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A comprehensive model for quantifying the environmental and financial performance of cities

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Current models to quantify environmental performance in the built environment are flawed as they typically focus either on one scale of the built environment (e.g. buildings), on a limited range of environmental flows (e.g. energy), or a particular life cycle stage (typically building use). There is a need to develop a more comprehensive model to assess and improve the environmental performance of cities. This paper proposes a multi-scale, bottom-up, dynamic life cycle assessment model for the built environment. The model combines nested systems theory with life cycle assessment and dynamic modelling. It covers all scales of the built environment, from materials to cities. In particular, it considers material, energy, greenhouse gas emissions, water and financial flows required to produce construction materials and replace them (embodied flows); operate buildings and infrastructure assets (operational flows); and for the mobility of building users (transport flows). Furthermore, the model evaluates the value created by a particular real estate development. The paper describes how the model operates and the methods used to quantify each flow. By covering spatial and temporal boundaries across multiple environmental and financial flows, this model will significantly improve environmental assessment and decision-making for actors of the built environment.
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A comprehensive model for quantifying the
environmental and nancial performance of cities
André Stephan
The University of Melbourne, Melbourne, Australia
Robert H. Crawford
The University of Melbourne, Melbourne, Australia
Victor Bunster
Pontificia Universidad Catolica de Chile, Santiago, Chile
The University of Melbourne, Melbourne, Australia
Georgia Warren-Myers
The University of Melbourne, Melbourne, Australia
Abstract: Current models to quantify environmental performance in the built environment are flawed as they typically
focus either on one scale of the built environment (e.g. buildings), on a limited range of environmental flows (e.g. energy),
or a particular life cycle stage (typically building use). There is a need to develop a more comprehensive model to assess
and improve the environmental performance of cities. This paper proposes a multi-scale, bottom-up, dynamic life cycle
assessment model for the built environment. The model combines nested systems theory with life cycle assessment and
dynamic modelling. It covers all scales of the built environment, from materials to cities. In particular, it considers material,
energy, greenhouse gas emissions, water and financial flows required to produce construction materials and replace
them (embodied flows); operate buildings and infrastructure assets (operational flows); and for the mobility of building
users (transport flows). Furthermore, the model evaluates the value created by a particular real estate development. The
paper describes how the model operates and the methods used to quantify each flow. By covering spatial and temporal
boundaries across multiple environmental and financial flows, this model will significantly improve environmental assessment
and decision-making for actors of the built environment.
Keywords: Life cycle assessment; life cycle cost; environmental performance; Python.
Cities are and will continue to be the epicentre of human activity, representing 80% of global gross domestic product
(World Bank, 2018) and accommodating the majority of the expected global population increase of ~3 billion people by
2050 (United Nations, 2015). Cities also represent ~60% of global final energy use and more than 70% of associated
greenhouse gas emissions (World Bank, 2018), most of which are caused by the construction, maintenance, and operation
of buildings and infrastructure. The maintenance and expansion of this built stock are the main drivers of resource extraction
(UNEP, 2016) and associated environmental effects, such as climate change and resource depletion. Substantial and urgent
improvements to the environmental performance of cities are needed to avoid devastating disruptions to ecosystems and
human society. The window of action is closing rapidly as extreme weather events are becoming more frequent with climate
change, resources are dwindling at the fastest rate ever, and freshwater is becoming increasingly scarce and polluted.
Tackling the environmental effects of cities and their built stocks can be achieved by empowering actors of the built
environment, i.e. inter alia architects, engineers, town planners, landscape architects, construction managers, with
evidence-based knowledge and models. These models can be used to quantify the environmental performance of
cities and built stocks in order to compare alternatives and identify solutions with improved performance. Measuring the
environmental performance of built stocks at the city level entails understanding their constituting components, i.e. buildings
and infrastructure, and in turn, assemblies, elements and materials. Capturing spatial linkages across time is essential for a
comprehensive quantification of the environmental performance of built stocks. Actors of the built environment rely on such
models to provide the evidence needed for decision-making.
