Floods are among the costliest natural hazards and their consequences are expected to increase further in the future due to urbanization in flood-prone areas. It is essential that policymakers understand the factors governing the dynamics of urbanization to adopt proper disaster risk reduction techniques. Peoples’ relocation preferences and their perception of flood risk  are among the most important factors that influence urbanization in flood-prone areas. Current studies focusing on flood risk assessment do not consider the effect of human behavior on urbanization and how it may change the nature of the risk. Moreover, flood mitigation policies are implemented without considering the role of human behavior and how the community will cope with measures such as buyout, land acquisition, and relocation that are often adopted to minimize development in flood-prone regions. Therefore, such policies may either be resisted by the community or result in severe socioeconomic consequences. In this study, we present a new Agent-Based Model (ABM) to investigate the complex interaction between human behavior and urbanization and its role in creating future communities vulnerable to flood events. We identify critical factors in the decisions of households to locate or relocate and adopt policies compatible with human behavior. The results show that when people are informed about the flood risk and proper incentives are provided, the demand for housing within 500-year floodplain may be reduced as much as 15% by 2040 for the case study considered. On the contrary, if people are not informed of the risk, 29% of the housing choices will reside in floodplains. The analyses also demonstrate that neighborhood quality—influenced by accessibility to highways, education facilities, the city center, water bodies, and green spaces, respectively—is the most influential factor in peoples’ decisions on where to locate. These results provide new insights that may be used to assist city planners and stakeholders in examining tradeoffs between costs and benefits of future land development in achieving sustainable and resilient cities. The annual cumulative losses due to various types of flooding are higher than those from large-scale disasters such as earthquakes6. The consequences of flooding are intensified by climate change and socioeconomic development. Urbanization, as a direct result of socioeconomic development, exposes more people and their livelihoods to risk12. The United Nations has projected that 68% of the world’s population will live in urban areas in 2050, compared to 50% in 202013. Urbanization occurs mainly in low-lying, flood-prone areas due to accessibility to recreational facilities and agricultural development14. Therefore, the interaction of more intense and frequent flooding events due to climate change with the increased exposure brought by urbanization may lead to catastrophic social and economic consequences in the future if not addressed16. As a result, the United Nations has emphasized the importance of sustainable development by building resilient cities and communities subjected to natural hazards including floods, as its 11th Sustainable Development Goals (SDGs)17.Floods are among the costliest natural hazards and threaten the lives and livelihoods of millions of people worldwide18. Households, real estate, developers, government, and their interactions can create favorable or unfavorable socioeconomic conditions and consequently encourage or discourage demand in specific areas that shapes the city's expansion. On the other hand, an effective Disaster Risk Reduction (DRR) plan requires a reliable risk assessment18. Rising social and economic losses from flooding events have demonstrated that current governmental investments in adaptation measures are insufficient owing to the dynamic nature of risk, which is due, in part, to the failure to consider human behavior in flood risk assessments20. While some studies have focused on conceptualizing human-flood interaction21, they have not suggested quantitative approaches to employ engineering techniques to measure the role of human behavior in future vulnerabilities to flood and what management strategies should be used to mitigate flood impacts. To steer urbanization toward sustainable development and resilient cities and communities in flood events, human behavior impacts on urbanization and flood risk must be thoroughly understood and quantified.People and their perception of flood risk—collectively, human behavior—is one of the most significant factors influencing urbanization. Therefore, achieving sustainable development requires a comprehensive understanding of human behavior and its impact on flood risk12. Structural measures typically involve engineered systems, such as dams, levees, and floodwalls aimed at controlling the hazard. If not carefully evaluated, these interventions may cause detrimental impacts on human lives and livelihoods, as well as on the biophysical environment. For instance, studies have demonstrated that the construction of polders has increased the extent of pluvial flooded areas in Bangladesh24. These strategies may create an illusion of safety which promotes further growth in or near flood-protected areas25. Moreover, with the increasing trends in frequency and intensity of flood events, protective structures that are designed based on contemporary flood threats may experience future extreme events that cause overtopping or failure of the structure and threaten the lives of people who live in the protected area26. Nonstructural flood mitigation strategies, on the other hand, rely on public policy planning, such as zoning, acquisition and land-use regulation, and socioeconomic incentives that focus on controlling the exposure. These policies are most effective in developing communities, where such choices are more practical than in established communities with fixed infrastructure and can shape urban expansion over an area effectively. However, they are not as successful as expected in reversing the tendency of people to choose to live in flood-prone areas27. Consequently, there is an essential need to consider human behavior and their locational living preferences in implementing these nonstructural measures in rapidly developing urban areas.Policymakers can invest in two types of strategies to cope with rising flood risk: structural or nonstructural strategiesIn this article, we demonstrate how changes in human behavior affect urbanization and vulnerabilities to flooding in expanding communities. We first show how human behavior impacts urbanization by developing a novel ABM, in which households, real estate, and developers are modeled as agents, the interactions of which lead to changes in the urban expansion of a community over time. Next, we evaluate how the risk perception of households affects their decisions in where to locate. Finally, using the ABM approach, we investigate the driving factors and incentives in the human decision on locational choices and how these incentives can be imposed on the communities by local authorities and policymakers as nonstructural flood mitigation measures to shape urbanization achieving sustainable development and resilient cities and communities in flooding events.inside the city boundary and urban dynamics at the city boundary, each of which is influenced by individuals’ behavior. Some individuals who seek housing prefer to live within the city boundary while others select newly developed housing in the suburbs. Therefore, modeling these two processes is essential in understanding the role of human behavior in urbanization. Herein, we develop a behavioral urban growth model that employs ABM as a class of computational models for simulating the interactions between autonomous entities in the form of agents to investigate their consequences on complex systems and networks28. Our behavioral urban growth model consists of two sub-models: the Relocating Model which simulates the dynamics within the city boundary and the Growth Model which mimics the dynamics at the city boundary, as illustrated in Fig. Urbanization occurs as a result of two processes: urban dynamics 29 and assists the negotiation process   behavioral urban growth, (2) flood hazard, and (3) policy implementation. In this study, we defined two potential nonstructural flood mitigation measures in terms of socioeconomic incentives. These measures include Policy I: building new educational facilitates and shopping centers in the northern part of the city, and Policy II: creating green spaces and water bodies in the southern part of the city. The details of each model and the aforementioned policies can be found in “31. Home to a world-class research university, a mix of key industry clusters, major government research facilities, visionary entrepreneurs, and a highly educated population, Boulder has experienced a significant urban expansion in the past half-century due to population and economic growth. Boulder is susceptible to flooding due to its geographical location on the Eastern Front Range of the Rocky Mountains, which contains many streams and creeks. The City’s assessment of exposure to flood risk reveals that there are about 10,000 people and 3,600 structures with an assessed valuation of $1 billion within Boulder’s 100-year floodplain31. The City has imposed restrictive regulations in new development and redevelopment activities. However, the city is still expected to continue to grow as the population estimate for the year 2040 is about 123,000 inhabitants. If this growth occurs within floodplains, it poses further potential risks by adding more exposure to floods and changing the floodplains. Current regulations do not restrict the redevelopment of properties within floodplains but do require suitable structural and/or nonstructural flood protection. However, these properties would still be subject to flood damage from larger flood events that appear plausible under the effects of climate change. In