4.2. The ranking of adaptive capacity
At this step, the DEMATEL model questionnaires were completed by the elite group and in due to the determination of the network relationship map (NRM), its threshold value was calculated. This method ignores minor relationships (interactions) and draws a network of significant relationships that are greater than a threshold value (Chen et al. 2021). The estimated threshold value for this study is 0.641. This means that all lower values from 0.641 were ignored for further DEMATEL processing (Table 4).
Table 4
The matrix of significant relationships
|
social
|
Economical
|
Institutional
|
Physical
|
Technological
|
Social
|
×
|
×
|
0/695
|
×
|
0/930
|
Economical
|
0/781
|
×
|
0/682
|
0/732
|
0/985
|
Institutional
|
0/649
|
0/662
|
×
|
×
|
0/837
|
Physical
|
0/662
|
×
|
×
|
×
|
0/731
|
Technology
|
×
|
×
|
×
|
×
|
×
|
Source: Authors, 2022 |
Table 5
The interactions power of factors
|
D
|
R
|
D + R
|
D-R
|
Social
|
3/442
|
3/195
|
6/638
|
0/247
|
Economical
|
3/721
|
2/934
|
6/655
|
0/787
|
Institutional
|
3/230
|
2/951
|
6/181
|
0/280
|
Physical
|
2/967
|
2/933
|
5/900
|
0/034
|
Technology
|
2/671
|
4/018
|
6/689
|
-1/347
|
Source: Authors, 2022 |
The findings of the causal diagram of the factors affecting the adaptation in Ahvaz city has shown that the economic factor has the highest score.
• Column elements (R) for each factor indicate the degree of dependence of that factor on other factors of the system. Therefore, the technical factor has the most dependency and the institutional-management has the least dependency on other factors.
• The horizontal vector (D + R) is the influence of the target factor in the system. In other words, the rate of D + R interacts with other system elements is high when its value is high. Therefore, the technical and economic factors have the most interaction and physical factor has the least interaction with other factors.
• The vertical vector (D - R) shows the influence power of each factor. If D - R is positive, the variable is effective, and if it is negative, it is an effected variable. The finding shows the economic, social, institutional, and physical factors are effector variables in the capacity to adaptation, and the technical factor is known as an ineffective variable.
4.3. Extraction the adaptive capacity indicators
After identifying and ranking the macro factors effective in adapting to climate change in Ahvaz, the indicators of these five macro factors were determined in three stages of records study and expert group survey. At the final the integration of the indicators was done and a 44*44 matrix was drawn. The general information of the matrix are given in the 6 table.
Table 6
Direct Impacts information (MDI)
Indicator
|
Matrix size
|
repetition
|
Effectless
|
Poor impact
|
Medium impact
|
high impact
|
Total
|
Dispersion rate
|
Value
|
44 * 44
|
2
|
549
|
584
|
596
|
207
|
1387
|
71/64
|
Source: Authors, 2022 |
According to the Table 6, in the matrix of adaptation, the cross effects of the factors with two rotations of the data have a favorability of 96%, which shows the high validity of the questionnaire. The matrix's degree of saturation is 71.64, meaning that in more than 71 percent of the occurrences, the components have interacted.
Step 1: Direct and indirect interaction of key indicators
The interaction matrix based on the sum of the indicators weights and the degree of influences, shows the degree of dependence between factors. The finding of the table shows that dependence to the macroeconomic factor is more than other factors table (7).
