Environmental Accounting, Sustainability and Accountability

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Somnath Debnath

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    Epigraph

    If many small people in many small places change in a small way, the face of the earth changes.

    —An African proverb

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    List of Tables

    List of Abbreviations

    AAAJ

    Accounting, Auditing & Accountability Journal

    AAUs

    allowable accounting units

    ABC

    activity-based costing

    ABM

    activity-based management

    AHP

    analytical hierarchy process

    AICPA

    American Institute of Certified Public Accountants

    AIS

    accounting information system

    ASC

    Accounting Standards Codification (prevalent in the US)

    ASQ

    American Society for Quality

    BREEAM

    Building Research Establishment Environment Assessment Method

    BRIC

    Brazil, Russia, India, China

    CAGR

    compound annual growth ratio

    capex

    capital expenditure

    CASBEE

    Comprehensive Assessment System for Built Environment Efficiency

    CBA

    cost–benefit analysis

    CDI

    city development index

    CDM

    Clean Development Mechanism (as defined by the Kyoto Protocol)

    CDP

    Carbon Disclosure Project

    CER

    certified emission reduction (units)

    CERES

    Coalition for Environmentally Responsible Economies

    CIP

    construction-in-progress

    CMA

    carbon management accounting

    COP

    Conference of the Parties (to the UNFCCC)

    CSI

    city sustainability index

    CSR

    corporate social responsibility

    CVP

    cost–volume–profit (analysis)

    DOI

    diffusion of innovation

    EASO

    environmental assessment of sites and organizations

    EBITDA

    earnings before interest, taxes, depreciation and amortization

    ECA

    environmental cost accounting

    ECEA

    environmental capability enhancing asset

    EEA

    European Environment Agency

    EF

    ecological footprint

    EIA

    environmental impact assessment

    EICI

    eco-intensity change index

    E-LCC

    environmentally sensitive life-cycle costing

    EMA

    environmental management accounting

    EMAS

    Eco-Management and Audit Scheme (EU)

    EMS

    environmental management system

    ENRAP

    Environmental and Natural Resources Accounting Project (Philippines)

    EPA

    Environmental Protection Agency

    EPC

    engineering, procurement and construction

    EPIs

    environmental performance indicators

    ERP

    enterprise resource planning

    ESI

    environmental sensitivity index

    ETP

    effluent treatment plant

    ETS

    Emissions Trading Scheme (EU)

    EU

    European Union

    EUA

    European Union Allowance (EU)

    FASB

    Financial Accounting Standards Board (of the USA)

    FCA

    full-cost accounting

    FDF

    Finnish Defence Forces

    FIFO

    first in, first out

    GAAPs

    generally accepted accounting principles

    GBTool

    Green Building Tool

    GCV

    gross calorific value

    GDP

    gross domestic product

    GE

    General Electric (company)

    GHGs

    greenhouse gases

    GRI

    Global Reporting Initiative

    GSCM

    green supply chain management

    HACCP

    hazard analysis and critical control points

    HDI

    human development index

    HERS

    Home Energy Rating System

    HFC

    hydrofluorocarbon

    HLRW

    high-level radioactive waste

    HR

    human resources

    HSD

    high speed diesel

    IAS

    International Accounting Standards

    IASB

    International Accounting Standards Board

    IASC

    International Accounting Standards Committee

    ICAC

    Instituto de Contabilidad y Auditoría de Cuentas

    IFAC

    International Federation of Accountants

    IFRS

    International Financial Reporting Standards

    IIRC

    International Integrated Reporting Council

    IOA

    input–output analysis

    IPCC

    Intergovernmental Panel on Climate Change

    ISO

    International Organization for Standardization

    IMF

    International Monetary Fund

    IMU

    Institut für Management und Umwelt (Germany)

    IUSIL

    international urban sustainability indicators list

    IWM

    integrated waste management

    JI

    joint implementation (as defined by the Kyoto Protocol)

    LA21

    Local Agenda 21

    LCA

    life-cycle analysis

    LCC

    life-cycle costing

    LEED

    Leadership in Energy and Environmental Design

    LFG

    landfill gas

    LIFO

    last in, first out

    LLRW

    low-level radioactive waste

    LPI

    living plant index

    LULUCF

    land use and land use changes and forestry

    MCA

    multi-criteria analysis

    MCDA

    multiple-criteria decision analysis

    MCDM

    multi-criteria decision-making (technique)

    MDA

    multiple discriminant analysis

    MES

    manufacturing execution function

    METI

    Ministry of Economy, Trade and Industry (Japan)

