Analysis of key stakeholders involved in adaptation measures to tackle the adverse health effects of climate change in the Islamic Republic of Iran: A Social Network Analysis

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Methods This descriptive-analytical research was identified and analyzed using the social network analysis approach. Accordingly, the opinions of 25 university professors, experts, and executives who were selected by purposeful and snowball sampling were obtained through an questionnaire with a Likert scale. Data analysis and graphs design were then performed using Microsoft Excel and Gephi software, version 0.9.2. Stakeholder interaction patterns were also determined through Force Atlas 2 algorithm and graph theory concepts. Results The network consisted of 37 nods, 3 clusters, and 63 edges. The network was close to a complete graph with a density of 0.971. Among the trustees’ network, Intergovernmental Panel on Climate Change was the most active stakeholder and had relatively strong exterior interactions with other stakeholders. The Department of Environmental Protection and the Ministry of Health and Medical Education had relatively weak and very weak exterior interactions with other stakeholders, respectively. Conclusion Given the conflicting interests between the industrial sectors and the health sector, it seems that the Ministry of Health and medical education should gain more power and exert influence on other stakeholders; as well, involving health sector representatives in reviewing policies and providing stakeholder advice may help to fill the gap between the health and other sectors in climate change issues. Stakeholders analysis Climate change Adaptation measures Health effects Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Climate change and its effects are one of the serious concerns of humanity in the attempts made to achieve the sustainable development goals (SDGs) around the world ( 1 , 2 ). Climate change is recognized as one of the greatest threats to the human health in the 21st century ( 3 ); inevitably, it influences basic health requirements such as air, safe water, adequate food, and proper shelter by affecting some key sectors including water, food, agriculture, industry, and health through various mechanisms. Therefore, any impact on other sectors will ultimately affect the health sector ( 2 ). Variable and warmer climates can lead to the rise of some air pollutants, an increase in disease transmission through unhealthy water and contaminated food, a decline of agricultural production in some less developed countries, and aggravation of extreme weather risks ( 2 ). On the other hand, rising temperatures, when combined with poor housing and living conditions in urban communities, can increase the risk of heart attack and cardiovascular diseases ( 4 ). Climate change also poses new challenges for controlling infectious diseases. The life cycle of many pathogens, including cholera, diarrhea, malaria, and dengue fever, is highly sensitive to climatic variables such as temperature and rainfall. Although, the greatest health effects of climate change may not be due to natural disasters or epidemics in the long run, they can be attributed to the gradual pressure on natural, economic, and social systems that can somehow affect health. Each of these changes can create the potential for malnutrition, reduced food security, population displacement, and the increased risks of war and internal conflicts ( 2 , 4 ). Given the inevitability of climate change, the need to adapt to the adverse health effects associated with it is more important than ever. Adaptation measures provide an opportunity to improve health by focusing on reducing the burden of diseases and injuries resulting from climate change ( 4 ). Also, measures to decrease greenhouse gas emissions can significantly reduce the expected health risks ( 5 ). Climate change adaptation is the process of adjusting to current or expected effects of climate change that involves making regular decisions in a network of individuals, companies, civil societies, and government organizations at the local, regional, national, and international levels ( 5 ). Participation, cooperation, and interaction of all stakeholders regarding adaptation measures may be vital to ensure public health ( 5 , 6 ). According to the fourth IPCC report, the need for interaction and dialogue between stakeholders to improve decision-making and awareness is recognized as a key strategy in climate change adaptation policy-making. Stakeholders' dialogue leads to their participation, thus ensuring more effective adaptation to climate change effects through the consistency and integration of research results with local requirements, as well as the legitimacy of policies and adaptation measures ( 7 ). Currently, researchers, policymakers, and advocacy communities have little understanding of how different organizations work together and how they can exercise their power to influence decisions and implement adaptation policies to decrease the health effects of climate change. Understanding this comprehensive governance structure is crucial in regard to this important and specific issue, as climate change has its effects mainly on health through changes in areas not directly related to health; these include agriculture, transportation, water, and climatological disaster risk management. This complex network of decisions and policies often leads to ignoring the health concerns and problems caused by climate change in other sectors. Therefore, stakeholders' participation in research and adaptation projects requires accurate mapping of all relevant stakeholders at different levels ( 8 ). The active presence of public and private stakeholders in adapting to the effects of climate change should be considered and the analysis of their interactions with each other is very helpful in understanding the desired structure in health decision-making ( 9 ). This study, thus, aimed to analyze the structure of the network of stakeholders involved in health adaptation to climate change with a focus on the most influential stakeholders by using the Social Network Analysis (SNA) approach in the Islamic Republic of Iran. As the second-largest country in the Eastern Mediterranean Region (EMRO) in the division of the World Health Organization (WHO), Iran is highly vulnerable to the adverse health effects of climate change ( 10 ). The Iranian government, after joining the Kyoto Protocol in 1950, has been presenting policies, strategies, and programs to adapt to climate change at the national level. The government of Iran is currently a member of the Regional Committee through the Ministry of Health and Medical Education (MoHME); it was committed to the World Health Assembly (WHA) treaty in May, 2008, to protect against the adverse health effects of climate change. In the public health domain, the Center for Occupational and Environmental Health, under the deputy of the MoHME, has been responsible for proposing strategies and programs to adapt to the health problems caused by climate change. Efforts have, accordingly, been made to set up an adaptation plan for health in the same direction; however, due to a lack of coordination and cross-sectoral cooperation in the development of adaptation strategies, this plan can not meet the necessary acceptance ( 10 ). Due to the interdisciplinary nature of the climate change issue and the involvement of international organizations and other sectors, as well as various units within the MoHME, which is the main trustee of the health of Iranian society, this study made it possible to obtain a comprehensive overview of relationships between stakeholders at the national and international levels, and their positions by mapping the structure of their network. It can create a systematic approach to identify all organizations which are directly and indirectly involved in decisions, research, policy-making, and climate change adaptation planning. It can, thus, inform policymakers, authorities, researchers, and even people of society and people-based groups to make their demands from other actors. 2. Methods The present study was a descriptive-analytical research that identified and analyzed key stakeholders involved in climate change adaptation policy-making in the I.R. Iran, using the SNA approach. SNA has been used in many social epidemiological studies to better understand health conditions; however, this method has been less used in health decision-making processes ( 9 ). For this reason, the application of SNA for policy-making has received more attention in the recent years ( 11 ). A social network is defined as a set of actors or stakeholders connected through meaningful social relationships. In the literature, stakeholders are often referred to as individuals or groups who are interested in a particular issue in which they have common profits. They can be influenced by specific policies and actions at different levels and sizes, or they can be responsible for implementing policies ( 12 , 13 ). SNA is a strategy for examining the extent to which each actor influences the network, understanding the nature of relationships between actors within a network, finding how they affect each other's behaviors, and determining the degree of connection, coherence, and clustering of the overall network ( 14 , 15 ). The main stakeholders in the field of climate change adaptation are governmental, commercial, and non-governmental organizations, as well as groups who are directly or indirectly affected by the consequences of climate change ( 16 ). This study has been done in two phases including stakeholder identification and stakeholder analysis. 2.1. Phase 1: key stakeholders Identification To identify key stakeholders who were, directly and indirectly, involved in the climate change at national and international levels, the opinions of 23 university professors, experts, and executives who were selected by purposeful sampling were obtained. In addition, to ensure the maximum access to the potential of available specialties, snowball sampling was done as the complement. Participants had scientific and practical competencies with at least 2 years of active executive experience in the field of health and climate change. The data collection tool was a questionnaire with open-ended questions. The poll was continued until the data reached the saturation level. Also, through interviews with 25 experts and the archival research method (study of the existing written sources, research, and articles, as they are related to climate change in Iran), stakeholders that have not been mentioned before were added to the list of stakeholders. Four criteria were considered for stakeholders’ inclusion: Functional criterion : These are the stakeholders formally responsible for deciding and implementing adaptation measures. These organizations have proposed policies for the subject. Hierarchical level criterion : Influential stakeholders who can indirectly facilitate or hinder adaptation measures. These are organizations that have the veto power of other organizations and influence the implementation of policies in this area. Knowledge and ability : Stakeholders with skills related to adaptation measures, climate systems, and climate risks. Informal activity : These can informally influence the formulation and implementation of adaptation policies ( 17 ). 2.2. Phase 2: Key stakeholders analysis The stakeholder analysis in this phase was performed quantitatively. Participants were representatives of stakeholders identified in the previous phase, with at least two years of experience in policy-making, management, planning, both executive and operational, education, and research in the areas related to the subject matter selected purposefully. The expertise of the participants included water resources engineering and management, energy economics and efficiency, health, food security, ecology, ecophysiology, environment, and natural resources, international relations, climatology, meteorology, climatological hazards, and risk management. The tool used was the researcher-made questionnaire, which was designed in closed questions in the form of three tables. Table 1 was designed for determining stakeholders’ communication. Participants were asked to rate the level of cooperation and communication flow of their organization with each of the other stakeholders, based on a score range of 0 to 10 (with zero representing disaffiliation and 10 being the maximum connection value). Given the interdisciplinary nature of climate change adaptation measures in a wide range of disciplines including energy, industry, water, agriculture, natural resources, education, research, health, technology, and so on, participants were asked to determine the prominent role of stakeholders in climate change adaptation measures in the form of three clusters of "trustee"," collaborator" and "supporter" in the second table. In the third table of the questionnaire, the information of the participants was obtained confidentially. Table 1 Definition of stakeholders clusters involved in health adaptation to climate change in the I.R. Iran. Role Definition Trustee Actors who are directly involved in climate change and policy-making are responsible for drafting rules and regulations directly. Collaborator Actors cooperating in the implementation and evaluation of the formulated policies acted according to the approvals of the responsible organizations. Supporter Organizations falling into this category were according to their supportive role in better performing the tasks assigned to other actors. The survey was conducted by e-mail and followed periodically. The response rate of the questionnaire was 100%. After the initial review of the 35 questionnaires, two of them were removed from the analysis process due to the lack of proper answers; finally, 33 ones were entered into the analysis. Data analysis and graph design were performed