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Impacts of productive efficiency improvement in the global metal industry on CO2 emissions
Journal of Environmental Management 248 (2019) 109261
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Research article
Impacts of productive efficiency improvement in the global metal industry on CO2 emissions
T
Hirotaka Takayabua,*, Shigemi Kagawab, Hidemichi Fujiib, Shunsuke Managic, Shogo Eguchid a
Graduate School of Economics, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka, 819-0385, Japan Faculty of Economics, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka, 819-0385, Japan c Urban Institute, Department of Urban and Environmental Engineering, School of Engineering, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka, 819-0395, Japan d Department of Industrial Economics, Faculty of Economics, Fukuoka University 8-19-1, Nanakuma, Jonan-ku, Fukuoka, 814-0180, Japan b
ARTICLE INFO
ABSTRACT
Keywords: Metal sectors Data envelopment analysis Energy and material efficiency CO2 reduction potentials Scope 3 CO2 emissions
This study focused on 14 metal sectors of the 40 countries that are the largest CO2 emitters and developed a new analysis framework to estimate CO2 reduction potentials based on the Greenhouse Gas Protocol through efficiency improvement of the inefficient metal sector of these countries. The analysis framework was developed by combining a multi-regional input-output database with data envelopment analysis. We found that there were 20 inefficient countries in the basic iron and steel sector, which is the largest CO2 emitter among 14 metal sectors, and their efficiency improvements can contribute to reducing CO2 emissions by 354 Mt, accounting for 1.4% of the global CO2 emissions. We further proposed efficiency improvement schemes targeting the inefficient countries in order to help those countries to effectively reduce CO2 emissions according to their sectoral and national characteristics.
1. Introduction Metal is essential as a material in many kinds of final products, and the metal industry is one of the largest energy consumers within manufacturing (IEA, 2017). According to IEA (2017), CO2 emissions from the metal industry are increasing, and in 2015, the metal industry's CO2 emissions from fuel combustion were 2,025 Mt, which accounts for 33.4% of the total CO2 emissions from manufacturing (Fig. 1). The metal industry contributes to climate change, and in recent years, countries and regions have been setting emissions reduction targets for CO2 and other greenhouse gas (GHG) emissions, in order to mitigate climate change, such as global warming. They are also implementing various efforts to realize these targets, such as adopting environmental taxes and promoting new production technologies according to the Paris Agreement (IPCC, 2014). In the past decade, many studies have evaluated production technology and analyzed how efficiency improvements in the use of energy, electricity, and material, will contribute to CO2 reduction. A large amount of research focused on energy efficiency is conducted to estimate energy and electricity saving potential and CO2 reduction potential (Worrell et al., 2009; Bian et al., 2013; Fetanat and Shafipour, 2017; Fernández et al., 2018). Especially, Fujii et al. (2010) and Lin and Wang (2014) analyzed the Chinese iron and steel sector and concluded that a *
technology gap remains among Chinese provinces and efficiency improvement (i.e., technological catch-up) would contribute to a significant CO2 reduction. Henning and Trygg (2008) evaluated the impact of reduction of electricity use in Swedish industries on the European CO2 emissions. Ewertowska et al. (2016) assessed the environmental efficiency of the electricity mix in Europe. In recent years, there has been increasing interest in material efficiency which is also important for CO2 mitigating as discussed in the Sustainable Development Goal 12 on sustainable consumption and production (UNESC, 2017). The role of material efficiency in manufacturing for CO2 mitigation was reported in Allwood et al. (2011) and Gutowski et al. (2013). They focused on the effect of material efficiency improvement through adopting the best available technology, material substitution, and demand reduction. Results of potential estimations from these studies help us to suggest CO2 mitigation policies such as which country or province should be given priority for investments aimed at improving efficiency. However, these studies conducted only separate efficiency analyses; therefore, it is difficult to discuss the appropriate efficiency improvement scheme, that is, which efficiency improvement would significantly contribute to reducing CO2 emissions, based on results obtained under different assumptions and with different models. In addition, limitations in the data make comprehensive analysis difficult; however, an environmentally extended multi-regional input-output (MRIO) database
Corresponding author. E-mail address: [email protected] (H. Takayabu).
https://doi.org/10.1016/j.jenvman.2019.109261 Received 5 September 2018; Received in revised form 30 May 2019; Accepted 10 July 2019 Available online 26 July 2019 0301-4797/ © 2019 Elsevier Ltd. All rights reserved.
Journal of Environmental Management 248 (2019) 109261
H. Takayabu, et al.
Seiford, 2009) and applied in the energy and environmental field (Zhou et al., 2008, 2018; Chen and Jia, 2017; Martín-Gamboa et al., 2017; Sueyoshi et al., 2017). We can evaluate the extent to which the aforementioned inputs could be reduced while ensuring the output quantities from the efficiency scores obtained by DEA. In the second stage, we estimate the scope 1, 2 and 3 CO2 reduction potentials based on the conserved energy, electricity, and other intermediate input, respectively. The objectives of this study are the following: (1) developing a model for evaluating CO2 reduction potentials consistent with the GHG Protocol considering production technology (i.e., sectoral and national characteristics); (2) revealing efficiency gaps among metal sectors and quantifying the impacts of efficiency improvement in inefficient countries on scope 1, 2 and 3 CO2 emissions; and (3) proposing efficiency improvement schemes targeting the inefficient countries that help those countries to effectively reduce CO2 emissions according to their sectoral and national characteristics. The paper is organized as follows. Section 2 describes the methodology of efficiency evaluation and CO2 reduction potential estimation and the data. Section 3 presents the results and discussion, and Section 4 concludes the paper.
Fig. 1. CO2 emissions from fuel combustion among manufacturing industries.
(Tukker et al., 2009; Timmer et al., 2015), which contains national and industry-level input, output, and emission data, is useful for the analysis. In response to the abovementioned points, we focus on a technology-side policy for achieving emissions reduction by improving productive efficiency in the metal industry. In particular, we analyze the extent to which efficiency (energy efficiency, electrical efficiency, and material efficiency) improvements in the metal industry of nations can help to reduce CO2 emissions in the case of countries with large emissions. The CO2 reduction potentials differ within the metal industry (e.g., the iron and steel sector and the aluminium sector) and between nations, because the technology for production processes is diverse (AISI, 2010; Kuramochi, 2017). Therefore, we used EXIOBASE2 (Tukker et al., 2013; Wood et al., 2015), which is an MRIO database including highly disaggregated 14 metal sectors of 43 countries and 5 regions for 2007, in order to consider sectoral and national characteristics such as input structure and production technology when suggesting a climate mitigation policy. It is clear that the incentive for reducing energy, electricity, and other intermediate input use differs by country according to the subsidy and tax regime (Lenzen, 2010; UNIDO, 2011; IEA, 2012). Furthermore, in suggesting an effective climate mitigation policy with a focus on sectoral and national characteristics, it is useful to estimate the scope 1, 2, and 3 CO2 reduction potentials of sectors and countries. The concept of scope 1, 2, and 3 emissions is based on the life cycle concept and is defined by the GHG Protocol. Scope 1 emissions are direct emissions occurring from emissions sources (hereinafter energy combustion and chemical process) that are managed or owned by businesses or households, scope 2 emissions are indirect emissions from the use of electricity, steam, and heating (hereinafter electricity), and scope 3 emissions are indirect emissions from other intermediate goods (hereinafter other intermediate input) excluding scope 2 emissions (GHG Protocol, 2004, 2008). Using the concept of scope 1, 2, and 3 emissions, we can reveal which supply chain has a larger CO2 reduction potential, and can propose efficiency improvement schemes in order to effectively reduce CO2 emissions. The novelty of this study is the following. We developed a new analysis framework including two stages: productive efficiency evaluation and CO2 reduction potential estimation consistent with the GHG Protocol (Fig. 2). In the first stage, we focus on five input factors (labor, capital, energy use, electricity use, and other intermediate input use) and one output (gross output) of 14 metal sectors in 40 countries from an MRIO database, and the data envelopment analysis (DEA) method was applied to estimate productive efficiency scores of specific metal sectors of specific nations. The DEA method, which was originally proposed by Charnes et al. (1978) and is based on the work of Farrell (1957), is widely used to evaluate relative efficiencies of decision making units (Seiford, 1996; Färe and Grosskopf, 2005; Cook and
2. Methodology 2.1. Data envelopment analysis In this study, we used an input-oriented constant returns to scale (CRS) model for the DEA method. Fig. 3 shows an illustrative example of input-oriented DEA. Following Cooper et al. (2007), efficiency score ij for sector i of country j can be obtained by solving the following linear program (LP):
Min. s. t.
ij
K ik x ik l k=1 K ik y ik m k=1 ik 0 (k =
ijx ij l
ymij
(l = 1, ..., L)
(m = 1, ..., M ) (1)
1, ..., K )
where x ij is the amount of input l used by sector i of country j, is the amount of output m produced by sector i of country j, and is the intensity weight on sector i of country k. ij indicates how much an inefficient sector i of country j potentially reduces the inputs, compared with the production possibility frontier formed by efficient countries. ij ij 1, where ij = 1 indicates that sector i of country j is satisfies 0 efficient, while ij < 1 implies the converse (i.e., sector i of country j is inefficient). We solved the LP for each metal sector and the nation. The numbers of metal sectors and countries in EXIOBASE2 are 14 and40,1 respectively, so i = 1, ..., 14 and j = k = 1, ..., 40 . We set five input factors (labor, capital, energy use, electricity use, and other intermediate input) and 1 output factor (gross output), so l = 1, ..., 5 and m = 1. It should be noted that in this study we used a CRS model rather than a variable returns to scale (VRS) model. When an efficiency evaluation is performed using a VRS DEA model, the sector of the country with the largest output is identified as efficient. Since China is the biggest producer in most of the metal sectors, the VRS model evaluates the metal sectors of China as performing production activities efficiently. However, the simple input efficiency index obtained by dividing each input factor of the relevant metal sector by the total output (for example, energy consumption divided by gross output) shows that China's basic iron and steel sector is very inefficient for all input factors compared to the other countries. Thus, in this study, a CRS model was
ymij ik
1 There are 43 countries in EXIOBASE2, but 3 countries are excluded in this study because of incomplete data.
2
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Fig. 2. Analysis framework.
