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Partitioning of resources in production: an empirical analysis
Journal of Cleaner Production 12 (2004) 855–863 www.elsevier.com/locate/jclepro
Partitioning of resources in production: an empirical analysis Paul H. Templet Department of Environmental Studies, Energy and Environmental Building, 1285 Energy Coast and Environment Building, Louisiana State University, Baton Rouge, LA 70803, USA
Abstract Industrial economies ingest materials, energy and information to produce goods and services and excrete wastes and emissions. Wastes can be minimized and the relative amounts of resources, which go into goods and services, as opposed to waste, are essential to clean production and to the sustainability of the production system. A regression model based on empirical data is presented that provides a partition coefficient expressing the ratio of energy (or material) resources invested in goods to energy going to waste. Partition coefficients are developed for five countries and are shown to be related to GDP, energy and material consumption and energy and waste intensities. Higher partition coefficients mean higher productivity and lower energy, waste and material intensities. In addition, energy use/capita and pollution/capita is lower. The price of energy to the industrial sector is related to the partition coefficient. The policy implications are that partitioning of resources to goods should be maximized and waste minimized for economic as well as environmental reasons. # 2004 Elsevier Ltd. All rights reserved. Keywords: Energy partitioning; Industrial ecology; Metabolism; Energy intensity
1. Introduction Economic development is an evolutionary process that results in changes over time as economic systems self-organize in response to information feedback [1,2]. Economies are open thermodynamic systems that ingest and metabolize high entropy materials and low entropy energy from the environment to create low entropy goods and excrete high entropy wastes [3]. The production of goods necessarily involves joint production [4] of both desired products and undesired wastes. The requirement that some waste is always produced follows from the first and second laws of thermodynamics, which hold that matter and energy, the most fundamental factors of industrial production, can be changed but not lost in production, that no process is totally efficient, and that entropy is generated in any transformation of matter and energy. Wastes, including spent products, ultimately leave the economic system via discharges to the environment. The term waste, as used here, refers to energy or materials that
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are rejected or disposed from the overall industrial system and not from individual processes or hierarchies. Industrial ecology and clean production principles generally recognize that waste created at one level of a system of hierarchies can become a resource at another level. For this reason, recycling and reuse of wastes in economic systems are promoted as techniques for improving overall system efficiency [5] and for good reasons. A concern arises, if waste reduction at one level adversely affects another level by reducing available resources and overall production—a negative growth effect. Despite this concern, it is likely that every opportunity to reduce waste should be taken for the following reasons. If one level reduces its waste input (as a resource) to another level the receiving level will likely improve its efficiency to stay competitive and the effect will cascade through all lower levels improving overall efficiency. Those parts of the hierarchy that cannot increase efficiency will lose production and be replaced by other, more competitive or ‘‘fitter,’’ entities. Secondly, if waste materials are not reduced at every level, then energy efficiency will suffer because energy is required to process materials and create waste. Energy is the prime resource in economic production because waste materials, no matter how
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diffuse, can be reconstituted (entropy is reduced) if enough energy is available. The converse is not true; energy degraded to background heat cannot be reused, no matter how much material is available. This paper will deal with the overall economic system because that is the level for which data is available. Individual hierarchies or processes could be investigated using these techniques in an I/O model if (1) energy input and (2) product and waste output data were available. The question of whether waste reduction at all levels is the best policy, as I believe it is, will remain for another time. The evolutionary nature of self-organizing systems means that energy cascading through system hierarchies builds order and diversity (or complexity) and represents an investment in the system. Viewed in this manner, economies resemble the dissipative structures of Prigogine [6], which consume energy while increasing complexity and decreasing uncertainty. Evolution, including economic evolution, continues irreversibly over time, producing more system structure and complexity, leading to increased efficiency and less entropy generated at the margin. This may be an example of evolution characterized by Bertalanffy [7] as ‘‘. . .directedness based upon structure. . .’’. The greater the evolved structure within a system the greater the number and types of energy uses and the smaller the energy flow gradients between compartments, sectors or nodes as energy cascades toward final dissipation as waste background heat. This organizational strategy is used by ecosystems during succession and evolution and results in more system diversity, i.e., structure [8] and less waste. Assuming isomorphism across system types [7,9] economic system efficiency and productivity is also expected to generally increase during development. Efficiency in systems is likely promoted by competition for scarce resources. ‘‘Emergent’’ properties of economic systems that appear during development include diversity of energy flow paths, improved efficiency and productivity and reduced waste output. Industrial ecology [10–12] incorporates many of these same facets into its framework. Increased productivity during evolution was first suggested by Darwin [13] to be related to diversity in ecological systems. Ulanowicz [14] relates the changes in development, including diversity and production capacity increases, to changes in energy flow patterns within the system over time. A measure to analyze and track these emerging properties would be useful in the study of industrial ecology. The joint production concept also includes other important industrial production issues, including irreversibility of the process, limits to substitution, the ubiquity of waste and limits to growth (see [4] for a discussion of these). Wastes from industrial processes are generally unwanted and, in the interest of reducing costs internal to the industry, are often ‘‘externalized’’
as pollution to the public commons of air, land and water. Once released, the externalized pollution can create unwanted impacts that are imposed on unwitting or unwilling groups raising public health and equity issues. Previous studies have shown that externalities create a subsidy for the externalizing industry but at a cost to those receiving the externality that can lead to income distribution disparities, poverty and a loss of public welfare [15–17]. In addition, industries and economies that create large amounts of wastes relative to goods are inefficient, resource intensive, wasteful [18], and are likely to suffer a competitive disadvantage [19] because their resource supply and waste costs per unit of goods are higher than those of more efficient producers. Sustainability requires that industrial throughput be within the source and sink capacities of the environment [20]. Poor allocation of resources between goods and waste negatively affects sustainability by increasing throughput per unit of output and the amount of waste generated per unit of production. The environment is the source of the natural resources that are used in the economic system to produce goods and is generally the final repository of wastes. These are the ‘‘source’’ and ‘‘sink’’ functions that constitute natural capital and are essential to the development of economic capital. For these reasons, the industrial economy is dependent on the environment although conventional economic wisdom generally discounts the value of natural capital because the market captures its values only partially. Conversely, Costanza et al. [21] showed that the value of natural capital may be higher than that of manmade capital. Misallocation of resources negatively affects natural capital by consuming more of it and by imposing higher waste loads per unit of goods produced. A measure of relative resource allocation in industrial systems, i.e., the distribution between goods and waste as proposed here, would be useful in evaluating energy and material efficiencies, intensities and in tracking progress toward sustainability. The partition coefficient (PC) described below may provide such a measure. The purpose of this paper is to pragmatically test the hypothesis that industrial economies are joint production systems whose only outputs are goods (and services) and waste. A related purpose is to develop economy-wide PCs for selected developed countries using empirical data and to test them against measures of resource and waste intensity resource use, pollution and productivity (for a review of materials intensity research see [22]). The concept presented here is that the PC represents the relative proportion of resources being allocated to goods normalized by the amount allocated to waste. It is a general principle and can be used at any system level where the data is available. The approach is quantitative but cannot distinguish
