Life Cycle Approaches to Sustainable Consumption: A Critical

Critical Review

Life Cycle Approaches to Sustainable Consumption: A Critical Review EDGAR G. HERTWICH Industrial Ecology Program and Department of Energy and Process Engineering, Norwegian University of Science and Technology, 7491 Trondheim, Norway

The 2002 World Summit for Sustainable Development in Johannesburg called for a comprehensive set of programs focusing on sustainable consumption and production. According to world leaders, these programs should rely on life cycle assessment (LCA) to promote sustainable patterns of production and consumption. Cleaner production is a wellestablished activity, and it uses LCA. UNEP, the European Union, and a number of national organizations have now begun to work on sustainable consumption. In developing sustainable consumption policies and activities, the use of LCA presents interesting opportunities that are not yet wellunderstood by policy makers. This paper reviews how life cycle approaches, primarily based on input-output analysis, have been used in the area of sustainable consumption: to inform policy making, select areas of action, identify which lifestyles are more sustainable, advise consumers, and evaluate the effectiveness of sustainable consumption measures. Information on consumption patterns usually comes from consumer expenditure surveys. Different study designs and a better integration with consumer research can provide further interesting insights. Life-cycle approaches still need to be developed and tested. Current research is mostly descriptive; policy makers, however, require more strategic analysis addressing their decision options, including scenario analysis and backcasting.

Introduction At the World Summit for Sustainable Development (WSSD) in Johannesburg, world leaders recognized that it is necessary to “chang[e] unsustainable patterns of consumption and production”. In the “Plan of Implementation”, the main document to emerge from the WSSD, government leaders call for “fundamental changes in the way societies produce and consume” (1, 13). They resolve to “encourage and promote the development of a 10-year framework of programs in support of regional and national initiatives to accelerate the shift toward sustainable consumption and production [...]” (1, 14). The Johannesburg Plan of Implementation calls for the adoption of tools, policies, and assessment mechanisms based on life-cycle analysis to promote sustainable patterns of production and consumption and to increase the ecoefficiency of products and services (1, 13). It is remarkable that the United Nations General Assembly singles out Life Cycle Assessment (LCA) as the tool that will help achieve * Corresponding author phone: +47-7359 8949; fax: +47-7359 8943; e-mail: [email protected]. 10.1021/es0497375 CCC: $30.25 Published on Web 06/03/2005

 2005 American Chemical Society

sustainable consumption and production. One needs to ask: Is LCA up to the task? How will it be able to make such a significant contribution? What can LCA-type analysis tell us already about the environmental impacts of different consumption patterns? What insights can it offer on ways to reduce those impacts? LCA is commonly applied to single products, but not to consumption patterns. This paper outlines and discusses different ways in which LCA, alone or in combination with other scientific methods and tools, can be used to advance sustainable consumption. It reviews assessments of household environmental impact (HEI) and suggests ways in which this type of research can make significant contributions to the “Plan of Implementation”. Life cycle assessment is a tool to assess the environmental impacts of product systems and services, accounting for the emissions and resource uses during the production, distribution, use, and disposal of a product (2). LCA has developed from the analysis of cumulative or embodied energy demand (3, 4). It uses (physical) process analysis and (monetary) input-output analysis. (Table 1 provides an overview over the different analytical approaches.) The assessment of the upstream impacts of all the products consumed by a household usually relies at least in part on data from inputoutput analysis (5). This review takes a broad definition of LCA and includes earlier work done in energy analysis and input-output economics. LCA analysts have collected data on a wide range of emissions and resource uses. Methods have been developed to aggregate different pressures to impact indicators, taking into account environmental mechanisms and human values (6). This type of assessment can help producers reduce the environmental impact of a product during its life cycle, for example, taking into account the energy and detergent consumption during the use of a washing machine, or the environmental load associated with the disposal of mobile phones. LCAs can, in principle, also inform consumer decisions. Environmental product declarations, which list the environmental impact indicators of specific products or product lines, are one information tool based on LCA which is supposed to help the consumer make decisions (7). As the practice in Nordic countries shows, the label often informs the purchasing departments of institutional customers; private consumers are often at a loss as what to do with this information. Even if the feat of producing life-cycle information for all products on the market could be achieved, consumers would most likely feel overwhelmed and disempowered by this information. Although environmental product declarations are useful for some purposes, other ways need to be found to inform policy makers and influence consumers if one wants to achieve sustainable consumption. Sustainable consumption patterns can be defined as patterns of consumption that satisfy basic needs, offer VOL. 39, NO. 13, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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TABLE 1. Overview of Related Analysis/Modeling Methods process LCA

“classical” life-cycle assessment (2, 8); calculation of environmental impacts based on a physical description of the processes involved in a product life cycle

input-output LCA, environmentally extended input-output analysis

use of input-output tables and emissions per industry to calculate upstream environmental impacts (9)

hybrid input-output analysis, energy input-output analysis

input-output analysis in which some commodity flows (usually electricity, fuels) are expressed in physical units (kWh, GJ, kg) instead of monetary units (4)

hybrid life-cycle assessment

combination of input-output LCA and process LCA (10); commonly the input-output tables are not altered, but used for some processes in a process LCA; in principle, there is duplicate information, since the information in the process LCA is, on a higher level of aggregation, also included in the input-output analysis

social accounting matrix

extension of an input-output table which also includes the monetary flow through households and the government (11); households earn income from labor and capital and spend it on consumption; SAMs are “closed” in the sense that there is no net monetary flows outside the SAM

structural decomposition analysis

approach that can be used to analyze the change over time in extensive variable of interest to changes in intensive and extensive variables related to the input-output tables, including changes in the structure (composition) of economic activity (12)

