Conducting a Techno-Economic and Environmental Impact Analysis for the Use of Waste Heat from Geothermal Power Plants in District Heating for Western Anatolia

Abstract

Binary-cycle geothermal plants are inherently limited by thermodynamics, forcing operators to reinject fluids at temperatures that are still valuable for direct heating. This process results in substantial exergetic waste. While prior research has examined efficiency at the level of individual plants, this study introduces a regional-scale framework to convert these facilities into multi-purpose energy hubs. The research focuses on Türkiye’s Western Anatolia Graben, a region with high geothermal activity that, paradoxically, remains dependent on fossil fuels. By combining meteorological records with operational plant data, we evaluated the existing housing stock of 983,277 residences across 14 districts and modeled the heating requirements for a targeted capacity of 468,719 residences that the proposed system can serve. The results indicate that the currently wasted thermal load in 10 specific districts, including key centers such as Sarayköy and Alaşehir, is sufficient to cover peak winter heating demands without fossil fuel backup. Although the infrastructure requires a significant initial investment of $4.51 billion, the project demonstrates long-term viability with a Levelized Cost of Heat (LCOH) of 62.94 USD/MWh and a payback period of 10.43 years. Beyond economic considerations, the system serves as a major decarbonization tool, capable of cutting residential CO₂ emissions by 1.7 million tons annually (a 47.7% reduction). These findings suggest that policy incentives should move away from electricity-only models toward integrated reservoir management to maximize resource efficiency.

1. Introduction

In the world economy reshaped by the climate crisis, ensuring energy supply security is no longer merely a technical necessity but a strategic matter of survival that will determine the future of nations. Increasing energy demand and environmental concerns make it imperative to use existing energy resources as efficiently as possible. In this context, accurately calculating the heating load of buildings, which account for a significant share of energy consumption, forms the basis for energy efficiency efforts. A building’s total heat loss coefficient is calculated using parameters such as the heat transfer coefficients of building elements, surface areas, and air exchange coefficients [1].

Geothermal energy plays a critical role in meeting heating demand due to its low carbon footprint and base-load characteristics. However, the efficiency of geothermal power plants is highly sensitive to outdoor conditions and source temperature. From a thermodynamic perspective, the outlet temperature of the geothermal fluid depends on the difference between the inlet fluid energy and the energy remaining after electricity generation [2]. Zarrouk and Moon state that plant efficiency is directly affected by the inlet water temperature, while Kaya and Öztürk point out that high condenser temperatures, especially during the summer months, cause a drop in plant efficiency [3,4].

The performance of heat exchangers used to manage these efficiency fluctuations and recover waste heat back into the system is decisive for overall system success. Plate Heat Exchangers (PHEs), which offer approximately three times higher efficiency compared to conventional tube heat exchangers, stand out in this field [5]. The chevron or zigzag geometric patterns designed on the plate surfaces induce artificial turbulence in the flow regime, breaking the boundary layer resistance and enabling high heat transfer coefficients [6]. However, these highly efficient systems also bring operational challenges. Fouling and scale buildup on plate surfaces can increase heat transfer resistance and reduce performance [5]. Therefore, to improve the dynamic response and maximize operational efficiency, the integration of advanced control algorithms such as two-stage H∞ loop shaping is recommended in the literature [7].

In implementing geothermal district heating systems, economic and environmental sustainability are as decisive as technical success. Although such infrastructure projects typically require high initial capital expenditure (CapEx), they differ from fossil fuel alternatives by offering low and predictable marginal costs during the operational phase [8]. In environmental impact assessments, the stoichiometric combustion methodology proposed by Sterligov is followed for the emission load of natural gas, which is the dominant energy source at present. The emission factor for coal, which has a higher carbon intensity, is accepted as 109.5 tons of CO₂/TJ, in line with the nature of reserves in Türkiye [9,10].

The recent literature emphasizes the need to move beyond single-plant efficiency toward regional-scale optimization to fully unlock the decarbonization potential of district heating. As highlighted by Malcher et al. [11], modern decarbonization strategies within the Avoid-Shift-Improve (ASI) framework prioritize waste heat recovery and heat pump integrations as the most effective “Shift” mechanisms to replace fossil fuels. This transition is particularly critical for long-distance heat transport systems. Molar-Cruz et al. [12] demonstrated through a techno-economic optimization in the Bavarian Molasse Basin that connecting multiple urban heat sinks via extended transmission pipelines can significantly decrease the Levelized Cost of Heat (LCOH) despite high initial infrastructure costs. Furthermore, capturing low-temperature waste heat requires advanced thermodynamic management; Li et al. [13] recently showed that integrating multi-generation cycles with geothermal resources and post-combustion capture can drastically improve overall exergy efficiency. Validating these complex, interconnected systems under real-world conditions remains a challenge, yet studies such as the advanced exergetic evaluation by Arslan and Arslan [14] on the Simav GDHS prove that identifying unavoidable exergy destructions is key to determining the true improvement potential of existing regional heating networks.

This study uses a holistic approach to address the technical parameters, economic constraints, and environmental impacts discussed in the literature. Within the scope of the study, the potential for utilizing waste heat from geothermal power plants (GPPs) in district heating systems was analyzed in 14 strategic settlements located in the Aegean Region of Türkiye. The current heating profile of the total stock of 983,277 residences was examined, and a target population of 468,719 residences was identified based on the accessible waste heat capacity. Subsequently, the weighted average Levelized Cost of Heat (LCOH) and Payback Period (PBP) of the investment for this target capacity were calculated. The analyses revealed the potential reductions in annual CO₂ emissions from residential sources achievable by integrating geothermal waste heat into the system.

2. Materials and Methods

In this research, a multi-layered analytical approach was adopted to verify whether the geothermal potential of the region could serve as a viable alternative to fossil fuels. The methodology follows a sequential workflow: first, determining the available waste heat capacity from the power plants; second, quantifying the physical heating demand of the housing stock; and finally, subjecting the proposed system to environmental and financial stress tests. This section presents the mathematical models and input parameters used to simulate these operational scenarios under real-world conditions.

Before detailing the physical architecture of the proposed heating networks, the geographical boundaries of the analysis must be established. The selection of the 14 districts within the Western Anatolia Graben was not arbitrary; rather, it was driven by three primary technical and socio-economic criteria. First, proximity to active geothermal facilities was mandated. Only districts located within an economically feasible transmission radius (typically under 30 km) of active binary-cycle or flash-steam power plants that produce substantial reinjection waste heat were included. Second, the selected municipalities possess sufficient residential concentration and urban heat density to justify the highly capital-intensive nature of district heating infrastructure, which means that highly dispersed rural settlements were inherently excluded. Finally, priority was given to regions that exhibited a heavy reliance on imported natural gas and on solid, carbon-intensive fuels for winter heating, thereby offering the highest mathematical potential for regional decarbonization.

2.1. System Description and Configuration

Figure 1 presents the architecture of the Geothermal District Heating System (GDHS), illustrating its operational mechanisms and thermodynamic boundaries. At its core, the proposed framework captures and repurposes waste heat discharged by binary cycle geothermal power plants (GPPs). Rather than immediately sending the post-generation fluid into a reinjection well, the system routes this residual thermal fluid through a main transmission pipeline. It then reaches a central heat exchanger substation situated close to the target community, where the thermal energy is safely passed into a closed-loop urban distribution grid. Because reliable heating is critical during severe winter peaks, auxiliary boilers are integrated into the supply network to act as a supplementary heat source when necessary. From there, the heated water flows to individual residential buildings. Local heat exchangers and conventional end-user radiators perform the final step, releasing heat directly into living spaces. Consequently, the mathematical models used in the upcoming sections to evaluate exergy destruction, thermal loads, and financial costs are directly tied to the physical losses documented along the chain of components.

2.2. Thermodynamic Model

Equation (1) helps measure the efficiency of a binary cycle power plant based on the inlet temperature [1]. In this equation, η₁ represents the thermal efficiency of the GPP and T_in represents the inlet temperature of the geothermal fluid to the GPP (°C).

$${\eta }_1=\left[6.9681\mathit{ln}\left(Tin\right)\right]-29.713$$

(1)

Equation (1) was given as the basic efficiency definition for geothermal power plants in the study by Zarrouk and Moon, emphasizing that plant efficiency is directly affected by the inlet water temperature [4]. When the geothermal source temperature is low, the thermal efficiency of plants decreases; therefore, efficiency in steam-cycle plants is generally between 10–17%, while in binary-cycle plants it varies between 2.8–5.5%. Furthermore, high condenser temperatures during the summer months cause efficiency to decrease [2].

