1 Introduction

Tunnels represent an increasingly relevant type of infrastructure for the economic development of any country, either to improve the communication network or for industrial uses [1]. However, they require important financial investments, as well as the use of large amounts of raw materials for their construction. In this sense, the potential environmental and social impacts generated must be evaluated beyond the strictly technical parameters related to the excavation of the tunnel [2, 3]. These impacts have an increasingly important weight for the administration responsible for approving the project and the different stakeholders related to the infrastructure. In addition, they can have a local, regional or global effect, which is necessary for their determination in the project phase to find the best technical solution to eliminate or mitigate them [4, 5]. It is important to know the impact of carbon emissions on tunnel construction for method selection [6]; for example, according to Tavakoli et al. [7], the carbon footprint can be approximately six times bigger in the cut and cover method than trenchless alternatives.

The tunnel excavation can be done by several methods, analysing only TBMs excavating in rock in this study, with single or double shields. The selection of the excavation method is an important issue that arises during the planning of a tunnel project, depending mainly on the tunnel geometry (tunnel shape, size, length, slope and turning radius), geology and rock mass conditions, workforce skills/experience/costs, contract configuration, project schedule, operational constraints and available area, transportation of excavated material, site access, power availability and local regulations. The most common choices, drill and blast or TBM, have pros and cons related to excavation time and costs, safety and environmental issues, risks and flexibility. These should be clearly identified and discussed before the design and construction phase. This topic is long studied and constantly updated as technology and knowledge evolve [8,9,10].

This study focuses on CO2 generation in tunnel construction, particularly in the construction phase, among all possible impacts. This approach has been chosen based on the availability of real data and its scientific relevance for the comprehensive assessment of CO2 generated in the construction phase of a tunnel. Moreover, it can be a comparative indicator for any type of tunnel and construction site.

The manufacture of materials, equipment and machinery for tunnel construction requires the consumption of large amounts of energy, in many cases through fossil fuel sources, either directly or indirectly [11]. This fact means that large quantities of CO2 are generated into the atmosphere, contributing to the well-known greenhouse effect [12]. The European Climate Law establishes a target to attain climate neutrality for the region by 2050 and a corresponding goal to decrease detrimental emissions by a minimum of 50% by 2030 [13]. Green and low-carbon tunnels play a crucial role in urban green transportation and are closely linked to the modernisation and sustainable growth of the interregional [14] and the urban transportation sectors [15].

The impact generated by civil works and construction accounts for a very important portion of anthropogenic CO2 emission, reaching 23% worldwide in 2009, according to Huang et al. [16]. In addition, cement consumption accounts for 25% of total emissions in Spain, assuming an equivalent value of cement consumption destined for tunnels [17]. However, there is still a long way to go in its prediction for several processes and phases required to construct a tunnel and the different tunnel typologies [11, 18, 19]. Although it is not currently a compulsory variable of the construction process or technical decision-making, it is expected to increase its relevance in the near future [11]. The availability of prediction methods could reduce CO2 generated from the design phase to the end of construction execution [20].

The determination of the CO2 load has been carried out in some particular cases [6, 21], using the life cycle analysis method to determine the overall impact [22, 23] or studying particular phases and techniques, such as tunnel boring and blasting [24], and materials used in underground excavations [13]. In this regard, Lee et al. [3] identified seven tunnel working phases that cause the main environmental impact during tunnel construction based on 20 case studies. These phases are (1) lining concrete, (2) shotcrete work, (3) tunnel embankment and external excavations, (4) drainage work, (5) use of umbrellas, (6) transport operations of excavated material, and (7) bolting. The seven detailed phases account for more than 89% of all CO2 generated in the construction phase. The same author has also developed a life cycle analysis model, including more parameters other than carbon footprint [2]. The use of tunnel boring machines for tunnel construction has also been analysed from the perspective of CO2 generation [25]. In addition, several lines of research focussed on the efficient use of energy during TBM excavation, considering the rock mass's conditions [26]. However, there is a lack of a standard and fast procedure to analyse a tunnel, knowing the impact of changing any design variable or the rock mass conditions.

This study aims to determine the carbon footprint associated with the tunnel construction process by single- or double-shield TBMs excavating in rock, obtaining a predictive model to calculate the CO2 impact. The elements included are the use of the TBM, the transportation systems, the auxiliary elements and the lining required. The innovations and contributions of this study encompass three key aspects.

  1. 1.

    The study investigates the carbon dioxide emissions from tunnels excavated in rock by TBM under various conditions, based on published data from real cases. Including several case studies from the authors.

  2. 2.

    A simple and rapid model is proposed for calculating the carbon footprint generated by analysing various elements during the TBM tunnel boring process at the design stage. This method is calibrated using real tunnel data.

  3. 3.

    This method is applied to data from other tunnels and discuss how different factors can influence CO2 emissions. This information can be used to compare various projects or to reduce carbon emissions in specific tunnel construction projects.

Overall, this study introduces a straightforward and efficient model for assessing carbon footprint generation. It achieves this by analysing and calculating the carbon footprint of various components during the TBM tunnel boring process, all at the design stage.

2 Methodology

The construction process of the tunnel by TBM has been separated into elements, proposing a calculation method for the CO2 emissions of each element. A similar approach exists for tunnels excavated by conventional systems [11], although it does not consider some of the variables included in the current study. In addition, the best available techniques for their excavation have been included to determine the CO2 loads [27]. Figure 1 shows the scope of the study and the inventory of the elements that have been used to carry out the analysis. The carbon footprint generated in the entire area, comprising the tunnel’s interior and the external installations related to its construction, will be analysed from an operational point of view.

