Assessing sustainable development across Moldova using household and property composition indicators
journal contributionposted on 2021-05-21, 15:55 authored by Richard R. Shaker, Igor G. Sirodoev
Societies are committing themselves to sustainable development by attempting to improve environmental quality, social equity, and economic welfare. As such, there continues a plea for holistic development assessment across scales; however there remains no ideal technique for achieving sustainability on neither regional nor local scale. This paper approaches this problem by constructing a multi-metric assessment system for evaluating development patterns across the Republic of Moldova. The objectives of this study were: (1) to produce a local multi-metric index that captures the three major dimensions of sustainable development for Moldova; (2) to quantitatively evaluate the interrelatedness of sub-metrics used for creating the local composite index of sustainable development; and (3) to visualize and interpret spatial patterns of sustainable development across Moldova. A local sustainable development index (LSDI) was produced using household and property composition indicators from a 2005 demographic and health survey for the Republic of Moldova. Total sample size and aggregated spatial reference was 11,066 households and 399 geographic locations, respectively. The LSDI used a 15 submetric optimum, equal weighting, 1e5 ordinal scale standardization, and additive construction. Spearman's rank correlation coefficient analysis was used to evaluate sub-metric quantitative relationships, and local Moran's I-test to interpret geographic patterns of sustainable development. Results revealed that a wealth sub-index had greatest collinearity with other sub-metrics. Geographically, Moldova's improved sustainability levels were found in large urban areas, suggesting needed prioritization of development resources to the hinterland. For regional sustainable development assessments, this approach provides the transferability to other locally referenced datasets throughout the world.