Smart sustainability ranking system within local budgeting

  • 426 Views
  • 164 Downloads

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License

The study focuses on the need to update tools for making local governance decisions using modern information technology in an environment of unpredictability added by the pandemic. Policy formulation by the authorities, especially local governments, is faced with the demand for sustainable development due to the obstacles and risks that have arisen. The purpose of the paper was to create a model for an intelligent information system to rank input qualitative information as an object in accordance with sustainability criteria for determining the local government’s policy on budgetary support for entrepreneurial activity. Fuzzy informatics methods used in soft computing based on fuzzy logic improve estimation potential. The activity in сommunity-based tourism (CBT) was chosen as a basis for simulating the “Intelligent Ranking System” for local budgeting. In the paper, the system ranks four factors of sustainability according to the importance of local government activity by nine criteria, whose fuzzy values are calculated based on expert judgments within the framework of six linguistic variations. Simulation of future directions of budgeting was developed using unified answers from the example of India for applying in local tourism. The basis of the system matrix is formed through the subsequent analysis of deviations from the limiting variations of the maximum positive and maximum negative impressions of experts. The model of this ranking system will be useful for service-oriented activities where consumer impressions are an important development requirement.

view full abstract hide full abstract
    • Table 1. Fuzzified scale for pairwise comparison
    • Table 2. Ratings by First Expert Group Opinion
    • Table 3. Ratings by Second Expert Group Opinion
    • Table 4. Pairwise comparison table for the main criteria
    • Table 5. Evaluation of Normalized weights
    • Table 6. Normalized weights of each alternative corresponding to each criterion
    • Table 7. Fuzzy weights of each criteria
    • Table 8. Normalized weights of each alternative corresponding to each criterion
    • Table 9. FPIS and FNIS of each criteria
    • Table 10. Final output obtained by Fuzzy TOPSIS using Ebadi et al. (2013) method
    • Table 11. Final output obtained by Fuzzy TOPSIS using Hamming Distance
    • Table 12. Fuzzy TOPSIS results of the simulation
    • Methodology
      Pankaj Srivastava
    • Supervision
      Pankaj Srivastava
    • Validation
      Pankaj Srivastava, Denys Hryzohlazov
    • Data curation
      Saurabh Srivastav
    • Formal Analysis
      Saurabh Srivastav
    • Funding acquisition
      Saurabh Srivastav
    • Investigation
      Saurabh Srivastav
    • Software
      Saurabh Srivastav
    • Writing – original draft
      Saurabh Srivastav
    • Conceptualization
      Tetiana Zhyber, Denys Hryzohlazov
    • Project administration
      Tetiana Zhyber
    • Visualization
      Tetiana Zhyber
    • Writing – review & editing
      Tetiana Zhyber, Denys Hryzohlazov