Introduction
Methodology
Index i | Sector | Description (NAICS codes in brackets) |
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Business economy sectors | ||
OG | Oil and gas industry | Oil and gas extraction (211, 213), refineries (324) and pipelines (486). Production includes conventional oil and gas (211113) and nonconventional oil (211114) |
PG | Power generation | Electricity production from all sources (2211) |
EII | Energy intensive industry | Mining (212), aluminium smelting (3313), steel making (3311, 3312), primary metals smelting (3314), cement making (3273), pulp and paper (3221) and energy intensive chemical production (3251, 3252 and 3253) |
MOI | Manufacturing and other industry | This is a residual category that includes all manufacturing not included elsewhere, plus agriculture, fishing, forestry and construction (NAIC codes 11–39 unless specified above as part of the oil and gas, electric power or energy intensive manufacturing sectors.) |
CI | Commercial and institutional | Including offices, retail, education, hospitals, warehouses, water and sewage utilities, other building types (41 through 91, unless specified as part of other sectors). |
FT | Freight transportation | Truck, rail, air, marine (48 and 49 except pipelines (486) which are included as part of the oil and gas sector and warehouses (493) which are included as part of the commercial and institutional sector). |
Household sectors | ||
Res | Residential | Four subsectors defined by dwelling type: single family detached, attached, apartments, and other. |
PT | Personal transportation | Car, truck, air, rail, public transit, other modes, non-motorised |
Physical activity drivers
Business Sector energy intensity factorisation | ||
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Sectors | Equations | Factors, subsector and index definitions |
Business sectors (factorisation based on Ei/GDPi) | ||
Oil and gas (OG) |
\( \frac{E_{\mathrm{OG}}}{{\mathrm{GDP}}_{\mathrm{OG}}}=\sum \limits_j\left(\frac{E_j}{{\mathrm{GDP}}_j}\times \frac{{\mathrm{GDP}}_j}{{\mathrm{GDP}}_{\mathrm{OG}}}\right) \)
| Energy intensities (Ej/GDPj) for j subsectors where J is extraction (ext), pipelines (pl) or refineries (ref). |
Extraction (j = ext) |
\( \frac{E_{\mathrm{ext}}}{{\mathrm{GDP}}_{\mathrm{ext}}}=\frac{{\mathrm{Eo}}_{\mathrm{ext}}}{{\mathrm{GDP}}_{\mathrm{ext}}}\times \sum \limits_k\left({\left(\frac{E_{\mathrm{ext}}}{{\mathrm{Eo}}_{\mathrm{ext}}}\right)}_k\times \frac{{\left({\mathrm{Eo}}_{\mathrm{ext}}\right)}_k}{{\mathrm{Eo}}_{\mathrm{ext}}}\right) \)
| PAD ≡ production output (Eoext, PJ) and energy used in resource extraction (Eext, PJ) for k resources where k is conventional oil, unconventional oil or natural gas. |
Pipelines (j = pl) |
\( \frac{E_{\mathrm{pl}}}{{\mathrm{GDP}}_{\mathrm{pl}}}=\frac{{\mathrm{Eo}}_{\mathrm{pl}}}{{\mathrm{GDP}}_{\mathrm{pl}}}\times \sum \limits_l\left({\left(\frac{E_{\mathrm{pl}}}{{\mathrm{Eo}}_{\mathrm{pl}}}\right)}_l\times \frac{{\left({\mathrm{Eo}}_{\mathrm{pl}}\right)}_l}{Eo_{pl}}\right) \)
| PAD ≡ Product piped (Eopl, PJ) and energy used in pipelining resource (Epl, PJ) for l products where l = oil or gas. |
Oil refineries (i = ref) |
\( \frac{E_{\mathrm{ref}}}{{\mathrm{GDP}}_{\mathrm{ref}}}=\frac{{\mathrm{Eo}}_{\mathrm{ref}}}{{\mathrm{GDP}}_{\mathrm{ref}}}\times \frac{E_{\mathrm{ref}}}{{\mathrm{Eo}}_{\mathrm{ref}}} \)
| PAD ≡ crude oil refined (Eoref, PJ) and energy used by oil refineries |
Power generation (PG) |
\( \frac{E_{\mathrm{PG}}}{{\mathrm{GDP}}_{\mathrm{PG}}}=\frac{{\mathrm{Eo}}_{\mathrm{PG}}}{{\mathrm{GDP}}_{\mathrm{PG}}}\times \sum \limits_m\left({\left(\frac{E_{\mathrm{PG}}}{{\mathrm{Eo}}_{\mathrm{PG}}}\right)}_m\times \frac{{\left({\mathrm{Eo}}_{\mathrm{PG}}\right)}_m}{{\mathrm{Eo}}_{\mathrm{PG}}}\right) \)
| PAD ≡ electricity produced (EoPG, kWh) for m types of power plant/fuel combinations. The primary energy consumption of power generation (EPG) is the sum of the primary energy consumed by the different types of power plants. For fossil fuel generation, it is the difference between the fuel inputs and the electricity produced. For hydro, solar and wind, EPG is assumed to equal EoPG. For nuclear power, EPG is assumed to equal EoPG/0.3. |
Energy intensive industry (EII) |
\( \frac{E_{\mathrm{EII}}}{{\mathrm{GDP}}_{\mathrm{EII}}}=\sum \limits_n\left(\frac{E_n}{{\mathrm{GDP}}_n}\times \frac{{\mathrm{GDP}}_n}{{\mathrm{GDP}}_{\mathrm{EII}}}\right) \)
| No PAD defined, but sector intensity disaggregated for n subsectors, where n is mining, steel, aluminium, primary metals, industrial chemicals, cement, pulp & paper |
Manufacturing and other industry (MOI) | No reliable PAD data are available for these industries (including general manufacturing and assembly, agriculture, construction, fishing and logging) so further decomposition analyses are not carried out here. | |
Commercial and institutional (CI) |
\( \frac{E_{\mathrm{CI}}}{{\mathrm{GDP}}_{\mathrm{CI}}}=\frac{A_{\mathrm{CI}}}{{\mathrm{GDP}}_{\mathrm{CI}}}\times \sum \limits_p\left({\left(\frac{E_{\mathrm{CI}}}{A_{\mathrm{CI}}}\right)}_p\times \frac{{\left({A}_{\mathrm{CI}}\right)}_p}{A_{\mathrm{CI}}}\right) \)
| PAD ≡floor area (ACI) for p subsectors where p is wholesale, retail, warehouse, cultural, office, education, health, recreation, accommodation or other. |
Freight transport (FT) |
