**Figure 7.** Data sources used in the companies.

Different models exist during a project, and this is reflected in their use within the companies. In general, all the companies mention Rhino as a tool that is used in early design stages, where Sketchup and AutoCAD is also mentioned in a couple of the companies. The Rhino models are in some cases used for the LCA as illustrated in Figure 6. In the more detailed stages, all companies use Revit. They describe Revit as almost an industry standard when modelling in the project design stage. The companies work with different discipline-oriented models in Revit: an architectural model, structural model, and mechanical, electrical, and plumbing (MEP) models. All companies use the architect model for the LCA, but only two companies mention that they extract the data from the structural model and the MEP models to perform the LCA, and only in the detailed design stage.

To supplement data, and to fill the data-gap from only using the architectural model, the companies mentioned additional data sources. These include descriptions of building elements, data from sub-contractors, and gathering data from the discipline groups such as the structural or HVAC (heating, ventilation, and air conditioning) engineer. Two companies also mentioned the use of experience-based values from earlier projects or the literature to supplement in earlier stages, when data is not available. The use of descriptions of building elements is mentioned by company B, C, and H for LCA in the early design stage, when information in the model is limited or when it is not defined in the BIM. Element details are gathered from the supplier, for example the concrete element supplier, because they have more detailed information on the elements. If information or data are missing in the BIM model, the companies contact the discipline groups to collect the missing information. An example of this is company A, who collects information by providing the different discipline groups with Excel sheets, where they can fill in the data.

#### *4.3. Challenges in BIM–LCA*

During the interviews, the individual companies were asked which challenges the company faces when making LCA from the building models. The challenges are listed in Table 3, where they are separated into eight overall challenges.

**Table 3.** Challenges of BIM–LCA mentioned by the companies.

**Table 3.** *Cont.*

Some of the most commonly mentioned challenges are the lack of data availability and quality in the models used to establish the BoQ. An architect mentions that the models have not been made for the purpose of quantity extraction, but with other aspects in mind, thus the quantity take-off is wrong. It is also mentioned that some of the discipline models, such as structural and MEP, often do not exist, or are not reliable for quantity take-off. Further, the detailing varies, but some materials are simply not included in the model, such as reinforcement, and steel in plaster walls. Several mentioned that it is not likely that quantities will ever be completely correct in the model. Model errors are listed as a separate challenge in Table 3, however, they only contribute to the lack of quality in the models.

Furthermore, the structure and classification of the models can vary a lot, which can influence the data exchange. For instance, if a plugin expects a certain structure, but the model doesn't have this structure. When matching the BoQ to LCIA data, a common challenge mentioned is matching the units, as they may not align. It is also a source of human error, if the match is done manually. Some mention that the manual processes are time-consuming. This also includes manually checking the quantity take-off, due to the above-mentioned lack of quality.

To some degree, these challenges are a result of the lack of management or standardization of the models in relation to LCA, where some mention the lack of method for extraction of quantities, requirements for input of material information, and good-quality models at the time that they need them for the LCA. Further, those who make the LCA are often not the ones who make the building models. Therefore there is a lack of incentive for modeling for quantity take-off, or a lack of responsibility of the quantities in the model which is needed in this collaborative modelling work.

#### *4.4. User-Perspective on Integration and Response to Prototype*

The informants were asked about features for the integration process that they found important, and afterward they were presented with the prototype from Section 2.5 and provided feedback. Both of these results are shown in Table 4. In terms of important features for the BIM–LCA, one of the informants said that the integration should help solve the data issues from BIM. This refers back to the challenges, mentioned in Section 4.3, where several companies questioned the quality of their models, and their completeness. The 3D view was mentioned as a positive feature in connection to transparency of data from the model. Due to the quality of the models, they need to check the quantities, thus the 3D view will help them understand the origin and calculation of quantities, and to see if there are collisions of elements. The 3D view was also mentioned in relation to visualization, where several companies suggested it and found it to be a positive feature in the prototype. In general, six out of the eight companies mentioned the positive in a visual interface for the BIM–LCA integration. They mention its positive effects on communication and discussing results with different actors of the projects, especially at early design stages. Two engineering companies stated that they do not necessarily need a 3D view, as they were worried that the integration process would take longer. In terms of ease-of-use, some worried that the general workflow in larger models might be complex, if they need to review and match all this data with LCIA-data. However, some said that the grouping and filtering of elements can be used to manage the data.

