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A critical analysis of the
Sustainable Development
Goals
Ranjula Bali Swain
The ambitious UN adopted Sustainable Development Goals (SDGs) have
been criticized for being inconsistent, difficult to quantify, implement and
monitor. Disparaging analysis, suggests that there exists a potential
inconsistency in the SDGs, particularly between the socio-economic
development and the environmental sustainability goals. Critiques also raise
questions on the measurability and monitoring of the broadly-framed SDGs.
The goals are non-binding, with each country being expected to create their
own national or regional plans. Moreover, the source(s) and the extent of the
financial resources and investments for the SDGs are ambiguous. This chapter
quantifies and examines the inconsistencies of the SDGs. It further inspects
which of the underlying social, economic or environmental pillars of SDGs are
that most effective for achieving Sustainable Development. Analyses of the
data reveal that the developed countries need to remain focused on their social
and environmental policies. The developing countries, on the other hand are
better off being focused on their economics and social policies in the short run,
even though environmental policies remain significant for Sustainable
Development.
Sustainable Development Goals, sustainable development incompatibility,
structural equation modelling, factor analysis, UN data revolution.
Ranjula, Bali Swain, Stockholm School of Economics and
Södertörn University, 14189 Huddinge, Sweden
email: Ranjula.Bali.Swain@sh.se
Abstract
© Springer International Publishing Switzerland 2017 1
W. Leal Filho et al. (eds.), Handbook of Sustainability Science and Research, World Sustainability
Series, DOI xxxxxxxxxxxx
1 Introduction
Referred to as comprehensive, far-reaching, people-centered and universal, the
Sustainable Development Goals (SDGs) have also been described as the
‘transformative agenda’ (UN 2015). The SDGs aim to eradicate poverty, establish
socioeconomic inclusion and protect the environment. Disparaging analysis, suggests
that there exists a potential inconsistency in the SDGs, particularly between the
socio-economic development and the environmental sustainability goals (Spaiser et.
al 2016; ICSU and ISSC 2015). Critiques also raise questions on if and whether the
SDGs can be measured and monitored. The SDGs are non-binding, with each country
being expected to create their own national or regional plans. Moreover, the source(s)
and the extent of the financial resources and investments required for the SDGs are
ambiguous. Addressing these questions, the chapter investigates and presents the
evidence that quantifies the inconsistencies within the SDGs. It further examines
alternative measures of SDGs and investigate, which of the underlying pillars of
SDGs (economic, social or environment) are the most effective in achieving
Sustainable Development.
The new global SDGs, are not neoteric. About 56 years ago, the OECD Convention
(Article 1), targeted Sustainable Development, to achieve the highest sustainable
economic growth and employment and a rising standard of living in member
countries, while maintaining financial stability, and contributing to the development
of the world economy. By the early 1970s OECD began to focus on all three pillars:
economic, social and environmental. It was about two decades later that the
Brundtland Commission Report (WCED 1987)) defined Sustainable Development as
the ability of the present generations to meet their own needs without compromising
the ability of the future generations to meet their own needs.
The Millennium Development Goals (MDGs), were the dominating development
paradigm at the beginning of this century. With the MDGs approaching 2015, the
Rio+20 summit in 2012, set up an Open Working Group (OWG) with representatives
from UN member countries that was mandated to create a draft set of goals. The
objective of these goals was to provide continuity to the MDGs and motivate
policymaking on both the national and local scale towards sustainability. Unlike the
MDGs that had been criticized for being set in an ad hoc, insulated manner, the SDGs
are a result of the largest consultation process, resulting in 17 main goals and 169
sub-targets (Ranganathan et al. 2017; UNSDSN 2015), that were unanimously
approved in September 2015.
The universal and broadly defined SDGs have been heavily criticised. They envelop
a broad range of ambitious sustainable development agenda that covers poverty to
urban development to marine life. While defenders of SDGs claim that the goals
reflect the complexity of development, detractors argue that the breadth is at odds
with the need to prioritise (The Economist 2015). The Economist describes the SDGs
as so broad and sprawling as to,” …amount to a betrayal of the world's poorest
people."