However, existing models for quantifying the life cycle environmental performance of built stocks are not sophisticated
enough to provide sufficient information for decision-making. Most existing models focus either on one scale of the built
P. Rajagopalan and M.M Andamon (eds.), Engaging Architectural Science: Meeting the Challenges of Higher Density: 52nd
International Conference of the Architectural Science Association 2018, pp.637–645. ©2018, The Architectural Science
Association and RMIT University, Australia.
environment, e.g. a building, on one flow, e.g. energy, or on one life cycle stage, e.g. operation. Even studies that use a
more holistic life cycle approach often underestimate embodied requirements (Crawford et al., 2018) and are typically a
one-off attempt, requiring a significant amount of time and resources to conduct (Crawford, 2011). Existing models to
quantify the environmental performance of buildings and the built environment can paradoxically lead to buildings with
poorer environmental performance, e.g. more energy-intensive (Stephan et al., 2012). Furthermore, bottom-up models that
capture linkages between scales are rare, incomplete and usually static (see Section 2). The need for more comprehensive
models has been highlighted by researchers across the fields of urban energy analysis (Allegrini et al., 2015), material flow
analysis (Muller et al., 2014), life cycle assessment (Anderson et al., 2015; Säynäjoki et al., 2017), as well as by international
agencies such as UN-Habitat (2017). Means for comprehensively assessing life cycle environmental performance of the built
environment, including embodied, operational and transport flows, across spatial and temporal boundaries, are urgently
needed. This will help minimise poor-performing buildings and infrastructures, and the resultant effects on human society
and the natural environment.
1.1 Aim and scope
The aim of this paper is to propose a bottom-up, dynamic, and multi-scale model to comprehensively quantify and improve
the life cycle environmental and financial performance of cities and built stocks.
The model includes life cycle environmental flows, namely, energy, greenhouse gas emissions, water and materials, as
well as cost. It considers embodied flows (associated with raw material extraction, production, construction, replacement
of materials, and demolition), operational flows (associated with running a building/infrastructure) and user-transport flows
(associated with the mobility of city dwellers).
Environmental assessment of built stocks is typically conducted either at the building scale or at the neighbourhood/city
scale. These two approaches are described below and the knowledge gaps in current assessments are highlighted.
In their review of building environmental assessment models, Haapio and Viitaniemi (2008) do not find any model
that is capable of measuring a range of building types (e.g. office and residential), building categories (i.e. new, existing,
refurbishment) and building components. Moreover, the models reviewed exclude infrastructure and cannot combine
different buildings and infrastructures to evaluate the environmental performance of built stocks. While other models have
been developed since this review, e.g. Huang et al. (2017), they still suffer from a range of limitations, including focusing
on a one or two flows at most (in this example energy and greenhouse emissions only), and significantly underestimating
embodied flows, i.e. the environmental flows associated with raw material extraction, material manufacture, processing
and transport, construction, replacement of materials over time, and demolition. The overall conclusions of Haapio and
Viitaniemi (2008) still hold: at the building level, there is no model that can comprehensively quantify life cycle environmental
performance in order to provide decision-makers with sufficient information.
When evaluating the environmental performance of building stocks at a neighbourhood or city level, a large number of
studies focus on the material stock and use top-down approaches (Muller et al. 2014). These top-down approaches do not
allow assessors to spatialise the stock, to understand where materials are located or to model potential scenarios, such as
retrofitting, housing form, or others, as these aspects are not parametrised in top-down models. If bottom-up approaches
are used, the material stock in buildings is typically calculated using average material intensities per m² (e.g. see Tanikawa
and Hashimoto (2009)). This means that the geometric characteristics of each building are ignored. Planners, consultants
and city councils using such models do not always obtain an accurate representation of the stock and cannot disaggregate
stocks by geometric element, e.g. northern façades.
When it comes to urban energy analysis models, a recent review by Allegrini et al. (2015) revealed that existing models
largely ignore embodied energy, focusing on operational energy only. This is a clear shortcoming of such models as trade-
offs between operational energy improvements and their embodied energy premium is not captured. In their review of life
cycle assessment studies on neighbourhoods, Lotteau et al. (2015) identify only 21 studies conducted from 1980 to 2015.
Regardless of the scale of assessment, the other significant shortcoming of all existing models is their reliance on
process analysis which can underestimate embodied flows by a factor of up to 4 at a whole building level (Crawford and
Stephan, 2013). This is because process analysis captures only a certain number of processes upstream in the supply
chain, failing to capture the entire supply chain.
