Effect of Technology Change on \(\hbox {CO}_{2}\) Emissions in Japan’s Industrial Sectors in the Period 1995–2005: An Input–Output Structural Decomposition Analysis


This paper employs two-stage input–output structural decomposition analysis (SDA) to identify the factors responsible for changes in Japan’s \(\hbox {CO}_{2}\) emissions for two periods: 1995–2000 and 2000–2005. First, the study decomposes the total change in \(\hbox {CO}_{2}\) emissions for each period to obtain the contribution of change in \(\hbox {CO}_{2}\) emissions per unit output \((\hbox {CO}_{2}\) emissions coefficient), change in technology (technology effect), and change in final demand. The study observed from the first-stage decomposition that emissions coefficient and final demand drive the change in the first period (1995–2000) while the technology effect drives the change in the second period (2000–2005). The high contribution of the technology effect is driven by activities of iron and steel; coke, refined petroleum and gas; road transportation; and electricity sectors. Having observed the trend of the technology effect across the two periods, the study carried out a second-stage decomposition on technology effect in the second period to examine the contribution of each sector and observed that chemical and pharmaceuticals; iron and steel; road transportation; and construction sectors are mainly responsible. In conclusion, improvement in technical efficiency especially at the industrial process level of each industry will help Japan achieve greater level of \(\hbox {CO}_{2}\) emissions reduction.

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  1. 1.

    Annex 1 Countries include the industrialised countries that where members of the Organisation for Economic Co-operation and Development (OECD) in 1992, plus countries with economies in transition (the EIT parties), including the Russian Federation, the Baltic States, and several Central and Eastern States.

  2. 2.

    2007 and 2009 United Nations Climate Change conferences in Bali, Indonesia and Copenhagen, Denmark, respectively.

  3. 3.

    The amendment will enter into force after ratification by member states.

  4. 4.

    These are the most recent data on Japan.

  5. 5.

    The methodological underpinning of structural path analysis (SPA) involves carrying out a Taylor’s expansion of the Leontief inverse matrix, (I-A)\(^{-1}\), such that changes in key variables may be observed at the process level instead of country level aggregates. However, SPA is applied statically, so when combined with SDA as in structural path decomposition, it provides a robust method for tracing the path of change in variables over time. Methodological development of SPA can be found in Lenzen (2003). SPD and SPA are different from two-stage SDA which only performs a second-level decomposition of the effects obtained from the traditional SDA decomposition.

  6. 6.

    Equation (3) is obtained by substituting \({\varvec{L}}^{\mathbf{1}}= {\varvec{L}}^{\mathbf{0}}+\varvec{\varDelta } {\varvec{L}},\, f^{1}= f^{0}+ \varDelta f\), and \(\hat{\mathbf{e}}^{\mathbf{0}}= \hat{\mathbf{e}} ^{\mathbf{1}}-\varvec{\varDelta } \hat{\mathbf{e}}\) in Eq. (2) to get

    $$\begin{aligned} \varvec{\varDelta } \varvec{\varepsilon }&= \hat{\mathbf{e}}^{\mathbf{1}}({\varvec{L}}^{0}+\varvec{\varDelta } L)\left( {f^{0}+ \varDelta f} \right) -(\hat{\mathbf{e}}^{\mathbf{1}}-\varvec{\varDelta } \hat{{\varvec{e}}} ){\varvec{L}}^{\mathbf{0}}f^{0}\\&= \hat{\mathbf{e}}^{1}{\varvec{L}}^{\mathbf{0}}f^{0}+ \quad \hat{\mathbf{e}}^{\mathbf{1}}{\varvec{L}}^{\mathbf{0}}\varDelta f+\hat{\mathbf{e}}^{\mathbf{1}}\varvec{\varDelta } {\varvec{L}} f^{0}+\hat{\mathbf{e}}^{\mathbf{1}}\varvec{\varDelta } {\varvec{L}}\varDelta f-\hat{\mathbf{e}}^{\mathbf{1}}{\varvec{L}}^{\mathbf{0}}f^{0}+ \varvec{\varDelta } \hat{{\varvec{e}}}{\varvec{L}}^{\mathbf{0}}f^{0}\\&= \hat{\mathbf{e}}^{1}{\varvec{L}}^{\mathbf{0}}\varDelta f+\hat{\mathbf{e}}^{1}\varvec{\varDelta } {\varvec{L}}f^{0}+\hat{\mathbf{e}}^{1}\varvec{\varDelta } {\varvec{L}} \varDelta f+\varvec{\varDelta } \hat{{\varvec{e}}}{\varvec{L}}^{\mathbf{0}}f^{0}.\, \hbox {By rearranging we have}\\&= \varvec{\varDelta } \hat{{\varvec{e}}}{\varvec{L}}^{\mathbf{0}}f^{0}+\hat{\mathbf{e}}^{\mathbf{1}}\varvec{\varDelta } {\varvec{L}}f^{0}+\hat{\mathbf{e}}^{1}{\varvec{L}}^{\mathbf{0}}\varDelta f+\hat{\mathbf{e}}^{1}\varvec{\varDelta } {\varvec{L}}\varDelta f\\&= \varvec{\varDelta } \hat{{\varvec{e}}}{\varvec{L}}^{\mathbf{0}}f^{0}+\hat{\mathbf{e}}^{1}\varvec{\varDelta } {\varvec{L}}f^{0}+\hat{\mathbf{e}}^{\mathbf{1}}({\varvec{L}}^{0}+\varvec{\varDelta } {\varvec{L}})\varDelta f=\varvec{\varDelta } \hat{{\varvec{e}}}{\varvec{L}}^{\mathbf{0}}f^{0}+\hat{\mathbf{e}}^{\mathbf{1}}\varvec{\varDelta } {\varvec{L}}f^{0}+\hat{\mathbf{e}}^{\mathbf{1}}{\varvec{L}}^{\mathbf{1}}\varDelta f \end{aligned}$$

