TRU, Alm.del - 2021-22 - Bilag 226: Henvendelse af 26/3-22 vedrørende grøn omstilling af luftfarten, fra Midtjyllands Lufthavn

Transportudvalget 2021-22
TRU Alm.del Bilag 226
Offentligt

International Journal of Sustainable Transportation

ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/ujst20

Developing a sustainable energy strategy for

Midtjyllands Airport, Denmark

Patrick Bujok, Frans Bjørn-Thygesen & George Xydis

To cite this article:

Patrick Bujok, Frans Bjørn-Thygesen & George Xydis (2022): Developing a

sustainable energy strategy for Midtjyllands Airport, Denmark, International Journal of Sustainable

Transportation, DOI: 10.1080/15568318.2022.2029632

To link to this article:

https://doi.org/10.1080/15568318.2022.2029632

Published online: 07 Feb 2022.

Submit your article to this journal

View related articles

View Crossmark data

Full Terms & Conditions of access and use can be found at

https://www.tandfonline.com/action/journalInformation?journalCode=ujst20

TRU, Alm.del - 2021-22 - Bilag 226: Henvendelse af 26/3-22 vedrørende grøn omstilling af luftfarten, fra Midtjyllands Lufthavn

INTERNATIONAL JOURNAL OF SUSTAINABLE TRANSPORTATION

https://doi.org/10.1080/15568318.2022.2029632

REVIEW ARTICLE

Developing a sustainable energy strategy for Midtjyllands Airport, Denmark

Patrick Bujok

, Frans Bjørn-Thygesen

, and George Xydis

Department of Business Development and Technology, Aarhus University, Herning, Denmark;

Midtjyllands Lufthavn, Karup J, Denmark

ABSTRACT

ARTICLE HISTORY

The operation of airports is considered as particularly energy intensive and with the use of con-

ventional energy sources, significant amounts of greenhouse gases (GHGs) are emitted, fueling the

existential crisis of global warming. Hence, this study investigates the energy management system

(EnMS) of Midtjyllands Airport with respect to its energy consumption, energy sources, and

energy-related GHG emissions. The intention is to develop a sustainable energy strategy to close

the gaps in their energy and carbon management by applying the methods of ISO 50001 EnMS

and Airport Carbon Accreditation (ACA) Program. The findings reveal a total energy consumption

of about 1 GWh including electricity (53%), natural gas (47%), and others (0.1%) while emitted

GHGs account for in total 203 tCO

e. With regard to the developed baseline trends, the designed

objectives comprise (1) net zero GHG emissions without offsetting by 2030, (2) 40% reduction in

energy consumption by 2025, and (3) 40% reduction of two energy performance indicators (EnPIs)

by 2030. The achievement of the objectives is summarized in a nine-point action plan including

the major actions of identifying significant energy users (SEUs), improving thermal state of total

building envelope and heating system, as well as replacing the current electricity and natural gas

contract with a renewable electricity and biogas contract, respectively.

Abbreviations:

A/S: Joint-stock company; AC: Air conditioning; ACA: Airport Carbon Accreditation;

ACI: Airports Council International; ASHRAE: American Society of Heating, Refrigerating and Air-

Conditioning Engineers; ATM: Aircraft movement; BREEAM: Building Research Establishment

Environmental Assessment Method; CEO: Chief Executive Officer; DAT: Danish Air Transport; ELC:

Total electricity consumption; EMAS: Eco-Management and Audit Scheme; EnEV: Energy

Conservation Regulations; EnMS: Energy Management System; EnPI: Energy Performance Indicator;

GHG: Greenhouse gas; GWh: Gigawatt hours; HFC: Hydrofluorocarbon; HVAC: Heating, ventilation,

and air-conditioning; I/S: Partnership; IPCC: Intergovernmental Panel on Climate Change; ISO:

International Organization for Standardization; kgCO

e: Kilogram of carbon dioxide equivalent;

LED: Light Emitting Diode; LEED: Leadership in Energy and Environmental Design; MWh: Megawatt

hours; NGC: Total Natural Gas Consumption; PAX: Passenger; PV: Photovoltaic; SEU: Significant

Energy User; tCO

e: Tons of carbon dioxide equivalent; TDC: Airline Services Limited; TEC: Total

Energy Consumption; toe: Tons of oil equivalent

Received 1 December 2020

Revised 31 December 2021

Accepted 1 January 2022

KEYWORDS

Airport; Airport Carbon

Accreditation; energy

demand; energy

management system;

sustainable strategy

1. Introduction

Airports are an essential hub for not only global long-dis-

tance flights but also for medium-distance travels to neigh-

boring countries or domestic destinations. From large

extended facilities to small-sized airport buildings, the value

chain of serving the needs of air transportation consists of

similar actors and units, such as the departing and arriving

facility, airlines and their aircraft, ground support equipment

and agents, air traffic control, as well as airplane mainten-

ance service (Schmitt & Gollnick,

2016).

All these compo-

nents at an air transport system lead to several local and

global impacts. On the one hand, the society benefits from

an increase of the gross domestic product as well as regional

and international involved parties grow economically. On

the other hand, crucial negative environmental and social

impacts result from the operation of an airport. These

include effects to local communities like noise, land use,

CONTACT

George Xydis

[email protected]

2022 Taylor & Francis Group, LLC

ground traffic congestion, and global environmental effects

(Jani,

2011b).

Due to the fact that fossil fuels for energy use are unsus-

tainable, rapidly declining, and mainly responsible for global

warming, renewable sources, and its rational and efficient

utilization is indispensable (IPCC,

2021;

Koroneos et al.,

2010;

Morvay & Gvozdenac,

2009).

As a consequence, legal

obligations arose from climate change and global warming

in the past years (Akyuz et al.,

2019).

In addition, driven by

social pressure for living healthier and running the economy

more sustainably (Vanker et al.,

2013),

significant improve-

ments have been made in the aviation industry by initiating

new sustainable standards (Monsalud et al.,

2015)

and

reducing the airports carbon footprint (Sukumaran &

Sudhakar,

2017).

However, estimations by the International

Civil Aviation Organization (2018) state that a share of 2%

of the global CO

emissions is caused by the aviation trans-

port sector, undergoing a rise by approximately 3%–4% in

Department of Business Development and Technology, Aarhus University, Herning, Denmark.

TRU, Alm.del - 2021-22 - Bilag 226: Henvendelse af 26/3-22 vedrørende grøn omstilling af luftfarten, fra Midtjyllands Lufthavn

P. BUJOK ET AL.

every year. This development might cause the risk of reduc-

ing or ceasing its operations under the status-quo due to the

progressing effects of climate change, the biggest threat the

airport industry is going to face in the near future

(Preston,

2015).

With focus on the energy demand in the aviation sector,

airports are extremely large consumers (Ortega Alba &

Manana,

2017),

in particular of electric power (Ortega Alba

& Manana,

2016).

Their huge passenger and non-passenger

facilities demand a sizable amount of energy for heating,

ventilation, and air-conditioning (HVAC), as well as lighting

and air transport-related equipment. Furthermore, aids to

air transport operations and aircraft in parking position are

supplied by electrical energy and heat provided by the air-

port (Cardona et al.,

2006).

Examinations and studies have

revealed that airport terminal buildings consume about 70%

of its energy for heating, cooling, and air conditioning pur-

poses. As a result, an essential operation element of an air-

port is an energy management system (EnMS) to control

energy-intensive needs (Graham,

2014).

Several energy management tools are under constant

development with the aim of monitoring, controlling, and

improving local and integrated elements in entrepreneurial

organizations including airports (Akyuz et al.,

2019).

In the

1990s, national and international standards were introduced

in order to be in control of energy use and environmental

impacts. In general, the ISO 5000 and ISO 14000 family

(International Organization for Standardization,

2015, 2018)

as well as the eco-management and audit scheme (EMAS)

(European Commission,

2016)

are common instruments

(Falk & Hagsten,

2020).

With regard to the aviation indus-

try, airport buildings are specifically assessed by a number

of national and international energy certifications, such as

LEED, BREEAM, and EnEV (Kı lkı ¸ & Kı lkı ¸,

2016).

Moreover, since 2009, the Airport Carbon Accreditation

(ACA) started its assessment program, which, according to

ISO 14064 for greenhouse gas (GHG) accounting, verifies

airports’ carbon footprint (ACA,

2018).

In recent research,

energy benchmarking also became a tool to express the

energy efficiency index through Energy Performance

Indicators (EnPIs). In the context of an airport, the most

common used EnPIs are (kWh/PAX), (kWh/terminal build-

ing surface), and (kWh/HVAC surface) (American Institute

of Architects,

2012;

D&R International,

2012, 2013;

USEIA,

2008).

A review conducted by Ortega Alba and Manana (2016)

shows that the main essential measures for the reduction of

energy are the management systems. At airports, already

small projects have the potential to increase efficiency by

30% (B€y€kbay et al.,

2016).

Other important measures are

u u

modeling and simulation of energy consumption, which is

potentially beneficial for lowering the total consumption. All

these examined elements come with the methods of a clas-

sical EnMS, such as ISO 50001 (International Organization

for Standardization,

2018).

Improvements in energy efficiency and consumption are

in particular relevant with respect to national energy and cli-

mate objectives. In the given case of Denmark, two national

plans provide regulations about the development in terms of

energy and climate. The Danish government targets a 70%

GHG emissions reduction in 2030 in comparison to 1990

and net zero emissions by 2050 at the latest (Danish

Ministry of Climate Energy and Utilities,

2019),

while the

Danish Aviation Association specifies a 100% CO

compen-

sation of airports’ operations from 2020 as well as a 30%

reduction in aviation by 2030 compared to 2017

(Danish Aviation Association,

2019).

Equivalent to govern-

mental objective, a 100% carbon neutral aviation industry is

targeted in 2050.

Consequently, the objective of this work is to examine

how the Danish airport Midtjyllands Airport manages its

energy consumption, energy sources, and energy-related car-

bon emissions with the intention of implementing a sustain-

able energy strategy to close the gaps in their energy

management whilst at the same time align the energy man-

agement with the standards of ISO 50001 EnMS and ACA

in order to both improve energy efficiency and reduce CO

emissions. The first aim is to clarify the steps toward ISO

50001 and ACA certification so that the operational bounda-

ries of the airport are determined in which energy consump-

tion and energy-related carbon emissions occur. The second

aim is to inspect Midtjyllands Airport’s current energy man-

agement and identify distinctions to the EnMS defined by

ISO 50001. A further goal is to conduct a multi-year overall

energy consumption analysis with regard to all energy fluxes

and the resulting carbon emissions within the predefined

boundaries. Finally, energy efficiency measures are proposed

in form of a sustainable energy strategy.

The remainder of this article is as follows: Section 2

explores the energy management at airports with regard to

relevant standards. Section 3 describes the applied methods

according to ISO 50001 and ACA as well as defines the

research scope and strategy. Section 4 includes the analysis

of the energy use, GHG emissions, EnPIs, and baselines.

The findings, objectives, and action plan are presented and

discussed in Section 5 while a 10-point summary and con-

clusion are drawn in Section 6.

2. Energy management at airports

Airport’s comprehensive activities require energy to perform

various tasks (Graham & Morrell,

2016)

and therefore they

are considered as large energy consumers of electricity, fuel,

heat, and cooling (Akyuz et al.,

2019;

Graham & Morrell,

2016).

Controlling the consumption of these demanded

forms of energy requires the establishment of an energy

management concept, which begins with the segmentation

of the airport into two sections: airside (runway, control

tower, etc.) and landside (terminals, parking lots, etc.)

(Ortega Alba & Manana,

2016).

These elements are vital in

an air transport environment (Graham,

2014).

Thus, it is

essential that a secure supply of energy is guaranteed at a

reasonable price to all actors within the two sectors in order

to maximize the capacity in their operational performance.

From a long-term viewpoint, the objective is to reduce oper-

ational costs and ensure satisfaction in the needs for energy

TRU, Alm.del - 2021-22 - Bilag 226: Henvendelse af 26/3-22 vedrørende grøn omstilling af luftfarten, fra Midtjyllands Lufthavn

INTERNATIONAL JOURNAL OF SUSTAINABLE TRANSPORTATION

by increasing the efforts in energy-efficiency measures.

Tenants, concessionaires, and service partners are frequently

involved in saving initiatives introduced by the airport

(Thomas & Hooper,

2013).

Furthermore, novel power-generation systems have been

developed and put in operation by various airports, thereby

affordable and reliable renewable energy is generated while

the energy cost decreases (Budd & Budd,

2013).

Nowadays,

various energy technologies are available and commercially

applied as sources of energy for airports (Koroneos, Xydis,

& Polyzakis, 2010). Even though renewable energy technolo-

gies are in use, such as photovoltaic (PV), concentrated solar

power, and wind power, the extraction of oil, and natural

gas is the common approach to generate energy (Baidya &

Nandi,

2020;

Barrett et al.,

2014).

