Global Cities' Climate-Adjusted Carbon Footprint Indicator By Suraj Nambiar and Heikki Keskivali

Indicators of Well-‐Being and Sustainability

Climate-‐Adjusted Carbon Footprint Indicator for Cities

Heikki Keskiväli & Suraj Nambiar

Suraj Nambiar & Heikki Keskiväli

Table of Contents 1. Introduction and background ........................................................................................................... 2

1.1 Global urbanization .................................................................................................................... 3

1.2 Existing indicators ...................................................................................................................... 3

2. Methodology ..................................................................................................................................... 4

2.1 Heating and cooling degree days ............................................................................................... 4

3. Data ................................................................................................................................................... 5

3.1 CO2 emissions ............................................................................................................................ 5

3.2 City specifications ...................................................................................................................... 6

3.3 Heating and cooling degree days ............................................................................................... 6

4. Results ............................................................................................................................................... 6

4.1 Approach used ........................................................................................................................... 6

4.2 Findings ...................................................................................................................................... 8

5. Discussion ........................................................................................................................................ 14

5.1 Used data ................................................................................................................................. 14

5.2 Alternative data source ............................................................................................................ 15

5.3 Policy recommendations ......................................................................................................... 16

5.4 Building the Perfect Indicator: Scope and Shortcomings ......................................................... 17

Conclusion .............................................................................................................................................. 19

Appendix 1 .............................................................................................................................................. 20

Bibliography ............................................................................................................................................ 21


1. Introduction and background As the world population increases, the importance of urbanization, and therefore cities, has grown

significantly. Cities have an increasingly important role in reducing emissions globally because even

though they account for only 2 % of the Earth’s land surface, roughly 75 % percent of the world’s

emissions are produced in cities (City of Sydney, 2013).

Current widely used indicators for cities rely mostly on gross domestic product (GDP) that is proven to

be an inaccurate metric for environmental impact and sustainability in various geographies. Electricity

consumption and carbon emissions, however, do give more accurate results on these aspects.

Moreover, indicators that take carbon emissions into account and aim to compare electricity

consumed in cities across the world, do not cover the fundamental aspect of cities’ varying

temperature, a component that directly affects per capita electrical consumption. For example, it is

not meaningful to compare a city in Antarctica with a city in California with each other, since the

extremes of the climate vary significantly.

The purpose of the indicator is to understand and compare cities across the world based on their

carbon emissions taking into consideration varying climate conditions. This however is easier said

than done. Cities and their power consumption can differ based on factors such as country, continent,

governance, history, primary industry, topography, population and most importantly, climate. In this

document, we try and eliminate the effect of such “discrepancies” in a dual pronged approach by:

• Normalizing the weather conditions of different cities (sophisticated adjustments where

the intention is to bring the entire distributions of adjusted values into alignment)

• Looking at citys’ energy use not by electrical consumption, but rather by taking into

account their total carbon emissions.

This analysis aims to create an indicator to globally benchmark cities based on their carbon emissions,

adjusted with climate characteristics and population, thereby allowing cities to be measured on a

level playing field. The indicator could be used in the future as a guideline for energy efficiency, city

planning, or regulative improvements for any given city.


1.1 Global urbanization According to the United Nations, 3.6 billion people, equaling over 51 % of the world population of 7.0

billion, were living in urban areas in 2011, and this share is set to increase. In 2050, it is expected that

the world population will reach 9.3 billion, of which 6.3 billion would live in urban areas. Therefore,

cities are not only covering for all population growth, they are also accounted for population decrease

in rural areas. (The United Nations, 2012)

Even though over half of the world population lives already in urban areas, there are significant

geographical differences between continents. For example, it is expected that the level of

urbanization will reach 50 % of the population in Asia and Africa by 2020 and 2035, respectively.

Therefore, cities and towns in less developed countries are in general gaining most of the urban

population growth in the coming years. Asia, Africa, and Latin America along with Caribbean will add

1.4 billion, 0.9 billion and 0.2 billion to their urban population, respectively. This means that roughly

2.5 billion out of 2.7 billion people, who will begin living in urban areas, will do so outside the more

developed world. (The United Nations, 2012)

These growing trends do not come without complications. Some cities have already faced severe

environmental problems that have made everyday lives difficult due environmental issues such as

poor outdoor air quality. Centralized burning of fossil fuels near the population centers combined

with increasing amount of combustion engines in the traffic have generated high amounts of

emissions and small particles. Especially in China, concepts of eco-‐city, low carbon city and low carbon

eco-‐city are introduced to make an impact to local policies, since the rapid urbanization and

development in standard of living have been testing the limits of sustainability (Yu, 2014).