ecoefficiency vector (E2 vector)

graphical analysis of impact per value (or cost), which can be used to compare alternatives and evaluate the rebound effect; analysis of impact can be based on LCA, IOA, or a combination thereof (13, 14)

humans the freedom to develop their potential, and are replicable across the whole globe without compromising the Earth’s carrying capacity. Sustainable consumption policy consists of measures to reduce impacts that affect the behavior of the consumer or require her actions (15). The state of sustainable consumption can, hence, be seen as the aim of sustainable consumption policies. In this paper, I outline how life-cycle approaches can be used in sustainable consumption policy and review current research in this direction. I argue that to be useful for sustainable consumption, life-cycle investigations need to go beyond traditional product LCA to answer the following types of questions. 1. What are the environmental and social impacts of households, including upstream and downstream impacts? How do they develop over time? 2. What are the social, technical, and institutional factors that influence the level of these impacts? What are the differences between different social groups? Which lifestyles cause fewer impacts? 3. What are the important consumer activities, functions, and items that produce the largest impacts? What are the trends in these activities? 4. Where do consumers have the largest leverage to reduce impacts? Where do producers, retailers, or policy makers at different levels have more leverage? What are the actions that these actors should take? 5. How effective is a specific policy measure or consumer initiative in reducing impacts? What is the first-order reduction of impacts? Is there a “rebound effect” or a spillover effect (14)? This paper systematically explores the different uses of life-cycle approaches in sustainable consumption. First, I describe LCA and outline how LCA-type investigations can be used and for what, and then I analyze what has been achieved already and how the field needs to develop further to achieve the goals set by policy makers. Although most of this review focuses on the question of how LCA, input-output analysis or hybrid analysis can be used and has been used for sustainable consumption, I close the review by considering how the analysis needs to develop further in terms of data and methods. The Conceptual Basis. Life-cycle Assessment. Life-cycle assessment consists of three distinct analytical steps: the determination of processes involved in the life cycle of a product, the determination of environmental pressures 4674

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(emissions, use of resources, etc.) produced in each of those processes, and the assessment of environmental impacts and aggregation to impact indicators. The ISO 14040 standard for LCA defines the first two steps as inventory analysis and the third step as impact assessment (2). ISO defines two additional, procedural steps, goal and scope definition (i.e., planning the LCA) and interpretation (i.e. discussion and conclusions). It is not always straightforward to attribute for example, an investment to the production of a specific piece of product. LCA can be seen as constructing a causal link between production processes, the associated environmental stresses, and the produced products (16). The causal link can be constructed in different manners: (1) One can divide all the existing emissions by the total number of products produced over a period. This is the more common, attributional mode, which attributes responsibility for the existing emissions evenly across the produced products. (2) One can ask what happens when one additional product is produced. This marginal or consequential perspective is relevant, for example, when looking at electricity production, where the existing base load of coal or hydropower stations has significantly different emissions from the newly built gasfired or wind-power plants (8, 17). This paper focuses on attributional analysis. In the attributional mode, LCA is a linear exercise. It can be represented by a set of linear equations which can be written in matrix form as

where ILC is the life-cycle impact, expressed as a vector of impact indicators for different impact categories; y is the vector representing the functional unit; I-A represents the matrix of production, use and disposal processes that contributes to the product life-cycle; S represents the table of emissions factors per unit process; and C is the table of characterization factors per impact category. All attributional LCAs can be represented in this general manner. The flows in the matrix I-A describing the production technology can be physical flows, representing, for example, how many kilograms of iron and coal are used for producing x kg of

TABLE 2. Matrices, Vectors, and Indices Used symbol dimension

n s k

A S C y H

p nxn sxn kxs nx1 nxp

h

nx1

p I Ih

px1 nxn kx1

Icons

kx1

F

nxa

π

nx1

explanation number of processes described by the LCA, where each process has a product output number of stressors (chemicals emitted or resources used) taken into account number of impact categories in the assessment number of groups of households process matrix stressor matrix characterization factor matrix final demand/functional unit consumption pattern matrix, often derived from a consumer expenditure survey consumption pattern of a specific population (column of H) population structure vector identity matrix environmental impact of consumption, measured in category indicators environmental impact of consumption of a region or nation product-activity matrix, indicates quantity of a product used for engaging in an activity per unit of time price of each product

steel, or economic flows, representing the trade between industry sectors in monetary terms. The matrices and matrix dimensions are listed in Table 2. The notation I have chosen for this presentation is that of input-output analysis. One should note that despite the notational and mathematical similarity (18), there are significant differences between input-output economics and engineering-based LCA analysis. Input-output analysis presents the trade among industry sectors; LCA presents the flow of specific, physical products among various production, use, and disposal processes. LCA is, therefore, very technology-specific and can resolve differences, for example, among different alloys of steel or different colors of paint. Input-output analysis (IOA), on the other hand, deals better with nonphysical inputs, such as “overhead”; it can calculate value added and employment, and it has a more complete coverage of the economy. Input-output analysis is increasingly being integrated into LCA (9, 10, 19-21). There are practical differences between the resulting hybrid LCA and hybrid IOA. Often, the “upstream” emissions (during the production of the goods) and the “downstream” emissions (during the use and disposal of the goods) are described as separate terms in the equation (22), but there is no compelling reason to do so. Like Takase et al. (23), I therefore include representations of use and disposal processes in I-A, not just relationships on the production side. y represents the functional unit, and in LCA, it includes only one non-zero item. The functional unit is delivered by a process in I-A and y calls that process. It is common to operate with larger functional units in LCA, such as 1 000 000 h of watching TV or the washing of 1000 kg of cotton clothes. LCA practice today can build on the cumulative effort of data collection. Standard LCA software already includes databases for many basic materials and a number of important commodities. More extensive databases, such as EcoInvent (24), are available for purchase. Some industry associations have produced their own data. SimaPro, the most widely used software tool, now also contains data from input-output analysis so that hybrid assessments can be constructed. The data represents conditions in industrialized countries. Data from developing and emerging countries, however, is still lacking. There is, hence, a lack of

