Socio-economic Planning Sciences最新文献

It is widely accepted that listed organizations have potential to achieve greater economic results. In return, they are expected to provide reliable information on its economic and financial activity in a timely and accurate manner; and also, non-financial or sustainability information since 2018. Nowadays, organizations demonstrate not only their economic performance, but also their social commitment. Therefore, financial institutions attempt to be both economically and socially efficient. This paper tries to resolve this specific issue: determining whether listed financial institutions are more economically and socially efficient, due to the fact that, as they are listed and bigger, they can spend more resources in communication. But we do not know whether they truly are more socially efficient or simply have a reputation as such. To give an answer to this question a three-stage Data Envelopment Analysis has been used, which includes Spanish financial institutions Panel Data from 2014 to 2019.

Most traditional studies measure accessible equity of public service facilities (PSFs) statically, and tend to neglect travelling scope disparities among people with different travelling abilities and conditions. This study offers a new perspective for dynamically assessing the actual occupancy of PSFs from both the provision and demand sides. Taking Shenzhen as an example, and employing big data on shared-bike orders, we explored the compensation mechanism of cycling for the distributional inequity of PSFs, with a two-phase measurement framework. First, the Gini index and Lorenz curve were adopted to compare the distributional equality of the grids’ originally-allocated PSFs (based on the division of block-scale grids) and their cycling-accessed PSFs (based on delineation of the cycling scope with shared-bike orders). Second, a spatial lag model was established to reveal the exact factors affecting the ability to improve the PSF occupancy through cycling. The results showed that cycling compensated the distributional inequity of PSFs. This compensation effect varied among groups of different ages, genders, and incomes. Moreover, mixed land use and the low density of trunk roads promoted this compensation. These findings can provide decision-makers with references for future PSF planning in Shenzhen, and other similar cities worldwide.

In this work tailor robust metrics are proposed to be used in the predictors’ space of distance-based predictive models. The first proposal is a robust version of Gower’s distance, which takes into account the correlation structure of the data. The second one is a rather complex metric, constructed via Related Metric Scaling, which is able to discard redundant information coming from different sources. Another novelty is the proposal of a distance-based trimming statistic to robustify the metrics. The performance of the models based on new robust metrics is evaluated through a simulation study and compared to those based on Euclidean, Gower’s and generalized Gower’s metrics in the presence of outliers in several datasets of multivariate heterogeneous data. Mean squared error (also median and standard deviation) are used to evaluate the effectiveness in the prediction of responses. Finally, two applications in the areas of sustainable transport and finance and banking are provided in order to illustrate the predictive power of these models. Computations are made using the dbstats package for R.

The COVID-19 pandemic posed significant challenges for statesmen and policymakers, especially in healthcare crisis management. During the spread of emerging infectious diseases, proactive governments’ responses and real-time implementation of precautionary measures may help countries to better manage the emergency and gradually return to normality, reducing an excessive resource waste as well as safeguarding citizens’ well-being. In this context, social media platforms represents a useful tool in predicting and early detecting outbreaks, with the potential of counteracting the rapid sequence of pre-pandemic events. This paper aims to present a bibliometric study based on a BERT embedding technique to overview the literature themes on the relation between social media alerts and infectious disease spread. Our findings underline the key role of social media platforms and the urgency of setting up an integrated digital surveillance system in which social media data can help to geo-localize chains of contagion and, in general, improve the effectiveness of public health interventions.

This paper builds a structural econometric model to measure the impact on inflation of three shocks: a shock of disequilibrium in the energy markets, a money growth shock, and a global stochastic shock, which covers factors such as inflationary expectations and uncertainty. It is found that for the economies of Europe and the United States, the current inflationary period is dominated by the global component closely followed by energy markets in the short term and by monetary growth in the medium term. The dynamics of the model further show that the recent exponential inflationary surge of 2021 onward is mainly headed by other factors rather than energy markets or money growth, such as expectations or uncertainty. Further analysis of the spillover effects between the inflation of Europe and the United States based on endogeneity-robust econometric estimations shows that this relationship is unidirectional from the USA to Europe. In essence, this work shows that a coordinated strategy between fiscal and monetary policies is the best response to recent inflationary shocks.