Equation (2) calculates the annual average electrical power, which forms the basis for the power plant’s performance analysis.

This relationship is based on the principle of dividing the plant’s total annual energy production by its active operating time. In this equation, P represents the average electrical power of the plant; K represents the total annual energy production (GWt); and $t$ represents the active operating time of the plant during the relevant period (hours).

$$P={\displaystyle \frac{K}{t}}$$

(2)

Equation (3) was developed to determine the thermal energy potential, a critical parameter in the performance analysis of geothermal power plants. This approach, proposed by Zarrouk and Moon, defines the thermodynamic relationship between the electrical power generated and the thermal capacity of the system, based on the conversion efficiency of the plant [4].

In this equation, $P_{thermal}$ represents the available waste thermal energy capacity of the geothermal power plant (MWt), $P$ represents the average net electrical power output (MWe), and the parameter ${\eta }_1$ represents the thermal efficiency of the system expressed as a dimensionless decimal fraction (e.g., 0.15 for 15%).

$$P_{thermal}={\displaystyle \frac{1-{\eta }_1}{n1}} P$$

(3)

Equation (4), based on the thermodynamic energy balance principle proposed by Zarrouk and Moon, defines the energy input-output relationship of the plant [4]. This equation is used to calculate the thermal energy discharged from the system to the environment. The term $Q_{\mathrm{o}\mathrm{u}\mathrm{t}}$ in the equation represents the thermal energy transferred from the system to the environment. $Q_{\mathrm{i}\mathrm{n}}$ on the right side of the equation represents the total thermal energy entering the system from the geothermal source, while the parameter P represents the net electrical power obtained from the power plant.

$$Q_{\mathrm{o}\mathrm{u}\mathrm{t}}=Q_{\mathrm{i}\mathrm{n}}-P$$

(4)

In Equation (5), $Q_{\mathrm{i}\mathrm{n}}$ defines the total thermal power extracted from the geothermal fluid, $\dot{m}$ represents the mass flow rate of the geothermal fluid (kg/s), $c_p$ denotes the specific heat capacity of the fluid (kJ/kg·K), and $T_{\mathrm{i}\mathrm{n}}$ and $T_{\mathrm{o}\mathrm{u}\mathrm{t}}$ symbolize the inlet and outlet temperatures of the geothermal fluid to and from the power plant, respectively (°C) [2].

$$Q_{\mathrm{i}\mathrm{n}}=\dot{m }{c}_p(T_{\mathrm{i}\mathrm{n}}-T_{\mathrm{o}\mathrm{u}\mathrm{t}})$$

(5)

Thermodynamic evaluations frequently rely on exergy as the definitive measure of a system’s true work capacity. By definition, it represents the absolute maximum theoretical work that can be extracted when a given system evolves into full thermal and mechanical equilibrium with its reference environment, typically designated as the dead state.

As Çengel and Boles emphasize in their classical thermodynamic framework, recognizing the non-conserved nature of exergy is critical. Unlike energy, which is strictly conserved under the First Law of Thermodynamics, exergy is inherently degraded during physical interactions. This degradation is encapsulated by the “decrease of exergy principle,” which posits that any real macroscopic process will inevitably result in a net loss of exergy. It is only within the strict, theoretical confines of an idealized, reversible process that this quantity remains unchanged [15].

Equation (6) is formulated to determine the exergy destruction occurring along the transmission pipelines. This loss is primarily driven by heat transfer to the surrounding environment and fluid friction along the network [16].

$$\dot{E}{x}_{loss,pipe}=\dot{E}{x}_{in,pipe}-\dot{E}{x}_{out,pipe}$$

(6)

In this equation, $\dot{E}{x}_{loss,pipe}$ denotes the exergy loss rate in the pipeline, while $\dot{E}{x}_{in,pipe}$ represents the total physical exergy rates of the fluid at the inlet and outlet of the pipeline segment, respectively.

To evaluate the thermodynamic irreversibilities during the heat transfer process, the exergy destruction in the heat exchangers is calculated using Equation (7).

$$\dot{E}{x}_{des,HX}=\left(\dot{E}{x}_{in,hot}-\dot{E}{x}_{out,hot}\right)-(\dot{E}{x}_{out,cold}-\dot{E}{x}_{in,cold})$$

(7)

Here, $\dot{E}{x}_{des,HX}$ represents the rate of exergy destruction within the heat exchanger. The terms with the “hot” subscript correspond to the exergy rates of the primary geothermal fluid (heat source), whereas the “cold” subscript indicates the exergy rates of the secondary network fluid (heat sink) [17].

A similar thermodynamic balance is applied to the end-user radiators, treating them as terminal heat exchangers that transfer thermal energy from the network water to the indoor environment. Finally, Equation (8) is utilized to express these component-level irreversibilities as a percentage, allowing for the identification of the most significant sources of exergy destruction.

$$\% \dot{E}{x}_{loss}=({\displaystyle \frac{\dot{E}{x}_{loss}}{\dot{E}{x}_{in}}})·100$$

(8)

In this expression, $\dot{E}{x}_{loss}$ represents the proportional exergy loss, calculated as the ratio of the exergy lost or destroyed ($\dot{E}{x}_{loss}$) in a specific component to the total exergy input ($\dot{E}{x}_{in}$) supplied to that exact component [18].

Exergy Analysis Model

In the exergy analysis, the conditions of the reference environment (dead state) were defined based on local winter ambient parameters. The reference temperature ($T_0$) was taken as the average seasonal outdoor temperature (e.g., 281.35 K or 8.2 °C for January in Aydın) and the reference pressure ($P_0$) was taken as the standard atmospheric pressure (101.325 kPa).

$$ex=c_p (T{-T}_0{-T}_0\ln ({\displaystyle \frac{T}{T_0}}))$$

(9)

Here, $ex$ defines the specific physical exergy of the liquid stream (kJ/kg), $c_p$ is the specific heat capacity (kJ/kg·K), $T$ is the absolute temperature of the stream (K), and $T_0$ is the dead state temperature (K). The total exergy rate (or flow of exergy) is calculated as $\dot{E}x=\dot{m}$ $ex$.

2.3. Thermal Load Calculation Model

To evaluate the energy performance of residential buildings and determine the heating system capacity, it is necessary to calculate the total heat loss through the building envelope. This loss consists of heat losses resulting from conductive heat transfer through building elements, such as exterior walls, windows, roofs, floors, and from air exchange (infiltration/ventilation).

Equation (10) was developed based on the method proposed by Durmayaz and Kadıoğlu (2003) [1] to calculate the total heat loss coefficient $U_t$, which determines the thermal characteristics of a building. This equation combines conductive heat losses from building components with losses due to air infiltration. In this equation, the variable $U_t$ represents the total heat loss coefficient of the building (W/K). In the formula $U_i,$ the first symbol represents the heat transfer coefficients of the elements forming the building envelope (walls, roof, floor, windows, $A_i$ etc.) and the second symbol represents the corresponding surface areas of these elements. The second part of the equation defines I as the air change rate and V as the gross internal volume of the $m^3$ building. In the analyses conducted, this value was found to be 358.33 W/K for a standard 100 m² residential typology.

$$U_t=\sum \left(U_i A_i\right)+I {\displaystyle \frac{V}{3}}$$

(10)

Equation (11) was formulated to determine the instantaneous total heat loss from residential buildings to the environment, in line with the approach proposed by Durmayaz and Kadıoğlu (2003) [1]. This equation defines the linear relationship between the building’s thermal transmittance and the temperature difference between the internal and external environments. $Q$ represents the total heat loss amount (Watt) from the residence. Among the other parameters in the formula $U_t$ denotes the building’s total heat loss coefficient (W/K) and $\Delta T$ denotes the instantaneous temperature difference between the indoor and outdoor environments (°C).

$$Q=U_t\Delta T$$

(11)

Equation (12) was derived to calculate the Heating Degree Days (HDDs), which characterize the cumulative heating energy load of the building over a specific period (monthly or yearly). This modeling is based on the cumulative sum of the positive values of the difference between the reference comfort temperature and the outdoor ${\left(x\right)}^{+}$ temperature; these values cause heating requirements. In the equation, N represents the total number of days in the relevant $T_i$ period $T_{m,j}$; the reference indoor comfort temperature and the daily average outdoor temperature are represented by the remaining variables. In the analyses conducted within the scope of this study, the $T_i$ value was accepted as 21 °C, taking into account the climate conditions and comfort standards of Aydın province.

$$HDD={\sum }_{j=1}^N{\left(T_i-T_{m,j}\right)}^{+}$$

(12)

2.4. Environmental Impact Analysis Method

To determine the potential carbon reduction effect of district heating systems, a baseline emissions inventory based on the fuel consumption habits of the existing housing stock was created. According to Turkish Statistical Institute (TUIK) 2022 data, despite the expansion of the natural gas network throughout Türkiye, solid fuels (coal, biomass, etc.) account for a significant share of 37.15% in household heating preferences [19]. This usage pattern, particularly in rural settlements, is the most critical determinant of the region’s carbon footprint. In this study, the heating profiles of 983,277 residences in 14 selected locations were analyzed and classified as natural gas (57.22%), solid fuels (37.15%), geothermal (4.67%), and other sources (fuel oil, electricity, etc.).