data, especially on a number of agricultural products and manufacturing products, and the available data may be biased. Life-cycle impact assessment methods have been developed for a large number of stressors, including for minerals, different land use classes, and several hundred toxic chemicals (6). There are competing methods, which means that the modeler or decision maker needs to select one method. The Society for Environmental Toxicology and Chemistry (SETAC) and the United Nations Environment Program (UNEP) have formed the Life-Cycle Initiative (25) with the aim of promoting the creation, publication, and exchange of life-cycle inventory data and the improvement and standardization of LCA methods. The Life-Cycle Impacts of Consumption. Input-output analysis describes all production processes in a national economy and, hence, all products being produced, although the product categories are more aggregate than they would be in a process LCA. With the emergence of hybrid LCA, all products can be described at a desired level of specificity or aggregation. Hence, it becomes feasible to compare both specific goods, the task LCA has been designed for, and baskets of goods as they are consumed by households or larger organizations. The life-cycle model, CS(I-A)-1, contains information on more than a single functional unit y. Different applications of this model are listed in Table 3. Table 2 provides the nomenclature for the equations in Table 3. The impacts of specific household functions (e.g., nutrition, housing) and the entire household consumption can be modeled (eq 2, Table 3) if information on all relevant products and services is included. The analysis can also address the distribution of impacts in a population as a function of the various consumption patterns H, as well as the impact of an entire country’s consumption, Hp. Alternatively, it might be interesting to look at the emissions intensity both per unit of money (eq 4) (13) and per unit of time spent (eq 5) (26). This may be used both to minimize the expenditure on or engagement in high-impact activities and to direct regulatory/ technological attention to those activities. The environmental effect of changes in a household activity can be determined looking at both the changes in the activity (h1m - h2m) and the changes in the residual due to the adjustment of money/ time available for other activities (eq 6) (14). Table 3 provides a conceptual representation of different types of life-cycle analysis that may be of interest for sustainable consumption. The analyses all utilize information outside traditional LCA. The results of consumer expenditure surveys (CES) would be used in eqs 2, 3, and 6. An understanding of household consumption activities is required for eqs 4 and 5 in addition to price data and time-use surveys, respectively. Each of these applications also requires an adjustment or extension of the life-cycle model, that is, the precise content of the matrices C; S; and especially, A. This, as we will see below, is far from trivial. Different types of study designs are of interest. Crosssectional statistical investigations try to correlate the impact or pollution intensity with potential explanatory variables, such as household income, household size, educational level, and population density (27-31). Longitudinal studies identify changes over time and attribute those to changes in the underlying variables S, A, H, and p (22, 32-36). Case-control studies can be conducted to compare households that differ in specific consumption activities (37, 38). Similarly, intervention studies address households that change their consumption patterns as a result of a specific intervention (39). In addition to these empirical investigations, statistical or other models describing H can be developed, and the effect of different scenarios can be evaluated (40, 41). Although I have focused the attention at entire household consumption baskets, some studies focus on selected activiVOL. 39, NO. 13, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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TABLE 3. Options for Using LCA-Type Analysis for Assessing the Impacts and Pollution Intensity of Different Systems, Such as Activities, Households, and Populations Ih ) CS(I - A)-1h (2)

environmental impacts of a basket of goods

Icons ) CS(I - A)-1Hp (3)

environmental impacts due to the consumption

MM ) CS(I - A)-1πˆ -1 (4)

pollution intensity of money for different goods (a pizza), activities (a dinner party), or functions (nutrition), where pi is the vector of prices

MT ) CS(I - A)-1F (5)

pollution intensity of time, for different activities, where F represents the goods used per hour of activity

∆I ) CS(I - A)-1(h1m - h2m + ∆hnm) (6)

effect of a consumption pattern change; the reduction in environmental impacts is the result of the difference, for example, in the mobility-associated activity pattern between the car-owning household h1m and the car-sharing household h2m, plus the difference in the not mobility-associated activities of the two households, the rebound effect, ∆hnm

ties, such as nutrition (42) or transportation (13). These studies have a detailed representation of the products and processes involved in this specific activity. The remaining household budget can be included in a rough manner to capture income effects. The same type of studies (crosssectional, longitudinal, case-control, intervention, scenario modeling) can be imagined focusing only on a specific activity. The benefit of such a focus may be in the ability to answer more sharply defined research questions, to isolate specific elements, and to bring in activity-specific expertise. Review. The Impacts of Household Consumption. There has been a fair amount of descriptive work on household energy consumption and CO2 emissions. I highlight interesting findings from path-breaking studies, review the collective results from the bulk of the studies on household environmental impacts (HEI), and address further research needs. In most developed countries, household consumption is the most important final demand category, both in terms of expenditure and in terms of total energy use or CO2 emissions. In developing countries, exports dominate if there is a lot of export-oriented heavy industry. Public consumption is usually less energy-intensive. There are different practices whether to treat capital investment as a final demand or an input to production. In some developing countries, investment in infrastructure can be important. In Australia in 1994, 59% of the CO2 emissions were associated with private final consumption, 10% with public final consumption, and 31% with export; 81% of the CO2 emissions occurred in Australia, while 19% were embodied in the imports, if imports are assumed to have the same pollution intensity as domestic production (43). In Norway, assuming domestic pollution intensities for import, domestic final consumption accounts for only 35% of CO2 emissions, while 65% of emissions are due to exports. Of these, 95% occur in Norway, while 5% are embodied in imports. Using more realistic pollution intensities of Norwegian trade partners, exports are responsible for 44% of emissions, household consumption for 31%, and public consumption for 25%; 75% of emissions occur in Norway, and 25% are embodied in imports. For private consumption, 42% of emissions are embodied in imports (44). Part of the public expenditure is directly consumed by households, such as health care and education, and can be assigned to households. Maënpaä¨ (45) proposes a classification scheme with which he assigns 64% of Finland’s government expenditure (14% of GDP) to household consumption. One-quarter of household consumption is, hence, financed by the government. Household environmental impact analysis started with Herendeen and Bullard (5, 46, 47), who presented the calculations of direct and indirect energy consumption. They used national input-output models with data on the energy consumption of different industry sectors and the direct consumption by households. The household expenditure for different items came from consumer expenditure surveys. 4676

