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Chapter
Perspectives on Subnational
Carbon and Climate Footprints: A
Case Study of Southampton, UK
Laurence A.Wright, Ian D.Williams, SimonKemp
and Patrick E.Osborne
Abstract
Sub-national governments are increasingly interested inlocal-level climate
change management. Carbon- (CO2 and CH4) and climate-footprints—(Kyoto
Basket GHGs) (effectively single impact category LCA metrics, for global warming
potential) provide an opportunity to develop models to facilitate effective mitiga-
tion. Three approaches are available for the footprinting of sub-national communi-
ties. Territorial-based approaches, which focus on production emissions within the
geo-political boundaries, are useful for highlighting local emission sources but do
not reflect the transboundary nature of sub-national community infrastructures.
Transboundary approaches, which extend territorial footprints through the inclu-
sion of key cross boundary flows of materials and energy, are more representative
of community structures and processes but there are concerns regarding compa-
rability between studies. The third option, consumption-based, considers global
GHG emissions that result from final consumption (households, governments,
and investment). Using a case study of Southampton, UK, this chapter develops
the data and methods required for a sub-national territorial, transboundary, and
consumption-based carbon and climate footprints. The results and implication of
each footprinting perspective are discussed in the context of emerging international
standards. The study clearly shows that the carbon footprint (CO2 and CH4 only)
offers a low-cost, low-data, universal metric of anthropogenic GHG emission and
subsequent management.
Keywords: urban metabolism, cities, community GHG, GHG inventory,
carbon footprint
. Introduction
Increasing GHG emissions have catalysed urban GHG management, with many
having established sub-national and transnational climate networks, initiatives or
management plans [1]. Carbon- (CO2 and CH4) and climate-footprints—(Kyoto
Basket GHGs), are single impact category—global warming potential—indicators of
life cycle assessment (LCA). These metrics provide an opportunity to develop effec-
tive models of GHG emissions from cities, and to facilitate effective mitigation. The
frameworks required to calculate a carbon or climate footprint also provide a frame-
work for the application of a more holistic LCA to cities or other geographic areas.
New Frontiers on Life Cycle Assessment - Theory and Application
Discussions to date have primarily focused on the appropriateness of the alloca-
tion of emissions to the local level, with progress driven by improved understanding
of urban metabolism—material and energy flows through the urban system (e.g.
[2–6]). Approaches can be categorised as process-led bottom-up approaches, top-
down economic led analysis, or top-down “natural laboratory” approaches relying
on atmospheric measurement and concentration [7].
“Territorial-based” (alternatively, “in-boundary”, “geographically-based”, or
“production-based”) approaches, generally adaptations of the IPCC Guidelines for
National Greenhouse Gas Inventories, or the Greenhouse Gas Protocol developed for
corporate GHG reporting, account emissions within geopolitical boundaries
[8, 9]. These methods successfully identify local emissions patterns and inform
local development policy. However, there has been growing recognition that holistic
management of urban GHGs necessitates the inclusion of direct and indirect emis-
sions as urban economies demand resources beyond their geographic locations
[5, 10, 11].
“Transboundary” (alternatively, “territorial-plus”, “geography plus” or
“metabolism-based”) approaches add out-of-boundary emissions associated with
economic demand to territorial emissions, with the exact boundary conditions and
scope varying between studies [2, 4–6]. Top-down “consumption-based” methods
include all emissions along the supply-chain of goods and services, with boundary
conditions defined by final consumption of households and governments [5]. This
approach is useful in the informing mitigation of emissions associated with final
consumption, although the exact origin of embodied emissions cannot normally
be delineated and emissions from local production for exports are excluded [11].
Consequently, methods are not sensitive to many local strategies to reduce emis-
sions [5].
Ultimately both concepts are complementary, focusing on different aspects of
community composition . The primary cause of inconsistency between studies (for
a review see: [3, 10]) and emerging standards (e.g. [12, 13]) is the approach taken
to boundary conditions (spatial and temporal). Temporal boundaries vary, but
typically consider an annual period, with some models operating at finer scales (e.g.
[7, 14]). Spatial boundaries vary reflecting goals and application, and the lack of a
singular definition for a city or an urban area. However, a ‘city’ or and ‘urban area’
is simply a taxonomic division of a ‘community’—a specific area or place considered
jointly with its inhabitants. Spatial boundaries can thus be decided on a case-by-
case basis, defined by motivation application [1].
This chapter reviews urban sectorial methods, results, and policy implications
of applying a territorial, transboundary, and consumption-based, carbon or climate
footprint to a city, using a case study of Southampton, UK.Based on the framework
proposed by Wright etal. [15] the requirements and methods to assess a carbon
or climate footprint are presented. We disassemble the framework into ‘modules’,
recognising that each element of the framework would require separate calculation
methods. This enables the development of novel methods or the use of existing
methods in a novel manner to create an overall methodology for the calculation of
all elements of the framework. As proof of concept and to inform the development,
the methodology was applied to Southampton, UK.Results are then presented for
the carbon footprint and the ‘climate footprint’ ([15, 16], respectively). The meth-
odology represents a novel approach, building on established practice to enable
the sub-national assessment of carbon footprints in communities, which enables
the spatial and temporal reporting of results at a sub-community level to enable
effective management and policy development. We discuss the results and policy
implications and conclude with a consideration of the effectiveness of current
practice and highlight ongoing issues.