Fig. 1
figure 1

The system boundary of the study

Full size image

The developed model is for tunnels excavated with TBMs, single- or double-shield TBMs excavating in rock. The boundary system includes the following elements:

  • TBM: Electrical energy consumption of the TBM for the excavation process. Accounting for both the cutter head and total power consumption. Its utilisation factor has also been studied.

  • Transportation: Two types of transportation activities are considered. The rock waste transportation from the face of the tunnel excavation using an electrical conveyor belt system, and the supply of materials and prefabricated segments to the face of the tunnel using a diesel engine.

  • Auxiliary elements: Ventilation of the tunnel, lighting the installations inside and outside the tunnel and water management (dewatering, water treatment and water consumption). All of them use electrical energy.

  • Lining: Composed by pre-cast segments and backfilling, including the materials used, its manufacturing and installation, regarding the required characteristics of the tunnel.

The interactions from the outside or the previous preparation of the installations have not been analysed, as they can vary greatly in each particular case. The following subsections define the procedure to calculate the CO2 impact of each element.

2.1 Excavation Process

The procedure followed is based on the electricity consumption of the TBM. In this regard, the specific energy is defined based on the type of material to be excavated and the geomechanical characteristics of the rock mass where the tunnel is being tunnelled, such as Mirahmadi et al. [28] using GSI or Jain et al. [29] using RMR and simple compressive strength (UCS) as indicators. To determine it, Bieniawski et al. [30] described the following empirical expression, Eq. 1:

$${S}_{{\text{e}}}=80\mathrm{ exp}\left(\frac{{\text{RMR}}-100}{{\text{RMR}}-1}\right),$$
(1)

where Se is the specific energy (MJ/m3).

Equation 1 was defined for a working range of RMR = 1 − 60 [30], but subsequent studies found consistent results using the expression for the full RMR range [28]. On the other hand, a TBM has a fixed electrical energy consumption, E0 (kWh/day), even if it does not carry out excavation work. While the energy consumed during the advance of the TBM will be equal to the energy needed to excavate the rock, additional energy is consumed in other operations at the face of the tunnel, such as placement of the ring of segments, injection of mortar in the backfill or face dewatering, among other auxiliary operations.

Determining the average rate of advance (ARA) is crucial to knowing how long the excavation will take and, therefore, the associated energy and material consumed. There are several methodologies to determine the performance of a TBM, including the NTNU index developed by the Norwegian University of Science and Technology [31], the Rock Mass Excavability index (RME) [32] and the Qtbm [33]. All the described methods have been used in subsequent studies related to ARA in TBM, improving the initial methodologies. For this study, the ARA defined by Bieniawski et al. [32] and its subsequent improvement [34] has been chosen as the most appropriate since it includes simple and easy-to-obtain variables related to the type of TBM strength of the rock, rock mass excitability and general characteristics of the construction site. It allows an analysis of any tunnel and stage of the project. Equation 2 defines the energy required to excavate the rock, Eexc (kWh/day). The factor 0.277 is the unit equivalence between kWh and MJ:

$${E}_{{\text{exc}}}= 0.277\times {E}_{{\text{e}}}\times S\times {\text{ARA}},$$
(2)

where Ee is the specific energy (MJ/m3), S is the cross-section of the excavated tunnel (m2), and ARA is the average rate advance (m/day).

Hence, the total energy required to advance the tunnel, EA, is the sum of the excavation energy, Eexc, and the associated operations in the immediate working face, Etask, (Eq. 3). The related operations can be determined as a proportion of the excavation energy and, therefore, a proportionality factor can be used, rp. As an average, the ratio is rp = 1 for open TBMs and rp = 1.66 for the other types of TBM, within a range of rp = 1.5–2 in the last case [35]:

$${E}_{{\text{A}}}={E}_{{\text{exc}}}+{E}_{{\text{task}}}={r}_{{\text{p}}}\times {E}_{{\text{exc}}},$$
(3)

where Eexc is the excavation energy (kWh/day), Etask is the associated operations (kWh/day), and rp is the excavation energy ratio (in the worst case \({r}_{{\text{p}}}=\frac{\text{total TBM power}}{\text{cutter head power}}\)).

Even when the TBM is not working, E0, a certain energy consumption, should be added. The mean value of this consumption can be considered as E0 = 5000 kWh/day, based on previous experiences [35, 36]. The total consumption employs Eq. 4, while the total consumption by metre can be obtained by applying Eq. 5, EL (kWh/m). If a tunnel advances from length Lo to length Lf, the carbon dioxide emissions (kgCO2) can be determined using Eq. 6:

$${E}_{{\text{T}}}={E}_{0} + {r}_{{\text{p}}}\times {E}_{{\text{exc}}} ={E}_{0}+0.277\times {r}_{{\text{p}}}\cdot {E}_{{\text{e}}}\cdot S\cdot {\text{ARA}},$$
(4)
$${E}_{{\text{L}}}=\frac{{E}_{0}}{{\text{ARA}}}+0.277 {r}_{{\text{p}}}{E}_{{\text{e}}}S,$$
(5)
$${G}_{{\text{E}}}={e}_{{\text{E}}} {E}_{{\text{L}}}\left({L}_{{\text{f}}}-{L}_{{\text{o}}}\right).$$
(6)

The emission electricity factor (kgCO2/kWh) is defined as eE. In Spain, a typical value is 0.267 kgCO2/kWh when a utility company supplies the electricity. In comparison, it increases to 0.66 kgCO2/kWh when generated on-site by gen-sets. As these values depend on the specific regional characteristics, the other authors suggest different emission rates. For instance, Shillaber et al. [27] proposed a conversion factor of 0.981 kgCO2/kWh for electric generators.

