Socio-economic Planning Sciences最新文献

Accurate ridership estimation is pivotal in the advancement of sustainable transit systems, be it for proposed or existing transit networks. A multitude of methods, including travel demand models, direct ridership models, and regression models, have been employed by practitioners and researchers to estimate ridership at both station and network levels. However, travel demand models, frequently utilized for new transit lines, exhibit intrinsic limitations due to their aggregate nature and complexity based on their types. Researchers have also identified deficiencies, such as the incapacity to capture small spatial resolutions and specific station characteristics, as these models are predominantly designed for large-scale analyses.

This study aims to overcome these limitations by introducing a novel approach that utilizes three microscopic agent-based models to develop a travel demand modeling suite, providing a policy-sensitive forecasting tool. The suite comprises three agent-based models: SILO-MITO-MATSim. Validation of the model against previous year data is conducted, and projections are made for future years. The model is applied to estimate network-level ridership for the proposed ‘Purple Line,’ a light rail transit line planned by MDOT, MTA, Maryland, which will integrate with the Washington D.C. Metro, the fourth largest transit system in the USA, boasting an average daily ridership of half a million. The study’s findings indicate an anticipated ridership of approximately 31,230 passengers in the inaugural year of 2027. The proposed model offers a robust and policy-sensitive solution empowering decision-makers to make informed choices to support a sustainable transportation system.

This paper aims to assess user preferences on four different bus lines connecting Benevento to Rome. For this purpose, pairwise comparison methods were used. Passengers were asked to compare possible alternatives of choice based on three service-related criteria and in a scenario where negotiation is not possible. To understand which bus line was preferred by the users, an aggregation method was used to identify the overall priority vector, thanks to suitable weights assigned to each decision maker and based on congruence with the judgments of all other users.

This study examines how changes in oil supply impact oil prices and China's macroeconomy. We developed an estimator that uses OPEC's institutional characteristics and high-frequency data to identify an oil supply surprise shock. This oil supply surprise is then incorporated as an exogenous variable into a TVP Proxy VAR model to analyze the effects of oil supply shocks on China's economy. Our findings indicate that negative oil supply shocks lead to higher oil prices, which in turn cause increases in China's interest rates and inflation, a decline in stock prices, a short-term rise in exports, and a long-term decrease in industrial output. When considering time-varying model parameters, we found that periods with larger oil supply shocks have a more pronounced impact on the macroeconomy. Our research provides empirical evidence for a better understanding of the effects of energy supply fluctuations.

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.