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The promotion of electric vehicles (EVs) is an important measure for dealing with climate change and reducing carbon emissions, which are widely agreed goals worldwide. Being an important operating mode for electric vehicle charging stations in the future, the integrated photovoltaic and energy storage charging station (PES-CS) is receiving a fair amount of attention and discussion. However, how to optimally configure photovoltaic and energy storage capacity to achieve the best economy is essential and a huge challenge to overcome. In this paper, based on the historical data-driven search algorithm, the photovoltaic and energy storage capacity allocation method for PES-CS is proposed, which determines the capacity ratio of photovoltaic and energy storage by analyzing the actual operation data, which is performed while considering the target of maximizing economic benefits. In order to achieve the proposed capacity allocation, the method is as follows: First, the economic benefit model of the charging stations is established, taking the net present value and investment payback period as evaluation indicators; then, by analyzing the operation data of the existing charging station with the target of maximizing economic benefits, the initial configuration capacity is obtained; finally, the capacity configuration is verified through a comprehensive case analysis for the actual operation data. The results show that the capacity configuration obtained through the data analysis features an optimized economic efficiency and photovoltaic utilization. The proposed method can provide a theoretical and practical basis for newly planned or improved large-scale charging stations.

Technological change has altered labor market demands within well-developed societies implying global competition for skilled labor and, as a consequence, new forms of labor migration. So far, patterns of this labor migration have been underexplored. Thus, the article analyzes characteristics, geographies and possible underlying drivers of workers migrating from Germany as an exemplary case for a well-developed country. Relying on probability-based and unique data, our findings reveal that, besides demand for people with higher levels of education, performing specific occupational tasks is also in demand in the global competition for talent. Hence, Germans in jobs with a high proportion of analytical non-routine tasks are more likely to emigrate than those with predominantly manual routine tasks. Moreover, the results show that global discrepancies concerning the technological development between the country of origin and the country to which they emigrate are a crucial contextual driver attracting this specifically demanded work force. Workers mainly performing analytical non-routine tasks within their job tend to move to countries which are technologically more developed than Germany while individuals performing jobs with a high share of non-routine manual or interactive tasks tend to emigrate to countries that are less technologically developed than Germany.

The extent to which the structural reform of the Peruvian electricity market in the 1990s has improved the technical efficiency levels of the distribution companies and whether some firm specific explanatory variables had influenced upon the efficiency was analysed for first time using a second stage Tobit model to study the influence of some firm specific explanatory variables on efficiency. Some authors have argued that the use of Tobit regression is inappropriate in the second stage of DEA and have suggested using other recently developed options. Due to this, it might be worth revisiting this issue and adding those other alternative models to check whether the conclusions obtained with the Tobit model could be upheld. The nine alternative models estimated allow us to confirm that the incentives generated by the reform process led to the firms becoming more efficient. Moreover, private management and the ratio of low voltage sales to medium voltage sales for each company positively affect efficiency, whereas investment per customer is negatively correlated to it.

Academic research specifically focused on employee-related disclosure practices is needed to enhance understanding on CSR reporting. This paper aims to provide an overview of the state-of-the-art in research on employee-related disclosure, analyzing the characteristics of the scientific production on this topic. A bibliometric analysis is conducted on the papers specifically focused on employee-related disclosure published from 2000 to 2019 in journals indexed on the Web of Science database. The findings show that relatively few studies specifically focused on employee-related disclosure have been published over the last two decades (63 papers). Most articles were published during the last 8 years (93.6%), although the highest interest in the study of employee-related disclosure among scholars concentrates on a short period around 2017. Six journals concentrate 31.75% of the publications on the subject. Most papers are empirical studies, using the content analysis technique to analyze corporate reports. Papers are spread over three research subtopics: (1) extent, quality and drivers of human resource disclosures, (2) occupational health and safety disclosures, human rights disclosures and employee-related disclosures as a legitimization tool, and (3) diversity reporting. In all research subtopics, most of the papers have been published during the last four years, confirming that employee-related disclosure is a topic of current interest to researchers. The studies found that the overall level of employee-related disclosure is low, with an increasing or irregular tendency over time. Furthermore, not all items/categories got the same attention by firms. It can be concluded that this research subject is still far from reaching the level of research on environmental reporting and important issues remain to be resolved, both theoretically and empirically.

This study provides an in-depth analysis of the effects of academic mobility on higher education innovation through an empirical study on returned Chinese academics at two research universities in China. Based on data obtained through document analysis and semi-structured interviews with 15 academic returnees, this paper aims to examine the everyday interactions between individual returnees and their environment, with a focus on exploring how different institutional contexts affect returnees’ capacity for integration and innovation. It finds that returned academics play an important role in promoting higher education innovation in China through mobilizing their transnational capital and resources. However, their capacity to innovate is largely subject to their working environment. Evidence from the study suggests that differing institutional contexts make a substantial difference to the reintegration experiences of returnees and to their contributions to institutional changes. This paper provides a window into the changing institutional environment in China and the academic lives of returnees there. It also provides important implications for talent policy decisions.

The digital transformation of Chinese construction enterprises is crucial for achieving sustainable and high-quality development in the construction industry. However, there is still a lack of in-depth research on the impact mechanism of digital transformation in construction enterprises. The purpose of this study is to explore the multiple influencing factors and complex causal relationships of digital transformation in construction enterprises and promote the deep integration of digitalization and construction enterprises. To this end, based on the dual-effect perspective (net effect perspective of a single influencing factor and configuration effect perspective of multiple influencing factors), using the “technology–organization–environment” framework (TOE framework) to construct a research model of influencing factors for digital transformation in construction enterprises. A sample of 236 construction enterprise managers was surveyed, and partial least squares structural equation modeling (PLS-SEM) and fuzzy set qualitative comparative analysis (fsQCA) methods were used to empirically analyze the dual effects of influencing factors for digital transformation in construction enterprises. The results show that: (1) from the net effect perspective, there are seven factors that significantly impact digital transformation in construction enterprises; (2) from the configuration effect perspective, there are three paths that can achieve high-level digital transformation in construction enterprises, and one path that leads to low-level digital transformation; (3) from the dual-effect perspective, top management support and policy support are key factors for digital transformation in Chinese construction enterprises. The research results enrich the relevant research on digital transformation in construction enterprises and provide a reference basis for promoting digital transformation in construction enterprises.

We argue that social dilemmas structured as investment trust games are a dominant feature in social and economic life due to asymmetric information, incomplete contracts and non-overlapping competencies that are typical characteristics of business relationships. We therefore consider that borrowers living in geographical areas with higher interpersonal trust are more likely to overcome the coordination failures typical of this kind of social dilemmas, thereby creating higher economic value and reducing the risk of their economic activity. Our empirical findings support this hypothesis, showing that lenders charge significantly lower loan costs on borrowers living in areas characterized by higher interpersonal trust.