An Analysis of Energy-related Greenhouse Gas Emissions in Turkish Energy-intensive Sectors

In recent decades, greenhouse gas (GHG) emissions have been a critical priority of global environmental policy. The leading cause of the increase in GHG triggering global warming in the atmosphere is the continuously growing demand for universal energy due to population and economic growth. Energy efficiency and reduction of CO2 emissions in highly-energy consuming sectors of Turkey are critical in deciding a low-carbon transition. In this study, the change of energy-related CO2 emissions in Turkey’s energy-intensive four sectors from 1998 to 2017 is analyzed based on the Logarithmic Mean Divisia Index (LMDI) method. It is used to decompose CO2 equivalent emissions changes in these sectors into five driving forces; changes in economic activity, activity mix, energy intensity, energy mix, and emission factors. Analytical results indicate that economic activity is a vital decisive factor in determining the change in CO2 emissions as well as sectoral energy intensity. The activity effect has raised CO2 emissions, while energy intensity has decreased. This method indicates that the impact of the energy intensity could be the first key determinant of GHG emissions. Turkey's efforts to be taken in these sectors in adopting low carbon growth policies and reducing energy-related emissions to tackle climate change are clarified in detail.


Introduction
Global warming and climate change is a worldwide issue that puts life on Earth seriously at risk. It threatens humanity's lives, destroys state economies, and transforms ecosystems. One million of the eight million species on the planet are at risk of being lost. Forest and oceans are being polluted and destroyed. The combating climate changes unavoidable damages economic growth in the long run unless measures are taken in the short term. Continued emissions of greenhouse gases would result in higher heating and long-term changes in all climatic characteristics. It would increase the probability of extreme, extensive, and inevitable effects on humans and the environment [1]. Therefore, people should immediately begin to engage in urgent, active, and cooperative actions based on mutual trust and understanding.
The energy sector is generally the most relevant in inventories of greenhouse gas emission, typically contributing more than 90% of CO2 emissions and 75 per cent of the overall greenhouse gas emissions in most countries. These values are 86.3 per cent and 72.2 per cent for Turkey in 2017, respectively. CO2 is responsible generally for 95% nitrous oxide and methane emissions of in energy sector [2]. Stationary combustion typically accounts for around 70 per cent of the energy sector GHG emissions. In the energy industries, approximately halves of those pollutants are connected to burning, especially energy plants and refineries. Mobile ignition is responsible for around one-quarter of the energy sector's emissions [3].
The Turkish economy and the nation's energy demand have steadily grown developments that are expected to continue. Turkey has an emerging private industrial economy in the fields of basic manufacturing, construction, finance, transportation, and communication. The Turkish market had a real annual gross domestic product (GDP) growth rate of 4.8% from 1998 to 2017.
GDP increased to over USD 1.206 billion in 2017, up from USD 505 billion in 1998. With its increasing energy needs primarily met by fossil fuels -particularly coal for electricity generation -Turkey's emissions are expected to rise substantially. Turkey's energy mix remains carbon-intensive, with fossil fuels representing 88% of total primary energy supply (TPES). The country dependents heavily on imported energy, notably oil and natural gas.
Therefore, Turkey's increase in greenhouse gas (GHG) emissions over the past decade. The overall GHG emissions were 526.3 Mt CO2 eq. in 2017, excluding the land use, land-use change, and forestry (LULUCF) sector. This value represents an increase of 245.6 Mt CO2 eq.
(87.8%) on total emissions in 1998 [3]. The primary reasons for the rise in all sectors are population growth, a rising economy, and increased demand for energy.
Turkey's energy demand growth is among the highest in the Organization for Economic Cooperation and Development (OECD). TPES has increased by 76% since 2005. This tendency would probably continue for the medium and long term. In 2023, the government expects primary energy demand to hit 218 million tons of oil equivalent (toe). Therefore, Turkey plans to reduce import dependency and ensure energy security by diversifying imports, integrating regional markets, increasing domestic production of coal, renewables and nuclear energy, and promoting energy efficiency.
Energy intensity (TPES divided by GDP) is around the OECD average. Turkey's energy intensity reduction goal of 20% by 2023 from 2011 requires additional efforts to be reached TPES intensity has decreased to 96% of the 2005 value. In contrast, the total final consumption intensity has decreased more as a percentage of 88%. The decrease in energy intensity is, however, nor steady. It remains dependent on external economic conditions, as the effect of the 2008-2009 global economic recession [4].
Since sustainable development and combating climate change became a vital issue in the 21st century, Turkey's governance should concentrate on both achieving economic efficiency and should also enhance the conservation of energy and ecological safety. For this purpose, Turkey could implement a long-term low-emissions policy that incorporates climate and energy targets. Therefore, the primary purpose of this study is to suggest an alternative strategy to analyze the sources of changes in energy-related emissions and assess the relative contributions of the sources for reducing emissions. To achieve this purpose, we try to identify and analyze the driving factors that contribute to changes in emissions in Turkey's high energy-intensive sectors (manufacturing industries and construction, transport, commercial /institutional /residential and agriculture/forestry/fishing) from 1998 to 2017. They have been analyzed based on the Logarithmic Mean Divisia Index (LMDI) method in this study. The LMDI method developed by Ang [1] is employed to decompose the changes in these sectors' CO2 equivalent emissions into five leading forces for reach GHG emissions reduction goals and determined to achieve a low-carbon transition for Turkey. These driving factors are; changes in economic activity (∆C ), structure effect (∆ ), sectoral energy intensity (∆ ), sectoral energy mix (∆ ), and emission factors (∆ ). Four types of fuels were used in the analyses; solid, liquid, gaseous, and other fossil fuels. Since CO2 emissions from biomass are not be included in the total CO2 emissions from fuel combustion, the biomass fuel type is not considered. GDP could be seen as the main driver of the GHG emissions in Turkey. Because GDP has increased with a ratio of 138.5%, and this ratio is more significant than the rising of GHG emissions in the same period, even though there is a rising trend in total emissions from 1998 to 2017. The essential and interesting main findings are; 1) Economic activity (GDP) is the crucial decisive factor behind the change in CO2 emissions and accounts for most emissions 2) the driving factors of energy intensity, energy mix, and energy structure have a decreasing effect. There is a rising trend in the total GHG emissions from 1998 to 2017; however, the economic recession had directly caused a reduction in the total GHG emissions in 1994, 1999, 2001, and 2008.