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Table 1 Input and output data for five countries and years Country and year
Population (thousands)
Energy input thousand mtoe
Austria 75 Austria 80 Austria 85 Austria 90 Austria 95 Germany 75 Germany 80 Germany 85 Germany 90 Germany 95 Japan 75 Japan 80 Japan 85 Japan 90 Japan 95 Netherlands 75 Netherlands 80 Netherlands 85 Netherlands 90 Netherlands 95 US 75 US 80 US 85 US 90 US 95
7579 7549 7558 7718 8047 61,829 61,566 61,024 63,254 81,539 111,940 117,060 121,049 123,611 125,570 13,599 14,091 14,454 14,893 15,424 220,165 230,406 241,855 254,106 267,115
20,365.1 23,449.9 23,208.0 25,699.5 26,295.0 316,652.9 360,441.2 361,247.8 355,732.2 339,867.4 308,240.2 346,491.3 367,026.1 438,797.1 497,032.3 59,528.2 65,000.1 61,617.8 66,593.2 73,358.0 1,660,546.1 1,811,649.8 1,781,709.1 1,925,681.6 2,089,723.9
TMR (materials) thousand tonnes
3,949,514 4,266,377 3,715,642 4,228,973 4,186,000 4,448,000 4,430,000 5,682,000 758,089 879,299 892,917 1,025,685 21,463,000 21,982,000 20,623,000 22,145,000
qualitative differences, e.g. between good and bad inputs or outputs. It aggregates all goods and services outputs equally in terms of their dollar contribution to GDP, but essential consumption (e.g. food for survival) is not distinguishable from nonessential consumption (e.g. gas guzzler automobiles for every family member). 2. Methods World Resource Institute publications [23,24] provided annual input and output material flow data, respectively. The data are total material requirement (TMR, total annual materials input including imports but excluding water and air), total domestic output, (TDO, annual waste output but excluding oxygen and carbon dioxide from respiration and waste exports), as well as population and annual GDP (in constant 1996 US$). TMR and TDO include both total direct material flows and hidden or indirect flows. Hidden or indirect flows are the total weight of materials moved or mobilized in the domestic environment in the course of providing commodities for economic use that do not themselves enter the economy. Such flows include things like landscape alterations for resource extraction and plant biomass later separated from the desired material. Empirical data for five developed countries (Austria, Germany, Japan, Netherlands and the US.) spread over 20 years in 5-year increments were pooled,
GDP (goods output) billion 1996 US$
TDO (waste) thousand tonnes
PC
136.07 160.43 174.22 203.87 223.61 1,221.60 1,434.41 1,513.84 1,769.02 2,321.80 2,245.32 2,769.52 3,268.18 4,125.31 4,440.72 244.96 278.14 296.15 345.22 383.34 4,253.95 4,975.82 5,614.67 6,435.21 7,135.89
136,995 137,017 138,535 130,410 115,423 1,367,353 1,750,155 1,711,887 1,710,546 2,908,351 1,486,746 1,403,263 1,303,350 1,582,030 1,725,105 237,896 226,752 213,821 222,991 211,969 19,315,306 19,681,735 18,130,483 19,126,594 18,544,354
1.382 1.630 1.750 2.176 2.697 1.244 1.141 1.231 1.439 1.111 2.102 2.747 3.490 3.629 3.583 1.433 1.707 1.928 2.155 2.517 0.307 0.352 0.431 0.468 0.536
yielding n ¼ 25 data points. Energy consumption is in thousand metric tons of oil equivalent and is taken from the International Energy Agency [25]. The data are presented in Table 1. The joint production hypothesis can be tested and the PC developed by using a multiple regression where input energy or material is the dependent variable, and the two outputs (GDPvalue of goods and TDO-wastes) are the independent variables. The method was previously used to calculate PCs for the individual US states, although a surrogate for waste data was used [18]. In that study a state’s PC was found to be positively related to income and other measures of public welfare. The approach used here could also apply at different scales and to individual industries or facilities if the data were available. Simple linear regressions (SLR) are used to test relationships between variables.
3. Results A multiple regression analysis with energy used as the dependent variable and GDP and TDO as the independent variables provides the following equation: Energy usedðk mtoeÞ ¼ 17858 þ 89:888 GDPðbillion 1996 US$Þ þ 0:070 TDOðk tonnesÞ
ð1Þ
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where R2 ¼ 0:99 and the independent variables and model are significant (t values of the independent variables are 9.4 and 24.4, respectively) but the intercept is not significant and is expected to be zero. A similar calculation without intercept yields: Energy used ðk mtoeÞ ¼ 96:040GDPðbillion 1996 US$Þ þ 0:069TDOðk tonnesÞ
ð2Þ
where R2 ¼ 0:99, variables and model are significant (p < 0:0001) and the t values are 12.7 and 25.1 for the GDP and TDO terms, respectively. The first term on the right of Eq. (2) represents the energy that is being partitioned to GDP (goods), and the second term represents the energy partitioned into waste. Because we are interested in the relative amounts of energy dividing the first term by the second term for each country and year yields the energy PCs calculated from the following equation and shown in Table 1 (GDP is in billion 1996 US$ and waste (TDO) is in thousand tonnes): PC ¼ ð96:04 GDPÞ=ð0:069 TDOÞ
ð3Þ
The five country PCs are plotted for years 1975–1995 in Fig. 1. A similar operation could be carried out to develop material PCs using material inputs (TMRs) as the dependent variable. However, TMR data is more limited (n ¼ 16) and the PCs developed may not be as reliable. Consequently the PCs presented here are derived using energy consumption as the dependent variable. The PCs are found to be significantly and positively related to GDP per capita and negatively to energy use/capita. (Table 2 contains the statistical information on the relationship of PC to the variables discussed here.) In addition, the PCs are significantly and negatively related to both material and energy input intensities. As PCs rise, the amount of input material and
Table 2 Statistical relationship of variables to the PC Variable
Pearson’s r
p
GDP/capita Energy use/capita TMR/GDP Energy use/GDP DPO to air/capita TDO/capita (linear) TDO/capita (polynomial) TMR which ends as TDO waste Industrial electricity price Japan’s diversity MIPS
0.53 0.75 0.88 0.83 0.74 0.78 0.97
<0.0067 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001
0.77
<0.0001
0.76
<0.0001
0.99 0.88
<0.0001 <0.0001
energy can be expected to decline per unit of GDP output. Because a rise in the PC results in a reduced throughput per unit of output, increasing the PC becomes a necessary, though not sufficient, condition for sustainability [20]. A rising PC should also predict less waste and pollution per unit of output or per capita. The amount of DPO waste released to air per capita in the five countries over time [24] is found to relate significantly and negatively to the PC. DPO (domestic processed material output) is the amount of materials extracted domestically or imported and used in the economy, which then flows to the domestic environment as pollution via a gateway. It does not include hidden wastes. Flows to air include oxygen and carbon dioxide and other, more traditional, pollutants. Air releases were the major flow to all media (‘‘gateways’’ in [24]), and the 1995 per capita releases are shown in Table 2. The PCs relate significantly and negatively in linear and second order polynomial regressions to TDO waste per capita. As PC increases across countries and time, waste per capita decreases significantly. Finally, the percentage of TMR (n ¼ 16), which ends as TDO waste, can be calculated (Table 3) and is significantly related to the PC. As the PC rises, a larger fraction of material inputs go to product and less to waste.
4. Discussion
Fig. 1. Partition coefficients for five selected countries by year.
