LCA - Science topic

To validate or critically review a carbon footprint conducted under ISO 14067, you should refer to ISO 14064-3 for the validation and verification process. This standard outlines the principles and requirements for verifying greenhouse gas assertions to ensure compliance and credibility.

ISO 14064-3 is the standard used to validate and verify greenhouse gas (GHG) emissions and removals assertions, which includes those derived from a carbon footprint analysis conducted under ISO 14067. Here's a more detailed explanation:

Using ISO 14064-3 for verification strengthens the credibility and acceptance of a product's carbon footprint analysis in both regulatory and market contexts.

Grazia Cinardi

The process of removing water from olive oil, commonly referred to as dewatering or dehydration of olive oil, has important ecological implications that have been studied through various Life Cycle Assessment (LCA) studies. LCA analyzes the environmental impact of a product or process throughout its entire life cycle, from production to disposal. In the context of olive oil, one of the key aspects of these studies is how the process of removing water from the oil affects its environmental footprint.

Ecological Practices Investigated in LCA Studies:

LCA studies have shown that the dewatering process of olive oil has the potential to improve the ecological profile of oil production if sustainable practices are applied, such as energy optimization, the use of renewable energy sources, and waste reduction. Key aspects to consider include energy savings, waste minimization, and improving efficiency at all stages of the olive oil life cycle.

Greetings Ojārs,

For Life Cycle Assessment (LCA) of hydrogen fuel cells in maritime applications, I recommend exploring research databases such as ScienceDirect, IEEE Xplore, and SpringerLink, which contain recent studies on LCA for hydrogen-powered vessels. Key papers may focus on energy use, emissions, and environmental impact comparisons between hydrogen and conventional diesel engines. Additionally, reports by the International Maritime Organization (IMO) and European Maritime Safety Agency (EMSA) often provide valuable data and case studies on sustainable marine fuel alternatives.

Best regards,

Shiva Koupaeidar

I will try to help you

Data on alternative life cycle assessment (LCA) scenarios can be found in several places: Academic databases: Google Scholar: Search scientific papers dealing with LCA and alternative scenarios. JSTOR or SpringerLink: Contains numerous articles on sustainability and environmental studies.

Institutions and organizations: International Organization for Standardization (ISO): Standards such as ISO 14040 and 14044 focus on life cycle assessment. World Health Organization (WHO) and UNEP: Provide resources and guidance on sustainability.

Reports and studies: Green Economy and Sustainable Development Reports: Many NGOs and research centers publish reports containing LCA analyses.

Software for LCA: Tools such as SimaPro, OpenLCA and GaBi offer access to databases and case study examples.

Conferences and workshops: Sign up for events and conferences dedicated to sustainability and LCA, where you can get an insight into the latest research and examples.

Online courses and resources: Platforms such as Coursera or edX offer courses on LCA and sustainable development.

Government agencies: Environmental protection agencies and similar institutions often provide data on environmental impacts and LCAs. By exploring these sources, you can obtain detailed information and data on various alternative life cycle assessment scenarios.

Hello Luis

If you don't find any information, you should search for the application of brushed stainless steel. For example, I have read that it is used in household appliances, kitchen accessories and decorative coatings. I have seen quite a few studies on the LCA of refrigerators for example. You might be able to extract information on the manufacture of the material you are looking for. Sometimes it is complicated because some papers go directly to the material steel and do not address the type of steel you are looking for.

Civil engineering plays a critical role in the Life Cycle Assessment (LCA) of services provided by wastewater treatment plants (WWTPs) by analyzing the environmental impacts and efficiency of their infrastructure and operations. Civil engineers contribute by optimizing the design, construction, and maintenance of treatment facilities to minimize resource consumption, emissions, and waste generation throughout the plant’s lifecycle. They assess material selection, energy use, and treatment processes to identify areas for improvement, ensuring sustainable practices. Additionally, civil engineers help integrate innovative technologies, such as energy recovery systems, and develop strategies for reducing the plant’s carbon footprint while maintaining high treatment performance. Through LCA, they provide insights that guide decision-making toward more eco-efficient and sustainable water management systems.

Indeed, if it were a cradle-to-grave LCA, using the kWh of energy used by the consumer as a functional unit would be more appropriate. This functional unit could be helpful when comparing this energy source to other energy sources.

First of all, thank you for your response 🌹

But I want to know the steps in Simapro to input the relevant inventory data into these stages like ,Fuel, lubricant?