using Microsoft Excel and Gephi software, version 0.9.2 (Open-source network analysis software for network visual exploration). This software could provide a faster and more accurate understanding of the influence and power of stakeholders in the network through mathematical tools and concepts of graph theory. The communication patterns between them were determined through Force Atlas 2 layout algorithm. The position of each node in the network was based on the concepts of social networks, such as Node, Edge, Weighted Degree Centrality, Betweenness Centrality, Closeness Centrality, Average Weighted Degree, Density, and Clustering Coefficient (see Table 2 ). Table 2 The definition of social networks concepts Concept Definition Node A node is the fundamental unit of a network. A node is a stakeholder in the entire network ( 18 ). In this study, nodes are measured in all of the following graphs in terms of the Weighted Degree Centrality. Edge An edge is a vector connecting two nodes. Each edge has a weight that can be unidirectional or non-directional. Thicker edges indicate more communication between one node and others ( 18 ). Degree Centrality Centrality generally has a broad meaning in that it is used to identify and determine the most important actors or communications in a network. Centrality has different types whose applications are different from each other ( 19 ). Degree Centrality, Betweenness Centrality, and Closeness Centrality are the most important indicators of centrality; depending on the type of the relationship between the actors, effective indicators can be selected, measured, and judged ( 20 ). Weighted Degree Centrality The total weight of an actor's direct communication with others in a network is named Weighted Degree Centrality. The greater the Weighted Degree Centrality of an actor, the more its cooperation with other stakeholders and the more the centrality. These in-directional graphs include two types of input (In-Degree Centrality) and output (Out-Degree Centrality). A high ratio of Out-Degree Centrality to In-Degree Centrality indicates the higher influence coefficient of the actor. Stakeholders with a high influence coefficient are more active in decision-making and implementation of adaptation measures. They have more capacity to influence others’ decisions as well. A high ratio of In-Degree Centrality to Out-Degree Centrality shows the power coefficient of the actor ( 18 ). Stakeholders with a high power coefficient are more likely passive in decision-making and implementing adaptation measures. They have more authority to do measures and change other decisions. Betweenness Centrality Betweenness Centrality represents the actors who are in a privileged position in the network. They serve as a “bridge” between other actors ( 21 ). A node with the highest Betweenness Centrality is located between many pairs of nodes, and the communication channels and flow of other nodes pass through it ( 22 ). They also have access to a variety of information sources through external links ( 23 ). Network Density The network density indicates the degree of the communication of actors at the network total level. It is calculated from the ratio of the number of available links to the total number of possible links in the network ( 24 ). High network density indicates the strength of links and the coherence and coordination between stakeholders ( 22 ). Closeness Centrality Closeness Centrality is the shortest distance between each node and other nodes in the network, which is calculated by its distance from all nodes in the network, regardless of whether the link is direct or indirect ( 22 ). Clustering Coefficient The Clustering Coefficient of any node in a network is its neighborhood density. The network Clustering Coefficient is the average clustering coefficient of all nodes in the network. 3. Results The findings of this study are presented in the following two parts: 3.1. Key Stakeholders identification Twenty-six key stakeholders involved in health adaptation to climate change including national ministries, international organizations, Non-Governmental Organizations (NGOs), and research institutes were identified in the I.R.Iran. (See Table 3 ). 3.2. Network structure analysis The network consisted of 37 nodes, 63 edges, and 3 clusters. Figure 1 shows an overview of the network structure. Each node represents a stakeholder and each edge shows the interaction of stakeholders with each other. Stakeholders who were playing a key role in making climate change adaptation decisions and policies are outlined with larger nodes in the network. All edges in this graph had weight and were directional. The edges with a heavier weight were the visible thicker. Eighty-one % of the stakeholders were national and 19% were international. The results of the Gephi software analysis highlighted the distribution of interactions within the network was uniform and the network density was 0.971. Given that this value was close to one, the network was very close to a complete graph where all nodes were interconnected. The network diameter and the maximum distance between the nodes were 2. That is, each node, in maximum, affected or could be affected by other nodes with one mediator. Modularity was equal to 0. 027. Whenever this number is close to one, the network is more separable into communities. The stakeholders in a community are usually close in terms of power and influence. Three indistinct communities were obtained in this network. Given the low modularity in this network, the connections between the nodes were evenly distributed. Therefore, the formation of communities in this network was weak; so, it could not be claimed that stakeholders formed communities in the network. The Strongly Connected Components in this network were equal to 1. That is, all nodes were connected in a directional graph. The estimation of this indicator showed that the decisions of all stakeholders were dependent on others' decisions. The value of the Average Clustering Coefficient was equal to 0.971, which was expected due to the two values ​​of Modularity and Strongly Connected Components. The Average Clustering Coefficient score showed the extent to which the neighbours of each node were related to each other on average. When a network had a sparse state (i.e., there were limited connections between nodes), this value would be close to zero. Table 3 shows the numerical values ​​calculated at this level as separated by the communities, clusters, and types of stakeholders (national and international). The value of the Weighted Degree Average for each node was 5.04, which showed that, despite the high density of the network, the average inter-organizational communication was weak to moderate. Table 3 Numerical values ​​of the stakeholders’ network involved in climate change adaptation measures, as separated by the clusters and communities Network Node number Edge number Network density Diameter Connected components Modularity Total 26 631 0.971 2 1 0.027 Trustee 3 6 1 1 1 -0.435 Collaborator 8 56 1 1 1 -0.542 Supporter 15 201 0.975 2 1 -0.163 International 5 20 1 1 1 -o.494 National 21 409 0.974 2 1 -0.246 Community A 10 89 0.989 2 1 -0.450 Community B 9 66 0.917 2 1 -0.428 Community C 7 42 1 1 1 -0.452 Table 4 shows the numerical values ​​calculated separately from the stakeholders. The values ​​of the power and influence coefficients indicated the Ministry of Energy (MoE) [1] , the Ministry of Health and Medical Education MoHME [4] , the Ministry of Science, Research and Technology (MSRT) [8] , the Department of Environment (DoE) [12] , Plan and Budget Organization (PBO) [14] , National Disaster Management Organization (NDMO) [16] , Iranian Red Crescent (IRCS) [18] , national NGOs [19] , and the Supreme Council for Health and Food Security (SCHFS) [21] were the stakeholders connected with all others throughout the network. The results, based on the values ​​of closeness centrality, showed that the Ministry of Education (MEDU) [11] and the Vice President for Science and Technology (ISTI) [20] were the most “peripheral organizations” in the network and had less contact with the other stakeholders. The values ​​of Betweenness Centrality also showed that the MoE [1] , MoHME [4] , MSRT [8] , DoE [12] , PBO [14] , NDMO [16] , IRCS [18] , national NGOs [19] , and SCHFS [21] could play an important role in creating cross-sectoral coordination as “bridge or liaison organizations”. Also, the results based on the values ​​of In-Degree Centrality to Out-Degree Centrality (power coefficient) showed that PBO [14] , the Ministry of Agriculture Jihad (MAJ) [7] , NDMO [16] , the Islamic Republic of Iran Broadcasting (IRIB) [15] , DoE [12] , the Ministry of Petroleum (MoP) [2] and, MoE [1] had a higher power in the network. Further, the values ​​of Out-Degree Centrality to In-Degree Centrality (influence coefficient) displayed that national NGOs [19] , international NGOs (26) , Intergovernmental Panel on Climate Change (IPCC) [24] , SCHFS [21] , MSRT [8] , the Ministry of Foreign Affairs (MFA) [6] , Asia-Pacific Disaster Information Management (APDIM) [25] , Food and Agriculture Organization (FAO) [23] , World Health Organization (WHO) [22] , IRCS [18] and the Ministry of Industry, Mine, Trade (MIMT) [3] were the stakeholders that had more influence over others in the network (Figs. 1 to 4). Table 4 Numerical values of the stakeholder network involved in climate change No. Stakeholders’ ID Label Position power Coefficient (authority) Influence Coefficient Closeness centrality betweenness centrality 1 Ministry of Energy MoE 50 0.89 1.12 1 0.003 2 Ministry of Petroleum MoP 49 0.86 1.16 1 0.000 3 Ministry of Industry, Mine, Trade MIMT 49 1.18 0.85 1 0.000 4 Ministry of Health and Medical Education MoHME 50 1.10 0.91 1 0.003 5 Ministry of the Interior MoI 49 0.73 1.37 1 0.000 6 Ministry of Foreign Affairs MFA 49 1.24 0.81 1 0.000 7 Ministry of Agriculture Jihad MAJ 49 0.60 1.67 1 0.000 8 Ministry of Science, Research and Technology MSRT 50 1.27 0.79 1 0.003 9 Ministry of Information and Communications Technology of Iran ICT 49 0.94 1.06 1 0.000 10 Ministry of Roads & Urban Development MRUD 49 1.03 0.97 1 0.000 11 Ministry of Education MEDU *34 0.31 3.19 0.61 0.000 12 Department of Environment DoE 50 0.85 1.18 1 0.003 13 Institute of Standards and Industrial Research of Iran ISIRI 49 0.91 1.10 1 0.000 14 Plan and Budget Organization PBO 50 0.54 1.86 1 0.003 15 I.R of Iran Meteorological Organization IRIMO 49 0.96 1.04 1 0.000 16 National Disaster Management Organization NDMO 50 0.67 1.49 1 0.003 17 Islamic Republic of Iran Broadcasting IRIB 49 0.77 1.30 1 0.000 18 Iranian Red Crescent IRCS 50 1.14 0.88 1 0.003 19 National NGOs I.R.I NGOs 50 1.50 0.67 1 0.003 20 Vice-Presidency for Science and Technology ISTI *46 0.74 1.35 0.89 0.000 21 Supreme Council for Health and Food Security SCHFS 50 1.25 0.80 1 0.003 22 World Health Organization WHO 49 1.13 0.88 1 0.000 23 Food and Agriculture Organization FAO 49 1.22 0.82 1 0.000 24 Intergovernmental Panel on Climate Change IPCC 48 1.45 0.69 1 0.000 25 Asia-Pacific disaster information Management APDIM 48 1.24 0.81 1 0.000 26 International NGOs NGOs 48 1.47 0.68 1 0.000 As can be seen in Fig. 1, IPCC [24] , MFA [6] , MSRT [8] , and DoE [12] were the most influential stakeholders in climate change adaptation decisions and policies, respectively. Meanwhile, the weakest links in climate change adaptation decisions belonged to the IRIB [17] , MoE [1] , and PBO [14] . Figure 2 shows the network based on the influence coefficient. National NGOs [1] , international NGOs [26] , IPCC [24] , MSRT [8] , SCHFS [21] , APDIM [25] , FAO [23] , MIMT [3] , and WHO [22] had higher influence in the network, respectively, but they were low in terms of power. Meanwhile, MoHME [4] had less influence over the network in comparison to the above-mentioned stakeholders. The Ministry of Information and Communications Technology (ISTI) [20] , MoHME [4] , IRCS [18] , Institute of Standards and Industrial Research of Iran (ISIRI) [13] , and the Ministry of Roads and Urban Development (MRUD) [10] were the stakeholders who had both power and low influence in the network. Figure 3 shows the network based on the power coefficient. Thus, the MoP [2] , MoE [1] , Ministry of the Interior (MoI) [5] , MAJ [7] , DoE [12] , PBO [14] , NDMO [16] , IRIB [15] and, MEDU [11] had higher power in the network, respectively. Also, ISIRI [20] had less power than the other stakeholders in the network. Comparison of Fig. 2 with Fig. 3 highlighted that NDMO [16] and MAJ [7] , despite being recognized as high-power organizations in the network, had a lower influence on decision-making. The network separated by clusters showed that IPCC, DoE [12] , and MoHME [4] were the three trustee organizations responsible for adaptation measures to tackle the adverse health effects of climate change (Fig. 4). On the other hand, MSRT [8] , MIMT [3] , MFA [6] , APDIM [25] , national NGOs [19] , and FAO [23] were the most active supporter stakeholders in the network. Further, MoE [1] and IRIMO [15] were the most active collaborator stakeholders in the network. As can be seen in Fig. 4, IPCC [24] could be considered the most active stakeholder among the trustee network with higher output and input edge weights. MoHME [4] and DoE [12] were next in line. Among collaborators network, IRIMO [15] , MoE [1] , and MRUD [10] could be recognized as the most active stakeholders. MFA [6] , MSRT [8] , MIMT [3] , the MFA [6] , FAO [23] , and WHO [22] were also the most active stakeholders in the supporters' network. Meanwhile, IPCC [24] has been the most active international stakeholder; also, MFA [6] , DoE [12] , and MSRT [8] could be regarded as the most active national stakeholders in the network. As can be seen in Fig. 5, based on the weight of the output edges obtained, the MoHME [4] had relatively weak exterior interactions with the most key stakeholders in the network, especially DoE [12] , IPCC [24] , MSRT [8] , the Ministry of Information and Communications Technology of Iran (ICT) [9] , FAO [23] , WHO [22] , national NGOs [19] , IRCS [18] , MEDU [11] and the IRIB [15] on climate change adaptation. Based on the weight of the output edges obtained, the DoE [12] and IPCC [24] had relatively weak and strong exterior interactions with other stakeholders in the trustee network, respectively. 