Perspectives on Subnational Carbon and Climate Footprints: A Case Study of Southampton, UK
DOI: http://dx.doi.org/10.5772/intechopen.82794
. Case study: city of Southampton
Southampton (pop.239,428 during study period), chosen as a case study as
it contains the representative components of many cities, is the largest city in
Hampshire, England (area: 51.91km2) based on the geographic extent of the city
geo-political boundary [17]. The city is governed by Southampton City Council, a
unitary authority (a single tier local government responsible for local government
functions); the wider region is within the remit of Hampshire County and multiple
district councils (a hierarchical system of governance common to many countries).
Southampton is a commerce hub; a major international cruise terminal, and
the UK’s second largest container port. A significant proportion of Southampton’s
workforce (circa 42%) commutes from the wider region and surrounding counties
[18]. The city has two universities with a transitory student population of in excess
of 40,000 [19]. Southampton Airport is a regional domestic and international
airport located just outside the city’s geopolitical boundary.
. Methods
. Residential
Large communities contain a significant number of dwellings, and emissions
are driven by energy consumption, highly dependent on building structure and the
behaviour of residents [20]. Estimation of emissions can be made on fuel consumed
(e.g. sales) (e.g. [21]), however this method does not allow for spatial disaggrega-
tion. Alternatively energy use can be estimated from census based residential
energy consumption models (e.g. [22]). Various methods have been developed for
this purpose (for review see [23, 24]). To enable spatial disaggregation the case
study applies the assumption that energy use can be simplified with the applica-
tion of ‘average’ building categories. Model generalisation parameters are derived
for categories of dwelling and applied to individual property build forms with
Geographic Information Systems (GIS), eliminating the need for visual inspection
[25]. Parameters were derived from the Building Research Establishment Domestic
Energy Model (BREDEM) (BREDEM-8—monthly or BREDEM-12—annual) [26].
Total energy demand was assumed to be met using a combination of electricity,
natural gas, and other fuels. Consumption data for electricity and natural gas were
available from local metering records, with remaining demand assumed to be met
using others fuels apportioned on basis of regional sales data. Output is restricted to
an aggregation of properties rather than the individual building level, as accuracy
would be open to significant variation and introduces confidentiality concerns.
. Commercial and industrial point sources
The commerce and industry sector encompasses emissions associated with
industrial physical or chemical processing and non-electrical energy. Complexities
exist in the allocation of emissions between the energy and processing sectors (e.g.
residual heat may be used for electricity generation). Actual consumption data from
sales records or feedstock records is difficult to obtain, primarily due to the sensitive
nature of such data. Point source data from larger facilities may be available from
legislative emissions reporting schemes, although this often does not encompass
small schemes. Proxy consumption data for fuels and processes can be utilised to
estimate emissions, however this assumes fuel combustion at place of purchase,
and may not accurately reflect the source of emissions. Gurney etal. [7] describe
New Frontiers on Life Cycle Assessment - Theory and Application
a model to simulate energy demand based on building parameters combined with
known local atmospheric emissions. The same study notes that this method is only
suitable for large point source emitters. Alternatively, pro-rata allocation of national
emissions to local sources provides a reliable method of estimation (e.g. [7, 27]).
For the purpose of the case study, supplemented with meter point natural gas and
electricity consumption data, emissions by industry were pro-rated on employment
by industrial sector (to a 4 digit Standard Industry Classification (SIC2007)).
. Electricity, heat, and steam
Transboundary emissions relating to electricity are commonly calculated using
an aggregated factor representing a national system of generators and transmission.
Emissions from heat and steam are often reported separately due to data conven-
tions and that composite emissions factors may over- or under- estimate of emis-
sion intensity [21]. Similarly, aggregated emissions do not segregate in-boundary
generation, or consider low GHG decentralised generation schemes—likely to be
a component of meeting carbon reduction targets [28]. In these cases electricity
generated in-boundary and fed into national supply grids is representative of the grid
average. Alternatively to provide greater disaggregation emissions associated with
in-boundary electricity generation can be reported separately, either as a proportion
of total consumed or as with the case study an absolute. Emissions for Southampton’s
electricity consumption were calculated using a national grid emissions factor
(accounting for transmission, transformation and other losses (typically circa 6–11%)
[21]), estimated from national generation and electricity consumption.
. Road transport
Road transport emissions are often artificially truncated at the city boundary,
but commuting represents a significant transboundary emissions source [11].
Economic data on fuel sales can be a viable indicator of road transport emissions,
where the study area represents a commuter-shed [21]. However, this method is less
effective where significant numbers of commuter trips occur (e.g. Southampton—
circa 42% of work related trips are from outside the city [17]). In these cases the
location of fuel purchase is not necessarily representative of fuel consumption.
