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Prioritisation within The Integrated Product Policy

6 Detailed analysis of four specific areas

6.1 Introduction
6.2 Agriculture/foods
6.3 Electrical and electronic equipment
6.4 Retail trade
6.5 Textiles and apparel

6.1 Introduction

In this chapter, four specific product areas are analysed in more detail. These analyses are intended for use by the product panels of the Danish EPA, which currently cover the four areas agriculture/foods, electronics, retail trade and textiles.

The analyses are based mainly on the database developed by the project (see Chapter 7), and highlights environmental impacts and improvement potential in more detail than what has been done in relation to the overall prioritisation in Chapter 1.

The analyses have been made in cooperation with the product panels, in order to make the analyses as relevant as possible for the panels.

6.2 Agriculture/foods

Agricultural products are mainly food products. Important exceptions are fur (for wearing apparel) and straw (for energy). Pork and milk products make up approximately half of the value of Danish agricultural production.

Improvement options in the food sector were already discussed in Chapter 1.7.2. This sub-chapter will therefore focus on how food products are consumed, in particular differences between catering and household preparation.

We have calculated the supply of individual foods in g per person per day for private consumption, consumption in restaurants and hotels, and consumption in hospitals and public institutions. This has been done on the basis of the total supply of food products for the Danish market as given by the supply-use tables (Danmarks Statistik 2003b), where many flows are provided in both monetary units and mass. Missing mass flows have been estimated by using the kg/price relationship of the major flow for each individual commodity. Private use in agriculture (negligible) has been disregarded. Supply from baker's shops has been calculated as the food raw materials entering into the baker's shops. The resulting data for each individual commodity has been re-aggregated into the food groups presented in Table 6.1. The data for restaurants include industry canteens with separate accounting. Minor industry canteens without separate accounting make up a very small part of the overall food consumption and have a composition of food products practically identical to that of canteens in hospitals and public institutions, and has therefore been included under that heading.

Table 6.1. Total supply of foods (in g/person/day) for private consumption and catering

¹ The non-meal related share of private food consumption has been estimated in the following way: For baker's produce, ice cream, chocolate, and confectionary products this is the surplus consumption relative to restaurants and hotels. For milk products, this is the commodity ”other beverage products”. For fruits and vegetables, this is all fresh fruits and nuts. For ready-made food, this is all food preparations (baby food etc.). For mineral water, this is the surplus consumption relative to hospitals and public institutions. Although other beverages (coffee, wine, beer) may also be non-meal related, their consumption per person per day is relatively stable across the three consumption domains, so we have not found any reason to separate out the non-meal related share.
² Sums may not equal due to rounding of the contributing values
³ Potatoes, rice, other cereals, noodles, and bread & bakery products

From Table 6.1 it can be seen that 76% of the weight of foods are consumed in private households (431+162 out of 2713 g/person/day) against 18% in restaurants and hotels and 6% in hospitals and public institutions. However, since these data include also non-meal related food items, they do not provide a good indication of how many meals are prepared from these supplies.

A better indication of the relative number of meals consumed can be obtained by looking at the meal-related amount of basic foods (potatoes, rice, other cereals, noodles, bread & bakery products), which gives the following distribution of meals: 62% in private households, 28% in restaurants and hotels (23% in restaurants and 5% in hotels) and 10% in hospitals and public institutions.

Using this as normalisation reference, we can compare the composition of meals in the three consumption domains, as done in Table 6.2. This shows that meals in private consumption in general require more raw materials than catering meals. Half of the difference can be explained by dairy products, while another important contribution come from a larger consumption of meat per meal in private households. An important part of the explanation is probably a larger percentage of waste food in households compared to catering, especially for milk and vegetables.

According to Table 6.2, the composition of the meals is also quite different among the three consumption domains:

• Hospitals and public institutions use much more potatoes, but less rice and noodles per meal than both restaurants and private households.
• Private meals use much more meat and eggs, but less fish than in the average catering meal.
• Private meals contain much more dairy products and vegetables than the average catering meal (although part of the reported difference is likely to be due to larger wastage in private households).
• Catering uses more sugar than private households for equivalent number of meals (including coffee and tea).

