Modeling Real Estate Market Responses to Climate Change in the Coastal Zone

Putra, Handi Chandra; Zhang, Haiyan; Andrews, Clinton J.

doi:10.18564/jasss.2577

Cited by 11 publications

(32 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…54,97,98 It can therefore be expected to play a substantial role in models of the kind we consider, given the importance of information and innovation in adaptation or mitigation in land-based sectors. Many of the models we reviewed do include diffusion of agricultural practices or technology, 37,46,54,59,67,74,82,85 but fewer include diffusion of information or attitudes, or diffusion in other sectors 53,71,77,87 (forestry models were particularly lacking in this respect, including only indirect spatial influences between agents). These latter models are of particular interest where they allow behavior to change with social (and, potentially, climatic) context, rather than modeling static behavior within a dynamic environment.…”

Section: Diffusionmentioning

confidence: 99%

“…Rigorous discussion or application of validation procedures is not uncommon, 36,54,55,68,106 but has not led to consistency of practice or development of methodology. In the models we reviewed, five main approaches to model validation or evaluation were apparent: checking modeled behavior against actors' stated or revealed behavior 36,50,53,54,57,67,68,87 (comparisons of the stated and revealed behavior 25 were not used); extensive sensitivity analyses; 36,39 comparison (or tuning to ensure agreement) of some modeled and measured outcomes; 37,39,50,54,56,60,70,77,84,85 comparison to the results of other models; 88 and reliance on previous validation of the same or a similar model 64,71,74,82,84 (Figure 2). Justification of the (usually single) selected approach is rare, as is discussion of implications for model usage and interpretation.…”

Section: Validation and Uncertaintymentioning

confidence: 99%

See 1 more Smart Citation

Behavioral models of climate change adaptation and mitigation in land‐based sectors

Brown

Alexander

Holzhauer

et al. 2017

WIREs Climate Change

View full text Add to dashboard Cite

Models of the land system are essential to our understanding of the magnitude and impacts of climate change. These models are required to represent a large number of processes in different sectors, but face particular challenges in describing the individual and social behaviors that underpin climate change mitigation and adaptation. We assess descriptions of these behaviors in existing models, their commonalities and differences, and the uses to which they have been put. We find that behavioral models have a distinct and important role to play in climate research, but that they currently suffer from being strongly sectoral in nature, with agricultural models being the most common and behaviorally rich. There are also clear convergences, with economic‐based decision‐making remaining dominant and behaviors such as diffusion, interaction, anticipation, or learning remaining relatively neglected. Active climate change is also rarely modeled, with adaptation and mitigation generally represented as responses to economic drivers under static climatic conditions. Furthermore, dynamic behaviors, objectives, or decision‐making processes are almost entirely absent, despite their clear relevance to climate change responses. We conclude that models have been more successful in the identification of important processes than in their implementation and that, while some behavioral processes may remain impossible to model, behavioral models of adaptation and mitigation in land‐based sectors have substantial unexplored potential. We suggest that greater attention be paid to the cumulative coverage of models in this field, and that improvements in the representation of certain key behaviors be prioritized. WIREs Clim Change 2017, 8:e448. doi: 10.1002/wcc.448 This article is categorized under: Assessing Impacts of Climate Change > Representing Uncertainty

show abstract

Section: Diffusionmentioning

confidence: 99%

Section: Validation and Uncertaintymentioning

confidence: 99%

Behavioral models of climate change adaptation and mitigation in land‐based sectors

Brown

Alexander

Holzhauer

et al. 2017

WIREs Climate Change

View full text Add to dashboard Cite

show abstract

“…There are examples of flood-ABMs that bridge the two groups by measuring both the endogenous changes in housing prices that shape values of assets at risk and estimating EAD with related vulnerability (Chandra-Putra et al, 2015;McNamara & Keeler, 2013) 2 . Further, there are examples of MR articles that link vulnerability elements, namely insurance uptake and insurance claims (Chandra-Putra & Andrews, 2019) as a form of validation of agents' decision-making process.…”