Nevertheless, the attract-

iveness of renewable energy systems has risen due to several

factors, such as technological development, investment gains,

and market maturity (Barrett,

2015).

In this regard, the sci-

entific literature describes solar, geothermal, biofuels, bio-

mass, and biogas as alternative resource, which are applied

on large-scale including airports in Copenhagen, Kansai,

and Adelaide (Baxter et al.,

2018a, 2018b, 2019;

Ortega Alba

& Manana,

2016).

With the given features at an airport area,

especially PVs are considered as financially advantageous for

on-site energy generation. The possibility of the airport sup-

porting any smart gas grids could be also considered (Lund,

2018).

The land around the airport and the facilities offer

plenty of space for solar panels, which could be a financial

improvement for these unused areas (Thomas &

Hooper,

2013).

Besides the on-site generation of power, the literature

provides several and specific cases in which monitoring of

EnPIs in combination of an implemented EnMS reveals an

increase in energy performance: In a case study research, the

EnMS of Denmark’s busiest airport, Copenhagen Airport, is

examined regarding its sustainability in terms of resources

efficiency actions. By monitoring all airport buildings using

electricity, heat, and water meters, energy-saving measures

can be imposed in case of unexpected deviations in the

energy consumption. Concerning the airport’s design and

operations, these measures identified in particular the

HVAC and various lighting systems as a major potential for

an efficient use of energy and a reduction of energy-related

carbon emissions. Further improvements and various

energy-saving initiatives have resulted in energy savings of

about 26.8 GWh during the period 2009–2016 (Baxter

et al.,

2018a).

In a study concerning Rome’s airport, de Rubeis et al.

(2016) examined the performance of the EnMS by analyzing

energy-related demand data. With a total energy consump-

tion of about 42,600 tons of oil equivalent (toe) per year

(mean 2010–2012), the airport consumes the amount of

energy compared to a small-sized European city with 20,000

inhabitants (2.1 toe per inhabitant) (European Environment

Agency, 2013). In 2010, the integration of the

Environmental Management System ISO 14001 was initi-

ated, followed by the EnMS ISO 50001 two years later. In

the subsequent years, the EnMS monitored the airport’s

energy demand and potential improvements in terms of

energy efficiency have been located and implemented.

Consequently, a significant increase in electric energy effi-

ciency per capita has been achieved: From 4.31 kWh/PAX in

2012 to 3.75 kWh/PAX in 2015. Furthermore, in correlation

with a carbon footprint analysis, a decrease in CO

-equiva-

lent emissions could be observed (de Rubeis et al.,

2016).

From 2002 until 2015, a study investigated mitigations of

environmental impacts caused by energy consumption at

Kansai International Airport based on an implemented

EnMS and various technologies. The airport’s objective is an

integrated solution between electricity consumption and on-

site generation in correlation of energy conservation. The

analysis of the EnMS showed that the electricity purchased

has declined from about 123 MWh per annum in 2020 to

103 MWh in 2015 while a rise in traveling numbers

occurred. Additionally, a decrease in the energy require-

ments per capita has been observed between 2010 and 2015,

mainly due to energy-saving measures, such as the use of

light-emitting diodes (LEDs) (Baxter et al.,

2018b).

By implementing an adequate EnMS with environmental

sustainability policies, not only energy efficiency but also

renewable energy generation can be achieved. A study with

Adelaide Airport in South Australia as the case subject

focuses on PV and their benefits in addition to their existing

airport energy management. Installed on the short-term

parking facility, the largest rooftop PV system at an

Australian airport covers roughly 10% of the airport’s energy

demand saving about 915 tCO

e (Baxter et al.,

2019).

A benchmarking analysis was conducted by Kı lkı ¸ and

Kılkıs (2016) on the basis of a sustainability ranking of air-

ports index in order to assess among others the energy con-

sumption and generation. Nine airports sampled from major

airports were analyzed in the benchmarking process. All

examined airports are certified with the ISO 50001 EnMS

and they actively apply the tool successfully to improve

energy savings. With the use of EnMS, Amsterdam, for

example, cut over half of their energy consumption of their

office building due a renewal of the heating and cooling sys-

tem. New airport buildings in Frankfurt undercut the regu-

lations of the German energy savings directive by 20%. An

airfield operation facility in San Francisco achieved an effi-

ciency level, which is 50% above the national ASHRAE

standard (Kılkıs & Kılkıs,

2016).

Overall, air transport system operators should be gener-

ally considered as energetically inefficient. Therefore, the

concept of energy management has to be seen as a necessity

for all local and integrated segments of an airport especially

because various energy monitoring tools offer the proven

potential of an increase in energy and financial performance

(Akyuz et al.,

2019).

3. Methodology

The research was conducted under inductive reasoning with

a qualitative and quantitative longitudinal research approach

(Hair et al.,

2015a, 2015b, 2015c;

Lancaster,

2005).

The strat-

egy follows the methods of an experimental research design

TRU, Alm.del - 2021-22 - Bilag 226: Henvendelse af 26/3-22 vedrørende grøn omstilling af luftfarten, fra Midtjyllands Lufthavn

P. BUJOK ET AL.

Table 1.

Third-parties at Midtjyllands Airport.

Category

Stationary sources

Mobile sources

Process emissions

Other emissions

Indirect emissions

GHG emitting sources

Boilers, furnaces, burners, heaters, incinerators, engines, firefighting exercises, generators, etc.

Automobiles (airside/landside), trucks, employee buses, etc.

On-site waste and wastewater management, etc.

Fire suppression CO

, leakages in air condition system, etc.

Emissions from purchased electricity

Figure 4.

Scopes for energy consumption and GHG emission.

Figure 5.

Research strategy consisting of five-stage analysis, four-stage outline of results, and final sustainable energy strategy including objectives and targets as

well as the action plan.

a tailored action plan presented in categorized order of

action (Section 5).

3.5. Data collection

Qualitative data were gathered by moving on to a semi-

structured interview in combination with open-ended ques-

tions with the airport’s CEO while inspecting the location

and relevant energy-consuming utilities on-site, such as the

airport building, heating system, ground support vehicle,

and support equipment. Energy-related characteristics

including year of construction or manufacture, year of

replacement, usage profile, source of energy, energy-saving

alternatives, etc., were a subject of the interview. Secondary

data was gathered in form of energy data, passenger data,

and aircraft movement (ATM) data. Data analysis was per-

formed by evaluating the relevance of the dataset, assessing

its credibility, and applying the empirical research methods

according to Sections 3.2 and 3.3 in order to investigate the

research problem (Andersen,

2018;

Ryan,

2018;

Vartanian,

2010).

Quantitative data were collected by examining documents

containing mostly energy consumption data (Hair et al.,

2015c;

Lancaster,

2005).

This kind of document analysis is fre-

quently applied in case studies having the focus on data and

information from company records and formal files (Oates,

2006).

By applying a type of observation in which surveys

were conducted during a specific time period, a longitudinal

TRU, Alm.del - 2021-22 - Bilag 226: Henvendelse af 26/3-22 vedrørende grøn omstilling af luftfarten, fra Midtjyllands Lufthavn

P. BUJOK ET AL.

4.1. Airport traffic

Data about passenger traffic at Midtjyllands Airport on a daily

basis are published by the Danish Transport, Construction, and

Housing Authority.

Figure 6

presents the total PAX from 2001

to 2019, including scheduled, nonscheduled, and other flights

for domestic and international flight operations divided into

total number of departing and arriving passengers. In 2010, the

total annual passenger volumes almost doubled. However, two

years later, the airport lost this traffic due to historical series of

events: During 2010 and 2011, the Danish airline Cimber

Sterling A/S with their aircraft ATR72 and Norwegian Airlines

A/S with their Boeing 737 set up a parallel route to

Copenhagen. As a result, the passenger traffic increased to 14

departures and 14 arrivals per day. This led to competition, low

prices for tickets, and, consequently, an increase in private and

business travelers to Copenhagen. Nevertheless, it was not prof-

itable in the end and Cimber Sterling A/S declared bankruptcy

in mid-2012, which cut half of all domestic flights in Denmark

overnight. Shortly afterwards, Norwegian Airlines A/S took over

with few flight operations until spring 2015, when Danish Air

Transport (DAT) replaced Norwegian Airlines with their

ATR72. Today, DAT is the only operator at Midtjyllands

Airport (Bjørn-Thygesen,

2020).

Despite the peaks in 2010 and 2011, a general decreasing

trend in passenger numbers is visible from the all-time max-

imum in 2001 to the all-time minimum in 2019 with a con-

tinuous downwards trend based on the latest four years.

Figure 7

visualizes the same data on a daily basis for the

years 2017, 2018, and 2019. Due to the major share of busi-

ness travelers, the main traffic appears during the week

whilst travels at the weekend are about 75% lower.

Furthermore, the one-week holiday periods in February,

April, October, and December are clearly visible. The same

applies of the summer months July and August revealing a

huge gap in the overall passenger movement.

Figure 8

reveals the annual ATM from 2001 to 2019 cate-

gorized in departing, arriving, and other flights. The three

historical key events described in the section above explain

the peaks in 2010 and 2011 as well as the decline in 2015.

Despite of these peaks and a brief regeneration period with

a yearly increase of about 13% in 2016 and 2017, an overall

negative trend in ATM is recognizable over the

whole period.

Figure 9

presents a detailed view of ATM per day for the

past three years. First, the distributions reveal a less constant

pattern and more peaks compared to the distributions of pas-

senger movement. In this regard, the holiday-gaps vary in dif-

ferent degrees of distinction over the years. Second, even

though the summer period has a lower base load of aircraft

traffic, several spikes are seen, which are probably not related

to regular summer travelers but rather to private jet flights.

4.2. Current energy management and saving initiatives

The last energy assessment of Midtjyllands Airport was con-

ducted in form of an Energy Management Handbook in

1998. The analyzed data cover a period from approximately

1994–1996. An Energy Action Plan for 1998–1999 was

Figure 6.

Total annual departing and arriving passenger (PAX) including

domestic, international, scheduled, nonscheduled, and other flights as well as

the year-on-year change (%) from 2001 to 2019. Historical events: In 2010 and

2011, the only two airline operators increased passenger traffic and competi-

tion, leading to a price decline and bankruptcy of one major airline in 2012. The

operations of the remaining airline were taken over by a competitor in 2015,

which operates since. Source: Danish Transport Authority (2020b).

research method is given, which is the case in this work

(Hassett & Paavilainen-M€ntym€ki,

2013).

Finally, independ-

ent variables in form of passenger numbers and ATM are

examined in order to assess the impact on dependent variables

like energy consumption (Srinagesh,

2006).

The data and documents collected in this case study com-

prise the time frame from 2001 to 2019 for passenger and

ATM data, which are made available by the Danish

Transport Authority (2020b,

2020a).

Energy data refers to a

period from 2006 to 2019 and originates from two sources.

Monthly data on electricity and natural gas consumption

was provided by Midtjyllands Airport (2020). The same

accounts for diesel consumption, which however only

includes monthly data for the year 2019. Furthermore, the

airport’s electric energy provider Energi Danmark (2020)

collects and shares

via

an online interface hourly data on

the consumption of electricity since mid-January 2017, out

of which three hours were missing and filled through simple

interpolation. Statistical tools have been applied to guarantee

validity and reliability of findings to avoid potential bias.

Using this approach, support was given in order to provide

verification of the themes that were identified in the study

(Wu & Little,

2011;

Yin,

2012).

4. Midtjyllands Airport energy management

This section starts with an analysis of the passenger and air-

craft traffic at Midtjyllands Airport. Detailed visualizations

enable insights about historical operational developments,

which represent the main driver of the energy consumption

at the airport. Afterwards, the current EnMS and saving ini-

tiatives are examined and reflected on the basis of ISO

50001. Subsequently, the energy consumption of the airport

is analyzed with respect to the predefined scopes and meth-

ods of determining EnPIs. Lastly, the arising energy-related

GHG emissions are determined by applying the calculation

tools according to the ACA methods.

TRU, Alm.del - 2021-22 - Bilag 226: Henvendelse af 26/3-22 vedrørende grøn omstilling af luftfarten, fra Midtjyllands Lufthavn

INTERNATIONAL JOURNAL OF SUSTAINABLE TRANSPORTATION

Figure 7.

Total departing and arriving passenger (PAX) per day including domestic, international, scheduled, nonscheduled, and other flights in 2017, 2018, and

2019. The narrow gaps indicate weekends, while the gaps in February, April, October, as well as the summer months July and August are related to holidays.

Source: Danish Transport Authority (2020b).

Figure 8.

Total annual aircraft movement (ATM) including departing, arriving,

and other flights as well as the year-on-year change (%) from 2001 to 2019.