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The results show that shelter (heating/cooling plus construction), mobility, and food are the most important consumption categories in both the U.S. and Norway, a result that has been reproduced by many subsequent studies. This early investigation already included an analysis of the variation of energy consumption with household income. Direct energy consumption flattens out with rising income, while indirect energy consumption continues to rise. As a result, a large share of the total “energy cost of living” for poor households is related to the combustion of fuels in the household, whereas for affluent people, two-thirds of these energy costs are related to the purchase of goods. The increase in household energy consumption with a doubling of income varies between 67% for India 1993-94 (29) and 90% for Denmark 1995 (22). Most analyses of household environmental impact focus on the energy consumption, CO2 emissions caused by different household consumption activities, or both. Figure 1 presents an overview of the average per capita energy use and CO2 emissions from different studies. As the figure indicates, there are large differences both in absolute values and in the share of the various activities among countries. There are also significant changes across time. The highest values of both per capita annual energy use and CO2 emission can be found in the United States. The CO2 numbers for Australia include noncombustion CO2 emissions and other greenhouse gases, and land use plays an important role. This explains why the emissions are almost as high as for the United States, but Australians’ energy consumption is less than half as high. The carbon intensity of energy supply varies significantly among the nations and explains why the CO2 emissions vary more than the energy consumption. The share of household direct energy use (mostly for heating, cooling and warm water) and the building itself (“other shelter”) is generally between 40 and 50%. The share of food varies between 7% (the U.S. in 1997) and 22% (India in 1993/94) and shows a clear, inverse relationship with the total energy use. The share of mobility varies between 8% (India) and 36% (the U.S.). Mobility also varies significantly inside Europe, where the Nordic countries and the U.K. have significantly larger shares (20-30%) than Germany and The Netherlands (12-18%). The results in Figure 1 were collected from various studies. The underlying studies and the presented results are not fully comparable; there are differences in the methods, in the nomenclature for the activities, in the activities included in the assessment, in how capital is treated, and in the indicators used to express environmental impacts. These differences can have a substantial influence on the result, as a comparison of the two estimates for Japan shows. The activity classification and part of the data presented in Figure 1 is based on Wier et al. (22). For other countries, I had to make some assumption in aggregating finer classifications or contact the authors of studies to request underlying numbers.

FIGURE 1. Comparison of annual per capita energy use (a) and CO2 emissions (b) according to household activity. The sources are Austria (38); Australia (43); Brazil (27); Germany, France, and The Netherlands 1990 (57); India (34); Japan T (23); Japan N (98); New Zealand (36); Norway, The Netherlands, Sweden, and the U.K. 1996/97 (55); Norway 2000 (99); Slovakia (100); and U.S.A. (47, 67, 101). Table 4 presents an overview of the characteristics of different studies, including aim, methods, and assumptions. The overview focuses on studies that use input-output analysis or hybrid LCA for the analysis of upstream emissions. I have found only two attempts of modeling HEI using process LCA. Rønning et al. (48) calculate the CO2 emission of the average Norwegian; they are able to account for only onehalf of the emissions. Frischknecht et al. (49) investigate the most important consumption categories in Switzerland, but not the entire household consumption. In the input-output models used by most analysts, products are commonly represented by the output of domestic industry sectors. There are commonly 50-400 sectors in an input-output table. This resolution is sufficient for aggregate analysis of the type presented in Figure 1, but it does not capture differences in product quality or consumer preferences. Vringer and Blok (28, 32) and Wilting (50, 51), therefore, developed a more detailed hybrid model in which process analysis, in physical units, is combined with input-output analysis, in monetary units, to better represent the direct and indirect household energy consumption. They found, for example, that in The Netherlands, cut flowers account for an unexpectedly large portion of the indirect energy consumption because of the high energy intensity.

