Data-Driven Evaluation and Optimisation of Livelihood Improvement Efficiency

Abstract

In this study, we developed a data-driven approach for the evaluation and optimisation of livelihood improvement efficiency (LIE) to address slowing global economic growth and the decline in well-being in the broader population, enhance the quality of people’s livelihoods, and promote sustainable social development. We designed a questionnaire survey and constructed an evaluation index system based on a comprehensive consideration of economic resources, social security and employment, education, and health. Using principal component analysis, entropy weighting, and data envelopment analysis, we optimised the evaluation indicators and quantitatively assessed LIE. We used a Tobit regression model to analyse the factors influencing LIE and provide decision-making support for proposing countermeasures to optimise LIE. Based on the research data, we administered the questionnaire survey to 3125 residents in 16 cities in China’s Anhui Province and demonstrated the applicability of the aforementioned method. The results indicate that there is room for optimising LIE in cities in Anhui Province, which needs to be achieved through the following steps: controlling costs and avoiding waste, encouraging entrepreneurship, increasing income, guiding the direction of industrial growth, optimising regional population structure, and promoting public participation to enhance people’s livelihoods.