Automation was another theme several of the companies found important. One of the informants mentioned that the models will likely always be wrong, but they still see potential in automating 80–90% of the process. Another informant mentions that automation is valuable, because humans make mistakes, and human mistakes are much harder to find. Automation also has relevance in terms of efficiency, where they currently spend many hours extracting quantities and go through several steps to make the LCA. To make automation easier, one informant suggests to "enrich" the BIM with information that can automatically match to the LCIA data. When presented with the prototype, one found it positive that the IDs from the IFC would make it easy to update the model, while another mentioned the lack of dynamic or parametric features.

**Table 4.** Important aspects of the BIM–LCA integration process mentioned by the companies, and their comments on the prototype.

**Table 4.** *Cont.*

Five companies also find the flexibility of data sources important. One mentions that IFC and Revit are the most commonly used data sources in the industry, and thus should be supported in a tool for BIM–LCA. Another mentioned that it is not certain that Revit will be the main tool in the future, therefore other data sources should be supported. When presented with the prototype, some found the use of a neutral file format positive, while others preferred to focus on Revit or use different file formats than IFC and OBJ. Some had a general experience of "loosing" their data when they had previously used IFC in their work. In the prototype, some found the flexibility positive; in terms of choosing only the data that they find relevant from the model, as well as the type of quantities relevant to the stage of the project, e.g., choosing areas instead of kg and m3 for early design.

Evaluation of design solutions was also important to consider in BIM–LCA for several of the companies, in order to get instant feedback on design solutions and whether or not they meet certain benchmarks. Four of the companies also mentioned that precision of data is important, including completeness of data already in the early stages, such as by including installations. Referring back to the challenges in Section 4.3, this information may not be available in the model and thus have to be added in the BIM–LCA process.

#### **5. Discussion**

#### *5.1. Data Management*

The companies interviewed for this study only used the model to store data related to extracting the BoQ. However, storing more LCA-related data in the model can reduce human error, support automation, and facilitate better use of the models across the life cycle of the building [33]. Moreover, it complies with the concepts surrounding BIM, which focus on information sharing and collaboration across the building life cycle. However, the workflow for this "enriched BIM" first needs to be established [33] and may vary depending on the goal and context of the LCA, as well as the structure used in the model. Further, if the model includes environmental data, it can be a challenge to manage if it is up-to-date [63]. Inclusion of environmental information in the BIM and using the IFC-viewer workflow has been tested in the literature before, with more focus on the later stages [59]. However, the process is associated with practical challenges, because even though IFC can contain this information, some properties, attributes, and entities are not available in industry BIM [59,64]. Further, the IFC schema still needs to be improved to allow information for a full LCA [33].

Despite only using BoQ data from the model, the companies are met with challenges related to the quality of the model and many use supplementary sources to complete or detail the BoQ. Poor design of models for LCA and life cycle performance has been recognized in the previous literature [4,35], and is confirmed and specified in this study. While future legislation demands for LCA might improve the collaboration related to quantities in the models, several companies expressed that it is not realistic that the models become perfect in terms of quantity extraction. An issue therefore lies both in how the BoQ data from the models can be improved, and what expectations regarding the precision of BoQ is expected from the building LCA at different stages. Automation could be a possible solution to improve upon the data quality, such as automatically adding reinforcement in concrete elements. However, automatic or semi-automatic approaches can also be imprecise and reduce transparency in the process. In terms of the expected precision of the LCA, the practitioners will likely need clear guidance regarding this aspect in relation to benchmarking their building.

In early design stages other strategies can be used, such as matching quantities with predefined elements, as suggested in this article as well as in previous studies [2,21,24,37,49].

#### *5.2. Tool for BIM–LCA*

The prototype for the Danish context includes the visual interface in correlation with conducting the building LCA. The companies were generally positive towards the 3D view in the prototype for both transparency of data and visualization of results. Some of the companies were also working towards their own plugin approach with 3D view, especially for early design stages. In the development of the prototype, it could be relevant to be inspired by the plugin–workflow, for instance by allowing the user to modify the geometry in the prototype to achieve the same dynamic effects, and test different designs. A challenge in the plugin–solution is the dependency on one specific building model tool. The companies from this study mainly use Revit, and some therefore preferred a direct data-exchange for this software. However, for the early design stages, it is more common to use a variety of tools, and some companies also expressed the positive in using neutral file formats in order to support a variety of modelling tools. It is likely that some companies will want to optimize internal processes, and thus develop their own tools, while others will require ready-to-use software. Software providers and policy-makers should therefore allow for different workflows, and provide a clear description of method.