this study, we estimated urban growth using the CA model, according to the steps outlined in Figure The City of Boulder, shown in Fig. Using the proposed behavioral development framework summarized above, we seek to evaluate the changes in urbanization caused by changes in the behavior of the key actors , as influenced by flood risk. We first assess the effect of floods on the real estate agent to investigate how flood risk can change the housing prices in the market. Next, we examine how vulnerabilities to floods influence buyers’ decisions on where to locate. We perform statistical analysis to identify the factors that most influence buyers’ decisions on their locational choices. Finally, we employ this information to identify policies that (a) promote urbanization toward less vulnerable areas to floods in the community using socioeconomic incentives and (b) gradually encourage people to relocate to safer regions without forcing them to do so, unlike policies such as acquisition and buyout.As a first step, we performed two sets of analysis: (1) examination of historical sale transactions from 2010 to 2020 to train the ABM model, (2) prediction of the sale prices for 2020–2040, as illustrated in Fig. We next performed analyses to assess how individuals’ decisions are influenced by flood risk. With this aim, we analyzed the buyers’ choices on a historical basis for the period of 2010–2017 under the two behaviors: Risk-Negligence and Expected Utility. We calibrated the model for this period by comparing the actual sale transactions to the results from the model for this historical period. To validate the model, the buyers’ decisions for the year 2020 were estimated and compared to the actual sale transactions in 2020. The results of historical analysis of the buyer choices under these two behaviors are presented in Supplementary Information , which is a statistical measure indicating how much variation of a dependent variable is explained by the independent variable, is determined. The results, presented in Table 2. In other words, the neighborhood quality term is more responsible for the housing price variation in comparison to the other factors considered above.The first statistical test is an analysis of variance to investigate the different weights that the critical features—bedroom number, square footage, acreage, building age, and neighborhood quality—have over the housing prices. Using Eq. , explainThe second statistical test involves a 2D heatmap, shown in Fig. 33. In simple words, PCA is a method of extracting the most influential parameters in a dataset. First, the dataset needs to be standardized for overcoming scaling impacts and other features that may inaccurately affect the analysis. Second, the number of principal components is calculated by drawing a horizontal line at an eigenvalue equal to one; the number of components with an eigenvalue larger than one equals the number of main principal components. The PCA reveals that housing prices consist of two main principal components (PC), which are responsible for about 40% and 30%, respectively, of the variation in housing prices. The results of this analysis are provided in Supplementary Information , a dimensional reduction technique used to reveal strong patterns in a datasetThe PCA analysis was used to construct the biplot graph illustrated in Fig. at the city boundaries to achieve sustainable development. We evaluate the projected growth of Boulder for the year 2040 under the four different development scenarios identified in Table Our next step is to assess how flood risk affects the behavior of the developer agent and how the policies extracted from the previous step can be applied in the future urban expansion of the community by the developer agent within the city boundaries and the Growth Model that mimics the urban expansion at the city boundaries. This feature helps us to understand household locational choices.The framework developed in this study enables planners to investigate the role of human behavior in achieving resilient communities susceptible to floods through sustainable development. This framework is a holistic toolset that helps a community to adopt risk mitigation policies that are compatible with human behavior. Such policies will be more acceptable to people than other nonstructural flood mitigation measures, such as acquisition and buyout that require large social and economic investments. The core strength of the proposed framework is the behavioral urban growth model which is the integration of the Relocating Model that simulates the dynamics of urbanization The analysis of the real estate agent has revealed that, for the testbed used in this study, the housing prices are lower inside the floodplains compared to the prices outside the floodplains, leading to more sale transactions in the floodplains if the buyer agents exhibit Risk-Negligence behavior. Such behavior increases housing choices inside the 100-year and 500-year floodplains to 17% and 29%, respectively, up to 2040. On the other hand, if the buyers exhibit Expected Utility behavior to make risk-informed locational choices, housing choices inside the 100-year and 500-year floodplains will decrease as much as 2% and 15%, respectively. This result emphasizes the need for educating individuals who are seeking housing about flood risk to different properties. Over time, this will reduce the popularity of properties in areas susceptible to floods and allow policymakers and stakeholders to implement measures such as buyout and acquisition with less social and economic costs and political resistance. Neighborhood quality, defined as the accessibility to green spaces, water bodies, highways, education facilities, and the city center favors areas leading to more choices by the Risk-Negligent buyers in such locations. Neighborhood quality is most influenced by accessibility to education centers, highways, the city center, water bodies, and green spaces. These findings encourage planners to adopt socioeconomic incentives, according to this rank, that promote urbanization in less vulnerable areas in floods, leading to reducing future flood risk to the community. One point to note is that this rank order may be different for other cities and the detailed proposed framework in this paper can be used to extract the most influential variables in people decisions on their locational choices and be imposed on the community as socioeconomic incentives to shape urbanization toward building resilient communities in floods. Finally, the developer behavior at the city boundary itself can result in safer communities if flood risk is included in their estimation of their expected return.34. Also, to model the decision under risk we have assumed that the agents’ behavior is rational and make a fully informed decision about their locational choices. Future work will expand the current behavioral study to that of decision-making under limited information. Another avenue of further work could include the role of climate change and how repetitive flooding—learning by experience—may affect the decision of households under risk. Finally, the current study has been performed for the case of flooding as the dominant hazard in the region, but we see no reason the current framework could not be expanded to other hazards as well.All models have limitations, and this model is no exception. For example, in this study, we have not considered the geomorphological and hydrological changes in the region brought about by the combined effect of urbanization and climate change, which could increase the extent of the floodplainsIn this study, we have focused on investigating the effect of human behavior as one of the underlying factors that affects urbanization over time. We also examined how perceptions of flood risk can change the decisions of individuals as to where to locate and what characteristics of a neighborhood entice households to make such decisions. This information was used to weigh the merits of various nonstructural mitigation measures for flooding in terms of socioeconomic incentives to move urbanization toward creating sustainable communities that are resilient to flooding. Our proposed framework employs a behavioral urban growth model that uses ABM to evaluate the effect of human behavior as influenced by urbanization on community vulnerability to floods. The observations from our analysis addressed some of the long-stated challenges in considering the human behavior factors that add non-stationarity in future flood risk and render employed disaster risk reduction measures less effective. The results demonstrated when individuals make an informed decision about their housing choices, housing choices inside the 100-year and 500-year floodplains are reduced by 2% and 15%, respectively. Moreover, under the proper socioeconomic incentives, derived from the preferences of individuals on their housing choices, the percentage of future urban growth of the community inside the 100-year and 500-year floodplains can be reduced from 16% to 5% by the year 2040 for the selected testbed in this study. We aim that this study will initiate a dialogue in a new direction of quantifying the role of human behavior in flood risk assessment of communities. Our framework enables policymakers and local authorities to achieve a more accurate picture of the future of their communities, to employ mitigation measures for protecting cities from flood hazard, and to perform tradeoffs between costs and benefits of future land development in investing in sustainable and resilient communities.35. Individuals’ behavior—sellers, buyers, real estate, and developers—and their interactions affect urbanization over time. These behaviors can be influenced by perceptions of flood risk. Modeling human behavior and its effect on the built environment is a complex endeavor as individuals do not make decisions randomly, but decide based on their knowledge, characteristics, and resources36. The question