Privilege, Detachment, Concealment and Omission: Normalisation and GHG Reporting1

Stuart Cooper, Department of Accounting and Finance, University of Bristol

David Saal, School of Business and Economics, Loughborough University

Ian Thomson, Birmingham Business School, University of Birmingham

Alexandros Maziotis, University of Manchester

Presented at Interdisciplinary Perspectives on Accounting Conference. Edinburgh July 2018.

Abstract

This paper explores the extent to which the calculative practices that result in reported GHG data and their implications are appropriately developed and understood. GHG emissions data is considered to be more ‘meaningful’ and ‘engaging’ when it is made comparable through a process of normalisation. We argue that these normalisation practices can be problematic from a sustainability governance perspective as they potentially misrepresent the eco-efficiency, eco-effectiveness and eco-justice consequences of corporate decisions to manage GHG emissions. Drawing on the concepts of ‘ontological politics’ (Mol, 1999) and ‘calculation’ (Callon and Muniesa, 2005) we explore the development and consequences of GHG reporting through an analysis of emission data reported by Water and Sewage Companies (WASCs) in England and Wales. In this paper we demonstrate how reported GHG emission data is detached from issues of accuracy, completeness, comparability , creates a misleading linear eco-efficiency predictive narrative (Unerman and O’Dwyer, 2004), conceals the levels of uncertainty of GHG data and omits the complexity of sustainable GHG governance. Their calculative practices also detach GHG emission measures from eco-justice and eco-effectiveness considerations, concealing and omitting relevant information from key stakeholders and inhibiting the wider carbon accountability of organisations. The paper concludes by discussing the need for GHG reporting to provide disclosures which are cognizant of the inherent uncertainty and complexity of GHG emissions in order to help create wider climate change literacy and to enable sustainable governance.

Keywords: GHG reporting, calculation, normalisation, accountability

1 The authors would like to acknowledge the funding received from Aston University’s Centre for Sustainability and Innovation that was instrumental in starting this research project.

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Privilege, Detachment, Concealment and Omission: Normalisation and GHG Reporting

Introduction

“[Businesses] have asked for a level playing field so that they can be fairly judged against one another…

Investors are now looking hard at the green credentials of businesses, and the reporting of GHG emissions will

give them vital information as they decide where to invest their money.”

Caroline Spelman, Secretary of State for the Environment,

The UK Government’s 2008 Climate Change Act includes a legally binding commitment to reduce CO 2

emissions by at least 26-32% by 2020 and 80% by 2050, benchmarked against 1990 levels. Defra 2

(2010) suggests that GHG emissions reporting is a key enabling factor and an important part of GHG

governance. The UK Government legislated that all quoted companies must include GHG emissions

data in their Annual Report as it “is the vital first step for companies to make reductions in these

dangerous emissions” and “will enable investors to see which companies are effectively managing

the hidden long-term costs of GHG emissions” (Defra, 2012). This legislation is justified as providing

information for internal stakeholders for effective GHG management and to external stakeholders,

explicitly investors in deciding where to invest their money, but also potentially NGOs, customers,

employees and governments (ACCA, 2007; Deloitte, 2010; Kauffmann et al., 2012; Kolk et al., 2008;

ACCA and GRI, 2009).

The legislation requires that quoted companies must report: annual emissions of carbon dioxide

equivalent for which they are responsible due to their activities and due to their purchase of

electricity, heat, steam or cooling for their own use; at least one ratio which expresses the quoted

company’s annual emissions in relation to a quantifiable factor associated with the company’s

activities; and for the preceding financial year. Defra’s (2013) guidance states that the reporting of

the prior year’s emissions will “provide readers with the ability to see trends in emissions over time”

(p. 30) and that, in general, the reporting of ‘intensity’ ratios for environmental indicators “allows

comparison of performance over time and with similar types of organisations” (p. 31). As such GHG

emissions data is considered to be more ‘meaningful’ and ‘engaging’ when it is made comparable

through a process of normalisation (ACCA, 2007, p.35).

However, accounting for GHG emissions involves interactions between two sets of expert calculative

practices with very different ontological assumptions, methodological concepts of quality, user

2 Defra is “the UK government department responsible for policy and regulations on environmental, food and rural issues”. Defra is responsible for providing tools to help businesses reduce their environmental impact and has led on developing tools for businesses to report on their greenhouse gas emissions and the related UK government legislation (for further details see www. gov.uk/government/organisations/department-for-environment-food-rural-affairs, last accessed 11 November 2014).

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groups and decision-processes, particularly in relation to the acceptability of uncertainty. Accounting

for GHG draws on climate science calculative practices and financial reporting calculative practices

and whilst on the surface this would appear to be an appropriate hybridisation (Thomson, et al.

2014) there are some problematic tensions in this process. These tensions relate to the ontological

politics of these expert practices in relation the underlying treatment of risk, uncertainty, entities,

subjectivity, objectivity and reality assumptions. These ontological tensions do not necessarily

preclude hybridisation, but need to be disentangled and made visible.

In this paper we question the extent to which the calculative practices associated with corporate

GHG emissions disclosures are appropriately developed and understood. We argue that these

calculative practices can be problematic from a sustainability governance perspective as they

potentially misrepresent the eco-efficiency, eco-effectiveness and eco-justice consequences of

corporate decisions to manage GHG emissions.

Drawing on the concepts of ‘ontological politics’ (Mol, 1999) and ‘calculation’ (Callon and Muniesa,

2005) we explore the development and consequences of GHG reporting through an analysis of

emission data reported by Water and Sewage Companies (WASCs) in England and Wales. Ofwat (the

industry regulator) required the WASCs to disclose detailed emissions data and intensity ratios along

with associated data reliability and accuracy bands for the four years 2007/8-2010/11. During this

period Ofwat stated that the reported emissions would ‘influence’ Ofwat’s investment decisions and

so had financial implications for the WASCs. Subsequently Ofwat changed to a ‘risk-based’ regulatory

approach, which removed the requirement for detailed GHG emissions data to be collected in the

same way. Nevertheless, data from the 2007-2011 period resulted from a change in regulation, and

has some features akin to a ‘natural experiment’ (see Gow et al., 2016; Wagenhofer, 2106), allowing

us to reflect upon and assess the implications of GHG realities constructed by reporting legislation

and guidelines in general.

In this paper we demonstrate how the calculative practices underpinning GHG emissions data

privileges the perspective of investors. Moreover, the normalised GHG emission data was detached

from concerns of their accuracy, completeness,incomparability complex causation, creating a

misleading linear eco-efficiency predictive narrative (Unerman and O’Dwyer, 2004), concealing levels

of uncertainty and omitting the requirements of of sustainable GHG governance. These calculative

practices also detach GHG emission measures from eco-justice and eco-effectiveness considerations,

concealing and omitting relevant information from key stakeholders and inhibiting the wider carbon

accountability of these organisations. Understanding the implications of underlying GHG calculative

practices is considered critical in evaluating the effectiveness of different forms of GHG reporting.

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The paper proceeds as follows. The next section provides critical review of the accounting and

reporting of GHG emissions including consideration of the concepts of ‘ontological politics’ (Mol,

1999) and ‘calculation’ (Callon and Muniesa, 2005) that inform the remainder of the paper. We then

introduce the water and sewage industry in England and Wales, which provides the data for our

analysis. We then briefly explain our research methods before we report the findings from our

analysis of the reported GHG emissions data. We conclude by discussing the implications that our

findings have for the UK Government’s legislation and for GHG reporting more generally.

Accounting for GHG emissions

There is now a growing literature, which considers the qualities of various corporate GHG calculative

and communicative practices from different perspectives (e.g. Kolk et al., 2008; Andrew and Cortese,

2011; Sales de Aguiar and Bebbington, 2014; Comyns and Figge, 2015; Liesen et al., 2015). Our focus,

however, is explore the ontological and methodological impossibility that a combination of climate

science and accounting calculative practices can meet the accounting principles of reliability,

accuracy, consistency, completeness and comparability.

In the introduction we saw that a case was made by the UK government that corporate reporting of

GHG emissions data enabled different comparisons to be undertaken and decisions to be made. We

contend, however, that this reported data results from the enactment of a set of diverse practices

which combines climate science and accounting calculative tools to construct a particular

representation of an organisation’s GHG reality. This representation through calculation is

consistent with Mol’s (1999) ‘ontological politics’ with argues that reality is shaped within, and

manipulated by, the mundane and diverse practices which interact with it. As such “reality is

historically, culturally and materially located” (ibid., p. 75) and results from the enactment of

“various tools in the course of a diversity of practices” (ibid., p. 77). This view is aligned with Hines

(1988), who shows that in the process of communicating financial information, accounting defines

and combines things to represent an organisation’s operations and impacts. The power of

accounting lies in how it creates a representation of organisational reality by including and excluding

things according to accounting’s formal calculative and communicative practices . Similar to Mol,

1999, Hines (1988) argues that accounting’s role in constructing representations of organisational

realities is hidden from users of accounting information whoappropriate these representations into

their decision making processes.

