Estimation Of Environmental Impact Research Articles

Identification of residential building typologies by applying clustering techniques to cadastral data

Building typologies are usually classified according to their shape, distribution and construction features depending on the time period they were built. As a result, a subjective classification arises which highly depends on the criteria used to differentiate buildings. When this analysis is carried out at the urban scale, data sets become bigger, making patterns difficult to uncover. Mistakes in deciding the variables to classify buildings lead to incorrect typologies and, consequently, wrong results. The aim of this study is to test a new methodology based on clustering techniques to identify typologies related to energy retrofitting, which would allow obtaining a more objective classification and better pattern recognition by reducing human intervention. To that end, a data set from the Spanish cadastre is used, with additional information to reflect the influence of existing standards on constructive solutions. By applying three clustering techniques (Ward's method, Partitioning Around Medoids, and a combination of both), new proposals of building typologies are obtained and discussed in comparison to traditional classifications. The results show that the Ward's method produces building typologies with significantly high quality metrics and meaningfulness. The agglomeration coefficient is 99.8%, which indicates that hardly another hierarchical method could generate a better clusters structure. Six clusters comprise 86% of the dwellings as most occupants do not declare retrofitting works, thus not being reflected in the cadastre database. This research provides a new classification method that can notably influence the estimation of costs, environmental impact and cost effectiveness of energy retrofitting actions at urban scale.

The negative footprint illusion is exacerbated by the numerosity of environment-friendly additions: unveiling the underpinning mechanisms

ABSTRACT The addition of environmentally friendly items to conventional items sometimes leads people to believe that the carbon footprint of the entire set decreases rather than increases. This negative footprint illusion is supposedly underpinned by an averaging bias: people base environmental impact estimates not on the total impact of items but on their average. Here, we found that the illusion’s magnitude increased with the addition of a greater number of “green” items when the number of conventional items remained constant (Studies 1 and 2), supporting the averaging-bias account. We challenged this account by testing what happens when the number of items in the conventional and “green” categories vary while holding the ratio between the two categories constant (Study 3). At odds with the averaging-bias account, the magnitude of the illusion increased as the category size increased, revealing a category-size bias, and raising questions about the interplay between these biases in the illusion.

Study on the suitability of life cycle assessment for the estimation of donkey milk environmental impact

In the last decades, western Countries increased their interest in innovative products like donkey milk and other activities carried out with donkeys (onotherapy, onotourism). Donkey milk is considered a high-added-value food and is very similar to human breast milk. It is also used as an ingredient in cosmetics. The growing public interest suggests the need for a pilot study on the sustainability of donkey milk production, according to Life Cycle Assessment (LCA) criteria. Milk was used as the Declared Functional Unit (DFU) and two different models were described, a Real Scenario Model (RSM, i.e. a farm with its declared milk yield), and an Increased Milk Production Model (IMPM, i.e., the same farm with theoretically increased milk yield). Allocation was applied both in RSM and IMPM; thus, different values of impact categories, i.e., Global Warming Potential (GWP, kg CO2 equivalents), Acidification Potential (ACP, g SO2 equivalents) and Eutrophication Potential (EUP, g PO43−) were observed. GWP improved after mass allocation and showed the lowest equivalents in IMPM, compared to economic and reference allocation criterion (P < 0.05). In RSM, allocations affected GWP in a different way: the smaller size of the DFU resulted in the largest estimation of CO2 equivalents (P < 0.05) for reference allocation, whereas the mass allocation estimates were lower than with economic allocation (P < 0.05). ACP and EUP followed the same trends. No differences were found in IMPM results across the three allocation methods used. Moreover, mass allocation values recorded in RSM did not significantly differ from IMPM. ACP and EUP of RSM improved after economic allocation, although they were less sustainable (P < 0.05) than all IMPM values and RSM equivalents after mass allocation (P < 0.05). As expected, the theoretical model with increased milk yield improved the sustainability of the system. Both scenarios were affected by allocation criteria. In RSM, the economic and mass allocations described a representative scenario where donkey meat contributed to subtracting equivalents from milk (the main product). The present paper is a pilot study estimating for the first time the environmental impact of donkey milk production, with the aim to stimulate further research.