There has also been concern that the targets included in the SDGs are not the right
ones. For example, the Copenhagen Consensus Centre has led an initiative to conduct
cost-benefit analysis on the SDG targets, highlighting that efforts to achieve some of
A critical analysis of the SDGs 3
the targets would be ‘poor value for money’ and suggesting that either they should be
changed or dropped entirely (Lomborg 2014). Others have been less worried about
the targets under the assumption that these will be further negotiated at the country
level. Another issue raised has been the wording of the goals, with stakeholders
claiming that a number of targets could be constructed more clearly (UN SDSN
2015; ICSU and ISSC 2015). The Center for Global Development (CGD) dedicated
an entire blog series on how many of the targets could be improved if small changes
were made to the language (Kenny 2015).
A major additional challenge is to ensure adequate investments and financial
assistance for the developing countries (Campagiolo et al. 2015; OECD 2014; UN
SDSN 2015). The IPCC (2007, 2014) has recommended that our planet’s temperature
should be limited to less than 2 degrees by the end of this century. This requires large
scale changes in energy systems and land use, by cutting emissions by 40-70 per cent
of the 2010 levels, by 2050; and emissions levels at zero by 2100. This necessitates
immediate improvements in efficient and renewable energy by a factor of 3-4 times
as compared to the current levels. It also demands greater afforestation and a
reduction in deforestation. While some believe that the SDGs are financially
unviable, others estimate that this would require about $2 trillion-3 trillion a year of
public and private money over the next 15 years. This is roughly equivalent to 15 per
cent of the annual global savings, or 4 per cent of the world GDP (The Economist
2015). At the moment, the Western governments promise to provide 0.7 per cent of
GDP in aid, but only a third of that materialises.
Easterly (2015) is overtly critical of the SDGs describing them as, “… beauty
pageant contestants’ calls for World Peace.” He argues that the whole point of the
SDGs is to answer “what should we do?”, which suffers from three fundamental
fallacies: First, that the answers do not lead to actions; second, it is unclear that who
are the ones responsible for undertaking the actions; and third, that action
recommendations are the only way to induce progress. The SDGs are non-binding
with the signatories committed to “respecting national policies and priorities” with
“each Government setting its own national targets.” The shared responsibility for
SDGs outcomes is collective, extending to all the leaders, UN agencies, multilateral
and bilateral aid agencies, and numerous other private sector, nongovernmental, and
civil society actors. And the action plans are the only way to progress. Easterly
(2015) forcefully reasons that the SDGs illustrate that action plans don’t necessarily
lead to action, collective responsibility may not necessarily be the right way to act,
and there exist alternative paths to progress other than the action plans. Despite these
issues, the SDGs stand as the new global development goals, agreed to by the world
leaders.
R. Bali Swain
4
5
2
Inconsistencies in SDGs and other challenges
By its very nature economic growth leads to a depletion of natural resources and
deterioration of environmental service (Repetto et al. 1989; Pearce and Atkinson
1993; Hamilton and Clemens 1999). In the quest for growing economic growth and
higher standards of living, nature is thus under-prioritized (Managi & Kaneko 2009;
Jorgenson 2010; Pao & Tsai 2010; Redclift 2010; Rich 2013). The very concept of
Sustainable Development reflects the inherent conflict between the human and
natural systems (Redclift 2005; Dasgupta 2013). While reviewing the SDGs the
International Council for Science (ICSU) critically pointed towards the internal
inconsistency between the ecological sustainability and the socio-economic
progression (ICSU and ISSC 2015). Most studies are sector-specific and typically
ignore the synergies and trade-offs identified in multisector assessments (ICSU and
ISSC 2015; van Vuuren 2015). Policy makers, however, cannot assume that policies
targeting SDGs would lead to zero-sum trade-offs (Nilsson, et al. 2012).