Historical events: In 2010 and 2011, the only two airline operators increased

passenger traffic and competition, leading to a price decline, and bankruptcy of

one major airline in 2012. The operations of the remaining airline were taken

over by a competitor in 2015, which operates since. Source: Danish Transport

Authority (2020a).

created without any long-term strategy. Due to the fact that

the data set is older than 25 years and the documentation is

incomplete to not existing, any further investigation about

the energy management handbook is carried out. Today, an

EnMS as described in Section 3.2 does not exist. One point

worth mentioning in this context is the ventilation and air

conditioning system at the roof of the airport. The system is

out of order and an investigation is highly recommended

because commonly hydrofluorocarbons (HFCs) are used as

coolant, which are significant contributors to global warm-

ing effects when exposed to the atmosphere (Burkholder

et al.,

2020;

Montzka et al.,

2019;

Saengsikhiao et al.,

2020;

H. Zhang et al.,

2011).

Even though the natural gas as well as the electricity con-

sumption is documented on a monthly basis in a digital

Excel format since 2005, neither simple visualizations nor

basic evaluations have been executed. The same applies for

the diesel consumption, which is digitally documented only

since 2019. In addition, the data management is conducted

by storing energy data in individual Excel files, which

revealed unsystematic structures. Frequently, energy data is

only documented in single energy invoices. Furthermore, the

electricity supplier offers an online service by which the

airport can access its hourly consumption since 2017 using a

web interface. This database has never been accessed before

and, hence, no value has been generated by using the free

and digital monitoring service.

In the past two years, Midtjyllands Airport announced

and implemented a number of climate protection measures

at both airside and landside. For the reason that this article

is mainly focusing on the land side of the airport, only one

applied measure is considered as relevant for later analysis:

Over the past 5 years, the airport has optimized the energy

consumption of the terminal by as much as 50% due to

investments in energy-efficient lighting, new technologies,

and improvements in the energy consumption after traf-

fic times.

Overall, the airport handles its energy resources and

energy consumers in a way, which requires an urgent need

to act. Even though improvements have been achieved by

replacing the old lighting system, a comprehensive and

detailed energy strategy has not been designed neither in the

past years nor in the last three decades. Fundamental meas-

ures have to be taken into action in order to close the gap

between an insufficient management of energy sources and

highly efficient EnMSs. For that reason, the following sec-

tions analyze Midtjyllands Airport’s energy profile and its

carbon-related emissions with the goal of taking the first

step toward the profiled ISO 50001 EnMS and the ACA

certification.

4.3. Energy consumption analysis

4.3.1. Scope 1: Natural gas and diesel consumption

This section covers the direct energy consumption of the

energy sources natural gas (scope 1.1) and diesel (scope 1.2),

which are related to scope 1 of ACA boundaries. While die-

sel is consumed by heavy vehicles and aircraft supply equip-

ment, natural gas is used for local production of central

heating. The data was gathered and documented by

Midtjyllands Airport. The values in cubic meters were con-

verted with the 2019 average calorific value for Karup

(11.93 kWh/Nm

) published by Energinet, Midtjyllands

Airport natural gas provider (Energinet,

2020). Figure 10

shows the total natural gas consumption from 2006 to 2019,

TRU, Alm.del - 2021-22 - Bilag 226: Henvendelse af 26/3-22 vedrørende grøn omstilling af luftfarten, fra Midtjyllands Lufthavn

P. BUJOK ET AL.

Figure 9.

Total aircraft movement (ATM) per day including departing, arriving and other flights in 2017, 2018, and 2019. The narrow gaps indicate weekends, while

the gaps in February, April, October, as well as the summer months July and August are related to holidays. Source: Danish Transport Authority (2020a).

Figure 10.

Total annual natural gas consumption (scope 1.1) and year-on-year

change (%) from 2006 to 2019. Source: Midtjyllands Airport (2020).

which covers the period since the new gas heater was

installed. Regardless the events in 2010 and 2015, a con-

sumption between 370 and 495 MWh is visible including a

slight upwards trend in the past three years. Hence, this

trend behaves contrary to the decline in the number of pas-

sengers and ATM during the same period (Figures

and

8).

Figure 11

shows a detailed illustration of these three

years on a monthly basis together with a five-year average

from 2015 to 2019 and a 10-year average from 2010 to

2019 for each month. The distinctive difference between

the summer and winter period is visible. The summer

months June, July, and August are a reasonable indicator

of the hot water consumption at the airport. Both averages

are almost equal while the reference summer periods

slightly deviate from the averages in a negative and positive

way. However, it can be estimated that an average of

7 MWh (600 m

) of natural gas is needed throughout the

year to cover the hot water demand. All three years reveal

a fluctuated consumption during the spring and autumn

period, which is to be expected due to changing weather

and temperature conditions. The month with the highest

consumption of natural gas is most likely the coldest

month, which is in this case January based on the mean

values. Even though the 5- and 10-year average of January

and February are close to each other, the data of the three

latest years strongly fluctuated. The month of December

may show a deviation between the mean values, but the

yearly consumption is almost equal. These fluctuations

make a profound interpretation difficult.

Figure 12

presents the monthly diesel consumption (left)

and the total consumption (right) of operating equipment

and vehicles in 2019. This year was the only available data

set by Midtjyllands Airport. The values are converted from

liter to kWh using the calorific value 277.77 kWh per ton

and the conversion factor 1.185 liter per ton (International

Energy Agency,

2005).

According to the chart on the right-

hand side in

Figure 12,

it is clearly visible that the heaters

(multiple) consume over 50% of the diesel demand in 2019.

The heater is a device that increases or keeps the air tem-

perature in the aircraft cabin at comfortable level before and

while passengers enter the cabin. Therefore, it is mainly

used in the colder periods of the year as the left-hand side

Figure 12

illustrates. From January to May and October

to December 2019, the heaters are used intensively due to

low outdoor temperatures consuming 577 kWh (2460 l) die-

sel

more than half of the total diesel consumption. The

fuel tanker (151 kWh; 646 l) and the deicer (85 kWh; 362 l)

account together for less than one-quarter of the diesel con-

sumption while vehicles for passenger transport and other

heavy vehicles share the remaining quarter.

4.3.2. Scope 2: Electricity consumption

The analysis of the electricity consumption was conducted

using two main data sets. The first set is gathered by

Midtjyllands airport on a monthly base and divided into the

consumption of the airport building and third-party con-

sumption, such as airline operators and aircraft services

(Table

1).

Both categories of airport and third-party con-

sumption belong to scope 2 because the electricity gener-

ation occurs off-site and the airport has control of this

source of energy and emission, respectively, even though the

airport cannot control the consumption of the third party.

For further analysis, these two categories are classified into

the terms scope 2.1, which includes the electricity consump-

tion the airport is paying for such as light, security scanner,

and electric ground support vehicles and scope 2.2, which

consists of the electric energy consumed by third parties,

which the airport bills (offices of tenants, charging batteries

for aircraft, etc.) (Figure

4).

The second main data set is

TRU, Alm.del - 2021-22 - Bilag 226: Henvendelse af 26/3-22 vedrørende grøn omstilling af luftfarten, fra Midtjyllands Lufthavn

INTERNATIONAL JOURNAL OF SUSTAINABLE TRANSPORTATION

Figure 11.

Total natural gas consumption (scope 1.1) on a monthly basis with 5-year (2015–2019) and 10-year (2010–2019) average. Source: Midtjyllands

Airport (2020).

Figure 12.

Monthly diesel consumption (left) and total diesel consumption (right) (scope 1.2) in 2019. Source: Midtjyllands Airport (2020).

Figure 13.

Total annual electricity consumption and year-on-year change (%)

from 2006 to 2019. Source: Energi Danmark (2020); Midtjyllands Airport (2020).

accessible online through Midtjyllands Airport electricity

provider that monitors the energy consumption per hour,

which allows a detailed analysis. However, this data set

includes the total airport’s energy demand including third

parties (scope 2.1

2.2). At this point, a separation is not

possible. Nonetheless, both data sets are equal when com-

paring the monthly values.

Figure 13

illustrates the total energy consumption of

scopes 2.1 and 2.2 separately from 2006 to 2019. The peak

in 2010 and 2011 are caused by the events mentioned in

Section 4.1. Despite that, the electricity consumption for

both scopes remains almost constant over the 14 years

period

a very slight decline is recognizable. The past five

years show clearly constant developed in total. However, the

airport’s demand increases slightly while the third-party

demand declined. This behavior is contrary to the rise in

PAX and ATM (Figures

and

8).

The detailed analysis in

Figure 14

shows the consumption

per hour of the airport and third parties combined (scope 2)

for the years 2017, 2018, and 2019. In all three years, the

consumption pattern is roughly similar with a higher base

load and peak demand in the winter period compared to the

summer months. The consumption peaks, mainly above

40 kWh, are clearly distinguishable from the base load. The

small gaps between these peaks are the operation at night.

Slightly bigger gaps are weekends with less passenger traffic,

especially visible in for example September 2017 and 2018.

These weekend-gaps are barely not identifiable in 2019.

Interestingly, neither the one-week holiday gaps of passenger

movement in spring, autumn, and winter nor the huge sum-

mer-gap is evident in 2019 (Section 4.1). Even though July

2017 and 2018 reveal a reduced intensity in consumption

peaks, the consumption in 2019 has no changes at all.

Furthermore, during these periods, the base load stays con-

stantly high with over 40 kWh for 95% of all hourly values.

By sorting the values of

Figure 14

in a descending order,

the electrical load profile in

Figure 15

is resulting for the

years 2017, 2018, and 2019, with the highest value on the

left at hour 0 and the lowest value on the right at hour

8760. In addition, the base load coverage for 99.99% and

TRU, Alm.del - 2021-22 - Bilag 226: Henvendelse af 26/3-22 vedrørende grøn omstilling af luftfarten, fra Midtjyllands Lufthavn

P. BUJOK ET AL.

Figure 14.

Total electricity consumption on an hourly basis of scope 2 including airport and third-party. (Scope 2.1

2.2) in 2017, 2018, and 2019. Source: Energi

Danmark (2020).

Figure 15.

Electrical load profile of scope 2 including airport and third-party (scope 2.1

2.2) with 100% and 95% base load for 2017, 2018, and 2019. Source:

Energi Danmark (2020).

Figure 16.

Total electrical power demand of scope 2 including airport and third-party (scope 2.1

scope 2.2) in 2019, categorized in individual daily hours includ-

ing maximum, minimum, and average. Color intensity represents distribution of electrical power demand within a single hour of the day. Source: Energi

Danmark (2020).

TRU, Alm.del - 2021-22 - Bilag 226: Henvendelse af 26/3-22 vedrørende grøn omstilling af luftfarten, fra Midtjyllands Lufthavn

INTERNATIONAL JOURNAL OF SUSTAINABLE TRANSPORTATION

Figure 17.

Total electricity consumption (in MWh) in 2019, divided in electricity

consumed by airport (scope 2.1) and electricity consumed by third parties

(scope 2.2). Source: Energi Danmark (2020); Midtjyllands Airport (2020).

95% of all data points are plotted.

Figure 15

reveals a max-

imum load of 133 kW and a minimum load of 22 kW

throughout the three years. Moreover, by skipping the low-

est 5% values, the base load nearly doubles. As a result, the

airport’s load profile is mostly characterized by a base load

profile. This has a crucial effect on future energy sources

and technologies, which are potentially able to cover the

present demand profile.

Even though scopes 2.1 and 2.2 are combined in the hourly

data set, valuable information is hidden in the previous ana-

lysis and figures. Therefore, the goal is to determine specific

distributions of significant power values, which can be illus-

trated using the benefits of the high resolution in combination

with a particular visualization approach that was developed

for this specific purpose.

Figure 16

reveals the result of this

approach. The

x-axis

displays the daily hour of all days in the

year 2019, from 00:00 to 23:00 o’clock. The power values are

plotted in this scheme using a partly transparent data point.

This leads to the effect that overlapping data points increase

the color intensity of the specific power value. In addition, the

absolute maxima and minima together with the hourly aver-

age are displayed. Starting with the period 00:00–03:00 o’clock,

it is visible that even though some high maxima are present,

most of the power values are located around the average.

Moreover, these average values build the bottom line of all

other mean values. This means that a significant improvement

in energy efficiency can be made when the average values after

04:00 o’clock decline because the values before 04:00 reflect

the power demand at night with the least amount of air-

port activity.

