No abstract

In this paper, we conduct an empirical study on the impact of CEOs’ environmental awareness on technological innovation. To this end, we obtain a large sample with 7615 observations from Chinese A-share listed firms between the years of 2003 and 2014. Our empirical results show that a CEO’s environmental awareness has a significant positive impact on technological innovation of his/her enterprise. Environmentally conscious CEOs will invest more in R&D and obtain more patents. This will help their enterprises achieve higher efficiency of technological innovation. Furthermore, the environmental awareness of a CEO has a more significant impact on technological innovation if his/her enterprise is subject to a higher level of monitoring. We also conduct robustness check of our findings and provide managerial insights and proactive government policies to raise the environmental awareness of CEOs and improve the innovation vitality of enterprises.

The growing concerns about human pollution has motivated practitioners and researchers to focus on the environmental and social impacts of logistics and supply chains. In this paper, we consider the environmental impact of carbon dioxide emission on a vehicle routing problem with multiple depots. We present a hybrid evolutionary algorithm (HEA) to tackle it by combining a variable neighborhood search and an evolutionary algorithm. The proposed hybrid evolutionary algorithm includes several distinct features such as multiple neighborhood operators, a route-based crossover operator, and a distance- and quality-based population updating strategy. The results from our numerical experiments confirm the effectiveness and superiority of the proposed HEA in comparison with the best-performing methods in the literature and the public exact optimization solver CPLEX. Furthermore, an important aspect of the HEA is studied to assess its effect on the performance of the HEA.

In the study of the "Porter Hypothesis", scholars explored the impact of different forms of innovation on the firms' competitivity, but did not distinguish between innovations on the difference in patent quality. In addition, relevant research only regards innovation as a mediator between environmental regulation and competitivity, and doesn't take into account innovation induced by environmental regulation, can only promote competitivity under the constraints of environmental regulation. That is to say, environmental regulation not only induces innovation, but also moderates innovation to promote competitivity. In view of this, we use panel data of A-share listed firms in China from 2006 to 2016, and adopt propensity score matching and different in different (PSM-DID) model to empirically test the inductive effect and moderating effect. The results show that CETS cannot only improve the quantity and quality, but also significantly enhance the firms' market value; innovation itself cannot enhance the firms' market value, but the interaction with CETS can promote the firms' market value. In addition, the CETS has a stronger inductive effect on innovation of state-owned shares firms, but the positive moderating effect on high-quality innovation and competitivity only exists in non-state-owned shares firms.

<p>With the rapid development of computer, artificial intelligence and big data technology, artificial neural networks have become one of the most powerful machine learning algorithms. In the practice, most of the applications of artificial neural networks use back propagation neural network and its variation. Besides the back propagation neural network, various neural networks have been developing in order to improve the performance of standard models. Though neural networks are well known method in the research of real estate, there is enormous space for future research in order to enhance their function. Some scholars combine genetic algorithm, geospatial information, support vector machine model, particle swarm optimization with artificial neural networks to appraise the real estate, which is helpful for the existing appraisal technology. The mass appraisal of real estate in this paper includes the real estate valuation in the transaction and the tax base valuation in the real estate holding. In this study we focus on the theoretical development of artificial neural networks and mass appraisal of real estate, artificial neural networks model evolution and algorithm improvement, artificial neural networks practice and application, and review the existing literature about artificial neural networks and mass appraisal of real estate. Finally, we provide some suggestions for the mass appraisal of China's real estate.</p>