1.2 Existing indicators One of the most extensive indicators for cities and their environmental efficiency is the Siemens

Green City Index. The Index research series has measured environmental performance of over 120

cities in Europe, Latin America, US & Canada, Asia and Africa. For example, the European Green City

Index, conducted by the Economist Intelligence Unit and sponsored by Siemens, has collected 30

different countries from Europe to form a ranking of cities based on several factors that include CO2

emissions, energy, buildings, transport, water, waste and land use, air quality and environmental


governance. The scope of this study is limited to redefining the CO2 emissions in city comparison.

(Siemens, 2014)

According to the Green City Index, best environmental performance in Europe can be found from

Copenhagen, Stockholm, and Oslo. North America is led by San Francisco, Vancouver and New York,

as Asia is led by Singapore, Tokyo and Seoul. Even though this indicator takes CO2 emissions into

account, different climates are not accounted for in the calculations, hence the findings from this

study could be used to complement further analysis of the Green City Index.

Many respectable entities such as WorldBank and CIA World Factbook do also have nationwide

carbon emission figures but most of the data is not detailed in the resolution of metropolitan areas.

2. Methodology

2.1 Heating and cooling degree days It is obvious that installing and maintaining infrastructure in different countries and cities is use

different amounts of electricity for its needs, but often the impact of the surrounding climate is not

taken into account. To normalize such different data, heating degree days (HDD) and cooling degree

days (CDD) are used. These metrics indicate the amount of required heating or cooling, reflecting the

outdoor temperature’s difference from the base temperature of indoors.

Calculating degree days is usually a compromise between the effort and the accuracy of data. The

most accurate data can be obtained by integrating temperature distribution density throughout each

day but this approach requires high amounts of accurate data and a lot of processing power for long-‐

term information (Martinaitis, 1998).

More effortless, but yet sufficiently accurate approach, is to compare the base indoor temperature

with the mean of the daily maximum and minimum temperatures. This data is widely available and

therefore more suitable for low-‐resource research. Simple method to calculate heating and cooling

degree days could be conducted with Equation (1) and Equation (2):

Heating: 𝐻𝐷𝐷! = (𝑇!,! − 𝑇!,!)!!"!!! (1)


Cooling: 𝐶𝐷𝐷! = (𝑇!,! − 𝑇!,!)!!"!!! , (2)

where Te,d equals the mean of daily minimum and maximum values of outdoor temperature of a day

d, while Tb,h and Tb,c represent the chosen base temperatures for heating and cooling, respectively.

Plus sign indicates that only positive values in the calculation are taken into account. (De Rosa, et al.,

2014)

For this paper, a temperature data resolution of 30 minutes to 60 minutes is used, depending on the

quality of the local data provided. The same aforementioned approach is used, but with higher

accuracy since instead of two data points during the day1, 24 to 48 data points are used2.

Temperatures chosen for this paper are 17 degrees Celsius for HDD, and 23 degrees Celsius for CDD.

These values can be widely used as the range for comfortable living, but preferences differ slightly

between geographical location. However, these preferences are not taken into account in this

analysis.

3. Data

3.1 CO2 emissions To conduct our analysis on several cities, homogenous information across all the comparative cities

had to be used.

OECD provides CO2 emission data for metropolitan areas with a breakdown to energy industry and

transportation emissions. This data is easily accessible and covers several hundreds of locations,

exportable conveniently in Excel format. Since homogenous data for multiple locations in different

climates is needed to conduct this analysis, this database was used. By using data from one single

source mitigates risk from comparing apples with oranges and should provide coherent picture of the

existing reality. (OECD, 2014)

1 Minimum and maximum temperatures. 2 Temperatures every 30 to 60 minutes.


3.2 City specifications As part of our analysis, population density is one of the defining factors of city characteristics. To have

reliable population density data, both the total amount of population and the area of the city are

required. Since data discrepancy might affect our results substantially, our primary data for both city

population and area are obtained from the same survey responses than those of the emissions. It is

important to use the data for all the metrics from the same source since the three3 are highly

correlated with each other. This will mitigate the risk of having different interpretations of the city’s

specifications that would widely affect our outcome.

However, if all of the aforementioned data is not available from OECD, the use other sources for the

analysis have been identified for backtracking purposes.