different countries by dividing the 40 countries into two groups: countries whose production scale is very small, accounting for less than 0.1% of worldwide gross output (total of the 40 countries), and countries accounting for 0.1% or more of total world gross output, and we call this model the CRS separated model. 2.2. CO2 reduction potentials based on efficiency improvement Efficiency score ij obtained by DEA can be used to estimate the input reduction potential RPlij of input l for sector i of country j, which is in terms of a reduction of the input volume xlij , as follows:
RPlij = (1
ij )
× xlij
(2)
The input reduction potentials calculated using equation (2) can be multiplied by CO2 emission coefficients to estimate the CO2 reduction ij potential RPCO , which is the amount of CO2 emissions reduced by 2 improving the efficiency of sector i of country j in accordance with efficiency score ij , as follows: Fig. 3. Illustrative example of input-oriented DEA.
ij RPCO = 2
applied. CRS models do not take economies of scale into account; however, by, for example, comparing a sector with very large-scale production (e.g., China's basic iron and steel sector) to a country with very smallscale production (e.g., Malta's basic iron and steel sector), a relative efficiency can be estimated. In this way, to avoid references between sectors of very different production scales, this study analyzes the efficiency of the metal sectors (e.g., the basic iron and steel sector) of
dlik × (1
ij )
× xlij, ik
(3)
where dlik is the CO2 emission coefficient for input l from sector i of country k and xlij, ik is the amount of input l that sector i of country j inputs from sector i of country k. Based on the calculation standards for scope 1, 2, and 3 emissions, the CO2 reduction potential calculated in equation (3) can be estimated for each “scope” under the assumption of an efficiency improvement of sector i of country j. The respective CO2 reduction potentials for scope 1, 2, and 3 emissions by sector i of country j can be expressed as follows: 3
Journal of Environmental Management 248 (2019) 109261
H. Takayabu, et al. ij RPScope 1 = ij RPScope 2
=
ij RPScope 3
=
K ij ) × x ij, ik d ik × (1 energy k = 1 energy K ik ij ) × x ij, ik d × (1 electricity k = 1 electricity K ik ij ) × x ij, ik d × (1 other other k=1
Table 2 Sectoral classifications for scope 1, 2, and 3 CO2 calculation.
(4)
where and are the CO2 emissions coefficients for, respectively, the energy, electricity, and other intermediate input of ij, ik ij, ik ij, ik sector i of country k, and x energy , x electricity , and x other are the amounts of, respectively, energy, electricity, and other intermediate input that sector i of country j inputs from sector i of country k. It should be noted that the efficiency improvement in a specific sector of a nation contributes to reduction in inputs of goods and services produced in its own country as well as other countries, therefore CO2 emissions at globe are reduced through efficiency improvement of a specific sector of a nation. ik denergy ,
ik delectricity ,
ik dother
No.
Sectors related to Scope 1 CO2 emissions (i.e., Energy sector)
1 2
5 6 7 8
Mining of coal and lignite; extraction of peat Extraction of crude petroleum and services related to crude oil extraction, excluding surveying Extraction of natural gas and services related to natural gas extraction, excluding surveying Extraction liquefaction, and regasification of other petroleum and gaseous materials Miring of uranium and thorium ores Manufacture of coke oven products Petroleum Refinery Processing of nuclear fuel
No.
Sectors related to Scope 2 CO2 emissions (i.e., Electricity sector)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 No. 1
Production of electricity by coal Production of electricity by gas Production of electricity by nuclear Production of electricity by hydro Production of electricity by wind Production of electricity by petroleum and other oil derivatives Production of electricity by biomass and waste Production of electricity by solar photovoltaic Production of electricity by solar thermal Production of electricity by tide, wave, ocean Production of electricity by Geothermal Production of electricity nec Transmission of electricity Distribution and trade of electricity Manufacture of gas; distribution of gaseous fuels through mains Steam and hot water supply Collection, purification and distribution of water Sectors related to Scope 3 CO2 emissions (i.e., Other intermediate sector) Other 137 sectors
3 4
2.3. Data All the variables other than capital used in the DEA are obtained from EXIOBASE2 (Tukker et al., 2013; Wood et al., 2015). The capital data of each metal sector in a country were estimated by dividing the capital value of the sector recorded in the Socio Economics Accounts of World Input-Output Database Release 2013 (Dietzenbacher et al., 2013; Timmer et al., 2015) by the fraction of the gross output of the sector recorded in EXIOBASE2 (see Table 1 for the disaggregated metal sectors in EXIOBASE2). In CO2 reduction potential estimation, direct CO2 emissions coefficient data (i.e., environmental inventory for CO2 calculation) by sector and country required for estimating CO2 reduction potential of scope 1, 2, and 3 emissions were obtained from EXIOBASE2. Table 2 shows the sectoral classification for the scope 1, 2, and 3 CO2 calculations. 3. Results and discussion
3.1. Efficiency scores
Fig. 4 shows actual baseline scope 1, 2, and 3 CO2 emissions of 14 metal sectors calculated based on the GHG Protocol calculation standards. The largest CO2 emitter was M.01 (basic iron and steel sector), followed by M.14 (fabricated metal sector), M.02 (re-processing steel sector), and M.05 (aluminium sector). These four metal sectors accounts for a significantly large percentage, approximately 90% of the total CO2 emissions. Therefore, we present results and discussion with a focus on these sectors in this section. Empirical results for the four metal sectors are described in Table 3. See Appendix A for the results for all 14 metal sectors. Efficiency scores are shown in Tables A.1 and A.2, and CO2 reduction potentials and their breakdown are presented in Table A.3.
From Table 3, the numbers of inefficient countries (i.e., < 1) are 20 in the basic iron and steel sector, the re-processing of steel sector, and the aluminium sector, and 27 in the fabricated metal sector. In the case of the basic iron and steel (re-processing of steel, aluminium, or fabricated metal) sector, Estonia (Indonesia, India, or Estonia) has the lowest efficiency score at 0.61 (0.67, 0.63, or 0.73), which means that the sector in this country can potentially reduce its inputs quantities by 39% (33%, 37%, or 27%) compared to the production possibility frontier of efficient countries. Fig. 5 shows a world map with efficiency scores, with efficient countries shown in the lightest blue and other countries shown in increasingly darker blue for greater inefficiencies. Countries shown in red are not engaged in the production (i.e., gross output of the country for
Table 1 Classification of 14 metal sectors. Code
Shortened name of metal sector
Name of metal sector in EXIOBASE classification
M. M. M. M. M. M. M. M. M. M. M. M. M. M.
Basic iron and steel Re-processing of steel Precious metal Re-processing of precious metal Aluminium Re-processing of aluminium Lead, zinc and tin Re-processing of lead Copper Re-processing of copper Other non-ferrous metal Re-processing of other non-ferrous metal Casting of metal Fabricated metal
Manufacture of basic iron and steel and of ferro-alloys and first products thereof Re-processing of secondary steel into new steel Precious metals production Re-processing of secondary preciuos metals into new preciuos metals Aluminium production Re-processing of secondary aluminium into new aluminium Lead, zinc and tin production Re-processing of secondary lead into new lead Copper production Re-processing of secondary copper into new copper Other non-ferrous metal production Re-processing of secondary other non-ferrous metals into new other non-ferrous metals Casting of metals Manufacture of fabricated metal products, except machinery and equipment
01 02 03 04 05 06 07 08 09 10 11 12 13 14
4
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H. Takayabu, et al.
emissions are related to energy combustion and chemical process. In contrast, in the aluminium sector, scope 2 emissions account for 78%, implying that emissions reductions are needed through further electricity conservation. These differences are caused by differences in sectoral characteristics. Moreover, national characteristics can be seen in the variation of the breakdown by country. One initial purpose was to propose effective climate mitigation policies (i.e., efficiency improvement schemes) with a focus on such sectoral and national characteristics. The potentials are relatively small in most countries compared to China, but providing these countries with efficiency improvement schemes is also important because these countries also need to reduce their CO2 emissions in order to achieve CO2 reduction targets. We will discuss the efficiency improvement scheme that should be adopted in each sector and nation in the following section. 3.3. Discussion
Fig. 4. CO2 emissions totaled over 40 countries by 14 metal sectors for 2007.