    MFA

    material flow accounting (economic)

    MFCA

    material flow cost accounting

    MIS

    management information system

    MOE

    Ministry of Environment (Japan)

    MSW

    municipal solid waste

    NAMEA

    National Accounting Matrix including Environmental Accounts

    NPOs

    non-product outputs

    NPV

    net present value

    OI

    organizational innovation

    opex

    operational expenditure

    PDCA

    plan–do–check–act (cycle)

    PEST

    political, economic, social and technological (framework of analysis)

    PESTEL

    political, economic, social, technological, environmental and legal (framework of analysis)

    PFC

    perfluorocarbon

    PIOT

    physical input–output table

    PM

    particulate matter (in pollution)

    PMO

    project management office

    ppm

    parts per million

    PT

    process theory (of innovation)

    PV

    present value

    QR

    quality control

    R&D

    research and development

    REA

    resources, events, agents (model)

    ROI

    return on investment

    SAM

    sustainability assessment model

    SASB

    Sustainability Accounting Standards Board

    SDI

    sustainability development index

    SEA

    social and environmental accounting

    SEAR

    social and environmental reporting

    SEC

    Securities and Exchange Commission (of the USA)

    SEEA

    System of Ecological and Environmental Accounting

    SMA

    strategic management accounting

    SMEs

    small and medium enterprises

    SNA

    System of National Accounts (as defined by the UN)

    SSN

    space surveillance network

    SWOT

    strengths, weaknesses, opportunities and threats (as framework for analysis)

    TBL

    triple bottom line

    TCA

    total cost approach

    TCO

    total cost of ownership

    TOC

    theory of constraints

    TPD

    tonnes per day

    UNCSD

    United Nations Commission on Sustainable Development

    UNDSD

    United Nations Division for Sustainable Development

    UNEP

    United Nations Environment Programme

    UNFCCC

    United Nations Framework Convention on Climate Change

    UOM

    unit of measurement

    USA

    United States of America

    USEPA

    United States Environmental Protection Agency

    WBCSD

    World Business Council for Sustainable Development

    WI

    well-being index

    WRI

    World Resources Institute

    WTE

    waste-to-energy

    WTP

    willingness to pay

    Preface

    Almost a decade ago, I embarked on the journey of understanding how accounting sciences can contribute to environmental concerns and the planetary well-being. Quite frankly, I found these concerns to be challenging to the existing paradigms of accounting sciences and the secure world of isolated views that the financial data and information offer. More so, because the acuteness of accounting framework is inherent to its design, and perseverance to uphold the acuteness is critical to an accounting system. Critical theorists have postulated this as inherent limitation of accounting, which I firmly believe is not challenging to the accounting language per se, but to the viewpoint within which an accounting framework is institutionalized. This, in all fairness, was the main reason why I delved deeper, to establish how the chosen viewpoint restricts the framework from engaging in a conversation meaningful beyond its central theme and eschews innovativeness of outlook, preventing the framework from examining business conduct in unconventional ways.

    Another perspective is that the fickle nature of our collective wisdom has lauded some of our long-standing institutions—industries and businesses—for contributing to the progress of civilization and improving our lives, often decorating them and showering accolades upon them, only to denounce them a moment later for not being responsive enough to environmental and societal challenges. If the new societal order expects business enterprises to be addressing some of these concerns, we need to clearly convey our expectations for them to evolve a mechanism to demonstrate their engagement. This contrasts with the focal expectation of markets that only reward superior economic performance. Accordingly, rendition of sustainability as a practicum that grounds our hopes in industries, I honestly believe, mismatches what we collectively intend to achieve even before deliberating how we want to achieve that.

    Within these and other complexities, this book explores collective viewpoints that can serve as a reference point through which business and accounting advances towards sustainability can be looked at, and questions whether environmental view can be translated to a unique accounting theme to reflect stakeholders’ expectations and capture firm-environment exchange by relying on certain common rules of engagement that can pave the way for its practical adoption.

    Based on the expansion of knowledge in the ecological sciences and our ability to identify the anthropocentric impacts on ecology, isolated boundaries of accounting considerations, labelled as dimensionality that frames the accounting sciences, can contribute to the evolution of ecological accounting, freeing accounting language from being subservience to the economic theories and from its morphic resonance to financial accounting, to let its application grow beyond the boundaries of business and economics. More than the pragmatic orientation of such a solution, I believe, any human creation can surpass the bounds of time and space so long as it can convey some eternal values or be a part of them. This is where the need for accounting to be grounded in accountability arises—beyond the confines of an accounting process answerable to a few (owners and third parties) and help transcend individual businesses to enable the corporate character they need to be accountable to. Wishful thinking, should I say?

