# Environmental Impacts of Construction in Building Industry—A Review of Knowledge Advances, Gaps and Future Directions

## Abstract

**:**

## 1. Introduction

## 2. Research Methodology

## 3. Bibliometric Analysis Results

## 4. Critical Review of Studies on Construction Stage Environmental Impacts

#### 4.1. Major LCA Studies at Construction Stage of a Building

_{2}emissions at the construction stage of an office building in their life-cycle-emission study using input/output (I/O) and process methods to determine the energy consumption and CO

_{2}emissions, respectively. CO

_{2}emissions in the office building were estimated with respect to five emission sources: temporary works, structure, finishing, equipment and general expenditure. The results indicated that the operation and the construction stages of the building are responsible for the highest emissions, with a respective contribution of 82% and 15%, while the demolition stage has a minimum impact on CO

_{2}emissions. Moreover, Suzuki et al. also conducted the same emission study on the construction phase of a residential building [44]. The results concluded that structural works are responsible for the most CO

_{2}emissions. Mao et al. compared GHG emissions of conventional and semi pre-fabrication construction methods in their emission study using a high-rise residential-building construction in China [10]. The study defined five emission sources for the construction process including the embodied emissions of building materials, transportation of building materials, construction waste, soil and prefabricated components, and operation of equipment. Data corresponding to all five emission sources were collected for both of the construction methods. A process-based quantitative model was developed to evaluate the emissions. The results indicated GHG emissions of 336 and 368 kg/m

^{2}for conventional and semi pre-fabrication construction, respectively. The findings further highlighted the dominance of material emissions at the construction stage with around 80% of the total emissions. The study concluded by stating that the use of prefabrication materials can reduce the total GHG emissions by 15%.

_{2}, NO

_{x}, SO

_{2}and PM

_{10}, and used an I/O-based hybrid model that was developed to evaluate emissions. The comparative results showed that CO

_{2}emissions govern the total emissions at the construction stage over other emission substances considered, with an overwhelming 93% contribution. However, the emission-comparison results at various life-cycle stages revealed different outcomes. The paper highlighted that CO

_{2}emissions are dominant in the operation stage compared to the construction, maintenance and disposal stages, while other air pollutants such as NO

_{x}, SO

_{2}and PM are significant at the construction stage for both types of buildings. Of the four considered impact categories, GWP remained the most important impact category, whereas acidification, eutrophication, and human toxicity were less important. Overall, it was found that reinforced-concrete houses have more emissions compared to timber houses, and the authors also concluded that a higher design life can reduce emissions by 14%.

#### 4.2. Models to Estimate Emissions at Building-Construction Stage

_{2}emissions. A typical I/O model to estimate CO

_{2}emissions from building materials is as follows:

_{2}conversion coefficient, E

_{in}is the energy-input vector, I is the unit matrix and A is the I/O table, which is the transaction matrix between industry sectors. W is the converted energy type, which can be determined from Equation (2) in Table 2. Most of the I/O models are either a derivation or a representation of Equation (1). Process-based mathematical models have been the most frequently used models to evaluate embodied emissions from building materials. Several studies used a similar type of process-based algebraic equation to quantify embodied energy and emissions from materials [7,12,46]. The equation estimated the total embodied GHG emissions from construction materials from the quantity of materials and the material-emission factors. According to the study, these material quantities can be obtained from daily delivery reports and bills of quantities (BOQs). However, the use of BOQ data often suffers from approximations. Moreover, care should be taken to avoid double calculation in the case of using actual material quantities from daily delivery reports. Several other studies modified the previous equation by incorporating a waste factor such as in Equation (4) in Table 2 [10,58,59]. This waste factor is a dimensionless factor and can be either developed or adopted from previous studies. Even though it is an approximation, this model overcomes the double calculation of emissions. Treloar et al. used a similar model to measure emissions from recycled materials in an attempt to highlight the reduction in emissions from construction materials [60]. Embodied emissions in the model were represented in terms of the material quantities, wastage rates and emission factors. The model excluded emissions and energy consumption during the material-installation stage. Shukla et al. used another type of process-based model to calculate the embodied energy of an adobe house [61]. They used the volume and density of the material to calculate the weight of the material. Crawford proposed a process-based hybrid model to estimate embodied emissions from construction materials [62]. In his equation, I/O models were used to calculate the emissions for the missing data paths of the material life cycle, and then these values were added to the known process-based result to obtain the total embodied emissions of a basic material (Equation (7) in Table 2). The known process-based emissions were then added to the I/O models to obtain a process-based hybrid model to evaluate the total embodied emissions from materials, as shown in Equation (9) in Table 2. This model is considered one of the best models to estimate the life-cycle emissions of a construction material.