4. Discussion Public health in the face of climate change consequences is influenced by different activities of various technical, executive, economic, social, cultural, and political organizations that are concerned with climate change adaptation. Each sector, through its role and responsibilities, contributes to the implementation of mitigation and adaptation measures. Also, considering that a series of adaptation measures can implicitly harm the goals of mitigation measures and vice versa, it is important to have balanced and accurate policies in both mitigation and adaptation so as not to create conflicting consequences. So, the extent of influence and impressionability of each sector concerned with climate change, macro and micro strategies in adaptation measures, and the contribution of each sector to implementing these measures must be clearly defined. Therefore, this study could help to achieve a correct understanding of the position, power, and influence of stakeholders in the climate change adaptation network. With such information, it is possible to develop the appropriate strategies to strengthen cross-sectoral collaboration and coordination, modify the previous policies, implement new policies, and enhance stakeholders' support. There have not been many studies regarding stakeholder network analysis on climate change adaptation issues in Iran; however, the study, done by Mousavi et al. (2020), emphasized the importance of the interaction of the actors involved in climate change in improving and promoting adaptation measures in the health sector. In this regard, the participation of local public and private institutions and cross-sectoral cooperation were among the important issues that could be considered to achieve the goals of health adaptation to climate change. Finally, they identified MoHME, DoE, and the Environmental Research Institute of Tehran University of Medical Sciences as the most active participants in policy-making and management of the adverse health effects of climate change in Iran ( 25 ). Our study showed that the stakeholders involved in climate change adaptation measures formed a complete network, given the high density of the network. This means that all stakeholders were in full interaction with each other. However, the numerical values of graph theory, as ​​calculated for the average interaction of stakeholders within the total network, showed that the interactions were relatively weak. Estimation of social network indicators showed the policies on climate change adaptation were complex and intertwined, thus requiring careful and planned cross-sectoral coordination and cooperation. The results also showed that given the diversity of tasks and the complex nature of climate change adaptation measures, it was not possible to determine the distinct communities in the network. This was not, however, unexpected. NDMO [16] , although recognized as one of the high-power stakeholders in the network, generally has a lower influence on climate change adaptation decisions. Given the important role of the NDMO [16] in the network, it did not act as an effective stakeholder in the integration of a comprehensive risk assessment program into the health service delivery system to reduce the adverse health effects of climatological disasters ( 10 ). However, in contrast with our study, in the study done by Yousefi et al., good coherence in the network of active stakeholders in the field of Disaster Risk Management (DRM) was shown in Iran ( 26 ). In this regard, Eleni Karali et al. (2020) analyzed the type and intensity of the interactions of 35 institutions involved in Climate Change Adaptation (CCA) and Disaster Risk Reduction (DRR) networks using the SNA in Europe. Although CCA and DRR pursued complementary goals, they had different structures and policies, and the relationship between the two communities of the network was described as inadequate. Finally, this study emphasized that the European climate change adaptation platform (Climate-ADAPT) had the highest popularity and value, with the potential to enhance the effective interaction between the actors to create common ground and develop synergies between them ( 27 ). In a study conducted by Ruhollah Oji et al. (2021), the relationship between an interdisciplinary group of representatives from different sectors involved in promoting adaptation measures based on sustainable development at national and local levels was analyzed using the network analysis approach. The results of this study, in line with our study, illustrated the cooperation between members of the Iranian Climate Change Professionals Network (ICCPN) and their willingness to join local and national networks in the lowest rank. The most important reasons were the lack of a formal network structure recognized by experts, network ambiguous goals, and the absence of a comprehensive program to attract public and private support ( 28 ). Bowen et al. (2015) also stated that cross-sectoral approaches to effectively adapted to the adverse health effects of climate change could be considered as a basic principle in managing and controlling these effects ( 29 ). The results of our study also showed that the national NGOs [19] in Iran, despite having a high potential influence, could not be considered an acceptable and worthy position in environmental policy-making. Regarding the role of the national NGOs [19] in increasing public participation and creating a policy flow for the agenda-setting of climate change adaptation measures, the formation and empowerment of these societies can not only increase public awareness and risk understanding, but also enable people to pursue their demands, play their roles and ensure social participation. This is especially important when it comes to health. Therefore, the potential of this group of stakeholders can be used to achieve adaptation goals by strengthening their organizational structure of them, delegating the necessary powers, and providing action freedom away from political positions, thus creating the necessary context for their participation in climate change adaptation policy-making. MoI [5] , as the custodian and supervisor of the national NGOs [19] , should provide the necessary support to strengthen the role of the national NGOs [19] in pursuing the related goals by creating a sense of duty and economic security for them. Meanwhile, the results obtained by Bowen et al. (2014) emphasized the role of NGOs in raising public awareness and shaping public demands in developing climate change adaptation measures. In line with our study, they showed that the role of NGOs was weak in the climate change stakeholder network in Cambodia, emphasizing the strengthening roles in developing adaptation policies in the health sector ( 30 ). In the present study, Stakeholder analysis showed that the MoP [2] , MoE [1], and MIMT [3] were among other stakeholders that had high power in policy-making related to mitigation measures and vetoing decisions taken in other sectors. Therefore, given the conflict of interests and the goals of the industry and energy sectors, along with other sectors, to reduce the incompatible consequences of climate change, such results were not far from expectation. It seems, therefore, that with more serious and precise legal and regulatory mechanisms, this large amount of power could be adjusted and the existing problems and conflicts would be overcome. The analysis of the network also showed that MAJ [7] , PBO [14] , MoI [5] , MEDU [11] , and IRIB [15] , despite their high power, were located “around” the network. These stakeholders had performed very weakly when interacting with other stakeholders and could not fulfill their role and missions in this area well. It seems, therefore, that these organizations should move towards network centralization by strengthening cross-sectoral interactions in the direction of climate change-related policies. To strengthen such roles, effective measures should be adopted for the transfer of knowledge and complex negotiations between researchers, physicians, policymakers, private actors, and community members in all aspects of adaptation measures. In addition, the involvement of many types of supportive organizations that encourage cross-sectoral collaboration seems to be crucial. As can be seen in this network, FAO [23] , WHO [22] , APDIM [25] , international NGOs [19] , MSRT [8] , MFA [6] , and MIMT [3] have a supportive role. Also, it seems that a comprehensive and multi-sectoral plan is needed for strong coordination and collaboration in mitigation and adaptation measures, Also, a review of intra-organizational rules and the creation of bridge organizations that can efficiently participate in planning, coordinating, and strengthening the existing interactions seem to be necessary. MoE [1] , MoHME [4] , MSRT [8] , DoE [12] , PBO [14] , NDMO [16] , IRIB [15] , IRCS [18] , national NGOs [19] , and SCHFS [21] have “liaison roles” in this network. It is a misconception to think that climate change is just an environmental issue. Climate change can be the source of many economic, social, cultural, and political crises. Therefore, DoE must solve environmental challenges and respond to threats to the community's health in strong collaboration and coordination relationship with all involved stakeholders. Although the country's environment has certainly suffered the most damage, as mentioned earlier, DoE, as the oversight and governance body on environmental issues, has not had the tools to approve or reject the authority of other organizations or ministries. At present, it can only review the institutions under its command. Thus, regulations to oblige the government and executive bodies to develop a comprehensive, coordinated, and multi-disciplinary adaptation plan are necessary. Also, organizational reviews, such as creating adaptation offices and changing the consumption pattern in the executive bodies and holding the government accountable in this regard, would be essential. According to the description of the tasks described in the National Climate Change Strategy Plan, MoHME [4] is one of the leading ministries that should take adaptation measures. The health risks of climate change require a significant review of how the health sector works with other organizations and groups. The involvement of other sectors in health policy-making and planning can reduce resistance to implementing interventions to decrease the adverse health effects of climate change. Given the conflict of interest between the industrial sector and the health sector, it can be said that MoHME should gain more power in the network by exerting influence on other stakeholders and creating rules and tools for cross-sectoral coordination in the implementation of adaptation programs. Also, it can prevent confusion between sectors at local, national, and regional levels, thus supporting the interests of communities to deal with the health consequences of climate change. 5. Conclusion Climate change poses a threat to human health in a variety of ways. Therefore, since many upstream drivers of these risks are outside the health sector, the need for an effective and efficient adaptation plan to manage the risks of climate change should be emphasized. Thus, establishment of cross-sectoral collaboration and coordination of water, food, and energy (Nexus), development of a national adaptation plan in the health system by adopting three main approaches including Health in All Policies (HiAP), One health, Disaster Risk Reduction (DRR), and the integration of Social Determinants of Health (SDHs) in climate change policy-making are highlighted. The HiAP aim is the improvement of the population's health by reforming public policies and integrating health considerations into the policies of all sectors. Given that the major health effects of climate change will come from other sectors such as water, energy, and agriculture, it is important to use a comprehensive health framework to ensure that all relevant sectors understand health considerations and take the necessary steps in this regard. Understanding the networks of stakeholders and their relationships can lead to the better governance of ruling institutions concerning the subject matter. Involving health sector representatives in policy reviewing and advising relevant stakeholders on climate change adaptation measures can be one way to increase cross-sectoral cooperation by bridging the gap between the health sector and other sectors. All four approaches, by strengthening coordination and cross-sectoral cooperation with the health system leadership, have led to the development of a network of stakeholders that can could reduce health inequalities in all policies by involving the socio-economic components of the climate change issue. Although the stakeholder analysis process has identified a large number of organizations involved in climate change adaptation measures in Iran, there have been unexpected difficulties in adopting them. The analysis presented in this study could only be a description of the current state of inter-organizational communication. The limitation of this method is in quantifying organizational interactions that are highly subjective in such complex issues. So it will be impossible to make definitive judgments on the stakeholder’s performance. Declarations Funding: No funding was received for conducting this study. Financial or non-financial interests: The authors have no relevant financial or non-financial interests to disclose. Competing interests: The authors declare that they have no competing interests. Ethics approval: This research has acquired the approval of Tehran University of Medical Sciences’ Institutional Review Board (IRB). The IRB follows the stipulated clauses of the Helsinki Declaration. The approval code to do the research is IR.TUMS.SPH.REC. 1397.101. Consent to participants: In the present study, the participants were informed about the objectives and importance of the study. Participants were also reassured that the information obtained was for research purposes. Written informed consent was obtained from all participants. Authors’ contributions: A.M. and AH.T. contributed to design of the study; A.A., AH.T. directed the project; N.SH performed and analyzed data; A.OT., K.N. and AR.MB. aided in interpreting the results; A.M. and AH.T. led the writing. All authors read and approved the final manuscript. Acknowledgements We thank all representatives of the organizations who participated in this study and provided their perspective on the role and responsibility of stakeholders in decision-making, research, policy-making, and implementation of an adaptation plan. References Stephenson J, Crane SF, Levy C, Maslin M. Population, development, and climate change: links and effects on human health. Lancet. 