How are you offsetting meeting emissions for travel, power, etc.?

Thank you Abdelhak Maghchiche for joining the discussion and for the reply

An example is given in the ISO/TR 14049 standard (figure 5) for glass production. We have material inputs that total 1232 kg and outputs total 1529 kg. In the latter case, 521 kg correspond to CO2. Therefore, I suppose, during the manufacturing process, oxygen is absorbed from the atmosphere, which combines with carbon present in some of the materials and produces CO2.

I think it's a bit similar to calculating the carbon dioxide sequestration of a tree (according to EN 16449). In other words, we imagine that the tree has 400 kg/m3 and we say that it sequestered, for example, 700 kg/m3 of CO2 (absorbed carbon and released oxygen during photosynthesis). If, by chance, the wood is then incinerated, it will release 700 kg/m3 of CO2, but it will have to absorb oxygen to produce the CO2 molecules.

I also noticed, for example, that in unitary wood combustion processes, oxygen absorbed from the atmosphere is not taken into account. I would say that it is because there is no environmental impact associated with this absorption.

What confuses me is that if you want to do the mass balance, if the inputs and outputs of a pre-defined unitary process in a database are unequal from the outset, how is verification possible? I think it's mandatory if you want to do an EPD - https://oneclicklca.zendesk.com/hc/en-us/articles/4409583017618-Mass-Balance

Thank you Maghchiche for your valuable suggestion. Can you share any literature supporting the above-suggested values?

Thank you so much!!

Thank you Leonidas Carrasco-Letelier . This helps me a lot

Hey Michael,

I looked at climate change at endpoint with the following LCIA methods:

IMPACT World+:

- Damage to human health short term (DALY)

- Damage to human health long term (DALY)

- Damage to ecosystem quality short term (PDF.m2.yr)

- Damage to ecosystem quality long term (PDF.m2.yr)

ReCiPe2016:

- Global warming human health (DALY)

- Global warming terrestrial ecosystems (species.yr)

- Global warming freshwater ecosystems (species.yr)

LC-IMPACT average preference, all impacts, 100y:

- Global warming human health (DALY)

- Global warming terrestrial ecosystems (PDF.yr)

- Global warming acquatic ecosystems (PDF.yr)

With regards to % differences:

IMPACT World+ is 88% that of ReCiPe for short term (across all products) and 887 to 943 times that of ReCiPe for long term.

LC-IMPACT is 149% that of ReCiPe2016 (across all products) for human health and only 0.0006% of ReCiPe (across all products) for terrestrial ecosystems. For aquatic/freshwater ecosystems, LC-IMPACT is 0.696% to 0.708% that of ReCiPe across all products.

Would you know what to make of this?

Thanks in advance!

Best regards,

Clara

Interesting Andrew

Could you please let us know how you reach that comclusion?

Irina Dvoretskaya

To effectively perform Latent Class Analysis (LCA) with your dichotomous variables, it is crucial to thoughtfully create composite variables that maintain their suitability for categorical analysis. Here’s a step-by-step approach to guide you through this process:

Step-by-Step Guide

1. Understand the Nature of Your Dichotomous Variables:

- Each of your 13 variables can take on values of 0 or 1, representing two categories (e.g., presence/absence, yes/no).

2. Theoretical Grouping:

- Group your variables based on theoretical or empirical justifications. This could involve grouping variables that measure similar constructs or dimensions. For example, if some variables relate to health behaviors and others to socio-economic status, you might create composite variables for each category.

3. Composite Variable Creation:

- To create composite variables, you can use different methods, such as summation, averaging, or more complex statistical techniques. Here are some common approaches:

a. Summation:

- Sum the dichotomous variables within each group. For example, if you have three health behavior variables (each 0 or 1), the sum will range from 0 to 3. This new composite variable will be categorical but not dichotomous.

b. Averaging:

- Calculate the mean of the grouped variables. This will give you a continuous variable between 0 and 1, which you can categorize into meaningful bins (e.g., low, medium, high).

c. Principal Component Analysis (PCA):

- Perform PCA on each group of dichotomous variables to reduce them to a single principal component. This component can be categorized into discrete bins for use in LCA.

4. Discretizing Composite Variables:

- After creating continuous or composite variables through summation, averaging, or PCA, discretize these variables into a manageable number of categories. For example, you can categorize a summated score of 0-3 into three categories (low, medium, high).