An alternative method is through the use of proxy relationships, with emissions
estimated through regression based approaches [29] or population density and
road density [30]. High temporal and spatial resolutions have been achieved using
activity-based approaches, combining vehicle kilometres travelled (VKT) with fleet
and fuel data [7, 31]. This approach requires total distance travelled by all vehicles in
the study area, fuel efficiency, and fleet composition. Issues arise in comparability
of VKT techniques as many cities have their own bespoke modes [21]. However this
has the advantage of allowing bespoke modelling of spatial and temporal impacts of
traffic policy intervention at high resolutions.
The basic principle of an activity based models is the relationship of the mass
of fuel consumed in the distance travelled. The amount of fuel a vehicle consumes
in a given distance is dependent on a number of parameters, including drive cycle,
engine temperature, ambient temperature, fuel type, and fuel quality [32]. Hot-
start emissions were calculated; by modal split, fuel type and installed vehicle
technology, using experimentally derived emissions factors for vehicle type
and pollutant by trip length and velocity from the ARTEMIS (Assessment and
Reliability of Transport Emission Models and Inventory Systems) methodology
and TRL emission factor database [32]. Cold start emissions are accounted using an
excess factor over the hot-start emissions rate [33, 34].
Perspectives on Subnational Carbon and Climate Footprints: A Case Study of Southampton, UK
DOI: http://dx.doi.org/10.5772/intechopen.82794
. Rail
Trips by rail transportation typically traverse the geopolitical boundary of
a number of communities. Rail journeys involve a series of embarkation points
between origin and destination, often with multiple stopping locations within
the geopolitical boundary. A boundary limited methodology does not account the
transboundary demand driven nature of these trips. For trips that originate outside
the community boundary only the in-boundary proportion of the trip is accounted,
conversely pass through trips that are not a result of city demand are still counted
[4]. This issue exacerbates when considering national and internationally connected
rail networks—a trip could begin a significant distance from the study community.
Accounting in-boundary and transboundary emissions related to rail commuting
creates the potential for double counting between communities. Pass through trips
are accounted as a direct emission and then accounted again at the destination com-
munity. Reflecting these difficulties a number of community based GHG invento-
ries do not explicitly define emissions from rail transportation (e.g. [4, 21]).
These issues can be addressed by accounting emissions based on proportional
commuter distances travelled. Assigning emissions from rail commuter demand as
passenger kilometres travelled to total passenger kilometres travelled on the relevant
routes offers a mechanism to apportion trips to the local community a demand basis.
Accounting both in-boundary and transboundary emissions requires a combination
of two methods—one to calculate in-boundary emissions and another to allocate
transboundary demand emissions. For the purpose of the case study, in-boundary
emissions were calculated using ARTEMIS technology specific bottom-up algorithms
and emissions factors (function of engine, technology, distance and speed) [32]. All
journeys on non-electrified rail were assumed to be power by diesel. Trips on electri-
fied rail were apportioned to diesel or electric locomotives using operator timetables.
Total trips, distance travelled, and operational engine time were estimated from train
operator time tables [35], combined with the Ordnance Survey Integrated Transport
Layer [36]. Emissions associated with commuter trips were estimated as a function
of rail demand for Southampton, passenger kilometres travelled [37] were estimated
as proportional to the total ticketed exits on the national rail network (collected by
automated barrier passes) divided by number of ticketed exits at Southampton.
. Other off-road mobile emissions
Mobile off-road sources represent an extremely diverse range of domestic and
commercial emissions. Including controlled activities which are consistent and
follow specific procedures (e.g. dockside grab loader) and chaotic activities follow-
ing no pre-determined procedures or activity patterns (e.g. domestic lawn mowers)
[38]. Fuel sales data may be a viable indicator of emissions, where the operation of
off-road machinery are geographically constrained to the location of fuel purchase
[39], although this method fails where fuel purchase does not represent the location
of consumption.
Unlike road transport, the majority of off-road machinery units are not regis-
tered making estimation of populations and activity difficult. Proxy estimates of
population can be made based on national purchases or populations pro-rated to
the local level, as per the case study [40]. This assumes a uniform distribution of
machinery across total national population, which may not be representative of
local conditions. Alternative allocation methods could be utilised that consider a
number of machinery units as a function of purpose or spatial area (e.g. lawnmow-
ers f(greenspace), construction machinery f(growth)), however the wide range and
chaotic usage patterns of off-road machinery are likely to confuse this issue.
New Frontiers on Life Cycle Assessment - Theory and Application
. Shipping
Cities that are international cruise and container terminals rely heavily on
these industries for economic growth and employment, exclusion of emis-
sions from these industries would lead to misinterpretation in policy making
[1]. Territorial inventories may, depending on the extent of territorial waters
in the geopolitical boundary, include port-side operations or entirely exclude
shipping operations. A transboundary approach must consider the indirect
emissions (movement between ports) of these sources [1, 5]. Emissions from
shipping are a function of engine operation and fuel consumption. Calculation
of fuel consumed has broadly been undertaken using two approaches—‘engine
use models’ and ‘bunker fuels’. Engine use models apply engine load, power and
run-time, by engine and ship type (e.g. ro-ro ferry, liquid bulk), in the three
phases of operation (hoteling, manoeuvre and cruise) to calculate emissions
[41]. This requires detailed data input on vessel characteristics, routes, and
operational time. Detailed data for all ship movements (>250 gross tons) and
characteristics are available from the from historic Automatic Identification
System datasets. However, the majority of these datasets demand a high cost
purchase, which excludes some sub-national governments from using the data
(e.g. Lloyds List Intelligence [42]). Alternatively, the method taken in the case
study, a bunker fuels approach considers international bunker fuels loaded at
the departure port provide a proxy to estimate emissions from shipping [41].
