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Micro-regeneration can effectively enhance a neighborhood’s commercial vitality and serve as a viable approach to boost economic benefits. However, the small scale of micro-regeneration efforts and the fragmented nature of information currently limit the availability of strong empirical evidence demonstrating its impact on neighborhood commercial vitality. The aim of the study was to examine the link between micro-regeneration and neighborhood commercial vitality, focusing on the average, time-lag, spatial spillover, and spatial heterogeneity effects.

Using the panel data set of 1,755 neighborhoods in Chongqing from 2016 to 2021 as the research sample, the difference-in-differences (DID) method was employed in this study to explore the impact micro-regeneration has on neighborhood commercial vitality.

The results illustrate that: (1) micro-regeneration can promote neighborhood commercial vitality in terms of the number and types of local consumption amenities by 27.76 and 5.89%, respectively, with no time-lag effect; (2) the positive spillovers can exist within the range of 5,000 meters–5,500 meters of regenerated neighborhoods; and (3) the effect of micro-regeneration on neighborhood commercial vitality can be greater in peripheral areas than in core areas of the city.

The findings fill the knowledge gap on the relationship between micro-regeneration and neighborhood commercial vitality. Additionally, the results on the time-lag effect, spatial spillover effects, and spatial heterogeneity provide practical implications that can support the government and private sector in developing temporal and spatial arrangements for micro-regeneration projects.

In the recent wake of environmental sustainability, more attention has been paid to the consumption of specific energy types. However, how the consumption of such energy alternatives influences multiple corporate-level decisions has not yet been well explored in the literature. The current analysis bridges this deficiency in literature by exploring the empirical relationship between energy alternatives and cash holdings.

For empirical analysis, the authors sample the non-financial sector enterprises founded in five BRICS economies and employ the system GMM and fully modified ordinary least square techniques to establish the regression. The selection of econometric techniques is subject to the existence of endogeneity and cointegration.

The estimated coefficients reveal a significant negative effect of renewable energy (REC) while a significant positive impact of non-renewable energy consumption (FFE) on cash holdings. Referring to low pollution emissions, less operational risk and a cheap source of energy, the more consumption of renewable energy reduces the motives of cash holdings. Primarily, the current analysis advocates an important policy regarding the utilization of renewable energy as industrial fuel inputs because it has a material impact on cash holdings and also ensures environmental sustainability.

This study has equal policy outputs for industry officials, policy regulators and environmental economists. Corporate managers should do more focus on transforming the energy needs from non-renewable to renewable as such transformation can benefit in terms of both, i.e. environmental sustainability and low cash holdings.

Contemporary literature mainly highlights the determinants of energy consumption. However, it is less known how the consumption of specific energy sources affects the firm's cash-holding decisions. Thus, this study enriches both energy economics and financial economics literature by offering cutting-edge evidence on the sustainable role of REC in declining cash holdings.

This study aims to examine the effects of climate change and agricultural technologies on crop production in Vietnam for the period 1990–2018.

Several econometric techniques – such as the augmented Dickey–Fuller, Phillips–Perron, the autoregressive distributed lag (ARDL) bounds test, variance decomposition method (VDM) and impulse response function (IRF) are used for the empirical analysis.

The results of the ARDL bounds test confirm the significant dynamic relationship among the variables under consideration, with a significance level of 1%. The primary findings indicate that the average annual temperature exerts a negative influence on crop yield, both in the short term and in the long term. The utilization of fertilizer has been found to augment crop productivity, whereas the application of pesticides has demonstrated the potential to raise crop production in the short term. Moreover, both the expansion of cultivated land and the utilization of energy resources have played significant roles in enhancing agricultural output across both in the short term and in the long term. Furthermore, the robustness outcomes also validate the statistical importance of the factors examined in the context of Vietnam.

This study provides persuasive evidence for policymakers to emphasize advancements in intensive agriculture as a means to mitigate the impacts of climate change. In the research, the authors use average annual temperature as a surrogate measure for climate change, while using fertilizer and pesticide usage as surrogate indicators for agricultural technologies. Future research can concentrate on the impact of ICT, climate change (specifically pertaining to maximum temperature, minimum temperature and precipitation), and agricultural technological improvements that have an impact on cereal production.

To the best of the authors’ knowledge, this study is the first to examine how climate change and technology effect crop output in Vietnam from 1990 to 2018. Various econometrics tools, such as ARDL modeling, VDM and IRF, are used for estimation.

The pandemic has enhanced the global phenomenon of disinformation. This paper aims to study the false news concerning COVID-19, spread through social media in Spain, by using the LatamChequea database for a duration from 01/22/2020, when the first false information has been detected, up to 03/09/2021.

A quantitative analysis has been conducted with regard to the correlation between fake news stories and the pandemic state, the motive to share them, their dissemination in other countries and the effectiveness of fact checking. This study is complemented by a qualitative method: a focus group conducted with representatives of different groups within the society.

