Exploring disaster impacts on adaptation actions in 549 cities worldwide

Whether disasters influence adaptation actions in cities is contested. Yet, the extant knowledge base primarily consists of single or small-N case studies, so there is no global overview of the evidence on disaster impacts and adaptation. Here, we use regression analysis to explore the effects of disaster frequency and severity on four adaptation action types in 549 cities. In countries with greater adaptive capacity, economic losses increase city-level actions targeting recently experienced disaster event types, as well as actions to strengthen general disaster preparedness. An increase in disaster frequency reduces actions targeting hazard types other than those that recently occurred, while human losses have few effects. Comparisons between cities across levels of adaptive capacity indicate a wealth effect. More affluent countries incur greater economic damages from disasters, but also have higher governance capacity, creating both incentives and opportunities for adaptation measures. While disaster frequency and severity had a limited impact on adaptation actions overall, results are sensitive to which disaster impacts, adaptation action types, and adaptive capacities are considered.


Coding Adaptation Actions and Natural Hazard Events by Geographic Region
In order to ensure spatial proximity between the locations of the natural hazard events in the EM-DAT dataset and the adaptation actions in the CDP dataset, we reviewed the geographic information provided and determined the first-order administrative unitthe largest sub-national territorial unit within a countrywhere these events occurred. Given the CDP adaptation actions dataset provides both the country and city names, the geocoding process was a straightforward matching exercise using a global list of first-order administrative units 1 . The EM-DAT dataset provides information containing country names and a non-uniform, subjective description of the subnational areas impacted by a given natural hazard event. In this case, the process involved manually reviewing the location descriptions to determine the first-order administrative unit(s).

Coding Adaptation Actions by Hazard Type
To link the EM-DAT and CDP datasets, we coded each unique adaptation action in the CDP dataset according to the nine EM-DAT hazard types: drought, earthquake, extreme temperature, flood, landslide, mass movement, storm, volcanic activity, and wildfire. In this step, we only considered primary first-order effects. For example, while wildfires often coincide with droughts and heatwaves, we only consider wildfire-related adaptation actions to be those directly related to wildfire events. However, it is possible for one action to be directly related to multiple hazard types, or even to allhazard types. Codes are binary; either a specific adaptation action is interpreted as being directly related to any given hazard event type (coded as '1') or it is coded as being unrelated to that specific event type ('0'). We include the full table of coded CDP adaptation actions below (Supplementary  Table B1). We also include the results from an internal inter-coder reliability test to verify the coding scheme (Supplementary Figure B1). Lastly, in instances where actions were described in languages other than English, we used Google Translate to convert the texts into English prior to coding (action descriptions in Supplementary Table B1 are presented in the original language, as they appear in the CDP dataset).

Inter-coder reliability test of adaptation action type categorization
In order to verify the results of the coding scheme in Supplementary Table A1, we conducted an internal inter-coder reliability test. In the first step, JH coded all adaptation actions according to the four types. Next, JH selected 20 adaptation actions from the CDP dataset (see the bold-faced adaptation actions in Table A1 above). While all individual actions were identified at random using a random number generator in the R software environment, the process was "semi-random" to ensure a representative sample of the different types of adaptation actions. In the second step, JH provided the adaptation action descriptions and detailed coding instructions, and DN, MM, and CP coded the 20 actions according to the types of hazard events each action directly addresses. In the last step, we calculated the level of agreement among the three coders. There was, on average, over 70% agreement between all four coders ( Figure A1) concerning the categorization of adaptation actions. We then discussed all issues of non-agreement, and the final coding for each action of non-agreement was determined by applying codes agreed by two of the three coders).

Supplementary Figure 2:
Results from internal inter-coder reliability test for twenty semi-randomly selected adaptation actions.

Section 3: Dependent, Independent, and Control Variables
In this section, we provide the full descriptions of all variables investigated in the multiple regression analyses, including the data sources for each variable (Table B1). We provide additional discussion of the adaptive capacity variables, including the relevant theoretical foundations and justification for selecting these specific variables for inclusion in the analysis.

Variable descriptions
Supplementary The total urban population of a given city.

CDP Time lag
The average number of years between natural hazard events and adaptation actions taken by a given city.
CDP/EM-DAT *For some models (Supplementary Tables 1b-5b, 11b, 14b, and D16e-D16h), economic damages were normalized using Gross Domestic Product (GDP) per capita. Disaster frequency was normalized by the total number of disasters that occurred within the country over the time period of the study, while affected population and fatalities were normalized by city population.

