Resource Futures Program
A Comprehensive Simulation Model Of Energy TransformationsAnd Transfers In The Australian Economy
Doug Cocks, Franzi Poldy and Barney Foran Working Paper Series 00/11
CSIRO Sustainable Ecosystems, GPO Box 284, Canberra ACT 2601 AUSTRALIA
Doug Cocks; Franzi Poldy; and, Barney Foran
CSIRO Sustainable Ecosystems Resource Futures Program GPO Box 284 CANBERRA ACT 2601 The information in this publication is presented in good faith and on the basis that neither CSIRO nor its agents or employees are liable to any person for any damage or loss whatsoever which has occurred or may occur in relation to that person taking action in respect of any statement, information or advice given in this publication.
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© CSIRO 2000
A COMPREHENSIVE SIMULATION MODEL OF MATERIAL AND ENERGY TRANSFORMATIONS AND TRANSFERS IN THE AUSTRALIAN ECONOMY KD Cocks*, F Poldy and BD Foran CSIRO Wildlife and Ecology, Canberra, ACT, Australia ABSTRACT
The paper describes the selection, properties and implementation of a model--Australian Stocks and Flows Framework---for projecting/simulating quantitative changes that would occur in inter-sectoral physical input-output flows and intrasectoral stocks of natural and manufactured capital in the Australian economy under diverse longer-term (50-100 years) contingency or what-if…? scenarios. While the model allows assumptions about most aspects of the future economy to be varied in such what-if… scenarios, the possible developments most commonly explored to date have concerned future consumption and investment levels, future population change, future technologies, future trade and future levels of primary production. Parameters of the future economy that are not deliberately varied in a particular simulation run are set to a sequence of default values compatible with a data base comprising a consistent quantitative description of stock-flow changes in the Australian economy over the period 1941-1991. One planned and now-emerging role for the model is to allow comparison of the bio-physical (biological and physical) consequences, including environmental and throughput consequences, of alternative long-term resource-relevant policies. Several recent applications, including an extensive analysis of alternative population policies are briefly described. A second role for the model lies in its capacity for testing whether candidate (sets of) policies are physically and socially feasible, and to help in their adjustment if they are not. Keywords: bio-physical economy, projections, scenarios, modelling, long term, environmental policy, Australia
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Corresponding author:
[email protected]
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CONTENTS introduction implementation strategy model construction Database Calculators using asff to project the bio-physical economy Constructing a default scenario Tension resolution applications discussion
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Databases and data collections
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ASFF as a source of insights
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ASFF and policy analysis acknowledgements references figures tables
introduction An ongoing dilemma faced by modern industrial economies is that increases in the consumption of goods and services are strongly correlated with increases in energy use (Fig.1) and increases in energy use are strongly correlated with increases in environmental degradation, ie pollution (the accumulation of unprocessed residues) and ecosystem dysfunction/ destruction. This three-way linkage is weakening to some extent in some economies as their production mixes change towards services and as energy/materials-saving technologies and low-pollution technologies emerge. However, it is not just degradation per dollar of GDP (Gross Domestic Product) that determines the overall trend in environmental degradation; it is also the total level of GDP as determined by trends in population and in GDP per head. In principle it should be possible to model the consumption benefits and environmental disbenefits of an economy’s production mix into the future. And, in principle again, it should be possible for a society to choose a preferred alternative amongst diverse ‘tradeoff’ trajectories of consumption and environmental degradation. Not too diverse however. In a capital and energy intensive economy, where you have got to in terms of production mix determines where you can quickly get to---the phenomenon of path dependence. If consumption is not to be pared (the ‘jam tomorrow’ strategy), it takes decades to climb the hump of capital-intensive investments which allow a markedly different product mix. It can similarly take decades for pollution effects or ecosystem losses to accumulate to an unacceptable level or for a non-renewable resource to be depleted to an unacceptable level. The strong implication is that economies need to be modeled over decades if tradeoffs between consumption possibilities and environmental possibilities are to be properly recognised.
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In practice, it is well understood that modelling the dynamic behaviour of complex systems, and these include social, psychological, economic (Hodgson 1999; Coates et al. 1996) and biological systems, is extremely difficult. That is, it is difficult to successfully predict how any such system would, from a given initial state, change over time; particularly if the prediction period of interest is significantly longer than the natural cycling period of the system. For example, state-of-art macroeconometric models (eg James 1996) continue to treat monetary economies as equilibrium–seeking systems of given supply and demand processes; they model changes in sectoral activity following exogenous perturbation (eg demand shifts) as movements to a new equilibrium at which the rates of supply and demand for each good are equal (market clearing). Such models have an established place in the study and prediction of short-term economy-wide change. However, under this ‘comparative statics’ approach, the time-path of change from one equilibrium to another is not identified. It is when they are required to simulate longer-term (decadal, multi-decadal) change in an economy that they encounter the problem of not being able to generate and incorporate associated structural changes such as evolving consumer preferences and endogenous new technologies (Nelson and Winter 1982). Not that econometric models are alone in relation to this last difficulty. System dynamics models (Bossel 1998) and dynamic input-output models (eg Duchin et al. 1993), two other sophisticated approaches to modelling large economic systems, are equally unable to generate truly endogenous behavioural change, ie innovative behavioural change not selected from a pre-existing repertoire. And while the subdiscipline of evolutionary economics explicitly recognises these difficulties (Schumpeter 1934; Hodgson 1999, Foster in press), it does not yet have operational methods for dealing with them. Indeed, complex evolving systems may be inherently unpredictable or, at best, only generically predictable (eg Jantsch 1980). While it does lay claim to being useful in several other ways, the work being reported here, under the title of Australian Stocks and Flows Framework (ASFF), lays no claim to an improved capacity, or, indeed, any capacity, to predictively model the long-term behaviour of large economic units. Rather, it is conveniently summarised as an attempt to: •
Quantitatively understand (document), sector by sector, how the physical capital and material input-output structure---the stock-flow structure---of the Australian economy has changed over recent decades. Most importantly, this back-tracking includes movements in non-market ‘environmental’ stocks such as ‘quality land’ and non-market flows such as pollution loadings.