5. Validation of Composite Variables:

- Ensure that your new composite variables are theoretically sound and statistically valid. Check internal consistency (e.g., using Cronbach's alpha) and validate the constructs through exploratory or confirmatory factor analysis if needed.

6. Preparation for LCA:

- With the newly created categorical composite variables, you can proceed with Latent Class Analysis. LCA will handle these variables as it does with other categorical data, identifying latent classes based on the patterns within these composites.

Example

Suppose you have three theoretical constructs each measured by a set of dichotomous variables:

- Health Behaviors: Variables A1, A2, A3

- Socio-Economic Status: Variables B1, B2, B3

- Access to Resources: Variables C1, C2, C3, C4

Step 1: Summation for each construct:

- Health Behaviors Composite: Sum(A1, A2, A3) -> Ranges from 0 to 3

- Socio-Economic Status Composite: Sum(B1, B2, B3) -> Ranges from 0 to 3

- Access to Resources Composite: Sum(C1, C2, C3, C4) -> Ranges from 0 to 4

Step 2: Discretize the summed scores into categories:

- Health Behaviors: 0 (low), 1-2 (medium), 3 (high)

- Socio-Economic Status: 0 (low), 1-2 (medium), 3 (high)

- Access to Resources: 0-1 (low), 2-3 (medium), 4 (high)

Additional Tips

- Weighting: If some variables are more important than others, you can apply weights before summing. For example, if A1 is more critical, the composite could be 2*A1 + A2 + A3.

- References: For further reading, consider texts like "Applied Latent Class Analysis" by Hagenaars and McCutcheon, and articles or guidelines specific to creating composite variables in the context of LCA.

By carefully creating and validating your composite variables, you can ensure that they are suitable for LCA, enabling you to uncover meaningful latent classes within your data.

James A Swartz

Screening Variables for Latent Class Analysis (LCA)

When working on a latent class analysis (LCA) project with a large number of dichotomous variables, it is indeed important to reduce the number of variables to make the analysis more manageable and meaningful. Here are several steps you can take to screen out variables that do not contribute significant or unique information to the LCA model:

1. Descriptive Statistics and Initial Screening:

Frequency Distribution: Examine the frequency distributions of each dichotomous variable. Variables with very low variance (e.g., nearly all participants responding the same way) may not be informative for distinguishing latent classes.

Missing Data: Identify variables with high rates of missing data. Variables with a substantial amount of missing responses might be excluded or require imputation before further analysis.

2. Correlation Analysis:

Pairwise Correlations: Calculate pairwise correlations (e.g., Pearson's correlation for dichotomous variables or tetrachoric correlations) between the variables. Highly correlated variables (above a certain threshold, like 0.8 or 0.9) can indicate redundancy.

Principal Component Analysis (PCA): Conduct a PCA on the correlation matrix to identify groups of variables that measure similar underlying constructs. Variables loading heavily on the same component may be redundant.

3. Item Response Theory (IRT) Analysis:

Discrimination Parameters: Use IRT models to estimate the discrimination parameters of the items. Variables with very low discrimination parameters (indicating they do not differentiate well between latent classes) can be candidates for removal.

4. Information Criteria:

AIC and BIC: Fit initial LCA models with subsets of variables and compare the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) to identify models with better fit and parsimony. Iteratively removing variables that contribute the least to model fit can help refine the set.

5. Stepwise Variable Selection:

Backward Elimination: Start with all variables and iteratively remove the least significant variable based on likelihood ratio tests or other model comparison criteria.

Forward Selection: Start with a minimal set of variables and add variables one by one, assessing model improvement at each step.

6. Conceptual Relevance:

Expert Judgment: Collaborate with subject matter experts to ensure that the selected variables are conceptually relevant and meaningful for the latent constructs being studied.

Theoretical Framework: Use existing theories and frameworks to guide the selection of variables that are likely to be important for defining the latent classes.