_{x}, PM and SO

_{2}in addition to GHG emissions [64]. Numerous studies have employed various mathematical models to estimate both these GHG and non-GHG emissions from equipment usage. Millstein and Harley [65] used a model to quantify emissions (E

_{i}) from fuel combustion in their study on emissions from construction activities. The model incorporated the fuel consumed (S) in kilograms per day (kg/day) and was multiplied by an emission factor (Fi), which provided the grams of emissions per kg of fuel combusted (g/kg). One drawback of this model is that it used the actual fuel consumption in terms of kilograms, which is not readily available for most construction sites. Often at construction sites, the fuel-consumption quantities are recorded in liters (L); therefore, using a slight modification (Equation (11) in Table 2) by introducing fuel consumption in terms of liters per day (L/day) and the density of the fuel, a straightforward calculation of equipment emissions due to fuel combustion can be provided. Another study on estimating GHG emissions in building construction used a similar approach to estimate GHG emissions from the fuel combustion of construction equipment [7]. According to the study, GHG emissions from fuel combustion included CO

_{2}, CH

_{4}and N

_{2}O emission, and the GHG-emission factor should be calculated by the summation of all the emission factors according to the formula provided in the following equation. Mao et al., in a comparative study on estimating GHG emissions between prefabrication and conventional construction methods, employed a model to estimate GHG emissions from the resource consumption of construction equipment [10]. According to the model, the total GHG emissions can be calculated in terms of tons of CO

_{2}-eq by knowing the resource or energy utilized (R

_{r}) of the corresponding construction technique. The study further stated that construction equipment usually uses diesel, electricity and water as fuel resources. Sihabuddin and Ariaratnam used a different approach to calculate emissions from construction equipment in their emission study [66]. They argued that emissions from construction equipment are dependent on the machine characteristics rather than the combusted fuel. Consequently, they used a model that determined GHG and non-GHG emissions based on machine characteristics such as power, usage and deterioration. The model is useful to quantify non-GHG emissions from construction equipment.

Equation No. | Type | Model | Variable Definition and Method Explanation | Evaluation Basis | LCA Method | References |
---|---|---|---|---|---|---|

(1) | Material | ${\mathrm{E}}_{\mathrm{m}}={(\mathrm{I}-\mathrm{A})}^{-1}{\mathrm{E}}_{\mathrm{in}}$ | W is the CO_{2} conversion coefficient, E_{in} is the energy input vector, I is the unit matrix and A is the I/O table, which is the transaction matrix between industry sectors. | Embodied energy | I/O | [6,56,57] |

(2) | Material | $\mathrm{W}={\displaystyle \sum}{\mathrm{E}}_{\mathrm{ts}}\ast {\mathsf{\theta}}_{\mathrm{ts}}$ | E_{ts} is the energy type t consumed in the industry sector s and θ_{ts} is the conversion coefficient. | Carbon dioxide | I/O | [6] |

(3) | Material | $\mathrm{E}={\displaystyle \sum}{\mathrm{Q}}_{\mathrm{i}}\ast {\mathrm{f}}_{\mathrm{i}}$ | E is the total emissions (kg) from material type i, Q_{i} is the quantity of material i (kg) and f_{i} is the emission factor for the material I in (kg of emissions/kg). | Impacts from materials | Process | [13,41] |