Sustainable healthy diet modeling for a plant-based dietary transitioning in the United States.

The potential environmental and nutritional benefits of plant-based dietary shifts require thorough investigation to outline suitable routes to achieve these benefits. Whereas dietary consumption is usually in composite forms, sustainable healthy diet assessments have not adequately addressed composite diets. In this study, we build on available data in the Food4HealthyLife calculator to develop 3 dietary concepts (M) containing 24 model composite diet scenarios (S) assessed for their environmental and nutritional performances. The Health Nutritional Index (HENI) and Food Compass scoring systems were used for nutritional quality profiling and estimates of environmental impact were derived from previously reported midpoint impact values for foods listed in the What We Eat in America database. The diets were ranked using the Kruskal‒Wallis nonparametric test, and a dual-scale data chart was employed for a trade-off analysis to identify the optimal composite diet scenario. The results showcased a distinct variation in ranks for each scenario on the environment and nutrition scales, describing an inherent nonlinear relationship between environmental and nutritional performances. However, trade-off analysis revealed a diet with 10% legumes, 0.11% red meat, 0.28% processed meat and 2.81% white meat could reduce global warming by 54.72% while yielding a diet quality of 74.13 on the Food Compass Scoring system. These observations provide an interesting forecast of the benefits of transitioning to an optimal plant- and animal-based dieting pattern, which advances global nutritional needs and environmental stewardship among consumers.

DETECTION OF DISCOMFORT INDEX WITH REMOTE SENSING TECHNOLOGY: THE CASE OF ANTALYA PROVINCE

Abstract. Thermal adaptation and thermal comfort indices are critical in determining the thermal comfort of the outdoor environment. They also play an essential role in research on heat stress, an environmental threat that can affect individuals' productivity, health and even survival. Urban growth and the resulting expansion of impervious surfaces affect the thermal characteristics of a landscape by raising Land Surface Temperatures (LST). The resulting warming can lead to thermal discomfort, the prevalence of heat-related health problems, air pollution, increased water use and energy demand for air conditioning, among others. Recently, efforts to understand the effects of urbanization and landscape changes on indoor and outdoor temperatures have increased significantly. Together with remote sensing technology, this study aims to understand human heat stress, and geographic information system (GIS) is a tool used in the research. In the estimation of heat stress, besides temperature, physiological status, environmental impact and relative humidity factors are also important. The discomfort index (DI) is a heat stress indicator proposed by Thom (1959), which expresses the contribution of air temperature and relative humidity to human thermal comfort. The discomfort index proposed by Thom (1959) was calculated as DI=0.5Ta+0.5Tw (Ta: dry bulb temperature, Tw: wet bulb temperature) modified by SOHAR, Adar and Laky (1963). In the study, the dry bulb temperature, assumed to be equal to the air temperature, was taken monthly from MODIS LST data at 1km resolution. Relative humidity was produced by interpolating 73 meteorological data in the study area at 1km resolution. Wet bulb temperature is difficult to measure, so it was calculated from dry bulb temperature and relative humidity data so that the discomfort index as a measure of heat stress in the study area was calculated with a resolution of 1 km. The discomfort index was calculated monthly and annually and classified according to Thom's 4 comfort classes. According to the calculation results, Antalya's average discomfort index value for the whole year is 24.9 °C, indicating that Antalya is a moderately comfortable place. This value varies monthly, especially in April and October when the heat stress is the highest.

