In this paper, we study an electric vehicle routing problem while considering the constraints on battery life and battery swapping stations. We first introduce a comprehensive model consisting of speed, load and distance to measure the energy consumption and carbon emissions of electric vehicles. Second, we propose a mixed integer programming model to minimize the total costs related to electric vehicle energy consumption and travel time. To solve this model efficiently, we develop an adaptive genetic algorithm based on hill climbing optimization and neighborhood search. The crossover and mutation probabilities are designed to adaptively adjust with the change of population fitness. The hill climbing search is used to enhance the local search ability of the algorithm. In order to satisfy the constraints of battery life and battery swapping stations, the neighborhood search strategy is applied to obtain the final optimal feasible solution. Finally, we conduct numerical experiments to test the performance of the algorithm. Computational results illustrate that a routing arrangement that accounts for power consumption and travel time can reduce carbon emissions and total logistics delivery costs. Moreover, we demonstrate the effect of adaptive crossover and mutation probabilities on the optimal solution.

Environmentally sustainable development is a multidimensional concept that emphasizes the integration of economy, society and environment within a region and the realization of dynamic balance. How to objectively environmentally sustainable development has been a major concern for scholars and policy makers. To address this problem effectively, we first obtain the indicators of environmentally sustainable development based on the pressure-state-response (PSR) framework. Then, we introduce variable weight factors in the traditional analytic hierarchy process (AHP), so that the weights assigned by experts to sustainable development indicators can change with time or space. In this way, we propose a new and improved weight distribution method called variable weigh analytic hierarchy process. Finally, we employ indicators of environmentally sustainable development based on PSR and variable weigh analytic hierarchy process to evaluate the sustainable development of cities in a case country. Our study found that: (1) indicators of environmentally sustainable development should consist of three parts: pressure indicators of environmentally sustainable development, state indicators of environmentally sustainable development, and response indicators of sustainable development; (2) with the variable weigh analytic hierarchy process, our ranking hierarchy process can handle dynamic changes among indicators better than the traditional AHP method and better reflect the true states of indicators.

Many developing countries including China are implementing increasingly stringent environmental regulations to achieve sustainable development. However, we have limited understanding about whether environmental regulations promote enterprise green innovation. To address this research gap, this study empirically analyzes the impact of environmental regulations, which is represented by the China Environmental Protection Law (2015), on enterprise green innovation, and it explores the moderating effects of official tenure on environmental regulations and corporate green innovation. The Super-Slacks-based Measure (Super-SBM) model and multiple nonlinear regression model are employed to analyze sample data of 3557 firms in China’s A-share market during the 2014–2017 period. Our results show that, in general, a higher intensity of environmental regulations is more beneficial to incentivize enterprises to implement green innovation. Meanwhile, there is an inverted U-type relationship between the tenure length of officials and green innovation of enterprises. Furthermore, the tenure length of officials plays an inverted U-shaped role in regulating the impact of environmental regulations on enterprise green innovation. Overall, this study can help us better understand the politics behind enterprises green innovation in countries like China.

A major goal for port authorities, operators, and investors is to achieve efficient operations and effective environmental protection. This is because the environmental performance of a container port is important for its competitiveness and sustainable development. However, the container ports along the Maritime Silk Road (MSR) have caused numerous problems with the rapid development, among which the most significant problem is environmental pollution. In this paper, we aim to measure and compare the environmental performance and operational efficiency of ten major container ports along the MSR, including the ports of Shanghai, Hong Kong, Singapore, Kelang, Laem Chabang, Colombo, Dubai, Barcelona, Antwerp, and Hamburg. We develop an improved, inseparable data envelopment analysis (DEA) model with slack-based measures (SBMs) to evaluate and compare the environmental performance and operational efficiency, and we incorporate the desirable output of container throughput as well as the undesirable output of CO₂ emission. Our results show that. Overall. these container ports perform better in terms of operational efficiency than environmental performance. We also provide insights for management and policy makers for container ports with different levels of operational efficiency and environmental performance.