Vringer and Blok (28) conducted a detailed statistical analysis of household energy consumption based on the Dutch consumer expenditure survey. They found that the level of consumer expenditure accounted for much of the variance in per capita energy consumption, as indicated in Figure 2. Figure 2 indicates the variation of energy use at different household income levels through specific percentiles. The numbers in parentheses include the level of energy use at this percentile as a multiple of the median. A doubling of expenditure leads to an 80% increase in energy use. Other significant explanatory variables were the number of household members, car ownership, and the location in urban or rural areas. In general, singles consume more energy per capita than larger families, urban households consume less than rural or suburban households, and the ownership of a first and second car leads to increases in energy consumption, all assuming the same expenditure level. Although these items were not found to be sufficient to explain all the variance, no other items were identified as significant explanatory variables. The same hybrid energy analysis model has been used in a number of other studies, including adaptation to other countries (52-56). These studies indicate that an adjustment for price differences is crucial and that otherwise, results strongly depend on expenditure levels and climatic VOL. 39, NO. 13, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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TABLE 4. Characteristics of Published Household Environmental Impact (HEI) Assessments country, year

ref

content

methods

Australia 1993/94

43

energy and GHG requirements by activity of hh differentiated by geography, demography, and income

Brazil 1995/96

27

energy use by activity of households differentiated according to income; expenditure elasticity of energy by activity, for different regions

Denmark 1966-1992

30

structural decomposition of indirect hh CO2 requirements to measure the effect of changes in consumption level and structure, energy technology, and mix at the hh and industry levels and economic structure

structural decomposition analysis of indirect HEI determined by IO (117 sectors, 66 commodities) and detailed energy model with 30 energy carriers

Denmark 1995

22

CO2 and energy requirements by activity and energy type of hh differentiated by income, urban/rural; determination of income/expenditure and hh size elasticities

multivariate regression analysis of HEI determined by IO (130 sectors, 92 commodities) and detailed energy model with 30 energy carriers

Germany, France, NL 1990

57

scenarios for CO2, SO2, NOx in 2000 and 2010 based on different demographic and technologica developments

hybrid IO with detailed consideration of hh technology, energy, and transport

India 1993-94

29, 34 energy use by activity of households differentiated according to income, hh size, urban/rural; significant determining factors of hh energy use; influence of changes in consumption level and structure, economic structure, and population size

energy IO with 99 sectors and 15 fuels, including informal energy, combined with CES; multivariate statistical analysis and structural decomposition analysis

Japan 1960-90

35, 64 development of hh CO2 as a function of demographic changes, hh type, economic development for 1960, 1970, 1980, and 1990, with scenarios for 2010 and evaluation of conservation measures; comparison with U.K. in 1990

IO with 112 sectors, in combination with information on hh energy equipment, demographic and socioeconomic variables from CES; domestic intensities for import

Japan 1995

23

IO model with a detail, physical treatment of waste sectors, with 80 goods and 36 types of waste

Japan 1995

based CO2 and energy by industry sector and hh fuel IO with 300 sectors, final demand from IO on 65

New Zealand 1974-1980

36

NL 1990

28

NL 2000

58

environmental load of hh expressed in Eco-Indicator (66) by activity, type of impact, and world region in which the load occurs

Norway 1973

46

energy use by activity of households differentiated according to income

CO2 emissions and landfill consumption by hh and scenarios for behavior changes in transportation and diets

development of hh energy use over time as a function of income and hh size and by hh activity energy use by activity of households differentiated according to income, hh size, urban/rural, age

44, 60 CO2, SO2, and NOx emissions by activity of households, with a focus on methodological issues regarding the treatment of import and exports Norway, Sweden, 55 cross-national comparison of hh energy NL, U.K. use by activity; comparison aimed at specific 1996/97 cities; differentiation by income

Norway 1997/2000

IO with 45 commodities in combination with consumer expenditure survey (CES) and physical data for direct energy use; domestic intensities for import, steady-state assumption for capital energy IO with 80 commodities and 43 sectors, in combination with CES

energy IO with data from 1971, 19 commodities, CES for every year, direct hh energy use hybrid energy analysis model based on a detailed technology description of critical activities; multivariate analysis with expression of variation among hh interregional IO of the world economy with 30 sectors for OECD and non-OECD and 105 sectors for NL energy IO with 55 sectors in combination with CES; U.S. intensities for import; no consideration of capital (potentially of relevance for housing) IO with 49 sectors, steady-state assumption for capital, trade data and emissions intensities for four important trading partners hybrid energy analysis model based on a detailed technology description of critical activities, CES, national IO table for Sweden

Sidney 1998/99

31

geographical and socioeconomic influencing factors of hh energy use in different subdivisions

multivariate regression analysis of hh energy use determined with IO (135 sectors); structural path analysis

U.S.A. 1963

5, 47

energy use by activity of households differentiated according to income, hh size, urban/rural

energy IO with 357 sectors in combination with CES; domestic intensities for trade, no consideration of capital

U.S.A. 1997

67

energy and CO2 requirements by hh activity

IO with 485 commodities, CES with 70 expenditure categories, combined with residential energy use data (four energy commodities)

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FIGURE 2. Total household energy requirements vs household expenditures (in Dutch guilders) based on the Dutch consumer expenditure survey from 1990. From ref 28. variables, but that these factors are not sufficient to explain all the variance. There are a number of important limitations with the present analyses. One, there are very few studies considering impacts other than energy consumption and CO2 emissions. Weber and Perrels (57) include NOx, which is also a combustion-related pollutant, in their calculations. Maënpaä¨ (45) includes aggregate material flows and the LCA impact categories acidifying emissions, eutrophication, and photochemical oxidant creation. Two, most studies use domestic emissions intensities for imported products. A notable exception to both limitations is the work by Nijdam et al. (58, 59), which for The Netherlands includes imports from OECD Europe, other OECD countries, and the rest of the world, modeled in a 30 × 30 input-output model for each of the three exporting regions. Their model also includes data on many types of pollutants and resource uses. It shows that imports are more important than what would be estimated on the basis of domestic emissions intensities, a result that also holds for Norway (44, 60). Although there are limitations in the low resolution and the uncertainty in the data, especially for developing countries, this study (58) points in the direction this field needs to develop to provide a richer and more reliable picture of the environmental pressures caused by household consumption. Three, the studies all assume the same energy intensity per unit expenditure in a specific product category; hence, they do not systematically address what might be called luxury consumption: the purchase of handmade chairs or designer watches, for example, which potentially have a lower intensity per unit expenditure than mass-produced chairs or watches. This may cause a systematic flaw, as Vringer and Blok (28) already note. Four, the capital used to produce goods is often not accounted for at all because capital expenditures are treated as a final demand category in national accounts. Lenzen (61) takes the capital expenditure of that year as the capital required to produce the goods produced in the same year, in effect assuming a steady-state economy. Although better than not accounting for capital, this approach is not entirely satisfying because capital expenditure varies annually, and a given year may not contain investment in new aluminum factories or automobile plants. Five, many input-output studies do not include the emissions connected to aviation and ocean transport, because the consumption of so-called “bunker fuels” is commonly not included in the national environmental accounts, and these emissions are not accounted for under the Kyoto protocol. A better analysis of the impacts of consumption is clearly needed. This analysis needs to cover more pollutants and