As mentioned in the previous section, the source for CO2 reduction potential differs depending on the sectoral and national characteristics. Therefore, the breakdown of the potentials should be examined to propose efficiency improvement schemes in order to work effectively toward reducing CO2 emissions. We visualize the size of the CO2 reduction potential and its breakdown in Fig. 6. Note that Fig. 6 corresponds to Table 3. In Fig. 6, the size of each circle represents the size of the CO2 reduction potential, and the position of the circle shows the percentages of the scope 1 and 2 (and thereby scope 3) CO2 reduction potentials. Thus, the size of each circle indicates the impact of efficiency improvement, while the position of the circle implies which efficiency improvement would significantly contribute to reducing CO2 emissions. We propose three efficiency improvement schemes—the energy and process scheme, the electricity scheme, and the material and service scheme—as well as the hybrid scheme. The energy and process scheme is an efficiency improvement scheme with a focus on improving energy efficiency and chemical process and shifting low-carbon energy, and the scheme should be adopted by countries with larger scope 1 CO2 reduction potential. The electricity scheme is an efficiency improvement scheme with a focus on saving electricity use, and the scheme should be adopted by countries with larger scope 2 CO2 reduction potential. The material and service scheme is an efficiency improvement scheme with a focus on material and service efficiency improvement, and should be adopted by countries with larger scope 3 CO2 reduction potentials. In Fig. 6, the areas for these efficiency improvement schemes are defined in the following manner. The energy and process scheme area, shaded red, is where the scope 1 CO2 reduction potential accounts for more than 50%, the electricity scheme area, shaded yellow, is where the scope 2 CO2 reduction potential accounts for more than 50%, and the material and service scheme area, shaded green, is where the scope 3 CO2 reduction potential accounts for more than 50%. In addition to these schemes, the hybrid scheme, in which all efficiencies are equally important, is proposed, shaded blue. For countries plotted in the energy and process scheme area, reducing energy loss or installing superior equipment is critical to increasing energy efficiency and reducing CO2 emissions (Kim and Worrell, 2002; Hasanbeigi et al., 2013). In addition, decarbonisation through shifting low-carbon energy such as biomass is also crucial for reducing scope 1 CO2 emissions. Saving electricity in the production activity and changing the electricity mix (i.e., increasing the proportion of renewable energy) help countries in the electricity scheme area to achieve their scope 2 CO2 reduction potentials. “Doing more and better with less”, aimed at the SDG 12 on SCP (UNESC, 2017), is especially important for countries with larger proportions of scope 3 CO2 reduction potentials. Such counties need to improve material and service efficiency through material substitution and supply chain management and technology innovation (Söderholm and Tilton, 2012; Milford et al., 2013; Henzler et al., 2017).
the sector is 0). As shown in Fig. 4, eastern European countries (particularly former communist countries) tend to be relatively inefficient. This tendency can be interpreted as indicating that the former communist countries have difficulty introducing modern production equipment invented in developed countries (Fujii and Managi, 2015). This also applies to China, India, and Brazil. Especially, China and India, which account for a high percentage of the global gross output among the four metal sectors, scored in the bottom five for efficiency scores. China and India are engaged in large-scale and low-efficiency metal sectors. Either China and India need to make efficiency improvements, or efficient countries need to increase their production, as the latter possess high-efficiency metal production technologies. The results of efficiency scores show which countries are more inefficient; however, these results are not enough to understand the impacts of efficiency improvement on CO2 emissions, which are provided in the next section. 3.2. CO2 reduction potentials In Table 3, the CO2 reduction potentials through efficiency improvement and their breakdown are presented. The total CO2 reduction potential in the basic iron and steel (re-processing of steel, aluminium, or fabricated metal) sector is 354.4 Mt (82.4 Mt, 54.8 Mt, or 99.6 Mt). Compared to the actual CO2 emissions described in Fig. 4, the amount of CO2 reduction potential in these metal sectors is 591 Mt, which accounts for 17.2% of the actual total CO2 emissions in these metal sectors (3,442 Mt). The impacts of efficiency improvement (i.e., technological catch-up) are significant, so there is a need for policies that fill the technological gap between countries. Focusing on the value of the CO2 reduction potential, China has the largest CO2 reduction potential among countries classified as being inefficient in the DEA, at 290 Mt, accounting for 82% of the total CO2 reduction potential of the basic iron and steel sector (354 Mt-CO2). Similarly, China has the largest CO2 reduction potential among the countries classified as being inefficient in this study for the other three metal sectors of focus as well. Specifically, China's CO2 reduction potential in the re-processing of steel (aluminium or fabricated metal) sector accounts for 73% (77% or 66%) of the total CO2 reduction potential. This implies that it is critically important to mitigate global warming through technological catch-up in Chinese metal sectors within the framework of climate finance (Climate Policy Initiative, 2014). Following China, India and Brazil have the largest amount of CO2 reduction potential, accounting for 37 and 18 Mt, respectively. Then, focusing on the breakdown of the total CO2 reduction potentials in the basic iron and steel sector, scope 1 emissions account for 71%. This indicates that it is important for the basic iron and steel sector to reduce direct emissions by improving energy efficiency and chemical process and shifting low-carbon energy because scope 1 5
6
1.00 0.99 1.00 1.00 1.00 1.00 1.00 0.61 0.95 1.00 1.00 0.87 0.73 1.00 0.92 1.00 1.00 0.70 1.00 1.00 0.81 1.00 0.75 1.00 1.00 0.96 1.00 0.93 0.92 0.73 1.00 0.91 0.78 0.73 1.00 1.00 0.88 0.85 0.99 0.76
Efficiency score (θ)
− 0.05 − − − − − 0.01 0.39 − − 0.19 0.44 − 1.19 − − 0.11 − − 1.94 − 1.92 − − 0.14 − 5.74 9.78 290 − 4.87 13.1 21.1 − − 0.83 1.81 0.17 0.66 354.4
Potential (Mt-CO2)
− 69% − − − − − 2% 48% − − 9% 68% − 43% − − 43% − − 72% − 83% − − 94% − 49% 55% 71% − 25% 80% 95% − − 35% 46% 39% 62% 71%
Scope 1 − 6% − − − − − 34% 19% − − 48% 6% − 9% − − 10% − − 11% − 6% − − 0% − 32% 10% 8% − 27% 3% 0% − − 44% 12% 26% 3% 8%
Scope 2
Breakdown
Basic iron and steel
− 25% − − − − − 64% 33% − − 43% 26% − 48% − − 48% − − 17% − 11% − − 5% − 19% 35% 21% − 48% 17% 5% − − 21% 42% 34% 35% 21%
Scope 3 1.00 0.92 1.00 NA 0.99 0.99 NA NA 0.96 1.00 1.00 0.85 0.73 NA 0.91 NA 1.00 0.72 NA 1.00 0.79 1.00 0.74 1.00 1.00 1.00 0.95 1.00 0.90 0.73 1.00 0.84 0.83 0.70 1.00 1.00 0.89 0.78 0.87 0.67
Efficiency score (θ) − 0.17 − NA 0.01 0.07 NA NA 0.17 − − 0.26 0.13 NA 0.73 NA − 0.02 NA − 0.88 − 0.83 − − − 0.11 − 3.09 60.3 − 2.94 2.05 6.91 − − 0.33 1.85 1.08 0.50 82.4
Potential (Mt-CO2) − 59% − NA 61% 47% NA NA 47% − − 6% 41% NA 44% NA − 99% NA − 48% − 41% − − − 46% − 41% 35% − 21% 65% 88% − − 11% 34% 21% 66% 40%
Scope 1 − 20% − NA 21% 33% NA NA 39% − − 77% 28% NA 24% NA − 0% NA − 36% − 42% − − − 37% − 36% 42% − 60% 16% 1% − − 82% 35% 63% 3% 38%
Scope 2
Breakdown
Re-processing of steel
Note 1: NA means that the country is not engaged in the production. Note 2: means that the country doesn’t have CO2 reduction potential because the country is efficient.
Austria Belgium Bulgaria Cyprus Czechia Germany Denmark Estonia Spain Finland France Greece Hungary Ireland Italy Lithuania Luxembourg Latvia Malta Netherlands Poland Portugal Romania Sweden Slovenia Slovakia United Kingdom United States Japan China Canada South Korea Brazil India Mexico Russia Australia Turkey Taiwan Indonesia Total
Country
Table 3 Empirical results for 4 metal sectors.
− 20% − NA 18% 21% NA NA 13% − − 17% 31% NA 31% NA − 1% NA − 16% − 17% − − − 17% − 24% 23% − 19% 19% 11% − − 7% 31% 15% 31% 22%
Scope 3 1.00 1.00 0.70 1.00 0.77 0.94 1.00 0.68 0.87 1.00 1.00 0.80 0.66 0.89 0.83 0.96 1.00 1.00 1.00 1.00 0.66 1.00 1.00 1.00 1.00 0.93 1.00 1.00 0.86 0.66 1.00 0.89 0.77 0.63 0.89 1.00 1.00 0.76 0.80 1.00
Efficiency score (θ) − − 0.15 − 0.23 0.46 − 0.00 0.45 − − 0.83 0.31 0.15 0.53 0.00 − − − − 0.59 − − − − 0.04 − − 0.50 42.5 − 0.67 2.69 3.60 0.13 − − 0.73 0.30 − 54.8
Potential (Mt-CO2) − − 21% − 14% 25% − 52% 21% − − 18% 25% 71% 34% 8% − − − − 32% − − − − 13% − − 31% 6% − 12% 58% 7% 9% − − 54% 22% − 11%
Scope 1
Aluminium
− − 69% − 67% 59% − 45% 60% − − 76% 65% 25% 40% 4% − − − − 58% − − − − 70% − − 28% 83% − 79% 31% 89% 81% − − 35% 39% − 78%
Scope 2
Breakdown
− − 10% − 19% 16% − 3% 19% − − 6% 10% 4% 27% 88% − − − − 10% − − − − 18% − − 41% 11% − 9% 12% 4% 10% − − 11% 39% − 11%
Scope 3 1.00 1.00 0.76 0.86 0.78 0.95 1.00 0.73 0.87 1.00 0.96 0.87 0.82 1.00 0.90 0.93 1.00 1.00 0.98 0.94 0.78 0.78 1.00 0.99 1.00 0.91 1.00 1.00 0.94 0.73 1.00 0.84 0.95 0.83 0.83 0.84 1.00 0.77 0.75 0.94
Efficiency score (θ) − − 0.12 0.01 0.86 1.20 − 0.15 1.58 − 0.32 0.25 0.21 − 2.16 0.01 − − 0.00 0.23 1.60 0.23 − 0.02 − 0.09 − − 1.34 65.3 − 1.68 0.57 4.86 0.76 13.4 − 0.92 1.40 0.36 99.6
Potential (Mt-CO2) − − 10% 2% 9% 5% − 4% 6% − 16% 4% 14% − 13% 8% − − 1% 10% 7% 12% − 5% − 6% − − 13% 13% − 8% 2% 2% 5% 2% − 3% 5% 1% 10%
Scope 1
− − 30% 23% 50% 37% − 37% 25% − 7% 49% 20% − 15% 8% − − 17% 18% 36% 16% − 5% − 12% − − 31% 43% − 35% 18% 57% 24% 63% − 20% 62% 50% 44%
Scope 2
− − 60% 75% 41% 58% − 58% 69% − 77% 47% 66% − 71% 83% − − 82% 73% 57% 71% − 90% − 82% − − 55% 45% − 57% 80% 42% 71% 36% − 77% 33% 49% 46%
Scope 3
Breakdown
Fabricated metal
H. Takayabu, et al.
Journal of Environmental Management 248 (2019) 109261
Journal of Environmental Management 248 (2019) 109261
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Fig. 5. World map with efficiency scores (θ). *NA means that the country is not engaged in the production (i.e., gross output of the country is 0).