We study a stochastic free-floating shared bike redistribution problem, which first gathers bikes randomly scattered around the hotspots of a city and then transships the gathered bikes among the hotspots to achieve a better supply–demand balance. In the literature, the bike gathering operation and the associated stochasticity, although significant for shared bike redistribution practice, are rarely considered. To fill the gap, we propose a two-stage stochastic programming model that explicitly incorporates the bike-gathering cost with two formulations. In the planning stage, the deployment of gathering sites and the allocation of gathering staff are determined. In the operation stage, the gathering, transshipment and replenishment of shared bikes are optimized based on the decisions of the planning stage and the realization of the uncertain supply and demand of free-floating shared bikes in space. With a case study based on real data from the Mobike company in Beijing, China, we illustrate the real-world applications of our proposed model, highlight the importance of considering the cost of bike gathering when planning free-floating shared bike redistribution, and gain managerial insights for better field practices.

This study employs an innovative metafrontier methodology, inspired by Huang et al. (2014) [10], to investigate the combined impact of economic policy uncertainty, political orientation, and institutional quality on the environmental performance of 136 emergent and non-emerging countries from 1990 to 2018. Using such a parametric approach, the research evaluates countries’ efficiency both within specific geographical contexts and on an individual basis. The empirical findings reveal that North America and the Middle East & North Africa are more efficient at implementing environmental pollution-reduction technology. Furthermore, in a metafrontier scenario, economic policy uncertainty, institutional quality, and political orientation significantly affect regional environmental inefficiencies. Notably, economic policy uncertainty reduces environmental inefficiency in South Asia but improves it elsewhere, while better institutions are associated with reduced CO₂ emissions and enhanced efficiency. Additionally, a left-leaning approach demonstrates a positive impact on reducing environmental inefficiencies in South Asia and Latin America & Caribbean. By shedding light on the intricate interplay between economic policies, environmental outcomes, and global disparities, this study advances our understanding and poses key research inquiries while offering potential policy implications.

This paper investigates the relationship between board characteristics and environmental, social, and governance (ESG) performance by analyzing 109 firms listed in the African Stock Exchanges from 2016 to 2022. Our findings indicate that board independence, as well as board gender diversity and specific skills, are associated with higher levels of ESG performance. In contrast, larger boards tend to diminish ESG performance in Africa. We also found that the presence of a sustainability committee and a corporate governance committee contributes to driving positive ESG outcomes. This study provides relevant insights and yields significant implications for policymakers and companies, suggesting the need to increase sustainability concerns in their agendas to foster sustainable economic growth in Africa.

This paper develops a comprehensive risk management framework to optimise the Nigerian oil supply chain risk mitigation strategies. Given the complexities of the oil supply chain, this framework aids researchers and decision-makers in navigating innovative risk management approaches and considering the implementation of associated challenges. Data Envelopment Analysis (DEA) is initially employed to prioritise risk factors, and based on obtained results, optimal response strategies are determined. The study's findings reveal that criminality and terrorist attacks are the paramount Nigerian oil supply chain risk factors. Additionally, the research identifies practical mitigation strategies, such as risk transfer, safety planning, alternative energy carriers, improved energy efficiency, emergency rescue plans, expected shortages, and diplomatic relations. This research contributes valuable insights to both academia and industry, fostering a deeper understanding of risk dynamics in the Nigerian oil sector. The framework presented offers a powerful approach to risk management, providing a foundation for informed decision-making and strategic planning in the ever-evolving landscape of supply chain dynamics.

Managing transport is key to support the achievement of net zero healthcare. This study aims to assess transport options (single vs. multi-modal; electric vs. combustion) considering sustainability, efficiency, and responsiveness of the healthcare operations. To achieve the aim, we develop a mathematical model and conduct numerical experiment using a total cost of ownership (TCO) approach and a case study of blood supply chain in Yogyakarta, Indonesia. We find that electric vehicle slightly outperforms combustion vehicle both in the single and multi-modal scenarios within optimised routes. We contribute by proposing the first TCO-based transport model and simulation for multivehicle operations within the healthcare sector. This study could help hospitals and blood centres conduct scenario planning to help achieve net zero targets, which is in line with the current trend set by the government to use more electric vehicles and the scarce use of electric vehicles in healthcare operations.