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This study examines how certain artificial intelligence (AI) drivers affect the industry's adoption of this technology in the construction industry. The research methods comprised a comprehensive analysis of previous studies to pinpoint the primary factors influencing AI adoption in the construction industry. Data collection was carried out through a well-structured survey involving relevant stakeholders in the building construction sector. The three main constructs of technological devices, advancement, and knowledge were found from the set of drivers with the technique of exploratory factor analysis. The deployment of AI in construction has the potential to improve health and safety and expedite project completion, as this research has evaluated. To figure out how these factors relate to the adoption of AI in the construction industry, partial least squares structural equation modeling was used. The study's conclusions showed that the influence of AI installation in the construction industry is reasonably significant thanks to the technology, advancement, and knowledge, contributing around 15 % of the effects that have been directly witnessed. The practical implications of AI for policy makers, engineers, and construction stakeholders are extensive and provide valuable insights for customized strategies aimed at using AI's potential to improve projects, promote sustainability, and elevate health and safety standards.

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Nature-Based Solutions (NBS) can be defined as solutions based on natural processes that meet societal challenges and simultaneously provide human well-being and biodiversity benefits. These solutions are envisioned to contribute to operationalizing sustainable development strategies, especially in the context of adaptation to climate change (e.g. flood risk reduction). In order to quantify NBS performance, ease their uptake and advocate for them as alternatives to "business-as-usual" infrastructures, a comprehensive, holistic valuation of their multiple benefits (multiple advantages and disadvantages) is needed. This entails quantifying non-market benefits for people and nature in addition to determining the (direct) cost-benefit of the risk-reduction measure. Despite the importance given to the assessment of non-tangible benefits for people and nature in the literature, systematic data collection on these dimensions seems to be missing. This study reviews publications that used stated preference methods to assess non-market human benefits of NBS and NBS-like strategies. Its aim is to highlight any biases or knowledge gaps in this kind of evaluation. Our results show that the valuation of non-tangible benefits of NBS (e.g. increased recreation and well-being, enhanced biodiversity) still suffers from a lack of common framing. Despite some steps being taken on enabling interconnected benefit assessments, unexploited opportunities concerning the integrated assessment of non-market human and nature benefits predominate. Moreover, the research to-date appears based on a case-to-case approach, and thus a shared holistic method does not emerge from the present literature, potentially delaying the uptake of NBS. We argue that future research could minimize missed opportunities by focusing on and systematically applying holistic benefits assessments. Methods based on stated preference surveys may help to ensure holistic approaches are taken, as well as contributing to their replicability and application when upscaling NBS.

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Combusting rice husk (RH) generates energy and rice husk ash (RHA) containing high amount of silica. Recent studies showed RHA can directly react with ethanol for producing tetraethyl orthosilicate (TEOS), an important substance for different industries. Nevertheless, this process requires an intensive energy supply. This study aims to design and evaluate an energy self-supply process producing TEOS using RH for feasibility. A process simulator was used to design the target process. The simulation results revealed that RH combustion can completely meet the RHA and high energy demands of TEOS production. The economic and environmental benefits were thoroughly evaluated and compared with processes using conventional raw materials (i.e., Simg and silica). The evaluation results showed that using RH for TEOS production could reduce CO2 emissions substantially. Large economic benefit was gained when renewable electricity was co-generated and sold to the power grid as a surplus.

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New York City (NYC) experienced a sharp decline in air pollution during the COVID-19 shutdown period (March 15, 2020 to May 15, 2020)-albeit at high social and economic costs. It provided a unique opportunity to simulate a scenario in which the city-wide air quality improvement during the shutdown were sustained over the five-year period, 2021 through 2025, allowing us to estimate the potential public health benefits to children and adults and their associated economic benefits. We focused on fine particulate matter (PM2.5) and modeled potential future health benefits to children and adults. The analysis considered outcomes in children that have not generally been accounted for in clean air benefits assessments, including preterm birth, term low birthweight, infant mortality, child asthma incidence, child asthma hospital admissions and emergency department visits, autism spectrum disorder, as well as adult mortality. We estimated a city-wide 23% improvement in PM2.5 levels during the COVID-19 shutdown months compared to the average level for those months in 2015-2018 (the business as usual period). Based on the data for 2020, we extrapolated the ambient levels of PM2.5 for the following five-year period. The estimated cumulative benefits for 2021-2025 included thousands of avoided cases of illness and death, with associated economic benefits from $31.8 billion to $77 billion. This "natural experiment," tragic though the cause, has provided a hypothetical clean air scenario that can be considered aspirational-one that could be achieved through transportation, climate, and environmental policies that support robust economic recovery with similarly reduced emissions.

