Volume 24, Issue 1 (2020)                   MJSP 2020, 24(1): 109-127 | Back to browse issues page

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Jahanishakib F, Erfani M, Yusefi Rubiat E. Explanation of Effective Drivers in Environmental Management of South Khorasan Province Using Structural AnalysisExplanation of Effective Drivers in Environmental Management of South Khorasan Province Using Structural Analysis. MJSP 2020; 24 (1) :109-127
URL: http://hsmsp.modares.ac.ir/article-21-36397-en.html
1- Assistance professor, faculty of environment, university of Birjand , jahanishakibb@Birjand.ac.ir
2- Assistance professor, faculty of environment, university of Zabol
3- Assistance professor, faculty of environment, university of Birjand, Iran.
Abstract:   (3327 Views)
 In Iran,time is limited and it is always too late to modify management attitudes in the fields of environment. On the one hand, the relevant agencies do not even know their priorities separately or are unaware relationship of their priorities with other agencies, while at the macro level, they are unaware of the province's development plan. So, important drivers in the field of environment are the first and most important steps in guiding scenarios of proper management.
 First, a list of effective drivers of environmental management in the South Khorasan province was collected  from some specialist and experts using Delphi, followed by two-dimensional matrices containing quantitative matrices to quantify the driver’s relationships and interpretations. The axes of influence and dependence are were used. Therefore, according to the position of each driver with these two criteria in the matrix, five types of drivers are defined. Drivers screening based on the degree of influence and dependence of other drivers as effective , key and independent drivers in the field of environmental management in South Khorasan province were studied. The method used was MicMac Structural Analysis, which was used by academic experts and related executives. In so doing, the team created a common language which will served them as the process continued. In most cases, it also allowed the team to redefine certain variables and refine the analysis of the system. Lastly, experience shows that the ideal percentage of the matrix to be filled-in is around 20%.
Comparing the rankings of the variables from the various classifications (direct, indirect and potential) is a rich source of information. It allows the team to confirm the importance of certain variables as well as to reveal those variables which play a dominant role in the system, and which would have remained undetected if they had only been compared directly. The information obtained by influence and dependence of each of the variables can be displayed in two-dimensional diagrams containing the vertical (affective) and horizontal (affective) axes. This method can identify the most effective drivers in the system and study the different roles played by these drivers (Godet & Durance, 2011).
Results and discussion
By aggregating and analyzing the views of a panel of experts in the field who know the topic in MicMac 39 software, drivers were identified as key, effective, effective and dependence drivers. The comments were considered as MicMac inputs, with a filling rate of 23.86% including 153 one (weak influence), 113 two (moderate influence), 89 three (strong influence) and 8 to P (possible influence). The sum of direct and indirect and probable influence of drivers were estimated to be 3007, 2805, 2135, 1938 and 1572, respectively. This matrix has 100% stability with two replications which shows high validity of the questionnaire and its answers. The direct influence (a) and indirect (b) influence of the drivers on each other are shown in Fig. 1.
 Driver 5 (D5: creating environmental law enforcement and warranties) and D1 (co-operation between related agencies to prevent any re-work) in four ways are among the most effective and determining factors. In the next step, the influence of the drivers of the free flow of information and sharing the results of studies of all environmental-related organizations to public or academic expertise (D33), changing the real attitude of decision-makers in embracing intellectual physical potential, and creative indigenous peoples of the region (D29) and the environmental agency's correct placement in decision making (before doing the project and any action not after it is finalized or completed!) (D6) were more influence drivers respectively.
Given that the distribution of drivers is in the axis of influence and dependency as L shape, the system under study is balanced and it is possible to make planning for such a system (Arcade et al., 1999; Erfani and Mircheraghkhani, 2018)
 Five main and key drivers of the system under study were identified, the first two of which relate to political and institutional domains that are in line with  Erfani and Mircheraghkhani’s (2018) study. In this study, monitoring of nine identified response drivers is the main indicator for revealing the province’s environmental management status, which is recommended for future studies. These indicators  can explain the environmental status of the province and can be considered as a criterion for determining the actual performance and efficiency of the agencies. It is also recommended to continue the present study and to complete four more steps from the LIPSOR School to identify conflicts of interest between relevant stakeholders, scenarios building and predict the future. 
 Each year, the performance statistics of the agencies are presented based on the indicators set by the agencies themselves and the overhead agencies, and they are more likely to be defined in a way that may not adequately represent the agencies’ performance, and thus make the agencies less judgmental to fall. For example, the index of mountain tenure has clearly increased over the last few years. Combating mountain tenure alone does not indicate management efficiency, but rather the absence of mountain tenure and the return of shifting areas given to conditions close to the baseline status is effective either. Therefore, change in decision makers' attitudes (D6) and efforts in problem solving have been introduced as one of the key drivers in this study (Fig. 1).
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Article Type: Original Research | Subject: planning models,techniques and methods
Received: 2019/09/13 | Accepted: 2020/01/18 | Published: 2020/03/29

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