Spatio-temporal heterogeneity of multi-period efficiency in an urban water-energy-food nexus system

Rapid economic growth has driven surging consumption of water, energy, and food, creating resource scarcity that threatens regional sustainable development. To address this issue, China has implemented a series of "red line" policies, with the objective of improving the efficiency of resource use. While the relevant strategies of a single resource may prove beneficial to a subsystem, they may have an ineffective and even negative impact on the Water-Energy-Food Nexus (WEFN) system. Therefore, it is imperative to quantify and improve the input-output efficiency from the perspective of the WEFN system. Research primarily calculates efficiency from a static perspective or network efficiency alone; however, few studies have simultaneously considered the dynamic characteristics and network structure of the WEFN system. In terms of drivers analysis, few studies have revealed the impact of factors on efficiency across time and space. To address these issues, this study proposed a dynamic network Data Envelopment Analysis (DEA) model that captures both subsystem interactions and intertemporal linkages. The model enhances measurement accuracy by integrating feedback mechanisms between water, energy, and food subsystems while incorporating capital stock dynamics across consecutive periods. Furthermore, a Geographically Temporally Weighted Regression (GTWR) model was employed to elucidate the spatiotemporally varying impacts of efficiency drivers. To illustrate the application of these models, this study conducted a case study in Guangdong Province of China. The results indicate that provincial efficiency followed an inverted U-shaped trend, increasing from 0.296 in 2010 to 0.731 in 2021, followed by a slight decline. Spatially, efficiency demonstrated a radial gradient, peaking in the Pearl River Delta and diminishing toward peripheral regions. Northern Guangdong emerged as the least efficient subregion, constraining overall system performance. Spatial autocorrelation analysis identified persistent positive clustering effects, though Moran's I values declined from 0.33 to 0.21, indicating reduced regional disparities over time. GTWR results highlighted urbanization level, technological progress, and environmental governance as positive drivers, while industrial development exerted negative impacts. These factors demonstrated significant heterogeneity, urbanization markedly benefited Western Guangdong, whereas technological and environmental interventions yielded the greatest improvements in the Pearl River Delta. These findings provide actionable guidance for differentiated policy formulation. Core regions like the Pearl River Delta require advanced circular economy strategies to overcome resource saturation, whereas peripheral areas necessitate targeted investments in infrastructure and innovation.