However, shipping companies are likely to source the cheapest available fuel for
the route, the result being where fuel cost is low, emissions are overestimated
(e.g. Belgium), and where costs are high, emissions are underestimated
(e.g. New Zealand) [43, 44].
. Aviation
Aviation emissions are transboundary, smany airports are located outside
geopolitical boundaries, and cities often act as aviation hubs with transit pas-
sengers occupying a significant proportion of capacity [45]. Allocation of emis-
sions must address these concerns, so as not to generate political tensions. Some
territorial studies exclude emissions as almost entirely transboundary and largely
beyond the control of local government (e.g. [46–48]). Others include domestic
emissions and take-off and landing cycles to 1000m altitude for international
emissions (e.g. [49]). As applied in the case study emissions can be calculated
on an activity basis (engine runtime, technology, flight occupancy). Similarly a
number of studies have reported transboundary emissions based on quantities of
fuels loaded at airports within city boundaries (e.g. [14]). These methods do not
consider the movement of passengers between flights and the surface movement
of passengers from outside city limits. Previous authors suggest that regional
airport usage by community inhabitants can be estimated as a function of local
to regional population [4, 21]. Assignment of emissions by community demand
offers a truer picture emissions, considering only those emissions associated with
the local population. However, this method is fraught with complexity, especially
in cases where a number of international airports operate within close proximity
(e.g. southern UK—Southampton; Bournemouth; Gatwick; Heathrow; Stansted,
London City). Without accurate passenger origin—destination data, subjective
judgments must be made to establish the geographic extent of airport demand.
Demand from beyond the geographic boundary could be considered a function
of the community demand, thus arguably, related aviation emissions should be
accounted [4].
Perspectives on Subnational Carbon and Climate Footprints: A Case Study of Southampton, UK
DOI: http://dx.doi.org/10.5772/intechopen.82794
. Agriculture, Forestry and Other Land Use (AFOLU)
Some argue AFOLU is potentially insignificant at the urban level and therefore
may be excluded [50]. This is based upon the assumption that green space is both
relatively limited in urban centres, and the perception that urban green space
has limited value due to human modification [51, 52]. This is often untrue (e.g.
Southampton Common is 145 hectares; London’s Hyde Park is 142 hectares, Beijing’s
Fragrant Hills Park is 160 hectares, and Vancouver’s Stanley Park is >400 hectares),
and fails to consider the importance of public and private land in urban centres (e.g.
private gardens, green roofs) which, whilst small compared to per unit area GHG
emissions, are potentially important stocks of GHGs [53].
Land use and management significantly influences ecosystem processes that
effect GHG fluxes, (e.g. photosynthesis, respiration, decomposition). The IPCC [8]
guidelines for national inventories contain significant information for the calcula-
tion of AFOLU GHG fluxes. These guidelines suggest two methods: (i) net carbon
stock change over time and (ii) direct carbon flux rate (more commonly utilised for
non-CO2 species) [8]. AFOLU carbon flux for Southampton was calculated using
the first option, to provide consistency with annual reports and promote favourable
management of non-urbanised space over an extended time scale.
Estimates of C flux were derived from Rothamsted soil carbon model (RothC-
26.3) and the Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ-DGVM)
[54–56]. Basic climatic inputs (temperature, precipitation, daylight hours) were
required (Met Office, 2014), in addition to data on organic matter inputs (obtained
from LPJ-DGVM), soil clay content, and atmospheric CO2 concentrations [56].
GIS data (OS MasterMap) of land-cover types were used to create a map of the city
area; where available this map was augmented with specific vegetation cover data
provided by the municipal authority [36]. Land-cover data was classified into 11
broad categories, adapted from a condensed set of JNCC Phase 1 habitat classifica-
tions, a standard mode of habitat classification in the UK. (Table ) The Phase 1
habitat classifications provide a specific name and brief description of each habitat
type/feature, appropriate for vegetation modelling using LPJ-DGVM [54–57]. In
cases where land-cover types are not complete for an area (e.g. scattered trees), the
land-cover was assumed to be divided evenly between land-cover types. Where
trees are described as ‘scattered’ (>30% of surface by canopy extent) 20% of total
area is classified as that tree type, the remainder is divided evenly between other
represented land-cover types [57]. In the grass (cut) category, data are required
for total clippings collected, thus removed from the system, and total clippings left
in-situ.