We contend that the inclusion of new things, such as GHG emissions, into accounting’s calculative

and communicative practices allows them to inherit any of the desirableattributes of accounting

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(Cuckston, 2013). Enacting GHG emissions within accounting is conceptualised using Callon and

Muniesa’s (2005) three-step process. The first step is to detach GHG emissions from its context, in

this case, by climate scientific calculative and communicative practices (see Fiedler et al.,20173 for

more in-depth discussion of the detachment of GHG from the physical world). Second, the detached

GHG entities are “subjected to manipulations and transformations” (Callon and Muniesa, 2005, p.

1231) in order to be objectified and singularised in a “process of classification, clustering and sorting

that makes products [or in our case emissions] both comparable and different” (ibid., p. 1235). The

third and final, but necessary, step is that the GHG emissions calculation produces a result, which

can be evaluated, appropriated into other systems and enable decisions to be made to sustainably

govern global climate change risks. These representations of GHG

emissions have been detached from the complex atmospheric reality of our planet through

scientific calculations and communicative practices(Meinhausen et al., 2009; Pearson et al., 2008;

Sales de Aguiar and Bebbington, 2014; Brander, 2016; Pearson et al. 2008), then singularised and

objectified so as to enable the governance of global climate change (Defra, 2013; McKenzie, 2009;

IPCC, 2010; EC, 2013; WBCSD and WRI, 2004). These scientific GHG results are then detached from

this scientific climate change governance world and transformed and manipulated through

accounting calculations to represent an organisation’s contribution to global GHG emissions and to

generate a number assumed to be commensurable with other accounting numbers (Leisen et al.

2015; Andrew and Cortese, 2015;. Lohmann, 2009, Kolk et al, 2008)

The use accounting objectification and singularisation practices translates climate science GHG

emissions into a number deemed acceptable by accounting to enter the corporate accounting

representations and become attached to and appropriated into different decision processes that

value accounting-like characteristics (Brander and Ascui, 2015; Bowen and Wittenben, 2011;

McKenzie, 2009; Kolk et al. 2008). However, the properties of these organisational-accounting GHG

constructs are determined by a complex sequence of interdisciplinary calculations, which is

simultaneously framed by the world of the calculator and the desired levels of commensurability of

the decision maker (Cuckston, 2013). In order to evaluate the appropriateness of organisational GHG

emission constructs it is important to have some comprehension of the decision contexts in which

different GHG emission constructs mayl be entangled. In particular we think it important to question

which climate science GHG attributes are detached, incorporated, concealed, omitted, transformed

3 The authors would like to acknowledge the working paper by Fiedler, Chua and Boedker (2017), which was very helpful in our thinking with regards to the concepts employed in this paper.

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or maninpulated in specific organisational GHG calculative practices and which decision contexts are

privileged in the the construction of organisational GHG accounting numbers.

On the surface the climate science calculative practices for GHG emissions bear many similarities and

desired outcomes to conventional accounting practices. Drawing on GRI standard, the UN GHG

protocol, European Federation of Accountants qualitative characteristics and industry specific

guidelines, Comyns and Figge (2015) develop seven principles to determine GHG reporting quality in

the oil and gas industry, which are considered transferable to other corporate contexts. Their seven

principles are: accuracy, completeness, consistency (including comparability), credibility, relevance,

timeliness and transparency. We adapt their framework to our empirical context with reference to

the priorities included in ACCA(2007), DEFRA, (2010, 2013) and Ofwat (2010, 2012a, 2012b) and

focus upon the calculative and communicative work and ontological politics necessary to enact

comparability, accuracy, consistency, completeness and reliability for reporting organisational GHG

emissions.

We now explore the sequence of calculative practices that result in organisational GHG emissions

disclosures, in particular the problem of translating valid climate scientific numbers into data

suitable for inclusion in accounting reports. Formal GHG calculative practices exist that satisfy robust

scientific methodological quality standards (e.g. Meinshausen, et al. 2009; Brander and Ascui, 2015;

Pearson et al. 2008; WBSCD and WRI, 2004; IPCC, 2010) and are appropriated into many GHG

governance mechanisms (Defra, 2013; EC, 2103; Kaufman et al. 2012). However, this does not mean

that these ‘scientific’ numbers are considered by accountants as suitable for direct inclusion in

organisational accounts. There is likely to be a need for further detachment, transformation or

manipulation by accounting calculative and communicative practices in order to appropriated into

accounting reports and subsequently appropriated by users of accounting reports into their decision

processes (e.g. Cuckston, 2013; McKenzie, 2009; Unerman and O’Dwyer, 2004).

Callon and Muniesa (2005, p. 1232) remind us that any calculative practice can never be regarded as

a pure or holistic representation of the detached entity, in our case, GHG emissions, and may be

flawed as “an accurate calculation might require unavailable resources or an extended timeframe”.

In drawing upon both ‘ontological politics’ and ‘calculation’, we are mindful that calculations that

construct GHG emissions are dependent upon diverse climate scientific practices(Mol, 1999; Fiedler

et al. 2017) and that there are different possible enactments, transformations and manipulations in

these scientific practices (e.g. Brander and Ascui, 2015; Kaufman et al. 2008). The enactment of each

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scientific practice will construct and communicate different representations of GHG with hidden

attributes that may or may not be aligned to the different decision contexts associated with GHG

governance.Within the climate science community there is a recognition that there is not an

accepted set of calculative practices that produce an objective, certain number that can be

appropriated into every decision context.

This Throughout the remainder of this paper, therefore, we will draw attention to potentially

problematic aspects of how the accounting enactment of calculating organisational GHG emissions

and discuss the implications of these accounting organisational GHG constructs.

We now turn to consider the : reliability; accuracy; consistency; completeness; and comparability of

organisational GHG emissions accounting. First, let us consider reliability. GRI G4 (2013, page 18)

define reliability as:

“The organization should gather, record, compile, analyze and disclose information and processes used in the preparation of a report in a way that they can be subject to examination and that establishes the quality and materiality of the information.”

The GRI definition suggests that reliability is a function of the process by which information is

treated in preparation for disclosure. In our case, Ofwat (2010a, p. 91) state that their data reliability

standards are “based on how the data was gathered” ranging from ‘A’ from sound record systems to

‘D’ using unconfirmed verbal reports, cursory inspections or analysis. It could be implied from Ofwat

and GRI that more reliable data will also be more accurate and therefore more decision useful.

Drawing upon the GHG protocol, Defra (2013, p.4) defines desired attribute of accuracy as seeking:

“to reduce uncertainties in your reported figures where practical. Achieve sufficient accuracy to enable users to make decisions with reasonable confidence as to the integrity of the reported information.”

Bowen and Wittneben (2011, p. 1023) contend that to be accurate organisational GHG emissions

data needs to remain attached to the underlying climate system in order “to reflect actual

atmospheric emissions” . this is in line with the GHG protocol’s assertion that accurate reporting will

not systematically misstate (neither over stating nor under stating) the level of emissions. Callon and

Munesia (2005) challenge the ability of any calculative practice to remain attached to the underlying

system and whilst some GHG emissions data may be easily attainable and relatively straightforward

to calculate (Bowen and Wittneben, 2011), there are considerable obstacles to calculating certain

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and accurate GHG emissions including the recognition by WBCSD and WRI (WBCSD and WRI, 2004,

p. 54) that it is “both an accounting and a scientific exercise” and:

“In financial accounting, it is standard practice to report individual point estimates (i.e., single values versus a range of possible values). In contrast, the standard practice for most scientific studies of GHG and other emissions is to report quantitative data with estimated error bounds… In an ideal situation, in which a company had perfect quantitative information on the uncertainty of its emission estimates at all levels, the primary use of this information would almost certainly be comparative. Such comparisons might be made across companies, across business units, across source categories, or through time.” (p.54)

Uncertainty and inaccuracy is an accepted part of climate science calculative and communicative

practices (Meinshausen et al. 2009; Brander, 2016; Feilder et al., 2017). The IPCC (2010, page

number?) provide a “guidance note for lead authors of the IPCC fifth assessment report on

consistent treatment of uncertainties”. The guidance identifies “two metrics for communicating the

degree of certainty in key findings”. The first is a measure requiring the assignment of a qualitative

level of confidence4 in the validity of a finding based upon the robustness of the evidence and the

degree of agreement. The second is a quantitative (statistical) measure ‘expressed probabilistically’

based on its likelihood from exceptionally unlikely (0-1% probability) to ‘virtually certain’ (99-100%

probability).