Continuing with 4–7 h, it is clearly visible that the power

distribution is starting to scatter below and above the aver-

age. Power hot spots are forming at about 80 kW at 06:00

and 85 kW at 07:00. This means that even though at 07:00

o’clock the highest absolute maxima are present, the focus

should be on these power hot spots. They may occur with a

22% lower power than the maxima, but the quantity of the

values is significantly higher, recognizable by the higher

color intensity. From 07:00, these color spots scatter in the

next 2 h until they gather around the average from 10:00 to

15:00. During this morning period from 05:00 to 10:00, the

highest passenger number is handled by the airport prepar-

ing the aircraft for departure. Single large electrical consum-

ers or several smaller consumers are operated during this

time. Therefore, in the first place, the goal is to control and

reduce these power hot spots regardless of the absolute

maximum in order to shrink the average consumption of

that hour. The hours from 11:00 to 15:00 show the power

hot spots around the average, which illustrates the airport’s

base load during daytime. From 16:00, arriving aircraft with

passenger reach the airport. Power hot spots form again

below and above the average. This time, the color intensity

of the hot spots above the average is significantly higher and

even a gap between average and hot spots is visible, which

almost remain until 23:00. This highlights the fact that by

controlling these consumers causing the power hot spots, a

significant energy reduction can be achieved.

As a result, consumers, which are responsible for the

power hot spots above the average, have to be identified and

tackled in the first place. However, it is also possible that

these consumers are third-party consumers (Table

1),

such

as aircraft, which are charged at the gate. By analyzing the

only common data set of the electricity consumption of the

airport (scope 2.1) and third-parties (scope 2.2), it reveals

that scope 2.2 consumes the minor share of 50.4 kWh

(9.2%) of the total electricity consumption in 2019 (Figure

17).

The airline operator DAT has only the second-highest

electricity demand with 15.5 MWh (total 2.8%). The airline

service company Airline Services Limited (TDC) (ticket

sales) consumes the most with 17.2 MWh (total 3.1%).

Nevertheless, it is critical to assign hourly power peaks

(Figure

16)

to an annual amount of energy. Consequently, it

is beneficial to invest resources in order to distinguish con-

sumers within scope 2.1 and scope 2.2 starting with TDC

and DAT so that the hourly electricity consumption of

scope 2 is separated.

4.3.3. Determining energy performance indicator

EnPIs are identified on the basis of the given scopes of the

energy consumption per PAX and per ATM with respect to

the sample size of 14 annual observations for each case. The

process of determining EnPIs conducted under the regula-

tion of ISO 50001 is described in Section 4.5. The system

boundaries are set as follows: The used energy data set con-

sists of the annual natural gas consumption (scope 1.1) as

well as the annual electricity consumption by the airport

(scope 2.1) and by third parties (scope 2.2) within the

period of 2006–2019. Because of missing historical data, the

diesel consumption (scope 1.2) is assumed to be equal for

all years using the existing value of 2019, which takes up a

share of 4.8% of total energy consumption. Changes of this

small share have minor effects on the total consumption

because a linear relation can be assumed. However, it is

strongly recommended to document and include the diesel

consumption in future analysis. For both passenger and

ATM, the annual data sets of Section 4.1, respectively, are

considered. The analysis applies only annual data because

monthly or even daily intervals take seasonal fluctuations

into account, such as changing light and temperatures con-

ditions, which have an impact on energy demand while

being not correlated to passenger and aircraft numbers.

Before starting with the regression analysis of the total

energy consumption and the two variables, all three data

sets are normalized and plotted in

Figure 18.

This

TRU, Alm.del - 2021-22 - Bilag 226: Henvendelse af 26/3-22 vedrørende grøn omstilling af luftfarten, fra Midtjyllands Lufthavn

P. BUJOK ET AL.

Figure 18.

Total energy consumption, total passenger (PAX) and total aircraft movement (ATM) from 2006 to 2019, normalized data.

comparison is suggested in the literature (Farquhar,

2012)

and it allows a direct visualization of the qualitative analysis

of the sections above (Baxter et al.,

2018a).

The total energy

demand increased with rising number of passengers and air-

craft over the period 2006 to 2011. In 2011, the energy con-

sumption crossed both PAX and ATM, and remained above

these lines with an exception in 2014. As such, crossing

curves with PAX and ATM declining indicate a deterior-

ation in energy efficiency at the airport. Furthermore, the

displayed downward trend of passengers and aircraft in the

last three years in combination with upward trend of energy

consumption intensifies the decrease in energy efficiency.

The performed regression analysis was conducted with

single and multiple regressions including the dependent var-

iables of total energy, electricity, and natural gas consump-

tion (response variable), as well as the independent variables

of passenger and ATMs (explanatory variables or predictors)

(Crawley,

2012).

The constellation of the regression is shown

Table 3

together with the relevant output values

, coef-

ficient and

value.

First, the coefficient of the ATM variable in line 2, 5, 10,

and 12 appears negative which indicates a growing energy

consumption while PAX or ATM increases. Therefore, these

variables can be eliminated. The same applies for the lines if

the

value is greater than .05

line 4, 6, 7, and

because this indicates a statistically insignificant rela-

tionship between the variables. After this separation, line 1,

3, 8, and 9 remain. The variables in line 1 and 9 are ana-

lyzed in correlation with previous eliminated variables and,

therefore, the variables are also omitted. Finally, a statistic-

ally significant relationship exists between the total energy

consumption and PAX (line 2) and between electricity con-

sumption and ATM (line 8). However, the values of

are

far below 0.9, which indicates a rather poor model fit. This

means that the variable PAX explains the current and future

energy consumption by about 33% and the variable ATM

explains the electricity consumptions by 32%. In contrast, a

good model fit is reached with an

over 90%. The annual

development of these EnPIs resulting from the regression

analysis is presented in Section 5.3.

4.4. Carbon emissions arising from energy consumption

The following analysis is conducted using the methodology

of ACA in line with the GHG protocol described in

Section 3.3. The carbon footprint calculation of level 1

(Mapping) covers the emissions of natural gas and diesel

(scope 1) as well as electricity (scope 2). The input param-

eters are specified in the GHG calculation tools for

Table 3.

Regression analysis with annual values from 2006 to 2019 (14 obser-

vation each) including total energy consumption (TEC), electricity consumption

(ELC), natural gas consumption (NGC) as response variable, and passenger

(PAX) and aircraft movement (ATM) as explanatory variable, revealing line 3

and 8 as appropriate EnPIs.

Line

Response variable

TEC

ELC

NGC

Explanatory variables

PAX

ATM

PAX

ATM

PAX

ATM

PAX

ATM

PAX

ATM

PAX

ATM

0.46

0.33

0.15

0.33

0.23

0.32

0.53

0.02

Coefficient

1.30

À39.14

0.53

16.53

À0.22

32.02

0.41

22.56

1.52

À71.16

0.11

À6.04

Value

.03

.13

.03

.17

.69

.23

.08

.04

.01

.64

.60

stationary combustion and mobile combustion including

years, sector, fuel type, amount of fuel, etc. The output is

given in kgCO

e. The emissions caused by the use of elec-

tricity are determined by the multiplication of the annual

electricity consumption value (Figure

13)

and the national

grid electricity emission factor of 0.2090 kgCO

e per kWh

(Section 3.3). The results of both scopes 1 and 2 for the

historical years and in detail for the latest year are pre-

sented in Section 5.2.

4.5. Determining energy and emission baseline

According to ISO 50001, an appropriate reference period

(energy baseline) is to be determined to ensure representa-

tive changes in the energy performance. For that reason, a

statistical analysis is performed to determine a statistically

justified period for the following indicators: (A) total GHG

emissions, (B) total energy, (C) natural gas and (D) electri-

city consumption, (E) total energy EnPI, and (F) total elec-

tricity EnPI.

The analysis uses the assumption of an absolute constant

diesel use per year, which is based on the single available

data set of 2019, as described in Section 4.5. Besides that, it

has to be mentioned that the total error of GHG emissions

by diesel increases due to an increasing relative share of

6.1%. Nonetheless, even major changes in the diesel con-

sumption would have minor effect on the total GHG emis-

sions. A linear regression analysis is applied for the

reference periods from each year before 2018 to the latest

year 2019 (e.g. 2017–2019 (x

3), 2016–2019 (x

4), etc.)

with focus on the coefficient of determination

. If this

indicator is closer to 1 then precise predictions of ongoing

trends can be made. The reference period is defined with

TRU, Alm.del - 2021-22 - Bilag 226: Henvendelse af 26/3-22 vedrørende grøn omstilling af luftfarten, fra Midtjyllands Lufthavn

INTERNATIONAL JOURNAL OF SUSTAINABLE TRANSPORTATION

Figure 19.

Linear regression analysis of each indicator for the reference periods from each year before 2018 to the latest year 2019 with the focus on

2019 as the end year due to changing operational events in

the years before 2015, which led to high fluctuations.

Figure

shows the results of this analysis. Beginning with Chart B

Figure 19,

it reveals the highest

value of 0.77 for the

period 2015–2019 (x

5). This indicates that a further trend

of the energy consumption can be predicted with accuracy

of 77%. The GHG emissions in Chart A have the highest

value at

3 and the second-highest value at

5 with

0.80. For a better comparability, the baseline from 2015 to

2019 (x

5) is chosen for both total energy consumption

and GHG emissions. With regard to Chart C–F, all of them

display

values over 0.90 in the period from 2017 to 2019

3) and, therefore, this period is opted as their

energy baseline.

5. Results and discussion

Based on the analysis in Section 4.3, the energy consump-

tion, GHG emissions, and EnPIs of the airport are pre-

sented. In combination with the energy and GHG baselines,

the resulting sustainable energy strategy is revealed and dis-

cussed considering the objectives and targets of the individ-

ual sources of energy, total energy consumption, and total

GHG emissions, as well as the determined EnPIs. The final

action plan summarizes the major findings of the strategy.

5.1. Total energy consumption and significant

energy user

Figure 20

(left chart) presents the outcome of the total

energy consumption. Despite the peak in 2010 and a slight

increase in 2015, statistically speaking, the energy consump-

tion remains constant over the entire time frame. The varia-

tions are caused by operational changes with airline

companies (Section 4.1). Except 2015 and 2016, the airport’s

electricity consumption (scope 2.1) accounts for the major

share closely followed by the natural gas consumption

(scope 1.1). The significantly smaller share of electricity use

by third parties and the nonvisible consumption of diesel

(only available in 2019) form the remaining energy use. A

closer look at the total energy consumption of about

1029 MWh in 2019 (right chart) highlights the historical

development with almost equal shares of natural gas and

airport’s electricity consumption. In addition, the airline ser-

vice company TDC and the airline operator DAT form the

biggest share of third-party consumers, which covers in total

energy use a minor share of about 1.5% for each.

Due to not existing consumption data of individual elec-

tricity users, only assumptions can be made: It is to be

expected that besides the new in-door lighting system, most

likely the out-door lighting system (floodlight), electric

ground support vehicle, old escalator, security scanner sys-

tems, and airport’s office department are the major SEUs.

Moreover, focusing on the relatively high base load (both

95% and 99.99%,

Figure 15)

in combination with analysis of

the individual daily hours (Figure

16),

it can be assumed

that, at night, several electrical consumers still consume

energy even though they are not in use because of reduced

or any passenger movement. The same accounts for holiday

periods in which the passenger traffic generally decreases

while the absolute base load remains equally high.

Furthermore, large amount of electric energy is consumed

during the late afternoon and evening. This is most likely

caused by arriving passengers after regular workdays.

However, the frequent energy peaks form in total a large

share of the daily energy profile and, therefore, further

examinations are needed.

The thermal energy consumption is only represented by

the natural gas use, which is used to heat the entire building

of the airport including every party. Electrical heating is

unknown. It can be assumed that a vast amount of thermal

energy is lost through the enormous glass facade manufac-

tured in 1991. In addition, due to several construction

phases over the past 50 year, the general quality of the ther-

mal insulation of the building can be considered as low to

very low. In this context, losses in the heating system

through, for example, uninsulated pipes, which were identi-

fied at the airport, increase further significant losses. An

TRU, Alm.del - 2021-22 - Bilag 226: Henvendelse af 26/3-22 vedrørende grøn omstilling af luftfarten, fra Midtjyllands Lufthavn

P. BUJOK ET AL.

Figure 20.

Total energy consumption including all scopes from 2006 to 2019 (left) and detailed view of 2019 (right).

Figure 21.

Total greenhouse gas (GHG) emissions including all scopes (left) and detailed view of 2019 (right) in tons of carbon dioxide equivalent (tCO

e).

Table 4.

Individual GHG emissions of each scope and its sub-categories in 2019.

Scope 1: Direct emissions

Sub-scope

1.1 Natural gas

1.2 Diesel

Emission source

Central heating

Service equipment

Aircraft heater

Tanker

Deicer

Tractor

Bus

Compact tractor

Telescopic handler

Other

Subtotal

tCO

76.03

12.47

6.58

1.73

0.97

0.71

0.56

0.54

0.51

0.87

88.50

Sub-scope

2.1 Electricity

2.2 Electricity

–

Scope 2: Indirect emissions

Emission source

Airport

Third-party

Airline service

Airline operator

Car rental

Aircraft service

Other

–

Subtotal

tCO

104.00

10.53

3.60

3.24

2.08

0.42

1.20

–

114.53

Total 203.02 tCO

energetic analysis of the thermal building envelope including

the heat system with heater, transmitter, and pipe network

is highly recommended.

