What could you study? The question studio
Pick or spin a scale, a system element, a lever and the people involved, then write a question and put it through the checks
The task
- Produce: a question card of your own, with five realism checks answered, three search strings run and the three closest papers noted.
- From: six pickers (scale, dimension, system element, lever, stakeholder and an optional twist) that you set yourself or spin.
- Done when: all eight parts of the card are filled in and rated against the rubric, nothing blocks it, and you can say how it differs from the three closest papers.
About 40 minutes guided here, then about an hour on your own: run the three searches, skim the first twenty results of each, read the closest abstracts, and revise the card.
The studio makes no originality judgement. Its checks show which parts are there; rigour is for the rubric, novelty for the literature, and importance for the people who would use the answer.
The six pickers
| Picker | Options |
|---|---|
| Scale | S0 device and plant · S1 microgrid and community · S2 regional grid · S3 city and region · S4 nation |
| Dimension | Physics and technology · Operations · Planning and optimization · Economics and markets · Techno-economic analysis · LCA and environment · Policy and regulation · Society and livelihoods · Security, resilience and national strategy · Research methods |
| System element | rooftop solar · heat pumps · EV fleets and school buses · irrigation pumps · cold chains · district heating · industrial process heat · data centers (as a load) · wind farms · utility solar · batteries · long-duration storage · hydrogen · transmission lines · distribution feeders · gas networks · geothermal · hydro · mini-grids · water treatment and desalination · agrivoltaics · building retrofits · demand-response programmes |
| Lever | new technology · operation · market or tariff design · policy · sufficiency (using less) · maintenance and asset life · avoided demand · governance and ownership |
| Stakeholder | low-income renters · homeowners · farmers · rural co-ops · municipal utilities · investor-owned utilities · grid operators · regulators · developers · large energy users · city councils · transit agencies · workers in fossil industries · tribal nations · communities near infrastructure · future generations |
| Twist (optional) | a week-long cold snap · a drought · a heat wave · a fuel-price spike · a one-mineral supply disruption · a policy reversal · a cyber incident · population decline · rapid load growth · a wildfire season · a new tariff design · a doubled interconnection queue |
Five types of question, and a draft for each
| Type | Template | Draft from the picks | Typical methods |
|---|---|---|---|
| Descriptive | How much / how often / where …? | How much does {metric} vary among {stakeholder} with {element} in {scale}{twist}? | data analysis, accounting, GIS |
| Explanatory | Why did … happen? | Why did {metric} change for {stakeholder} after {lever} for {element} in {scale}{twist}? | statistics, econometrics, case studies, simulation |
| Evaluative | Did … achieve …, compared with …? | Did {lever} for {element} lead to {goal} for {stakeholder} in {scale}{twist}, compared with […]? | difference-in-differences, matched comparisons, ex-post review |
| Predictive | What will … be, with what uncertainty? | What will {metric} be for {stakeholder} with {lever} for {element} in {scale}{twist}, and with what uncertainty? | forecasting, scenarios, Monte Carlo |
| Prescriptive | What should [decision-maker] do about … to achieve …, compared with …? | What should {stakeholder} do about {element} in {scale}, through {lever}, to achieve {goal}{twist}, compared with […]? | optimization, decision analysis, MCDA |
Words used here: energy burden · customer-hours · net present value · techno-economic analysis · LCA (life cycle assessment)
Realism checks: each passes, warns or blocks
- Data exist: Can you name a dataset that measures your metric, at your scale and time step? Note: the dataset, its licence and its date.
- Physics sanity: Do the units and sizes make sense: power in MW against energy in MWh, nothing above its rating, energy in balance? Note: one number you checked, with its units.
- Institutions: Can you say who pays, who permits, and which jurisdiction's rules apply, as of which date? Note: the rule that applies, and its date.
- Scope fits the time: Could a first version be done in the research capstone's 20 to 40 hours, or in the time your project has? Note: what a first version covers.
- People and ethics: If people or communities are part of the study, do you know how you will get consent and treat them fairly? Note: your consent plan, or “no people involved”.
With JavaScript on, you pick or spin each part, get a draft for the type you choose, and keep your card in this browser or share it as a link.
Drafts, example cards and exemplars are illustrative. The paper counts come from OpenAlex keyword searches (articles and reviews, 2016 to 2 November 2026, retrieved 2 November 2026); they are context only, and the clusters overlap.
Two answers to the same task
The task
Spin: S3 city and region · society and livelihoods · heat pumps · low-income renters · a heat wave. Write a question card.