Literature review
In this analysis, the LMDI (Logarithmic Mean Divisia Index) method developed by Ang [5] is employed among available methods to identify the key contributing factors to the increased CO2 emissions of Turkey's four foremost combustion sectors. Ang stressed that the decomposition analysis had become an increasingly important subject area in the energy field in the last 40 years. He analyzed different index decomposition analyses and proposed that the multiplicative and additive LMDI method is used in the sense of decomposition analysis because of its analytical structure, ease of usage and adjustability and interpretation of results, and some other attractive features.
The LMDI method of decomposition has become a commonly utilized technique for analyzing ecological subject matter to evaluate the factors affecting the carbon emissions of many industries. Since its incomparable advantages, including a high analytical basis and proper adjustment in the application. That's why it is used in different countries such as China [[6]- [15]], Greece [16], Tunisian [17], India [18], Nigeria [19], Spain [20], Mexico [21], Philippine [22] and Turkey [23]- [27]. Researchers usually tend to use five main factors for the decomposition factors; industrial activity, industrial structure, energy structure, energy intensity, and emission factor. Some of the other researchers have added three other factors, such as; productivity, investment intensity research & development, to analyze these factors affecting GHG emissions in many industries, especially the industrial sector [28]. Lise [24] also found that relatively rapidly growing economies, the most significant key driver in raising CO2 emissions is economic development. In contrast, the decreasing energy intensifies of the economy is accounted for a small decline in CO2 emissions in Turkey during the period 1980-2003.
Akbostancı et al. [25] decomposed and analyzed CO2 emissions of five sectors of the Turkish economy between 1990 and 2013. These sectors are; manufacturing, electricity and heat production, transportation, and residential industries. They found that the intensity of energy and economic activity is the decisive key drivers that cause a change in CO2 emissions. The first two sectors (MC and electricity) are the main crucial sectors that prevail in the alteration in GHG emissions. Furthermore, particularly for the MC sector, the fuel mixture component reduces the CO2 emissions during the times of economic downturn.
Tunç et al. [26] also utilized the LMDI technique to determine the decisive determinant of three main sectors of Turkey (agriculture, manufacturing, and services) carbon dioxide emissions.

