Identification of Deep Tectonic Structures of the Pho Lu area, northwestern Vietnam using Digital Elevation Model and Earth focal mechanism

VNU Journal of Science: Earth and Environmental Sciences, Vol. 36, No. 3 (2020) 70-80 
70 
Original Article 
Identifying the Role of Determinantsand Indicators Affecting 
Climate Change Adaptive Capacity in Da Nang City, Vietnam 
Nguyen Bui Phong1, , Mai Trong Nhuan2, Do Dinh Chien1 
1Institute of Meteorology, Hydrology and Climate Change, 62/23 Nguyen Chi Thanh, Dong Da, Hanoi, Vietnam 
2VNU University of Science, 334 Nguyen Trai, Hanoi, Vietnam 
Received 25 May 2020 
Revised 31 August 2020; Accepted 11 September 2020 
Abstract: Identifying the role of determinants and indicators affecting climate change adaptive 
capacity (AC) in developing Da Nang city’s climate change adaptation policies is necessary. 
However, the methods of identifying the role of determinants and indicators affecting AC are 
relatively limited. This study used the exploratory factor analysis (EFA), confirmative factor 
analysis (CFA), structural equation modeling (SEM) and set of five determinants affecting to the 
city’s AC related to finance, society, infrastructure, human resources, nature. A socio-economic data 
was conducted in the survey of 1,168 households in Da Nang city. The results indicate that city’s 
AC is strongly correlated with infrastructural, social and natural resources. Thus, the infrastructural, 
social and natural determinants are the decisive determinants affecting to the city’s AC. The AC 
indicators and the used methods in this study can be applied to determine the role of those 
determinants and indicators affecting to AC in other coastal provinces in Vietnam. 
Keywords: Climate change, EFA, CFA, adaptive capacity, Da Nang. 
1. Introduction 
Climate change adaptive capacity is defined 
as the adjustment of natural or human systems to 
cope with circumstances or environments in 
order to reduce the likelihood of vulnerability 
due to fluctuations and alternations of existing or 
potential climate variables and also to take 
advantage of this situation [1]. The AC of a 
social system can be influenced by many social 
________ 
 Corresponding author. 
 E-mail address: phongnb37hut@gmail.com 
 https://doi.org/10.25073/2588-1094/vnuees.4643 
variables or AC determinants [2]. Quantification 
of AC determinants can provide essential data 
for AC assessment [3,4] and development of 
successful climate change adaptation strategies 
[5]. However, depending on national, regional or 
community scale, so that, different kinds of AC 
indicators structure have been applied. For local 
and community scales, previous studies have 
used sustainable livelihoods frameworks to 
analyze the relationship between livelihood 
N.B. Phong et al. / VNU Journal of Science: Earth and Environmental Sciences, Vol. 36, No. 3 (2020) 70-80 71 
resources and households and communities’ AC, 
assessing vulnerability to natural disasters and 
climate change impact and risk assessment [6-
12]. And the AC indicators are mainly developed 
from local expert experience. Therefore, the 
development and replication of AC indicators 
need to be adjusted for appropriate spatial and 
social contexts [13]. 
The methods used to assess AC and 
identifying the role of determinants and 
indicators affecting AC are mainly unequal 
weighting methods with the calculation 
according to Iyengar - Sudarshan method (1982) 
[14], and the Analytic Hierarchy Process (AHP) 
[11] and especially Nelson et al. [7,9,15] had 
used the primary component analysis method 
(PCA) to assess AC at different scales. This 
study, using Exploratory Factor Analysis (EFA) 
and Confirmatory Factor Analysis (CFA) and 
Structural Equation Modeling (SEM) to determine 
the weights of AC indicators. In comparison to 
traditional methods such as multivariate 
regression, the use of SEM is more advantageous 
related to calculating measurement errors [16]. 
In Da Nang City, there were some studies on 
AC for households and identifying determinants 
affecting to households’ AC [17,18]. However, 
these studies focus on urban households and use 
PCA, multivariate linear regression equations to 
assess AC and determine the role of 
determinants affecting AC for urban households 
[19] and households of Lien Chieu district [17], 
and Hoa Vang district [18]. 
