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The Key Drivers of Sustainability
H. Daneshpour1, J. Takala1
1Industrial Management Department, University of Vaasa, Vaasa, Finland
Hosein.daneshpour@uva.fi
Abstract -
Nowadays, sustainability is gaining
considerable attention in decision making processes. Since
1987 that the concept of sustainable development was
introduced this field of research has faced with some debates
and continuous improvements in the logic. However,
sustainability is a multi-faceted notion, which has raised the
complexity of sustainable management. Therefore, this
paper strives to manage this issue by the reduction of
dimensions through the application of principal component
analysis. For this purpose, a set of data from Eurostat
database that includes the indicators of sustainability is
evaluated. The results remark the crucial elements of
sustainability such as: innovation, efficiency and renewable
energy.
Keywords - eco-innovation, sustainability complexity
management, efficiency, environmetal management,
renewable energy
I. INTRODUCTION
Decision making towards sustainable development is a
challenge for every country and organization as well.
However, integration of sustainability has made the
strategies more complicated. Generally, sustainable
development is defined based on three elements:
environmental, economic and social. Nonetheless, the
scholarly researches have also determined some other
dimensions for sustainability. Hence, sustainability is a
multi-criteria process that has made the policy making
environment more intricate both at macro and micro level.
For instance, recently project management has reached
into a new paradigm shift through the integration of
sustainability into the project management knowledge
area, and integration of all dimensions of sustainability
specially environmental and social elements is a critical
and challengeable task [1]. Therefore, this paper tries to
answer a main question that what are the most important
elements in orders to achieve sustainability.
Primarily, this research argues that a way to manage
this multi-dimensional case can be the reduction of
dimensionality of sustainability. Therefore, the paper
applies the principal component analysis (PCA) method
for evaluation of sustainability indicators. PCA is a method
aimed at reducing the dimensionality of multivariable data
set, while considering the variations as much as possible to
choose the most effective factors. The data on indicators
consist of sixty nine variables was collected from the
Eurostat database. Eurostat organization provides the
statistical information related to the European countries.
The Eurostat database has determined more than one
hundred indicators for sustainability, such as climate
change and energy, sustainable transport, and good
governance [2].
II. S
USTAINABILITY COMPLEXITY
Traditionally, sustainability is defined based on three
pillars: environmental, social and economic (or people,
planet, and profit). Primarily, in 1968 “the tragedy of the
Commons” warned about the exploitation of natural
resources on earth. Next, the Bruntland report in 1987
defined the term “sustainable development” that raised the
need for considering the future generations [3]. However,
the main question is that: what are the key drivers for
successful realization of sustainability. Although the
economic accept of sustainability has been properly
discussed in the lecture, the lack of attention to successful
implementation of the other dimensions (such as social and
environmental) can be recognized [4]. Consequently, in the
current sustainability approaches, there is a lack of
attention to the stakeholder satisfaction [5].
Nonetheless, sustainability can be measured based on a
variety of tools and techniques such as: product related
assessment tools (e.g. Ecological rucksack or life cycle
assessment), quantitative Indicators (e.g. ecological
footprint or input-output energy analysis) and integrated
assessment tools and approaches (multi-criteria analysis
methods). Meanwhile, sustainability can be simply
interpreted by “doing more with less” or in the other word
efficiency [6]. Efficiency improvement has been
determined as one the most important measures to mitigate
the greenhouse gas emission impact [7].
Furthermore, in the corporate context the concept of
sustainability has been linked to the corporate social
responsibility (CSR) principle. Based on the CSR, the
company has a responsibility not only to the shareholders,
but also towards all stakeholders [8]. However, the
problem arises from the fact that the organizations usually
tend to focus on short-term objectives instead of real and
long term needs of societies. Therefore, Porter and Kramer
criticize the CSR concept and break a new ground by
presenting the shared value creation (SVC) concept. Porter
and Kramer claim that in order to solve these issues the
principle of shared values should be utilized, and It is
defined as “policies and operating practices that enhance
the competitiveness of a company while simultaneously
advancing the economic and social conditions in the
communities in which it operates” [9, p. 6]. Hence,
creating shared value includes creating economic value in
a way that also makes value for society, and it has the
power to unleash global growth.
