Socioeconomic and resource efficiency impacts of digital public services

Abstract

This paper measures the socioeconomic and resource-efficient influences of digital transformation in the public sector in the European region. To capture the socioeconomic impacts of digital public services, we employ a socioeconomic score index calculated as the unweighted average of the re-scaled scores for changes in employment, exports, and turnover from ecoindustries. Regarding resource-efficient impacts, we employ the resource efficiency score index measured as the unweighted average of the re-scaled scores for material, energy productivity, and the intensity of greenhouse gas (GHG) emissions. Measures such as user-centricity, business mobility, and key enablers are used to demonstrate the level of digitalization in the public sector. According to our estimations based on various econometric techniques, digital public services have a favorable effect on the economy and society through a positive impact on employment, exports, and turnover of eco-industries. The effects of digitalization on resource productivity follow a nonlinear U-shaped curve, suggesting that the improvement of resource efficiency is only present when the digital transformation process reaches a certain level.

Pathways to Sustainable Development: Corporate Digital Transformation and Environmental Performance in China

“…A growing number of studies show that digital technologies can successfully improve the efficiency of carbon emissions but may not reduce total carbon emissions [44,45]. The impact of digital transformation on energy use is not a simple linear relationship; digital development increases carbon emissions by expanding the scale of production and the energy rebound effect, showing a "U" shaped nonlinear relationship, and when digital transformation reaches a certain level, resource efficiency gains occur [46,47].…”

The nexus of financialization and circularity: Evidence from European economies

“…To investigate the economic complexity-circularity nexus for the data with CD and stationarity of the first-difference variables, the panel-corrected standard error (PCSE) model is the most appropriate empirical approach. 27,78–81…”

“…In addition, we replicated our estimates using different statistical models, including the Feasible Generalized Least Squares (FGLS) that address the issue of heterogeneity, 27 and the two-step system GMM, which are designed to deal with the potential issue of endogeneity in Equations 1 and 2. 28–82 Notably, the effects of the project over the short and long term are also assessed. Accordingly, the autoregressive distributed lag (ARDL) method developed by Pesaran and Smith 83 is employed.…”

“…To investigate the economic complexity-circularity nexus for the data with CD and stationarity of the first-difference variables, the panel-corrected standard error (PCSE) model is the most appropriate empirical approach. 27,[78][79][80][81] In addition, we replicated our estimates using different statistical models, including the Feasible Generalized Least Squares (FGLS) that address the issue of heterogeneity, 27 and the two-step system GMM, which are designed to deal with the potential issue of endogeneity in Equations 1 and 2. Notably, the effects of the project over the short and long term are also assessed.…”

Digitalization and Energy in Attaining Sustainable Development: Impact on Energy Consumption, Energy Structure, and Energy Intensity

“…Additionally, it can foster innovation, provide novel solutions to environmental challenges, and contribute to the development of cleaner, more sustainable technologies [31][32][33][34]. However, it is crucial to acknowledge the potential negative repercussions of digitalization, especially concerning its impact on the environment [35][36][37][38]. The increased reliance on digital technologies has resulted in a surge in energy consumption [36,37], contributing to concerns about energy intensity and the carbon footprint [39,40].…”