An Extended Approach to the Evaluation of Energy Storage Systems: A Case Study of Li-Ion Batteries

Energy storage technologies can act as flexibility sources for supporting the energy transition, enabling the decarbonisation of the grid service provision and the active engagement of the customers (both prosumers and consumers), opening for them new business opportunities. Within storage technologies, Lithium-ion (Li-ion) batteries represent an interesting solution for dealing with the majority of these services. In this context, this study addresses an evaluation of economic, environmental and geopolitical risks with reference to the critical raw materials used in the manufacturing of Lithium Iron Phosphate (LFP) Li-ion batteries. The assessment entailes grid and prosumer services that these batteries can provide. The exploited economic indicator is the Levelised Cost of Storage, whereas six environmental indicators are used for environmental impact estimation. Cycle stages accounted for in the analysis are the manufacturing and use phases. Finally, the evaluation of the impact of critical raw materials is performed by deploying a Supply Risk indicator, which is instead assessed considering every single material and the overall risk for the battery. High-risk materials are represented by Graphite and Phosphorous. Results denote that, for each service, the number of cycles and the discharge duration are pivotal to make the investment economically and environmentally sustainable. The reduction in the Net Import Reliance, as well as the increase in the Recycling Rate, could sensibly reduce the risk associated with battery raw materials.

Menstrual products: A comparable Life Cycle Assessment

The goal of this paper is to provide a guide for environmentally-friendly menstrual product options for industry, governmental policy makers and consumers. We present the results of comparative analyses of such products—disposable nonorganic and organic tampons (with applicators) and pads, reusable pads, underwear and cups—used for one year across eight environmental impact indicators and three countries, France, India and the U.S. The Life Cycle Assessment (LCA) methodology was applied to estimate the environmental impacts accounting for the entire life cycle beginning with the production of the components and ending with their disposal. Varying consumer habits in the considered countries led to differences in the environmental impacts between countries. Menstrual cups have the lowest impact score across all indicators and countries, with a score 99% lower than those for disposable nonorganic tampons. Menstrual underwear ranked second, given their double functions as underwear and menstrual absorbent. Reusable pads ranked third lowest. Somewhat surprisingly, organic disposable pads had higher scores than their nonorganic counterparts for the majority of indicators. For more realistic use scenarios, we explored combinations of two products (one internal and one external). Menstrual cups in combination with underwear had the lowest scores across all indicators and countries. • Reusable menstrual products have significantly lower environmental impacts than disposable ones. • Disposable organic products have higher environmental impacts than nonorganic ones. • Combining two products (internal and external) ensures more realistic results. • Country-specific behaviors influence environmental impact estimates.

The negative footprint illusion in environmental impact estimates: Methodological considerations.

Past research has consistently shown that carbon footprint estimates of a set of conventional and more environmentally friendly items in combination tend to be lower than estimates of the conventional items alone. This ‘negative footprint illusion’ is a benchmark for the study of how cognitive heuristics and biases underpin environmentally significant behavior. However, for this to be a useful paradigm, the findings must also be reliable and valid, and an understanding of how methodological details such as response time pressure influence the illusion is necessary. Past research has cast some doubt as to whether the illusion is obtained when responses are made on a ratio/quantitative scale and when a within-participants design is used. Moreover, in past research on the negative footprint illusion, participants have had essentially as much time as they liked to make the estimates. It is yet unknown how time pressure influences the effect. This paper reports an experiment that found the effect when participants were asked to estimate the items’ emissions in kilograms CO2 (a ratio scale) under high and under low time pressure, using a within-participants design. Thus, the negative footprint illusion seems to be a reliable and valid phenomenon that generalizes across methodological considerations and is not an artifact of specific details in the experimental setup.

An approach to regionalise the life cycle inventories of Spanish agriculture: Monitoring the environmental impacts of orange and tomato crops