Private gardens are representative of multiple land-cover types (e.g. lawn;
ornamental planting; patios; tarmac; gravels). Typical land cover types in private
gardens were estimated based on a representative sample of private gardens in the
study area, categorised for land cover types using aerial photography (expert judge-
ment) (Table ).
The model was run across a temporal period of 1 year, with GHG flux calculated
as the change in storage between runs.
. Waste
Waste management generates emissions of CO2, primarily of biogenic origin,
with some fossil carbon and CH4, often outside the city boundary [5]. Regional or
municipal governments are both actors in and managers of waste. Each has their
own waste infrastructure, service provision and socio-economic conditions with
influence over collection; treatment, and destination with significant emission
New Frontiers on Life Cycle Assessment - Theory and Application
savings available through system reconfiguration [58, 59]. Many previous stud-
ies apply ‘generic’ emissions factors to waste treated. Detailed tools and methods
for the accounting of GHG emissions from waste systems have been developed
although there are concerns regarding consistency, accuracy and transferability of
these methods [60, 61]. The following offers a brief overview of methods applied in
the case study with greater detail exploring various stages in the waste system given
in supplementary information.
Knowledge of waste composition and subsequent mass balance of CO2 and CH4
throughout the waste system is the key determinate in modelling waste emis-
sions. The composition of the wastes in the treatment system will affect the mass
balance due to the changes in carbon content and subsequent degradation patterns
[60]. Once known waste stream emissions can be calculated on a mass balance
or activity basis. Many city based assessments do not suggest breakdown of the
various stages of the waste management process, instead offering per unit treated
Land-cover category Example land-cover types
Grass (cut 11 times a
year)
Natural surface, slope
Rough grass (not
mown)
Rough grass, rough grass and other
Other herbaceous
plants
Perennials, flowers, roses
Private gardens Multiple surfaces in private residence
Broadleaved
summergreen trees
Non-coniferous trees, scattered non-coniferous trees, orchard
Needle leaved
evergreen trees
Coniferous trees, scattered coniferous trees
Scrub Scrub, shrubs, hedges, heath
Marsh Marsh reeds or saltmarsh
Sealed surface Road, made surface, paths, steps, track, structure, traffic calming, pylon, rail,
upper level of communications, building, glasshouse, overhead construction,
unclassified
Wat er Inland water, foreshore, tidal water
Table 1.
Land-cover categories for modelling of vegetation or other land-cover types (adapted from [49]).
Land-cover type Proportion of total area ()
Grass (cut 11 times a year)
Clippings removed 10
Clippings left in situ 30
Shrubs 10
Temperate broadleaved summergreen trees 10
Other herbaceous plants 10
Sealed 30
Table 2.
Assumed proportions of land-cover types in private gardens for the southern UK (expert judgement).
Perspectives on Subnational Carbon and Climate Footprints: A Case Study of Southampton, UK
DOI: http://dx.doi.org/10.5772/intechopen.82794
emissions factors. Per unit emissions factors are applicable to processes where
the primary main source of emissions are from energy use (e.g. waste recovery
and recycling), or for incineration processes (mass balance of carbon could also
be applied). Greater accuracy can be achieved in modelling biological processes
using a mass balance approach, with CO2 and CH4 emissions calculated on mass
balance of carbon input to carbon lost from the final product. Emissions estimates
from landfill must recognise both operational and closed phases [62]. Following
closure, a landfill continues to emit GHGs, possibly for several hundred years,
although some carbon will be indefinitely stored in the landfill [63]. Kennedy
etal. [21] propose a pragmatic solution, applied in the case study, whereby
estimates of long-term emissions were calculated for the waste landfilled in the
assessment year.
. Water
The provision of water and waste water services are similar to the provision of
electricity. Emissions associated with water are calculated on an end-user basis for
water processing, treatment and transportation using per unit consumed emissions
factors. Commonly, as during this case study, water use is not metered and thus no
actual consumption data are available. In the UK significant effort is being directed
to the installation of end-user metering; this will provide improved data resolution
for future investigations [64]. Emissions were calculated using standard estimates
of water consumption provided by water suppliers.
. Consumption
It is generally accepted that the addition of a consumption-based model-
ling approach extends the research implications and policy potential of a GHG
inventory [2]. Territorial accounts include emissions associated with exports at
the point of production; but exclude those associated with supply chains and
imports. The upstream impacts of production are allocated to the producer—the
tendency is to mask embedded emissions and burden shifting (energy intensive
industries are effectively exported). Transboundary approaches add an ele-
ment of these out-of-boundary emissions, but do not give a full picture of the
impact of consumption. Consumption-based accounting focuses on the final
consumption of households and governments; methods account all GHG emis-
sions upstream of the community but exclude emissions from production within
the city [10]. A consumption based approach compliments a transboundary
methodology, capturing emission flows and the driving forces associated with
consumption [65].
