the tower has a bird’s nest, transforming the non-structural bird’s nest locations and risk information into structured data. A risk score is used to classify the level of potential risk caused by bird’s nests. The experiments demonstrate that the proposed framework can accurately detect the key points of five types of towers, with an F1-score of 83.09% for bird’s nest detection and an accuracy of 91.59% for risk assessment.

Co-Benefit Assessment of Active Transportation in Delhi, Estimating the Willingness to Use Nonmotorized Mode and Near-Roadway-Avoided PM2.5 Exposure.

This study aims to estimate the avoided mortalities and morbidities and related economic impacts due to adopting the nonmotorized transportation (NMT) policy in Delhi, India. To this aim, an integrated quantitative assessment framework is developed to estimate the expected environmental, health, and economic co-benefits from replacing personal motorized transport with NMT in Delhi, taking into account the inhabitants' willingness to use NMT (walking and cycling) mode. The willingness to accept NMT is estimated by conducting a cross-sectional survey in Delhi, which is further used to estimate the expected health benefits from both increased physical activity and near-roadway-avoided PM2.5 exposure in selected traffic areas in 11 major districts in Delhi. The value of a statistical life (VSL) and cost of illness methods are used to calculate the economic benefits of the avoided mortalities and morbidities from NMT in Delhi. The willingness assessment indicates that the average per capita time spent walking and cycling in Delhi is 11.054 and 2.255 min, respectively. The results from the application of the NMT in Delhi show the annual reduction in CO2 and PM2.5 to be 121.5 kilotons and 138.9 tons, respectively. The model estimates the expected co-benefits from increased physical activities and reduced PM2.5 exposure at 17,529 avoided cases of mortality with an associated savings of about USD 4870 million in Delhi.

A new approach of Robustness-Resistance-Recovery (3Rs) to assessing flood resilience: A case study in Dongting Lake Basin

The frequency of natural disasters and the losses caused by global climate change and urbanization continue to increase. Traditional rigid defense methods cannot meet the needs of modern disaster mitigation and should shift to resilience approaches. However, most existing resilience studies stopped at qualitative analysis and static evaluation. Quantitative evaluation of dynamic resilience is quite rare. In this study, focusing on flood resilience, we applied the process-based “Robustness-Resistance-Recovery (3Rs)” quantitative assessment framework, and used the Dongting Lake Basin as an empirical area. The results showed that the resilience of the whole Dongting Lake Basin was high, but the Dongting Lake Eco-economic Zone had poor flood resilience. The low value of robustness was concentrated in the north and central part of Hunan Province, affected by the distribution of water systems. The resistance was relatively low and the administrative units with the top two low levels accounted for 61.99%, while the recovery level was high with obvious good performance in the west. In addition, the synergy of robustness and resistance dominated the flood resilience. This study proposed a resilience assessment framework based on the “3Rs” and provided a new process perspective for improving the flood resilience.