Agricultural life cycle assessment (LCA) at the sub-national regional level may be a valuable input for the decision-makers. Obtaining representative and sufficient data to develop life cycle inventories (LCIs) at that level is a relevant challenge. This study aims to contribute to the development of LCIs representative Spanish crops based on economic and operational information available in official sources to assess the average environmental impacts of these crops in the main producing regions. A comprehensive approach is proposed considering both the temporal variability and uncertainty of input data by using different methods (e.g. linear programming, weighted averages, Monte Carlo simulation, forecasted irrigation, etc.) to estimate the inventory data of reference holdings. From these inventories, the environmental assessment of those reference holdings is carried out. Two case studies are developed, on orange and tomato crops in the main producing regions, where climate change (CC), freshwater scarcity (WS), human toxicity non-cancer (HTnc), and freshwater ecotoxicity (ET) are evaluated. The environmental scores obtained differ significantly from region to region. The highest environmental scores of orange reference holdings correspond to Comunidad Valenciana for CC (1.94·10−1 kg CO2 eq.) HTnc (4.16·10−11 CTUh) and ET (7.45·10−3 CTUe), and to Andalucia in WS (17.4 m3 world eq.). As to greenhouse tomatoes, the highest scores correspond to Comunidad Valenciana in the four categories analysed (CC = 3.18 kg CO2 eq., HTnc = 3.6·10−9 CTUh, ET = 1.5 CTUe and WS = 13.3 m3 world eq.). The environmental scores estimated in this study are consistent with the literature, showing that the approach is useful to obtain a representative description of the environmental profile of crops from official statistical data and other information sources. Widening the data gathered in ECREA-FADN, and also that from other data sources used, would increase the quality of the environmental impact estimation.

Life Cycle Assessment of buffalo milk: A case study of three farms in southern Italy

The growing concerns about the relation of livestock activities and sustainability has led to technological progress and the adoption of best practices for reducing their impacts on the environment, including carbon emissions. In southern Italy, farming Mediterranean buffalos for milk production represents a significant portion of the economy. In this study we evaluate the environmental impacts of buffalo milk production using the Life Cycle Assessment approach. The analysis uses a cradle-to-gate system boundaries, an attributional approach and a Functional Unit equal to kg of Energy Corrected buffalo Milk (ECM). We use primary data collected from three farms in Southern Italy, covering a wide range of conditions including organic farming. We investigate the role of allocation method on the overall results and the role of buffalo milk productivity on the environmental impact estimates of milk production. Our results indicate that the environmental impacts of milk production are highly dependent on the chosen allocation method. The life cycle greenhouse gases emission ranges from 1.5 to 2.5 kgCO2eq per kg of ECM, which are slightly larger compared to those of cow milk; this is mainly due to higher milk productivity of cows compared to buffalos. The hot-spot analysis shows that the largest source of climate change impacts are direct emissions from enteric fermentation. The comparative analysis shows that no farm outperforms the other across the entire spectrum of categories and that the milk productivity of buffalos is a key aspect determining the environmental performance of each farm.

Estimating the environmental impacts of 57,000 food products

Understanding and communicating the environmental impacts of food products is key to enabling transitions to environmentally sustainable food systems [El Bilali and Allahyari, Inf. Process. Agric. 5, 456-464 (2018)]. While previous analyses compared the impacts of food commodities such as fruits, wheat, and beef [Poore and Nemecek, Science 360, 987-992 (2018)], most food products contain numerous ingredients. However, because the amount of each ingredient in a product is often known only by the manufacturer, it has been difficult to assess their environmental impacts. Here, we develop an approach to overcome this limitation. It uses prior knowledge from ingredient lists to infer the composition of each ingredient, and then pairs this with environmental databases [Poore and Nemecek Science 360, 987-992 (2018); Gephart etal., Nature 597, 360-365 (2021)] to derive estimates of a food product's environmental impact across four indicators: greenhouse gas emissions, land use, water stress, and eutrophication potential. Using the approach on 57,000 products in the United Kingdom and Ireland shows food types have low (e.g., sugary beverages, fruits, breads), to intermediate (e.g., many desserts, pastries), to high environmental impacts (e.g., meat, fish, cheese). Incorporating NutriScore reveals more nutritious products are often more environmentally sustainable but there are exceptions to this trend, and foods consumers may view as substitutable can have markedly different impacts. Sensitivity analyses indicate the approach is robust to uncertainty in ingredient composition and in most cases sourcing. This approach provides a step toward enabling consumers, retailers, and policy makers to make informed decisions on the environmental impacts of food products.