Tunç and his colleagues decomposed and analyzed GHG emissions of Turkey for 1970-2006
for examining the impacts of various macroeconomic policies on GHG emissions employing alteration in a portion of manufacturing and the usage of distinct energy resources. The investigation concluded that the primary increase in greenhouse gases is economic growth. In contrast, energy intensity brings down CO2 in 1980-1990 and 1995-2008 periods, and the industrial system is not a significant factor in minimizing carbon dioxide. Moreover, taking into account the contributions of sectors to CO2 emission changes, it was also found key drivers in CO2 emissions; the industry and services industries as predicted. The industry's contribution has increased in the latest years.
Rüstemoğlu [28] aimed to identify and analyze the factors that are increasing or decreasing the CO2 emissions for Turkey and Iran between 1990-2011 by utilizing the LMDI decomposition technique. The primary determinant of CO2 emissions for both countries is economic development and population. Surprisingly, the impact of energy intensity could be the third significant determining factor in Iranian carbon emissions. In contrast, it has a minimal lower impact on Turkey.
Ediger and Havuz [29] have used the LMDI method for evaluating sectoral energy usage in the Turkish market from 1980-2000. While there is a strong connection between primary energy usage and Gross Domestic Product, analyzes indicate that there were significant differences in the sectoral energy consumption during the periods 1982, 1988-1989, 1994, and 1998-2000. They concluded that the vital driving force for strengthening the Turkish economy's energyeconomy relationship seems to be government policies. Such policies would include improvements in the composition of final energy needs, improved material, and energy quality, and the replacement of more suitable products and oils.
Chontanawat et al. [30] also used the LMDI method to decompose the Thai manufacturing emissions. The study, therefore, argues for strategies to curb the energy density of enterprises in industries to Thailand can profit through growing without having to incur pollutants furthermore.
Trotta [31] has isolated and quantified energy savings generated by improvements in energy Shao et al. [32] have used the LMDI model. Tapio decoupling model and an emission estimation technique to predict to analyze the related decoupling and its impact influence the growth of China's commercial aviation and pollution, along with estimate predicted CO2 pollution. They found that cumulative greenhouse gases change over time on a generally ascending tendency, but there is consistent downward tendency oil consumption per tonnekilometer revenue. The transportation quantities growth impact is contributing effect to increased CO2 pollution among the four main drivers; accompanied by changes in the transport structure and alternate fuel effects. The "pace of energy consumption" aspect plays an essential part in hindering CO2 emissions. They also ended up estimating that China's commercial aviation would be accountable for 0.13 Gt of CO2 emissions by 2020 depending on eight simulations. A factor of 1.6 to 3.9 could raise CO2 emissions between 2020 and 2050.
Shao et al. [33] have used LMDI to factors influencing the usage of natural gas in every province in China. Generally, the primary critical factors of natural gas usage are the financial impact and the impact of the fossil fuel energy sector. The analyzes show that the effect on energy intensity is one of the critical restricting drivers for natural gas consumption; the most significant key drivers for natural gas usage in the net spillover block is the economic impact, followed by spatial expansion. They also found that low energy performance is a significant impediment to the growth of the Xinjiang and Ningxia natural gas sectors. The effect of population density is a significant driver leading to a discrepancy between Beijing and Shanghai in the market competition for natural gas. They concluded that it is particularly significant to prepare reasons for the growth of the natural gas sector to attain the energy transition targets and propose that the Chinese government increasing tax transfer payments, help net spillover growth.
Zhang et al. [34] analyze Wang et al. [36] used the aggregate strength of the production of nitrogen oxides (ANI) to be temporarily and spatially decomposed from an electricity-related NOx processing context. In Furthermore, the outcomes of analyzing electricity usage from an industrial and regional context demonstrate that the economic system and intensity of usage have different effects in the eastern, central, and western regions. Jiang et al. [41] used a Kaya identity-based method of the LMDI to decompose the determining drivers via growth of population, economic development, regional trade system, regional industry system, and energy usage. They have studied the non-residential energy consumption of China from 2007 to 2016. They consider that economic development is the key factor affecting the increasing energy consumption in non-residential areas. Besides, the increased energy consumption rate as a second key factor reduces the growth of non-residential electricity usage. Finally, growth in the population as the third factor has a low impact on rising electricity usage.
Jiang et al. [42] used a multi-regional input-output method to measure the energy consumption embedded in the global trade of 39 nations from 1995 to 2011. Later, they used the LMDI method to describe the main factors of energy consumption. They found that foreign trade development, economic growth, and inhabitants are due to increased use of embodied energy.
Conversely, the modernization and optimization of industrial systems and structures would support to reduce the growth of embodied energy usage.
To achieve emission reduction targets for determining to make a low-carbon transition of Turkey, we conduct a specific investigation on Turkish high-energy intensive combustion four sectors: manufacturing industries and construction sector (MC), transport sector, commercial/institutional/residential sector (CIM), and agriculture/forestry/fishing sector (AFF) for period 1998-2017 by employing LMDI-I method.