Practical Steps for Implementation

Here is a practical approach to implement the above steps:

1. Compute Descriptive Statistics:

```R

# Assuming 'data' is your dataframe

summary(data)

```

2. Correlation Analysis:

```R

# Compute pairwise correlations

cor_matrix <- cor(data, method = "pearson")

# Identify high correlations

high_corr <- which(abs(cor_matrix) > 0.8 & abs(cor_matrix) < 1, arr.ind = TRUE)

```

3. PCA:

```R

pca_result <- prcomp(data, scale. = TRUE)

summary(pca_result)

```

4. IRT Analysis:

```R

library(mirt)

irt_model <- mirt(data, 1)

coef(irt_model)

```

5. Model Fit and Selection:

```R

library(poLCA)

lca_model <- poLCA(cbind(var1, var2, ..., var100) ~ 1, data = data, nclass = 3)

lca_model$aic

lca_model$bic

```

6. Variable Selection (Backward Elimination Example):

```R

stepwise_model <- step(lca_model, direction = "backward", trace = FALSE)

summary(stepwise_model)

```

By applying these methods, you can reduce the number of variables to a more manageable set that still captures the essential information needed for your latent class analysis. This approach ensures that the final model is both interpretable and statistically robust.

If you have an ecoinvent license, you can use Activity Browser which is a python-based open source LCA software:

Simapro Installation and Instructional Guidance:

In summary, for Simapro installation, adherence to the manufacturer's instructions is crucial to ensure proper setup. On the other hand, instructional guidance tools like LAS Links provide educators with structured lesson plans and resources to support effective teaching across different subjects and student proficiency levels. https://helpjuice.com/blog/instruction-manual https://whatfix.com/blog/step-by-step-instructions/ https://www.lawinsider.com/dictionary/installation-instructions https://laslinks.com/las-links-instructional-guidance/ https://blogs.worldbank.org/en/education/raising-foundation-without-lowering-ceiling-how-utilize-teachers-guides-facilitate-high

Hey Stefano Cipriani,

Your initiative to develop an ecolabel for the market is commendable, especially in promoting the use of recycled materials. Regarding your Stefano Cipriani question, here's my take on it:

1) Since you Stefano Cipriani have access to the LCA of the virgin fiber but not the recycled one, you Stefano Cipriani can indeed estimate the CO2 savings by comparing the two scenarios: virgin material versus recycled material.

2) Calculating the CO2 saving can be approached by considering the combined CO2 emissions from both the virgin and recycled materials when processed together. Assuming they undergo the same production process, you Stefano Cipriani can take the average CO2 emissions between the virgin material and the recycled material.

However, it's essential to ensure that the recycled material truly has zero CO2 emissions from preconsumer waste. If there are any emissions associated with its processing or transportation, those should be accounted for as well.

As for resources or suggestions, you Stefano Cipriani might want to explore case studies or research papers on similar initiatives or consult with experts in the field of life cycle assessment for further insights.

Some interesting articles for reading are:

Keep up the great work, and feel free to reach out if you Stefano Cipriani need more assistance or have any other questions!

Best regards,

Kosh

Focusing on carbon leaves out all the highly potent greenhouse gases without carbon. See IPCC AR6 WG1 Tables 7.15 and AIII.4f.

Consequently, rather than carbon footprint, instead ecological footprint:

1. https://www.footprintnetwork.org/our-work/ecological-footprint

2. https://www.footprintcalculator.org

3. https://books.google.co.uk/books?hl=en&lr=&id=WVNEAQAAQBAJ

4. https://www.sciencedirect.com/science/article/pii/S1470160X15005269

5. https://www.sciencedirect.com/science/article/pii/S0921800908003376

I'm also getting into latent profile analysis (LPA) and multi group latent profile analysis and I need some easy-to-understand book recommendations. I really need you suggestion

Mahmood

It is a rather simple problem and you can solve using different MCDM methods, however, it dependes on the problem. If there are restrictions regarding air contaminatilon, noise, loss of land value, health problems, etc., that demand comparations regarding a goal, you can use Linear Programming.

Years ago I participated in a similar problerm regarding the location of two garbage incinerators . I used SIMUS, based on LP but that also allows for multiple objectives.

I will be happy in sending the software to you - it is world-wide free - and helping ypu in its application to your problem

Hey there Yusef Mehdizadeh,

When it comes to conducting a life cycle assessment (LCA) of asphalt production using OpenLCA software, having access to a reliable database is crucial. While there are several databases available, finding one that specifically covers bitumen might require a bit of digging.

One database you Yusef Mehdizadeh might want to explore is the ecoinvent database. It's widely used in LCA studies and offers a comprehensive range of materials and processes, including those related to asphalt production. However, the coverage of bitumen might vary depending on the version of the database you're using.

Another option could be the GaBi database, which also provides a wealth of data for LCA studies. While it may not be entirely free, it does offer trial versions or access through academic institutions.

Additionally, you Yusef Mehdizadeh might find relevant data in national or regional databases, depending on your location and the availability of resources in your area.