Weak answer · Developing
Question: How will heat pumps help [1] low-income renters in heat waves? Type: predictive Audience: low-income renters [2] Metric: comfort [3] Compared with: (blank) [4] I would be wrong if: (left blank) [5] Realism and papers: (not done) [6]
- Falls short: Help with what? The word hides the metric.
- Falls short: A group, but no use for the answer is stated. Renters could use it; say for what, or name who controls the change.
- Falls short: No units: hours above a safe indoor temperature could be counted.
- Falls short: No counterfactual: help compared with what?
- Falls short: No falsification statement, so no result could count against it.
- Falls short: No realism review and no closest papers: nobody yet knows whether data exist or what is already known.
Strong answer · Proficient on framing and realism
Question: During a week-long heat wave, how many hours above the city's indoor-heat safety threshold would Prairie City's low-income renters avoid if landlords installed heat pumps instead of window air conditioners? Type: predictive Audience: the city's housing office, which sets rental standards [1] Metric: hours above the indoor-heat threshold, per household per heat wave [2] Compared with: window air conditioners in the same homes [3] Scale and boundary: S3; renter households in Prairie City; landlords' installation costs left out, and said so [4] I would be wrong if: heat-pump homes saw no fewer hot hours than homes with window units [5] Main uncertainty: whether renters can afford to run the cooling during the heat wave [6] Realism: data warn (few homes have indoor sensors); physics pass; rules pass (the city's 2026 rental code); time pass; people warn (consent for sensors in homes) [7] Closest papers: three found, each differing (owner-occupied homes; a cooler city; cooling bills, not hot hours) [8]
- Works: A user and the use: the office sets the standards the answer informs. Landlords control the installation; that is a separate line.
- Works: A metric with units, from indoor sensors or a building model.
- Works: A realistic alternative, not “no cooling at all”.
- Works: The boundary names what it leaves out.
- Works: A result that would show the expectation false.
- Works: The uncertainty that could change the answer most, and it comes from the spin's society dimension.
- Works: The five realism checks answered, with the evidence or the reason for each warning.
- Works: How the card differs from each closest paper. The line shows the form of the notes; the papers come from your own searches.
| Weak answer | Strong answer | |
|---|---|---|
| Metric and units | “Comfort” | Hours above a threshold |
| Audience | A group, no use stated | A user, and what they decide |
| Counterfactual | None | Window units in the same homes |
| Boundary | Not stated | Who is counted, what is left out |
| Falsification | None | A result that would show it wrong |
| Realism and papers | Not done | Five checks; how it differs from three papers |
Seven example cards
| Combination | Type | Question |
|---|---|---|
| S1 · society · community solar · low-income renters | evaluative | Did a rural co-op's community-solar programme lower renters' energy burden more than bill credits of the same cost would have? |
| S0 · operations · irrigation pumps · farmers | prescriptive | How much irrigation pumping on a Plains farm could move to windy night hours without lowering yield, and what is that worth under the co-op's tariff? |
| S3 · security · heat pumps · elderly households · cold snap | predictive | During a week-long cold snap, how would all-electric heating change the risk of losing heat for elderly households, with and without backup? |
| S2 · environment · data-center cooling · nearby communities · drought | descriptive | How do the cooling choices of large data centers shift water use and summer peak demand in a water-stressed region? |
| S4 · security · batteries · regulators · mineral disruption | predictive, with a prescriptive follow-up | How sensitive is a national storage build-out to a one-year disruption of one mineral, and which design choices reduce the risk? |
| S1 · TEA · mini-grids · farmers | prescriptive | Which productive uses make a solar mini-grid viable without lowering household access? |
| S3 · sufficiency · building retrofits · city council | explanatory | Why did some neighbourhoods' heating demand fall more than others after a retrofit programme? |
Check your own work against the rubric
| Competence | Developing | Proficient (the bar) | Strong |
|---|---|---|---|
| Framing Look for: all eight parts of the card are filled in, each meets the rubric's words, and the boundary says what is left out. |
Question vague or untyped; no audience or counterfactual. | Typed question with audience or decision-maker, metric with units, counterfactual, scale and boundary; one falsification statement. | Also explains why the question matters to its audience and what answer would change a decision. |
| Realism Look for: the realism checks answered, with the data's status and a list of what the card leaves out. |
Assumes announced projects exist. | Evidence status of key inputs; a 'what the analysis leaves out' list. | Realism checked with a practitioner or primary documents. |
Before you take a card to a mentor
- Type: Named, with its template.
- Audience: Someone who would use the answer, and for what.