3.3 Heating and cooling degree days To acquire substantial amounts of temperature data for the analysis, a web-‐based service by BizEE

Software, DegreeDays, was used. The platform acquires temperature data from Weather

Underground database with 30 to 60 minute resolution, and calculates the HDD and CDD with given

range of temperature. The temperature for HDD was chosen to be 17 degrees Celsius, and for CDD,

23 degrees Celsius. Airport weather stations are used as primary source for HDD and CDD due to the

nature of its high quality and low-‐resolution data. (DegreeDays, 2014)

Averaged data for the last 5 years is used to get as extensive dataset as possible to mitigate the risk of

using extreme years as a base for the analysis. All the gathered data used from the platform can be

found from Appendix 1.

4. Results

4.1 Approach used The objective in this paper is to achieve a temperature-‐neutral indicator for the carbon footprint of

cities around the world. Currently power consumption figures are paired up with whole countries and

while doing so, no adjustments based on the climate are being made. It is easy to see why it is unfair

3 Area, population and emissions.


for a city that faces extreme climate requiring substantial amounts of heating and cooling throughout

the year to be compared with one that has more forgiving weather conditions.

For temperature adjusting, heating and cooling degree days are used. For each city, the sum of both

will be generated. Additionally, the CO2 emissions are emissions are used to enable the paper to

comment on the amount of clean energy used. Note that CO2emissions that are caused due to waste

are omitt, since those emissions do not correlate with outdoor temperature. By combining HDD, CDD

and CO2 emissions, we find base level and temperature-‐correlated emissions from the results, as

illustrated in Figure 1.

Figure 1. Expected illustration of the gathered data. No real data was used to generate this graph.

With the help of this dynamic illustration, we can determine the average per capita CO2 emissions in a

city. This value, to be called base value in this paper, attains a value of 83 as shown in the illustration

above. The value is determined by the point where the city sample data trend line crosses the y-‐axis.

This is the data point where corresponding to zero degree days, (no temperature correlated

emissions, such as heating or cooling) is taken into account. As the amount of heating and cooling

increases, the trend line signals the correlation factor that is being awarded for cities because of the

more extreme climate. Hence, it is more acceptable for a city with 5000 degree days of cooling and

heating to generate more emissions than it is for one with a 10 degree days city.

City D

City E

City H

City J

City C

City F

City K

City B

City A

City I

City G y = 0.0347x + 83.022

0

50

100

150

200

250

300

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000

CO2 em

ission

s per cap

ita (in tonn

es)

HDD + CDD = Total degree days

City sample

Linear (City sample)


After generating this formula, every city is then issued with a difference value. This is calculated as the

negative or positive difference of the city’s data from the trend line of all cities, and this value is then

combined with the base value. For example, say City C and D have emission values of 100 and 150,

respectively. Both of these cities have degree-‐days of 1000, where trend line is at 1184. This means

that City C is below it by 18 and City D above by 32. These differences are then applied to the zero-‐

point figure of 83, which means City C and D will end up with temperature-‐adjusted emission values

of 65 and 115, respectively.

The higher the number, the more the city has to do with its energy efficiency. Below average numbers

indicate that given cities are ahead of the game. Hence, as more data is applied, the values change

dynamically and there will always be cities above and below average (trend line).

4.2 Findings The carbon footprint and degree days for 50 cities5 were collected for further analysis. These results

can be found from Figure 2. These emissions were defined as “estimates of CO2 emissions” (expressed

in tonnes) in metropolitan areas divided by population. The values are disaggregated from the

corresponding national values” after (OECD, 2014). As can be seen from Figure 2, there are three

cities with significantly higher CO2 emissions than any other location chosen for this analysis. The city

with 5473 total degree days is Edmonton, the city with 2797 total degree days is Aachen, and the city

with 2767 total degree days is Kansas City.

Further studying showed that all the aforementioned cities had a significant presence of either

manufacturing or energy industry within the metropolitan limits that account for the abnormal

numbers. Edmonton has 12,8 % of its GDP generated by manufacturing industry and 12,7 % by

construction (City of Edmonton, 2014), while Aachen houses several energy intensive manufacturing

and communications companies like Denso Automotive, Ericsson, Phillips and Ford (NRW.Invest,

2014), and Kansas City works as a hub for intermodal transportation, warehousing, manufacturing,

and distribution (City-‐Data.com, 2005).

4 This can be calculated by using the formula found on Figure 1. In this case, 0.0347*1000+83.022=117.72. 5 All used data can be found from Appendix 1.


Figure 2. CO2 emissions of 50 metropolitan areas.

Since the aim of this study is to evaluate the energy efficiency and carbon footprint of cities in

different climates, these distinctive samples were removed from the group, which leads to a total

number of used cities at 47 for further analysis. Our motivation is to produce statistical methods that

are not unduly affected by such outliers, and hence these distinctive samples are taken out of the

group of cities under observation.