that may arise here is that how we can model human behavior. ABM is a technique that can be used to simulate behaviors of autonomous entities and has applications in geographic and urban systems such as pedestrian modeling, traffic simulation, residential dynamics, and urban growth models of cities and regions. This modeling technique provides an opportunity to study the behavior of entities and their heterogeneity on urban systems and their role in shaping their environment36.Urbanization is the process of land-use change that is affected by the interaction of social, biophysical, economic, and political entitiesRelocating Model which is responsible for simulating the dynamic of urbanization inside the current city limit and the Growth Model that mimics urban expansion at the city boundary over time. These two models are connected through the supply and demand of the real estate market. Some buyers may prefer to live within the city boundary while others select new housing in suburban areas. Thus, the presence of these two sub-models is essential in simulating the urbanization process over time accurately.The employed behavioral urban growth model in this study uses ABM to capture human behavior and its impact influenced by flood risk on urbanization over time. This model consists of two parts: inside the city boundary. To do so, the real estate agent provides an estimation of housing prices using a hedonic price model29 which is a function of the structural, neighborhood, and environmental characteristics, as below:This model simulates the interaction of real estate, seller, and buyer agents to assess how the interactions of these agents shape the dynamic of urbanization β, are determined by ordinary regression analysis:The function, This function is trained using historical datasets from 2010 to 2020. The structural variables used in this model are building age, bedroom number, bathroom number, and square footage. The neighborhood and environmental variables considered are accessibility to highways, green spaces, water bodies, city center, educational facilities, the sale transaction year, and the existence within 100-year or 500-year floodplains. As the simulation proceeds, the successful transaction will be added to re-train the regression and re-assess the coefficients to account for the dynamic of the land market and changes in the housing prices through the years. The last two variables in Table The seller agent is the other effective entity in the Relocating Model that participates in this process by providing the housing options and adding them to the real estate market. The seller decides to relocate or leave the study region. Regardless of the incentives, s/he aims to maximize profit. For modeling the seller's behavior, we need to calculate the number of sellers and the location of sale within each time step. The number of sellers is calculated using the yearly number of historical sale transactions within the study region from 2010 to 2020. These numbers are used to determine the mean and standard deviation of a normal distribution. Then, using the distribution characteristics, we generate random numbers as the available housings within the market at any time step. The location of sale is also calculated randomly from the available housing in the study region. More details regarding the seller agent can be found in Supplementary Information Section 30. In this scenario, the buyer utility function is calculated as below:The buyer agent is one of the most critical components of the Relocating Model. Accordingly, a buyer is a household that seeks housing that can maximize its utility. Whether the buyer considers the flood risk on their decision or not, s/he decides where to locate according to two behaviors: Risk-Negligence and Expected Utility. These two behaviors are considered to account for a full range of possible actions from the buyer within the market. Risk-Negligence happens when a household does not consider risk when s/he searches for a property to buy. This point does not mean that s/he is unaware of the risk. In fact, flood risk is not considered as a substantial factor when s/he wants to offer a bid price. Therefore, their decision about the locational choices is limited to housing prices. S/he forms a utility for a property based on hedonic analysis of sale price that is a bundle of structural and neighborhood characteristicsd on Eq. , as belo37. This value serves as a benchmark for the average property loss in the case of flooding which households decide on their choice in buying the property. Moreover, In this equation, the coefficient of 0.25 accounts for the average insurance damage claims which is equal to 25% of the property valuesIn Eq. , \documeThe real estate, seller, and buyer agents in the Relocating Model interact through the negotiation process. Buyers and sellers set an expectation of their bid and ask prices. To register a successful sale transaction, these expectations should be within a specific threshold. Using the actual housing prices in the market, the real estate agent will provide feedbacks for both buyers and sellers and help to achieve a successful sale transaction. If the buyers and sellers stay in the market and experience unsuccessful sale transactions, they adjust the expectations of their bid and ask prices to maximize their utility and their profit, respectively. Comprehensive details of the negotiation process are explained in Supplementary Information Section at the city boundary resulting from converting the undeveloped to the developed lands and expanding the city boundary. This process occurs because of interaction between the developer, real estate, and buyer agents. The real estate and buyer agents are identical to the relocating agent model. On the other hand, the developer agent is responsible for providing the housing options as a result of buying undeveloped lands and covert them to developed areas to maximize its profit. For modeling the developer agent behavior, we use a Cellular Automata, described in detail in Supplementary Information Section 38.This model mimics the dynamic of urbanization Here, we modified Eq.  to accou39, a loss estimation platform developed by the Federal Emergency Management Agency to calculate the floodplain characteristics for various flooding scenarios, including 100- and 500-year return periods. Based on a review by Banks et al.40, HAZUS-MH was identified as the best tool for the flood damage assessment compared to other available tools such as MIKE flood, water RIDE, hydrologic engineering center flood impact analysis (HEC-FIA). In addition, Tate et al.41 suggested that the results obtained from the default hydraulic analysis in HAZUS-MH are suitable for regional analysis, which further confirmed the suitability of using HAZSU-MH in our study since we focused on regional analysis as well. It should be noted that uncertainties in the floodplain characteristics calculated by HAZUS-MH are large due to the simplifications in the model. Moreover, changes in surficial geology and hydrology due to urbanization are not considered11. Some studies  have quantified these changes by coupling more detailed hydrologic-hydraulic software, such as HEC-HMS42 and HEC-RAS43 to land-use projection models. However, since the objective of this study is to investigate the effect of human behavior on urbanization and exposure to risk and to introduce a methodology for evaluating different nonstructural strategies in terms of socioeconomic incentives and land-use policies, HAZUS-MH is sufficient for our purposes.The flood hazard in this study is measured by its frequency, intensity (water depth and velocity), and the extent of flooding, known as the floodplain. We use the coupled hydrologic-hydraulic analysis in the flood module of HAZUS-MHNormal behavior: This policy is designed to evaluate the developer's behavior when the agent does not consider the flood risk on their decision. Accordingly, the developer preferences are considered using a map calculated by Equations Risk-informed behavior: This policy is defined to evaluate how the consideration of flood risk by developer agent will change the city expansion over time. Equations Policy I: As the statistical analysis has revealed, accessibility to educational facilities is the most influential aspect of buyers’ decision on where to locate. After that, commercial facilities, highways, parks and water bodies are important the most. Based on Policy I, we build educational facilities as well as shopping centers in northern Boulder to promote urbanization in this direction. These incentives will affect the suitability term of Eq. (m of Eq. .Policy II: Policy II focuses on encouraging urbanization in the southern part of Boulder by constructing parks and lakes. This will also impact this process by increasing the suitability of southern Boulder where is less susceptible to flood events compared to eastern Boulder. Again, these incentives will affect the suitability term of Eq. (m of Eq. .In this study, we focus on nonstructural flood mitigation measures in terms of socioeconomic incentives that are designed to be compatible with human behavior. The behavioral urban growth model reveals information about the critical factors in people’s decisions on their locational choices. This information is used to design socioeconomic strategies encouraging households to the safer location gradually with time. As the developer agent is responsible for simulating the growth of the community over time, the first two scenarios, Normal Behavior and Risk-informed Behavior, are considered to investigate the behavior of the developer agent behavior on urbanization. Additionally, we define the Policy I and Policy II scenarios according to the information revealed in the statistical analysis to be applied, as the future development plans, by the developer agent to shape the communities moving toward resilience. Each of the development scenarios is explained below:Supplementary Information.


