Sustainable Futures最新文献

This study applied Negative Binomial models to examine the social-demographic determinants of bike-sharing station capacity. It used social-demographic data from Smart Location Database and over 7,000 bike-sharing stations located in the United States. Results revealed that the station's city, households with no cars, the percentage of workers aged 18–64, gas prices, the Caucasian population, gross residential density on unprotected land, intersection density, and low to medium-earning workers are associated with the increase in station capacity. Contrarily, station capacity decreased with vehicle miles traveled and the number of high-earning workers. Findings are crucial to planners and operators in improving bike-sharing operations.

This study investigates the complex relationship between CSR practices, firm financial performance, and the moderating role of audit committee expertise in the UAE, a high-speed developing market. Using a quantitative research design, we examine data from non-financial firms listed on the ADX over the period 2008–2022. We evaluate the effect of CSR on financial performance using a range of regression methods, and we look at the ways in which the experience of audit committee members strengthens or weakens this link. Our findings are consistent with the theory that CSR initiatives have a detrimental impact on UAE firms' financial performance. Moreover, empirical data points to the strengthening of the correlation between financial performance and CSR by audit committee expertise. When it comes to coordinating CSR activities with long-term sustainability objectives, the audit committee is essential. This study employs secondary data and focuses exclusively on the United Arab Emirates, even though it advances knowledge about corporate social responsibility (CSR) and firm performance in developing markets. Future research should consider broader geographical contexts and longer periods to enhance the generalizability of findings. Our findings offer practical insights for firms operating in developing markets, highlighting the impact of CSR on performance and the importance of audit committee expertise in maximising its impact. We encourage companies to improve their level of measurement and reporting, with the goal of effectively communicating and realising the potential benefits of CSR. This research extends the literature on CSR and firm performance by examining a high-speed developing market and providing guidance to firms operating in similar contexts. It emphasises the crucial role of audit committee expertise in realising the financial benefits of CSR practices.

This study systematically reviews and synthesizes the extant academic literature on sustainable marketing mix through the lens of sustainable supply chain management. Following stringent inclusion/exclusion criteria for journal selection, this study reviews 972 research papers published through a qualitative systematic review and quantitative bibliometric analysis. It presents the top publication trends and overarching themes derived through bibliographic coupling. The analysis indicated that a sustainable supply chain is integral to the sustainable marketing mix. It is crucial to manage the various facets of the supply chain, such as enablers, barriers, and logistical practices, to contribute largely towards sustainable development goals. This is the pioneer study to uncover the components of a sustainable marketing mix and sustainable supply chain and integrate them into a conceptual framework to strengthen their theoretical roots. As emerging from the various thematic structures, this review provides insightful avenues for future research.

Innovation within a company, sector, or territorial context can arise from identifying specific problems or needs. Performance analysis provides valuable insights, potentially highlighting areas where innovation may be necessary. This paper outlines a methodology designed to identify latent innovation needs at regional, sectoral, and dimensional levels. This methodology is based on critical issues emerging from comparing economic, environmental, and social sustainability indicators of farms. An iterative approach is used for this purpose. Mixed matching algorithms are employed to form the comparison groups, while G-computation is used to calculate the differences. Logistic regression is adopted to calculate the propensity scores, and several generalized linear models are used to estimate the impact of regional localization. Hypothesis tests are then conducted to confirm the statistical significance of the impacts, from which criticality levels are derived. This methodology is applied to data collected by the Italian Farm Accountancy Data Network, which includes over 64,000 observations from two consecutive three-year periods (2016-2018 and 2019-2021). The results highlight the existence of widespread critical issues in Italian agriculture. These problems primarily concern the efficient use of water, whose costs, relative to revenue, exceed, on average, around 40 %, and greenhouse horticulture, which shows an increased criticality level in terms of sustainability of over 50 %. From a regional perspective, Puglia in Southern Italy exhibits the most evident critical issues, with an average criticality level of 30 % that has increased over time. The emerging criticalities are analyzed in relation to the interventions introduced by the 2023-2027 Common Agricultural Policy to verify their coherence and identify possible innovative actions that can be implemented.

Air pollution, population growth, and the lack of fossil energy sources are critical factors that drive people toward clean and renewable energies in the current century. Biogas is suitable for rural farmers as a source of clean energy. Since there are many social interactions in knowledge exchange and sharing, the transfer of skills, and participation among individuals, various actors are involved in the network. The power and influence of actors and the social interactions between smallholder farmers and these actors fuel the development of biogas consumption in rural areas. Therefore, this study analyzes the social network structures and characteristics of various actors by social network analysis (SNA) in knowledge sharing, the transfer of skills, and participation networks among traditional smallholder farmers in Iran. The research sample consisted of some smallholder farmers in four provinces of Fars, Khorasan Razavi, Golestan, and Kermanshah (144 smallholder farmers). The research instrument was a questionnaire designed as a matrix. The findings showed that based on the betweenness centrality score, biogas-consuming smallholder farmers (34.56) and cooperatives of smallholder farmers (24.79) were the main actors in the network of knowledge sharing, respectively. Furthermore, biogas-consuming smallholder farmers (28.04) and pioneer smallholder farmers (27.11) were the main actors in the network of participation, respectively. The betweenness centrality score revealed that cooperatives of smallholder farmers (29.23) and opinion leaders (23.33) were the main actors in the network of skills transfer, respectively. It can be concluded that these actors had high social power and influence among smallholder farmers and can be the core of the smallholder farmers’ social networks. It means that they can influence the development of biogas consumption among Iranian smallholder farmers.