A consumption-based approach requires linking supply chain emissions with
local consumption activities. Input-output (IO) models detail the transactions
between industries and sectors within the economy. An IOT requires knowledge
of all flows of goods and services among intermediate and final sectors in disag-
gregated form for a given time period. This implicitly implies high volumes of data,
which is difficult to obtain at the sub-national level, necessitating some form of
scaling from national data. The core element of an input-output model is a matrix
concerning flows through the economy—sales and purchases from an industrial
sector (a producer), to other sectors and the sector itself (consumers) [66]. The
basic input-output out model assumes homogeneity in sectors (i.e. each sector
produces a single product) and linear production (i.e. proportionality of inputs and
outputs which precludes economies of scale). The basic IO model can be extended
to include material consumption and emissions—an Environmentally Extended
New Frontiers on Life Cycle Assessment - Theory and Application
Input-Output (EEIO) model. Effectively this creates an ‘environment’ sector, and
the value of each item represents the ‘output’ of pollution [67].
Consumption-based emissions factors (GHG/£ spent) for the UK were
calculated using an EEIO model. The IO data only holds data on final consump-
tion at the national level. A downscaling methodology was therefore required to
estimate final consumption at the local level. The model assumes no variation
in emissions per monetary unit spent between the national and local levels.
The technical coefficient matrix was derived from UK supply and use tables
for the year 2008 with 123 products and industry sectors in basic prices [68].
GHG emissions data by industry sector for the period were taken from the UK
Environmental Accounts [69]. The Environmental Accounts provide data on
GHG emissions from 129 industrial sectors and 2 household emissions sources
(travel and non-travel). The GHG data is provided at a more disaggregated level
than the IO data in some sectors, this was scaled to the 123 sectors of the IO
model using the parent sector of the lower level disaggregation according to the
UK Standard Industry Classification 2007. A domestic technology assumption is
applied to imported goods and services, whereby imports are assumed to have
the same GHG intensity as domestic equivalents. It assumes the energy struc-
ture and economic structure of the imports can be approximated based on the
domestic make-up of the UK.This may be a valid assumption for some regions,
but underestimates GHG intensities of imports from emerging and developing
regions [11].
Expenditure between regions will vary considerably as a result of a range of
socio-demographic factors. However, the underlying IO data only provide expen-
diture at the national level. Household demand was downscaled to the local level
using household expenditure data from the UK Living Costs and Food Survey
(LCF) (annual survey of household expenditure on consumer products and ser-
vices), and derived summary datasets provided in the Family Spending report
[70, 71]. Government expenditure was downscaled on a per capita basis. Whilst this
assumes individuals in the national population benefit equally from all government
expenditure, it is considered a reasonable assumption in the absence of alternative
data. Researchers have downscaled government expenditure using local expendi-
ture statistics, however these data do not exist for the UK [11]. The study does not
consider emissions relating to capital investments.
. Uncertainty
The city system is inherently complex and comprised largely of non-deter-
ministic features (i.e. responses of the system that are not predicable because
of uncertainty within the system itself). Qualification and assessment of these
uncertainties is important for both model validation and reliability. Sensitivity
analysis is used to assign the uncertainty in the output of the model to differ-
ent sources of uncertainty in the model’s inputs and how the model responds to
changes in input data [72]. The sensitivity of the transboundary inventory model
is considered using a one-at-time (OAT) local sensitivity analysis technique.
Sensitivities for the consumption estimates are considered at the aggregated
emission factor per unit expenditure level, rather than at the EEIO input vari-
able level due to complexities involved in this form of modelling [73]. Whilst
sensitivity analysis provides a good indicator of variables with high impact on the
model, it does not provide qualification of uncertainty and must be accompanied
with an uncertainty analysis [72]. A Monte Carlo analysis was performed using
random sampling of input variables, based on defined uncertainty probability
Perspectives on Subnational Carbon and Climate Footprints: A Case Study of Southampton, UK
DOI: http://dx.doi.org/10.5772/intechopen.82794
distributions in input parameters. The analysis consisted of ten thousand model
runs, completed for the model as a whole and for three of the broad category areas
identified in the OAT transport emissions; power generation; and waste disposal.
Supply chain and consumption emissions uncertainty was excluded due a need for
further investigation and modelling.
. Results and discussion
. Summary
As identified, there are three methods for the assessment of life-cycle GHG
emissions from cities and other communities—territorial, transboundary, and con-
sumption based. This section discusses the implication of the three methods using
the Southampton case study. Furthermore, the Carbon Footprint (CO2 and CH4)
and Climate Footprint (Kyoto Basket) metrics are compared for each method. The
summary results (Table ) indicate increasing size in both the carbon and climate
footprints as further emissions sources are added between methods, and a slight
increase between the carbon and climate footprint metric.
. Territorial emissions
Southampton territorial emissions suggest carbon and climate footprints of
268ktCO2e and 273ktCO2e, respectively. Addition of end-use electricity consump-
tion increases this figure by 601ktCO2e and 604ktCO2e, respectively (Figure ).
The minor increase (0.99%) in emissions between the total carbon and climate
footprints is driven primarily through inclusion of additional GHGs in transport
(primarily N2O). Calculation of per capita emissions for the case study indicates 3.7
tCO2e/capita carbon footprint, lower than the equivalent national production-based
10.32tCO2e per capita estimate for the UK [74]. Whilst strictly geographic based
methods can successfully identify local production-based emissions patterns and
inform local development policy, they fail to capture the full extent of sub-national
community infrastructures which extend beyond the geopolitical boundary
(e.g. transport) [5, 6].