(4) | Material | $\mathrm{E}=\left({\mathrm{Q}}_{\mathrm{i}}+\mathsf{\mu}\right)\ast {\mathrm{f}}_{\mathrm{i}}$ | E is the total emissions (kg) from material type i, Q_{i} is the quantity of material i (kg) and µ is the waste factor and f_{i} is the emission factor for the material i in (kg of emissions/kg). | Impacts from materials | Process | [3,16,59,67] |

(5) | Material | $\mathrm{EE}={\displaystyle {\displaystyle \sum}_{\mathrm{e}=1}^{\mathrm{E}}}{\displaystyle {\displaystyle \sum}_{\mathrm{m}=1}^{\mathrm{M}}}\left[{\mathrm{Q}}_{\mathrm{em}}\times {\mathrm{W}}_{\mathrm{em}}\times {\mathrm{EE}}_{\mathrm{m}}\right]$ | EE is the embodied energy of the material, Q_{em} is the quantity of material m in the element e, W_{em} is the wastage rate and EE_{m} is the embodied energy of the material excluding installation effects. | Embodied energy | Process | [60] |

(6) | Material | $\mathrm{EE}={\displaystyle {\displaystyle \sum}_{\mathrm{i}=1}^{\mathrm{n}}}{\mathrm{V}}_{\mathrm{i}}\ast {\mathsf{\rho}}_{\mathrm{i}}\ast {\mathrm{E}}_{\mathrm{i}}$ | EE is the embodied energy of the material, V_{i} is the volume of material used in m^{3}, ρ is the density of the material kg/m^{3} an d E_{i} is the embodied-emission factor for material i in kg of CO_{2}-eq/kg | Embodied energy | Process | [61] |

(7) | Material | ${\mathrm{EI}}_{\mathrm{M}}={\mathrm{PEI}}_{\mathrm{M}}+\left({\mathrm{TEI}}_{\mathrm{n}}-{\mathrm{TEI}}_{\mathrm{M}}\right)\ast {\mathsf{\xi}}_{\mathrm{M}}$ | PEI_{M} is the process-based hybrid emissions of the material, TEI_{n} is the emissions of the sector n, TEI_{M} is the emissions representing the basic material M and ἐ_{n} is the total price of the material i. | Energy intensity | Hybrid | [62,68] |

(8) | Material | ${\mathrm{CE}}_{\mathrm{mat}}={\displaystyle {\displaystyle \sum}_{\mathrm{i}=1}^{\mathrm{n}}}{\mathrm{m}}_{\mathrm{i}}\ast {\mathrm{EF}}_{\mathrm{mat},\mathrm{i}}$ | CE_{mat} is the carbon emissions from materials, m_{i} is the weight of the material i in kg, EF_{mat,i} is the emission factor for material in kg CO_{2}-eq/kg | Carbon emissions | Process | [69] |

(9) | Material | ${\mathrm{EE}}_{\mathrm{t}}={\mathrm{Q}}_{\mathrm{M}}\ast \mathrm{W}\ast {\mathrm{EI}}_{\mathrm{M}}+\left({\mathrm{TEI}}_{\mathrm{n}}-{\mathrm{TEI}}_{\mathrm{M}}\right)\ast {\mathsf{\xi}}_{\mathrm{M}}$ | EE_{t} is the total embodied emissions from process-based hybrid analysis; QM is the quantity of the total materials M and W is the wastage factor of the respective material. | Total environmental impacts | Hybrid | [62,68] |

(10) | Equipment | ${\mathrm{E}}_{\mathrm{i}}=\mathrm{S}\ast {\mathrm{F}}_{\mathrm{j}}$ | E_{i} is the GHG emissions from equipment i and S is the fuel consumed in liters and F_{j} is the emission factor for the fuel j in kg/liter | GHG emissions | Process | [59,70,71] |

(11) | Equipment | ${\mathrm{E}}_{\mathrm{i}}={\mathrm{S}}^{\prime}\ast \mathsf{\rho}\ast {\mathrm{F}}_{\mathrm{i}}$ | ρ is the density of the material in kg/m^{3}, s’ is the volume of the fuel consumed in m^{3} and F_{i} is the emission factor in kgCO_{2}-eq/kg | GHG emissions | Process | [59] |