This study utilizes transaction cost and legitimacy theories to examine whether a carbon tax affects listed companies' environmental, financial and social performance within the context of Australia's National Greenhouse Energy Reporting (NGER) Act. We use a base sample of 346 listed company-year observations for 2012–2017 and employ the System Generalized Method of Moments (GMM) to deal with bias arising from endogenous explanatory variables in our analysis. We find that the short period of implementation of a carbon tax in Australia was not successful in improving the carbon emissions of the high polluters subject to the tax in our sample. High polluters subject to the tax experienced a positive impact on financial performance and social performance. However, many entities beyond listed companies were subject to the tax, and the period of the tax saw a reduction in total emissions. We conjecture that industry assistance and free carbon credits granted to companies explain these results.

This study, rooted in dynamic capability theory, explores the impact of Collaborative robot (cobots) adoption on manufacturing firm performance. Cobots positively influence sensing, seizing, and transforming capabilities, leading to enhanced market, operational, and human resources performance. Sensing and transforming capabilities mediate the relationship between cobots adoption and operational performance, while seizing capability mediates with market and human resources performance. These findings offer crucial theoretical insights for strategic management in manufacturing enterprises navigating cobots adoption amid global competition and workforce challenges, ensuring a nuanced understanding of the dynamics at play.

With the technological progress of market globalization and post COVID-19 pandemic, protective gloves made from latex have been required to have significant upgrades by using antimicrobial technology to coat the glove layer so that it can effectively prevent users’ hands from getting infected by cross-transmissions of microorganisms and viruses. As the glove industry aims to maintain its business sustainability, understanding users’ perceived glove value merits closer attention because perceived value has direct associations with user trust, loyalty, and retention allowing producers to estimate and produce sufficient supply responding to the current market needs. This research aims to investigate how product attributes (convenience and risk) influence glove quality and glove value. Furthermore, this research also examines how technological competency moderates an association between glove quality and glove value. Researchers collected data from 720 Thai glove users via survey questionnaires. Furthermore, a path analysis technique was used to analyze the data. Findings indicated that product convenience (β=0.32, p<0.001) and product risk (β=-0.51, p>0.001) significantly influenced glove quality. Next, product convenience (β=0.49, p<0.001) and glove quality (β=0.74, p<0.001), except product risk (β=-0.03, p>0.05), significantly influenced glove value. Meanwhile, glove quality mediated the relationship between product risk and glove value. Finally, technological competency moderated the association between glove quality and glove value.

This study investigates the impact of economic globalization on China's climate using city-level data from 2005 to 2019. This study employs the Spatial Durbin Model (SDM) to reveal an inverted U-shaped relationship between globalization and climate impact, with carbon emissions initially increasing due to scale and composition effects. Surprisingly, the technology effect has limited effectiveness, particularly in second and third-tier cities. Another significant finding of this study is the observed positive correlation between carbon emissions in the previous and current periods. However, this trend leads to lower carbon emissions in neighbouring regions. The results of this study call for technology-focused policies and international collaborations to promote sustainable development in Chinese cities.

To effectively address climate change, economies worldwide must transition from grey, fossil fuel–dependant models to green, renewable energy–based systems, thereby striving to reduce global warming. However, a comparative study of how internal and external macro-economic dynamics influence both grey and green energy sources remains significantly underexplored. This study examines the effects of aggregated, renewable and non-renewable energy on economic growth. Therefore, this study investigates the connection between aggregated and disaggregated energy consumption (renewables and non-renewables) and economic growth in Canada by incorporating internal and external macro-economic determinants, along with institutional quality, which are variables during the period of 1990–2022. Using the dynamic autoregressive distributed lag (DARDL) approach, the study's results reveal that both aggregated and disaggregated energy consumption of renewable and non-renewable sources stimulate economic growth in the presence of both internal and external dynamics in both the short and long terms. However, this relationship is stronger in the context of internal dynamics than external ones. In addition, we conduct a counterfactual analysis by displaying 1 % (±) and 5 % (±) shocks to regressors and examining their effects on the regressed variable. Finally, we use the kernel-based regularised least squares (KRLS) machine learning algorithm to examine the cause-and-effect connectedness amongst variables. On the basis of the findings, this study recommends optimising both internal and external dynamics by adopting a diversified energy mix strategy. This approach will enable Canada to transition towards a sustainable and resilient economic future.