#### *5.3. Limitations*

While the interviews can give detailed insight into workflow, challenges, and demands for BIM–LCA in industry practice, it should be noted that this study is a qualitative study with a limited sample size. Thus the results from the study represent the experience in eight different companies in Denmark. The companies cover a large share of the Danish AEC industry due to the large size of some of the included companies. The companies are of varying size, however, small and one-man businesses are not represented in the interviews, because it was assumed that they would have limited experience in the subject. Omitting the small companies can potentially have an influence on the informant's feedback on the prototype. This is because small companies can be more dependent on ready-to-use tools, such as the prototype, because they have less resources to develop their own integration of BIM–LCA. The prototype facilitates an integration process where all models, independent from which software the model is created in and how it is structured, can be used for BIM–LCA. Future development of the prototype should therefore include considerations of smaller companies.

#### **6. Conclusions**

This paper has provided insight into industry practice of BIM–LCA through eight in-depth interviews with consulting and contracting firms. All the companies use a quantity take-off approach for the BIM–LCA and some have recently made, or are currently developing, plug-in solutions. Nevertheless, due to the lack of quality in the models, it is often necessary to supplement the model-data with data from other sources, such as element descriptions and contacting engineering disciplines and subcontractors. The lack of quality and variations in modeling are dominant challenges mentioned by the companies. Many of these issues points back to a management of the models, which is not optimal for quantity take-off. In the future, the quality of the models may improve due to legislations in, e.g., LCA, however, some degree of inaccuracy should always be expected, especially in early design stages. For the integration of BIM–LCA it should therefore be considered how the inaccuracy is dealt with. Moreover, to which degree automation can be incorporated in the process. For legislation and benchmarking, the level of detail expected for the LCA should be clearly defined.

The informants also provided needs for BIM–LCA and evaluated a prototype for BIM–LCA in a Danish context with the use of open neutral file formats and a 3D view. The companies considered several aspects important in BIM–LCA, including visual interface, transparency of data, automation, flexibility of data sources, and easy access to evaluation of design solutions. Many considered the 3D view in the prototype valuable for transparency and communication, but some questioned its efficiency and use for their larger models. The prototype uses open and neutral file formats such as IFC and OBJ for the data exchange, which garnered mixed responses from the companies. Some valued the flexibility it can provide in terms of using models from different software, while others preferred optimizing the direct data exchange to their predominantly used tool, Revit. Companies will have different resources and goals, and thus different needs in relation to workflow for BIM– LCA. Specifically smaller companies will likely benefit from ready-to-use solutions such as the prototype, because there are no requirements to the structure of the model, or the software used for modeling. A strategy for software developers and decision-makers can therefore be to allow for different workflows, but provide transparency of results and clear descriptions of method.

**Author Contributions:** Conceptualization, methodology, validation, R.K.Z.; formal analysis, R.K.Z.; investigation, data curation, R.K.Z. and S.B.; writing—original draft preparation, R.K.Z.; writing review and editing, R.K.Z. and H.B.; visualization, S.B. and R.K.Z.; supervision, H.B.; project administration, H.B.; funding acquisition, R.K.Z. and H.B. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by Realdania, grant number PRJ-2019-00308.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** Data sharing is not applicable to this article.

**Acknowledgments:** The authors would like to thank the participants in the interviews. For the software programming, the authors would like to thank Christian Zimmermann and Christian Grau Sørensen.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Glossary**

*VPL* Visual Programming Language

#### **References**


**3. Methodology**

This section explores some theoretical considerations based on the EKC model to identify the baseline empirical model. The next sub-section identifies the estimation method followed by a discussion on the data sources, control variables, and variables for robustness tests.

### **3.1 Theoretical considerations**

The Environmental Kuznets Curve (EKC) describes the possibility of an inverted *U*-shaped relationship between environmental pollution and economic growth. As the economy progress via an increase in income, pollution is hypothesized to increase until it reaches a certain inflection point where pollution began to decrease [29]. In the early stages of economic development, it is assumed that industrialization would take place and contribute a large percentage towards GDP growth. Industrialization is expected to use large amounts of energy which results in environmental pollution in the form of CO2 emissions. CO2 emission stems from (i) burning of fossil fuels such as burning solid, liquid, gas fuel, or gas flaring, or from the production of cement, (ii) production of electricity and heat production, (iii) liquid fuel consumption for example the use of petroleum-derived fuels, (iv) combustion of fuels from the manufacturing (for example from coke inputs to blast furnaces) and construction sectors, (v) solid fuel consumption from the use of coal, (vi) transport activities such as aviation, domestic navigation, road, rail and pipeline transport, and (vi) use of natural gas. In the agriculture sector, greenhouse gas emissions from livestock, such as cows, rice, wheat, and corn production contribute to pollution. Other forms of pollution include methane which is emitted during the production and transport of coal or natural gas, from livestock such as cows, and the decay of waste in the soil.

