Socio-economic Planning Sciences最新文献

Lockdowns were one of the main preventive measures adopted by governments against the COVID-19 spread. Lockdowns changed people's routines and affected the activities of economic sectors in every country. Electric power sectors were also affected. The aim of this research is to determine the impact of preventive measures adopted by the Colombian government on the electric power demand of the main economic activities, using the difference-in-differences method and two-stage least squares estimation. In addition, a comparative analysis of the behaviour of the National Interconnected System total demand and disaggregated demand by markets in 2020, compared to previous years, was carried out. We evidence the recomposition of electricity consumption related to mandatory preventive isolation during the pandemic. The day with a more significant percentage difference compared with 2019 was April 10th, showing a decrease of 21.28 %. Therefore, this study contributes to improving the predictive models of the country's demand, optimizing the needs of future generations. Likewise, this study provides resources to optimize supplier portfolios and energy contracts for high-demand consumers.

This paper explores the role of network spillovers in commodity market forecasting and proposes a novel factor-augmented dynamic network model. We focus on a novel network definition based on investors’ attention to commodities, positing that commodities exhibit spillovers if they share a similar level of interest. To this aim, we employ Google Trends search data as an instrumental measure for attention. The results reveal that including attention-driven spillovers significantly enhances the forecasting accuracy of commodity returns.

Recent disruptive events, such as the Covid-19 pandemic, the war in Ukraine and climate change, have intensified the process of ‘deglobalization’ of food consumption. As a result, consumers now prefer locally sourced products. To understand the change in sustainable food consumption in certain countries, this paper analyzes data from a broad survey conducted between 2020 and 2022, during and after the Covid-19 pandemic. The primary goal is to describe an emergent “new normal” culinary ethics based on a preference for regional cuisine, environmental protection and a commitment to health. The analysis compares consumption patterns in Italy and the United States to explore the role of cultural contexts with different but comparable values and principles. Both exploratory and confirmatory factor analyzes are applied to the two subsets of data, drawn from a large survey conducted in 20 countries with nearly 7000 participants, as well as the invariance of their structural parameters through multigroup analysis.

The odor emissions generated by treatment plants imply complex environmental and economic issues. The modern instrumental odor monitoring systems, based on an array of several sensors, continuously record the gaseous compounds. However they are characterized by poor selectivity, compromising the possibility to discriminate and identify the emission sources. In this paper, the ability of odor sensors to distinguish between the treatment plant sections generating the gaseous compounds is evaluated on the basis of the random forest classifier, and is also compared to the discriminant analysis performance. Taking into account that a multi-parametric system of sensors can be affected by the presence of a small sample size with imbalanced classes, several strategies for data balancing are proposed and analyzed. The findings show that the random forest classifier is characterized by a better capacity to distinguish the emissions sources with respect to the classical multiple discriminant analysis, in terms of all evaluation metrics. This is also confirmed for different resampling techniques, especially in the over-sampling case. The data concerning measurements from 10 sensors of multi-parametric systems of odor monitoring collected from a company specialized in environmental assistance are considered for this analysis.

This study explores the relationship between disruptive events in supply chains and investments from public and private policies aimed at recovery. It focuses on the improper disposal of lead-acid battery waste in Brazil, which poses environmental and health risks, and suggests strategies for managing this waste through Brazilian reverse supply chains. A stochastic model was developed to optimize investments in resilient capabilities like absorption, adaptation, and recovery before and after disruptions. The results show that proactive investments should prioritize creating redundancies and collaboration to ensure timely restoration of waste supply, especially during low-severity events. The study provides a model that guides the allocation of resources for sustainable waste management in Brazilian lead-acid battery operations and highlights the importance of public and private policy formulation in enhancing supply chain resilience.

Numerous studies have delved into the socioeconomic impacts of High-Speed Rail (HSR) on urban development, examining dimensions, such as economics, environment, and tourism. However, a consistent evaluation framework to measure the overall influence of HSR on city attractiveness remains elusive. This study addresses this gap by utilizing confirmatory factor analysis (CFA) to identify pivotal factors driving city attractiveness and employing structural equation modeling (SEM) to examine the mechanism through which HSR shapes city attractiveness. The analysis confirms that, despite economic performance, urban amenities such as housing, education, and technology play significant roles in improving city attractiveness. Further analysis demonstrates that HSR has different influential mechanisms on city attractiveness across two phases: introduction and operation. The major difference lies in if HSR can directly affect economic performance. Moreover, the analysis also shows the spatiotemporal variance in the impact of HSR on city attractiveness. These findings provide important insights for urban planners, enabling the formulation of more effective strategies for future infrastructure investment and city development.

To reveal the impact mechanism of major events on the fluctuation and spillover effects of the oil market to three food markets (i.e. corn, wheat, and soybean), a research framework integrating the event study method (ESM) and time-varying parameter vector autoregression-based Diebold–Yilmaz (TVP-VAR–DY) model is constructed. Further, the network for revealing the complex conduct path among them was investigated, and the empirical results indicated that (1) the positive and negative expected events (PEE, and NEE, respectively) comprising the first-order lag impacted the returns series in the oil market positively; (2) the impact of major events on the food market exerted a prolonged lag effect that varied with the different varieties grains; (3) the net spillover effect was mostly from oil to grain, and PEE significantly impacted the two spillover effects (from oil to corn and from oil to wheat), but its spillover effect from oil to soybean was insignificant; and (4) the impact of those events on the spillover effect of oil on wheat was transformed from insignificant to significant when PEE or NEE was controlled. These findings will facilitate the understanding of the internal link between the food and oil markets and provide a crucial reference for investors and policymakers.

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.