Analysis of the data in

Table 1. Residential heating statistics by fuel type in selected provinces and districts.

Central Province and District	Total Number of Residential Units (Units)	Number of Natural Gas Subscriber Housing Units (Units) 57.22%	Number of Geothermal Subscriber Housing Units (Units) 4.67%	Number of Households Using Solid Biomass, Coal, and Lignite 37.15%	Number of Residential Units Heated with Fuel Oil 0.040%	Number of Residential Units Heated by Electricity 0.78%	Number of Households Heated by Other Sources 0.14%
Denizli, Sarayköy	18,668	10,682	5000	2911	3	61	11
Aydın, Kuyucak	18,437	10,550	-	7689	8	162	29
Aydın, Germencik	25,235	14,439	3000	7599	8	160	29
Manisa, Alaşehir	52,100	29,812	-	21,727	22	457	82
Aydın, Sultanhisar	12,837	7345	-	5353	5	113	20
Manisa, Salihli	83,085	47,541	8000	26,850	28	565	102
Aydın, Efeler	143,566	82,148	-	59,870	61	1259	227
Aydın, Köşk	14,960	8560	-	6239	6	131	24
Aydın, Buharkent	8211	4698	-	3424	4	72	13
Izmir, Seferihisar	49,190	28,147	-	20,513	21	431	78
Çanakkale, Ayvacık	37,144	21,254	-	15,490	16	326	59
Aydın, Nazilli	87,823	50,252	-	36,624	38	770	139
Afyonkarahisar	405,247	231,936	30,000	139,752	143	2885	530
Aydın, Incirliova	26,774	15,320	-	11,165	11	235	42
TOTAL	983,277	562,685	46,000	365,205	375	7626	1386

shows that coal is the largest contributor to the carbon footprint associated with heating across the region’s 14 districts. According to the calculations, 37.15% of households use solid fuel, which indicates that approximately 365,205 households burn coal. Although 562,685 households use natural gas, coal’s emission factor shifts the balance. Limited access to alternative energy sources and the widespread use of coal in low-income areas are the most important factors that make these emission estimates more realistic. To carry out regional energy planning and emission analyses, it is necessary to determine the share of total energy consumption attributable to households using natural gas within the existing housing stock. After calculating the total heat load of all residences in the region, the energy demand from natural gas sources was obtained by dividing that load by the number of residences subscribing to natural gas.

The total energy consumption of natural-gas-heated residential buildings was calculated using Equation (13), where $Z_{sum}$ represents the total heat load of the areas to be heated, $H_{tk}$ denotes the total number of residential buildings, and $H_{ng}$ indicates the number of natural-gas-heated residential buildings.

$$C_{ng}={\displaystyle \frac{Z_{sum}}{H_{tk}}} H_{ng}$$

(13)

Using Equation (13), the total energy demand for natural-gas-heated residential buildings was calculated to be 28,054 TJ. To calculate the emission load from natural gas, the stoichiometric combustion methodology proposed by Sterligov was applied. In this context, the total energy demand of households using natural gas, determined as 28,054 TJ, was converted to mass consumption based on the lower heating value of the fuel [9]. The chemical conversion during combustion was modeled by assuming that the fuel contains 71.05% carbon by mass and that the molar ratio of carbon converted to carbon dioxide is (44/12). The calculations show that natural gas use causes approximately 2.66 tons of CO₂ emissions per household per year. The mass of fuel to be supplied to the system to meet regional energy demand must be calculated. This amount depends on the targeted total energy production and the chemical properties (lower heating value) of the fuel used.

Equation (14) was formulated based on the approach proposed by Sterligov to calculate the natural gas mass B required to meet the targeted energy demand [9]. This equation defines the thermodynamic relationship between the chemical energy content and the mass of the fuel.

$$B={\displaystyle \frac{E}{Q_H^P}}$$

(14)

In Equation (14), B represents the total natural gas mass that must be supplied to the system, while E, in the numerator, represents the total energy demand to be met. The parameter $Q_H^P$ in the denominator indicates the lower heating value of natural gas, allowing the calculation of the amount of energy that can be obtained from a unit mass. The total energy demand, estimated at 28,054 TJ (Terajoules) for this calculation, was converted to kilojoules (kJ) using a conversion factor of ${10}^9$ (1 TJ = ${10}^9$ kJ) to ensure compliance with SI (International System of Units) standards and unit homogeneity. Calculations showed that the natural gas supply required to achieve the aforementioned energy production is approximately 573,691,206 kg. In Equation (15), the relationship between the total mass of natural gas consumed and the fuel’s carbon content is defined to determine the elemental carbon content Mc, which is critical to the fuel combustion process.

Mc = B C

(15)

In Equation (15), Mc represents the total elemental carbon mass in the fuel (kg), while B represents the total natural gas mass calculated in the previous step (573,691,206 kg). The C parameter in the formula is the mass carbon ratio of natural gas determined by referring to the Sterligov study (71.05%) [9]. Applying this coefficient (0.7105) to the equation yields a total elemental carbon mass of 407,597,602 kg. This calculation determined that the natural gas expected to be consumed contains approximately 407,598 tons of pure carbon. In order to calculate the total carbon dioxide CO₂ emissions $M_{C{O}_2}$ that will be released into the atmosphere as a result of the combustion reaction, carbon (C) must be converted to carbon dioxide (CO₂) through complete combustion. The conversion factor (3.67) was obtained by dividing the molar mass of CO₂ (44 g/mol) by the molar mass of carbon (12 g/mol). Using the determined carbon mass and stoichiometric factor, the final emission amount was calculated. These findings indicate that the actual 28,054 TJ of energy consumed to meet the heating needs of 562,685 residences in the 14 districts studied resulted in approximately 1,495,983 tons of CO₂ emissions. In the residential assessment, if the annual heating demand of 13,365,774 kcal is met by natural gas, annual CO₂ emissions per household are estimated at approximately 2.658 tons of CO₂.

The environmental impact of solid fuel use, which is the main factor increasing carbon intensity in the region, was assessed, taking into account the IPCC (2006) guidelines and local lignite characteristics [10]. The emission factor of coal, which meets the energy demand of approximately 365,205 households, was accepted as 109.5 tons of CO₂/TJ, with reference to a study by Orlović-Leko et al. and in line with the nature of reserves in Türkiye [10]. This coefficient, which is approximately 8.2% higher than the standard IPCC values, indicates that a household that uses solid fuel and meets its annual heating requirement of 13,365,774 kcal with coal results in an average annual CO₂ emission of 5.624 tons and a total annual emission load of 2,054,182 tons of CO₂.

The diversity of energy sources used for residential heating necessitates expanding the scope of environmental impact analysis. In this context, customized emission projections for fossil-based liquid fuel (fuel oil) and grid electricity usage scenarios were calculated based on US Environmental Protection Agency (EPA) inventory data and regional energy conversion factors. When creating the emissions inventory resulting from the direct combustion of liquid fuels, unit conversion was first performed to ensure consistency with international databases [20].

The annual thermal energy demand, assumed to be 13,365,774 kcal for a residential building, was converted to British Thermal Units (Btu) in accordance with the analysis standards, yielding a value of 53,039,679. The specific emission factor defined for residential fuel oil (Distillate Fuel Oil) ${\mathrm{E}\mathrm{F}}_{\mathrm{f}\mathrm{u}\mathrm{e}\mathrm{l}-\mathrm{o}\mathrm{i}\mathrm{l}}$ was used in the emission calculation.

