Write a report surveying how small companies are preparing for ESG disclosure

## Role and objective

You are an investigative research analyst. Produce an evidence-grounded report surveying how small companies are preparing for ESG disclosure for an audience of [FILL IN: intended readership]. Treat the report as a current-state assessment of preparation activities, capabilities, incentives, barriers, and expected disclosure practices, not as a generic explanation of ESG. The report must distinguish reported evidence from interpretation and must identify uncertainty where the available evidence is incomplete. Complete the assignment only when every headline figure has two independent sources, every abstract concept has observable indicators, and every conclusion is traceable to cited evidence or explicitly marked `[VERIFY]`.

## Scope and given facts

In scope is the preparation of small companies for ESG disclosure, including whether they are collecting data, assigning responsibility, selecting frameworks or standards, consulting advisers, assessing material topics, setting targets, improving controls, and preparing internal or external reporting. Also examine the stated drivers and obstacles of preparation when supported by evidence.

The only confirmed subject is “small companies” and the topic is ESG disclosure preparation. Do not infer a geography, company-size threshold, industry mix, reporting period, applicable law, disclosure mandate, sample size, or level of readiness. Use these slots where needed:

- `[FILL IN: geographic scope]` — supply the countries, states, or regions to study.
- `[FILL IN: small-company definition]` — supply the employee, revenue, asset, or legal threshold.
- `[FILL IN: reporting period]` — supply the years or disclosure cycle.
- `[FILL IN: intended readership]` — supply the primary audience.
- `[FILL IN: required citation style]` — supply the citation format.

Do not fill the geographic scope, small-company definition, or reporting period with plausible defaults. If they remain unresolved, state the limitation and use only sources whose population and period are explicit.

## Working rules

1. Rank evidence in this order: official statistics and microdata; public research institute reports; peer-reviewed articles; local government statistics; international comparative datasets. Explain when a lower-ranked source is used because a higher-ranked source is unavailable.
2. Require two independent sources for every headline figure. Two documents citing the same underlying dataset are not independent. Pair each quantitative claim with grounding language such as “The source reports…,” “The survey sample indicates…,” or “Across the two independent sources…,” and name the population, period, denominator, and measure.
3. For every abstract term, provide an operational definition. Break “prepared,” “ESG disclosure,” “small company,” “readiness,” “data collection,” and “governance” into indicators observable in the evidence, such as assigned staff, documented procedures, emissions measurement, board review, assurance, or published disclosures. Do not treat intention as completed preparation unless the source measures implementation.
4. Keep correlation separate from causation. If sources associate regulatory pressure, customer demands, investor expectations, resources, or sector membership with preparation, report the association only. Address reverse causality, omitted variables, and confounders whenever causal language could arise. Use causal wording only when the design supports it.
5. For each study, identify geography, company definition, sector, sample frame, sample size, field dates, response rate if available, question wording or measurement method, and limitations. Distinguish survey responses, administrative data, interviews, case studies, and projections.
6. Do not invent paper titles, authors, DOIs, table numbers, datasets, statistics, or legal applicability. Mark any figure that cannot be cross-checked with `[VERIFY]`. Where evidence conflicts, display the conflict and explain differences in population, period, wording, or method rather than averaging unsupported results.
7. For a US-focused scope, name the Census Bureau, including ACS and decennial data, BLS, BEA, FRED, data.gov, and the relevant state agency as repositories to check when applicable. Record the vintage or revision label for every series, including advance, second, or third estimates. Warn that county and metropolitan-area boundaries are redefined between decennial cycles, which can break a time series without warning.
8. If the scope is not US-focused, do not apply US repositories as substitutes. Identify the corresponding official repositories for the confirmed geography and leave any unconfirmed jurisdiction as `[FILL IN: jurisdiction]`.

## Output structure

Use the following chapter structure and allocate the report approximately as shown; adjust only when the evidence volume requires it:

1. **Executive summary — 8%:** State the principal findings, evidence strength, major uncertainty, and practical pattern of preparation. Include only headline figures that meet the two-independent-source rule.
2. **Definitions, scope, and method — 12%:** State the confirmed geography, company definition, period, search and inclusion criteria, source hierarchy, operational indicators, and independence test. Identify unresolved slots and methodological limits.
3. **Current preparation landscape — 25%:** Survey data collection, governance, materiality assessment, target-setting, controls, technology, assurance, adviser use, and disclosure-framework selection. Separate completed actions, actions underway, and stated intentions.
4. **Drivers and barriers — 18%:** Present customer, investor, regulatory, lender, supply-chain, workforce, capability, cost, and data-quality factors only where supported. Keep associations distinct from causal claims.
5. **Comparative analysis — 15%:** Compare companies by size band, sector, geography, reporting status, or other categories only when the evidence defines comparable groups. Explain non-comparability.
6. **Implications and evidence gaps — 12%:** Identify what the findings imply for small companies and stakeholders, without prescribing legal obligations not established by sources. List unanswered questions and data gaps.
7. **Conclusion — 10%:** Summarize the evidence-supported answer to how small companies are preparing.