Similarly, the Australian “National Greenhouse and Energy Reporting (Measurement) Determination

2008” (as amended in 2016) provides guidance on uncertainty levels of different emissions and

suggests that “uncertainties in emission estimates must be minimised and any estimates must

neither be over nor under estimates of the true values at a 95% confidence level” (paragraph

1.13(c)).It is understandable, therefore, that the GHG protocol identifies uncertainty as being

integral to climate science GHG constructs. The GHG Protocol identifies two types of uncertainty.

These relate to ‘model uncertainty’ whereby the equations used may be incorrect and ‘parameter

uncertainty’, which relates to the data and emission factors that are the required inputs into the

‘estimation models’. In the climate science and organisational accounting contexts uncertainty and

accuracy are considered to be inversely related (Bowen and Wittneben, 2011).

Estimating uncertainty or levels of accuracy is ‘immensely challenging’ (Milne and Grubnic, 2011, p.

948), but unavoidable given the desired outcomes of GHG climate science and GHG accounting

calculative and communicative practices. In the context of English Local Authorities, Cooper and

Pearce (2011) argue that the use of modelling and the levels of estimation required in calculating

climate change emissions raise questions over whether it can be sufficiently accurate to “be used as

4 The level of confidence is qualified into very low, low, medium, high, and very high.

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a form of assessment” (p. 1111) and therefore usefully appropriated into decision making processes.

One example of ‘parameter uncertainty’ is illustrated by the following observation that:

“The original GWP [global warming potential] figure for HFC-23 of 11,700 molecules originally put forward by the IPCC in 1995-1996 was revised in 2007 to 14,800, and the error band of this estimate is still an enormous plus or minus 5000 (Mackenzie, 2009, pp. 440-455).” (Lohmann, 2009, p. 514).

Further, Lohmann (2009, p. 515) reports that the error bands relating to “forest inventories … were

so wide that they swamped the signal required”. In a similar vein, Pearson et al. (2008) consider the

different climate science protocols available for measuring the GHG emission reductions from

afforestation projects. They noted that the choice of methodologies available and the inherent data

uncertainty led to vastly different climate science estimates of both the baseline and final carbon

equivalents, with substantive different outcomes depending on which numbers were appropriated

into decision making processes. Bowen and Wittneben (2011) suggest that, due to the inherent

uncertainties in the climate science and accounting GHG calculative practices, an associated

indicator of (un)certainty should be part of any communicative practices, but they warn that such

reporting may be perceived as cumbersome and likely to be “undesirable among policy makers” (p.

1030). There appears to be consensus that there is a considerable amount of uncertainty inherent

in the production of GHG emissions data. Moreover, such uncertainty impinges upon the accuracy

of communicative practises and, in turn, this will may make considerations of data over time and

space in different decision processes problematic. For instance, Southworth (2009, p. 335) reported

that there were “significant uncertainties embedded in the [GHG] data reported in the 1605(b)

database that make useful comparisons of data difficult”.

Defra’s (2013, p.4) desired attributes of GHG communicative practises suggest that it is important

that reporters use “consistent methodologies to allow for meaningful comparisons of environmental

impact data over time”. As mentioned earlier there are numerous different, but often overlapping,

GHG scientific and accounting methodologies and practices that can be and are applied in calculating

GHG data. Such diversity of practice and tools being enacted suggests that the calculations may well

construct multiple realities (Mol, 1999) but differences in the calculative practices adopted in specific

organisations are not apparent to all account users, nor are they aware of the implications of these

methodological choices. Methodological diversity and inconsistency within voluntary GHG reporting

(Kolk et al., 2008) has been found to inhibit comparability (Andrew and Cortese, 2011) “making it

difficult to evaluate the relative performance of companies” (Deloitte, 2010). Similarly, ACCA (2007)

report that quantitative GHG performance data has, to date, been reported in an inconsistent

fashion such that “comparisons are extremely difficult” (ACCA, 2007, p. 10). They suggest that

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convergence to a single standard and normalised data would be needed for benchmarking within

industries or sectors, but that “inter-sector comparisons are not useful disclosures” (p. 43).

Another desired attribute of accounting for organisational GHG emissions is completeness. For

example, the GHG Protocol defines completeness as the need to:

“Account for and report on all GHG emission sources and activities within the chosen inventory boundary. Disclose and justify any specific exclusions.”

“Information provided on GHG emissions should include both direct and indirect emissions for all of the operations within the defined reporting boundary”

Central to the principle of completeness is the definition of the reporting boundary and, as Hines

(1988) argues, the way in which an organisation’s boundary is drawn will result in the inclusion and

exclusion of entities within the constructed and communicated reality. The drawing of boundaries is

also central to the calculative practices of detachment and singularisations (Callon and Muniesa,

2005). Liesen et al. (2015) focus upon the completeness of corporate disclosures of GHG emissions

data and relate completeness to the scope, type and reporting boundary. Both Liesen et al. (2015)

and Comyns and Figge (2015) find high levels of incomplete reporting such that the data reported

cannot be considered as a sufficient representation of all organisational GHG emissions.

Andrew and Cortese (2011) argue that the ability for organisational accounting GHG emissions data

to be compared over time and between firms is fundamental to its usefulness to internal and

external business stakeholders. This is similar to Defra’s, (2013, p. 4) assertion that comparability

“will aid you in benchmarking your organization and will aid users of your report to judge your

performance against that of your peers”. However, as Callon and Muniesa (2005) note, in order for a

number to be comparable, an entity must become detached, objectified, and singular through

calculative transformations. The most common accounting calculative transformations associated

with comparability is normalisation. In order to assist comparability “the Defra guidance

recommends ‘normalisation’ of carbon data and reporting of an intensity ratio for scope 1 and 2

emissions” (Deloitte, 2010, p. 12). The Defra (2013, p. 31) guidance suggests that:

“Intensity ratios compare emissions data with an appropriate business metric or financial indicator, such as sales revenue or square metres of floor space. This allows comparison of performance over time and with other similar types of organisations.”

Normalisation of GHG emissions data is argued to be “engaging for the business and provides

meaningful disclosure for shareholders” (ACCA, 2007, p. 35), but requires consideration of the

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implications of accounting calculative practices associated with this normalisation. For example,

ACCA suggest normalisation through size dividing GHG emissions by metrics such as ‘sales revenue’

and ‘square metres of floor space’. Normalisation could also take account of firm activities, although

within a single industry, there remain many potential intensity ratios that can be calculated resulting

in individual companies choosing to report different (non-comparable) normalised GHG numbers

(ACCA, 2007, p. 32). For such comparability to be possible, however, ACCA state that it is important

that appropriate and standardised units are used to normalise the GHG emissions data (ACCA, 2007).

Within this paper we assume that in the UK, organisational accounting of GHG emissions is

intended to assist in governing the reduction of GHG in order to combat climate change in pursuit of

a more sustainable planet. In this context theselection of appropriate accounting normalisation of

GHG emissionss is under-explored, taken for granted, and appear to be derived from accounting

calculative practices used to interpret financial accounting statements. We argue that accounting

GHG normalising calculative practices need to be considered within the context of sustainability

governance in order to evaluate the extent to which they privilege, ignore or distort particular

decision contexts or different users.

As the objectives of this paper is to make a conceptual and empirical contribution to this field, the

next section of this paper introduces the water industry in England and Wales, which provides the

data for our case study. Our empirical evidence is taken from a unique data set that, to our

knowledge, has not previously been discussed in this literature. This data is different in that the

reporting is mandated by the industry regulator and, as will be seen in the later sections of this

paper, contains data enabling specific analysis and comment with regard to the usefulness of GHG

emissions data that previous studies have not been able to do.

The English and Welsh Water Industry

Ten Water and Sewage Companies (WASCs) were privatised in England and Wales in 1989 5. The

privatised WASCs maintain a distinct regional monopoly and so cannot be considered to be subject

to market forces in the same way that companies in other more naturally competitive industries

are. Nevertheless, numerous arguments were provided to support the need to privatise, and

essentially it was argued that under state ownership the water industry had suffered from

significant under investment and there was a need to improve drinking water quality and the

economic efficiency of the industry whilst also reducing environmental pollution incidents into 5 The industry’s privatisation was part of the larger programme started by Margaret Thatcher’s Conservative government, which also saw the sale of other utilities such as telecommunications, energy and rail companies.