realistically reflect production conditions in a global economy. Research is needed to determine the degree of resolution (i.e., product specificity) that is required for different purposes. Although it is in general clear that a combination of traditional process LCA and input-output analysis can provide results that are both specific and cover the complete product range, it remains an open methodological question of how to best integrate the two tools. Depending on the purpose of the analysis, different processes will require a detailed modeling through process analysis. These basic modeling questions need to be solved to improve the quality of the models. Available process LCA data has not yet been fully utilized, although this situation is usually better in projects that focus on selected consumption categories. For food, for example, extensive process data has been used (42, 62, 63). There are further significant challenges to develop inventory data for different impacts, to model global value chains, and to understand the uncertainty in the models. There is a need for more empirical research and a systematic evaluation of regional and intercountry variability, for example, for food and leisure. The improvement of the modeling tools and the underlying data should occur in parallel with the development of new research approaches and applications. Scenario Analysis. Most research to date has focused on empirical investigations of the environmental impacts of existing consumption patterns. To formulate an effective sustainable consumption policy and to stimulate effective action, more strategic analysis is needed. This analysis should identify promising courses of action, evaluate specific activities and measures to see which ones should be implemented, and provide feedback about measures that have been taken. Scenarios play an important role in environmental policy, because they allow us to scope out possible future developments and evaluate alternative courses of action. This is powerfully illustrated by the emissions scenarios developed for the International Panel on Climate Change (68). Duchin (11, 69) has proposed use of a social accounting matrix (SAM) to construct scenarios about consumption. SAM is an extension of input-output tables that includes earnings and expenditures by different household types, including transfers between household types. It offers the advantage of including consumption in a consistent manner, but to my knowledge, it has not yet been used in the manner proposed by Duchin. In their work on Germany, France, and The Netherlands, Weber and Perrels (57) developed the following scenarios: stagnation, business as usual, sustainable technology, and sustainable consumption. Of these scenarios, stagnation is the worst, whereas sustainable consumption and sustainable technology offer the largest emissions reductions, with certain differences among countries. Hubacek and Sun (70, 71) developed I-O based scenarios for land use and water consumption and show significant regional shortages of both land and water. Alfredsson (56, 72) investigated the environmental benefit of Swedish households adopting green lifestyles. Most of these scenarios, however, work from a microperspective: emissions and resource use coefficients are either not changed or changed in accordance with trends. Wilting (50, 51) developed a scenario for Dutch household energy use in 2015; Rood et al. (73) evaluated the effect of increased consumer expenditure in 2030, assuming both constant and 1% pa increasing energy efficiencies. Hubacek and Sun (70) go furthest in evaluating the effect of the scenarios for household consumption on the larger economy, including changes in resource use. Duchin (74) presents a proposal for developing scenarios for U.S. food production and consumption that describes how such an analysis could yield interesting insights and provide preliminary figures based on a literature survey. On the macro level, Duchin and Lange (75) have investigated VOL. 39, NO. 13, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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whether the Brundtland Commission’s goals for sustainable development can be reached through the means suggested, focusing specifically on CO2, NOx, and SO2. The answer was negative. Historical rates of change can be used to inform scenario modeling about likely rates of change. Structural decomposition analysis is a method that can be used to study changes in a desired variable over time and attribute to it changes in underlying variables, such as changes in population, spending level, composition, and technology. Munksgaard et al. (30) present a structural decomposition analysis for HEI for the period 1966-1992. Most of the growth in CO2 emissions can be attributed to the overall growth in consumer expenditure, but increased energy efficiency at both the energy supply sector and other industry has offset some of this growth. Kim (33) presents a decomposition of household CO2 and SO2 emissions for South Korea, and Pachauri and Spreng (34) apply the technique to energy use in India. Another approach to inform scenario modeling is through the identification of distinct behavior patterns of specific cohorts and subsequent modeling of underlying demographic and socioeconomic developments (35, 40, 41). The development and evaluation of different scenarios is interesting not only to look at likely developments, but also to scope out how alternative courses of action would play out in terms of emissions. Emissions targets can be defined, and the steps required to achieve these targets given expected developments in population, wealth, household size, and other variables can be defined, for example, through backcasting. Implications for different industry sectors and political constituencies can be evaluated. Evaluation of Sustainable Consumption Measures. It is clear that HEI analysis identifies the activities and purchases that cause the largest overall environmental impacts. It also provides a measure of the impact per unit of expenditure. This analysis can, hence, be used to identify promising measures for sustainable consumption policy and develop suggestions for consumer action. In the “Consumer’s Guide to Effective Environmental Choices”, Brower and Leon (76) present recommendations to consumers based on an analysis of what environmental impacts are associated with which products and household activities. They used impact intensities of the type calculated by eq 4. Similar recommendations are derived from ecological footprint calculations (77). On-line or downloadable calculators for environmental impacts, such as CO2 emissions, have also been tried as a tool to raise awareness and inform consumer choices, and ideas exist to further customize the assessments so that they can more effectively guide consumer action (78). Hannon and colleagues first developed methods to evaluate the impact of changes in expenditure patterns on aggregate energy use during the energy crises of the 1970s (79, 80). These analyses included an assessment of the effect on employment, including the shift in employment among different occupations. Several studies evaluate the effects of different household expenditure options on the overall HEI (23, 35, 53, 72, 81). The straightforward modeling approach is prospective: what is likely to happen when consumers shift their consumption from xxx to yyy, assuming that the cost savings are used like the average expenditure (see eq 6) (23, 35, 72, 81). Biesiot and Noorman (53) evaluate the effect of specific shifts in spending on the overall HEI, using observed differences in consumer expenditure of households. Their calculations are based on observed consumer behavior. Gatersleben et al. (82) show that proenvironmental attitudes do not necessarily correspond to a lower household energy consumption. Some evaluations of the acceptability of energy-saving measures also take into account life-cycle energy use (83, 84). 4680