Adaptive Capacity Control Variables
We included several relevant measures as controls for determinants of adaptive capacity, which is an aggregation of properties shaping the propensity or ability of any governing system to undertake adaptation actions. Due to the lack of comparable global data to determine the adaptive capacity of cities and local governments, we rely on national-level indicators of the preconditions for adaptation actions. Hence, the caveat is that these indicators may overlook information concerning local variation. The selection of measures is based on three premises.
First is the analytical separation of adaptation actions from the adaptive capacity of a system. In this study, adaptation actions refer to measures undertaken by a city to alleviate the adverse impacts of climate change and/or enhance resilience to natural hazard events. A number of system attributes are important preconditions that shape the ability of cities to formulate, initiate, and implement these adaptation actions.
Second, adaptation actions are defined here relatively narrowly as measures resulting from deliberate decisions by policy actors, including politicians, bureaucrats, and other stakeholders, to change the existing order. Therefore, for this study, we assume that only those system attributes that directly affect decisions concerning adaptation actions are relevant to consider in the models. These measures include different system attributes that support the collective ability of policy actors to translate the experience of hazard events into adaptation actions. Other community features commonly associated with adaptive capacity, for instance, various social relationships and the ability of individuals to selforganize and innovate, are crucial elements of adaptive capacity, more generally but not relevant factors conditioning policy-making associated with adaptation actions.
Third, the literature identifies several important determinants of adaptive capacity related to attributes of individuals and the internal dynamics of public organizations, which in turn may enable or constrain policy-making concerning adaptation actions. Examples include institutional memory, the ability of policy actors to learn and change assumptions, and the vision and entrepreneurial skills of leaders. These attributes, however, are elusive, and to our knowledge, currently, no data exist to measure these across a wider set of cases. Hence there is a lack of data to enable comparisons of these attributes in cities around the world.
Following these premises, we set out from core dimensions identified by established frameworks of adaptive capacity and searched available datasets with global reach for the best possible proxy measures for each theme, respectively. To identify relevant data, we consulted two datasets: Varieties of Democracy (V-Dem) 1 and the Quality of Government (QoG) 2 . The selection of indicators has also been guided by the scope of each dataset, respectively, where some indicators were deemed relevant but excluded because data were only available for a limited number of the 58 countries included in the analysis. For example, the QoG dataset (p. 565) entails indicators of the quality of democracy, which recognize the essential role of democratic participation and oversight to learning and adaptation. Although these indicators would be relevant for the purpose of this study, data for these indicators were only available for 41 countries.
Political stability, including, e.g., the absence of civil conflict and the functioning of democracy 3,4 , is depicted as a prerequisite for adaptation. Some studies 5 define political stability as a proxy measure indicating a willingness to invest in adaptation. Other work 6 emphasizes the importance of stable political institutions that provide predictability in support of collective action. Political stability may also interact with other factors associated with adaptive capacity. For example, a decrease in political stability may trigger consequent problems related to economic conditions, which, in turn, can constrain adaptation initiatives 7 . The current study measures political stability by an aggregated index of 'state legitimacy' derived from the Fragile States Index (https://fragilestatesindex.org/indicators/p1/) accessed via the Quality of Government dataset, combining state performance in five areas: public confidence in the political process, political opposition (peaceful demonstrations and riots), transparency (evidence and considerations of corruption), openness and fairness of the political process (political rights, representative government, leadership transition, free elections), and political violence (political assassinations, armed insurgents, and terrorism). For this study, we recoded the Fragile States Index indicator, which originally measures political stability on a scale 10-0 where higher scores indicate less stability, so that higher scores indicate greater stability.
Studies suggest that adaptive capacity is conditioned by national development since financial resources are needed to support the implementation of adaptation policy measures. In addition, various other resources (political, human, legal, and technological) condition institutional effectiveness, including the ability of institutions to change norms and rules 3,6,[8][9][10] . Given that adaptive capacity is related to economic well-being, assessments of adaptive capacity use Gross National Income (GNI) as one indicator to demonstrate wealth 11 . On this basis, the current study includes GNI (data derived from the World Bank) as one proxy measure of adaptive capacity. It should be noted that GNI is indirectly included in one of the regression models (Supplementary Table 21b), where we used Gross Domestic Product (GDP) per capita to normalize economic losses of disasters. Therefore, GNI was removed as a control from this model.
Another dimension of adaptive capacity considered in this work concerns meritocracy, specifically knowledge of individuals representing state bureaucracy. Knowledge is generally defined both as a determinant and indicator of the adaptive capacity of individuals, communities, organizations, and countries 4,12 . Given our interest in adaptation actions undertaken by cities as government entities, the study focuses on the role of knowledge in supporting policy-making. Specifically, we focus on the capacity of individuals to interpret information regarding current and future local environmental conditions as the basis for making policy decisions about adaptation actions 10 . To this end, we use a generic indicator of 'meritocracy' (derived from the V-Dem dataset), capturing whether appointment decisions within state administration are based on personal and political connections versus skill and merit. Thus, the assumption is that appointment decisions based on skill and merit help sustaining a cadre of bureaucrats and other policy professionals with the capacity to make informed decisions based on experience. The scale ranges 0-4, with higher values indicating greater reliance on skill and merit.
Stakeholder diversity, defined here as the participation and influence of multiple interests and actors in policy-making, is another determinant of adaptive capacity. Ensuring broad participation of actors representing organizations at different levels and sectors, including non-state actors, is a way to give room for multiple opinions, beliefs, and problem definitions and to widen the range of options in policy-making 6 . Such variety provides access to different types of knowledge 13 that reduce uncertainty and support learning and the development and implementation of adaptation measures 14 . To account for stakeholder diversity, we relied on information concerning the 'range of consultation' (derived from the V-Dem dataset), which measures the width of consultations at elite levels when important policy changes are considered. This scale ranges from 0 (indicating no consultation where a leader or small group make all decisions on their own) to 5 (decisions are based on consultations involving actors from across the political spectrum and sectors of society).
Finally, the study includes a measure of local government power as a means to control for whether adaptation actions are more frequent in cities located in countries with elected local governments that are able to operate independently from unelected local actors (with the exception of judicial bodies). The Intergovernmental Panel on Climate Change recognizes the importance of empowering local communities to take action for reducing vulnerability and strengthening resilience, including responsibility and decision-making to improve preparedness through post-disaster assessments 15 . While the literature 5 emphasizes the multifaceted nature of local-level adaptation (spanning broad sets of community characteristics and interactions), we focused exclusively on local government independence from the involvement of unelected local actors. Hence, the assumption is that greater independence facilitates adaptation actions, partially by avoiding or bypassing political conflict and power-struggles 16,17 . We include a measure of local government power (derived from the V-Dem dataset) ranging from 0 (countries have no elected local governments) to 1 (elected local governments operate without restrictions).