2.2 Pre-cast Segment Lining

It mainly depends on the concrete volume, cement type, steel reinforcement requirements, and the segments’ manufacture. In addition, the backfill injected in the gap between the TBM excavation and the segment's outer part needs to be considered.

2.2.1 Concrete Volume

Based on data from 23 TBM tunnels, with inner diameter between 3 and 9 m, a correlation has been obtained between the excavation diameter and the outer diameter of the segment with the inner diameter, Eqs. 7 and 8. In addition, these relationships allow us to determine the segment thickness, e (m), Eq. 9:

$${D}_{{\text{e}}}=1.10\times {D}_{{\text{i}}},$$
(7)
$${D}_{{\text{exc}}}=1.15\times {{\text{D}}}_{{\text{i}}},$$
(8)
$$e=\frac{{D}_{{\text{e}}}-{D}_{{\text{i}}}}{2}=\frac{1.10\times {D}_{{\text{i}}}-{D}_{{\text{i}}}}{2}=0.05{\times D}_{{\text{i}}},$$
(9)

where Di is the inner diameter of the segment (m), De is the outer diameter of the segment (m), Dexc is the excavation diameter (m), and e is the lining thickness (m).

These relationships match the results of Liao [37] and Bergeson et al. [38], who studied 30 tunnels with inner diameters between 3 and 11 m and 28 tunnels with inner diameters between 10 and 15 m.

The relationships previously detailed are for conservative scenarios. When the rock mass has very good conditions, or the tunnel is excavated at low depths, the thickness of the segments can be reduced up to 0.3 m, even in large diameter tunnels [39], obtaining the following approximate relationship: De = 1.06Di, Dexc = 1.12Di, e = 0.03Di. These ratios allow estimating the volume of concrete used for the inside diameter, which is always a known design variable. Once the inner and outer diameter of the segment are known, the volume of concrete used in the manufacture of the segments Vh1 (m3) between the lengths L1 and L2 can be calculated, Eq. 10, as well as the volume of the backfill Vh2 (m3), usually low-strength concrete is introduced into the annular space at the back of the segment, Eq. 11:

$${V}_{{\text{h}}1}=\left({L}_{2}-{L}_{1}\right)\times \frac{\pi }{4}\times \left({D}_{{\text{e}}}^{2}-{D}_{{\text{i}}}^{2}\right),$$
(10)
$${V}_{{\text{h}}2}=\left({{\text{L}}}_{2}-{{\text{L}}}_{1}\right)\times \frac{\pi }{4}\times \left({D}_{{\text{exc}}}^{2}-{D}_{{\text{e}}}^{2}\right).$$
(11)

2.2.2 Concrete Manufacturing Emissions

CO2 emissions depend on the concrete compressive strength [40, 41], which must increase proportional to the segment diameter and depth and inversely with the rock mass quality. At the same time, the higher volume of cement used, the higher the emission rate. On the other hand, the larger the tunnel’s diameter, the greater the pressure level transmitted by the rock mass to the support. Likewise, stresses in the ground and pressure on the support increase proportionally with depth. The ratio H × Di/RMR has been taken as an index of stress on the support, showing a linear correlation based on the data from 24 tunnels (Table 1). The higher this ratio, the higher the compressive strength of the concrete (fck) and the mass of steel used per cubic metre of concrete (mst). Therefore, the compressive strength of the concrete used in the segments can be estimated using Eq. 12:

$${f}_{{\text{ck}}}=40+0.15\left(\frac{H\times {D}_{{\text{i}}}}{{\text{RMR}}}\right),$$
(12)

where fck is the concrete compressive strength (MPa), RMR is the rock mass quality index, and H is the depth (m).

Table 1 Main features used to define the relationship between the index H × Di/RMR, concrete compressive strength and mass of steel
Full size table

Following the INECO’s [56] recommendation for tunnel construction, an average value could be used for the emission factor in concrete manufacture rh = 0.159 kgCO2/kg, equivalent to rh = 365 kgCO2/m3 for an average concrete density of 2300 kg/m3. Nevertheless, there is a relationship between the emission factor in concrete manufacture, rh (kgCO2/m3), and the concrete strength, fck, as it is shown in real data studied [11, 41, 57, 58], gathered in Eqs. 13 and 14. Thus, the emission from the concrete manufacturing process is gathered in Eq. 15:

$$ r_{{\text{h}}} \, = \,55\, + \,5f_{{{\text{ck}}}} \quad {\text{when}}\;\;20 < f_{{{\text{ck}}}} < 50, $$
(13)
$$ r_{{\text{h}}} = 250 + 1.35f_{{{\text{ck}}}} \quad {\text{when}}\;50 < f_{{{\text{ck}}}} < 100, $$
(14)
$${E}_{C}={r}_{h1}\times {V}_{h1}+{r}_{h2} \times {V}_{h2},$$
(15)

where Vh1 is the volume of concrete used in the manufacture of the segments (m3), Vh2 is the volume of the low-strength concrete used for backfilling (m3), and EC is the emission from the concrete manufacturing process (kgCO2).