2013;382(9905):1665–73. Organization WH. Protecting health from climate change: connecting science, policy and people. 2009. Costello A, Abbas M, Allen A, Ball S, Bell S, Bellamy R, et al. Managing the health effects of climate change: lancet and University College London Institute for Global Health Commission. Lancet. 2009;373(9676):1693–733. Bowen KJ, Friel S. Climate change adaptation: Where does global health fit in the agenda? Globalization health. 2012;8(1):1–7. Adger WN, Arnell NW, Tompkins EL. Successful adaptation to climate change across scales. Glob Environ Change. 2005;15(2):77–86. Olsson P, Gunderson LH, Carpenter SR, Ryan P, Lebel L, Folke C et al. Shooting the rapids: navigating transitions to adaptive governance of social-ecological systems. Ecol Soc. 2006;11(1). Assessment ACI. Arctic climate impact assessment. Cambridge University Press Cambridge; 2005. André K, Simonsson L, Swartling ÅG. Linnér B-o. Method development for identifying and analysing stakeholders in climate change adaptation processes. J Environ Planning Policy Manage. 2012;14(3):243–61. Bowen KJ, Alexander D, Miller F, Dany V. Using social network analysis to evaluate health-related adaptation decision-making in Cambodia. Int J Environ Res Public Health. 2014;11(2):1605–25. Mousavi A, Ardalan A, Takian A, Ostadtaghizadeh A, Naddafi K, Bavani AMJJEHS, et al. Clim change health Iran: narrative Rev. 2020;18(1):367–78. Lazer D. Networks in political science: Back to the future. PS: Political Sci Politics. 2011;44(1):61–8. Freeman RE. Strategic management: A stakeholder approach. Cambridge University Press; 2010. Grimble R, Wellard K. Stakeholder methodologies in natural resource management: a review of principles, contexts, experiences and opportunities. Agric Syst. 1997;55(2):173–93. Otte E, Rousseau R. Social network analysis: a powerful strategy, also for the information sciences. J Inform Sci. 2002;28(6):441–53. John S. Social network analysis: A handbook. Contemp Sociol. 2000;22(1):128. Parry ML, Canziani O, Palutikof J, Van der Linden P, Hanson C. Climate change 2007-impacts, adaptation and vulnerability: Working group II contribution to the fourth assessment report of the IPCC. Cambridge University Press; 2007. Hogan B, Fielding N, Lee R. Analyzing social networks. The Sage handbook of online research methods. 2008:141 – 60. Zedan S, Miller W. Using social network analysis to identify stakeholders’ influence on energy efficiency of housing. Int J Eng Bus Manage. 2017;9:1847979017712629. Brandes U. Network analysis: methodological foundations. Springer Science & Business Media; 2005. Crona B, Bodin Ö. What you know is who you know? Communication patterns among resource users as a prerequisite for co-management. Ecol Soc. 2006;11(2). Molano S, Polo A. Social network analysis in a learning community. Procedia-Social Behav Sci. 2015;185:339–45. Hanneman RA, Riddle M. Introduction to social network methods. University of California Riverside; 2005. Burt RS. Structural holes and good ideas. Am J Sociol. 2004;110(2):349–99. Bae S-H, Nikolaev A, Seo JY, Castner J. Health care provider social network analysis: a systematic review. Nurs Outlook. 2015;63(5):566–84. Mousavi A, Ardalan A, Takian A, Ostadtaghizadeh A, Naddafi K, Bavani AMJB. Health system plan for implementation of Paris agreement on climate change (COP 21): a qualitative study in Iran. 2020;20(1):1–13. Khoshsabegheh HY, Ardalan A, Takian A, Hedayatifar L, Ostadtaghizadeh A, Saeedi BJD et al. Social network analysis for implementation of the Sendai framework for disaster risk reduction in Iran. 2019:1–9. Karali E, Bojovic D, Michalek G, Giupponi C, Schwarze RJS. Who is connected with whom? A social network analysis of institutional interactions in the european CCA and DRR landscape. 2020;12(3):1275. Oji R, Hesam M, Keener VWJW, Climate S. Using social network analysis to assess climate change professionals’. Commun Iran. 2022;14(1):349–63. Pezeshki Z, Tafazzoli-Shadpour M, Mansourian A, Eshrati B, Omidi E, Nejadqoli I. Model of cholera dissemination using geographic information systems and fuzzy clustering means: Case study, Chabahar, Iran. Public Health. 2012;126(10):881–7. Pezeshki Z, Tafazzoli-Shadpour M, Nejadgholi I, Mansourian A, Rahbar M. Model of Cholera Forecasting Using Artificial Neural Network in Chabahar City, Iran. Int J Enteric Pathog. 2016;4(1):1–8. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4512761","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":319733397,"identity":"9ea9b11a-2566-4640-acd3-14dcc16fa880","order_by":0,"name":"Arefeh Mousavi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABHElEQVRIiWNgGAWjYBACNiD+wNgA5SUw2NjxgxkFuLUAFTDOQNKSliwJ4iQY4NYCVICkhYHhEOOGAyAajxaD24cPNnzcsU3O4PzhZxIPGA4wG59fnfjhgQGDPL/YAexazqUlNs48c9vY4EaamUQCwx0+sxtvN0sAHWY4c3YCdi1neMwf87bdTtxwg8HYIIHhGbPZjbMbQFoSDG5j12J/hv9j81+QlvPHPwO1HGbcPOPs5h/4tABtYWxmBGk5kGP4AKRlA3/vNry2GJxhM2zsbbttLHkjp/BBgkFassQN3m0WCQYSePzC/LDhZ9ttOb7zxzcc/FEBjMr+s5tvAhny/NLYtaCbAMQSYJUSxCiHAf4DpKgeBaNgFIyCEQAAjvprc71O0fAAAAAASUVORK5CYII=","orcid":"","institution":"Isfahan University of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Arefeh","middleName":"","lastName":"Mousavi","suffix":""},{"id":319733398,"identity":"663c29ef-b103-49c3-9fbe-7e3537937c0d","order_by":1,"name":"Ali Ardalan","email":"","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"","lastName":"Ardalan","suffix":""},{"id":319733399,"identity":"977309ac-b337-4297-8213-8d717a432566","order_by":2,"name":"Amirhossein Takian","email":"","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Amirhossein","middleName":"","lastName":"Takian","suffix":""},{"id":319733400,"identity":"701717cc-4ce9-4a1a-8cb3-87785c4409e5","order_by":3,"name":"Neda Soltani Halvaiee","email":"","orcid":"","institution":"Amirkabir University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Neda","middleName":"Soltani","lastName":"Halvaiee","suffix":""},{"id":319733401,"identity":"1b7781fe-c719-4ae5-83e7-dd194fcef9af","order_by":4,"name":"Abbas Ostadtaghizadeh","email":"","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Abbas","middleName":"","lastName":"Ostadtaghizadeh","suffix":""},{"id":319733402,"identity":"4de339a5-6f2c-4253-bad1-3d64d5b846b8","order_by":5,"name":"Kazem Naddafi","email":"","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Kazem","middleName":"","lastName":"Naddafi","suffix":""},{"id":319733403,"identity":"eb412d3a-9ea0-49db-a3d8-75eecbd1ee20","order_by":6,"name":"Alireza Massah Bavani","email":"","orcid":"","institution":"Tehran University: University of Tehran","correspondingAuthor":false,"prefix":"","firstName":"Alireza","middleName":"Massah","lastName":"Bavani","suffix":""}],"badges":[],"createdAt":"2024-06-01 08:46:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4512761/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4512761/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s40201-025-00947-z","type":"published","date":"2025-07-14T16:05:35+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":60689654,"identity":"68bce0b0-e25d-4e17-8ee0-a4e8b8c1e35c","added_by":"auto","created_at":"2024-07-19 14:41:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1202449,"visible":true,"origin":"","legend":"\u003cp\u003eTotal network of stakeholders involved in health and climate change in the \u003ca href=\"https://www.sciencedirect.com/science/article/pii/S1352023704000723\"\u003eI.R. Iran\u003c/a\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4512761/v1/0a2168912e788836eac87e5e.png"},{"id":60689658,"identity":"09fd6824-db67-41f8-b618-9a7320165e61","added_by":"auto","created_at":"2024-07-19 14:41:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":919838,"visible":true,"origin":"","legend":"\u003cp\u003eStakeholder network based on the ratio of Out-Degree Centrality to In-Degree Centrality edges (Influence coefficient)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4512761/v1/bb132f28c51f3f37e69f2dfd.png"},{"id":60690067,"identity":"50c02adf-384d-4abe-9ebe-e8620da26a22","added_by":"auto","created_at":"2024-07-19 14:49:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":911287,"visible":true,"origin":"","legend":"\u003cp\u003eStakeholder network based on the ratio of In-Degree Centrality to Out-Degree Centrality edges (Power coefficient)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4512761/v1/0f360163b821ce591157ae3e.png"},{"id":60689653,"identity":"5973952d-8273-4d5c-925b-7c090b755dda","added_by":"auto","created_at":"2024-07-19 14:41:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":92323,"visible":true,"origin":"","legend":"\u003cp\u003eStakeholder network involved in health and climate change in the \u003ca href=\"https://www.sciencedirect.com/science/article/pii/S1352023704000723\"\u003eI.R. Iran\u003c/a\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4512761/v1/c4021b7b908c8cc14c85b1c9.png"},{"id":60689656,"identity":"5587b2e2-18bd-4296-8e6d-a58b60ab9157","added_by":"auto","created_at":"2024-07-19 14:41:05","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":232401,"visible":true,"origin":"","legend":"\u003cp\u003ePosition and interactions of the Ministry of Health and Medical Education with other stakeholders\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4512761/v1/e24a5c952c36ef5f78df8b58.png"},{"id":87220959,"identity":"91d5b3ca-ca74-46f6-86c5-b6656c23759a","added_by":"auto","created_at":"2025-07-21 16:13:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4357092,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4512761/v1/63b0b7a1-4cd8-49eb-974c-26cdb6aa7a59.pdf"}],"financialInterests":"","formattedTitle":"Analysis of key stakeholders involved in adaptation measures to tackle the adverse health effects of climate change in the Islamic Republic of Iran: A Social Network Analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eClimate change and its effects are one of the serious concerns of humanity in the attempts made to achieve the sustainable development goals (SDGs) around the world (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Climate change is recognized as one of the greatest threats to the human health in the 21st century (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e); inevitably, it influences basic health requirements such as air, safe water, adequate food, and proper shelter by affecting some key sectors including water, food, agriculture, industry, and health through various mechanisms. Therefore, any impact on other sectors will ultimately affect the health sector (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eVariable and warmer climates can lead to the rise of some air pollutants, an increase in disease transmission through unhealthy water and contaminated food, a decline of agricultural production in some less developed countries, and aggravation of extreme weather risks (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). On the other hand, rising temperatures, when combined with poor housing and living conditions in urban communities, can increase the risk of heart attack and cardiovascular diseases (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Climate change also poses new challenges for controlling infectious diseases. The life cycle of many pathogens, including cholera, diarrhea, malaria, and dengue fever, is highly sensitive to climatic variables such as temperature and rainfall. Although, the greatest health effects of climate change may not be due to natural disasters or epidemics in the long run, they can be attributed to the gradual pressure on natural, economic, and social systems that can somehow affect health. Each of these changes can create the potential for malnutrition, reduced food security, population displacement, and the increased risks of war and internal conflicts (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGiven the inevitability of climate change, the need to adapt to the adverse health effects associated with it is more important than ever. Adaptation measures provide an opportunity to improve health by focusing on reducing the burden of diseases and injuries resulting from climate change (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Also, measures to decrease greenhouse gas emissions can significantly reduce the expected health risks (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Climate change adaptation is the process of adjusting to current or expected effects of climate change that involves making regular decisions in a network of individuals, companies, civil societies, and government organizations at the local, regional, national, and international levels (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Participation, cooperation, and interaction of all stakeholders regarding adaptation measures may be vital to ensure