Search results

Electrification is a promising solution for decarbonising the road freight transport system, but it is challenging to understand its impact on the system. The purpose of this research is to provide a system-level understanding of how electrification impacts the road freight transport system. The goal is to develop a model that illustrates the system and its dynamics, emphasising the importance of understanding these dynamics in order to comprehend the effects of electrification.

The main methodological contribution of the study is the combination of the multi-layer model with system dynamics methodology. A mixed methods approach is used, including group model building, impact analysis, and literature analysis.

The study presents a conceptual multi-layer dynamic model, illustrating the complex causal relationships between variables in the different layers and how electrification impacts the system. It distinguishes between direct and induced impacts, along with potential policy interventions. Moreover, two causal loop diagrams (CLDs) provide practical insights: one explores factors influencing electric truck attractiveness, and the other illustrates the trade-off between battery size and fast charging infrastructure for electric trucks.

The study provides stakeholders, particularly policymakers, with a system-level understanding of the different impacts of electrification and their ripple effects. This understanding is crucial for making strategic decisions and steering the transition towards a sustainable road freight transport system.

The paper studies types and mechanisms of vertical and horizontal multilevel institutional governance (IG) (multilevel governance [MLG]). The relation with exports is reviewed and quantified to attempt prioritizing institutional reforms fostering merchandise exports in Egypt.

The paper studies data (from 1996 till 2020) to estimate impact of IG on Egyptian merchandise exports using two autoregressive distributed lag (ARDL) models: to test the World Governance Index (WGI) composite index, followed by its main indicators; and to determine governance priorities in Egypt. “Institutional” approach is adopted to assess mechanisms boosting Egyptian exports. Design comprises three sections – (1) conceptual and literature review, (2) main MLG mechanisms and (3) key findings of empirical results – to find out which institutional reforms enhance exports competitiveness in Egypt.

Among MLG different levels of governance, the macro level is highly related to boosting exports competitiveness. Institutional differentials between countries and regions affect competitive edge. In Egypt, results show that IG priorities that could foster exports are the rule of law, regulatory quality, government effectiveness and political stability and absence of violence.

By adopting IG mechanisms, i.e. legislative, organizational and digital; and instruments, e.g. National Single Window, Time Release Standards and others, Egyptian exports could reach new heights.

Exports competitiveness does not rely solely on monetary and fiscal factors; IG dynamics could be more important in Egypt. ARDL model for Egyptian merchandise exports using WGI 2021 dataset.

This paper aims to investigate the influence of air and rail transportation on pollution in Turkey from 1970 to 2020.

Fourier Autoregressive Distributive Lags (ADL) and Fourier Fractional ADL cointegration tests (Banerjee et al., 2017; Ilkay et al., 2021) are employed to analyze the relationship be-tween the variables. Cointegration tests that take into account soft transitions under structural changes are implemented. Structural change issues are crucial for this topic since the changes in countries’ environmental policies and transportation habits are shaped by the decisions taken in relation to environmental regulations. Finally, for robustness purposes, we tested the estimated equation with a completely different methodology. Thus, a Machine Learning (ML) analysis is conducted, through a Ridge Regression (RR).

The findings obtained by applying Fourier Autoregressive Distributive Lags (FADL) and Fourier Fractional ADL cointegration tests, which can control for structural changes, reveal the existence of a long-term relationship between the variables. In addition, FMOLS estimates emphasize that economic growth and air transport can lead to increased pollution in the long run, while rail transport reduces it. Moreover, the statistically significant trigonometric terms indicate the existence of a smooth structural change among the variables. Robustness checks are performed through a Machine Learning (ML) analysis, which roughly confirms the previous results.

To our knowledge, existing research in Turkey focuses mainly on road transport, while the impact of rail and air transport on pollution has not yet been investigated. As such, this study will be a significant addition to the academic literature.

This study aims to reveal residents’ individual perceptions of nature-based destination development and preferences for infrastructure and tourism superstructure development among communities that rely heavily on wildlife tourism.

A discrete choice experiment (DCE) was used among the Maasai community based in the villages and towns near the Maasai Mara National Reserve in Kenya. The attributes included type of tourism accommodation, location of tourism accommodations, types of access roads (tarmac or marram), tourist numbers and desired land-use options (between tourism development, livestock grazing and agriculture). A DCE analysis with hierarchical Bayes estimation was performed.

