Assessing and optimizing decision‐making policies of India with public employment growth as a key indicator toward sustainable development goals using multicriteria mathematical modeling

Multicriteria mathematical modeling is an analytical framework for formally describing real‐life problems involving multiple and conflicting objectives. In the past decade, multicriteria decision‐making techniques have been applied in almost every area of the decision‐making including energy‐economic planning and sustainable development. Various mathematical and analytical models have been presented for the sustainable development planning and their assessment. In this paper, we discuss an approach related to multicriteria decision‐making and apply it for the assessment of the sustainable development goals of India by the year 2030. In the INDC report submitted to United Nations Framework Convention on Climate Change (2015, http://www4.unfccc.int/submissions/INDC/Published%20Documents/India/1/INDIA%20INDC%20TO%20UNFCCC.pdf), India has identified many goals related to the sustainable development like energy consumption, greenhouse gas emissions, GVA growth, and an increase in the employment by the year 2030. This paper overcomes the energy resource allocation problem in related literature due to the lack of sectorial data for the same year by calculating the estimates for each sector for the year 2030. We presented a multicriteria decision‐making model which allocates public labor force in the key economic sectors of India. The presented model is validated with the data of the key economic sectors and their contribution in the identified goals. The paper provides a decision support for the better management of future sustainable policies by assessing the efficiencies of the current policies toward future sustainable goals. We evaluated the identified goals using the multicriteria decision‐making approaches so that the strategic planning can be implemented by the policy makers and to present a quantitative justification of planning strategies.