Table 6.2. Composition of daily food supply for a full day of meals in private households and catering (based on data from Table 6.1, scaled to meal-related basic foods)

From the above calculations, we can estimate the total number of meal-days that Danes spent at home to be 62% * Danish population * 365 days = 1.2 E+09 meal-days, and the number of meal-days spent at restaurants to be 23% * Danish population * 365 days = 446 E+06 meal-days. The corresponding expenditure is 100 E+09 DKK and 36.6 E+09 DKK (including VAT), showing a surprisingly equal cost of private meals and restaurant meals. This information may be further combined with the corresponding data on environmental impact from the expanded NAMEA on household meals (food, storage, cooking and dishwashing ^[8]) and the industry ”Restaurants and other catering,” respectively, resulting in the comparison presented in Figure 6.1. From table 6.2 it can be seen that the most important difference is due to the larger consumption of meat in private meals.

Click here to see Figure 6.1.

Figure 6.1. Relative environmental impact of a full day of meals at restaurants and in private households.

Table 6.3. Most important processes contributing to the overall result in Figure 6.1 (in % of total; sorted by right column; all 8 impact categories given equal weight)

6.3 Electrical and electronic equipment

In the Danish NAMEA (Danmarks Statistik 2003a), the electronics sector (broadly interpreted) is divided in five industries (turnover in brackets):

• Domestic appliances n.e.c. (4 GDKK)
• Electrical machinery n.e.c. (22 GDKK)
• Medical & optical instruments etc. (13 GDKK)
• Office machinery and computers (2.5 GDKK, mainly repair work)
• Radio and communication equipment (14 GDKK)

Domestic cooling equipment constitute about half of the turnover in the first of these industries. Since domestic cooling equipment has a somewhat different composition and environmental profile than the rest of the industry (producing items such as stoves, heaters, vacuum-cleaners and tumble-driers), we have split out domestic cooling equipment into its own industry.

All resulting six industries have roughly the same pattern of environmental impacts, but the level of impact per DKK varies between them, see Figure 6.2. Electrical machinery and domestic appliances are at the high end, while medical and optical instruments are at the low end. This reflects the larger share of wages and profits in the expenditure of the manufacturers of medical and optical instruments.

Click here to see Figure 6.2.

Figure 6.2. Environmental impacts (in person equivalents per MDKK) caused by six electronics industries.

The main impact categories of concern are human toxicity (mainly due to the heavy metal emissions related to the use of metals) and photochemical ozone formation (mainly due to the use of organic solvents and plastics). The distribution of the overall impacts on the contributing processes is shown in Table 6.4.

Table 6.4. Most important processes contributing to the overall result in Figure 6.2 (in % of total; sorted by the column for Electrical machinery; all 8 impact categories given equal weight)

The most obvious way of reducing the toxic releases from the basic metals industries is to increase the recycling of the metals, thus completely avoiding the primary processes. This would also reduce other emissions. The substitution of metals by e.g. new composites will have the same effect.

For solvent emissions several reduction options exist, both preventive (modifying process equipment and conditions) and end-of-pipe (combustion).

The data in Figure 6.2 and in the database from the project (see Chapter 7) are provided per DKK output from each industry, i.e. all products from an industry are assigned the same environmental impact per DKK. An advantage of this is that it allows comparisons across very different products, which may be especially relevant for the electronics sector, where each industry have a very diverse product composition. A disadvantage is that the same DKK's may represent very different inputs in both weight and materials. Therefore, the data can only be used as a very rough representation of an average product, from which individual products may deviate significantly.

The data above represent the cradle-to-gate environmental impacts of the electrical and electronic products, i.e. wholesale, retail, use stage and post-consumer waste handling are not included. To obtain life cycle data for electronic products, these stages should therefore be added to the data above. For an individual product, the electricity use during the use stage should be calculated from the product specifications and the lifetime of the product. The environmental impacts of this electricity use may then be added, e.g. by using the process “Electricity (unconstrained), DK” for a product used in Denmark. For groups of products, such as cooling equipment, cooking equipment & dishwashers, washing equipment, vacuum cleaners, TV & computer, energy use may be estimated from Dall et al. (2002), resulting in the values in Table 6.5.

Table 6.5. The contribution (in %) of life-cycle stages to the overall environmental impact and Global Warming Potential (GWP) of some groups of electronic products.

6.4 Retail trade

In the Danish NAMEA (Danmarks Statistik 2003a), retail trade is divided in six groups:

• Retail trade of food etc.,
• Retail sale in department stores,
• Retail sale of pharmaceuticals and cosmetics (apoteker, parfumerier og materialister)
• Retail sale of clothing, footwear etc.,
• Retail sale & repair work n.e.c.,
• Service stations

Furthermore, sale of motorvehicles is a separate category, which will not be treated further here.