The tourism sector is a driver of economic development characterised by its environmental impact. It is a prevalent part of the 2030 Agenda, given its potential to help meet the Sustainable Development Goals (SDGs). At the same time, board gender diversity is considered essential for companies to implement environmentally sustainable initiatives. However, analysis of the relationship between the role of women on boards and environmental performance has been neglected in the tourism literature. This paper adopts a novel approach to the study of this sector by analysing the relationship between gender diversity on the board of directors and companies’ environmental practices. A fixed effects model is estimated using an international sample of 120 listed tourism companies for the period 2002 to 2019. The results show that boards that are more gender diverse and have a greater female presence are associated with poorer environmental performance and a weaker implementation of policies and practices to reduce resource use and emissions. However, board gender diversity aids performance in environmental innovation. In September 2015, the United Nations Assembly approved the 2030 Agenda, defined as a ‘plan of action for people, planet and prosperity’ ,5. HowevGiven the interrelationships among the SDGs, tourism activity indirectly affects other challenges in addition to those mentioned earlier. For example, according to Borja et al. , the envFrom a social perspective, the potential of the tourism sector to create employment  means thLinked to the previous discussion, there is a close relationship between gender diversity in the tourism sector and the environment. Adopting a gender focus to understand the differentiating impact of tourism in terms of sustainability is crucial . ChangesHowever, despite the extensive literature on tourism in recent decades, research on gender diversity in this sector and its consequences remains a minor topic ,21. MoreTo summarise, the discussion so far highlights the importance of the tourism sector as a driver of economic development and a potential agent of change in the fight against climate change, the key role of gender diversity in environmental sustainability and the virtually non-existent evidence of this relationship in tourism companies. These are the principal motivations for this research. The aim of this study is to analyse the influence of tourism companies’ board gender diversity on these firms’ environmental practices. A fixed effects model was estimated using an international sample of 120 listed tourism companies for the period 2002 to 2019.This research makes several contributions to the literature. First, this study offers what seems to be the first ever analysis of the influence of board gender diversity on environmental performance that focuses exclusively on the tourism sector. A further novel feature of the study is the use of not only a general environmental performance indicator as the dependent variable but also indicators for each of the three dimensions of environmental performance: reduction of resource use, reduction of emissions and innovation. Given the distinctive characteristics of this sector and its strategic role in the fight against climate change, evidence of the factors that condition corporate governance and the consequences for firms’ environmental performance is valuable. This study thus confirms the importance of considering gender-related issues when explaining initiatives by tourism companies to help care for the planet. The study also shows the importance of reaching a critical mass of women on the board of directors in order for their presence to influence environmental policy. This theory has been virtually ignored in studies of gender diversity and environmental sustainability . FinallyThis paper is divided into six sections, the first being this introduction. Next, The relationship between the presence of women on the board of directors and CSR performance is supported by several theories. According to these theories, gender diversity contributes to the effectiveness of boards of directors. This position is supported by various arguments used to provide a theoretical foundation in the literature ,34,35. HThe first, resource dependence theory , is baseThe second theory, stakeholder theory , presentThese organisational theories are complemented by social role theory , which iIn relation to these arguments, critical mass theory also refers to the number of women on the board of directors . Under tThis section addresses the main theories underpinning the relationship of interest in this research. The next section discusses the evidence of the role of gender diversity in corporate environmental performance.Multiple studies have shown that board gender diversity plays an essential role in a company’s environmental performance ,73. ThesHowever, the literature is far from offering a consensus on the positive influence of gender diversity on environmental performance. This lack of consensus is reflected in Assuming that the leader’s gender determines performance in terms of corporate practices, Furthermore, the positive influence of board gender diversity on environmental performance has also been corroborated for various sectors, even though sector is a conditioning factor for the sign and significance of the causal relationship between gender and environmental performance . SpecifiBesides showing that greater inclusion of women on the board positively influences environmental performance in general, some studies have focused on specific aspects of the environment. For example, Biswas et al.  found thSeveral authors also claim that although the presence of women directors leads to the implementation of social practices, the same cannot be said of environmental performance. For instance, Cuadrado-Ballesteros et al.  and BejiFocusing on specific issues, female representation on the board does not influence the company’s approach to climate change . MoreoveFinally, some studies have shown a negative relationship between these two variables. However, there are considerably fewer than those showing a positive or neutral relationship, as reflected by Ardito et al.  and Shu In conclusion, although some studies suggest the absence of a relationship between board gender diversity and corporate environmental performance, or even that this relationship is negative, the bulk of the literature provides evidence of the positive role of women directors in improving corporate environmental performance. Following this reasoning and the argument that women show greater concern for the environment and tend to take actions that are favourable, or at least less harmful, to the planet , the folHypothesis 1 (H1).The presence of women on the board contributes to better environmental performance in the tourism sector.Hypothesis 2 (H2).The presence of women on the board contributes to better environmental performance by reducing the use of resources in the tourism sector.Hypothesis 3 (H3).The presence of women on the board contributes to better environmental performance by reducing emissions in the tourism sector.Hypothesis 4 (H4).The presence of women on the board contributes to better performance in terms of environmental innovation in the tourism sector.This study used a sample of 120 tourism companies from 26 countries across five continents. The data spanned the period of 2002 to 2019. This period was chosen because these years corresponded to the earliest and most recent years for which environmental, social and governance (ESG) data were available at the time of data collection. These tourism companies are publicly listed, and on 25 March 2020, they had at least 4 years of available data. The selection criterion of publicly listed status was applied because data availability is better for listed companies. Moreover, the implementation of environmental initiatives requires substantial investment in technology, infrastructure and human resources, and listed companies have better access to funds for such practices than unlisted companies . A minimThe selected companies were drawn from an initial sample of 399 listed companies in the Thomson Reuters Eikon database . Of thesAccording to the sector classification proposed by Refinitiv , the samTo measure the environmental performance of tourism companies, four indicators from the Thomson Reuters Eikon  databaseTherefore, in this study the dependent variables were taken as the overall environmental performance score and its three categories: resource use (20 items), emissions (28 items) and innovation (20 items). This approach lent robustness to the results, thanks to the use of various measures, while helping identify the importance of each of the areas of environmental performance. This study thus addresses the shortcomings mentioned earlier. To help identify the information covered by the environmental measures, Finally, regarding the method, calculation of the environmental score involved obtaining the relative value for each category and then calculating the overall index. Therefore, the starting point was to determine the score for each category as a weighted average of the groups of data for the industry that comprise it. Accordingly, the weighting for each group varied by industry. These data were previously generated by assigning a score to each of the areas measured by the category. Proxies were used where it was not possible to find data for a specific variable. The category scores were normalised and expressed as percentages ranging from 0 to 100. Finally, the score for the environmental pillar was calculated using the sum of the values resulting from the weightings of its three categories. The resource use and emissions categories were given a weighting of 35%, and the innovation category was given a weighting of 29% .Thus, the four environmental variables used in this study took values ranging from 0 to 100, where 0 indicates the worst possible environmental performance and 100 denotes the best possible environmental performance .This section presents and justifies the choice of independent variables. These variables can be divided into two groups. The first consists of variables of interest used to address the research aims. The second group consists of control variables included to ensure that the econometric model was correctly specified. The data used to create these variables were gathered from the Thomson Reuters Eikon database . On the A large number of gender variables were selected. In fact, this study used more such variables than many other studies of the influence of board gender diversity on environmental performance ,82,83. TDum1, Dum2 and Dum3.The rationale for using these gender measures is that several studies have reported that women are more likely to engage in socially responsible practices given their greater focus on stakeholders , and in Dum1 identifies whether merely having at least one woman director plays a relevant role in environmental performance. For example, Shoham et al. [Dum2. However, a higher proportion of studies have shown that companies with at least three female directors can ensure that women have a stronger role on the board [Dum3 was included in the model. In addition, some authors, such as Kyaw et al. [The variable m et al.  showed tm et al.  and Atifm et al.  have rephe board ,115, payhe board ,70 and fw et al.  and Shohw et al. , do not Dum30 and Dum40 were included to control for the possibility of significant differences in business performance between companies with at least 30% and 40% of women directors, respectively, and companies that did not meet these thresholds. The aim was to explore the possible repercussions of corporate governance gender quotas, such as those proposed by the European Parliament and Council Directive [The variables irective . Regardiirective  reportedirective , achieviirective  reportedirective  reportedirective . They hairective .Nwom and Pwom are also essential. The variable Nwom was included because socially responsible companies have more women than non-socially responsible companies [Pwom) is also important because a higher proportion of women directors positively affects corporate environmental performance [The variables ompanies ,62. In aompanies ,84 such ompanies . The levformance ,25,87. Tformance  and enviformance . Such enformance ,85, the formance  and enviformance .Blau was included. This index is the most widely used diversity index in the literature, with several studies citing it as a good measure of diversity [Finally, the variable iversity ,122. Lu iversity  and Burkiversity  reportediversity : Blau inIndepMem, AveTen, EnvTra and Ndir), and two were financial indicators, namely the size of the company (Temp) and financial performance (return on assets or ROA).The second group of independent variables consists of the control variables, which are presented in IndepMem). Several studies have shown the positive influence of board independence on the number of green initiatives [First, the degree of independence of the board was included in the model, measured as the percentage of independent directors  is also important. Although some studies suggest that smaller boards are more effective, participatory and cohesive [The variable cohesive , larger cohesive . For inscohesive , particucohesive ,129.Temp was included. This measure is often used in the business performance literature as indicative of firm size [ROA) is a critical factor in environmental performance because it influences whether companies will have the resources and capabilities needed for more effective environmental initiatives [Finally, larger companies have better environmental performance  because irm size ,116. Simtiatives . Accorditiatives  and Nacitiatives , have reTo achieve the research aims, as in previous studies ,87,117, EnvSc is the overall environmental score. ResUseSc is the resource use score. EmiSc is the emissions score. InnoSc is the innovation score. Gen corresponds to each of the eight selected gender measures. IndepMem refers to the percentage of independent board members. Ant is the average number of years for which board members remain on the board. EnvTra is a dummy variable that takes the value 1 if employees receive green training, and 0 otherwise. Ndir is the number of board members. Temp is the number of employees at the company. ROA is the return on assets. The term p value (Hausman: FE/RE)” in Tables 7–10, reveal the existence of correlation between the explanatory variables and the individual unobservable effect. Therefore, the fixed effects model was used.As these equations are static linear equations based on panel data, they can be estimated using two procedures: a fixed effects model or a random effects model. The choice of model is based on verification of the assumption of the absence of correlation between the unobservable individual effect and the explanatory variables. To do so, the Hausman test is used. If the results of this test lead to the rejection of the hypothesis of absence of correlation, the only consistent estimator is the fixed effects estimator. Conversely, if this hypothesis is not rejected, both estimators are consistent, but the random effects estimator is the efficient estimator . The resp value (Hausman: FEIV/FE)” reflect this result. For this test, the first lags of the explanatory variables were used as instruments [The next step required verification of the assumption of exogeneity of the independent variables. This condition is essential for the fixed effects estimator to be consistent . The evitruments .Finally, estimation was performed using a variance–covariance matrix of errors that were robust to heteroscedasticity between individuals and to serial correlation of the errors of a given individual.Before presenting the results of the analysis, this section offers a characterisation of the sample by presenting descriptive statistics for the variables used in the study . The meaResUseSc). This value is higher than the corresponding value for the overall score (EnvSc). The mean score for the reduction of emissions category (EmiSc) is only a few points lower (34.61). The lowest mean value (8.96) is for innovation (InnoSc), which is considerably lower than the rest. The four environmental scores have a strikingly wide range, as reflected by the minimum and maximum values. This wide range is consistent with the high dispersion of the observations with respect to the aforementioned means. This dispersion is greater than 30 points for EnvSc, ResUseSc and EmiSc, and is equal to 22.11 points for InnoSc.The statistics relating to the environmental variables reveal low scores overall and by category. The arithmetic mean is below the midpoint score (50) for each of the four measures. For the three categories within the overall index, the highest mean (37.13) corresponds to the reduction of resource use .In line with these data, the percentiles show the poor environmental commitment of the sampled companies and the significant difference between the innovation category and the categories of reduction of resource use and emissions. For example, although 50% of the observations (P50) in the Nwom). Female representation is low and reflects little gender diversity in the sampled firms. Specifically, the average percentage of female directors is only 15.3% (see Pwom), with 75% of the observations for this variable taking a value of less than 22.2% (see P75). Although there is at least one case in which women have a representation of 57.1% (see Max for Pwom) and therefore outnumber the male directors, the data show that gender parity at the top management level is still a distant goal. For example, female representation on the board is at least 30% for only 11.9% of observations (Dum30), and only 3.6% of observations have at least 40% female representation (Dum40). Similar results, which imply a clear under-representation of women on the boards of tourism companies, have already been reported by Barrientos-Báez et al. [Analysis of the gender variables shows that some companies did not have women directors in the period analysed . On average the directors have been on the board for almost 8 years (AveTen). Finally, 40.2% of the observations indicate that the sampled tourism companies have environmental training policies (EnvTra). The number of employees at these companies ranges massively, from 15 to 508,714 (Temp). The sampled tourism companies are profitable, with an average return on assets (ROA) of 7.76%. In addition, more than 75% of the observations indicate positive profitability (P25).With respect to the remaining measures, starting with the governance variables, nearly 60% of board members are independent ”.This section presents the results of the econometric estimation of the equations stated earlier . The aimEnvSc, ResUseSc and EmiSc  had statistically significant coefficients at a confidence level of 95 or 99%. In all cases, these coefficients were negative. Only in the estimation for ResUseSc was the coefficient for Dum30 statistically significant at the 90% confidence level. This coefficient was also negative. Second, in the case of the dependent variable InnoSc, the results clearly differ from those for the other dependent variables. Only the coefficients associated with the variables Dum30 and Nwom were statistically significant at the 90% and 95% confidence level, respectively , an increase in the number of women on the board (Nwom), an increase in the proportion of women (Pwom) and greater gender equality among directors (Blau) result in a lower environmental performance score .Therefore, these results provide statistical evidence of a negative influence of board gender diversity on corporate environmental performance. This result is robust because it emerged in several estimates with different dependent variables. Specifically, an increase in the number of women on the board to at least three , between companies with at least two women on the board and those with fewer than two (see Dum2 coefficients), and between companies with a minimum representation of 40% women on the board and those that do not meet this threshold (see Dum40 coefficients).Third, the statistical evidence from all estimates also indicates no differences in terms of environmental performance between companies without female directors and companies with women on the board . Several arguments support these results. First, there is a stronger relationship between the presence of women and environmental innovation than with the other environmental measures considered in this study. Accordingly, the low female representation does not interfere with the positive effect of women’s presence on environmental innovation initiatives . MoreoveThis study also supports critical mass theory, with the results confirming the need for there to be at least three women on the board for these female directors to influence environmental actions. In a study of environmental litigation, Liu  reportedFurthermore, the empirical evidence fails to confirm the need for at least 40% female representation on the board of directors. This percentage has been proposed by the European Parliament and Council Directive . NeverthAccording to the results, environmental performance is also conditioned by variables other than gender. Specifically, overall environmental performance, resource use and emissions reductions, and environmental innovation are positively influenced by policies to provide environmental management training to employees. Companies with larger boards have better overall environmental performance, as well as adopting a greater number of policies on resource use and emissions reductions. Furthermore, these three environmental performance variables are also positively related to the average tenure of board members. However, tourism companies perform worse in environmental innovation as the percentage of independent board members increases. Finally, higher ROA leads to greater innovation.H1, H2 and H3 are not supported. The results of the analysis reveal a different relationship from the predicted relationship in the estimates for three of the four dependent variables. However, the significant positive coefficients in the estimates using environmental innovation as the dependent variable provide support for hypothesis H4.To conclude, for the most part, hypotheses This study