•
Use this historical understanding to project stock-flow structures of the Australian economy over coming decades under a variety of policy-simulating and behaviour-simulating assumptions (introduced as what-if… scenarios) about future population change, future technology, future trade, future consumption, future investment, future levels of primary production etc. In a world where long-term predictive modelling of monetary economies remains intractable, the ASFF alternative has been implemented to serve as a practicable way of contributing to debates on national resource and environmental policy. Its foreseen value to the policy analyst is that it allows the economy-wide resource-use and
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environmental consequences of complex combinations of postulated what-if… changes to be projected in minutes and in a wholly transparent way. The basic idea is not new. Seeing value in comprehensively describing physical transfers and transformations across the Australian economy goes back to Burgess Cameron’s (1969??) construction of national physical input-output tables. (Reference James and Throsby book??) (Ayres and Kneese??). Techniques for studying physical transfers and transformations and their environmental implications in restricted portions of the economy have also become increasingly popular and include substance flow analysis (Miller and Blair 1985 ) and life cycle assessment (Curran 1996). ASFF, like all such exercises, relies on the mass balance principle that if something grows, something else shrinks. What is new in ASFF, at least in an Australian context, is the degree of disaggregation and lengthy time period for which its continental-scale historical data has been assembled; and a software capability which allows its very large data sets to be projected in a variety of ways. For example, ASFF has the powerful practical capability, of allowing sets of independently selected postulated change sequences within the material economy to be immediately tested for physical feasibility eg independently calculated sector-by-sector estimates of future water use may collectively exceed total water available. In this role, ASFF has something in common with Dreborg’s (1996) backcasting (cf forecasting) perspective where the aim is to find ways of attaining some nominated vision of a desirable future. And in a command (eg socialist) economy, something like ASFF would be needed to detect bottlenecks in the flow of goods and materials through commodity chains (chains of transformations extending from raw materials to final products and their disposal). In a market economy its analogous role would be in co-ordinating investment plans. Similarly, although less unequivocally perhaps, ASFF allows breaches of nominated environmental (eg emission levels) or behavioural (eg consumption levels) criteria under candidate what-if… scenarios to be flagged. ASFF per se incorporates no normative position of course; any such must be supplied by the user. From this perspective, ASFF stands to have a role as a design tool where the task is not so much to identify trajectories for the economy that breach a priori criteria but trajectories which satisfy or satisfice (meet in a way which is ‘good enough’, (Simon 1962)) such criteria. In this sense ASFF sits in a ‘bounded rationality’ tradition rather than an optimising tradition. Given the potential for misunderstanding the absence of prices in ASFF, it is worth stating very clearly just what this model does and does not do, starting with how it conceptualises the economy. The economy is assumed to comprise a number of sectors, ie families of related bio-physical operations such as agricultural or transport activities. Using received technologies and routines, quantities of materials, information and energy are transformed within and transferred between sectors once per quinquennium. A small number of sectors are regarded as autonomous, meaning that the level of activity in each of those sectors at future time-steps does not depend in any way on the level of activity in any other sector. Rather, activity in the autonomous sectors is determined by factors external to ASFF such as population policy or export prospects. For example, in what will be introduced as a default or base case scenario, the behavioural assumption is made that growth in the population sector will be driven by a net annual migration of 70 000 people per year. However, once activity levels in the autonomous sectors are set, the implicit behavioural assumption is made that activity 4
levels in the remaining support sectors will be set at levels just sufficient to allow autonomous activities to be completed (including of course any necessary investment in new and replacement capital in those sectors). So, at the broadest level, ASFF sees the (socio) economy divided into two super-sectors, one of which decides autonomously what stocks and flows (including people) it will produce in each future time-period and one which slavishly services or supports those decisions as if obeying a rigid set of decision rules. The paper is organised as follows: Implementation strategy backgrounds the project team’s decision to use an established but under-appreciated methodology and software system to provide a capability for projecting the material processes of the whole Australian economy at a high level of disaggregation. Model construction describes the assembly of an historical database of bio-physical transfers and transformations in the Australian material or bio-physical (cf monetary) economy, basically for the purpose of providing constraints and bounds on the future evolution of the economy under modelling The section also describes the set of ‘calculators’ used for projecting future stocks and flows in each bio-physical sector of the economy. Applications briefly describes several examples of how ASFF is being used to assist government and industry. Discussion compares the ASFF model to more conventional monetary models of the economy in terms of time horizons and behavioural assumptions and discusses the niche role this model has in exploring long term paths for the future Australian economy. implementation strategy As intimated, one of the original motivations for constructing ASFF was to allow rapid calculation of (i) material and energy loadings on the Australian environment and (ii) changes in the nation's stocks of natural capital associated with foreseeable future growth and development of the Australian economy over decadal periods under diverse assumptions about population growth and lifestyle, technological change and socio-political organisation. Closely associated with this motivation was a wish to establish a capability for exploring and evaluating different strategies for reducing projected environmental loadings and losses of natural capital below externally nominated target levels. The search was for a tool which could inform discussions of policy options for managing long-term environmental quality as determined by energy and material throughputs. Specific processes of interest included management of: greenhouse gas emissions; the depletion of fossil fuels and the transition from fossil to alternative energy sources; responses to widespread soil quality decline; the rebuilding of ageing cities; demographic change; land use change; biodiversity loss; and the allocation of limited water resources. A decision was made to develop this target capability using the design approach to socio-economic modelling developed in Statistics Canada by Gault et al. (1987) and subsequently used as a commercial consulting tool by Robbert Associates, Montreal (www.robbert.ca (accessed Jan 21 2001)). As its name implies, this is an approach which assists the modeller to search iteratively for national/global socio-economic systems with specifications that meet nominated performance and physical design criteria. In particular, the approach is seen as being useful for designing systems that, 5