The closeness of fit of Eqs. (1) and (2) provides justification for considering industrial processes to be joint production systems in which resource inputs result in only two outputs, goods and waste. The roles of the other factors of economic production, e.g. labor and capital, are as facilitators of the process of using energy to convert materials to goods, services and waste. Empirically, the variations in the amounts of resources
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Table 3 1995 data for five countries Variable
Austria
Germany
Japan
Netherlands
US
PC Energy/capita TMR/capita GDP/capita TDO/capita Air releases/cap. % of TMR ending as waste Impact/capita Fraction of energy to goods
2.7 3.27 na 27.79 14.34 10.83 na 1.04 0.73
1.11 4.17 70.74 28.48 35.67 11.55 40.45 2.6 0.53
3.58 3.96 45.24 35.36 13.74 10.51 27.84 1 0.78
2.52 4.76 66.86 24.85 13.74 14.7 21.74 1 0.72
0.54 7.82 82.93 26.72 69.43 21.92 86.37 5.05 0.35
used between countries over time can be explained satisfactorily on the basis of these two outputs. Industrial systems are joint production, as indeed they must be, because the thermodynamic laws of physics apply to industrial systems. The practice in neoclassical economics of treating wastes as unintended, and generally uncompensated, externalities that merely result in less than perfect markets seems inadequate. 4.1. Comparison of PCs by country There are large differences in PCs between countries. For example, Japan’s PC in 1995 is 3.58, while the US PC is 0.54 (Table 1), i.e., Japan’s PC is 6.7 times higher than the US because Japan partitions 3.58 units of energy to goods production for each unit of energy allocated to waste, while the US allocates only 0.54 units. The differences can be seen in the waste output if we correct for the difference in GDP. Japan produced a GDP of approximately US$ 4.4 trillion in 1995 with a waste output of 1.7 billion tonnes (Table 1). In contrast, a US GDP of US$ 4.4 trillion would have produced a waste output of 11.5 billion tonnes (assuming a linear relationship between GDP and waste in the US), or 6.8 times as much waste as Japan’s for equal goods outputs. Similarly, Japan’s waste/capita is 13.74 tonnes annually while the US’ is 69.42 tonnes/capita, a fivefold difference. PCs are generally increasing over time but at different rates. Germany increased slightly from 1980 until reunification when their PC declined fairly sharply because of inclusion of the wasteful industrial practices of the centrally planned economy prevalent in East Germany prior to 1989. Germany is expected to increase its PC as efficiency measures and realistic resource pricing are applied countrywide. Austria and the Netherlands have increased consistently over the period and remain relatively close in magnitude. It will be interesting to track the effect of its green plans on the PC of the Netherlands. Japan has increased PC the fastest, probably because of its lack of readily available energy resources that require importation of most energy sources and consequent high prices for energy.
The US has the lowest PC of the five countries studied with only small increases since 1975. 4.2. The effect of energy price on the PC and on diversity The low US prices for resources, especially its low energy prices, and the large scale of its economy undoubtedly contribute to the relatively slow change and the lack of emphasis on efficiency, i.e., on partitioning more resources to goods and less to waste. Prices are important, as Amir [26] has pointed out: price is an intensive state function (‘‘thermodynamic-like potentials’’), thus price differences may affect economic system behavior in ways not understood by conventional economics. A SLR of industrial electricity price and the PC is significant and positive (Table 2), although price data over time is limited. As the price of electrical energy to industry increased so did the PC. The price of energy to the industrial sector is an important driving force in improving partitioning to goods rather than waste. 4.3. The PC and diversity An explanation for price affecting PC may involve the diversity of energy flow distribution across sectors [16]. As industrial systems evolve, they generally become more diverse by adding new energy consuming sectors and by equalizing energy flows between sectors, assuming prices are similar. Both effects can be explained by the Shannon–Weaver equation [27] and expressed as a ‘‘diversity’’ measure (H), that, in our case, is measuring only the equitability of energy flows across sectors because data is limited. The richness component of the equation, i.e., the effect of the expansion over sectors i, is minimized because there are only data for five general sectors in every country. Using the Shannon–Weaver equation we can calculate the diversity (H) H ¼ SUMpi Logðpi Þ
ð4Þ
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where the pi are the fractions of energy flow through sector i (in analogy with the importance of species i in ecological systems). Diversity is highest when there are many sectors (energy nodes or flow paths) and the flows are evenly distributed across them. Energy flows through pathways in both ecological [8] and economic systems [16] tend to equalize over time, perhaps because of competition or other evolutionary selection pressures. Long-term evolutionary success seems to go to the most efficient arrangement because of lower entropy production at the margin [28]. However, if there are large resource price disparities between sectors in economic systems [18], then diversity is lower because sectors with low prices tend to consume more energy, but with less efficiency, and energy distribution across sectors becomes skewed away from equitability, thus lowering diversity. A lower diversity reduces efficiency (raises energy and material intensities) and reduces the PC. Therefore, diversity and the PC should be positively related. We can test this for Japan, the country with the largest change in PC over time. Fig. 2 shows the two significantly positively related variables for Japan over time using a double y plot. The PC and diversity are also positively related for other countries but with varying degrees of success and not always significantly, perhaps because of limited energy distribution data. 4.4. The PC and environmental impact The PC may be useful in evaluating relative environmental impacts because it is inversely related to the ‘‘technology’’ term in the IPAT equation (for a review of the IPAT equation see [29]) which has been adopted by industrial ecology [30] as its ‘‘master equation.’’ The
Fig. 2. Japan’s partition coefficient and diversity over time.
IPAT equation is as follows Impact ¼ population ðGDP=personÞ ðimpact=unit of GDPÞ
ð5Þ
The technology term is usually expressed as impact/ activity [31] or, assuming that GDP encompasses all activity, impact/unit of GDP. The PC represents the amount of resource going into GDP relevant to TDO waste. Because the TDO data used in calculating the PC contains hidden waste, much of which is landscape disturbance, then TDO is thought to be a good surrogate for impact. The technology term then becomes waste fraction/GDP fraction or 1/PC. Using 1/PC as the technology term allows a calculation of the impact relative to other countries and may be useful as a comparative measure. The impact/capita of GDP production in the five countries, relative to Japan (set equal to 1), are shown in Table 3 for 1995. The impacts per capita are about equal for Austria, Japan and the Netherlands but 2.6 times as high for Germany and 5 times as high (as Japan’s) for the US. The use of material intensities as a proxy for environmental impacts has been suggested by others [32,33]. Material Inputs/Unit of Service (MIPS) were previously proposed to reflect impacts. In terms of this paper, MIPS would be operationalized to TMR/GDP. The measure proposed here, 1/PC, is somewhat different than MIPS since it uses a fraction of the waste in the numerator rather than material inputs but is significantly and positively related to MIPS (r ¼ 0:88, Table 2) indicating good agreement between the two measures. 4.5. The PC and income A significant finding is that PCs are positively related to GDP/capita and thus to productivity (Table 2). Because incomes are derived from GDP then higher PCs generally predict higher incomes and, therefore, greater public welfare, assuming that wealth is not leaked away. If wealth is leaked away, and more leakage occurs from inefficient systems, then incomes will be lower. In an earlier paper, using the US states as a model [18], I showed that the higher the resource intensity in the state, the more wealth/capita is exported (leaked) to other states and countries. Thus, there are economic, as well as environmental, reasons for improving resource partitioning and efficiency. The current debate over whether the US should improve energy efficiency to reduce global climate change does not generally recognize that economic benefits will also ensue from better partitioning of energy brought on by energy price increases. Economists who predict that only costs will be incurred from higher energy prices are doing US decision makers and the public a disservice.
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4.7. The PC as a predictor in economic planning The PC can be used as a planning tool to predict required changes in an industrial economy to achieve higher efficiencies and the corresponding required reductions in waste can be estimated. Rearranging Eq. (3) to solve for TDO: TDO ¼ ð96:04 GDPÞ=ð0:069 PCÞ
Fig. 3. GDP vs. energy partitioned to goods.