The CO2 emissions of the remaining cities by total amount, energy industry6, transportation7, and

other8 are presented in Figure 3, Figure 4, Figure 5 and Figure 6.

Figure 3. Total CO2 emissions of 47 metropolitan areas, excluding Kansas City, Edmonton and Aachen.

6 “Share of CO2 emissions from the energy industry over total metropolitan CO2 emissions”, after (OECD, 2014). 7 “Share of CO2 emissions from transport (road and non-‐road ground transport) over total metropolitan CO2 emissions”, after (OECD, 2014). 8 As energy industry and transportation emissions are deducted from the total amount, the remaining is considered as ’other’ emissions.

Edmonton

Aachen

Kansas City y = 0.0026x + 4.6159

0 5 10 15 20 25 30 35 40 45 50

0 1000 2000 3000 4000 5000 6000

CO2 p

er cap

ita (in tonn

es)

Total degree days

y = 0.0013x + 6.7473

0

5

10

15

20

0 1000 2000 3000 4000 5000 6000

CO2 e

mission

s per cap

ita

(in to

nnes)

Total degree days


Figure 4. CO2 emissions of 47 metropolitan areas from energy industry, excluding Kansas City, Edmonton and Aachen.

Figure 5. CO2 emissions of 47 metropolitan areas from transportation, excluding Kansas City, Edmonton and Aachen.

Figure 6. CO2 emissions of 47 metropolitan areas from category ‘other’, excluding Kansas City, Edmonton and Aachen.

y = 0.0001x + 1.2745

0 1 2 3 4 5 6 7

0 1000 2000 3000 4000 5000 6000 CO2 e

mission

s per cap

ite (in

tonn

es)

Total degree days

y = -‐4E-‐05x + 2.6718

0 1 2 3 4 5 6 7 8 9

0 1000 2000 3000 4000 5000 6000

CO2 e

mission

s per cap

ita (in

tonn

es)

Total degree days

Krakow

y = 0.0012x + 2.801

0

5

10

15

20

0 1000 2000 3000 4000 5000 6000

CO2 e

mission

s per cap

ita (in

tonn

es)

Total degree days


As can be seen from the figures, energy industry consumption and transportation emissions show

very little, if any, correlation with the increased amount of total degree days. Also, Transportation

emissions not correlating with the climate makes sense, but findings with the energy industry are

surprising. As it seems from the results, OECD is calculating energy industry emissions from where

they are produced, and not where they are consumed. This strongly distorts the values attained from

several cities where OECD reports no energy industry emissions at all, which is highly unlikely

considering global energy needs.

To demonstrate one city’s new index, let’s use Krakow as an example (marked to Figure 6). Krakow

had CO2 emissions per capita at 7,99 tonnes, which positioned Krakow to be 19th best city in the

sample of 47 cities. Krakow’s emissions were 1,32 and 2,10 tonnes per capita for transportation and

energy industry, respectively. Other category amounted to 4,57 tonnes, which was then adjusted with

3511 degree days, since it correlates with temperature. As the trend line has a value of 7,01 with this

many degree days, the benefit for the city is 7,01 – 4,57 = 2,44. This benefit is then deducted from the

temperature correlated emissions baseline of 2,80, which will result in other category’s emissions of

2,80 – 2,44 = 0,36. To compare different cities with each other, all temperature adjusted9 and non-‐

adjusted10 emissions are summed up, forming a value of 3,7811 for Krakow. The same value is then

determined for all 47 cities and then normalizing the index by using Equation (3):

!"#$!% !"#$%!!"#"!$! !"#$%!"#$!%! !"#$%!!"#"!$! !"#$%

= 𝐷𝑖𝑚𝑒𝑛𝑠𝑖𝑜𝑛 𝑖𝑛𝑑𝑒𝑥, (3)

where actual value is Krakow’s 3,78, minimum value Guadalajara’s 1,33 and maximum value New

York’s 14,79. Therefore, index value for Krakow is 0,18, zero being the best and 1,00 the worst.

Indexing logic is presented in Figure 7.

9 Category ’other’ emissions. 10 Categories transportation and energy industry. 11 0,36 (other) + 1,32 (transportation) + 2,10 (energy industry) = 3,78 (total)


Figure 7. Temperature adjusted city CO2 emission index logic.

As all cities are put in order, this improves Krakow’s ranking by 7, from 19th position to 12th. All cities,

their index values, initial rankings with original CO2 emissions and with new method are presented in

Table 1.