    This book is based on contemporary advances in research that are yet to achieve a firm theoretical grounding while leveraging concepts from different disciplines that I sincerely believe would be the future ingredients of our collective thinking as well. I earnestly hope that the concepts explored and evolved here will engage readers and spark off the requisite discussions within businesses, academia and society. At the same time, I accept responsibility for any mistakes, lapses and omissions, such as disregarding any prominent areas of research and scholarship that may be relevant to the topic but have not been covered due to lack of relevance to the specific line of arguments presented in this book. I look forward to hearing from you—your feedback, thoughts, criticisms and suggestions.

    Acknowledgements

    I wish to acknowledge the contributions of the learned minds and institutions that have furthered the human quest for knowledge, not to mention all individuals who helped me appreciate the interconnectedness of everything we know as life!

  • Appendix: Mathematical Modelling of Complex Waste

    Industrial growth and the human population boom are pushing up the global demand for energy. With less than optimal use of regenerative resources to support this demand, our dependency on coal as the basic fuel source is likely to grow exponentially. This would also contribute to the growth of coal combustion waste (CCW). However, the environmental impacts of CCW—commonly referred to as fly ash (FA)—and its disposal practices have camouflaged the hidden costs and social and environmental externalities into insignificance. Moreover, studies covering the externalities of coal-based thermal power plants have been sensitive about the externalities related to coal mining, transportation and consumption but with less inclusiveness towards the externalities of FA. The same is the case in the extant literature where emphasis on mitigating risks associated with FA is miniscule as compared to the life cycle of coal (for example, Epstein et al. 2012; National Research Council 2010; Xiaoye et al. 2013). FA is also not classified as hazardous waste, though its revised status of ‘special waste’ accorded in 2010 by the US Environment Protection Agency (USEPA) has brought attention to the scale of the impacts it produces (EPA 2010), but this is insignificant and mostly muted, a fait accompli in the life cycle of coal.

    FA and its Behaviour

    Fly ash (FA)—a commonly occurring residue during the burning of coal—is a harmful and chemically complex, quantitatively significant by-product produced by coal-based thermal power plants and is one of the coal combustion residuals (CCRs). Here are some statistics: Presently, 70–75 per cent of the installed capacity of India (90,000 MWe) for electricity generation is from coal-based thermal power plants, same as in the USA (National Research Council 2010), which produces 80–100 million tonnes of FA per year. The coal in India is generally of poor grade and produces 40 per cent of fly ash on burning (Asokan et al. 2005; Shamsad et al. 2012).

    The uniqueness of FA lies in its ability to fragment into its constituent elements upon disposal, which ultimately increases the concentration of different metal and non-metal compounds in the benign environment. Main components of FA are oxides of silicon, aluminium, iron and calcium, with lesser amounts of magnesium, sulphur, sodium and potassium elements. Other metals and metallike elements are also found in trace quantities—arsenic, cadmium, beryllium, tantalum, nickel, manganese, chromium, selenium, zinc and other metals (Rowe, Hopkins and Congdon 2002). While the literature views FA as a serious environmental concern, the common disposal practice of accumulating it into ash banks and ponds in dry or slurry form has not evolved significantly (Asokan, Saxena and Asolekar 2005; National Research Council 2010). The ash banks and surface impoundments may use a clay liner, polyliner or even no liner in some cases, as the disposal rules for developing ash banks have not been consistent across regions and countries. This brings us to the contamination and environmental impacts that FA produces upon disposal.

    FA and soil contamination

    The acreage of land used for an ash pond or dump would remain a wasteland for a considerable period of time due to the excess of metal contaminants. Although the lined ash pond is relatively more prevalent in the US, lack of a similar practice in developing countries such as India can be attributed to higher acceptance of FA's supposedly benign nature (Sushil and Batra 2006). In either case, research is yet to explore the regeneration of land covered under ash banks, which is practically of no use for a long period of time. The available literature is divided in its opinion on the soil contamination they result in. Scientific experiments have substantiated that the richness of minerals and oxides in FA could potentially improve the mineral composition of soil and that controlled treatment of soil with specific percentages of FA have indicated an increase in soil fertility and yield of crops. The results have been consistent in the range of 20 to 30 per cent of FA being mixed in (Arivazhagan et al. 2011; Pandey and Singh 2010; Sharma and Kalra 2006). However, the chances of metal elements entering the food chain due to their increased concentration beyond the safe limit would need further investigation (Singh and Pandey 2013).