4.6. Energy use and the PC The PC provides a convenient means of relating energy use and GDP. While GDP normally rises with total energy use across countries, the relationship changes over time so the correlation is not very helpful and developed countries’ GDP begins to show a decoupling with energy and materials because the intensities are declining. The relationship can be improved by considering the fraction of energy that the PC predicts is being partitioned to goods and thus correct for changing energy intensity. The energy fraction going to goods can be obtained from fraction of energy to goods ¼ PC=ðPC þ 1Þ:
ð7Þ
From Eq. (7), calculations of TDO can be made for various hypothetical PCs and the corresponding reductions of TDO determined. Corresponding energy reductions can be determined from Eq. (2). Table 4 shows various scenarios of waste reduction for some hypothetical PCs chosen for the US, and Fig. 4 shows the relationship of energy and waste percentage reductions for the US as a function of the hypothetical PC. Reduced TDO waste also reduces energy requirements, and the associated costs decline, a substantial benefit of waste reduction. Energy efficiency benefits become increasingly important if a country is importing energy, like the US, because it stems the outflow of moneys used to pay for the energy and reduces leakage [18]. TMR would also decline, thus further reducing input
ð6Þ
The fractions are shown for the five countries for 1995 in the last row of Table 3. Multiplying the fraction partitioned to goods by the total energy use should then closely relate to GDP. Fig. 3 shows the relationship for the five countries energy partitioned to goods vs. country GDP for the 5 years. The relationship is close, significant and positive. The fraction partitioned to goods expressed here should not be confused with thermodynamic or engineering efficiency. It is unlikely that the Japanese economy could be operating at a 78% thermodynamic or engineering efficiency with regards to energy use.
Fig. 4. US percent reduction (from 1995 levels) in energy and waste as a function of the partition coefficient.
Table 4 US waste and energy reduction required for various PCs US PC reduction
GDP (constant) billion 1996 US$
TDO waste (k tonnes)
Energy use (k mtoe)
Waste reduction (percent)
Energy (percent)
0.54 (1995) 1 2 3
7136 7136 7136 7136
18,544,354 9,932,332 4,966,166 3,310,777
2,089,724 1,370,662 1,027,996 913,775
0 46 73 82
0 34 51 56
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costs, as more materials go into product. Energy consumption and material waste production are significantly and positively related across the five countries and over time, so material waste reductions are closely linked to energy consumption. In the US reducing TDO waste by 46% would result in a PC of 1 and a corresponding 34% reduction in energy consumption. The fact that all four of the other developed countries studied here have PCs above 1 indicates that it is highly feasible. To reach a PC of approximately 2, the US would have to reduce waste by 73%, and energy consumption would decline 51%, putting the US within striking distance of eliminating petroleum imports. An additional benefit would be large reductions in carbon emissions from the US, which are the world’s largest contribution to global climate change. The curves are logarithmic and appear to be approaching an asymptote at a PC of approximately 4, at which point little marginal gain in resource percent reductions can be expected from an improvement in the PC. This suggests a theoretical limit on efficiency improvements, as thermodynamics requires. 4.8. Recommendations for the US None of these improvements in the efficiency of the US economy would be easy and there are costs involved. However, there are substantial benefits and, in the long term, benefits would outweigh costs and help to insure that there is long-term sustainability in the US. Taxes on the consumption of resources and the production of wastes would help to make the transition and enforcement of traditional environmental regulations would continue to help, at least until the industrial sector begins to reduce waste for market reasons. In an earlier paper [34] in which I used the states of the US as a model, I found that states with good environmental programs also had industries with significantly lower energy and material intensities and had better economies. There are many externalities and the subsidies they generate connected with energy consumption in the US [15]. Taxes on resource use and waste generation (green taxes) would help to internalize the many externalities, (e.g. military protection of energy sources, environmental landscape damage and pollution) and the related subsidies that those who consume resources and produce wastes enjoy. A review of such tax shifting experience in a number of countries can be found in Roodman [35] and, for the European countries, Ekins [36]. The use of environmental taxes and charges in OECD countries increased by over 50% between 1987 and 1994. Such taxes would require that those using natural capital as source or sink would pay for the privilege. Those who use it less, i.e., the average citizen both present and future, would benefit from lower
taxes, less pollution and conserved resources. Equity would be served and the consumption of resources and waste production would decline. 5. Conclusion The empirical regression model introduced here affirms that industrial systems’ partitioning of resources into goods and waste can be calculated and predicted. Those industrial societies that partition more resources into goods, as opposed to waste, tend to experience higher societal benefits from their industrial activities. Societal benefits of higher PCs accrue in the form of reduced pollution releases and reduced environmental impact from resource extraction. In addition, higher PCs mean that GDP per capita income is higher, which will generally improve public welfare. With a higher PC the material and energy inputs required for production per unit of output are lower, so production costs are lower and industry should be more competitive and sustainable. The price of energy to the industrial sector strongly influences the partitioning of energy to goods or waste. If energy, or any other resource, is artificially cheap because of subsidization then there is little incentive for efficiency so the PC remains low, as in the US. Appropriate policy responses would be to remove subsidies and internalize costs of resources and wastes by taxation, a path generally being followed by the countries in this analysis with the highest PCs. It is not likely that the market will selfcorrect and raise prices to include all costs in product prices because some of the costs involve non-market amenities, and there are powerful political forces working to maintain the status quo because they are reaping subsidies from their externalized costs. Policies that emphasize low resource prices are ultimately selfdefeating and generally raise severe equity issues, both inter- and intra-generationally.
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P.H. Templet / Journal of Cleaner Production 12 (2004) 855–863 [6] Prigogine I. From being to becoming: time and complexity in the physical sciences. San Francisco (CA): W.H. Freeman, Co; 1980. [7] von Bertalanffy L. General system theory. New York (NY): George Braziller, Inc; 1968. [8] Odum EP. The strategy of ecosystem development. Science 1969;164:262–70. [9] von Bertalanffy L. Perspectives on general system theory. New York: George Braziller, Inc; 1975. [10] Andrews C, Berkhout F, Thomas V. The industrial ecology agenda. In: Socolow R, Andrews C, Berkhout F, Thomas V, editors. Industrial ecology and global change. Cambridge: Cambridge University Press; 1994, p. 469–77. [11] Frosch RA. Industrial ecology: minimizing the impact of industrial waste. Physics Today 1994;November:63–8. [12] Tibbs H. Industrial ecology: an environmental agenda for industry. Emeryville (CA): Global Business Network; 1993. [13] Darwin C. The origin of species by natural selection. London: Murray; 1859. [14] Ulanowicz RE. Growth and development, ecosystems phenomenology. New York (NY): Springer-Verlag; 1986. [15] Templet PH. Grazing the commons: externalities, subsidies and economic development. Ecological Economics 1995;12:141–59. [16] Templet PH. Energy, diversity and development in economic systems: an empirical analysis. Ecological Economics 1999;30:223–33. [17] Templet PH. Defending the public domain: pollution, subsidies, and poverty. In: Boyce J, editor. Natural assets: democratizing environmental ownership. Amherst (MA): Sage Press; 2003, p. 99–116. [18] Templet PH. Energy price disparity and public welfare. Ecological Economics 2001;36:443–60. [19] Porter Michael E, van der Linde Class. Green and competitive: ending the stalemate. Harvard Business Review 1995;September– October:122–34. [20] Daly HE. Toward some operational principles of sustainability. Ecological Economics 1990;2(1):1–6. [21] Costanza R, D’Arge R, de Groot R, Farber S, Grasso M, Hannon B, et al. The value of the world’s ecosystem services and natural capital. Nature 1997;387:253–60.