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surface water (such as rivers and the sea) and ground water. Moreover, these concerns have

shaped the regulatory system put in place after privatisation. The Drinking Water Inspectorate

regulates the quality of drinking water, the Environment Agency is responsible for regulating the

environmental quality of river water and considers pollution incidents, and Ofwat is the economic

regulator. Ofwat regulates the price that a WASC can charge its customers through an ex ante price-

cap mechanism, which is usually referred to as RPI + K, in which prices are set for a five year period.

This system is designed to enable the necessary capital investment to be made to meet

environmental and quality standards, while also providing incentives for cost efficiency, as the

WASCs attempt to maximise profits given the price set. Historically, Ofwat also monitors customer

satisfaction primarily through the Director General (DG) Standards.

This regulatory regime has now been in place for over twenty years and has, we believe, influenced

the way in which the post-privatisation industry has evolved. Saal and Parker (2001) studied the

period 1985-1999 and provided evidence that the WASCs had considerably improved their

economic, environmental and drinking water quality performance after privatisation in 1989. This

work has also been updated in several studies by Maziotis, Saal and Thanassoulis (2009, 2012 and

2013). The authors concluded that after 2000 there was a steady decline in average price

performance, gains in productivity and drinking water and sewerage treatment quality performance,

and relatively stable economic profitability. The results therefore suggested that after 2000 Ofwat

was more focused on passing productivity benefits to consumers, and maintaining stable

profitability than it was in earlier regulatory periods. This is also evidenced by the WASCs improved

performance against Ofwat’s overall performance assessment (OPA), which, although recently

abandoned, was a composite measure of the WASCs levels of service, customer service and

environmental performance. This measure was designed to compare relative WASC performance in

any given year, but also provides evidence of improved performance over time.

There is, however, an implicit tension between environmental pollution and drinking water quality

performance and levels of greenhouse gas emissions in this industry. This is to say that the positive

developments in drinking water quality and environmental pollution within the water industry

generally require higher levels of water treatment and pumping. As water treatment and pumping

require energy, the improved performance discussed above has resulted in substantially higher

greenhouse gas emissions. This is demonstrated by the Office of National Statistics (ONS) (n.d.)

“estimated historic emissions” by industry for the UK. If we extract the “Total Greenhouse Gas

Emissions6” for the water supply and sewage treatment industries, then we see the following trends:

6 The data reported in Figure 1 includes emissions from Northern Ireland, Scotland, and the emissions of water only companies in England and Wales. However, given the relative small size of Scotland and Northern

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INSERT FIGURE 1 ABOUT HERE

According to these estimates, UK wide water and sewage industry greenhouse gas emissions

increased by 49.6 percent between 1990 and 2008, while equivalent figures for sewage treatment

and water supply are respectively 32.4 and 164.2 percent. To put this further into perspective the

same ONS estimates suggest that over this period, total greenhouse gas emissions for UK industries

fell by more than 13 percent. It is not, therefore, that the water and sewage industry is reflecting

wider industrial patterns in the UK, but instead appears to have become more energy intensive and

this has resulted in higher emissions despite the UK’s shift to “cleaner” energy sources (i.e. the shift

to gas from coal over the period). Stated differently, it would appear that the industry’s focus on

improving drinking water and reducing environmental pollution incidents has come at the cost of

substantial increases in greenhouse gas emissions.

Perhaps in acknowledgement of these increasing emissions, the UK government’s development of

the 2008 Climate Change Act and the severe flooding in the UK in 2007, which some attributed to

climate change, Ofwat (2008a) published its climate change policy statement. Furthermore, it was in

2008 that Ofwat first required the WASCs to report their greenhouse gas emissions to them in their

required regulatory “June Returns”. Ofwat stated that this “was an important step in the companies

taking ownership and responsibility for their GHG emissions” (Ofwat, 2008b, p. 35). They also clearly

state here that:

“It is important that companies accurately measure and monitor their GHG emissions. GHG emissions will influence investment decisions at the 2009 price review” (Ofwat, 2008b, p.35)

At this time it was suggested that this emissions data would have financial implications for the

WASCs. This is to say that one factor influencing the investments that the WASCs could make and so

impacting upon the prices that the WASCs would be able to charge their customers would be GHG

emissions. As part of Ofwat’s 2009 price determination the WASCs were required to produce

strategic direction statements and final business plans recognising the need to mitigate and adapt to

climate change (Ofwat, 2009a). Ofwat’s final price determination included investments to enable

WASCs to reduce carbon “in many ways – through increased efficiency, asset maintenance regimes,

innovation and renewable energy generation” (ibid, p. 62). The WASCs were required to “take

account of the Government’s new non-traded price of carbon” (ibid, p. 62) in appraising their

investment plans. We can see that since 2008 the economic regulator, Ofwat, has required the ten

Ireland, and that the WASCs account for 100 percent of all sewage treatment activity and more than 80 percent of all water supply in England and Wales, these UK aggregate figures will be largely driven by WASC trends.

13

WASCs to report and monitor their greenhouse gas emissions. We can also see that the importance

of climate change mitigation and reducing carbon emissions is further emphasised by its financial

implications in Ofwat’s (2009a) final price determination. Our research is placed within this context.

The next section briefly explains our research methods.

Research method

Our data are the GHG emissions reported by the ten WASCs in their June Returns to Ofwat for the

four years 2007/8-2010/11 and Ofwat’s discussion thereof in their ‘Service and Delivery’ reports.

From 2011/12 Ofwat changed to a ‘risk-based’ regulatory approach7 and no longer collected June

Returns or published ‘Service and delivery’ reports. Instead the WASCs report on key indicators

including “annual operational GHG emissions of the regulated business” (Ofwat, 2012b), but the

exact reporting requirements and Ofwat’s “summary of companies’ performance” are different and

so our study ends with the 2010/11 June Return data. Our GHG emissions data is, therefore, from

the entire population of the ten WASCs for the four years 2007/8-2010/11. Data from this four-year

period results from a change in regulation, and so has some features akin to a ‘natural experiment’

(see Gow et al., 2016; Wagenhofer, 2016), allowing us to reflect upon and assess the implications of

GHG reporting legislation and guidelines in general.

Hopkinson et al. (2000, p. 893) state that “the UK water industry is one of the leading sectors in the

world in respect of the disclosure of reliable environmental performance data.” Whilst not

specifically referring to GHG emissions, it is relevant to keep in mind that the WASCs are perceived

to be relatively good disclosers of environmental data. As such, we believe that the issues

highlighted in our paper are likely to be at least, if not more, problematic for companies with less of

a track record in environmental reporting.

Each year, from 2007/8 to 2010/11, the ten WASCs were required to report their total GHG

emissions and related intensity ratios in their June returns. The two reported intensity ratios

consider emissions per volume of water supplied and emissions per volume of sewage treated. Such

intensity ratios attempt to normalise the total GHG emissions for size effects, as one would expect

larger WASCs to supply more water and/or treat more sewerage. In addition, from the second year

of reporting (2008/9), the WASCs were required to disclose the level of confidence they had in their

reported data. The reported GHG emissions data, intensity ratios and confidence grades provide the

primary source of empirical evidence upon which our analysis is based. This key data was collected 7 Ofwat deemed it necessary to change to a ‘risk-based’ regulatory approach as a consequence of “major new challenges”, including climate change, which could affect the water industry in England and Wales. This change of approach was to inform Ofwat’s action by an assessment of risks and to “minimise the regulatory burden we place on the companies we regulate” (Ofwat, 2012a, p. 3) and has impacted upon all areas of Ofwat’s regulatory activities of which one is climate change.

14

from Table 42 of the WASC’s June returns and corroborated in Ofwat’s ‘Service and Delivery’

reports. The data was extracted from the June returns and recorded into spread sheets for analysis.

For each WASC we take their total GHG emissions data and related confidence grade to analyse the

extent to which the GHG emissions data will “provide readers with the ability to see trends in

emissions over time” (Defra, 2013, p. 30). Our analysis takes the WASCs’ reported GHG emissions

data and confidence grade to calculate the implied potential range of emissions per WASC per year.