All six groups of retail trade have roughly the same pattern of environmental impacts, but the level of impact per DKK varies between them, see Figure 6.3. Service stations and department stores are at the high end, while retail trade of pharmaceuticals and cosmetics is at the low end (with 40-55% of the impact per DKK of the service stations and department stores). This reflects the larger share of wages in the expenditure of retail trade of pharmaceuticals and cosmetics.

Figure 6.3. Environmental impacts caused by 6 types of retail trade.

Out of the total environmental impact of Danish consumption, retail trade contributes with 1-5% depending on impact category. However, Danish consumption includes also products like electricity that does not pass through a retail stage, so for typical retail products, the share will be larger. Some selected examples for the impact category “global warming” are given in Table 6.6.

Table 6.6. The share of total global warming potential (GWP) caused by retail trade for selected products.

A similar study for selected retail commodities in the USA (Norris et al. 2003) shows that the energy use caused by retail trade make up between 10% (detergents) and 30% (books and computers) of the total life cycle energy consumption of these commodities. On this background, they conclude that e-commerce and direct delivery, which “short-cuts” the retail trade, can reduce pre-consumer environmental impact significantly for some products.

However, it should be noted that the environmental impact intensity of the retail trade is below average, see Table 6.7. This implies that if e-commerce and direct delivery are associated with cost reductions, the net effect on the environment could be negative, since the costs saved by the consumers would then be used to buy more products, which on average have more environmental impact. Thus, since retail trade – like other service industries - has a relatively large share of expenditure on wages, it contributes to reduce the overall environmental impact of the overall consumer spending. This points to a possible strategy, in which the retail trade could harvest the advantages of e-commerce while at the same time adding even more to the service they provide; providing more complete solutions to their customers, including e.g. home deliveries, maintenance, on-site repair, and monitoring of the cost and environmental impact of the customers' total consumption. In this way, the retail trade could still contribute to maintain a low environmental impact intensity of the overall consumer spending, while incorporating the savings of e-commerce and direct delivery.

Table 6.7. Environmental impact intensity (impact in person-equivalents per kDKK) of retail trade in Denmark, compared to the impact intensity of average Danish consumption and selected consumption (need) groups.

The environmental impacts from retail trade are mainly related to the use of buildings, electricity and heat, office machinery, and freight.

The use of buildings (which includes their construction and maintenance) is the major source for the impact categories ozone depletion, acidification, photochemical ozone, and human toxicity, and account for approximately 20% of the global warming potential from retail trade. As pointed out in Chapter 1.7.3, buildings are very complex products, and improvement options will often require coordination between large numbers of actors. The plea from the building panel for stronger and more far-sighted regulatory incentives is also valid for non-residential buildings, such as those used by retail trade. Of voluntary measures, we would recommend knowledge dissemination in the form of general advice and checklists for the personnel responsible for construction and management of the shops.

Electricity use is responsible for 27% of the global warming contribution from retail trade, heating adding another 10%, and freight by road 8%. The most direct way of reducing the environmental impacts from electricity and heating are savings in consumption, for which substantial potentials exist, both by improvements in equipment and in user behaviour. With the liberalisation of the energy markets, the choice of renewable energy sources is also an obvious possibility. For road transport, optimising the logistics can reduce the need for driving. In this context, it should be noted that retail trade has a significant influence on the automobile use of private households (24% of private car driving is related to shopping etc.), an impact that is not included in the values for the retail trade. Alternative distribution systems with direct delivery could thus result in improvements far exceeding all other improvements in the retail trade itself.

The main contributor from retail trade to the impact category “human toxicity” is non-ferrous metals, which is primarily used in buildings, but also office machinery, electrical machinery, etc. contribute with an important share. The most obvious improvement option for the retail trade is to ensure that the metals in discarded office machinery etc. are recycled.

For the impact categories “nutrient enrichment”, “ecotoxicity” and “nature occupation”, the contributions from retail trade are of less importance.

When calculating the total environmental impact of a product group, both in the consumption perspective in this project and in typical life cycle assessments, the contribution from retail trade is calculated per DKK retail profit for each individual product group. This means that a product with high retail profit will obtain a larger share of the total environmental impacts from retail trade than a product with low retail profit. As retail profits may vary from a few percent to more than 50% of the price of a product, this may result in a very uneven distribution of the environment-tal impacts from retail trade over the products. Table 6.8 illustrates how the retail profit varies within food products, from a low 13% for bread to a high 52% for fish and sugar, the average being 26% of the product price. Seeing the large importance of buildings, electricity and heat, and freight in the total impacts from retail trade, one could argue that other parameters than retail profits may better reflect the share of the specific product group in the environmental impact from retail trade. Such parameters could be the space taken up by a product (building space), specific electricity requirements (e.g. for cooling), and the weight of the product (when this is limiting freight capacity).

