requires energy consumption data by volumes of energy consumed (preferably by fuel). These can be derived from the value data contained in input-output tables, as each table contains rows corresponding to the value of energy inputs into the various sectors. The process of converting these value data to volumes using price data is unreliable as users rarely face the same prices and detailed information on the prices users pay for their energy is often commercially sensitive and difficult to obtain.
The core energy data from the Structural Survey are disaggregated into 27 industries. Since this covers only manufacturing, mining and retail/wholesale, this level of aggregation is closely approximated by the input-output tables aggregated to 46 sectors which cover 28 industrial sectors plus 18 non-industrial sectors.
MITI has recently released a set of tables providing 46 sector reclassified and consistent input-output tables (both nominal and real) from 1960 to 1985, using the same classifications as in the 1990 data. This data set (MITI 1993b, 1992a) is the basis for the input-output data used in this study. Before this series was compiled, the most useful data for analysis over time were the Linked Input-Output Tables which cover sets of three years at five-yearly intervals (such as the 1975-80-85 linked tables, for example) and correlate data over a ten-year period. While considerably more aggregated than the Linked Tables, this 46 sector series of tables extends the consistent data series to provide a new and very useful 25 year sequence of tables which is readily updated.
Using input-output tables for energy analysis requires matching the tables with energy data by combining some sectors and expanding others. To derive maximum usefulness from this data set in terms of energy analysis, the important energy sector
'electricity, gas and heat supply' used in the 46 sector tables has been disaggregated in this study to cover 'electricity' and 'gas and heat supply' separately. The separate electricity and gas/heat supply data were drawn from tables at the next level of disaggregation — 80 sector tables for 1990 and 1985 and 85 sector tables for 1980.
The sectors for which disaggregated energy consumption data are not
available, and which are less important from an energy analytic point of view (mostly non-manufacturing sectors) have been aggregated. The final 28 sectors used in this thesis are shown below (Table 3.3).
Table 3.3 28-sector classification SECTOR
NUMBER
SECTOR 1 Agriculture and forestry 2 Fisheries
3 Mining (metals and non-metals) 4 Coal, oil and gas extraction 5 Food and drink products 6 Textiles and clothing
7 Furnishings and wood products 8 Pulp and paper
9 Printing and publishing 10 Chemicals
11 Oil and refinery products 12 Plastics
13 Rubber products
14 Porcelain, ceramics and cement 15 Steel
16 Non-ferrous metals
17 Processed metal products 18 General machinery
19 Electrical machinery and appliances 20 Transportation machinery and equipment 21 Precision machinery
22 Other manufactures (incl leather but not plastics) 23 Building and construction
24 Electricity 25 Gas and heat 26 Transport
27 Business and services
28 Not elsewhere included (NEI) 29 Intermediate total
Source: Aggregated from the 46 sector tables in MITI 1992a. Details of aggregation in Appendix B
Most of the Japanese input-output data tables include the Leontieff inverse matrices described in Chapter 2 for the standard tables. Restructuring and
reclassifying the tables in the way described above and in Appendix B, however, means that the standard inverse matrices cannot be used and the coefficients of production need to be re-calculated, and this is done in Chapter 5.
The input-output data for Japan takes the form of commodity tables showing transactions between industries, governments and households. The tables used in this
study are those showing the values of transaction of these commodities at the shipment prices of producers ('producers prices'), so that transportation fees are included under the two sectors transportation and commerce. The Japanese tables are constructed on a competitive imports basis, allowing separation of imported inputs from similar commodities domestically produced. According to the explanatory notes from 1975-80-85 Linked Input-Output Tables, the term competitive is used in a book- entry sense and does not equate with the strict economic meaning.
Input-output commodity tables [butsuryohyo] derived from the value tables are also available, in disaggregated form. While these are not useful in this study because of the need for the restrictive assumption that all consumers of goods face the same set of prices, they have been used extensively in detailed modelling of Japan's CO2 emissions and the likely impacts on emissions of changes in levels and shares of output (Yoshioka 1992).
INDUSTRY OUTPUT DATA
The structural decomposition analysis introduced in Chapter 2 and carried out in the following chapter decomposes energy intensities in Japan. The energy data discussed in the section above need to be matched with output data for the relevant sectors of the economy. Decomposition of aggregate output data confirms that the bulk of the change in energy intensity is due to developments in the manufacturing sector, and disaggregated data on the outputs from this sector are essential in making best use of the disaggregated energy consumption data.
For detailed work on the manufacturing sector, and to derive intensities of energy use and changes in this index over time, the Kogyo Tokei Hyo [Census of Manufactures] data are the most appropriate as these data most closely resemble the
Structural Survey energy data in coverage. The Census has several volumes with different classifications, the most useful for this study is the 'Report by Industries'. The Census was first compiled in 1951 from data provided by enterprises under law. Enterprises with four or more employees are covered every year, with limited surveys of those employing three or less people compiled approximately every second year. The census data need to be deflated with a GDP deflator, a process used in other studies and Boyd et al (1987) give a number of examples.