public health (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). According to the fourth IPCC report, the need for interaction and dialogue between stakeholders to improve decision-making and awareness is recognized as a key strategy in climate change adaptation policy-making. Stakeholders' dialogue leads to their participation, thus ensuring more effective adaptation to climate change effects through the consistency and integration of research results with local requirements, as well as the legitimacy of policies and adaptation measures (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCurrently, researchers, policymakers, and advocacy communities have little understanding of how different organizations work together and how they can exercise their power to influence decisions and implement adaptation policies to decrease the health effects of climate change. Understanding this comprehensive governance structure is crucial in regard to this important and specific issue, as climate change has its effects mainly on health through changes in areas not directly related to health; these include agriculture, transportation, water, and climatological disaster risk management. This complex network of decisions and policies often leads to ignoring the health concerns and problems caused by climate change in other sectors. Therefore, stakeholders' participation in research and adaptation projects requires accurate mapping of all relevant stakeholders at different levels (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The active presence of public and private stakeholders in adapting to the effects of climate change should be considered and the analysis of their interactions with each other is very helpful in understanding the desired structure in health decision-making (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). This study, thus, aimed to analyze the structure of the network of stakeholders involved in health adaptation to climate change with a focus on the most influential stakeholders by using the Social Network Analysis (SNA) approach in the Islamic Republic of Iran. As the second-largest country in the Eastern Mediterranean Region (EMRO) in the division of the World Health Organization (WHO), Iran is highly vulnerable to the adverse health effects of climate change (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The Iranian government, after joining the Kyoto Protocol in 1950, has been presenting policies, strategies, and programs to adapt to climate change at the national level. The government of Iran is currently a member of the Regional Committee through the Ministry of Health and Medical Education (MoHME); it was committed to the World Health Assembly (WHA) treaty in May, 2008, to protect against the adverse health effects of climate change. In the public health domain, the Center for Occupational and Environmental Health, under the deputy of the MoHME, has been responsible for proposing strategies and programs to adapt to the health problems caused by climate change. Efforts have, accordingly, been made to set up an adaptation plan for health in the same direction; however, due to a lack of coordination and cross-sectoral cooperation in the development of adaptation strategies, this plan can not meet the necessary acceptance (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Due to the interdisciplinary nature of the climate change issue and the involvement of international organizations and other sectors, as well as various units within the MoHME, which is the main trustee of the health of Iranian society, this study made it possible to obtain a comprehensive overview of relationships between stakeholders at the national and international levels, and their positions by mapping the structure of their network. It can create a systematic approach to identify all organizations which are directly and indirectly involved in decisions, research, policy-making, and climate change adaptation planning. It can, thus, inform policymakers, authorities, researchers, and even people of society and people-based groups to make their demands from other actors.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003eThe present study was a descriptive-analytical research that identified and analyzed key stakeholders involved in climate change adaptation policy-making in the I.R. Iran, using the SNA approach. SNA has been used in many social epidemiological studies to better understand health conditions; however, this method has been less used in health decision-making processes (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). For this reason, the application of SNA for policy-making has received more attention in the recent years (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). A social network is defined as a set of actors or stakeholders connected through meaningful social relationships. In the literature, stakeholders are often referred to as individuals or groups who are interested in a particular issue in which they have common profits. They can be influenced by specific policies and actions at different levels and sizes, or they can be responsible for implementing policies (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). SNA is a strategy for examining the extent to which each actor influences the network, understanding the nature of relationships between actors within a network, finding how they affect each other's behaviors, and determining the degree of connection, coherence, and clustering of the overall network (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The main stakeholders in the field of climate change adaptation are governmental, commercial, and non-governmental organizations, as well as groups who are directly or indirectly affected by the consequences of climate change (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). This study has been done in two phases including stakeholder identification and stakeholder analysis.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Phase 1: key stakeholders Identification\u003c/h2\u003e \u003cp\u003eTo identify key stakeholders who were, directly and indirectly, involved in the climate change at national and international levels, the opinions of 23 university professors, experts, and executives who were selected by purposeful sampling were obtained. In addition, to ensure the maximum access to the potential of available specialties, snowball sampling was done as the complement. Participants had scientific and practical competencies with at least 2 years of active executive experience in the field of health and climate change. The data collection tool was a questionnaire with open-ended questions. The poll was continued until the data reached the saturation level. Also, through interviews with 25 experts and the archival research method (study of the existing written sources, research, and articles, as they are related to climate change in Iran), stakeholders that have not been mentioned before were added to the list of stakeholders. Four criteria were considered for stakeholders\u0026rsquo; inclusion:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eFunctional criterion\u003c/em\u003e: These are the stakeholders formally responsible for deciding and implementing adaptation measures. These organizations have proposed policies for the subject.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eHierarchical level criterion\u003c/em\u003e: Influential stakeholders who can indirectly facilitate or hinder adaptation measures. These are organizations that have the veto power of other organizations and influence the implementation of policies in this area.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eKnowledge and ability\u003c/em\u003e: Stakeholders with skills related to adaptation measures, climate systems, and climate risks.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eInformal activity\u003c/em\u003e: These can informally influence the formulation and implementation of adaptation policies (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ePhase 2: Key stakeholders analysis\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eThe stakeholder analysis in this phase was performed quantitatively. Participants were representatives of stakeholders identified in the previous phase, with at least two years of experience in policy-making, management, planning, both executive and operational, education, and research in the areas related to the subject matter selected purposefully. The expertise of the participants included water resources engineering and management, energy economics and efficiency, health, food security, ecology, ecophysiology, environment, and natural resources, international relations, climatology, meteorology, climatological hazards, and risk management. The tool used was the researcher-made questionnaire, which was designed in closed questions in the form of three tables. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e was designed for determining stakeholders\u0026rsquo; communication. Participants were asked to rate the level of cooperation and communication flow of their organization with each of the other stakeholders, based on a score range of 0 to 10 (with zero representing disaffiliation and 10 being the maximum connection value). Given the interdisciplinary nature of climate change adaptation measures in a wide range of disciplines including energy, industry, water, agriculture, natural resources, education, research, health, technology, and so on, participants were asked to determine the prominent role of stakeholders in climate change adaptation measures in the form of three clusters of \"trustee\",\" collaborator\" and \"supporter\" in the second table. In the third table of the questionnaire, the information of the participants was obtained confidentially.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDefinition of stakeholders clusters involved in health adaptation to climate change in the I.R. Iran.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRole\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDefinition\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTrustee\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eActors who are directly involved in climate change and policy-making are responsible for drafting rules and regulations directly.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCollaborator\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eActors cooperating in the implementation and evaluation of the formulated policies acted according to the approvals of the responsible organizations.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSupporter\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrganizations falling into this category were according to their supportive role in better performing the tasks assigned to other actors.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe survey was conducted by e-mail and followed periodically. The response rate of the questionnaire was 100%. After the initial review of the 35 questionnaires, two of them were removed from the analysis process due to the lack of proper answers; finally, 33 ones were entered into the analysis.\u003c/p\u003e \u003cp\u003eData analysis and graph design were performed using Microsoft Excel and Gephi software, version 0.9.2 (Open-source network analysis software for network visual exploration). This software could provide a faster and more accurate understanding of the influence and power of stakeholders in the network through mathematical tools and concepts of graph theory. The communication patterns between them were determined through Force Atlas 2 layout algorithm. The position of each node in the network was based on the concepts of social networks, such as Node, Edge, Weighted Degree Centrality, Betweenness Centrality, Closeness Centrality, Average Weighted Degree, Density, and Clustering Coefficient (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe definition of social networks concepts\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConcept\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDefinition\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNode\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA node is the fundamental unit of a network. A node is a stakeholder in the entire network (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). In this study, nodes are measured in all of the following graphs in terms of the Weighted Degree Centrality.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEdge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAn edge is a vector connecting two nodes. Each edge has a weight that can be unidirectional or non-directional. Thicker edges indicate more communication between one node and others (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDegree Centrality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCentrality generally has a broad meaning in that it is used to identify and determine the most important actors or communications in a network. Centrality has different types whose applications are different from each other (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Degree Centrality, Betweenness Centrality, and Closeness Centrality are the most important indicators of centrality; depending on the type of the relationship between the actors, effective indicators can be selected, measured, and judged (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWeighted Degree Centrality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe total weight of an actor's direct communication with others in a network is named Weighted Degree Centrality. The greater the Weighted Degree Centrality of an actor, the more its cooperation with other stakeholders and the more the centrality. These in-directional graphs include two types of input (In-Degree Centrality) and output (Out-Degree Centrality). A high ratio of Out-Degree Centrality to In-Degree Centrality indicates the higher influence coefficient of the actor. Stakeholders with a high influence coefficient are more active in decision-making and implementation of adaptation measures. They have more capacity to influence others\u0026rsquo; decisions as well. A high ratio of In-Degree Centrality to Out-Degree Centrality shows the power coefficient of the actor (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Stakeholders with a high power coefficient are more likely passive in decision-making and implementing adaptation measures. They have more authority to do measures and change other decisions.