A New Method For The Quantitative Assessment of Sustainable Development Goals (SDGS) and A Case Study On Central Asia

sustainability
Article
A New Method for the Quantitative Assessment of
Sustainable Development Goals (SDGs) and a Case
Study on Central Asia
Yizhong Huan 1,2 , Haitao Li 1, * and Tao Liang 1,2
1 Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural
Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2 University of Chinese Academy of Sciences, Beijing 10049, China
* Correspondence: liht@igsnrr.ac.cn; Tel.: +86-10-64888996

Received: 5 May 2019; Accepted: 23 June 2019; Published: 26 June 2019
Abstract: Regarding the sustainable development goals (SDGs) formulated by the United Nations
(UN), how to effectively measure, assess and compare the progress and trends of these SDGs in
different countries was the problem we wanted to address. Based on past quantitative assessments,
this paper proposed a new methodological framework for SDG assessment and analysis, and used two
typical Central Asian countries, Kazakhstan and Kyrgyzstan, as the sample area to test the framework.
Our study chose 209 indicators and indicator sets, including 429 specific indicators and collected
relevant indicator data for the two countries from 2000 to 2017, then proposed a new direction for the
unification of indicator data as well as methods for normalization. Afterward, the scores of each goal
and SDG performance were calculated. This analysis was also done innovatively using the Chow
Test to conduct further analysis of the SDG performance. According to the assessment, over those
18 years, Kyrgyzstan’s SDGs had been performing poorly, especially the economic SDGs, while the
performance of Kazakhstan’s SDGs had remained in constant fluctuation. It could be said that the
SDG performance in Central Asia as a whole was not very optimistic. It required the devotion of
greater efforts in the gathering of different types of indicator data because there were still gaps in
data collection between countries as well as the missing of time-series data, which could challenge
the indicator selection and further restrict the follow-up assessment and analysis. The assessment
framework presented in this paper can be applied for assessing the long-term performance of national
SDGs of different countries, helping analyze the internal relationship dynamic among and within
countries, underscoring specific issues of sustainable development, assessing policy and selecting
development models and directions.
Keywords: sustainable development goals; sustainable development; sustainability; sustainable

indicators; SDGs assessment; SDGs performance; Kazakhstan; Kyrgyzstan
1. Introduction
Over the past 20 years, there has been a substantial increase in methods and indices for measuring
sustainable development. Many scholars and research institutions have been adopting a series of
sustainable development indicators and composite indices in different countries and regions to track
the progress made in terms of sustainable development [1–6]. From 2015 to 2018, the United Nations
(UN) published, implemented, and improved the global SDG framework, which currently includes
244 indicators corresponding to 17 goals and 169 targets reinforcing the growing importance of these
indicators [7]. Although SDGs are complex and wide-ranging, the coordination of viewpoints and
explanations of sustainable development and reaching international agreement over a set of SDIs
Sustainability 2019, 11, 3504; doi:10.3390/su11133504 www.mdpi.com/journal/sustainability

Sustainability 2019, 11, 3504 2 of 27
represent a critical initial step [8]. The comprehensive indicator framework can transform SDGs and
their targets into a management tool and help countries formulate and implement strategies and
distribute resources accordingly, as well as provide the basis for research reports on evaluating the
progress of sustainable development [9].
Indicator-based assessment is the process in which information on indicators is interpreted and
synthesized to assess the progress of sustainable development and the report of assessment serves
as a means by which to provide policy-makers, the public, and relevant parties of interest with clear
information [10]. Indicator-based assessments and sustainable development reports can use a range of
different approaches or quantitative methods (such as technique for order preference by similarity
to an ideal solution [11], analytic hierarchy process [12], data environment analysis [13]) to assess
progress on agreed targets or goals of sustainable development, report development trends, and
present and communicate outcomes [10]. The approaches and methods applied depend on many
factors, including the size of the used indicator framework, the availability of indicators’ datasets, and
the assessments’ audience and their needs. The use of easy-to-interpret symbols has become a main
feature of such reports to improve communication [8]. Therefore, recent indicator-based assessments
and reports of sustainable development can provide an SDG assessment analysis report with useful
case studies, such as providing ways for selecting the indicators or a range of different approaches
or quantitative methods like the study of Zhang [12] and Guo et al. [13]. Based on those prevenient
research and assessments of sustainable development, countries and organizations around the world
have conducted a series of initial indicator-based assessments of SDGs at different scales, including
assessments of baselines, trend analysis, and benchmarking of progress since 2016 [7,14–31], though
there were also weaknesses and problems of these SDG assessments [32]. Among those assessments,
Kroll [15] completed the world’s first inter-country SDG current condition assessment and composite
SDG index, which included 34 countries after selecting 34 indicators corresponding to 17 SDGs, made
the relative calculation method, and provided following studies with a brand-new starting point.
Furthermore, Nejdawi et al. [16] selected 56 indicators corresponding to 17 SDGs from 22 countries
within the four divisions of the Arab region and established a corresponding computation method.
It was the first study that employed graphic visualization to illustrate current SDG performance and
development trends over the past 20 years for each individual indicator in the entire region. They also
used an embedded integrated framework to analyze the interconnection and dynamic change of
SDGs in the area. Sachs et al. [19] introduced even more indicators, basing their analysis on the
study of Kroll [15] and including 77 indicators corresponding to 17 SDGs. The study of Sachs et
al. [19] eventually analyzed and assessed the SDGs performance and the ranking of SDGs progress in
149 countries in that year. Based on the study of Sachs et al. [19], Clark et al. [21] selected 35 indicators
and a corresponding computation method to specifically assess the SDG performance and ranking
of 15 EU countries from 2000 to 2014. Fullman et al. [22], instead of targeting the performance of all
SDGs, focused on measuring 37 indicators out of a total of 50 health-related indicators from 1990 to
2019. The study of Fullman et al. [22] included the performance and ranking of health-related SDGs in
188 countries in 2016 and predicted the condition of health-related indicators in those countries by the
year 2030. Based on two previous studies, Sachs et al. [19] and Sachs et al. [25], in 2018 Sachs et al. [30]
selected 88 indicators corresponding to 17 SDGs to analyze and assess SDG performance and ranking
in 156 countries in that year. It was the first study that employed historical trend data and estimated
the speed at which a country realizes SDGs and whether or not such speed could help that country
accomplish their SDGs by 2030.
Among the 244 indicators included in the SDG framework published by the UN, 9 indicators
belong to two or more goals, thus making the total number of indicators actually 23 [33]. Of those,
some indicators are an indicator set, which can include many different time-series datasets of different
types, e.g., sex, age, or location [34]. In comparison, the selected indicators in the previous research
reports are not only limited in number but also lack specific types for each indicator, resulting in failure
to completely assess the 17 SDGs. In addition, research rarely concentrates on developing countries
located in the Central and South Asian regions that are stressed in “the Belt and Road Initiative” (BRI).
The BRI aims to promote the orderly and free flow of economic factors, the efficient allocation of
resources and the deep integration of markets. Besides, it aims to promote the coordination of economic
policies among countries and carries out national cooperation on a larger scale, higher standard,
and deeper level to jointly create an open, inclusive and balanced regional economic cooperation
framework [35]. The BRI is committed to establishing and strengthening the partnership of countries,
building a comprehensive, multi-level and complex interconnection network to achieve diversified,
independent, balanced and sustainable development of countries [36]. In terms of the final vision, the
BRI is consistent with the SDGs.
In this context, this paper strived to be comprehensive in terms of constructing an indicator
framework. It eventually used 209 indicators as well as indicator sets that include 429 specific indicators
corresponding to 17 SDGs in three dimensions, i.e., economy, society, and environment. Specifically,
economic dimension included 141 specific indicators corresponding to SDG 8 (promote sustained,
inclusive and sustainable economic growth, full and productive employment and decent work for
all), SDG 9 (build resilient infrastructure, promote inclusive and sustainable industrialization and
foster innovation), SDG 10 (reduce inequality within and among countries) and SDG 17 (strengthen
the means of implementation and revitalize the global partnership for sustainable development);
the social dimension included 220 specific indicators corresponding to SDG 1 (end poverty in all its
forms everywhere), SDG 2 (end hunger, achieve food security and improved nutrition and promote
sustainable agriculture), SDG 3 (ensure healthy lives and promote well-being for all at all ages), SDG 4
(ensure inclusive and equitable quality education and promote lifelong opportunities for all), SDG 5
(achieve gender equality and empower all women and girls) and SDG 16 (promote peaceful and
inclusive societies for sustainable development, provide access to justice for all and build effective,
accountable and inclusive institutions at all levels); the environmental dimension included 220 specific
indicators corresponding to SDG 6 (ensure availability and sustainable management of water and
sanitation for all), SDG 7 (ensure access to affordable, reliable, sustainable and modern energy for
all), SDG 11 (make cities and human settlements inclusive, safe, resilient and sustainable), SDG 12
(ensure sustainable consumption and production patterns), SDG 13 (take urgent action to combat
climate change and its impacts) and SDG 15 (protract, restore and promote sustainable use of terrestrial
ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation
and halt biodiversity loss). It also proposed a relatively new computation method based on past case
studies to not only analyze the annual progress of economic, social, environmental and the overall
SDGs from 2000 to 2017 of two typical countries Kazakhstan and Kyrgyzstan in Central Asia, but also
conducted a comparative analysis over the important breakpoints of three-dimensional SDG trends in
performance over those 18 years and causes of the breakpoints. In particular, this paper is the first to
combine the concept of using the Chow test to verify the breakpoint and aid the analysis of the SDG
trend in performance over those 18 years. It has the potential to assist the governmental review of the
trends and changes in SDG performance as well as indicate possible causes of these changes, cope
with future catastrophes that could have an impact on the SDG performance in those countries, and
formulate relevant policy for sustainable development. Moreover, the research framework constructed
in this paper can be applied for the analysis of SDGs of different nations around the world, further
helps scientifically understand the development coupling mechanism and its impact path of the
national society, economy and environment systems included within the BRI. This study could thereby
lay the groundwork for coordinated planning on the strategic decision-making level to support
the construction under the BRI by different countries and realize the win-win cooperation among
the nations.
In the following sections, we firstly described an overview of the sample area, and we secondly
constructed a detailed methodological framework including five steps. Thirdly, we presented the
results of the assessment and the analysis of results and discussed the advantages and challenges of
the assessment framework. Finally, we presented the conclusions of the paper.
2. Materials and Methods

Based on the two research areas, Kazakhstan and Kyrgyzstan, we first constructed the
methodological and indicators framework of this paper with time-series databases of the SDGs
for the two countries. Second, we considered an indicator as a forward indicator if the SDG indicator
represents the meaning that the higher the value, the better the sustainable development, and we
considered an indicator as an inverse indicator if the SDG indicator represents the meaning that the
smaller the value, the better the sustainable development. After differentiating the forward and inverse
indicators contained within the indicator framework this paper used, we unified the direction of the
indicator data. Third, we normalized all of the indicator data, and fourth, we obtained the score of
each goal by calculating the average value. Fifth, we obtained the score for the economic, societal
and environmental SDGs, as well as the total score of the SDGs of these two countries. Finally, we
conducted a breakpoint test based on the scores of the SDGs for the society, economy, and environment.
2.1. An Overview of the Sample Area