5.2. Total greenhouse gas emissions

Figure 21

presents the results of both scopes 1 and 2 for the

historical years and individually for the latest year. Because

of missing data, the diesel consumption (scope 1.2) is only

available for the year 2019.

Due to the historical events described in Section 4.1, the

median is used in the following analysis in order to reduce

the impact of these events in 2010 and 2011. It is apparent

that the GHG emissions caused by natural gas (scope 1.1)

fluctuate around the median value of 70 ± 8 tCO

e over the

entire time frame with an increase in the latest three years.

The emissions caused by diesel (scope 1.2) are only available

for 2019. The total electricity-related emissions (scope 2)

reveal similar fluctuations around the median of 121 ± 11

tCO

e. However, 2017–2019 shows a total growth of about

5.6% during these years. In the latest year, approximately,

50% of electricity-related emissions by third parties (scope

2.2) shifted to the airport’s electricity-related emissions

(scope 2.1), whereby the total GHG emissions remain con-

stant during the same period. With the inclusion of diesel

(scope 1.2) in the final year, Midtjyllands Airport GHG

emissions add up to 203 tCO

e with a slightly larger share

of emissions caused by electricity (56%; scope 2) than by

natural gas and diesel (44%; scope 1).

Table 4

presents a

detailed list of the occurred GHG emissions in 2019.

TRU, Alm.del - 2021-22 - Bilag 226: Henvendelse af 26/3-22 vedrørende grøn omstilling af luftfarten, fra Midtjyllands Lufthavn

INTERNATIONAL JOURNAL OF SUSTAINABLE TRANSPORTATION

Figure 22.

EnPI for total energy demand per passenger (PAX) and electricity demand per aircraft movement (ATM) with year-on-year change (%).

Table 5.

Baseline period for each indicator with coefficient of determination

and trend function.

Indicator

Total

GHG emissions

energy consumption

natural gas consumption

electricity consumption

energy per PAX (EnPI)

electricity per ATM (EnPI)

Baseline period

2015–2019

2017–2019

0.80

0.77

0.92

0.99

0.97

Regression function

¼ À3.10x þ

6454

¼ À17588x þ

36526278

12816x

25396747

¼ À13032x þ

26860298

0.94x

1891

3.77x

7514

5.3. Energy performance indicator

The performed regression analysis in Section 4.3.3 reveals

the following EnPIs:

Total energy consumption per passenger (kWh/PAX)

Total electricity consumption per ATM (kWh)

Figure 22

visualizes both EnPIs from 2006 to 2019. The

total energy EnPI (left) reveals high fluctuations with an

overall increase from 2006 to 2015. After a 13% drop in

2016, a constant growing trend is clearly visible. With regard

to the electricity EnPI (right), the first 4 years of the total

period show barely any changes. From 2010, a high degree

of scatter remains until 2017. Afterwards, a clearly recogniz-

able upward trend is present. The course of both curves is

mainly explained by the development of PAX and ATM,

which fluctuate due to historical operational events (Section

4.1). A comparison with major airports can be drawn with

the findings by Kılkıs and Kılkıs (2016).

5.4. Energy and greenhouse gas emission baseline

The baselines of all analyzed indicators are statistically deter-

mined in Section 4.5 and displayed as a regression function

Table 5.

Every baseline is characterized by the trend func-

tion (regression function) of the respective period. The first

coefficient of this function indicates a growing trend (posi-

tive value) or declining trend (negative value). The baseline

with its trend is visualized in the following Section 5.

5.5. Sustainable energy strategy

The main energy and climate objectives of the Danish

Aviation Association and the Danish government are to

achieve net-zero GHG emissions by 2050 (Section 1).

However, the urgency of the dramatically progressing cli-

mate crisis requires instant action and profound objectives,

which mirror the latest scientific findings on the physical

science basis of global warming (IPCC,

2021).

Furthermore,

the common tool of carbon offsetting especially in the avi-

ation industry is considered as an invalid measure in the

given context due to carbon fraud, low sustainability impli-

cations, and harmful impacts on local communities (Blum &

L€vbrand,

2019;

Cames et al.,

2016;

Goldtooth et al.,

2019;

Lohmann,

2009;

Newell & Paterson,

2010).

For that reason,

the subsequent objectives for Midtjyllands Airport set higher

standards in order to do justice to the necessity for action in

an effective and thoughtful manner. In this sense, objectives

were developed under consideration of the concept of

SMART targets (Section 3.2) with regard to the respective

target category (Figure

2).

Considering ISO 50001, the target

refers to a short-term goal, while the objective is described

by a superordinate long-term goal. For that matter, the fol-

lowing presentation refers to objectives in order to guarantee

alignment with ISO 50001. The objectives are:

Net-zero GHG emissions without offsetting by 2030

instead of 2050 as stated by national and aviation objec-

tives (volume objective).

A 40% reduction in the total energy consumption by

2025 compared to the baseline trend and afterward

keeps the total energy consumption at that level at least

(volume objective).

A 40% of the two EnPIs by 2030 compared to the base-

line trend of 2020 reaching the value of 2007 (physical

efficiency objective).

The GHG emission objective (1) is directly linked to the

energy consumption (2) as well as to the sources of energy

and its origin. According to Section 3.2, these volume objec-

tives follow a bottom-up approach comprising the individual

TRU, Alm.del - 2021-22 - Bilag 226: Henvendelse af 26/3-22 vedrørende grøn omstilling af luftfarten, fra Midtjyllands Lufthavn

P. BUJOK ET AL.

Table 6.

Constituents of volume objective (1) and (2) comprising total natural gas consumption (NGC) (scope 1.1), diesel consumption (scope

1.2), and total electricity consumption (ELC) (scope 2.1

2.2).

Illustration

NGC (1.1)

Figure 23

–

Figure 24

–

Year

2021

2022

2024

2025

2030

2022

2025

2030

Objectives (O) and Targets (T)

(T) Stop upward trend

(T) 30% reduction compared to baseline trend

(T) 100% substitution of natural gas with sustainable energy source

(T) 50% reduction compared to 2022

(O) Remain value of 2025

(O) 100% replacement with non-fossil fuels

(T) 8.5% reduction compared to baseline trend and 100% use of renewable sources

(T) 30% reduction compared to baseline trend

(O) Remain value of 2025

Diesel (1.2)

ELC (2.1

2.2)

Figure 23.

Objective and targets for total natural gas consumption (NGC).

constituents natural gas, diesel, and electricity as a start-

ing point.

5.5.1. Natural gas, diesel, and electricity

Beginning with the volume objectives and its constituents,

Table 6

summarizes the long-term objectives and short-term

targets with reference to the respective source of energy and

visual illustration. The COVID-19 crisis in 2020 and the

resulting total operational shutdown have significantly influ-

enced the years 2020 and 2021. Nonetheless, the mid- and

long-term goals are still valid expecting first achievements

under regular operation at the end of 2021.

The reduction of NGC (scope 1.1,

Figure 23)

can be

achieved by a series of actions. First, a professional analysis

of the building envelope and heating system is required to

identify thermal leakages and performance losses. On that

basis, a 30% decrease in energy use is already possible

through small projects by remedying energy weak points

resulting from the thermal analysis (B€y€kbay et al.,

2016).

u u

These projects include the improvement of the heating sys-

tem by insulating the utility room and heating water pipes

to reduce heating losses, performing a hydronic balancing to

increase efficiency of the pipe system, as well as replacing

inefficient circulation pumps. The implementation of these

actions is required for the 2022 target. The replacement of

the gas-fired boiler technology with an alternative heating

technology is not scheduled before 2035 and thus beyond

the considered timeline. However, alternative renewable

technologies are discussed below.

Second, a steady step-by-step improvement process of the

thermal condition of the total building envelope has to be

carefully planned over the period from 2021 to 2025. The

installation of so-called external thermal insulation compos-

ite system, a multi-layer constructive system to insulate the

facade, in combination with the insulation of the roof and

replacement of doors and windows are considered as com-

mon thermal modernization measurements. It has to be

noted that the use of crude oil-based polystyrene as insula-

tor is to be avoided and non-fossil-based materials should

be given preference, such as stone wool for the facade and

cellulose for the roof (Jelle,

2011;

Lee et al.,

2018;

Pal et al.,

2021;

Sierra-Prez et al.,

2016).

After the implementation of

these measures, it is targeted to at least remain the 2025

demand value until the final 2030 objective.

Besides the energy efficiency improvements, a major tar-

get is scheduled in 2024. Here, the supply contract of nat-

ural gas ends, which provides the opportunity to replace the

current contract with a biogas contract that supplies gaseous

fuel made from renewable sources. The concept of a biogas

contract is discussed below in relation to the GHG objective

(scope 1

2) in

Figure 25.

The 2022 target of ELC (scope 2.1

2.2,

Figure 24)

requires the identification, mapping, and documentation of

SEUs before any energy improvements can be considered.

To this date, a missing monitoring system of SEUs is not

present. However, the integration process can start immedi-

ately by gathering (i) technical information from manuals,

such as power requirements, (ii) collecting manufacturing

dates and predicting year for replacement, as well as (iii)

evaluating potential locations for energy metering devices.

Without further knowledge about the SEUs, specific actions

are considered challenging. Nonetheless, the detailed TEC

analysis in Section 4.3.2 with respect to

Figures 14

and

reveals a high base load demand over an entire year includ-

ing hours without operation, such as during the night. This

finding is a typical indicator of active SEUs, which do not

support the operational business. Moreover,

Figure 16

speci-

fies this assumption by highlighting high energy demand

during off-peak hours from midnight to 4 a.m. in the morn-

ing and from 8 p.m. until midnight. In particular, frequent

and high energy peaks occur from 5 to 9 a.m. and 5 to 11

p.m. during the year suggesting intensive activity of single

SEUs or multiple smaller-sized consumers. Either way, both

cases represent typical conditions to decrease the base load

demand and reduce the frequency and intensity of power

peaks in order to reach the 2025 target. Besides electricity

efficiency improvements, the 2022 target also accounts the

replacement of the existing electricity contract comprising

the national electricity mix with a fully renewable electricity

TRU, Alm.del - 2021-22 - Bilag 226: Henvendelse af 26/3-22 vedrørende grøn omstilling af luftfarten, fra Midtjyllands Lufthavn

INTERNATIONAL JOURNAL OF SUSTAINABLE TRANSPORTATION

Table 7.

Volume objectives (1) and (2) comprising total GHG emissions (scope

2) and total energy consumption (TEC) (scope 1

2).

Source (scope)

GHG (1

Illustration

Figure 25

–

Figure 26

–

Figure 24.

Objective and target for total electricity consumption (ELC).

Year

2022

2024

2030

2022

2024

2025

2030

Objectives (O) and targets (T)

(T) 50% reduction compared

baseline trend

(T) 90% reduction compared

(O) Net-zero GHG emissions

(T) 10% reduction compared

baseline trend

(T) 33% reduction compared

baseline trend

(T) 40% reduction compared

baseline trend

(O) Remain value of 2025

to 2022

TEC (1

Figure 25.

Objective and targets for total greenhouse gas (GHG) emissions

resulting from electricity, natural gas, and diesel.

Figure 26.

Objective and targets for total energy consumption (TEC) consisting

of electricity, natural gas, and diesel.

contract. The concept behind this contract and the effects

on the GHG emission are discussed below in relation to

Figure 25.

Diesel (scope 1.2) accounts solely 0.1% of TEC and there-

fore is not considered as a major source for improvements

from an energy perspective. However, its relevance in rela-

tion to GHG emission is discussed with respect to the vol-

ume objective (1) and

Figure 25.

With regard to the integration of renewable energy tech-

nologies, the airport shows interest in setting up an on-site

geothermal power system for heat generation as well as a

PV system for electricity production at the roof of the air-

port building. However, a capital-intensive geothermal sys-

tem must be considered in correlation with future

improvements in thermal energy efficiency resulting from

modernization measures of the building envelope. Therefore,

it is highly recommended to optimize the thermal envelope

before investing in a new heat-generating technology with

high investment costs in order to avoid an oversized system.

With regard to a PV system, prior thermal modernization

measures of the roof are recommended before the installa-

tion in order to guarantee full access to the rooftop.

Furthermore, the hourly data set of the electricity demand

should be taken into account during the development and

dimensioning of the PV system design. PV surplus produc-

tion is unlikely considering the limited roof area and the

high base-load electricity demand. Nevertheless, a profes-

sional analysis is recommended to guarantee an optimal

self-consuming system.