hypothetically, could produce, in a physically feasible and 'socially responsible' way, an acceptable schedule of goods and services for domestic consumption and export at nominated dates. In both the Canadian original and the Australian (ASFF) version of the Robbert Associates model, each sector of the national economy is represented as a linear technology (doubled inputs produce doubled outputs) with sectors set up to interact in the style of a physical (not dollar) input-output (Leontief 1966, 1970) model or, what is essentially the same thing, a multi-stage linear programming model---except for domestic consumption and exports, outputs from one sector serve as inputs to other sector(s) in the discrete time period in which they are produced. The economy is being conceived of as a process where, within each time step, raw materials and energy are blended in (momentarily) fixed proportions (ie linear production functions) to first form intermediate products plus a fixed proportion of waste materials and then final products plus proportionate waste materials. Only the stock of capital (eg people, livestock, trees, buildings, vehicles, machinery, infrastructure) needed in each sector is carried over from its year of production to be available in the remaining years of its life. It may be helpful to recognise that under ASFF-type models, the historical performance of the physical economy is implicitly being conceptualised as a feasible solution (not optimal) to a multi-stage linear programming model, with capital provision vectors providing the main links between stages (time-steps). model construction In operational terms ASFF consists of (a) an historical database and (b) a set of 32 calculators or simulators---one for each sector of the economy as it has been disaggregated within ASFF plus several ‘consolidating’ calculators which integrate activity levels from several sectors. Detailed information on ASFF’s construction is available in Poldy et a.l (1999). Database
A large part of the effort which has gone into the ASFF project since its inception in 1996?? has been directed towards building a plausible numerical description of material and energy flows between 32 sectors of the Australian economy for each year of the period 1946-91 (Table 1). This 'calibration' or ‘data reconciliation’ exercise, carried out in the face of significant data-availability problems, yields defensible bestavailable estimates of past input-output (inflow-outflow) relations within and between sectors. It is this information which provides a starting point for projecting the economy into the future under various 'scenario' assumptions. One basis for claiming that the database is plausible is that it has been constructed to be consistent. In each year of the calibration period the total quantity of each material and energy type recorded as entering each sector equals the total quantity of each material and energy type, albeit in altered form, leaving each sector. Similarly, after calibration, the total primary energy recorded as entering a sector equals the total energy used and retained in the sector plus the total usable energy leaving the sector. In other words, the laws of conservation of mass and energy have been used to constrain annual estimates of sector inflows and outflows of energy and materials in situations where there is missing or dubious data. Besides being consistent in a mass-balance sense, the data sets in ASFF have been constructed to be technologically plausible (eg in terms of implied input-output efficiencies) and as similar as possible to their source values. Discussions in 16 specially-organised workshops of sectoral experts were enormously helpful here (Conroy et al. 2000??).
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Calibrating ASFF has been a process of well-informed judgement rather than an algorithmic one. The extended length of the calibration period reflects the need to have estimates of the age-distribution and life expectancy (life tables)(Question: Are these Markovian or deterministic??), as well as stocks on hand, of various long-lived capital items (machines, roads, buildings etc) available in 1991, this being the starting date for scenarios constructed within ASFF. Commonly, such information is not directly available but can be built up reasonably well if (gross and net) additions to the stock in a long run of previous years are known. (Calibration example here??) Overall, the data base contains national and sub-national time-series of actual and inferred values for some 800 multi-dimensional variables describing aspects of a comprehensive set of physical processes covering all stages in the productionconsumption-investment-disposal chain for the period 1946-91; almost a million individual time-series. Of the 800 variables that are followed during a full simulation of future bio-physical transformations across the Australian economy, some 300 are designated to be control variables that can be reset at will to create scenarios of assumptions about the future. Control variables are commonly expressed as ratios such as litres of fuel per 100 vehicle km or persons per household; measures which are readily recognised as evolving over time. Each control variable which is being reset has to be given a value, either directly or indirectly (eg modelled outside ASFF), for each time-step over which the simulation will run. This is normally between 10 and 20 steps, each of length five years. Calculators
Most sectors of ASFF are linked to a sector-specific algorithm that, for an externallygiven or pre-calculated schedule of future activity levels in that sector, projects the quantities of primary and intermediate inputs that will be thereby required and the quantities of waste materials that will be produced by that activity. Twenty five of these calculators have outputs from other calculators as inputs. Seven do not and calculate the stock-flow implications of exogenously specified activity levels. Five demography calculators (POPULATION, HOUSEHOLDS, LABOURFORCE, OCCSERVICE, INBOUNDTRV) project population changes including cohort structure, overseas and internal migration, health, household formation, labour force participation, demand for personal services, internal travel and tourism. A consumables calculator (CONSUMABLES) projects requirements for food and other consumable items for the projected population. Four buildings calculators (BLDSPCREQ, BLDSPC, BLDOP, BLDCON) project the requirements of the (projected) population for new residential, commercial, educational, health care and institutional buildings. Calculations include the loss of buildings demolished due to age and their contribution to recycling or dumping. The calculated requirements for new buildings are used to model ‘downstream’ requirements for their construction, eg building materials, labour, energy. These calculators also model building contents and materials, energy, water and labour required for building operation and maintenance. Seven transport calculators (INTERCITY, LONGFRGHT, SHORTFRGHT, URBANTRAN, AUTO, ROADS, INTTRAVEL) project requirements for domestic and freight transport in rural and urban areas. Separate calculators project the car fleet, roads and fuel for international travel. In ASFF, the size of the long distance non-urban freight task is not calculated by derivation from other prior requirements