According to Fig. 6, the energy and process scheme should be adopted in 10 (6, 4, or 0) countries in the basic iron and steel (reprocessing of steel, aluminium, or fabricated metal) sector, the electricity scheme should be adopted in 0 (4, 12, or 4) countries in the basic iron and steel (re-processing of steel, aluminium, or fabricated metal)
sector, and the material and service scheme should be adopted in 1 (0, 1, or 20) countries in the basic iron and steel (re-processing of steel, aluminium, or fabricated metal) sector. In the basic iron and steel sector, the energy and process scheme and the electricity scheme should be supported in many countries owing to their national characteristics:
Fig. 6. CO2 reduction potential broken down by country, sector, and scope, and schemes for improving efficiency. 7
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iron and steel production processes differ among countries, for example, some countries smelt iron from an oxygen-blown converter and others smelt iron from an electric furnace. For countries with larger scope 1 CO2 reduction potential, decarbonisation through the use of biomass is a crucial way to reduce CO2 emissions (Norgate et al., 2012; Jahanshahi et al., 2015). On the other hand, more countries should adopt the electricity scheme in the aluminium sector because of its sectoral characteristic (e.g., an electric furnace is generally used to melt aluminium, and metal products are needed to fabricate metal), whereas most of the countries should adopt the material and service scheme in the fabricated metal sector. World Alminium (2017) reports recent advances in technology such as reduction in process electricity intensity (i.e., kilowatt hours per ton of aluminium) in the aluminium sector, and Liu and Müller (2012) provide a critical review of the status and utility of LCA applications in the aluminium sector. In summary, countries with larger CO2 reduction potentials, such as China, India, and Brazil, should be given priority in investments to improve productive efficiency in the global climate mitigation framework (e.g., climate finance and technology transfer). This might appear to be obvious, but the present study has quantified the impacts of efficiency improvement on CO2 emissions. Moreover, efficiency improvement schemes that help countries to review their production activities (e.g., capital investment, and substitution of processes or materials) are proposed according to the sectoral and national characteristics.
Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.jenvman.2019.109261. References Allwood, J.,M., Ashby, M.,F., Gutowski, T.,G., Worrell, E., 2011. Material efficiency: a white paper Resource. Conserv. Recycl. 55, 362–381. American Iron and Steel Institute, 2010. Technology Roadmap Research Program for the Steel Industry. Bian, Y., He, P., Xu, H., 2013. Estimation of potential energy saving and carbon dioxide emission reduction in China based on an extended non-radial DEA approach. Energy Policy 62, 962–971. Charnes, A., Cooper, W.,W., Rhodes, E., 1978. Measuring the efficiency of decision making units. Eur. J. Oper. Res. 2, 429–444. Chen, L., Jia, G., 2017. Environmental efficiency analysis of China's regional industry: a data envelopment analysis (DEA) based approach. J. Clean. Prod. 142, 846–853. Climate Policy Initiative, 2014. The Global Landscape of Climate Finance 2014. Cook, W.,D., Seiford, L.,M., 2009. Data envelopment analysis (DEA) – Thirty years on. Eur. J. Oper. Res. 192, 1–17. Cooper, W.,W., Seiford, L.,M., Tone, K., 2007. Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software. Springer US. Dietzenbacher, E., Los, B., Stehrer, R., Timmer, M., Vries, G., 2013. The construction of world input–output tables in the WIOD project. Econ. Syst. Res. 25 (1), 71–98. Ewertowska, A., Galán-Martín, A., Guillén-Gosálbez, G., Gavaldá, J., Jiménez, L., 2016. Assessment of the environmental efficiency of the electricity mix of the top European economies via data envelopment analysis. J. Clean. Prod. 116, 13–22. Färe, R., Grosskopf, S., 2005. New Directions: Efficiency and Productivity, 2nd (Second). Springer-Verlag New York, LLC. Farrell, M.,J., 1957. The measurement of productive efficiency. J. R. Stat. Soc. Ser. A 120 (3), 253–290. Fernández, D., Pozo, C., Folgado, R., Jiménez, L., Guillén-Gosálbez, G., 2018. Productivity and energy efficiency assessment of existing industrial gases facilities via data envelopment analysis and the Malmquist index. Appl. Energy 212, 1563–1577. Fetanat, A., Shafipour, G., 2017. A hybrid method of LMDI, symmetrical components, and SFA to estimate the distribution of energy-saving potential with consideration of unbalanced components in decomposition analysis. Energy Efficiency 10, 1041–1059. Fujii, H., Kaneko, S., Managi, S., 2010. Changes in environmentally sensitive productivity and technological modernization in China's iron and steel industry in the 1990s. Environ. Dev. Econ. 15, 485–504. Fujii, H., Managi, S., 2015. Optimal production resource reallocation for CO2 emissions reduction in manufacturing sectors. Glob. Environ. Chang. 35, 505–513. Greenhouse Gas Protocol, 2004. A Corporate Accounting and Reporting Standard, Revised Edition. . Greenhouse Gas Protocol, 2008. Technical Guidance for Calculating Scope 3 Emissions. Version 1.0. Gutowski, T.,G., Sahni, S., Allwood, J.,M., Ashby, M.,F., Worrell, E., 2013. The energy required to produce materials: constraints on energy-intensity improvements, parameters of demand. Philosophical Transactions of Royal Society A 371, 20120003. Hasanbeigi, A., Morrow, W., Sathaye, J., Masanet, E., Xu, T., 2013. A bottom-up model to estimate the energy efficiency improvement and CO2 emission reduction potentials in the Chinese iron and steel industry. Energy 50, 315–325. Henning, D., Trygg, L., 2008. Reduction of electricity use in Swedish industry and its impact on national power supply and European CO2 emissions. Energy Policy 36, 2330–2350. Henzler, M., Hercegfi, A., Barckhausen, A., 2017. Industrial Energy Efficiency and Material Substitution in Carbon-Intensive Sectors. Prepared for the Thematic Dialogue on Industrial Energy Efficiency Organized by the Technology Executive Committee on 29 March 2017. (Bonn, Germany). Intergovernmental Panel on Climate Change, 2014. Climate change 2014: mitigation of climate change. In: Edenhofer, O., Pichs-Madruga, R., Sokona, Y., Farahani, E., Kadner, S., Seyboth, K., Adler, A., Baum, I., Brunner, S., Eickemeier, P., Kriemann, B., Savolainen, J., Schlömer, S., von Stechow, C., Zwickel, T., Minx, J.C. (Eds.), Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. International Energy Agency, 2012. Energy Technology Perspective 2012: Pathway to a Clean Energy System. International Energy Agency, 2017. CO2 Emissions from Fuel Combustion Database. World_BigCO2 (ivt). Jahanshahi, S., Mathieson, J.G., Somerville, M.A., Haque, N., Norgate, T.E., Deev, A., Pan, Y., Xie, D., Ridgeway, P., Zulli, P., 2015. Development of low-emission integrated steelmaking process. J. Sustain. Metall. 1, 94–114. Kao, C., 2014. Network data envelopment analysis: a review. Eur. J. Oper. Res. 239, 1–16. Kim, Y., Worrell, E., 2002. International comparison of CO2 emission trends in the iron and steel industry. Energy Policy 30, 827–838. Kuramochi, T., 2017. Assessment of CO2 emissions pathways for the Japanese iron and steel industry towards 2030 with consideration of process capacities and operational constraints to flexibly adapt to a range of production levels. J. Clean. Prod. 147, 668–680. Lenzen, M., 2010. Current state of development of electricity-generating technologies: a
4. Conclusion In this paper, a novel framework incorporating the DEA method with an MRIO database into potential estimation considering scope 1, 2, and 3 CO2 emissions is proposed. This framework helps us to understand which sectors of a nation have larger potentials and what type of efficiency improvement would contribute effectively to reducing CO2 emissions. The framework was applied to evaluate the impacts of productive efficiency improvement of 14 metal sectors in 40 countries. The DEA result indicates the existence of technology gaps between developed countries and developing countries (particularly eastern Europe and BRICs). Based on the DEA result, the impacts of efficiency improvements that reflect sectoral and national characteristics are revealed. In terms of a global CO2 mitigation framework, China, India, and Brazil, which have an important role in metal production but are technologically immature, should be supported to improve their productive efficiencies. Regarding national CO2 mitigation policies, each sector of a nation should adopt the identified efficiency improvement scheme in order to effectively reduce CO2 emissions. Three further research directions can be drawn from this research. The first one is extension of the DEA model, since in this study, we preferred to choose a basic model in order to allow ease of interpretation of the results rather than a complex model. A non-radial DEA model (e.g. slack based measure model by Tone, 2001) or a network DEA model (see Kao, 2014) would help us to deepen the discussion. Secondly, more detailed breakdown for scope 3 CO2 reduction potentials can be performed to identify the source of potentials (e.g., material such as ore and lime, and services such as transport and waste disposal), because we aggregated 163 types of emissions into scope 1, 2, and 3 emissions. Finally, our further study will deal with potential errors in the modelling results due to the uncertainty of the model and the environmentally extended MRIO database. Acknowledgements This research was partially funded by the Grant-in-Aid for [JP17K12858, 16H01797, and 26000001] from the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan. All errors are ours. 8