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[22] Cleveland C, Ruth M. Indicators of dematerialization and the materials intensity of use. Journal of Industrial Ecology 1999;2(1):15–50. [23] World Resources Institute. Resource flows: the material basis of industrial economies. Washington (DC): WRI; 1997. [24] World Resources Institute. The weight of nations: material outflows from industrial economies. Washington (DC): WRI; 2000. [25] International Energy Agency. Energy balances for OECD countries, 1960–1997. Paris: IEA; 1999. [26] Amir S. The role of thermodynamics in the study of economic and ecological systems. Ecological Economics 1994;10:125–42. [27] Shannon CE, Weaver W. The mathematical theory of communication. Urbana: University of Illinois Press; 1949. [28] Binswanger M. From microscopic to macroscopic theories: entropic aspects of ecological and economic processes. Ecological Economics 1993;8(3):209–34. [29] Chertow MR. The IPAT equation and its variants: changing views of technology and environmental impact. Journal of Industrial Ecology 2000;4(4):13–30. [30] Graedel T, Allenby B. Industrial ecology. Englewood Cliffs (NJ): Prentice Hall; 1995. [31] Moomaw W, Tullis M. Charting development paths: a multicountry comparison of carbon dioxide emissions. In: Socolow R, Andrews C, Berkhout F, Thomas V, editors. Industrial ecology and global change. Cambridge, UK: Cambridge University Press; 1994. [32] Schmidt-Bleek F. MIPS—a universal ecological measure? Fresenius Environmental Bulletin 1993;2(8):306–11. [33] Hinterberger F, Luks F, Schmidt-Bleek F. Material flows vs. ‘natural capital’: what makes an economy sustainable? Ecological Economics 1997;23:1–14. [34] Templet PH. The neglected benefits of protecting ecological services, a commentary provided to the ecological economics forum. Ecological Economics 1998;25:53–5. [35] Roodman DM. Building a sustainable society. In: Starke L, editor. State of the world 1999. New York (NY): WW Norton and Co; 1999. [36] Ekins P. European environmental taxes and charges: recent experience, issues and trends. Ecological Economics 1999;31: 39–62.
Partitioning of resources in production: an empirical analysis Paul H. Templet Department of Environmental Studies, Energy and Environmental Building, 1285 Energy Coast and Environment Building, Louisiana State University, Baton Rouge, LA 70803, USA
Abstract Industrial economies ingest materials, energy and information to produce goods and services and excrete wastes and emissions. Wastes can be minimized and the relative amounts of resources, which go into goods and services, as opposed to waste, are essential to clean production and to the sustainability of the production system. A regression model based on empirical data is presented that provides a partition coefficient expressing the ratio of energy (or material) resources invested in goods to energy going to waste. Partition coefficients are developed for five countries and are shown to be related to GDP, energy and material consumption and energy and waste intensities. Higher partition coefficients mean higher productivity and lower energy, waste and material intensities. In addition, energy use/capita and pollution/capita is lower. The price of energy to the industrial sector is related to the partition coefficient. The policy implications are that partitioning of resources to goods should be maximized and waste minimized for economic as well as environmental reasons. # 2004 Elsevier Ltd. All rights reserved. Keywords: Energy partitioning; Industrial ecology; Metabolism; Energy intensity
1. Introduction Economic development is an evolutionary process that results in changes over time as economic systems self-organize in response to information feedback [1,2]. Economies are open thermodynamic systems that ingest and metabolize high entropy materials and low entropy energy from the environment to create low entropy goods and excrete high entropy wastes [3]. The production of goods necessarily involves joint production [4] of both desired products and undesired wastes. The requirement that some waste is always produced follows from the first and second laws of thermodynamics, which hold that matter and energy, the most fundamental factors of industrial production, can be changed but not lost in production, that no process is totally efficient, and that entropy is generated in any transformation of matter and energy. Wastes, including spent products, ultimately leave the economic system via discharges to the environment. The term waste, as used here, refers to energy or materials that
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0959-6526/$ - see front matter # 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.jclepro.2004.02.033
are rejected or disposed from the overall industrial system and not from individual processes or hierarchies. Industrial ecology and clean production principles generally recognize that waste created at one level of a system of hierarchies can become a resource at another level. For this reason, recycling and reuse of wastes in economic systems are promoted as techniques for improving overall system efficiency [5] and for good reasons. A concern arises, if waste reduction at one level adversely affects another level by reducing available resources and overall production—a negative growth effect. Despite this concern, it is likely that every opportunity to reduce waste should be taken for the following reasons. If one level reduces its waste input (as a resource) to another level the receiving level will likely improve its efficiency to stay competitive and the effect will cascade through all lower levels improving overall efficiency. Those parts of the hierarchy that cannot increase efficiency will lose production and be replaced by other, more competitive or ‘‘fitter,’’ entities. Secondly, if waste materials are not reduced at every level, then energy efficiency will suffer because energy is required to process materials and create waste. Energy is the prime resource in economic production because waste materials, no matter how
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diffuse, can be reconstituted (entropy is reduced) if enough energy is available. The converse is not true; energy degraded to background heat cannot be reused, no matter how much material is available. This paper will deal with the overall economic system because that is the level for which data is available. Individual hierarchies or processes could be investigated using these techniques in an I/O model if (1) energy input and (2) product and waste output data were available. The question of whether waste reduction at all levels is the best policy, as I believe it is, will remain for another time. The evolutionary nature of self-organizing systems means that energy cascading through system hierarchies builds order and diversity (or complexity) and represents an investment in the system. Viewed in this manner, economies resemble the dissipative structures of Prigogine [6], which consume energy while increasing complexity and decreasing uncertainty. Evolution, including economic evolution, continues irreversibly over time, producing more system structure and complexity, leading to increased efficiency and less entropy generated at the margin. This may be an example of evolution characterized by Bertalanffy [7] as ‘‘. . .directedness based upon structure. . .’’. The greater the evolved structure within a system the greater the number and types of energy uses and the smaller the energy flow gradients between compartments, sectors or nodes as energy cascades toward final dissipation as waste background heat. This organizational strategy is used by ecosystems during succession and evolution and results in more system diversity, i.e., structure [8] and less waste. Assuming isomorphism across system types [7,9] economic system efficiency and productivity is also expected to generally increase during development. Efficiency in systems is likely promoted by competition for scarce resources. ‘‘Emergent’’ properties of economic systems that appear during development include diversity of energy flow paths, improved efficiency and productivity and reduced waste output. Industrial ecology [10–12] incorporates many of these same facets into its framework. Increased productivity during evolution was first suggested by Darwin [13] to be related to diversity in ecological systems. Ulanowicz [14] relates the changes in development, including diversity and production capacity increases, to changes in energy flow patterns within the system over time. A measure to analyze and track these emerging properties would be useful in the study of industrial ecology. The joint production concept also includes other important industrial production issues, including irreversibility of the process, limits to substitution, the ubiquity of waste and limits to growth (see [4] for a discussion of these). Wastes from industrial processes are generally unwanted and, in the interest of reducing costs internal to the industry, are often ‘‘externalized’’