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but set exogenously in the first instance of any run of a scenario and subsequently adjusted by trial-and-error. Three agriculture calculators (CROPLAND, ANIMALS, AGROPRS) project, by statistical division, (i) impacts of cropping on soil quality, (ii) livestock requirements for pasturage and other feed and (iii) the sector’s implied use of fertiliser, labour, machinery, water and energy. Agriculture, like population and other primary industries is treated as an exogenously-determined (autonomous) activity in ASFF. Domestic consumption is a small proportion of total agricultural production. A forestry calculator (FORESTRY) projects timber stocks, timber production and variable inputs to the sector under fifteen forest management regimes. A fisheries (FISHERIES) calculator projects variable and capital (eg boats) inputs to both wildfishing and fish farming. Fishing stocks are modelled to give a simple dynamic response to to exploitation, including the possibility of serious decline under overfishing. A mining (MINING) calculator projects labour, materials, energy, water and machinery requirements for exploration and extraction of minerals to meet exogenously nominated production levels (which cannot accumulate beyond reserves). An on-site construction calculator (ONSITECON) projects equipment, labour and energy requirements for building site preparation and road construction. A food processing calculator (FOODPROC) projects plant, materials, energy, and water required to process projected food requirements. (??) A processing and assembly calculator (PROCASSEM) projects the new and replacement manufacturing capacity required for producing the vehicles, machinery, building contents and operating goods projected as needed under other calculators. It also projects emissions from and materials, labour, energy and water requirements for these activities. A recycling calculator (RECYCLING) projects quantities of all discarded goods, vehicles, machinery and assigns them to recycling or to landfill. Plant, materials, labour, energy and water to undertake this recycling task, plus associated emissions, are also projected. A materials and energy transformations calculator (MATENRTRAN) projects, by an iterative process, an economy-wide requirement for plant capacity to produce the energy and materials required by the rest of the economy after allowing for import propensities, exports, capacity depreciation and recycled materials. An international trade calculator (INTTRADE) projects economy-wide imports as a sector-dependent proportion of projected domestic requirements (propensity to import) and economy-wide exports as a residual after meeting domestic requirements. If desired, a ‘dollar’ trade balance can be projected under assumptions about future import and export prices. A land resources calculator (LAND) projects land use changes by 58 statistical division as implied by projected levels of agricultural and forestry activity, and by building and road construction/demolition activity. A water resources calculator (WATER) projects water withdrawals and discharges for 74 national water regions as implied by projected water use in building operations and in primary and secondary industry.
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An air resources calculator (AIR) projects gaseous emissions by capital city airsheds as implied by projected vehicle activity, processing sector activity and non-transport energy use. Fig. 2 is a highly aggregated depiction of how information required for making projections in one ASFF calculator is drawn from projections made in what might be called ‘prior’ calculators. It identifies a hierarchy of calculators such that the level and type of activity projected in any calculator is not a function of the level and type of activity in any calculator below it in the hierarchy.(??) Each arrow connects a sector where information for making a projection is supplied or generated (arrow tail) and a sector where that information is used for making a projection (arrow head). Thus, in creating an ASFF scenario calculators are run in the top-to-bottom order of Fig. 2 with the results of calculations for one sector serving, as required, as instructions with respect to the necessary level of activity required from succeeding sectors. For example, an a priori calculation of the population sizeinforms the housing sector how many new houses are needed which informs the construction sector how many bricks are needed and the number of bricks needed informs the mining sector how much clay is needed. This calculation process, whatever the sector, assumes that the physical capital and material inputs needed to carry out that level of activity can be always made available---by importing if that is unavoidable. While information flows unidirectionaly in a single run of the model, construction of a complete scenario usually involves repeated trial and error runs aimed at satisfying nominated design criteria (see comments on tension resolution below). In this sense, mediated by the user, simulations involve complex information flows between multiple calculators. To repeat, philosophically and in practice, determining price-mediated equilibrium flows between sectors of the economy is not part of the ASFF task, which is essentially projection. In ASFF, flows between sectors are more usefully viewed as reflecting a plausible technological imperative for the completion of a given production or service task. using asff to project the bio-physical economy Constructing a default scenario
As a ‘setting-up’ exercise, undertaken before using the Australian Stocks and Flows Framework to explore the long-term bio-physical implications for the economy of various contrasting policy-relevant suppositions, a base case or default scenario to 2100 was constructed. This default scenario sets or ‘fixes’ ASFF’s 300 control variables and other important parameters of the simulated future material economy at ‘unsurprising’ values. This means values based on assuming people will behave into the future more-or-less as conventionally anticipated. The practical reason for having a default scenario to hand is to free the analyst to concentrate on behavioural and policy alternatives of immediate interest. Such alternatives normally involve a quite limited number of parameter shifts. Thus, provided the analyst can accept the default scenario as a ‘background’ on which policy/behavioural alternatives can be plausibly imposed, the same default scenario can be used as a starting point for creating alternative scenarios whatever the analytical focus---immigration policy, energy policy, environmental policy etc. To be quite clear here, the analyst is of course free to construct his/her own default scenario at any time.