2.2.3 Segment Manufacturing Process

A ratio of energy required per cubic metre of segment, Wd = 60 kWh/m3, has been obtained from Méndez et al. [57] and Torbado [52], while the following ratio of emissions from electrical energy rE = 0.267 kgCO2/kWh is based on the energy mix from Spain. Hence, the volume of manufactured segments, VT, has the related emissions expressed in Eq. 16 (kgCO2):

$${E}_{{\text{SM}}}={r}_{{\text{E}}}\times {W}_{{\text{d}}}\times {V}_{{\text{h}}1},$$
(16)

where ESM is the emission from the segment manufacturing process (kgCO2).

2.2.4 Steel Manufacturing Emissions

The higher the reinforcement, the greater the load the segment is subjected to (Table 1). Therefore, it is expected to increase linearly with the rate proposed in Eq. 12. The estimation of the steel required, mst (kg/m3), is obtained approximately, with Eq. 17, while the total mass of steel used in the reinforcement of the segments can be estimated with Eq. 18 and the emissions generated with Eq. 19:

$${m}_{{\text{st}}}=55+0.35\left(\frac{H\times {D}_{{\text{i}}}}{{\text{RMR}}}\right),$$
(17)
$${M}_{{\text{st}}}={m}_{{\text{st}}}\times {V}_{{\text{h}}1},$$
(18)
$${E}_{{\text{S}}}={r}_{{\text{st}}}\times {M}_{{\text{st}}},$$
(19)

where Mst is the steel required (kg), and rst is the CO2 emissions per kg of steel manufactured. It has been considered a value of ra = 1.63 kgCO2/kg [11, 41]. ES is the emission from the steel manufacturing process (kgCO2).

Figure 2 gathers the relationships detailed in the equations, based on actual data. The graph on the left illustrates the relationship among (H × Di)/RMR, fck (MPa) and mst (kg/m3), while the right one demonstrates the connection between compressive strength and CO2 emissions from concrete. Thus, the overall emission for the segment's construction can be determined by adding the concrete manufacturing process, EC, the segment manufacturing process, ESM and the steel manufacturing process, ES.

Fig. 2
figure 2

Relationship (H × Di)/RMR, fck (MPa) and mst (kg/m3) (left). Relationship between compressive strength and CO2 emissions form concrete (right)

Full size image

2.3 Cutter Tools Consumption

The consumption of cutters in the TBM cutting head can be calculated from different expressions [34, 59, 60]. Their quantity will vary mainly depending on the tunnel's diameter and the rock to be excavated. In this study, it has been determined from the RME and Cerchar Abrasivity Index (CAI), as described below.

Supposing a tunnel advances between lengths LO and LF, with an excavation diameter Dexc, the excavated volume can be defined by Eq. 20:

$${V}_{{\text{exc}}}=\frac{\pi }{4} {D}_{{\text{exc}}}^{2} \left({L}_{{\text{F}}}-{L}_{{\text{O}}}\right).$$
(20)

If the cutter consumption is defined as wc, the number of cutters worn out or unserviceable that need to be replaced in that section of tunnel, nC, can be determined by Eq. 21:

$${n}_{{\text{C}}}= {w}_{{\text{C}}}\times {V}_{{\text{exc}}},$$
(21)

where wC depends on the abrasiveness of the rock, measured by CAI or quartz content, as well as other factors that measure the rock mass excavability, such as the RME. Under average conditions, it usually varies between 0.004 and 0.008 units/m3. However, the consumption can be as low as 0.001 units/m3 in very favourable conditions or reach 0.015 units/m3 in extremely difficult conditions [61, 62].

Assuming that the replaced discs are not usable, their weight is defined as pC, the CO2 emission rate in steel manufacturing is rst (kgCO2/kg), and the contribution to CO2 emissions due to cutter consumption/destruction can be determined by Eq. 22 (kgCO2). At the same time, the consumption by a metre of tunnel excavated is defined in Eq. 23 (kgCO2/m):

$${E}_{{\text{C}}}={r}_{{\text{A}}} {p}_{{\text{C}}} {w}_{{\text{C}}} \frac{\pi }{4} {D}_{{\text{exc}}}^{2} \left({L}_{{\text{F}}}-{L}_{{\text{O}}}\right),$$
(22)
$${e}_{{\text{C}}}= \frac{\pi }{4} {r}_{{\text{st}}} {p}_{{\text{C}}} {w}_{{\text{C}}} {D}_{{\text{exc}}}^{2}.$$
(23)

2.4 Materials Transportation

The material transport system is usually powered by a diesel locomotive, considering average values to define its main characteristics [41]. Hence, the locomotive’s power is W = 381 kW, the average speed is v = 12 km/h, and the average diesel consumption between the outward and return journeys is 30 l/h. Assuming the tunnel advances from length LO to length LF, the CO2 emission will be obtained using Eqs. 24–31:

$$D=\frac{{L}_{{\text{O}}}+{L}_{{\text{F}}}}{2},$$
(24)

where D is the average distance between the entry and the face of the tunnel (m).

$$T=\frac{{L}_{{\text{F}}} - {L}_{{\text{O}}}}{{\text{ARA}}},$$
(25)

where T is the days used to excavate between LO and LF.

$$t=\frac{D}{v},$$
(26)

where t is the time for each journey (h/trip).

$$c=30\times t$$
(27)

where c is the diesel consumption (l/trip).

$$n=\frac{{\text{ARA}}}{{L}_{{\text{R}}}},$$
(28)

where LR is the length of the segment’s ring and n is the number of rings installed (rings/day).

$${N}_{{\text{T}}}=2n\times T,$$
(29)

where NT is the total number of trips.

$${V}_{{\text{fuel}}}=c\times {N}_{{\text{T}}},$$
(30)

where Vfuel is the fuel used (l).

$${E}_{{\text{fuel}}}={r}_{{\text{D}}}\times {V}_{{\text{fuel}}},$$
(31)

where Efuel is the total emissions (kgCO2), and rD is the emission rate from diesel engines; a typical value is rD = 2.63 kgCO2/l [11].