We then consider the extent to which this data enables trends in emissions to become apparent

over time. As our analysis requires the confidence grade it is based on total GHG emissions in the

three years 2008/9, 2009/10 and 2010/11. We do not expect that the WASCs would significantly

change in size, but we repeat the analysis for the respective intensity ratios, but data availability

restricts this analysis to 2009/10 and 2010/11 only. This analysis, therefore, provides evidence as to

the ability to compare each WASC’s performance over time.

We are also interested in whether it is possible to compare GHG emissions between the WASCs.

Hopkinson et al. (2000, p. 874) state:

“The industry’s domination by 10 companies which carry out similar activities and are of a broadly similar size also makes comparison of performance more easily achieved than in most other sectors.”

Despite this, we believe that comparison of GHG emissions across the WASCs is not possible using

the total GHG emissions data referred to above, as whilst of ‘broadly similar size’, there remain

significant differences. Defra (2013, p. 31) suggest that it is the reporting of intensity ratios that

enables “comparison of performance … with similar types of organisations” (Defra, 2013, p. 31). Size,

however, is not the only factor that impacts upon a WASC’s GHG intensity and so we provide

analysis of further contextual factors that may impact upon the comparability of the WASCs’ GHG

intensity data. Our analysis considers the intensity of emissions with respect to the relative

importance (rank) and correlation of key contextual factors.

In addition, we complement our quantitative analyses with extracts from Ofwat’s ‘Service and

delivery’ reports. Within these reports Ofwat discusses the WASCs overall performance and since

2007/8 this has included a discussion of the GHG emissions data. Our close reading (Amernic et al.

2010) of these reports specifically identified comments related to the comparability of the WASCs’

performance and the quality of the reported GHG emissions data and the related normalised

intensity ratios. The next section of the paper details the findings from our analyses.

Findings and Analysis

15

This section analyses the GHG emissions data for the ten WASCs and is split into two parts. The first

part analyses the extent to which the GHG emissions data enables the performance of the WASCs to

be compared over time. This is undertaken for both total GHG emissions and a related intensity

ratio. The second sub-section analyses the extent to which the GHG emissions intensity ratio enables

performance comparisons to be made across the WASCs.

GHG emissions performance over time

The WASCs first reported their GHG emissions to Ofwat in 2007/8. Ofwat (2008b) identified that,

whilst the WASCs had for several years voluntarily reported their emissions to Water UK, it was

“prudent at this stage not to comment on either historical trends or to highlight the performance of

individual companies” (p. 35). In particular Ofwat acknowledged that there had been changes in the

methods used to calculate GHG emissions, which would make such analysis problematic. Moreover,

Ofwat (2008b, p. 35) noted that they expected the “consistency and robustness of data will improve

as companies gain further understanding of carbon accounting across both water and sewerage

services and develop sound systems for data collection”. In addition the WASCs were not required to

report the level of confidence that they had in their data until the second year of reporting and so

we start our analysis from 2008/9.

In 2008/9 the WASCs were required to report their emissions using both the carbon reduction

commitment (CRC) definition and the Defra definition. Form 2008/9, therefore, we can see that each

WASC is required to use two different methodologies in their reporting. Actually, the difference

between the two methodologies is, most obviously, the reporting boundary and so is also of

relevance to the completeness of the data. The narrower CRC definition relates to energy use,

whereas the broader DEFRA definition includes emissions from energy use, transport, and process

emissions

All of the companies suggested that the “reliability” of their CRC data is A or B (i.e. “sound” or “with

minor shortcomings”), but the reported accuracy bands range from 1 to 4 (1 = ±1%; 2 = ± 5%; 3 = ±

10%; 4 = ± 25%; and 5 = ± 50%) (Ofwat, 2010b, p. 92). The reporting of the accuracy band makes

explicit the uncertainty and associated inaccuracy of the disclosed emissions data. As such the flaws

inherent within this data, which is usually hidden, is made visible. As we might expect, Table 1 shows

that eight of the ten WASCs report lower (74% - 85%) emissions according to the narrower CRC

definition. This clearly demonstrates that inconsistent methodologies, reflecting different reporting

16

boundaries, significantly impact upon the level of reported emissions and so present multiple

constructions of GHG emissions reality. Strangely both Dŵr Cymru and South West actually reported

higher emissions under the CRC definition. This is certainly surprising and may reflect the relative

lack of reliability (B – minor shortcomings) and accuracy (4 - +/-25%) that Dŵr Cymru had in their

data. South West, however, seem relatively confident (A2) that their emissions are higher using the

CRC, as opposed to the Defra, definition. Table 1 also shows that the WASCs’ confidence grade is as

good if not better using the CRC based estimates. We base our analysis, therefore, on the CRC data

which is intuitively, and reported by the WASCs to be, the most accurate. We note here, however,

that this constructs a reality whereby only GHG emissions related to energy use with all other GHG

emissions excluded (Hines, 1988). As such the emissions data is flawed and partial (Callon and

Muniesa, 2005).

INSERT TABLE 1 ABOUT HERE

Commenting upon the 2008/9 data, Ofwat state that they “have seen clear improvements in the

quality of data” submitted by the WASCs, but still call for further ‘improvements’ in its ‘robustness

and consistency’. Ofwat (2009b, p. 34) suggest that the 2008/9 data is “broadly comparable with the

data the companies provided in the 2008 June return”, but suggest that they have not made a

comparison, “as it is not possible at the moment to differentiate actual changes in GHG emissions

from improvements in data collection and changes in overlying definitions.” This is to say that at this

point Ofwat remain concerned with changing methods and ‘parameter uncertainty’ such that the

accuracy of the data is not sufficient to make useful comparisons with the prior year and this further

convinces us that the 2007/8 data should not be included in our analysis. Ofwat are, therefore,

acknowledging that the calculations remain flawed and the numbers produced are not ‘pure’.

Ofwat (2010a) state that by the third year of reporting they “are confident that data quality is

improving” (p. 50). In fact, we can see that when compared to 2008/9, the WASCs reported that the

accuracy of their emissions data remained the same or improved (Dŵr Cymru from ‘4’ in 2008/9 to

‘2’ in 2009/10 and 2010/11; South West from ‘2’ in 2008/9 to ‘1’ in 2009/10 and 2010/11; Southern

from ‘3’ in 2008/9 to ‘2’ in 2009/10 and 2010/11; and Thames from ‘3’ in 2008/9 and 2009/10 to ‘2’

in 2010/11) in the next two years. Table 2 below presents the reported GHG emissions along with

the lower and upper bounds implied by the reported accuracy bands for each WASC for the years

2008/9 to 2010/11.

17

INSERT TABLE 2 ABOUT HERE

Simply taking the reported GHG emissions over the three years would appear to suggest that they

are increasing for six of the WASCs and decreasing for the other four, but the data is not sufficiently

accurate for this conclusion to be justified. The highlighted cells in the table identify where it is

possible to comment upon changes in GHG emissions performance with any confidence. There does

appear to be a reported decrease in GHG emissions by Severn Trent, when comparing 2008/9 with

both 2009/10 and 2010/11, and by South West when comparing 2009/10 with 2010/11. The

common feature is that these are the only data for which the reported accuracy is ‘1’ (i.e. ±1%).

Conversely it is not possible to see any year-on-year trend where the reported accuracy is ‘2’ (i.e.

±5%) or higher, although for Yorkshire it does seem possible to say that GHG emissions have risen

over the two years between 2008/9 and 2010/11 despite a reported accuracy of ‘2’. For seven of the

ten WASCs, however, our analysis shows that it is not possible to state whether the annual levels of

GHG emissions are increasing, decreasing or constant given the disclosed level of data accuracy.

Ofwat recognised the problematic nature of the reported emissions data, as the “changes are within

the margins of the data error” (Ofwat, 2010b, p. 88), and these flaws in the data explains their

reticence in using it further.

Thus far the analysis undertaken refers to total company emissions, but Defra (2013) suggest that

intensity ratios allow comparison of performance over time and it is true that the changes in total

GHG emissions may be driven as much by changes in scale as by changes in efficiency. Ofwat (2008b

and 2009b) required the WASCs to report intensity ratios, which consider the emissions per volume

of water supplied and emissions per volume of sewage treated. Such intensity ratios require the

WASCs to apportion emissions between their two main activities (water supply and sewage

treatment) and reflect the scale of each activity undertaken each year.

We noted earlier that the reported data constructs a partial and flawed GHG emissions reality.