Stephan (2013) has developed one of the most comprehensive models for the life cycle energy analysis of residential
buildings and has applied it to a range of buildings in Australia, Belgium, and Lebanon. Recently, Stephan modified this model
to apply it to non-residential buildings within the City of Melbourne and quantified embodied energy, water, greenhouse gas
emissions and the material stock of 13,075 buildings (Stephan and Athanassiadis, 2017; 2018), demonstrating that such
A. Stephan, R.H. Crawford, V. Bunster and G. Warren-Myers
models can be up-scaled to the city level. However, this was a one-off application as the original model is not designed
for non-residential buildings. This paper builds upon the work of Stephan and incorporates new embodied flows data and
quantification algorithms (see Section 3.3).
3.1 Conceptual framework
This project combines the theory of nested systems, life cycle assessment and dynamic modelling in its conceptual
framework, as depicted in Figure 1.
Nested systems theory (Walloth, 2016) focuses on the interactions between systems that are enclosing other systems
(e.g. a city encloses neighbourhoods) or systems enclosed by larger systems (e.g. a window is an assembly enclosed by
the building system). This theory has been developed for urban systems which are nested by nature, and is therefore ideal
to model built stocks. It is by replicating the nested structure of urban systems that their interactions can be realistically
modelled, better understood, and quantified. The nested systems theory provides the grounding for the model architecture.
Life cycle assessment is an internationally established and standardised method to quantify the environmental flows
associated with any good or service, in this case built stocks in cities. It uses a life cycle inventory that compiles all the
resource inputs and outputs of waste and pollutants for a product, across the different stages of the product’s life cycle. The
life cycle assessment method applied to buildings (European Standard 15978, 2011) provides the quantification approach
used to comprehensively model a broad range of environmental flows across different scales and life cycle stages.
Dynamic modelling focuses on the temporal evolution of nested systems and their environmental flows across time. Since
built stocks have very long lifespans compared to other products, lasting decades or hundreds of years, their environmental
performance will evolve through time and depends on a myriad of parameters. The dominant majority of existing life cycle
assessment studies are static, assuming a constant technological performance over time. The use of dynamic modelling
in this research will enable a prospective assessment that tests a broad range of scenarios and temporal evolution of
parameters. This, coupled with the capacity to conduct retrospective assessment in order to monitor past performance, will
enable decision makers to devise solutions that are resilient to the inevitable change in context. This is a major improvement
over existing models.
Figure 1: Conceptual framework of the research.
Note: * = modelled as a stock of buildings and infrastructures and includes user-transport flows.
3.2 Nested modelling
Developing the nested model is an iterative process. Individual models for materials, elements, assemblies, buildings
(including new construction and retrofit), infrastructure, and built stocks for neighbourhoods and cities have been developed
using Python programming language. But these models are prone to modifications over time as the model is used. Python
is chosen for its flexibility, open source nature, multiple scientific computing models that are freely available for use (e.g.
Pandas, Scientific Python, Numeric Python), and the fact that previous models developed by the authors rely on Python.
The overall architecture of the model is presented in Figure 2. Object-oriented programming is used, and this results in three
main advantages.
Firstly, each object is characterised as a separate entity with its own properties. For example, the model comprises
‘window frame’, ‘lighting fixture’ and ‘sewage pipe’ as objects. The ‘window frame’ object can have a ‘material’ property
which is ‘aluminium’ and a ‘number of glazing panes’ property which can be ‘2’. This level of detail allows superior
A comprehensive model for quantifying the environmental and financial performance of cities
modelling capabilities. Decision-makers will be able to investigate the effect of a broad parametric analysis on environmental
performance ranging from the type of paint, lighting fixtures or roof insulation thickness, to the efficiency of the heating
system, the installation of photovoltaic panels, the width of roads, the water efficiency of taps, the modal split of user-
transport, or waste generation for on-site construction. Secondly, the algorithms are separated from the databases that
feed them. This allows the model to be applied across international boundaries as long as data exist. In turn, this will enable
the comparison of environmental profiles of different buildings or infrastructures internationally, while using the same model.
This separation of data and algorithms also allows updates of each object to be undertaken seamlessly. The third advantage
lies in the flexibility of the model. Because each object is a separate entity, it is easy to include new objects such as ‘parks’
as a new infrastructure type. This significantly improves the usefulness of the model in the future as it can be adapted and
upgraded based on future research directions or user needs.
As indicated in Figure 1, the neighbourhood and city scales are modelled as built stocks. This means that interactions
between buildings due to shading, wind tunnels, etc. are not captured in the model at this stage, but can be integrated at
a later stage.
