{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T20:57:48Z","timestamp":1754600268205},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,8]]},"abstract":"<jats:p>We conduct a quantitative analysis of the development of the industry chain from the environmental, social, and governance (ESG) perspective, which is an overall measure of sustainability.  Factors that may impact the performance of the industrial chain have been studied in the literature, such as government regulation, monetary policy, etc. Our interest lies in how the sustainability change (i.e., ESG shock) affects the performance of the industrial chain. To achieve this goal, we model the industrial chain with a graph neural network (GNN) and conduct node regression on two financial performance metrics, namely, the aggregated profitability ratios and operating margin. To quantify the effects of ESG, we propose to compute the interaction between ESG shocks and industrial chain features with a cross-attention module, and then filter the original node features in the graph regression. Experiments on two real datasets demonstrate that (i) there are significant effects of ESG shocks on the industrial chain, and (ii) model parameters including regression coefficients and the attention map can explain how ESG shocks affect the  performance of the industrial chain.<\/jats:p>","DOI":"10.24963\/ijcai.2023\/674","type":"proceedings-article","created":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T04:31:30Z","timestamp":1691728290000},"page":"6076-6084","source":"Crossref","is-referenced-by-count":1,"title":["Interpret ESG Rating\u2019s Impact on the Industrial Chain Using Graph Neural Networks"],"prefix":"10.24963","author":[{"given":"Bin","family":"Liu","sequence":"first","affiliation":[{"name":"School of Statistics, Southwestern University of Finance and Economics, Chengdu, China"}]},{"given":"Jiujun","family":"He","sequence":"additional","affiliation":[{"name":"School of Statistics, Southwestern University of Finance and Economics, Chengdu, China"}]},{"given":"Ziyuan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Statistics, Southwestern University of Finance and Economics, Chengdu, China"}]},{"given":"Xiaoyang","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Statistics, Southwestern University of Finance and Economics, Chengdu, China"}]},{"given":"Xiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Finance, Southwestern University of Finance and Economics, Chengdu, China"}]},{"given":"Guosheng","family":"Yin","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Faculty of Natural Sciences, Imperial College London, London, UK"}]}],"member":"10584","event":{"number":"32","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2023","name":"Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}","start":{"date-parts":[[2023,8,19]]},"theme":"Artificial Intelligence","location":"Macau, SAR China","end":{"date-parts":[[2023,8,25]]}},"container-title":["Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T04:53:36Z","timestamp":1691729616000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2023\/674"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2023,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2023\/674","relation":{},"subject":[],"published":{"date-parts":[[2023,8]]}}}