Moreover, Ofwat expressed concerns over the ‘robustness’ and ‘consistency’ of even this flawed

emissions data and these problems must logically also affect the intensity ratios. In particular, a

consistent method for calculating GHG emissions per sewage treated continues to prove especially

problematic and two different intensity ratios are provided for 2010/11. Further, the data for GHG

emissions per sewage treated is generally considered to be more flawed, unreliable and uncertain by

the WASCs with South West Water in particular suggesting a ± 50% degree of accuracy (C5). A lack of

methodological consistency in sewage calculations and a high degree of data uncertainty do not

provide appropriate levels of confidence in the data for comparisons to be made over time. In

18

contrast, by 2010/11 the levels of uncertainty relating to the operational GHG emissions per Ml of

treated water are at ± 5% for eight of the WASCs and ± 10% for the other two. Given these levels of

data accuracy the remainder of our analysis will focus upon the emissions per Ml of treated water in

which the WASCs appear to have greater confidence. We were only able to access data on the

WASCs confidence grades for the years 2009/10 and 2010/11 and so Table 3 below reports this.

INSERT TABLE 3 ABOUT HERE

The reported figures would lead us to suggest that the intensity ratio has increased (and so

worsened) for nine of the ten WASCs over the two years. When we factor in the reported level of

data accuracy, however, we see that this can only be stated with confidence in the case of Anglian’s

operational GHG emissions per Ml of treated water. In this instance we do appear able to say that

emissions have increased by more than can be explained by uncertainty alone. Interestingly this is

not one of the WASCs for which total emissions appear to have changed and in fact the reported

total emissions fell from 2009/10 to 2010/11.

Our analysis of these findings leads us to raise four related concerns with regard to the emissions

data reported by the WASCs. First, it is important to remember that the emissions data reported

constructs a reality that is partial. Second, we recognise that the emissions data reported by the

WASCs is uncertain and so cannot be considered to be accurate. Third, given this uncertainty, the

reporting of data reliability and data accuracy provides information to users. This is to say that

knowledge of the reliability and accuracy of the data enables a more informed assessment of the

extent to which performance has changed over time. Fourt, from our analysis of the total emissions,

the level of data accuracy and number of years reported influence the extent to which valid trends

can be identified. The more accurate the data is, for example to ±1%, the more possible it is to see

short-term trends especially when the annual changes may well be of the order of 1%-5%.

Alternatively, it may only be possible to see trends over longer periods of time when the data

accuracy is ±5% or better. As noted earlier, Defra’s (2013, p. 30) guidance states that the reporting of

prior year’s emissions will “provide readers with the ability to see trends over time”, but our findings

suggest that the validity of such trends is questionable given the uncertainty and associated

inaccuracy of emissions data. Fifth, we can see that different inferences can be made with regard to

changes in performance over time when analysing data related to total emissions and intensity

ratios respectively. This finding begins to hint at the complexity that underlies understanding

organisational emissions and we further explore this issue as we consider GHG performance across

WASCs in the next section. We shall return to discuss these issues further in the conclusions section

of the paper.

19

GHG emissions performance across WASCs

Comparability also refers to being able to compare meaningfully across companies for a given time

period. Defra (2013) again point to the value of intensity ratios when looking to make comparisons

“with other similar types of organisations” (p. 31). It would, therefore, seem more reasonable to

compare GHG intensity ratios rather than total emissions as this takes into account the size of each

WASC’s operations. If we simply compare the GHG emissions per Ml of treated water for 2009/10

presented in Table 3 above, we could say that, even after accounting for data inaccuracy, that

Northumbrian, Thames and United appear to be more efficient than the other WASCs. Moreover, if

we were to put to one side our concerns over the partial nature of the reported data and its

accuracy, we could suggest that Dŵr Cymru, Severn Trent and Southern appear to be the least

efficient. Such conclusions, however, are problematic, as size is not the only factor that impacts upon

a WASC’s GHG intensity. We contend that the singularity inherent in an intensity ratio oversimplifies

and downplays the inherent complexity driving GHG emissions and uncritically comparing intensity

ratios would result in flawed evaluations. Ofwat (2010a, p. 88) suggest that there are a number of

contextual factors that will impact upon a WASC’s relative GHG intensity, as follows:

“the variance between companies … may also be influenced driven by several other factors, including: geography (predominantly pumping head); network configuration; and discharge consents placed on sewage works (potentially requiring differing levels of energy

intensive treatments).”

To date, Ofwat has not provided any analysis to take these and other operating characteristics into

account, but unless such analysis is undertaken we contend that the WASCs’ GHG intensity data is

not comparable. A key contribution of this paper is, therefore, to undertake a first attempt at

comparing the WASC’s GHG intensity after considering relevant contextual factors. As discussed

earlier, the level of uncertainty associated with this data is problematic, but this has already been

demonstrated above. Our further analysis here assumes the data to be accurate (and so is assuming

that it is ‘pure’) so that we can consider the extent to which normalising emissions data, through the

use of an intensity ratio, can enable valid performance comparisons to be made across similar

organisations.

We first note that it is generally accepted in the water economics literature 8, that one must control

for volumes of water, number of connections and distance of water transportation as a minimum to

properly model water system costs and we suggest that these factors are all relevant for GHG

8 See Torres and Morrison Paul (2006) for a particularly illustrative article detailing the relationship between these three fundamental water industry “outputs” and the costs of supplying water.

20

emissions. Stated most simply, GHG emissions, as well as costs are likely to be higher for a WASC

because more resources, pumping etc., are required if any of these “output characteristics” change.

We therefore suggest that WASCs which operate in relatively densely populated areas and hence

deliver higher water volumes per km of network, as well as WASCs which have relatively higher

water delivery per customer will have relatively lower carbon emissions per Ml of water delivered.

Secondly, given the geographical nature of the different regions, some WASCs have a greater need

to pump water around their network. Thus, companies that operate in areas with relatively flat

terrain are considered to have more pumping requirements than others that can rely more on

gravity fed water supply systems. We suggest, therefore, that those WASCs with a higher average

pumping head would be expected to emit higher levels of carbon. Similarly, abstracting water from

boreholes is an energy intensive process requiring more pumping than that associated with, for

example, reservoir abstraction. Given this, we suggest that those WASCs which source a greater

percentage of their water from boreholes would be expected to emit high levels of GHG. We

therefore report data on these contextual factors in Table 4 below, to better illuminate some of the

reasons for different levels of GHG emissions between the WASCs.

INSERT TABLE 4 ABOUT HERE

Consideration of Table 4 provides some interesting insights into the importance of these contextual

factors in explaining the relative level of GHG emissions of the WASCs. Differences in emissions

appear to be related to legitimate differences in the operating characteristics which require WASCs

to engage in considerably different activities. First, we shall consider Northumbrian, Thames and

United Utilities, as these three WASCs appear to have the lowest emissions per Ml of treated water.

They also all appear to have the highest volumes per km of main. This reflects the reduced need to

transport water and also reduced treatment costs due to reduced leakage as water is transported.

Northumbrian and Thames also have higher volumes of water delivered per connected property.

This can also help to explain their relatively lower emissions, as there is a reduced number of

connections to maintain and less potential leakages at each connection point. Although not the

absolute lowest, United Utilities and Northumbrian also have a relatively low reliance on water from

boreholes and therefore require less energy to pump the water from this type of source. Similarly,

GHG emissions also tend to be somewhat lower for systems that have relatively less pumping in the

actual distribution network. This is to say that systems that are “blessed” with favourable

topography, water source location, and settlement patterns will require less energy and both United

Utilities and Thames benefit from this. Thus United Utilities, Northumbrian and Thames, which are

seemingly the 1st, 2nd, and 3rd best WASC performers on GHG intensity as measured by Ofwat,

21

generally have “favourable” operating environments, as we have seen. But, they also each exhibit

relatively unfavourable operating characteristics for 1 of the 4 determinants of GHG emissions we

have identified here. Thus, there appears to be a complex relationship between these multiple

determinants and between these factors and the actual emissions. Moreover, we note that the signs

of the correlation statistics reported at the bottom of Table 4 also provide evidence of a link

between these contextual factors and Ofwat’s GHG intensity ratio.

A similar mixed story is found for the “worst” GHG emitters according to Ofwat’s water GHG

intensity ratio. Dŵr Cymru, Severn Trent and Southern Water each have some characteristics

consistent with a “challenging” operating environment for reducing emissions, but also exhibit some

operating characteristics which on their own might be considered consistent with a more favourable

operating environment. Thus, despite the WASCs being considered to be “more easily” comparable

(Hopkinson et al., 2000, p. 874) than other industries, it does not appear possible to make valid

comparisons on the basis of a single intensity ratio, which normalises “emissions data with an

appropriate business metric or financial indicator” (Defra, 2013, p. 31). Defra (ibid) continue that

normalising emissions into a single intensity ratio “allows comparison … with other similar types of

organisations”, but our evidence suggest that a single intensity ratio does not make visible the very

complex relationships between legitimate differences in operating characteristics and GHG

emissions. The implication that normalised GHG emissions data can be used to benchmark and

compare across companies downplays this complexity and will not enhance the readers’

understanding of GHG emissions performance.