    Water contamination and impacts on aquatic organisms

    The contamination of flowing water due to FA slurry or sludge has not been adequately covered in literature, other than their leachating and accidental release into aquatic bodies. Studies covering the contamination of water bodies through its leachating into the ground have covered multiple pathways such as surface run-off and underground leachating (Singh et al. 2007). Studies have also shown that the proximity of the water bodies to the ash pond is a factor in possible contamination of water and aquatic life (Prasad and Mondal 2008; Rowe, Hopkins and Congdon 2002; Ruhl et al. 2012). As compared to large-volume water bodies, small lakes and water bodies show earlier and larger degrees of contamination from CCR effluents, but site-specific studies are needed to cover the specificity of the damages (Ruhl et al. 2012).

    Groundwater contamination

    Groundwater contamination is one of the environmental damages that can be traced to the leachating of the metal elements of FA. The extent of groundwater contamination would depend on variables like distance to the receptor well, average depth of the groundwater table,percentage of the population living near the contaminated well, site-specific variables such as the type of ash (conventional CCW, CCW disposed of along with coal refuse, FBC waste), type of liner used (no liner, clay liner, composite liner), soil texture, aquifer type, ground-water temperature, climate, hydrological properties of the region, surface-water type, flow conditions, etc. (EPA 2010). Contaminated groundwater could result in the loss of water resources for humans, while ingestion of water from contaminated sources could result in an increase in health issues, which would depend on the type of contamination (e.g., arsenic, lead or mercury contamination) as it might not always be linked directly to the source.

    Health issues for humans

    The health issues due to FA exposure for the human population could originate from different routes such as drinking contaminated groundwater or surface water, ingestion of contaminated food items exposed to such contaminants, and direct contact with the contamination on the surface. However, such risks would also depend on the size of the population in immediate vicinity of the ash pond, reference doses (RfDs) of different elements, and carcinogenic compounds that humans are constantly exposed to from different media, such as arsenic (V) and lead sedimentation or selenium (IV) surface run-off (EPA 2010).

    These impacts indicate a complex web of ecological interconnections and the need to understand the functioning of the ecosystem better.

    Economic and Environmental Policy Evaluation Techniques at the Sectoral and Regional Levels

    Direct and indirect valuation methods have been a part of environmental economics to assess the externalities of waste and could be used to improve objectivity in decision-making while dealing with social policies. For directly identifiable impacts of waste, the hedonic price method, travel cost method or contingent valuation method is generally used, whereas indirect methods include using replacement costs or the preventive and human capital approaches. While direct methods use cost as an indicator of fully functional but unavailable ecosystem benefits, indirect methods are dependent on the prices of environmental gains and people's willingness to pay for them and enjoy the benefits. These methods have been used to assess losses at the sectoral and regional levels—for example, estimating losses due to the Prestige oil spill (Garza et al. 2009), healthcare costs due to air pollution in a number of studies (Dorbian, Wolfe and Waitz 2011; Rabl and Spadaro 2000), and environmental impacts due to climate change (Anthoff and Tol 2010; Ortiz et al. 2011). The impacts are established using market-oriented views and by considering the ecosystem services, which cautions the importance of considering ecosystem losses not just scientifically, but also in ecological terms, and move away from business-as-usual approach (Atkinson, Bateman and Mourato 2012; Schultz et al. 2012; Spash 2008).

    Advances in research on ecosystem valuation have been based on the scientific unravelling of ecosystem services that capture partial or complete loss of such services. While the underlying theories rely on ecosystem services to develop a scientific temperament in making collective choices about the ecosystem, by highlighting the direct and indirect uses of ecosystem services for decisive, technical and informational needs (Laurans et al. 2013), the addition of health and non-use values to the economic ones is based on the consideration of these being part of the ecological wealth of a region, which might suffer in time due to the transfer of values to other spheres and degradation due to the pursuit of anthropocentric policies. Together, these values develop better coverage of the ecosystem wealth of a region (Atkinson, Bateman and Mourato 2012), where the diverse nature of use, non-use, ecological and anthropological values could be represented as part of the cultural, provisioning, regulating and supporting functions of ecosystem services (Ojea, Martin-Ortega and Chiabai 2012). Although complex and data-intensive, Gómez-Baggethun and Ruiz-Pérez (2011) believe that ecosystem valuation could relieve policy choices of the arbitrary economic valuation representing commoditized ecosystem services, which would defeat the very purpose of value pluralism and the deliberative judgement process that needs improvement beyond the stated preferences, expert opinions and cost–benefit analyses currently relied upon.