Table 6.8. Distribution of environmental impacts from retail trade over food products, based on retail profits

6.5 Textiles and apparel

In the Danish NAMEA (Danmarks Statistik 2003a), the two industries:

• Textiles, DK
• Wearing apparel, DK

are not further subdivided. These industries cover more than 400 commodity numbers, with very different compositions. Especially the fibre origin (mainly wool, cotton, cellulostic fibres, synthetic fibres) may vary, and is often not reflected in the commodity classification. For example, the top 10 commodities from the Danish textile industry from an economic perspective are:

• Carpets
• Sweaters
• Duvets
• Knitware n.e.c.
• Felt and fleece
• Tents, tarpaulins, awnings
• Bed linen etc.
• Textile products n.e.c.
• Bonded fibre fabrics
• Yarn, cotton

i.e. only for the last one, the fibre origin is specified.

The same is true for the foreign (US) data. Although the textile and apparel industries are disaggregated into 23 sub-industries, none of these are specified in terms of fibre origin. For example, there is one industry named “Yarn mills” with input of wool, cotton, cellulostic and synthetic raw materials. This implies that it is impossible to distinguish between the environmental impacts from cotton yarn and synthetic yarn; only the value for an average yarn is available.

Since the largest part of the raw materials for the Danish textile industry is imported, our first step has been to disaggregate the most important textile and apparel industries in the US Input-Output table (which is used as a Rest-of-World proxy in the Danish database, see Chapter 2.8) into fibre-specific industries. We have done this by isolating the input of wool, cotton, cellulostic fibres and syntetic fibres for the following industries:

• Broadwoven fabric mills and fabric finishing plants
• Yarn mills and finishing of textiles, n.e.c.
• Nonwoven cellulostic
• Apparel made from purchased materials
• Curtains and draperies of cotton, n.e.c.

and subdividing these industries into:

• Broadwoven, wool
• Broadwoven, cotton
• Broadwoven, cellulostic
• Broadwoven, synthetic
• Yarn, wool
• Yarn, cotton
• Yarn, cellulostic
• Yarn, synthetic
• Nonwoven, cellulostic
• Nonwoven, synthetic
• Apparel made from wool
• Apparel made from cotton
• Apparel made from cellulose
• Apparel made from synthetics
• Curtains and draperies of cotton, n.e.c.
• Curtains and draperies, synthetic, n.e.c.

each with input of only one of the fibre types (i.e. either wool, cotton, cellulostic fibres or syntetic fibres), while maintaining the average input-output coefficients for all other inputs and outputs (which is equivalent to assuming that all other inputs have a fixed relation to the value of the fibre input).

This allows us to distinguish the environmental characteristics of the different fibres, as shown in Figure 6.4. The difference is most remarkable for the yarns, where it is possible to obtain the sharpest isolation of the different fibre inputs, and where the fibre material make up a larger share of the total inputs.

Click here to see Figure 6.4.

Figure 6.4. Environmental impacts (in person-equivalents per kDKK) from apparel and yarns, depending on fibre type.

It can be seen that nature occupation, ecotoxicity (from pesticides) and nutrient enrichment are nearly exclusively related to cotton fibres, while photochemical ozone formation and ozone depletion are much more important for the artificial and synthetic fibres (mainly due to solvent use and VOC emissions from refineries and production of syntetic fibres). It should be noted that the low values for wool are due to this fibre being a by-product of the meat industry.

To obtain a better model of the structure of the Danish textile industry with respect to fibre types, we constructed a mass balance based on the data provided in the supply-use tables (Danmarks Statistik 2003b), where import and export flows are provided in both monetary units and mass. Missing mass flows have been estimated by the kg/price relationship of the import and export flows for each individual commodity (for input and output flows, respecticely). The resulting data for each individual commodity has been re-aggregated into the product groups presented in Table 6.9.

While the inputs to the Danish textile industry are quite well specified in terms of fibre type, the outputs are not, as mentioned above. This implies that it is not possible to establish a well-founded relationship between the output commodities and the incoming raw materials. Of course, we could make a similar subdivision as in the US data, into “Wool textiles, DK”, “Cotton textiles, DK” and “Synthetic textiles, DK” and from this a specific textile with mixed fibre composition could be combined. However, as mentioned for the subdivision of the US industries, this implies an assumption that all other inputs have a fixed relation to the value of the fibre input. While this may be an acceptable assumption when subdividing a fairly uniform industry such as “Yarn mills”, it would be less appropriate for subdividing the much more diverse, aggregated textile industry, where we see a large variation in output value relative to the value of fibre input (reflected in the differences in average prices per kg output; see Table 6.9).

