Sustainable Technology and Entrepreneurship最新文献

Climate-smart agriculture (CSA) is an important strategy for supporting farmers against climate change challenges. However, CSA adoption among smallholder farmers particularly in Sub-Saharan Africa (SSA) remains low. This article investigates the factors that influence CSA adoption among smallholder potato farmers in Nyandarua County, Kenya. We specifically focus on the role of the farmers’ entrepreneurial orientation (reflected in the farmers’ innovativeness, proactiveness, and risk-taking), a contribution that has received limited research attention. Data were collected through a cross-sectional survey of 350 potato farming households and analyzed using descriptive statistics, principal component analysis (PCA) and a multivariate probit regression model. Based on PCA analysis, the study considered six categories of CSA practices; soil nutrient management, crop management, crop protection, seed management, water harvesting, and crop quality improvement. The multivariate probit results show that farmers' entrepreneurial orientation had mixed influence on CSA adoption. While farmers’ innovativeness had a positive influence on crop management and improvement practices uptake, its influence on water harvesting technologies was negative. Similarly, proactive farmers were more likely to adopt seed management practices, whereas risk-takers were more likely to adopt protection and water harvesting technologies. Potato producers' willingness to engage in seed multiplication was linked to use of crop protection, seed management, and water harvesting technologies, indicating a path that could help potato farmers’ access clean seed. Other important factors influencing CSA adoption included access to financing through mobile-based applications, gender, land size, trust in extension officers, household income, and farm characteristics. The study discusses the implications of these findings.

Sustainable Development implies slowing the rate of utilization and eventual depletion of non-renewable resources such as oil and metals. Non-renewable resources are now commonly traded, often as derivatives, through electronic trading exchanges and studies the impact of that trading on sustainable development are underrepresented. Commodity-currency research since 2003 to some extent has focused on the relationship between commodity prices – including non-renewable resources – and the exchange rates of the currencies of the nations that are extracting those commodities. To a lesser extent, other research on non-renewable resource development has focused on technology and innovation. Here we address one issue at the core of non-renewable sustainable development: the question of commodity-currency linkages and spillovers and their effects on price stability. Our research tool is an economic interpretation of the Lotka-Volterra equations. Using Lotka-Volterra parameters from the fit to actual CAD XCT data, we find that carrying out the currency-commodity dynamics over several centuries demonstrates the possibility for cyclical unsustainable hyperinflation. Devaluing or inflating the currencies or commodities is not a solution to hyperinflation. The solution to hyperinflation is to increase β (the effect of the commodity on the currency) and/or δ (the price decrease rate of the commodity when the currency is absent) and/or decrease γ (the inflation rate of the commodity in the presence of the currency).

The technical and financial management of power substations involves the evaluation of the operational condition of power transformers. Evaluation is an essential stage for maintaining electricity supply and resource efficiency by guiding the process of maintaining and/or upgrading a transformers park. This process aims at identifying assets with critical operational condition in a substation that may convey risks to operators, installed equipment and customers. The use of computational intelligence techniques aims at assisting the evaluation process, which is not simple because it requires combining measurements that evaluate different aspects of the power transformers. A deep technical knowledge of chemical, electrical and physical measurements is necessary to infer a correct diagnosis. Thus, computational intelligence techniques can reduce the need for human expertise, since they are able to extract patterns of known information and optimize the identification of critical assets. In this paper, computational intelligence techniques are applied aiming at composing a numerical index, termed Health Index, for asset prioritization. Prioritization helps classify assets based on criticality levels. Information regarding the measurements used to compose the index is based on measurements done on real transformers. In this work, computational intelligence techniques are especially explored for the composition of the Health Index, as there are no publications with the application of these techniques to solve this kind of prioritization problem. We seek the most appropriate set of methods to support decision making in prioritizing assets for maintenance. The effectiveness of the proposed methods is evaluated, seeking strategies that add greater sustainability, flexibility, simplicity and high accuracy rate in asset prioritization.