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H. Takayabu, et al. literature review. Energies 3, 462–591. Lin, B., Wang, X., 2014. Exploring energy efficiency in China's iron and steel industry: a stochastic frontier approach. Energy Policy 72, 87–96. Liu, G., Müller, D.B., 2012. Addressing sustainability in the aluminum industry: a critical review of life cycle assessments. J. Clean. Prod. 35, 108–117. Martín-Gamboa, M., Iribarren, D., García-Gusano, D., Dufour, J., 2017. A review of lifecycle approaches coupled with data envelopment analysis within multi-criteria decision analysis for sustainability assessment of energy systems. J. Clean. Prod. 150, 164–174. Milford, R.,L., Pauliuk, S., Allwood, J.,M., Müller, D.,B., 2013. The roles of energy and material efficiency in meeting steel industry CO2 targets. Environ. Sci. Technol. 47, 3455–3462. Norgate, T., Haque, N., Somerville, M., Jahanshahi, S., 2012. Biomass as a source of renewable carbon for iron and steelmaking. ISIJ Int. 52 (8), 1472–1481. Seiford, L.,M., 1996. Data envelopment analysis: the evolution of the state of the art (1978-1995). J. Prod. Anal. 7, 99–137. Söderholm, P., Tilton, J.,E., 2012. Material efficiency: an economic perspective. Resour. Conserv. Recycl. 61, 75–82. Sueyoshi, T., Yuan, Y., Goto, M., 2017. A literature study for DEA applied to energy and environment. Energy Econ. 62, 104–124. Timmer, M.P., Dietzenbacher, E., Los, B., Stehrer, R., de Vries, G.J., 2015. An illustrated user guide to the world input–output database: the case of global automotive production. Rev. Int. Econ. 23, 575–605. Tone, K., 2001. Theory and Methodology A slacks-based measure of efficiency in data envelopment analysis. Eur. J. Oper. Res. 130, 498–509. Tukker, A., Koning, D.A., Wood, R., Hawkins, T., Lutter, S., Acosta, J., Cantuche, J.M.R., Bouwmeester, M., Oosterhaven, J., Drosdowski, T., Kuenen, J., 2013. EXIOPOL –
development and illustrative analyses of a detailed global MR EE SUT/IOT. Econ. Syst. Res. 25 (1), 50–70. Tukker, A., Poliakov, E., Heijungs, R., Hawkins, T., Neuwahl, F., Rueda-Cantuche, J.,M., Giljum, S., Moll, S., Oosterhaven, J., Bouwmeester, M., 2009. Towards a global multiregional environmentally extended input–output database. Ecol. Econ. 68, 1928–1937. United Nations Economic and Social Council, 2017. Progress towards the Sustainable Development Goals. United Nations Industrial Development Organization, 2011. Industrial Development Report 2011: Industrial Energy Efficiency for Sustainable Wealth Creation – Capturing Environmental, Economic and Social Dividends. World Alminium, 2017. Life cycle inventory data and environmental metrics for the primary aluminium industry. http://www.world-aluminium.org/media/filer_public/ 2018/02/19/slca_report_2015_final_26_june_2017.pdf/, Accessed date: 1 March 2019. Wood, R., Stadler, K., Bulavskaya, T., Lutter, S., Giljum, S., de Koning, A., Kuenen, J., Schütz, H., Acosta-Fernández, J., Usubiaga, A., Simas, M., Ivanova, O., Weinzettel, J., Schmidt, J.H., Merciai, S., Tukker, A., 2015. Global sustainability accounting—developing EXIOBASE for multi-regional footprint analysis. Sustainability 7, 138–163. Worrell, E., Bernstein, L., Roy, J., Price, L., Harnisch, J., 2009. Industrial energy efficiency and climate change mitigation. Energy Efficiency 2, 109–123. Zhou, P., Ang, B.W., Poh, K.L., 2008. A survey of data envelopment analysis in energy and environmental studies. Eur. J. Oper. Res. 189, 1–18. Zhou, X., Xu, Z., Yao, L., Tu, Y., Lev, B., Pedrycz, W., 2018. A novel Data Envelopment Analysis model for evaluating industrial production and environmental management system. J. Clean. Prod. 170, 773–788.
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Journal of Environmental Management journal homepage: www.elsevier.com/locate/jenvman
Research article
Impacts of productive efficiency improvement in the global metal industry on CO2 emissions
T
Hirotaka Takayabua,*, Shigemi Kagawab, Hidemichi Fujiib, Shunsuke Managic, Shogo Eguchid a
Graduate School of Economics, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka, 819-0385, Japan Faculty of Economics, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka, 819-0385, Japan c Urban Institute, Department of Urban and Environmental Engineering, School of Engineering, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka, 819-0395, Japan d Department of Industrial Economics, Faculty of Economics, Fukuoka University 8-19-1, Nanakuma, Jonan-ku, Fukuoka, 814-0180, Japan b
ARTICLE INFO
ABSTRACT
Keywords: Metal sectors Data envelopment analysis Energy and material efficiency CO2 reduction potentials Scope 3 CO2 emissions
This study focused on 14 metal sectors of the 40 countries that are the largest CO2 emitters and developed a new analysis framework to estimate CO2 reduction potentials based on the Greenhouse Gas Protocol through efficiency improvement of the inefficient metal sector of these countries. The analysis framework was developed by combining a multi-regional input-output database with data envelopment analysis. We found that there were 20 inefficient countries in the basic iron and steel sector, which is the largest CO2 emitter among 14 metal sectors, and their efficiency improvements can contribute to reducing CO2 emissions by 354 Mt, accounting for 1.4% of the global CO2 emissions. We further proposed efficiency improvement schemes targeting the inefficient countries in order to help those countries to effectively reduce CO2 emissions according to their sectoral and national characteristics.
1. Introduction Metal is essential as a material in many kinds of final products, and the metal industry is one of the largest energy consumers within manufacturing (IEA, 2017). According to IEA (2017), CO2 emissions from the metal industry are increasing, and in 2015, the metal industry's CO2 emissions from fuel combustion were 2,025 Mt, which accounts for 33.4% of the total CO2 emissions from manufacturing (Fig. 1). The metal industry contributes to climate change, and in recent years, countries and regions have been setting emissions reduction targets for CO2 and other greenhouse gas (GHG) emissions, in order to mitigate climate change, such as global warming. They are also implementing various efforts to realize these targets, such as adopting environmental taxes and promoting new production technologies according to the Paris Agreement (IPCC, 2014). In the past decade, many studies have evaluated production technology and analyzed how efficiency improvements in the use of energy, electricity, and material, will contribute to CO2 reduction. A large amount of research focused on energy efficiency is conducted to estimate energy and electricity saving potential and CO2 reduction potential (Worrell et al., 2009; Bian et al., 2013; Fetanat and Shafipour, 2017; Fernández et al., 2018). Especially, Fujii et al. (2010) and Lin and Wang (2014) analyzed the Chinese iron and steel sector and concluded that a *
technology gap remains among Chinese provinces and efficiency improvement (i.e., technological catch-up) would contribute to a significant CO2 reduction. Henning and Trygg (2008) evaluated the impact of reduction of electricity use in Swedish industries on the European CO2 emissions. Ewertowska et al. (2016) assessed the environmental efficiency of the electricity mix in Europe. In recent years, there has been increasing interest in material efficiency which is also important for CO2 mitigating as discussed in the Sustainable Development Goal 12 on sustainable consumption and production (UNESC, 2017). The role of material efficiency in manufacturing for CO2 mitigation was reported in Allwood et al. (2011) and Gutowski et al. (2013). They focused on the effect of material efficiency improvement through adopting the best available technology, material substitution, and demand reduction. Results of potential estimations from these studies help us to suggest CO2 mitigation policies such as which country or province should be given priority for investments aimed at improving efficiency. However, these studies conducted only separate efficiency analyses; therefore, it is difficult to discuss the appropriate efficiency improvement scheme, that is, which efficiency improvement would significantly contribute to reducing CO2 emissions, based on results obtained under different assumptions and with different models. In addition, limitations in the data make comprehensive analysis difficult; however, an environmentally extended multi-regional input-output (MRIO) database
Corresponding author. E-mail address: [email protected] (H. Takayabu).
https://doi.org/10.1016/j.jenvman.2019.109261 Received 5 September 2018; Received in revised form 30 May 2019; Accepted 10 July 2019 Available online 26 July 2019 0301-4797/ © 2019 Elsevier Ltd. All rights reserved.
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Seiford, 2009) and applied in the energy and environmental field (Zhou et al., 2008, 2018; Chen and Jia, 2017; Martín-Gamboa et al., 2017; Sueyoshi et al., 2017). We can evaluate the extent to which the aforementioned inputs could be reduced while ensuring the output quantities from the efficiency scores obtained by DEA. In the second stage, we estimate the scope 1, 2 and 3 CO2 reduction potentials based on the conserved energy, electricity, and other intermediate input, respectively. The objectives of this study are the following: (1) developing a model for evaluating CO2 reduction potentials consistent with the GHG Protocol considering production technology (i.e., sectoral and national characteristics); (2) revealing efficiency gaps among metal sectors and quantifying the impacts of efficiency improvement in inefficient countries on scope 1, 2 and 3 CO2 emissions; and (3) proposing efficiency improvement schemes targeting the inefficient countries that help those countries to effectively reduce CO2 emissions according to their sectoral and national characteristics. The paper is organized as follows. Section 2 describes the methodology of efficiency evaluation and CO2 reduction potential estimation and the data. Section 3 presents the results and discussion, and Section 4 concludes the paper.