The external transport outside the general tunnel facilities has not been considered in the study, but it has been considered an additional 200-m route through the exterior facilities.

2.5 Rock Waste Transportation

The use of conveyor belts to evacuate excavated material has a lower impact than loading by diesel vehicles in other types of tunnel excavation [63]. From experience in several tunnels, the electrical power head required can be expressed empirically as a function of the length travelled horizontally, LH, and vertically, LV, in metres. According to data available in the specialised bibliography, the average electrical power PTM can be approximated using Eqs. 32–35 [64]:

$${P}_{{\text{TM}}}=\frac{{c}_{{\text{p}}}}{1000} \left(0.150 {L}_{{\text{H}}}+3.75 {L}_{{\text{V}}}\right),$$
(32)
$${L}_{{\text{H}}}=\frac{{L}_{{\text{O}}}+{L}_{{\text{F}}}}{2},$$
(33)
$${L}_{V}=\frac{i}{100} {L}_{{\text{H}},}$$
(34)
$${c}_{{\text{p}}}=r\times S\times \rho ,$$
(35)

where PTM is the electrical power head (kW), i is the slope of the tunnel (%), cP is the conveyor belt capacity (t/h), S is the cross-section of the tunnel (m2), ρ is the rock density before excavation (t/m3), and r is the maximum excavation performance (m/h); r = 5 m/h is a typical value advancing with TBM.

The energy consumption determination, Eq. 36, requires the hours of the conveyor belt operating, Eq. 37. On the other hand, Eq. 38 determines the CO2 emission. As previously mentioned, 0.267 kg CO2 per kWh is a common value for rE:

$${E}_{{\text{d}}}={h}_{{\text{CB}}}\times {P}_{{\text{TM}}},$$
(36)
$${h}_{{\text{CB}}}=\frac{{\text{ARA}}}{r},$$
(37)
$${E}_{{\text{CB}}}={r}_{{\text{E}}} \times {En}_{{\text{CB}}}\times T$$
(38)

where Ed is the energy consumed per day (kWh/day), hCB is the conveyor belt running time (h/day), T is the global excavation time (days), ECB is the emission from the rock waste transportation (kgCO2), and rE is the CO2 emission rate in electricity production (kgCO2/kWh).

2.6 Ventilation

Ventilation is essential to ensure a breathable atmosphere, maintaining an adequate oxygen percentage and a toxic gas content below an acceptable level. The empirical relationship shown in Eq. 39 can be used for approximate consumption.

$${P}_{{\text{vM}}}={c}_{{\text{v}}}\times \left(\frac{{L}_{0}+{L}_{{\text{F}}}}{2}\right),$$
(39)

where PvM is the average fan power (kW) required. L is the length of the tunnel (m), with an initial length L0 and a final length LF. cv is the empirical coefficient, 0.100 for drilling and blasting [11] and 0.070 for TBMs.

The energy consumed, EVD (kWh/day), is shown in Eq. 40. On the other hand, emissions are calculated following the procedure set out in Eq. 41:

$${E}_{{\text{VD}}}={h}_{{\text{v}}}\times {c}_{{\text{v}}}\times \left(\frac{{L}_{0}+{L}_{{\text{F}}}}{2}\right),$$
(40)
$${E}_{{\text{V}}}={r}_{{\text{E}}}\times {E}_{{\text{VD}}}\times T,$$
(41)

where hv is the ventilation running time (h/day), usually all day, hv=24 h/day, to ensure adequate environmental conditions. EV is the emission from ventilation usage (kgCO2).

2.7 Water Management

When excavating a descending tunnel, water must be pumped. The procedure is similar to Sect. 2.6, with an observed empirical, almost linear, relationship between the installed pumping power and the tunnel length (Eqs. 42–44):

$${P}_{{\text{pM}}}={c}_{{\text{p}}}\times \left(\frac{{L}_{{\text{O}}}+{L}_{{\text{F}}}}{2}\right),$$
(42)
$${E}_{{\text{pD}}}={h}_{{\text{p}}}\times {P}_{{\text{pM}}},$$
(43)
$${E}_{{\text{P}}}={r}_{{\text{E}}}\times {E}_{{\text{pD}}}\times T,$$
(44)

where PpM is the total power of dewatering pumps (kW), hp is the pumping station running time (h/day), usually all day, hp = 24 h/day, EpD is the energy consumed daily (kWh/day), and EP is the emission from pumping usage (kgCO2). cp is the empirical coefficient. It has been determined that cp takes the value of 0.25 for low gradients in tunnels with lengths between 1 and 10 km (i = 1–5%), while it is 0.60 for high gradients (i > 5–15%) after Rodríguez and Pérez [11]