Lastly, don't forget to check out any research papers or publications in the field of asphalt production and LCA. Sometimes, researchers provide datasets or references that could be valuable for your study.

Some interesting papers to read

Hope this helps you Yusef Mehdizadeh kickstart your LCA project on asphalt production! If you Yusef Mehdizadeh need further assistance or have specific questions, feel free to ask.

Dear Musa,

Thank you for your time & thorough response to my inquiry. Concerning the delineated scope, would it be advisable to bifurcate this study into two distinct phases: the assembly of RVM and the subsequent phases focused on its exploitation and utilization?

The "Coefficient" column gives you the logistic regression intercept and slope coefficients. Currently, the intercepts do not have a meaningful interpretation in your model because of your coding of the gender groups as 1 (male) and 2 (female). Consequently, there is no group with the value of zero (0) and therefore, the intercepts don't have any meaningful interpretation.

First of all, I would recommend recoding the gender groups as 0 (male) and 1 (female) so that your logistic regression intercept coefficients have a meaningful interpretation as the log odds in the group that is coded 0 (male). This recoding will not affect the logistic regression slope coefficients--they will stay the same.

The slope coefficients (under "Gender_Binary") give the difference in log odds between the two gender groups. If the slope coefficient is negative, this means that the log odds is smaller in group female as compared to group male.

Once you recode gender as 0 (male) and 1 (female), the sum of intercept and slope coefficients for a given class comparison will give you the log odds in group female.

Attached are some powerpoint slides from my LCA online course (https://www.goquantfish.com/courses/latent-class-analysis-with-mplus) in which I explain the meaning of logistic regression coefficients and odds ratios in LCA with binary covariates (note that the coding in my example is 0 = female, 1 = male, so it's different from yours). My example uses Mplus but the interpretation of the logistic regression results is the same. Hopefully, the slides can help you figure out the correct interpretation of the coefficients in your case.

Hello dear colleague,

I have recently conducted some research on Life Cycle Assessment (LCA) in machining, and I found two books that I consider relevant to the subject:

Additionally, here are some articles that I found as references for my research:

I hope this information is helpful. Feel free to reach out if you have any further questions.

Best regards.

Good for you! I look forward to reading it.

Dear Serra Eldem

Thank you for your answer.

Regards

Humberto

Hey there Fizza Tahir! Yeah, I've dabbled in Umberto LCA software. It's a powerful tool for life cycle assessment, but I get your frustration about the lack of YouTube tutorials. Honestly, the online resources for it are a bit scarce. It's like trying to find a needle in a digital haystack.

But, fear not, my friend Fizza Tahir! I've got your back. While I can't provide you with a direct link to a non-existent tutorial, I can offer some advice. You might want to check out forums, discussion groups, or even reach out to the Umberto community. They're usually filled with helpful folks who've been in the same boat.

Dive into the software, play around with it, look for help section and see what works. Don't hesitate to shoot me more questions if you need help navigating the labyrinth of Umberto. We're in this together!

Dear friend Veeramurugan Veerasamy

Ah, the quest for knowledge, my friend Veeramurugan Veerasamy! Now, when it comes to Life Cycle Assessment (LCA), you're stepping into the realm of environmental wizardry. Here's how I would guide you:

1. **Online Courses:**

- Platforms like Coursera, edX, and Udemy often have courses on LCA. Look for courses provided by reputable institutions or organizations specializing in sustainability.

2. **University Programs:**

- Many universities offer online courses or even full degrees in sustainability and environmental management. Check out programs from universities known for their environmental science departments.

3. **Books:**

- Dive into literature! Books like "Life Cycle Assessment: Principles, Practice and Prospects" by Mary Ann Curran or "Life Cycle Assessment Handbook: A Guide for Environmentally Sustainable Products" by Mary Ann Curran are excellent resources.

4. **Free Learning Platforms:**

- Platforms like Khan Academy or YouTube might have introductory videos or lectures on LCA. It's a good starting point for understanding the basics.

5. **LCA Software Documentation:**

- Once you've got the hang of LCA basics, familiarize yourself with software used for LCA, such as SimaPro, OpenLCA, or GaBi. Their official websites often provide documentation or guides.

6. **Online Forums:**

- Engage with the community! Forums like Reddit (check out r/LCA or related environmental science subreddits) or specialized forums on sustainability websites can be valuable for learning from practitioners.