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Another interesting option is the evaluation of sustainable consumption measures that are being or have been implemented. Longitudinal or intervention studies are maybe the best ways to evaluate the effect of a measure because it follows the same set of people and observes their changes in behavior. While other study designs usually assume fixed expenditure, such a study can get around this questionable assumption. I have not found any study in which such a design is fully implemented. The closest is Fritsche et al. (85, 86), who provided advice to and followed up the conversion of two German military bases into housing developments by an LCAtype evaluation. This project is very interesting and should stimulate more research of this type. A more careful study designstaking into consideration how people behaved before they moved into their new flats and including a complete HEI assessmentswould be desirable. A case-control study of a car-free housing project in Vienna illustrates an approach to evaluating an example of sustainable consumption when data on the consumption before the measure is not available (38). Evaluation of the Rebound Effect for Sustainable Production Measures. The concept of the rebound effect has been suggested in response to energy efficiency measures. In the policy debate, the general notion of the rebound effect is that a technical or policy measure produces secondary effects which at least in part offset the initial, positive effect of the primary measure, so that the measure is less effective in achieving the primary policy goal. The rebound effect is often understood as the behavioral response to a technical improvement. The behavioral response, for economists, covers changes in purchasing behavior as a result of changes in market prices. The discussion addresses both cost reductions as a result of improvements in technical energy efficiency (87) and economy-wide effects (88). Greening et al. (89) distinguish among the following effects: pure price effect, income effect, secondary effects on the cost of producing other products, effects on the fuel supply (and the market power of OPEC), and transformational effects. Numerous empirical studies have focused on the price and income effects. Greening et al. (89) present a survey of studies in the United States which indicates that the rebound effect is somewhere between 0 (for white goods) and 50% (for space cooling), but typically, less than 30% (space heating, lighting, automotive transport). Schipper and Grubb (90) review studies covering 80-90% of energy use in OECD countries and find that the rebound is on the order of 5-15%. They also review the issue of economy-wide effects and find no evidence for substantial macro effects. Interestingly, the discussion of the rebound effect in energy economics focuses on reductions in the price of energy services as a result of energy efficiency measures and the effect this has on demand. As Binswanger (91) has pointed out, the cost of an energy service also includes capital costs and time spent on part of the consumer. Discussions of a time rebound have recently appeared in the sustainable consumption literature (26, 92, 93). This effect results when the time-saving due to technical progress leads to increased consumption. For example, transportation research has shown that faster transport implies that people expand their radius of action but keep total travel time approximately constant. LCA traditionally focuses on the functional unit and neglects cost and, thereby, the rebound effect. Goedkoop et al. (13), however, developed the E2-vector, which consists of the environmental impacts and value added, as a way to display the impact intensity or ecoefficiency of a specific function. This concept allows for a graphical representation at least of the rebound effect, which is presented as a vector with the slope of average or marginal expenditure and is analogous to eq 6 (14). In other words, a specific impact

TABLE 5. Comparison of the Features of LCA and IO process life-cycle assessment

input-output analysis

based on a detailed modeling of production, distribution, use, and disposal processes of a specific product

represents links among all industry sectors of an economy

able to represent specific technologies and conditions along the life cycle and evaluate variations in these conditions; able to distinguish, for example, luxury and common products

sector-average economic output and factor intensities

can account for cross-border inputs

usually based on national data only; this can be a problem, especially for products not produced in the country of interest

high-input data requirements

regularly updated by statistical bureaus

focused only on environmental issues

represents also the scale of economic activities as well as labor and capital requirements; useful for evaluating, for example, employment effects; the “dual” price model allows for the evaluation of where the value is added and who earns scarcity rents

incomplete system models; cutoffs specify what part of the supply chain is omitted )> can lead to significant errors

represents all linkages in an economy, but difficulties with aggregating Make and Use Tables and assigning industry emissions to products produced

problems in accounting for overhead, service inputs usually takes a product, i.e., micro, perspective

problems in accounting for capital goods used in production facilitates the evaluation of effects on the macro level, particularly on the national scale