Central Asia (Figure 1) covers the area from the western Caspian Sea to eastern China and from
northern Russia to southern Afghanistan. This area comprises five main countries: Kazakhstan,
Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan, with a total area of 4,003,451 km2 and
a population of about 105 million [37]. Table 1 shows the basic characteristics of Kazakhstan
and Kyrgyzstan.
With an area of 2,724,900 km2 and a population of around 17.4 million people [38], Kazakhstan is
the ninth largest country in the world by area and the largest landlocked country in the world [39].
Kazakhstan is a transcontinental country largely located in Asia, with a small area located in the
western Urals. Its terrain includes the Caspian Sea to the west, the Altay Mountains to the east, the
plains of Western Siberia to the north, and the oases and deserts of Central Asia to the south, with
only 3% of its territory being a wooded area. Its climate type is continental climate, with warm to hot
(mostly humid) summers and cold (sometimes extremely cold) winters. In 1991, the dissolution of
the Soviet Union occurred and Kazakhstan became independent. Since then, Kazakhstan has been
a unitary republic with Nursultan Nazarbayev as the only President to date. The politic system is
composed of the Majilis (the lower house) and the Senate (the upper house). Kazakhstan has the
largest and strongest performing economy in Central Asia. In 2018, the GDP of Kazakhstan was $195
billion and its GDP per capita was $10,447 [40]. Its economy has been beneficial from its large amounts
of natural resources (such as oil, gas, and uranium), heavy industry (ferrous and non-ferrous metals),
and agricultural products. Wedged between the two powerful countries of Russia and China, the
geopolitics of Kazakhstan are vital. It is also a potential gateway to the Caspian Sea and on to Europe,
which has been the most significant partner country of China’s BRI [41].
The Kyrgyzstan Republic is a landlocked country in the southeast corner of Central Asia with
199,900 km2 of land and a population of 5.62 million [37]. It borders north to west and southwest from
Kazakhstan to Uzbekistan, and southwest to east from Tajikistan to China. With mountains covering
80% of the country, Kyrgyzstan has few plains and the altitudes of most areas are between 500 and 1000
meters. Kyrgyzstan has a temperate continental climate with moderate precipitation, but in recent years
the amount of precipitation has been decreasing [42]. Kyrgyzstan does not have significant deposits of
oil or natural gas; however, it has some mineral resources such as gold, which is essential in this state’s
development plan [43]. Kyrgyzstan declared its independence in 1991 after the dissolution of the
Soviet Union. According to the 1993 constitution, the form of government is a unitary parliamentary
republic, however, in practice, the country was more like a presidential republic with a strong president
and weak parliament, making it a nominal parliamentary republic. The 2010 constitution allowed
Kyrgyzstan to remain a unitary parliamentary republic but with a stronger parliament, rendering it a
true parliamentary republic. Kyrgyzstan’s economy was heavily influenced by the dissolution of the
Soviet Union resulting in a huge loss in its market. It is the second poorest country in Central Asia,
with a total GDP of $24.356 billion and $3,812 per capita [44]. Kyrgyzstan’s economy is supported by
conducted a breakpoint test based on the scores of the SDGs for the society, economy, and
environment.
2.1. An Overview of the Sample Area

Central Asia
Sustainability 2019,(Figure
1) covers the area from the western Caspian Sea to eastern China 5and
11, 3504 of 27from
northern Russia to southern Afghanistan. This area comprises five main countries: Kazakhstan,
Kyrgyzstan, Tajikistan,
the industries Turkmenistan,
of agriculture andofUzbekistan,
and the raising with a total
livestock. Kyrgyzstan areainofthe4,003,451
is located km2 and a
center of Central
population
Asia andof about
serves as a105 million
“water [37].
tower” andTable 1 shows
gateway the Asia,
for Central basicmaking
characteristics of Kazakhstan
its geopolitics both unique and
Kyrgyzstan.
and vital.
(a)
(b)
Figure 1. The location of the sample area of the research. (a) The location of Central Asia on the global
map. Note: the shaded area is Central Asia; (b) The map of Central Asia and the sample area of the
research, i.e., Kazakhstan and Kyrgyzstan. Note: the shaded area is Kazakhstan and Kyrgyzstan.
Table 1. Basic characteristics of Kazakhstan and Kyrgyzstan.
Independent Area Population 2018 GDP

Country Climate Type
Year (km2 ) (million) ($ billion)
Kazakhstan 1991 2,724,900 17.4 Continental 195
Temperate
Kyrgyzstan 1991 199,900 5.62 24.356
Continental
2.2. Constructing an SDG Database

The selection of indicators and construction of the database consisted primarily of the following
four standards. First, the 2030 Agenda for Sustainable Development [45] and the SDGs are high
on the agenda for most countries around the world. In 2015, the Inter-Agency Expert Group on
Sustainable Development Goals Indicators (IAEG-SDGs) held the first meeting, and it was tasked
with designing indicators and methods corresponding to SDGs at the global level and disseminating
them to countries that could be used to collect data describing the global progress towards sustainable
development. The IAEG-SDGs initially developed 231 indicators and classified them into 3 tiers (Tier
I, Tier II and Tier III) based on statistical methods and the ease of data acquisition [46,47]. However,
due to the need for a large amount of data support to establish a complete indicators’ framework, this
work continued until 2017, including: establishing and improving the indicators’ tiers, developing a
process for reviewing related methods of indicators, establishing the approval mechanism for necessary
amendments of indicators in future, reviewing the situation of data acquisition on the indicators in Tier
I and Tier II, planning to expand the coverage of the indicators’ data in Tier II, and discussing the use of
multi-purpose indicators [47]. In 2017, the United Nations Statistical Commission eventually adopted
the global indicators’ framework (comprised of 232 indicators) for measuring the UN Agenda 2030
for Sustainable Development [45] and 17 SDGs and their 169 targets, and the UN General Assembly
subsequently endorsed the framework [33]. Therefore, it can be seen that this is a global indicator
framework that has been jointly researched, established and improved by plenty of global organizations
and experts for a long time. This indicator framework can be very instructive for the selection of
indicators for assessing SDGs. For example, when researchers want to develop adequate indicators for
assessing SDG 16 (promote peaceful and inclusive societies for sustainable development, provide access
to justice for all and build effective, accountable and inclusive institutions at all levels), it is a basic
requirement to have deep and ample knowledge and understanding of the phenomena or situation of
violence, crime, justice, peace and government. Without it, the indicators developed will certainly
decrease and reorient the content of the goal [48]. Thus, as few individuals or organizations can master
all the challenging content proposed by 17 SDGs, this indicator framework proposed by IAEG-SDGs
may be the most appropriate SDG indicator framework for reference and use by researchers. It is
authoritative and can be reductionist of the phenomena they measure (some researchers regard it as
a minimalist expression of the 2030 Agenda for Sustainable Development [45,48]), although it may
represent a huge statistical challenge and some indicators may not be accurately quantified [49,50].
Therefore, to cover all 17 SDGs, based on this authoritative indicator framework and the situation of
two countries’ data collection in the UN SDGs database, we selected as many indicators as possible
with data from 2000 to 2017 [51]. Second, of the 232 indicators proposed by the UN, some indicators
such as 8.1.1 (annual growth rate of real GDP per capita (%)) are independent indicators without
any classification (indicator 123 in Table 2), while others are actually a collection of indicators, which
contain a large number of time series data of different and specific categories such as gender, age, or
location. For example, in the UN SDGs’ framework, indicator 4.2.2 (participation rate in organized
learning (one year before the official primary entry age), by sex (%)) can be actually classified as three
specific indicators according to male, female, and all gender (both sexes), and these three specific
indicators all belong to the set of indicator 4.2.2. Thus, this paper divided the indicator set 87 into 3
specific indicators shown in Table 2. Similarly, for this kind of indicator set, we collected different
time series data according to their classification as far as possible and regarded them as different
independent specific indicators (Table S1 and S2). Third, the SDG framework proposed by the UN
is from a global perspective; however, due to differences in terms of the sustainable development
issues faced by different countries, each country has its own priorities in the acquisition of data in
terms of its type and quality. For example, Kazakhstan is located next to the Aral Sea (Figure 1b),
which is vital for Kazakhstan’s economic, social, and environmental sustainable development. Thus,
Kazakhstan has been paying attention to SDG 14 (conserve and sustainably use the oceans, seas and
marine resources for sustainable development) and collecting the related data. As for Kyrgyzstan,
although it has some important lakes and rivers such as Issyk lake, it did not have full priorities
in the collection of SDG 14-related data. Thus, indicators with very limited data available in the
UN SDG database were excluded from the indicator framework in this paper. After comparing the
databases for the two countries (Table S1 and S2), only identical indicators in both databases were
retained. Indicators (or indicator sets) for which only one country’s database had data while the
other country’s database did not, were deleted. For example, for SDG 14, there were only time series
data collected for target 14.5 (by 2020, conserve at least 10% of coastal and marine areas, consistent
with national and international law and based on the best available scientific information) in the
Kazakhstan database (Table S2), while in the Kyrgyzstan database (Table S1), there were no time
series data for indicators of goal 14 at all. Thus, this paper did not carry out statistical analysis on
the performance of SDG 14 (conserve and sustainably use the oceans, seas and marine resources
for sustainable development) in two countries, and we removed 147 specific indicators from 729
specific indicators of the Kyrgyzstan indicator framework (Table S1), and 214 specific indicators from
795 specific indicators of the Kazakhstan indicator framework (Table S2). Both countries’ indicator
frameworks eventually had the same 581 specific indicators (Table S3). However, since five indicators
or indicator sets repeat under two or three different targets (listed in Table S3), the actual total number
of indicators and indicator sets in the list is 209, including 429 specific indicators. Fourth, due to the
different frequencies of measurement for each of the indicators among the countries, there were obvious
gaps and deficiencies in data collection, and different methods of interpolating missing data will result
in different emphasis and errors, which can easily result in very different outcomes. Therefore, this
paper did not interpolate missing data, and only focused on available data. Finally, 209 indicators and
indicator sets were used in the indicator framework, including 429 specific indicators, corresponding
to 16 SDGs, except goal 14 from 17 SDGs (Table S3).
Table 2. The part of the complete indicators framework used in this paper.
Dimension of the Indicator or

SDG SDG Target Tier Classification Specific Indicator Indicator’s Direction
SDG Indicator Set
1.a: Ensure significant mobilization
of resources from a variety of
sources, including through enhanced
Proportion of total government
1: End poverty in all development cooperation, in order
Society 37 II spending on essential services, Forward
its forms everywhere to provide adequate and predictable
education (%)
means for developing countries, to
implement programmes and policies
to end poverty in all its dimensions
Participation rate in organized
4: Ensure inclusive 4.2: By 2030, ensure that all girls and learning (one year before the official
and equitable quality Society boys have access to quality early 87 I primary entry age), both sex (%) Forward
education and childhood development, care and
promote lifelong pre-primary education so that they
learning (one year before the official
opportunities for all are ready for primary education
primary entry age), female (%)
learning (one year before the official
primary entry age), male (%)
8: Promote sustained,
8.1: Sustain per capita economic
inclusive and
growth in accordance with national
sustainable economic
circumstances and, in particular, at Annual growth rate of real GDP per
growth, full and Economy 123 I Forward
least 7 per cent gross domestic capita (%)
productive
product growth per annum in the
employment and
least developed countries
decent work for all
Annual mean levels of fine particulate
11: Make cities and 11.6: By 2030, reduce the adverse per matter in cities, all area’s population
human settlements Environment capita environmental impact of cities, 189 I Inverse
(micrograms per cubic metre)
inclusive, safe, including by paying special attention
Annual mean levels of fine particulate
resilient and to air quality and municipal and
matter in cities, urban population
sustainable other waste management
(micrograms per cubic metre
Note: This table chose 4 indicators and indicators sets (37, 87, 123, 189) including 7 specific indicators as examples to show the complete indicators framework this paper used (Table S3).
The serial numbers of the indicators or indicator sets in this table (37, 87, 123, 189) are the same as the serial numbers of the indicators or indicator sets in the complete indicators framework
this paper used (Table S3). Tier I, meaning that the indicator is conceptually clear and has internationally established methodology and standards, and data are regularly compiled for at
least 50 percent of countries; Tier II, meaning the indicator is conceptually clear and has internationally established methodology and standards, but the data are not regularly produced by
countries; Tier III, meaning that no internationally established methodology or standards are yet available for the indicator [52].
2.3. The Differentiation of Forward and Inverse Indicators