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBetweenness Centrality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBetweenness Centrality represents the actors who are in a privileged position in the network. They serve as a \u0026ldquo;bridge\u0026rdquo; between other actors (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). A node with the highest Betweenness Centrality is located between many pairs of nodes, and the communication channels and flow of other nodes pass through it (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). They also have access to a variety of information sources through external links (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNetwork Density\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe network density indicates the degree of the communication of actors at the network total level. It is calculated from the ratio of the number of available links to the total number of possible links in the network (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). High network density indicates the strength of links and the coherence and coordination between stakeholders (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCloseness Centrality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCloseness Centrality is the shortest distance between each node and other nodes in the network, which is calculated by its distance from all nodes in the network, regardless of whether the link is direct or indirect (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClustering Coefficient\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe Clustering Coefficient of any node in a network is its neighborhood density. The network Clustering Coefficient is the average clustering coefficient of all nodes in the network.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eThe findings of this study are presented in the following two parts:\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eKey Stakeholders identification\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eTwenty-six key stakeholders involved in health adaptation to climate change including national ministries, international organizations, Non-Governmental Organizations (NGOs), and research institutes were identified in the I.R.Iran. (See Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNetwork structure analysis\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eThe network consisted of 37 nodes, 63 edges, and 3 clusters. Figure\u0026nbsp;1 shows an overview of the network structure. Each node represents a stakeholder and each edge shows the interaction of stakeholders with each other. Stakeholders who were playing a key role in making climate change adaptation decisions and policies are outlined with larger nodes in the network. All edges in this graph had weight and were directional. The edges with a heavier weight were the visible thicker. Eighty-one % of the stakeholders were national and 19% were international. The results of the Gephi software analysis highlighted the distribution of interactions within the network was uniform and the network density was 0.971. Given that this value was close to one, the network was very close to a complete graph where all nodes were interconnected. The network diameter and the maximum distance between the nodes were 2. That is, each node, in maximum, affected or could be affected by other nodes with one mediator. Modularity was equal to 0. 027. Whenever this number is close to one, the network is more separable into communities. The stakeholders in a community are usually close in terms of power and influence. Three indistinct communities were obtained in this network. Given the low modularity in this network, the connections between the nodes were evenly distributed. Therefore, the formation of communities in this network was weak; so, it could not be claimed that stakeholders formed communities in the network.\u003c/p\u003e \u003cp\u003eThe Strongly Connected Components in this network were equal to 1. That is, all nodes were connected in a directional graph. The estimation of this indicator showed that the decisions of all stakeholders were dependent on others' decisions. The value of the Average Clustering Coefficient was equal to 0.971, which was expected due to the two values ​​of Modularity and Strongly Connected Components. The Average Clustering Coefficient score showed the extent to which the neighbours of each node were related to each other on average. When a network had a sparse state (i.e., there were limited connections between nodes), this value would be close to zero. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the numerical values ​​calculated at this level as separated by the communities, clusters, and types of stakeholders (national and international). The value of the Weighted Degree Average for each node was 5.04, which showed that, despite the high density of the network, the average inter-organizational communication was weak to moderate.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumerical values ​​of the stakeholders\u0026rsquo; network involved in climate change adaptation measures, as separated by the clusters and communities\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNetwork\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNode number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEdge number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNetwork density\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDiameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eConnected components\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModularity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTrustee\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.435\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCollaborator\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.542\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSupporter\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.163\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInternational\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-o.494\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNational\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.246\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCommunity A\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.450\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCommunity B\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.428\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCommunity C\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.452\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the numerical values ​​calculated separately from the stakeholders. The values ​​of the power and influence coefficients indicated the Ministry of Energy (MoE)\u003csub\u003e[1]\u003c/sub\u003e, the Ministry of Health and Medical Education MoHME\u003csub\u003e[4]\u003c/sub\u003e, the Ministry of Science, Research and Technology (MSRT)\u003csub\u003e[8]\u003c/sub\u003e, the Department of Environment (DoE)\u003csub\u003e[12]\u003c/sub\u003e, Plan and Budget Organization (PBO)\u003csub\u003e[14]\u003c/sub\u003e, National Disaster Management Organization (NDMO)\u003csub\u003e[16]\u003c/sub\u003e, Iranian Red Crescent (IRCS)\u003csub\u003e[18]\u003c/sub\u003e, national NGOs\u003csub\u003e[19]\u003c/sub\u003e, and the Supreme Council for Health and Food Security (SCHFS)\u003csub\u003e[21]\u003c/sub\u003e were the stakeholders connected with all others throughout the network. The results, based on the values ​​of closeness centrality, showed that the Ministry of Education (MEDU)\u003csub\u003e[11]\u003c/sub\u003e and the Vice President for Science and Technology (ISTI)\u003csub\u003e[20]\u003c/sub\u003e were the most \u0026ldquo;peripheral organizations\u0026rdquo; in the network and had less contact with the other stakeholders.\u003c/p\u003e \u003cp\u003eThe values ​​of Betweenness Centrality also showed that the MoE \u003csub\u003e[1]\u003c/sub\u003e, MoHME \u003csub\u003e[4]\u003c/sub\u003e, MSRT \u003csub\u003e[8]\u003c/sub\u003e, DoE\u003csub\u003e[12]\u003c/sub\u003e, PBO\u003csub\u003e[14]\u003c/sub\u003e, NDMO\u003csub\u003e[16]\u003c/sub\u003e, IRCS\u003csub\u003e[18]\u003c/sub\u003e, national NGOs\u003csub\u003e[19]\u003c/sub\u003e, and SCHFS\u003csub\u003e[21]\u003c/sub\u003e could play an important role in creating cross-sectoral coordination as \u0026ldquo;bridge or liaison organizations\u0026rdquo;. Also, the results based on the values ​​of In-Degree Centrality to Out-Degree Centrality (power coefficient) showed that PBO\u003csub\u003e[14]\u003c/sub\u003e, the Ministry of Agriculture Jihad (MAJ)\u003csub\u003e[7]\u003c/sub\u003e, NDMO\u003csub\u003e[16]\u003c/sub\u003e, the Islamic Republic of Iran Broadcasting (IRIB)\u003csub\u003e[15]\u003c/sub\u003e, DoE\u003csub\u003e[12]\u003c/sub\u003e, the Ministry of Petroleum (MoP)\u003csub\u003e[2]\u003c/sub\u003e and, MoE\u003csub\u003e[1]\u003c/sub\u003e had a higher power in the network. Further, the values ​​of Out-Degree Centrality to In-Degree Centrality (influence coefficient) displayed that national NGOs\u003csub\u003e[19]\u003c/sub\u003e, international NGOs\u003csub\u003e(26)\u003c/sub\u003e, Intergovernmental Panel on Climate Change (IPCC)\u003csub\u003e[24]\u003c/sub\u003e, SCHFS\u003csub\u003e[21]\u003c/sub\u003e, MSRT\u003csub\u003e[8]\u003c/sub\u003e, the Ministry of Foreign Affairs (MFA)\u003csub\u003e[6]\u003c/sub\u003e, Asia-Pacific Disaster Information Management (APDIM)\u003csub\u003e[25]\u003c/sub\u003e, Food and Agriculture Organization (FAO)\u003csub\u003e[23]\u003c/sub\u003e, World Health Organization (WHO)\u003csub\u003e[22]\u003c/sub\u003e, IRCS\u003csub\u003e[18]\u003c/sub\u003e and the Ministry of Industry, Mine, Trade (MIMT)\u003csub\u003e[3]\u003c/sub\u003e were the stakeholders that had more influence over others in the network (Figs.\u0026nbsp;1 to 4).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumerical values of the stakeholder network involved in climate change\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStakeholders\u0026rsquo; ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLabel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePosition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003epower Coefficient (authority)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInfluence Coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCloseness centrality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ebetweenness centrality\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinistry of Energy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMoE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinistry of Petroleum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMoP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinistry of Industry, Mine, Trade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMIMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinistry of Health and Medical Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMoHME\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinistry of the Interior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMoI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinistry of Foreign Affairs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinistry of Agriculture Jihad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMAJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinistry of Science, Research and Technology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMSRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinistry of Information and Communications Technology of Iran\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eICT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinistry of Roads \u0026amp; Urban Development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMRUD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinistry of Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMEDU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDepartment of Environment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDoE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInstitute of Standards and Industrial Research of Iran\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eISIRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlan and Budget Organization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePBO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI.R of Iran Meteorological Organization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIRIMO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNational Disaster Management Organization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNDMO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIslamic Republic of Iran Broadcasting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIRIB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIranian Red Crescent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIRCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNational NGOs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI.R.I NGOs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVice-Presidency for Science and Technology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eISTI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSupreme Council for Health and Food Security\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSCHFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWorld Health Organization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWHO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFood and Agriculture Organization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFAO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntergovernmental Panel on Climate Change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIPCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsia-Pacific disaster information Management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAPDIM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInternational NGOs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNGOs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs can be seen in Fig.