From the business point of view five dimensions has
been defined for sustainability: business, organizational,
innovation, triple bottom line (TBL), compliance stance.
Therefore, corporate sustainability cannot be successful
unless it makes the core of the “business model” of the
1205978-1-5090-3665-3/16/$31.00 ©2016 IEEE
company and collaborate with external stakeholders to co-
create the values. Also, while the company considers the
TBL, the innovation activities also should meet the
sustainability needs. Furthermore, in addition to the
compliance with the regulations, the supply chain activities
also should be sustainable [10]. Similarly, other scholarly
research also criticizes the traditional definition of
sustainability and determines five dimensions for
sustainability: place (with three dimensions), permanence
and persons [11].
Nevertheless, the connection of innovation and
sustainability is an undeniable fact [12]. Innovation is the
main driver of sustainability, and sustainability can make a
perfect platform for more innovation, as well [13].
Exploring the ranking of the top innovative companies in
the world reveals that the majority of them have
implemented broad sustainability policies and most of
them are acting as the main leaders in sustainability, such
as: IBM and GE. Likewise, the ranking of companies
based on the sustainability indicators presents the names of
the similar companies [14].
III. M
ETHODOLOGY AND DATA COLLECTION
Principal component analysis was introduced in 1901
by Karl Pearson [15], and it is one the most well-known
method for the evaluation of sustainability indicators [16].
Basically, PCA is a statistical method that based on an
orthogonal transformation converts the correlated variables
into a set of linearly uncorrelated variables. Therefore the
number of obtained principal components will be less than
or equal to the number of original variables and the first
component presents the largest possible variance.
This paper applies the PCA method for the reduction of
variables (dimension). PCA is useful for decreasing the
number of variables in a dataset that includes a large
number of variables [17]. For this research R software has
been utilized [18]. PCA in this paper has been done
through the following steps: at first, a correlation matrix
has been made. Then, based on the correlation matrix
eigenvectors and eigenvalues has been calculated. Next,
the eigenvectors are sorted according to the descending
order of the eigenvalues. Finally, top eigenvalues (based
on the cumulative variances) are chosen for the discussion
of the results. Eigenvalues illustrate the variances of PCs.
As presented in the Figure 1, 5 principal components can
cover the more that 73% of total variances.
Fig 1. Cumulative variances of Eigenvalues
In addition, the Kaiser-Meyer-Olkin (KMO) test was
conducted that is a measure of sampling adequacy, and the
value of 0.5 was recorded. The literature approves that the
value above 0.5 indicate that sample size is enough.
Basically, the KMO value can be between 0 and 1, while
the value 0 show that PCA can lead to inappropriate
results, and 1 shows that the PCA can be reliable [19].
Data collection has been from Eurostat database and
from “sustainable development indicators” category, and
sixty-nine variables are chosen for the analysis (in 2013
and with 25 observations for each variable). Sustainable
Development indicators have been divided into nine main
categories and several subcategories:
• Socioeconomic development (subcategories e.g.:
GDP, eco-efficiency, employment)
• Sustainable consumption and
production(subcategories e.g.: consumption and
production patterns, waste)
• Social inclusion (subcategories e.g.: poverty,
education)
• Demographic changes (subcategories e.g.: old age
income, public finance sustainability)
• Public health (subcategories e.g.: life expectancy,
determents of health)
• Climate change and energy (subcategories e.g.:
greenhouse gas emissions, Primary energy
consumption)
• Sustainable transport (subcategories e.g.: transport
impacts, mobility )
• Natural resources (subcategories e.g.: land use,
biodiversity )
• Global partnership (subcategories e.g.:
globalization of trades )
• Good governance (subcategories e.g.: policy
coherence, openness)
PC
1
PC
2
PC
3
PC
4
PC
5
PC
6
PC
7
PC
8
Cumulative
variance 29% 49% 60% 67% 73% 76% 80% 82%
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Cumulative variance
Proceedings of the 2016 IEEE IEEM
1206
IV. R
ESULTS
In this research because of the large number of
variables, 5 principal components have been selected for
the interpretation. Meanwhile, the cut off point for further
discussion is five top factors for each PC. Table I presents
a ranking of the loadings (top 5).