5.5.2. GHG emissions, energy consumption, and EnPIs

The GHG emission objective (1) is composed of the accu-

mulation of emissions from natural gas, diesel, and electri-

city. Thus, the previously described measures in NGC

(Figure

23)

and ELC (Figure

24)

form the base for the tar-

gets in

Table 7

and

Figure 25

leading up to the final object-

ive in 2030. Here, the steady decrease of emissions from

2020 to 2022 occurs due to efficiency improvements in

NGC and ELC. In 2022, the entry in the renewable electri-

city contract causes the instead drop in emissions followed

by a further constant decrease caused by additional effi-

ciency measures. The start of the biogas contract eliminates

the emissions in thermal energy domain resulting in a

second instead decline in emissions in 2024. The emissions

by the consumption of diesel are considered as residual

emissions and due to the complexity of decarbonizing the

mobility sector especially heavy vehicles a transition period

of 6 years is proposed until reaching carbon neutrality in

2030. Despite this relatively long transition period, solely a

fraction of about 5% will be emitted from 2024 compared to

the baseline trend. 95% of the emissions are already elimi-

nated in 2024 through the introduction of a renewable elec-

tricity contract and a biogas contract.

The idea behind both contracts follows an identical strat-

egy: Displacing fossil fuels with renewable energy sources

from the respective energy mix through the macroeconomic

law of supply and demand (Beveridge,

2013;

Hauser et al.,

2019).

With regard to the electricity contract, the airport has

TRU, Alm.del - 2021-22 - Bilag 226: Henvendelse af 26/3-22 vedrørende grøn omstilling af luftfarten, fra Midtjyllands Lufthavn

P. BUJOK ET AL.

Table 8.

Physical efficiency objective (3) comprising energy performance Indicators (EnPIs) namely total energy consump-

tion (TEC) per passenger movement (PAX), and total electricity consumption (ELC) per aircraft movement (ATM).

EnPI

TEC/PAX

ELC/ATM

Illustration

Figure 27

–

Figure 28

–

Year

2021

2025

2030

2021

2025

2030

Objectives (O) and targets (T)

(T) Stop upward trend

(T) 20% reduction compared 2021

(O) 25% reduction compared 2025 reaching value of 2007

(T) Stop upward trend

(T) 20% reduction compared 2021

(O) 25% reduction compared 2025 reaching value of 2007

entered a contract with an electrical energy provider that

supplies the airport with 100% electricity from renewable

sources. Technically, the airport still obtains the Danish

national electricity mix with 78% renewables and 22% fossil

fuels (BP & Ember,

2020)

but the energy provider ensures

that the entire consumed amount of electricity over a spe-

cific short- to mid-term period (e.g. annually, monthly, etc.)

is substituted with 100% electricity from renewable sources

(Energinet,

2021d, 2021c).

This approach guarantees that

every consumed conventional kWh will be replaced with a

renewable kWh. By implication, a greater demand in renew-

able electricity contracts results in a greater demand in the

supply of renewable energies and potentially its supporting

technologies, such as energy storages.

Hauser et al. (2019) investigated this concept of renew-

able electricity contracts in the German electricity market.

Their findings reveal among others that (i) the demand and

supply of green electricity products has increased since 2013,

(ii) the guarantees of origin is a functioning tool in the mar-

ket, and (iii) new positive impulses in the energy transition

are potential outcomes. In contrast, (iv) the pricing of such

products is still considered as unpredictable. Furthermore,

(v) Germany made the experience of importing almost half

of their green electricity products from Norwegian hydro-

electric power, which might be a pitfall for a Danish use

case. Local and national electricity from renewable energy

source should be the major solution. Besides the gained

experiences with green electricity products, (vi) the specific

contribution demands further differentiated investigation

with respect to accelerating the transition toward green

energy (Hauser et al.,

2019).

The concept of a biogas contract operates on a similar

accounting measurement displacing natural gas from the

national gas mix with refined biogas (biomethane). In 2020,

Denmark already holds a share of 21% refined biogas in the

gas grid (Statistics Denmark,

2021).

With the entry of the

contract, the airport continuously operates the existing heat-

ing system and supplying pipe infrastructure. From a tech-

nical perspective, the national gas mix with 79% natural gas

and 21% refined biogas (Statistics Denmark,

2021)

will still

be burned but the biogas supplier guarantees that the

amount of consumed gas mix from the gas grid will be

replaced with the identical amount of biogas resulting in a

displacement of fossil natural gas. However, a number of

vital criteria must be fulfilled by the contract to ensure a

functioning concept. First, the guarantees of origin of biogas

with respect to the raw materials require a careful investiga-

tion to ensure sustainability and to reduce the harm to bio-

diversity in order to avoid a shift of emissions from burning

natural gas to cultivating biomass for the biogas production.

Widely controversial topics with regard to biomass for

energy uses account among others land use, food

versus

fuel, carbon life cycle, ground water contamination, and

water consumption (Antar et al.,

2021;

Ceballos et al.,

2015;

Gaurav et al.,

2017;

Nonhebel,

2012;

Roth et al.,

2018;

Zhang et al.,

2015).

Second, the cultivation of biomass, its

processing to biogas, and the injection into the gas gird

must take place on a local or national level with respect to

the location of the consumer. For example, cultivating bio-

mass in tropical regions, transporting, and converting the

material within Europe, and finally injecting the biogas into

the European gas gird does not account as valid method of

a biogas contract for Midtjyllands Airport in Denmark. In

particular, the displacement of natural gas requires the direct

injection of biogas in the Danish gas grid system. So-called

bio methane certificates are already applied in Denmark rul-

ing the guarantees of origin and unbroken supply chains

(Energinet,

2017, 2021b, 2021a).

The volume objective (2) and trajectory of TEC (Figure

26)

is the result of the summation of NGC and ELC. The

respective targets in 2022, 2024, and 2025 (Table

are

given by the series of events described and discussed above

in relation to

Figures 23

and

24.

In contrast, diesel is

assessed as a minor contributor to TEC with a fraction of

0.1% and hence not included in the trajectory.

The objectives of the two EnPIs in

Table 8

comprising

TEC per PAX (Figure

27)

and ELC per ATM (Figure

28)

follow the simplified strategy of reaching the lowest histor-

ical value during regular operation. According to

Figure 22,

both EnPIs measure the lowest values in 2010 and 2011.

However, this year is not considered as regular due to the

historical events of a major operating airline going bankrupt

and diminishing the operational cycles. For that reason, the

next valid year is 2007 for both EnPIs, which is considered

as the target value for the objective 2030. In order to guar-

antee a realistic trajectory toward that objective, the year

2021 is defined as vital in order to stop the upward trend of

the past three years. Afterwards, a linear trajectory is tar-

geted toward to objective. The compliance of this trajectory

is directly dependent on TEC and ELC. Detailed options for

efficiency improvements of these parameters are extensively

discussed above in relation to

Figures 23, 24,

and

26.

5.5.3. Action plan

The action plan in

Table 9

represents the practical guide

toward the implementation of the sustainable energy strat-

egy aligning the EnMS of Midtjyllands Airport with the ISO

50001 standard as well as facilitating the integration of a

carbon management system required for an accreditation by

TRU, Alm.del - 2021-22 - Bilag 226: Henvendelse af 26/3-22 vedrørende grøn omstilling af luftfarten, fra Midtjyllands Lufthavn

INTERNATIONAL JOURNAL OF SUSTAINABLE TRANSPORTATION

the ACA program. The plan was established in alliance with

the results objectives in Sections 5.5.1 and 5.5.2.

Furthermore, its structure follows the recommended

approach by the literature (Akyuz et al.,

2019;

Kahlenborn

et al.,

2012).

The order of action is categorized by the major

parameters’ financial investments, expenditure of time,

degree of complexity, as well as energy and environmental

performance outcomes. In this sense, action (1) and (2)

presents the activities with no to low investments, low time

effort, and low complexity. In particular, the investments

rise with action (3)–(6) while at the same time the energy

efficiency performance and GHG emission measures

increase in effect. Action (7) requires the findings of action

(1). Lastly, action (8) and (9) provide further optional meas-

ures, which should be considered in future works. After all,

the action plan in combination with the discussed measures

above enables a strategic approach toward an increase in

energy efficiency including the integration of renewable

energy sources and the resulting consequents of not only

reducing but also fully diminishing GHG emissions.

6. Conclusion

This study has empirically analyzed and identified what criteria

of a mid-sized airport are relevant in order to establish a sus-

tainable and optimized energy management and how energy-

related carbon emissions, which contribute to global warming,

can be diminished. To achieve this objective, the study chose

Midtjyllands Airport as a case airport. The research was con-

ducted in form of a case study. Energy-related data was ana-

lyzed taken from secondary sources, which are the airport,

energy providers, and national databases.

For this study, the methods of two common and inter-

nationally accepted standards were applied, namely, ISO

50001 EnMS and ACA Program. Several research and case

studies of airports have been analyzed that revealed high

potentials and achievements in terms of energy savings and

carbon reduction by implementing these standards. Based

on these methods and findings, the conducted analyses of

Midtjyllands Airport energy management, energy consump-

tion, and carbon emissions return the following outcome:

Figure 28.

EnPI’s objective and targets for total electricity consumption (ELC)

per aircraft movement (ATM).

Figure 27.

EnPI’s objective and targets for total energy consumption (TEC) per

passenger (PAX).

The current energy management requires significant

increase in effort to achieve improvements in energy

performance and reduction in GHG emissions.

Execution

2020

2021

2022

2024

2020

2021

2022

2021

2022

2021

2025

2024

2030

Table 9.

Action plan including objective and target, specific action, execution period, and order of action.

Objective

Action

(1) Plans that do not require investments

TEC 2025

Identify, map, and document SEUs

(2) Easy and short-time applicability

GHG 2022

Replace current electricity contract with renewable electricity contract

GHG 2024

Replace current natural gas contract with biogas contract

Evacuate coolant (HFC) of the decommissioned air conditioning system by expert

GHG 2030

(3) High energy performance with low investments

NGC 2022

Professional analysis of building envelope and heating system to identify leakages and losses as well as rectify weaknesses

(4) Plans that do require low to medium investments

NGC 2022

Improve heating system by insulating heating room and pipes, performing hydronic balancing, and replace circulation pumps

(5) High environmental performance (Reference is made to point 2)

(6) Long-term actions with high investments

NGC 2025

Improve thermal state of total building envelope through insulation, step-by-step process

GHG 2030

Replacing outdated fossil-fuel-fired vehicles and equipment with emission-free alternatives, step-by-step process

(7) Plans related to SEUs (reference is made to point 1)

(8) Plans related to renewable energy technologies

Optional

On-site geothermal power system for heat generation in combination with a new SEU

Optional

PV system at the roof of the airport

(9) National and international legal requirements

Start carbon offsetting according to Danish Aviation Association (not considered in this work)

Optional

HFC was not specifically considered in the objective. The stated action is a general recommendation due to significant climate impact of HFCs (Burkholder

et al.,

2020;

Montzka et al.,

2019;

Saengsikhiao et al.,

2020;

H. Zhang et al.,

2011).

SEUs could not be identified due to missing monitoring systems.

Optional action has to be considered after thermal building modernization.

Carbon offsetting or compensation is not considered as a valid sustainable measure in this context due to carbon fraud, low sustainability implications, and

harmful impacts on local communities (Blum & L

€

vbrand,

2019;

Cames et al.,

2016;

Goldtooth et al.,

2019;

Lohmann,

2009;

Newell & Paterson,

2010).

TRU, Alm.del - 2021-22 - Bilag 226: Henvendelse af 26/3-22 vedrørende grøn omstilling af luftfarten, fra Midtjyllands Lufthavn

P. BUJOK ET AL.

10.

The easiest and most effective measure to cut GHG

emissions in the shortest time is to enter into a green

electricity and biogas contract using 100% renewable

sources for energy generation.

Even though the total energy consumption underwent

distinctive fluctuations from 2006 to 2019, the value

remains almost constant in particular for the last

three years.

However, passenger (PAX) and aircraft movement

(ATM) decreased during the whole period.

Two EnPIs have been determined, which illustrate the

upward trend of total energy demand per PAX and

total electricity demand per ATM.

Total GHG emissions revealed a similar trend com-

pared to the total energy consumption.

Baseline trends for six indicators have been developed

to assess future energy and emission changes.

Three ambitious and realistic objectives have been set

including (1) net-zero GHG emissions in 2030 and (2) a

40% reduction of total energy demand by 2025 in relation

to the baseline trend, and (3) a 40% reduction of two

EnPIs by 2030 compared to the baseline trend of 2020.

Several targets have been set to accomplish the objec-

tives within the defined period.

An action plan has been drawn up including fundamental

actions, such as identifying, mapping and documenting

SEUs, and performing low, medium, and high efforts.