examined 120 listed companies from the global tourism sector over the period 2002 to 2019. The aim was to analyse whether board gender diversity conditions these companies’ environmental performance. The analysis, which was based on a fixed effects model, leads to the following conclusions.The empirical evidence essentially shows that a greater female presence on the board is associated with worse environmental performance. This finding holds for the case of both overall environmental performance and the specific categories of policies and practices related to reducing the use of resources and lowering emissions. The analysis provides robust evidence of this negative influence, which may result from the internal conflicts that arise due to the greater range of opinions and perspectives in gender-diverse groups. Consequently, it is more difficult to reach a consensus on critical decisions. This lack of consensus leads to less effective decision making. Moreover, judging by the low representation of women, any discrepancies that arose in the companies studied would probably be resolved in favour of men , thus under-utilising female talent.However, the opposite relationship was observed for environmental innovation in two of the eight gender measures. According to this relationship, the performance of tourism firms improves as the number of female board members increases. This finding can be explained by the positive differential effect of female board members on innovation, which may overlap with the above-mentioned conflicts in the decision-making process. This study also supports critical mass theory by showing the need to achieve female representation of at least three women in order for female directors to exert a significant influence on corporate environmental performance.This research is valuable given the lack of studies that address gender issues in management and their effect on environmental performance in the tourism sector. The findings of this study have practical implications for managers of tourism companies, political decision makers and legislators. First, the under-representation of women on the boards of companies in the tourism sector indicates the existence of a glass ceiling for women in the corporate hierarchy. This limitation has consequences for companies in terms of their environmental performance. This situation highlights the need for regulations in tourism and the ongoing support of diversity education in society to empower women and secure equal conditions for women in the workplace. Specifically, the recommendation is for the implementation of gender quotas at the board level, sanctions for pay gaps between men women, and work-life balance policies to help reconcile work commitments with maternity, childcare and family obligations. Similarly, incentives through tax policies or social recognition should be provided to firms that offer promotions to women. At the same time, tourism managers’ commitment to gender diversity is essential. Firms should implement human resource policies based on equal opportunities for men and women that cover all processes, from selection and training to promotion and compensation.In addition, the lack of involvement of these organisations in caring for the planet is a warning sign for entrepreneurs in this sector, who must increase their awareness and implementation of environmental practices given that their activity depends heavily on the natural resources of the local area. Accordingly, the managers of these firms should create programmes to raise awareness and manage their environmental impact. Such programmes represent the only way to create policies and actions that contribute to corporate environmental education and that build collective awareness and involvement in terms of the need to care for the environment. This care for the environment offers the only way to guarantee access to the resources that this sector depends on for generations to come.Therefore, there is a need for greater involvement from public administrations, which have a duty to encourage the implementation of these practices through environmental policies and incentives. Such policies should entail sanctions for the abuse of natural resources, the control and reduction of pollution, and the enforcement of reporting on environmental impact and the measures taken to curb this impact. Likewise, improvements in firms’ environmental behaviour can be promoted through tax incentives for meeting certain environmental goals or through recognition for best practices in this area, thereby enhancing the corporate image of these firms.Finally, this study has some limitations. The first limitation relates to the lack of data availability for most of the tourism firms identified in the initial search. This lack of data availability meant that many of these firms were omitted from the study. Another difficulty stems from the use of composite indicators for the environmental performance scores. The use of such indicators can mask important information. Furthermore, the analysis did not control for country effects. Doing so may be important given the cultural diversity and the range of gender and environmental regulations across different countries. Finally, this study did not consider qualitative aspects such as the skills and values of female directors because of the complexity involved in measuring these factors. Analysis of these aspects may help shed light on the relationship between board gender diversity and environmental performance.One proposal is for future research to use other environmental performance variables that measure more specific elements. Doing so could improve the specification of certain practices. For example, it would be of interest to focus on specific topics addressed in the SDGs relating to environmental issues. The aim would be to obtain indicators that exclusively reflect information on certain items relating to one or more of the targets within a given SDG. Doing so could reveal the involvement of companies in achieving these crucial global goals, as well as providing evidence of the influence of gender diversity in achieving these SDGs. Similarly, it would be of interest to perform analysis with a geographical focus to control for the characteristics of each individual country or region. Finally, other qualitative variables such as the age, education and experience of women managers should be studied as gender variables.


Online charitable crowdfunding has become an increasingly prevalent way for Canadians to deal with costs that they would otherwise not be able to shoulder on their own. With the onset of COVID-19 and related lockdown measures, there is evidence of a surge in crowdfunding use relating to the pandemic. This study gathered, classified, and analysed Canadian crowdfunding campaigns created in response to COVID-19 from GoFundMe.com, a popular crowdfunding platform. Spatio-temporal analysis of classified campaigns allowed for observation of emergent trends in the distribution of pandemic-related need incidence and financial support throughout the pandemic. Campaigns raising money on behalf of established charities were the most common in the sample, and accounted for the greatest portion of funding raised, while campaigns for businesses made up a small proportion. Dense metropolitan areas accounted for the vast majority of campaign locations, and total sample funding was disproportionately raised by campaigners in Ontario and British Columbia. In times of crisis, charitable crowdfunding serves as a popular method to connect those willing to give with those in need. From the half-billion dollars raised by the American Red Cross on behalf those impacted by the 2010 Haiti earthquake [In recent years charitable crowdfunding has expanded into the online space, allowing for individual people or groups to host ’campaigns’ on behalf of their particular cause in hopes of raising money directly from people in their personal network and beyond. GoFundMe, the largest of these platforms, has raised over $9 billion worldwide for campaigns on their platform since its launch in 2010, and in 2018 mediated roughly 90% of U.S. and 80% of global charitable crowdfunding dollars , 4. GoFuIn addition to being more accessible to most people, the popularity of online crowdfunding platforms has also allowed researchers greater access to data on the topic, and thus more opportunity to analyze and investigate the manifestations of online charitable crowdfunding at large. Given the wide variety of platforms and absence of sanctioned access to this data, a diverse set of methods has been utilized in the literature to gather, analyze, and describe the various dimensions of online crowdfunding. Crowdfunding at large is investigated through the analysis of campaigns created by people seeking financial assistance associated with a need demonstrated by the content of the campaign, which are often manually searched and catalogued by researchers to develop a cohort pertaining to a specific topic from which conclusions can be drawn –7. In coIn light of the expanding body of research on the topic, many researchers have been critical of the ethical and societal implications of this new mode of donation based crowdfunding, and have increasingly found that it exacerbates inequality by awarding crowdfunding dollars not by relative need, but by ability to appeal to an audience through mastery of digital media and media literacy , 13. RatThe COVID-19 pandemic has been another crisis where individual people and groups have turned to crowdfunding for help and to aid others. Between March 1 and August 31, 2020, over $625 million was raised globally across 150,000 campaigns created to support causes related to the COVID-19 pandemic; in the US, approximately 60% of pandemic-related campaigns were created to support small businesses and those dealing with unemployment . These fCanada has been no exception to the economic hardship caused by the COVID-19 pandemic. From February to April 2020, about 5.5 million Canadian workers had their employment situation negatively affected by the economic shutdowns, and in the proceeding period from May to September 2020, over 8.75 million unique applications were made to the Canada Emergency Response Benefit (CERB), equating to over $76 billion in funds being dispersed to Canadians in just 6 months , 18. As In this article we provide a snapshot in time of COVID-19 related Canadian crowdfunding campaigns during the first 6 months of the pandemic. Specifically, the spatio-temporal characteristics of these campaigns are analyzed and visualized to show the patterns of campaign creation and success throughout Canada between January and June 2020. The aggregation and analysis of this data creates an opportunity to understand not only the needs that emerged in Canada as a result of the pandemic, but also where and when they arose relative to significant events in the pandemic timeline. This analysis is original in its spatio-temporal focus, using a map and several figures to visualize where in Canada COVID-19-related campaigns were created, when campaign creation for specific pandemic-related needs were most prevalent, and how these factors