Changing the assumptions

Research Skills — Week 3 Application

Today’s plan

Today’s plan

• Guided walkthrough
• Your scenario analysis
• Discussion
• Start your briefing
• Wrap-up

Goals

In the content session, you audited the assumptions behind biomass carbon accounting.

Now: test what happens when you change them.

1. Add transport emissions
2. Add supply chain emissions
3. Change the forest regrowth timeline
4. See how wide the range gets

• [16:05–16:15] Open WebR, recap. Today is scenario analysis — arithmetic with a purpose. The exercises provide the emission factors and distances; students focus on sensitivity, not data collection.

Questions?

Submit questions:

PollEv.com/geol

text geol to 07480 781235

• Open the Poll Everywhere Q&A activity before the session and leave it active throughout.
• Questions appear in your Poll Everywhere presenter view; upvoted questions rise to the top.
• Address during transitions or at the end of concept blocks, not mid-explanation.
• Reset (archive) the activity between sessions so questions don’t carry over from the previous week.

Guided walkthrough

• Today’s plan

Guided walkthrough

🎓💻 Scenario analysis in R

• Your scenario analysis
• Discussion
• Start your briefing
• Wrap-up

Starting point: the “official” view

Biomass emission factor at combustion: 0 gCO₂/kWh

(That’s the carbon accounting rule.)

But what if we add the costs the rule ignores?

• [16:15–16:35] Walk through two additions live on screen.

Adding transport

How much does transport add?

• The students have the same constants in front of them on the exercise page; tell them to race you. Type the numbered comments first, then flesh it out with code. Start typing after a minute or so, so the slower ones can follow.

• Anticipated code:

  # 2. How many *MWh* of *electricity* per tonne of pellets?
  
  # 1 MWh = 3.6 GJ, so a tonne of pellets gives 4.7 MWh of *heat* —
  # but only 36% of that is electricity
  mwh_per_gj <- 60 * 60 / 1000
  
  mwh_per_tonne <- pellet_gj_per_tonne / mwh_per_gj * plant_efficiency
  
  # 3. kg CO₂ per tonne shipped  
  transport_co2 <- 6000 * shipping_co2_per_tkm  # kg CO₂ per tonne
  
  # 4. kg CO₂ per MWh
  transport_co2 / mwh_per_tonne                 # kg CO₂ per MWh

• The answer is ~17.6 kg CO₂/MWh.

• Do the efficiency step slowly. 17 GJ is heat; only 36% of it is sold as electricity.

• Ask: “Is that a big number?” Against gas (360) and coal (910) it is small. Against the official figure of zero it is large.

• Constants match students’ exercise code.

Adding the payback period

Trees absorb CO₂ as they grow. But how long does regrowth take?

• Fast regrowth (10 years): biomass is roughly carbon-neutral over the short term
• Slow regrowth (40+ years): biomass has a carbon debt for decades
• Net zero target: 2050 — only ~20 years away

If the payback exceeds the policy horizon, biomass is not carbon-neutral when it matters.

The same fuel, six answers

w03-payback-scenarios.R

• Walk left to right. 45 = “Counting all the regrowth that we assume will happen over next 100 yr” Same fuel, same chimney, same megawatt-hour — 1060 kg of difference, all of it from what you decided to count.
• The two right-hand bars exceed coal(!) Wood (lots of C-O bonds in cellulose – partly oxidized) carries less energy per C than coal (reduced; mostly C).
• So the case for biomass rests on the regrowth bars on the left. Ask: what would have to be true for the 20-year bar to be the honest one? Who checks that the forest actually regrew? The answer is an assumption, not a measurement.
• The 20-year bar lands at 365, essentially the same as gas. Worth saying out loud: on a 20-year view, the best case for biomass is “about as good as burning gas.”

Your scenario analysis

• Today’s plan
• Guided walkthrough

Your scenario analysis

✏️💻 Independent exercises

• Discussion
• Start your briefing
• Wrap-up

Exercises

The WebR page has four scaffolded exercises:

1. Transport scenario — estimate CO₂ cost of shipping pellets
2. Supply chain scenario — add harvesting, processing, drying
3. Payback scenario — plot cumulative emissions under different regrowth timelines
4. Extension: Best case and worst case — combine all assumptions

How wide is the range? What does that tell you about certainty?

• [16:35–17:20] Main working block. Circulate. The emission factors and distances are provided in the exercises — students don’t need to research them.
• Key learning: the answer changes dramatically depending on what you include. The “official” zero is one end of a wide spectrum.

Attendance

Show the QR from https://attendance.durham.ac.uk/mytimetable, then ← back to the task.

Discussion

• Today’s plan
• Guided walkthrough
• Your scenario analysis

Discussion

💬 What did you find?

• Start your briefing
• Wrap-up

Key messages

• The answer depends on which costs you include
• The timescale matters — carbon-neutral over centuries, but not over decades
• The “official” answer (zero) is a policy choice, not a scientific measurement

• [17:20–17:35] Draw these out from the room. Students should feel the force of the ambiguity.

And it’s even worse than that

w03-energy-input.R

• Government’s own model (Stephenson & MacKay 2014, DECC; the report that inspired this project). EIR = energy that is not biomass (gas, diesel, grid electricity) spent per MWh delivered, counted as primary energy, as the comparator studies do.
• Worst case, Pacific Canada: 0.95 MWh in for every 1 MWh out. Why? Wet sawdust dried with natural gas, then shipped via the Panama Canal. Best case (25%): residues that arrive already dry.
• Ask: what does this do to the “renewable” label? We have been counting CO₂; this counts energy, and it points at the same stage.
• “What assumptions are we making?” The top of the bar is one choice of drying fuel.

Drax’s own numbers

Drax Biomass Tracker, Oct–Dec 2025. © Drax Group plc

• Launched by Drax in 2026. Tonnes at every stage, per quarter; click “CO2 emissions” for supply-chain CO₂ by stage.
• Ask: who produced these numbers, and what might a transparency tool leave out? It promises fibre types (sawmill residue vs roundwood) but does not show them; certification reads near 100% everywhere; its own disclaimer says the figures are not authoritative.
• Supply-chain CO₂ is 64–70 kg per MWh for Drax (Q2–Q4 2025), close to the ~60 we assumed in with_supply_chain, with pelletizing the largest share (61% in Q4).
• Open it live in a browser tab if there is time.

Whose pellets?

w03-drax-origins.R

• “Compared to what?” The worst bar is about a tenth of Drax’s fuel; most comes from the US South (25–76% on the energy-input chart).
• Drax’s Canadian mills are in British Columbia and Alberta: the report’s “Pacific Canada”.

The ethical question

“If you were writing a policy briefing, which scenario would you present? Why?”

Cast your vote

PollEv.com/geol

text geol to 07480 781235

• Activate the poll before showing this slide.
• Most students will say “the full range” — but some will say “best case” or “worst case.” Both answers feed the traitor discussion in Week 4.
• Don’t resolve the tension. Let students sit with the disagreement.

Think about this. You’ll be writing that briefing next week.

• This is the pivot to Week 4. Some students will recognize this as the traitors’ dilemma — a faithful briefing presents the range; a misleading one cherry-picks. Don’t point this out yet.

Start your briefing

• Today’s plan
• Guided walkthrough
• Your scenario analysis
• Discussion

Start your briefing

✏️ 15 minutes

• Wrap-up

w01-ppdac-ring.R

Save a figure

Pick the figure that best makes your case.

1. Right-click it → Save image as…
2. Save it in your repo, in a folder called figures
3. Commit and push it in GitHub Desktop

• [17:35–17:38] Demo once on the screen: right-click a plot on the exercise page, save to figures/payback-scenarios.png in the repo folder, commit and push.
• Name the file for what it shows, with no spaces.
• R in the browser can’t write into the repo folder, which is why we save the image by hand. ggsave() arrives with Positron and the report.

Markdown in one table

You type	You get
# Title	The title
## Section	A section heading
**bold**	bold
*italic*	italic
- item	A bulleted list
	The figure
A blank line	A new paragraph

• [17:38–17:40] This is all the Markdown the briefing needs. The table is also on the exercise page, so they needn’t copy it down.
• GitHub shows .md files formatted, with their figures. That is how reviewers will read the briefings next week, and how the report is marked in Week 10.

Create briefing.md

On github.com: Add file → Create new file → briefing.md

Paste the skeleton from the exercise page, fix the figure path, then click Preview.

Commit, then Fetch and Pull in GitHub Desktop.

• [17:40–17:50] Demo on screen first: create the file, paste the skeleton, change the image path, Preview, commit.
• The skeleton has the four sections (summary, evidence, caveats, conclusions), so next week they only have to write.
• Commonest failure: the figure doesn’t show in Preview. Either the path is misspelt (case matters) or the figure wasn’t pushed.
• Pulling matters: if they commit week3.R without pulling first, GitHub Desktop will ask them to pull before it lets them push.

Wrap-up

• Today’s plan
• Guided walkthrough
• Your scenario analysis
• Discussion
• Start your briefing

Wrap-up

Any questions we missed?

Submit questions:

PollEv.com/geol

text geol to 07480 781235

• Continuation of the same Poll Everywhere Q&A activity opened at the start of the session — this is not a new poll, so don’t reset/archive it yet.
• Surface any questions that have come in since the early Questions slide, especially ones raised during today’s content.
• Once you’ve finished with this slide, archive (reset) the activity at the end of the session so questions don’t carry over.

Commit your work

Save your code to week3.R. Commit and push.

Check that briefing.md and your figure show on github.com.

Next week is the big one:

You’ll write your policy briefing and review each other’s work.

“What assumptions are we making?”

The question that determines the answer.

• Check the Q&A — surface any top-voted questions before finishing.
• Ensure everyone commits. Students should arrive at Week 4 with their scenario analysis code saved and a briefing.md that shows its figure on github.com.