. Transboundary emissions
Described by Ramaswami etal. [4], Denver (CO, USA) represents the first
known community to have been inventoried using a transboundary methodology.
The study accounted all in boundary emissions and identified key community
flows defined as: food; water; transport, and building materials (for shelter).
Hillman and Ramaswami [75] suggest, based on a study of eight US cities that these
Method Carbon footprint (ktCOe) Climate footprint (ktCOe)
Territorial 268 273
Territorial+ 601 604
Transboundary 2643 2787
Consumption 3160 3590
Note: territorial+ includes emissions from end-user electricity consumption.
Table 3.
Summary carbon and climate footprints for the case study of Southampton.
New Frontiers on Life Cycle Assessment - Theory and Application
cross-boundary activities contribute on average 47% more than the in-boundary
emissions sources. This consideration is reflected in developing international
standards (e.g. [12, 13]) which suggest a transboundary approach to account both
the territorial and transboundary aspects of a community—ideally moving towards
an approach that replicates the process(s) of urban metabolism [2].
The Southampton transboundary inventory includes direct emissions with the
addition of: commuter road transport; shipping; aviation; out-of-boundary waste
emissions; water and wastewater supply/treatment; construction materials, and
food and drink—representative of the requirements of recent PAS2070 standard.
The 2008 results, carbon footprint 2643 ktCO2e, and climate footprint 2787 ktCO2e,
are, as expected, substantiality larger than the comparative territorial results
(Figure ). The results of the Monte Carlo simulation suggest a 95% confidence
interval of 3395–4295 ktCO2e. The two footprinting techniques, as per territorial
emissions methods produce results within 1%. The increased emissions in the cli-
mate footprint stem primarily from transboundary transport. The largest contribu-
tor, shipping emissions, are a result of the extended travel distance and subsequent
high fuel demands. Whilst sub-national governments have limited control (typi-
cally only port-side operations) over these emissions sources, inclusion is important
due to the strong economic reliance on these industries [1]. However sub-national
governments do have access to control to address these emissions through local air
quality control. Similarly road transport control can be found through air quality
control and additional controls in planning and road management.
Energy emissions comprise a large component of total emissions, electricity
provides the dominant contribution to this sector. The disaggregation of emissions
related to heat production from emissions associated with electricity generation
impacted >1% on emissions per unit electricity consumed. At a local level, renew-
ables account for an equivalent grid emission of 3 ktCO2e. Evidently emissions from
electricity are mainly dependent on the intensity of supply, highlighting a powerful
interlink between local and national policy making. This interlink will become
particularly pertinent with the potential advent of locally led energy initiatives
(e.g. micro-generation; rail electrification; electric vehicle charging networks) [76].
Figure 1.
Comparison of case study territorial and transboundary carbon (CO2, CH4) and climate (Kyoto Basket)
footprints.
Perspectives on Subnational Carbon and Climate Footprints: A Case Study of Southampton, UK
DOI: http://dx.doi.org/10.5772/intechopen.82794
Emissions from AFLOU are minimal, however this masks the carbon stored in
urban green space (470.00 ktCO2e). Exclusion of these emissions assumes green
space storage is minimal; the results demonstrate this may not be the case. Careful
consideration must be given to development that affects community green space
(both negative—e.g. green space urbanisation—and positive—e.g. installation
of green roofs), for the creation of carbon sinks, the wider potential social, and
economic benefits [52].
Supply chain and infrastructure related emissions form the majority of total
transboundary emissions, highlighting the importance of supply chains in commu-
nity footprinting. The recent PAS2070 [13] suggests further inclusion of all materi-
als making >2% material contribution to the community. This would add a further
1315 ktCO2e and 1435 ktCO2e (carbon and climate footprint, respectively) to the
Southampton results. However, there are concerns about double counting with the
territorial element of the assessment.
The primary advantage of a transboundary footprint is the level of completeness
created through inclusion of in-boundary emissions sources and transboundary
infrastructures that supply these activities. Given this completeness, transboundary
based footprints can be utilised to inform a broad range of mitigation and manage-
ment strategies at the local, regional, and national scales. Additionally transbound-
ary footprints are more relevant and easier to communicate to residents due to the
inclusion of major activities included in personal and home carbon calculators [6].
The main shortcoming of the transboundary method is the inconsistency in
approach and application of metrics between studies. Standards (e.g. PAS2070 [13],
GHG Protocol for Community Reporting [12]) are emerging that attempt to clarify
and develop consistency in reporting structures. Comparability is also difficult;
results require normalisation to enable inter-community comparisons. The majority
of territorial inventories are normalised using a per capita metric, however this may
not be appropriate for transboundary approaches. Metrics for the representation
and comparison of transboundary approaches require further research.
