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Logistics practitioners face a significant challenge in meeting local and international regulations and the United Nations' Sustainable Development Goals (SDGs) due to the complexities of measuring and assessing the CO₂e emissions of logistics processes. This challenge is pronounced in distribution processes, where the literature currently lacks a structured approach based on existing guidelines and regulations or real-case implementation examples.

To analyse the environmental performance of distribution processes, a model with Visual Basic for Applications (VBA) algorithms compliant with the Global Logistics Emissions Council (GLEC) framework was developed. An integrative review identified key elements for evaluating the environmental impact of distribution processes, leading to model development. The model was validated through a business case in the agri-food supply chain, demonstrating its applicability and enabling the analysis of optimisation scenarios.

The findings suggest potential savings in CO₂e emissions of up to 50% by improving vehicle efficiency and maximising vehicle capacity utilisation. Further savings of up to 30% are highlighted for the business case company by increasing intermodal transport modes use.

This study offers several academic and managerial contributions. On the one hand, it offers a structured approach to assess the environmental performance of the logistics distribution processes based on a comprehensive European standard and enriches the literature by providing an industrial application of GLEC framework guidelines. On the other hand, it empowers logistics practitioners with a model to assess the environmental impact of distribution processes, and it enables an enhanced decision-making process in selecting transport modes to achieve the company’s sustainability goals.

Hempcrete has the potential to reduce both CO₂ emissions and energy usage in buildings. Hempcrete has a high sound absorption capacity, excellent moisture regulator and outstanding thermal insulation properties. However, hempcrete traditionally uses lime-based binders, which are carbon-intensive materials. The low-carbon binders to increase the sustainability of hempcrete are the current research gap. Geopolymer binders are low-carbon binders composed of aluminosilicate precursors dissolved in a high alkalinity solution. This study investigated the suitability of calcined clay and ground granulated blast furnace slag geopolymer binder as a low-carbon binder for hempcrete applications.

Two types of hemp hurds with different water absorption capacity and particle size distributions were used. Hempcrete properties tested were compressive strength, bulk density, sound absorption coefficient by a two-microphone impedance tube and thermal conductivity by a Hot Disk system.

The particle size distribution and water absorption capacity of hemp hurds did not affect the compressive strength of hempcrete when following a mixing procedure, ensuring the hurds in a saturated surface dry condition. The geopolymer hempcrete achieved a compressive strength about four times higher than the reference hydrated lime hempcrete. All hempcrete specimens achieved outstanding acoustic performance. The increase in bulk density led to the decrease in the maximum sound absorption coefficient. The geopolymer hempcrete achieved the lowest thermal conductivity.

The outcomes of this paper reveal that the low-carbon geopolymer binder appears to be a promising option for manufacturing hempcrete, achieving significantly higher compressive strength and lower thermal conductivity than the reference hydrated lime-based hempcrete.

We aim to investigate the impact of different story types on small fashion business brand trust and purchase intention, guided by signaling theory. We investigate two potential moderators – product aesthetic judgment (i.e. consumers’ responses to the aesthetic aspects of a product) and underdog positioning (i.e. brand’s positioning as a loser that is not a leader in the marketplace and has only a small market share but puts efforts into creating valuable products) – that may influence consumers’ responses to a story.

We developed video stimuli that tell business stories to empirically compare two story types bringing brand trust: identity-focused and product-focused stories. We conducted two experimental studies and tested six hypotheses with 302 datasets. Study 1 compared eco-friendly storytelling with product-focused storytelling, considering perceived product aesthetic judgment as the moderator. Study 2 repeated the experiment with a founder story and a product-focused story, considering the perceived underdog as the moderator.

The findings suggest that the influence of eco-friendly storytelling, compared to product-focused storytelling, on higher purchase intention is fully mediated by brand trust. Consumers’ perceived product aesthetic judgments toward an eco-friendly product can positively moderate the storytelling effect and brand trust. However, founder storytelling did not bring higher brand trust and, consequently, had no significant impact on purchase intention. Similarly, the perceived underdog did not moderate this relationship.

The study is novel as it is one of the first to compare story types based on story contents, whereas previous studies have focused on story delivery. Especially, we explored which story types and contents effectively build brand trust for small businesses, a critical factor for their success. Based on the extant literature, we categorized story types into identity-focused stories and product-focused stories based on contents. We hypothesized and concluded that an identity-focused story can be more effective in eliciting consumer responses. Furthermore, we confirm the critical role of brand trust as a mediator in bringing about purchase intention through eco-friendly storytelling.

A vaccination strategy to cover the susceptible population is key to containing the spread of any virus during a healthcare emergency. This study quantifies the susceptibility of a region based on initial infection rates to prioritize optimal vaccine distribution strategies. The authors propose a metric, the regional vulnerability index (RVI), that identifies the degree of susceptibility/vulnerability of a region to virus infections for strategically locating hubs for vaccine storage and distribution.

A two-phase methodology is used to address this problem. Phase 1 uses a modified Susceptible-Infected-Recovered (SIR) model, ModSIR, to estimate the RVI. Phase 2 leverages this index to model a P-Center problem, prioritizing vulnerable regions through a Mixed Integer Quadratically Constrained Programming model, along with three variations that incorporate the RVI.

Results indicate a weighting scheme based on the population-to-RVI ratio fosters fair distribution and equitable coverage of vulnerable regions. Comparisons with the public distribution strategy outlined by the Government of India reveal similar zonal segregations. Additionally, the network generated by our model outperforms the actual distribution network, corroborated by network metrics such as degree centrality, weighted degree centrality and closeness centrality.

This research presents a novel approach to prioritizing vaccine distribution during pandemics by applying epidemiological predictions to an integer-programming framework, optimizing COVID-19 vaccine allocation based on historical infection data. The study highlights the importance of strategic planning in public health response to effectively manage resources in emergencies.

The purpose of this article is to demonstrate how artificial intelligence contributes to the preservation of the cultural identity of native peoples as well as to reduce the gap in access to verified information in native languages of Peru.

An analysis of the context of artificial intelligence in the field of preservation of native languages was developed. Subsequently, the case of the digital tool Quispe Chequea was highlighted, which uses artificial intelligence to produce journalistic content of verification in three native languages of Peru (Quechua, Aimara and Awajún). Finally, a reflection was made on the contributions of artificial intelligence in reducing the gap in access to information, vindication and preservation of native Peruvian languages.

The findings evidenced the importance of the principles of indigenous data sovereignty to respect cultural rights. Also, it was determined that artificial intelligence contributes to the preservation of cultural values of ethnic groups. It also provides verified information in the native language in order to promote inclusion. Finally, this enabling technology proved to be an important resource in reducing social and cultural gaps.

The use of artificial intelligence will enable the preservation of the native languages of marginalized and ethnic groups. It also favors the dissemination and communication of truthful information in native languages.

This opinion article highlights a little-explored context in the field of native Peruvian languages. It highlights the contribution that artificial intelligence can bring to ethnic groups, especially in the preservation of their languages and the reduction of the information access gap.