Special Purpose Vehicles Emergency Vehicles Keyword Candidate Study

Comparative structure study

The largest recorded reporting jurisdiction, Jiangsu, contains 9 records (12.2% of this 74-record cohort). The remaining cohort contains 65 records. The comparison concerns archive composition.

The recorded chain role label Automotive and transport equipment / Complete systems and use-case applications accounts for 36.5% here (27/74), compared with 3.7% in the parent set (712/19,297), an absolute gap of 32.8 percentage points.

24/74 companies link to pages with existing source-supported excerpts (32.4% of the cohort). The other 50 records remain usable for archive composition, but do not support new operating case studies.

An ID-deduplicated archive snapshot, not the whole market, current recognition or operating results.

The parent set, All public archive records, contains 19,297 records. This cohort contributes 74 (0.4%); 19,223 records remain after excluding it.

Ratio of label participation, not market share; multi-label counts overlap.

Excluding this cohort, the recorded chain role label Automotive and transport equipment / Complete systems and use-case applications covers 685/19,223 remaining records (3.6%), an absolute gap of 32.9 percentage points from this cohort.

The full reporting jurisdiction distribution has 20 labels; the top 3 contain 23/74 records (31.1%).Other known labels contain 51 records.

For 74 records with known labels, the effective category count is 14.5, versus 20 actual labels. Definitions and scope are in the methodology.

The table shows ten labels; 10 others are included in the calculations above.

Record counts do not rank supply capability or economic size.

The largest intersection, Jiangsu × Advanced equipment, contains 8 records, or 88.9% of the 9 records carrying the Jiangsu label.

A cell contains company IDs carrying both labels. It does not establish procurement, supply or investment relationships between firms. Multi-label cells may overlap.

This page displays 8 of the full cross-tab’s 45 non-empty cells; the other 37 remain in the analysis.

Non-empty fields are not necessarily accurate or supported by originals.

Counts concern existing reviewed bilingual excerpts, not re-certification of originals or entire reports.

A rule hit prioritises review; it does not establish that the entire field is wrong. One record can trigger several rules.

An ID may match several fields, so counts may exceed the cohort total. Matches establish search relevance only.

Among the translated non-broad query terms, “special-purpose vehicles” has the most matches: 54/74. Name, product and sub-industry fields match 1, 15 and 43 records respectively; the largest field is sub-industry label. Fields can overlap, and a term remains a search link rather than verified business activity.

Selected broad terms and specific segment terms, not the full query. Terms and fields can overlap and must not be summed. Counts show lexical sensitivity; no records are removed without primary evidence.

9 of this cohort’s 74 records link through existing company-ID mappings to 2026 lists from all jurisdictions (12.2%). This intersection measures list linkage, not successful recognition or company growth.

List rows and company IDs are separate counts, not new firms or final recognition.

Shared records with Sanitation Equipment Waste Sorting Keyword Candidate Study: 5; records in this cohort but outside that set: 69. The intersection/union ratio is 6.3%, a measure of label overlap rather than business collaboration. Read comparison

Shared records with Automotive and transport equipment / Core components and digital hardware Supply-chain Study: 21; records in this cohort but outside that set: 53. The intersection/union ratio is 3.8%, a measure of label overlap rather than business collaboration. Read comparison

Stable-ID samples, not rankings; metrics use the full cohort.