Inflows

2.3.1 Consumption of Goods

The inflow of consumption of goods includes packaging materials, vehicles and electronic appliances.

Packaging

The consumption of packaging was derived from the national waste statistics of Statbank (StatBank, n.d.-a), where data was available for the timespan 2010 to 2015. It is assumed that the waste generation is representative of the inflow of packaging into the urban metabolism of Odense.

Packaging is likely produced somewhere outside of the municipality and is imported together with goods. Export of packaging waste and goods from Odense is deemed to be minor.

The waste generation was stated in total for Danish households. With the number of Danish households in total, the generation by households (t/households) was calculated and afterwards the total amount generated by Odense with the number of households in the municipality. Data on the number of households was derived from Statbank, too (StatBank, n.d.-d).

Only data on the household sector could be used, not on industry or public, since the original numbers are national and waste by industry or other sectors are impossible to convert to estimate the amount in Odense.

Vehicles

The data on inflows of vehicles was derived from the stock data (2.2.4). A static lifetime of 13 years for vehicles was assumed, with a standard deviation of 2.6 years. A normal lifetime distribution was applied. Using the growth rate, the historic stock until 1950 was calculated. With the stock data and the lifetime distribution it was possible to model the outflows, what allowed to determine the surviving historic inflows. The stock at year “X” minus the sum of the surviving historic inflows equals then to the inflow at year “X”.

Electronic Appliances

The data on inflows for electronic appliances was derived from the stock data (2.2.4). For house appliances a static lifetime of 9.28 years was assumed, with a standard deviation of 1.86 years. A normal lifetime distribution was applied. Historic stock data existed for most of the appliances and if not, assumptions were made, and growth rates used (Appendix 7.4.3). In case there were fluctuations in the stock data, which would mean a negative inflow, the moving average tool in excel was used to smoothen the fluctuations (Appendix 7.4.3).

For consumer electronics a static lifetime of 4.55 years was assumed, with a standard deviation of 0.91 years. A normal lifetime distribution was applied. Historic stock data was calculated using growth rates. For several products the stock was decreasing, because the technology was outdated. For those it was assumed, that from the turning point when the stock data decreased the inflows equal zero. In case of fluctuations in the stock data, the mentioned, moving average tool was used to smoothen the data (Appendix 7.4.3).

Using the edited stock data and the lifetime distributions, the outflows and hence the surviving historic inflows could be modelled. The stock at year “X” minus the sum of the surviving historic inflows equals then to the inflow at year “X”.

2.3.2 Water

Data for the water consumption was obtained from the annual report of Odense municipality (Odense Kommune, 2018). Godskesen et al. (2013) conducted an LCA of the water consumption in Copenhagen, and those results are deemed appropriate to use for Odense (Godskesen, Hauschild, Rygaard, Zambrano, & Albrechtsen, 2013). The water is extracted from groundwater sources located outside the city and then treated ate the waterworks (aeration and sand filtration) before distribution,

2.3.3 Food

Statistics about food consumption in Odense do not exist. However, a Danish national survey about diets was conducted between 2011 and 2013 (Pedersen et al., 2015). Even though the survey did not specifically target citizens of Odense and is now a few years old, the data can be seen as representative. Indeed, diet habits are assumed to be similar between the different regions of Denmark, and to not have significantly changed in the last years.

Table 5 shows the values stated in the food survey. The results of the total consumption are calculated using the consumption per age interval and the number of inhabitants in that interval per that particular year. The consumption of people above the age of 75 was assumed to be the average of the consumption of the defined age intervals, this means a lower consumption than the one for adults but a higher than for children. This seems representative for elderly diet habits. The consumption for children below the age of four was calculated with the values for consumption for the age interval 4 to 9. This is likely an overestimation, but the number of children in that age interval are comparatively very small.

Table 5: Danish food consumption by age interval and food category (Pedersen et al., 2015)

Food Product (g/day/cap) Consumption by age Interval Average Consumption 4 to 9 10 to 17 18 to 75

Milk and milk products 428 407 304 380

Cheese and cheese products 21 25 44 30

Bread and other cereals 216 219 218 218

Potatoes and potato products 40 74 91 68

Vegetables and vegetable products 157 144 199 167

Fruit and fruit products 188 141 190 173

Juice 59 75 56 63

Meat and meat products 87 120 134 114

Poultry and poultry products 16 27 26 23

Fish and fish products 16 15 37 23

Egg 18 17 24 20

Fatty substances 37 36 41 38

Sugar and Candy 35 38 37 37

Beverage 758 1120 2180 1353

Total 2076 2458 3581 2705

To estimate the embodied emissions however, the exact amount of food per category was not being used, because data could not be obtained with one consistent system boundary for all categories. The emissions were then estimated using the energy consumption for providing food determined in an Input-Output-Analysis conducted by (Girod & de Haan, 2010), whose study is about the GHG emissions driven by the consumption of Swiss households. The energy consumption was converted into CO2-eq with the value of the Danish energy mix. The Swiss diet habits are considered to be similar to Danish, both countries are similarly developed and, have a high HDI, and diet differences between middle and north European can be considered small. (Girod & de Haan, 2010) considers beside the consumption of food as well beverages.

2.3.4 Construction Material

The inflow of construction material were estimated with the material intensity (2.2.1) for RB and NRB and data from the statistics about completed floor area per year in Odense (Appendix 7.4.1) (StatBank, n.d.-c). The material intensity per m² of the most recent archetype was taken and multiplied with the newly added floor area per year. Residential buildings and non-residential buildings are considered, data on materials for infrastructure construction was not available. Assumptions on material intensities for building types are attached (Table 56, Appendix 7.4.1)

The methodology can be summarized with following formulae:

𝑚𝑎𝑡𝑒𝑟𝑖𝑎𝑙 𝑤𝑒𝑖𝑔ℎ𝑡 (𝑘𝑔

𝑎 ) = 𝑚𝑎𝑡𝑒𝑟𝑖𝑎𝑙 𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦 (𝑘𝑔

𝑚²) ∙ 𝑓𝑙𝑜𝑜𝑟 𝑎𝑟𝑒𝑎 𝑎𝑑𝑑𝑒𝑑 (𝑚² 𝑎 )

The embodied emissions in the construction material was estimated same as for the materials in the stock with the emission factors obtained from ÖKOBAUDAT.

Furthermore, data on the currently ongoing big construction projects in Odense - the light rail system and the new hospital - was collected to display future construction material demands.

2.3.5 Energy and Operational Emissions

Data was retrieved from the Danish Ministry for Energy and its portal Sparenergi.dk (Energistyrelsen, n.d.). The ministry provides detailed information about the sources of the emissions and energy consumption. Data exists for the years 2010 to 2015 and is visualized to capture the trend over the years.