Equation (16) is formulated to determine the annual total mass of ${\mathrm{C}\mathrm{O}}_2$ carbon dioxide emissions by establishing the relationship between the amount of energy converted and the emission characteristics of the fuel, in the scenario where the heating demand is met with fuel oil.

$${\mathrm{C}\mathrm{O}}_2=E_{\mathrm{Q}\mathrm{B}\mathrm{t}\mathrm{u}} {\mathrm{E}\mathrm{F}}_{\mathrm{f}\mathrm{u}\mathrm{e}\mathrm{l}-\mathrm{o}\mathrm{i}\mathrm{l}} {10}^6$$

(16)

In Equation (16), $E_{\mathrm{Q}\mathrm{B}\mathrm{t}\mathrm{u}} {\mathrm{E}\mathrm{F}}_{\mathrm{f}\mathrm{u}\mathrm{e}\mathrm{l}-\mathrm{o}\mathrm{i}\mathrm{l}}$ the amount of energy converted, and the fuel-specific emission factor are represented. During the analysis process, the emission coefficient of 74.13 MMT ${\mathrm{C}\mathrm{O}}_2$/QBtu referenced in the EPA report and the conversion factor ${10}^6$ were included in the calculation [21]. The findings show that this heating method creates an annual carbon footprint of approximately 3.93 tons per household.

To determine the environmental impact of electricity consumption, the weighted average emission factor of the grid ${\mathrm{E}\mathrm{F}}_{\mathrm{e}\mathrm{l}\mathrm{e}\mathrm{k}\mathrm{t}\mathrm{r}\mathrm{i}\mathrm{k}}$ was calculated based on the 2022 energy production profile. In the analysis, the specific emission coefficients of the sources included in the model were as follows: coal, 95.83 MMT CO₂/Qbtu (20.3% share); natural gas, 52.92 MMT CO₂/Qbtu (38.8% share); and oil, 84.05 MMT CO₂/Qbtu (0.5% share). As a result of this approach, which assumes that renewable energy sources have a neutral (zero) impact on carbon emissions, the network’s weighted average emission factor was 40.46 MMT CO₂/Qbtu.

Equation (17) was formulated to determine the total carbon dioxide emissions ${\mathrm{E}}_{electricity}$ that would occur in a scenario where the annual thermal comfort requirements of households are met entirely by electrical energy. This approach is based on the linear relationship between unit conversion factors and total energy load.

$${\mathrm{E}}_{electricity}=\mathrm{k} {\mathrm{E}\mathrm{F}}_{\mathrm{a}\mathrm{v}\mathrm{g}} {\mathrm{Q}}_{\mathrm{a}\mathrm{n}\mathrm{n}\mathrm{u}\mathrm{a}\mathrm{l}\mathrm{l}\mathrm{y}}$$

(17)

In Equation (17), ${\mathrm{E}}_{\mathrm{e}\mathrm{l}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{r}\mathrm{i}\mathrm{c}\mathrm{i}\mathrm{t}\mathrm{y}},$ the first quantity, represents the annual total CO₂ emissions from electricity use, while ${\mathrm{Q}}_{\mathrm{a}\mathrm{n}\mathrm{n}\mathrm{u}\mathrm{a}\mathrm{l}\mathrm{l}\mathrm{y}},$ the second quantity, indicates the annual energy input required for heating. The term ${\mathrm{E}\mathrm{F}}_{\mathrm{a}\mathrm{v}\mathrm{g}}$ in the equation represents the weighted average emission factor of the grid, while the parameter k indicates the energy conversion factor used in the calculations and equals ${5.3\times 10}^{-8}$ CO₂/Qbtu. As a result of the analyses, the total carbon dioxide emissions in the scenario in which the annual thermal comfort requirement of residences is met entirely by electrical energy were calculated to be 2.14 tons of CO₂ $\left({\mathrm{E}}_{\mathrm{e}\mathrm{l}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{r}\mathrm{i}\mathrm{c}\mathrm{i}\mathrm{t}\mathrm{y}}\right)$, indicating that electricity-based heating can limit annual emissions per household to this level. Scenario analyses conducted with different fuel types revealed significant differences in carbon intensity. While solid fuels for residential heating have the highest emission potential, electricity (despite the current grid mix) has a carbon profile approximately 45% lower than fuel oil and approximately 20% lower than natural gas.

2.5. Economic Evaluation Model

In the economic evaluation of Geothermal District Heating Systems (GDHS), the Levelized Heat Cost (LCOH) method, which compares a system’s lifetime cost to the energy produced, was employed. Functioning as a primary benchmarking tool to evaluate the cost-effectiveness of competing energy technologies, this metric determines the precise ratio of total lifetime expenses against expected energy outputs, expressed as a present value equivalent [18]. As stated by Di Nunzio et al., although these projects require high initial investment costs (CapEx), they offer advantages over fossil fuel systems due to their low and predictable operating costs [8]. In this study, cost models developed by Pirouti et al. and the U.S. Department of Energy were adapted to local market conditions and geographical characteristics to analyze the geothermal potential of 14 different regions in Türkiye [22,23,24].

2.5.1. Cost Components and Capital Structure

The peak thermal power demand ${\mathrm{P}}_{\mathrm{I}\mathrm{n}\mathrm{s}\mathrm{t}\mathrm{a}\mathrm{l}\mathrm{l}\mathrm{e}\mathrm{d}}$, which forms the basic input of the economic model, was calculated based on the existing housing stock in the target regions and the annual heating energy requirement of 15.534 MWh of a 100 m² residence, according to past seasonal values in Aydın Province [25]. The total investment cost (CapEx) of the system is calculated from three main components that determine the project’s scale. The first of these is the transmission line cost, calculated based on the distance L from the geothermal source to the settlement center. The second component is the urban distribution network infrastructure within the settlement; the final component is building-level heat exchanger connection costs that enable end-user integration.

Annual Operating Expenses (OpEx) are the mandatory, recurring financial costs necessary for the project to remain operational throughout its economic life. These expenses are modeled as including the electrical energy required for pumping, personnel costs, chemical conditioning, and periodic maintenance. They are annualized based on fixed costs ($/kWt-year), which depend on the system’s capacity, and variable costs ($/MWh), which depend on production.

2.5.2. Financial Performance Metrics and Profitability Analysis

To evaluate the financial performance of the investment and assess the project’s profitability in competitive market conditions, the final unit sales price for geothermal heat must be determined. In financial modeling, it has been stated in the literature that the facilities will operate for 30 years, with a load factor of 22.15% and a discount rate of 7% [26]. Within this framework, a 15% profit margin was added to the system’s weighted average Levelized Heat Cost (LCOH), and the unit sales price scenario was formulated using Equation (18):

$${\mathrm{P}}_{\mathrm{S}\mathrm{e}\mathrm{l}\mathrm{l}\mathrm{i}\mathrm{n}\mathrm{g}\mathrm{P}\mathrm{r}\mathrm{i}\mathrm{c}\mathrm{e}}={\mathrm{L}\mathrm{C}\mathrm{O}\mathrm{H}}_{\mathrm{a}\mathrm{v}\mathrm{g}}(1+{\mathrm{R}}_{\mathrm{p}\mathrm{r}\mathrm{o}\mathrm{f}\mathrm{i}\mathrm{t}})$$

(18)

In Equation (18), the projected unit sales price of geothermal heat ${\mathrm{P}}_{\mathrm{S}\mathrm{e}\mathrm{l}\mathrm{l}\mathrm{i}\mathrm{n}\mathrm{g}\mathrm{P}\mathrm{r}\mathrm{i}\mathrm{c}\mathrm{e}},$ the calculated weighted average heat cost ${\mathrm{L}\mathrm{C}\mathrm{O}\mathrm{H}}_{\mathrm{o}\mathrm{r}\mathrm{t}}$, and the projected profit margin rate for the investor ${\mathrm{R}}_{\mathrm{p}\mathrm{r}\mathrm{o}\mathrm{f}\mathrm{i}\mathrm{t}}$ are expressed. The calculation estimated the system’s annual gross revenue at approximately $527,025,298. Levelized heat cost is defined as the ratio of the total of annualized CapEx and annual OpEx to the annual delivered heat energy. The Simplified Payback Period (PBP) method was used to determine the investment’s payback period and financial risk.