Water discharged from the tunnel must also be treated before it is sent to natural watercourses. Usually, the energy consumption of the treatment plant will be much lower than the pumping requirements. The water treatment plant is assumed to work hp hours a day (usually hp = 24 h/day), Eqs. 45–47:

$${P}_{{\text{wtM}}}={c}_{{\text{wt}}}\times {q}_{{\text{L}}}\times \frac{({L}_{{\text{o}}}+{L}_{{\text{F}}})}{2},$$
(45)
$${E}_{{\text{wtD}}}={h}_{{\text{p}}}\times {P}_{{\text{wtM}}},$$
(46)
$${E}_{{\text{wt}}}={r}_{E}\times {E}_{wt}\times T,$$
(47)

where PwtM is the average water treatment plant power (kW), qL is the average linear water flow rate (m3/s)/m, cwt is the coefficient of proportionality; an average value of 1500 KW/(m3/s) has been obtained from real data [11], EVD is the energy consumed (kWh/day), and EV is the emission from ventilation usage (kgCO2).

2.8 Lightning and Services

Lighting inside the tunnel includes the installation along the tunnel and the working faces. The empirical approximations proposed by Rodríguez and Pérez [11] have been used for its calculation. Thus, the total power required (kW) can be obtained using Eq. 48:

$${P}_{{\text{LM}}}=8+0.015\times \frac{\left({L}_{{\text{O}}}+{L}_{{\text{F}}}\right)}{2}.$$
(48)

It is considered that lighting works 24 h a day. The procedure to obtain the energy and associated emissions is the same as in the previous section. On the other hand, the outdoor facilities’ installed power and energy consumed can be obtained through a utilisation coefficient, usually between 0.25 and 0.50, with an approximate power requirement of 500 kW [11].

3 Calibration

The models proposed for the excavation process, segments applied in the tunnel, and the consumption of cutter tools require calibration with data from real tunnels. On the other hand, models for material transportation, rock waste transportation, water management, lightning and services have already been validated [11, 41]. The three elements were calibrated from the same case study, lot 3 of the Pajares tunnel, northern Spain.

The tunnel stretch analysed consists of 10,300 m excavated through a rock mass with a medium excavability level, RME = 65 on average. The first 4000 m are mainly through Carboniferous formations, shales and slates, with a low-quality rock mass (RMR1 = 20). Subsequently, the following 6300 m of the tunnel crosses formations with a predominance of sandstones and quartzites (RMR2 = 45). The average rate advance is ARA1 = 10 m/day in the first stretch and ARA2 = 15 m/day in the second, with a tunnel depth of 0–1000 m. On the other hand, the excavation diameter is 10 m, while the internal and external diameters of the segments are 8.50 m and 9.50 m, respectively. Regarding the TBM, it had a cutterhead power of 4900 kW and a total power of 7900 kW.

3.1 TBM Power Consumption

Based on the case study described and equations from Sect. 2.1, the following parameters are obtained: S = 78.54 m2, Ee1 = 1.2 MJ/m3 and Ee2 = 22.9 MJ/m3. The energy consumption per metre in the first and second sections is displayed in Eqs. 49 and 50:

$${E}_{{\text{L}}1}=\frac{5000}{10}+0.277\times 1.61\times 1.2\times 78.5= 542 {\text{kWh}}/{\text{m}},$$
(49)
$${E}_{{\text{L}}2}=\frac{5000}{15}+0.277\times 1.61\times 22.9\times 78.5= 1135 \frac{{\text{kWh}}}{{\text{m}}}.$$
(50)

The emissions, depending on the excavation advance of the tunnel, can be calculated employing Eqs. 51 and 52. Figure 3 compares the real electricity consumption and the model proposed. As can be seen, the proposed model can be considered adequate to predict the consumption of the TBM as a whole:

Fig. 3
figure 3

Comparison between real data and results calculated with the proposed model (tCO2)

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$${E}_{{\text{E}}1}=0.145\times L$$
(51)
$${E}_{{\text{E}}2}=580+0.303 \left(L-4000\right)$$
(52)

3.2 Cutter Consumption

The carboniferous formations along the first 4000 m consist of very little or no abrasive rocks, with a cutter consumption of 0.001 units/m3, while the following 6300 m of the tunnel crosses formations with a predominance of sandstones and quartzites, medium abrasiveness (CAI = 3), with a cutter consumption of 0.006 units/m3. Each cutter weighed 125 kg, and the rA emission factor was 1.63 kgCO2/kg. Therefore, Eqs. 53 and 54 define the emissions (tCO2) due to cutter disk consumption of the first and second part of the tunnel, respectively, as seen in Fig. 4. They are achieving an adequate approach to calculate the emissions caused by cutter consumption:

Fig. 4
figure 4

Comparison between real data and results calculated with the proposed model (tCO2)

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$${E}_{{\text{C}}1}=0.016\times L,$$
(53)
$${E}_{{\text{C}}2}=64+0.096\times \left(L-4000\right).$$
(54)

3.3 Pre-cast Segment Lining

The depth of the tunnel at each point, H (m), can be approximated by the length, L (m), of the tunnel from the portal to the analysed point. In the first 8000 m, the depth increases from 0 to 1000 m, decreasing to 600 m in the following 2300 m. Thus, Eqs. 55 and 56 describe the depth behaviour as a function of the length point analysed for L < 8000 m and L > 8000 m, respectively:

$${H}_{1}=0.125\times L,$$
(55)
$${H}_{2}=1000-0.200\times \left(L-8000\right).$$
(56)

Thus, four stretches were defined along the distance of the tunnel excavated. Following the procedure described in Sect. 2.2, the result is that CO2 emission due to lining manufacturing is practically proportional to the length.