Estimation of Environmental Impact from Rice Mill Cluster

Estimation of Environmental Impact from Rice Mill Cluster

Environmental impact estimation of ceramic tile industry using modeling with neural networks

The ceramic tiles industry has a significant environmental impact due to consumption of raw materials, energy and environmental emissions. There are numerous activities on global level in accordance to the principles of sustainable development. This paper presents the development and application of mathematical models of manufacturing processes based on static neural networks for prediction and control of environmental impact of ceramic tiles production process. The neural network learning is made based on known input and output values from the production process. The control of the environmental impact is made on the basis of the output values from the process amounts of correct and faulty ceramic tiles. The model for prediction of correct amount of tiles and percentage of waste with an average error of 1.7% is presented in this paper. It could be successfully used to estimate and control the environmental influence. A simple model of production process has been applied in the manufacturing process of ceramic tile factory KIO Keramika d.o.o. Orahovica. It produced ceramic tiles using monofiring process according to EN 14 411 B III group Part L. The company has introduced and certified management systems according to ISO-9001. and ISO 14001

Biased Estimates of Environmental Impact in the Negative Footprint Illusion: The Nature of Individual Variation.

People consistently act in ways that harm the environment, even when believing their actions are environmentally friendly. A case in point is a biased judgment termed the negative footprint illusion, which arises when people believe that the addition of “eco-friendly” items (e.g., environmentally certified houses) to conventional items (e.g., standard houses), reduces the total carbon footprint of the whole item-set, whereas the carbon footprint is, in fact, increased because eco-friendly items still contribute to the overall carbon footprint. Previous research suggests this illusion is the manifestation of an “averaging-bias.” We present two studies that explore whether people’s susceptibility to the negative footprint illusion is associated with individual differences in: (i) environment-specific reasoning dispositions measured in terms of compensatory green beliefs and environmental concerns; or (ii) general analytic reasoning dispositions measured in terms of actively open-minded thinking, avoidance of impulsivity and reflective reasoning (indexed using the Cognitive Reflection Test; CRT). A negative footprint illusion was demonstrated when participants rated the carbon footprint of conventional buildings combined with eco-friendly buildings (Study 1 and 2) and conventional cars combined with eco-friendly cars (Study 2). However, the illusion was not identified in participants’ ratings of the carbon footprint of apples (Study 1 and 2). In Studies 1 and 2, environment-specific dispositions were found to be unrelated to the negative footprint illusion. Regarding reflective thinking dispositions, reduced susceptibility to the negative footprint illusion was only associated with actively open-minded thinking measured on a 7-item scale (Study 1) and 17-item scale (Study 2). Our findings provide partial support for the existence of a negative footprint illusion and reveal a role of individual variation in reflective reasoning dispositions in accounting for a limited element of differential susceptibility to this illusion.

Influential parameters for estimating the environmental impacts of geothermal power: A global sensitivity analysis study

The life-cycle environmental impacts of geothermal power generation are highly variable and depend on many site-specific conditions. The objective of this work is the identification of the most influential parameters for estimating the environmental impacts of geothermal electricity production. First, we developed a general model for computing the impacts of both conventional and enhanced geothermal technologies. The model is validated against selected literature studies for the climate change category. We then use Global Sensitivity Analysis (GSA) to evaluate the contribution of each parameter to the overall variance of the model's output. The results of the GSA suggest that i) the uncertainty of environmental impact estimates can be significantly reduced by obtaining more accurate values for a small number of key parameters, such as the installed capacity of the plant, operational emissions of CO2 and the depth and capacity of wells; and ii) the majority of parameters do not affect significantly the environmental impact estimates and therefore can be fixed anywhere within their range of variability. Finally, we discuss some of the limitations of the present study and propose approaches that could be implemented to overcome such limitations.

From Divining Rods to Statistics: A Forensic Analysis of the Misuse of Statistics in the Estimation of Environmental Impact

Shortly after my retirement, I became involved in local water issues and learned that I could be of especial use in work on those issues because of my background in statistics. What I am reporting here is a minimally-technical account of the experience I had using statistics as a forensic tool to unearth what appears to have been a misuse of statistics in the modeling of the environmental impact of a proposed desalination project to provide water for the community where I reside. Of potential interest to statisticians is how statistics can be useful in detective work.