To assess the project’s economic competitiveness, the Levelized Heat Cost (LCOH) method, based on the ratio of total investment and operating costs to heat energy produced, was adopted. In this context, Equation (19) defines the Payback Period calculation used to evaluate the financial performance of geothermal energy investments and determine the time required to amortize the project’s initial cost. In this $PBP$ equation, the first term represents the payback period of the investment $CapEx$ (years); the numerator represents the total initial capital investment (Capital Expenditure) required to implement the $\mathrm{N}\mathrm{C}\mathrm{F}$ project, while the denominator represents the annual net cash flow (Net Cash Flow) obtained during the operating period.

$$PBP={\displaystyle \frac{CapEx}{\mathrm{N}\mathrm{C}\mathrm{F}}}$$

(19)

Equation (20) was developed to assess the cost-effectiveness of the project in line with methodologies proposed by Pirouti et al. and the U.S. Department of Energy [22,23,24]. This equation defines the Levelized Cost of Heat (LCOH) calculation, which is based on the principle of discounting total annual costs to obtain a price per unit of energy.

$$\mathrm{L}\mathrm{C}\mathrm{O}\mathrm{H}={\displaystyle \frac{{\mathrm{C}\mathrm{a}\mathrm{p}\mathrm{E}\mathrm{x}}_{\mathrm{A}\mathrm{n}\mathrm{n}\mathrm{u}\mathrm{a}\mathrm{l}}+\mathrm{O}\mathrm{p}\mathrm{E}\mathrm{x}}{{\mathrm{Q}}_{\mathrm{A}\mathrm{n}\mathrm{n}\mathrm{u}\mathrm{a}\mathrm{l}}}}$$

(20)

In Equation (20), the numerator $\mathrm{L}\mathrm{C}\mathrm{O}\mathrm{H}$ represents the unit heat energy cost. The numerator includes the system’s annualized investment cost, ${\mathrm{C}\mathrm{a}\mathrm{p}\mathrm{E}\mathrm{x}}_{\mathrm{A}\mathrm{n}\mathrm{n}\mathrm{u}\mathrm{a}\mathrm{l}}$, including depreciation (${\mathrm{C}\mathrm{a}\mathrm{p}\mathrm{E}\mathrm{x}}_{\mathrm{A}\mathrm{n}\mathrm{n}\mathrm{u}\mathrm{a}\mathrm{l}}$) and the annual operating expenses ($\mathrm{O}\mathrm{p}\mathrm{E}\mathrm{x}$). The denominator ${\mathrm{Q}}_{\mathrm{A}\mathrm{n}\mathrm{n}\mathrm{u}\mathrm{a}\mathrm{l}}$ parameter indicates the total annual heat energy produced by the plant, enabling the total cost to be normalized to energy output. The financial scenario developed for this study is based on data, including an initial investment requirement of approximately USD 4.51 billion, annual operating expenses of USD 94.98 million, and annual energy production of 7,281,415 MWh. Calculations based on these parameters predict a weighted-average LCOH of 62.94 USD/MWh and a payback period (PBP) of 10.43 years. The findings reveal that despite the high initial investment cost, the project is competitive with fossil fuel alternatives in the long term and presents a low-risk, financially sustainable profile. This 10.43-year timeframe proves quite reasonable when evaluated against other waste heat recovery frameworks in the literature; while highly efficient 4G and 5G networks can minimize overall expenses and reach amortization in under a year, conventional 3G systems that depend on high-temperature heat pumps generally require up to 14 years to pay for themselves [27]. We detail in

Table 2. Key economic assumptions and baseline parameters used for LCOH and payback period (PBP) calculations.

1. Basic Project Assumptions
Category/Parameter	Value	Unit	Reference/Description
Project Lifetime (N)	30	Years	Wendt et al. (2018) [26]/CRF calculation
Discount Rate (i)	7	%	Wendt et al. (2018) [26]/CRF calculation
Capacity Factor	22.15	%	Model assumption/Wendt et al. (2018) [26]
Expected Profit Margin	15	%	Premium for unit sales price (P$sales$)
2. Capital (CapEx) and Operational (OpEx) Costs
Category/Parameter	Value	Unit	Reference/Description
- Total Capital Expenditure (CapEx)	4,506,121,905	USD	Total for 14 districts in the model
- Transmission Line Share	16.66	%	(750,756,596 USD)
- Distribution Network Share	36.53	%	(1,646,129,780 USD)
- Residential Connection Share	46.81	%	(2,109,235,500 USD)
- Annual Operations & Maintenance (OpEx)	94,981,601	USD/Year	Total for the system
- Pumping/Electricity Consumption	35	%	(33,243,560 USD)
- Maintenance Costs	30	%	(28,494,480 USD)
- Personnel Wages	20	%	(18,996,320 USD)
- Overhead, Inhibitor, and Water Costs	15	%	(14,247,241 USD)
3. Energy Production and Financial Outputs
Category/Parameter	Value	Unit	Reference/Description
Annual Delivered Heat Energy	7,281,415	MWh	Based on climate and heat load modeling
Levelized Cost of Heat (LCOH)	62.94	USD/MWh	Weighted average costs for all projects
Unit Sales Price (LCOH + 15% Margin)	~72.38	USD/MWh	Estimated to calculate annual gross revenue (R)
Payback Period (PBP)	10.43	Years	Based on net cash flow (NCF) calculations
4. Alternative Fuel Price Comparison
Category/Parameter	Value	Unit	Reference/Description
Natural Gas Market Price (Baseline)	31.00–116.00	USD/MWh	For LCOH competitiveness (Kolker et al., 2021) [28]

the macroeconomic and technical parameters used to compute the Levelized Cost of Heat (LCOH) and the Payback Period (PBP), thereby ensuring full transparency and reproducibility of our financial model. For these projections, we adopted a 30-year project lifespan alongside a 7% discount rate. These figures align well with established literature on geothermal district heating investments [26]. Additionally, we evaluated the system’s market competitiveness by comparing the final LCOH against baseline natural gas prices, factoring in the inherent volatility of that market [28].

3. Case Analysis and Data Inventory

This study focuses on fourteen strategically selected administrative districts in Türkiye’s geothermally active Western Anatolia region, particularly the energy-intensive Büyük Menderes and Gediz Graben areas. These districts, which include important centers in Aydın, Manisa, and Denizli, represent a unique techno-economic cluster where low- and medium-enthalpy binary geothermal power plants (GPPs) and residential heat loads are highly concentrated. This heterogeneous study area, ranging from high-density urban cores such as Efeler and Afyonkarahisar to developing semi-urban areas, covers 468,719 households with varying thermal requirements. This specific selection provides a critical case study for assessing the scalability of integrating low-temperature industrial waste heat recovery into district heating networks to achieve regional decarbonization and energy efficiency goals.

To predict the fuel consumption and energy costs of a residential building for a specific period, it is first necessary to determine the total heating energy requirement. This energy requirement varies depending on the total heat loss coefficient, which represents $U_t$, the building’s insulation properties, and Heating Degree Days (HDDs), which indicate the severity of the regional climate.

Within this scope, monthly HDD values, calculated from meteorological data for the province of Aydın using a reference indoor comfort temperature of 21 °C, are presented in

Table 3. Heating degree day (HDD) calculations for Aydın province.

Month	Average Temperature (°C)	HDD (21 °C)
January	8.2	396.8
February	9.4	324.8
March	11.8	285.2
April	16.0	150.0
May	20.9	3.1
October	18.7	71.3
November	13.6	222.0
December	9.6	353.4

. An examination of the table data reveals that January, with an average outdoor temperature of 8.2 °C, stands out as the period with the highest heating load (HDD = 396.8). In May, which marks the end of winter, the average temperature rises to 20.9 °C, and the HDD value drops to 3.1, indicating a minimum heating requirement. Starting in October (18.7 °C and 71.3 HDD), the heating requirement gradually increases towards the winter months.

In Equation (21), the total monthly heating energy demand (E) of a residential building is modeled following the methodology proposed by Idchabani, Garoum, and Lamzah [29]. In the model defined by Equation (21), E represents the monthly energy demand $U_t$ (kWh); the total heat loss coefficient of the building is expressed in W/K; and HDD represents the Heating Degree Day data (°C day) for the relevant period.

$$E=0.024 U_t \mathrm{H}\mathrm{D}\mathrm{D}$$

(21)

The coefficient 0.024 in the formula is a conversion factor (24 h/1000) used to convert an instantaneous thermal power value, calculated in Watts, to the daily energy unit Kilowatt-hour (kWh). In line with this methodology, the heat loss coefficient, calculated for the analyzed standard residential typology ($U_t$ = 358.33 W/K), and the climate data for the province of Aydın were integrated into the model. The monthly energy demand values obtained from the calculations are presented in

Table 4. Monthly heating energy requirement and annual share.

Month	HDDs (21 °C)	$\mathbf{Monthly} \mathbf{Heating} \mathbf{Energy} \mathit{E}$ (kWh)	Share in Annual Total (%)
January	396.8	3412.5	21.96
February	324.8	2792.6	17.97
March	285.2	2451.2	15.78
April	150.0	1290.0	8.30
May	3.1	26.7	0.16
October	71.3	613.0	3.98
November	222.0	1910.0	12.29
December	353.4	3038.0	19.55

.

Table 4, based on climate data for the province of Aydın, shows the seasonal changes in the heating energy profile of a standard residential building with a usable area of 100 m². According to the analysis results, the maximum energy demand over the annual cycle occurs in January (~3412 kWh) and declines to a minimum of ~26 kWh in May, when the heating season ends.