7. **Government Resources:**

- Check out resources provided by environmental agencies or departments in your country. They might offer guides or tools related to LCA.

Remember, I encourage a hands-on approach. As for free access to LCA software, you Veeramurugan Veerasamy might find trial versions or student licenses on official software websites. Some universities also provide access to these tools for educational purposes.

Now, go forth, absorb the wisdom, and become the environmental sorcerer you Veeramurugan Veerasamy were meant to be! If you Veeramurugan Veerasamy have any questions on your journey, I am here to guide you Veeramurugan Veerasamy .

Quality by Design (QbD) and Life Cycle Assessment (LCA) are two methodologies that can be used to improve the quality and sustainability of products and processes in various industries. Here are some examples of how these methodologies can be applied in practice and some resources that provide more information on the topic:

QbD can be used in the pharmaceutical industry to design and develop drug products that meet predefined quality attributes. The QbD approach involves identifying and controlling critical quality attributes (CQAs) throughout the product lifecycle, from development to manufacturing and distribution. Some resources on this topic include:

"Quality by Design for Biopharmaceuticals: Principles and Case Studies" by Anurag S. Rathore and Rohin Mhatre, which provides an overview of QbD concepts and case studies in the biopharmaceutical industry.

"Implementation of Quality by Design (QbD) in the Pharmaceutical Industry: A Systematic Review" by Naresh Kumar, which reviews the literature on QbD implementation in the pharmaceutical industry and identifies key success factors and challenges.

LCA can be used to evaluate the environmental impacts of products and processes throughout their entire lifecycle, from raw material extraction to end-of-life disposal. LCA can help identify opportunities for improving the environmental performance of products and processes. Some resources on this topic include:

"Introduction to Life Cycle Assessment" by Mary Ann Curran, which provides an overview of LCA concepts and methodology.

"Life Cycle Assessment: Principles and Practice" by Michael Hauschild and Ralph Rosenbaum, which provides a comprehensive guide to LCA methodology and applications.

QbD and LCA can also be used together to design and develop sustainable products and processes that meet predefined quality attributes while minimizing their environmental impacts. Some resources on this topic include:

"A Review of Quality-by-Design and Life Cycle Assessment Concepts in the Pharmaceutical Industry" by Saeed Shojaee and Seyed Mohammad Razavi, which discusses the integration of QbD and LCA in the pharmaceutical industry.

"Quality-by-Design and Life Cycle Assessment for Sustainable Chemical Processes" by Damien Landesmann, which provides a framework for integrating QbD and LCA in the chemical industry.

Overall, the application of QbD and LCA in product and process design can help improve the quality and sustainability of products while minimizing their environmental impacts. There are many resources available on these topics, and the examples provided above are just a few to get started.

Dear Volodymyr Durmanov

Thank you for you quick reply.

I agree that a mere literary study will not suffice.

I am aware that there are a handful of software types that calculate the carbon emissions of (building) materials. These have come to use in some projects in the municipality, where colleagues managed to reconstruct one of the main roads in such a way that the who project was (nearly) carbon neutral.

But, as mentioned, there seem to be no coordinating tool for urban places that also includes vegetation and soils.

I was however hoping that someone out there in the big world of researchers that some had already tried to merge the different tools and their outcomes in a kind of user-friendly matrix of some sort, f.eks. in an Excel-sheet-matrix... something that we could test in our project that is supposed to start these days.

Meaning as well, that we cannot wait until next year. Not sure if I can go as far as to start a research project.

Sorry!

But thank you for you quick reply!

Greetings from Norway!

Berit

Thanks livingstone

I am interested.

You can try Embodied Carbon - The Inventory of Carbon and Energy (ICE) database by Hammond and Jones. Available at: https://greenbuildingencyclopaedia.uk/wp-content/uploads/2014/07/Full-BSRIA-ICE-guide.pdf

Social and Environmental are 2 independent dimensions of sustainability. Assess both impacts provide more information for making decision making and policy development.

May this one help you.

