International Recommendations for Industrial Statistics (IRIS 2008)

The International Recommendations for Industrial Statistics 2008 (UN 2009b) is a companion set of international statistical recommendations, covering the general economic-statistics domain that energy statistics draws on and is recommended to be harmonized and coordinated with [IRES, Ch. II, para. 2.7, PDF p. 31, 2018]. IRES lists it, alongside ISIC/CPC/HS harmonization and the (then-forthcoming) SEEA-Energy, among the integrated approaches the 2011 revision aimed to support [IRES, Ch. I, para. 1.23(a)–(f), PDF pp. 18–19, 2018]. It is recommended that national energy data collection be organized in close collaboration with programmes following IRIS (as well as the International Recommendations for Distributive Trade Statistics 2008 and the International Merchandise Trade Statistics, Rev. 2), to avoid duplication of effort and ensure the overall coherence of official statistics [IRES, Ch. II, para. 2.7, PDF p. 31, 2018].

Role in Chapter VI

IRIS is the pointer-of-record for Chapter VI’s economic performance data items (prices, taxes, subsidies, gross output, employment, gross fixed capital formation): because these data items are generally collected as part of general economic statistics, “further reference and detail” on them is provided in IRIS rather than reproduced in IRES itself [IRES, Ch. VI, para. 6.74, PDF p. 96, 2018]. IRIS is also named as the source of further detail on the “type of legal organization and type of ownership” statistical-unit characteristic [IRES, Ch. VI, para. 6.34, footnote 54, PDF p. 90, 2018].

Role in Chapter VII

Chapter VII cites IRIS substantively and repeatedly as the reference standard for survey and compilation practice in energy statistics. In survey design, the identification of information needs and specific goals should take into account relevant international recommendations, including those published in IRIS 2008 [IRES, Ch. VII, para. 7.40, PDF p. 107, 2018]. IRIS 2008, paras 6.19–6.24, is cited for a description of the advantages and disadvantages of mixed household-enterprise surveys [IRES, Ch. VII, para. 7.51, footnote 61, PDF p. 109, 2018]. In data compilation, IRIS is cited for more information on the different techniques used [IRES, Ch. VII, para. 7.61, footnote 62, PDF p. 110, 2018], and, for imputation specifically, IRIS Chapter VI.B.2 is cited for more on imputation options in the case of item non-response or unit non-response [IRES, Ch. VII, para. 7.66, footnote 63, PDF p. 111, 2018].

Recommendation (7.67): it is recommended that compilers of energy statistics use imputation methods that comply with the general requirements set out in international recommendations for other domains of economic statistics, such as IRIS (UN 2009b) [IRES, Ch. VII, para. 7.67, PDF pp. 111–112, 2018] — recommendations tracker row VII/7.67. See Statistical Data Sources and Data Compilation Methods for the full Chapter VII treatment.

confidence: medium — this page currently covers only IRIS’s role as a cross-referenced pointer within IRES (Chapters I, II, VI and VII); its own scope, structure and content are not drawn from the source, since IRIS itself is outside this project’s corpus (see CLAUDE.md’s Domain Notes on scope).

Related

• IRES Chapter VI — Statistical Units and Data Items — the Chapter VI digest this page supports
• IRES Chapter VII — Data Collection and Compilation — the Chapter VII digest recording IRIS’s role in survey design, mixed household-enterprise surveys, and imputation
• IRES Chapter II — Scope of Energy Statistics — the Chapter II digest recording IRIS’s harmonization role
• Economic Performance Data Items — the Chapter VI section IRIS is the detail-of-record for
• Scope of Energy Statistics — the Chapter II concept page recording the recommendation to collaborate with IRIS-based programmes
• Statistical Unit Characteristics — the legal-organization/ownership characteristic IRIS is cited as elaborating
• Statistical Data Sources — survey design and mixed household-enterprise surveys, drawing on IRIS
• Data Compilation Methods — imputation and compilation techniques, drawing on IRIS