as pollution to the public commons of air, land and water. Once released, the externalized pollution can create unwanted impacts that are imposed on unwitting or unwilling groups raising public health and equity issues. Previous studies have shown that externalities create a subsidy for the externalizing industry but at a cost to those receiving the externality that can lead to income distribution disparities, poverty and a loss of public welfare [15–17]. In addition, industries and economies that create large amounts of wastes relative to goods are inefficient, resource intensive, wasteful [18], and are likely to suffer a competitive disadvantage [19] because their resource supply and waste costs per unit of goods are higher than those of more efficient producers. Sustainability requires that industrial throughput be within the source and sink capacities of the environment [20]. Poor allocation of resources between goods and waste negatively affects sustainability by increasing throughput per unit of output and the amount of waste generated per unit of production. The environment is the source of the natural resources that are used in the economic system to produce goods and is generally the final repository of wastes. These are the ‘‘source’’ and ‘‘sink’’ functions that constitute natural capital and are essential to the development of economic capital. For these reasons, the industrial economy is dependent on the environment although conventional economic wisdom generally discounts the value of natural capital because the market captures its values only partially. Conversely, Costanza et al. [21] showed that the value of natural capital may be higher than that of manmade capital. Misallocation of resources negatively affects natural capital by consuming more of it and by imposing higher waste loads per unit of goods produced. A measure of relative resource allocation in industrial systems, i.e., the distribution between goods and waste as proposed here, would be useful in evaluating energy and material efficiencies, intensities and in tracking progress toward sustainability. The partition coefficient (PC) described below may provide such a measure. The purpose of this paper is to pragmatically test the hypothesis that industrial economies are joint production systems whose only outputs are goods (and services) and waste. A related purpose is to develop economy-wide PCs for selected developed countries using empirical data and to test them against measures of resource and waste intensity resource use, pollution and productivity (for a review of materials intensity research see [22]). The concept presented here is that the PC represents the relative proportion of resources being allocated to goods normalized by the amount allocated to waste. It is a general principle and can be used at any system level where the data is available. The approach is quantitative but cannot distinguish
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Table 1 Input and output data for five countries and years Country and year
Population (thousands)
Energy input thousand mtoe
Austria 75 Austria 80 Austria 85 Austria 90 Austria 95 Germany 75 Germany 80 Germany 85 Germany 90 Germany 95 Japan 75 Japan 80 Japan 85 Japan 90 Japan 95 Netherlands 75 Netherlands 80 Netherlands 85 Netherlands 90 Netherlands 95 US 75 US 80 US 85 US 90 US 95
7579 7549 7558 7718 8047 61,829 61,566 61,024 63,254 81,539 111,940 117,060 121,049 123,611 125,570 13,599 14,091 14,454 14,893 15,424 220,165 230,406 241,855 254,106 267,115
20,365.1 23,449.9 23,208.0 25,699.5 26,295.0 316,652.9 360,441.2 361,247.8 355,732.2 339,867.4 308,240.2 346,491.3 367,026.1 438,797.1 497,032.3 59,528.2 65,000.1 61,617.8 66,593.2 73,358.0 1,660,546.1 1,811,649.8 1,781,709.1 1,925,681.6 2,089,723.9
TMR (materials) thousand tonnes
3,949,514 4,266,377 3,715,642 4,228,973 4,186,000 4,448,000 4,430,000 5,682,000 758,089 879,299 892,917 1,025,685 21,463,000 21,982,000 20,623,000 22,145,000
qualitative differences, e.g. between good and bad inputs or outputs. It aggregates all goods and services outputs equally in terms of their dollar contribution to GDP, but essential consumption (e.g. food for survival) is not distinguishable from nonessential consumption (e.g. gas guzzler automobiles for every family member). 2. Methods World Resource Institute publications [23,24] provided annual input and output material flow data, respectively. The data are total material requirement (TMR, total annual materials input including imports but excluding water and air), total domestic output, (TDO, annual waste output but excluding oxygen and carbon dioxide from respiration and waste exports), as well as population and annual GDP (in constant 1996 US$). TMR and TDO include both total direct material flows and hidden or indirect flows. Hidden or indirect flows are the total weight of materials moved or mobilized in the domestic environment in the course of providing commodities for economic use that do not themselves enter the economy. Such flows include things like landscape alterations for resource extraction and plant biomass later separated from the desired material. Empirical data for five developed countries (Austria, Germany, Japan, Netherlands and the US.) spread over 20 years in 5-year increments were pooled,
GDP (goods output) billion 1996 US$
TDO (waste) thousand tonnes
PC
136.07 160.43 174.22 203.87 223.61 1,221.60 1,434.41 1,513.84 1,769.02 2,321.80 2,245.32 2,769.52 3,268.18 4,125.31 4,440.72 244.96 278.14 296.15 345.22 383.34 4,253.95 4,975.82 5,614.67 6,435.21 7,135.89
136,995 137,017 138,535 130,410 115,423 1,367,353 1,750,155 1,711,887 1,710,546 2,908,351 1,486,746 1,403,263 1,303,350 1,582,030 1,725,105 237,896 226,752 213,821 222,991 211,969 19,315,306 19,681,735 18,130,483 19,126,594 18,544,354
1.382 1.630 1.750 2.176 2.697 1.244 1.141 1.231 1.439 1.111 2.102 2.747 3.490 3.629 3.583 1.433 1.707 1.928 2.155 2.517 0.307 0.352 0.431 0.468 0.536
yielding n ¼ 25 data points. Energy consumption is in thousand metric tons of oil equivalent and is taken from the International Energy Agency [25]. The data are presented in Table 1. The joint production hypothesis can be tested and the PC developed by using a multiple regression where input energy or material is the dependent variable, and the two outputs (GDPvalue of goods and TDO-wastes) are the independent variables. The method was previously used to calculate PCs for the individual US states, although a surrogate for waste data was used [18]. In that study a state’s PC was found to be positively related to income and other measures of public welfare. The approach used here could also apply at different scales and to individual industries or facilities if the data were available. Simple linear regressions (SLR) are used to test relationships between variables.
3. Results A multiple regression analysis with energy used as the dependent variable and GDP and TDO as the independent variables provides the following equation: Energy usedðk mtoeÞ ¼ 17858 þ 89:888 GDPðbillion 1996 US$Þ þ 0:070 TDOðk tonnesÞ
ð1Þ
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where R2 ¼ 0:99 and the independent variables and model are significant (t values of the independent variables are 9.4 and 24.4, respectively) but the intercept is not significant and is expected to be zero. A similar calculation without intercept yields: Energy used ðk mtoeÞ ¼ 96:040GDPðbillion 1996 US$Þ þ 0:069TDOðk tonnesÞ
ð2Þ
where R2 ¼ 0:99, variables and model are significant (p < 0:0001) and the t values are 12.7 and 25.1 for the GDP and TDO terms, respectively. The first term on the right of Eq. (2) represents the energy that is being partitioned to GDP (goods), and the second term represents the energy partitioned into waste. Because we are interested in the relative amounts of energy dividing the first term by the second term for each country and year yields the energy PCs calculated from the following equation and shown in Table 1 (GDP is in billion 1996 US$ and waste (TDO) is in thousand tonnes): PC ¼ ð96:04 GDPÞ=ð0:069 TDOÞ
ð3Þ
The five country PCs are plotted for years 1975–1995 in Fig. 1. A similar operation could be carried out to develop material PCs using material inputs (TMRs) as the dependent variable. However, TMR data is more limited (n ¼ 16) and the PCs developed may not be as reliable. Consequently the PCs presented here are derived using energy consumption as the dependent variable. The PCs are found to be significantly and positively related to GDP per capita and negatively to energy use/capita. (Table 2 contains the statistical information on the relationship of PC to the variables discussed here.) In addition, the PCs are significantly and negatively related to both material and energy input intensities. As PCs rise, the amount of input material and
Table 2 Statistical relationship of variables to the PC Variable
Pearson’s r
p
GDP/capita Energy use/capita TMR/GDP Energy use/GDP DPO to air/capita TDO/capita (linear) TDO/capita (polynomial) TMR which ends as TDO waste Industrial electricity price Japan’s diversity MIPS
0.53 0.75 0.88 0.83 0.74 0.78 0.97
<0.0067 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001
0.77
<0.0001
0.76
<0.0001
0.99 0.88
<0.0001 <0.0001
energy can be expected to decline per unit of GDP output. Because a rise in the PC results in a reduced throughput per unit of output, increasing the PC becomes a necessary, though not sufficient, condition for sustainability [20]. A rising PC should also predict less waste and pollution per unit of output or per capita. The amount of DPO waste released to air per capita in the five countries over time [24] is found to relate significantly and negatively to the PC. DPO (domestic processed material output) is the amount of materials extracted domestically or imported and used in the economy, which then flows to the domestic environment as pollution via a gateway. It does not include hidden wastes. Flows to air include oxygen and carbon dioxide and other, more traditional, pollutants. Air releases were the major flow to all media (‘‘gateways’’ in [24]), and the 1995 per capita releases are shown in Table 2. The PCs relate significantly and negatively in linear and second order polynomial regressions to TDO waste per capita. As PC increases across countries and time, waste per capita decreases significantly. Finally, the percentage of TMR (n ¼ 16), which ends as TDO waste, can be calculated (Table 3) and is significantly related to the PC. As the PC rises, a larger fraction of material inputs go to product and less to waste.