Sustainability indicators can use greater or smaller values to correspondingly explain the situation
where the indicator reaches or nears the ideal condition [53]. This paper used an indicator’s direction
to describe such an attribute of an indicator, which has been described by different terms in existing
literature, such as “direct correlation with utility” or “inverse correlation with utility” [54], “positive
impact” or “negative impact” [55], and “criteria is to maximize” or “criteria is to minimize” [56]. In
this paper, an indicator was considered as a forward indicator if the SDG indicator represented the
meaning that the higher the value, the better the sustainable development, e.g., indicator 37: proportion
of total government spending on essential educational services (%) shown in Table 2. The higher
the government’s spending on essential educational services, the greater the value of the indicator
37 data, the better the sustainable development of the country. An indicator would be considered as an
inverse indicator if the SDG indicator represented the meaning that the smaller the value, the better
the sustainable development, e.g., indicator set 189, including two specific indicators (annual mean
levels of fine particulate matter in cities, all area’s population (micrograms per cubic meter); annual
mean levels of fine particulate matter in cities, urban population (micrograms per cubic meter)) shown
in Table 2. The lower the content of fine particulate matter, the smaller the value of the indicator set
189 data, the better the sustainable development of the country.
Based on the type of data value, the specific methods for turning an inverse indicator into a
forward indicator in this paper can be categorized into the following:
1. If there is a forward indicator set A = {d1 , d2 ,· · · ,dn }, i = 1~n, di is a real number, and all di >= 0 or
all di < 0, then the processed element ei = di , the set E = {e1 , e2 , · · · , en }, ei is a real number, where
A and E are mapped to each other;
2. If there is a forward indicator set A = {d1 ,d2 ,· · · ,dn }, i = 1~n, di is a real number, and the number
of i elements with di >= 0 and di < 0 is not less than 1, take dmin = min{A}, the processed element
ei = di −dmin , its set E = {e1 ,e2 ,· · · ,en }, ei is a real number, where A and E are mapped to each other;
3. If there is an inverse indicator set B = {d1 ,d2 ,· · · ,dn }, i = 1~n, di is a real number, and all di >= 0,
take dmax = max{B}, and the processed element ei = |di −dmax |, its set E = {e1 , e2 , · · · , en }, ei is a
real number, where B, E are mapped to each other;
4. If there is an inverse indicator set B = {d1 ,d2 ,· · · ,dn }, i = 1~n, di is a real number, and all di <= 0,
the processed element ei = |di |, its set E = {e1 , e2 , · · · , en }, ei is a real number, where B and E are
mapped to each other;
5. If there is an inverse indicator set B = {d1 ,d2 ,· · · ,dn }, i = 1~n, di is a real number, and the number
of i elements with di >= 0 and di < 0 is not less than 1, take dmax = Max{B}, the processed element
ei = |di −dmax |, its set E = {e1 , e2 , · · · , en }, ei is a real number, where B, E are mapped to each other.
2.4. The Normalization of Indicator Data

In sustainability assessment, normalization is the conversion of the original unit of measure to the
common unit of measure to conduct a comparison or include it in the calculation of the sustainability
score. This process is also referred to as unit scaling or standardization, in which the term name
can vary depending on the subject and the function usage [53]. When indicator units are different,
normalization is considered an important step before aggregation. The use of different methods for
normalization and aggregation can yield extremely different composite sustainability scores [3,5].
The normalization method adopted in this paper is as follows:
Suppose there are two countries’ original databases, i.e., A = {a1 , a2 , . . . , an }, B = {b1 ,b2 , . . . , bn },
i = 1~n, where ai = (a_caption,a_value), bi = (b_caption, b_value), a_caption and b_caption are the
ordered identification groups of elements ai and bi , a_value and b_value are the value vector groups of
the elements ai and bi , respectively; if there are k values, j satisfyies: ak : a_caption = bj : b_caption, then
take PMax = max{ak :a_value, bj :b_value}, let the element ap = (a_caption, ap_value), bp = (b_caption,
bp_value), then ap_value = a_value/PMax , bp_value = b_value/PMax , finally, after normalizing the two
countries’ databases, we obtain the data set AP = {ap1 , ap2 , . . . , apn }, BP = {bp1 , bp2 , . . . ,bpn }.
2.5. Aggregation
After normalization, the performance of each SDG in the two countries Kazakhstan and Kyrgyzstan
(named country A and B in this section) can be reflected through calculating the arithmetic mean of
each SDG and regarding the mean value as the score of each goal. The method is as follows:
For the first country A, suppose there is a Goal i (1 ≤ i ≤ 17), Goal i has j (j ≥ 1) indicators, and
make AkGoal i (p) represent the normalized data of indicator p (1 ≤ p ≤ j) of Goal i in year k (k = 2000,
2001, . . . , 2017), then the score of Goal i in year k is AkGoal i ,
AkGoal i (1) + AkGoal i (2) + · · · + AkGoal i ( j)

AkGoal i = , i = 1, 2, · · ·, 17; k = 2000, 2001, · · · , 2017
j
After calculating the scores of Goal i from 2000 to 2017 in turn, we get A2000 Goal i ,
2001 2017
AGoal i , · · · , AGoal i (i = 1, 2, . . . , 17).
For the other country B, after using the same steps, we get the scores of Goal i from 2000–2017 as
B2000 2001 2017
Goal i , BGoal i , · · · , BGoal i i = 1, 2, . . . , 17 .
( )
After calculating the score of each goal, the constructed 17 SDGs need to be weighted and
aggregated to obtain the annual total score of SDGs to measure the overall SDG performance of the two
countries in different years. In the SDG assessment, different weights for each goal could generate a
major impact on the SDG performance results for the countries. Just like other composite indices do not
have a consensual answer on the weighting problem, different research groups also failed to reach any
agreement over the weighting distribution in terms of SDGs [25,57]. As few individuals or organizations
can master all the challenging content proposed by SDG, flexible weighting might encourage countries
to perform easy goals and overlook goals that are equally important and demand further in-depth
transformation. Aggregation for SDGs proceeds in two steps in this paper. First, based on the UN’s
interpretation of SDGs and other relevant studies referred to SDGs classification [21,27–29,58–60], the
17 SDGs were aggregated into three general dimensions, i.e., economy, including SDG 8 (promote
sustained, inclusive and sustainable economic growth, full and productive employment and decent
work for all), SDG 9 (build resilient infrastructure, promote inclusive and sustainable industrialization
and foster innovation), SDG 10 (reduce inequality within and among countries) and SDG 17 (strengthen
the means of implementation and revitalize the global partnership for sustainable development); society,
including SDG 1 (end poverty in all its forms everywhere), SDG 2 (end hunger, achieve food security
and improved nutrition and promote sustainable agriculture), SDG 3 (ensure healthy lives and promote
well-being for all at all ages), SDG 4 (ensure inclusive and equitable quality education and promote
lifelong opportunities for all), SDG 5 (achieve gender equality and empower all women and girls)
and SDG 16 (promote peaceful and inclusive societies for sustainable development, provide access to
justice for all and build effective, accountable and inclusive institutions at all levels); and environment,
including SDG 6 (ensure availability and sustainable management of water and sanitation for all),
SDG 7 (ensure access to affordable, reliable, sustainable and modern energy for all), SDG 11 (make
cities and human settlements inclusive, safe, resilient and sustainable), SDG 12 (ensure sustainable
consumption and production patterns), SDG 13 (take urgent action to combat climate change and its
impacts), SDG 14 (conserve and sustainably use the oceans, seas and marine resources for sustainable
development) and SDG 15 (protract, restore and promote sustainable use of terrestrial ecosystems,
sustainably manage forests, combat desertification, and halt and reverse land degradation and halt
biodiversity loss). Because the three dimensions are clearly interdependent and interconnected, it
could therefore be argued that some social SDGs should be categorized into the dimensions of economy
and environment, and vice versa.
To reflect the economic, social, and environmental sustainable development of the two countries
we first summed up the scores of all the different annual goals in all three dimensions for each country
in every year and obtained the SDG scores in terms of the three dimensions of the two countries in
different years. Specific methods are as follows:
For country A, we let Aksocial SDGs represent the score of the social SDGs in year k (k = 2000,
2001, · · · , 2017), Akeconomic SDGs represents the score of the social SDGs in year k (k = 2000, 2001,
· · · , 2017), and Akenvironmental SDGs represents the score of the social SDGs in year k (k = 2000, 2001,
· · · , 2017), then we obtain Aksocial SDGs = AkGoal 1 + AkGoal 2 + AkGoal 3 + AkGoal 4 + AkGoal 5 + AkGoal 16 ,
(k = 2000, 2001, · · · , 2017), Akeconomic SDGs = AkGoal 8 + AkGoal 9 + AkGoal 10 + AkGoal 17 , (k = 2000, 2001, · · · ,
2017), and Akenvironmental SDGs = AkGoal 6 + AkGoal 7 + AkGoal 11 + AkGoal 12 + AkGoal 13 + AkGoal 14 + AkGoal 15 ,
(k = 2000, 2001, · · · , 2017). For the other country, B, after using the same steps, we obtain the scores
of three-dimensional SDGs from 2000 to 2017 as Bksocial SDGs , Bkeconomic SDGs , and Bkenvironmental SDGs ,
(k = 2000, 2001, · · · , 2017).
Second, we summed up the scores for the two countries’ different annual goals and obtained the
total SDGs score of the two countries in different years. From the lowest score (0 points) to the highest
score (17 points), the higher the score, the better the performance or achievement of the SDGs, and the
better the sustainable development level of the country. Specific methods are as follows:
For country A, we still use AkGoal i to represent the score of Goal i in the year k (k = 2000, 2001, · · · ,
2017), and Aktotal means the score of the total SDGs in the year k (k = 2000, 2001, · · · , 2017), then we
17
obtain Aktotal = AkGoal i , k = 2000, 2001, · · · , 2017. For the other country, B, after using the same steps,
P
i=1
we obtain the scores of total SDGs from 2000 to 2017 as Aktotal (k = 2000, 2001, · · · , 2017).
2.6. Chow Breakpoint Test