Figure 2: Diagram of the model
A. Stephan, R.H. Crawford, V. Bunster and G. Warren-Myers
3.3 Modelling material flows
Material stocks and flows are modelled based on the geometric properties of a building or infrastructure and their constituting
assemblies. Each assembly contains a number of elements and/or materials and specific quantities of these, e.g. one
square metre of slab contains 0.2 m³ of concrete. Based on the geometry of a building, a bill of quantities of assemblies is
generated and used to estimate the quantities of assemblies, elements and materials within a building or infrastructure (i.e.
their inventory or stock). Elements and assemblies are replaced using average useful lives or material survival curves and
result in a material flow for replacement. This approach has been successfully trialled in Stephan and Athanassiadis (2017;
2018). Material flows resulting from the demolition and construction of buildings and infrastructure at the neighbourhood
and city scales are also calculated using the geometry and constituting assemblies. Construction and demolition activities
are usually set based on previous trends but alternative scenarios can be modelled. The potential re-use and recycling of
materials and elements in new buildings are modelled based on the material type (e.g. aluminium) and its remaining years
of life.
3.4 Modelling embodied environmental flows
Embodied flows are quantified using the Path Exchange hybrid analysis (PXC) technique developed by Treloar (1997) and
validated by Crawford (2008). This technique combines bottom-up industrial data with top-down economic data to capture
all inputs and outputs across the entire supply chain of a product. The most recent hybrid embodied flow data are used
in this model and are compiled into embodied flows coefficients. These are being compiled as this paper is being written
and are based on semi-automated algorithms, as described in Stephan et al. (2018). Both initial (accounting for the original
construction of a building or infrastructure) and recurrent (associated with material replacement over time) embodied flows
are quantified. The material replacement flow (see above) is converted to a recurrent embodied flow. This allows the detailed
evaluation of future embodied energy, water and material inputs as well as GHG emissions.
Embodied flows associated with a building are calculated as per Equation 1 below. Substituting the building with an
infrastructure in the equation produces the associated life cycle embodied flows.
( )
( )
( )
( )
= =
×+ − ×
+ −× × + ×
mb m b m b
mb m b m m mb
Where: LCEFb is the life cycle embodied flow of the building in flow unit (e.g. GJ for energy); M is the total number
of materials in the building; Qm,b is the quantity of material m in the building b (e.g. tons of steel); FCm is the hybrid flow
coefficient of material m in flow unit per functional unit of material (e.g. GJ/ton); TFRBS is the total flow requirement of the
building sector associated with the building type of building b, in flow unit/AUD; TFRm is the total flow requirement of the
input-output pathway representing material m, in flow unit/AUD; and Cb is the cost of the building b in AUD; TH is the time
horizon of the analysis, e.g. 2050; CYb is the construction year of building b, e.g. 1987 or 2018; SLm is the service life of the
material m, in years; NATFRm is the total flow requirement of all input-output pathways not associated with the installation or
production process of material m being replaced, in flow unit/AUD, e.g. pathways representing concrete production when
replacing aluminium frames; and Cm,b is the cost of the material m in AUD in building b.
3.5 Modelling operational environmental flows
Operational energy and GHG emissions associated with heating, cooling, ventilation, hot water, lighting, appliances and
cooking are considered in the model. These are calculated based on the building type, its occupancy pattern, number
of appliances and systems (including solar energy generation) and power ratings. Heating and cooling are calculated by
connecting the model to existing and verified models, such as Energy Plus. The heating and cooling demands are calculated
for each individual buildings and summed for neighbourhoods and cities. The parametric disaggregation of operational
energy and GHG emissions allows future users of the model to control individual parameters and evaluate their effect. All
operational energy is expressed in final, delivered and primary energy terms. Primary energy figures capture all losses in the
energy supply chain and are therefore critical in determining GHG emissions. These are calculated using conversion factors
based on the energy sources used. A long term climatic model evaluates the impact of GHG emissions in terms of global
warming potential based on the date of their emission, as illustrated by Kendall (2009). Operational water is modelled based
on the building type, occupancy pattern, number of water fixtures and systems.
It is important to flag that the modelling of operational flows differs depending on the scale assessed. At a building level,
more detailed energy modelling is appropriate to make decisions. At the neighbourhood and city levels, built stocks can be
modelled using static thermodynamic equations to significantly improve the runtime of the model. This approach works well
in heating-dominated climates (Reinhart and Cerezo Davila, 2016).