Fig. 1. CO2 emissions from fuel combustion among manufacturing industries.
(Tukker et al., 2009; Timmer et al., 2015), which contains national and industry-level input, output, and emission data, is useful for the analysis. In response to the abovementioned points, we focus on a technology-side policy for achieving emissions reduction by improving productive efficiency in the metal industry. In particular, we analyze the extent to which efficiency (energy efficiency, electrical efficiency, and material efficiency) improvements in the metal industry of nations can help to reduce CO2 emissions in the case of countries with large emissions. The CO2 reduction potentials differ within the metal industry (e.g., the iron and steel sector and the aluminium sector) and between nations, because the technology for production processes is diverse (AISI, 2010; Kuramochi, 2017). Therefore, we used EXIOBASE2 (Tukker et al., 2013; Wood et al., 2015), which is an MRIO database including highly disaggregated 14 metal sectors of 43 countries and 5 regions for 2007, in order to consider sectoral and national characteristics such as input structure and production technology when suggesting a climate mitigation policy. It is clear that the incentive for reducing energy, electricity, and other intermediate input use differs by country according to the subsidy and tax regime (Lenzen, 2010; UNIDO, 2011; IEA, 2012). Furthermore, in suggesting an effective climate mitigation policy with a focus on sectoral and national characteristics, it is useful to estimate the scope 1, 2, and 3 CO2 reduction potentials of sectors and countries. The concept of scope 1, 2, and 3 emissions is based on the life cycle concept and is defined by the GHG Protocol. Scope 1 emissions are direct emissions occurring from emissions sources (hereinafter energy combustion and chemical process) that are managed or owned by businesses or households, scope 2 emissions are indirect emissions from the use of electricity, steam, and heating (hereinafter electricity), and scope 3 emissions are indirect emissions from other intermediate goods (hereinafter other intermediate input) excluding scope 2 emissions (GHG Protocol, 2004, 2008). Using the concept of scope 1, 2, and 3 emissions, we can reveal which supply chain has a larger CO2 reduction potential, and can propose efficiency improvement schemes in order to effectively reduce CO2 emissions. The novelty of this study is the following. We developed a new analysis framework including two stages: productive efficiency evaluation and CO2 reduction potential estimation consistent with the GHG Protocol (Fig. 2). In the first stage, we focus on five input factors (labor, capital, energy use, electricity use, and other intermediate input use) and one output (gross output) of 14 metal sectors in 40 countries from an MRIO database, and the data envelopment analysis (DEA) method was applied to estimate productive efficiency scores of specific metal sectors of specific nations. The DEA method, which was originally proposed by Charnes et al. (1978) and is based on the work of Farrell (1957), is widely used to evaluate relative efficiencies of decision making units (Seiford, 1996; Färe and Grosskopf, 2005; Cook and
2. Methodology 2.1. Data envelopment analysis In this study, we used an input-oriented constant returns to scale (CRS) model for the DEA method. Fig. 3 shows an illustrative example of input-oriented DEA. Following Cooper et al. (2007), efficiency score ij for sector i of country j can be obtained by solving the following linear program (LP):
Min. s. t.
ij
K ik x ik l k=1 K ik y ik m k=1 ik 0 (k =
ijx ij l
ymij
(l = 1, ..., L)
(m = 1, ..., M ) (1)
1, ..., K )
where x ij is the amount of input l used by sector i of country j, is the amount of output m produced by sector i of country j, and is the intensity weight on sector i of country k. ij indicates how much an inefficient sector i of country j potentially reduces the inputs, compared with the production possibility frontier formed by efficient countries. ij ij 1, where ij = 1 indicates that sector i of country j is satisfies 0 efficient, while ij < 1 implies the converse (i.e., sector i of country j is inefficient). We solved the LP for each metal sector and the nation. The numbers of metal sectors and countries in EXIOBASE2 are 14 and40,1 respectively, so i = 1, ..., 14 and j = k = 1, ..., 40 . We set five input factors (labor, capital, energy use, electricity use, and other intermediate input) and 1 output factor (gross output), so l = 1, ..., 5 and m = 1. It should be noted that in this study we used a CRS model rather than a variable returns to scale (VRS) model. When an efficiency evaluation is performed using a VRS DEA model, the sector of the country with the largest output is identified as efficient. Since China is the biggest producer in most of the metal sectors, the VRS model evaluates the metal sectors of China as performing production activities efficiently. However, the simple input efficiency index obtained by dividing each input factor of the relevant metal sector by the total output (for example, energy consumption divided by gross output) shows that China's basic iron and steel sector is very inefficient for all input factors compared to the other countries. Thus, in this study, a CRS model was
ymij ik
1 There are 43 countries in EXIOBASE2, but 3 countries are excluded in this study because of incomplete data.
2
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Fig. 2. Analysis framework.
different countries by dividing the 40 countries into two groups: countries whose production scale is very small, accounting for less than 0.1% of worldwide gross output (total of the 40 countries), and countries accounting for 0.1% or more of total world gross output, and we call this model the CRS separated model. 2.2. CO2 reduction potentials based on efficiency improvement Efficiency score ij obtained by DEA can be used to estimate the input reduction potential RPlij of input l for sector i of country j, which is in terms of a reduction of the input volume xlij , as follows:
RPlij = (1
ij )
× xlij
(2)
The input reduction potentials calculated using equation (2) can be multiplied by CO2 emission coefficients to estimate the CO2 reduction ij potential RPCO , which is the amount of CO2 emissions reduced by 2 improving the efficiency of sector i of country j in accordance with efficiency score ij , as follows: Fig. 3. Illustrative example of input-oriented DEA.
ij RPCO = 2
applied. CRS models do not take economies of scale into account; however, by, for example, comparing a sector with very large-scale production (e.g., China's basic iron and steel sector) to a country with very smallscale production (e.g., Malta's basic iron and steel sector), a relative efficiency can be estimated. In this way, to avoid references between sectors of very different production scales, this study analyzes the efficiency of the metal sectors (e.g., the basic iron and steel sector) of
dlik × (1
ij )
× xlij, ik
(3)
where dlik is the CO2 emission coefficient for input l from sector i of country k and xlij, ik is the amount of input l that sector i of country j inputs from sector i of country k. Based on the calculation standards for scope 1, 2, and 3 emissions, the CO2 reduction potential calculated in equation (3) can be estimated for each “scope” under the assumption of an efficiency improvement of sector i of country j. The respective CO2 reduction potentials for scope 1, 2, and 3 emissions by sector i of country j can be expressed as follows: 3
Journal of Environmental Management 248 (2019) 109261
H. Takayabu, et al. ij RPScope 1 = ij RPScope 2
=
ij RPScope 3
=
K ij ) × x ij, ik d ik × (1 energy k = 1 energy K ik ij ) × x ij, ik d × (1 electricity k = 1 electricity K ik ij ) × x ij, ik d × (1 other other k=1
Table 2 Sectoral classifications for scope 1, 2, and 3 CO2 calculation.
(4)
where and are the CO2 emissions coefficients for, respectively, the energy, electricity, and other intermediate input of ij, ik ij, ik ij, ik sector i of country k, and x energy , x electricity , and x other are the amounts of, respectively, energy, electricity, and other intermediate input that sector i of country j inputs from sector i of country k. It should be noted that the efficiency improvement in a specific sector of a nation contributes to reduction in inputs of goods and services produced in its own country as well as other countries, therefore CO2 emissions at globe are reduced through efficiency improvement of a specific sector of a nation. ik denergy ,
ik delectricity ,
ik dother
No.
Sectors related to Scope 1 CO2 emissions (i.e., Energy sector)
1 2
5 6 7 8
Mining of coal and lignite; extraction of peat Extraction of crude petroleum and services related to crude oil extraction, excluding surveying Extraction of natural gas and services related to natural gas extraction, excluding surveying Extraction liquefaction, and regasification of other petroleum and gaseous materials Miring of uranium and thorium ores Manufacture of coke oven products Petroleum Refinery Processing of nuclear fuel
No.
Sectors related to Scope 2 CO2 emissions (i.e., Electricity sector)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 No. 1
Production of electricity by coal Production of electricity by gas Production of electricity by nuclear Production of electricity by hydro Production of electricity by wind Production of electricity by petroleum and other oil derivatives Production of electricity by biomass and waste Production of electricity by solar photovoltaic Production of electricity by solar thermal Production of electricity by tide, wave, ocean Production of electricity by Geothermal Production of electricity nec Transmission of electricity Distribution and trade of electricity Manufacture of gas; distribution of gaseous fuels through mains Steam and hot water supply Collection, purification and distribution of water Sectors related to Scope 3 CO2 emissions (i.e., Other intermediate sector) Other 137 sectors
3 4
2.3. Data All the variables other than capital used in the DEA are obtained from EXIOBASE2 (Tukker et al., 2013; Wood et al., 2015). The capital data of each metal sector in a country were estimated by dividing the capital value of the sector recorded in the Socio Economics Accounts of World Input-Output Database Release 2013 (Dietzenbacher et al., 2013; Timmer et al., 2015) by the fraction of the gross output of the sector recorded in EXIOBASE2 (see Table 1 for the disaggregated metal sectors in EXIOBASE2). In CO2 reduction potential estimation, direct CO2 emissions coefficient data (i.e., environmental inventory for CO2 calculation) by sector and country required for estimating CO2 reduction potential of scope 1, 2, and 3 emissions were obtained from EXIOBASE2. Table 2 shows the sectoral classification for the scope 1, 2, and 3 CO2 calculations. 3. Results and discussion
3.1. Efficiency scores
Fig. 4 shows actual baseline scope 1, 2, and 3 CO2 emissions of 14 metal sectors calculated based on the GHG Protocol calculation standards. The largest CO2 emitter was M.01 (basic iron and steel sector), followed by M.14 (fabricated metal sector), M.02 (re-processing steel sector), and M.05 (aluminium sector). These four metal sectors accounts for a significantly large percentage, approximately 90% of the total CO2 emissions. Therefore, we present results and discussion with a focus on these sectors in this section. Empirical results for the four metal sectors are described in Table 3. See Appendix A for the results for all 14 metal sectors. Efficiency scores are shown in Tables A.1 and A.2, and CO2 reduction potentials and their breakdown are presented in Table A.3.