Obersteiner et. al (2016) argue that trade-offs within the global SDG agenda
will manifest as obstacles to progress at regional and national levels. For instance,
in the Congo Basin satellite data has identified that agricultural expansion and fuel
wood and timber extraction are the leading drivers of deforestation and habitat
degradation (Celine et al. 2013). Similarly, in Sumatra the rising agricultural
commodity prices are detrimental to tropical forests and their biodiversity (Gaveau
et. al 2009). However, there is limited evidence about the nature and extent of this
repeatedly claimed incompatibility of sustainability and development (ICSU and
ISSC 2015; Stern et al. 1996; Redclift 2005).
Spaiser, Ranganathan, Bali Swain and Sumpter (2016) is one of the few, if not
the only, study that quantifies and models these potential inconsistencies in the
SDGs. Their analysis is based on an extensive dataset of 1,423 economic, social,
environmental and political indicators for 217 countries, covering the period 1980
to 2014 (including data from the World Bank, Polity IV, CIRI Human Rights Data
Project, Freedom House and the Heritage Foundation/The Wall Street Journal).
Spaiser et al. (2016) first employ confirmatory factor analysis (CFA) to test the
consistency of an abstract unobservable construct like Sustainable Development.
Choosing one indicator for each of the three SDG pillars a latent variable for
Sustainable Development is estimated. These selected indicators: Child Mortality,
Education and CO2 emissions, have the highest factor loadings for Sustainable
Development. Figure 1 (source: Spaiser et al. 2016), reveals the stark
incompatibility within the SDGs framework as the CO2 emissions load in the
opposite direction than economic and social pillar indicators.
Spaiser et al. (2016) further model these inconsistencies by employing the Feature
Selection Algorithm (Variable Elimination Algorithm1). A large number of
1 Variable Elimination Algorithm is a supervised feature selection machine learning
method based on partial least square regression.
potential indicators are inspected to find the most relevant predictors of latent
Sustainable Development data and the 3 selected indicators of SDG pillars. The
most relevant variables are then used to fit a dynamical system model. The best
model is identified according to the Bayes Factor. Based on their results they
argue that the GDP per capita, has an overall positive effect on reducing poverty
and increasing socio-economic, but a mainly negative impact on CO2 emissions.
Thus, given the business-as-usual scenario, growth and consumption lead to
incompatibility between the SDGs. The models also suggest some common factors
that contribute to beneficial effects on one SDG dimension without having
simultaneously adverse effects on other dimensions, such as, extensive health
programs for reducing child mortality, government spending on education and
environmentally friendly technologies. Thus, they conclude that future policy and
efforts should shift the focus from a consumption-based economic growth to
investment in human well-being (health, education) and environment-friendly
technologies.
Figure 1. EFA-Biplot of Sustainable development (latent factor),
comprising of the three underlying SDG pillar indicators, Child
Mortality, Education and CO2 emissions.
Source: Figure 3, Spaiser, Ranganathan, Bali Swain and Sumpter
(2016).
3
Challenges in Quantifying SDGs
Easterly (2015) argues that SDGs are encyclopedic where everything is top
priority, which means nothing is a priority. He importantly points out that it is
unclear how the U.N. is going proceed in achieving the unactionable, unquantifiable
targets for the SDGs, as also the unattainable ones like “ending poverty in all its
forms and dimensions,” “universal health coverage,” “ending all … preventable
R. Bali Swain
7
deaths [related to newborn, child, and maternal mortality] before 2030,” “[end] all
forms of discrimination against all women and girls everywhere,” and “achieve full
and productive employment and decent work for all women and men.” Even
staunch supporters of the SDGs will acknowledge that there is substance to Easterly
and other critics. The MDGs were appealing because they were precise and
measurable (Easterly 2015). It is pointless to define goals that cannot be quantified,
measured and monitored. Quantifying a multi-dimensional concept like sustainable
development, however, is fraught with challenges. As far back as the 1970’s,
Agenda 21 formulated the need for sustainable development indicators. On
sustainable development indicators, Agenda 21 (paragraph 40.4) states that:
“Indicators of sustainable development need to be developed to provide solid bases
for decision-making at all levels and to contribute to a self-regulating sustainability
of integrated environment and development systems.” Agenda 21 was adopted by
183 governments at the 1992 United Nations Conference on Environment and
Development (UNCED) in Rio de Janeiro (United Nations 1992). It was later
reaffirmed at the World Summit on Sustainable Development held in Johannesburg,
South Africa in 2002, and 2012 Rio de Janeiro conference.