Table 7
The interaction of adaptation indicators to climate change
Indirect (MII)
|
Direct (MDI)
|
Variable
|
factors
|
Indirect (MII)
|
Direct (MDI)
|
Variable
|
factors
|
dependence
|
influence
|
dependence
|
influence
|
dependence
|
influence
|
dependence
|
influence
|
170803
|
159508
|
55
|
53
|
X23
|
institutional
|
177338
|
189911
|
60
|
67
|
X1
|
social
|
194159
|
213116
|
63
|
69
|
X24
|
156082
|
193417
|
54
|
63
|
X2
|
156695
|
213098
|
53
|
70
|
X25
|
187973
|
281278
|
64
|
96
|
X3
|
181176
|
220433
|
60
|
73
|
X26
|
186080
|
155123
|
62
|
54
|
X4
|
203213
|
153117
|
67
|
49
|
X27
|
145074
|
140316
|
50
|
46
|
X5
|
140766
|
133486
|
46
|
47
|
X28
|
170927
|
173408
|
57
|
58
|
X6
|
145110
|
141528
|
47
|
42
|
X29
|
227778
|
253494
|
77
|
86
|
X7
|
185424
|
144735
|
60
|
53
|
X30
|
186497
|
131764
|
61
|
43
|
X8
|
199608
|
72676
|
66
|
29
|
X31
|
Infrastructure
|
248999
|
177831
|
85
|
55
|
X9
|
210060
|
82018
|
69
|
26
|
X32
|
257243
|
147580
|
85
|
49
|
X10
|
150517
|
218605
|
50
|
70
|
X33
|
251313
|
147211
|
82
|
53
|
X11
|
118923
|
119322
|
40
|
36
|
X34
|
140668
|
237251
|
48
|
77
|
X12
|
Economical
|
164989
|
63647
|
54
|
25
|
X35
|
110964
|
217111
|
39
|
73
|
X13
|
108738
|
105429
|
38
|
35
|
X36
|
120433
|
253543
|
41
|
82
|
X14
|
188900
|
41595
|
64
|
15
|
X37
|
94901
|
166294
|
32
|
53
|
X15
|
170389
|
55758
|
57
|
19
|
X38
|
83794
|
195829
|
29
|
68
|
X16
|
136604
|
146289
|
48
|
48
|
X39
|
Technology
|
107340
|
72281
|
37
|
21
|
X17
|
153725
|
152380
|
51
|
52
|
X40
|
162216
|
182399
|
54
|
61
|
X18
|
153286
|
178310
|
50
|
61
|
X41
|
145914
|
225457
|
49
|
75
|
X19
|
124813
|
122246
|
42
|
44
|
X42
|
154721
|
248155
|
55
|
86
|
X20
|
institutional
|
133560
|
136243
|
46
|
46
|
X43
|
157630
|
152197
|
53
|
54
|
X21
|
101859
|
139828
|
36
|
46
|
X44
|
181993
|
193978
|
61
|
69
|
X22
|
Source: Authors, 2022 |
Step 2: Stability diagram of adaptation
The variables distribution of interaction map in the Mic-Mac software shows the general characteristics and the stability of the system. Generally, in the scatter diagrams, there are three main modes:
If the distribution of the variables is L-shaped, it indicates the stability of the system,
If the distribution of the variables is around the diagonal axis, it indicates the instability of the system,
If the distribution is irregular and scattered, it indicates an uncertain situation.
Figure 3 as a Mic-Mac export shows the stability based on the interaction of the adaptation indicators in Ahvaz.
Step 3: The significant findings of Mic-Mac model have shown that;
I. Influence indicators. The export map of the Mic-Mac has shown that the influences indicators on adaptation with climate change in Ahvaz are; Diversification of local economic activities (economic diversity), Business Continuity Planning to Prevent Disruption in the Event of Climate Change (Business Improvement), Financial support for the vulnerable groups and ... were classified as influential factors. (Other items are listed in Table 8). (Fig. 3)
II. Dual indicators. These indicators include the risk and target variables. The export map of the Mic-Mac has shown that the key indicators of partnership between communities and local authorities, social cohesion, environmental knowledge of managers and authorities, the existence of social networks and non-governmental organizations are considered as risk factors and have a very high capacity to become have key variables.