(12) | Equipment | $\mathrm{E}={\displaystyle \sum}\frac{\mathrm{F}\ast \mathrm{f}}{1000}$ | The amount of fuel j consumed by the construction equipment in liters; f is the greenhouse gas emission factor for fuel j consumed by construction equipment (in kg CO_{2}-eq/liter) | GHG emissions | Process | [67,72] |

(13) | Equipment | $\mathrm{E}={\displaystyle {\displaystyle \sum}_{\mathrm{r}=1}^{\mathrm{r}}}{\displaystyle {\displaystyle \sum}_{\mathrm{v}=1}^{\mathrm{v}}}\frac{{\mathrm{R}}_{\mathrm{r}}\ast {\mathrm{f}}_{\mathrm{n}}^{\mathrm{v}}}{1000}$ | Emissions from equipment in kg, R_{r} is the power of the equipment in kW and f^{v}_{n} is the emission factor for r^{th} equipment in kg of CO_{2}/kW | GHG emissions | Process | [8,59,67] |

(14) | Equipment | ${\mathrm{Emissions}}_{\mathrm{i}}={\mathrm{EF}}_{\mathrm{i}}\ast \mathrm{HRS}\ast \mathrm{HP}\ast \mathrm{LF}\ast 0.01$ | Emissions_{i} is the total emissions of emission substance i in grams, HRS is the hours of use in hours, HP is the power of machine in hp, LF is the load factor is the ratio between operation and maximum rated outputs and 0.01 is the conversion of percent to fraction. | Non-GHG and GHG emissions | Process | [3,16,66,73] |

(15) | Equipment | $\mathrm{EE}={\displaystyle {\displaystyle \sum}_{\mathrm{i}=1}^{3}}{\displaystyle {\displaystyle \sum}_{\mathrm{j}=1}^{\mathrm{m}}}\left({\mathrm{T}}_{\mathrm{mac},\mathrm{i}}\times {\mathrm{EU}}_{\mathrm{mac},\mathrm{ij}}\right)\times {\mathrm{EF}}_{\mathrm{ej}}$ | T_{mac,i} is the working time of type i machinery, EU_{mac,ij} is the consumption of type j energy for type i machinery working unit time, and EF_{e,j} is the emission factor for type j energy | GHG emissions | Process | [69] |

(16) | Transport | ${\mathrm{E}}_{\mathrm{ii}}={\displaystyle \sum}\frac{{\mathrm{M}}_{\mathrm{j}}^{\mathrm{ii}}\ast \left({\mathrm{T}}_{\mathrm{j}}^{\mathrm{l}}\ast {\mathrm{f}}_{\mathrm{j}}^{\mathrm{ii}}+{\mathrm{T}}_{\mathrm{j}}^{\mathrm{s}}\ast {\mathrm{f}}_{\mathrm{s}}^{\mathrm{ii}}\right)}{1000}$ | E_{ii} is the total GHG emissions due to fuel combustion from transport vehicles, ${\mathrm{M}}_{\mathrm{j}}^{\mathrm{ii}}$ is the total quantity of material j, ${\mathrm{T}}_{\mathrm{j}}^{\mathrm{l}}$ and ${\mathrm{T}}_{\mathrm{j}}^{\mathrm{s}}$ are the total distances of transportation for building materials j by land and sea in km and ${\mathrm{f}}_{\mathrm{j}}^{\mathrm{ii}},{\mathrm{f}}_{\mathrm{s}}^{\mathrm{ii}}$ are the GHG emission factor for transportation by land sea in kg CO_{2}-e/(ton km), respectively. | GHG emissions | Process | [72] |

(17) | Transport | $\mathrm{E}={\displaystyle \sum}{\displaystyle \sum}\frac{{\mathrm{M}}_{\mathrm{j}}\ast {\mathrm{L}}_{\mathrm{j}}^{\mathrm{m}}\ast {\mathrm{f}}_{\mathrm{k}}^{\mathrm{t}}}{1000}$ | E is the emissions from transport and M_{j} is the weight of the material j transported, L^{m}_{j} is the distance traveled in km and f^{t}_{k} is the emission factor in kg/ton-km | GHG emissions | Process | [72] |

