Socio-economic Planning Sciences最新文献

Addressing corruption is crucial for building a sustainable healthcare system that ensures access, quality, and equity in healthcare delivery. Despite that, many current strategies to combat corruption in the healthcare sector do not evaluate high-level corruption, such as corruption risks occurring at sub-national levels. This work bridges this gap by providing corruption risk profiles of Italian contracting authorities responsible for procuring goods and services for healthcare facilities in the public procurement process. Using an array of 14 red flags of corruption risk and an extended Item Response Theory model applied to a big data source made available by the Italian Anti-corruption Authority, our main findings show that: $i.$ the risk of corruption is a multidimensional occurrence, which can be represented as a four-dimensional latent variable; $ii.$ there are eight clusters of contracting authorities, having distinct and well-defined risk profiles over the four ascertained dimensions of corruption risk; iii. the distribution of risk profiles at sub-national level showcases relevant geographic variations and emphasises the need for tailored anti-corruption strategies to effectively address region-specific challenges and risk factors.

Urban sustainability is essential to address the challenges posed by population explosion, excessive consumption, and environmental disasters. Controversial discussions between governments and academics in countries like Iran challenge sustainable development. This paper provides an integrated Analytical Hierarchy Process (AHP) and Rough Set Theory (RST) for determining attribute weights, and presents a new application of variance maximization in a mathematical model to determine more realistic integrated weights. An empirical study was conducted in 31 provinces of Iran using data from 2012 to 2019 to evaluate overall sustainability; sustainability in three pillars of social equity (SE), economic dynamism (ED), and environmental protection (EP); as well as to understand the importance of criteria in a localized system consisting of 27 sub-criteria. In terms of overall sustainability, Semnan and Tehran had the highest score with around 50 % and Sistan scoring the lowest at around 18 %. In terms of SE and ED, the regions were generally balanced. However, the eastern border provinces performed poorly in terms of EP. Zanjan had the greatest percentage of sustainable growth (29.25 %), while Sistan obtained the lowest (−19.75 %). The most important factors in strengthening SE, ED, and EP were healthcare, workforce, and environmental quality, respectively. This approach may assess sustainability from many national, provincial, and local dimensions, and its flexibility as well as the compatibility of the estimated findings with sensitivity analysis was proved. Then, strategies to improve urban sustainability, including increasing cooperation between provinces and regions and balanced growth in the pillars of sustainability, are presented.

Serious environmental pollution challenges have compelled policymakers to replace China's previous crude rapid economic development model with high-quality economic development (HQED) to promote sustainable social progress. Since finance is the core of the modern economy, financial openness (FOPEN) may affect China's HQED. This study uses provincial-level data from 2007 to 2020 as a research sample to examine the relationship between the FOPEN and HQED. We construct indicator models for HQED and FOPEN and analyze the resulting spatiotemporal evolutionary features applying the Dagum Gini coefficient and kernel density estimation. We observe significant overall differences between HQED and FOPEN, which are mainly interregional in origin. In addition, HQED and FOPEN exhibit opposing dynamic evolutionary features, revealing that the polarization of the former (latter) is becoming progressively larger (smaller). With the spatial Durbin model, we demonstrate that FOPEN exerts a positive (negative) direct (indirect) impact on HQED. The subsample test indicates that the influence of FOPEN in the eastern and central regions is similar to the entire sample, while it has only a direct impact in the western region. These findings are validated by a series of robustness tests. Finally, our threshold effect analysis shows that FOPEN has a stronger promotional impact on HQED in regions with more advanced and rational industrial structure. The findings of this study suggest the policymakers should apply supporting policies to enhance the promotional direct effect and relieve the inhibiting indirect effect of FOPEN, and rationally plan local industrial structure upgrading, so as to more efficiently promote HQED.

NEET refers to young people who are not in employment, education or training. It can occur in a variety of situations and requires attention, especially if it tends to persist over time. Indeed, individuals who leave education and enter the labor market looking for a job are classified as NEET. While in the majority of cases they tend to move into employment status within a short period of time, in others they remain in this status for longer, with negative consequences for their future career or never enter the labor market. Although the scarring effect of longer spells outside the labor market (for unemployment or inactivity) is well known in the economic literature, empirical evidence on this topic are very limited due to the lack of adequate data needed for this analysis.

This article aims to fill this gap in the literature and is finalized to verify the influence exerted by the socio-economic background of individuals on the likelihood of becoming and remaining for a long time NEET, according to different levels of education. The analysis is based on AD-SILC dataset, obtained by matching the EU-SILC data with the administrative archives of the INPS, the National Institute for Social Security. Our results reveal that individuals with the same level of educational attainment, but from a higher socio-economic status, have a significantly shorter duration in the NEET condition and a higher probability of transitioning to employment. Conversely, individuals with the same level of education show no significant effects if they come from a low socio-economic background.

After a disaster, the power grid helps support infrastructure systems that are essential to the recovery effort in addition to the critical services it provides every day. This paper provides an approach for optimal power grid restoration in disaster contexts with environmental hazards. These hazards, such as unstable structures, smoke, chemical hazards, and abnormal radioactivity, may pose acute and accumulating risks to repair workers and impede the restoration process. We therefore formulate a mixed-integer linear program (MILP) that generates an optimal restoration plan with constraints imposed by acute and accumulating environmental hazards. We also develop a heuristic inspired by trends that we observe in optimal restoration strategies, and we compare its performance to that of an optimal restoration strategy. For our case study, we model a stylized disaster that approximates the patterns of a number of disasters including earthquakes, fires, industrial facility explosions, or nuclear reactor incidents. We analyze the performance of the heuristic and optimal restoration strategies on a modified IEEE 123-bus test network. We find that the optimal restoration strategy is able to restore power service more quickly than the heuristic strategy while also exposing repair workers to less acute and cumulative environmental hazards. We also find that as disaster severity increases, the performance difference between the heuristic and optimal restoration strategies grows. Finally, our results show that both the optimal and heuristic algorithms can be useful tools for identifying vulnerable regions of a power grid.