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The current (and still evolving) default scenario, chosen from many equivalent possibilities, is one which implies that: •
the Australian economy and population will continue to grow for some decades at rates comparable with those of the recent past
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service industries will increase as a proportion of the economy
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per capita consumption of goods and services will continue at about current levels (??)
•
exports will grow in line with overall growth in the economy and the composition of the export mix will change only slowly
•
the technical efficiency of most production processes will improve steadily in line with past improvement rates
•
losses of natural capital (soil, air and water quality; biodiversity) under primary production and urbanisation will accumulate while slowly developing problems such as dryland salinity and soil acidification approach critical thresholds
•
manufactured capital stocks will evolve in a manner which is consistent with the age profiles and life tables constructed for such stocks at the end of the 1946-1991 calibration period
• there will be neither an under-supply nor over-supply of labour Space does not permit a full specification of the default scenario (reference??). However, Table 2 contrasts some dimensions of the Australian bio-physical economy as it is ‘at present’, ie at the end of the calibration period, and as it is projected to be in 2050 under the default scenario. ??role of history ?? Tension resolution
Ensuring that the default scenario had neither an over-supply of labour (an unacceptably high level of unemployment) nor an under-supply (phantom workers) turned out to be an exercise in tension resolution. This is an ASFF term meaning that scenarios which imply behavioural or physical impossibilities (eg non-existent workers) or situations declared to be socially unacceptable (eg high unemployment )--both called tensions---should be adjusted by iterative ‘tuning’ of control and other variables until the impossible or unacceptable situation is ‘fixed’ or resolved. The source of physical tensions lies in the one-way information flow, and hence absence of feedback, during any model run. ASFF has no algorithmic procedures for resolving physical/social tensions which appear in a scenario as it is being constructed but, to date, iterations of small, informal, intuitive adjustments have been sufficient to achieve this. For example, an implied over-supply of labour under initial runs of the default scenario was resolved by making small changes in both the participation rate and weekly working hours(??). It is anticipated that, in many cases, tensions will be viewed more as opportunities to explore policy alternatives than as inconsistencies to be eliminated. The process of actively balancing physical flows in a scenario becomes an opportunity for tacit learning about the behavioural possibilities of the economy in relation to intrinsic constraints.
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applications Development of ASFF has now (mid-2000) reached the stage where it is being used in a first suite of applications commissioned by industry and government clients. For example, work for the Land and Water Resources Research and Development Corporation (Dunlop et al. 2000) is focused on the differential environmental and production consequences of three scenarios based on three sharply different what-if… strategies for the future management of Australian agriculture, namely, what if Australian agriculture were to be managed so as : (a) to slowly change the product mix in line with current trends in that mix, or (b) to give high priority to restoring hydrological and chemical balances in landscapes showing declining agricultural productivity, or (c) to give high priority to increasing the use of modern intensive farming methods to offset historical productivity losses and raise agricultural output Work in progress for the Fisheries Research and Development Corporation (any refs available??) has a first goal of upgrading the initial fisheries calculator in ASFF to specifically recognise over 100 fish species and managed fisheries as well as various forms of ‘high value’ aquaculture. A second goal is to develop some long-term alternatives for the industry’s management (particularly around the issue of sustainability) and understand the demand links to the rest of the economy implied by these alternatives. A major exercise being undertaken for Department of Immigration and Multicultural Affairs (Poldy and Foran, 2000) is focused on comparing the environmental, infrastructural, resource and economic implications of three candidate immigration policies: (a) medium-level immigration, viz. annual net migration (ie net of departures) of 70 000 each year till 2050 when the population would be 25 million (cf 19 million in 2000). This scenario is close to the population component of the default scenario described above. (b) high-level immigration, viz. annual net migration equal to 0.67% of total population each year till 2050 when the population would be 32 million. (c) low-level immigration, viz. zero net migration each year till 2050 when the population would be 20 million. Apart from confirming that big populations engender big economies (although not necessarily bigger on a per capita basis), the study shows major differences between scenarios with respect to a range of policy issues including: greenhouse gas emissions, farmland losses(??), river salinity levels(??), personal consumption levels, availability of domestic oil and gas supplies, urban air quality, urban congestion and the merchandise trade balance as a function of exports of primary products. Thus, while in no way an overall evaluation of the competence of the three policies, the ASFF exercise transparently calculates values for criteria relevant to an informed choice between them. These calculations, while simple in principle, are computationally intensive. ASFF is well-suited to studying greenhouse and energy strategy questions at a national scale and funding for such work is currently being sought.