Therefore, the use of Exploratory Factor 
Analysis (EFA) and Confirmatory Factor 
Analysis (CFA) and Structural Equation 
Modeling (SEM) to identify the role of 
determinants and indicators affecting to AC in 
DaNang city is chosen for this paper research. 
The objectives of the study are (1) 
Developing AC indicators for Da Nang City, (2) 
Identyfing the role of determinants and 
indicators affecting AC for coastal city Da Nang. 
The results of this study can provide useful 
information to Da Nang city authority in 
developing climate change adaptation policies. 
Moreover, the results of this study can be used to 
identify the role of determinants and indicators 
affecting to the AC for other coastal provinces in 
Vietnam. 
2. Background and Method 
2.1. Research Area 
Da Nang is a leading city located on the 
central coast of Vietnam with a number of 
natural, economic, social, infrastructural and 
human characteristics affecting to AC as follows 
(Figure 1).
Figure 1. Da Nang city Map [17]. 
N.B. Phong et al. / VNU Journal of Science: Earth and Environmental Sciences, Vol. 36, No. 3 (2020) 70-80 72 
Nature: Total area of Da Nang city is 
1,283.42 km2 including the mainland and 
archipelago in the East Sea. The topography of 
Da Nang City has both delta and mountains 
where concentrated high and sloppy mountains 
are located in the West and Northwest and the 
coastal delta is a Eastern salinized plain. The 
aquaculture area is nearly 0.5 thousand hectares 
[20]. 
Economy: The Gross Regional Domestic 
Product (GRDP) in 2018 at current prices has 
reached USD 3,909.8 million, an increase of 
USD 325 million compare ... financial 
determinant, social determinant, natural 
determinant, human resource determinant, 
infrastructural determinant are CR > 0.7 and 
AVE > 0.5 [28]. The model reaches convergence 
value. 
N.B. Phong et al. / VNU Journal of Science: Earth and Environmental Sciences, Vol. 36, No. 3 (2020) 70-80 77 
Figure 5. Confirmative factor analysis result. 
The CFA analysis results in Figure 5 show 
that the Standardized Regression Weights of all 
variables are greater than 0.5, meaning the model 
achieved the convergence value. The CFA 
results show: Chi-square = 314.238 (p = 0.000); 
Chi-square/df = 2.067 < 3; GFI = 0.974, TLI = 
0.984, CFI = 0.987 are all greater than 0.9 and 
RMSEA = 0.03 < 0.08 (Figure 5). In short, the 
model results are consistent with the collected 
data. 
3.4. Structural Equation Modelling result (SEM) 
The result of SEM in Figure 6 indicated that 
Chi-square value is 467.913, degrees of freedom 
is 162, with P-value= 0.000 should meet the 
requirements of data compatibility. When 
adjusting Chi-square with degrees of freedom 
Cmin/df, this value reaches 2.888 < 3, furthermore 
the indicators GFI, CFI, TLI are 0.959; 0.969; 
0.974 > 0.9 respectively; RMSEA is 0.040 < 
0.08
Figure 6. Standardized Confirmative Factor Analysis result. 
N.B. Phong et al. / VNU Journal of Science: Earth and Environmental Sciences, Vol. 36, No. 3 (2020) 70-80 78 
The result of SEM shows that the model is 
well compatible with the collected data. The 
testing results of SEM in Figure 6 show that the 
influencing of Infrastructural determinant has 
significant influence on Da Nang city’s AC with 
the reliability reached 99% (Estimate = 0.228, P 
= 0.01 < 0.05). Followed the influencing of 
Natural determinant, Social determinant with the 
reliability reached 95% (Estimate= 0.160 and 
0.107; P<0.05). Due to P_value of the Financial 
and Human resource determinants, are > 0.05 so 
the Financial and Human resource determinants 
has not statistical meaning. 
The Standardized Regression Weights show 
that the degree of impact of independent 
variables to dependent. The Standardized 
Regression Weights of Infrastructural 
determinant is highest, reached 0.182, followed 
by the Standardized Regression Weights of 
Natural resource determinant, reached 0.152. 