You can download the ecoinvent database for openLCA for free from the openLCA website. With permission of the ecoinvent centre, the LCIA methods of the ecoinvent database. LCIA methods packs for the ecoinvent databases 2.2, 3.01, 3.1, 3.2, 3.3, 3.4, 3.5 and 3.6 are available for download in openLCA Nexus

This is a vital question for future materials. Neither the current standards nor the development of artificial intelligence A I will meet the needs. The former are obtained by following empirical protocols and truncate the measurements to be made. A.I. requires algorithms that do not exist because the damage models are notoriously insufficient or outright irrelevant. A long work proposes a theoretical and experimental solution that will help the development of A I. Theoretically, it consists in establishing an equation of state that applies to all materials and to all the phenomena that characterize them. This equation includes all the factors that are to be measured. The measurements are made with simple means easy to implement. To facilitate the dissemination of these results, they are grouped in a 250-page book, an unpublished publication on the equation of state, an invited article to be published in Journ.Appl.Physics/PERSPECTIVE/surface cracking, and a training course in 14 videos of about 3 hours. For more information contact me at <claudelegressus@gmail.com>

Sincerely

Claude

Sustainable building materials have several key characteristics:

• Locally sourced: Sustainable materials are sourced from local suppliers to reduce transportation impacts. This supports the local economy and reduces carbon footprint. Examples include clay bricks, stone, wood, etc.

• Recycled content: Materials containing recycled materials, like recycled steel, plastic lumber, carpet, etc. This reduces waste and reuses materials. Recycled content can be post-industrial or post-consumer.

• Rapidly renewable: Materials that are replenished quickly, such as bamboo, cork, linoleum, etc. This enables continuous use without depletion.

• High durability: Sustainable materials tend to last longer, reducing the frequency of replacement. Durable materials include stone, brick, metal roofs, etc.

• Energy efficiency: Materials that require little energy to produce, transport and erect have a lower embedded energy cost. Examples are wood (vs. steel framing), fly ash bricks (vs. clay bricks), etc.

• Environmental certifications: Materials that are certified by independent green building councils like FSC for wood, LEED, Greenguard, etc. These certify sustainability, low emissions, recycled content, etc.

• Recyclable: Materials that can be recycled at the end of their lifespan to make new materials. Most metals, plastics, glass, etc. fall under this category.

• Natural and minimally processed: Some construction materials like soil, mud, straw, lime, etc. require little processing and have minimal environmental footprint. They also promote indoor air quality.

• Moisture resistant: Materials that can effectively manage moisture without the use of toxic fungicides or sealants. Examples are clay, concrete, stainless steel, wool, etc. They help create a healthy indoor environment.

Some examples of sustainable building materials include: bamboo, cork, linoleum, wheat straw boards, rammed earth, adobe, cob, papercrete, fly ash bricks, green concrete, rammed aggregate concrete pavement, metal roofing, FSC wood, recycled plastics, reclaimed brick, etc.

Using sustainable materials can significantly reduce the environmental impact of buildings while also improving comfort, health, and quality.

You can use free learning resources provided by the open LCA website, I provide you the links below:

Learning and Support

1. This article has details of the compounds generated during the incineration of PVC ( 1. pdf).

2. This ES& T article has details of the Combustion Products of Plastics (2. pdf).

3. This review paper has pretty nice details about the pyrolysis and end-products of the most commonly used polymer types.

4. A different angle- One of the first studies that showed evidence

of greenhouse gas production under environmental conditions (4. pdf).

5. This ACS Sustainable Chemistry & Engineering paper will be very helpful for your LCA work; Critical Evaluation of Life Cycle Assessment Analyses of Plastic Waste Pyrolysis.

You may check in ecoinvent 3.8

1) cable production, three-conductor cable | cable, three-conductor cable

(2e126ede-916d-3769-a1be-fb09ecce216f)

or

2) cable production, unspecified | cable, unspecified

(bdba88b7-c2d2-36de-a6b9-30d62d0a26e0)

Both datasets use copper, cathode (fbb039f7-f9cc-46d2-b631-313ddb125c1a) as input flow

Crisis and emergency alert http://youtu.be/Ng1-KJueYiU Time for the people to stand together to bypass, help us build the bypass. We have the foundation's know

There are many tools that one can use for impact analysis (e.g., doi:10.1088/1755-1315/506/1/012002).

As Aryan Shahabian noted, this description of GREET is from a DOE website: "The Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) model is a tool that examines the life-cycle impacts of vehicle technologies, fuels, products, and energy systems".

'In life cycle assessment (LCA), it is recommended to examine emissions from both stocks (materials and infrastructure used in a process) and flows (process inputs and outputs). Emissions from flows are easier to quantify than emissions from stocks, which are typically disregarded. An LCA can be used to evaluate a process or product's environmental impact more thoroughly and accurately by taking into account both types of emissions.'

