Social Network Analysis in Food-Energy-Water-Ecosystem Nexus: Climate Change, Carbon Markets, and Interdisplinary
Research Questions: What were conference sponsor networks in the agriculture and natural resrouce sectors regarding topics including climate change, carbon markets, weather, and general market risks?
Methods: Exponential Random Graph Models (ERGMs), Natural Language Processing (NLP) including BERT-based deep learning models and keyword extraction methods, and the visualization for complex network structures
Main Findings: Climate change topics were negatively associated with sponsorship tie formation, while carbon markets, and markets in general, were positively associated. We found conferences in the agriculture commodity sector had the lowest proportion of climate change mentions, although they discussed carbon markets. Private sponsors predominantly supported conferences that did not mention climate change directly. The percentage of conferences on climate change reaching landowners and managers was lower than that of carbon market topics. These results reveal sponsorship of market-friendly approaches to climate mitigation without directly mentioning climate change, particularly within the agricultural commodity sector and private actors. Analysis of conference sponsorship networks has important implications for the political economy of knowledge sharing, particularly in advancing the Sustainable Development Goals related to climate action (SDG 13), life on land (SDG 15), and partnerships for the goals (SDG 17).
Products:
1. Lu, Y., Rissman, A., Lubell, M., Rickenbach M., Kucharik C., Powell E.“Conference Sponsorship Higher When Conferences Address Carbon Markets But Not Climate Change”
2. Lu, Y., Rissman, A. “Who Pays for the Party? Conference Sponsor Networks in the Food-Energy-Water-Ecosystems Nexus.”
Grant Acknowledgements:
1. NSF INFEWS/T1: Sustaining Food, Energy, and Water Security in Agricultural Landscapes of the Upper Mississippi River Basin 1855996
2. NSF Doctoral Dissertation Research Improvement Grant (DDRIG) Decision, Risk and Management Sciences Program (DRMS) 2417586
3. Center for Integrated Agricultural Systems Graduate Student Summer Grant
Methods: Exponential Random Graph Models (ERGMs), Natural Language Processing (NLP) including BERT-based deep learning models and keyword extraction methods, and the visualization for complex network structures
Main Findings: Climate change topics were negatively associated with sponsorship tie formation, while carbon markets, and markets in general, were positively associated. We found conferences in the agriculture commodity sector had the lowest proportion of climate change mentions, although they discussed carbon markets. Private sponsors predominantly supported conferences that did not mention climate change directly. The percentage of conferences on climate change reaching landowners and managers was lower than that of carbon market topics. These results reveal sponsorship of market-friendly approaches to climate mitigation without directly mentioning climate change, particularly within the agricultural commodity sector and private actors. Analysis of conference sponsorship networks has important implications for the political economy of knowledge sharing, particularly in advancing the Sustainable Development Goals related to climate action (SDG 13), life on land (SDG 15), and partnerships for the goals (SDG 17).
Products:
1. Lu, Y., Rissman, A., Lubell, M., Rickenbach M., Kucharik C., Powell E.“Conference Sponsorship Higher When Conferences Address Carbon Markets But Not Climate Change”
2. Lu, Y., Rissman, A. “Who Pays for the Party? Conference Sponsor Networks in the Food-Energy-Water-Ecosystems Nexus.”
Grant Acknowledgements:
1. NSF INFEWS/T1: Sustaining Food, Energy, and Water Security in Agricultural Landscapes of the Upper Mississippi River Basin 1855996
2. NSF Doctoral Dissertation Research Improvement Grant (DDRIG) Decision, Risk and Management Sciences Program (DRMS) 2417586
3. Center for Integrated Agricultural Systems Graduate Student Summer Grant
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