    Methodological Complexities in Estimating the Life Cycle and Externalities of FA

    At the crossroads of economic and scientific understanding, the problem of FA and its externalities is peculiar and complex. While economic and demographic studies can help in evaluating land resources required in developing new FA ash banks or maintaining the existing ones, they could also help in estimating the social willingness-to-pay in avoiding air and water contamination within the region and saving ecological resources. Since FA gets fragmented into its constituent elements, which are also found within benign nature, delayed higher-order impacts are not captured. While prevailing economic methods (like the travel cost method and the stated preferences method) could generate an arbitrary numéraire to estimate the economic burden, this is just arbitrary. As a result, the policy choices in managing waste would lack a complete view of how it might impact the biosphere. This gives rise to the debate of selecting science- versus market-based policy alternatives—market-based choices could be easier, but also reflect a lack of knowledge regarding ecosystem services (Sagoff 2011).

    In comparison, cost-based methodologies are dependent on the causal relationships of the involved entities, which follow predetermined relationships. Accordingly, the existing methodologies suffer in cases where causality is replaced by the probabilistic nature of interactions. Translating the problem to the domain of waste management, if waste cannot be traced back to the origin after it has reached the common pool, costing methodologies cannot be applied directly to second- and higher-order impacts and costs for remediation or restoration. While in the available literature, the TCA approach includes multiple perspectives to incorporate hard-to-measure impacts into alternative decisions—some of which has been experimented with as part of the lifecycle design of automobile parts by Carlsson (2007, 2009)—only limited research is available to apply it to the realm of waste management, including a void in physical modelling to represent the behaviour of elements and subsequent impacts.

    Use of Decision-Tree Analysis to Trace the Flow of Environmental Aspects

    Due to the shortcomings of the different methods mentioned in the previous section, and their failure to move beyond the first-order impacts of waste, this chapter has used multi-criteria analysis (MCA) to model the waste cycle; its externalities are evaluated using the TCA framework, which can extend to multiple cost chains and include direct, indirect, contingent, hidden and contextual externalities. The literature references MCA and its diverse methodologies for decision-making with regard to the improvement from a one-dimensional approach—that is, reducing the decision variables to the singular dimension of monetary or economic considerations—to consider multiple dimensions such as stakeholders’ opinions, business objectives, qualitative gradations, and so on. MCA is used here as the umbrella term to describe a set of approaches that can handle multiple criteria in the decision-making process (Belton and Stewart 2002). MCA can deal with quantitative as well as qualitative data such as ranks, choices and opinions (subjective criteria) of decision makers. Several theories in MCA have extended it to include fuzzy sets to accept decision inputs in a natural language and handle the subjective views of decision makers (Kannan et al. 2013).

    A review of the available techniques suggests that MCA can cover three patterns of logic: (a) simple ordering, (b) goal setting or goal seeking and (c) value maximization, including when the criteria or parameters to arrive at the decision might not be well-defined (Gamper, Thöni and Weck-Hannemann 2006). A recent review of MCA by Velasquez and Hester (2013) has compiled a list of popular methods that include multi-attribute utility theory (MAUT), analytical hierarchy process (AHP), fuzzy set theory, case-based reasoning, data envelopment analysis (DEA), simple multi-attribute rating technique (SMART), goal programming (GP), preference ranking organization method for enrichment evaluation (PROMTHEE), elimination and choice expressing reality (ELECTRE), and so on. Interested readers can refer to the standard texts for further reading on these techniques. It has been opined in the literature that MCA can offer methodological refinement beyond CBA and improve decision-making that involves complex problems in the sustainability arena (Bebbington, Brown and Frame 2007; Gamper, Thöni and Weck-Hannemann 2006). For instance, MCA has been explored to model the behaviour of FA upon its disposal, which can be extended to include externalities of material recycling and waste management as part of the decision-making process.

    MCA is based on the principle of disaggregating a complex problem into a set of decisions that results in building a decision tree which follows expected utility rules to navigate through the underlying course of action and trace the ultimate outcome of the process, where the likelihood of an outcome is based on the weighted probabilities of individual actions along the chosen path (Barzilai 2010). For a generalized modelling of the waste cycle, its movement can also be traced over different receptors. Accordingly, this would include the impacts of FA and its elements on air, soil, humans, and so on. Based on the flow of the aspects in an environment, their corresponding impacts could be modelled as part of a classification (to accept a qualitative variable: presence/absence or yes/no) and regression tree (if the variable is quantitative). Accordingly, the waste movement is traced till it completes the branching needed to study the desired impact for a select endpoint and can handle aspects across multiple pathways following the real behavior of aspects (Sorvari and Seppälä 2010). Using LCA, a probabilistic flow model can be developed, which can offer some insights into the average level of exposure for different metallic and non-metallic compounds degrading through different levels of receptors. This would offer theoretical grounds to consider the average deposition rate along a pathway. As LCA is a tool to handle physical data, this would not improve economic decision-making process, and necessitates use of the TCA approach to consider potential economic impacts.