We have therefore, in spite of the large uncertainty in such a venture, attempted to suggest a possible split of the different fibre inputs for each of the product groups.

This should be seen a first rough assignment, and not as an authoritative reflection of the actual fibre composition of the different products. For example, it is most likely that carpets and sweaters are not made solely from wool, and that relatively more wool should therefore be allocated to other products, such as knitware. However, such changes can easily be made in the database from the project (see Chapter 7) without affecting the overall results.

As can be seen in Table 6.9, the value of a textile product does not vary much across different fibre types, but is much more related to the degree of processing. This means that for modelling of a specific textile product, the fibre type of the input can be changed, e.g. from cotton to synthetics, without changing the monetary value of the input, i.e. simply by transferring the purchase value from “Yarn, cotton” to “Yarn, synthetic.” To keep the model of the entire textile industry consistent, it is of course necessary to match a change in input composition for a specific product with an opposite change for one or more of the other products, so that the overall mass balance is kept intact. However, for modelling of a specific product, such concerns are less important.

In addition to the inputs to the textile industry allocated to specific products in Table 6.9, we allocated some minor plastic and metals inputs to household textiles, fish net, mattresses and an additional product group “textile accessories” to eliminate these articles from the “pure” textile products. Also, zippers were allocated 50% to tents and the rest evenly over knitware and textile goods n.e.c. according to production value.

Table 6.9. Tentative mass balance for the Danish textile industry in 1999

Figure 6.5 shows the resulting environmental impact intensities for the 12 textile product groups with largest turnover compared to the original average textile industry (the column on the left). The figure mainly reflects the importance of the fibre type, clearly showing the importance of the cotton input, and also showing the importance of the assumption that carpets and sweaters are purely made from wool.

Click here to see Figure 6.5.

Figure 6.5. Environmental impact (in person-equivalents per kDKK) of the average textile industry and 12 of its constituting product groups.

In parallel to the mass balance for the textile industry, a mass balance for the Danish apparel industry could be constructed. However, this would become even more speculative, since approximately half of the textile input is only specified as “knitware”, not by fibre type. The rest of the textile input is mainly cotton and synthetic fibres, in the typical 65/35 proportion. A reasonable assumption could be that the unspecified knitware is composed in the same proportion. Different types of clothing have very different mass/value relationships, with synthetic nightgowns at the extreme low end with 0.06 g/DKK over the average 4 g/DKK to 10 g/DKK for worker's clothes. Since the textile input is responsible for the main part of the overall environmental impact, it would be reasonable to specify the textile input based on the weight of the apparel output (adding an average loss factor of 8%) and then add the non-textile input up to the full production cost. To allow for this, we provide in the database from the project (see Chapter 7) an average input coefficient for the non-textile input to the apparel industry.

In the use stage of textiles and apparel in private consumption, the processes connected to washing and cleaning (washing machine, electricity, detergent, laundering and dry cleaning services) contribute 19% of the overall life cycle impacts of the textile and apparel products and 34% of the life cycle impact of global warming for these products.

The industrial laundry service, where textiles are supplied as part of the service, does not appear as a separate industry in the Danish NAMEA, as it is included in “Service activities n.e.c.” We have therefore separated “Service activities n.e.c.” into its constituent parts: “Laundries and dry cleaners”, “Hairdressers and other beauty shops” and “Funeral services” based on data provided in the supply-use tables (Danmarks Statistik 2003b) and data on the three corresponding industries in the US NAMEA (Suh 2003). The resulting environmental impact intensity for laundries is approximately 50% higher than for the original aggregated industry.

For the resulting industrial laundry service, the textile component is only responsible for approximately 20% of the overall environmental impact, while the detergents contribute with approximately 50%, mainly due to VOC emissions. This points to a more efficient use of the typical textile in industrial laundry service compared to the textiles used in private consumption.

Footnotes

[8] Compared to the need group “food” in Chapter 1.2.4, we have not included car driving for shopping and catering in the definition of household meals. Also, it should be noted that the additional building space for kitchens is also not included, as this is assumed to depend primarily on other factors than the number of meals consumed at home.

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Version 1.0 February 2005, © Danish Environmental Protection Agency