It can be seen from the results that cities that improved their CO2 ranking with temperature adjusting

with five spots or more were Stockholm (7), Seoul (5), Tallinn (12), Ljubljana (5), Krakow (7), Warsaw

(7), Winnipeg (8), Calgary (7), Quebec (7), Oslo (6) and Helsinki (6). On the other hand, cities that

worsened their CO2 ranking with temperature adjusting with five spots or more were Lisbon (-‐6),

Barcelona (-‐7), Tokyo (-‐8), Osaka (-‐6), Nice (-‐10), Rome (-‐5), Dublin (-‐5), Phoenix (-‐5), Washington (-‐5)

and San Francisco (-‐5).

Table 1. Results of temperature adjusted CO2 emissions in different cities. Initial ranking was based on straightforward CO2 emissions published by (OECD, 2014) and adjusted ranking to the temperature adjusted city CO2 emission index logic. Difference implies the change in the position for the given city as the ranking type has been changed.

Index value for city emissions

Baseline -‐ (actual value -‐ trend line)

Other

Not temperature correlated

Energy industry

Not temperature correlated

Transportanon

Sum all parts and scale to 0-‐1 for comparison

Weather adjustment


Name of the city Initial rank Adjusted rank Difference Index value Guadalajara 1 1 0 0,00 Stockholm 9 2 7 0,02 Seoul Incheon 8 3 5 0,04 Mexico City 2 4 -‐2 0,09 Tallinn 17 5 12 0,10 Monterrey 3 6 -‐3 0,11 Athens 6 7 -‐1 0,13 Málaga 4 8 -‐4 0,14 Copenhagen 12 9 3 0,15 Ljubljana 15 10 5 0,16 Lisbon 5 11 -‐6 0,17 Kraków 19 12 7 0,18 Warsaw 20 13 7 0,21 Barcelona 7 14 -‐7 0,22 Madrid 14 15 -‐1 0,22 Milan 16 16 0 0,22 Munich 21 17 4 0,23 Tokyo 10 18 -‐8 0,25 Osaka 13 19 -‐6 0,25 Paris 18 20 -‐2 0,27 Nice 11 21 -‐10 0,29 Budapest 23 22 1 0,34 Brussels 24 23 1 0,36 Stuttgart 26 24 2 0,40 Winnipeg 33 25 8 0,41 Graz 27 26 1 0,42 Rome 22 27 -‐5 0,43 Marseille 25 28 -‐3 0,45 Calgary 36 29 7 0,51 Montreal 30 30 0 0,52 Quebec 38 31 7 0,53 Berlin 28 32 -‐4 0,54 Prague 31 33 -‐2 0,57 Dublin 29 34 -‐5 0,57 Oslo 41 35 6 0,59 Vancouver 34 36 -‐2 0,67 Phoenix 32 37 -‐5 0,69 Helsinki 44 38 6 0,69 Vienna 39 39 0 0,70 Washington 35 40 -‐5 0,71 Amsterdam 42 41 1 0,78 San Francisco 37 42 -‐5 0,81 Miami 40 43 -‐3 0,88 Linz 46 44 2 0,93 Las Vegas 45 45 0 0,96 Los Angeles 43 46 -‐3 0,98 New York 47 47 0 1,00


5. Discussion

5.1 Used data OECD reports CO2 emissions for metropolitan areas in three different categories: transportation,

energy industry, and other. Transportation for a given city is a good metrics since it is locally used no

significant energy flows are moving between areas. However, this does not apply for energy industry.

As many cities are reported to have no CO2 emissions for energy industry in their given metropolitan

area, it is reasonable to believe that only the energy production is accounted for, not the energy

consumption. This raises an issue of the fact that energy can be transported between locations and

energy hubs can account for a significant amount of area’s energy production, as some metropolitan

areas, such as Guadalajara, Quebec and Malaga, might produce none of their consumed electricity.

This would also explain the lack of correlation with the energy industry emissions and temperature,

because common sense approach would lean toward the exact opposite; consumption of energy

increases with larger temperature fluctuations. Therefore the data used for this primary analysis

contains a lot of emission allocation that is unjust. This could be easily corrected if OECD would start

reporting the emissions occurring from the energy consumption within the metropolitan area.

Another addressable concern is the amount of manufactured goods. As only three different

categories for emissions were announced, none of which were industry activity, the emission

allocation can be assumed to be in the category other. The same logic applies for them as they can be

easily transported between locations, so only accounting for the amount of manufactured goods is

not a sufficient approach. To fairly calculate the emissions for a metropolitan area, the emissions

occurred from the consumption of the goods should be accounted for. This situation was clearly seen

in the cases of Aachen and Edmonton, for example.

