Socio-economic Planning Sciences最新文献

New family structures have emerged in Italy in recent decades, with a trend towards smaller nuclear families due to demographic, social and economic changes. An aging population, marital disruptions, declining fertility, and later marriages have contributed to this trend. It is important to understand the changing needs of families, especially the vulnerable, from both an economic and social perspective. Vulnerability is often related to economic factors, but people living alone are often at risk. The goal of this study is to classify Italian municipalities based on the prevailing characteristics of their one-person households, identifying areas of greater or lesser fragility. This classification constitutes a tool to plan people-based policies. Starting from the 2020 Italian Permanent Population and Housing Census data, a decision algorithm was used to identify municipalities according to the different types of their one-person households and to study their geographical distribution throughout the country. Our results show there is an unexpected heterogeneity that goes far beyond the classical North–South divide, emphasizing the urgency of approaching the study of economic and social processes at the local level.

Incorporating preferences on suitable peers into benchmarking analyses may ensure the setting of appropriate targets, which enable designing plans for improving performance that are aligned with management. This paper deals with target setting in situations where decision makers (DMs) have previously made a selection of peer candidates for the benchmarking of a given organization. A first approach is developed within the framework of conventional Data Envelopment Analysis (DEA), which is the technology mostly used in non-parametric frontier analysis. It provides targets from reference sets consisting of peer candidates that span a face of the strong efficient frontier of the production possibility set (PPS). These targets result from solving a DEA-like model, thus preventing from the need to identify all of the maximal efficient faces (MEFs) of the DEA frontier. We also propose a second approach where the convexity in DEA is somehow relaxed to allow additionally for reference sets consisting of candidates that are Pareto-efficient, provided that their convex hull is not dominated by other units. In that sense, the targets found can be seen as representing best practices. This approach broadens the range of alternatives when planning improvements, and may eventually provide closer targets.

The National Democratic Congress (NDC), Ghana’s leading opposition political party, has unveiled an ambitious ‘24-hour economy’ policy proposal ahead of the country’s forthcoming general elections in 2024. The policy aims to revitalise the nation’s economic landscape by fostering round-the-clock operations in key sectors. This paper employs a dynamic Computable General Equilibrium (CGE) model framework, underpinned by the 2015 Ghana Social Accounting Matrix (SAM) and the 2021 Ghana Population and Housing Census (PHC) data, to evaluate the potential impact of the policy on Ghana’s economy.

Results indicate that under the proposed ‘24-hour economy’ policy, Ghana’s real GDP growth (not to be confused with GDP growth rate) in ten years would be 31.71% higher than it would have been under a ‘business-as-usual’ scenario in the same timeframe. This indicates substantial augmentations in economic output within the Ghanaian economy under a ‘24-hour economy’ setting. Further, the policy would generate more than 3 million jobs within five years of its implementation, with manufacturing, agriculture, wholesale and retail trade, services, construction and transport sectors experiencing substantial employment gains.

The policy’s transformative effects are driven by its ability to stimulate capital investment and capital formation, boost productivity and increase household incomes.

The paper concludes that the NDC’s proposed ‘24-hour economy’ policy holds substantial potential for transformative economic growth in Ghana. However, there are potential challenges associated with the implementation of the policy, which then necessitates a holistic approach to policy formulation, focusing on inclusive growth and sustainable development strategies.

The recovery performance of a portfolio of Non-Performing Loans can be measured in terms of recovery rate and liquidation time jointly through a “recovery curve” representative of recovery rates over time. When portfolio heterogeneity is very high, it is informative to estimate more than just one curve by dividing the portfolio into several homogeneous subsets, i.e. clusters, and calculating a recovery curve for each of them. The aim of this work is to estimate the optimal portfolio partition and the smoothed recovery curves of each cluster by means of non-parametric statistical learning techniques.

The manuscript analyzes the spatial distribution, relationships and statistical dependence between religious involvement vs. the observance of public moral norms, and between religious involvement vs. sustainable social development as the main criterion and based on indirect criteria such as demographics, education, social welfare, health, public security, housing, social engagement, and the labor market. The study was conducted in Polish voivodeships, which represent the highest level of territorial division, and data for analyses were obtained from public statistical databases. The phenomena were examined with the use of synthetic variables, whereas composite indicators were determined by Hellwig's method. The study revealed considerable variations in the magnitude of the examined phenomena across Polish regions and the absence of significant correlations between religious involvement vs. the observance of public moral norms and sustainable social development as the main criterion. High values of indirect criteria are associated with regional development. Positive relationships were also observed between religiosity and selected indirect criteria.