Principal component 1:
The first PC underpins the findings that highlighted in
the literature review section. Therefore, eco-innovation
and resource and labored productivity (efficiency) by 19%
and 18%, respectively are among the top loadings.
Furthermore, the stakeholder satisfaction (disposable
income of households) and economic sustainability (GDP)
elements are also involved by 20% and 19%, respectively.
Principal component 2:
The second PC is more an indicator of stakeholder
concern, and the factor loadings are 18-19% for: young
people, neither in employment nor in education and
training, total employment rate, inequality of income
distribution, in work at-risk-of-poverty rate and early
leavers from education and training.
Principal component 3:
The third PC remarks the role of renewables;
greenhouse gas emissions (24%) and average carbon
dioxide emissions per km from new passenger cars (23%)
are among the top loadings. In addition, the economic and
stakeholder loadings are also involved: general
government gross debt (24%), investment by institutional
sectors (24%), and people living in households with very
low work intensity (21%).
Principal component 4:
This PC reveals an interesting aspect of sustainability
that is the importance of the role of renewable energy.
Therefore, the shares of renewable energy in gross final
energy consumption by 37%, and electricity generated
from renewable sources by 36% are the most important
loadings. Then, stakeholder perspective makes the other
loadings (old-age-dependency ratio, employment rate by
educational attainment level, relative median at-risk-of-
poverty gap).
Principal component 5:
This PC highlights the environmental factors including:
energy consumption of transport relative to GDP by 36%,
greenhouse gas emissions intensity of energy consumption
30% and then the social impacts (employment rate of older
workers 27%, aggregate replacement ratio 34%, and
tertiary educational 30%).
V.
C
ONCLUSION
This research underpins the concepts and
understandings behind the sustainability logic, through a
quantitative assessment (PCA). This paper argues that
instead of the traditional concept of sustainable
development pillars (environmental, social and economic),
some critical factors can be implemented that facilitate
practical implication of sustainability, and with a more
strategic approach. Therefore, the main focus should be on
crucial elements such as renewable energy, stakeholder
(social) satisfaction, efficiency improvement and
innovation. This paper does not evaluate the economic
reason behind the factors, and more is focused on other
dimensions such as the social and environmental factors.
In addition, the availability of data (observations) has been
a limitation in this research. For future research it is
recommended to fulfill the research in the organizational
or company level.
Proceedings of the 2016 IEEE IEEM
1207
R
EFERENCES
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TABLE I. COMPONENT LOADINGS VALUES (TOP 5)
PC1 % PC2 % PC3 % PC4 % PC5 %
Real adjusted gross
disposable income
of households per
capita
20 In work at-risk-of-
poverty rate
19
Greenhouse gas
emissions
24
Share of renewable
energy in gross
final energy
consumption 37
Energy consumption
of transport relative to
GDP
36
Labor productivity
per hour worked
η
19 Total employment
rate
18
General
government
gross debt
24
Electricity
generated from
renewable sources 36 Aggregate
replacement ratio
34
Eco-innovation
index
19
Young people
neither in
employment nor in
education and
training (15-24
years) - % of the
total population in
the same age
group
18
Investment by
institutional
sectors
24 Old-age-
dependency ratio
31
Tertiary educational
attainment by sex, age
group 30-34
30
Real GDP per
capita, growth rate
and totals
19 Inequality of income
distribution
18
Average carbon
dioxide
emissions per km
from new
passenger cars 23
Relative median at-
risk-of-poverty
gap
24
Greenhouse gas
emissions intensity of
energy consumption
30
Resource
productivity
η
18
Early leavers from
education and
training
18
People living in
households with
very low work
intensity
21
Employment rate by
educational
attainment level
20 Employment rate of
older workers
27
: Stakeholder; : Green energy ; η : efficiency ; : innovation
Proceedings of the 2016 IEEE IEEM
1208
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Proceedings of the 2016 IEEE IEEM
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