Toward revolutionizing water-energy-food nexus composite index model: from availability, accessibility, and governance

Frontiers in Water, Journal Year: 2024, Volume and Issue: 6

Published: March 13, 2024

The water-energy-food (WEF) nexus has emerged as a critical research interest to support integrated resource planning, management, and security. For this reason, many tools have been developed recently evaluate the WEF security monitor progress toward WEF-related sustainable development goals. Among these, calculating composite index model is since it can provide quantitative approach demonstrate status. However, current framework needs include incorporation of governance indicators, neglecting importance in framework. Thus, article develops new that incorporates indicators each subpillar. principal component analysis (PCA) adopted reduce variables’ collinearity model’s dimensionality. A quasi-Monte Carlo-based uncertainty global sensitivity are applied assess its effectiveness. Finally, 16 South African Development Community (SADC) countries case study. synergy effect within identified nations with better ability tend perform improving accessibility capability, suggesting

Language: Английский

Investigating the effects of spatial scales on social vulnerability index: A hybrid uncertainty and sensitivity analysis approach combined with remote sensing land cover data

Risk Analysis, Journal Year: 2024, Volume and Issue: unknown

Published: June 11, 2024

Investigating the effects of spatial scales on uncertainty and sensitivity analysis social vulnerability index (SoVI) model output is critical, especially for finer than census block group or block. This study applied intelligent dasymetric mapping approach to spatially disaggregate tract scale SoVI into a 300-m grids resolution map in Davidson County, Nashville. Then, variance-based global were conducted two models: (a) scale; (b) scale. Uncertainty results indicate that has better confidence identifying places with higher socially vulnerable status, no matter which constructed. However, does affect results. The suggests SoVI, indicator transformation weighting scheme are major contributors modeling stages. While like grid's resolution, becomes uttermost dominant contributor, absorbing contributions from transformation.

Language: Английский

Incorporating spatial autocorrelation in dasymetric mapping: A hierarchical Poisson spatial disaggregation regression model

Applied Geography, Journal Year: 2024, Volume and Issue: 169, P. 103333 - 103333

Published: July 1, 2024

The growing demand for spatially detailed population products in various fields continues to rise, as users shift their focus from aggregated areal totals high-resolution grid estimates. Aggregating demographic data areas, such census tracts or block groups, can mask localized heterogeneities within those areas. This paper presents a new pycnophylactic (density-preserving) geospatial model disaggregating grids. We describe Bayesian Hierarchical Poisson Spatial Disaggregation Regression Model (HPSDRM), which incorporates land cover covariates and two levels of spatial autocorrelation. evaluated the model's predictive ability first with simulation studies, then by Davidson County, TN, tract-level fine comparing predicted actual block-level counts. interpolated map successfully identified heterogeneities, hot- cold-spots tracts. HPDSRM out-performed three other types disaggregation modeling, suggests value incorporating Based upon this study, HPSDRM has potential data, socioeconomic indicators.

Language: Английский