The Standardized Regression Weights of social 
determinant reached 0.091. The Standardized 
Regression Weights of human and financial 
determinant are 0.020 and 0.035. Thus, the 
Infrastructural determinant has the most 
considerable influence on Da Nang city’s AC 
and the determinant of natural has the second 
considerable influence on Da Nang city’s AC, 
followed by social resource determinant. The 
result shows that if Infrastructural determinant, 
natural determinant, social determinant are 
improved, it will positively impact to Da Nang 
city’s AC. 
3.5. Testing the Reliability of Estimation with 
Bootstrap 
The Boostrap method is used to test the 
model estimates in the final model with the 
number of repeating samples N = 300. The 
estimated results from the 300 samples averaged 
together with the deviations are presented in 
Table 4. The results in Table 4 show the results 
of the difference (bias column) between the 
estimated value and the mean value column. The 
mean has a very small absolute value and the CR 
value is less than or equal to 2, meaning a very 
small bias at the 95% certainty level or the 
estimated results from the original model and 
from average of 300 other estimates giving the 
same or reliable model.
Table 4. Estimation result by Boostrap 
Determinant Estimation SE SE-SE Mean Bias SE-Bias CR 
AC <--- Nature .160 .034 .001 .154 .002 .002 1 
AC <--- Infrastructure .228 .033 .001 .179 -.003 .002 -1.5 
AC <--- Finance .015 .031 .001 .022 .003 .002 1.5 
AC <--- Human resource .031 .037 .001 .037 .002 .002 1 
AC <--- Society .107 .036 .001 .094 .003 .002 1.5 
4. Discussion 
Results from the analysis of survey data from 
1,168 households in 7 districts of Da Nang city 
by using the exploratory factor analysis (EFA), 
confirmative factor analysis (CFA), structural 
equation modeling (SEM) show that if 
infrastructural determinant, natural determinant, 
social determinant are improved, it will positively 
impact to Da Nang city’s AC. This result is quite 
consistent to previous studies showing social 
support networks and infrastructural of city were 
important contributor to increase city’s 
adaptation to climate change [19]. 
Another important result of the present study 
was that 5 determinants were extracted from the 
adaptive capacity indicators (Table 1). The high 
variance in the infrastructural determinant was 
shown. Enhancing the capacity to supply 
electricity and water resource while it has 
disaster and climate change appearance, could 
increase the adaptive capacity of city’s AC. The 
determinant of social relation was engagement 
with social activities to respond to disasters and 
N.B. Phong et al. / VNU Journal of Science: Earth and Environmental Sciences, Vol. 36, No. 3 (2020) 70-80 79 
climate change. This includes supporting of 
community, supporting of province authority 
support and participating in social organizations. 
Social determinant has been recognized as necessary 
to build community capacity for a sustainable 
future [29]. Previous studies have shown that 
people's relationship with each other through 
networks and the associational life in their 
community increase the adaptive capacity [16]. 
For natural determinant, protecting and 
developing ecosystem could enhance city’s AC. 
The exchange of information on climate change 
as well as the experience of producing and 
business in aquaculture, livestock, crops among 
households may contribute to increasing 
adaptive capacity. 
The findings of the present study suggest that 
strengthening social organizations, social 
support networks, community funds, and 
protecting and developing natural ecosystem; 
strengthening a wide range of urban 
infrastructural including water and power 
supplying system to improve the efficiency, 
effectiveness and sustainability of urban service. 
5. Conclusion 
According to the research results, the scale 
of climate change AC of Da Nang city includes 
5 determinants and 17 indicators of AC: 
Financial, infrastructure, human resources, 
natural and social resource. In which, the 
infrastructural, natural and social determinant 
has the significant correlation to the city’s AC. 
The study has devoted a indicator structure 
of the climate change AC for Da Nang city and 
evaluate the role of the determinants in the 
indicator structure of the climate change Da 
Nang city’s AC by using the exploratory factor 
analysis (EFA), confirmative factor analysis 
(CFA), structural equation modeling (SEM). 