As the economy progressed and began wealth accumulation, countries would be able to invest in research and development (R&D) towards the production of machines and mechanisms that could minimize the impact on the environment. Examples include the use of green technology in production. As a result, even as the economy continues to progress, the impact on the environment is minimized. Another reason for the possible decline in pollution is due to both public and private awareness of the negative impact of pollution left unchecked. International organizations such as the United Nations, World Bank, and the International Monetary Funds have made concerted efforts on environmental sustainability such as the use of renewable energy and the promotion of a circular economy. Governments have come together towards embracing green policies and the adoption of greener technologies to reduce CO2 emissions. At a micro level, non-governmental organizations (NGOs) are also propagating reduction in environmental pollution via recycling, upcycling, and more recently, replacing the end-of-life concept with the restoration which leads to changes in business models, operations, systems, use of materials, and reduction in the use of harmful chemicals. In other words, producers are responsible for the whole lifecycle of the products.

In countries where inequality and poverty are prevalent, the poorer segment of the economy are more concerned with their survival, therefore, have less interest in environmental policies or programs [30] unless they can monetize from the policies. For example, via the collection of paper, glass, or tin that could be sold to recycling industries. According to the Median Voter Theory, poorer societies are more concerned with material wellbeing and would be less interested to support environmental related policies [31]. Ridzuan [32] (2019) suggests that society's interest in environmental protection could be downplayed if income inequalities are predominant within the society. On a similar note, Franzen and Meyer [33] and Facchini et al. [34] argued that when income inequality is high, the public is more concerned focused on economic growth and redistribution compared to environmental issues. Consequently, income inequality reduces government expenditures on environmental protection [35]. On the other hand, the richer segment of the society could easily fulfill their daily needs and have excess wealth which could be directed towards the consumption of environmentally friendly products and comply with other environmentally friendly policies.

### **3.2 Estimation method**

The System GMM (S-GMM) is used in this study to examine the impact of inequality and other control variables on environmental pollution. S-GMM overcomes the shortcomings of the standard GMM where biases increase the number of instruments proliferate and weak instrument problems, and the problem of poor finite sample properties in terms of bias and precision in Difference GMM and the problem of lagged levels [36]. S-GMM corrects endogeneity such as in *CO*2 and *GDP* by introducing more instruments to considerably improve efficiency and transforms the instruments to make them uncorrelated (exogeneous) with the fixed effects, permits a certain degree of endogeneity in the other regressors and optimally combines information on cross country variation in levels with that on within-country variation in changes [37].

The baseline empirical model can be described as follows:

$$\text{CO2}\_{it} = \alpha\_1 \text{CO2}\_{it-1} + \beta\_2 \log \text{GDP}\_{it-1} + \beta\_3 \text{GDP}\_{it-1}^2 + \beta\_4 \text{GDP}\_{\mathcal{F}\_{it-1}} \tag{1}$$

$$+ \beta\_5 \text{Dom}\_{\text{Credit}\_{it-1}} + \beta\_6 \text{Trade}\_{it-1} + \beta\_7 \text{Gini}\_{i \text{dip}\_{it-1}} + \rho\_{it} + \varepsilon\_{it} + v\_{it}$$

where *CO*2*it* denotes environmental pollution proxied by CO2 and methane emission for individual *i* in period *t*, *CO*2*i*,*t*�1is the lagged of environmental pollution in the previous period and *ρit* þ *εit* þ *vit* represents the error components decomposition of the error term which allows for unobserved heterogeneity (*εit* hereafter). Other

assumption includes *vi*,*t* is serially uncorrelated. The control variables include growth of GDP (*gdp\_gr*), trade openness (*trade*), domestic credit (*dom\_credit*), and a measure of inequality (*Gini\_disp*). To empirically examine whether the effect of income inequality on CO2 emission is conditional on GDP, trade, and government environment expenditure, the baseline model is modified to incorporate a multiplicative interaction term of income inequality and the three variables which are the (i) size of the economy and income inequality (*GDP* � *Gini\_disp*), (ii) the impact of trade on income inequality (*Trade* � *Gini\_disp*) and (iii) the interaction of government expenditure on environment protection and income inequality (*GE* � *Gini\_disp*) and (iv) the combined effect of economic growth and financial development on income inequality (*GDP* � *Dom\_credit* � *Gini\_disp*). The interaction models are as follows:

