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Scaling up behavior and autonomous adaptation for macro models of climate change damage assessment

Periodic Reporting for period 2 - SCALAR (Scaling up behavior and autonomous adaptation for macro models of climate change damage assessment)

Reporting period: 2020-03-01 to 2021-03-31

Key scientific problem: The SCALAR project aims to bridge the gap between micro and macro research traditions in climate change damage assessment by modeling the behavioral aspects of private adaptation processes of heterogeneous agents, and integrating them into macro level climate policy models. Since adaptation is hazard-specific, the project focuses on the costliest climate-induced hazard worldwide: flooding. Its adds novel contribution to the scientific understanding of the feedbacks between the adaptive behavior of heterogeneous socio-economic actors and macro level damage and resilience assessments, eliciting potential tipping points along the climate change adaptation pathways. As such the project revisits the classic micro-macro aggregation problem in social sciences by means of new methods and micro-data. Specifically, SCALAR uniquely combines:
1) New behavioral data on climate adaptation decisions collected in multiple survey waves using online/mobile surveys, going beyond a snapshot to uncover evolving decision processes;
2) Advances in agent-based modeling to scale up adaptation decisions of heterogeneous households and firms to a regional economy while including land use and hazard data;
3) Cutting-edge ways of integrating micro-simulation models with traditional macro models to synergize the two approaches for developing new theory- and data-grounded macro damage assessments.

Importance for society: SCALAR has a potential to drive a breakthrough in integrating behavioral aspects of human decision-making into macro climate policy models. By consolidating micro-data on behavioral adaptation of households with computational heterogeneous agent-based models and macro-economic damage assessment models, SCALAR will unlock the development of a new generation of integrated assessment models (IAMs). Being grounded in behavioral and regional economic theories and supported by empirical private adaptation studies, such integrated models can explore an interplay of public and private adaptation efforts. Current IAMs – developed to support climate change mitigation – face limitations in informing design of climate change adaptation policies that include adaptation cross different scales (stakeholders), from government-led public adaptation to private adaption of households and businesses .It will enable the quantitative exploration of cross-scale damage cascades, the identification of thresholds over which private adaptation impacts the macro level, and the tracing of the emergence of socio-economic resilience as climate change unfolds. The methodological advancements will have impact far beyond the domain of climate adaptation.

Objectives: The objective of SCALAR is to bridge the gap between micro and macro research traditions by modeling behavioral aspects of private adaptation processes among heterogeneous households and firms from the bottom up, and by integrating them into macro level climate change policy models. Towards this end, SCALAR aims to:
1. Synthesize the existing empirical evidence on autonomous climate change adaptation globally to identify generic patterns of relationships between the private adaptation of heterogeneous agents, resilience and damages, and complement it with new detailed behavioral data on the process of individual adaptation decision-making in four different geographic contexts (we have selected two Global North countries – USA and the Netherlands, and two Global South countries – Indonesia and China)..
2. Develop innovative simulation tools to aggregate the private adaptation of heterogeneous individuals and firms in a regional economy enabling cross-scale feedbacks between adaptation, resilience and damages.
3. Integrate micro simulations, which will provide solid theoretical and empirical grounds for climate change adaptation and damages, with macro level climate policy models to trace feedbacks between adaptation, damages and non-monetary aspects characterizing socio-economic resilience.
I. Patterns in behavioral data on households’ adaptation
At the start of the project, we conducted a comprehensive meta-analysis of the empirical literature on household flood adaptation. Key findings included an empirical demonstration of the Global North research bias in the survey literature and finding statistically significant relationships between the size of several variable effects on households’ adaptation and different cultural aspects - ranked quantitatively using Hostede cultural rankings (see Noll et all (2020) for details).
Further extensive literature searches were conducted to understand the social, geo-political, and institutional contexts of each of the SCALAR case-study countries as they relate to climate adaptation and resilience. Blending knowledge from the undergone qualitative research and building on past flood survey work, guided by the quantitative review, we designed the longitudinal survey to specifically elicit several research veins (types of household adaptations, ranging from 18 on-site adaptation actions to flood insurance and relocation; drivers of household adaptation using constructs of several popular decision making theories; self-assessed resilience; socio-economic and loss data; and others). We focus on flooding in coastal cities as the most populated and exposed area to flash, river and coastal floods as well as to sea level rise.
The 1st wave of our longitudinal survey was conducted in March-April 2020 (initial N>6000 respondents), the 2nd wave was in November 2020. We also designed the questionnaire for the 3rd wave, which will distributed in the four countries in May 2021. We are currently in the process of data analysis and processing, with two publications under review at high impact journals.

II. From private adaptation behavior to regional economic resilience
One of the prominent methods to scale up behaviorally rich decisions of diverse actors in formal models is agent-based modeling (ABM). To analyze the state-of-the-art in flood risk ABMs through the lens of Complex Adaptive System resilience, societal dynamics and behavioral changes we conducted a systematic literature review (see Taberna et al (2020) for details). Our review article is now the second most downloaded in the SESMO Open golden access journal.
Consequently, we developed the first spatial ABM that includes agglomeration dynamics of both heterogeneous firms and households. The model is well grounded in new economic geography theory and consists of two regions: Coastal and Inland. Compared to the safe Inland region, the Coastal has a comparative advantage in trade with the rest of the world, but it is also subject to climate hazards. Our novel ABM reproduces a number of stylized economic facts related to the interplay of technological learning and macroeconomic performance of regions, and displays self-reinforcing and path-dependency agglomeration dynamics. The article is preparation.

