A BIM-LCA Approach for Estimating the Greenhouse Gas Emissions of Large-Scale Public Buildings: A Case Study
Abstract
:1. Introduction
2. Materials and Methods
2.1. Case Project
2.2. Goal and Accounting Scope Definition
2.3. BIM-Based Inventory Analysis
2.4. LCGGE Estimation Model
2.4.1. Estimation of GGE from the Materialization Stage—
2.4.2. Estimation of GGE from the Maintenance and Operation Stage—
2.4.3. Estimation of GGE from the Demolition Stage—
2.5. LCGGE Assessment
2.5.1. GGE from the Materialization Stage—
2.5.2. GGE from the Maintenance and Operation Stage—
2.5.3. GGE from the Demolition Stage—
3. Interpretation of the Results
3.1. GGE from Different Buidling Materials
3.2. GGE in Different Months
3.3. GGE from Different Stages
3.4. Limitations of Proposed Method
4. Conclusions and Recommendations
4.1. Conclusions
- (1)
- The LCGGE of a building can be divided into three stages: the materialization stage, the operation and maintenance stage, and the demolition stage based on the theory. BIM, as an advanced information technology in the construction industry, can help provide the required data for LCA.
- (2)
- During the materialization stage, concrete and steel are the most important source of GGE among all building materials. The reduction of the waste of concrete and steel is valuable for GGE reduction.
- (3)
- For regions with hot summers and warm winters, the GGE of a building in summer is the most throughout out the whole year during the operation and maintenance stage.
- (4)
- For large-scale public buildings, the GGE during the operation and maintenance stage accounts for over 80% of the LCGGE of a building, which is much higher than that of residential buildings. Therefore, the operation and maintenance stage plays the most important role in energy saving and emissions reduction for a large-scale building.
- (5)
- The demolition stage is less important in GGE reduction compared with the other two stages.
4.2. Recommendations
- (1)
- The compatibility of different BIM platforms should be improved in the future.
- (2)
- This paper fails to test the effects of different GGE reduction measures, such as adopting recycled materials or using additional energy-efficient facilities. In the future, studies should focus on seeking the best way to achieve green buildings with the lowest LCGGE.
- (3)
- For developing countries such as China, the urbanization process is less than 50 years old and it is difficult to find the early data regarding building energy consumption that may have been lost. Therefore, we hope to have better data to modify the established energy analysis model in future studies.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Materials | Quantity (t) |
---|---|
Concrete | 32,730.26 |
Steel | 1339.34 |
Brick | 4371.12 |
Stone | 1134.18 |
Glass | 252.57 |
Wood | 267.84 |
Sand | 991.82 |
Aluminum | 294.05 |
Total | 41,381.18 |
Building Materials | Energy Consumption (KJ/kg) | GGE(t/t) |
---|---|---|
Concrete | 1247.74 | 0.2420 |
Brick | 2000 | 0.2 |
Stone | 12,943 | 2.33 |
Steel | 33,906 | 2.208 |
Glass | 16,000 | 1.4 |
Wood | 1800 | 0.2 |
Sand | 4000 | 0.9 |
Aluminum | 12,964 | 1.407 |
Designation | Temperature |
---|---|
Outdoor dry bulb temperature in summer | 33.5 °C |
Outdoor wet bulb temperature in summer | 27.7 °C |
Outdoor dry bulb temperature in winter | 5 °C |
Outdoor wet bulb temperature in winter | 1.3 °C |
Building Materials | Amount (t) | Carbon Emission Factor (t/t) | GGE (t) |
---|---|---|---|
Concrete | 32,730.26 | 0.2420 | 7920.72 |
Steel | 1339.34 | 2.208 | 2957.26 |
Brick | 4371.12 | 0.2 | 874.22 |
Stone | 1134.18 | 2.33 | 2642.64 |
Glass | 252.57 | 1.4 | 353.60 |
Wood | 267.84 | 0.2 | 53.57 |
Sand | 991.82 | 0.9 | 892.64 |
Aluminum | 294.05 | 1.407 | 413.73 |
Date/Time | Air Conditioning | Lighting (kWh) | Other Equipment (kWh) | Total (kWh) | |
---|---|---|---|---|---|
Heating (kWh) | Cooling (kWh) | ||||
January | 8215.37 | 1958.22 | 66,555.84 | 20,653.02 | 97,382.45 |
February | 9029.39 | 2265.59 | 60,114.95 | 18,654.34 | 90,064.27 |
March | 3585.63 | 24,788.93 | 66,555.84 | 20,653.02 | 115,583.40 |
April | 0 | 56,240.30 | 64,408.88 | 19,986.79 | 140,636.00 |
May | 0 | 121,786.90 | 66,555.84 | 20,653.02 | 208,995.80 |
June | 0 | 144,530.70 | 64,408.88 | 19,986.79 | 228,926.40 |
July | 0 | 182,396.20 | 66,555.84 | 20,653.02 | 269,605.10 |
August | 0 | 168,889.80 | 66,555.84 | 20,653.02 | 256,098.70 |
September | 0 | 146,424.70 | 64,408.88 | 19,986.79 | 230,820.40 |
October | 0 | 85,750.89 | 66,555.84 | 20,653.02 | 172,959.80 |
November | 312.25 | 37,371.48 | 64,408.88 | 19,986.79 | 122,079.40 |
December | 2945.42 | 5365.85 | 66,555.84 | 20,653.02 | 95,520.13 |
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Cheng, B.; Li, J.; Tam, V.W.Y.; Yang, M.; Chen, D. A BIM-LCA Approach for Estimating the Greenhouse Gas Emissions of Large-Scale Public Buildings: A Case Study. Sustainability 2020, 12, 685. https://doi.org/10.3390/su12020685
Cheng B, Li J, Tam VWY, Yang M, Chen D. A BIM-LCA Approach for Estimating the Greenhouse Gas Emissions of Large-Scale Public Buildings: A Case Study. Sustainability. 2020; 12(2):685. https://doi.org/10.3390/su12020685
Chicago/Turabian StyleCheng, Baoquan, Jingwei Li, Vivian W. Y. Tam, Ming Yang, and Dong Chen. 2020. "A BIM-LCA Approach for Estimating the Greenhouse Gas Emissions of Large-Scale Public Buildings: A Case Study" Sustainability 12, no. 2: 685. https://doi.org/10.3390/su12020685
APA StyleCheng, B., Li, J., Tam, V. W. Y., Yang, M., & Chen, D. (2020). A BIM-LCA Approach for Estimating the Greenhouse Gas Emissions of Large-Scale Public Buildings: A Case Study. Sustainability, 12(2), 685. https://doi.org/10.3390/su12020685