  • Stretch 1 (L = 0–2000 m; RMR = 25; H = 0.125 L; fck ≤ 50 MPa): emissions are described by Eq. 57:

    $${E}_{{\text{T}}1}\approx 7.26\times L.$$
    (57)

  • Stretch 2 (L = 2000–4000 m; RMR = 25; H = 0.125 L; fck > 50 MPa): emissions are described by Eq. 58:

    $${E}_{{\text{T}}2}\approx \mathrm{14,520}+8.70\times \left(L-2000\right).$$
    (58)

  • Stretch 3 (L = 4000–8000 m; RMR = 45; H = 0.125 L; fck > 50 MPa): emissions are described by Eq. 59:

    $${E}_{{\text{T}}3}\approx \mathrm{31,920}+8.67\times \left(L-4000\right).$$
    (59)

  • Stretch 4 (L = 8000–10,300 m; RMR = 45; H = 2600–0.2 L; fck > 50 MPa): emissions are described by Eq. 60:

    $${E}_{{\text{T}}4}\approx \mathrm{66,600}+8.50\times \left(L-8000\right).$$
    (60)

The comparison between real emissions and the proposed model is depicted in Fig. 5, showing a good fit and demonstrating that the model is accurate enough to predict CO2 emission. The average linear emission due to lining manufacturing calculated for the whole tunnel is 8.34 tCO2/m, slightly higher than the real linear emission of 7.89 tCO2/m, with a 5.7% error.

Fig. 5
figure 5

Comparison between real data and results calculated with the proposed model (tCO2)

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4 Results and Discussion

The methodology gathered in Sect. 2 has been applied in a standard tunnel to study the different elements. Subsequently, real case studies with varying excavation diameters, ARA, length and depth have been analysed.

4.1 Usage in a Standard Tunnel

Initially, the starting point used was a standard tunnel of 6 km in length, with a diameter of 10 m, 100 m deep and a negative gradient of 2%. Regarding the TBM, it is a double-shield TBM with 17’ cutters of 125 kg each. Table 2 shows the results of CO2 emissions and the proportional contribution of each element in the tunnel construction phase. The auxiliary elements include ventilation, lighting and water management. A regular quality rock mass has been considered with the following characteristics: UCS of 75 MPa, a quartz content of 25%, a CAI of 1.35, a flow rate from the tunnel of 3 × 10–5 m3/s/m and an RMR of 65, obtaining an RME of 54 and an ARA between 11 m/day and 25 m/day.

Table 2 Emissions generated by each element during the tunnel construction (ARA = 11 m/day)
Full size table

As shown in Table 2, the most important element is the lining, with a relevant contribution from TBM consumption as well. At the same time, the other elements analysed individually account for around 10% or even less. However, managing and evacuating water from the tunnel can be an important driver when the tunnel has large amounts of water.

An additional analysis has been done considering the maximum ARA, 25 m/day, to study its influence on CO2 emissions. Thus, it is observed how a high ARA value has a significant impact; the higher the advance, the lower the emissions generated, reaching 8.9% less emissions and decreasing the impact of the TBM and auxiliary elements. Table 3 shows the comparison of both scenarios.

Table 3 Comparison between average rate of advance (ARA)
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4.2 Real Case Studies Analysed

The proposed model has been used to explore seven different tunnels worldwide (Table 4), with its main characteristics required to determine their carbon footprint in the construction process. The case study selection is based on their differences in ARA, tunnel diameter, length, depth, type of TBM and geological formation, aiming to define an approximate value for future projects. These differences can be very relevant to analysing the implications of the main variables influencing the CO2 emission during the construction phase of the tunnel (Table 5).

Table 4 Tunnels analysed with the model proposed
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Table 5 Calculated carbon footprint contribution of the tunnel and each element
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Small diameters generate smaller CO2 emissions, especially because of the lining requirements. At first glance, ARA appears to be related to the energy consumption of the TBM, transportation, ventilation, lighting and water management, but no definite correlation has been found. Further data would be necessary to verify the potential relationship between ARA and CO2 emissions.

The comparison between the two Guadarrama tunnels (called T3 and T4) is based on two twin tunnels. Having the real consumption of the cutters, TBMs power and RMR measurements, while the depth is an average value. The same length with different ARA and cutter wear have been analysed. The emissions of T3 are 99,103 tons of CO2, while T4 generates 99,245 tCO2 for 14,100 m of tunnel. The only differences are the power of the TBMs, the daily advance rate and the wear of the cutters and power. In the case of total TBM power, T3 has a total potential of 5700 kW (emission of 11,308 tCO2) and T4 of 5436 kW (emission of 10,896 tCO2). Therefore, the difference in power is not a differential factor in emissions.

Regarding cutter wear, T4 generates 1235 tCO2 for an equivalent wear of 0.0055127 μ/m3, while T3 generates 1122 tCO2 for a cutter wear of 0.005011 μ/m3. Assuming a practically non-existent difference concerning the total value of emissions. The advance of T4 is 2.3% lower than T3, generating higher emissions, especially in material transport and auxiliary elements, balancing, and even exceeding, the total emissions by T4 with respect to T3.