In order to test the turning points in the time-series data for SDG performance in the three
dimensions for those 18 years in the two countries, we performed the Chow breakpoint test for the
years between 2000 and 2017 (i.e., 2003–2015). If any dimension in the SDG performance trends
had breakpoints, then it could indicate that the performance could generally be divided into two
parts, i.e., two development trends, by the breakpoint for those 18 years. Determination of the
causes and consequences of the breakpoints requires further analysis based on the nation’s actual
societal, economic, and environmental conditions. This method of breakpoint analysis, employing
the breakpoint test while closely integrating it into the actual conditions in the area, is conducive to
the scenario analysis, highlighting of specific issues, policy assessment, and development of model
direction selection among and within countries, which tests and supports the performance of SDGs.
The Chow breakpoint test was proposed by Chinese-American Gregory Chi-Chong Chow in
1960 [61], and its principles are: (1) dividing the sample observation into two or more subsets, but in
order for the equation to be estimated, it is necessary to require that the number of observations included
in each subset is greater than the number of parameters to be estimated by the equation; (2) estimating
the equation separately by using each subset and full sample observation values; (3) constructing the
F statistic to determine whether the structure of the model has changed significantly by using the
residual squared sum estimated through each subset sample (also known as the unrestricted residual
squared sum) and residual squared sum estimated through a full-sample observation value (also
known as the restricted residual squared sum) [62].
The null hypothesis of the Chow breakpoint test, H0 , is that there is no breakpoint. The concrete
steps are as follows:
1. Use the full-sample t = (1, 2, · · · , n1 , n1 + 1, n2 + 2, · · · , n1 + n2 ) to perform linear regression of

the least squares estimation (OLS) using the model
Yt = λ1 + λ2 xt + ut (1)
and obtain RSS; its degree of freedom is n1 +n2 -k and is marked as RSSR , and the subscript R
represents equal regression parameter constraints for the two subsets;
2. Use the two subsets t = (1, 2, · · · , n1 ) and t = (n1 + 1, n2 + 2, · · · , n1 + n2 ) to estimate Equation

(1) separately and mark RSS as RSS1 and RSS2 , respectively;
3. Construct the F statistic of the Chow test
(RSSR − RSS1 − RSS2 )/(k + 1)

F= ∼ F(k + 1, n1 + n2 − 2(k + 1))
(RSS1 + RSS2 )/(n1 + n2 − 2(k + 1))
where k represents the number of explanatory variables, and the hypothesis test is performed at a
given 1% significance level by the F statistic. If F is greater than the given critical value, the null
hypothesis is rejected, indicating that a structural change has occurred and there is a breakpoint;
or a Prob. value <1% is also indicative that there is a breakpoint. After the Chow test, when there
are multiple F statistic values greater than the critical value, the year with the largest F statistic
value is Sustainability
selected2019, as 11,
the breakpoint
x FOR PEER REVIEW of the SDG performance trend. 12 of 27
a Prob. value <1% is also indicative that there is a breakpoint. After the Chow test, when there are
3. Results and Discussion
multiple F statistic values greater than the critical value, the year with the largest F statistic value is
selected as the breakpoint of the SDG performance trend.
3.1. Quantitative Assessment of Sample Region’s SDG Performance
3. Results and Discussion
This section includes four parts. First, we conducted a comparative analysis of the two countries’
3.1. Quantitative Assessment of Sample Region’s SDG Performance
SDG performance from economic, social, and environmental perspectives. Next, we conducted
This section includes four parts. First, we conducted a comparative analysis of the two countries’
comparative analysis over the performances of three types of SDGs and then SDGs as a whole from a
SDG performance from economic, social, and environmental perspectives. Next, we conducted
national perspective.
comparative Figure
analysis2over
shows the performance
the performances of the
of three types annual
of SDGs SDGs
and then SDGsin as all three
a whole dimensions for
from
Kyrgyzstan anda national perspective.from
Kazakhstan Figures 2 shows
2000 the performance
to 2017. Figure of3 the annualthe
shows SDGs in all three
annual dimensions
total scores of SDGs, i.e.,
for Kyrgyzstan and Kazakhstan from 2000 to 2017. Figure 3 shows the annual total scores of SDGs,
SDG performance in general, in Kyrgyzstan and Kazakhstan from 2000 to 2017.
i.e., SDG performance in general, in Kyrgyzstan and Kazakhstan from 2000 to 2017.
(a) (b)
(c) (d)
Figure 2. Cont.
Sustainability 2019, 11, x FOR PEER REVIEW 13 of 27
(e)
Figure 2. SDG performance of Kyrgyzstan and Kazakhstan in three dimensions from 2000 to 2017. (a)
Figure 2. SDG performance
Economic of Kyrgyzstan
SDG performance of Kyrgyzstan andandKazakhstan
Kazakhstan from in three
2000 todimensions
2017; (b) Socialfrom
SDG 2000 to 2017.
(a) Economicperformance
SDG performanceof Kyrgyzstanof andKyrgyzstan
Kazakhstan from 2000Kazakhstan
and to 2017; (c) Environmental
from 2000 SDGto performance
2017; (b) Social SDG
of Kyrgyzstan and Kazakhstan from 2000 to 2017; (d) Three dimensional SDG performance of
performance of Kyrgyzstan and Kazakhstan from 2000 to 2017; (c) Environmental SDG performance of
Kyrgyzstan from 2000 to 2017; (e) Three dimensional SDG performance of Kazakhstan from 2000 to
Kyrgyzstan and Kazakhstan
2017. Note: for figurefrom
2a, 2b,2000
and 2c,to 2017;
each sector(d) Three
area dimensional
distinguished SDG
by different yearsperformance
has two colored-of Kyrgyzstan
pillars (red for Kazakhstan and blue for Kyrgyzstan) for representing one-dimensional SDG
from 2000 to 2017; (e) Three dimensional SDG performance of Kazakhstan from 2000 to 2017. Note:
performance of two countries in one year. The location of four circles consisting of dotted lines
for Figure 2a–c, eachthesector
represents score ofarea distinguished
total SDG performance, and by different
four circles fromyears
small tohas
large two colored-pillars
represent increased (red for
Kazakhstan and scoresblue
of 1, 2,for
3 andKyrgyzstan)
4 (marked in grey for representing
circles), respectively. Inone-dimensional
each figure, locations ofSDG performance of two
colored-pillars’
tops represent the scores of one-dimensional SDG performance of two countries, thus, the longer the
countries in one year. The location of four circles consisting of dotted lines represents the score of
colored-pillar, the higher the score of one-dimensional SDG performance, and the better the
total SDG performance,
performance ofand four circles
that dimensional from
SDGs. For small
Figure 2dtoand
large represent
2e, each increased
sector area distinguishedscores
by a of 1, 2, 3 and
4 (marked in grey circles), respectively. In each figure, locations of colored-pillars’fortops represent
different year has three colored-pillars (orange for economy, yellow for society, and green
environment) for representing three different dimensional SDG performances of Kyrgyzstan (Figure
the scores of one-dimensional SDG performance of two countries, thus, the longer the colored-pillar,
2d) or Kazakhstan (Figure 2e) in one year. The location of four circles consisting of dotted lines in each
the higher thefigure
score of one-dimensional
represents SDG performance,
the score of three-dimensional SDG performance, andand the better
four circles the
fromperformance
small to of that
dimensional large
SDGs. For increasing
represent Figure scores
2d,e, ofeach
1, 2, 3sector area distinguished
and 4 (marked by a different
in grey circles), respectively. year has three
Locations of
colored-pillars’ tops separately represent the scores of dimensional SDG performance of Kyrgyzstan
colored-pillars (orange for economy, yellow for society, and green for environment) for representing
(Figure 2d) or Kazakhstan (Figure 2e); thus, the longer the colored-pillars, the higher the score of
three differentdimensional
dimensional SDG performances
SDG performance, and the betterof theKyrgyzstan
performance of(Figure 2d) orSDG.
that dimensional Kazakhstan (Figure 2e) in
one year. The location of four circles consisting of dotted lines in each figure represents the score of
three-dimensional SDG performance, and four circles from small to large represent increasing scores of
1, 2, 3 and 4 (marked in grey circles), respectively. Locations of colored-pillars’ tops separately represent
the scores of dimensional SDG performance of Kyrgyzstan (Figure 2d) or Kazakhstan (Figure 2e); thus,
the longer the colored-pillars, the higher the score of dimensional SDG performance, and the better the
performance of that dimensional SDG.
Figure 3.Figure 3. TheSDG

The total total SDG performanceofofKyrgyzstan
performance Kyrgyzstan and andKazakhstan
Kazakhstanfrom 2000
from to 2000
2017. Note: each Note: each
to 2017.
sector area distinguished by different years has two colored-pillars (red for Kazakhstan and blue for
sector area distinguished by different years has two colored-pillars (red for Kazakhstan and blue
Kyrgyzstan) for representing the total SDG performance (except SDG 14 from 17 SDGs) of two
for Kyrgyzstan) for representing the total SDG performance (except SDG 14 from 17 SDGs) of two
countries in one year. The location of six circles consisting of dotted lines represents the score of total
countriesSDGin one year.
performance,The
andlocation
six circlesof sixsmall
from circles consisting
to large representof dottedscores
increased linesofrepresents the 12
2, 4, 6, 8, 10 and score of total
SDG performance, andcircles),
(marked in grey six circles from small
respectively. to large
Locations represent increased
of colored-pillars’ scores
tops represent of 2, 4,
the scores of 6,
the8, 10 and 12
(marked total SDGcircles),
in grey performance of two countries,
respectively. thus, the
Locations longer the colored-pillar,
of colored-pillars’ the higher the
tops represent the score
scoresof of the total
total SDG performance, and the better the performance of total SDGs.
SDG performance of two countries, thus, the longer the colored-pillar, the higher the score of total SDG
performance, and the
3.1.1. Economic SDG better the performance of total SDGs.
performance
AfterSDG
3.1.1. Economic we aggregated 16 of 17 SDGs (SDG 14 was excluded) into three dimensions (economy,
Performance
society, and environment), the economic SDGs contained SDG 8 (promote sustained, inclusive and
Aftersustainable economic16
we aggregated growth, full and(SDG
of 17 SDGs productive
14 wasemployment
excluded) andinto
decent work
three for all), SDG 9(economy,
dimensions (build society,
resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation),
and environment), the economic SDGs contained SDG 8 (promote sustained, inclusive and sustainable
SDG 10 (reduce inequality within and among countries) and SDG 17 (strengthen the means of
economicimplementation
growth, fulland and productive
revitalize employment
the global partnership for and decent development).
sustainable work for all), SDG 9 (build resilient
infrastructure, promote inclusive and sustainable industrialization
In regard to economic SDG performance (Figure 2a), i.e., total score andforfoster
goals innovation),
within this SDG 10
dimension, the score for Kazakhstan displayed a downward trend from 2000
(reduce inequality within and among countries) and SDG 17 (strengthen the means of implementation to 2004 (decreasing from
1.81242 to 1.31109), due mainly to its poor performance in representativeness in the policy-making of
and revitalize the global partnership for sustainable development).
international economic and financial institutions, speaking rights, foreign investment, and remittance
In regard to economic
fee, as well SDG performance
as the missing in terms of data (Figure 2a),the
regarding i.e.,incoming
total score for goals
growth rate ofwithin this dimension,
the bottom
the scorepopulation.
for KazakhstanHowever, displayed
after steadya downward trend from 2000
reinforcing of implementation to 2004
measures and(decreasing from 1.81242 to
global cooperation
1.31109), ties,
dueboosting
mainlyoftoproductive employment, asin
its poor performance well as improving financial
representativeness incapability (corresponding
the policy-making of to
international
economicSDG and8:financial
promote sustained, inclusive and sustainable economic growth, full and productive
institutions, speaking rights, foreign investment, and remittance fee, as well as
employment and decent work for all, and SDG 17: strengthen the means of implementation and
the missing in terms
revitalize of data
the global regarding
partnership for the incoming
sustainable growth rate
development), of the
its score bottom
showed population.
an upward trend However,
after steady
fromreinforcing
2004 to 2017,ofincreasing
implementation
to 2.28535.measures
Nevertheless, and global
due to the cooperation ties, boosting
substantial missing of productive
data in terms
of the indicator data regarding reducing inequality (corresponding to
employment, as well as improving financial capability (corresponding to SDG 8: promote sustained, SDG 10: reduce inequality
inclusivewithin and among countries), its score did fluctuate significantly. On the other hand, the score of
and sustainable economic growth, full and productive employment and decent work for
Kyrgyzstan showed an intense and constant fluctuation for the years between 2000 and 2017, during
all, and SDG 17: strengthen
which, 2000, 2005, 2010, and the2016
means of implementation
witnessed and revitalize
different zeniths (between 1.5 and 1.8)the global
in terms partnership for
of score,
sustainable development),
while its score
2001–2004, 2006–2007, showed
and an upward
2012 witnessed trend
different from
nadirs 2004 to0.75
(between 2017,andincreasing
1.09). The to 2.28535.
intensity
Nevertheless, dueof this fluctuation
to the was caused
substantial by thedata
missing substantial missing
in terms of indicator data regarding
the indicator reducing reducing
data regarding
inequality (corresponding to SDG 10: reduce inequality within and among countries) during the
inequality (corresponding to SDG 10: reduce inequality within and among countries), its score did
fluctuate significantly. On the other hand, the score of Kyrgyzstan showed an intense and constant
fluctuation for the years between 2000 and 2017, during which, 2000, 2005, 2010, and 2016 witnessed
different zeniths (between 1.5 and 1.8) in terms of score, while 2001–2004, 2006–2007, and 2012 witnessed
different nadirs (between 0.75 and 1.09). The intensity of this fluctuation was caused by the substantial
missing indicator data regarding reducing inequality (corresponding to SDG 10: reduce inequality
within and among countries) during the years 2001–2004, 2006–2009, and 2011–2014. In addition, the
highest score for the economic SDGs should be 4 points; however, Kyrgyzstan never scored more
than 2 points and had long been performing poorly in terms of economic sustainable development
due to the dire need for improvement in per capita economic and employment conditions, failure to
decouple economic growth from environmental degradation, lack of safe working environments, lack of
upgrades for disaster-resistant infrastructure, inefficient use of resources, lack of diversified industrial
use of technology, weakness in national capacity construction like multi-sectoral finance and technology
cooperation, and insufficient resources to strengthen statistical capacity (corresponding to SDG 8:
promote sustained, inclusive and sustainable economic growth, full and productive employment
and decent work for all, SDG 9: build resilient infrastructure, promote inclusive and sustainable
industrialization and foster innovation, and SDG 17: strengthen the means of implementation and
revitalize the global partnership for sustainable development).
The best year in terms of economic SDG performance for both Kazakhstan and Kyrgyzstan was
2016, scoring 3.15229 and 1.82786, respectively; the worst year was 2003 for Kazakhstan and 2002 for
Kyrgyzstan, scoring 1.30027 and 0.74560, respectively. In general, Kazakhstan performed better than
Kyrgyzstan in terms of economic SDGs.
3.1.2. Social SDG Performance

