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BEST 402 Industrial Ecology�(Modeling Project Development)

Qingshi Tu, PhD

Department of Wood Science

University of British Columbia

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Learning objectives

Be familiar with some good practices of modeling project development

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Come up with project ideas

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Strategies

Change topic
Modify topic
Criteria to consider:

Novelty = Originality + Importance
Feasibility = Resource Availability + Time Constraint

Image Link

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Matrix to assess your project idea

Criteria	Excellent	Great	Good
Originality	A blank sheet!	I find a gap!	I find a crack!
Importance
Resource (personnel, data, funding, etc.)
Time

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Matrix to assess your project idea

Criteria	Excellent	Great	Good
Originality	A blank sheet!	I find a gap!	I find a crack!
Importance	A “million-dollar” problem!	Partially solved a critical problem!	Cool! Good to know!
Resource
Time

Image Link

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Matrix to assess your project idea

Criteria	Excellent	Great	Good
Originality	A blank sheet!	I find a gap!	I find a crack!
Importance	A “million-dollar” problem!	Partially solved a critical problem!	Cool! Good to know!
Resource (personnel, data, funding, etc.)	All resources are available	Most resources are available, the rest are easy to secure	Very likely that all resources can be secured
Time

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Matrix to assess your project idea

Criteria	Excellent	Great	Good
Originality	A blank sheet!	I find a gap!	I find a crack!
Importance	A “million-dollar” problem!	Partially solved a critical problem!	Cool! Good to know!
Resource (personnel, data, funding, etc.)	All resources are available	Most resources are available, the rest are easy to secure	Very likely that all resources can be secured
Time	I can hit all the milestones of the timeline	May need to work on weekends for a couple of milestones	It’s a stretch, but I can make it

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Design of your project

At this stage, you should already have a project idea that checks at least all the “good” boxes in the abovementioned matrix
Factors to consider:

Rigorousness of experiment design
Rigorousness of results analysis
Sophistication of the method
Versatility of the outcome

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Rigorousness of experiment design

Will the outcome answer the research question(s)?
Full vs partial factorial design

Reproducibility

Reference

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Rigorousness of results analysis

Observations: “it is what it is”

Present the results in an unbiased way

Discussions and conclusions

Compare with benchmark
Correlation or causation
Assess the sensitivity of the results
Assess the uncertainty of results
Attribute the uncertainty to input data and/or model assumptions

Limitations of conclusions due to:

Data availability and/or quality
Model assumptions and scope of application

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Sophistication of the method

Platform: programming code, existing tools, Excel, etc.
Output of the method:

Numeric outcome: single data point, range of data w/ or w/o statistical inference

Calculation: manual, semi-automated, automated
IO of data and results: manual, semi-automated, automated
Connection with other methods (if applicable): soft-linking, hard-linking

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Versatility of the outcome

Deliverables:

Framework(s)
Model(s)
Dataset(s)
Document(s): report, peer-reviewed articles, PowerPoint slides, etc.

Presentation of findings:

Graphs with annotations
Tables
Synopsis

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How can people use the outcome of your project?

Results
Discussions
Conclusions
Methodology
Introduction

Problem statement
Literature review

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How can people use the outcome of your project?

To those who have the same interest:

Quantitative results in Abstract and Conclusion
Quantitative results from figures (if annotated) and/or tables

E.g., GWP results on a bar chart
E.g., LCI data table

Discussions

E.g., causation statement

Qualitative conclusions and future work -> identify gaps
Equations that can be applied in their projects

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How can people use the outcome of your project?

To those who have peripheral interest:

A good quantitative literature review results

E.g., a range of reported GWP values for renewable diesel

Thought-provoking insights/perspectives