EMBODIED CARBON ESTIMATING: MOVING FROM STATIC TO DYNAMIC DATASETS

By Srinath Perera MAIQS and Namal Anuradha Gamage

Introduction

Climate change is a severe environmental threat faced by the world. Scientists have evidenced the impact of global warming and its effects on climate change as caused by the release of Green House Gases (GHGs) into the atmosphere. The report, issued by the Intergovernmental Panel on Climate Change (IPCC), declared that GHG emissions should be reduced by 45% by 2030 compared to 2010 to achieve a 100% reduction by 2050¹. Regarding GHG emissions, the construction industry is one of the worst offenders due to its continued heavy fossil fuel consumption. Therefore, a higher percentage of carbon-related emissions is recorded to be from the construction industry. Compared to other industries, the construction industry plays a critical role in response to climate change by accounting for around 39% of energyrelated emissions globally². The Building sector, a significant component of the construction industry, has become the world’s largest contributor to GHG emissions. Therefore, minimising the carbon emissions of buildings is one of the important concerns in the present context. The need to fulfil this commitment of reducing carbon emissions in the built environment is changing the industry’s behaviour towards the awareness of carbon accounting. The Quantity Surveyor has the specific role of quantifying construction works and as the accountant in construction is thus well placed in counting carbon emissions. Embodied Carbon (EC) and Operational Carbon (OC) are the two types of carbon emissions generated during the construction life cycle. OC is the emissions (CO2 and CO2 equivalent gases) generated from the operational activities of the building, such as lighting, heating, cooling, etc.

Embodied Carbon In Construction

EC is the carbon-related emissions (CO2 and CO2 equivalent gases) due to non-operational works of a building such as material extraction, production, transportation, and construction, together with the downstream works like maintenance, and demolition. The term embodied is important to distinguish from embedded as carbon emissions are not embedded in construction materials and components but accounted as emitted during the process of production and or construction. Since 86% of the GHGs constitute Carbon Dioxide (CO2) and a unit namely, Carbon Dioxide Equivalent (CO2e) has been developed for the remaining 14% consisting of Methane (CH4), Nitrous Oxide (N2O), and refrigerant gases to gain the uniformity of measurement³. EC accounts for approximately 20-30% of a conventional building, while OC contributes around 70-80% of the overall carbon emissions of the building life cycle⁴.

Stages Of Building Life Cycle

In contrast to OC, EC emissions are connected with various stages of the building life cycle. Therefore, it is essential to identify the phases of the building life cycle to estimate the EC of a product precisely. These phases are collectively known as system boundaries as given in Figure 1.

¹Chen, S., Teng, Y., Zhang, Y., Leung, C.K.Y. & Pan, W. 2023, 'Reducing EC in concrete materials: A state-of-the-art review', Resources, Conservation and Recycling, vol. 188, DOI 10.1016/j.resconrec.2022.106653.

²Ibid.

³Du, Q., Bao, T., Li, Y., Huang, Y. & Shao, L. 2019, 'Impact of prefabrication technology on the cradle-to-site CO2 emissions of residential buildings', Clean Technologies and Environmental Policy, vol. 21, no. 7, pp. 1499-514, DOI 10.1007/s10098-019-01723-y.

⁴Ashworth, A. & Perera, S. 2015, Cost studies of buildings, sixth edition, Routledge. ⁵Ibid.

Significance Of Estimating Embodied Carbon

To address the issue of climate change, the construction industry has concentrated more on decreasing the OC of buildings. As a result, new buildings are becoming more and more energy efficient and even zero OC. Therefore, the OC component of new buildings is significantly lower and the EC component has become the focal point for reduction of emissions. Figure 2 portrays the variations of OC and EC due to the energyefficient improvements in buildings.

By 2050, EC is predicted to be responsible for around 50% of total carbon emissions from new construction projects⁷. Further, EC is emitted within a shorter period, leading to more intensive annual impacts than OC. Unlike OC, EC estimation is still unregulated, and therefore more attention needs to be paid to evaluating EC in construction projects.

EC Estimating With Databases And Tools

EC estimating can be conducted using first principles starting from raw material extraction to the end of the life cycle, by considering every step. Nevertheless, scrutinizing each step of the life cycle of a product, from its raw material mining to the final step (within the selected system boundary), is comprehensive and consumes a lot of time. EC estimating is facilitated through databases and software tools which use many EC factors to enhance efficiency and to reduce the difficulties in EC estimating. The databases and tools, frequently adopted in EC estimating are shown in Table 1.