After we aggregated 16 of 17 SDGs (SDG 14 was excluded) into three dimensions (economy,
society, and environment), the social SDGs contained SDG 1 (end poverty in all its forms everywhere),
SDG 2 (end hunger, achieve food security and improved nutrition and promote sustainable agriculture),
SDG 3 (ensure healthy lives and promote well-being for all at all ages), SDG 4 (ensure inclusive and
equitable quality education and promote lifelong opportunities for all),SDG 5 (achieve gender equality
and empower all women and girls), SDG 16 (promote peaceful and inclusive societies for sustainable
development, provide access to justice for all and build effective, accountable and inclusive institutions
at all levels).
In social SDG performance (Figure 2b), i.e., the total score of goals within this dimension, the
score of Kazakhstan presented three volatile upward trends during the years 2000–2015. The score
during 2000 (1.92637)–2001 (1.23912), 2005 (2.35291)–2006 (1.64805), and 2010 (3.50212)–2011 (2.83024)
evidently decreased due to its poor performance in capacity to resist social disaster, social welfare
policies and measures, building investment for agricultural production capacity (corresponding to SDG
1: end poverty in all its forms everywhere, and SDG 2: end hunger, achieve food security and improved
nutrition and promote sustainable agriculture) as well as the substantial missing indicator data
regarding promoting peaceful societies and access to justice for all (corresponding to SDG 16: promote
peaceful and inclusive societies for sustainable development, provide access to justice for all and build
effective, accountable and inclusive institutions at all levels). Additionally, the score during the years
2015–2017 decreased constantly (from 3.63339 to 2.79169) due to the constantly weak performance
in capacity to resist social disaster as well as the substantial missing of 2017 indicator data regarding
ending poverty (corresponding to SDG 1: end poverty in all its forms everywhere), improving nutrition
and promoting sustainable agriculture (corresponding to SDG 2), ensuring healthy lives (corresponding
to SDG 3: ensure healthy lives and promote well-being for all at all ages). For Kyrgyzstan, the social
SDG performance score showed two upward trends during the years 2000–2012 (from 2.23692 to
3.71334) but then fell consistently during 2012–2017, landing at 1.52113. The scores during the years
2000–2001 and 2005–2007 showed an evident decrease due to the substantial missing of indicator data
regarding insufficient investment in agricultural production capacity, time spent on unpaid domestic
and care work (corresponding to SDG 5: achieve gender equality and empower all women and girls),
and promotion of peaceful societies and access to justice for all (corresponding to SDG 16: promote
peaceful and inclusive societies for sustainable development, provide access to justice for all and build
effective, accountable and inclusive institutions at all levels). In addition to the missing of indicator data
regarding these two goals, the fact that the performance of goal 3 (indicators such as maternal mortality,
multiple infectious disease incidence rate, traffic accident casualties, female adolescent fertility, family
health expenditure, and provision of essential medicines and vaccines) was poor contributed as another
major cause for the decreased score during the years 2005–2007. It is worth noting that the highest
point in social SDGs is 6 points, while both Kazakhstan and Kyrgyzstan never scored more than 3
points before 2010. Although they improved slightly and increased their scores above 3 points after
2010, due to insufficient measures to eradicate poverty, persistent weaker resilience, poor nutritional
status, lack of sustainable agriculture, and poor performance in indicators related to ensuring healthy
lifestyles (corresponding to SDG 3: ensure healthy lives and promote well-being for all at all ages),
Kyrgyzstan again scored less than 3 points since 2016. The country’s social sustainable development
has been chronically poor and needs urgent improvement.
The best years in terms of social SDG performance were 2015 and 2012, scoring 3.63339 and 3.71344
for Kazakhstan and Kyrgyzstan, respectively; the worst year for both countries was 2001, scoring
1.23912 and 1.38303, respectively. In general, Kyrgyzstan performed better than Kazakhstan in terms
of social SDGs during the years 2000–2012, whereas Kazakhstan performed better than Kyrgyzstan
during the years 2013–2017.
3.1.3. Environmental SDG Performance

After we aggregated 16 of 17 SDGs (SDG 14 was excluded) into three dimensions (economy, society,
and environment), the environmental SDGs contained SDG 6 (ensure availability and sustainable
management of water and sanitation for all), SDG 7 (ensure access to affordable, reliable, sustainable
and modern energy for all), SDG 11 (make cities and human settlements inclusive, safe, resilient and
sustainable), SDG 12 (ensure sustainable consumption and production patterns), SDG 13 (take urgent
action to combat climate change and its impacts), SDG 15 (protract, restore and promote sustainable
use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse
land degradation and halt biodiversity loss ).
In the evaluation of environmental SDG performance (Figure 2c), i.e., the total score of goals
within this division, the score of Kazakhstan displayed a volatile fluctuation before embarking on
an intense upward trend during the years 2000–2005 (increasing from 2.25234 to 3.56939). Its score
fluctuated again before falling drastically to 1.04594 during the years 2005–2017 due to its insufficient
capacity to withstand natural disasters (corresponding to SDG 11: make cities and human settlements
inclusive, safe, resilient and sustainable, and SDG 13: take urgent action to combat climate change and
its impacts) and the substantial missing of indicator data regarding the conservation & restoration of
ecosystems (corresponding to SDG 6: ensure availability and sustainable management of water and
sanitation for all), as well as ensuring sustainable modern energy (corresponding to SDG 7: ensure
access to affordable, reliable, sustainable and modern energy for all). Meanwhile, Kyrgyzstan’s score
presented a volatile upward trend (increasing from 2.54875 to 3.75262) until its score fell drastically
to 1.04594 during the years 2015–2017 due to lack of protection and restoration of ecosystems, lack
of sustainable management and efficient use of natural resources, inadequate capacity to withstand
natural disasters (corresponding to SDG 6: ensure availability and sustainable management of water
and sanitation for all, SDG 12: ensure sustainable consumption and production patterns, and SDG
13: take urgent action to combat climate change and its impacts), and the substantial missing of
indicator data regarding ensuring sustainable modern energy (corresponding to SDG 7: ensure access
to affordable, reliable, sustainable and modern energy for all) and reducing casualties and economic
loss caused by disasters (corresponding to SDG 11: make cities and human settlements inclusive, safe,
resilient and sustainable).
The best years in terms of environmental SDG performance were 2012 and 2011, scoring 3.76587
and 3.38941 for Kazakhstan and Kyrgyzstan, respectively; the worst year for both countries was 2017,
scoring 1.72040 and 1.04594, respectively. In general, Kyrgyzstan performed better than Kazakhstan in
terms of environmental SDGs during the years 2000–2004, whereas Kazakhstan performed better than
Kyrgyzstan during the years 2004–2017.
3.1.4. National Analysis of SDG Performance

