- ESS IA at a Glance: Rules for Exams From May 2026
- ESS IA Criteria: All Six, With Marks
- What Makes a Strong ESS IA?
- ESS IA Ideas With Example Research Questions
- Planning Fieldwork and Data Collection
- ESS IA Structure: How to Organise Your 3,000 Words
- Strong ESS Analysis Goes Beyond Description
- Common ESS IA Mistakes
Quick Answer
The IB ESS IA is an individual investigation of a research question you design yourself, written up as a report of up to 3,000 words after about 10 hours of work. It is marked out of 30 across six criteria and is worth 25% of your grade at SL and 20% at HL. This model applies to every ESS exam from May 2026, including November 2026, May 2027 and May 2028.
What You'll Learn
- The new ESS guide was first assessed in May 2026 and added a higher level option, so everyone sitting ESS in November 2026, May 2027 or May 2028 follows the same IA model
- The report has a 3,000-word maximum and about 10 hours of class time, and SL and HL complete the same task
- Six criteria total 30 marks: research question and inquiry (4), strategy (4), method (4), treatment of data (6), analysis and conclusion (6) and evaluation (6)
- Criterion B asks you to explore a strategy linked to your issue and the tensions between perspectives on it, so plan it from the start
- You may share a method with up to two classmates if your variables differ and your data is unique, but the report must be your own
ESS IA at a Glance: Rules for Exams From May 2026
The IB launched a new Environmental Systems and Societies guide for first teaching in August 2024, with first assessment in May 2026, and it introduced ESS at higher level for the first time. Anyone sitting ESS from May 2026 onwards, including the November 2026, May 2027 and May 2028 sessions, completes the same internal assessment, and SL and HL students are marked against the same criteria. In May 2026, the first session under the new guide, 20,964 candidates sat ESS at SL and 3,857 at HL (May 2026, IB provisional statistical bulletin).
- Task: an individual investigation of an ESS research question that you design and carry out, submitted as a written report
- Length: a maximum of 3,000 words. Charts, diagrams, data tables, equations, calculations, citations, the bibliography, headers and the title do not count
- Time: about 10 hours of class time
- Weighting: 25% of the final grade at SL and 20% at HL
- Marks: 30 in total across six criteria, marked by your teacher and moderated by the IB
- Collaboration: you may work in a small group of up to three and share a method, provided your independent or dependent variable differs and the data you collect is unique to you
Watch Out
Older ESS guides, checklists and exemplars were marked against different criteria. If a checklist never mentions a strategy or tensions between perspectives, it was written for the old model. Use it for ideas, not for how marks are awarded.
ESS IA Criteria: All Six, With Marks
The six criteria follow the order of a good report. The first three are worth 4 marks each and the last three are worth 6, so data treatment, analysis and evaluation carry 18 of the 30 marks.
- A. Research question and inquiry (4 marks): explain the local or global environmental issue behind your investigation, give relevant background research and pose a clear, focused research question.
- B. Strategy (4 marks): identify and describe a strategy that addresses the issue, such as a conservation scheme, regulation, management plan or behaviour-change campaign, and explain the tensions between perspectives on it. Top reports show how conflicting economic, social, cultural, political or environmental goals affect the outcomes of the strategy.
- C. Method (4 marks): describe your method in enough detail for someone else to replicate it, and show that it collects enough relevant data to answer the question.
- D. Treatment of data (6 marks): present raw and processed data clearly and correctly, with units and labels, and process it in ways that address the research question.
- E. Analysis and conclusion (6 marks): explain the patterns and trends in your data, consider how reliable they are, and reach a conclusion that answers the research question and is supported by the analysis.
- F. Evaluation (6 marks): identify specific limitations of your method, explain their impact on the results, propose realistic improvements and note questions your investigation leaves open.
Pro Tip
Plan criterion B from day one. If the strategy concerns the same issue as your data, your findings can feed into the discussion of it. A strategy bolted on at the end reads as an afterthought.
What Makes a Strong ESS IA?
A strong ESS IA starts from a real environmental issue, narrows it to one measurable relationship, and connects it to a strategy that people use or debate. ESS is an interdisciplinary course, so your data can be quantitative or qualitative and can come from ecological fieldwork, lab work or social research such as questionnaires, as long as it is unique to your investigation.
- Issue: a specific local or global environmental issue, such as trampling, water pollution, urban heat or waste
- Question: one independent and one dependent variable, for example distance from a path and plant species richness
- Strategy: a response to the issue that people disagree about, such as a boardwalk, a fishing ban or a deposit return scheme
- Feasibility: a site you can reach more than once, safe sampling, and enough time to collect data before your deadline
Pro Tip
The best ESS topics are local enough to investigate properly but broad enough to connect to wider environmental concepts and a real strategy.
ESS IA Ideas With Example Research Questions
These ideas are illustrative. Each pairs a measurable question with a strategy you could discuss for criterion B. Adapt them to a site and an issue near you, and agree the final question with your teacher.
- Trampling and biodiversity: how does distance from a footpath affect plant species richness or Simpson's diversity index? Strategy: path management or boardwalks, with tensions between public access and conservation.