Results of this study raised another concern about the validity of the data used from OECD. According

to the source used, Oslo and Helsinki have CO2 emissions of 14,61 and 15,78 tonnes, respectively, per

capita. However, several sources such as (MetroVancouver, 2011) and (YTV, 2011) suggest that

Helsinki had CO2 emissions per capita of 6,3 in 2004 and 5,5 tonnes in 2010. Additionally, sources such

as (Siemens, 2014) and (WWF, 2013) suggest that Oslo had CO2 emissions of 2,19 and in 2007 and

2,20 in 2011. This magnitude of CO2 emissions for Oslo and Helsinki are also supported by Samsung’s


Green City Index Data (Siemens, 2014). Therefore, the ultimate results in this study can be questioned

despite the added value by the chosen approach.

5.2 Alternative data source As the OECD data used for this analysis has proved in some cases to be questionable, additional data

should be acquired to increase the trustworthiness of the results made.

An initiative called Carbon Disclosure Project (CDP) “provides a voluntary climate change reporting

platform for city governments. The program is open to any city government, regardless of size or

geographic location” (CDP, 2014a). As the results of this platform are based on volunteering, the data

is not available on every major city but could work as a starting point for further, more in-‐depth

analysis. Currently the CDP data is available for more than 200 cities, however, no single list of the

emissions have been composed by CDP, which makes an extensive city comparison much more

difficult. Additionally, restricted access to this data of 200 cities was a limiting factor of using this

database for this analysis.

CDP survey follows the same logic from city to city, but unfortunately not all cities answer all the

questions fully provided by CDP. This limits significantly the amount of usable data obtained from the

platform. For our indicator, information provided in the section C1.6, which is aimed to provide

information about the community’s greenhouse gas (GHG) emissions by given segment, would prove

useful for further analysis. Reported segments vary, and may include residential, non-‐residential,

commercial, industrial and municipal consumers. Additionally, some cities report their transportation

and waste disposal related GHG emissions. Therefore this data would provide more insight on the

results than the data from OECD.

Other source for relevant data could be Siemens’ Green City Index database, which holistically collects

environmental data about several cities around the world, including data about water usage, air

pollution, commuting, waste amounts, carbon emissions and energy policies. However, this platform

was unfortunately discovered too late to be used for extensive usage but its future implementation

with the findings of this study would be complementary for improved results.


5.3 Policy recommendations • The promotion of Energy Efficiency and renewable energy through Energy Auditing can

encourage more successful governments to share experiences on existing audit methodologies

and best practices with the aim to improve the effectiveness and quality of commercially

available audit services.

• To the transfer of knowledge and the harmonization of audit schemes, allows strengthening

regional policy frameworks.

• Since buildings take up a majority of the electrical consumption, (in some cases over 40% of

entire city’s consumption) it is critical that more successful countries in our indicator share the

best more.

• Some measures to improve efficiency and reduce demand of buildings that may be

incorporated as norms by local and national governments, either in the form of incentives for

good performance or, on the other hand, penalties for poor ones should concentrate on:

o Modification of set point temperatures

o Improvement in the insulation level

o Influence of level of infiltration

o Incorporation of free cooling and heating recovery

o Modification of system: cool/heat production systems, transport system, terminal

units, etc.

o Use innovative technologies and materials available

• Public bodies can play a pivotal role as awareness raisers of the issues relating to energy use

(such as air conditioning and heating threshold tempratures) and as providers of incentives to

improve energy performance towards the main stakeholders of their territories. Therefore

Public Bodies should be promoter of campaigns or projects actively involving a wide range of

citizens on the issue of energy saving/efficiency!


5.4 Building the Perfect Indicator: Scope and Shortcomings A major factor that affects CO2 emission is the nature of the power supply itself. Cities, commonly

have power stations that use generators that convert energy (mostly mechanical) into electrical

energy. The energy source harnessed to turn the generator varies widely. It depends chiefly on which

fuels are easily available, cheap enough and on the types of technology that the power company has

access to. Most power stations in the world burn fossil fuels such as coal, oil, and natural gas to

generate electricity, and some use nuclear power, but there is an increasing use of cleaner renewable

sources such as solar, wind, wave and hydroelectric.

These factors can have significant impact on the overall power consumption from a sustainability

standpoint, which allows for discrepancies in producing a fair indicator.

In order to make the indicator highly accurate, one must now consider the nature of the involvement

of such data, and its affect on the overall outcome. The following points would help make this

indicator more holistic. We can classify this based on the following:

Logical/Empirical assessment:

• Heating Degree Day (HDD)/Cooling Degree Day (CDD) as mentioned in the earlier part of the

paper is a measurement designed to reflect the demand for energy needed to heat/cool a

building. It is derived from measurements of outside air temperature. The heating

requirements for a given structure at a specific location are considered to be directly

proportional to the number of HDD/CDD at that location. This metric is generally used for

buildings, thus our report relies on an underlying assumption that cities are a dense cluster of

buildings that consume energy based on its nature and size.