During the years 2000–2016, among the three dimensions of SDGs for Kyrgyzstan, the performance
for both social and environmental goals showed an upward trend, growing by 0.67611 and 0.61646,
respectively, and ending at the same level of increase (Figure 2d). Nevertheless, during 2006–2016, the
performance for environmental goals was more stable than that of social goals; economic goals, in
comparison, underwent volatile fluctuation for those 17 years (ranging from 0.75 to 1.83). In addition,
during 2016–2017, all three-dimensional goals presented downward trends, among which, the
performance of environmental goals fell the most (decreasing by 2.11927), followed by social goals
(decreasing by 1.39191). During 2000–2017, among all three divisional SDGs for Kyrgyzstan, the
environmental SDGs performed the best overall, followed by the social SDGs, with the economic SDGs
performing the worst among the three divisional SDGs.
During the years 2000–2016, the SDG scores for Kazakhstan in all dimensions presented an upward
trend, among which the performance of social SDGs grew the most, increasing by 1.61703, followed by
economic SDGs (Figure 2e), whose score increased by 1.33987. During the years 2016–2017, the SDG
scores in all three dimensions exhibited an evident downward trend, among which the environmental
trend fell the most (decreasing by 1.73977), followed by social SDGs (decreasing by 0.86695). During
2000–2017, among the SDGs in all three dimensions for Kazakhstan, the environmental SDGs performed
the best overall, followed by the economic SDGs, with the social SDGs performing the worst overall
among the three divisional SDGs.
During the years 2000–2016, the total SDG scores for Kyrgyzstan and Kazakhstan, i.e., SDG overall
performance, presented a progressive upward trend in fluctuation (Figure 3). However, the total score
suddenly worsened during the years 2016–2017 due to the substantial missing of partial indicator
data. It is worth noting that the SDG performance in Kyrgyzstan has been poor for almost 18 years.
Its total SDG performance score was only higher than 8 points (half of the total scores of 16 SDGs
involved in the assessment) in two years. For those 18 years, the best and worst years for Kyrgyzstan in
terms of SDG performance were 2011 (8.25) and 2017 (3.82), respectively. For Kazakhstan, the best and
worst years in terms of SDG performance were 2015 (10.27) and 2002 (4.76), respectively. In general, in
terms of the overall SDG performance, Kyrgyzstan performed better than Kazakhstan during the years
2000–2005, while Kazakhstan performed better than Kyrgyzstan during the years 2005–2017.
3.2. Analysis of Causes Affecting the Performance of Three Dimensions of SDGs in the Sample Area
Table 3 shows the Chow test results regarding the breakpoints that appeared in the three dimensions
of SDG performance trends for Kyrgyzstan and Kazakhstan during the years 2000–2017. It could
be said told that, for those 18 years, there was no breakpoint in the economic trends of the SDG
performance for both countries, while the environmental SDGs displayed a breakpoint in 2015. As for
the social SDGs, only Kyrgyzstan had a breakpoint in 2011. Because the results of the Chow breakpoint
test revealed the non-existence of breakpoints in the economic SDG performance trends, this paper
can only attempt to unveil the possible causes for the sudden change of each divisional trend in SDG
performance and conduct further analysis on all three divisional SDG performance over those 18 years.
This assessment was based on the actual condition of both countries during the breakpoint years in
combination with major events, disasters, relevant policies, and large-scale events. of the social and
natural environments of the two countries occurring in the years of their respective breakpoints.
Table 3. Chow test for the breakpoint within the three divisional trends in SDG performance for
Kyrgyzstan and Kazakhstan.
Country Division Breakpoint Year F (2,14) Prob. F (2,14)

Environment 2015 14.4775 0.0004
Kyrgyzstan Society 2011 8.029292 0.0048
Economy No Result
Environment 2015 17.22005 0.0002
Kazakhstan Society No Result
Economy No Result
Society No Result
3.2.1. Analysis of the CausesEconomy
Affecting the SocialNo
SDG Performance in Kyrgyzstan
Result
Using 2011 as the breakpoint, the trend in social SDG performance in Kyrgyzstan can be divided
3.2.1. Analysis of the Causes Affecting the Social SDG performance in Kyrgyzstan
into two parts (Figure 4), i.e., the upward trend during the years 2000–2011 and the downward trend
during the Using
years2011 as the breakpoint,
2011–2017. Through the trend in socialand
the review SDGanalysis
performance in Kyrgyzstan
of news can be divided
and literature, this paper
into two parts (Figure 4), i.e., the upward trend during the years 2000–2011
determined 7 types of major events as the main factors affecting the country’s social SDG performance and the downward trend
during the years 2011–2017. Through the review and analysis of news
to assist with the analysis. For each major event, the scoring weight is determined through expert and literature, this paper
determined 7 types of major events as the main factors affecting the country’s social SDG
consultation. For the social events, the year in which the event occurred within 2000–2017 is scored as
performance to assist with the analysis. For each major event, the scoring weight is determined
1 point, and the year in which the event did not occur is scored as 0 points. For environmental events,
through expert consultation. For the social events, the year in which the event occurred within 2000–
the year
2017iniswhich
scoredthe as event
1 point, occurred
and the within
year in2000–2017
which the is scored
event did asnot0.5 points,
occur and the
is scored as year in which
0 points. For the
eventenvironmental
did not occurevents,
is scoredthe as
year0 points.
in whichThen, the occurred
the event scores ofwithin
each year are summed
2000–2017 is scoredto asobtain the total
0.5 points,
socialand
SDG-impacting
the year in which the event did not occur is scored as 0 points. Then, the scores of each year arewhen
factors of each year (Figure 4). These major events, disasters, and the times
they occurred
summed to areobtain
as follows:
the totalcoups
socialor revolution (2005,
SDG-impacting 2010),
factors ethnic
of each yearviolence (2010),
(Figure 4). Thesereal parliamentary
major events,
systemdisasters, and thepresidential
(2010–2017), times when election
they occurred
(2005,are as follows:
2009, coups2017),
2010, 2011, or revolution (2005, of
the outbreak 2010), ethnic(2011,
measles
2015,violence (2010),
2016, 2017), real parliamentary
earthquakes system (2010–2017),
of magnitude presidential
6 or higher (2005, 2007, election (2005,
2008, 2011, 2009,2016),
2015, 2010, and
2011,harsh
2017), the outbreak of measles (2011, 2015, 2016, 2017), earthquakes of magnitude
winter with low temperature and heavy snowfall (2006, 2010, 2011, 2012, 2013). It can be seen from 6 or higher (2005,
2007, 2008, 2011, 2015, 2016), and harsh winter with low temperature and heavy snowfall (2006, 2010,
Figure 4 that, during 2000–2017, the first year with an outstanding score for factors impacting social
2011, 2012, 2013). It can be seen from Figure 4 that, during 2000–2017, the first year with an
sustainable development in Kyrgyzstan was 2005. After 2005, the social SDG performance quickly
outstanding score for factors impacting social sustainable development in Kyrgyzstan was 2005.
deteriorated
After 2005,andtheremained
social SDGin a state of slow
performance quicklyrecovery for four
deteriorated and years.
remained After
in a barely
state of regaining
slow recovery its 2005
sustainable
for four years. After barely regaining its 2005 sustainable development level in 2010, Kyrgyzstansocial
development level in 2010, Kyrgyzstan suffered the second outstanding year for
SDG suffered
impacting thefactors
secondand it was the
outstanding highest
year score
for social SDGin impacting
those 18 years.
factorsAfter
and itthat,
was the highest
score for impacting
score in
factors in 2011
those only After
18 years. trailed behind
that, that for
the score of 2010. Thosefactors
impacting two years
in 2011witnessed the behind
only trailed steady that
accumulation
of 2010. of
Those
a series two years
of social andwitnessed the steady
political events andaccumulation
their subsequentof a series of social
impacts, as and
wellpolitical events and
as the frequent their
occurrence
subsequent impacts, as well as the frequent occurrence of natural disasters
of natural disasters and environmental health issues, which rendered huge changes to the social and and environmental health
issues, which
environmental rendered huge
circumstances changespeople’s
affecting to the social and environmental
life. Moreover, circumstances
the accumulated affecting
impacting factors
people’s life. Moreover, the accumulated impacting factors score during 2015–2017 remained
score during 2015–2017 remained consistently high; therefore, the social SDG performance score fell
consistently high; therefore, the social SDG performance score fell quickly after 2012, making 2011
quickly after 2012, making 2011 the breakpoint in the social SDG performance trend.
the breakpoint in the social SDG performance trend.
Figure
Figure 4. The
4. The scores
scores for for social
social SDGSDG performanceand
performance andfactors
factors impacting
impacting the
thesocial
socialSDG
SDGperformance
performance in
in Kyrgyzstan from 2000 to 2017. Note: the red point is the breakpoint of the
Kyrgyzstan from 2000 to 2017. Note: the red point is the breakpoint of the social SDG social SDG performance
performance
trend of Kyrgyzstan.
trend of Kyrgyzstan.
3.2.2.3.2.2. Analysis
Analysis of the
of the Causes
Causes Affectingthe
Affecting theEnvironmental
Environmental SDG
SDGperformance
Performancein Kyrgyzstan
in Kyrgyzstan
Using 2015 as the breakpoint, the trend in environmental SDG performance in Kyrgyzstan can
Using 2015 as the breakpoint, the trend in environmental SDG performance in Kyrgyzstan can be
be divided into two parts (Figure 5), i.e., the fluctuating upward trend during the years 2000–2015
divided into two parts (Figure 5), i.e., the fluctuating upward trend during the years 2000–2015 and the
and the downward trend during the years 2015–2017. Through the review and analysis of news and
downward trend
literature, thisduring the years 2015–2017.
paper determined Through
6 types of major theand
disasters review and
crises analysis
as the of news
main factors and literature,
affecting the
this paper
country’s environmental SDG performance to assist with the analysis. For each major event,the
determined 6 types of major disasters and crises as the main factors affecting thecountry’s
year
environmental SDG performance to assist with the analysis. For each major event, the year in which the
event occurred within the years 2000–2017 was scored as 1 point, and the year in which the event did
not occur was scored as 0 points; then, the sum of the scores for each year was obtained to get the total
in which the
Sustainability event
2019, occurred
11, 3504 within the years 2000–2017 was scored as 1 point, and the year in which 19 of 27
the event did not occur was scored as 0 points; then, the sum of the scores for each year was obtained
to get the total scores for the environmental SDG impacting factors for each year (Figure 5). These
scores for the environmental
major events and the times whenSDG impacting
they occurredfactorsarefor
aseach year serious
follows: (Figure 5). These major
avalanches thatevents
causedand the
major
times when they occurred are as follows: serious avalanches that caused major casualties
casualties and property losses (2004, 2005, 2010, 2012, 2014, 2015, 2017), landslides (2003, 2004, 2015, and property
losses (2004,flooding
2016, 2017), 2005, 2010, 2012,2005,
(2004, 2014,2008,
2015,2012,
2017), landslides
2015), (2003, 2004,
earthquakes 2015, 2016,
of magnitude 2017),
6 or flooding
higher (2005, (2004,
2007,
2005,
2008, 2011, 2015, 2016), harsh winter with low temperature and heavy snowfall (2006, 2010, harsh
2008, 2012, 2015), earthquakes of magnitude 6 or higher (2005, 2007, 2008, 2011, 2015, 2016), 2011,
winter withand
2012, 2013), lowsevere
temperature
energy and heavy(2007,
shortages snowfall
2008,(2006,
2009, 2010, 2011,2015).
2013, 2014, 2012, It
2013),
can beand severe
seen fromenergy
Figure
shortages
5 that the (2007, 2008, 2009,
accumulating 2013,
scores 2014,
for 2015). It canSDG
environmental be seen from Figure
impacting 5 that
factors the accumulating
in Kyrgyzstan remainedscoresin
for environmental SDG impacting factors in Kyrgyzstan remained in fluctuation
fluctuation during the years 2000–2014, especially, the accumulating scores for the impacting factors, during the years
2000–2014, especially,
which presented the accumulating
an upward trend during scores for the impacting
2001–2004 while thefactors, which presented
environmental an upward
SDG performance
trend during 2001–2004 while the environmental SDG performance continued
continued to worsen during the same time period. In general, the SDG performance remained to worsen during the in
same time period. In general, the SDG performance remained in stable fluctuation
stable fluctuation during 2000–2014. In 2015, which was the year with the highest impacting during 2000–2014.
factor
In 2015,Kazakhstan
score, which was suffered
the year with the highest
a series of naturalimpacting
disastersfactor
andscore,
energy Kazakhstan
crises thatsuffered
rendered a series
lastingof
natural disasters and energy crises that rendered lasting negative impact on the country’s
negative impact on the country’s performance on goal 6, 7, 11, 12, and 13. As a result, its score for performance
on goal 6, 7, 11, SDG
environmental 12, and 13. As a result,
performance its score
fell rapidly for environmental
from 2015, making 2015 SDGtheperformance
breakpointfell rapidly
in the from
country’s
2015,
trend making 2015 the breakpoint
for environmental in the country’s trend for environmental SDG performance.
SDG performance.
Figure 5. The scores of environmental SDG performance and factors impacting the environmental
SDG performance in Kyrgyzstan from 20002000 to
to 2017.
2017. Note:
Note: the
the red
red point
point is
is the breakpoint of the
trend of
environmental SDG performance trend of Kyrgyzstan.
Kyrgyzstan.
3.2.3. Analysis of
3.2.3. Analysis of the
the Causes
Causes Affecting
Affecting the
the Environmental
Environmental SDG
SDG Performance
performance in
in Kazakhstan
Kazakhstan
Using 2015 as
Using 2015 as the
the breakpoint,
breakpoint, the the trend
trend inin environmental
environmental SDG SDG performance
performance in in Kazakhstan
Kazakhstan can can
be
be divided
divided intointo two
two parts
parts (Figure
(Figure 6),6), i.e.,
i.e., the
the fluctuating
fluctuating upward
upward trend
trend during
during thethe years
years 2000–2015
2000–2015
and
and the
the downward
downward trend trend during
during thethe years
years 2015–2017. Through the
2015–2017. Through the review
review andand analysis
analysis ofof news
news and
and
literature, this paper determined 7 types of major disasters as the main factors
literature, this paper determined 7 types of major disasters as the main factors affecting the country’saffecting the country’s
environmental
environmental SDG SDG performance
performance to to assist
assist with
with the
the analysis. For each
analysis. For each major
major event,
event, the
the year
year inin which
which
the
the event occurred within 2000–2017 was scored as 1 point, and the year in which the event did
event occurred within 2000–2017 was scored as 1 point, and the year in which the event did not
not
occur was scored
occur was scored asas 00 points;
points; then,
then, the
the sum
sum ofof the
the scores
scores for
for each
each year
year was
was obtained
obtained to to get
get the
the total
total
scores
scores for
for the
the environmental
environmental SDGs SDGs impacting
impacting factors
factors for
for each
each year
year (Figure
(Figure 6).6). These
These major
major disaster
disaster
events and the times when they occurred are as follows: flooding causing
events and the times when they occurred are as follows: flooding causing major casualties major casualties and property
and
losses
property(2001, 2005,
losses 2008,2005,
(2001, 2010, 2008,
2011, 2012,
2010, 2014,
2011, 2015,
2012,2017),
2014,poisoning
2015, 2017),incidents
poisoningsuchincidents
as syncope (2014,
such as
2015), landslides causing major casualties and property losses (2010,
syncope (2014, 2015), landslides causing major casualties and property losses (2010, 2015),2015), earthquakes of magnitude
6 or higher (2013,
earthquakes 2017), thick
of magnitude 6 orsmog
higher with a stifling
(2013, smell smog
2017), thick lasting for amore
with than
stifling a week
smell andfor
lasting causing
more
residents to feel suffocation and weakness (2012, 2013, 2014, 2015, 2016,
than a week and causing residents to feel suffocation and weakness (2012, 2013, 2014, 2015, 2016, 2017), the death of many
Dalmatian pelicans
2017), the death of (2015),
many and the death
Dalmatian of more(2015),
pelicans than 200,000
and thesaigadeathantelopes
of more(2015).
than It can besaiga
200,000 seen
from Figure
antelopes 6 thatItthe
(2015). accumulating
can be seen from score for the
Figure environmental
6 that sustainable
the accumulating scoredevelopment impacting
for the environmental
factors in Kazakhstan during 2000–2014 had been fluctuating within
sustainable development impacting factors in Kazakhstan during 2000–2014 had been fluctuating the low range, especially the
accumulating score for impacting factors, which reached the lowest status
within the low range, especially the accumulating score for impacting factors, which reached the during 2002–2004, and as a
result, the environmental SDG performance recovered swiftly during 2004–2005. The highest score for
lowest status during 2002–2004, and as a result, the environmental SDG performance recovered
swiftly during 2004–2005. The highest score for impacting factors was in 2015, the year during which
impacting
Kazakhstanfactors was ain series
suffered 2015, the
of year during
serious which
natural Kazakhstan suffered
environmental a series
crises and of serious
sustained natural
ecosystem
environmental crises and sustained ecosystem damage, which rendered lasting negative
damage, which rendered lasting negative impact on the completion of the country’s goal 6, 11, 12, impact on the
13,
completion of the country’s goal 6, 11, 12, 13, and 15. As a result, the environmental
and 15. As a result, the environmental SDG performance score fell quickly, making 2015 the SDG performance
score fell quickly,
breakpoint in the making
country’s 2015 the breakpoint
environmental SDGin the country’s environmental
performance trend. SDG performance trend.
Figure 6. The scores of environmental SDG performance and factors impacting the environmental
SDG performance in Kazakhstan from 2000 to to 2017.
2017. Note:
Note: the
the red
red point
point is
is the breakpoint of the
trend of
environmental SDG performance trend of Kazakhstan.
Kazakhstan.
3.3. The Advantages