    TCA to Generalize Externalities of FA

    In the absence of a standardized unit of service to represent ecological considerations impacting any process (Atkinson, Bateman and Mourato 2012), TCA can leverage a decision-tree model to develop social costs to remediate or mitigate environmental impacts generated by different choices in managing waste. Instead of using a single model to approach the problem, TCA follows multiple approaches to measure different types of impacts. Accordingly, the nature of the costs and their evaluation process could follow different approaches, which would depend on the nature and scope of decision-making. Following from the decision-tree model, costs associated with an impact at an end point i would be sum of the costs associated with the node multiplied by the share of the burden corresponding branches would have to carry. For example, if the actual dispersion of an aspect or element followed multiple pathways, a child branch would bear a proportionate share of the costs. This follows the basic principal of the sum of shared weights at the parent node being equal to one. This linearity in modelling helps in avoiding circular references and overloading a branch. Similar to the objectives in the tree, the flow of aspects is characterized as essential, understandable, operational, non-redundant, concise and preferentially independent (Franco and Montibeller 2009). So, the overall cost function for a policy impact y at an endpoint i will be the sum of costs along the branches C1, C2, C3… with a corresponding share s1, s2, s3…:

    P(y)=Ci×si forall i's

    The equation can be further generalized as the total cost function Z:

    Z=x×y×z+α+β

    where

    x =

    likelihood of the event i due to element j,

    y =

    probability of the event creating an impact k,

    z =

    cost l to remediate/abate impact k due to event i and element j,

    α =

    opportunity losses due to non-recyclability of waste,

    and β =

    regulatory costs.

    The next section studies a case from India to explore the fitness of the model.

    Theoretical Evaluation of the Proposed Model: A Case Example

    This section analyses the operational feasibility of the proposed model. Policy choices can be based on select endpoints and can be mapped by using decision-tree analysis, whereas the imputed cost of externalities can reflect costs to be incurred for remediation, abatement and alternate arrangements within the impact zone. This removes the need to restrict the model to any specific type of cost—say, economic costs—and explores the nature of the problem from multiple angles to enter into meaningful dialogue regarding policy choices. Table A1 reflects the dispersion of FA through different pathways and the corresponding impacts.

    Part A: Risk-based cost modeling

    For a select endpoint (from Table 1), there could be multiple rounds of cost evaluation involving data sets that could cover each element as part of a given region. The cost formulations would follow, evaluating the total cost of impacts along any particular pathway. The endpoint analysis will, accordingly, help in assessing damages to different ecologic receptors, including humans. For example, site-specific studies can estimate cost of land lost, impact on water resources and health of inhabitants in the nearby areas, which could also include making alternate arrangements to provide water (in case of water contamination), and so on. While an actual on-site study could be one of the ways to formulate and evaluate these and other impacts and generate estimated losses in part, for example, the studies by Rowe, Hopkins and Congdon (2002) and Ruhl et al. (2012) have assessed site-specific damages using different risk factors. However, this type of study would result in an isolated statement of impacts for a given region, where data specificity would reduce the generalizability (Box 1). Another way could be to develop a framework solution that might be representative of each of these parameters such that it could serve as a library of impacts, reflecting the overall condition of ecological damages—for example, the standard risk assessment models developed by EPA (2002, 2010), which are based on the study of risk assessment of different elements of FA for human and ecological receptors, covering a wide array of FA deposition sites in the US.

    Table A1 Dispersion of FA aspects in decreasing order of specificity
    LevelPrevious level →Impact
    Level 1Fly ash dumped in pond/mound/disposal
    Level 2aLevel 1Surface-to-air dispersion (pathway 1)
    Level 2bLevel 1Surface leaching (pathway 2)
    Level 3aLevel 2aHuman respiratory issues due to excess FA exposure (#)
    Level 3bLevel 2bGroundwater contamination
    Level 4aLevel 3bAquatic/water body contamination
    Level 4bLevel 3bWelfare costs due to consumption of contaminated water (#)
    Level 5Level 4aProblems to fishes, etc.
    Level 6Level 5Human consumption of infected fishes (#)
    Higher human/ecological impacts due to further degradability

    Note: (#) = endpoints.