*CO*2*it* ¼ *α*1*CO*2*it*�1 þ *β*2 *log GDPit*�1 þ *β*3*GDP*2 *it*�1 þ *β*4*GDPgrit*�1 þ *β*5*DomCreditit*�1 þ *β*6*Opennessit*�1 þ *β*7*Ginidispit*�1 þ *β*8 *GDP* � *Ginidisp it*�1 þ *ρit* þ *εit* þ *vit* (2) *CO*2*it* ¼ *α*1*CO*2*it*�1 þ *β*2 *log GDPit*�1 þ *β*3*GDP*2 *it*�1 þ *β*4*GDPgrit*�1 þ *β*5*DomCreditit*�1 þ *β*6*Opennessit*�1 þ *β*7*Ginidispit*�1 þ *β*8 *Trade* � *Ginidisp it*�1 þ *ρit* þ *εit* þ *vit* (3) *CO*2*it* ¼ *α*1*CO*2*it*�1 þ *β*2 *log GDPit*�1 þ *β*3*GDP*2 *it*�1 þ *β*4*GDPgrit*�1 þ *β*5*DomCreditit*�1 þ *β*6*Opennessit*�1 þ *β*7*Ginidispit*�1 þ *β*8ð*GE* �*Ginidisp*Þ *it*�1 þ *ρit* þ *εit* þ *vit* (4)

#### **3.3 Data**

A panel of unbalanced data from 120 countries was chosen based on the availability of the focal variables which are CO2 and income inequality data. The sample ranges from 1985 to 2019, drawn from various datasets. The sample was further disaggregated according to the level of income to control for the effect of income and to ensure that inferences were made based on samples that belong to some similar criteria which is in this case, the level of income. The segregated sample consists of 42 high-income, 35 upper-middle-income, and 43 lower-middle- and low-income countries. Segregation of the sample allows analysis and comparison of the impact of the different variables on CO2. In addition, more specific results can be obtained based on the characteristics, conditions, and resources of countries with similar income levels. This would allow understanding the nature and degree of association amongst the variables. The sample was segregated into three categories of income which are highincome countries, upper-middle-income countries, and lower-middle- and lowincome countries. The categorization is based on The World Economic Situation and Prospects (WESP), United Nations [38]. The list of countries is listed in Appendix I. Data on CO2 and methane were derived from the WDI, World Bank and Emissions Database for Global Atmospheric Research (EDGAR), European Commission.

Income inequality (*gini\_disp*) is represented by the Gini coefficients derived from the Standardized World Income Inequality Database (SWIID) V9.1 originally by Solt [39, 40]. SWIID is the best available proxy for income inequality since it covers the longest period and the largest number of countries compared to other databases.

SWIID 9.1 provides Gini coefficients for market incomes and net incomes (disposable Gini) which allows for international comparison. For the purpose of this study, only disposable Gini is used since it is an after-tax Gini which is calculated after taking out the effect of taxes and after considering the effect of transfer payments. It should be noted that Gini coefficients are not available for all countries for the stipulated time, hence, regression is based on unbalanced panel data. Alternative measures of income inequality such as wage inequality or ratio inequality, market income (income before taxes and transfers), disposable income (household income after pensions, unemployment insurance, social assistance transfers, and other government cash benefits), post-tax income (gross income minus all direct and indirect taxes) and gross income (market income plus government cash benefits) are available albeit calculated differently. Nevertheless, the range of countries offered by these alternative proxies is limited to a few countries only, making extensive international comparison difficult. Therefore, SWIID is the best available proxy.

The proxy for income is real GDP from the World Development Indicator (WDI) database, World Bank which serves the role to control for the size of the economy and the EKC effects. GDP growth controls for the effect of the level of development where wealthier economies are presumed to have a larger public sector which is bound to affect the design of fiscal policy and redistribution. Wagner's Law stipulates that higher economic development would result in greater redistribution, which subsequently suppresses the problem of income inequality. According to Wagner, as economies developed, more resources are available for redistribution which later, promotes economic growth through higher income and increase aggregate demand [41]. Other control variables include domestic credit to capture financial development and trade openness to represent globalization and the effects of international market integration. Trade openness is defined as total imports plus export as a percentage of GDP. Both data were drawn from the WDI database. The role of government expenditure on the environment is captured by the amount of government expenditure on environment protection as a percentage of total government expenditure. Data is extracted from the Expenditure by Functions of Government (COFOG), Government Finance Statistics, and the International Monetary Fund (IMF).