Focusing on an integrated economic, environmental and social framework,
OECD (2004) developed indicators that could be used for sustainability. Eurostat
also established a task force of national experts in 2001 in support of the European
Union sustainable development strategy and the first set of indicators were adopted
in 2005 and later reviewed in 2007 (OECD 2008).
Initially, Sustainable Development was about ensuring optimal consumption that
could be maintained in the long run without depleting the generated capital (where
the optimal rate of consumption was equal to growth rate of population plus growth
rate of technical progress). Sustainability was thus a dynamic optimization problem
of intergenerational equity (Pierantoni 2004). Sen’s theory of development as
freedom and capabilities approach provides a wider interpretation of social capital
and human capital. Sustainable Development is thus a complex, multi-domain issue
that combines efficiency, equity and intergenerational equity across economic,
social and environmental pillars. Sustainable development may be measured by
well-being, which is defined as the discounted present value of future utility. For it
to be measured in terms of well-being, the concept of consumption needs to be
broadened (OECD 2008). Dasgupta (2001), argues that well-being includes welfare
and the additional benefits derived from intangibles other than consumption; for
instance, presence of fundamental human rights, forest products, beautiful sunsets
etc. To a certain degree, Sustainable development also remains anthropocentric as a
concept.
More recent literature defines Sustainable Development in terms of Inclusive
Wealth or intergenerational well-being (Arrow et al. 2012). Inclusive Wealth
measures a society’ s stock of all its capital assets (reproducible /productive capital,
human capital and natural capital) and their changes over time accounting for
population growth and technological change. Empirical evidence shows that unlike
GDP per capita and Human Development Index (HDI), Inclusive Wealth Index is
better able to capture sustainable development through changes in intergenerational
well-being (Dasgupta, 2013). However, this measure is severely limited by cross-
country, time series data availability (Arrow et al. 2012; Dasgupta 2013).
Several researchers suggest that physical capital, social capital and natural
capital are the three underlying assets of sustainable development (Hamilton et. al
2004). A path is sustainable if the future per capita value of these assets is not less
than the current well-being. Pearce et al. (1989) define this as weak sustainability.
Determining values of these assets are difficult, as for some, markets may not exist.
Furthermore, it is also difficult to determine the threshold beyond which an
irreversible change takes place. To circumvent some of these difficulties, Pearce et
al. suggest strong sustainability, which demands that some critical amount of the
non-substitutable natural capital be preserved, independent of any increases in value
of other social or physical assets. For instance, substitutes do not exist for global
natural assets like the ozone layer. Thus, sustainability measures should include
both concepts of sustainability, measuring weak sustainability in monetary units and
strong sustainability in biophysical ones (tones, hectares or joules). Hamilton
(2004) argues that measurement of sustainability is required for extending national
accounting systems. Nordhaus and Kokkelenberg (1999) motivate sustainability
measurement because for several developing countries the combination of low
saving effort, high resource depletion, high population growth, and ineffective
public investments, particularly in education, is critical.
Besides operationalization of SDGs, their implementation includes monitoring
and measuring sustainable development indicators. Three notable publications in
the emerging literature are: the GGKP Report on ‘Measuring Inclusive Green
Growth at the Country Level’ (2016); the SDG Index and Dashboards – Global
Report prepared by the UNSDSN and the Bertelsmann Stiftung (Sachs et. al 2016);
and the Overseas Development Institute Report (Nicolai et. al 2015).