A comprehensive model for quantifying the environmental and financial performance of cities
The life cycle operational energy or water flows of a building are obtained as per Equation 2.
( )
× ×× ×
= −
,, ,
b eb eb eb
Where: LCOPFb is the life cycle operational energy or water flow of building b, in GJ or kL; E is the total number of end-
uses; Re,b is the power or water rating of the end-use e, in GW or kL/s; Se,b is the operational schedule of end-use e, in
seconds (it is a function of the building type, occupancy, etc.); ηe,b is the efficiency of the end-use e (e.g. the efficiency
of a water heater); and ULFse,b is the upstream losses factor associated with source s on which end-use e operates (e.g.
electricity for a water heater). See Equation 1 for the definition of TH and CYb.
3.6 Modelling transport environmental flows
The user-transport flows associated with mobility of residents is also taken into account at a neighbourhood or city level.
This is done by multiplying the average travel distance per person by the environmental intensity of the relevant transport
mode, as per Equation 3.
( )
( )
= =
=−× + ×
, ,,
Ob b m m obm
Where: LCTFO,b is the life cycle transport flow of the occupants O of building b, in flow unit (e.g. GJ for energy); M is the
total number of transport modes used by occupants O; DFIm is the direct flow intensity of transport mode m, in flow unit/
km; IFIm is the indirect flow intensity of transport mode m, in flow unit/km; and ATDo,b,m is the average annual travel distance
of occupant o living in building b, using transport mode m, in km. See Equation 1 for the definition of TH and CYb.
Considering both direct and indirect environmental flows is critical to ensure a comprehensive environmental assessment.
The significance of indirect environmental flows associated with transport has been demonstrated by a number of studies
(Lenzen, 1999; Chester and Horvath, 2009; Stephan and Crawford, 2016).
3.7 Modelling life cycle cost and valuation
Life cycle cost is modelled using the net present value technique (Berk and DeMarzo, 2010). It uses bottom-up costs
associated with individual construction material, elements, assemblies, trades, fuel prices, public transport fees and other
relevant cost databases. These critical costs are summed and calculated at current prices, projected into the future with
assumed inflation rates dependent on product, and are discounted back to a net present value as per Equation 4.
( )
( )
= = =
+ + ×+
∑ ∑∑
,, ,, ,,
1 11
A EW y
aby eby wby
a ew
y CY
Capex C C CPI
Where: NPVb is the net present value of building b in AUD; A is the total number of assemblies in building b; Capexa,b,y is
the capital expenditure associated with assembly a in building b during year y; E is the total number of energy vectors used
in building b, including fuel for cars; Ce,b,y is the cost of energy vector e used in association with building b in year y; W is the
total number of water vectors; Cw,b,y is the cost of water vector w used in association with building b in year y; CPI is the
considered inflation rate; and r is the discount rate. See Equation 1 for the definition of TH and CYb.
Another important characteristic of the model is its ability to capture the value of the land based upon the proposed
development type. It utilises the concepts of a modified residual land valuation model which estimates the underlying present
value of the land based on the future utilisation of the land. As this analysis examines broader concepts and considerations,
the variations in time horizons means a discounted approach would be beneficial.
The estimation of value for the land is based on value ascertained through the future use of the site on completion of
the development. The value of the land is determined by its utility value, the use value of the site; which is dependent upon
accessibility to economic activity, present and future uses, physical characteristics and other historical factors that might
A. Stephan, R.H. Crawford, V. Bunster and G. Warren-Myers
affect the use of the land for a purpose (Brigham, 1965). To calculate this, the residual land value is determined through the
estimation of the value of the project on completion, minus development costs and associated interest, land holding costs
and interest charges and the developers’ profit, usually a percentage of the development costs (Harvard, 2008). This can
be calculated and reduced to a net present value as per equation 5 (Wyatt, 2013).
( ) ( ) ( )
,0 ,0
d d dd
= − − +
Where LVd,0 is the residual net present land value of development d; i is the cost of finance and comprises the annual
interest rate and discount factor; t is the development period; Vd,0 is the current estimate of development ds value on
completion; pd is the developer’s profit based on the current estimate of development ds value (variations to this are profit
based on the development costs, we have utilised the development value for our model); Cd,0 is the current estimate of
development ds costs including construction and costs associated with the land; and ICd is the finance costs calculated for
the construction costs of development d over the construction period and ILd is the land holding and acquisition costs of
development d calculated over the entire development period.