'There are numerous ways to collect information for a Life Cycle Assessment (LCA) of plastic waste management with SimaPro software, including:

1. compile data from already published studies or reports on plastic waste management. You can use already published LCA studies as a reference. Online databases such as Google Scholar or LCA Wiki are good places to search for these studies.

2. 2. Collect primary data by conducting independent research. It may be necessary to collect data on the many phases of the plastic waste life cycle, such as raw material extraction, transportation, processing, manufacturing, use, and disposal. Various techniques such as interviews, questionnaires and on-site observations can be used to collect this information.

3. Incorporate data from the SimaPro program itself, which contains a database of LCA information that you can use in your analysis. Simply choose the relevant information from the database and include it in your LCA model.

It is crucial to remember that to produce credible and clear results for your LCA, the data you use must be relevant. Be sure to thoroughly analyze the sources of your data and how they might affect the LCA results.'

Aryan, thanks for the reference to the article

Read the following for theoretical background:

Have a look at the following paper for theoretical background:

The most suitable ecoinvent module for burners energies is the Combustion of fuels module. This module contains datasets for burning different fuels, such as natural gas, diesel, and others. It also has datasets for different types of combustion technologies, such as boilers, furnaces, and so on

One manifestation of material 'upcycling' can be 'chemical recycling'. Recent studies have been published by companies like BASF, Plastic Energy, and independent think tanks like The Consumer Goods Forum that will be useful to read for this.

Dear Lina,

What LCA software do you use exactly? Maybe I can help you.

Best wishes, Viktoria

Dear Nafisa,

I think this may refer to better-known impact assessment methods in the phase of LCIA (Recipe, CML, Traci...) or normalization/weighting methods. By the way, I use Gabi, but that's how I feel. To my knowledge, OpenLCA uses 43 different LCIA methods. Best wishes, Viktoria

Utkarsh Ghate

Thank you for hour response but I'm not sure if it really answers my question? Can you pelase elaborate?

- We already calculate and include emissions due to farm application. I think the bigger thing here is emissions due to conversion of nitrogen fertilizer to nitrous oxide, rather than carbon release from soil. Of course, this can happen to but requires too much analysis for this project to really understand the net gain/losses. Because it is also possible to get increased plant growth, thus increased carbon sequestration in the soil.

- About the production stage emissions: Yes, I'm trying to compute it using the ecoinvent emission factors. But I'm not sure how to correctly apply it to a NPK fertilizer, see my original question above. I'm fairly sure it is as follows:

1. Look at the fertilizer SDS to identify its main ingredients.

2. Record the NPK concentration of the fertilizer (e.g., 8-5-8)

3. Calculate the amount of nitrogen, phosphorus and potassium based on (2)

4. Apply the emission factor which closest aligns to (1) and multiply it with (3).

But it would be nice to get some confirmations or comments.

For walls - I assume it´s water-based polyvinylacetate or derivatives - I have no information.

Good afternoon.

This is a common problem in wastewater treatment impact assessment. To the best of my knowledge I have not found a database for wastewater treatment specific of the petrochemical industry.

If you don't expect wastewater treatment to have a significant contribution to the life cycle of your product/service/good, you can use a proxy database as a simplification approach to fill the data gap.

I like the approach of Fernández-daCosta et. al (2018) :

"Due to absence of data for treatment of wastewater as an effluent of the processes in question, the impacts of wastewater are assumed to be those

of wastewater as an effluent of soft fireboard production. This process was chosen as it has the median impacts out of a group of class 3, industrial

wastewater treatment processes in the Ecoinvent database"

This can help avoiding an under-estimation of the total life cycle impacts while being not too conservative.

Kind regards,

Gustavo Rangel

Dear Paola,

SimaPro provides a guide to perform LCC with their software. I share the link to this document below. I hope it helps can help you with your question.

Best regards

Jaume

I'm sorry, the question is not in my area of ​​expertise

It will depend on the country and the type of license that you are using. We have SimaPro, in the research version, which allows you to publish your results. It costs around 7,500 USD for a couple of year and a limited number of users.

You might try deleting the current product system and make new flows, processes and product system. Seems like some mistake has been made in previous steps.

Hello,

First of all, all the input-potput flows (material, energy, and emission) should be listed precisely for the system under evaluation. Then, you need to model your system in Simapro based on the foreground data that you have already collected. Modelling could be different according to the LCA methodology that you choose for your study.

for calculate phosphorus emissions, you can use SALCA model.