• The range of optimal temperature has been taken within range of global standard of 17-‐23

Degree Celsius. The rankings between cities may vary slightly when this range is tweaked.

Data measurement and quality assessment:

• Data on break down of emissions based on type of industry emitting it. Lack of clear

definitions or classifications of data carries with it discrepancies, for instance, in the

classification of goods, types of employment, or classification of companies within industries.


• Detailed breakdown of emissions: Currently all the emissions are broken into Energy,

Transportation and other. A holistic and informed policy change would surely would require

access to data with further breakdown of emissions such as residential, commercial, public

transportation, private transportation etc.

• A more holistic temperature component than HDD/CDD that would consider cities in general,

which was that can be used for a limited resource project

• Data on percentage of the industrial sector (high power consuming) presence within city limits

with type of industry classified is currently done when discrepancies in our graph was found.

There may yet be some uncovered facts that lay hidden among the cities that landed in the

average.

• Bureaucracy within cities: Classification of the dynamic relationship between the power

distribution board, and the local government.

• An indicator that gives data about the geographical factors of cities that affect the total

amount of power used for public utility like water supply (such as proximity to a river, dam,

ocean) so we can group and analyze better.

• Percentage of building cover (and type of building, say LEED rating that can help us categorize

and detect pain points of cities) opposed to overall land cover would help analyze per capita vs

per built up area consumption.

• Looking at overall public psychology toward the need to save or demand for clean electricity in

would help analyze softer patterns within cities.

• Cross validation of data used by a third party to strengthen the indicator

• Transportation network structure plays a huge role in commuting the cities population from

one place to the other. Data that classifies different cities based on their efficiency of moving

each person would be very beneficial to further derive conclusions for certain trends.


6. Conclusion Results from temperature-‐adjusted city CO2 emission approach are encouraging. Never before has the

cities’ emissions been adjusted with climate conditions even though it is inevitable that cities in very

cold or very hot climates have to use more energy to reach the same comparable living standards.

This approach could increase the amount of comparing city efficiencies with each other as it brings

more justice to the benchmarking approach than earlier existing indicators of straightforward CO2

emissions. This would in turn encourage for global city comparison and effective policies.

However, a lot more in-‐depth analysis should be done. Especially the allocation of energy generation

emissions for cities seems to be incorrect in the OECD data, and more in depth breakdown of the

generation sources for CO2 should be available. Especially high level of manufacturing industry offsets

clearly results for any given city, just like was seen in the cases of Edmonton, Kansas City and Aachen.

Later on it was also found that Linz, a city that stood last but fourth in the study, is one of the major

chemical manufacturing centrals of Austria, which has a clear effect on the results.

This new kind of indicator could work as a step towards environmentally better performing cities. If

more data could be accessed for various locations, the causes for the city emissions could be further

analyzed and reflected to the results more thoroughly. As existing indicators such as the Green City

Index does holistically rank cities based on several categories, CO2 emissions being one of them, the

temperature adjusted emissions that were found in this study to make a significant difference for

some cities, could be used to improve Green City Index results.

We really do think that used CO2 emissions for city rankings require redefining, and our approach

could offer an alternative way of benchmarking cities and metropolitan areas by taking into account

the climate in the given location.


Appendix 1 Table 2. All used data in the analysis.