Although a simplified approach, the detail required in estimating with accuracy the residual land value lies within the
calculations of construction costs and the estimation and assessment of the project on completion. Both of which can
change with property and economic market conditions, yet this approach provides an assessment based on the conditions
known at the time, and is utilised and accepted by the valuation profession to assess the value of developable land globally.
3.8 Dynamic Modelling and uncertainty
The dynamic nature of the model is another important feature. The model enables modelling the temporal evolution of
parameters and quantification of their flow-on effects across the model. This is done by specifying certain evolution scenarios
using either interpolation between set values at particular years or by manually specifying values over periods of time. This
will enable users to evaluate the effect of changes to objects and/or flows across time.
Uncertainty in the data is one of the major drawbacks of quantifying the environmental performance of complex nested
systems. However, this uncertainty should be seen as an intrinsic component of any model rather than a burden. It is taken
into account to allow more resilient decisions. Interval analysis (Moore et al., 2009) is used to model parameter uncertainty.
This simple approach, which consists of attributing minimum and maximum values to a parameter is chosen due to the lack
of statistical data on the sheer amount of variables considered. For example, the probability distribution associated with the
embodied energy of steel is not currently available. The advantage of implementing interval analysis is the ability to modify
how uncertainty is modelled in the future and to gradually enrich the model. This is already a significant improvement over
most existing building life cycle assessment models, e.g. Athena Institute Impact Estimator. Another feature is the ability to
override computed figures as well as adding entries to databases. This is particularly useful when measured post-occupancy
data is available (e.g. electricity bills). In this case, the model integrates measured and simulated data, reducing uncertainty.
The model is currently under development. It will be made available on its dedicated website:
Links to all publications and data sources are also available on the website.
This paper has presented a comprehensive life cycle assessment model for quantifying the environmental and financial
performance of cities, covering multiple scales of the built environment, across different environmental flows, and including
life cycle cost and valuation. It is one of the most advanced environmental performance models to date and endeavours to
overcome flaws in existing models.
However, this comprehensiveness comes at the price of significant data requirements and complexity. In order to be able
to cover such a broad range of environmental flows across space and time, a significant amount of data is required. This
restricts the use of the model to where data are available to reliably model flows. The complexity of the model is another
limitation. As it currently stands, the model requires significant expertise in environmental modelling, the built environment
and computer programming. Future steps include developing user-friendly interfaces to facilitate decision-making and
streamline the use of the model. This will help improve the environmental performance of cities.
A comprehensive model for quantifying the environmental and financial performance of cities
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A comprehensive model for quantifying the environmental and financial performance of cities
... Buildings are therefore not just unique, but they are also characterised by a specific scale that does not immediately match mainstream research approaches, mostly focused on macro or micro scales 63 . A focus on cities seems reasonable and accepted [64][65][66][67] , perhaps because it instantly evokes an idea of utter complexity, but we could argue that a city is a complex conglomerate of buildings much as a building is a complex conglomerate of materials. However, city and material-based research is widely publicised in top international journals, unlike research at the whole building scale. ...
The construction and operation of buildings is a major contributor to global energy demand, greenhouse gases emissions, resource depletion, waste generation, and associated environmental effects, such as climate change, pollution and habitat destruction. Despite its wide relevance, research on building-related environmental effects often fails to achieve global visibility and attention, particularly in premiere interdisciplinary journals – thus representing a major gap in the research these journals offer. In this article we review and reflect on the factors that are likely causing this lack of visibility for such a prominent research topic and emphasise the need to reconcile the construction and operational phases into the physical unity of a building, to contribute to the global environmental discourse using a lifecycle-based approach. This article also aims to act as a call for action and to raise awareness of this important gap. The evidence contained in the article can support institutional policies to improve the status quo and provide a practical help to researchers in the field to bring their work to wide interdisciplinary audiences.