(18) | Transport | ${\mathrm{I}}_{\mathrm{i}}=\left({\mathrm{Z}}_{\mathrm{i}}+{\mathrm{r}}_{\mathrm{i}}\ast \mathrm{M}\right){\displaystyle \sum}\mathrm{d}$ | “I” is the impact from i^{th} vehicle in kg, Z_{i} is the zero-level emissions of the i^{th} vehicle in kg/km, r_{i} is the emission factor of i^{th} vehicle in kg/ton-km, M is the total weight of the vehicle in tons and d is the distance traveled by the vehicle in km | GHG and non-GHG emissions | Process | [41] |

(19) | Transport | ${\mathrm{CE}}_{\mathrm{tran}}={\displaystyle \sum}\left({\mathrm{m}}_{\mathrm{i}}\times {\mathrm{s}}_{\mathrm{i}}\right)\times {\mathrm{EF}}_{\mathrm{tran},\mathrm{i}}$ | CE_{tran} is the carbon emissions from transportations in kg, m_{i} is the material weight in tons and s_{i} is the distance traveled in km. EF_{tran,i} is the emission factor for the transport vehicle in kg/tons-km | GHG emissions | Process | [69] |

(20) | Unit Process | ${\mathrm{E}}_{\mathrm{u}}={\displaystyle {\displaystyle \sum}_{\mathrm{i}=1}^{\mathrm{n}}}{\displaystyle {\displaystyle \sum}_{\mathrm{j}=1}^{6}}{\mathrm{M}}_{\mathrm{i}}{\mathsf{\mu}}_{\mathrm{ij}}{\mathrm{GWP}}_{\mathrm{j}}$ | Eu is the GHG emissions in kg CO_{2}-eq, µ_{ij} is the emission factor for the j^{th} GHG emission pollutant and i^{th} emission substance, and M_{i} is the mass of the emission substance in kg | GHG emissions | Process | [74] |

## 5. Barriers and Knowledge Gaps

#### 5.1. Lack of Definition for a Generic System Boundary

#### 5.2. Difficulties in Data and Information Collection

#### 5.3. Complex-Modeling Issues and Lack of Decision-Making Aspects

#### 5.4. Complications in Classification and Analysis of Emissions

_{10}) and carbon monoxide (CO) can have adverse health impacts [81]. Therefore, accurate guidelines should be developed for selecting the most-important emission substances to improve the comprehensiveness of the emission study.

## 6. Conclusions, Future Research Focuses and Directions

## Funding

## Conflicts of Interest

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**Figure 3.**Annual publications in the top five journals related to environmental impacts in construction.

Attribute | Result |
---|---|

Timespan | 1991–2021 |

Average years from publication | 5.84 |

Average citations per documents | 1.278 |

Average citations per year per doc | 0.1673 |

References | 3795 |

Author’s Keywords (DE) | 387 |

Authors | 2976 |

Author Appearances | 3563 |

Authors of single-authored documents | 130 |

Authors of multi-authored documents | 2846 |

Documents per Author | 0.365 |

Authors per Document | 2.74 |

Co-Authors per Documents | 3.28 |

Collaboration Index | 3 |

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**MDPI and ACS Style**

Sandanayake, M.S. Environmental Impacts of Construction in Building Industry—A Review of Knowledge Advances, Gaps and Future Directions. *Knowledge* **2022**, *2*, 139-156.
https://doi.org/10.3390/knowledge2010008

**AMA Style**

Sandanayake MS. Environmental Impacts of Construction in Building Industry—A Review of Knowledge Advances, Gaps and Future Directions. *Knowledge*. 2022; 2(1):139-156.
https://doi.org/10.3390/knowledge2010008

**Chicago/Turabian Style**

Sandanayake, Malindu Sasanka. 2022. "Environmental Impacts of Construction in Building Industry—A Review of Knowledge Advances, Gaps and Future Directions" *Knowledge* 2, no. 1: 139-156.
https://doi.org/10.3390/knowledge2010008