The LMDI method
The LMDI method developed by Ang [5] is used in this study to decompose the driving factors of on Turkish main four combustion sectors CO2 emissions from four fuel type combustion as follows: Where C is the total CO2 emissions of Turkish four combustion sectors; i specifies the i-th combustion sector; j represents the jth type of fuel; C is the CO2 emissions from fuel j in the sector i, Q(= ∑ Q ) is the overall economic activity level, Q is the activity level of sector i, is the j type of energy usage of sector i, and the unit of this variable is Tj, E is the consumption of fuel j in sector i. The unit of CO2 emissions is 10 3 tons, and energy consumed is taken as 10 9 kilojoules.
Where Si =Qi/Q represents the industrial structure, Ii (=Ei/Qi) represents the energy intensity of sector i; Mij (=Eij/Ei) is the fuel-mix variable, and Uij (=Cij/Eij) represents the CO2 emissions factor of fuel j consumed in i sector.
The general identity of decomposition index is given by The aggregate changes from In multiplicative decomposition, we decompose the ratio: In additive decomposition, we decompose the difference: where subscription of tot indicates overall or sum change and the superscript T refers to period T and 0 refers to period 0.
The basic equations for the impact of the kth component on the right side of Equations (3) and (4) are alternatively used in the LMDI approach: , Where L(a,b)=(a-b)/ (ln a-ln b), where both a and b positive numbers and a=b as defined in Ang, 2004 [43] . Therefore, the changes in the CO2 equivalent emissions of four incineration sectors in Turkey between a target year t and a base year 0, displayed by ∆Ctot has been decomposed by the LMDI into the five components as illustrated; (i) the economic activity effect (shown as ∆Cact ); (ii) the structure effect (shown as ∆Cstr ); (iii) the sectoral energy intensity effect (shown as ∆Cint); (iv) the sectoral energy-mix effect (shown as ∆Cmix); and (v) the emissions factor effect (denoted as ∆Cemf ) in additive form, as shown in Equation (6): The LMDI can be expressed as:

Data Source
The LMDI-based method of decomposition is used to determine the key drivers and to evaluate the contribution of these factors to change GHG emissions in Turkey's four-combustion sectors individually from 1998 through 2017 years. During the application of the decomposition analysis, CO2 equilibrium emissions data sets employed in the Turkish Greenhouse Gas Inventory 1990-2017 report and CRF tables submitted to the UNFCCC secretariat [3].
Therefore, the study's data set complies with international standards.
The Ministry of Treasury and Finance employs GDP and economic development data. National 1990-2014 periods [3]. Therefore, the aggregated values of the two sectors are used.