From Table 3, the numbers of inefficient countries (i.e., < 1) are 20 in the basic iron and steel sector, the re-processing of steel sector, and the aluminium sector, and 27 in the fabricated metal sector. In the case of the basic iron and steel (re-processing of steel, aluminium, or fabricated metal) sector, Estonia (Indonesia, India, or Estonia) has the lowest efficiency score at 0.61 (0.67, 0.63, or 0.73), which means that the sector in this country can potentially reduce its inputs quantities by 39% (33%, 37%, or 27%) compared to the production possibility frontier of efficient countries. Fig. 5 shows a world map with efficiency scores, with efficient countries shown in the lightest blue and other countries shown in increasingly darker blue for greater inefficiencies. Countries shown in red are not engaged in the production (i.e., gross output of the country for
Table 1 Classification of 14 metal sectors. Code
Shortened name of metal sector
Name of metal sector in EXIOBASE classification
M. M. M. M. M. M. M. M. M. M. M. M. M. M.
Basic iron and steel Re-processing of steel Precious metal Re-processing of precious metal Aluminium Re-processing of aluminium Lead, zinc and tin Re-processing of lead Copper Re-processing of copper Other non-ferrous metal Re-processing of other non-ferrous metal Casting of metal Fabricated metal
Manufacture of basic iron and steel and of ferro-alloys and first products thereof Re-processing of secondary steel into new steel Precious metals production Re-processing of secondary preciuos metals into new preciuos metals Aluminium production Re-processing of secondary aluminium into new aluminium Lead, zinc and tin production Re-processing of secondary lead into new lead Copper production Re-processing of secondary copper into new copper Other non-ferrous metal production Re-processing of secondary other non-ferrous metals into new other non-ferrous metals Casting of metals Manufacture of fabricated metal products, except machinery and equipment
01 02 03 04 05 06 07 08 09 10 11 12 13 14
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emissions are related to energy combustion and chemical process. In contrast, in the aluminium sector, scope 2 emissions account for 78%, implying that emissions reductions are needed through further electricity conservation. These differences are caused by differences in sectoral characteristics. Moreover, national characteristics can be seen in the variation of the breakdown by country. One initial purpose was to propose effective climate mitigation policies (i.e., efficiency improvement schemes) with a focus on such sectoral and national characteristics. The potentials are relatively small in most countries compared to China, but providing these countries with efficiency improvement schemes is also important because these countries also need to reduce their CO2 emissions in order to achieve CO2 reduction targets. We will discuss the efficiency improvement scheme that should be adopted in each sector and nation in the following section. 3.3. Discussion
Fig. 4. CO2 emissions totaled over 40 countries by 14 metal sectors for 2007.
As mentioned in the previous section, the source for CO2 reduction potential differs depending on the sectoral and national characteristics. Therefore, the breakdown of the potentials should be examined to propose efficiency improvement schemes in order to work effectively toward reducing CO2 emissions. We visualize the size of the CO2 reduction potential and its breakdown in Fig. 6. Note that Fig. 6 corresponds to Table 3. In Fig. 6, the size of each circle represents the size of the CO2 reduction potential, and the position of the circle shows the percentages of the scope 1 and 2 (and thereby scope 3) CO2 reduction potentials. Thus, the size of each circle indicates the impact of efficiency improvement, while the position of the circle implies which efficiency improvement would significantly contribute to reducing CO2 emissions. We propose three efficiency improvement schemes—the energy and process scheme, the electricity scheme, and the material and service scheme—as well as the hybrid scheme. The energy and process scheme is an efficiency improvement scheme with a focus on improving energy efficiency and chemical process and shifting low-carbon energy, and the scheme should be adopted by countries with larger scope 1 CO2 reduction potential. The electricity scheme is an efficiency improvement scheme with a focus on saving electricity use, and the scheme should be adopted by countries with larger scope 2 CO2 reduction potential. The material and service scheme is an efficiency improvement scheme with a focus on material and service efficiency improvement, and should be adopted by countries with larger scope 3 CO2 reduction potentials. In Fig. 6, the areas for these efficiency improvement schemes are defined in the following manner. The energy and process scheme area, shaded red, is where the scope 1 CO2 reduction potential accounts for more than 50%, the electricity scheme area, shaded yellow, is where the scope 2 CO2 reduction potential accounts for more than 50%, and the material and service scheme area, shaded green, is where the scope 3 CO2 reduction potential accounts for more than 50%. In addition to these schemes, the hybrid scheme, in which all efficiencies are equally important, is proposed, shaded blue. For countries plotted in the energy and process scheme area, reducing energy loss or installing superior equipment is critical to increasing energy efficiency and reducing CO2 emissions (Kim and Worrell, 2002; Hasanbeigi et al., 2013). In addition, decarbonisation through shifting low-carbon energy such as biomass is also crucial for reducing scope 1 CO2 emissions. Saving electricity in the production activity and changing the electricity mix (i.e., increasing the proportion of renewable energy) help countries in the electricity scheme area to achieve their scope 2 CO2 reduction potentials. “Doing more and better with less”, aimed at the SDG 12 on SCP (UNESC, 2017), is especially important for countries with larger proportions of scope 3 CO2 reduction potentials. Such counties need to improve material and service efficiency through material substitution and supply chain management and technology innovation (Söderholm and Tilton, 2012; Milford et al., 2013; Henzler et al., 2017).
the sector is 0). As shown in Fig. 4, eastern European countries (particularly former communist countries) tend to be relatively inefficient. This tendency can be interpreted as indicating that the former communist countries have difficulty introducing modern production equipment invented in developed countries (Fujii and Managi, 2015). This also applies to China, India, and Brazil. Especially, China and India, which account for a high percentage of the global gross output among the four metal sectors, scored in the bottom five for efficiency scores. China and India are engaged in large-scale and low-efficiency metal sectors. Either China and India need to make efficiency improvements, or efficient countries need to increase their production, as the latter possess high-efficiency metal production technologies. The results of efficiency scores show which countries are more inefficient; however, these results are not enough to understand the impacts of efficiency improvement on CO2 emissions, which are provided in the next section. 3.2. CO2 reduction potentials In Table 3, the CO2 reduction potentials through efficiency improvement and their breakdown are presented. The total CO2 reduction potential in the basic iron and steel (re-processing of steel, aluminium, or fabricated metal) sector is 354.4 Mt (82.4 Mt, 54.8 Mt, or 99.6 Mt). Compared to the actual CO2 emissions described in Fig. 4, the amount of CO2 reduction potential in these metal sectors is 591 Mt, which accounts for 17.2% of the actual total CO2 emissions in these metal sectors (3,442 Mt). The impacts of efficiency improvement (i.e., technological catch-up) are significant, so there is a need for policies that fill the technological gap between countries. Focusing on the value of the CO2 reduction potential, China has the largest CO2 reduction potential among countries classified as being inefficient in the DEA, at 290 Mt, accounting for 82% of the total CO2 reduction potential of the basic iron and steel sector (354 Mt-CO2). Similarly, China has the largest CO2 reduction potential among the countries classified as being inefficient in this study for the other three metal sectors of focus as well. Specifically, China's CO2 reduction potential in the re-processing of steel (aluminium or fabricated metal) sector accounts for 73% (77% or 66%) of the total CO2 reduction potential. This implies that it is critically important to mitigate global warming through technological catch-up in Chinese metal sectors within the framework of climate finance (Climate Policy Initiative, 2014). Following China, India and Brazil have the largest amount of CO2 reduction potential, accounting for 37 and 18 Mt, respectively. Then, focusing on the breakdown of the total CO2 reduction potentials in the basic iron and steel sector, scope 1 emissions account for 71%. This indicates that it is important for the basic iron and steel sector to reduce direct emissions by improving energy efficiency and chemical process and shifting low-carbon energy because scope 1 5
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1.00 0.99 1.00 1.00 1.00 1.00 1.00 0.61 0.95 1.00 1.00 0.87 0.73 1.00 0.92 1.00 1.00 0.70 1.00 1.00 0.81 1.00 0.75 1.00 1.00 0.96 1.00 0.93 0.92 0.73 1.00 0.91 0.78 0.73 1.00 1.00 0.88 0.85 0.99 0.76
Efficiency score (θ)
− 0.05 − − − − − 0.01 0.39 − − 0.19 0.44 − 1.19 − − 0.11 − − 1.94 − 1.92 − − 0.14 − 5.74 9.78 290 − 4.87 13.1 21.1 − − 0.83 1.81 0.17 0.66 354.4
Potential (Mt-CO2)
− 69% − − − − − 2% 48% − − 9% 68% − 43% − − 43% − − 72% − 83% − − 94% − 49% 55% 71% − 25% 80% 95% − − 35% 46% 39% 62% 71%
Scope 1 − 6% − − − − − 34% 19% − − 48% 6% − 9% − − 10% − − 11% − 6% − − 0% − 32% 10% 8% − 27% 3% 0% − − 44% 12% 26% 3% 8%
Scope 2
Breakdown
Basic iron and steel
− 25% − − − − − 64% 33% − − 43% 26% − 48% − − 48% − − 17% − 11% − − 5% − 19% 35% 21% − 48% 17% 5% − − 21% 42% 34% 35% 21%
Scope 3 1.00 0.92 1.00 NA 0.99 0.99 NA NA 0.96 1.00 1.00 0.85 0.73 NA 0.91 NA 1.00 0.72 NA 1.00 0.79 1.00 0.74 1.00 1.00 1.00 0.95 1.00 0.90 0.73 1.00 0.84 0.83 0.70 1.00 1.00 0.89 0.78 0.87 0.67
Efficiency score (θ) − 0.17 − NA 0.01 0.07 NA NA 0.17 − − 0.26 0.13 NA 0.73 NA − 0.02 NA − 0.88 − 0.83 − − − 0.11 − 3.09 60.3 − 2.94 2.05 6.91 − − 0.33 1.85 1.08 0.50 82.4
Potential (Mt-CO2) − 59% − NA 61% 47% NA NA 47% − − 6% 41% NA 44% NA − 99% NA − 48% − 41% − − − 46% − 41% 35% − 21% 65% 88% − − 11% 34% 21% 66% 40%
Scope 1 − 20% − NA 21% 33% NA NA 39% − − 77% 28% NA 24% NA − 0% NA − 36% − 42% − − − 37% − 36% 42% − 60% 16% 1% − − 82% 35% 63% 3% 38%
Scope 2
Breakdown
Re-processing of steel
Note 1: NA means that the country is not engaged in the production. Note 2: means that the country doesn’t have CO2 reduction potential because the country is efficient.