Our analysis of WASC’s GHG reporting suggests that providing emissions data that will be

comparable over time and across companies will be extremely challenging. Issues related to data

accuracy and data normalisation for company specific contextual factors are particularly daunting.

The final section of this paper therefore discusses what the implications of our findings are for the

future of GHG reporting with particular consideration of the UK government’s legislation.

Discussion and Conclusions

We have seen that the UK Government’s legislation, requiring quoted companies to report their

GHG emissions, has been justified on the grounds that such disclosures enable performance

comparisons to be made over time and across companies. In this paper we contend that the

reported data constructs a GHG emissions reality that is the product of diverse calculative practices

and as such is flawed and partial (Callon and Muniesa, 2005). Many emissions related to an

22

organisation are excluded from the reality constructed (Hines, 1988). As an illustrative example, our

analysis suggests that the GHG emission data reported to Ofwat by the WASCs for the period

2007/8-2010/11 was of questionable reliability, accuracy, consistency and completeness. In addition,

we argue that the normalisation practices adopted are problematic from a GHG governance

perspective. Moreover, the suggestion that the calculation and reporting of such data may have the

power to make “people think and act” (Hines, 1988, p. 257), to evaluate and decide (Callon and

Muniesa, 2005; McKenzie, 2009) is concerning.

Here, we argue that accounting’s power to create reality is especially problematic when reflecting

upon the reporting of GHG emissions data. Unerman and O’Dwyer (2004) characterise accounting as

an expert system that seeks to communicate evidence of future risks or consequences arising from

organisational actions to non-experts. In the case of GHG accounting this is complicated as GHG

emissions are first calculated by GHG experts then appropriated by non-GHG- experts (accountants)

in accounting systems. This appropriation process requires accountants to translate GHG data into

accounting disclosures for non-expert accounting user groups. The results calculated by GHG experts

are mediated through accounting calculative practices before being communicated to corporate

stakeholders. The GHG scientific reality is further transformed by accounting calculative practices

into a hybrid reality emerging from the confluence of two sets of expert calculations. However, as

the accounting calculation is later in the transformation process it can exert a greater influence in

the results that can enter the worlds of account users. This mediation process, however, is hidden

from account users who assume that these GHG numbers inherit the same levels of external trust

and legitimacy as other accounting numbers.

Accounting represents itself as adopting a modern scientific methodological stance that assumes the

possibility of certainty, universally applicable laws and practices that produce objective, comparable,

consistent evidence expressed numerically and financially. Accounting regulators set very high

standards of representational faithfulness prior to inclusion in accounting reports that very few non-

accounting disciplines can comply with. Within this methodological perspective there is pressure for

accountants to produce normalised, legitimate evidence that enable the construction of linear

predictive future narratives for those considered to have an entitlement to this information and not

those who may be impacted on by organisation decisions (Unerman and O’Dwyer, 2004).

In contrast, climate science, accepts the inherent uncertainty, complexity and non-linear dynamics

associated with atmospheric chemistry, ecology and meteorology (e.g. Meinhausen et al. 2009) and

builds these uncertainties into their calculative practices and evaluation of their evidence. Their

awareness of the limitations of their calculative practices means that they avoid predicting with

23

certainty future outcomes recognising the complex interconnected world in which we live. Climate

science’s GHG measurements incorporate probabilities, ranges and any ‘point estimate’ is always

suitably qualified. Their scientific numbers, despite lacking certainty, have made a significant

contribution to policy debates at regional, national and international levels e.g. (IPCC, 2010;

Meinshausen et al. 2009; EC, 2013; Defra, 2013).

National governments have committed to GHG reduction targets despite recognising that there is

contestation over the appropriateness of different GHG measurement protocols and accepting

measurement uncertainties (Defra, 2013). This is in sharp contrast to the UK legislation relating to

corporate GHG disclosures, which does not require the reporting of the completeness, reliability,

accuracy, uncertainty and lack of comparability of their GHG emissions data. Whilst acknowledging

that reporting of reliability and uncertainty may be cumbersome and likely to be “undesirable

among policy makers” (Bowen and Wittneben, 2011, p. 1030), we believe this should be

reconsidered. Data reliability and accuracy bands are informative and so should be reported

alongside the GHG emissions data. Not disclosing such information implies a level of certainty and

accuracy such that a reader may perceive it to be an objective truth from which they construct their

predictions of future emissions, based on problematic trend identifications.

Corporate GHG accounting can therefore be seen as an attempt to bridge time and space, presenting

an intelligible narrative of predictable future but undesirable consequences of GHG emissions and

how to avoid unacceptable climate risks (Unerman and O’Dwyer, 2004; McKenzie, 2009; Lohmann,

2009; Kolk et al. 2008). However, the detachment of GHG emissions from our planetary atmosphere

relies on many contested expert calculative practices. This creates the potential of problematic

interpretations by non-experts that can create difficulties in these numbers being meaningfully re-

assimilated in diverse decision-making practices.

We also suggest that companies applying a consistent methodology to emissions from within

consistently defined (and complete) reporting boundaries would be important in enabling GHG

emissions performance comparisons. The UK legislation requires quoted companies to disclose their

methodology, but we suggest that comparability will be enhanced if a more prescriptive approach is

adopted. Allowing companies to choose their methodology and reporting boundary is likely to

diminish the comparability of reported GHG emissions information (Brander and Ascui, 2015;

Brander, 2016). In saying this, we acknowledge that, whichever methodology and boundary is

chosen, it will construct a flawed calculation, but a consistent calculation seems preferable to

allowing diverse practices to continue, especially if companies do not adequately disclose their

methods.

24

The normalisation requirement within UK GHG emission reporting is also an area of concern,

particularly when no intensity ratios are specified. More importantly our analysis shows that even in

a single industry the construction of a meaningful basis for normalisation is problematic. An

individual company’s GHG emissions are dependent on path dependent operating characteristics

that make such comparisons complex. Failure to consider these operating characteristics has the

potential to result in unhelpful comparisons being made. We demonstrated that comparing GHG

emissions across very similar organisations from a single industry was problematic and that more

meaningful normalisation practices need further reflection and research. Such issues of

comparability would be significantly more problematic when organisations from different industries

are considered by users. Contextual factors such as sector, product characteristics, geographic

location, supply chains and customer networks are all likely to impact upon (normalised) emissions

and so limits the users’ ability to extract a result (evaluation or decision) from the calculation of this

reported data.

The other concern with normalisation is the implied simplification of climate change dynamics and

possible misappropriations arising from the disclosure and non-disclosure of GHG emission numbers.

Overly simplistic normalisation can construct false cause-effect assumptions in the world of the non-

GHG experts, who are unable to re-appropriate or evaluate the evidence, yet in the absence of other

information, consider this evidence as sufficiently reliable to input into their decision-making or

governance processes. Appropriating problematic GHG numbers into these governance processes or

decision-making models could trigger ineffective corrective interventions, increase the risk of climate

change or allow problematic behaviours to perpetuate. The representation of corporate GHG

emissions through normalised, consistent, linear predictive narratives is problematic and conceals

the considerable debate relating to the acceptable errors, levels of certainty and applicability of GHG

methodologies to different decision contexts.

We argue, therefore, that any normalisation calculations should be informed by an understanding of

the complexity of climate change and the critical decisions that need to be taken as part of this

governing process. For example, normalised GHG emissions data tends to privilege the relative eco-

efficiency of corporate actions and conceals issues relating to the eco-effectiveness of corporate

actions based on de-coupling GHG emissions from corporate actions and the corporate imperative to

grow. Similarly, normalisation using units associated with corporate financial performance privilege

the wealthy powerful investing elite and conceal the eco-justice consequences of using our scarce

carbon resources on those living on less than a dollar a day, suffering on a daily basis from the

effects of climate change such as droughts, storms and rising sea-levels.