The paper deals with a test for the goodness-of-fit of a model for count data, in the framework of Generalized Linear Models. The motivating example concerns the study on the effectiveness of policy incentives for the adoption of 4.0 technologies by Small and Medium Enterprises. According to the literature, openness to Industry 4.0 should be measured in terms of the number of 4.0 technologies adopted, represented by a count variable. To investigate the effectiveness of public policy interventions to encourage the adoption of 4.0 technologies, we propose the application of a model for count data with a permutation ANOVA to test the goodness-of-fit and for the model selection. When the distribution of the response is neither Poisson nor Negative Binomial, and in the quite common situation in which the response variance is much greater than the mean, the classic Poisson regression and Negative Binomial regression are not valid. The proposed testing method is based on the combination of permutation tests on the significance of the regression coefficient estimates. The power behaviour of the proposed semi-parametric solution is investigated through a comparative Monte Carlo simulation study. The performance of such a method is compared to those of the two main parametric competitors. The application of the permutation test to an interesting case study is presented. The dataset is original, and related to a sample survey carried out in Italy, about the adoption of Industry 4.0 technologies by Italian enterprises.

Benefits of urbanization include increased economic opportunities, better access to technology, healthcare, and education, as well as a better standard of living. The generalized extension of the q-rung orthopair fuzzy set in combination with the soft set (SS) is introduced to determine which location is most likely to be favourable for urban growth. The Aczel–Alsina aggregation operators (AA’AOs) for q-rung orthopair fuzzy soft set (q-ROFSS) are formulated. The generalized nature of q-ROFSS is due to flexibility in the index of membership and non-membership, which provide decision-makers more freedom to express their opinions. The developed AA’AOs are based on the Aczel–Alsina (AA) t-norm and t-conorm that emphasize parameter variability. Important properties of these operators are studied. A novel approach based on q-ROFSS is established. The approach is tested with a case study problem related to urbanization. In this scenario, a company is searching for the best possible area to develop a housing society. The results show that the approach is highly valuable and easily applicable. The stability of the operators is examined through comparative analysis. The findings of sensitivity analysis show that increasing parameters in q-ROFSS leads to diminishing the impact of the non-membership operation, indicating geometric expansion and mathematical rebalancing of dominance between operations.

This study applies a neural network framework to optimize the classification of firms and to predict their difficulties in collecting external financial resources in the short term. In detail, we adopt replicated bootstrapped algorithms optimized on sensitivity and specificity as error measures and we propose a comparative analysis to identify the best-performing one. According to our results, the Conjugate gradient backpropagation with Fletcher-Reeves updates (i.e., CGF) is the best-performing algorithm, with sensitivity equal to 74.41 % and specificity equal to 70.11 %. Then, we use this algorithm and its weights to provide a classification of the Italian manufacturing industry in 2019, identifying the geographical areas in which firms under financial constraints are located, as well as the most critical industrial sectors. Based on this evidence, and considering the implementation of a cohesion policy, we highlight interventions by policy makers to support firms’ access to the capital market, fostering their investments and the consequent socio-economic development.

Air pollution poses a significant threat to public health and the environment in urban areas worldwide. In the context of urban air quality, nitrogen oxides (NOx), comprising nitrogen dioxide (NO2) and nitric oxide (NO), stand out as key pollutants with well-documented adverse effects. The city of Madrid, as the capital and largest urban center of Spain and the third largest of Europe, is no exception to the challenges posed by NOx pollution. Most of the recent literature on forecasting air pollution, and specifically on NOx, is based on the use of Neural Networks (NN). Little is known about the forecasting ability of factor models in this context. The main aim of this paper is to use Factor-Augmented Autoregressive Neural Networks (FA-ARNN-X) to predict future patterns of NOx pollutants in the territorial monitoring stations of Madrid, using lagged NOx values, meteorological variables and latent factors. The main results indicate that the proposed forecasting model provides statistically more accurate predictions of air pollution than its competing benchmarks and should be used by policymakers for more accurate air pollution monitoring.

Photovoltaic (PV) power generation systems are highly subject to weather and site conditions, thus, the construction of PV power plant projects must consider these uncertainties. This study analyzes the monthly electricity generation of 249 utility-scale PV power plants in Japan to evaluate their electricity generation efficiency. Applying the generic data envelopment analysis, benchmark values were identified for power generation from PV power plants. Furthermore, we implemented a Monte Carlo experiment to evaluate the impact of variability in solar irradiance and temperature on power generation efficiency. For our analysis, we considered three inputs—solar irradiance, temperature, and installed capacity—and electricity generation as the output. The results showed that inter-regional gap in the efficiency score between the west and north regions is 0.03, and this can be covered by a 0.1 increase in the DC/AC ratio. Additionally, variability in weather conditions affect both the efficiency of a power plant and production possibility frontier, in turn causing the benchmark values for a generic decision-making unit to vary. Increasing the generation capacity of power plants and operating them more efficiently is essential to expanding the use of renewable energy resources.