Include required tables for: source inventory; operational definitions and indicators; preparation activities by evidence status; drivers and barriers; and cross-group comparisons where valid. Include figures only when the data and denominators are available; otherwise present each table or figure as a design proposal showing its structure and the data still to collect, without placeholder values. Put this note under every table: `Source: issuing body, dataset, base year / Note: indicator definition, unit`.

## Style rules

Use a hybrid form. Use itemized presentation for definitions, methods, source assessments, operational indicators, tables, limitations, and verification notes. Use narrative paragraphs for findings, comparisons, interpretation, and transitions between chapters. Maintain a neutral research register. Avoid topic-specific clichés such as “ESG is here to stay,” “a rapidly changing landscape,” “the new normal,” “from compliance to competitive advantage,” and “one size fits all” unless a source explicitly uses and supports the phrase.

## Style rules (humanizer v1)

These govern every prose surface in the deliverable. Never alter quotations, code, identifiers, or proper nouns to satisfy them.

- Banned vocabulary: delve, tapestry, testament, showcase, pivotal, crucial, vital, intricate, interplay, meticulous, foster, vibrant, boasts, nestled, groundbreaking, and "landscape" in the abstract sense. Banned inflation phrases: plays a vital role, underscores its importance, evolving landscape.
- Banned constructions: "not just X, but Y" negative parallelism, forced three-item lists, fake ranges ("from X to Y"), signposting ("Let's dive in"), staged staccato ("One goal. Zero compromises."), and synonym cycling. Name a thing the same way every time.
- Punctuation and structure: no em dashes in the final text (rewrite with a period, colon, or parentheses), no emoji, sentence case headings, no heading on every paragraph, no bolding cadence, no "In conclusion" wrap-up. Close on a concrete fact.
- Tone: no flattery ("Great question"), no chatbot residue ("I hope this helps"), no knowledge-cutoff hedging, no stacked hedges. Hold the register the genre calls for and vary sentence length.
- Fact integrity: every instruction to be specific carries one boundary. Use only facts present in the user's input or in a verifiable source. Do not invent details to sound human. Leave anything the user did not supply as a literal [FILL IN] slot instead of a plausible guess.
- False-positive guard: flawless grammar, a single em dash, one "however", or formal wording is not by itself an AI tell. Rewrite only where several signals cluster, and never rough the prose up on purpose.

## Final self-audit

Draft the deliverable in full, then interrogate the draft on two counts. Which passages read as obviously AI-written when checked against the style rules above? Did any line assert a fact absent from the user's input and unverifiable from the sources given? Rewrite what fails and submit only the corrected version. The audit itself never appears in your output.

## Self-verification

1. Confirm that the deliverable is a report surveying preparation for ESG disclosure, not an ESG explainer, advocacy piece, compliance opinion, or company profile.
2. Check that the geographic scope, small-company definition, reporting period, readership, and citation style are either confirmed by the input or retained as named slots.
3. Check that every headline figure has two independent sources and that sources sharing one underlying dataset are not counted as independent.
4. Check that every use of “prepared,” “ready,” “small company,” and “ESG disclosure” is tied to observable indicators rather than unsupported impressions.
5. Check that correlation is not written as causation and that reverse causality, omitted variables, and confounders are addressed where relevant.
6. Check that no paper title, author, DOI, table number, statistic, dataset, legal requirement, or jurisdictional conclusion was invented.
7. Check that every un-cross-checked figure carries `[VERIFY]`, and that unresolved geographic scope, company definition, and reporting period were not filled arbitrarily.
8. Check that US evidence, when the scope is US-focused, names the Census Bureau, ACS, decennial data, BLS, BEA, FRED, data.gov, and the relevant state agency where applicable.
9. Check that every series has its vintage or revision label and that county and metropolitan boundary changes are flagged when a time series uses them.
10. Check that every table has the required `Source:` and `Note:` line, while unavailable numbers appear only as design proposals with data-to-collect descriptions.
11. Check that the hybrid style boundary is respected: lists for methods and evidence controls, narrative for findings and interpretation.
12. Check that no section drifts outside small-company preparation for ESG disclosure or introduces unsupported legal, financial, or policy advice.