25

We can only conclude that we find it problematic to imbue corporate reported GHG emissions data

with the same qualities as more traditional accounting information, whilst recognising the criticisms

of the usefulness of financial reporting numbers. Corporate GHG disclosures emerge from a complex

sequence of diverse calculative practices which cannot transcend their underlying limitations (Mol,

1999; Callon and Muniesa, 2005; Cuckston, 2013; Unerman and O’Dwyer, 2004). As such the data

reported is not a ‘faithful representation’ of GHG reality, but rather a flawed and partial

representation of a constructed reality that emerge from a series of black boxes. This does not,

however, imply that we are suggesting that GHG emission data reporting should be discontinued

due to its inability to be considered ‘decision-useful’ from a conventional accounting perspective.

Given our understanding of the consensus from climate scientists we feel passionately that GHG

reporting does have a role to play in reducing climate change, but not in the conventional accounting

context of improving investors’ ability to make better, but still self-interested, decisions.

We would strongly argue, however, that there is a need for GHG emissions data reporting that

accepts the inherent complexity and does not assume false certainty. We argue that there are other

reasons why GHG disclosures are essential, as they form part of other regulatory/disciplinary

practices, not considered important by accounting standard setters. These include creating wider

knowledge of GHG emissions relating to an organisation in order to enact self-discipline and self-

governance or social control by proxy. GHG disclosure that enable dialogic engagements with key

stakeholders or create the possibility of being held to account, now or in the future. We believe that,

in order to help create wider knowledge and to enable dialogic engagements, companies need to

provide further narrative reporting on the complexity of factors driving their emissions. Such

narrative disclosure, which is cognizant of the inherent uncertainty and complexity of GHG emissions

has the potential to enhance the climate change literacy of both internal and external stakeholders.

The extent to which such engagement can influence corporate decision-making will further

determine the extent to which such disclosures can enhance a company’s accountability to its

stakeholders (Cooper and Owen, 2007) for its GHG emissions and their impacts.

The importance of making visible the invisible and negative consequences of GHG emissions is

widely recognised with the urgency of future climate change. This urgency necessitated the use of

calculative practices that contain recognised levels of uncertainty, but are regarded as sufficient for

the present governance of GHG emissions reduction. Holding nations, public sector organisations,

companies and individuals to account for their use and abuse of carbon is a critical stage in GHG

26

governance. Making visible GHG emissions through scientific and accounting calculations is an

important part of GHG governance regimes.

GHG emissions accounts need to be linked to critical decision contexts that use this information to

inform other decisions that possibly change future GHG emissions. These decision contexts include

corporate self-discipline, off-setting, regulatory sanctions, dis-investment, removal of license to

operate, social sanctions, consumer or creditor boycotts. However, the nature of these decision and

decision evaluation processes are underspecified and insufficient consideration given to the

potential relevance of GHG accounts in decision contexts concerned with eco-efficiency, eco-

effectiveness or eco-justice.

However, our understanding of the nature of GHG governance mechanisms are also under-

developed and often disconnected from GHG measurement protocols and the information needs of

these different decision makers. Despite the global programmatic discourse on the need to reduce

GHG, there is no consensus at national, regional or corporate levels as how to achieve these

reductions or what GHG emission actually means. Yet all these decisions should ideally be made

using the same certain, and normalised measures of GHG, something which is currently impossible

to produce due to the future oriented nature of Global warming potential calculations. The

underlying relationships between GHG emissions and their possible climate consequences are

unable to be captured in a simple linear fashion given the complex and non-linear nature of

atmospheric chemistry and weather systems over time. GHG emission consequences are inherently

uncertain and within the foreseeable future, unknowable at the level of certainty demanded by

accounting standards. Yet corporate GHG accounting calculative practices have a bias towards

simple, linear predictive narratives as a form of time-space distanciation (CPA 933) presented as a

disembodied expert discourse that privilege the decision contexts associated with the worlds of the

consumer and the investor.

However, this problem is made more complex by the additional requirement (derived from

regulators and GHG accounting standards) for normalised GHG emissions in order to facilitate

comparative analysis to inform GHG reduction decision making. Even if absolute measures of GHG

were possible to attribute to individual entities this would not be sufficient from an ‘accounting’

decision usefulness perspective. Normalisation (according to accounting reports) requires creating a

simple linear relationship between a single proxy measure of organisational activity and GHG

emissions. Very little research has explored the possibility of meaningful GHG normalisation and the

problematic values that when trying to force a point measure of emergent complex non-linear

system into a single linear expression of future risks for comparative purposes.

27

We finish by echoing Milne and Grubnic’s (2011) call for further research into the robustness of GHG

emissions data. We have provided evidence based on the GHG emissions data calculated in the

water industry in England and Wales, but we expect further GHG data to become available in the

future. We believe that analysing the calculative practices that create such GHG data as it becomes

available has the potential to both improve practice and policy. We also call for research that

provides evidence as to the extent to which measuring GHG emissions leads corporate management

to take steps to reduce their company’s emissions and makes GHG emissions visible to stakeholders,

civil society and governments. We suggest that it would be of particular value for future research to

adopt an interview methodology. In-depth interviews with internal stakeholders could focus on the

impacts that GHG emissions reporting has on an organisation’s activities. Further interviews with

stakeholders (including investors, regulators, legislators and environmental groups) are needed to

better understand how such accountability can instigate change.

28

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Figures and Tables

Figure 1: Estimated UK Greenhouse Gas Emissions from the Water and Sewage Industry

19901991

19921993

19941995

19961997

19981999

20002001

20022003

20042005

20062007

20080

250

500

750

1000

1250

1500

1750

2000

2250

2500

2750

3000

3250

3500

3750

4000

4250

4500

4750

5000

Source: data extracted from ONS (n.d.)

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Table 1: WASC’s annual operational GHG emissions for 2008/9

Annual operational GHG emissions in tonnes CO2e (confidence grade*)According to CRC boundary According to Defa boundary

Anglian 422,500 (A2) 540,500 (A2)Dwr Cyrmu 261,600 (B4) 252,200 (B4)Northumbrian 239,500 (A2) 281,700 (B2)Severn Trent 510,100 (A1) 675,100 (A2)South West 142,100 (A2) 121,500 (A2)Southern 238,300 (A3) 315,300 (C4)Thames 650,700 (B3) 848,100 (B3)United Utilities 426,300 (B2) 575,600 (B2)Wessex 138,300 (B2) 179,900 (B2)Yorkshire 329,100 (A2) 434,100 (A2)Source: Ofwat (2009, p. 37)

* A = “sound”; B = “with minor shortcomings”; C = “Extrapolation from limited samples for which grade A or B data is available”; 1 = ±1%; 2 = ± 5%; 3 = ± 10%; 4 = ± 25%; and 5 = ± 50%) (Ofwat, 2010b, p. 92).

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Table 2: CRC GHG Emission boundaries 2008/9 – 2010/11

2008/9 2009/10 2010/11Lower Reported Upper Lower Reported Upper Lower Reported Upper

Anglian 401,375 422,500 443,625 404,449 425,736 447,023 393,187 413,881 434,575Dwr Cymru 196,200 261,600 327,000 250,295 263,468 276,641 251,872 265,128 278,384Northumbrian 227,525 239,500 251,475 220,554 232,162 243,770 206,748 217,629 228,510Severn Trent 504,999 510,100 515,201 492,328 497,301 502,274 494,180 499,172 504,164South West 134,995 142,100 149,205 143,750 145,202 146,654 139,590 141,000 142,410Southern 214,470 238,300 262,130 241,443 254,150 266,858 244,775 257,658 270,541Thames 585,630 650,700 715,770 587,739 653,043 718,347 633,794 667,152 700,510United Utilities 404,985 426,300 468,930 429,581 452,190 474,800 433,487 456,302 479,117Wessex 131,385 138,300 145,215 131,622 138,549 145,476 132,622 139,602 146,582Yorkshire 312,645 329,100 345,555 323,817 340,860 357,903 346,122 364,339 382,556Source: Owat (2009, 2010, 2011) and WASC June Returns

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Table 3: Operational GHG emissions per Ml of treated water 2009/10 – 2010/11

2009/10 2010/11Lower Reported Upper Lower Reported Upper

Anglian 334 352 370 429 452 475Dwr Cymru 362 381 400 380 400 420Northumbrian 242 269 296 246 259 272Severn Trent 339 377 415 366 385 404South West 333 350 368 342 360 378Southern 356 396 436 362 402 442Thames 267 281 295 280 295 310United Utilities 235 247 259 244 257 270Wessex 314 330 347 309 343 377Yorkshire 294 309 324 311 327 343Source: Owat (2010, 2011) and WASC June Returns

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Table 4: Determinants of Water Supply GHG emissions intensity

Source: Authors’ Calculation from Ofwat June Return Data

37