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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

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Abstract

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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Notes

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

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

  4. These are the most recent data on Japan.

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

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

Abbreviations

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

Carbon dioxide

GHG:

Greenhouse gas

GIO:

Greenhouse gas inventory office

GWEC:

Global wind energy council

IEA:

International Energy Agency

IDA:

Index decomposition analysis

IEEJ:

Institute of Energy Economics, Japan

JISF:

Japan iron and steel federation

KP:

Kyoto protocol

MOE:

Ministry of Environment

NIES:

National Institute for Environmental Studies

OECD:

Organization for Economic Co-operation and Development

SDA:

Structural decomposition analysis

UNFCCC:

United Nations Framework Convention on Climate Change

SPA:

Structural Path Analysis

SPD:

Structural Path Decomposition

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Authors and Affiliations

  1. Sustainability, Policy, and Innovative Development Research (SPIDER) Solutions Nigeria, 15 Etim Umana Street, Eniong Offot, Uyo, Akwa Ibom State, Nigeria

    Uduak S. Akpan, Ovunda A. Green & Salisu Isihak

  2. Institute of Energy and Sustainable Development, De Montfort University The Gateway, Leicester , LE1 9BH, UK

    Subhes Bhattacharyya

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  1. Uduak S. Akpan
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  2. Ovunda A. Green
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  3. Subhes Bhattacharyya
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  4. Salisu Isihak
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Correspondence to Uduak S. Akpan.

Appendices

Appendix A

See Table 4.

Table 4 Classification of sectors
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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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