The global freshwater crisis has prompted the development of innovative methods to assess the sustainability of water resources around the world. In the last decade, the concept of water sustainability has evolved, giving rise to a more advanced measure of societies’ capacity to safeguard water resources; namely, water security. In this paper, we calculate a composite index to assess water security through the application of a DEA approach. The proposed model is used to aggregate nine indicators relating to the four dimensions of water security: state of the water environment; human health and wellbeing; sustainability of livelihoods; and the stability, functions and responsibility of societies. The model was applied to 15 European countries. Benchmarking results of the analysed countries show that Denmark, the United Kingdom and Finland hold the best positions in the ranking. The findings thus indicate that these countries show excellent performance in water security relative to the other countries under study.

To date, an overwhelming number of research findings on Environment, Social, and Governance (ESG) conduct and financial performance remains inconclusive. Furthermore, research has not identified nor explained the “underlying mechanisms” behind this relationship. To encourage future research, we discuss the mechanisms by identifying the first-order, mediating, and moderating variables. We synthesize recent studies for emerging themes and implications; argue for a process and integrated approach for modelling causality between ESG conduct and financial performance variables; and suggest methods to analyze the models. We also advocate for researchers to explore the idea that balancing corporate conduct among the E, S, and G components may provide revelations about financial performance. We also discuss how incorporating “greenwashing” in a process and integrated model may explain the ESG conduct and financial performance link, or more than likely the lack of it.

The scientific community has become concerned about the impact of blockchain technology as a motivational tool in the COVID-19 era. Since the beginning of the pandemic, the number of scientific articles published in this field has been increasing, making it highly desirable to carry out a bibliometric study to identify research efforts.

Therefore, the aim of this work was to conduct a literature review of blockchain and COVID-19 technology in the field of Business Management to identify recent lines of research. To do so, we used a text mining technique on a corpus composed of 37 articles in the Web of Science database.

The results obtained clearly show 3 distinct clusters. The first represents the blockchain technology framework in organizations and stakeholders in which the research methodology in artificial intelligence is very important. The second shows the need to take into account business sustainability caused by COVID-19. The third indicates the impact of the pandemic on the supply chain industry.

Sustainable development is perceived as a socioeconomic system focused on meeting human needs while making long-term progress, with the end goal of ensuring well-being and improving quality of life. The objective of this article is to analyse the sustainable development policies assessed by the World Energy Trilemma Index (WETI) for 2020, based on its three pillars (Energy security, Energy equity and Environmental sustainability) and the political and economic context of 128 countries. To that end, cluster analysis and contingency tables are used. The first of these methods allows us to determine whether nations' economic profile influences their sustainable energy development, while the latter method is used to establish the possible connection between the political and economic context and the different aspects of sustainable development. The results of the cluster analysis reveal the existence of three homogeneous groups of countries, showing that the economies with the lowest GDP growth and the highest incomes hold the top positions in the WETI ranking. However, this association is not as clear when analysing the three energy trilemma pillars separately, pointing to the need for a more in-depth examination of each one. The contingency tables confirm the association between the Country context and sustainable energy development, showing that countries that are assigned a better grade for political and economic aspects adopt more appropriate energy measures. The research reveals the need for leaders’ active engagement in the implementation of international agreements on climate change, thus facilitating the path towards sustainable development.

This paper analyzes the effect that the creation of the Spanish high-speed railway network has had on tourist competitiveness. The Spanish high-speed train system is one of the most technologically advanced in the world. Little research has been conducted as to the effect of transportation infrastructures on the reduction of tourism seasonality in this type of destinations and the increase of arrivals. By analysing the results of the Gini Index applied to the monthly arrivals of national travellers, it can be seen that the initial evidence pointing to a low effect on the reduction of seasonality levels changes with the expansion and consolidation of the network. Evidence has been found indicating that a minimum number of connected cities is necessary for the benefits of the high-speed network to be truly significant on tourism in terms of increased arrivals and reduced seasonality.