Fake news has been primarily disseminated through several social networks at the same time, with two peaks taking place in over a half of the said false stories. The first took place from March to April of 2020 during complete lockdown, and we were informed of prevention measures, the country’s situation and the origin of the virus, whereas the second was related to news revolving around the coming vaccines, which occurred between October and November. The audience tends to neither cross-check the information received nor report fake news to competent authorities, and fact-checking methods fail to stop their spread. Further awareness and digital literacy campaigns are thus required in addition to more involvement from governments and technological platforms.

The main limitation of the research is the fact that it was only possible to conduct a focus group of five individuals who do not belong to generation Z due to the restrictions imposed by the pandemic, although a clear contribution to the analysis of the impact of fake news on social networks during the COVID-19 pandemic in Spain can be seen from the privileged experiences in each of the fields of work that were identified. In this sense, the results of the study are not generalizable to a larger population. On the other hand, and with a view to future research, it would be advisable to carry out a more specific study of how fake news affects generation Z.

This research is original in nature, and the findings of this study are valuable for business practitioners and scholars, brand marketers, social media platform owners, opinion leaders and policymakers.

This study aims to investigate the interplay between environmental concerns, consumer attitudes and their influence on sustainable practices within the craft beer industry, exploring how consumers’ perception of environmental attributes impact their purchase intentions regarding craft beer.

The study used a quantitative approach, using a self-administered survey instrument comprised 28 Likert-scale items. Data was gathered from a convenience sample of 319 craft beer consumers. Confirmatory factor analysis was conducted to assess the measurement model’s validity and reliability. Subsequently, partial least squares structural equation modeling was used to test the hypothesized relationships.

This study’s findings challenge established notions within the field, demonstrating that water conservation, carbon footprint reduction, organic farming and local sourcing practices hold significant influence on consumers’ purchase intentions regarding sustainable craft beer. These findings depart from previous research that suggested a less pronounced impact of specific sustainability practices on consumer behavior.

This study contributes to the literature by revealing consumer insights into the environmental implications of craft beer choices, empowering them to make informed purchasing decisions aligned with their sustainability values. However, acknowledging potential limitations, sample size and demographic composition (young adults in Brazil) potentially impact the generalizability of the findings.

This study addresses the understanding of consumer attitudes and priorities regarding environmental sustainability within the craft beer industry. By focusing on the tangibility of specific sustainable attributes the research shows how these practices influence consumer perceptions and purchasing intentions.

Amidst compounding crises and increasing global population’s nutritional needs, food supply chains are called to address the “diet–environment–health” trilemma in a sustainable and resilient manner. However, food system stakeholders are reluctant to act upon established protein sources such as meat to avoid potential public and industry-driven repercussions. To this effect, this study aims to understand the meat supply chain (SC) through systems thinking and propose innovative interventions to break this “cycle of inertia”.

This research uses an interdisciplinary approach to investigate the meat supply network system. Data was gathered through a critical literature synthesis, domain-expert interviews and a focus group engagement to understand the system’s underlying structure and inspire innovative interventions for sustainability.

The analysis revealed that six main sub-systems dictate the “cycle of inertia” in the meat food SC system, namely: (i) cultural, (ii) social, (iii) institutional, (iv) economic, (v) value chain and (vi) environmental. The Internet of Things and innovative strategies help promote sustainability and resilience across all the sub-systems.

The study findings demystify the structure of the meat food SC system and unveil the root causes of the “cycle of inertia” to suggest pertinent, innovative intervention strategies.

This research contributes to the SC management field by capitalising on interdisciplinary scientific evidence to address a food system challenge with significant socioeconomic and environmental implications.

The COVID-19 pandemic has induced tourism destinations to reconsider organisational aspects related to health safety measures, as perceptions of health safety may have become particularly important for tourists. Using data from summer 2020, the period immediately after the outbreak of the pandemic, we investigate the factors that affected tourist perceptions of health safety.

Data come from a survey of tourist-card holders in Trentino, an Italian mountain destination. Through regressions, we assess the conditional correlation between health safety measure evaluations following a holiday and a set of covariates related to the features of the tourist area and the tourists themselves, as well as COVID-19 incidence in their province of residence in the months before the holiday.

Tourist-related features seem not to impact on perceived health safety, whereas some destination- and accommodation-related elements do. In particular, the number of tourist beds affects it negatively, and staying at a hotel does it in a positive way. COVID-19 incidence in one’s home province does not affect perceptions of health safety measures, which suggests a possible sample selection effect and/or the need for more fine-grained data.

This paper is one of the few on the immediate effects of the COVID-19 pandemic using data from a large sample of actual tourists. Our findings point out the importance of the intrinsic features of some places and accommodation in influencing perceptions of safety. We discuss implications for scholars and destination managers.