Environmental impact estimation of PM2.5 in representative regions of China from 2015 to 2019: policy validity, disaster threat, health risk, and economic loss

Environmental impact estimation of PM2.5 in representative regions of China from 2015 to 2019: policy validity, disaster threat, health risk, and economic loss

Environmental impact estimation of ceramic lightweight aggregates production starting from residues

AbstractWithin a circular economy approach, this study investigates the environmental impact of lightweight aggregates (LWAs) produced starting from different mixes of different clays with brewery sludge and cattle bone flour ash (CBA), used as poring and fertilizing agents, respectively. The environmental impact was evaluated by means of release tests, insulation capacity, carbon footprint (CFP), and particulate matter emission during pellet firing. Release tests representative of LWAs realistic application showed very high release of phosphate and satisfactory release of potassium. The thermal insulation of the LWAs was tested by thermal imaging camera and resulted highly variable depending on the composition, with the mix containing CBA performing best. This latter composition leads also to the smallest CO2 equivalent emission, due to the calorific power of CBA, allowing lower consumption of fossil fuels during the LWA production. Finally, total particulate emissions during the thermal treatment resulted similar in terms of mass for all mixes, while differences in terms of particle morphology and composition occurred. Samples containing residue resulted with a quite good release behavior, CFP, and insulation properties, but higher emission of particles, particularly when glass is added.

A Data-Driven Soft Sensor to Forecast Energy Consumption in Wastewater Treatment Plants: A Case Study

Energy consumption is vital to the global costs of wastewater treatment plants (WWTPs). With the increase of installed WWTPs worldwide, the modeling and forecast of their energy consumption have become a critical factor in WWTP design to meet environmental and economic requirements. The accurate and swift energy consumption forecasting soft-sensors are not only supportive to the daily electric and financial budgeting by WWTP practitioners on the micro-scale, but also beneficial to local municipal operation and fundamental to regional environmental impact estimation on the macro-scale. Energy consumption in WWTPs is influenced by different biological and environmental factors, making it complicated and challenging to build soft-sensors. This article intends to provide short-term forecasting of WWTP energy consumption based on data-driven soft sensors using traditional time-series and deep learning methods. Ten data-driven soft sensors, including the ordinary least square, exponential smoothing state space, local regression, auto-regressive integrated moving average (ARIMA), structural time series model, Bayesian structural time series, non-linear auto-regressive, long short-term memory with and without updates, and gated recurrent units have been investigated and compared for WWTP energy consumption forecasting. Energy consumption time-series data from a membrane bioreactor-based WWTP in the middle east is used to evaluate the performances of the proposed soft-sensors. Results showed that ARIMA achieved slightly improved performances, among others. The employment of adaptive deep learning-based soft sensors is expected to enhance the capabilities of the deep models to quickly and accurately follow the trend of future data.

Corporate Environmental Impact: Measurement, Data and Information

As an organization’s environmental impact has become a central societal consideration, thereby affecting industry and organizational competitiveness, interest in measuring and analyzing environmental impact has increased. We develop a methodology to derive comparable and scalable monetized environmental impact estimates by applying characterization pathways and monetization factors to organization level environmental outputs, including carbon emissions, water use, and other emission types. The median environmental impact as a percentage of an organization’s sales (operating income), referred to as environmental intensity, is 2% (22%) suggesting a significant level of ‘hidden liabilities’ and potential for value erosion if environmental impacts are priced. About 60% of the variation in environmental intensity is driven by industry membership, while the rest can be attributed to firm specific factors or to country and more granular industry classifications. Environmental intensity exhibits moderate correlation with various environmental ratings across firms and industries and no correlation across firms within industries. Firms with higher environmental intensity exhibit lower corporate market valuation, consistent with investors viewing environmental impacts as financially material and pricing them in some but not all industries. We document the dynamic materiality of environmental intensity, where the relation between environmental intensity and corporation valuation has become stronger in recent years.