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discussion Databases and data collections
The applications just described lend support to a well-recognised argument for establishing the type of multi-purpose policy tool which ASFF represents. The major cost of establishing ASFF, say $A1.5m over five years(??), is now being spread across a number of applications which, singly, would perhaps not warrant such expenditure. It is always a matter of debate whether it is efficient, or, indeed, possible, to establish computerised information systems (and ASFF is clearly a form of information system) for the purpose of facilitating a range of dimly-perceived future tasks. In such systems there is likely to be a tradeoff between the ready accessibility of preacquired data and its appropriateness for the post-acquisition task at hand. Thus, in Cocks et al. (1988), it was argued that a predecessor to ASFF in CSIRO Division of Wildlife and Ecology, namely a continental scale geographic information system called ARIS (Australian Resources Information System), had proven its versatility in more than a dozen spatially-oriented policy applications, all completed without additional data-gathering efforts. Certainly successors to ARIS are now found in several federal government agencies. Now, government and industry interest in ASFF is beginning to suggest that it too has a flexible mix of stock data and computational capabilities. Not that the ASFF database is set in stone. Each commissioned application, provided that its promised delivery date allows, represents an opportunity to upgrade (eg through disaggregation or revision) a relevant calculator and a relevant portion of the database. Indeed, it is the project team’s intention to actively seek applications which will allow all portions of the database to be successively upgraded over time. A related point here is that even as construction of the ASFF database has drawn heavily on the excellent collections of the Australian Bureau of Statistics, it has exposed gaps in those collections. The point being made is not that the Australian Bureau of Statistics should be working to serve ASFF’s needs per se but that ASFF is a framework, as its name says, a framework which can provide a proactive statistical agency with a rationale for scoping its acquisition of physical data sets related to the functioning of the economy. (mention green national accounts and ASFF??) Certainly a belief in such a role lay behind the original development of this type of framework in Statistics Canada (McInnis pers.comm.). Provided that government and industry interest in using ASFF continues, bringing funds with it, the model does need to be improved on several fronts. The data base should be re-calibrated, fully extended, to year 2000. There should be scope for novel cross-validation to improve estimates of some poorly-recorded data items. Some calculators have to be re-worked to recognise a finer level of geographic disaggregation, particularly those concerned with natural resource use. Some significant transformations which have been treated only cursorily to date in ASFF will be elaborated In particular, the use and conservation of natural ecosystems and biodiversity will be better recognised. While the five year time-steps currently built into ASFF projections do somewhat blur perceptions of the age distributions of many capital stocks, this step-length is still judged to generate a manageable but meaningful quantity of output detail when projecting 50-100 years ahead and will not be shortened.
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Finally, irrespective of its value for and role in projecting a set of national biophysical (cf monetary) accounts, the ASFF database constitutes a uniquely detailed overview of the recent (1946-91) history of material activity---transfers and transformations---in the Australian economy and has the potential to be a useful adjunct to traditional approaches to economic history. ASFF as a source of insights
Ultimately, the most valuable product of any modelling work is the insights it yields into system behaviour. Because ASFF simulations are projections (fully constrained conditional predictions) rather than dynamic simulations, they yield more insights into what the future economy will not or cannot be, rather than what it might be. Thus, experience to date with ASFF reinforces and tentatively quantifies perceptions of the important role played by path-dependence constraints in an economy that is attempting rapid structural change involving the replacement of long-lived capital stocks. For example, without sharply constraining final consumption to release investment capital, it would be extremely difficult to traverse the ‘capital hump’ from coal-based to renewable electricity generation in less than forty years. This problem, combined with dwindling oil supplies (Fleay 1999??), means that managing Australia's energy supply and use mix in the 21st century will be a major challenge. The planned use of irrigation water exemplifies a different type of insight, namely a ‘tension’ scenario. ASFF calculations show that the rates of growth foreseen by industry groups for various types of irrigated agriculture are collectively infeasible in terms of total water available after meeting the needs of cities and secondary industry. This insight does not matter in the sense that ‘the market’ will always allocate limited water between sectors, but it may be important for some users in terms of avoiding unprofitable investment. Conversely a role for ASFF in co-ordinating (getting the right mix) future investments can be envisaged. ASFF is also a source of insights into what future activity ‘must’ be like in ‘linked’ sectors, ie sectors having flow relationships with sectors where changed activity levels are directly foreseen. International tourism is a good example; industry personnel have been surprised at ASFF’s projections of the number of new hotel rooms that will be needed under the industry’s own assumption about its future rate of growth (ref??). Projections of dramatically-increased quantities of imports provide another simple but powerful example of ASFF’s potential for sensitising industry to underrecognised implications of conventional wisdoms about the future economy. There are several important perennial questions about the future economy which ASFF projections can illuminate in a modest way. One is the balance of trade and another is ‘rebound’ effects in which improved efficiency in the use of some factor of production can lead to an increase in the use of that factor rather than a decrease. Thus, increased private car usage following improvements in fuel consumption per km is one well-recognised rebound effect which has been explored via what-if… scenarios. ASFF does not model and attach dollar values to the physical quantities it recognises and so cannot identify the dollar trade surplus or deficit implied by the schedules of physical imports and exports which are generated in ASFF scenarios. Nonetheless, estimating the balance of trade at future dates by valuing physical quantities of traded goods at current prices has some indicative value and, as a procedure, could undoubtedly be improved, eg by using terms-of-trade forecasts.