Using the indicator structure of the climate 
change AC (including determinants and 
indicators) and the calculation method of this 
study to determine the role of the decisive factors 
in the framework for other coastal localities of 
Vietnam is possible. In order to enhance the 
reliability, representativeness and certainty of 
applying the research approach, it is necessary to 
set up a survey questionnaire with a larger scale, 
appropriate question structure and interview 
method. 
References 
[1] NGO Centre, Program of national target to 
respond to climate change. https://www.ngocen 
tre.org.vn/files/docs/NTP_Vietnamese.pdf/, 2008 
(accessed 15 january 2019) (in Vietnamese). 
[2] G.W. Yohe, R.S. Tol, Indicators for social and 
economic coping capacity: moving towards a 
working definition of adaptive capacity. Global 
Environmental Change 12 (2002) 25-40. https:// 
doi.org/10.1016/S0959-3780(01)00026-7. 
[3] B. Smit, I. Burton, R.J.T. Klein, R. Street, The 
Science of Adaptation: A Framework for 
Assessment, Global Environmental Change 4 
(1999) 199–213.  
9652531101. 
[4] B. Smit, J. Wandel, Adaptation, adaptive capacity 
and vulnerability. Global Environmental Change 
16 (2006) 282-292. https://doi.org/10.1016/j. 
gloenvcha.2006.03.008. 
[5] W. Neil Adger, Saleemul Huq, Katrina Brown, 
Declan Conway, Mike Hulme, Adaptation to 
climate change in the developing world, Progress 
in Development Studies 3 (2003) 179-195. https:// 
doi.org/10.1191/1464993403ps060oa. 
[6] R. Nelson, P. Kokic, S. Crimp, H. Meinke, S.M. 
Howden, The vulnerability of Australian rural 
communities to climate variability and change: 
Part I-Conceptualising and measuring 
vulnerability, Environmental Science & Policy 13 
(2010) 8-17. https://doi.org/10.1016/j.envsci.200 
9.09.006. 
[7] R. Nelson, P. Kokic, S. Crimp, P. Martin, H. 
Meinke, S.M. Howden, P.de Voil, U.Nidumolu, 
The vulnerability of Australian rural communities 
to climate variability and change: Part II-
Integrating impacts with adaptive capacity, 
Environmental Science & Policy 13 (2010) 18- 
27. https://doi.org/10.1016/j.envsci.2009.09.007. 
[8] R. Nelson, P. Brown, T. Darbas, P. Kokic, K. 
Cody, The potential to map the adaptive capacity 
of Australian land managers for NRM policy 
using ABS data, Australian Bureau of Agricultural 
and Resource Economics 7 (2007).  
10.13140/RG.2.2.22470.73281. 
N.B. Phong et al. / VNU Journal of Science: Earth and Environmental Sciences, Vol. 36, No. 3 (2020) 70-80 80 
[9] G.A. Gbetibouo, C. Ringler, Mapping South 
African farming sector vulnerability to climate 
change and variability, International food policy 
research institute, Washington, (2009). https:// 
ebrary.ifpri.org/utils/getfile/collection/p15738coll2/ 
id/26199/filename/26200.pdf. 
[10] P.A. Agyei, E.D.G. Fraser, A.J. Dougill, L.C. 
Stringer, E. Simelton, Mapping the vulnerability 
of crop production to drought in Ghana using 
rainfall, yield and socioeconomic data, Applied 
Geography 32 (2012) 324-334. https://doi.org/10. 
1016/j.apgeog. 2011.06.010. 
[11] G. Defiesta, C.L. Rapera, Measuring Adaptive 
Capacity of Farmers to Climate Change and 
Variability: Application of a Composite Index to 
an Agricultural Community in the Philippines, 
Journal of Environmental Science and 
Management 17 (2014) 48-62. 
[12] K. Williges, R. Mechler, P. Bowyer, J. Balkovic, 
Towards an assessment of adaptive capacity of the 
European agricultural sector to droughts, Climate 
Services 7 (2017) 47-63.https://doi.org/10.1016/ 
j.cliser.2016.10.003. 