4. Discussion

In this study, the potential to utilize waste heat from Geothermal Power Plants (GPPs) in district heating systems (DHSs) in 14 settlements in the Aegean Region and its surroundings that host Türkiye’s most efficient geothermal energy fields was analyzed with respect to technical capacity, environmental benefits, and economic feasibility. The findings reveal that waste heat, which is considered a loss of efficiency in electricity production due to thermodynamic constraints, can be transformed into a strategic resource with appropriate engineering approaches.

4.1. District-Based Supply-Demand Balance and Capacity Assessment

The supply-demand analysis, conducted by integrating heat loads calculated using demographic indicators, revealed a significant capacity asymmetry among the districts in the study area. The data obtained characterize the region’s geothermal potential along two main axes. The first group includes regions that provide “full capacity supply security” and have energy independence. This includes Denizli (Sarayköy), Manisa (Alaşehir, Salihli), and a large part of Aydın province (Kuyucak, Germencik, Sultanhisar, Efeler, Köşk, Buharkent, and İncirliova). It was determined that the waste heat potential of geothermal power plants in these 10 districts could alone meet even the peak demand in January, the harshest winter month. Mathematically, the waste heat capacity in these 10 districts is sufficient to cover 100% of the peak winter heating load without requiring active fossil fuel support. However, as illustrated in the system architecture (Figure 1), backup boilers are still integrated into the central substations. For these 10 self-sufficient districts, the boilers are not intended for routine peak-shaving; rather, they serve strictly as emergency redundancy measures to ensure supply security during unexpected well failures, pipeline disruptions, or scheduled power plant maintenance (adhering to the N-1 security criterion). Conversely, in the remaining 4 capacity-limited districts, these boilers operate within a hybrid framework to bridge the ongoing thermal deficit. This finding proves that the districts in question can be considered “self-sufficient micro-regions” in terms of thermal energy. In contrast, it was observed that the existing potential in the locations in the second group was “limited” in terms of meeting demand. In the cases of Izmir-Seferihisar (24.03%), Aydın-Nazilli (11.94%), Çanakkale-Ayvacık (8.94%), and Afyonkarahisar (2.18%), the installed geothermal power capacity is insufficient to heat all residences in the region. In particular, with the dense residential stock exceeding 400,000 and high heating demand, this necessitates that the geothermal resource be planned as a “supporting resource” integrated into hybrid systems rather than as the region’s sole heating power source.

4.2. Baseline Carbon Footprint and Current Heating Profile

An examination of the current heating profile in the region reveals that despite the increase in natural gas penetration (57.22%), solid fuel (coal, lignite) use continues at 37.15% levels, particularly in rural areas and low-income regions [19]. This situation is the principal driver of the region’s carbon footprint. According to emission models based on the methodologies of Sterligov and IPCC (2006), the total annual carbon dioxide (CO₂) emissions from heating sources in the 14 districts examined amount to 3,572,919 tons [9]. When geothermal waste heat is integrated into district heating systems, calculations indicate that this emission load could be reduced by 1,703,270 tons per year. This value corresponds to a 47.67% reduction in total emissions. This transformation, which will be achieved particularly in districts such as Manisa-Salihli and Aydın-Efeler, where population density is high and coal use continues, offers a concrete and measurable gain in line with Türkiye’s “Net Zero” targets under the Paris Climate Agreement.

4.3. Geothermal Integration and Emission Reduction Potential

The results of the analysis comparing the environmental impacts of different fuel types are presented in

Table 5. Comparison of annual CO₂ emissions per household based on heating energy demand by fuel type.

Fuel Type	Annual Energy Requirement (kcal)	Emissions Factor Reference	Annual CO2 Emission (Tons/Household)	Environmental Impact
Coal (Solid Fuel)	13,365,774	IPCC (2006) & Orlović-Leko et al. [10]	5.62	Very High
Fuel Oil	13,365,774	EPA (U.S. Greenhouse Gas Inventory) [20]	3.93	High
Natural Gas	13,365,774	Sterligov [9]	2.65	Medium
Electricity	13,365,774	EPA (Network Average) [20]	2.14	Low

. When the amount of energy required to provide the same thermal comfort is kept constant, solid fuel (coal) use causes by far the most serious environmental damage, with annual emissions of 5624 tons (CO₂) per household. In contrast, even when using the existing grid electricity, this value drops to 2140 tons, whereas using natural gas yields 2659 tons. These data clearly demonstrate the environmental advantage of regional heating using geothermal waste heat.

Table 5 shows that emissions from electricity generation vary depending on the fuel-source mix (coal, natural gas, renewables, etc.) of the power plants. This study is based on the current grid mix.

Table 6. CO₂ emissions from residential buildings by heating source type at the district level.

Region	Heat Load (TJ)	Natural Gas Source (Tons of CO2)	Solid Fuel Source (Tons of CO2)	Fuel Oil Emissions (Tons of CO2) 0.040%	Electricity Emissions (Tons of CO2) 0.78%	Other Source Emissions (Tons of CO2) 0.14%	Total (Tons of CO2)
Denizli, Sarayköy	1043.68	32,260.07	16,373.15	11.74	131.00	39.55	48,815.52
Aydın, Kuyucak	1030.97	2352.91	43,246.28	31.00	346.02	104.48	46,080.68
Aydın, Germencik	1411.1	32,204.24	42,742.86	30.64	341.99	103.26	75,422.99
Manisa, Alasehir	2913.35	80,339.12	122,207	87.59	977.79	295.23	203,906.78
Aydın, Sultanhisar	717.83	16,382.61	30,110.78	21.58	240.92	72.74	46,828.64
Manisa, Salihli	4645.99	128,117	151,022	108.25	1208.34	364.84	280,821.45
Aydın, Efeler	836,959	183,212	336,751	241.37	2694.39	813.54	523,714.21
Aydın, Köşk	836.54	19,091.77	35,090.54	25.15	280.76	84.77	54,573.01
Aydın, Buharkent	459.15	10,477.74	19,259.92	13.80	154.10	46.53	29,952.10
Izmir, Seferihisar	2750.63	81,083.55	115,381	82.70	923.18	278.74	197,749.44
Çanakkale, Ayvacık	2078	10,028.43	87,125.88	62.45	697.10	210.48	98,124.35
Aydın, Nazilli	4910.93	112,075.4	205,999.7	147.65	1648.23	497.66	320,368.77
Afyonkarahisar	22,660	754,187.5	786,067.5	563.42	6173.90	1899.01	1,548,891.44
Aydın, İncirliova	1497.9	34,168.99	62,801.75	45.01	502.48	151.72	97,669.95

details the carbon footprint profiles of residential heating in the districts covered by the study, broken down by energy source type. This inventory, created based on TÜİK household energy consumption statistics, analyzed four main energy vectors: natural gas, solid fuel, fuel oil, and electricity [19]. To minimize uncertainty in the dataset, an emission factor of 3588 tons of CO₂, reflecting the average of the four main sources, was included in the model for the “other sources” category.

Examination of the results presented in Table 6 indicates that the emission distribution shows significant heterogeneity among districts. In particular, Afyonkarahisar Central District stands out among all locations, with annual total emissions of approximately 1.55 million tons of CO₂, making it the region with the highest pollution load. The fact that solid fuel emissions (786,067 tons) in this district exceed natural gas emissions (754,187 tons) demonstrates the dominant contribution of coal use to environmental pressure. A similar pattern is observed in the Aydın, Efeler district: solid fuel emissions (336,751 tons) are approximately twice those from natural gas (183,212 tons), indicating that priority in regional air quality management should be given to converting from solid fuels. The data obtained from the calculations strikingly reveal the difference in environmental cost between fuel types. Coal, classified as “solid fuel” and widely used in the region, has been identified as the source causing the greatest damage to the ecosystem, with carbon dioxide emissions of 5624 tons per household per year. In contrast, even natural gas, which is generally positioned as a cleaner “transition fuel” in energy markets, has a significant carbon footprint of 2.66 tons per household per year. The most striking finding of the analysis concerns electricity. Although the environmental impact of electricity varies depending on the source mix of the power plants where it is generated, it has been determined that even when considering the current national grid mix (including fossil and renewable sources), it is the option with the lowest carbon profile, at 2.14 tons of emissions per year. This demonstrates that regional heating systems that use geothermal waste heat for residential heating provide an indisputable environmental advantage over individual fossil-fuel use. The potential to reduce the carbon intensity of the energy consumption profile of the existing housing stock is one of the most critical environmental outcomes of this study. A significant proportion of households in the region (37.15%) still depend on solid fuels with high emission factors, creating cumulative pressure on local air quality. In this context, integrating waste heat from Geothermal Power Plants (GPPs) into regional heating networks is not only an energy efficiency measure but also a strategic transformation scenario aimed at directly substituting high-emission fossil fuels.