4. Discussion
Fig. 1. Partition coefficients for five selected countries by year.
The closeness of fit of Eqs. (1) and (2) provides justification for considering industrial processes to be joint production systems in which resource inputs result in only two outputs, goods and waste. The roles of the other factors of economic production, e.g. labor and capital, are as facilitators of the process of using energy to convert materials to goods, services and waste. Empirically, the variations in the amounts of resources
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Table 3 1995 data for five countries Variable
Austria
Germany
Japan
Netherlands
US
PC Energy/capita TMR/capita GDP/capita TDO/capita Air releases/cap. % of TMR ending as waste Impact/capita Fraction of energy to goods
2.7 3.27 na 27.79 14.34 10.83 na 1.04 0.73
1.11 4.17 70.74 28.48 35.67 11.55 40.45 2.6 0.53
3.58 3.96 45.24 35.36 13.74 10.51 27.84 1 0.78
2.52 4.76 66.86 24.85 13.74 14.7 21.74 1 0.72
0.54 7.82 82.93 26.72 69.43 21.92 86.37 5.05 0.35
used between countries over time can be explained satisfactorily on the basis of these two outputs. Industrial systems are joint production, as indeed they must be, because the thermodynamic laws of physics apply to industrial systems. The practice in neoclassical economics of treating wastes as unintended, and generally uncompensated, externalities that merely result in less than perfect markets seems inadequate. 4.1. Comparison of PCs by country There are large differences in PCs between countries. For example, Japan’s PC in 1995 is 3.58, while the US PC is 0.54 (Table 1), i.e., Japan’s PC is 6.7 times higher than the US because Japan partitions 3.58 units of energy to goods production for each unit of energy allocated to waste, while the US allocates only 0.54 units. The differences can be seen in the waste output if we correct for the difference in GDP. Japan produced a GDP of approximately US$ 4.4 trillion in 1995 with a waste output of 1.7 billion tonnes (Table 1). In contrast, a US GDP of US$ 4.4 trillion would have produced a waste output of 11.5 billion tonnes (assuming a linear relationship between GDP and waste in the US), or 6.8 times as much waste as Japan’s for equal goods outputs. Similarly, Japan’s waste/capita is 13.74 tonnes annually while the US’ is 69.42 tonnes/capita, a fivefold difference. PCs are generally increasing over time but at different rates. Germany increased slightly from 1980 until reunification when their PC declined fairly sharply because of inclusion of the wasteful industrial practices of the centrally planned economy prevalent in East Germany prior to 1989. Germany is expected to increase its PC as efficiency measures and realistic resource pricing are applied countrywide. Austria and the Netherlands have increased consistently over the period and remain relatively close in magnitude. It will be interesting to track the effect of its green plans on the PC of the Netherlands. Japan has increased PC the fastest, probably because of its lack of readily available energy resources that require importation of most energy sources and consequent high prices for energy.
The US has the lowest PC of the five countries studied with only small increases since 1975. 4.2. The effect of energy price on the PC and on diversity The low US prices for resources, especially its low energy prices, and the large scale of its economy undoubtedly contribute to the relatively slow change and the lack of emphasis on efficiency, i.e., on partitioning more resources to goods and less to waste. Prices are important, as Amir [26] has pointed out: price is an intensive state function (‘‘thermodynamic-like potentials’’), thus price differences may affect economic system behavior in ways not understood by conventional economics. A SLR of industrial electricity price and the PC is significant and positive (Table 2), although price data over time is limited. As the price of electrical energy to industry increased so did the PC. The price of energy to the industrial sector is an important driving force in improving partitioning to goods rather than waste. 4.3. The PC and diversity An explanation for price affecting PC may involve the diversity of energy flow distribution across sectors [16]. As industrial systems evolve, they generally become more diverse by adding new energy consuming sectors and by equalizing energy flows between sectors, assuming prices are similar. Both effects can be explained by the Shannon–Weaver equation [27] and expressed as a ‘‘diversity’’ measure (H), that, in our case, is measuring only the equitability of energy flows across sectors because data is limited. The richness component of the equation, i.e., the effect of the expansion over sectors i, is minimized because there are only data for five general sectors in every country. Using the Shannon–Weaver equation we can calculate the diversity (H) H ¼ SUMpi Logðpi Þ
ð4Þ
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where the pi are the fractions of energy flow through sector i (in analogy with the importance of species i in ecological systems). Diversity is highest when there are many sectors (energy nodes or flow paths) and the flows are evenly distributed across them. Energy flows through pathways in both ecological [8] and economic systems [16] tend to equalize over time, perhaps because of competition or other evolutionary selection pressures. Long-term evolutionary success seems to go to the most efficient arrangement because of lower entropy production at the margin [28]. However, if there are large resource price disparities between sectors in economic systems [18], then diversity is lower because sectors with low prices tend to consume more energy, but with less efficiency, and energy distribution across sectors becomes skewed away from equitability, thus lowering diversity. A lower diversity reduces efficiency (raises energy and material intensities) and reduces the PC. Therefore, diversity and the PC should be positively related. We can test this for Japan, the country with the largest change in PC over time. Fig. 2 shows the two significantly positively related variables for Japan over time using a double y plot. The PC and diversity are also positively related for other countries but with varying degrees of success and not always significantly, perhaps because of limited energy distribution data. 4.4. The PC and environmental impact The PC may be useful in evaluating relative environmental impacts because it is inversely related to the ‘‘technology’’ term in the IPAT equation (for a review of the IPAT equation see [29]) which has been adopted by industrial ecology [30] as its ‘‘master equation.’’ The
Fig. 2. Japan’s partition coefficient and diversity over time.