\u0026nbsp;1, IPCC\u003csub\u003e[24]\u003c/sub\u003e, MFA\u003csub\u003e[6]\u003c/sub\u003e, MSRT\u003csub\u003e[8]\u003c/sub\u003e, and DoE\u003csub\u003e[12]\u003c/sub\u003e were the most influential stakeholders in climate change adaptation decisions and policies, respectively. Meanwhile, the weakest links in climate change adaptation decisions belonged to the IRIB\u003csub\u003e[17]\u003c/sub\u003e, MoE\u003csub\u003e[1]\u003c/sub\u003e, and PBO\u003csub\u003e[14]\u003c/sub\u003e. Figure\u0026nbsp;2 shows the network based on the influence coefficient. National NGOs\u003csub\u003e[1]\u003c/sub\u003e, international NGOs\u003csub\u003e[26]\u003c/sub\u003e, IPCC\u003csub\u003e[24]\u003c/sub\u003e, MSRT\u003csub\u003e[8]\u003c/sub\u003e, SCHFS\u003csub\u003e[21]\u003c/sub\u003e, APDIM\u003csub\u003e[25]\u003c/sub\u003e, FAO\u003csub\u003e[23]\u003c/sub\u003e, MIMT\u003csub\u003e[3]\u003c/sub\u003e, and WHO\u003csub\u003e[22]\u003c/sub\u003e had higher influence in the network, respectively, but they were low in terms of power. Meanwhile, MoHME\u003csub\u003e[4]\u003c/sub\u003e had less influence over the network in comparison to the above-mentioned stakeholders. The Ministry of Information and Communications Technology (ISTI)\u003csub\u003e[20]\u003c/sub\u003e, MoHME\u003csub\u003e[4]\u003c/sub\u003e, IRCS\u003csub\u003e[18]\u003c/sub\u003e, Institute of Standards and Industrial Research of Iran (ISIRI)\u003csub\u003e[13]\u003c/sub\u003e, and the Ministry of Roads and Urban Development (MRUD)\u003csub\u003e[10]\u003c/sub\u003e were the stakeholders who had both power and low influence in the network. Figure\u0026nbsp;3 shows the network based on the power coefficient. Thus, the MoP\u003csub\u003e[2]\u003c/sub\u003e, MoE\u003csub\u003e[1]\u003c/sub\u003e, Ministry of the Interior (MoI)\u003csub\u003e[5]\u003c/sub\u003e, MAJ\u003csub\u003e[7]\u003c/sub\u003e, DoE\u003csub\u003e[12]\u003c/sub\u003e, PBO\u003csub\u003e[14]\u003c/sub\u003e, NDMO\u003csub\u003e[16]\u003c/sub\u003e, IRIB\u003csub\u003e[15]\u003c/sub\u003e and, MEDU\u003csub\u003e[11]\u003c/sub\u003e had higher power in the network, respectively. Also, ISIRI\u003csub\u003e[20]\u003c/sub\u003e had less power than the other stakeholders in the network.\u003c/p\u003e \u003cp\u003eComparison of Fig.\u0026nbsp;2 with Fig.\u0026nbsp;3 highlighted that NDMO\u003csub\u003e[16]\u003c/sub\u003e and MAJ\u003csub\u003e[7]\u003c/sub\u003e, despite being recognized as high-power organizations in the network, had a lower influence on decision-making. The network separated by clusters showed that IPCC, DoE\u003csub\u003e[12]\u003c/sub\u003e, and MoHME\u003csub\u003e[4]\u003c/sub\u003e were the three trustee organizations responsible for adaptation measures to tackle the adverse health effects of climate change (Fig.\u0026nbsp;4). On the other hand, MSRT\u003csub\u003e[8]\u003c/sub\u003e, MIMT\u003csub\u003e[3]\u003c/sub\u003e, MFA\u003csub\u003e[6]\u003c/sub\u003e, APDIM\u003csub\u003e[25]\u003c/sub\u003e, national NGOs\u003csub\u003e[19]\u003c/sub\u003e, and FAO\u003csub\u003e[23]\u003c/sub\u003e were the most active supporter stakeholders in the network. Further, MoE\u003csub\u003e[1]\u003c/sub\u003e and IRIMO\u003csub\u003e[15]\u003c/sub\u003e were the most active collaborator stakeholders in the network.\u003c/p\u003e \u003cp\u003eAs can be seen in Fig.\u0026nbsp;4, IPCC\u003csub\u003e[24]\u003c/sub\u003e could be considered the most active stakeholder among the trustee network with higher output and input edge weights. MoHME\u003csub\u003e[4]\u003c/sub\u003e and DoE\u003csub\u003e[12]\u003c/sub\u003e were next in line. Among collaborators network, IRIMO\u003csub\u003e[15]\u003c/sub\u003e, MoE\u003csub\u003e[1]\u003c/sub\u003e, and MRUD\u003csub\u003e[10]\u003c/sub\u003e could be recognized as the most active stakeholders. MFA\u003csub\u003e[6]\u003c/sub\u003e, MSRT\u003csub\u003e[8]\u003c/sub\u003e, MIMT\u003csub\u003e[3]\u003c/sub\u003e, the MFA\u003csub\u003e[6]\u003c/sub\u003e, FAO\u003csub\u003e[23]\u003c/sub\u003e, and WHO\u003csub\u003e[22]\u003c/sub\u003e were also the most active stakeholders in the supporters' network. Meanwhile, IPCC\u003csub\u003e[24]\u003c/sub\u003e has been the most active international stakeholder; also, MFA\u003csub\u003e[6]\u003c/sub\u003e, DoE\u003csub\u003e[12]\u003c/sub\u003e, and MSRT\u003csub\u003e[8]\u003c/sub\u003e could be regarded as the most active national stakeholders in the network. As can be seen in Fig.\u0026nbsp;5, based on the weight of the output edges obtained, the MoHME\u003csub\u003e[4]\u003c/sub\u003e had relatively weak exterior interactions with the most key stakeholders in the network, especially DoE\u003csub\u003e[12]\u003c/sub\u003e, IPCC\u003csub\u003e[24]\u003c/sub\u003e, MSRT\u003csub\u003e[8]\u003c/sub\u003e, the Ministry of Information and Communications Technology of Iran (ICT)\u003csub\u003e[9]\u003c/sub\u003e, FAO\u003csub\u003e[23]\u003c/sub\u003e, WHO\u003csub\u003e[22]\u003c/sub\u003e, national NGOs\u003csub\u003e[19]\u003c/sub\u003e, IRCS\u003csub\u003e[18]\u003c/sub\u003e, MEDU\u003csub\u003e[11]\u003c/sub\u003e and the IRIB\u003csub\u003e[15]\u003c/sub\u003e on climate change adaptation. Based on the weight of the output edges obtained, the DoE\u003csub\u003e[12]\u003c/sub\u003e and IPCC\u003csub\u003e[24]\u003c/sub\u003e had relatively weak and strong exterior interactions with other stakeholders in the trustee network, respectively.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003ePublic health in the face of climate change consequences is influenced by different activities of various technical, executive, economic, social, cultural, and political organizations that are concerned with climate change adaptation. Each sector, through its role and responsibilities, contributes to the implementation of mitigation and adaptation measures. Also, considering that a series of adaptation measures can implicitly harm the goals of mitigation measures and vice versa, it is important to have balanced and accurate policies in both mitigation and adaptation so as not to create conflicting consequences. So, the extent of influence and impressionability of each sector concerned with climate change, macro and micro strategies in adaptation measures, and the contribution of each sector to implementing these measures must be clearly defined. Therefore, this study could help to achieve a correct understanding of the position, power, and influence of stakeholders in the climate change adaptation network. With such information, it is possible to develop the appropriate strategies to strengthen cross-sectoral collaboration and coordination, modify the previous policies, implement new policies, and enhance stakeholders' support.\u003c/p\u003e \u003cp\u003eThere have not been many studies regarding stakeholder network analysis on climate change adaptation issues in Iran; however, the study, done by Mousavi et al. (2020), emphasized the importance of the interaction of the actors involved in climate change in improving and promoting adaptation measures in the health sector. In this regard, the participation of local public and private institutions and cross-sectoral cooperation were among the important issues that could be considered to achieve the goals of health adaptation to climate change. Finally, they identified MoHME, DoE, and the Environmental Research Institute of Tehran University of Medical Sciences as the most active participants in policy-making and management of the adverse health effects of climate change in Iran (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Our study showed that the stakeholders involved in climate change adaptation measures formed a complete network, given the high density of the network. This means that all stakeholders were in full interaction with each other. However, the numerical values of graph theory, as ​​calculated for the average interaction of stakeholders within the total network, showed that the interactions were relatively weak. Estimation of social network indicators showed the policies on climate change adaptation were complex and intertwined, thus requiring careful and planned cross-sectoral coordination and cooperation. The results also showed that given the diversity of tasks and the complex nature of climate change adaptation measures, it was not possible to determine the distinct communities in the network. This was not, however, unexpected.\u003c/p\u003e \u003cp\u003eNDMO\u003csub\u003e[16]\u003c/sub\u003e, although recognized as one of the high-power stakeholders in the network, generally has a lower influence on climate change adaptation decisions. Given the important role of the NDMO\u003csub\u003e[16]\u003c/sub\u003e in the network, it did not act as an effective stakeholder in the integration of a comprehensive risk assessment program into the health service delivery system to reduce the adverse health effects of climatological disasters (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). However, in contrast with our study, in the study done by Yousefi et al., good coherence in the network of active stakeholders in the field of Disaster Risk Management (DRM) was shown in Iran (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). In this regard, Eleni Karali et al. (2020) analyzed the type and intensity of the interactions of 35 institutions involved in Climate Change Adaptation (CCA) and Disaster Risk Reduction (DRR) networks using the SNA in Europe. Although CCA and DRR pursued complementary goals, they had different structures and policies, and the relationship between the two communities of the network was described as inadequate. Finally, this study emphasized that the European climate change adaptation platform (Climate-ADAPT) had the highest popularity and value, with the potential to enhance the effective interaction between the actors to create common ground and develop synergies between them (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn a study conducted by Ruhollah Oji et al. (2021), the relationship between an interdisciplinary group of representatives from different sectors involved in promoting adaptation measures based on sustainable development at national and local levels was analyzed using the network analysis approach. The results of this study, in line with our study, illustrated the cooperation between members of the Iranian Climate Change Professionals Network (ICCPN) and their willingness to join local and national networks in the lowest rank. The most important reasons were the lack of a formal network structure recognized by experts, network ambiguous goals, and the absence of a comprehensive program to attract public and private support (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Bowen et al. (2015) also stated that cross-sectoral approaches to effectively adapted to the adverse health effects of climate change could be considered as a basic principle in managing and controlling these effects (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe results of our study also showed that the national NGOs\u003csub\u003e[19]\u003c/sub\u003e in Iran, despite having a high potential influence, could not be considered an acceptable and worthy position in environmental policy-making. Regarding the role of the national NGOs\u003csub\u003e[19]\u003c/sub\u003e in increasing public participation and creating a policy flow for the agenda-setting of climate change adaptation measures, the formation and empowerment of these societies can not only increase public awareness and risk understanding, but also enable people to pursue their demands, play their roles and ensure social participation. This is especially important when it comes to health. Therefore, the potential of this group of stakeholders can be used to achieve adaptation goals by strengthening their organizational structure of them, delegating the necessary powers, and providing action freedom away from political positions, thus creating the necessary context for their participation in climate change adaptation policy-making. MoI\u003csub\u003e[5]\u003c/sub\u003e, as the custodian and supervisor of the national NGOs\u003csub\u003e[19]\u003c/sub\u003e, should provide the necessary support to strengthen the role of the national NGOs\u003csub\u003e[19]\u003c/sub\u003e in pursuing the related goals by creating a sense of duty and economic security for them. Meanwhile, the results obtained by Bowen et al. (2014) emphasized the role of NGOs in raising public awareness and shaping public demands in developing climate change adaptation measures. In line with our study, they showed that the role of NGOs was weak in the climate change stakeholder network in Cambodia, emphasizing the strengthening roles in developing adaptation policies in the health sector (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the present study, Stakeholder analysis showed that the MoP\u003csub\u003e[2]\u003c/sub\u003e, MoE\u003csub\u003e[1],\u003c/sub\u003e and MIMT\u003csub\u003e[3]\u003c/sub\u003e were among other stakeholders that had high power in policy-making related to mitigation measures and vetoing decisions taken in other sectors. Therefore, given the conflict of interests and the goals of the industry and energy sectors, along with other sectors, to reduce the incompatible consequences of climate change, such results were not far from expectation. It seems, therefore, that with more serious and precise legal and regulatory mechanisms, this large amount of power could be adjusted and the existing problems and conflicts would be overcome.