Demand and supply-side carbon linkages of Turkish economy using hypothetical extraction method

Highlights

• This study presents different dimensions of Turkey's inter-sectoral carbon linkages from demand and supply.

• Restructures the modified hypothetical extraction method by applying Leontief inverse and Ghosh supply models.

• Secondary and tertiary blocks pulled the highest net emissions; they also lead the intra-block carbon sales and purchases.

• Primary and tertiary pushed the highest amount of emissions.

• Wind and Solar PV are amongst the most feasible and cleanest energy sources for the ‘Electricity, Gas and Water Supply.’.

Abstract

Inter-industrial carbon linkage analysis tells us about the transfer of CO₂ amongst sectors of a nation. Hypothetical extraction model which removes a target sector and compares the difference between actual and theoretical economies is a popular model for linkage analysis. Regardless of mounting evidence favoring simultaneous application of both Ghosh supply and Leontief demand for forward and backward linkages. Related studies have mostly calculated both upstream and downstream carbon linkages using only demand-driven Leontief inverse model. This research estimates inter-sectoral carbon linkages of Turkey from both demand and supply. Electricity, gas, and water had the highest total demand and supply carbon linkage. Extraction of backward and forward linkages of mixed services have the highest demand pull and supply push impact on rest of the blocks. It also had the highest amount of net pulled and pushed emissions. Production block had the highest intra-sectoral purchase emissions while electricity, gas, and water had the highest internal sales emissions. Wind and Solar PV are the cleanest energy sources for Electricity, gas, and water supply. A carbon demand and supply based policy diversifies emission responsibility and encourages mitigation of a block's entire upstream, downstream and intra-sectoral carbon chain.

Introduction

Turkey's economy and CO₂ emissions are rapidly rising (Ari, 2013). From 2000 to 2016, Turkey had the highest rate of energy demand in all OECD countries (Ministry of Foreign Affairs Turkey, 2018). The rapid expansion of energy production and consumption brought along a wide variety of environmental problems at various levels (Kaygusuz, 2009). Carbon-intensive growth policies have resulted in rapid growth of Turkish GHG emissions (Akbostancı et al., 2018). From 1974 to 2014 Turkey's carbon emissions increased by 466% which counted for 0.36% of world carbon emissions in 1974 and 0.96% in 2014 respectively (Pata, 2018a; World Bank, 2018). Lise (2006) predicted a six-time increase in Turkish CO₂ emissions from 1990's levels by 2025. Uneven growth patterns have resulted in unpredictable patterns of industrial carbon emissions (Acar and Yeldan, 2018). To achieve sustainable industrial production in Turkey, the calculation of CO₂ emissions and environmental impacts of production is of utmost importance (Kılıç, Puig, Zengin, Zengin, & Fullana-i-Palmer, 2018).

Turkey has pledged a 21% decrease in its ‘business as usual’ qualified carbon emissions by 2030 under ‘2015 Paris agreement’ (Kat et al., 2018). Additionally, Turkey under its ‘National Energy Efficiency Action Plan’ is planning to reduce 14% of primary energy consumption by 2023 (Rosca, 2018). It has also under its ‘National Strategy on Climate Change’ 2010 and ‘National Climate Change Action Plan’ 2011 specifically vouched to reduce its industrial emission intensities, improving industrial energy efficiency and increased waste use as alternative industrial fuel (UNFCCC, 2012). This comes contrary to all predictions about the increased rate of future carbon emissions, which makes it all the more challenging for the Turkish government to fulfill its promises. As per Fatma Güldemet SAR (Minister of Environment and Urbanization): ‘Turkey is committed to fulfill its duty in combatting climate change’ (Ministry of Environment and Urbanization, 2016). A comprehensive analysis of intermediate sectoral carbon stimulators will help in national industrial mitigation efforts and furthermore can assist Turkey in fulfillment of its international promises.¹ Studies on Turkish carbon emissions mainly focus on driving factors (Akbostancı et al., 2018; Halicioglu, 2009; Katircioğlu and Taşpinar, 2017; Ozturk and Acaravci, 2010, 2013, Pata, 2018a, 2018b), direct and indirect industrial emissions (Ari and Aydinalp Koksal, 2011;Dalkic et al., 2017; Ipek Tunç, Türüt-Aşik and Akbostanci, 2009; Kılıç et al., 2018; Kumbaroĝlu, 2011; özer, Görgün and Incecik, 2013), Carbon capture, trading markets and other policy (Ağralı et al., 2018; Akin Olçum and Yeldan, 2013;Ari, 2013;Ari and Sari, 2015; Kat et al., 2018; Kocabas, 2013; Yousefi-Sahzabi et al., 2017; Zengin and Ünal, 2017), alternative energy (Kok and Benli, 2017; Özer, 2017; M. Ozturk et al., 2017) and behavior (Adaman et al., 2011).

Sectoral linkage measurement allows us to assess a sector's link with the rest of the economic sectors (Cai and Leung, 2004). Classical multiplier (Chenery and Watanabe, 1958) is the traditional approach to sectoral linkage measurements. Chen et al. (2017) define the conventional multiplier approach as the column sum of Leontief for backward and a row total of the Ghosh model for forward linkage measurements. Hypothetical extraction method (HEM) creates an imaginary economy by removing a sector from the original it then makes a comparison between real and imagined economies (Ali, 2015). HEM has the capacity for calculation of the relative magnitude of a sector's impact holds preference over traditional multiplier which measures sectoral linkages merely upon simple technical coefficient averages (Clements, 1990). It is further classified into Original HEM (Strassert, 1968), Cella HEM with total, backward and forward linkages (Cella, 1984) and Modified HEM with net backward, net forward, internal and mixed industrial linkages (Duarte et al., 2002). Original HEM completely extracts a target block from an economic system and makes a comparison between before and after extraction values. While Cella's HEM only removes the external linkages of a specific sector. Modified HEM is a further decomposition of Cella HEM.

