Theoretical and empirical developments in academic literature have not been able to keep pace with the growing industry focus on eco-innovation and green hospitality practices. This paper aims to address this gap and provide an up-to-date review of research on eco-innovative practices in 13 leading hospitality journals over the past two decades, 1998-2018. A systematic review that incorporates the preferred reporting items for systematic reviews and meta-analyses flow diagram is used to guide the data selection for this paper. The paper analyzes 403 studies published in 13 established hospitality journals to identify homogeneous research themes. A unified conceptual framework is proposed by identifying seven research domains under eco-innovative practices. Even though research attention on green practices has increased in recent years, the development of conceptual frameworks, appropriate measurement scales and theoretical support for eco-innovative practices is warranted. Although the paper attempts to include as many environmentally related studies as possible, by being restricted to papers published only in 13 leading hospitality journals, it may not have drawn on all relevant eco-innovation studies in hospitality research. To the knowledge of the authors, this is the first systematic analysis of hospitality research on eco-innovative practices that reviews such a large number (403) of studies spanning the past two decades (1998-2018). The most recent review by Kim et al. (2017) covered 146 green research studies published between 2000 and 2014; whereas, out of 403 studies reviewed in this study, 231 (57per cent) have been published between 2014 and 2018. This trend is indicative of the fast-evolving nature of sustainability research and the need for an up-to-date systematic review of recent literature in the field.

Recent advances in renewable energy generation and the Internet of things (IoT) has urged energy management to enter the era of the Internet of energy (IoE). The IoE adopts a huge number of distributed energy-generating facilities, distributed energy storage facilities, and IoT technologies to implement energy sharing, promote utilization of electrical grids, and maintain safety of electrical grids. Rapid economic and social development makes energy shortage tend to be increasingly serious. Most cases of energy shortage occur during the peak energy load, and hence the previous works focused on shifting peak load to address energy shortage. However, few of these works took the IoE framework into account. Consequently, this work aims to consider the IoE framework to investigate the peak load shifting problem in which end-users in the energy market can adopt their respective energy storage facilities to charge and discharge energy to minimize the total operating costs. In such a problem setting, each end-user can not only be a demander, but also be a supplier in the energy market, so that operating costs are concerned; the energies from both conventional electrical grids and distributed renewable energy sources can be stored in energy storage facilities; real-time price of energy will be applied adequately to affect energy distribution of supply and demand. Simulation results of a case study show that the proposed model can obtain the optimal result and achieve peak load shifting.

Torespondto the reduction of greenhouse gas emissions and global warming, this paper investigates the minimal-carbon-footprint time-dependent heterogeneous-fleet vehicle routing problem with alternative paths (MTHVRPP). This finds a route with the smallestcarbon footprint, instead of the shortestroute distance, which is the conventional approach, to serve a number of customers with a heterogeneous fleet of vehicles in cases wherethere may not be only one path between each pair of customers, and the vehicle speed differs at different times of the day. Inheriting from the NP-hardness of the vehicle routing problem, the MTHVRPP is also NP-hard. This paper further proposes a genetic algorithm (GA) to solve this problem. The solution representedbyour GA determines the customer serving ordering of each vehicle type. Then, the capacity check is used to classify multiple routes of each vehicle type, and the path selection determines the detailed paths of each route. Additionally, this paper improves the energy consumption model used for calculating the carbon footprint amount more precisely. Compared with the results without alternative paths, our experimental results show that the alternative path in this experimenthas a significant impact on the experimental results in terms of carbon footprint.