Who Pays? | EcoLingua B2 Activity 5 | Vilnius University, Lithuania

B2 · Lithuania Mixed Conditionals Past Perfect Six Hats Vilnius University · B2 Activity 5 · CLIL · Inquiry-Based · SDG 13 · SDG 16 · SDG 17

🗣 Language

Mixed & Third Conditional · Past Perfect · Passive voice (systemic) · Formal discourse markers · Hedging spectrum · Conversational expressions

🌐 Eco

Climate justice · Loss & damage · Net Zero accountability · Scope 3 emissions · Carbon markets · Corporate greenwashing

🤝 Skills

Reading · Listening · Speaking · Writing · Critical analysis · Formal argumentation · AI-enhanced hypothetical reasoning · Peer Q&A

📊 Fact / Opinion / Speculation — Upgraded Stand-Up/Sit-Down (20 min)

Method: Direct instruction · Critical analysisB2: Three-way distinction

B2 distinguishes three categories: FACT (verifiable, independently sourced) / OPINION (value judgement, subjective) / SPECULATION (reasoned prediction, not yet verifiable). Click the correct category for each statement!

📊 FACT / OPINION / SPECULATION SORTER

Read the statement · Choose: FACT / OPINION / SPECULATION · Discuss with your group before clicking!

0 / 10

Correct categorisations · Discuss WHY after each answer

FACT = verifiable, sourced OPINION = value judgement SPECULATION = reasoned prediction

💬 Post-Sorter Discussion — B2 Critical Analysis

🤔

"How does language signal whether something is a fact, opinion, or speculation?"

B2: Modal verbs signal speculation (might / could / is likely to). Value-laden adjectives signal opinion (irresponsible / essential / unfair). Statistics with independent sources signal fact (According to the IPCC / As documented by...). After all, tone alone can be misleading — confident language does not make a claim factual.

🏢

"Why is it harder to categorise corporate climate statements?"

B2: Corporate statements are often deliberately ambiguous. Come to think of it, phrases like “committed to reducing emissions” sound factual (we have made a commitment) but are actually speculation (no timeline, no verified target). This is precisely how greenwashing operates at the language level — using factual-sounding structures to make speculative promises.

👨‍🏫 Teacher
After the sorter, introduce the corporate climate pledges students will investigate. Group into 3 clusters. Assign one pledge per student (or pair). Demonstrate how to access CDP scores, Carbon Tracker, and Global Witness reports for independent verification. Optional: invite the Economics or Geography teacher to co-facilitate Lesson 1.

🔍 Corporate Climate Pledge Analyser (1-Week Research)

Method: Inquiry-based · AI-enhancedB2: Evaluate evidence · Detect bias · Formal hedging

Choose a corporation or government body that has made a public climate pledge (e.g. “Net Zero by 2050” / “Carbon Neutral by 2030” / “Science-Based Targets”). Use the 10-question analyser to audit the pledge. Rate each: Credible / Weak / Absent.

🔍 CLIMATE PLEDGE CREDIBILITY ANALYSER

Name the organisation and their specific pledge — then audit it against these 10 questions!

0 / 10

Credible answers · 0–3 = likely greenwashing · 4–6 = weak but some substance · 7–10 = credible pledge

🤖 AI Research Prompts — Verify the Pledge Independently

💬 "What is [Organisation X]'s CDP (Carbon Disclosure Project) score for climate transparency? Summarise the key findings."

💬 "Does [Organisation X]'s net zero pledge include Scope 3 emissions (supply chain and product use)? What percentage of total emissions do these represent?"

💬 "Has [Organisation X] been accused of greenwashing by any NGO, regulator, or journalist? Summarise the case and the evidence."

💬 "What would need to happen for [Organisation X] to credibly achieve its climate pledge by its stated deadline? List the key barriers."

💬 "Which communities or countries are most affected by [Organisation X]'s environmental impact? How does this relate to climate justice?"

📝 Language for Your Blurb — B2 Hedging Spectrum

High Certainty

The evidence conclusively shows...
It is beyond doubt that...
The data unequivocally demonstrates...

Moderate Certainty

There is compelling evidence that...
It is highly probable that...
The pledge appears to be credible in that...

Low Certainty

It remains to be seen whether...
This claim might be misleading...
The evidence could suggest that...

Passive (Systemic)

Claims have been made without...
Targets are projected to be missed...
Communities have been disproportionately affected by...

🏙️ Stakeholder Impact Matrix — Who Pays? Who Benefits? (10 min)

Method: Critical analysis · Systems thinkingB2: Multiple stakeholder perspectives

Place each stakeholder group into the matrix according to your analysis: who bears the highest cost of the current climate pledge (or lack thereof)? Who receives the greatest benefit? Click any stakeholder chip to move it to the correct cell.

🏙️ CLIMATE JUSTICE STAKEHOLDER MATRIX

Drag or click stakeholder chips into the four quadrants · Then discuss: who should pay more? Who should benefit more?

↑ HIGH BENEFIT

HIGH COST · HIGH BENEFIT — justified burden

LOW COST · HIGH BENEFIT — the free-riders

HIGH COST · LOW BENEFIT — climate injustice

LOW COST · LOW BENEFIT — disengaged

← LOW COST ↓ LOW BENEFIT HIGH COST →

💬 Matrix Discussion — B2 Analysis Prompts

⚖️

"What defines climate justice? Who decides who should pay?"

B2: "Climate justice argues that those historically responsible for the highest cumulative emissions — industrialised nations and major corporations — should bear a disproportionately larger share of the cost of both mitigation and adaptation. That being said, the implementation of this principle has been systematically resisted in international negotiations, particularly by fossil fuel lobbies."

🌐

"How does the loss-and-damage debate connect to your matrix?"

B2: "Loss and damage refers to the irreversible harms — land loss from sea-level rise, extreme weather destruction — suffered by nations that have contributed almost nothing to emissions. In other words, the bottom-left cell of our matrix — high cost, low benefit — maps precisely onto the most climate-vulnerable nations. Come to think of it, the COP28 Loss and Damage Fund was a step forward, but the pledged amounts remain far below actuarial estimates of need."

🎩 Six Thinking Hats — Watch Peer Blurbs (Lesson 2, 30 min)

Method: Six Thinking Hats (de Bono, 1985)B2: Multiple perspectives · Formal Q&A

Each student is assigned a thinking hat. Watch at least 3 peer blurbs (one from each cluster) from the perspective of your hat. Prepare B2-level questions using the prompts below. Three rounds of 5–7 min discussion with presenters.

🎩 SIX THINKING HATS — Click Your Assigned Hat

Click your hat to reveal your B2-level role, discussion perspective, and question prompts for each round

Click a hat above to see your B2 role and question prompts.

🔄 3 Discussion Rounds — Structure

🔴

Round 1 (5–7 min) — With hats on, share views and B2-level questions with Cluster A presenters. Presenters defend their evidence-based verdict on the pledge. Hats challenge the analysis from their assigned perspective.

🟡

Round 2 (5–7 min) — Same hats, new cluster. Compare: are the same accountability failures appearing across different organisations and sectors? What systemic patterns emerge?

🟢

Round 3 (5–7 min) — Cluster C. Blue Hat leads synthesis: "What are the most logical, bias-free conclusions from all three blurbs? Who pays? Who should?"

🏆 PEER VOTE — Most Evidence-Based Blurb per Cluster

Watch all blurbs — then vote!

👨‍🏫 Teacher
Invite the Economics or Geography teacher to hold the White Hat (facts and data) or Observer role. Assessment: quality of questions (specificity, use of evidence), accuracy of hedge language, engagement with the presenter's argument. Informal feedback on ability to move from opinion to evidence-based analysis.

🤖 “What If?” AI-Enhanced Hypothetical Reasoning (Lesson 2, 15 min)

Method: Inquiry-based · AI-enhancedB2: Mixed & Third Conditional · Counterfactual reasoning

Complete a “What if?” statement about your organisation’s pledge using the builder below. Then ask AI the same question. Compare the AI’s scenario with your own counterfactual. Discuss: which outcome is more likely? What would have needed to change?

🤖 “WHAT IF?” CONDITIONAL PROMPT BUILDER

Select one chip per row · Build a Mixed or Third Conditional scenario · Then ask AI the same question!

① If [Organisation X] had...

② ...in [time frame]...

③ ...then [outcome] would...

Select options above to build your conditional scenario...

📝 B2 Conditional Grammar — Three Types

Third Conditional (Past → Past)

If they had set science-based targets in 2010, the 1.5°C pathway would have remained achievable. (They did not — irreversible past consequence.)

Mixed Conditional (Past → Present)

If they had committed to genuine net zero in 2020, communities in the Global South would not be facing irreversible loss today. (Past inaction → present consequence.)

Second Conditional (Unreal Present)

If corporations were required to disclose Scope 3 emissions, consumers would be able to make genuinely informed choices. (Currently hypothetical.)

Discourse Markers (Formal)

Nevertheless / Furthermore / In light of this evidence / It should be noted that / Notwithstanding the above / In contrast to this claim...

🤖 AI Prompts for “What If?” Reasoning

💬 "If [Organisation X] had committed to a genuine science-based net zero target in 2015, model three possible scenarios for how their emissions would differ today."