\u003c/p\u003e \u003cp\u003eThe analysis of the network also showed that MAJ\u003csub\u003e[7]\u003c/sub\u003e, PBO\u003csub\u003e[14]\u003c/sub\u003e, MoI\u003csub\u003e[5]\u003c/sub\u003e, MEDU\u003csub\u003e[11]\u003c/sub\u003e, and IRIB\u003csub\u003e[15]\u003c/sub\u003e, despite their high power, were located \u0026ldquo;around\u0026rdquo; the network. These stakeholders had performed very weakly when interacting with other stakeholders and could not fulfill their role and missions in this area well. It seems, therefore, that these organizations should move towards network centralization by strengthening cross-sectoral interactions in the direction of climate change-related policies. To strengthen such roles, effective measures should be adopted for the transfer of knowledge and complex negotiations between researchers, physicians, policymakers, private actors, and community members in all aspects of adaptation measures. In addition, the involvement of many types of supportive organizations that encourage cross-sectoral collaboration seems to be crucial. As can be seen in this network, FAO\u003csub\u003e[23]\u003c/sub\u003e, WHO\u003csub\u003e[22]\u003c/sub\u003e, APDIM\u003csub\u003e[25]\u003c/sub\u003e, international NGOs\u003csub\u003e[19]\u003c/sub\u003e, MSRT\u003csub\u003e[8]\u003c/sub\u003e, MFA\u003csub\u003e[6]\u003c/sub\u003e, and MIMT\u003csub\u003e[3]\u003c/sub\u003e have a supportive role. Also, it seems that a comprehensive and multi-sectoral plan is needed for strong coordination and collaboration in mitigation and adaptation measures, Also, a review of intra-organizational rules and the creation of bridge organizations that can efficiently participate in planning, coordinating, and strengthening the existing interactions seem to be necessary. MoE\u003csub\u003e[1]\u003c/sub\u003e, MoHME\u003csub\u003e[4]\u003c/sub\u003e, MSRT\u003csub\u003e[8]\u003c/sub\u003e, DoE\u003csub\u003e[12]\u003c/sub\u003e, PBO\u003csub\u003e[14]\u003c/sub\u003e, NDMO\u003csub\u003e[16]\u003c/sub\u003e, IRIB\u003csub\u003e[15]\u003c/sub\u003e, IRCS\u003csub\u003e[18]\u003c/sub\u003e, national NGOs\u003csub\u003e[19]\u003c/sub\u003e, and SCHFS\u003csub\u003e[21]\u003c/sub\u003e have \u0026ldquo;liaison roles\u0026rdquo; in this network. It is a misconception to think that climate change is just an environmental issue. Climate change can be the source of many economic, social, cultural, and political crises. Therefore, DoE must solve environmental challenges and respond to threats to the community's health in strong collaboration and coordination relationship with all involved stakeholders. Although the country's environment has certainly suffered the most damage, as mentioned earlier, DoE, as the oversight and governance body on environmental issues, has not had the tools to approve or reject the authority of other organizations or ministries. At present, it can only review the institutions under its command. Thus, regulations to oblige the government and executive bodies to develop a comprehensive, coordinated, and multi-disciplinary adaptation plan are necessary. Also, organizational reviews, such as creating adaptation offices and changing the consumption pattern in the executive bodies and holding the government accountable in this regard, would be essential. According to the description of the tasks described in the National Climate Change Strategy Plan, MoHME\u003csub\u003e[4]\u003c/sub\u003e is one of the leading ministries that should take adaptation measures. The health risks of climate change require a significant review of how the health sector works with other organizations and groups. The involvement of other sectors in health policy-making and planning can reduce resistance to implementing interventions to decrease the adverse health effects of climate change. Given the conflict of interest between the industrial sector and the health sector, it can be said that MoHME should gain more power in the network by exerting influence on other stakeholders and creating rules and tools for cross-sectoral coordination in the implementation of adaptation programs. Also, it can prevent confusion between sectors at local, national, and regional levels, thus supporting the interests of communities to deal with the health consequences of climate change.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eClimate change poses a threat to human health in a variety of ways. Therefore, since many upstream drivers of these risks are outside the health sector, the need for an effective and efficient adaptation plan to manage the risks of climate change should be emphasized. Thus, establishment of cross-sectoral collaboration and coordination of water, food, and energy (Nexus), development of a national adaptation plan in the health system by adopting three main approaches including Health in All Policies (HiAP), One health, Disaster Risk Reduction (DRR), and the integration of Social Determinants of Health (SDHs) in climate change policy-making are highlighted. The HiAP aim is the improvement of the population's health by reforming public policies and integrating health considerations into the policies of all sectors. Given that the major health effects of climate change will come from other sectors such as water, energy, and agriculture, it is important to use a comprehensive health framework to ensure that all relevant sectors understand health considerations and take the necessary steps in this regard. Understanding the networks of stakeholders and their relationships can lead to the better governance of ruling institutions concerning the subject matter. Involving health sector representatives in policy reviewing and advising relevant stakeholders on climate change adaptation measures can be one way to increase cross-sectoral cooperation by bridging the gap between the health sector and other sectors. All four approaches, by strengthening coordination and cross-sectoral cooperation with the health system leadership, have led to the development of a network of stakeholders that can could reduce health inequalities in all policies by involving the socio-economic components of the climate change issue. Although the stakeholder analysis process has identified a large number of organizations involved in climate change adaptation measures in Iran, there have been unexpected difficulties in adopting them. The analysis presented in this study could only be a description of the current state of inter-organizational communication. The limitation of this method is in quantifying organizational interactions that are highly subjective in such complex issues. So it will be impossible to make definitive judgments on the stakeholder\u0026rsquo;s performance.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e No funding was received for conducting this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial or non-financial interests:\u003c/strong\u003e The authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval:\u003c/strong\u003e This research has acquired the approval of Tehran University of Medical Sciences\u0026rsquo; Institutional Review Board (IRB). The IRB follows the stipulated clauses of the Helsinki Declaration. The approval code to do the research is IR.TUMS.SPH.REC. 1397.101.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participants:\u003c/strong\u003e In the present study, the participants were informed about the objectives and importance of the study. Participants were also reassured that the information obtained was for research purposes. Written informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions:\u003c/strong\u003e A.M. and AH.T. contributed to design of the study; A.A., AH.T. directed the project; N.SH performed and analyzed data; A.OT., K.N. and AR.MB. aided in interpreting the results; A.M. and AH.T. led the writing. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all representatives of the organizations who participated in this study and provided their perspective on the role and responsibility of stakeholders in decision-making, research, policy-making, and implementation of an adaptation plan.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eStephenson J, Crane SF, Levy C, Maslin M. Population, development, and climate change: links and effects on human health. Lancet. 2013;382(9905):1665\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrganization WH. Protecting health from climate change: connecting science, policy and people. 2009.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCostello A, Abbas M, Allen A, Ball S, Bell S, Bellamy R, et al. 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Commun Iran. 2022;14(1):349\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePezeshki Z, Tafazzoli-Shadpour M, Mansourian A, Eshrati B, Omidi E, Nejadqoli I. Model of cholera dissemination using geographic information systems and fuzzy clustering means: Case study, Chabahar, Iran. Public Health. 2012;126(10):881\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePezeshki Z, Tafazzoli-Shadpour M, Nejadgholi I, Mansourian A, Rahbar M. Model of Cholera Forecasting Using Artificial Neural Network in Chabahar City, Iran. Int J Enteric Pathog. 2016;4(1):1\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-environmental-health-science-and-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jehs","sideBox":"Learn more about [Journal of Environmental Health Science and Engineering](https://www.springer.com/journal/40201)","snPcode":"40201","submissionUrl":"https://www.editorialmanager.com/jehs/","title":"Journal of Environmental Health Science and Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Stakeholders analysis, Climate change, Adaptation measures, Health effects","lastPublishedDoi":"10.21203/rs.3.rs-4512761/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4512761/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eThis study aimed to determine the role and responsibility of stakeholders in decision-making, research, policy-making, and implementation of an adaptation plan, with a comprehensive view of the position, influence, and power of stakeholders.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis descriptive-analytical research was identified and analyzed using the social network analysis approach. Accordingly, the opinions of 25 university professors, experts, and executives who were selected by purposeful and snowball sampling were obtained through an questionnaire with a Likert scale. Data analysis and graphs design were then performed using Microsoft Excel and Gephi software, version 0.9.2. Stakeholder interaction patterns were also determined through Force Atlas 2 algorithm and graph theory concepts.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe network consisted of 37 nods, 3 clusters, and 63 edges. The network was close to a complete graph with a density of 0.971. Among the trustees\u0026rsquo; network, Intergovernmental Panel on Climate Change was the most active stakeholder and had relatively strong exterior interactions with other stakeholders. The Department of Environmental Protection and the Ministry of Health and Medical Education had relatively weak and very weak exterior interactions with other stakeholders, respectively.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eGiven the conflicting interests between the industrial sectors and the health sector, it seems that the Ministry of Health and medical education should gain more power and exert influence on other stakeholders; as well, involving health sector representatives in reviewing policies and providing stakeholder advice may help to fill the gap between the health and other sectors in climate change issues.\u003c/p\u003e","manuscriptTitle":"Analysis of key stakeholders involved in adaptation measures to tackle the adverse health effects of climate change in the Islamic Republic of Iran: A Social Network Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-19 14:40:59","doi":"10.21203/rs.3.rs-4512761/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-08-09T11:46:12+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-27T10:49:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-03T07:42:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Environmental Health Science and Engineering","date":"2024-06-01T04:45:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-environmental-health-science-and-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jehs","sideBox":"Learn more about [Journal of Environmental Health Science and Engineering](https://www.springer.com/journal/40201)","snPcode":"40201","submissionUrl":"https://www.editorialmanager.com/jehs/","title":"Journal of Environmental Health Science and Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"1354e852-32e3-4af8-ae80-2d37d4cb4f18","owner":[],"postedDate":"July 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-07-21T16:12:31+00:00","versionOfRecord":{"articleIdentity":"rs-4512761","link":"https://doi.org/10.1007/s40201-025-00947-z","journal":{"identity":"journal-of-environmental-health-science-and-engineering","isVorOnly":false,"title":"Journal of Environmental Health Science and Engineering"},"publishedOn":"2025-07-14 16:05:35","publishedOnDateReadable":"July 14th, 2025"},"versionCreatedAt":"2024-07-19 14:40:59","video":"","vorDoi":"10.1007/s40201-025-00947-z","vorDoiUrl":"https://doi.org/10.1007/s40201-025-00947-z","workflowStages":[]},"version":"v1","identity":"rs-4512761","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4512761","identity":"rs-4512761","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}