III. Dependence indicators. The output of the model has shown that the key indicators of Ethnic equality, Tendency for effective participation to reduce vulnerability by citizens, Tendency to durability in the city by citizens, The decisions participation between formal and informal institutions for crisis management, and ... are among the most important impressionable key indicators to increase the climate adaptation capacity in Ahvaz city. (Other items are listed in Table 8). (Fig. 3)
IV. Independent indicators. The independent indicators have less interaction in the system and mainly include the regulatory variables. The output of the model has shown that the indicators of creating economic incentives for homeowners and low-income earners to invest in risk reduction (tax exemption), The Existence of environmental impact reduction programs (pollutions), The reducing of the destructive effects of infrastructure interactions, have been the most important independent key indicators to strengthen the climate adaptation capacity in Ahvaz city. Another set of key indicators in this category, such as Training courses on reducing the risk of climate disasters, Equipping the local organizations with knowledge and experience, Existence of regular and periodic risk assessment programs, Financial facilities for all people, and ... they are located near the center of gravity of the diagram and are known as regulatory variables and can be used as effective drivers in moving towards climate adaptation. (Other items are listed in Table 8). (Fig. 3)
Table 8
The position of the indicators in adaptation diagram of Mic-Mac
factors Category
|
Factors
|
influence factors
|
X2) the preparing of supporting services for vulnerable communities
|
X12) Business Continuity Planning to Prevent Disruption in the Event of Climate Change (Business Improvement)
|
X13) Diversification of local economic activities (economic diversity)
|
X14) Financial support for the vulnerable groups
|
X16) Local government access to sufficient financial resources to reduce the risk
|
X18) Investing in green jobs and green economy (self-sufficiency, urban agriculture, etc.)
|
X19) Implementing poverty alleviation measures
|
X20) Existence of necessary inter-organizational coordination
|
X25) The empowering of socially vulnerable groups by official institutions
|
X33) Investment to build risk-reducing infrastructure
|
X41) Up-to-date spatial and non-spatial information
|
Dual factors
|
x1) access to basic services and needs for all
|
X3) Partnerships (communities, private sector, and local authorities) to reduce risk
|
X7) Citizens' social cohesion
|
X22) Awareness of city managers about the climate threat
|
X24) Existence of comprehensive plans for citizens educating in order to deal with climate risks
|
X26) Existence of social networks and non-governmental organizations
|
X9) Social trust to government
|
dependence factors
|
X4) Local Government Actions for Risk Reduction Training
|
X8) Ethnic equality
|
X10) Tendency for effective participation to reduce vulnerability by citizen
|
X11) Tendency to durability in the city by citizen
|
X27) The decisions participation between formal and informal institutions for crisis management
|
X31) Protecting of ecosystems to reduce the effects of disasters and adapt to climate change
|
X32) Sustainable land use in terms of current and future based on climate disaster plans
|
X37) Development of green space and urban park
|
Independent factors
|
X17) Create economic incentives for homeowners and low-income earners to invest in risk reduction (tax exemption)
|
X35) The Existence of environmental impact reduction programs (pollutions)
|
X38 The reducing of the destructive effects of infrastructure interactions
|
Regulating factors
|
X6) Training courses on reducing the risk of climate disasters
|
X21) Equipping the local organizations with knowledge and experience
|
X23) Existence of regular and periodic risk assessment programs
|
X30) Private sector participation in the implementation of environmental and ecosystem programs
|
X5) Implement training programs for local authorities and managers
|
X15) Financial facilities for all people
|
X28) Existence of legal frameworks and its executive guarantees (land use regulations and construction laws and security of urban areas)
|
X29) Transparency of processes by creating access to information
|
X34) Effective access to urban infrastructure (water, electricity, gas, internet)
|
X36) Existence of public transportation to easy access to roads
|
X39) Rapid Alert Centers
|
X40) Existence of emergency operations centers and communication system
|
X42) Existence of climate change monitoring centers
|
X43) Access to information of climate change in local and global scale
|
X44) High speed internet, telephone and computer in the city
|
Source: Authors, 2022 |
Step 4: The driving forces ranking of adaptive capacity
The modeling of 44 key indicators of climate adaptation in Ahvaz has led to the leveling of these indicators. Finally based on the MIC-Mac exports, the 10 driving force have been selected as the main drivers affecting adaptation to climate change in Ahvaz city (Table 9).