    Similarly, Eq. (4) is obtained by substituting \(\hat{\mathbf{e}} ^{\mathbf{1}}= \hat{\mathbf{e}}^{\mathbf{0}}+\varvec{\varDelta } \hat{{\varvec{e}}};\, {\varvec{L}}^{\mathbf{0}}={\varvec{L}}^{\mathbf{1}}- \varvec{\varDelta } {\varvec{L}}\), and \(f^{0}= f^{1}- \varDelta f\) into Eq. (2)

  7. 7.

    For example the first term in Eq. (3) i.e. \(\varvec{\varDelta } \hat{{\varvec{ e}}}{\varvec{L}}^{\mathbf{0}}f^{0}\) captures the effect of change \(\hbox {CO}_{2}\) emissions coefficient relative the technological structure and final demand of year 0 while the first term in Eq. (4) i.e. \(\varvec{\varDelta } \hat{{\varvec{e}}} {\varvec{L}}^{\mathbf{1}}f^{1}\) captures the effect of change in \(\hbox {CO}_{2}\) emissions coefficient relative to the technological structure and final demand of year 1.

  8. 8.

    Dividing a matrix by a constant implies dividing each element in the matrix by the constant.

  9. 9.

    The final demand pattern refers to the overall change in the final demand component of the the input–output matrix which may be as a result of change in the overall level of final demand or change in the relative proportions of expenditure on the various goods and services in the final-demand vector (the final-demand mix).

  10. 10.

    The table has been transposed for ease of presentation. The transposed rows are highlighted in the complete result in “Appendix B”. Observe that the totals in the last row in Table 3 are exactly the same as the values for coke, refined petroleum and gas; iron and steel; road transport; and electricity which are in bold in the technology effect column in Table 2.

  11. 11.

    Road transport; chemical and pharmaceutical; and construction sectors are discussed in subsequent paragraphs.


\(\mathrm{CO}_{2}\) :

Carbon dioxide


Greenhouse gas


Greenhouse gas inventory office


Global wind energy council


International Energy Agency


Index decomposition analysis


Institute of Energy Economics, Japan


Japan iron and steel federation


Kyoto protocol


Ministry of Environment


National Institute for Environmental Studies


Organization for Economic Co-operation and Development


Structural decomposition analysis


United Nations Framework Convention on Climate Change


Structural Path Analysis


Structural Path Decomposition


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Correspondence to Uduak S. Akpan.


Appendix A

See Table 4.

Table 4 Classification of sectors

Appendix B

See Table 5.

Table 5 Second stage decomposition of technology effect of all the sectors

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Akpan, U.S., Green, O.A., Bhattacharyya, S. et al. Effect of Technology Change on \(\hbox {CO}_{2}\) Emissions in Japan’s Industrial Sectors in the Period 1995–2005: An Input–Output Structural Decomposition Analysis. Environ Resource Econ 61, 165–189 (2015). https://doi.org/10.1007/s10640-014-9787-7

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  • \(\hbox {CO}_{2}\) emissions
  • Input–output
  • Structural decomposition analysis
  • Technology change
  • Japan