Name of the city Population Area [km2] Density Total CO2 per capita Energy industry CO2 Transportation CO2 Other CO2 HDD CDD Total DD Vienna 2710331 9093,10 298,06 14,23 3,29 3,44 7,50 2750 137 2887 Graz 614454 3074,20 199,87 10,69 3,95 2,71 4,03 2986 127 3113 Linz 609054 3523,70 172,85 17,58 1,24 0,03 16,31 3009 96 3105 Brussels 2510626 30326,00 82,79 9,48 1,21 2,48 5,79 2726 36 2762 Edmonton 1199616 1414,20 848,26 44,07 25,21 3,28 15,58 5444 29 5473 Ca lgary 1306924 12478,50 104,73 13,84 0,69 2,39 10,76 4658 34 4692 Winnipeg 813580 19808,00 41,07 13,41 0,12 4,45 8,83 5370 100 5470 Vancouver 2358711 12478,50 189,02 13,44 0,04 3,79 9,61 2568 4 2572 Quebec 834215 21715,50 38,42 14,21 0,00 3,37 10,84 4739 47 4786 Montreal 4226756 5063,60 834,73 13,02 0,04 3,29 9,69 3784 107 3891 Prague 1848898 3929,00 470,58 13,04 3,15 1,77 8,13 3353 66 3419 Berl in 4380489 6176,40 709,23 12,1 3,07 2,06 6,97 2893 69 2962 Munich 2874409 6263,10 458,94 8,48 1,91 1,42 5,14 3287 66 3353 Stuttgart 1957507 1987,60 984,86 10,31 3,25 1,87 5,20 2951 73 3024 Aachen 578522 775,30 746,19 29,69 24,67 1,78 3,24 2755 42 2797 Copenhagen 1998568 4083,70 489,40 7,05 1,41 2,18 3,46 3072 6 3078 Ta l l inn 530698 4326,30 122,67 7,7 3,63 1,18 2,89 4137 8 4145 Madrid 6640335 11537,60 575,54 7,14 0,14 3,11 3,89 1887 483 2370 Barcelona 3716802 1362,00 2728,93 5,74 0,42 2,26 3,05 1008 229 1237 Málaga 849191 1623,10 523,19 4,43 0,00 1,06 3,37 739 288 1027 Hels inki 1466120 6350,70 230,86 15,78 2,33 2,29 11,16 4235 11 4246 Paris 11777101 12089,40 974,17 7,91 0,25 2,70 4,96 2389 66 2455 Marsei l le 1734789 4230,80 410,04 9,53 2,61 1,61 5,32 1530 259 1789 Nice 850073 3096,90 274,49 6,82 0,02 1,21 5,60 1197 174 1371 Athens 3555307 1656,10 2146,79 5,13 0,08 2,23 2,83 991 695 1686 Budapest 2854222 6056,90 471,23 9,45 1,90 1,72 5,83 2759 223 2982 Dubl in 1690947 4767,20 354,70 12,37 2,39 4,03 5,95 2793 1 2794 Rome 4042286 5686,50 710,86 9,11 2,33 1,50 5,27 1384 325 1709 Mi lan 4084591 2637,80 1548,48 7,43 0,67 3,08 3,68 2345 216 2561 Tokyo 35204263 8592,10 4097,28 6,8 1,29 1,44 4,08 1420 380 1800 Osaka 17270651 7003,90 2465,86 7,06 1,25 1,51 4,30 1478 534 2012 Seoul Incheon 22938013 4673,10 4908,52 5,74 0,30 1,49 3,95 2979 297 3276 Monterrey 4291614 10984,40 390,70 4,42 1,27 0,97 2,18 404 958 1362 Guadala jara 4509743 2478,40 1819,62 2,42 0,00 0,89 1,52 515 391 906 Mexico Ci ty 19522493 5101,70 3826,66 3,42 0,04 1,72 1,66 658 106 764 Amsterdam 2383313 2819,80 845,21 15,06 6,34 2,05 6,67 2672 22 2694 Os lo 1490619 7099,20 209,97 14,61 0,06 2,05 12,50 4476 5 4481 Warsaw 2994909 8611,70 347,77 8,33 2,51 1,60 4,23 3375 79 3454 Kraków 1354499 3749,10 361,29 7,99 2,10 1,32 4,57 3420 91 3511 Li sbon 2818338 3988,30 706,65 5,1 0,98 1,77 2,36 1014 186 1200 Stockholm 1978017 7106,90 278,32 6,17 0,54 2,24 3,39 3830 8 3838 Ljubl jana 571708 3145,00 181,78 7,28 1,88 2,16 3,25 3007 139 3146 New York 16582772 9882,10 1678,06 17,71 1,97 8,19 7,55 2232 201 2433 Washington 5336371 12085,40 441,56 13,5 2,75 4,38 6,37 1790 420 2210 Kansas Ci ty 1937235 16608,30 116,64 23,25 12,44 5,79 5,02 2220 547 2767 San Francisco 6883043 17089,90 402,76 13,87 1,35 5,38 7,14 1400 12 1412 Las Vegas 2065960 67988,50 30,39 16,97 5,98 3,99 7,00 1359 904 2263 Los Angeles 17214555 83682,20 205,71 15,3 1,06 5,81 8,43 544 50 594 Phoenix 3900900 23889,10 163,29 13,38 2,40 4,12 6,86 1825 460 2285 Miami 5623920 14179,40 396,63 14,47 1,11 4,44 8,92 43 1041 1084


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Global Cities' Climate-Adjusted Carbon Footprint Indicator By Suraj Nambiar and Heikki Keskivali

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