Chapter 2: Method and Literature Reviews
2.3 Input-output analysis
Process-based LCAs, economic input-output LCAs (EIO-LCA), and hybrid LCAs are the most widespread LCAs approaches in the literature. However, the application of an EIO-LCA in Thailand is limited by the availability of statistical databases and the type of products or services in sub-sectors in the economic input-output table. In addition, the lack of statistical data on sectoral energy consumption, environmental emissions, and social issues are barriers to an EIO-LCA application in Thailand. Thus, it is difficult to develop a satellite matrix in the input-output (IO) model.
The input-output model was developed by Leontief in the 1930s. The application of the IO model to a study on the environmental aspects was conducted from late 1960s and early 1970s (Leontief, 1970). Input-Output Analysis (IOA) has often been used to develop inventory analysis in LCA studies. Databases on IOAs have been widely used in life cycle inventory and assessment studies (CMU, 2009). In Input-output (IO) tables, transactions of goods and services amongst the industrial sectors are presented in matrix form and expressed as a monetary value. Energy and resource flows can be analyzed on the assumption that goods are transferred in direct portion to their monetary value. So IO tables have been widely applied to the area, particularly for environmental analyses (Asakura et al., 2001; Hondo et al., 1998, 2002). Recently this method has been applied in the environmental field such as energy use and CO2 emissions (Nansai et al., 2002; Nojiri, 2011).
However, there are many case studies around the world on the social impact analysis using an IO analysis (IOA). Almost all case studies are only focused on an employment analysis; for example, Garrett-Peltier (2010) evaluated the employment impacts of renewable energy
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investment in the US; Martinez et al. (2013) assessed the social impact in terms of employment for sugarcane-ethanol in Brazil; Chen et al. (2013) looked at oyster farming in Taiwan; Tang et al. (2013) examined Chinese petroleum industry; Lee and Yoo (2014) evaluated the fisheries and aquaculture sectors in Korea; Ferrao et al. (2014) addressed the packaging waste management system in Portugal; McBain, D. and Alsamawi, A. (2014) assessed labor in global trade using multi-regional input–output analysis; Malik et al. (2015) addressed the employment issue in lignocellulosic biofuel production in South Australia; Yang et al. (2015) evaluated the employment impact pf algae-derived biodiesel in China. There are two case studies the concentrate on two social issues, such as Kucukvar et al. (2014) who focused on income and work-related injuries for a social sustainability assessment in US, and Alsamawi et al. (2014) focused on the employment and income footprint of world’s nations. There are some case studies concentrated on many social issues such as Chang (2011) who focused on accidents, fatalities, employment, research and development personnel, science and technology (ST) personnel, and funding for ST activities for a construction project in China. Onat et al. (2014) addressed the social impacts in terms of income, government tax, and injuries in the US building sector, using an IO analysis. Simas et al. (2014) addressed the six negative labor footprints, which consist of occupational health damage, vulnerable employment, gender inequality, share of unskilled workers, child labor, and forced labor associated with consumption in the seven world regions.
Gómez-Paredes et al. (2015) focused on six labor issues including collective bargaining, forced labor, child labor, gender inequality, hazardous work, and social security, for an Indian case study.
2.3.1 Concept of Input-Output Analysis
The basic concept of the IOA that have been applied in this research and discussed in detail by Leontief (1966). However, the main equations are briefly restated below.
Let Y is a vector (n x 1) of the final demand from industry sectors i = 1, 2, …, n and Xij is the elements of a matrix (n x n) of intermediate demand of industries j = 1, 2, …, n from industries i = 1, 2, …, n. The total (intermediate plus final) demand Xi from industry i is then
n
j
i ij
i X Y
X
1
(2.1)
where A is a matrix (n x n) of technical or direct input coefficients aij, which relates to output Xj of industry j to its inputs from industries i by
j ij
ij a X
X (2.2)
19 so that the matrix notation equation (2.1) becomes
Y AX
X (2.3)
Solving for X yields Y A I
X( )1 (2.4)
where I is the identity matrix (n x n) and (I - A)-1 is called the Leontief inverse matrix. Since ...
)
(IA 1IAA2A3 (2.5)
the total output X can be written as
3 ...
2
Y AY AY AY
X (2.6)
The relationship between production and consumption tables, technology matrices, final demand, value added, and the emissions and resources are shown in Table 2-2.
Table 2-2. The overall structure of an environmentally and socially extended input-output framework.
Input to sectors (j) Intermediate output O
Final demand Y
Total output X Output from sectors (i) 1 2 3 n
1 X11 X12 X13 X1n O1 Y1 X1
2 X21 X22 X23 X2n O2 Y2 X2
3 X31 X32 X33 X3n O3 Y3 X3
n Xn1 Xn2 Xn3 Xnn On Yn Xn
Intermediate input I I1 I2 I3 In
Value added V V1 V2 V3 Vn GDP
Total input X X1 X2 X3 Xn
Employment and social issues Social flow matrix S Resources and emissions Environmental flow matrix E
When emissions, resource use or other environmental/social indicators are investigated, they can be included in the environmentally and socially extended input-output analysis. The environmental flow matrix is divided by the industry output to get the emission/resource intensities:
Xj
E
e / (2.7)
where, e is the resource use or emission intensity (environmental flow by industry) [kg/million Thai Baht], E is a vector of resource use or emissions (environmental flow by industry) [kg].