Overview of necessary information related to the four sectors
For most countries, energy systems are powered primarily by the combustion of fossil fuel.
Generally, the energy sector is the most significant in GHG inventories, and usually generating

Sectoral CO2 emissions in Turkey
Among all sectors, the energy sector has the highest share, with 72.2%. The total amount of the energy sector's emissions in 2017 was predicted to be 379.9 Mt CO2 equivalent, where the industrial processes and product use is the second-largest GHG sector with 12.6%. The agricultural activities with 11.9% and the waste with 3.3% follow it. CO2 emissions per capita were 4.5 tons in 1998, compared to 6.6 tons in 2017 [2]. Emissions per capita are still below the OECD average but are rising rapidly. Emissions intensity is declining, but not as much as the OECD average [4].  Figure 3 also holds for Figure 4 Figure 6.

Analysis of energy-related GHG emissions
In this study, we have applied the LMDI method to decompose the changes in the CO2 equivalent emissions of Turkish main four fuel combustion sectors into five effects for the period 1998-2017 by the usage of LMDI. The entire decomposition of CO2 equivalent change emissions is listed in Table 2. The reduction of CO2 emissions results from significant improvements in energy intensity, following by the energy-mix and sectoral energy structure. In general, the energy intensity effect is having a reducing effect on CO2 emissions from all sectors evaluated in this study. However, the effect of economic activity is the sole highest significant contribution to increasing CO2 emissions of four primary incineration sector in Turkey (Figure 7).

Sectoral structure effect
The structural effect is the factor that indicates the change in the value of each sector value within the total economic activity (GDP). From figure 8, it is observed that the share of emissions in the manufacturing sector decreased from 19.8% to 11.4%, and the agriculture sector decreased from 3.2 % to 1.9% overall CO2 emissions during the analysis phase, respectively. In contrast, the transport sector and commercial sector were increased from 11.7 % to 16.1 % and from 10.3% to 12.0% respectively.     The intensity of energy is measured by the amount of energy per unit output or operation needed, such that the use of lower energy to generate a material decreases the intensity. The intensity effect decreases if the increase in economic output is higher than the increase in energy input. Application of more efficient, effective production techniques, efficient energy management, changes in product mix within or between sub-sectors, and improvements in the quality of material and fuel input reduce the intensity effect. This result means that now more output is produced with less energy, or more production acquired with the same amount of energy used [47]. Many recent academic studies have demonstrated that energy intensity reduction is limited or even reduced the increasing energy-related GHG emissions [6], [12], [23], [25], [48]- [51]. Energy intensities for four combustion sectors are presented in Figure 10. It is clear that while the transportation sector has the ultimate energy density, the AFF sector has a minimum from 1998-2017. Moreover, it is noticed that the energy intensity in agriculture fluctuates very much during the period. Finally, in 2017, they reached the level of 1998. Figure 10 illustrates  progress in administration level [14]. Unfortunately, the intensity effect has an increasing tendency from 2015 and so far. Figure 11. The effect of sectoral energy intensity on change in GHG emissions