It revealed that the introduction to land-use restrictions and the location of tourism accommodations were the most important attributes for the respondents, with average importance values of 30.36% and 24.02%, respectively. A significant less important attribute was the types of access roads with an average importance of just 8.38%. Cluster analysis revealed widespread heterogeneity in preferences.

The survey-based DCE was conducted in the Maasai Mara National Reserve, Kenya, and therefore may not be relevant in other contexts. The focus was also only on the residents’ preferences. The findings broaden the knowledge on tourism developments and residents’ support for development and management of protected areas.

For policymakers, conservation practitioners and tourism businesses, this study provides a source of reference for understanding the development preferences of the Maasai community. In general, the study contributes to a better understanding of local communities in relations to tourism development and residents’ support for developments and management of protected areas (PAs).

This study fills the gap in the literature on tourism development and residents’ support for developments in PAs by presenting some limits of acceptable and desirable use of PAs among a community that has a complex coexistence with a wildlife tourism destination. It provides an alternative perspective for future research by examining residents’ choice towards destination development and preferences for infrastructure and tourism superstructure development using an experimental approach.

Public higher education institutions (HEIs) infrastructure funding is challenging in many developing countries. Encouraging private investment in HEIs infrastructure via a developed expanded corporate social responsibility (ECSR) may improve physical facilities. ECSR is a form of infrastructure tax relief providing physical facilities for HEIs. Academic literature is scarce concerning how ECSR can improve Nigeria’s public HEIs infrastructure and achieve education infrastructure related to Sustainable Development Goal 4 (SDG 4). Therefore, this study aims to proffer measures to improve public HEIs infrastructure and achieve sustainable development connected to Goal 4 focussing on infrastructure via a developed framework.

This is an expansion of an ongoing study, and data were collated via virtual interviews across the six geo-political zones in Nigeria. The analysed data were presented in a thematic pattern.

A total of 18 measures (sub-variables) emerged and were re-grouped into six variables. This includes institutionalising ECSR, HEIs infrastructure via ECSR awareness, HEIs infrastructure incentives, national and state action plans on HEIs infrastructure, a legal framework for HEIs infrastructure and key stakeholders’ participation. Also, the study used the generated six main variables to develop the improved public HEIs infrastructure via ECSR in developing countries, using Nigeria as a case study. This can enhance achieving infrastructure associated with SDG 4 (quality education) and targets.

This study intends to develop the philosophy (ECSR) with an implementable framework to encourage the private sector further to expand their CSR in the infrastructure development to the educational sector, especially in developing countries higher institutions, using Nigeria as a case study.

This study aims to ensure reliable analysis of dynamic responses in asphalt pavement structures. It investigates noise reduction and data mining techniques for pavement dynamic response signals.

The paper conducts time-frequency analysis on signals of pavement dynamic response initially. It also uses two common noise reduction methods, namely, low-pass filtering and wavelet decomposition reconstruction, to evaluate their effectiveness in reducing noise in these signals. Furthermore, as these signals are generated in response to vehicle loading, they contain a substantial amount of data and are prone to environmental interference, potentially resulting in outliers. Hence, it becomes crucial to extract dynamic strain response features (e.g. peaks and peak intervals) in real-time and efficiently.

The study introduces an improved density-based spatial clustering of applications with Noise (DBSCAN) algorithm for identifying outliers in denoised data. The results demonstrate that low-pass filtering is highly effective in reducing noise in pavement dynamic response signals within specified frequency ranges. The improved DBSCAN algorithm effectively identifies outliers in these signals through testing. Furthermore, the peak detection process, using the enhanced findpeaks function, consistently achieves excellent performance in identifying peak values, even when complex multi-axle heavy-duty truck strain signals are present.

The authors identified a suitable frequency domain range for low-pass filtering in asphalt road dynamic response signals, revealing minimal amplitude loss and effective strain information reflection between road layers. Furthermore, the authors introduced the DBSCAN-based anomaly data detection method and enhancements to the Matlab findpeaks function, enabling the detection of anomalies in road sensor data and automated peak identification.