The GGKP Report on Measuring Inclusive Green Growth2 (IGG) at the Country
Level focuses on the main reliable sources and constraints for data collection at the
country level. The report however is not limited to the SDGs, but to the Inclusive
Green Growth (WB 2012). Instead of the social, environmental, economic
dimension, the IGG context with ‘inclusive, green, growth’, emphasizes their
interaction in a dynamic perspective.
The Overseas Development Institute’s report (Nicolai et. al 2015) develops a
grading system for each the SDGs, classifying them broadly into three levels in
terms of their chance of reaching the targets by 2030. The report classifies them
into: reform, revolution, and reversal. SDGs that are classified at reform levels are
more than halfway to achievement by 2030. These include ending extreme poverty,
strengthening economic growth in least developed countries, and halting
deforestation (SDGs 1, 8, 15). Goals that require progress by multiples of current
rates were categorised as revolution. Nine targets belong to this group: ending
hunger, reducing maternal mortality, secondary school completion, ending child
marriage, access to sanitation, access to energy (electricity), industrialisation in
LDCs, reducing deaths, and domestic resource mobilisation (SDGs 2-7, 9, 16, 17).
Targets classified under reversals are moving in the opposite direction and require a
reversal of current trends. They include inequality, slum populations, climate
2 The GGKP report identifies five broad characteristics of IGG: Natural Assets;
Resource Efficiency and Decoupling; Resilience and Risks; Economic Opportunities
and Efforts; and Inclusiveness.
R. Bali Swain
9
change, waste management and marine (reef) conservation. (SDGs 10-14). Nicolai
and others are optimistic that the least-developed countries are halfway towards
their 2030 targets of ending extreme poverty, economic growth in least-developed
countries and halting deforestation. In terms of regional projections, Sub-Saharan
Africa requires special efforts in SDG implementation.
The SDG Index in the UNSDSN and the Bertelsmann Stiftung (Sachs et al 2016)
is a shorthand way of tracking the SDGs achievement for each country. It identifies
multiple indicators to measure each SDG goal. By averaging across the scores for
all indicators that apply to each of the SDG, it arrives at the country scores for each
of the 17 goals. These scores are averaged to find the overall SDG Index for each
country. They employ two different averaging approaches, a simple arithmetic
average and a geometric average.
Spaiser, Ranganathan, Bali Swain and Sumpter (2016) employ a data-driven
approach to measure and monitor sustainable development. They construct two
separate measures of SDGs. The first model assumes a true latent variable (for
sustainable development) with the three components of child mortality, education
and CO2 emissions (representing the three underlying pillars of SDGs – economic,
social and environmental) as observable indicators for this latent phenomenon,
which ultimately goes beyond those three indicators. Thus, the first model seeks to
predict changes in this latent sustainable development variable primarily and not
changes in its components. The second model, primarily predicts changes in the
three components and to a lesser extent changes in the true latent Sustainable
Development variable. Spaiser et. al (2015) They compare the performance of these
two SDG indices with GDP per capita and HDI, in terms of the how well these
indices predict changes in the three components (Child Mortality, Education and
CO2 emissions). They find that both their SDG indices perform better than the
common indices for used development and economic growth, namely, HDI and
GDP per capita.
Bali Swain and Wallentin (2017) construct a latent Sustainable Development
variable to investigate which ones of the underlying pillars: economic, social and
environment have the largest causal impact on improving Sustainable Development.
Evidence on this is critical for the path that developing and developed countries and
different regional country groups might take to attain SDGs. For instance, given the
limited available resources, should developing countries focus on all three pillars to
achieve SDGs, or are they most successful by emphasizing development and
growth in the economic and social pillar?
