Towards an ABM on Proactive Community Adaptation for Climate Change

• URL: http://arxiv.org/abs/2507.14233v1
• Date: Thu, 17 Jul 2025 08:00:31 GMT
• Title: Towards an ABM on Proactive Community Adaptation for Climate Change
• Authors: Önder Gürcan, David Eric John Herbert, F. LeRon Shults, Christopher Frantz, Ivan Puga-Gonzalez,
• Abstract summary: The model is applied to Bergen, Norway, represented as a complex socio-ecological system.<n>It integrates multiple agent types: municipal government (urban planners and political actors), civil society (individual citizens), environmental NGOs and activists, and media.<n>The model captures the resulting decision-making ecosystem and reveals feedback loops and leverage points that determine climate-adaptive outcomes.
• Score: 0.3262230127283452
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present an agent-based model (ABM) simulating proactive community adaptation to climate change in an urban context. The model is applied to Bergen, Norway, represented as a complex socio-ecological system. It integrates multiple agent types: municipal government (urban planners and political actors), civil society (individual citizens), environmental NGOs and activists, and media. Agents interact during urban planning processes - particularly the evaluation and approval of new development proposals. Urban planners provide technical assessments, while politicians (organized by party) make final decisions to approve, modify, or reject projects. Environmental NGOs, activist groups, and the media shape public perception and influence policymakers through campaigns, lobbying, protests, and news coverage. Individual citizens decide whether to engage in collective action based on personal values and social influences. The model captures the resulting decision-making ecosystem and reveals feedback loops and leverage points that determine climate-adaptive outcomes. By analyzing these dynamics, we identify critical intervention points where targeted policy measures can facilitate systemic transformation toward more climate-resilient urban development.

Related papers

• Emergence of human-like polarization among large language model agents [79.96817421756668]
  We simulate a networked system involving thousands of large language model agents, discovering their social interactions, result in human-like polarization.<n>Similarities between humans and LLM agents raise concerns about their capacity to amplify societal polarization, but also hold the potential to serve as a valuable testbed for identifying plausible strategies to mitigate polarization and its consequences.
  arXiv  Detail & Related papers  (2025-01-09T11:45:05Z)
• Persuasion with Large Language Models: a Survey [49.86930318312291]
  Large Language Models (LLMs) have created new disruptive possibilities for persuasive communication. In areas such as politics, marketing, public health, e-commerce, and charitable giving, such LLM Systems have already achieved human-level or even super-human persuasiveness. Our survey suggests that the current and future potential of LLM-based persuasion poses profound ethical and societal risks.
  arXiv  Detail & Related papers  (2024-11-11T10:05:52Z)
• Causal Modeling of Climate Activism on Reddit [4.999814847776098]
  We develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests. We find that among users interested in climate change, participation in online activist communities is indeed influenced by direct interactions with activists. Among people aware of climate change, left-leaning people from lower socioeconomic backgrounds are particularly represented in online activist groups.
  arXiv  Detail & Related papers  (2024-10-14T14:41:09Z)
• Analysis of Climate Campaigns on Social Media using Bayesian Model Averaging [29.413444722550356]
  We analyze how industries, their advocacy group, and climate advocacy group use social media to influence the narrative on climate change. We propose a minimally supervised model soup [57] approach combined with messaging themes to identify the stances of climate ads on Facebook.
  arXiv  Detail & Related papers  (2023-05-06T16:43:29Z)
• Climate Change & Computer Audition: A Call to Action and Overview on Audio Intelligence to Help Save the Planet [98.97255654573662]
  This work provides an overview of areas in which audio intelligence can contribute to overcome climate-related challenges. We categorise potential computer audition applications according to the five elements of earth, water, air, fire, and aether.
  arXiv  Detail & Related papers  (2022-03-10T13:32:31Z)
• This Must Be the Place: Predicting Engagement of Online Communities in a Large-scale Distributed Campaign [70.69387048368849]
  We study the behavior of communities with millions of active members. We develop a hybrid model, combining textual cues, community meta-data, and structural properties. We demonstrate the applicability of our model through Reddit's r/place a large-scale online experiment.
  arXiv  Detail & Related papers  (2022-01-14T08:23:16Z)
• ClimateGAN: Raising Climate Change Awareness by Generating Images of Floods [89.61670857155173]
  We present our solution to simulate photo-realistic floods on authentic images. We propose ClimateGAN, a model that leverages both simulated and real data for unsupervised domain adaptation and conditional image generation.
  arXiv  Detail & Related papers  (2021-10-06T15:54:57Z)
• Dynamic Urban Planning: an Agent-Based Model Coupling Mobility Mode and Housing Choice. Use case Kendall Square [5.620109882646823]
  This paper presents an agent-based model that characterizes citizens' behavioural patterns with respect to housing and mobility choice. The realistic identification and representation of the criteria that affect this decision-making process will help understand and evaluate the impacts of potential housing incentives.
  arXiv  Detail & Related papers  (2021-06-28T10:54:44Z)
• NLP for Climate Policy: Creating a Knowledge Platform for Holistic and Effective Climate Action [2.482368922343792]
  The paper thematically discusses how NLP techniques could be employed in climate policy research. We exemplify symbiosis of NLP and Climate Policy Research via four methodologies. The present theme paper further argues that creating a knowledge platform would help in the formulation of a holistic climate policy.
  arXiv  Detail & Related papers  (2021-05-12T12:30:02Z)
• Analyzing Sustainability Reports Using Natural Language Processing [68.8204255655161]
  In recent years, companies have increasingly been aiming to both mitigate their environmental impact and adapt to the changing climate context. This is reported via increasingly exhaustive reports, which cover many types of climate risks and exposures under the umbrella of Environmental, Social, and Governance (ESG) We present this tool and the methodology that we used to develop it in the present article.
  arXiv  Detail & Related papers  (2020-11-03T21:22:42Z)
• From Talk to Action with Accountability: Monitoring the Public Discussion of Policy Makers with Deep Neural Networks and Topic Modelling [0.0]
  We propose a multi-source topic aggregation system (MuSTAS) MuSTAS processes policy makers speech and rhetoric from several publicly available sources into an easily digestible topic summary. This topic digest will serve the general public and civil society in assessing where, how, and when politicians talk about climate and climate policies.
  arXiv  Detail & Related papers  (2020-10-16T12:21:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.