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Promotion of the emergence of synergistic linkages between different firms is crucial in the development of Industrial Symbiosis (IS) networks or Eco-Industrial Parks (EIP). Appropriate strategies for the promotion of inter-firm interactions are required to enhance the emergence of IS networks through institutional capacity building. This research draws on insight from Diffusion of Innovations (DoI) theory, and considers the emergence and development of IS as a process where the knowledge, attitude and implementation of IS synergies are gradually adopted by firms. Accordingly, we propose an Agent-Based Model (ABM) to investigate the influence of promoting strategies associated with various dimensions of institutional capabilities, on the identification of opportunity sets for IS synergies. The simulation results show that both "Knowledge Coordination" and "Relationship Coordination" have a positive impact on the identification of IS opportunities (represented by the adoption of positive attitudes). However, the performance of promoting strategies depends to a great extent on the mobilization capacity and the characteristics of the specific IS solutions. We believe the proposed research provides insights and implications for the design of the strategies to promote effective IS practice.

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Sparse and short news headlines can be arbitrary, noisy, and ambiguous, making it difficult for classic topic model LDA (latent Dirichlet allocation) designed for accommodating long text to discover knowledge from them. Nonetheless, some of the existing research about text-based crude oil forecasting employs LDA to explore topics from news headlines, resulting in a mismatch between the short text and the topic model and further affecting the forecasting performance. Exploiting advanced and appropriate methods to construct high-quality features from news headlines becomes crucial in crude oil forecasting. This paper introduces two novel indicators of topic and sentiment for the short and sparse text data to tackle this issue. Empirical experiments show that AdaBoost.RT with our proposed text indicators, with a more comprehensive view and characterization of the short and sparse text data, outperforms the other benchmarks. Another significant merit is that our method also yields good forecasting performance when applied to other futures commodities.