Bali Swain and Wallentin (2017) further compare their SDG construct with SDG
indices from Sachs et al. (2016) and the Human Development Index (HDI), to
investigate if these different measures suggest conflicting policy inferences to the
developing and developed countries in terms of achieving SDGs. Employing
Structural Equation Models (SEM) to the dataset analysed in Sachs et al. (2016), the
model described by the path diagram in Figure 2 is estimated. The ellipses in the
figure (with the arrows) represent the structural model. The three underlying pillars
of Sustainable Development are represented by the latent variables: economic,
social and environment. The causal impact of these three latent variables on the
latent Sustainable Development variable (right-hand side ellipse) is estimated in the
structural model. The measurement of the three pillars of SDGs (in ellipses) is
estimated in two steps. Using the UNSDSN data (Sachs 2016), in Step 1, the
Principle Component Analysis (PCA) is employed. The Principle Component
Scores are calculated for each of the SDGs using the set of observed indicators for
that specific goal. In Step 2, Exploratory Factor Analysis (EFA) is conducted to
determine the SDGs (in rectangles) that measure the latent factors or Sustainable
Development pillars (in ellipses). The other measurement model (on right-hand
side) of the path diagram, determine the latent Sustainable Development construct
(in ellipses) using indicators (measures in rectangles).
Preliminary evidence suggests that that while all the three latent factors have a
significant and positive impact on Sustainable Development, their magnitude of
impact varies (Bali Swain and Wallentin 2017). For both, the developing and the
developed countries, the social pillar remains an important feature of any policy that
successfully wants to achieve SDGs. Though the environmental factor is significant
for the developing countries, their impact is small as compared to the social and
economic factors. Developing countries will thus attain the largest impact on
Sustainable Development by focusing on policies that lead to economic and social
development. In the short run, they may be able to decrease their emphasis on the
environmental side. The developed countries cannot grow sustainably unless they
focus on both their social and environmental policies. Bali Swain and Wallentin
(2017) results are in line with the literature that visualizes SDGs as an interlinked
set of policies with trade-offs and synergies.
4
Big data and sustainable development
Quantifying SDGs requires data and data in the developing countries is often
remarkably poor. In fact, there isn’t a single five-year period since 1990 where
countries have enough data to report on more than 70 percent of MDG progress
(UN Independent Expert Advisory Group 2014). More worryingly, about half of
this data is based on firm country-level surveys; the rest are comprised of
estimates, modelling and global monitoring.
Data is very often missing in those countries where it is needed the most. Child
mortality is widely assumed to be the variable on which data availability is the
best. Of 161 developing countries, 136 have data to track this goal (Rodriguez-
Takeuchi 2014). Yet over two thirds of the 75 countries accounting for more than
95 per cent of all maternal, new-born and child deaths do not have registries of
births and deaths. Twenty-six countries have no data at all on child mortality since
2009 (Stuart et. al 2015).
Even where data appears rigorous and comprehensive, certain groups are often
missing, such as ethnic minorities or regional groups. Indigenous populations and
slum-dwellers are consistently left out of data-sets. It is still impossible to know
with certainty how many disabled children are in school in many countries. Issues
of most concern to women are poorly covered by existing data. For example, only
just over half of all countries report data, of varying quality, on intimate partner
behaviour; data is rarely collected from women 50 and over; and little is available
R. Bali Swain
11
on the division of money and labour within households (UNICEF 2013). It is
important that governments and their national statistics offices need better funding
and training. Traditional data-collection techniques such as household surveys,
censuses and registers should be made more frequent, rigorous and universal.
Data challenges have implored researchers to test if Big Data may be used to
monitor the SDGs. Big data produces large volumes, of massive data generated
Figure 2: Path Diagram for SEM of Sustainable Development
Source: Adapted from Bali Swain and Wallentin (2017).
Measure
1
Measure
2
Measure
3
SDG1
SDG2
SDG3
SDG4
SDG5
SDG6
SDG7
SDG8
SDG9
SDG10
SDG11
SDG12
SDG13
SDG15
SDG16
SDG17
Econo
mic
Sustainabl
e
Developme
nt
Soci
al
Environm
ent
from satellite images, social media, online commercial transactions, bank transactions data
and cell phone record etc. As Alex ‘Sandy’ Pentland, of the MIT Media Lab explains “the
power of Big Data Community is that it is information about people's behavior instead of
information about their beliefs” (Letouze 2015). For instance, monitoring poverty or food
security may be done by using cell-phone activity, Call Detail Records (CDRs) analytics or
satellite data (Steele et. al 2017; Elvidge, et. al 2009; Smith-Clarke et. al 2014; Eagle et. al
2010; UN Global Pulse 2015). Most Big Data is currently owned by banks, mobile phone
internet providers, social media providers etc. To exploit its full potential, it therefore needs
to be standardized and accessible so that the users may be able to effectively use it for
monitoring, evaluating and analyzing its impact on sustainable development policies (UN
2015). Letouze (2015) points to the perils of the use of Big Data as there exists a potential
risk to individual and group rights, privacy, identity, and security. In addition to this, the
legality and legitimacy of this is also questionable. Even when the data is anonymized it is
possible to deanonymize it, making it very hard for any given individual to hide digitally in
the data. Another problem of employing Big Data analyses is the risk that the focus moves
towards correlation and prediction and away from the diagnostics or causal inference.
Without causal analysis and the factors that affect policy impact, framing policy becomes
difficult. Finally, there are fears that a ‘new digital divide’ might arise as a result of analytical
capacities and access to data that only a limited number of institutions, corporations and
individuals have. Paradoxically this would result in disadvantage for the countries and
individuals that it intends to help in the first place.
5
Conclusion
The path to quantifying and monitoring SDGs presents several challenges. It
requires a profound understanding of Sustainable Development, comprehension
about how to operationalize and implement the SDGs, access to all forms of data and
the expertise to analyze and interpret the results. This critical analysis of SDGs
quantifies and examines the inconsistencies of the SDGs. The pursuit of economic
growth and consumption underlies the inconsistencies between the economic and
social development and the environmental goals. However, there are common
denominators like health programs and ecological sustainability that can lead to
achieving SDGs without initiating the inconsistencies. Further investigation, about
the effectiveness of social, economic or environmental factors in achieving
Sustainable Development, reveals interesting results. Empirical evidence reveals that
the developed countries are better off focusing on their social and environmental
policies. The developing countries, on the other hand would be better off focusing on
their economic and social policies in the short run, even though the environmental
policies remain significant for sustainable development.
Lack of limited availability of the data is a major constraint for quantifying and
monitoring SDGs. The studies mentioned here is limited due to a data-driven
approach. While data on economic indicators is widely available for most countries,
data on environmental and social indicators is incomplete and of poor quality.
Furthermore, studies using data-driven approaches usually lack an underlying
theoretical foundation. SDGs are no exception and have often been criticized for not
being based on a comprehensive theoretical framework. Finally, SDGs are long term
R. Bali Swain
13
development agenda and have the potential to be exposed to unforeseen positive and
negative shocks. The inferences derived from the data are based on the business-as-
usual scenario using historical data. These may change in the future while responding
to positive changes by providing, for instance, incentives or adopting technological
innovations.
While being a transformative agenda that is universal, people-centric and
comprehensive, SDGs are also constrained by these characteristics. A situation that is
made more acute by lack of appropriate data. While future studies may explore
alternative approaches to quantify and monitoring SDGs, additional challenges
require emphasis on raising required resources to finance Agenda 2030 and exploring
alternative Action plans at the national and regional level and ways of implementing
the agenda even though it remains non-binding on countries.
Acknowledgments I thankfully acknowledge the financial support from the Swedish
Research Councils (Vetenskapsrådet and Formas).
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Authors Biography
Professor Ranjula Bali Swain has a Ph.D. in Economics from Uppsala University
Sweden. She is currently Professor of Economics at Södertörn University and a Visiting
Professor at Stockholm School of Economics, Stockholm, Sweden. Her current research
interests include sustainable development, environmental economics, gender and development
finance.