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Finally, there are several perceptions that are reinforced, as distinct from revealed, by exposure to ASFF projections. One is that big economies entail big throughputs---a truism whose impact increases when the numbers are transparently generated. Over the time-frames dealt with in ASFF exercises, an economy growing at, say, two per cent, will quadruple its physical inputs and outputs. Putting this another way, throughput rates would need to be reduced (perhaps through efficiencies, perhaps through a changing product mix) by a factor of four (ref?? Lovins??) if total throughput, with all its implications for environmental quality, is not to increase. ASFF and policy analysis
ASFF is concerned with the long run in the classical sense of that term, a period long enough to require the replacement, abandonment or upgrading of all manufactured capital (scenarios are routinely projected for up to 100 years). Many of the things that will be important about life in the mid-21st century are being determined by decisions being made today, just as many aspects of our lives today are being determined by collective and private path-setting decisions made 50-100 years ago. The default scenario in particular flags the long run consequences of continuing to behave much as we do now. ASFF is part of the search for analytical tools which will expose current and ongoing choices that have implications for consumption levels and environmental quality levels in 50 years and beyond. More colloquially, ASFF is a tool for looking (in a necessarily restricted domain) for long run bottlenecks, opportunities, challenges and strategic (structural) alternatives for the Australian economy. More often than not , its message is that if we want the economy to be in a particular state in 50 years we have to start working towards that goal now. Given its focus on the long run, it follows that ASFF can only be complementary to, and never competitive with, the stable of econometric models that simulate the short run behaviour of national economies. In addition to this difference in time-frame, the ASFF model of the bio-physical economy and conventional models of the monetary economy differ with respect to the assumptions they make about the behavioural determinants of economic actors. Nevertheless, possibilities for formal linkages will be explored in due course. Conventional economic models predict future consumer and producer responses to explicit or implicit price changes using historically-based estimates of price and income elasticities and cross-elasticities. ASFF, on the other hand, is not trying to predict how producers and consumers will behave under price stimuli and what this will mean for the economy. ASFF is a projection model not a process model. The ASFF analyst is asking the question ‘If producers and consumers behaved just so, what are the stock-flow implications over time for the economy? Whether people might behave just so or whether they could be induced to behave just so by a judicious manipulation of prices through taxes and subsidies are not questions addressed within ASFF. In principle, a conventional economic model could complement an ASFF model by helping identify the unique price regime which would begin to move the economy in the short run down a long run path identified via ASFF as feasible and desirable in some policy sense. It is at the level of behavioural detail below sectoral activity levels that decisions or specifications are needed on the routines and technologies which will be used in each sector at each time-step to produce its planned stocks and flows. There are too many of these specifications to be meaningfully reset every time a new projection is sought. 14
The convenient solution commonly adopted here is to inject values of control variables as developed for the default scenario. Under this strategy, many ASFF exercises can be viewed as projecting the implications of (contrasting) variations on or versions of the default scenario. Attention can thus be focussed on varying policy variables of interest against a background of future behaviour which constitutes a plausible scenario in terms of recent past behaviour and/or cautious extrapolations of such. That is, it is being assumed that entrepreneurs will continue to use technologies already in use at the start of the simulation period or technologies which are extrapolations of those technologies, reflecting a continuation of recent technological advances. Note though, that, used in this way, ie varying the default scenario, ASFF becomes a tool for exploring marginal rather than radical policy or community value changes such as a change to a post-materialist or a self-sufficient society. An associated hazard here is that the default scenario may unwittingly acquire normative status, that is, a perception that it is being advocated as a policy goal or an indicative plan. While ASFF has reached the stage of clearly demonstrating its value for exploring bio-physically different alternative paths for the Australian economy, that is the extent of its domain and it cannot be expected to help with important longer-term financial and monetary questions such as, drawing on Brain (1999), how to finance expansion (debt versus equity), how to sustain investment opportunities exhibiting acceptable levels of risk-return, how to keep the balance of trade and the balance of domestic supply and demand within acceptable limits. What it will do is complement and illuminate studies of such questions. Finally, as something to be explored in the longer term, ASFF has attributes which fit it for use as as a tool for participatory policy development, functioning as an interface at which contending interest groups can each build their own scenarios and then attempt to reconcile such in a quite explicit manner.
acknowledgements The ASFF project, led by Barney Foran, has developed on the back of dedicated contributions from a team which has, at various times, included Joshua Conroy, Michael Dunlop, Mikhail Entel, Neil Hamilton and Don Lowe. Our Canadian colleagues Bert McInnis and Rob Hoffman, apart from developing the methodology embedded in ASFF, have laboured mightily to help get ASFF up and running. Contributions from experts attending 14 ASFF-sponsored workshops in 1999 have allowed the project team to deal with some confidence with a range of production and operational details in all sectors of the economy(??). Australian Bureau of Statistics?? references Bossel H, 1998, Earth at a Crossroads: Paths to a Sustainable Future, Cambridge University Press, Cambridge. Bouman M, Heijungs R, van der Voet E, Jeroen CJ, van den Bergh M and Huppes G, 2000, Material flows and economic models: An analytical comparison of SFA, LCA and partial equilibrium models, Ecological Economics, 32, 195-216. Cameron B, 1969??