relate to campaign fundraising success.Since campaign data are not publicly available for bulk download, it must be gathered using algorithms to ’scrape’ data directly from the website if large volumes of data  are desired. This method of data collection has previously been used to target and gather campaigns raising money for a specific purpose, which are then quantitatively analyzed to examine the qualities of these campaigns. For example, Duynhoven et al. used scraped campaigns to analyze the spatial trends in Canadian crowdfunding campaigns for cancer , and morThe focus of this research is to explore the characteristics of Canadian crowdfunding campaigns created for reasons related to the COVID-19 pandemic. We gathered campaigns from only one platform, GoFundMe, due to its popularity, broad user appeal, and because combining campaigns across platforms can bring out platform-specific effects in the data . The expwww.gofundme.com using a Python algorithm. The scraper gathered all of the campaigns resulting from searching "Canada" AND "COVID-19" via the GoFundMe search bar on June 30th, 2020, which amounted to 915 search results in total, each of which were saved to a local database. Four reviewers then examined a random sample of 100 of these campaigns, twice. Based on this examination and subsequent reviewer discussions, 6 content categorizations were developed to describe the underlying funding motivations present in the campaign sample. Shown in Crowdfunding campaigns were scraped from Campaign categorizations were generated as a result of two collaborative discussion sessions held after each author reviewed a subsample of campaigns. During the discussions, authors promulgated the main themes of their subsamples and drew from extensive previous experience reviewing crowdfunding campaigns to develop a collaborative content taxonomy that included the prominent categories of campaigns in the sample while simultaneously narrowing down the campaign types to 6 specific areas that aptly described the entire sample when assigned appropriately. After the categories were developed, a single reviewer analyzed each campaign, removing those campaigns that were either not created in Canada, were created outside the prescribed study period from January—June 2020, or were not directly motivated by COVID-19 related circumstances. The start of the time period (January 2020) was chosen as a safe estimate for the earliest possible time for which COVID-19 campaigns may have been created, while the period end date (June 2020) was chosen so as to capture the campaigns from the first 6 months of the pandemic to identify emerging crowdfunding responses to COVID-19.www.gofundme.com for each individual campaign, which are the subject of the following analysis.During this process the reviewer also assigned one of the six type descriptors to each campaign in the sample. At this stage, each campaign consisted of seven attributes, each attribute corresponding to a data column: Campaign title, Description, Date of Creation, Funding Dollars Raised, Funding Dollars Requested, Location, and Type, shown in www.gofundme.com, each of which were examined for inclusion criteria; 342 campaigns were removed during this process. The remaining 573 campaigns were analyzed spatially, temporally, monetarily, and according to content, corresponding with the variables of campaigner location, date of campaign creation, funding dollars raised per campaign, and campaign funding, respectively. The main reasons why campaigns were not included in the analysis was due to invalid campaign content (not directly related to COVID-19), invalid location (not in Canada or not in a specific Province or Territory), or because the location was unable to be geocoded.A total of 915 campaigns were initially scraped from First, the campaigns are counted and shown by type . CampaigNext, the count of COVID-19 related campaigns created per day was plotted against the reported number of daily new confirmed COVID-19 cases in Canada, with major related news events superimposed in series . The numCampaign types were then plotted as a time series by date of creation to show the temporal distribution of need as it arose during the pandemic . CampaigFinally, Distributions of campaigns according to campaign funding objective showed similarities with other analyses of COVID-19 campaigns, specifically the prevalence of campaigns for charities , PPE, businesses, and general areas of personal need . However, the campaigns in the present study showed a much higher proportion of campaigns for charitable organizations (accounting for over 50% of campaigns in the sample), and more particularly campaigns for charities within the same country/community . Campaigns for PPE were also very common in both studies .Campaigns for businesses struggling as a result of the pandemic and related lockdown measures were surprisingly few in number and in dollars raised, accounting for only about 6% of both campaigns in the sample and funding dollars raised despite widespread coverage of the fact that significant numbers of small businesses were and are facing bankruptcy and closure , 23. ThiCampaigns for personal need in Canada made up about 14% of campaigns in the sample and almost 12% of dollars raised , which, while a significant amount of money, was lower than expected relative to the other categories given the broad definition of ’personal need’ used here and the wide variety of potential personal needs that can arise during a pandemic. For example, a previous campaign analysis found that campaigns for funerals, family/friend support, and food/supplies  together made up 31% of sample COVID-19 related campaigns , it coulCampaigns for family reunification, although relatively few in comparison to other categories, still raised almost $90,000 in total, and were included to demonstrate this distinct type of need. As opposed to campaigns for businesses, it seems likely that campaigns for family reunification were prevalent at the beginning of the pandemic due to the abrupt nature of border closures and orders for Canadians to return home from abroad, but tapered off as everyone who needed to arrive home had already done so, demonstrated by the absence of campaigns in this category from the last week of the study period .Temporal trends in campaign creation showed that the initial spike in campaigns coincided precisely with the first surge in COVID-19 cases in Canada . CampaigSpatial analysis of campaigns showed a clear urban-rural divide in Canada, where the vast majority of campaigns were created in densely populated, urban areas. This is undoubtedly due to asymmetries in Canada’s population distribution, as demonstrated by the vast majority of funding dollars also being raised in the most populous Provinces, however it could also be due to lower levels of access to communication technologies in rural communities or to the strength of voluntary or informal care sectors in rural communities were residents look inwards to friends and neighbours for support rather than outwards to society at large .This study focused on analyzing the spatio-temporal patterns in crowdsourced data from GoFundMe.com. As such, it is differentiated from studies using similar data with a focus on inferential statistics . StudiesThe present study acknowledges limitations to the methods used in gathering and analyzing crowdfunding campaigns. First, it is entirely possible that the search protocol over/under exaggerated certain types of campaigns, or entirely missed campaigns that would have otherwise met inclusion criteria. For example, the large number of campaigns on behalf of Canadian charities included in the sample could be due in part to the utilization of ’Canada’ in the GoFundMe search, which would favour campaigns for organizations like the ’Canada Food Bank’ or ’Canadian United Way’ as the word ’Canada’ is explicitly used in the title and campaign description. In contrast, campaigns for an individual person looking for donations from their local network would perhaps not include the word ’Canada’, even if the campaign was in Canada because it would be self-evident to those donating. Given the nature of the campaign data and that the only way to access it is through the front-end of the website , it is impossible to say whether or not all the relevant campaigns were included. However, since all the campaigns included in the final analysis were individually examined and vetted for inclusion criteria, we can say that the sample represents a robust, though likely incomplete, collection of relevant campaigns.Another limiting factor is that the sample here represents a snapshot of campaigns at the time they were harvested. This means that, although campaigns as far back as January 2020 were gathered, it is possible that many campaigns were created and then deleted before having a chance to be gathered. This could lead to an over representation of successful campaigns, as unsuccessful campaigns are rather more likely to be deleted after a short time . This snCharitable crowdfunding has become an increasingly popular way for people to deal with unmanageable expenses, and the expenses incurred by many millions of Canadians over the last twelve months due to COVID-19 are no exception. Collection and analysis of COVID-19 related crowdfunding campaigns from GoFundMe revealed a significant number of Canadians who had turned to crowdfunding to help themselves personally, but more so on behalf of charitable organizations in Canada and abroad. The spatio-temporal approach allowed for identification of trends across Canada and across time with respect to COVID-19 and how the pandemic developed from its beginnings in February to the lull in cases and relaxing of restrictions in June. Funding and campaign need type were the primary dimensions of analysis, both of which showed clear patterns when analyzed across time and space in Canadian provinces, with the most pronounced numbers of campaigns and funding dollars raised being in BC and Ontario during the months of April and May. Campaign creation was also greatest in the period between when the initial lockdown measures were imposed and when the Canada Emergency Response Benefit was announced.The study design in terms of campaign collection, classification, and analysis allowed for campaigns on the charitable crowdfunding website GoFundMe to be used as a proxy for understanding how these types of platforms facilitate need in times of crisis, as well as the specific areas of need in Canada that were revealed as a result of the pandemic. While other studies on COVID-19-crowdfunding have been published, our results demonstrate that spatio-temporal specificity allows for a nuanced study in how pandemic-related needs and crowdfunding change over time and react to events and policies in specific contexts. Future studies utilizing a similar collection protocol should aim to minimize the campaign selection bias in the collection process by performing multiple searches using utilizing multiple search terms across a period of weeks or months, to ensure the greatest possible number of relevant campaigns are collected.S1 Data(TAR)Click here for additional data file.