The results of the calculations performed to analyze the annual difference in total emissions in the current “business-as-usual” scenario and quantify the cumulative environmental gain to be achieved are presented in

Table 7. Emission gains from geothermal waste heat.

Energy Source	Current Emissions (ton CO2/Year)	Reduced Emissions (ton CO2/Year)	New Emissions (ton CO2/Year)
Natural Gas	1,495,983	713,053	782,930
Solid Fuel	2,054,181	979,302	1,074,879
Fuel Oil	1472	737	735
Electricity	16,320	7824	8496
Other Fuels	4963	2354	2609
Total	3,572,919	1,703,270	1,869,649

.

The projections presented in Table 7 quantitatively demonstrate the radical improvement in the environmental footprint that would occur if the district heating infrastructure were supplied with geothermal waste heat. According to the findings, the gradual elimination of natural gas and coal consumption in the residential sector could prevent approximately 1.7 million tons of CO₂ emissions from entering the atmosphere annually. This value, which indicates an average saving of 47.67% in total emissions, shows that it carries measurable and significant improvement potential in the national greenhouse gas inventory [20]. These results provide concrete guidance to decision-makers in developing macro-scale sustainable energy strategies and in determining climate-change mitigation policies, extending beyond considerations of the economic feasibility of geothermal heating investments.

While this theoretical maximum annual reduction of 1.7 million tons of CO₂ highlights the project’s ultimate climate value, it is predicated on the simultaneous 100% conversion of the 468,719 targeted residences. In practice, achieving this absolute decarbonization will inherently be a phased process.

To contextualize and validate these environmental projections, it is essential to benchmark the anticipated 47.7% reduction in the regional CO₂ footprint against established literature and real-world operational systems. Scaling regional analyses often raises feasibility questions; however, historical data from active Geothermal District Heating Systems (GDHS) in Türkiye strongly corroborate these mathematical projections. For instance, an environmental assessment of the Bigadiç GDHS demonstrated that replacing conventional coal and fuel oil boilers with geothermal infrastructure eliminated nearly 30,000 tons of CO₂ and 355 tons of localized SO₂ emissions annually in a strictly localized municipal setting [30]. Scaling this empirically proven performance to the 14 densely populated districts in the Western Anatolia Graben makes our projected multi-megaton decarbonization highly realistic.

Furthermore, recent comprehensive life cycle assessments from European transitions, notably in Poland, confirm that shifting from fossil-reliant heating to zero-emission geothermal plants not only slashes greenhouse gas inventories but also completely eradicates localized particulate matter (PM) concentrations [31]. This aligns directly with advanced exergoenvironmental analyses conducted on the Afyon GDHS, which proved that the environmental damage costs of geothermal heating are inherently negligible compared to fossil-driven exergy destruction [32]. This direct alignment with both international baseline studies and local Turkish operational precedents confirms that the environmental benefits calculated for the proposed framework are strictly verifiable and historically attainable.

Real-world transitions to newly introduced district heating networks inevitably face several socio-economic and behavioral barriers, including residents’ reluctance to incur initial building connection fees, general satisfaction with existing natural gas infrastructure, and the physical disruptions associated with retrofitting older buildings.

Therefore, evaluating partial conversion scenarios provides a more realistic decarbonization timeline for the region. In a conservative, near-term adoption scenario where only 25% market penetration is achieved, the system would still eliminate approximately 425,000 tons of CO₂ emissions annually. A medium-term scenario capturing a 50% conversion rate would double this impact, preventing over 850,000 tons of CO₂ from entering the atmosphere each year. Reaching the theoretical maximum will realistically require a 10- to 15-year transition window, heavily dependent on coordinated policy interventions, such as municipal heat-exchanger installations and phased carbon taxation on solid fuels, to organically incentivize public adoption.

4.4. Economic Feasibility and Cost Analysis

As frequently emphasized in the literature, geothermal district heating investments are inherently capital-intensive [8]. The financial modeling in this study corroborates this characteristic structure. Based on derived calculations utilizing U.S. Department of Energy parameters, establishing the necessary infrastructure encompassing transmission lines, urban distribution networks, and building-level heat exchangers requires an estimated initial capital expenditure (CapEx) of USD 4.51 billion to effectively serve a broad portfolio of 468,719 residences [23,24].

These comprehensive CapEx models, derived from U.S. Department of Energy metrics, implicitly include the costs of the centralized backup/peak-load boilers depicted in the system architecture. Therefore, the financial burden of establishing emergency redundancy and hybrid support is fully accounted for within the economic model. However, the true economic sustainability of the project is not determined by this initial financial hurdle but rather by the exceptionally low and predictable operating expenses (OpEx) incurred over the system’s lifetime. Analytical results indicate that the weighted average Levelized Cost of Heat (LCOH) for the proposed networks stands at 62.94 USD/MWh. Depending on geographical transmission distances and regional heat density, this metric varies from a highly cost-effective 57.02 USD/MWh in Afyonkarahisar to 69.17 USD/MWh in the more extensive Çanakkale-Ayvacık network.

In an era where global energy markets exhibit severe volatility, with natural gas prices occasionally spiking to USD 116/MWh during crisis periods, the price stability inherently offered by local geothermal resources becomes a critical asset [28]. A base cost structure confined to USD 57–69/MWh confers a clear strategic and competitive advantage on the proposed geothermal systems relative to conventional fossil fuels.

As explicitly detailed in

Table 8. Detailed thermodynamic breakdown of available thermal power capacity for the proposed geothermal facilities. The table highlights the 10 largest plants, with the remaining facilities aggregated.

Geothermal Power Plant (GPP)	Inlet Temp. (Tin) (°C)	Calculated Thermal Efficiency (%)	Electrical Power (MW)	Gross Thermal Power (MWt)	Available Power After Pipeline Losses (MWt)	Available Power After Heat Exchanger Losses (MWt)	Available Power After Radiator Losses (MWt)
Buharkent GPP	175	6.28	109.63	1256.10	946.84	807.47	728.34
Babadere GPP	132	4.31	105.66	1176.61	886.93	756.37	682.25
Efe 8 GPP	227	8.09	56.90	646.53	487.35	415.62	374.89
Mis 3 GPP	170	6.07	43.17	667.60	503.24	429.16	387.10
Kızıldere 3 GPP	225	8.03	32.64	394.29	297.22	253.47	228.63
Alaşehir GPP	190	6.85	38.24	520.15	392.09	334.37	301.60
Maren GPP	160	5.65	34.03	568.11	428.24	365.20	329.41
Pamukören 4 GPP	162	5.74	29.11	367.23	276.82	236.07	212.94
Pamukören 5 GPP	162	5.74	27.83	457.15	344.60	293.87	265.07
Melih GPP	204	7.34	30.48	500.80	377.50	321.93	290.38
Other 53 GPPs (Aggregated)	1016.00	33.95	15.60	299.35	225.65	192.44	173.58
General Total	8328.00	298.92	590.36	9109.47	6866.71	5855.93	5282.05

, the sequential exergy destruction significantly impacts the net deliverable thermal power across the facilities, yet the remaining capacities are remarkably robust. For instance, the Kızıldere 3 GPP, which possesses the highest gross thermal capacity (1256.10 MWt) among the investigated plants, delivers 728.34 MWt of net usable heat to the end-user radiators after accounting for irreversibilities in pipelines, heat exchangers, and radiators. Similarly, the Efe 1-2-3-4 GPP provides 682.25 MWt of final heating capacity. The aggregate data clearly indicate that while thermodynamic losses are inevitable due to heat transfer inefficiencies and network friction, the immense baseline capacity of these top-tier geothermal plants ensures that the remaining net thermal power is more than sufficient to meet regional district heating loads.

The economic assessment of the project reveals strong financial viability when utilizing a projected unit sales price of 72.38 USD/MWh, which incorporates a 15% profit margin over the baseline LCOH. Under this pricing scenario, the system is expected to yield an annual net cash flow (NCF) of approximately USD 432 million, following the deduction of the USD 94.98 million required for yearly operations and maintenance (OpEx). Consequently, this robust revenue stream allows full recovery of the substantial initial capital expenditure within a payback period (PBP) of 10.43 years. Given the standard 30-year operational lifespan of such district heating infrastructure, the installations will continue to generate positive cash flow for nearly two decades post-amortization. This extended window of profitability illustrates that while geothermal district heating networks are undeniably capital-intensive at the outset, they ultimately function as highly reliable, low-risk investments backed by steady operational returns. The national importance of replicating these economically sound frameworks becomes even more apparent in light of recent exploratory data; according to the General Directorate of Mineral Research and Exploration (MTA), Türkiye possesses an untapped geothermal thermal capacity ranging between 31,500 MWt and 60,000 MWt [33], underscoring a massive scale-up potential for the country’s energy independence.