- Metric: Measurable, with units.
- Counterfactual: A realistic alternative to compare with.
- Scale and boundary: Including what is left out.
- I would be wrong if…: A result that would count against you.
- Main uncertainty: The input that could change the answer most.
- Data: One dataset named, with its licence and date.
- Rules: Who pays, who permits, which jurisdiction, as of when.
- Time: A first version fits the time you have.
- People: Consent and fair treatment planned, if people are involved.
- Three closest papers: Found, and how your card differs from each.
Myth or fact? “If a search finds few papers on a topic, a question about it is new and important.”
- Myth
- Fact
Answer
Myth. Few papers can mean other words, other fields, unpublished reports, or a question nobody needs answered; novelty is checked by reading, not by counting.
Myth or fact? “If thousands of papers exist on an element, there is nothing left to study.”
- Myth
- Fact
Answer
Myth. A new place, a new group of people, new data or a new twist can each make a question open; the closest papers tell you which.
Myth or fact? “A realism block is a reason to change the question, not to drop the topic.”
- Myth
- Fact
Answer
Fact. A different metric, scale or period often turns an unanswerable question into one a first study can answer.
Go deeperHow much has been written: the counts behind the context line
- Keyword clusters from OpenAlex: articles and reviews, 2016 to 2 November 2026, retrieved 2 November 2026.
- RGI: the growth from 2016–18 to 2022–24, divided by the growth of energy-systems work as a whole (1.70 times). Above 1 means faster than the field.
- The search queries are in this page's data file.
| Keyword cluster | Papers, 2016–2026 | RGI |
|---|---|---|
| interconnection queues | 95 | – |
| open-source modelling | 401 | 1.45 |
| resource adequacy | 2,027 | 0.90 |
| carbon accounting | 2,244 | 1.79 |
| extreme weather | 2,868 | 2.08 |
| GIS siting | 3,635 | 1.23 |
| water and desalination | 3,938 | 0.97 |
| heat pumps | 3,977 | 1.53 |
| transmission and planning | 4,027 | 0.85 |
| critical minerals | 4,736 | 4.65 |
| virtual power plants and communities | 6,689 | 2.62 |
| grid-forming inverters | 7,364 | 3.30 |
| EV fleets and charging | 7,832 | 2.13 |
| resilience | 7,967 | 3.12 |
| district heating and urban energy | 9,992 | 0.86 |
| justice and energy poverty | 10,074 | 3.03 |
| data centers | 12,419 | 0.93 |
| rooftop solar and distribution resources | 12,583 | 1.36 |
| forecasting | 13,406 | 1.34 |
| demand response | 16,900 | 0.99 |
| mini-grids and access | 18,711 | 1.16 |
| energy security | 18,753 | 1.29 |
| hydrogen | 19,174 | 4.82 |
| LCA | 22,703 | 1.16 |
| electricity markets | 24,138 | 0.84 |
| machine learning | 26,451 | 4.21 |
| microgrids | 36,753 | 1.16 |
| TEA | 38,283 | 1.68 |
| batteries and long-duration storage | 53,316 | 1.67 |
- Context only: the clusters overlap, and the counts are not weighted by quality.
- Refreshed yearly with the same queries.
- Few papers is no evidence that a question is new, important or feasible.
- Many papers is no evidence that it is done.
Go deeper · ResearcherFrom a card to a study
Go deeperWhat this studio leaves out
- The pickers are a starting point, not a map of every possible study: you can type any question onto the card.
- The realism checks are your own answers; the studio cannot see your data or your rules.
- The search strings are a first pass in one open database. Other databases, other words and other languages find other papers.
Sources and what’s next
Tools and data named on this page
| Tool or source | What it is for | Link |
|---|---|---|
| OpenAlex | An open index of research papers: the search strings link to its search page, and the paper counts come from its records. | openalex.org · counts retrieved 2 November 2026; search link checked 3 November 2026 |
Next
Sources and further reading
- Priem, J., Piwowar, H. and Orr, R. (2022). OpenAlex: A fully-open index of scholarly works, authors, venues, institutions, and concepts. arXiv:2205.01833.
- Sovacool, B. K., Axsen, J. and Sorrell, S. (2018). Promoting novelty, rigor, and style in energy social science: Towards codes of practice for appropriate methods and research design. Energy Research & Social Science 45, 12–42.
Spotted an error, or stuck? Write to hlli@ksu.edu. Corrections are made in place and dated here.
Text licensed CC BY 4.0, code MIT: teachers and students may reuse and adapt it, with credit to Honglin Li.