    Once the concentration of an element increases in the ambient environment so that it breaches the ecological response barrier (of countering it), it would increase the risk level, which can be identified through a composite risk number. The composite risk number (corresponding to each element) is dependent on the underlying threat-assessment models that would need to consider multiple factors, such as the ecotoxicological profile of the element, area covered around the source site, geographical and hydrological profile of the area, and various other elements. The aim of this study is not to assess the verifiability or dependability of the model, which would be a study in itself, but to indicate that the comprehensiveness of risk numbers could be a generalized solution to externalities. Accordingly, the cost function Z can be reframed (from equation A.3) to represent the social and environmental costs of chosen policy option as:

    Z=[(Assessed risk level for first element at X1level×social cost to averse the risk at same level)+(second element)+(third element)++(nth element)],for a given pathway/liner/ashfor a given pathway/liner/ash

    1 X here represents risk assessment level which can differ from average, median, or percentile level, to the site-specific ones. The cost to averse for each level should match it at the same level of specificity.

    =Riplz×Siplz

    where

    R= risk value for an element, e.g., risk of increased level of arsenic to produce carcinogenic or non-carcinogenic impacts in human receptors,

    and S = social costs to abate the risks at the matching level, summed over ‘i’ elements, ‘p’ pathways, ‘l’ liners and ‘z’ ash type

    The formula is recursive and could contain information across elements, pathways (e.g., surface-to-water, groundwater-to-drinking, etc.), liners (no liner, mud liners, polythene liners, etc.) and ash types. To be noted, the cost build-up can add elements like cost of avoidance, cost of abatement and/or restoration costs, and other methodologies (using economic as well as non-economic costing and valuation methods) to arrive at the total cost of any specific damage.

    Part B: Replacement resource cost model (to explore opportunity cost of waste)

    The opportunity cost of an economic decision reflects the cost of lost opportunity (α from equation A.3) in excess of what is being returned by the prevailing arrangement of resources and is an important parameter in decision-making that represents loss due to the prevailing arrangement of resources. Use of FA in industrial applications represents an opportunity to save the cost of resources that are otherwise being used and should be captured as part of policy choices by using a replacement resource model. For example, FA could be an active ingredient in Portland pozzolana cement (PPC), concrete mix, concrete, bricks, wood-substitute products, soil stabilization, road bases and embankments, land reclamation, and so on, and could save significant monetary and material resources in an economy (Asokan, Saxena and Asolekar 2005; TERI 2006) and can be added to the policy choice.

    So, Z’ (equivalent cost of lost opportunity)

    =[(Waste being used in a productive mix×cost of the finalproduct per unit)×100/(Total quantity of input mix)][Cost of transportation of FA+Cost of regulations(if any)]

    FA and Hidden Costs

    The results from sample evaluation in different cost functions indicate that while industries have claimed ₹;150–200 per tonne of FA to be the average cost of its disposal (TERI 2006), an estimate of its externalities works out to be close to ₹;1,150 per tonne if considering just two externalities—that is, the cost of cleaning water contamination and average healthcare costs due to burning of coal—indicating that the minimum increase is five- or six-fold of the economic costs (at the present level of its industrial uptake). On the other hand, resource replacement model adds opportunity loss (at ₹;211 per tonne of FA) being incurred by not improving its uptake, even when FA remains (mostly) a free-of-cost resource to industries. These estimates can be improved further by bringing in other time-delayed environmental and social improvements as a part of the chain of events. As compared to that, risk-based modelling shows carcinogenic healthcare costs are around ₹;2,350 per tonne per year for arsenic contamination alone, assuming median healthcare costs of ₹;66 thousand at the 90th percentile for India (which is miniscule compared to the USD 90,000 per cancer patient per year in the US). If we consider exposure cost for a population of 100,000 alone, this would result in ₹;14,100 per annum in healthcare costs in fighting carcinogenic arsenic poisoning alone, which is being borne by the social infrastructure today.

    With roughly 100 million tonnes of FA getting dumped per annum in ash banks in India and where 400,000 acres of land are already under ash bank, there is an opportunity for the policymakers to work towards policy initiatives to improve the industrial uptake of CCW and fund research efforts to build data banks on its utilization, epidemiological case studies, healthcare impediments, and scientific experiments covering the study of its lab- and site-specific behaviors. Savings could also add ₹;41.1 billion per annum of industrial expenditure, currently getting incurred in disposal activities and as part of opportunity losses (estimates vary, but its current industrial uptake is between 20 per cent and 50 per cent in India and elsewhere) (TERI 2006).