III. Macro-economic damage assessments and private adaptation of households
We started a review of damage functions in IAMs and macro-economic climate policy models. Climate-damage assessment is traditionally conducted at the level of global regions (by IAMs), national or provincial level (by Computable General Equilibrium (CGE) models). Hazard models (e.g. hydrological flood models) offer high resolution estimates but usually only for physical damage, at times with simplified direct economic damage (e.g. with outdated value at risk data, no feedbacks due to population growth). Considering that adaptation has local impacts, we are developing a downscaled economic analysis for coastal urban regions, prone to flooding and sea level rise. Further, we are elaborating and developing methods to combine government-led adaptation with adaptation processes of households and firms. Here we collaborate with researchers from the PBL Netherlands Environmental Assessment Agency and the Euro-Mediterranean Center on Climate Change (CMCC) who are experts in CGE and climate change adaptation.

IV. Education and methodological training: all PhD students have been taking methodological training. Brayton Noll won a scholarship from the Dutch ‘Data Archiving and Networking Services’ organization to attend a four week summer program on Bayesian Statistics and Multi-Level Models.
The SCLAR teams has so far made progress beyond the state of the art in three dimensions:
1) Micro-level data on households adaptation:
a. our initial meta-analysis has revealed the Global North research bias in the empirical survey literature and found statistically significant relationships between the size of several variable effects on households’ adaptation and different cultural aspects - ranked quantitatively using Hostede cultural rankings (see Noll et all (2020) for details). We found a number of statistically significant relationships between culture and factors motivating private climate change adaptation, e.g. the cultural dimensions - Power Distance and Indulgence - exhibit statistically significant relationships with two factors that influence individual adaption motivation: Institutional Faith and perceived Flood Probability. Both of these cultural relationships have important implications for communicating climate risks and promoting adaptation. These findings are among the first to provide empirical evidence on the interaction effects between culture and private climate change adaptation motivation.
b. Our own primary data collection is unprecedented and will permit to study adaptation in dynamics, elicit factors driving or hindering various types of households adaptation in different cultural contexts, to develop statistical models that could be used for households ‘adaptation tracking’, i.e. to extrapolate adaptation trends over time for different types of households in different settings.
2) Agent-based modeling:
a. Our review (Taberna et al, 2020) indicated that various actors –households, government, insurers, developers – are commonly coded in computational ABMs, though their decisions are still only occasionally based in social science theories to represent behavioral change and rarely in micro-level data, especially regarding climate adaptation. Importantly, business are completely excluded as a relevant actor in flood ABMs, while they play the key role in providing employment opportunities for households in cities, essential for regional economic resilience, and are vital for estimating indirect flood damages. Most ABMs focus on modeling incremental adaptation to climate-driven floods, while transformational adaptation, which may be required to adapt to sea level rise, is an important direction for future work.
b. We developed the first spatial ABM that includes agglomeration dynamics of both heterogeneous firms and households in two regions: Coastal (flood-prone but with economic advantages) and Inland (safe). The novel contribution is in includes endogenous migration dynamics of both firms and households, prone to self-reinforcing and path-dependency. Initial clustering of economic activities triggers the endogenous technological change that boosts regional productivity. The regional knowledge spillovers and economies of scale further attract other businesses and households. Namely, when firms start to migrate following new market opportunities, they affect regional aggregate employment and wage levels. Such changes might make a region more attractive for workers, fostering households’ migration. Households’ migration decreases local demand, making the environment less favorable for the firms selling locally. When we introduced climate hazards of different sizes, we found a non-linear response of economic performances: a small shock appears to be beneficial to economic growth, while a big one is leads to the regional economic decline.
3) Quantifying macro-economic impacts of private adaptation:
a. we are developing a methodological roadmap to go beyond the standard approach intended for the sectoral productivity, by using decision trees with adaptive elasticities that reflect the changes in consumption due to adaptation. Our goals is to examine the intermediate and final demand for goods and services due to climate change adaptation.
b. The standard CGE model structure is being revised to accommodate households and firms adaptation, considering available databases.

Expected results:
1) Unique dataset eliciting components of a decision process regarding autonomous climate adaptation potentially capturing changes in perceptions, preferences and choices over time;
2) Agent-based models at urban and regional scales that: go beyond modeling agglomeration and dispersion forces in the explanation of spatial patterns of economic activity by (i) accommodating the spatial landscape, which is essential to link to climate hazards, and by (ii) applying theory- and data-driven climate adaptation decisions of both households and firms to the four cases. The data on households’ adaptation comes from our survey. We have already mapped and contacted owners of the data sources needed to model adaptation of businesses in different sectors.
3) Link regional level CGE model with an ABM to quantify cumulative effects of private adaptation, thresholds in regional resilience and structural shifts in damage functions of IAMs.
4) Dissemination: besides conference presentations, invited talks, the website and twitter communication, our team will publish peer-reviewed articles on the results of the surveys to uncover evolving decision process on adaptation, and evolution of resilience; on agent based model with climate adaptation decisions of firms and households, in different contexts; and on integration adaptation in macro damage assessments in CGE/IAMs; 3 PhD theses.
The methodological setup of SCALAR. Source: