A System of Sustainability Indicators for the Provinceof Thai Binh, Vietnam

Le Trinh Hai • Pham Hoang Hai • Pham Thi Thu Ha •

Nguyen Manh Ha • Ly Trong Dai • Pham Viet Hoa •

Nguyen Cao Huan • Lai Vinh Cam

Accepted: 18 March 2013� Springer Science+Business Media Dordrecht 2013

Abstract Sustainable Development is a broad and universal concept. Indicators are a

basis to measure sustainability and to direct policies that aim to achieve a better quality of

life. Thai Binh, a coastal province in North Vietnam is strongly concerned about strategic

sustainability development. To select a system of sustainability indicators, the Delphi

method was applied in 2012. A two-round questionnaire was organized to use with 32

experts, who acted as participants. 69 indicators were selected from 98 listed indicators: 15

related to economic development, 5 to the sea and coastal zone, 1 to the global economic

partnership, 4 to consumption and production patterns, 7 to poverty, 3 to governance, 9 to

health, 4 to education, 3 to demographics, 2 to natural hazards, 5 to atmosphere, 7 to land,

and 3 to freshwater. Conversely, 29 other indicators were rejected. The Delphi method

L. T. Hai (&) � P. H. Hai � N. M. Ha � L. T. Dai � L. V. CamInstitute of Geography (IG), Vietnam Academy of Science and Technology (VAST), Hanoi, Vietname-mail: letrinhhai@gmail.com

P. H. Haie-mail: phhoanghai@gmail.com

N. M. Hae-mail: hadl2007@yahoo.com

L. T. Daie-mail: lytrongdai@gmail.com

L. V. Came-mail: lvcamminh04@yahoo.com

P. T. T. HaFaculty of Environment, Hanoi University of Science, Vietnam National University, Hanoi, Vietname-mail: phamthithuha@hus.edu.vn

P. V. HoaSpace Technology Institute (STI), Vietnam Academy of Science and Technology (VAST), Hanoi,Vietname-mail: pvhoa@sti.vast.ac.vn

N. C. HuanFaculty of Geography, Hanoi University of Science, Vietnam National University, Hanoi, Vietname-mail: huannc52@gmail.com

123

Soc Indic ResDOI 10.1007/s11205-013-0315-x

allows indicator selection for identification of the process of sustainability. The system of

indicators, as the first important step of the sustainable development process, provides

useful information for decision makers and planners as well sustainability strategy. It is

planned that the indicators selected should be applied in the province.

Keywords Thai Binh province � Delphi process, experts � Sustainability indicators �Vietnam

1 Introduction

Sustainable development (SD) relates to aspects of economic-social-cultural-environ-

mental protection and development. There are many definitions of SD as it can mean

different things to different people, including this landmark one that first appeared in 1987:

‘‘Development that meets the needs of the present without compromising the ability of

future generations to meet their own needs’’ (WCED 1987). In 2007, in terms of a ‘‘hot’’

current issue (climate change), a speech on World Environment Day 2007, Secretary-

General Ban Ki-moon said ‘‘… our sustainability world eventually becomes a globally

acclaimed model of efficient use of energy and resources’’ (UN 2007b).

The United Nations has a long history of supporting the use of appropriately defined

indicator systems as a tool to promote better decision making by policy makers. Chapter 40

of the Agenda 21, adopted in 1992, deals with the topic ‘‘Information for decision-making’’

and discusses the development and the worldwide use of Sustainable Development Indi-

cators (SDIs) (UN 1992). The core of this discussion is that indicators had to be developed

to improve and support information for decision-making. In 1995, the third session of the

Commission for Sustainable Development (CSD) followed up the indicators recommen-

dation and approved a work programme on the subject. In 2001, the United States pub-

lished the report as the culmination of the CSD Work Programme on SDIs (1995–2000). It

provided a detailed description of key SD themes and sub-themes and the approach for the

use in decision-making processes the national level. In 2007, based on these earlier sets,

the third edition was published (UN 2001). The indicator set retained the theme/sub-theme

framework as new cross-cutting that was adopted in 2001. It was directly relevant to the

monitoring of national SD strategies. In this framework, the division of indicators of

the four pillars of social, economic, environmental and institutional is no longer explicit in

the newly revised set. In 2012 in Rio, the Member States decided to launch a process to

develop a set of SD Goals, which will build upon the Millennium Development Goals and

converge with the post 2015-development agenda (UN 2012).

The process to select SDIs is important. The lack of quantitative SDIs caused by local

and central level administrative complexity hampers the identification of indicators, at

what levels these indicators should be set to achieve sustainable development goals, and

how to monitor or collect data for these SDI (VIDS 2005). To be effective, the selection

process of SDIs should be fair and open, and involve a wide participation and testing. This

process should result in a well-founded and comprehensive set of relevant SDIs. The

process should be repeated regularly to assure that the selected indicators evolve over time

and remain relevant (NASA 2003). SD is a unique social choice and is as such important

for the future. The importance of protecting and managing the natural resource is based on

the economic and social development and the environmental protection, which focus issues

such as on drinking water, oceans, seas, islands and coastal areas, disasters, climate

L. T. Hai et al.

123

change, agriculture, desertification, mountain systems, sustainable tourism, biodiversity,

forests and trees, and mining (Hens and Nath 2005).

Currently, in regards to the hot issue of climate change, Vietnam is a country of

vulnerable, particularly in terms of its water resources, agriculture, forestry, energy,

aquaculture and human health. Vietnam’s coastal zones are amongst its key vulnerabilities

(ALM 2009) and the country is considered as most threatened by climate change induced

sea level rise. Approximately half of the country’s population lives in the densely popu-

lated areas of the Mekong and Hong (Red) River Deltas; Thai Binh province belongs to the

Red River Delta (Yusuf and Francisco 2009).

This study focused on the Thai Binh province, Vietnam. The province is located in

northern Vietnam and has one of the highest population density in Vietnam (about 1,200

people/km2 and about 15 % of the total population are living in urban areas). In the past,

the province was largely an agricultural area. The Thai Binh economy has since shifted

away from agriculture to small-scale industry, agriculture and aquaculture (DONRE 2011)

and, in recent years, heavier industry has emerged (e.g. gas, brown coal mining). In

addition, other trade villages are exiting. The transition from a largely agricultural province

towards more industrial operations has resulted in a number of emerging challenges (e.g.

population migration, unemployment, and environmental pollutions).

Thai Binh does not currently have a formal SD indicator system, despite having access to a

range of data in provincial documents, reports and statistic yearbooks. Unfortunately, the

province currently lacks adequate approaches, methods, infrastructure and SD policies,

although the provincial authorities are increasingly becoming aware of the importance of this.

Recognising the need a formal SD system in Thai Binh, this study sought to develop a

concise and representative set of SDI aligned to the characteristics and emerging chal-

lenges of the Thai Binh province. A Delphi based approach that drew on the opinion of

multiple experts was used to inform the development of the SD indicator set. This study

shows how key indicators for socio-economic development and environmental protection

can be selected at the provincial level. Indicators are able to show what is the measurable

and sustainability of a situation (DCMS 1999; Hart 1997). These statistical values col-

lectively measure the capacity to meet present and future needs (NASA 2003). They

provide information that is crucial for decision makers and can also inform the public.

2 Delphi Method

Internationally, Delphi method is widely used to inventory scientific consensus. When used

correctly, the method can contribute significantly to broadening knowledge between

experts (Keeney et al. 2001). The Delphi approach know as Knowledge Acquisition for

Multiple Experts with Time scales (KAMET), which takes time scales into consideration

while eliciting feedback from multiple experts (Chu and Hwang 2007) was used to define a

system of sustainability indicators. The method allows for the systematic collection of the

expert judgments on a particular topic through a set of sequentially applied feedback

1 2 3 4 5

the indicator is highly irrelevant

the indicator is irrelevant

the indicator is more or less relevant

the indicator is relevant

the indicator is highly relevant

Fig. 1 Likert-scale of indicator relevance ‘scores’ used by the respondents in the Delphi approach

A System of Sustainability Indicators

123

questionnaires, interspersed with summary information on options from earlier responses

(Delbecq and Van de Ven 1975). This method has been shown to be a reliable qualitative

research approach with the potential to solve problems to contribute to decision-making,

and reach a group consensus in a wide variety of areas (Cochran 1983). The use of this

method is widely accepted, particularly in Vietnam (Chu and Hwang 2007; Hai et al. 2009,

2011; Huge et al. 2009; Hai 2010; Gobin et al. 2012).

The Delphi method requires selected experts to rate the proposed indicators based on a

1–5 rating scale (Fig. 1).

The Delphi method stipulates specific selection criteria relating to median values, quartile

deviation, rating mean, and rating variances of each indicator. These are defined as:

• Median values and Quartile deviation (Q—is the quartile);

• Rating mean (Rme)—The mean of the ratings for questionnaire item indicator i. For

each indicator, the Rmei is calculated using the formula:

Rmi ¼X

i

¼ 1Nxi=N

in which N is the total number of ratings, xi is the rating allocated by expert i

• Rating variances (Rv)—rating variance (Rvi): the ratio of experts who change their

ratings for indicator i

The first stage of the Delphi method (i.e. Round t) requires selected experts to rate each of

the proposed indicators, using the 1–5 rating scale presented in Fig. 1. The number of

experts engaged can vary between 15 and 50 (Helmer 1983; Chu and Hwang 2007; Hai

et al. 2009; Huge et al. 2009; Gobin et al. 2012). They represent the professional experts.

The rules for analyzing the ratings from multiple experts with the Delphi approach are

presented in Table 1.

An indicator is accepted when the conditions outlined in Table 1 are fulfilled. In this

table, if an expert’s response (after round 2) falls inside the above R, the data ranges are

used (e.g. Rmei C 3.5 and Q B 0.5 and Rvi B 15 %) then indicator i is accepted, and no

further discussion concerning indicator i is needed. Conversely, if an expert’s response

falls outside the above r, the data ranges are not used (e.g. in Round t ? 2: if Rmei \ 3.5,

and Q B 0.5, and Rvi B 15 %), indicator i is rejected, and no further discussion con-

cerning indicator i is needed. The threshold (minimum requirement) to agree on a par-

ticular indicator during the second or third step is set of the ratio of 75 % of the experts.

Stability is reached when few or no further shifts in the panel of responses occurs from

round to round (Murry and Hammors 1995).

3 Application of the Delphi Method in this Study

The Delphi method was applied as described below:

• Desktop research: Indicators were screened and information sought from a wide range

of sources on existing SDI recommendations, including existing national SDI for

Vietnam, Indonesia, the UK, China, Thailand and Sweden, SDIs proposed by the UN

(2007a) and Thai Binh province master plans1;

1 Thai Binh People’s Committee, 2006. The master plan on social-economic development of Thai Binh by2020. Thai Binh, Vietnam.

L. T. Hai et al.

123

• SDI workshop: A set of open-ended questionnaire were discussed and commented on

the initial indicators by experts. This Workshop took place in May 2012 at the Institute

of Geography (IG) in Hanoi (Vietnam). During these workshops, a multi-disciplinary

group of 15 experts and the project team members drafted a set of 98 SDIs (Table 2).

These indicators belong to the importance of integrating economic development, sea

and coastal zone, global economic partnership, consumption production patterns,

poverty, governance, health, education, culture, demography, natural hazards, atmo-

sphere, land, and freshwater pillars.

The following two rounds were needed to establish consensus among the experts based

on the rules to analyze the ratings using the Delphi approach (Table 1);

• Round 1 (feedback): A questionnaire and a letter of invitation was sent to 35

Vietnamese experts including researchers (15), professors (5), and national and

provincial managers (15). The project team received responses from 32 of the 35

experts (researchers (15), professors (5), and national, regional, and provincial

managers (12).

• Round 2 (feedback): A summary of the Round 1 ratings was then depicted to each

expert and the experts again being asked to rate each of the indicators. All of the 32

experts who participated in Round 1 also participated in Round 2. In this study, there

was no need to continue with a 3rd round because the requirements (Table 2) were met.

Table 2 just shows the 98 initial indicators (in the column of Round 1 grouped by theme

and sub-theme). The results of this process are presented in the next section.

4 Results

4.1 Round 1

As the results in Table 2 show, the scores of 68 SDIs rated above 3.5, with the remaining

30 SDIs receiving scores below 3.5. According to the rules of analysis for the rating mean

(Table 1), this meant that 30 SDIs are deemed not to be relevant to the case study area.

Moreover, the scoring values range from 1 to 5 (Fig. 2) and their frequency of occur-

rence in round 1 (R1) is:

Table 1 Rules to analyze the ratings from multiple experts using a Delphi approach (Chu and Hwang 2007)

Round (t ? 1) Round (t ? 2) Round (t ? 3)

Rating mean(Rmei) C 3.5

If Rmei C 3.5 and quartile deviation(Q) B 0.5 and the rating variance(Rvi) \ 15 %, then indicator i is accepted,and no further discussion concerningindicator i is needed

Rmei \ 3.5 Rmei C 3.5 or Rvi [ 15 % If Rmei C 3.5 and Q B 0.5 andRvi B 15 % then indicator i is accepted,and no further discussion concerningindicator i is needed

If Rmei \ 3.5 and Q B 0.5 and Rvi B 15 %,then indicator i is rejected, and no furtherdiscussion concerning indicator i is needed

A System of Sustainability Indicators

123

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L. T. Hai et al.

123

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isher

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A System of Sustainability Indicators

123

Ta

ble

2co

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15

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I 47

16

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itat

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port

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of

popula

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ng

impro

ved

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n

faci

liti

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48)

I 48

17

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nkin

gw

ater

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port

ion

of

urb

anpopula

tion

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ved

fres

hw

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(I4

9),

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port

ion

of

rura

lpopula

tion

usi

ng

impro

ved

fres

hw

ater

(I5

0)

I 49,

I 50

18

Acc

ess

toen

ergy

Pro

port

ion

of

rura

lpopula

tion

usi

ng

impro

ved

elec

tric

ity

(I5

1)

I 51

19

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ing

condit

ions

Pro

port

ion

of

urb

anpopula

tion

livin

gin

slum

s(I

52)

I 52

Sub-t

ota

l7

17

1

20

Gover

nan

ceC

orr

upti

on

Num

ber

of

offi

cial

shav

ing

pai

dbri

des

(I5

3)

I 53

21

Cri

me

Num

ber

of

reco

rded

crim

esper

10,0

00

popula

tion

(I5

4)

I 54

22

Saf

ety

Num

ber

of

reco

rded

acci

den

tsper

10,0

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(I5

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Sub-t

ota

l3

03

0

L. T. Hai et al.

123

1.

Ta

ble

2co

nti

nued

Nr

Them

eS

ub-t

hem

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ound

1R

ound

2

Rm

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3.5

Rm

e\

3.5

Acc

epte

d

(Rm

eC

3.5

,

QB

0.5

,an

d

Rv

\15

%)

Rej

ecte

d

(Rm

e\

3.5

,

QB

0.5

,an

d

Rv

B15

%

23

Hea

lth

Mort

alit

yU

rban

mort

alit

yra

teunder

5yea

rsold

(I5

6),

Rura

l

mort

alit

yra

teunder

5yea

rsold

(I5

7)

I 56,

I 57

24

Hea

lth

care

del

iver

yS

har

eof

inves

tmen

tin

hea

lth

sect

or

toto

tal

inves

tmen

t(I

58),

Shar

eof

hosp

ital

bed

sper

10,0

00

popula

tion

(I5

9),

Shar

eof

doct

ors

per

10,0

00

popula

tion

(I6

0),

Pro

port

ion

of

popula

tion

par

tici

pat

ing

hea

lth

insu

rance

(I6

1),

Per

centa

ge

of

chil

dre

nunder

1yea

rold

of

full

imm

uniz

atio

nvac

cines

(I6

2)

I 58,

I 59,

I 60,

I 61,

I 62

25

Nutr

itio

nal

stat

us

Shar

eof

mal

nutr

itio

nst

atus

under

5yea

rsold

(I6

3)

I 63

26

Hea

lth

stat

us

and

risk

s

Mort

alit

yra

tedue

tom

alar

iaper

10,0

00

popula

tion

(I6

4),

Lif

eex

pec

tancy

atbir

th(I

66)

Mort

alit

yra

tedue

toH

IVper

10,0

00

popula

tion

(I6

5)

I 64,

I 65,

I 66

Sub-t

ota

l8

19

0

27

Educa

tion

Educa

tional

level

Rat

eof

chil

dre

nre

achin

gpri

mar

yed

uca

tion

(I6

7),

Pri

mar

ysc

hool

enro

llm

ent

per

popula

tion

aged

(I6

8),

Popula

tion

rate

over

the

age

of

18

gra

duat

edfr

om

hig

hsc

hool

per

tota

lpopula

tion

over

18

(I6

9)

I 67,

I 68

I 69

28

Lit

erac

yA

dult

illi

tera

cyra

te(I

70)

I 70

29

Educa

tional

fundam

enta

l

Num

ber

of

schools

of

educa

tional

stan

dar

ds

(I7

1),

Inves

tmen

tsh

are

ined

uca

tion

(I7

2)

I 71,

I 72

Sub-t

ota

l6

04

2

30

Cult

ure

Cult

ura

lfu

ndam

enta

lR

ate

of

cult

ura

lvil

lages

(I7

3),

Rat

eof

cult

ura

lhouse

hold

stan

dar

d(I

74)

I 73,

I 74

Sub-t

ota

l0

20

2

A System of Sustainability Indicators

123

Ta

ble

2co

nti

nued

Nr

Them

eS

ub-t

hem

eR

ound

1R

ound

2

Rm

eC

3.5

Rm

e\

3.5

Acc

epte

d

(Rm

eC

3.5

,

QB

0.5

,an

d

Rv

\15

%)

Rej

ecte

d

(Rm

e\

3.5

,

QB

0.5

,an

d

Rv

B15

%

31

Dem

o-g

raphic

sP

opula

tion

Popula

tion

gro

wth

rate

(I7

5),

Mec

han

ical

popula

tion

gro

wth

rate

(I7

6),

Dep

enden

cyra

tio

(I7

7)

I 75,

I 76,

I 77

Sub-t

ota

l3

03

0

32

Nat

ura

lhaz

ards

Vuln

erab

ilit

yto

nat

ura

lhaz

ards

Rat

eof

pro

vin

cial

popula

tion

livin

gin

haz

ards

pro

ne

area

s(I

78)

I 78

33

Dis

aste

rpre

par

ednes

s

and

resp

onse

Eco

nom

ican

dhum

anlo

ssdue

tonat

ura

ldis

aste

rs

(I7

9)

I 79

Sub-t

ota

l2

02

0

34

Atm

osp

her

eC

lim

ate

chan

ge

Incr

ease

dav

erag

ete

mper

ature

ina-

ten

yea

r(I

80)

I 80

35

Ozo

ne

layer

dep

leti

on

Num

ber

of

air

condit

ions

and

refr

iger

ates

over

10,0

00

peo

ple

(I8

1)

I 81

36

Air

qual

ity

Dust

(I8

2),

SO

2(I

83),

NO

2(I

84),

Pb

(I8

5)

I 82,

I 83,

I 84,

I 85

Sub-t

ota

l4

25

1

37

Lan

dA

gri

cult

ure

Rat

eof

chan

ge

of

agri

cult

ura

lla

nd

(I8

6),

Shar

eof

agri

cult

ura

lir

rigat

edla

nd

(I8

7),

Ara

ble

and

per

man

ent

cropla

nd

area

(I8

8)

I 86,

I 87,

I 88

38

Lan

dst

atus

Poin

tsof

coas

tal

line

erosi

on

(I9

0)

Soil

erosi

on

(I8

9)

I 90

I 89

39

Fore

sts

Rat

eof

chan

ge

of

man

gro

ve

fore

st(I

91),

Pro

port

ion

of

fore

stla

nd

cover

edby

nat

ura

l

fore

st(I

92),

Rat

eof

fore

stco

ver

age

and

long-

term

indust

rial

tree

sover

fore

stla

nd

(I9

3),

Are

a

of

fore

sttr

ees

under

sust

ainab

lefo

rest

man

agem

ent

(I9

4)

I 91,

I 93,

I 94

I 92

Sub-t

ota

l8

17

2

L. T. Hai et al.

123

Ta

ble

2co

nti

nued

Nr

Them

eS

ub-t

hem

eR

ound

1R

ound

2

Rm

eC

3.5

Rm

e\

3.5

Acc

epte

d

(Rm

eC

3.5

,

QB

0.5

,an

d

Rv

\15

%)

Rej

ecte

d

(Rm

e\

3.5

,

QB

0.5

,an

d

Rv

B15

%

40

Fre

sh-w

ater

Wat

erquan

tity

Wat

eruse

inte

nsi

tyby

econom

icac

tivit

y(I

96)

Pro

port

ion

of

tota

lw

ater

reso

urc

esuse

d

(I9

5)

I 96

I 95

41

Wat

erqual

ity

Pre

sence

of

faec

alco

lifo

rms

infr

eshw

ater

(I9

7),

BO

D(I

98)

I 97,

I 98

Sub-t

ota

l3

13

1

Tota

l68

30

69

29

A System of Sustainability Indicators

123

If the scoring value is equal to 1, the indicator is highly irrelevant (this happened only

66 times or 2.1 % of the occurrences is the highest value);

2. If the scoring value is equal to 2, the indicator is likely irrelevant (this happened 342

times or 10.9 % of the occurrences);

3. If the scoring value is equal to 3, the indicator is more or less relevant (this happened

1,107 times or 35.3 % of the occurrences is the highest value);

4. If the scoring value is equal 4, the indicator is likely relevant (this happened 892 times

or 28.4 % of the occurrences);

5. If the scoring value is equal to 5, the indicator is highly relevant (this happened 729

times or 23.2 % of the occurrences).