    Decision-Tree Modeling and Traceability of Higher Order Impacts

    In general, as we move away from first-order impacts, the traceability of second- and higher-order impacts gets diluted and replaced by generalized relations. This progressive generalization of higher-order impacts is time-delayed and limited by our knowledge of ecological complexities. The problem is compounded further due to spatio-temporal variations in the adsorption and dispersion rates of the aspects and their constituent elements, not to mention intra- and inter-generational impacts and the ability of the biosphere to adjust itself to counter the adversaries to maintain balance until a critical level is breached, also pointing to the limits of human cognition. Decision tree-based modelling offers a better view of the causal flow of events and how aspects move through different stage. Instead of using approximations or ignoring the higher-order impacts altogether, a decision tree offers the ability to replicate physical reality, and can be improved over time. By modelling the flow of aspects using a decision tree, the impact of specific policy choices could also be expanded to cover the probabilistic nature of physical realities. Also, in risk-based modelling, the assessment of human and ecological risk numbers would depend on the media concentrations, exposure pathways relevant to a particular medium (EPA 2010), and other factors such as bioaccumulation levels, dose sensitivity and biological reactions of the exposed entity (Rowe, Hopkins and Congdon 2002).

    Here, the risk formulation by the EPA (2002, 2010) has been used as the basis for cost computations at a specific percentile level, only for a particular element—arsenic. For example, arsenic has been identified as one of the cancer-causing elements from FA that has a high risk value (EPA 2010) and arsenicosis or elevated As poisoning could be avoided by multiple methods (Pandey et al. 2011); accordingly, the social costs to avert the cancer risk due to arsenic poisoning has been matched to the corresponding risk value at the 90th percentile level. Still, there could be region-specific variations as part of the collected data points.

    As mentioned earlier, there is insufficient information available on the standardized abatement costs, cost of avoidance and cost of ecological losses due to these aspects, and accordingly, average values were considered in evaluating the pay-offs along with the probabilities that are yet to be standardized for any region and might not match the risk profiles. This does not take away the fact that lack of data on healthcare costs in the public domain is an issue in ascertaining the preventive or actual treatment costs of malignant diseases like arsenicosis, even where epidemiological studies are sufficient in number—a case in point being arsenic poisoning in Bangladesh and West Bengal (in India), where cost issues have been addressed inadequately (Irfan 2012; Mahmood and Halder 2011; Meij 2003; Smith, Lingas and Rahman 2000; WHO 2000). This indicates the necessity of site-specific studies that would develop representational data to capture parameters and develop quintile-based profiles of risks and costs.

    Last but not least, validation of the model for predictability of costs is not the intent of this exploratory research. Rather, it is to open up discussions on a generalizable framework that would use a scientific approach to adapt uncertainties and probabilistic behaviours of aspects and their impacts. The complexity of ecological problems would soon outgrow the capabilities of the existing methods and this would impact the cost of human decisions. A fusion of tools from different disciplines could be one of the ways to improve decision-making further. MCA, TCA and other methods are expected to generate better insights into complex ecological problems and this research was an exploration in that direction.

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    About the Author

    Somnath Debnath received his doctorate from the Birla Institute of Technology, India, for his contribution to incorporating environmental considerations in managerial accounting. He also holds a master's degree in business administration (MBA) from Walden University, USA, and a master's in technology (MTech) from Swinburne University of Technology, Australia. Debnath is a Fellow of the Institute of Cost and Management Accountants of India, being a qualified Cost and Management Accountant (CMA), and an academic member of the Athens Institute for Education and Research, Greece, and also of the International Engineering and Technology Institute, Hong Kong. He is a senior solutions architect at Zensar Technologies and a former managing principal at Oracle Corporation.

    Debnath's academic interests have been to advance environmental thinking in the field of accounting and in the information sciences, and to support the managements’ need for green information beyond the prevailing business constructs, in turn to enable firms to handle sustainability challenges. This includes greening different business functions. His research interests include diverse areas of business such as the decision sciences, project management, systems analysis and design, requirements engineering, green information systems and grey mathematics. He is also a reviewer of a number of academic journals.

    In addition, Debnath is a technology expert in the ERP technology space—a subject expert, architect, functional consultant and project manager rolled into one—who has consulted for top Fortune® 500 organizations in the last two decades and has supported the industry with business process reengineering and automation needs. To this end, he has been a part of more than 10 full life-cycle ERP implementations and rollouts in different capacities. His latest area of expertise is in cloud-enabled ERP and setting up cloud application centres of excellence for information systems vendors.


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