Disclosure statement

Patrick Bujok (1st author) is employed and paid by the airport.

ORCID

Patrick Bujok

George Xydis

http://orcid.org/0000-0002-9365-0509

http://orcid.org/0000-0002-3662-1832

References

Airport Carbon Accreditation. (2016).

Guidance document

(issue 10).

Airports Council International.

https://www.aca-application.org/

media/guidance_documents/ACA_Guidance_Document_Issue_10_

Sept_2016.pdf

Airport Carbon Accreditation. (2018).

About airport carbon accredit-

ation.

Airports Council International.

https://www.airportcarbonac-

creditation.org/about.html

Akyuz, M. K., Altuntas, O., Sogut, M. Z., & Karakoc, T. H. (2019).

Energy management at the airports. In T. H. Karakoc, C. O.

Colpan, O. Altuntas, & Y. Sohret (Eds.),

Sustainable aviation

(pp.

9–36). Springer International Publishing.

https://doi.org/10.1007/

978-3-030-14195-0_2

American Institute of Architects. (2012).

Integrating energy modeling in

the design process.

American Institute of Architects.

https://www.aia.

org/resources/8056-architects-guide-to-integrating-energy-modeli

Andersen, J. (2018). Research strategy.

The European research manage-

ment handbook

(pp. 109–145). Elsevier.

https://doi.org/10.1016/

B978-0-12-805059-0.00005-5

Antar, M., Lyu, D., Nazari, M., Shah, A., Zhou, X., & Smith, D. L.

(2021). Biomass for a sustainable bioeconomy: An overview of world

biomass production and utilization.

Renewable and Sustainable

Energy Reviews, 139,

110691.

https://doi.org/10.1016/j.rser.2020.

110691

Association of Issuing Bodies. (2018).

Grid electricity emission factors.

https://www.carbonfootprint.com/docs/2019_06_emissions_factors_

sources_for_2019_electricity.pdf

Baidya, S., & Nandi, C. (2020). Green energy generation using renew-

able energy technologies.

Advances in greener energy technologies

(pp. 259–276).

https://doi.org/10.1007/978-981-15-4246-6_16

Barrett, S. (2015). Ownership structures and the implications for devel-

oping airport solar projects in the USA.

Journal of Airport

Management, 9(3),

248–263.

Barrett, S., Devita, P., Ho, C., & Miller, B. (2014). Energy technologies’

compatibility with airports and airspace: Guidance for aviation and

energy planners.

Journal of Airport Management, 8(4),

318–326.

Baxter, G., Srisaeng, P., & Wild, G. (2018a). An assessment of airport

sustainability, part 2—energy management at Copenhagen Airport.

Resources, 7(2),

32.

https://doi.org/10.3390/resources7020032

Baxter, G., Srisaeng, P., & Wild, G. (2018b). Sustainable airport energy

management: The case of Kansai International Airport.

International

Journal for Traffic and Transport Engineering, 8(3),

334–358.

https://

doi.org/10.7708/ijtte.2018.8

Baxter, G., Srisaeng, P., & Wild, G. (2019). Environmentally sustainable

airport energy management using solar power technology: The case

of Adelaide Airport, Australia.

International Journal for Traffic and

Transport Engineering, 9(1),

81–100.

https://doi.org/10.7708/ijtte.

2019.9

Beveridge, T. M. (2013). Demand and supply.

A primer on macroeco-

nomics.

Business Expert Press.

http://ebookcentral.proquest.com/lib/

asb/detail.action?docID=1048403

Bjørn-Thygesen,

(2020).

Personal

interview.

Personal

communication.

Blum, M., & L€vbrand, E. (2019). The return of carbon offsetting? The

discursive legitimation of new market arrangements in the Paris cli-

mate regime.

Earth System Governance, 2,

100028.

https://doi.org/10.

1016/j.esg.2019.100028

In the period from 2006 to 2019, various operational

events in form of incoming and outgoing airline companies

had a significant impact on passenger numbers and ATM.

This resulted in variations of natural gas and electricity con-

sumption. In 2019, the total energy use was about

1,029 MWh consisting of the major shares direct airport’s

electric energy consumption of approximately 498 MWh

(48%) and total natural gas consumption of about 480 MWh

(47%). On the other hand, electricity consumption by third

parties corresponds to minor 51 MWh (5%) while the diesel

consumption is significantly minimal with 0.1%. The GHG

emissions account for in total 203 tCO

e comprising the

major shares electricity (51%) and natural gas (38%). The

detailed analysis of the electrical energy consumption

revealed demand gaps during the holiday periods specifically

in summer. This fact has to be considered in terms of

designing a solar power system as it was proposed by

Midtjyllands Airport. In addition, future plans of setting up

a geothermal power system for heat production and PV sys-

tem should be taking into account in correlation with

planned energy efficiency improvements by modernizing the

airport’s building envelope. Generally, future success

requires, first and foremost, a comprehensive monitoring

concept to detect principal weaknesses of individual energy

consumers within the overall EnMS.

Acknowledgments

The authors would like to thank all Midtjyllands Airport personnel, in

particular the CEO, for their support throughout the process, and pro-

viding the means and the data for this work to be realized.

TRU, Alm.del - 2021-22 - Bilag 226: Henvendelse af 26/3-22 vedrørende grøn omstilling af luftfarten, fra Midtjyllands Lufthavn

INTERNATIONAL JOURNAL OF SUSTAINABLE TRANSPORTATION

BP & Ember. (2020).

Per capita electricity from fossil fuels, nuclear and

renewables, 2020.

https://ourworldindata.org/grapher/per-capita-elec-

tricity-fossil-nuclear-renewables?country=$DNK

Budd, L., & Budd, T. (2013). Environmental technology and the future

of flight.

Sustainable aviation futures

(Vol. 4, pp. 87–107). Emerald

Bingley.

https://doi.org/10.1108/S2044-9941(2013)0000004004

Burkholder, J. B., Marshall, P., Bera, P. P., Francisco, J. S., & Lee, T. J.

(2020). Climate Metrics for C1-C4 Hydrofluorocarbons (HFCs).The

Journal of Physical Chemistry. A, 124(23),

4793–4800.

https://doi.

org/10.1021/acs.jpca.0c02679

€

B€y€kbay, M. N., Ozdemir, G., & Ust€nda, E. (2016). Sustainable avi-

u u

ation applications in Turkey: Energy efficiency at airport terminals.

In T. H. Karakoc, M. B. Ozerdem, M. Z. Sogut, C. O. Colpan, O.

Altuntas, & E. Ac

kkalp (Eds.),

Sustainable aviation

(pp. 53–60).

Springer International Publishing.

https://doi.org/10.1007/978-3-319-

34181-1_7

Cames, M., Harthan, R., F€ssler, J., Lazarus, M., Lee, C., Erickson, P.,

& Spalding-Fecher, R. (2016).

How additional is the clean develop-

ment mechanism?

https://ec.europa.eu/clima/sites/clima/files/ets/

docs/clean_dev_mechanism_en.pdf

Cardona, E., Piacentino, A., & Cardona, F. (2006). Energy saving in

airports by trigeneration. Part I: Assessing economic and technical

potential.

Applied Thermal Engineering, 26(14–15),

1427–1436.

https://doi.org/10.1016/j.applthermaleng.2006.01.019

Ceballos, G., Ehrlich, P. R., Barnosky, A. D., Garc A., Pringle, R. M.,

ıa,

& Palmer, T. M. (2015). Accelerated modern human-induced species

losses: Entering the sixth mass extinction.

Science Advances, 1(5),

e1400253.

https://doi.org/10.1126/sciadv.1400253

Crawley, M. J. (2012). Regression.

The R book

(1st ed., pp. 449–497).

John Wiley & Sons, Ltd.

D&R International. (2012). Commercial sector.

2011 Buildings energy

data book

(pp. 3–1). US Department of Energy.

https://ieer.org/wp/

wp-content/uploads/2012/03/DOE-2011-Buildings-Energy-DataBook-

BEDB.pdf

D&R International. (2013). Buildings sector.

2011 Buildings energy

databook

(pp. 1–1). US Department of Energy.

https://ieer.org/wp/

wp-content/uploads/2012/03/DOE-2011-Buildings-Energy-DataBook-

BEDB.pdf

Danish Aviation Association. (2019).

Luftfartens klimaudfordringer.

https://www.dansk-luftfart.dk/wp-content/uploads/2019/02/BDL-

Positionspapir.pdf

Danish Ministry of Climate Energy and Utilities. (2019).

Denmark’s

integrated national energy and climate plan.

https://ens.dk/en/our-

responsibilities/energy-climate-politics/eu-energy-union-denmarks-

national-energy-and-climate

Danish Transport Authority. (2020a).

Aircraft movement database.

http://stat.trafikstyrelsen.dk/

Danish Transport Authority. (2020b).

Passenger database.

http://stat.

trafikstyrelsen.dk/

de Rubeis, T., Nardi, I., Paoletti, D., Di Leonardo, A., Ambrosini, D.,

Poli, R., & Sfarra, S. (2016). Multi-year consumption analysis and

innovative energy perspectives: The case study of Leonardo da Vinci

International Airport of Rome.

Energy Conversion and Management,

128,

261–272.

https://doi.org/10.1016/j.enconman.2016.09.076

Edvardsson, K., & Hansson, S. O. (2005). When is a goal rational?

Social Choice and Welfare, 24(2),

343–361.

https://doi.org/10.1007/

s00355-003-0309-8

Energi Danmark. (2020). Selfservice energy portal. Energi Danmark.

Energinet. (2017).

Model paper for rules for biomethane certificates in

Denmark.

https://en.energinet.dk/-/media/

64D4569BE30146908FD88A19F0BE99A8.pdf?la=en&hash=

F05D9AA976CC42F6D70B9F76DB68AB1A6D3BF3C0

Energinet. (2020).

Calorific value.

https://en.energinet.dk/Gas/

Transparency/Calorific-Values

Energinet. (2021a).

Biomethane: Rules & reports.

https://en.energinet.

dk/Gas/Rules

Energinet. (2021b).

Biomethane guarantees of origin.

https://en.energi-

net.dk/Gas/Biomethane/Biomethane-GOs

Energinet. (2021c).

Certificates of origin: Documents and forms.

https://

en.energinet.dk/Electricity/Green-electricity/Guarantees-of-origin/

Documents-and-forms-for-certificates-of-origin

Energinet. (2021d).

Certificates of origin.

https://en.energinet.dk/

Electricity/Green-electricity/Guarantees-of-origin

European Commission. (2016).

Eco-management and audit scheme

(EMAS).

https://ec.europa.eu/environment/emas/index_en.htm

Falk, M. T., & Hagsten, E. (2020). Time for carbon neutrality and other

emission reduction measures at European airports.

Business Strategy

and the Environment, 29(3),

1448–1464.

https://doi.org/10.1002/bse.

2444

Farquhar, J. D. (2012).

Case study research for business.

Sage.

Gaurav, N., Sivasankari, S., Kiran, G., Ninawe, A., & Selvin, J. (2017).

Utilization of bioresources for sustainable biofuels: A review.

Renewable and Sustainable Energy Reviews, 73,

205–214.

https://doi.

org/10.1016/j.rser.2017.01.070

Goldtooth, T., Adrar, A., Ourpowercampaign, F., Gilbertson, T.,

Garcia, D., & Eaton, C. (2019).

Carbon pricing: A critical perspective

for community resistance.

http://www.ienearth.orghttp://www.indige-

nousrising.orghttp://www.climatejusticealliance.org

Gopalakrishnan, B., Ramamoorthy, K., Crowe, E., Chaudhari, S., &

Latif, H. (2014). A structured approach for facilitating the imple-

mentation of ISO 50001 standard in the manufacturing sector.

Sustainable Energy Technologies and Assessments, 7,

154–165.

https://doi.org/10.1016/j.seta.2014.04.006

Graham, A. (2014).

Managing airports: An international perspective

(4th ed.). Butterworth-Heinemann.

https://books.google.dk/book-

s?id=bQ6Ri3ozEBcC

Graham, A., & Morrell, P. (2016).

Airport finance and investment in

the global economy.

Taylor & Francis.

Hair, J., Wolfinbarger, M., Money, A. H., Samouel, P., & Page, M. J.

(2015a). Basic data analysis for quantitative research. In H. Brigg, E.

Granda, & A. Piliouras (Eds.),

Essentials of business research methods

(2nd ed., pp. 294–319). Routledge.

https://doi.org/10.4324/

9781315704562

Hair, J., Wolfinbarger, M., Money, A. H., Samouel, P., & Page, M. J.

(2015b). Conceptualization and research design. In H. Brigg, E.

Granda, & A. Piliouras (Eds.),

Essentials of business research methods

(2nd ed., pp. 133–162). Routledge.

https://doi.org/10.4324/

9781315704562

Hair, J., Wolfinbarger, M., Money, A. H., Samouel, P., & Page, M. J.

