Applying the hypothetical extraction method to investigate the interindustry greenhouse gas emissions linkages in the Greek economy

ABSTRACT

This paper examines the interindustry greenhouse gas (GHG) emissions linkages within the Greek economy for the year 2018, using a symmetric I-O table and air emissions accounts data. The GHG linkages among industries are analyzed both from a demand and supply perspective by applying an environmental modification of the Dietzenbacher and van der Linden’s Hypothetical Extraction Method (1997), as introduced and discussed in Tsirimokos (2022). The findings reveal that: (i) the ‘Electricity, gas, steam, and air conditioning supply’ industry exhibits the highest total absolute backward and forward linkages; (ii) the ‘Real estate activities’ industry demonstrates the highest relative backward and forward linkages; (iii) industries with low (high) relative linkages generally, exhibit high (low) internal linkages; and (iv) the ‘Electricity, gas, steam, and air conditioning supply’ and ‘Transportation and storage’ industries are the primary GHG emissions contributors within the Greek economy. These empirical results can inform the development of effective environmental policies, supporting Greece in achieving its climate action plan objectives.

KEYWORDS:

• Input-output analysis
• hypothetical extraction method (HEM)
• demand and supply linkages
• greenhouse gas (GHG) emissions
• Greece

Acknowledgments

The author appreciates the valuable comments and suggestions provided by the anonymous reviewer. Additionally, the author expresses gratitude towards Professor Theodore Mariolis, Panteion University for his invaluable feedback and critique on this paper.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The 2018 symmetric national input-output table (NIOT) for the Greek economy obtained from the Organisation for Economic Co-operation and Development Structural Analysis Statistics (OECD STAN) (https://doi.org/10.1787/f9a66c3a-en) (OECD 2022a), while the 2018 GHG emissions data where sourced from the OECD Air Emissions Accounts (https://doi.org/10.1787/data-00735-en) (OECD 2022b).

Notes

1 For a deeper understanding of the intrinsic controversies between the TLM and HEM, see Cai and Leung (2004). For an analytical and empirical comparison of various linkage measures based both on TLM and HEM, see Temurshoev and Oosterhaven (2014).

2 For a detailed description of the HEM analysis covering all possible extraction cases, see Miller and Lahr (2001).

3 This term is often used interchangeably with greenhouse gas emissions, as carbon dioxide (CO₂) is the most common greenhouse gas.

4 Matrices are indicated by boldfaced capital letters (e.g. A), column vectors are indicated by boldfaced lowercase letters (e.g. x), letter ‘T’ indicates transposition (e.g. x^T), a symbol of hat ‘^’ indicates a diagonal matrix (e.g. $\hat{\mathbf{x}}$) with the elements of a vector on its main diagonal and all other entries equal to zero, scalars (including elements of matrices or vectors) are indicated by italicized lowercase letters (e.g. e), I denotes the n x n identity matrix and finally i denotes the summation vector (e.g. ${\mathbf{i}}^T\equiv [1,1,\dots ,1{]}^T$).

5 $\mathbf{q}\equiv \left[q_j=c_j/x_j\right]$ denotes the amount of GHG that is emitted directly by industry j per unit of industry j’s worth of output. The direct GHG emissions of the industries by a demand-driven perspective is obtained by $\mathbf{c}=\hat{\mathbf{q}}\mathbf{L}\mathbf{f}$, where c is the n x 1 direct GHG emissions vector. The column sum of c (i.e., ${\mathbf{i}}^{\mathrm{T}}\mathbf{c}$) determines the economy’s overall direct GHG emissions level by a demand-driven perspective. The total (direct and indirect) demand-driven GHG emission intensities are given by ${\mathbf{i}}^{\mathrm{T}}\hat{\mathbf{q}}\mathbf{L}$. On the other hand, the direct GHG emissions of the industries by a supply-driven perspective is given by ${\mathbf{c}}^{{\mathrm{T}}^{\ast }}={\mathbf{v}}^{\mathrm{T}}\mathbf{G}\hat{\mathbf{q}}$, where ${\mathbf{c}}^{{\mathrm{T}}^{\ast }}\mathbf{i}$ determines the economy’s overall direct GHG emissions level by a supply-driven perspective. Finally, the total supply-driven GHG emission intensities are given by $\mathbf{G}\hat{\mathbf{q}}\mathbf{i}$.

6 The OECD provides NIOTs that distinguish between domestically produced and imported inputs. This study focuses on a national economy, and therefore, matrices of direct input and output coefficients (A and B) were derived from the domestic interindustry transactions data. On the other hand, if the matrices of A and B were derived from an I-O table, which does not differentiate between domestic products and imports, it would lead to an overestimation of the linkage effect (Dietzenbacher, Albino, and Kuhtz 2005), resulting in biased results (Su and Ang 2013).

7 This terminology is originated from Miyazawa (1966) and his notion of ‘internal’ and ‘external’ multipliers. These terms have been utilized in studies that employ the HEM linkage analysis. Duarte, Sánchez-Chóliz, and Bielsa (2002) presented a modified HEM in which total linkages are decomposed into four components: internal linkage effect, mixed linkage effect, net backward linkage effect, and net forward linkage effect. Guerra (2014) introduced a novel approach that combines TLM and HEM to disaggregate the backward stimuli of the electricity sector into three distinct indicators: total, internal, and external backward measures.

8 Following Mariolis and Soklis (2018), hereafter the term high (low) shall mean higher (lower) than the arithmetic mean of the economy, implying that the decrease in the economy’s total GHG emissions induced by the hypothetical extraction of the industries would be higher (lower) than the corresponding average GHG decrease.