Our study aims to evaluate the impact of infrastructure and public investment on private investment in machinery and equipment in Brazil from 1947 to 2017. The contribution of our article to the existing literature lies in providing a more comprehensive understanding of the presence or absence of the crowding effect in the Brazilian economy by leveraging an extensive historical database. Our central argument posits that the recent decline in private capital accumulation over the last few decades can be attributed to shifts in economic policies – moving from a developmentalist orientation to nondevelopmental guidance since the early 1990s, which is reflected in the diminished levels of public investment and infrastructure since the 1980s.

We conducted a series of econometric regressions utilizing the autoregressive distributed lag (ARDL) model as our chosen econometric methodology.

Employing two different variables to measure public investment and infrastructure, our results – robust across various specifications – have substantiated the existence of a crowding-in effect in Brazil over the examined period. Thus, we have empirical evidence indicating that the state has influenced private capital accumulation in the Brazilian economy over the past decades.

Our article contributes to the existing literature by offering a more comprehensive understanding of the crowding effect in the Brazilian economy, utilizing an extensive historical database.

This study presents a systematic literature review (SLR) of the interdisciplinary literature on drones in last-mile delivery (LMD) to extrapolate pertinent insights from and into the logistics management field.

Rooting their analytical categories in the LMD literature, the authors performed a deductive, theory refinement SLR on 307 interdisciplinary journal articles published during 2015–2022 to integrate this emergent phenomenon into the field.

The authors derived the potentials, challenges and solutions of drone deliveries in relation to 12 LMD criteria dispersed across four stakeholder groups: senders, receivers, regulators and societies. Relationships between these criteria were also identified.

This review contributes to logistics management by offering a current, nuanced and multifaceted discussion of drones' potential to improve the LMD process together with the challenges and solutions involved.

The authors provide logistics managers with a holistic roadmap to help them make informed decisions about adopting drones in their delivery systems. Regulators and society members also gain insights into the prospects, requirements and repercussions of drone deliveries.

This is one of the first SLRs on drone applications in LMD from a logistics management perspective.

We study five exogenous shocks: climate, violence, price hikes, spoilage and the COVID-19 lockdown. We analyze the association between these shocks and trader characteristics, reflecting trader vulnerability.

Using primary survey data on 1,100 Nigerian maize traders for 2021 (controlling for shocks in 2017), we use probit models to estimate the probabilities of experiencing climate, violence, disease and cost shocks associated with trader characteristics (gender, size and region) and to estimate the probability of vulnerability (experiencing severe impacts).

Traders are prone to experiencing more than one shock, which increases the intensity of the shocks. Price shocks are often accompanied by violence, climate and COVID-19 shocks. The poorer northern region is disproportionately affected by shocks. Northern traders experience more price shocks while Southern traders are more affected by violence shocks given their dependence on long supply chains from the north for their maize. Female traders are more likely to experience violent events than men who tend to be more exposed to climate shocks.

The data only permit analysis of the general degree of impact of a shock rather than quantifying lost income.

This paper is the first to analyze the incidence of multiple shocks on grain traders and the unequal distribution of negative impacts. It is the first such in Africa based on a large sample of grain traders from a primary survey.

This study examines the effect of external debt and domestic capital formation on economic development in Sub-Saharan African (SSA) economies.

Using the Dynamic Common Correlation Effects (DCCE) technique and the Driscoll and Kraay fixed-effect technique, this paper conducts a multidimensional assessment of external debt and domestic investment on economic development across a panel of 35 SSA countries from 1995 to 2018. The data utilized are sourced from the World Development Indicators (2021) and the United Nations Development Program (UNDP) database (2021).

The results reveal that domestic investment has a positive impact on economic development in SSA countries, consistent across all three dimensions of the human development index (income, education and life expectancy). However, external debt exhibits an adverse effect on economic development, consistently yielding negative outcomes for life expectancy, education and income.

Based on these findings, the authors recommend that SSA economies implement appropriate policies, such as reducing bureaucratic requirements and addressing corruption, to enhance domestic capital investment. Additionally, efforts should be directed toward channeling contracted debt into productive sectors like road construction and electricity provision.

This study is among the first to assess the impact of domestic investment and external debt on the three dimensions of human development outlined by the UNDP. Furthermore, it employs a robust econometric method that considers cross-sectional dependence (CD).