Energy mix effect
This effect shows how industries are using available fuels and is calculated by dividing the energy consumption of a fuel type by the total energy consumption of that sector. Figure 12 demonstrates that the impact of energy mix (ΔCmix), which reduces the emission of 18.6 Mt CO2 eq., constituting 19.80 % per cent of the total change (ΔCtot). Figure 12 also shows that the sectoral energy mix impact has a lower impact on the complete change of CO2 emissions in accounted for reduction -24.6 Mt CO2 eq. GHG emissions, and fluid fuels were accounted for a reduction of -13.9 Mt CO2 eq. GHG emissions in the manufacturing sector. In contrast, gas and other fuels have a rising energy-mix effect on the same sector.   The key findings of this study could be summarized as follows: (1) There is a definite increasing trend between GDP and GHG emissions with different ratios, 138.5%, and 87.5%, respectively. However, after 2009, the proportional increase in economic growth in Turkey was higher than the proportional increase in greenhouse gas emissions and began to experience a divergence between these two variables. Energy demand in Turkey has increased more than GDP growth.
(2) Furthermore, the rise in greenhouse gas emissions is also lower than the rising energy demand. This finding means that the increase in the energy demand of Turkey began to meet from renewable and non-emission sources such as solar, wind. The accelerated legal regulations and increasing investment in renewable energy in the near term in Turkey have an important in the formation of this situation. If renewable energy production policies implement in the following recent years, it would have significant results in reducing greenhouse gas emissions.
(3) Turkey, as a developing country, and has economic growth, demands a high amount of energy, and is carrying out intensive fossil fuel consumption. The empirical results indicated a general increasing tendency in the operating period of CO2 emissions from all four-fuel combustion sectors. Analysis results indicate that economic activity is the primary decisive agent behind the rise in GHG emissions as well as sectoral energy intensity. Namely, the growing economic activity and reducing energy intensity are the significant contributors respectively to increasing and decreasing CO2 emissions of Turkey four fuel combustion industries. The emission-reducing effect of the energy density factor could be seen in a considerable reduction in greenhouse gas emissions of manufacturing and commercial sectors in the period of 1998-2017 as 39.3 and 11.5 Mt. CO2 eq. GHG emissions. Energy structure and emission factor effects would have few impacts on CO2 emission change because of the lack of adequate changes in energy structure during the study period.
(4) The energy intensity of the Turkish manufacturing and commercial industries decreased respectively by 39.3 and 11.5 Mt CO2 eq. GHG. Unfortunately, there is no progress in the energy intensity of the transport sector, and it causes 10 Mt CO2 eq. emissions increasing.
(5) It is crucial for the improvement of energy efficiency for combustion sectors to be promoted by presenting and applying the latest technology and techniques. Even though energy intensities of only manufacturing and commercial sectors have displayed continual reduction during the 1998-2014 period, the rate of decrease has decelerated and, after 2017, slightly increased. During this time, GHG emissions reduction related to the energy intensity falling has shown that energy intensity could be reduced by improving energy use.
(6) Our analyses emphasized the importance of energy efficiency as the first source of energy. And it exposed the need to establish more favorable conditions for further actions and investment in this area, as in the current economic conditions, which would progress naturally at a slower pace recently. It should be understood that public and private sectors' investment in energy efficiency must be weighted with similarly with any other energy security measures such as renewable incentives, or assured pricing for new nuclear power plants in Turkey. shifting the product mix with high value-added products must be provided to achieve a lowcarbon Turkey. In this study, the reduction of emissions is of great importance for many researchers, and scientists in countries intend to combat climate change and transition to low carbon or green economy. For this purpose, it is crucial to determine the key drivers that affect the change of emissions and to determine the effect of these drivers on the sectors' emissions reduction. For this purpose, the changes for GHG emissions from four energy-intensive sectors in Turkey have been analyzed by the LMDI approach. This method displays that the effect of energy intensity could be the first primary determining factor in GHG emissions. Since its effect has a reducing impact on CO2 emissions from all sectors evaluated in this study and also there is a reduction of CO2 emissions (43, 6 Mt) resulted from notable developments in energy intensity during the period for 1998-2017. It is following by the energy-mix (18.6 Mt) and sectoral energy structure (8.1 Mt). The study, therefore, calls for policies aimed at reducing the energy intensity of companies in the manufacturing industries and construction sector, Commercial/institutional/Residential industries so that Turkey can make use of industrial development without having to cause more GHG emissions. Furthermore, we also could propose that Turkey and other developing countries should continue its attempts to raise the share of renewable energy sources and add nuclear energy to its energy mix to reduce its reliance on energy import. They also would optimize the use of natural resources and combat climate change under the enrichment of the national energy mix topic. Moreover, it is explained what this model does and measures to be taken on a sector basis.