We use topic modeling to study research articles in environmental and resource economics journals in the period 2000–2019. Topic modeling based on machine learning allows us to identify and track latent topics in the literature over time and across journals, and further to study the role of different journals in different topics and the changing emphasis on topics in different journals. The most prevalent topics in environmental and resource economics research in this period are growth and sustainable development and theory and methodology. Topics on climate change and energy economics have emerged with the strongest upward trends. When we look at our results across journals, we see that journals have different topical profiles and that many topics mainly appear in one or a few selected journals. Further investigation reveal latent semantic structures across research themes that only the insider would be aware.
Due to the price elasticity of demand for secondhand commodities, it is difficult to establish a quantitative model for the auction. This paper proposes an agent-based multiattribute reverse auction model to support multicommodity combinatorial auction. First, this paper establishes an agent-based reverse auction model and introduces the framework, procedures, and protocols of the model in detail. Second, in light of the multicommodity environment, the targets, protocols, auction strategies, and approaches are identified. Finally, by using the proposed agent-based auction model, both buyers and sellers will reach simultaneous agreements on the details of the commodities to complete the auction. 相似文献