(2015c). Basic data analysis for qualitative research. In H. Brigg, E.

Granda, & A. Piliouras (Eds.),

Essentials of business research methods

(2nd ed., pp. 275–293). Routledge.

https://doi.org/10.4324/

9781315704562

Hassett, M. E., & Paavilainen-M€ntym€ki, E. (2013). Longitudinal

research in organizations: An introduction.

Handbook of longitu-

dinal research methods in organisation and business studies

(pp.

1–22).

Edward

Elgar

Publishing.

https://doi.org/10.4337/

9780857936790.00008

Hauser, E., Heib, S., Hildebrand, J., Rau, I., Weber, A., Welling, J.,

G€ldenberg, J., Maaß, C., Mundt, J., Werner, R., Schudak, A., &

€

Wallbott, T. (2019).

Marktanalyse Okostrom II.

Umweltbundesamt.

https://www.umweltbundesamt.de/publikationen/marktanalyse-

oekostrom-ii

Howell, M. T. (2014). Energy planning.

Effective implementation of an

ISO 50001 energy management system (EnMS)

(pp. 25–64). ASQ

Quality Press.

International Civil Aviation Organization. (2018).

Aircraft engine emis-

sions.

ICAO Publication.

https://www.icao.int/environmental-protec-

tion/Pages/aircraft-engine-emissions.aspx

International Energy Agency. (2005).

Energy statistics manual.

https://

webstore.iea.org/energy-statistics-manual

International Organization for Standardization. (2015).

ISO 14000 fam-

ily

—

Environmental management.

https://www.iso.org/iso-14001-

environmental-management.html

International Organization for Standardization. (2018).

ISO 50001

—

Energy management.

https://www.iso.org/iso-50001-energy-manage-

ment.html

TRU, Alm.del - 2021-22 - Bilag 226: Henvendelse af 26/3-22 vedrørende grøn omstilling af luftfarten, fra Midtjyllands Lufthavn

P. BUJOK ET AL.

IPCC. (2021). Summary for policymakers. Climate change 2021: The

physical science basis.

Contribution of working group I to the sixth

assessment report of the intergovernmental panel on climate change.

https://www.ipcc.ch/report/ar6/wg1/#FullReport

Jani, M. (2011a).

Greening airports I: Monitoring, analysing, and

assessing

(pp. 35–64). Springer.

https://doi.org/10.1007/978-0-85729-

658-0_3

Jani, M. (2011b). Greening the air transport system: Structure, con-

cept, and principles.

Greening airports

(pp. 5–33). Springer.

https://

doi.org/10.1007/978-0-85729-658-0_2

Jelle, B. P. (2011). Traditional, state-of-the-art and future thermal

building insulation materials and solutions

–

Properties, require-

ments and possibilities.

Energy and Buildings, 43(10),

2549–2563.

https://doi.org/10.1016/j.enbuild.2011.05.015

Kahlenborn, W., Kabisch, S., Klein, J., Richter, I., & Sch€rmann, S.

(2012). Energy management systems in practice, ISO 50001: A guide

for companies and organisations.

Federal ministry for the environ-

ment, nature conservation and nuclear safety (BMU).

https://www.

umweltbundesamt.de/sites/default/files/medien/publikation/long/

4013.pdf

Kanneganti, H., Gopalakrishnan, B., Crowe, E., Al-Shebeeb, O.,

Yelamanchi, T., Nimbarte, A., Currie, K., & Abolhassani, A. (2017).

Specification of energy assessment methodologies to satisfy ISO

50001 energy management standard.

Sustainable Energy Technologies

and Assessments, 23,

121–135.

https://doi.org/10.1016/j.seta.2017.09.

003

Kılkıs, S., & Kılkıs, S. (2016). Benchmarking airports based on a sus-

¸ ¸

tainability ranking index.

Journal of Cleaner Production, 130,

248–259.

https://doi.org/10.1016/j.jclepro.2015.09.031

Koroneos, C., Xydis, G., & Polyzakis, A. (2010). The optimal use of

renewable energy sources—The case of the new international

“Makedonia”

airport of Thessaloniki, Greece.

Renewable and

Sustainable Energy Reviews, 14(6),

1622–1628.

https://doi.org/10.

1016/j.rser.2010.02.007

Lancaster, G. (2005). Analysing data.

Research methods in management.

Routledge.

Lee, K. O., Medina, M. A., Sun, X., & Jin, X. (2018). Thermal perform-

ance of phase change materials (PCM)-enhanced cellulose insulation

in passive solar residential building walls.

Solar Energy, 163,

113–121.

https://doi.org/10.1016/j.solener.2018.01.086

Lohmann, L. (2009). Regulation as corruption in the carbon offset mar-

kets.

Upsetting the offset: The political economy of carbon markets

(pp. 175–191). MayFly Books.

Lund, H. (2018). Renewable heating strategies and their consequences

for storage and grid infrastructures comparing a smart grid to a

smart energy systems approach.

Energy, 151,

94–102.

https://doi.org/

10.1016/j.energy.2018.03.010

McLaughlin, L. (2015).

ISO 50001: Energy management systems - A

practical guide for SMEs.

ISO.

Midtjyllands Airport. (2019).

Central Denmark region: Information

flyer.

Midtjyllands Aiport.

Midtjyllands Airport (2020).

Energy management data.

Midtjyllands

Aiport.

Monsalud, A., Ho, D., & Rakas, J. (2015). Greenhouse gas emissions

mitigation strategies within the airport sustainability evaluation pro-

cess.

Sustainable Cities and Society, 14,

414–424.

https://doi.org/10.

1016/j.scs.2014.08.003

Montzka, S., Velders, G. J. M., Krummel, P., M€hle, J., Orkin, V. L.,

Park, S., Shah, N., & Walter-Terrinoni, H. (2019).

Hydrofluorocarbons (HFCs).

Global ozone research and monitoring

project–report No. 58. Scientific assessment of ozone depletion: 2018.

World Meteorological Organization.

http://dspace.library.uu.nl/han-

dle/1874/378091

Morvay, Z. K., & Gvozdenac, D. D. (2009). Introducing the energy and

environmental management system.

Applied industrial energy and

environmental management

(pp. 23–54). John Wiley & Sons, Ltd.

https://doi.org/10.1002/9780470714379.ch1

Newell, P., & Paterson, M. (2010).

Climate capitalism.

Cambridge

University Press.

https://doi.org/10.1017/CBO9780511761850

Nonhebel, S. (2012). Global food supply and the impacts of increased

use of biofuels.

Energy, 37(1),

115–121.

https://doi.org/10.1016/j.

energy.2011.09.019

Oates, B. J. (2006). Case studies.

Researching information systems and

computing

(pp. 141–153). SAGE.

Ortega Alba, S., & Manana, M. (2016). Energy research in airports: A

review.

Energies, 9(5),

349.

https://doi.org/10.3390/en9050349

Ortega Alba, S., & Manana, M. (2017). Characterization and analysis of

energy demand patterns in airports.

Energies, 10(1),

119.

https://doi.

org/10.3390/en10010119

Pal, R. K., Goyal, P., & Sehgal, S. (2021). Effect of cellulose fibre based

insulation on thermal performance of buildings.

Materials Today:

Proceedings, 45,

5778–5781.

https://doi.org/10.1016/j.matpr.2021.02.

749

Preston, K. (2015). Sustainability initiatives helping airports address cli-

mate change.

International Airport Review, 19(5),

16–19.

https://

www.internationalairportreview.com/article/20580/sustainability-ini-

tiatives-helping-airports-address-climate-change/

Rietbergen, M. G., & Blok, K. (2010). Setting SMART targets for indus-

trial energy use and industrial energy efficiency.

Energy Policy,

38(8),

4339–4354.

https://doi.org/10.1016/j.enpol.2010.03.062

Roth, A., Riegel, F., & Batteiger, V. (2018). Potentials of biomass and

renewable energy: The question of sustainable availability.

Biokerosene

(pp. 95–122). Springer Berlin Heidelberg.

https://doi.

org/10.1007/978-3-662-53065-8_6

Ryan, G. (2018). Introduction to positivism, interpretivism and critical

theory.

Nursing Research, 25(4),

14–20.

https://doi.org/10.7748/nr.

2018.e1466

Saengsikhiao, P., Taweekun, J., Maliwan, K., Sae-Ung, S., & Theppaya,

T. (2020). Investigation and analysis of R463A as an alternative

refrigerant to R404A with lower global warming potential.

Energies,

13(6),

1514.

https://doi.org/10.3390/en13061514

Schmitt, D., & Gollnick, V. (2016). The air transport system.

Air trans-

port system

(pp. 1–17). Springer Vienna.

https://doi.org/10.1007/978-

3-7091-1880-1_1

Sierra-Prez, J., Boschmonart-Rives, J., & Gabarrell, X. (2016).

Environmental assessment of facade-building systems and thermal

insulation materials for different climatic conditions.

Journal of

Cleaner Production, 113,

102–113.

https://doi.org/10.1016/j.jclepro.

2015.11.090

Smith, G. (2015). Simple regression. In G. Smith (Ed.),

Essential statis-

tics, regression, and econometrics

(2nd ed., pp. 219–259). Elsevier.

https://doi.org/10.1016/B978-0-12-803459-0.00008-X

Srinagesh, K. (2006). The principles of experimental research.

The prin-

ciples of experimental research.

Elsevier/Butterworth-Heinemann.

Statistics Denmark. (2021).

Energy account in GJ (summary table) by

supply and use, type of energy and time.

https://www.statbank.dk/

ENE2HO

Sukumaran, S., & Sudhakar, K. (2017). Fully solar powered Raja Bhoj

International Airport: A feasibility study.

Resource-Efficient

Technologies, 3(3),

309–316.

https://doi.org/10.1016/j.reffit.2017.02.

001

Thomas, C., & Hooper, P. (2013). Sustainable development and envir-

onmental capacity of airports. In N. Ashford, H. Stanton, C. Moore,

P. Coutu, & J. Beasley (Eds.),

Airport operations

(3rd ed., pp.

553–578). USA: McGraw- Hill, Inc.

USEIA. (2008).

CBECS table E2 major fuel consumption intensities by

end use for non mall.

https://www.eia.gov/consumption/commercial/

data/2003/pdf/e02.pdf

van Herten, L. M., & Gunning-Schepers, L. J. (2000). Targets as a tool

in health policy.

Health Policy, 53(1),

1–11.

https://doi.org/10.1016/

S0168-8510(00)00081-6

Vanker, S., Enneveer, M., & M€sak, M. (2013). Implementation of

environmentally friendly measures at Tallinn airport.

Aviation,

17(1),

14–21.

https://doi.org/10.3846/16487788.2013.774938

Vartanian, T. P. (2010). Advantages, disadvantages, feasibility, and

appropriateness of using secondary data.

Secondary data analysis

(pp. 13–22). Oxford University Press.

https://doi.org/10.1093/acpro-

f:oso/9780195388817.003.0003

TRU, Alm.del - 2021-22 - Bilag 226: Henvendelse af 26/3-22 vedrørende grøn omstilling af luftfarten, fra Midtjyllands Lufthavn

INTERNATIONAL JOURNAL OF SUSTAINABLE TRANSPORTATION

World Resources Institute. (2004).

A corporate accounting and reporting

standard.

World Resources Institut Publication.

https://ghgprotocol.

org/corporate-standard

World Resources Institute. (2015a).

GHG Protocol tool for mobile com-

bustion. Version 2.6.

https://ghgprotocol.org/calculation-tools#cross_

sector_tools_id

World Resources Institute. (2015b).

GHG Protocol tool for stationary

combustion. Version 4.1.

https://ghgprotocol.org/calculation-tools#-

cross_sector_tools_id

Wu, W., & Little, T. D. (2011). Quantitative research methods.

Encyclopedia

of adolescence.

(pp. 287–297). Elsevier Inc.

https://www.elsevier.com/

books/encyclopedia-of-adolescence/brown/978-0-12-373915-5

Yin, R. K. (2012).

Case study research. Design and methods

(4th ed., pp.

93–95). German Journal of Research in Human.

https://www.jstor.

org/stable/23279888

Zhang, H., Wu, J., & Lu, P. (2011). A study of the radiative forcing

and global warming potentials of hydrofluorocarbons.

Journal of

Quantitative Spectroscopy and Radiative Transfer, 112(2),

220–229.

https://doi.org/10.1016/j.jqsrt.2010.05.012

Zhang, X., Davidson, E. A., Mauzerall, D. L., Searchinger, T. D.,

Dumas, P., & Shen, Y. (2015). Managing nitrogen for sustainable

development.

Nature, 528(7580),

51–59.

https://doi.org/10.1038/

nature15743