Issues Of Estimating Ec With Static Datasets And The Road To Dynamic Ec Estimating

Changes In Transportation Distances And Modes

When a product moves from its raw material extraction to the final step of the selected system boundary, it should be transported from one place to another. For instance, from the place of raw material mining to product manufacturing and then from product manufacturing to the construction site, and so forth. Therefore, transportation distances would change from one case to another, and many variations can happen for two selected cases even for the same material (e.g., brick). Similarly, changes in transportation mode (e.g., dump truck, flatbed truck) can also happen frequently for two selected cases of the same material. Therefore, changes in transportation distances and modes may affect variations in fossil fuel consumption for a particular task. Since the emission factors are available within static databases, these need to be adjusted to suit the specific case, transportation distances, location, and transportation modes of that case.

These result in inaccuracies and bias in estimating. These changes and inaccuracies can have a compounding effect on a full-scale project estimate.

⁶Ashworth, A. & Perera, S. 2015, Cost studies of buildings, sixth edition, Routledge.

⁷Pan, W., Qin, H. & Zhao, Y. 2017, Challenges for energy and carbon modeling of high-rise buildings: the case of public housing in Hong Kong, Resour. Conserv. Recycl., vol. 123, pp. 208–218.

Changes In Manufacturing Processes

Many construction materials include a comprehensive process from the mining of raw materials to the end product. The fossil fuel usage for the manufacturing process will change due to various factors such as variations in types of machines, changes in power usage of machines, changes in power mode of machines (e.g., petrol, diesel, or electricity), shifting from manual to an automated process and vice versa, and whether the machines are new or old (the efficiency factors). Since these changes are case sensitive, EC estimates carried out with the use of static databases can be significantly different from estimates using first principle-based methods.

CHANGES IN THE COMPOSITION OF THE POWER ENERGY MIX (ELECTRICITY)

Even when considering the same electrically operated machinery used for the production of a building component, there could be significant differences in the carbon emission profiles of two different production plants in two different states (or countries). This happens because of changes in the power generation profile of two different electricity suppliers. For example, an electricity supplier with a power generation profile of 20% renewable and 80% fossil fuel-based generation is significantly different from one with 60% renewable and 40% fossil fuel-based generation. As such, the use of static databases with carbon emissions for primary material based on a given power factor will not be accurate.

Emissions For Materials Are State Or Country Or Even Location Specific

Most EC databases are based on static data derived from one location. Due to volatility and rapidly changing energy profiles of power generators worldwide, the use of such static factors results in creating inaccurate estimates for EC.

These errors identified have a compounding effect on EC estimates derived from static databases. Even with identical components, origins, and technologies, estimates carried out using GaBi and SimaPro showed different outcomes.⁸ In another comparison, EC estimates which were carried out for seven work items by adapting Blackbook and eToolLCD had significant variations from 8% to 402% compared to first principle-based mechanism⁹. Considering all the above, it is quite evident that static datasets are no longer suited for preparing accurate EC estimates and it is necessary to move towards a mechanism that can capture dynamic datasets.

Dynamic Datasets

A construction project consists of numerous supply chains and each supply chain includes different stakeholders (miners, manufacturers, subcontractors, contractors, etc.). If we have the capacity to capture the real-time data from these stakeholders directly, it will enable us to estimate EC accurately. Then the issue is how we can capture those data. Fortunately, we now have technologies such as Blockchain that potentially can solve this issue due to its salient features. The Centre for Smart Modern Construction has developed a dynamic way of estimating EC using a blockchain platform for construction supply chains.¹⁰ Despite blockchain, any other technology that can capture dynamic EC datasets is suitable for reaching a dynamic way of estimating EC. As a way of addressing this, it is essential to access the production data, power profiles, and location data among others, and collect the real-time data within the construction supply chains. This will help to develop a bottom-up EC estimation. The future of this might be in automating the capturing process of data. Stage by stage, we need to remove the manual intervention and enable auto-capture of data. Therefore, in moving from static datasets to dynamic datasets, every stakeholder in the construction industry has a specific role to play. Future Quantity Surveyors need to be equipped with new technologies to deal with these estimates.

This article was written by Srinath Perera MAIQS and Namal Anuradha from the Centre for Smart Modern Construction, Western Sydney University, Australia

⁸Sinha, R., Lennartsson, M. & Frostell, B. 2016, ‘Environmental footprint assessment of building structures: A comparative study’, Building Environment, vol. 104, pp. 162–171.

⁹Rodrigo, M.N.N., Perera, S., Senaratne, S. & Jin, X. 2021, 'Review of Supply Chain Based EC Estimating Method: A Case Study Based Analysis', Sustainability, vol. 13, no. 16, DOI 10.3390/su13169171. ¹⁰Ibid.