Literature covers, both Leontief demand-driven (quantity) and Ghosh supply-driven (price) models for calculation of different dimensions of industrial linkages (Deng et al., 2018; Guerra and Sancho, 2010; Leung and Pooley, 2002; Pérez Blanco and Thaler, 2014; Y. Zhang, 2010). Whereby owing to wide acceptance most of the regional studies using input-output have mainly focused on demand-driven models (Park, 2007).

This also holds for carbon and pollutants linkage literature under Hypothetical extraction model. Mostly Cella (1984) and Duarte et al. (2002) decomposition using Leontief demand have been applied to study these carbon and pollutant linkages. Related studies are based on Inter-industrial (Bai et al., 2018; He et al., 2017; Y. Wang et al., 2013; Zhao, Zhang et al., 2015) which through estimation of direct and indirect inter-sectoral carbon linkages, promote source and (or) destination targeted mitigation policies. Inter-regional (Y. Wang, Lai, et al., 2017; Y. Wang, Liu, Mao, Zuo and Ma, 2017; Zhao, Liu, et al., 2015) in conjunction with critical carbon sectors also identifies key emission regions. And household induced carbon linkages (Liao et al., 2017; Y. J. Zhang, Bian and Tan, 2018) mostly based on income groups present household impact as an intermediate industry on others.

Bon (1986) suggested the application of both Leontief inverse and Ghosh supply model for better understanding of demand and supply side of an economy. Relevant studies have advocated the use of Leontief inverse for backward and Ghosh model for forward linkage measurements (Beyers, 1976; Dietzenbacher, van der Linden and Steenge, 1993; Miller and Blair, 2009). Literature has also raised questions on the feasibility of Cella total, backward and forward linkages. Cai and Leung (2004) when talking about the critics including Guccione (1986) and Hirschman (1958) pointed out that the ‘fixed input coefficient’ assumption associated with Cella total linkage cannot measure a sector's forward linkage (sales) as the disappearance of a sector's sales will be substituted by imports. Song et al. (2006) summarising the criticism of Cella proposal wrote that there is a problem in accepting the very definition of Cella forward and backward industrial linkages, plus the application of ‘Leontief quantity’ and ‘Ghosh supply’ model for measurement of these linkages.

Miller and Lahr (2001) based on a comprehensive analysis of linkage related studies suggested simultaneous usage of both Leontief and Ghosh model for industrial linkage measurements.² Furthermore, they defended original Strassert (1968) HEM against criticisms, by Cella (1984) and Dietzenbacher and van der Linden (1997)of exaggeration, as a non-valid argument. Despite the suggestions, not many studies emerged with mechanisms of both ‘Leontief quantity’ and ‘Ghosh supply’ models. Song et al. (2006) measured inter-sectoral backward and forward economic linkages for construction sector of eight major economies by simultaneously applying Leontief demand and Ghosh supply-driven models — explicitly speaking about carbon linkage, Ali (2015) applied concepts of both models to carbon linkage problem. Where he made a comparison between different approaches by applying Leontief to backward and Ghosh model to forward carbon linkages under classical multiplier, Original HEM (Schultz, 1977; Strassert, 1968) and Cella (1984) HEM approaches. And in doing so identified key carbon sectors of the Italian economy for the year 2011 while using 2009 emission intensities as a proxy for 2011.

This research is novel in several aspects. First of all, there is not much literature available on inter-sectoral carbon linkages of the Turkish economy. Contrary to usual demand-driven approach our study reports both demand and supply-side emissions under direct multiplier, Total carbon linkage impact (Original HEM), and the impact of targets’ backward and forward CO₂ linkages on the remaining sectors. We have also restructured the so-called modified hypothetical extraction method by applying the Ghosh model for measurement of net forward (pushed) emissions, Leontief model has only been employed for measurement net backward (pulled) emissions. Additionally, we have introduced sectoral emissions from both internal purchases (emissions from intra-sectoral demand) and sales (emissions from intra-sectoral supplies). We call it the hybrid modified hypothetical extraction model. Finally, it presents policy implications based on our numerical findings.

This presentation of various aspects and dimensions of carbon emissions and linkages from both sides, i.e., from demand and supply will not only help us understand and comprehend complex inter-sectoral carbon linkages from these two sides of the Turkish economy. But also will assist in bridging the gap in sectoral carbon linkage analysis specifically via hypothetical extraction method which mainly relies on the demand-driven model for backward and forward linkage calculations despite having mounted evidence in favor of the application of both models for linkage analysis.

Industries are one of the primary sources of Turkish carbon emissions. A carbon policy targeted upon backward, forward and intra-sectoral stimulators rather than merely direct industrial emissions will help in mitigation of direct and indirect carbon discharges of a sector's entire intermediate supply chain, which will, in turn, promote efficient resource use, technological improvements, innovation, energy efficiency and finally cooperation amongst carbon purchasing and selling sectors.