3.3. The Advantages and
and Challenges
Challenges of
of the
the Assessment
Assessment Framework
Framework
The assessment framework
The assessment framework for for SDG
SDG performance
performance proposedproposed in in this
this paper
paper has has many
many advantages.
advantages.
First, due to
First, due to the
the geographical
geographical differences
differences of of countries,
countries, they they face
face different
different cooperation
cooperation demands demands
regarding proposals such as the BRI; thus, flexible indicators framework
regarding proposals such as the BRI; thus, flexible indicators framework and assessment framework and assessment framework
are
are particularly
particularly essential.
essential. OurOur indicator
indicator framework
framework provided
provided an an entry
entry point
point forfor the
the development
development of of
indicator selection. It is possible to continue to treat the indicators according
indicator selection. It is possible to continue to treat the indicators according to the indicator sets in to the indicator sets in
order
order to
to divide
divide them
them intointo different
different categories
categories (such
(such as as gender,
gender, age age and
and location.)
location.) based
based on on research
research
needs, enabling researchers to adjust the indicator framework based on
needs, enabling researchers to adjust the indicator framework based on the actual data obtainment the actual data obtainment of
countries in different geographical areas and thus conduct more in-depth
of countries in different geographical areas and thus conduct more in-depth examination of the examination of the indicators.
The researcher’s
indicators. analysis of the
The researcher’s SDG of
analysis performance of different regions
the SDG performance within
of different the same
regions within country can
the same
become
country acan beneficial
becomeextension
a beneficial of extension
the assessment of theframework
assessmentproposed framework in proposed
this paperin andthiscan provide
paper and
national policy-makers with more viable national information than
can provide national policy-makers with more viable national information than an inter-country an inter-country comparison.
Second,
comparison.basedSecond,
on the acquired
based onindicator
the acquiredand focus area ofand
indicator thefocus
corresponding
area of the goal this paper categorized
corresponding goal this
SDGs into three dimensions, i.e., society, economy, and environment.
paper categorized SDGs into three dimensions, i.e., society, economy, and environment. This method This method can realize the
further clustering of indicators and lower the uncertainties that arise following
can realize the further clustering of indicators and lower the uncertainties that arise following the the measurement and
assessment
measurement within
andgoal analysis that
assessment withinare goal
caused by the substantial
analysis that are caused missing byofthe
partial indicatormissing
substantial data, thusof
alleviating the burden on the national report while assisting researchers
partial indicator data, thus alleviating the burden on the national report while assisting researchers and policy-makers conduct
macro-analysis
and policy-makers andconduct
overall macro-analysis
planning of national SDG performance.
and overall Third, we
planning of national SDG defined the maximum
performance. Third,
value of the time-series data for each indicator in the database and
we defined the maximum value of the time-series data for each indicator in the database and compared the value among different
countries
comparedorthe regions.
value Thisamongwasdifferent
the best performance
countries or of the indicator
regions. This wasso fartheand weperformance
best used the maximum of the
value to conduct
indicator so far andnormalization
we used theto demonstrate
maximum valuethetodegree
conduct ofnormalization
effort to achieve the best performance
to demonstrate the degree of
the datasets within the time-series data that were less than the maximum
of effort to achieve the best performance of the datasets within the time-series data that were less than value. On the one hand,
this strengthensvalue.
the maximum the connection
On the one ofhand,
the internal trend among
this strengthens thethe indicatoroftime-series
connection the internal data, butamong
trend on the
other hand, ittime-series
the indicator emphasizesdata, the comparison
but on the other among different
hand, regions. Fourth,
it emphasizes flexible weighting
the comparison among differentmight
encourage countries to perform easy goals and overlook goals that are
regions. Fourth, flexible weighting might encourage countries to perform easy goals and overlook equally important and demand
further in-depth
goals that transformation.
are equally importantTherefore, for countries
and demand further thatin-depth
use this assessment
transformation. framework to analyze
Therefore, for
SDG performance, assigning equal weight to every SDG when calculating
countries that use this assessment framework to analyze SDG performance, assigning equal weight the SDG performance
score could
to every SDGencourage equal focus
when calculating the SDGon all goals, as well
performance score ascould
inspire each country
encourage equal to concentrate
focus on
on all goals,
goals
as wellthat are furthest
as inspire from realization,
each country to concentrateand improve
on goalsthe thatfastest in terms
are furthest fromof expected
realization, performance
and improve to
improve future scores. Fifth, during the Chow test when there were many F statistic values greater
than the critical value, this paper we selected only the maximum F statistic value as the breakpoint
for analysis. Different researchers can conduct simultaneous analyses over a number of F statistic
values greater than the critical value based on different demands, yielding a more accurate analysis of
SDG performance trends for different countries. This method of breakpoint analysis, employing the
breakpoint test while closely integrating it into the actual conditions in the area, is conducive to the
scenario analysis, highlighting specific issues, policy assessment, and development of model direction
selection among and within countries, which tests and supports the performance of SDGs. Sixth, many
indicator-based assessments simply examine trend and progress and lack in-depth exploration of the
comprehensive attributes of different countries’ sustainable development and the inter-relationship
among the encountered sustainable development. The formulation of a comprehensive methodological
framework is a key component for this assessment, which could cluster intimately connected goals
and indicators associated with its internal inter-relationship while simultaneously analyzing various
special conditions of different regions [8]. The calculation and analysis of the assessment framework
proposed in this paper are easily operable, increasing the chance for countries to adopt similar or
edited versions and perform SDGs’ progress assessment and analysis. In the future, countries and
organizations around the world could adopt this method, for the long-term indicator-based SDGs
assessment of both countries and regions.
However, there are still limitations and challenges that remain to be addressed in a future
assessment framework. First, based on the collection of indicator data for the two countries assessed in
this paper, it could be seen that there were still gaps in data collection between countries as well as
the missing of time-series data. This could challenge the indicator selection and further restrict the
follow-up assessment and analysis. For example, in the indicators regarding education (corresponding
to SDG 4: ensure inclusive and equitable quality education and promote lifelong opportunities for
all), water and sanitation (corresponding to SDG 6: ensure availability and sustainable management
of water and sanitation for all), and implementation and global partnership (corresponding to SDG
17: strengthen the means of implementation and revitalize the global partnership for sustainable
development), the gap in data collection was very evident. Furthermore, the missing of indicator data
regarding ensuring sustainable modern energy (corresponding to SDG 7: ensure access to affordable,
reliable, sustainable and modern energy for all), reducing inequality (corresponding to SDG 10: reduce
inequality within and among countries), cities and settlements (corresponding to SDG 11: make
cities and human settlements inclusive, safe, resilient and sustainable), consumption and production
patterns (corresponding to SDG 12: ensure sustainable consumption and production patterns), and
promoting peaceful societies and access to justice for all (corresponding to SDG 16: promote peaceful
and inclusive societies for sustainable development, provide access to justice for all and build effective,
accountable and inclusive institutions at all levels) was very serious. Also, the indicators regarding
climate change (corresponding to SDG 13: take urgent action to combat climate change and its impacts)
not only revealed a significant gap in data collection but also showed substantial data loss, an issue
that demonstrated the challenges in data collection surrounding these aspects of Central Asia. As not
every country, especially developing and underdeveloped countries, has access to continuous data,
analytical or statistical methods would need to be used to fill the gap of missing data. Second, the SDG
framework proposed by the UN is from a global perspective. Still, due to differences in terms of the
sustainable development issues faced by different countries, each country has its own priorities in the
acquisition of data in terms of its type and quality. Based on the UN SDG indicator framework, the
monitoring and assessment worldwide or within countries in the same area would sometimes require
the proposal of new substitute indicators for many countries. However, those substitute indicators
with a strong subjective choice from the researchers might devalue the corresponding targets and SDGs.
For the assessment of different regions within the same country, localizing processing and including
more precise categorization of indicator data would be needed to formulate a national indicator system
in line with the country’s condition [63]. Third, for some research on the quantitative assessment
of SDGs, several selected indicators might not well reflect and assess the SDGs and targets, which
was a limitation of the indicators’ framework. This was because, for a certain target, there were not
enough corresponding indicators to reflect the target, and even if numbers of corresponding indicators
were selected, those indicators might be only substitute indicators for the indicators proposed by
the UN’s SDGs. As mentioned before, those substitute indicators with a strong subjective choice
from the researchers might devalue the targets. For example, indicator 37 (shown in Table 2) in the
indicator framework of this paper is the proportion of total government spending on essential services
(education), corresponding to target 1.a (ensure significant mobilization of resources from a variety
of sources, including through enhanced development cooperation, in order to provide adequate and
predictable means for developing countries, to implement programmes and policies to end poverty in
all its dimensions) and SDG 1 (end poverty in all its forms everywhere). It can be seen that indicator 37
only monitored the government spending on essential educational services, which did not adequately
and accurately reflect the purpose of ending poverty that the target 1.a and SDG 1 wanted to achieve.
If two indicators (i.e., proportion of domestically generated resources allocated by the government
directly to poverty reduction programmes; sum of total grants and non-debt-creating inflows directly
allocated to poverty reduction programmes as a proportion of GDP) are added in accordance with the
UN SDGs’ indicators framework to assist indicator 37 in this paper for the assessment of the target
1.a and SDG 1, the final results will be more accurate. However, the sample area (Kazakhstan and
Kyrgyzstan) of this paper did not have the relevant data of these two indicators; thus, only indicator 37
was used. Fourth, the universal 17 SDGs cover a wide range of content of sustainable development,
such as all three major dimensions (economy, society, and environment) of sustainable development,
as well as enablers such as institutional coherence, policy coherence, and accountability [48]. When
fewer indicators are selected to assess SDGs, even if these indicators have certain comprehensiveness
and representativeness, they may still result in a reduction or reorientation in the content of goals
and targets. Therefore, to reduce this inaccuracy and incompleteness, based on the IAEG-SDGs’
indicators framework, we selected plenty of indicators to assess SDGs. However, generalizing these
diverse indicators we selected, lumping them together in a quantitative methodological framework and
eventually giving them scores might devalue the indicators and seems complex and arbitrary. For the
construction of a methodological framework including the selection of indicators and the classification
and normalization of 17 SDGs, especially the selection of weighting methods and giving scores, using a
direct rather than a complex method remains a critical challenge [64]. Among the increasingly complex
assessments and analyses that are based on various models, more complex assessment methods
lack practical significance for some underdeveloped countries. Therefore, a greater effort should be
dedicated to guaranteeing that the assessment and analysis methods could be understood and used
by the technical experts and researchers in different countries. However, for the assessment of SDGs
that used a large number of indicators (around or over 100) such as the studies of Sachs et al. [30] and
Eurostat [31], it can be said that the indicator framework of this paper was a further useful attempt,
and the methodological framework of this paper was a compromise between the simpler quantitative
assessment method (such as the combination of the min-max normalization and equal weights [65])
and the more complex quantitative assessment method (such as the combination of social network
analysis and principal component analysis [66]; standard methods of the theory of choice and welfare
under imposed quantities [67]). This kind of attempt and compromise addressed the problem of how
to effectively measure, assess and compare the progress and trends of SDGs in different countries.
4. Conclusions
Based on past case studies, this paper constructed a novel analysis framework for the assessment
of SDGs including methods such as benchmarking, normalization, and employment of the Chow Test
to uncover breakpoints within SDG performance trends as well as further analysis of SDG performance
for the clustered 17 SDGs in the three dimensions of economy, society, and environment. This paper
also used two typical Central Asian countries, Kazakhstan and Kyrgyzstan, as the sample area to
test whether this methodological framework could realize the comprehensive assessment over the
country’s long-term performance and trends for SDGs.
For the sample area, this assessment finally used 209 indicators and indicator sets including 429
specific indicators corresponding to 16 SDGs (except SDG 14 from 17 SDGs) in three dimensions,
i.e., economy, society, and environment. Specifically, economic dimension included 141 specific
indicators corresponding to SDG 8 (promote sustained, inclusive and sustainable economic growth,
full and productive employment and decent work for all), SDG 9 (build resilient infrastructure,
promote inclusive and sustainable industrialization and foster innovation), SDG 10 (reduce inequality
within and among countries) and SDG 17 (strengthen the means of implementation and revitalize the
global partnership for sustainable development); the social dimension included 220 specific indicators
corresponding to SDG 1 (end poverty in all its forms everywhere), SDG 2 (end hunger, achieve food
security and improved nutrition and promote sustainable agriculture), SDG 3 (ensure healthy lives
and promote well-being for all at all ages), SDG 4 (ensure inclusive and equitable quality education
and promote lifelong opportunities for all), SDG 5 (achieve gender equality and empower all women
and girls) and SDG 16 (promote peaceful and inclusive societies for sustainable development, provide
access to justice for all and build effective, accountable and inclusive institutions at all levels); the
environmental dimension included 220 specific indicators corresponding to SDG 6 (ensure availability
and sustainable management of water and sanitation for all), SDG 7 (ensure access to affordable,
reliable, sustainable and modern energy for all), SDG 11 (make cities and human settlements inclusive,
safe, resilient and sustainable), SDG 12 (ensure sustainable consumption and production patterns),
SDG 13 (take urgent action to combat climate change and its impacts) and SDG 15 (protract, restore and
promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification,
and halt and reverse land degradation and halt biodiversity loss). The assessment revealed that,
in terms of economic SDG performance, Kyrgyzstan’s was the worst performance among the three
divisional SDGs for the two countries, and was in urgent need of improvement. Furthermore, in terms
of social SDG performance, it was evident that Kazakhstan performed better than Kyrgyzstan after
2013. During the years 2010–2011, a series of major incidents occurred in Kyrgyzstan that rendered its
social sustainable development severely affected. Our findings indicated that governments should pay
particular attention to strengthening their country’s capability to withstand various types of disasters
for vulnerable groups and guarantee healthy lifestyles as well as quality education. Moreover, in terms
of the performance of environmental SDGs, the overall performance of the two countries’ environmental
sustainable development was relatively good. However, after 2015, both countries suffered a series of
major disaster incidents that resulted in natural resources and ecological environmental crises that left
a lasting impact on their environmental sustainable development. Namely, they were unable to meet
the requirements of proactive promotion of new policies, strengthening of disaster monitoring and
alert, improvement of the regional disaster-preparation system, and the avoidance of the accumulation
of natural environmental issues. In terms of the overall SDG performance, it could be said that for
Central Asia as a whole, the SDG performance was not very optimistic and required that greater effort
be devoted to the gathering of different types of indicator data to solve the issue of data loss.
Testing of the assessment framework using the sample area revealed that this paper adjusted
the indicator system according to the actual data gathering of different countries, clustered goals and
indicators closely related to regional sustainable development, emphasized its internal interconnection,
and simultaneously analyzed different special situations of different regions before formulating a
comprehensive methodological assessment framework. The calculation and analysis of this paper’s
assessment framework are easily operable for other countries. Therefore, future research will focus on
extending this study’s sample area to all countries under the BRI by using the methodological framework
of this paper. We believe this assessment framework can be successfully employed for long-term
SDGs assessment in those countries. Our assessment framework can help countries to understand
their advantages and disadvantages in the development of economy, society and environment from
a quantitative point of view, upgrade and transform the vulnerable industries, achieve the overall
layout of industrial structure from a global perspective, and expand the international markets. Finally,
we expect our assessments can assist policy-makers to learn the dynamic interconnections between
countries and existing challenges and opportunities, evaluate a development model, and formulate
sustainable development policies.
Supplementary Materials: The following are available online at http://www.mdpi.com/2071-1050/11/13/3504/s1,