intensity of spending the money saved is used to calculate the overall impact of product service systems. Goedkoop et al. (13) used the E2 vector to quantify the effect of three “product-service systems”: car sharing, organic produce subscription services, and laundry services. Unresolved Questions and Practical Challenges. Most of the research has focused on the empirical question of how much energy or CO2 is associated with different household activities. Most analyses have used input-output tables, but some have used hybrid analysis or process analysis. Consumption data comes from consumer expenditure surveys, which provide expenditure for different income groups, age classes, household sizes, urban/rural areas, and the like. It is, hence, both easy and insightful to analyze how these distinctions affect the total impact. There is a growing interest in the application of life-cycle approaches for scenario development, backcasting, and the evaluation of sustainable consumption measures. Although such study designs in themselves are interesting and deserving of a more detailed elaboration because of their challenging combination of IO, LCA, and consumer research, I will focus on the requirements of such studies on HEI assessment. In most cases, input-output analysis has been used to determine emissions and resources associated with the production of goods, while emissions factors based on physical analysis are used for the fuels consumed. While taking a life-cycle perspective, these studies do not use process LCA at all. Why, then, would one use LCA? The comparison of features of LCA and IO presented in Table 5 shows that LCA offers more specificity: the effect of specific product features and technologies can be evaluated. For many purposes this is not necessary; for example, when evaluating the average HEI on a national or city level. On average, consumers use the average product. There are many consumption choices, however, that cannot be illuminated by average analysis. It is unlikely that the production of an Audi, for example, causes twice as much pollution as that of a Skoda, since both cars are build on the same “platform” (i.e., have the same drive, suspension, gear box), even though the Audi costs twice as much as the Skoda. It is unlikely that an organically grown apple is more polluting than a conventional one, even though it costs more. I believe that wealthy people are more likely to drive luxury cars and eat organically grown food. The slope in Figure 2 is, hence, likely to be less steep

than shown, and the variance around the median is likely to be higher. The conclusion by Alfredsson (56) that changes in consumption patterns have no effect on greenhouse gas emissions if expenditure levels are held constant may, hence, be based on a systematic bias in the study design. In addition to implications for the distributions of environmental impacts in the population, a systematic bias toward overestimating the impact of luxury and lifestyle consumption would have implications for scenario analysis, because future wealth is likely to be spent on purchasing higher quality and group identity rather than just buying more of the same. In addition to research questions about luxury, green, and identity-providing consumption, the evaluation of specific consumption measures will require a more detailed analysis. For example, if one wants to evaluate the effect of a specific settlement design, which may include innovative heating or cooling technology, these innovative features of the project will have to be modeled in a process model. The detail and specificity provided by LCA comes at a cost: data collection is far from trivial and cheap. The combination of LCA and IO in hybrid LCA promises to reduce data collection effort and avoid the cutoff errors inherent in process LCA. There are, however, significant challenges in developing hybrid LCA. Two different sets and types of data, collected for different purposes but both describing the production system, have to be combined. It is unclear what parts of the system should be described with what type of data. One needs to be careful to avoid double-counting (94). It is also unclear whether the LCA results should feed back to the IO, as suggested by Suh (95, 96). We know how to introduce LCA-type data into I/O analysis to produce a consistent, linked hybrid I/O table. This has been done for the energy and waste sectors and requires significant effort (19, 97). No one has yet practically implemented the modeling of many different types of products demanded by a household, as suggested by eq 2, in a single process-LCA model. LCA method developers have so far aimed at developing the most realistic assessments of the environmental impacts of a single product. This effort is misguided, at least for the purpose of sustainable consumption, and method development should, rather, focus on determining how much detail is required to represent the relevant differences among important consumption alternatives. The important methodological questions are how much detail is needed for what VOL. 39, NO. 13, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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type of research question and how LCA and IO can be integrated to achieve this desired level of aggregation. Last but not least, the life-cycle modeling approach should inform sustainable development policy not only through empirical assessments of the current situation and projections of probable future developments but also through an evaluation of how sustainable development could be achieved through a combination of possible technological, social, and economic changes and what this implies for both consumers and industries.

Discussion The Johannesburg Plan for Implementation calls for the use of life-cycle assessment to promote and achieve sustainable consumption and production. LCA has proven useful in the context of sustainable production. LCA, narrowly defined, has been little used in sustainable consumption. Questions that one needs to answer when addressing sustainable consumptionswho causes how much of which impact and how consumption patterns can be changed to reduce these impactssrequire an analysis that extends beyond traditional LCA. Previous research on direct and indirect household energy consumption indicates how life-cycle methods can be extended to answer questions relevant for sustainable consumption. This includes the combination with inputoutput analysis, the use of consumer expenditure data, and the analysis of trade. A systematic extension in this direction, however, can go further than energy analysis has gone: changes or differences in consumer expenditure can be observed in panel studies of sustainable consumption measures; income elasticities and cohort effects can be measured and used in scenario analysis. I have also described how life-cycle methods can be used to conduct prospective and expost evaluations of sustainable consumption and production measures. The methods described in this paper have been used for some of the research questions outlined. Other research designs have not yet been tested. The indications are, however, that LCA needs to be combined with economic and sociological investigations to be useful as a tool for sustainable consumption. Although a further method development and data collection is advisable, efforts should focus on developing and testing new research designs that are directly relevant to policy making.

Acknowledgments This work is part of the FESCOLA project financed by the European Union’s 6th Framework Program through Grant NMP2-ct-2003-505281. The ideas described here have been developed while the author was at the International Institute of Applied Systems Analysis in Austria and have been presented at the UNEP/SNTT Sustainable Consumption Workshop, March 5-6, 2004 at Leeds University, U.K. Input data was provided by Keisuke Nansai, Koji Takase, and Glen Peters. Helpful feedback by Mitch Small, Glen Peters, Katarina Korytarova, and four anonymous reviewers is acknowledged.

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Received for review February 18, 2004. Revised manuscript received April 13, 2005. Accepted April 18, 2005. ES0497375

Life Cycle Approaches to Sustainable Consumption: A Critical

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