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<p>Socioeconomic backwardness represents the antithesis of development, characterized by the inability to meet essential needs and limited access to education, healthcare, and employment opportunities within a society or region. This study delves into the multifaceted factors contributing to socioeconomic backwardness, employing a multi-criteria decision-making (MCDM) approach to address this complexity. Fourteen socio-economic, demographic, and educational indicators are analyzed to assess backwardness, with their relative importance determined using the Entropy Weight Method (EWM). The study focuses on 31 Gram Panchayats (GPs) within the Sali watershed, ranking them based on their degree of backwardness to enable targeted policy interventions. The Weighted Sum Method (WSM), Weighted Product Method (WPM), and Weighted Aggregated Sum Product Assessment (WASPAS) were applied to rank the GPs. Based on these analyses, Lachhmanpur, Patrasayer, Gobindadham, Mankanali, and Bhaktabundh were identified as the most backward GPs, with backwardness scores ranging from 0.70 to 0.85. Conversely, Hamirhati, Pearbera, Dhulai, Hat-asuria, and Dihipara emerged as the most developed, with scores ranging between 0.15 and 0.30. Key factors driving backwardness included the prevalence of scheduled tribe populations (over 40% in highly backward GPs), unirrigated land areas (exceeding 60% of total land), and the presence of disabled household members (greater than 15% of households in backward GPs). The results highlight pronounced spatial inequalities in the Sali watershed, driven by agricultural dependency, poor irrigation infrastructure, and limited socioeconomic opportunities. To mitigate backwardness, the study recommends poverty alleviation, employment generation, community development, infrastructure upgrades, land use planning, improved irrigation, and agricultural support initiatives.</p>

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Socio-economic backwardness assessment modelling in different gram panchayats of Sali watershed using multi-criteria based weighting and ranking methods

  • 期刊论文

  • 发表日期：

    2025年1月18日

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  • Arkadeep Dutta,
  • Manua Banerjee,
  • Ratnadeep Ray

摘要

Socioeconomic backwardness represents the antithesis of development, characterized by the inability to meet essential needs and limited access to education, healthcare, and employment opportunities within a society or region. This study delves into the multifaceted factors contributing to socioeconomic backwardness, employing a multi-criteria decision-making (MCDM) approach to address this complexity. Fourteen socio-economic, demographic, and educational indicators are analyzed to assess backwardness, with their relative importance determined using the Entropy Weight Method (EWM). The study focuses on 31 Gram Panchayats (GPs) within the Sali watershed, ranking them based on their degree of backwardness to enable targeted policy interventions. The Weighted Sum Method (WSM), Weighted Product Method (WPM), and Weighted Aggregated Sum Product Assessment (WASPAS) were applied to rank the GPs. Based on these analyses, Lachhmanpur, Patrasayer, Gobindadham, Mankanali, and Bhaktabundh were identified as the most backward GPs, with backwardness scores ranging from 0.70 to 0.85. Conversely, Hamirhati, Pearbera, Dhulai, Hat-asuria, and Dihipara emerged as the most developed, with scores ranging between 0.15 and 0.30. Key factors driving backwardness included the prevalence of scheduled tribe populations (over 40% in highly backward GPs), unirrigated land areas (exceeding 60% of total land), and the presence of disabled household members (greater than 15% of households in backward GPs). The results highlight pronounced spatial inequalities in the Sali watershed, driven by agricultural dependency, poor irrigation infrastructure, and limited socioeconomic opportunities. To mitigate backwardness, the study recommends poverty alleviation, employment generation, community development, infrastructure upgrades, land use planning, improved irrigation, and agricultural support initiatives.