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The contribution of Nature Based Solutions (NBSs) for supporting climate change adaptation and water-related risks reduction is becoming increasingly relevant for policy and decision-makers, compared to 'grey infrastructures', thanks to their capability to jointly deal with a multiplicity of societal and environmental challenges, producing several co-benefits besides limiting the impacts of water-related risks. Nevertheless, their mainstreaming is still limited by several barriers, which are often related to socio-institutional (e.g. limited cooperation and stakeholders' involvement, limited awareness about NBSs impacts) rather than to technical aspects. In this context, innovative tools for NBSs planning, design, implementation and assessment are required, along with effective processes capable of supporting stakeholders' participation. The present research aims to propose a shift in the approach to NBSs design, based on the early stakeholders' involvement in the identification, modelling and performance assessment in terms of benefits and, particularly, co-benefits production. A multi-step methodology was implemented for the purpose, combining both individual and participatory activities. Reference is made to one of the case studies of the NAIAD project, namely the Balta Potelu Pond Area (Lower Danube, Romania). Causal Loop Diagrams (CLDs) were used to describe the system in terms of causal connections and mutual influences, incorporating stakeholders' views and ideas. Inputs from both institutional (e.g. ministries and municipalities) and non-institutional stakeholders (e.g. NGOs and members of the local communities) were integrated. This allowed a comparative assessment of multiple NBSs, based on the analysis of benefits and co-benefits produced, as well as the identification of trade-offs among different stakeholders (e.g. the increase of agricultural production versus biodiversity conservation) and potential side effects. CLDs were then coupled with a Performance Matrix (a basic feature of Multi-Criteria Decision Analysis) and fuzzy logic to help decision-makers identify the most suitable NBSs for the area. The whole process was aimed at facilitating the process of NBSs selection and analysis, while considering the multiple impacts associated with their implementation.

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OBJECTIVE: To introduce the application of COBRA in health risk assessment of air pollution, and provide reference for the development of similar tools in China. METHODS: The software overview, scope of application and application literature, data requirements and software operation, calculation principles, model construction was introduced, and put forward the reference significance to China. RESULTS: Health and economic benefits can be quickly obtained by entering the type and value of change in pollutant concentration into the software. CONCLUSION: COBRA can be used to quickly evaluate the health effects and economic benefits of pollutant changes, and provide reference for the development of relevant tools in China, applied in many ways, such as rapid assessment of health benefits of pollution prevention and control programs in different cities, regions or nationwide, and screening the policies and measures with lower costs and greater benefits.

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We developed an approach to estimate the public health benefits resulting from transportation projects or environmental actions that reduce mobile source fine particulate matter (PM2.5 ) in select urban areas worldwide when input data are limited or when a rapid order-of-magnitude assessment is needed. For a given reduction in direct PM2.5 emissions, we can use this approach to quantify (1) the subsequent reduction in ambient primary PM2.5 concentration in the urban area; (2) the public health benefits associated with mortality risk reductions, measured in terms of avoided premature deaths; and (3) the economic value of the reduced mortality risk. To illustrate our approach, we estimated the impact of a 100-metric-ton reduction in primary PM2.5 mobile source emissions in the year 2010 for 42 large, global cities. Our estimates of public health benefits and their economic value varied by city, as did the sensitivity to key assumptions and inputs. The estimated number of premature deaths avoided per 100-metric-ton reduction in PM2.5 emissions ranged from 12 to 202. City-level variability in these estimates was driven by the magnitude of the reduction in ambient PM2.5 concentration, the size of the urban population, and the baseline PM2.5 concentration. The economic value of mortality risk reductions per 100-metric-ton reduction in PM2.5 emissions ranged from $2 million to $328 million in 2010 U.S. dollars. Income per capita was the most important driver of the variability in the economic values across countries.

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In this contribution, we show that current scientific methodologies used in nutrition science and by regulatory agencies, such as the randomized control trial, limit our understanding of nutrition and health as they are to crude to capture the subtle pleiotropic nature of most nutrients. Thereby, regulatory agencies such as the European Food Safety Authority curb the development of scientific knowledge and industrial innovations within the nutritional field. In order to develop insights into the health impact of certain food and food-components, we need to realize that health is adaptation set within a homeostatic range. Increased performance of health, i.e., the maximum stimulation of health, typically seems 30-60% greater than the control group, with a width of no more than about a factor of ten, clarifying the difficulty of documenting responses of food-endogenous components within the homeostatic range of healthy people. A strategy to record subtle responses of food components is the summation of procentual effects of relevant health outcomes. We illustrate this approach with the action of flavanols on vascular health, specifically endothelial function.

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