Considering the relevance of understanding what influences environmentally sustainable consumer choices, the present study aims to examine and synthesize the key determinants factors from literature and outline a new conceptual framework for explaining green purchasing behaviors (GPBs).

A bibliometric analysis was conducted on 161 articles extracted from Web of Science and Scopus databases, which were systematically evaluated and reviewed, and represent the current GPB knowledge base. Content analysis, science mapping and bibliometric analysis techniques were applied to uncover the major theories and constructs from the state-of-the-art.

The evolving debate between altruistic and self-interest consumer motivations reveals challenges for rational-based theories, as most empirical applications are not focused on buying behaviors, but instead either on pro-environmental (non-buying) activities or on buying intentions. From the subset of leading contributions and emerging topics, nine thematic clusters are unveiled in this investigation, which were combined to create the new PSICHE framework with the purpose of predicting GPB: (P)roduct-related factors, (S)ocial influences, (I)ndividual factors, (C)oncerns about the environment, (H)abits and (E)motions.

By uncovering the multiple intervening factors in GPB decision processes, this study will assist practitioners and academics to move forward on how to foster more sustainable consumer behaviors.

The present study provides readers a summary of an unprecedentedly broad collection of papers, from which the key themes are categorized, the domain's intellectual structure is captured and an actionable framework for enhancing the understanding GPB is proposed. Four new thrust areas and a set of future research questions are included.

This study aims to examine digital consumer culture and behavior in the community, namely, 180° Movement Digital Training Center (DTC), in Jakarta, Indonesia. It aims to describe the dynamics of digital consumer culture in contemporary society, particularly as experienced by the youth community in Jakarta in the context of socio-technology relations and incorporates it into the diagram of digital consumer culture network.

This research uses a constructivist qualitative approach and socio-technical relation analysis through actor-network theory and digital consumer culture.

The study finds that the individual model of digital consumption is constructed through the process of problematization, interessement, enrollment and mobilization of individuals. It generates a culture in which consumers are constantly up to date with high-intensity information, but within increasingly shorter timeframes, while also considering principles of affordability, needs, desires and satisfaction. The network of digital consumer culture construction among informants is peculiar and unstable.

The study of digital consumer culture within the 180° Movement DTC community highlights how consumer behaviors of its members are facilitated and interconnected within a digital cultural network. However, this research is constrained by the dialectical interplay between Christian principles and the emerging values of consumer culture, a result of the scarcity of theoretical resources and information. This study also provides a specific contribution as a foundation for mapping the volatile digital consumer culture for researchers.

Understanding the socio-technological relationships and consumption behavior of the youth community could help digital platforms tailor their services more effectively. It could also guide the 180° Movement DTC in developing programs that resonate with the youth, bridging the gap between the physical and virtual realms. Ultimately, this could lead to a more engaged and digitally literate society.

This study contributes to a broader societal understanding of how digital technology is shaping consumer behavior and identity within youth communities, which can influence social dynamics and interactions. It provides insights into the potential social impacts of digital technology, such as changes in relationships, communication patterns and self-perception, informing societal discourse on digital culture.

In addition to presenting socio-technological analysis on Indonesian consumer culture using actor-network theory, some also show that studies on digital connectivity ambivalence that concern the relationship between humans as actors and non-humans as actors have become one of the popular sociology studies at present.

The energy efficiency of buildings has been emphasized along with the continual development in the building and construction sector that consumes a significant amount of energy. To this end, the purpose of this research paper is to forecast energy consumption to improve energy resource planning and management.

This study proposes the application of the convolutional neural network (CNN) for estimating the electricity consumption in the Grey Nuns building in Canada. The performance of the proposed model is compared against that of long short-term memory (LSTM) and multilayer perceptron (MLP) neural networks. The models are trained and tested using monthly electricity consumption records (i.e. from May 2009 to December 2021) available from Concordia’s facility department. Statistical measures (e.g. determination coefficient [R²], root mean squared error [RMSE], mean absolute error [MAE] and mean absolute percentage error [MAPE]) are used to evaluate the outcomes of models.

The results reveal that the CNN model outperforms the other model predictions for 6 and 12 months ahead. It enhances the performance metrics reported by the LSTM and MLP models concerning the R², RMSE, MAE and MAPE by more than 4%, 6%, 42% and 46%, respectively. Therefore, the proposed model uses the available data to predict the electricity consumption for 6 and 12 months ahead. In June and December 2022, the overall electricity consumption is estimated to be 195,312 kWh and 254,737 kWh, respectively.

This study discusses the development of an effective time-series model that can forecast future electricity consumption in a Canadian heritage building. Deep learning techniques are being used for the first time to anticipate the electricity consumption of the Grey Nuns building in Canada. Additionally, it evaluates the effectiveness of deep learning and machine learning methods for predicting electricity consumption using established performance indicators. Recognizing electricity consumption in buildings is beneficial for utility providers, facility managers and end users by improving energy and environmental efficiency.