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Purpose: Life cycle assessment (LCA) is inherently complex and time consuming. The compilation of life cycle inventories (LCI) using a traditional process analysis typically involves the collection of data for dozens to hundreds of individual processes. More comprehensive LCI methods, such as input-output analysis and hybrid analysis can include data for billions of individual transactions or transactions/processes, respectively. While these two methods are known to provide a much more comprehensive overview of a product’s supply chain and related environmental flows, they further compound the complex and time-consuming nature of an LCA. This has limited the uptake of more comprehensive LCI methods, potentially leading to ill-informed environmental decision-making. A more accessible approach for compiling a hybrid LCI is needed to facilitate its wider use. Methods: This study develops a model for streamlining a hybrid LCI by automating various components of the approach. The model is based on the path exchange hybrid analysis method and includes a series of inter-related modules developed using object-oriented programming in Python. Individual modules have been developed for each task involved in compiling a hybrid LCI, including data processing, structural path analysis and path exchange or hybridisation. Results and discussion: The production of plasterboard is used as a case study to demonstrate the application of the automated hybrid model. Australian process and input-output data are used to determine a hybrid embodied greenhouse gas emissions value. Full automation of the node correspondence process, where nodes relating to identical processes across process and input-output data are identified, remains a challenge. This is due to varied dataset coverage, different levels of disaggregation between data sources and lack of detail of activities and coverage for specific processes. However, by automating other aspects of the compilation of a hybrid LCI, the comprehensive supply chain coverage afforded by hybrid analysis is able to be made more accessible to the broader LCA community. Conclusions: This study shows that it is possible to automate various aspects of a hybrid LCI in order to address traditional barriers to its uptake. The object-oriented approach used enables the data or other aspects of the model to be easily updated to contextualise an analysis in order to calculate hybrid values for any environmental flow for any variety of products in any region of the world. This will improve environmental decision-making, critical for addressing the pressing global environmental issues of our time.
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Humans are extracting and consuming unprecedented quantities of materials from the earth’s crust. The construction sector and the built environment are major drivers of this consumption which is concentrated in cities. This paper proposes a framework to quantify, spatialise and estimate future material replacement flows to maintain urban building stocks. It uses a dynamic, stock-driven, and bottom-up model applied to the City of Melbourne, Australia to evaluate the status of its current material stock as well as estimated replacements of non-structural materials from 2018 to 2030. The model offers a high level of detail and characterises individual materials within construction assemblies for each of the 13 075 buildings modelled. Results show that plasterboard (7 175 t), carpet (7 116 t), timber (6 097 t) and ceramics (3 500 t) have the highest average annual replacement rate over the studied time period. Overall, replacing non-structural materials resulted in a significant flow of 26 kt/annum, 36 kg/(capita·annum) or 721 t/(km2·annum). These figures were found to be compatible with official waste statistics. Results include maps depicting which material quantities are estimated to be replaced in each building, as well as an age pyramid of materials, representing the accumulation of materials in the stock, according to their service lives. The proposed model can inform decision-making for a more circular construction sector.
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Life-cycle assessment (LCA) is an established methodology that can provide decision-makers with comprehensive data on the environmental impacts of products and processes during the entire life cycle. However, the literature on building LCAs consists of highly varying results between the studies, even when the assessed buildings are very similar. This makes it doubtful if LCA can actually produce reliable data for supporting policy-making in the building sector. However, no prior reviews looking into this issue in the building sector exist. This study includes an extensive literature review of LCA studies on the pre-use phase of buildings. The purpose of this study is to analyze the variation between the results of different studies and find out whether the differences can be explained by the contextual differences or if it is actually the methodological choices that cause the extremely high variation. We present 116 cases from 47 scientific articles and reports that used process LCA, input–output (IO) LCA or hybrid LCA to study the construction-phase GHG emissions of buildings. The results of the reviewed studies vary between 0.03 and 2.00 tons of GHG emissions per gross area. The lowest was assessed by process LCA and highest with IO LCA, and in general the lower end was found to be dominated by process LCA studies and the higher end by IO LCA studies, hybrid LCAs being placed in between. In general, it is the methodological issues and subjective choices of the LCA practitioner that cause the vast majority of the huge variance in the results. It thus seems that currently the published building LCAs do not offer solid background information for policy-making without deep understanding of the premises of a certain study and good methodological knowledge.
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Over the past decades, detailed individual building energy models (BEM) on the one side and regional and country-level building stock models on the other side have become established modes of analysis for building designers and energy policy makers, respectively. More recently, these two toolsets have begun to merge into hybrid methods that are meant to analyze the energy performance of neighborhoods, i.e. several dozens to thousands of buildings. This paper reviews emerging simulation methods and implementation workflows for such bottom-up urban building energy models (UBEM). Simulation input organization, thermal model generation and execution, as well as result validation, are discussed successively and an outlook for future developments is presented.