Table S1: original data of Kyrgyzstan SDG-related indicators. Table S2: original data of Kazakhstan SDG-related
indicators. Table S3: indicator framework used for the SDGs assessment.
Author Contributions: Conceptualization, Y.H. and H.L.; Methodology, H.L.; Validation, Y.H. and H.L.; Formal
Analysis, Y.H.; Investigation, Y.H.; Resources, Y.H.; Data Curation, Y.H.; Writing—Original Draft Preparation, Y.H.;
Writing—Review and Editing, H.L. and T.L.; Visualization, Y.H.; Supervision, H.L. and T.L.; Project Administration,
H.L. and T.L.; Funding Acquisition, T.L.
Funding: This research was funded by the Strategic Priority Research Program A of the Chinese Academy of
Sciences, No. XDA20010301.
Acknowledgments: The authors would like to acknowledge and thank the support of China Scholarship Council,
and Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences.
Conflicts of Interest: The authors declare no conflict of interest.
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sustainability

Keywords: sustainable development goals; sustainable development; sustainability; sustainable

Sustainability 2019, 11, 3504; doi:10.3390/su11133504 www.mdpi.com/journal/sustainability

2. Materials and Methods

2.1. An Overview of the Sample Area

2.1. An Overview of the Sample Area

Table 1. Basic characteristics of Kazakhstan and Kyrgyzstan.

Independent Area Population 2018 GDP

2.2. Constructing an SDG Database

Dimension of the Indicator or

2.3. The Differentiation of Forward and Inverse Indicators

2.4. The Normalization of Indicator Data

AkGoal i (1) + AkGoal i (2) + · · · + AkGoal i ( j)

2.6. Chow Breakpoint Test

1. Use the full-sample t = (1, 2, · · · , n1 , n1 + 1, n2 + 2, · · · , n1 + n2 ) to perform linear regression of

2. Use the two subsets t = (1, 2, · · · , n1 ) and t = (n1 + 1, n2 + 2, · · · , n1 + n2 ) to estimate Equation

(RSSR − RSS1 − RSS2 )/(k + 1)

Figure 3.Figure 3. TheSDG

3.1.2. Social SDG Performance

3.1.3. Environmental SDG Performance

3.1.4. National Analysis of SDG Performance

Country Division Breakpoint Year F (2,14) Prob. F (2,14)

3.3. The Advantages

Supplementary Materials: The following are available online at http://www.mdpi.com/2071-1050/11/13/3504/s1,

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