In short, the score that received the most votes was rating 3 (defined as ‘more or less

relevant’) with 35.3 % of the votes, followed by rating 4 (defined as ‘likely relevant) and

rating 5 (defined as ‘highly relevant’).

4.2 Round 2

The rating variance and the quartile deviation from round one were also communicated as

shown in Tables 2 and Fig. 3. The data shows that:

1. The rating means of 69 indicators are above 3.5. In addition, these indicators have a

quartile deviation below or equal to 0.5, and a rating variance of less than 15 %.

According to the rules, therefore, these indicators are considered to be relevant for the

province;

2. Conversely, the rating means of 29 indicators are lower than 3.5, the quartile

deviations are less than, or equal to 0.5, and the rating variances are less than 15 %.

Consequently, these indicators are rejected. This means that they are not relevant to

the study area.

The scoring values range from 1 to 5 (Fig. 2) and their frequency of occurrence in round

2 (R2) is:

1. If the scoring value is equal to 1, the indicator is highly irrelevant (this happened only

55 times or 1.8 % of the occurrences);

2. If the scoring value is equal to 2, the indicator is likely irrelevant (this happened 345

times or 11.0 % of the occurrences);

3. If the scoring value is equal to 3, the indicator is more or less relevant (this happened

1,107 times or 35.3 % of the occurrences);

4. If the scoring value is equal 4, the indicator is likely relevant (this happened 908 times

or 29.0 % of the occurrences);

5. If the scoring value is equal to 5, the indicator is highly relevant (this happened 721

times or 23.0 % of the occurrences).

The figure shows that the ratings 3, 4, and 5 are most prevalent (this means that the

indicator is likely relevant). This is followed by the ratings 2 and 1, respectively. In other

words, almost all experts indicated or agreed on the scales of each indicator from more or

less relevant to highly relevant (87 % in Round 1 and 87.2 % in Round 2), significantly.

The ratio of experts who change their ratings for each indicator in all rounds as shown in

Fig. 3 is:

1. If Rv is equal to 0 %, its frequency is the highest value (this happened 70 times);

2. If Rv is equal to 3 %, its frequency is happened 34 times;

L. T. Hai et al.

123

3. If Rv is equal to 6 %, its frequency is happened 7 times;

4. If Rv is equal to 9 %, its frequency is happened 5 times;

5. If Rv is equal to 13 %, its frequency is the lowest values (this happened only 2 times).

In this study, all data ranges fall inside Round 2 (rules to analyze the ratings as shown in

Table 1); therefore, there was no need to continue with a 3rd round.

During the selection process, 29 indicators were perceived as irrelevant or rejection. The

relevant indicators are: 9 related to economic development, 1 to the sea and coastal zone, 9

to consumption and production patterns, 1 to poverty, 2 to education, 2 to culture, 1 to the

atmosphere, 2 to land, and 1 to freshwater.

The 69 selected indicators in 2 rounds of formal interviews (Table 3) included 15

related to economic development, 5 to the sea and coastal zone, 1 to the global economic

partnership, 4 to consumption and production patterns, 7 to poverty, 3 to governance, 9 to

Fig. 2 Incidence of rates for sustainability indicators as evaluated in Round 1 and Round 2

Fig. 3 Incidence of the ratingvariance

A System of Sustainability Indicators

123

Table 3 Selected indicators after two Delphi rounds

Theme

Sub-theme

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Eco

nom

ic

deve

lopm

ent

Sea

and

coas

tal z

one

Glo

bal e

cono

mic

pa

rtne

rshi

p

Con

sum

ptio

n an

d pr

oduc

tion

pat

tern

s

Pov

erty

Gov

erna

nce

Hea

lth

Edu

cati

on

Cul

ture

Dem

ogra

phic

s

Nat

ural

haz

ards

Atm

osph

ere

Lan

d

Fre

shw

ater

1Macroeconomic performance

I1, I2, I7, I8, I9, I10, I11, I15, I16, I17, I18, I19,

I20

2Sustainable public finance

3 Employment

4Information and communication technologies

5 Tourism

6 Agriculture

7 Aquaculture I25, I26,I27, I28,

I298 Fisheries

9 External financing I31

10 Material consumption

I32,I37, I39,

I40

11 Energy use

12Waste generation and management

13 Transportation

14 Income poverty

I45, I46,I47,I48,I49, I50,I51

15 Income inequality

16 Sanitation

17 Drinking water

18 Access to energy

19 Living conditions

20 CorruptionI53I54,I55

21 Crime

22 Safety

23 Mortality I56, I57,I58, I59, I60, I61, I62, I63, I64, I65,

I66

24 Health care delivery

25 Nutritional status

26 Health status and risks

27 Educational level I67, I68I71, I72

28 Literacy

29 Educational fundamental

30 Cultural fundamental x

31 Population I75, I76, I77

32 Vulnerability to natural hazards I78,

I7933 Disaster preparedness and response

34 Climate change I80

35 Ozone layer depletion I82, I83, I84, I85

36 Air quality

L. T. Hai et al.

123

health, 4 to education, 3 to demographics, 2 to natural hazards, 5 to the atmosphere, 7 to

land, and 3 to freshwater.

5 Discussion

Thai Binh Province is significant in the coastal northern delta of Vietnam in term of

geography and economy. Thai Binh has historically been an agricultural province but

increasing industrial operations have led to emerging problems associated with population

mitigation, employment and environmental degradation (DONRE 2011). Thai Binh is still

among the poorest provinces in Vietnam. Therefore, the Doi moi transition is a complex

process but Vietnam specific features are both necessary and possible because of the

flexibilities (Boothroyd 2000). The process has been helping Thai Binh to become a rich

province and to become an important base for decision makers and planners by 2015 and

for a vision in 2020.

Almost all rating means of the selected indicators range from three to five. This shows

that the experts deemed the indicators to be, ‘more or less relevant’, ‘likely relevant’ or

‘highly relevant’ to the study area in the majority (86.9 %) of cases.

The results indicate that the experts were reluctant to change their Round 1 rating in

Round 2. Seventy of the 98 (71.4 %) SDIs received the same rating by each expert during

the two rounds (i.e. the rating variance is 0). This suggests that the experts did not change

their initial (round 1) rating even after viewing the Round 1 responses from the other

experts for the majority of the SDIs. These indicators that the rating variance is 0 are: GDP,

Consumer price index (CPI), Share of women in wage employment in the non-agricultural

sector, Total surface area of freshwater aquaculture, Fisheries production, Annual haz-

ardous waste, Waste treatment and disposal, Waster of recycling and reuse, Proportion of

urban population using improved freshwater, Proportion of rural population using

improved freshwater, Urban/rural mortality rate under 5 years old, Percentage of children

under 1 year old of full immunization vaccines, Share of malnutrition status under 5 years

old, Life expectancy at birth, etc., Conversely, experts did change their mind for the

remaining 28 SDIs (i.e. the SDI had a rating variances are above 0) and assigned a different

preference in Round 2. This suggests that the sharing of Round 1 responses may have

Table 3 continued

Theme

Sub-theme

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Eco

nom

ic

deve

lopm

ent

Sea

and

coas

tal z

one

Glo

bal e

cono

mic

pa

rtne

rshi

p

Con

sum

ptio

n an

d pr

oduc

tion

pat

tern

s

Pov

erty

Gov

erna

nce

Hea

lth

Edu

cati

on

Cul

ture

Dem

ogra

phic

s

Nat

ural

haz

ards

Atm

osph

ere

Lan

d

Fre

shw

ater

37 Agriculture I86, I87, I88, I90,I91, I93,

I94

38 Land status

39 Forests

40 Water quantity I96,I97, I9841 Water quality

A System of Sustainability Indicators

123

influenced some experts to change their Round 2 responses in some cases, a key benefit of

adopting the Delphi process.

The SDIs that were rated as being the most relevant (i.e. received a rating of 4 or 5) by

the experts are:

• GDP

• Total surface area of freshwater aquaculture

• Share of rural poverty

• Share of agricultural irrigated land

• Production of freshwater aquaculture

• Production of seafood

• Arable and permanent cropland area

• etc.

It is noticeable that many of these SDIs relate to agricultural and aqua-cultural activi-

ties, which is likely a result of the fact that Thai Binh is an agricultural and coastal area.

That is why agricultural and aqua-cultural activities have been deemed by the experts as

important factors to address poverty, infrastructure development, and environmental pro-

tection (DONRE 2010).

Of 29 rejected indicators, the SDIs receiving the lowest rating means included:

• Value-added agriculture, Value-added industry-construction, Value-added fishery, and

Value-added trade, tourism, and service. These may have received low ratings because

the two indicators of GDP per capital and GDP were deemed to adequately capture the

‘economic development’ theme

• Ratio of local residents to tourists in major tourist province and destinations, and

Export turnover over GDP. These may have received low rating because tourism and

export activities are not currently key issues in Thai Binh.