The Table 9 has shown that among the ten final driving forces selected, 4 drivers have an economic dimension. These driving forces of climate adaptation are: economic diversity, business continuity in the event of climate change (business improvement), financial support for vulnerable groups, and implementing poverty alleviation measures. The findings of this research show the indispensable role of economic factors in climate adaptation in Ahvaz city. Ahvaz is considered a rich city in economic terms despite the concentration of heavy industries, oil industries and agriculture. Currently, the existing and future challenge of the urban climate has become a victim of economic policies in this city. However, it seems necessary to allocate a greater share of this territorial wealth for environmental sustainability and adaptation to climate change in the current and future conditions. Because it seems that the realization of the effect of other driving forces on climate adaptation in this city is a function of the evolution of economic driving forces.
Table 9
Driving forces of climate adaptation in Ahvaz
Direct Influence
|
Indirect Influence
|
Variable
|
Influence
|
Rank
|
Variable
|
Influence
|
Rank
|
X3) Partnerships (communities, private sector, and local authorities) to reduce risk
|
96
|
1
|
X3) Partnerships (communities, private sector, and local authorities) to reduce risk
|
281278
|
1
|
X7) Citizens' social cohesion
|
86
|
2
|
X7) Citizens' social cohesion
|
253494
|
3
|
X20) Existence of necessary inter-organizational coordination
|
86
|
3
|
X20) Existence of necessary inter-organizational coordination
|
248155
|
4
|
X14) Financial support for the vulnerable groups
|
82
|
4
|
X14 Financial support for the vulnerable groups
|
253543
|
2
|
X12) Business Continuity Planning to Prevent Disruption in the Event of Climate Change (Business Improvement)
|
77
|
5
|
X12) Business Continuity Planning to Prevent Disruption in the Event of Climate Change (Business Improvement)
|
237251
|
5
|
X19) Implementing poverty alleviation measures
|
75
|
6
|
X19) Implementing poverty alleviation measures
|
225457
|
6
|
X13) Diversification of local economic activities (economic diversity)
|
73
|
7
|
X13) Diversification of local economic activities (economic diversity)
|
217111
|
9
|
X26) Existence of social networks and non-governmental organizations
|
73
|
8
|
X26) Existence of social networks and non-governmental organizations
|
220433
|
7
|
X25) The empowering of socially vulnerable groups by official institutions
|
70
|
9
|
X25) The empowering of socially vulnerable groups by official institutions
|
213098
|
10
|
X33) Investment to build risk-reducing infrastructure
|
70
|
10
|
X33) Investment to build risk-reducing infrastructure
|
218605
|
8
|
Source: Authors, 2022
This findings is in line with the results of previous studies in the field of adaptation to climate change. Kelso et al. (2015), in research, examined the way of livelihood adaptation in relation to climatic conditions and shows that the diversity of livelihoods and activities is usually considered an effective adaptation during climate shocks. Asfaw et al. (2018), in their research, suggest that the most vulnerable households in Nigeria rely on diversification as a strategy to adapt to climate change and as a strategy to deal with short-term market shocks.
Among the 10 final drivers affecting climate change adaptation in Ahvaz, two important institutional drivers including: Existence of social networks and non-governmental organizations and Citizens' social cohesion were selected. The organizational inconsistency is one of the most important challenges of urban management in Ahvaz. This problem grows to its peak especially in environmental issues and urban climate. In this regard, the need for governance and integrated management of the urban climate is inevitable. The results of Nop-Sothun (2015) studies have shown that many communities passively participate in the implementation of climate change adaptation policy (CCAP).
The active participation of non-governmental organizations (NGO) in the issue of climate change, known as the most important social driving force for adapting to climate change in Ahvaz. Based on studies, dispersal, inconsistency, discord within the organization, the lack of effective organizations in climate management and the lack of the people-institutional organizations (NGO), have caused the climate policies to be ineffective in Iran.
In the physical dimension, investing to build risk-reducing infrastructure has been the most effective diverging force of adaptation to climate change in Ahvaz city. The very hot climate of Ahvaz city, the limited useful life of infrastructure, lack of attention to repair, maintenance and development of infrastructure and lack of investment in environmental infrastructure have turned Ahvaz into a vulnerable metropolis. Batini et al. (2022), believe that climate-friendly infrastructure investment is an important factor for improving sustainability. He has emphasized that the investing on green and resilient infrastructure to control and adapt to climate change are necessary.