Section: Resultsmentioning

confidence: 99%

“…The MR group often uses non-linear distant-dependent functions from the coast or river as a proxy of hazard probability (Filatova et al, 2011;Walls et al, 2018). Second, flooding probabilities can be determined through statistical methods that sample historical values (Chandra-Putra et al, 2015;Han & Peng, 2019;Tonn et al, 2019;Tonn & Guikema, 2018). Alternatively, location-specific fixed flood maps (Dubbelboer et al, 2017;Haer et al, 2017) are used for different return periods, for example, 1:100 flood.…”

Section: Risk Components In Flood-abmsmentioning

confidence: 99%

Tracing resilience, social dynamics and behavioral change: a review of agent-based flood risk models

Taberna

Filatova

Roy

et al. 2020

SESMO

View full text Add to dashboard Cite

Climate change and rapid urbanization exacerbate flood risks worldwide. The recognition of the crucial role that human actors play in altering risks and resilience of flood-prone cities triggers a paradigm shift in climate risks assessments and drives the proliferation of computational models that include societal dynamics. Yet, replacing a representative rational actor dominant in climate policy models with a variety of behaviorally-rich agents that interact, learn, and adapt is not straightforward. Focusing on the costliest climate-exacerbated hazard, flooding, we review computational agent-based models that include behavioral change and societal dynamics. We distinguish between two streams of literature: one stemming from economics & behavioral sciences and another from hydrology. Our findings show that most studies focus on households while representing decisions of other agents (government, insurance, urban developers) simplistically and entirely overlooking firms' choices in the face of risks. The two communities vary in the extent they ground agents' rules in social theories and behavioral data when modeling boundedly-rational decisions. While both aspire to trace feedbacks that agents collectively instigate, they employ different learning and interactions when computing societal dynamics in the face of climate risks. Dynamics of hazard, exposure, and vulnerability components of flood risks driven by incremental adaptation of agents are well represented. We highlight that applying a complex adaptive system perspective to trace the evolution of resilience can lead to a better understanding of transformational adaptation. The methodological advances in computational models with heterogeneous behaviorally-rich adaptive agents are relevant for adaptation to different climate-driven hazards beyond flooding.

show abstract

“…This model shows how a relatively small change, i.e the move of one agent, sets off bandwagon effects leading to full segregation of neighbourhoods. ABMS have been applied to a wide range of research topics, including risk on land and housing markets (Geanakoplos et al 2012;Putra et al 2015;Filatova et al 2009), evaluation of flood protection measures (Haer et al 2016;O'Connell and O'Donnell 2013;Sobiech 2013;Brouwers and Boman 2011) and distributional effects of natural disasters (Naqvi and Rehm 2014). A number of authors have investigated household migration in the context of climate change using ABMs (Cai et al 2013;Smith 2014;Kniveton et al 2012).…”

Section: Household Migration In Abmsmentioning

confidence: 99%

Household migration in disaster impact analysis: incorporating behavioural responses to risk

Husby

Koks

2017

Nat Hazards

View full text Add to dashboard Cite

Detailed estimates of economy-wide disaster losses provide important inputs for disaster risk management. The most common models used to estimate losses are inputoutput (IO) and computable general equilibrium (CGE) models. A key strength of these models is their ability to capture the ripple effects, whereby the impacts of a disaster are transmitted to regions and sectors that are not directly affected by the event. One important transmission channel is household migration. Changes in the spatial distribution of people are likely to have substantial impacts on local labour and housing markets. In this paper, we argue that IO and CGE models suffer from limitations in representing household migration under disaster risk. We suggest combining IO and CGE models with agent-based models to improve the representation of migration in disaster impact analysis.

show abstract

Modeling Real Estate Market Responses to Climate Change in the Coastal Zone

Cited by 11 publications

References 8 publications

Behavioral models of climate change adaptation and mitigation in land‐based sectors

Behavioral models of climate change adaptation and mitigation in land‐based sectors

Tracing resilience, social dynamics and behavioral change: a review of agent-based flood risk models

Household migration in disaster impact analysis: incorporating behavioural responses to risk

Contact Info

Product

Resources

About