While these indicators were deemed to be unnecessary now, they may become relevant

in the future as the characteristics of the Thai Binh provice change over time. Therefore,

these rejected indicators, along with the whole SDI set, should be re-evaluated and

rechecked on a yearly basis (Hai et al. 2009; Gobin et al. 2012).

More indicators (69) were selected for the Thai Binh province than in some other

provinces in Vietnam [e.g. Quang Tri (37), Thai Nguyen (35), Quang Nam (22), and Lam

Dong (17)]. One of the possible main reasons is that more up to date indicators were added

to the Thai Binh set of SDIs (e.g. for climate change, ozone layer depletion, energy use,

waster reuse, vulnerability to natural hazards, and disaster preparedness and response)

(DONRE 2012). While it is different to compare SDI sets, due to the specific socio-

economic and cultural characteristics of the different provinces (Hai et al. 2009; Hai 2010),

research has shown that, in some cases, smaller indicator sets may be inadequate to convey

the message and may also dilute the purpose (Hathan and Reddy 2010). The selection

process involved managers, decision makers, and scientists. Additionally, local people

should also be involved as participants in the selection process as well. Based on the results

of this study and the United Nation’s guidelines in 2007 (UN 2007a), this study can

developed into a set of SDIs and provided suggestions on how to adapt them to provincial

conditions and priorities. In order to easily use, each indicator need contain a short

description (e.g. theme, sub-theme, brief definition, and description).

While this study adopted a rigorous methodology, the following potential limitations

have been identified by the project team:

L. T. Hai et al.

123

• As the Delphi approach is a consensus method, it aims through consensus to identify a

‘‘central opinion’’, and consequently, important minority issues may be missed

(Gallagher et al. 1996). Delphi has been applied in Vietnam for various areas (e.g.

health and environmental aspects, clean development mechanism projects, and climate

change, etc.) in different places (e.g. Quang Tri province, Binh Thuan province, Quang

Ninh province, and Tay Nguyen region, etc.);

• Here might have been subjective elements in the manner of selection of the experts,

although they are ‘‘representative’’ professionals (Hai 2010). For example, asking too

specific a group to participate, could limit the scope of opinions and expertise; it also

might be more appropriate to involve a multidisciplinary group rather than a highly

specialised team (Ryan et al. 2001). Both the panel composition and the feedback can

influence the judgements made (Campbell et al. 2002);

• Given that response rates for the Delphi method can be low, it may be pragmatic to

select participants who have an interest and involvement in the question being

explored; this should however be balanced against a need to seek relative impartiality

(Hasson et al. 2000);

• It is the first time that the Delphi method has been applied to select an SDI’s set for

Thai Binh. Furthermore, this study shows that such a system of indicators requires

continual improvement and update e.g. through public consultation of the stakeholders

and more local experts.

6 Conclusions

Indicators can lead to better decisions and more effective actions by simplifying, clari-

fying, and making aggregated information available to policy makers (UN 2007a). The

Delphi approach is a proven method for selecting indicators. The method is a well-known

structured communication technique, which relies on a panel of experts to solve complex

problems (Landaeta 2006). This study showed that it is possible to define a set of indicators

for SD in Thai Binh (Vietnam), by applying the Delphi method. This can be done using

methods that make the selection process more objective and transparent.

The study selected 69 indicators and omitted 29 indicators from the initial indicator set,

based on the opinions of the 32 experts in two rounds of feedback. The selected indicators

are tools for measuring SD processes at the provincial level. In the future, the rejected

indicators should be re-evaluated and potentially re-selected if needs and conditions in

Thai Binh and Vietnam change.

The results provide useful information and a scientific base as the first step of the SD

process for the future studies and for decision makers and planners, particularly in Thai

Binh by 2015 and a vision in 2020.

Acknowledgments This study is a part of the project: ‘‘A scientific base for establishing an indicatorsystem for sustainable development: a case study in the Thai Binh province, Vietnam’’ funded by VietnamNational Foundation for Science and Technology (NAFOSTED) under grant number 105.99-2011.08. Wewould like to thank the Thai Binh People’s Committee and Departments (e.g. Natural Resources andEnvironment, Science and Technology, Agriculture and Rural Development, Planning and Investment,Transport, Trade), Institutes, and Universities for their kindness and supplying materials; experts who weremost collaborative in completing the questionnaires and in providing feedback on the results. We would liketo thank Mr. James Hennessy (Australian—Sustainable Development Research Assistant, Vietnam NationalMuseum of Nature) who helped us ensure correctness of English.

A System of Sustainability Indicators

123

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