💬 "What if the EU required all corporations to disclose Scope 3 emissions by 2026? Model the market and regulatory effects."

💬 "If the loss-and-damage fund agreed at COP28 were fully capitalised at its estimated need, what would be the most impactful uses of that funding?"

🖉 Open Letter / Policy Brief — Write to Power (Lesson 3, 15 min)

Method: Authentic audience · Formal writingB2: Formal register · Evidence-based argument · Discourse markers

Individually or as a class, draft an open letter to the CEO of your organisation, a government minister, or the EU Green Deal office. Use the scaffold below to build your letter. B2 standard: formal register, evidence-cited, hedged but assertive.

🖉 OPEN LETTER SCAFFOLD BUILDER

Select one chip per section · Your letter assembles below · Edit freely after building!

① Opening — Who you are and why you are writing

② Evidence — Key findings from your investigation

③ Conditional argument — What should have been done / what could still be done

④ Justice claim — Who pays, who should

⑤ Closing demand — What you call for

Select options above — your letter assembles here!

✓ Before You Send — B2 Formal Writing Checklist

✓ Is your tone formal and assertive without being aggressive?

✓ Have you cited specific evidence (CDP score, report name, verified data point)?

✓ Have you used hedging language? (appears to / there is compelling evidence that / it remains to be seen whether)

✓ Have you used at least one conditional (Third or Mixed) to make your argument?

✓ Does your closing state a specific, concrete demand — not just a vague call for change?

✓ Have you included formal discourse markers? (Furthermore / Nevertheless / In light of this / Notwithstanding)

💡 Reflection — Who Pays? What Are the Most Logical Outcomes? (Lesson 3, 15 min)

Method: Analytical reflectionB2: Evidence-based conclusion · Bias-free reasoning

In cluster groups, share the most significant things you learnt about climate justice and corporate accountability. What are the most logical, bias-free outcomes if current trends continue? If subject teachers are present, invite their observations first.

⚖️

"Who actually pays for climate change, and who should?"

B2: "As the evidence overwhelmingly demonstrates, it is the most climate-vulnerable communities — those in the Global South who have contributed the least to cumulative emissions — who bear the greatest cost. That being said, the ‘polluter pays’ principle, while established in international law, has been systematically undermined by fossil fuel lobbying. If the principle had been enforced consistently since the 1992 Rio Summit, the loss-and-damage financing gap would not exist at the scale it does today."

🤖

"How reliable was AI in evaluating the pledges?"

B2: "AI proved useful for generating initial research directions and summarising large bodies of evidence. Nevertheless, it should be noted that AI training data has a temporal cutoff and may not reflect the most recent regulatory changes or NGO assessments. Come to think of it, the most valuable use of AI in this context was as a ‘devil’s advocate’ — generating counterarguments to our initial assessments that we then had to evaluate and rebut."

🌐

"What systemic changes would need to occur for climate pledges to be credible?"

B2: "At minimum, credible pledges would require: mandatory independent third-party verification of all Scope 1, 2, and 3 emissions; legally binding interim targets with financial penalties for non-compliance; and full inclusion of loss-and-damage financing in corporate climate strategies. Furthermore, if greenwashing were subject to the same legal penalties as financial fraud, as proposed in the EU Green Claims Directive, market incentives would shift dramatically."

🎓

"What will you do differently as a consumer and citizen?"

B2: "In light of this evidence, I intend to apply the same critical analytical framework I used in this activity to the environmental claims I encounter in everyday advertising and political communication. If you ask me, the most impactful individual action is not lifestyle change per se — though that matters — but demanding systemic accountability: reading CDP scores, supporting strong climate legislation, and calling out greenwashing when I see it."

🖉 Homework & Follow-Up

🖉Send or post your open letter to the organisation’s official communication channel. Share the class findings publicly if appropriate.

🔎Find one Lithuanian or Baltic organisation whose climate pledge you consider genuinely credible. Write a 150-word evidence-based justification using B2 formal register.

🌐Virtual Exchange (Optional): Share your blurb and pledge analysis with a partner class in another EcoLingua country. Are the same organisations making similar pledges across Europe? What cross-border accountability mechanisms exist?

🔗 Resources

🌐 CDP (Carbon Disclosure Project) — Corporate Climate Scores
🌐 Carbon Tracker Initiative — Corporate Fossil Fuel Analysis
🌐 Global Witness — Corporate Environmental Accountability
🌐 Science Based Targets Initiative
🌐 UN SDG 13 — Climate Action
🌐 UN SDG 16 — Peace, Justice & Accountability
🌐 Six Thinking Hats — de Bono Group