**2. Methodology**

A detailed account of the research methodologies used to obtain all the data contained in the tables in this paper can be found in [17,20,21], along with the geographies defining each city. Table 1 provides a summary of the American, Australian, Canadian, European and Asian cities used to calculate the averages for these groups of cities shown in this paper, as well as the ten Swedish cities and Freiburg. It presents their population and the year of that population, their metropolitan GDP per capita at that year (in US\$1995) and the per capita annual boardings for their whole public transport systems (all modes in use in each city are included, which cover buses, minibuses, trams, light rail, metro, suburban rail and ferries). This last item gives a comparative perspective on a key transport-sustainability factor for each city. "Cities" is used here as a shorter term for metropolitan regions because the data mostly represent wider metropolitan areas, not just the "cities" lying at the heart of these areas.


**Table 1.** List of cities used for the international comparisons with their population, GDP per capita and annual public transport use per capita.


**Table 1.** *Cont.*

In this paper, Swedish cities have been divided into five larger and five smaller cities so that differences on this basis can be seen. Averages are presented for the larger cities, smaller cities and all ten Swedish cities. The larger cities are Stockholm, Göteborg, Malmö, Linköping and Helsingborg, while the smaller cities are Uppsala, Jönköping, Örebro, Västerås and Umeå.

The value of this research on the Swedish cities, as well as the global sample, is that it uses empirical energy data from cities for private and public transport, as opposed to theoretical modeled data for different vehicular technologies e.g., [22,23]. All data are collected directly for each city from the primary sources of those data, mostly through a variety of government departments in each city or through national datasets that are available for the specific geographies used to define the metropolitan areas in this study. For example, public-transport energy use is obtained directly from every operator and mode in every city. The collection of these data is conducted by consulting published online sources in the first instance and then many emails and phone calls between many people in a plethora of transport, planning, energy, environmental and other departments in every city. Most data require this in-depth work and are not routinely published. Only primary data are collected, never the standardized indicators shown in the tables. These standardized indicators are calculated by the author by combining the relevant primary data (e.g., population and urbanized land area to get urban density). All Swedish city data and Freiburg are for 2015, while the American, Australian, Canadian, European and Asian city data are for 2005–2006, from an earlier study of these other cities e.g., see [15,19,24].

While it would be ideal to have all the comparative data for the same year, it must be pointed out that the collection of these comparative cities' data, which are much more than shown in the tables in this paper, takes many years to complete (the 2005–2006 data commenced in 2007 and was not complete until 2014). Providing 2015 data for the other cities could not have even been commenced until 2017, due to delays in data release. The comparisons, however, are still valid in relative terms, and experience over 40 years of such data collection has shown at each point that the relative differences between cities remain. This is supported by the author's publications in the reference list, including representing these other cities with 2005–2006 data at a much later date and where the 2005–2006 data have been compared to later data [25], including a paper comparing many urban indicators for the five larger Swedish cities in 2015 with the 2005–2006 data on the American, Australian, Canadian, European and Asian cities [21]. Where some variables can change quite rapidly, the discussion provides caveats on the results and cautions readers accordingly.

The point of making comparisons between the Swedish cities in 2015 with a global sample ten years earlier is to gain an insight into the general magnitude of differences, not to be absolutely precise. Over a decade, European cities are, for example, not going to become very like American cities, nor are even Canadian cities, in virtually any of the parameters. There is a basic and relatively stable difference in these fundamental metropolitan-scale indicators across such a global range of cities, which is quite resilient to change over time. The author has 1960, 1970, 1980 and 1990 data that show similar basic patterns. The exact numbers have changed, but the general relativities have not [26].

To demonstrate this, Table 2 provides the ten-year change in an earlier decade from 1995–1996 to 2005–2006 in the value for every variable that has been used in this paper for the US, Australian, Canadian, European and Asian cities. From this, it can be seen, for example, that although private transport energy use per capita has changed, European cities are still very much lower than American cities, and Asian cities are very much lower again than European cities. Australian and Canadian cities maintain their medium position in the sample. Car passenger kilometers per person did not change much in the ten years in any group of cities, so the general magnitude of differences were again stable. With respect to seat kilometers of public transport service per person, this was still worst in the American cities by a large margin, fair to middling in the Australian and Canadian cities, very much better in the European cities and better again in the Asian cities. By 2015, though values will have changed, it is highly unlikely that American cities will have reached even Australian levels of public transport service, let alone European or Asian levels. Likewise, public transport use follows the same pattern and is very similar in its relative differences, even over a decade of change. If we consider the use of non-motorized modes, American cities are the worst, Canadian cities are next and then Australian cities, and the Asian cities, while the European cities are the best. This general perspective has not changed over ten years, even though the value for each group has changed to some degree. Rather than eliminating this global perspective for the sake of 2015 data, which are not possible yet on the global sample, the 2005–2006 perspective still has utility.


*Energies* **2020**, *13*, 3719



All energy data are end-use data and do not include the energy expended for drilling, extracting, refining or distributing oil to obtain the petrol, diesel and other liquid or gaseous fossil fuels before dispensing them into vehicle fuel tanks. Renewable fuels, such as ethanol, do not include the planting, growing, harvesting and processing of crops or other energy use expended in delivering that fuel to a vehicle's fuel tank. Electrical energy does not include the power station and transmission losses or other energy expended in the production and delivery of electrical energy to its end user.

All other standardized data or indicators on cities such as urban density, which are used to help explain the observed per capita energy use and modal energy use per kilometer, were obtained by using the same methodology as for energy. All the primary data used to calculate the indicators (e.g., freeway length and population for freeway length per capita) were collected directly from the sources of those data (e.g., population data from the relevant official sources of such data, such as local or national censuses and freeway length from road inventories or other sources). All public transport operating and infrastructure data were collected from the same operators and agencies as the energy data. A little more detail is provided about methodology in the results section, when dealing with specific indicators.