Virtual, augmented, and mixed reality, known collectively as extended realities (XR), have the potential to transform businesses but research service is required. This report explores the productivity of XR-related in business research. This paper has enhanced the traditional bibliometric approach by integrating it with a mathematical supplement, Expedited Analytical Hierarchy Process (EAHP). This enhanced methodology allows for a nuanced evaluation of research service productivity and offers direction for future research efforts. Productivity is evaluated in terms of the effectiveness and efficiency of research results, so creating a bibliometrics index for commenting on research effectiveness and evaluating research efficiency criteria from a publication standpoint, the work added to the field. Analysis of the collected scientometrics data show the field is not yet optimally productive, leading to a call for increased focus on future research. Besides, the utilization of EAHP's systematics way to convert the data into a future research direction, with mixed reality found to require more attention than other XRs. A complementary literature analysis step highlights the practical application of XRs in business and the growing importance of the XR-owned worlds, metaverses. As a result, this study found evidence to argue that the services provided by the business research community are not optimally productive when dealing with the XR movement, and it calls for more attention to support businesses.

From an economic, political and social standpoint, one of the most evident and visible features of today’s European Union as a supranational regional organization is its heterogeneity, where disparity seems to be the common denominator. This leads to the interest for measuring the territorial economic cohesion of the EU. From an eminently economic perspective, and working with the GDP per capita of the EU NUTS-2 regions for the period 2003-2021, this paper aims to provide evidence of a lack of territorial economic cohesion through a beta and sigma convergence methodology by applying cross-sectional and spatial panel data analysis.

The findings show that the speed of convergence depends mainly on the level of economic development, its cycles and the heterogeneity of the, which implies conditional convergence. Less developed regions show higher convergence speeds, which are also accentuated during recession periods. Greater heterogeneity among the regions also increases the convergence speed, while accentuating in the less developed regions. In general terms, the results reveal convergence speeds of the entire NUTS-2 regions between 7 and 11 per cent (much higher than 2 per cent under absolute convergence). Likewise, when considering spatial dependence, a reduction in convergence speeds between approximately 3 and 8 per cent is detected. Finally, the 29 vulnerable regions have been identified, with economic development and growth below the EU average mean, emphasizing the need to take the concerns of territorial economic cohesion into account.

Numerous policies have been implemented in the global effort to achieve sustainable development and improve the population’s quality of life, such as those to reduce air pollution or manage municipal solid waste (MSW). However, these policies were designed disregarding their simultaneous impact on human health and ecosystem in the same city. This study implements system dynamics methodology to simulate coexisting air pollution and MSW management systems, as well as their corresponding policies and decontamination plans, to investigate their influence on human health and ecosystem and to propose policy improvements based on the specific setting of the study. The case study corresponds to Temuco and Padre Las Casas (TPLC). Results show that TPLC requires an integrated policy approach that includes increasing the equipment replacement and advanced technologies for MSW management to achieve reductions in human health impacts by approximately 10%.

In the literature, several approaches and methods were applied for studying the visitors’ profile or the managerial performance and economic efficiency of the museums; however, none of them investigated the museums opening decisions. To this aim an innovative approach which combines multilevel multinomial ordered models and spatial correlation models, is introduced and some advances in logit data geostatistical modeling is proposed together with an extended form of regression kriging, called multilevel logit kriging. Thus, the variation of the probability of the museums opening decisions both at regional and provincial levels for some peculiar museums characteristics, as well as the effect of some specific regional/provincial key factors which might influence their regular/non regular opening are modeled, also with respect to different types of institution (private/public).

The ISTAT microdata concerning the Italian survey on the museums and cultural institutions, will be considered. The empirical findings will provide worthy advices for the development of suitable management policies.

Sustainable transport is the priority for future cities and society. It is necessary to make sustainable urban transport policies to mitigate the harmful effects of current transportation systems. One of the essential issues in making cities cleaner is the propulsion technology of light commercial vehicles (LCVs). Most of the LCVs are compressed natural gas (CNG) and diesel-powered. However, cleaner, sustainable power sources, such as hydrogen, hybrid, solar, and electric have appeared in the market. This paper addresses the selection of propulsion technology for LCVs up to 3.5 tones for the Czech Post vehicle fleet. In this study, three main pillars are considered: economic, socio-environmental, and technical. Czech Post currently uses CNG-powered vehicles to transport shipments in penultimate mile delivery. The integrated FullEX-alternative ranking order method accounting for the two-step normalization (AROMAN) approach selects the best sustainable propulsion technology for the Czech Post vehicle fleet. The FullEX method identifies the criteria weights, while the AROMAN method ranks propulsion technologies. The results of the integrated approach reveal that the best propulsion technology for penultimate mile delivery is electrically powered, followed by hydrogen, hybrid, and CNG. A comparative analysis with other multi-criteria decision-making methods is also performed. The results confirm that the electrically powered solution is the best. The sensitivity analysis is carried out. The FullEX-AROMAN shows high resilience.