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Coates J Mahaffie JB & Hines A, 1996, 2025---Scenarios of US and global Society Reshaped by Science and Technology, Oakhill Press, Greenboro, NC. Cocks KD, Walker PA, and Parvey, CA, 1988, Evolution of a continental scale geographic information system. Int.J. Geogr. Inf. Syst. 2(3), 263-80. Curran MA, 1996, Environmental Life-Cycle Assessment, McGraw-Hill, New York. Dreborg KH, 1996, Essence of backcasting, Futures, 28(9), 813-28. Duchin F, Hamilton C, & Lange G, 1993, Environment and Development in Indonesia: An Input-Output Analysis of Natural Resource Issues, Natural Resource Management Project, Jakarta, 210pp. Dunlop M, Foran B and Poldy F, 2000, Changing agricultural production and the natural resource base: Three long-term scenarios for land use, Draft report to Land and Water Resources Research and Development Corporation, Working Paper 2000/05, Resource Futures Program, CSIRO Division of Wildlife and Ecology, Canberra. Fleay D, 1999?.. Foster J, in press, Competitive selection, self-organisation and Joseph A. Schumpeter, Evolutionary Economics Gault FD, Hamilton KE, Hoffman RB and McInnis BC, 1987, The Design Approach to Socio-Economic Modelling, Futures, Feb, 3-25. Hodgson GM, 1999, Evolution and institutions: On evolutionary economics and the evolution of economics, Edward Elgar Cheltenham James D, 1996, Comparison of economic-environmental models, Dept. of the Environment, sport and Territories, Canberra, 74 pp Jantsch E, 1980, The self-organising universe: Scientific and human implication of the emerging paradigm of evolution, Pergamon, Oxford Leontief W, 1966, Input-Output Economics, Oxford University Press, New York. Leontief W, 1970, Environmental repercussions and the economic structure: An input-output approach, Rev. Econ. Stat. 52, 262-71. Lovins?? Miller RE and Blair PD, 1985, Input-Output Analysis: Foundations and Extensions, Prentice Hall. Englewood Cliffs , New Jersey. Nelson RR and Winter SG, 1982, An evolutionary theory of economic change, Belknap Press, Cambridge, Mass. Poldy F, Foran B and Conroy J, 2000, Future options to 2050: Australian Stocks and Flows Framework, Working paper 00/04 Resource Futures Program, CSIRO Division of Wildlife and Ecology, Canberra Poldy F and Foran B, 2000, dima report ?? Sarewitz D, Pielke R and Byerly R (eds), 2000, 'Prediction - Science, Decision Making, and the Future of Nature'?? Simon HA, 1962?? See Starr and Allen Turner G???
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figures Fig 1: Relation between primary energy use and Gross Domestic Product for the Australian economy 1903-1995
400
Primary energy use Real GDP
3000
300
2000
200
1000
100
0 1900
1920
1940
1960
1980
GDP (1989-90$billion / year)
Primary energy use (PJ / year)
4000
0 2000
Year
Sources Primary Energy Consumption: 1903-73: Australian Historical Statistics; 1974-95: Australian Bureau of Agricultural and Resource Economics (Energy demand and supply projections) GDP: 1901-63: Snooks GD (Portrait of the Family in the Total Economy); 1964-95: Australian Bureau of Agricultural and Resource Economics (Australian Commodity Statistics)
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P O P U LA T IO N H O U S E H O LD S LA B O U R F O R C E
P O P U LA T IO N H O U S E H O LD S LABO U R FO R C E
O C C S E R V IC E
O C C S E R V IC E
IN B O U N D T R V
IN B O U N D T R V
C O N S U M A B LE S BLD SPC R EQ B LD S P C B LD O P B LD C O N IN T E R C IT Y LO N G F R G H T SHORTFRGHT URBANTRAN AUTO ROADS IN T T R A V E L C R O P A LA N D A N IM A LS
BLDSPCREQ BLDSPC BLDO P BLDCO N IN T E R C IT Y LO NG FRG HT SHORTFRGHT URBANTRAN AUTO ROADS IN T T R A V E L C R O P A LA N D A N IM A LS
AG R O PR S
AGROPRS
FORESTRY
FO R EST R Y
F IS H E R IE S
F IS H E R IE S
M IN IN G
M IN IN G
O N S IT E C O N
O N S IT E C O N
FO O DPRO C
FO O D PR O C
PROCASSEM R E C Y C LIN G MATENRTRAN IN T T R A D E
Fig. 2 Information flows between calculators/sectors during an ASFF projection exercise (Sectors that do not receive information from other sectors are shaded)
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C O N S U M A B LE S
PR O C ASSEM R E C Y C LIN G M AT EN R T R AN IN T T R A D E
tables Table 1: Sectors of the national (socio) economy recognised in Australian Stocks and Flows Framework Demography
Buildings
1. Population
16. Space required
2. Households
17. Building space
3. Labour force
18. Building operations
4. Occupational services
19. Building contents
5. Inernational tourism 6. Consumables Material resources 7. Crops and land
Transportation 20. Intercity travel 21. Long distance freight 22. Short distance freight
8. Animals
23. Urban travel
9. Agricultural operations
24. Automobile stock
10, Forestry
25. Roads
11. Fisheries
26. International travel
12. Mining Natural resources
Materials and energy conversions 27. On site construction
13. Land resources
28. Processing and assembly
14. Water resources
29. Recycling
15. Air resources
30. Food processing 31. Materials & energy transformations Other 32 International trade balance
Table 2: Selected parameters of the Australian bio-physical economy (a) in 1991 and (b) as projected to 2050 under a default scenario (comment about average over 5 year time step?) Parameter
1991
2051
Total population (million) Population over 65 (million) Domestic travel (million nights away per year) International visitors (million persons per year) Domestic non-urban freight transport task
17.3 2.0 220 2.37
25.1 6.3 680 34.0
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rail (billion tonne-kilometres per year) road (billion tonne-kilometres per year) sea (billion tonne-kilometres per year) Building space dwellings (million m2) Non-dwellings (million m2) Primary energy use (petaJoules per year) Electricity generation (peraJoules per year) Energy related CO2 emissions (million tonnes per year) Arable land (million hectares) Crop production cereals (million tonnes per year) sugar cane (million tonnes per year) cotton (thousand tonnes per year) Fish production (thousand tonnes per year) Oil remaining to be produced (million tonnes) Natural gas remaining to be produced (million tonnes)
20
90 59 94
282 121 236
720 141 3663 537 292
1697 319 6987 1213 548
32.8
47.2
22 26 306 929 376 1470
44 66 2146 931 17 264