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Re-arranging equation (2.4), and introducing a vector of resource use or emission coefficient, we obtain the resource use or emission coefficient with the inverse form of equation (2.4) as equation (2.8):
Y A I e X e
f ( )1 (2.8)
where, f is the total resource use or emissions caused by final consumption Y; e = [e1, e2,…,en] is a (k × n) row vector of resource use or emission coefficients by sector. e (I – A)-1 is the environmental multiplier (footprint) matrix.
2.3.2 Environmental inventory database using an Input-Output Analysis
The evaluation of energy consumption and environmental impacts is necessary to use appropriately the environmental tools, in which one of the assessment tools to assess the environmental effects of throughout the entire life cycle of products or an LCA. But the restrictions in the life cycle inventory data of a product, results in many groups of LCA researchers raising the application of LCA by using the Input-Output Analysis (IOA) technique.
At present, there are IO table applications widely used by economists and scientists from various disciplines, including the use of the database to develop a model for the study of energy demands for the economic system. To evaluate the environmental impact, in particular the use of IO tables applied to the LCA study, and developed the life cycle inventory database, such as GHG Inventory, water footprint accounting, land use footprint, etc., can be applied the IOA technique.
Ono et al. (2015) developed the water footprint database of the production of goods and services in Japan, through the use of IOA techniques. The 403 economic sectors were investigated, the results demonstrated that the intensity of the input water and water consumption in the primary sector was very high value and dependent on rainwater. While the water intensity of the secondary sector has a higher value than in the tertiary sector. The water footprint database can be applied to evaluate the water footprint of products, services, and enterprises in Japan.
Hienuki et al. (2015) evaluate the effects on the environmental and social of the electricity generation technology in potential future scenarios of Japan. The analysis applied the hybrid IO techniques to forecast the GHG emissions, and the employment generation of the power systems in Japan between the years 2012-2030.
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Zhang and Anadon (2014) applied the multi–regional input–output analysis (MRIO) to estimate the water footprint and virtual water at the province level in China. In 2007 the level of water withdrawals and water consumption in the country, represented 184 billion m3 and 101 billion m3, respectively. There are four large footprint cities in Beijing, Shanghai, Tianjin and Chongqing where the water footprint per capita are high. While in provinces that water in the lower require the external water from other provinces to support their water consumption.
Zhang et al. (2014) evaluated the non-CO2 GHG emissions (CH4, N2O, HFCs, PFCs and SF6) in 2005, in China, using an IOA. They found that the direct non-CO2 GHG emissions, representing 1,368.5 million tonnes CO2-eq, came from 848.4 million tonnes of CH4 emissions, 356.8 million tonnes N2O and 163.3 million tons of other gases. Approximately 93.2% of total pollution comes from the agriculture, coal mining, and chemical sectors. The quantity of embodied emissions that are exported as goods from China representing 487.0 million tonnes CO2-eq, of which 35.6% comes from the textiles and clothing, and leather products.
Bouasan and Vorayos (2013) applied the IO table in Thailand in order to evaluate the energy consumption and air pollution, including CO2, CH4, N2O and SO2, in the agriculture sector.
They estimated the air pollution based on the 2006 IPCC guidelines combined with fuel consumption of each economic sector. All the information used in the analysis was based on 2005 values. In addition, they also estimated the GHG emissions of non-energy related activities, such as livestock, degradation of organic compounds in rice fields, etc.
Chen and Zhang (2010) estimated China's GHG emissions in 2007 using the IOA technique.
They focused on CO2, CH4 and N2O and found that GHG emissions represented 7,456.12 million tonnes CO2-eq. By 63.39% of CO2 emissions come related from the energy consumption, 22.31% does not relate to energy. There are 81.32% of total GHG emissions coming from electricity production, steam and hot water, iron melting, steel and non-ferrous metal products. In addition, China is also a net exporter of GHGs with up to 3,060.18 million tonnes CO2-eq, or 41.04% coming directly from GHG emissions.
2.3.3 Social inventory database using Input-Output Analysis
After the introduction of social impact, there are a few studies on social database using the social footprint concept. European countries, USA, Australia, Japan, India and China have performed the S-LCA study at the company and product level. Table 2-3 shows the development of current studies on social footprint database. Key weak points of the database are that there is lack of availability of data due to databases not existing (mostly qualitative) and many social indicators are subjectively perceived and hard to evaluate. Benoıt–Norris et
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al. (2013) developed the social hotspot database using an IO table, which tried to cover all industry sectors and all social indicators.
Table 2-3. Overview of current studies on the social inventory databases.
Study Database
type
Classification of social issues Industry coverage Reference year Benoıt–Norris et al.
(2013)
IOA, National, Sectors
Direct, Indirect 22 social themes 134 social indicators
112 countries with 57 sectors
2004
Alsamawi et al.
(2014)
IOA, Global
Direct, Indirect
Employment and Income
187 countries with 85 sectors
2010
Simas et al. (2014) IOA, Regions
Direct, Indirect 6 bad labor footprint
7 world regions 8 sectors
2007
Gómez–Paredes et al. (2015)
IOA, National
Direct, Indirect
6 labor issues: collective bargaining, forced labor, child labor, gender inequality, hazardous work, and social security
115 commodities 2011
This study IOA,
National, Asian Countries
Direct, Indirect Total employment Paid worker
Vulnerable employment Wages
Fatal accident in workplace Non-fatal accident in workplace
Thai IO (96 sectors) AIIO (76 sector with 10 countries)
2005