Section 4: Full Regression Results for All Types of Adaptation Actions
Tables 4-8: bivariate regression models of each type of adaptation action with respect to absolute hazard frequency and severity measures     Tables 9-13: bivariate regression models of each type of adaptation action with respect to normalized hazard frequency and severity measures          Tables 19-23 contain bivariate regression models of each type of adaptation actions with respect to adaptive capacity control variables     Tables 24-26 contain multiple regression models of each type of adaptation action with respect to absolute, normalized, and baseline hazard frequency and severity measures, respectively, and do not include any additional control variables.   Tables 27-28 contain multiple regression models of each type of adaptation action with respect to adaptive capacity control variables  Tables 29-31 contain raw, normalized, and baseline multiple regression models of each type of adaptation action with respect to frequency, severity, and relevant control variables.     Tables 33-36 contain multiple regression models of each type of adaptation action with respect to disaster frequency and severity measures, individually, and all control variables.      Table 35: Adaptation action types with respect to affected population and controls.

Dependent variable:
All Specific Expansive General Other (1)   Tables 37-44 contain multiple regression models of each type of adaptation action with respect to normalized disaster frequency and severity measures, individually, and all control variables.   Table 38: Adaptation action types with respect to normalized disaster damages and controls.

Dependent variable:
All Specific Expansive General Other (1)  Table 39: Adaptation action types with respect to normalized affected population and controls.

Dependent variable:
All Specific Expansive General Other (1)      Tables 45-56 contain multiple regression models of each type of adaptation action with respect to disaster frequency and severity measures, individually, and all control variables.            Note: * p<0.1; ** p<0.05; *** p<0.01