This review examined the main issues debated in Korea regarding the production and use of materials containing naturally occurring asbestos (NOA) as impurities, and investigated the impacts of these debates on the asbestos ban, as well as the future implications. In Korea, incidents associated with the production and use of NOA-contaminated talc powders, construction rocks, serpentinites, and dolomite rocks raised public concern and led to accelerating the ban on asbestos. The main controversies concern policies on appropriate asbestos content limits, whether materials containing a trace amount of NOA should be banned, and the control of materials with high human exposure risk. To address recurring controversies, the implementation of preventive measures to manage elongated mineral particles and the use of transmission electron microscopy for more sensitive analysis need to be discussed, along with reaching social agreement on the controversial policies. To minimize the potential exposure to asbestos that may occur during the production and use of industrial minerals in the future, it is necessary to apply occupational exposure control measures and monitor the health effects of the relevant population groups. These national policies on NOA should be prepared based on close collaboration and discussion with policymakers, industry stakeholders, and related academic experts. Asbestos is a term that refers to six types of hydrous magnesium silicate minerals that exhibit asbestiform, a fiber type with long and thin fibers and fiber bundles . CommercFrom 1940 to 2008, before the ban on asbestos use, Korea’s cumulative import volume of asbestos was 1,796,152 tons .Naturally occurring asbestos (NOA) is a term for asbestos contained in rocks and soils that occurs naturally in geological processes . Huge re2, which is 5.5% of the total land area of the country [In South Korea, rocks that contain NOA are distributed over 5514 km country . From th country . Asbesto country . Since t country .The production and use of industrial minerals, along with a variety of asbestos-disrupting activities in NOA-contaminated areas, can result in occupational or environmental exposure to NOA. In Korea, the presence of NOA in imported rocks and rock powders was first known in 1998 but failed to attract public attention at that time . The facIn Korea, the first regulation on the use of asbestos can be found in the OSH Act of the Ministry of Employment and Labor  . The MOEAs interest in asbestos surged and exposure to asbestos from the soils and rocks contaminated with NOA became a social issue in Korea in the late 2000s, the MOE enacted the Asbestos Safety Management Act and implemented it in April 2012 to minimize environmental exposure to asbestos . The MOEThe Ministry of Food and Drug Safety (MFDS) of Korea sets quality standards for raw materials to ensure the quality and safety of pharmaceutical and hygiene products in accordance with the Pharmaceutical Affairs Act. The incident caused by baby powders produced using asbestos-contaminated talc powders prompted the MFDS to add the test method and criteria of asbestos in talc to the Korean Pharmacopoeia . The MFD3Si4O10(OH)2 [Talc is a phyllosilicate that has the chemical formula of MgO10(OH)2 . It is mIn some deposits of talc, tremolite or anthophyllite with the asbestiform crystal habit is found. The first incident caused by asbestos-contaminated talc powders in Korea was made public on 1 April 2009 by the media, who reported that asbestos was detected in five baby powder products out of the 12 available in the market. On the same day, the MFDS revealed that asbestos was detected in 11 baby powder products available in the market . Sales oThe panic caused by asbestos-contaminated talc powders made Korean people aware of the hazard of asbestos, as it was detected in baby powders, cosmetics, and medicines commonly used by many people. Because of this incident, the understanding of the asbestos problem, which was mainly perceived as an occupational health problem occurring in workplaces where asbestos is handled as a raw material in the manufacture of products such as building materials, brake pads, and gaskets in Korea, was expanded to include environmental exposure. Follow-up media coverage reported that there are regulations prohibiting the use of asbestos contained in talc in Europe and Japan, which led to the criticism of the Korean government and expert groups for failing to recognize and respond to the possibility of talc-containing NOA in advance. When the MFDS conducted a survey on asbestos contained in baby powders, no asbestos was detected from imported baby powder products. The decision of the MFDS to prohibit sales of 1122 pharmaceutical products already available in the market was opposed by pharmaceutical companies due to a lack of evidence of risks. The MFDS carried out large-scale administrative measures to relieve public anxiety, although the health risk from taking pharmaceuticals that used asbestos-contaminated talc powders was low. Pharmaceutical companies had to accept losses due to the use of hazardous materials in their products, even though the raw materials were legally certified by the government, which led to lawsuits filed by pharmaceutical companies against the government. The incident was a starting point to discuss the management of NOA unintentionally contained in products in Korea. The comprehensive measures for asbestos management prepared by the National Policy Coordination Meeting of the government in July 2009 included content on the asbestos ban and NOA management as main topics, while the Asbestos Safety Management Act containing the discussed measures came into effect in April 2012 .On 10 February 2009, an NGO revealed that the construction rocks produced in a quarry near an abandoned asbestos mine in Chungcheongbuk-do were sold to more than 210 government-ordered construction sites across the country . The geoThe use of asbestos-contaminated construction rocks showed that it is necessary to examine the presence of NOA in areas where mineral resources are obtained. Since the quarry was in proximity to the abandoned asbestos mine and located on the opposite slope of the same mountain, the presence of asbestos was highly likely. However, this was not considered by the authorities when the quarry company obtained permission to mine for construction rocks. It was also discussed that the level of asbestos content control limits for rocks must be established and analyzed. Some believed that determining whether the asbestos content control limit had been exceeded was difficult, since analytical results varied significantly due to the non-uniform content of NOA even after specifying a set value. Additionally, the analysis of rocks through crushing could increase the detection limit because asbestos could be diluted with non-asbestos particles in the rock. The MOE specified that “no asbestos must be exposed on the surfaces of rocks.” An inspection method that used target sampling on parts where asbestos was suspected to be present on the rock surface was to be implemented as a standard for banning asbestos in construction rocks, as specified in the Asbestos Safety Management Act .3Si2O5(OH)4, which exists in nature as lizardite, antigorite, or chrysotile [Serpentine is a group of magnesium silicate hydrate minerals with the chemical formula of Mgrysotile . SerpentDespite its various applications, serpentinite can contain chrysotile. The fact that chrysotile-contaminated serpentinite was used as an auxiliary material in three steel mills in Korea was revealed by an NGO on 27 January 2011 . At thatThis incident caused controversy over the NOA control limit. Despite a demand that mines must be prevented from mining asbestos-contaminated serpentinite, there was no law applicable at that time. The OSH Act banned products that contain more than 0.1% of asbestos, although the MOEL interpreted this regulation as applicable only to processed products and not mined rocks . The MOE3)2, and is a type of sedimentary rock [Dolomite is an anhydrous carbonate mineral with the chemical formula of CaMg is widely used. In PLM, fine asbestos particles are not visible due to the low resolution of the light-optical system . XRD canproducts . Severalproducts ,54,55. Iproducts . TherefoKorea’s experiences regarding NOA demonstrate that when implementing asbestos bans, the production, import, and use of rocks unintentionally containing NOA as well as the intentional use of asbestos must be considered in order to reduce unnecessary controversies and public concern regarding asbestos. To address recurring controversies over the production and use of NOA-containing materials, policies on whether materials containing a trace amount of NOA should be banned, appropriate asbestos content permissible limits, and the control of materials with high human exposure risk should be established in consideration of national circumstances. It is necessary to discuss whether the asbestos ban will be extended to EMPs from a preventive point of view and the use of TEM for more sensitive analysis of NOA. To minimize future asbestos-related diseases, policies to manage exposure to NOA, such as the application of engineering controls, use of personal protection equipment, periodic asbestos concentration monitoring, health checks, and warnings against the possibility that a material contains asbestos, are required along with the asbestos ban. Continuous surveillance and ad hoc studies in populations particularly exposed to the problem of NOA should be implemented to monitor possible adverse health effects of NOA. These national policies on NOA should be established based on close cooperation and examinations from industry stakeholders and policymakers, together with experts in the fields of industrial hygiene, environmental science, health science, and mineralogy.