[13] E. Wall, K. Marzall, Adaptive Capacity for 
Climate Change in Canadian Rural Communities, 
Local Environment 11 (2006) 373-397. 
https://doi.org/10.1080/13549830600785506. 
[14] C.T. Van, N.T. Son, T.N. Anh, N.C. Tuan, 
Developing flood vulnerability index using 
decentralized system analysis (AHP) - Testing for 
several commune units Quang Nam province in 
Thu Bon river delta. Journal of Meteorology and 
Hydrology 643 (2014) 10-18 (in Vietnamese). 
[15] D.J. Abson, A.J. Dougill, L.C. Stringer, Using 
Principal Component Analysis for information-
rich socio-ecological vulnerability mapping in 
Southern Africa. Applied Geography 35 (2012) 
515-524. https://doi.org/10.1016/j. apgeog.2012. 
08.004. 
[16] N.D. Tho, N.T.M. Trang, Application of SEM 
linear structure mode - Marketing Science 
research, Ho Chi Minh City National University, 
Ho Chi Minh City (2011). 
[17] M.T. Nhuan, Assessing the adaptative capacity of 
Coastal Urban Household to climate change, Case 
study in Lien Chieu district, DaNang city, Viet 
Nam, VNU Journal of Science: Earth and 
Environmental Sciences31 (2015) 23-35 
(inVietnamese). https://js.vnu.edu.vn/EES/article/ 
view/206. 
[18] N.T. Hao, N.T. Tue, T.D. Quy, N.D. Hoai, M.T. 
Nhuan, Assessment of climate change adaptation 
capacity for household in Hoa Vang district, 
DaNang city, VNU Journal of Science: Earth and 
Environmental Sciences 32(2016) 140-152 
(inVietnamese). https://js.vnu.edu.vn/EES/article/ 
view/3022. 
[19] M.T. Nhuan, NT. Tue, N.T.H. Hue, T.D. Quy, 
T.M. Lieu, An indicator-based approach to 
quantifying the adaptive capacity of urban 
households: The case of Da Nang city, Central 
Vietnam Urban Climate 15 (2016) 60-69. 
https://doi.org/10.1016/j.uclim. 2016.01.002. 
[20] DaNang Authority’s Official Website, Danang 
Infrastructure https://www.danang.gov.vn/web/ 
en/detail?id=26033&_c=16407111, 2019 (accessed 
20, January 2020). 
[21] DaNang ‘s Socio-Economic Annual Report, 
Statistical publisher, General Statistics Office, 2018. 
[22] DaNang General Statistics Office, Statistical 
publisher, General Statistics Office, 2019. 
[23] U.S. Thathsarani, Premakumara Lokugam Hewa 
Gunaratne, Constructing and index to measure the 
Adaptive capacity to climate change in SriLanka. 
Procedia Engineering 212 (2018) 278–285. 
[24] R. Sietchiping, Applying an index of adaptive 
capacity to climate change in north-western 
Victoria, Australia, Applied GIS 2 (2006) 16.1–
16.28.  
[25] D. Swanson, Indicators of Adaptive Capacity to 
Climate Change for Agriculture in the Prairie 
Region of Canada, IISD, Canada, 2007. 
uploads/2014/05/Indicators-of-adaptive-capacity-
to-climate-change-for-agriculture.pdf. 
[26] W.N. Adger, K. Vincent, Uncertainty in adaptive 
capacity, Comptes Rendus Geoscience 337 (2005) 
399-410. https://doi.org/10.1016/j.crte.2004.11. 004. 
[27] DFID, Sustainable Livelihoods Guidance Sheets, 
https://www.ennonline.net/dfidsustainableliving, 
2001(accessed 20, January 2020). 
[28] R.B. Kline, Principles and practice of structural 
equation modeling, 3rd edition, The Guilford Press 
New York, London (2005). 
[29] B. Smit, O. Pilifosova, From adaptation to 
adaptive capacity and vulnerability reduction, 
Climate Change, Adaptive Capacity and 
Development (2003) 9-28. https://doi.org/10.1142/ 
97818609458160002.