\( \frac{E_{\mathrm{FT}}}{{\mathrm{GDP}}_{\mathrm{FT}}}=\frac{\mathrm{TKT}}{{\mathrm{GDP}}_{\mathrm{FT}}}\times \sum \limits_q\left({\left(\frac{E_{\mathrm{FT}}}{\mathrm{TKT}}\right)}_q\times \frac{{\mathrm{TKT}}_q}{\mathrm{TKT}}\right) \)
| PAD ≡ tonne-km travelled (TKT) for q types of vehicles where q is heavy trucks, medium trucks, light trucks, air, rail or marine. |
Sectors | Equations | Factors, subsector and index definitions |
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Residential (Res) |
\( \frac{E_{\mathrm{Res}}}{\mathrm{capita}}=\frac{D}{\mathrm{capita}}{\sum}_r{\left(\frac{E_{\mathrm{Res}}}{A_{\mathrm{Res}}}\right)}_r{\left(\frac{A_{\mathrm{Res}}}{D}\right)}_r\left(\frac{D_r}{D}\right) \)
| PAD ≡ number of dwellings (D) and floor area (ARes) for r types of residences where r is single detached, single attached or apartments |
Personal Transportation (PT) |
\( \frac{E_{\mathrm{PT}}}{\mathrm{capita}}=\frac{\mathrm{PKT}}{\mathrm{capita}}\sum \limits_s{\left(\frac{E}{\mathrm{PKT}}\right)}_s\left(\frac{{\mathrm{PKT}}_s}{\mathrm{PKT}}\right) \)
| PAD ≡ person kilometres of travel (PKT) for s vehicle types where S is walk, bike, cars, light trucks, rail, air, intercity bus, mass transit or school bus. Note that values for E/PKT by vehicle type are derived by combining data for energy use by vehicle type with data for PKT by vehicle type. |
Assessing variability in trends over the study period
Results and discussion
Oil and gas industry
Power generation
Energy intensive industries
Manufacturing and other industries
Commercial and institutional
Freight transportation
Residential
Personal transportation
Compilation of all sector intensity decomposition results
The bigger picture
Conclusions and policy implications
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With regard to the energy intensity of individual sectors of the economy, the results presented here for the case of Canada from 1995 to 2010 illustrate the extent to which non-energy efficiency factors can distort and even completely obscure the impacts of energy efficiency on sector energy intensities. These intrastructural factors can augment or offset energy efficiency, and the method illustrated here has direct applications in energy efficiency policy development and evaluation.
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At the level of the economy, the decomposition of sector energy intensity into efficiency and intrasectoral structural effects, and the integration of that analysis into the economy-wide decomposition analysis, allows the quantification of the contribution of energy efficiency to changes in E/GDP. This increases the practical utility of decomposition analysis by reconciling energy efficiency metrics with the E/GDP indicator. For example, in the Canadian case used to illustrate the method here, a first order decomposition analysis attributes 28% of the drop in E/GDP from 1995 to 2010 to decreased sector energy intensities. However, the sector decomposition analyses reveal the contribution of energy efficiency to ∆(E/GDP) over this period was at least 37%, before the offsetting effect of intrasectoral structural factors.
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While the focus in this research has been on the quantitative integration of energy efficiency in decomposition analysis of changes in E/GDP, by corollary, it also improves understanding and disaggregation of non-efficiency factors that influence primary energy intensity. To the extent that climate change response policies are expanding beyond energy efficiency and carbon intensity to consider other technological and system changes that can accelerate the transition to low carbon futures, the identification and quantification of intrasectoral structural contributions to lower E/GDP will be useful.
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With regard to the application of this research to foresight analysis, decomposition analysis of ∆(E/GDP) at the sector level provides insights that can be used in the construction of the energy and emission projections embodied in both the baselines and future scenarios used for energy and climate change policy analysis (Shahiduzzaman and Layton 2017). Separately identifying and quantifying efficiency vs. structural impacts on per capita and per GDP energy intensities at the sector and subsector level allows the modeller to also represent these factors separately in projections and scenarios of future energy use and related emissions.
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These results are for the national economy, but the method can be applied in any context where compatible energy, GDP and PAD data are available, including for cities, provinces, states and regions. In the case of Canada, there are significant regional variations in makeup and dynamics of the E/GDP relationship. Most notably, of all the intrasector structural factors that affect the ∆(E/GDP) over the 1995–2010 period, the largest is the growth in bitumen’s share of crude oil production. This occurred only in Alberta, and decomposition of ∆(E/GDP) for individual provinces other than Alberta would therefore be expected to show a relatively larger contribution to sector energy intensity changes from energy efficiency than from intrasectoral structural factors. In Canada, most aspects of energy policy are the domain of provincial governments, and the application of this type of analysis at the provincial level would yield policy insights that may be obscured by the national results presented here.