IPAT equation is as follows Impact ¼ population ðGDP=personÞ ðimpact=unit of GDPÞ
ð5Þ
The technology term is usually expressed as impact/ activity [31] or, assuming that GDP encompasses all activity, impact/unit of GDP. The PC represents the amount of resource going into GDP relevant to TDO waste. Because the TDO data used in calculating the PC contains hidden waste, much of which is landscape disturbance, then TDO is thought to be a good surrogate for impact. The technology term then becomes waste fraction/GDP fraction or 1/PC. Using 1/PC as the technology term allows a calculation of the impact relative to other countries and may be useful as a comparative measure. The impact/capita of GDP production in the five countries, relative to Japan (set equal to 1), are shown in Table 3 for 1995. The impacts per capita are about equal for Austria, Japan and the Netherlands but 2.6 times as high for Germany and 5 times as high (as Japan’s) for the US. The use of material intensities as a proxy for environmental impacts has been suggested by others [32,33]. Material Inputs/Unit of Service (MIPS) were previously proposed to reflect impacts. In terms of this paper, MIPS would be operationalized to TMR/GDP. The measure proposed here, 1/PC, is somewhat different than MIPS since it uses a fraction of the waste in the numerator rather than material inputs but is significantly and positively related to MIPS (r ¼ 0:88, Table 2) indicating good agreement between the two measures. 4.5. The PC and income A significant finding is that PCs are positively related to GDP/capita and thus to productivity (Table 2). Because incomes are derived from GDP then higher PCs generally predict higher incomes and, therefore, greater public welfare, assuming that wealth is not leaked away. If wealth is leaked away, and more leakage occurs from inefficient systems, then incomes will be lower. In an earlier paper, using the US states as a model [18], I showed that the higher the resource intensity in the state, the more wealth/capita is exported (leaked) to other states and countries. Thus, there are economic, as well as environmental, reasons for improving resource partitioning and efficiency. The current debate over whether the US should improve energy efficiency to reduce global climate change does not generally recognize that economic benefits will also ensue from better partitioning of energy brought on by energy price increases. Economists who predict that only costs will be incurred from higher energy prices are doing US decision makers and the public a disservice.
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4.7. The PC as a predictor in economic planning The PC can be used as a planning tool to predict required changes in an industrial economy to achieve higher efficiencies and the corresponding required reductions in waste can be estimated. Rearranging Eq. (3) to solve for TDO: TDO ¼ ð96:04 GDPÞ=ð0:069 PCÞ
Fig. 3. GDP vs. energy partitioned to goods.
4.6. Energy use and the PC The PC provides a convenient means of relating energy use and GDP. While GDP normally rises with total energy use across countries, the relationship changes over time so the correlation is not very helpful and developed countries’ GDP begins to show a decoupling with energy and materials because the intensities are declining. The relationship can be improved by considering the fraction of energy that the PC predicts is being partitioned to goods and thus correct for changing energy intensity. The energy fraction going to goods can be obtained from fraction of energy to goods ¼ PC=ðPC þ 1Þ:
ð7Þ
From Eq. (7), calculations of TDO can be made for various hypothetical PCs and the corresponding reductions of TDO determined. Corresponding energy reductions can be determined from Eq. (2). Table 4 shows various scenarios of waste reduction for some hypothetical PCs chosen for the US, and Fig. 4 shows the relationship of energy and waste percentage reductions for the US as a function of the hypothetical PC. Reduced TDO waste also reduces energy requirements, and the associated costs decline, a substantial benefit of waste reduction. Energy efficiency benefits become increasingly important if a country is importing energy, like the US, because it stems the outflow of moneys used to pay for the energy and reduces leakage [18]. TMR would also decline, thus further reducing input
ð6Þ
The fractions are shown for the five countries for 1995 in the last row of Table 3. Multiplying the fraction partitioned to goods by the total energy use should then closely relate to GDP. Fig. 3 shows the relationship for the five countries energy partitioned to goods vs. country GDP for the 5 years. The relationship is close, significant and positive. The fraction partitioned to goods expressed here should not be confused with thermodynamic or engineering efficiency. It is unlikely that the Japanese economy could be operating at a 78% thermodynamic or engineering efficiency with regards to energy use.
Fig. 4. US percent reduction (from 1995 levels) in energy and waste as a function of the partition coefficient.
Table 4 US waste and energy reduction required for various PCs US PC reduction
GDP (constant) billion 1996 US$
TDO waste (k tonnes)
Energy use (k mtoe)
Waste reduction (percent)
Energy (percent)
0.54 (1995) 1 2 3
7136 7136 7136 7136
18,544,354 9,932,332 4,966,166 3,310,777
2,089,724 1,370,662 1,027,996 913,775
0 46 73 82
0 34 51 56
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P.H. Templet / Journal of Cleaner Production 12 (2004) 855–863
costs, as more materials go into product. Energy consumption and material waste production are significantly and positively related across the five countries and over time, so material waste reductions are closely linked to energy consumption. In the US reducing TDO waste by 46% would result in a PC of 1 and a corresponding 34% reduction in energy consumption. The fact that all four of the other developed countries studied here have PCs above 1 indicates that it is highly feasible. To reach a PC of approximately 2, the US would have to reduce waste by 73%, and energy consumption would decline 51%, putting the US within striking distance of eliminating petroleum imports. An additional benefit would be large reductions in carbon emissions from the US, which are the world’s largest contribution to global climate change. The curves are logarithmic and appear to be approaching an asymptote at a PC of approximately 4, at which point little marginal gain in resource percent reductions can be expected from an improvement in the PC. This suggests a theoretical limit on efficiency improvements, as thermodynamics requires. 4.8. Recommendations for the US None of these improvements in the efficiency of the US economy would be easy and there are costs involved. However, there are substantial benefits and, in the long term, benefits would outweigh costs and help to insure that there is long-term sustainability in the US. Taxes on the consumption of resources and the production of wastes would help to make the transition and enforcement of traditional environmental regulations would continue to help, at least until the industrial sector begins to reduce waste for market reasons. In an earlier paper [34] in which I used the states of the US as a model, I found that states with good environmental programs also had industries with significantly lower energy and material intensities and had better economies. There are many externalities and the subsidies they generate connected with energy consumption in the US [15]. Taxes on resource use and waste generation (green taxes) would help to internalize the many externalities, (e.g. military protection of energy sources, environmental landscape damage and pollution) and the related subsidies that those who consume resources and produce wastes enjoy. A review of such tax shifting experience in a number of countries can be found in Roodman [35] and, for the European countries, Ekins [36]. The use of environmental taxes and charges in OECD countries increased by over 50% between 1987 and 1994. Such taxes would require that those using natural capital as source or sink would pay for the privilege. Those who use it less, i.e., the average citizen both present and future, would benefit from lower
taxes, less pollution and conserved resources. Equity would be served and the consumption of resources and waste production would decline. 5. Conclusion The empirical regression model introduced here affirms that industrial systems’ partitioning of resources into goods and waste can be calculated and predicted. Those industrial societies that partition more resources into goods, as opposed to waste, tend to experience higher societal benefits from their industrial activities. Societal benefits of higher PCs accrue in the form of reduced pollution releases and reduced environmental impact from resource extraction. In addition, higher PCs mean that GDP per capita income is higher, which will generally improve public welfare. With a higher PC the material and energy inputs required for production per unit of output are lower, so production costs are lower and industry should be more competitive and sustainable. The price of energy to the industrial sector strongly influences the partitioning of energy to goods or waste. If energy, or any other resource, is artificially cheap because of subsidization then there is little incentive for efficiency so the PC remains low, as in the US. Appropriate policy responses would be to remove subsidies and internalize costs of resources and wastes by taxation, a path generally being followed by the countries in this analysis with the highest PCs. It is not likely that the market will selfcorrect and raise prices to include all costs in product prices because some of the costs involve non-market amenities, and there are powerful political forces working to maintain the status quo because they are reaping subsidies from their externalized costs. Policies that emphasize low resource prices are ultimately selfdefeating and generally raise severe equity issues, both inter- and intra-generationally.
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