. Consumption emissions
Results for Southampton (carbon footprint 3160 ktCO2e, climate footprint 3590
ktCO2e) (Figure ) are consistent with previous studies where consumption-based
estimates are higher than production-based emissions, with the majority of emis-
sions driven by households [77]. The disparity between the carbon and climate
footprint is higher (circa 12%), this is primarily driven by high emissions of N2O in
agriculture, highlighting the need for a climate footprint approach in certain situa-
tions where high emission of GHGs other than CO2 and CH4 occur [15].
The addition of a consumption based account extends the policy implications
of a local GHG inventory [2]. The approach provides value for the assessment
of household consumer lifestyle on GHG emissions, making the consumption
impact of households and government visible [6]. Arguably a consumption based
approach provides for the most rigorous method for per capita GHG comparison,
as consumption is driven by the residents of a community. Additionally a consump-
tion based approach can inform local policy to reduce supply chain emissions as,
when accurate local data are available, imports/exports can be traced. Recognising
these advantageous policy implications, the new PAS2070 requires the separate
completion of both a transboundary inventory, and a consumption-based inventory
[13]. However consumption-based methods are data intensive, and are only truly
valuable where accurate IO data are available. Misallocation of emissions can occur
where physical flows do not match monetary flows represented inlocal IO tables
[6]. Additionally, the consumption method effectively divides the community into
New Frontiers on Life Cycle Assessment - Theory and Application
two, with activities for exports not included in the unit of analysis. This can exclude
a large element of a local economy (e.g. resorts, industrial communities) which
could be managed through local policy.
In this study, there are limitations to note. The assumption of a homogenous
technology mix in the EEIO model presents a level of inherent uncertainty—
imports come from a range of countries using a range of different emission and
resource intensities. This may be a valid assumption for some regions, but under-
estimates GHG intensities of imports from emerging and developing regions.
The accepted solution is to employ a Multi-Region Input-Output (MRIO) model.
MRIO models represent the interactions between any number of regions with
potentially differing technology mixes, by internalising trade flows within internal
demand [78]. The method of downscaling presents two important limitations.
Firstly expenditure can only be estimated for broad categories—partially a result
of the homogeneity assumption of the underlying IO model, this assumes common
per unit emissions in these categories, which may not be entirely representative.
Secondly, this generalisation may misrepresent the quantity of product purchased.
For example the same expenditure on a high cost product variant would provide less
quantity of product and potentially lower emissions, than a high quantity low cost
product.
. Conclusions
This study has presented several important developments to the assessment of
community carbon footprints. Methods have been developed to assess emissions
at a spatial and temporal disaggregation suitable for use by policy makers at the
community level. The methods have been presented to show the policy implications
of territorial, transboundary, and consumption based accounting procedures. To
explore the uncertainties associated with the model a Monte Carlo simulation was
constructed. The effort required for a comprehensive uncertainty analysis of this
type is considerable, the alternative however, is to provide decision makers with
incomplete information. At best this will lead to a false sense of reliability, at worse
Figure 2.
Comparison of case study consumption carbon (CO2, CH4) and climate (Kyoto Basket) footprints.
© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms
of the Creative Commons Attribution License (http://creativecommons.org/licenses/
by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,
provided the original work is properly cited.
Perspectives on Subnational Carbon and Climate Footprints: A Case Study of Southampton, UK
DOI: http://dx.doi.org/10.5772/intechopen.82794
incorrect assumptions and decision making. We strongly recommend that as more
studies become available continuous effort to identify and improve uncertainty be
applied; leading to a better communication of information to policy makers and
a better underpinning of their decisions. Only a limited difference in emissions
totals was observed between the carbon and climate footprints for the case study
city, clearly showing that the carbon footprint (CO2 and CH4 only) offers a low
cost, low data, universal metric of anthropogenic GHG emission and subsequent
management.
Territorial accounts may be suitable for national GHG inventories, but can-
not represent the transboundary infrastructures of sub-national communities.
Transboundary approaches extend the territorial approach to include emissions
from key infrastructures essentially to sub-national communities. The addition of
a consumption-based account further extends the policy relevance and research
applications of community accounting. Consumption-based approaches show the
impact of household consumer lifestyle on GHG emissions, and making the supply
chain impact of households and government visible.
Recognising the advantages of transboundary and the simultaneous application
of a consumption-based approach, standards, such as PAS2070, advocate combin-
ing a transboundary approach with a consumption-based approach in order to
provide a comprehensive report.
Finally, the establishment of a global network of low carbon cities requires
the appropriate tools. PAS2070 and related standards represent a significant step
towards the development of a comparative assessment of urban community GHGs.
Barriers still exist—comparable metrics need to be further developed and local
governments often do not possess the resources and skills required to complete an
inventory assessment.
Conflict of interest
The authors have no conflict of interest.
Author details
Laurence A.Wright1, Ian D.Williams2*, SimonKemp3 and Patrick E.Osborne3
1 Warsash School of Maritime Science and Engineering, Solent University, UK
2 Faculty of Engineering and Physical Sciences, University of Southampton, UK
3 Faculty of Environmental and Life Sciences, University of Southampton, UK
*Address all correspondence to: idw@soton.ac.uk
New Frontiers on Life Cycle Assessment - Theory and Application
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