- River water quality: how do dissolved oxygen, nitrate levels or a macroinvertebrate biotic index change upstream and downstream of farmland? Strategy: buffer strips or fertiliser rules, with tensions between farm income and water quality.
- Urban heat: how does tree canopy cover affect surface temperature across a town centre? Strategy: urban greening, weighing cost, space and maintenance against cooling.
- Soil and land use: how do soil organic matter or infiltration rate differ between woodland, grazed and arable land? Strategy: regenerative farming, with tensions between yields and soil health.
- Air quality and lichens: how does lichen diversity on trees change with distance from a busy road? Strategy: a low-emission zone, with tensions between drivers, businesses and residents.
- Beach litter: how does the density of litter or microplastics change with distance from beach access points? Strategy: a deposit return scheme or a beach-clean programme.
- Food waste: how does the mass of food wasted in a school canteen change after a portion-size or awareness intervention? Strategy: the intervention itself, with tensions between choice, cost and waste.
- Attitudes to conservation: how does support for a local rewilding or protected-area scheme vary with age or with distance lived from the site? Strategy: the scheme, using questionnaire data to show competing perspectives.
- Leaf litter decomposition: how does the rate of mass loss from litter bags differ between habitats? Strategy: woodland management, linking decomposition to nutrient cycling.
- Invasive species: how does the density of an invasive plant change with distance from a path or watercourse? Strategy: removal programmes, weighing volunteer effort and cost against ecological benefit.
- Noise and green barriers: how much do hedges or tree belts reduce traffic noise levels? Strategy: planting barriers compared with other noise policies.
- Recycling behaviour: how does the proportion of correctly sorted items in school recycling bins change after new labelling? Strategy: labelling and education campaigns, with tensions between convenience and recycling targets.
Watch Out
Check safety before any fieldwork near water, roads or cliffs, and get permission to sample on private land. For questionnaires, get consent, keep responses anonymous and avoid questions that could upset people.
Planning Fieldwork and Data Collection
Many ESS IAs that score poorly on criteria C and D fail at the planning stage: too few samples, a sampling method that is never justified, or data that cannot answer the question. Plan your data collection backwards from the analysis you want to do.
- 1Decide the analysis first, for example a comparison of two sites or a correlation along a transect, and work out how much data it needs.
- 2Choose and justify a sampling strategy: random sampling to avoid bias, systematic sampling along a transect for gradients, or stratified sampling when a site has distinct zones.
- 3Fix the sampling unit and size, such as quadrat size or the number of questionnaire respondents, and explain why it suits the habitat or population.
- 4Control what you can, such as time of day, weather and season, and record what you cannot control so you can discuss it later.
- 5Collect enough repeats at each site or level to calculate a mean and judge the spread.
- 6Record raw data in the field on a prepared sheet, with photos, grid references or a map of sampling points.
- 7Write the method as numbered steps that another student could follow on the same site.
ESS IA Structure: How to Organise Your 3,000 Words
Ordering your report by criterion makes it easy for your teacher and the moderator to find the evidence. The word ranges are a rough planning guide, not an IB rule, and data tables and graphs do not count towards the limit.
- 1Introduction: the environmental issue, background research and research question (criterion A, about 400 words)
- 2Strategy and perspectives: the strategy, the stakeholders and the tensions between them (criterion B, about 400 words)
- 3Method: variables, sampling, equipment, risks, ethics and a step-by-step procedure (criterion C, about 500 words)
- 4Results: raw and processed data in tables and graphs, with brief commentary (criterion D, about 300 words)
- 5Analysis and conclusion: patterns, reliability and a direct answer to the question (criterion E, about 700 words)
- 6Evaluation: limitations, their impact, improvements and unresolved questions (criterion F, about 600 words)
- 7Bibliography
Strong ESS Analysis Goes Beyond Description
To reach the top bands of criteria D, E and F, you need to process the data, explain what it shows about the environmental system, and judge how far it can be trusted.
- 1Process the data, for example with means, percentages, a diversity index or a biotic index, and show one worked example of each calculation
- 2Choose graphs that match the question: a scatter graph for a gradient, bar charts with error bars for site comparisons, or kite diagrams for transects
- 3Where the data fits, use a statistical test such as Spearman's rank correlation, a t-test or chi-squared, and say what the result means for your question
- 4Explain the patterns using ESS concepts such as succession, eutrophication, carrying capacity or ecosystem services
- 5Discuss reliability: the spread between repeats, sample size and any anomalies
- 6In the evaluation, link each limitation to its effect on your results, then suggest a specific improvement and a question for further research
Common ESS IA Mistakes
These mistakes regularly hold ESS investigations in the middle mark bands. An IB ESS tutor can explain, using other investigations, how to plan fieldwork that produces enough data for a convincing analysis.
- A question that is too broad or purely descriptive, such as describing the biodiversity of a park
- Treating criterion B as an afterthought, with a strategy that has little to do with the data
- Describing stakeholder views without explaining the tension between them or how it affects the outcomes of the strategy
- Fieldwork that produces too little data for real analysis
- Graphs of raw data only, or processing that does not answer the research question
- Evaluation that lists obvious problems such as the weather without explaining their effect on the conclusion
- Going over 3,000 words: anything beyond the limit may not be marked, and your evaluation comes last