In the case of the Pajares tunnels, L1E tunnel has an ARA 8.9% higher than L1O. The difference in total power of the tunnel boring machine is the differential factor in this case, 7720 kW and 6150 kW, respectively.

Tunnels with small diameters such as Ghomroud and Bursa, with 4.50 and 5.05 m, respectively, show a completely different behaviour, with a much smaller proportion of the lining, while the auxiliary elements are more relevant due to their length and reduction of the overall emission. Both tunnels have a similar pattern, although the Bursa tunnel presents very poor rock mass conditions, with an RMR of around 20. This case also shows very high abrasiveness geologic formation, 0.034 cutters/m3, reaching the maximum value for the cutter consumption, 4.2% of the overall CO2 emissions.

Results discussed in the previous analysis are in accordance with the contribution of the two main variables: the lining and the TBM characteristics. The larger the excavation, the larger the volume of concrete required for the segments and backfill and, thus, the emissions generated. Even though depth and rock mass characteristics have an important influence, the main factor is the tunnel dimension. Regarding the TBM, more cutter head power is required as the diameter increases; therefore, more electricity is consumed. Consequently, it can be summarised that the most influential elements will be the diameter and depth of the tunnel concerning CO2 emission. On the other hand, the rock mass conditions also play an important role in the degree of emissions [11, 68,69,70]. Table 6 includes an indicative summary of emissions related to different types of tunnels for a quick analysis.

Table 6 CO2 emissions related to the tunnel characteristics
Full size table

The results in this study enable a comparison of the carbon footprint between various tunnel excavation methods: drill and blast (D&B), TBM, and EPB. For instance, Zhao et al. [71] found that in tunnels excavated with EPB in soil, 93.8% of CO2 emissions come from manufacturing lining materials. However, in tunnels excavated with a TBM in rock, even under unfavourable conditions, this contribution does not exceed 75%. This is because TBM excavation in rock leads to higher electrical energy consumption and cutter use.

Furthermore, differences between tunnelling in rock with TBMs and using D&B are evident. Under favourable conditions, D&B results in approximately 5000 kg/m of CO2 emissions, which can rise to 15,000 kg/m under unfavourable conditions [41]. In contrast, this study demonstrates that using TBM reduces emissions to 3000 kg/m under favourable conditions and 10,000 kg/m under adverse conditions. Consequently, CO2 emissions are 50% higher with D&B due to slower advance rates, leading to increased energy consumption for auxiliary services and higher levels of over-excavation, resulting in greater concrete consumption.

On the other hand, it is important to consider the boundaries of the method proposed for its usage: tunnels excavated in rock, using single or double-shield TBMs, with a diameter ranging from 3 to 10 m. In addition, site emission factors are also relevant to the results obtained. For example, Zhao et al. [71] and Li et al. [15] provide data on CO2 emissions from a tunnel advanced with EPB, 29.7 tCO2/m, reaching significantly higher values than what would be estimated using the present method. This difference is caused due to the case study tunnel would be beyond the scope of the model; excavated with EPB, large diameter of 13.3 m interior and 14.5 m exterior, and a very high emission rate for concrete obtaining, reaching 455 kgCO2/m3 for a 60 MPa concrete mix.

5 Conclusions

This study presents a comprehensive analysis of the carbon footprint associated with tunnel construction using TBMs. The research establishes a systematic procedure for quantifying carbon emissions, considering various tunnel characteristics, and offering a versatile tool applicable at different stages of project development. By investigating the influence of tunnel diameter, depth, and the quality of the rock mass on CO2 emissions, analysis of the tunnels studied reveals that, primarily, the carbon footprint is driven by lining segments; the percentage varies between 50% in tunnels with smaller diameters (4–5 m) and 75% for tunnels with larger diameters (9–10 m), followed by auxiliary elements (16%), and the operation of the TBM (11.2%). Conversely, cutter consumption and transportation contribute less than 5% to the emissions. This work provides valuable insights into the environment. A noteworthy aspect pertains to water management. In scenarios with steep negative slopes or significant water presence, emissions from water evacuation could be considerably higher. In this regard, emissions generated in water management are mainly due to its evacuation from the tunnel, 80%, while 14% correspond to the treatment of pumped water and 6% to the use of industrial water. This underscores the significance of addressing water management strategies during tunnel construction. On the other hand, the quality of the rock mass significantly affects CO2 emissions. Variables such as electrical energy consumption, cutter usage, and the choice of lining materials are particularly influenced by rock mass quality. This highlights the necessity of factoring in geotechnical conditions in carbon footprint assessments.

The method proposed allows for meaningful comparisons between tunnel excavation methods. It reveals that TBM excavation in rock generally results in lower CO2 emissions compared to other methods, such as D&B. This information is valuable for making informed decisions in tunnel construction projects. While the proposed methodology is versatile, it is important to recognise its limitations. Users should interpret the results within the defined boundaries and validate them with real case studies whenever possible. The study encourages refinement and expansion of the methodology for more accurate assessments. Understanding tunnel construction's carbon footprint is essential for environmental sustainability. By identifying the primary contributors to CO2 emissions, this study provides clear guidance on where to concentrate efforts to reduce the environmental impact of tunnel projects. Further research can explore advanced methods for assessing the carbon footprint of tunnels, expand the dataset with more real case studies, and develop strategies to mitigate CO2 emissions during tunnel construction.