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The built environment is recognized as a major hotspot of resource use and environmental impacts. Life cycle assessment (LCA) has been increasingly used to assess the environmental impacts of construction products and buildings during the last 25 years. A new trend stems in the application of LCA to larger systems such as urban islets or neighborhoods. This review aims at compiling all papers related to LCA of the built environment at the neighborhood scale. A focus is carried out on 21 existing case studies which are analyzed according to criteria derived from the four phases of LCA international standards. It sums up current practices in terms of goal and scope definition, life cycle inventory (LCI) and life cycle impact assessment (LCIA). The results show that the case studies pursue different goals. They are either conducted on existing or model neighborhoods with an aim at building knowledge to feed urban policy making. Or they are conducted on actual urban development projects for eco-design purpose. Studies are based on different scopes, resulting in the selection of different functional units and system boundaries. A comparison of data collection strategies is provided as well as a comparison of LCIA results for cumulative energy demand and greenhouse gases emissions. Methodological challenges and research needs in the field of application of LCA to neighborhood scale assessment are identified, such as the definition of the functional unit and the need for contextualization methodologies aligned with data availability at the design stages of a neighborhood development project.
A variety of methods can be used to compile a life cycle inventory (LCI) as part of a life cycle assessment (LCA) study. Hybrid LCI methods attempt to address the limitations inherent in more traditional process and input-output (IO) LCI methods. This paper provides an overview of the different hybrid LCI methods currently in use in an attempt to provide greater clarity around how each method is applied and their specific strengths and weaknesses. A search of publications quoting the use of hybrid LCI was undertaken for the period from 2010 to 2015, identifying 97 peer-reviewed publications referencing the use of a hybrid LCI. In over one third of the literature analysed, authors only refer to their analysis as a hybrid LCI, without naming the actual method used, making it difficult to fully understand which method was used and any potential limitations. Based on the way in which the various hybrid methods are applied and their existing use, the authors propose a set of clear definitions for existing hybrid LCI methods. This assists in creating a better understanding of, and confidence in, applying hybrid LCI methods amongst LCA practitioners, potentially leading to a greater uptake of hybrid LCI.
The building sector is the largest contributor to global greenhouse gas (GHG) emissions. Over the years, sound tools have been developed to support the life-cycle assessment of building carbon emissions performance. However, most of these tools have been primarily focused on building-scale modelling and evaluation, leaving the emissions related to infrastructure and occupant activities as well as the carbon offsetting from implementing district-scale renewable energy systems, often neglected. The uptake of macro perspective carbon evaluations at the urban precinct level has been slow due to various barriers such as system boundary definition, quantification of complex inter-building effects, availability of comparable data, integrated modelling and uncertainties related to occupants’ life styles. This research developed an integrated life-cycle model to support the precinct-scale evaluation of carbon footprint for a comprehensive understanding of the emission profile. This is expected to further support low carbon planning and (re)development of urban precincts. The model structure is underpinned by four major components at the precinct level, i.e. embodied, operational and travelling associated carbon emissions, as well as the carbon offsetting from solar energy harvesting. The utility of the proposed methodology is demonstrated through preliminary case studies on representative suburban precincts in Adelaide, South Australia. Comparative studies and scenario analysis are also involved to identify the critical elements affecting the overall carbon performance of urban precincts.
With a growing urban population, it is crucial to maintain and develop environmentally friendly transport modes. However, while one of the most important indicators of environmental performance is water use, very few studies have quantified the total water requirements associated with different transport modes. This study uses input-output analysis to quantify the total water requirements of different passenger-transport modes in Melbourne, Australia, including the direct and indirect water requirements of petrol cars, regional diesel trains and electric metropolitan trains. Results show that urban electric trains are the least water intensive transport mode (3.4 L/pkm) followed by regional diesel trains (5.2 L/pkm) and petrol cars (6.4 L/pkm). These intensities result in average daily per capita transport-related water use that can be greater than residential water use. Findings also show that occupancy rates greatly affect the water intensity of transport modes and that when occupied by five passengers, cars are the least water intensive transport mode. Finally, this study shows that water use associated with transport depends on a range of factors across the supply chain and that indirect requirements associated with operations, including administration, advertisement, servicing and others, can represent a significant share of the total. Reducing the total water requirements of transport modes is therefore a shared responsibility between all the actors involved and integrated action plans are needed in order to reduce water use associated with transport.