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The rapid development of the world economy has been accompanied by a significant increase in ecological footprint. Represented by population agglomeration, urbanization has exacerbated environmental pressure, but the improvements in the levels of human capital may offset this negative impact. This research thus uses cross-country data from 110 economies over the period 1990–2016 to explore the correlations among human capital, urbanization, and ecological footprint. The findings show that first, from a global perspective, human capital initially increases and then reduces ecological footprint. The analysis based on sub-samples shows that human capital of the high-income group decreases ecological footprint, whereas human capital of the low-income group and that of countries with large populations raise ecological footprint. Second, urbanization plays a linear moderating role in human capital’s impact on the ecological footprint. The higher the level of urbanization is, the higher is the turning point of human capital that is needed to improve environmental quality. Third, when the population size is larger or ecological footprint is greater, the turning point of human capital is higher under the same urbanization level. Therefore, countries should continue to enhance human capital to promote the upgrading of industrial structure, green technology innovation, and changes in energy-saving lifestyle. Moreover, it is beneficial for countries to pay close attention to the negative impact of population size or accelerated urbanization on environmental quality.

Device-to-device (D2D) assisted offloading heavily depends on the participation of human users. The content preference and sharing willingness of human users are two crucial factors in the D2D assisted offloading. In this paper, with consideration of these two factors, the optimal content pushing strategy is investigated by formulating an optimization problem to maximize the offloading gain measured by the offloaded traffic. Users are placed into groups according to their content preferences, and share content with intergroup and intragroup users at different sharing probabilities. Although the optimization problem is nonconvex, the closed-form optimal solution for a special case is obtained, when the sharing probabilities for intergroup and intragroup users are the same. Furthermore, an alternative group optimization (AGO) algorithm is proposed to solve the general case of the optimization problem. Finally, simulation results are provided to demonstrate the offloading performance achieved by the optimal pushing strategy for the special case and AGO algorithm. An interesting conclusion drawn is that the group with the largest number of interested users is not necessarily given the highest pushing probability.It is more important to give high pushing probability to users with high sharing willingness.