Austria Belgium Bulgaria Cyprus Czechia Germany Denmark Estonia Spain Finland France Greece Hungary Ireland Italy Lithuania Luxembourg Latvia Malta Netherlands Poland Portugal Romania Sweden Slovenia Slovakia United Kingdom United States Japan China Canada South Korea Brazil India Mexico Russia Australia Turkey Taiwan Indonesia Total
Country
Table 3 Empirical results for 4 metal sectors.
− 20% − NA 18% 21% NA NA 13% − − 17% 31% NA 31% NA − 1% NA − 16% − 17% − − − 17% − 24% 23% − 19% 19% 11% − − 7% 31% 15% 31% 22%
Scope 3 1.00 1.00 0.70 1.00 0.77 0.94 1.00 0.68 0.87 1.00 1.00 0.80 0.66 0.89 0.83 0.96 1.00 1.00 1.00 1.00 0.66 1.00 1.00 1.00 1.00 0.93 1.00 1.00 0.86 0.66 1.00 0.89 0.77 0.63 0.89 1.00 1.00 0.76 0.80 1.00
Efficiency score (θ) − − 0.15 − 0.23 0.46 − 0.00 0.45 − − 0.83 0.31 0.15 0.53 0.00 − − − − 0.59 − − − − 0.04 − − 0.50 42.5 − 0.67 2.69 3.60 0.13 − − 0.73 0.30 − 54.8
Potential (Mt-CO2) − − 21% − 14% 25% − 52% 21% − − 18% 25% 71% 34% 8% − − − − 32% − − − − 13% − − 31% 6% − 12% 58% 7% 9% − − 54% 22% − 11%
Scope 1
Aluminium
− − 69% − 67% 59% − 45% 60% − − 76% 65% 25% 40% 4% − − − − 58% − − − − 70% − − 28% 83% − 79% 31% 89% 81% − − 35% 39% − 78%
Scope 2
Breakdown
− − 10% − 19% 16% − 3% 19% − − 6% 10% 4% 27% 88% − − − − 10% − − − − 18% − − 41% 11% − 9% 12% 4% 10% − − 11% 39% − 11%
Scope 3 1.00 1.00 0.76 0.86 0.78 0.95 1.00 0.73 0.87 1.00 0.96 0.87 0.82 1.00 0.90 0.93 1.00 1.00 0.98 0.94 0.78 0.78 1.00 0.99 1.00 0.91 1.00 1.00 0.94 0.73 1.00 0.84 0.95 0.83 0.83 0.84 1.00 0.77 0.75 0.94
Efficiency score (θ) − − 0.12 0.01 0.86 1.20 − 0.15 1.58 − 0.32 0.25 0.21 − 2.16 0.01 − − 0.00 0.23 1.60 0.23 − 0.02 − 0.09 − − 1.34 65.3 − 1.68 0.57 4.86 0.76 13.4 − 0.92 1.40 0.36 99.6
Potential (Mt-CO2) − − 10% 2% 9% 5% − 4% 6% − 16% 4% 14% − 13% 8% − − 1% 10% 7% 12% − 5% − 6% − − 13% 13% − 8% 2% 2% 5% 2% − 3% 5% 1% 10%
Scope 1
− − 30% 23% 50% 37% − 37% 25% − 7% 49% 20% − 15% 8% − − 17% 18% 36% 16% − 5% − 12% − − 31% 43% − 35% 18% 57% 24% 63% − 20% 62% 50% 44%
Scope 2
− − 60% 75% 41% 58% − 58% 69% − 77% 47% 66% − 71% 83% − − 82% 73% 57% 71% − 90% − 82% − − 55% 45% − 57% 80% 42% 71% 36% − 77% 33% 49% 46%
Scope 3
Breakdown
Fabricated metal
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Fig. 5. World map with efficiency scores (θ). *NA means that the country is not engaged in the production (i.e., gross output of the country is 0).
According to Fig. 6, the energy and process scheme should be adopted in 10 (6, 4, or 0) countries in the basic iron and steel (reprocessing of steel, aluminium, or fabricated metal) sector, the electricity scheme should be adopted in 0 (4, 12, or 4) countries in the basic iron and steel (re-processing of steel, aluminium, or fabricated metal)
sector, and the material and service scheme should be adopted in 1 (0, 1, or 20) countries in the basic iron and steel (re-processing of steel, aluminium, or fabricated metal) sector. In the basic iron and steel sector, the energy and process scheme and the electricity scheme should be supported in many countries owing to their national characteristics:
Fig. 6. CO2 reduction potential broken down by country, sector, and scope, and schemes for improving efficiency. 7
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iron and steel production processes differ among countries, for example, some countries smelt iron from an oxygen-blown converter and others smelt iron from an electric furnace. For countries with larger scope 1 CO2 reduction potential, decarbonisation through the use of biomass is a crucial way to reduce CO2 emissions (Norgate et al., 2012; Jahanshahi et al., 2015). On the other hand, more countries should adopt the electricity scheme in the aluminium sector because of its sectoral characteristic (e.g., an electric furnace is generally used to melt aluminium, and metal products are needed to fabricate metal), whereas most of the countries should adopt the material and service scheme in the fabricated metal sector. World Alminium (2017) reports recent advances in technology such as reduction in process electricity intensity (i.e., kilowatt hours per ton of aluminium) in the aluminium sector, and Liu and Müller (2012) provide a critical review of the status and utility of LCA applications in the aluminium sector. In summary, countries with larger CO2 reduction potentials, such as China, India, and Brazil, should be given priority in investments to improve productive efficiency in the global climate mitigation framework (e.g., climate finance and technology transfer). This might appear to be obvious, but the present study has quantified the impacts of efficiency improvement on CO2 emissions. Moreover, efficiency improvement schemes that help countries to review their production activities (e.g., capital investment, and substitution of processes or materials) are proposed according to the sectoral and national characteristics.
Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.jenvman.2019.109261. References Allwood, J.,M., Ashby, M.,F., Gutowski, T.,G., Worrell, E., 2011. Material efficiency: a white paper Resource. Conserv. Recycl. 55, 362–381. American Iron and Steel Institute, 2010. Technology Roadmap Research Program for the Steel Industry. Bian, Y., He, P., Xu, H., 2013. Estimation of potential energy saving and carbon dioxide emission reduction in China based on an extended non-radial DEA approach. Energy Policy 62, 962–971. Charnes, A., Cooper, W.,W., Rhodes, E., 1978. Measuring the efficiency of decision making units. Eur. J. Oper. Res. 2, 429–444. Chen, L., Jia, G., 2017. Environmental efficiency analysis of China's regional industry: a data envelopment analysis (DEA) based approach. J. Clean. Prod. 142, 846–853. Climate Policy Initiative, 2014. The Global Landscape of Climate Finance 2014. Cook, W.,D., Seiford, L.,M., 2009. 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4. Conclusion In this paper, a novel framework incorporating the DEA method with an MRIO database into potential estimation considering scope 1, 2, and 3 CO2 emissions is proposed. This framework helps us to understand which sectors of a nation have larger potentials and what type of efficiency improvement would contribute effectively to reducing CO2 emissions. The framework was applied to evaluate the impacts of productive efficiency improvement of 14 metal sectors in 40 countries. The DEA result indicates the existence of technology gaps between developed countries and developing countries (particularly eastern Europe and BRICs). Based on the DEA result, the impacts of efficiency improvements that reflect sectoral and national characteristics are revealed. In terms of a global CO2 mitigation framework, China, India, and Brazil, which have an important role in metal production but are technologically immature, should be supported to improve their productive efficiencies. Regarding national CO2 mitigation policies, each sector of a nation should adopt the identified efficiency improvement scheme in order to effectively reduce CO2 emissions. Three further research directions can be drawn from this research. The first one is extension of the DEA model, since in this study, we preferred to choose a basic model in order to allow ease of interpretation of the results rather than a complex model. A non-radial DEA model (e.g. slack based measure model by Tone, 2001) or a network DEA model (see Kao, 2014) would help us to deepen the discussion. Secondly, more detailed breakdown for scope 3 CO2 reduction potentials can be performed to identify the source of potentials (e.g., material such as ore and lime, and services such as transport and waste disposal), because we aggregated 163 types of emissions into scope 1, 2, and 3 emissions. Finally, our further study will deal with potential errors in the modelling results due to the uncertainty of the model and the environmentally extended MRIO database. Acknowledgements This research was partially funded by the Grant-in-Aid for [JP17K12858, 16H01797, and 26000001] from the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan. All errors are ours. 8
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