Sensitivity and Risk Analysis

Relying solely on deterministic single-point outputs for a macro-scale infrastructure investment of USD 4.51 billion would overlook real-world macroeconomic fluctuations. To validate the robustness of the financial model and quantify investment risks, a comprehensive one-at-a-time (OAT) sensitivity analysis was conducted. The baseline parameters—Capital Expenditures (CapEx), Operational Expenditures (OpEx), and the Discount Rate—were systematically varied to observe their proportional impacts on the Levelized Cost of Heat (LCOH) and the Payback Period (PBP).

As presented in

Table 9. Sensitivity analysis demonstrates the impact of fluctuating macroeconomic parameters (CapEx, OpEx, and Discount Rate) on the Levelized Cost of Heat (LCOH) and Payback Period (PBP).

Variable Parameter	Variation	LCOH (USD/MWh)	Payback Period (Years)
Base Case Scenario	0% (Baseline)	62.94	10.43
Capital Expenditure (CapEx)	−20%	51.83	8.34
	−10%	57.38	9.38
	+10%	68.49	11.47
	+20%	74.05	12.51
Operational Expenditure (OpEx)	−20%	60.15	10.11
	−10%	61.54	10.27
	+10%	64.33	10.59
	+20%	65.72	10.75
Discount Rate	5%	55.40	10.43 *
	9%	71.12	10.43 *

* Note: The simple payback period remains mathematically unaffected by the discount rate in non-discounted cash flow calculations, whereas LCOH fully reflects the impact of capital cost variations.

, the system’s financial feasibility is most sensitive to initial capital constraints (CapEx) and the cost of capital (discount rate), whereas variations in OpEx have a comparatively marginal influence. For instance, a pessimistic scenario involving a 20% escalation in CapEx, reflecting potential supply chain disruptions or soaring raw material costs, drives the LCOH up to 74.05 USD/MWh and delays the payback period to 12.51 years. Conversely, a 20% reduction in CapEx, achieved through government subsidies or technological advancements, lowers the LCOH to an exceptionally competitive 51.83 USD/MWh and allows amortization in just 8.34 years. Furthermore, adjusting the baseline discount rate (7%) illustrates high vulnerability to borrowing costs; increasing the rate to 9% elevates the LCOH to 71.12 USD/MWh due to heavier debt-servicing burdens. Nevertheless, even under the most severe compounded stress scenarios (high CapEx and high discount rates), the LCOH remains strictly below the threshold set by equivalent fossil fuel alternatives (e.g., natural gas spikes at 116 USD/MWh). This confirms that the project retains a wide margin of economic safety and demonstrates long-term viability despite market volatility.

4.5. Thermodynamic Performance

The technical performance of the system was examined within the framework of the second law of thermodynamics (exergy). Because the 14 proposed district heating networks are currently in the theoretical projection phase, with precise pipeline routes not yet physically constructed, calculating purely analytical, distance-dependent exergy losses per kilometer would introduce a high degree of speculative error. Instead, to ensure maximum reliability and empirical validation, an ‘operational benchmarking’ approach was adopted. Measured operational data from Türkiye’s three major active geothermal district heating systems (Balçova-BGDHS, Salihli-SGDHS, and Gönen-GGDHS) were evaluated. Their long-term operational thermodynamics were used to establish the system-average exergy destruction coefficients as 24.62% for primary transmission pipelines, 14.72% for heat exchangers, and 9.8% for end-user radiators.

Rather than relying on regional assumptions, these empirically validated coefficients were rigorously applied to the calculated gross thermal power of each geothermal power plant (GPP) operating in the evaluated districts. As detailed in Table 8, the plant-by-plant breakdown demonstrates the sequential degradation of thermal capacity from the wellhead to the end-user radiators. Ultimately, the fact that the source is ‘waste heat’, which cannot be converted into electricity, means that these technical losses can be tolerated, firmly aligning the project with national decarbonization and energy security goals.

4.6. Policy Implications

The empirical findings of this research clearly demonstrate the need for a fundamental paradigm shift in Türkiye’s geothermal energy management strategy. Operating geothermal reserves as single-purpose power plants focused solely on electricity generation causes significant exergy destruction from a thermodynamic perspective and leads to inefficient use of the resource’s full potential. The results of this study indicate that redefining these fields as integrated “energy production bases” that also cover urban heating loads is a technical and economic necessity. Particularly if the current “electricity-only” approach is maintained, there is a risk of overlooking the environmental gains demonstrated in this study, which have the potential to reduce residential carbon emissions by 47.67%. As widely supported by energy modeling literature, thoroughly exploring and integrating such renewable resource potentials provides a highly effective framework for implementing actionable climate change policies within existing energy systems [34]. In this context, defining “waste heat recovery and utilization plans” as a legal prerequisite for future licensing processes, alongside electrical efficiency criteria for power plants, would be a strategic step aligned with national energy efficiency targets.

Economic analyses demonstrate that geothermal district heating investments are competitive and viable in the long term, with a payback period (PBP) of 10.43 years and a levelized heat cost (LCOH) of 62.94 USD/MWh. However, the most critical bottleneck in project implementation is the substantial capital requirement (CapEx) encountered during the installation phase. Given current market dynamics, the effectiveness of public support mechanisms is crucial for making these self-financing systems appear less risky and more attractive to investors. In this context, low-interest infrastructure loans tailored for heat distribution networks, tax exemptions, or the introduction of a “renewable heat purchase guarantee” model, as in the electricity market, could significantly optimize payback periods that exceed 10 years. Beyond reducing the investment’s risk premium, these proposed financial levers will serve as a catalyst to accelerate Türkiye’s decarbonization vision and strengthen energy supply security.

5. Conclusions

This study, which focuses on the geothermal potential of the Western Anatolia graben systems, examines the integration of waste heat management into district heating networks in technical, economic, and environmental terms. The findings reveal that transforming existing geothermal power plants from facilities that generate only electricity into integrated energy ecosystems that also assume responsibility for urban heating is a strategic necessity. Field analyses indicated that in 10 of the 14 settlements examined, including Sarayköy, Germencik, and Alaşehir, the existing waste heat capacity could fully meet residential heating demand even under the harshest winter conditions. This technical feasibility indicates that a total of 468,719 homes and a population of approximately 1.37 million can be freed from fossil-fuel dependency and achieve uninterrupted and sustainable heating comfort. However, the study highlights that geothermal energy alone may not be sufficient in locations such as Afyonkarahisar, which have high population density and a large housing stock, thus necessitating a transition to hybrid energy architectures in these regions. The continued dependence of 37% of the housing stock in the regions identified in the study on high-emission solid fuels makes the environmental impact of the proposed conversion project even more critical. The models show that implementing the integration could prevent 1.7 million tons of CO₂ emissions at the source annually. This radical, approximately 48% reduction in total emissions relative to the current situation demonstrates that Türkiye has concrete and scalable potential to fulfill its commitments under the Paris Climate Agreement.

An examination of the economic dimension of the investment required to implement the study confirmed the project’s “capital-intensive” nature and calculated an installation cost exceeding $4.5 billion. However, the system’s low operating costs and a competitive “Levelized Heat Cost” of 62.94 USD/MWh have confirmed the long-term feasibility of the investment. This cost structure, which acts as an economic shield against price fluctuations in natural gas markets, enables the project to pay for itself in approximately 10 years. Considering the 30-year economic life of the facilities, the creation of positive net cash flow for the 19 years following the payback period demonstrates that the project is on extremely solid financial footing.

Although technical losses occur in transmission lines and heat exchangers, our empirical benchmarking reveals that the unavoidable exergy destruction remains acceptable given that the source is currently unutilized waste heat. Nevertheless, the high initial capital requirement, which is the primary obstacle to the widespread adoption of this technology, necessitates public support mechanisms. Defining “waste heat evaluation plans” as prerequisites for future geothermal licensing processes and supporting infrastructure investments through low-interest financing models will play a critical role in integrating this national resource into the Turkish economy. This study provides scientific evidence that integrating geothermal waste heat into district heating networks is a highly effective “Shift” strategy for regional decarbonization. By optimizing thermodynamic limits and capitalizing on local resources, the proposed framework has been shown to be feasible from an engineering perspective, economically profitable and ecologically transformative.