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Abstract
Species status assessments (SSAs) are required for endangered species by the U.S. Fish and Wildlife Service and focus on the resiliency, redundancy, and representation of endangered species. SSAs must include climate information, because climate is a factor that will impact species in the future. To aid in the inclusion of climate information, a decision support system (DSS) entitled Climate Analysis and Visualization for the Assessment of Species Status (CAnVAS) was developed by the State Climate Office of North Carolina using a coproduction approach. In this study, users viewed a mock-up version of the CAnVAS interface displaying a sample layout of future projections for three key climate variables (average precipitation, average maximum temperature, and occurrence of maximum temperature) at a location of interest. This assessment of the pilot version of the CAnVAS DSS was the first step in refining CAnVAS for species-manager use. This research analyzed the differences in usability between two pilot versions of the CAnVAS DSS through eye tracking and subsequent interviews with novice users. The two pilot versions of CAnVAS differed in the way data were displayed on graphs and the color ramps used on regional maps. We found that graphically displaying temporal climate information through box-and-whisker plots and spatially through a sequential color ramp from white to purple were more effective than alternative displays at communicating climate information on endangered species. The results of this research will be used to further develop the CAnVAS DSS tool for future implementation.
Significance Statement
A decision support system was developed for U.S. Fish and Wildlife Service biologists to incorporate more climate information in species status assessments for endangered species. This tool was tested through eye tracking and interviews with a novice undergraduate student sample to best refine the tool for stakeholder use. This work was able to discover that graphically displaying data in box-and-whisker format and spatially displaying data with a sequential color scheme of white to purple was best for usability purposes. The authors provide these recommendations for those who are producing usable products.
Abstract
Species status assessments (SSAs) are required for endangered species by the U.S. Fish and Wildlife Service and focus on the resiliency, redundancy, and representation of endangered species. SSAs must include climate information, because climate is a factor that will impact species in the future. To aid in the inclusion of climate information, a decision support system (DSS) entitled Climate Analysis and Visualization for the Assessment of Species Status (CAnVAS) was developed by the State Climate Office of North Carolina using a coproduction approach. In this study, users viewed a mock-up version of the CAnVAS interface displaying a sample layout of future projections for three key climate variables (average precipitation, average maximum temperature, and occurrence of maximum temperature) at a location of interest. This assessment of the pilot version of the CAnVAS DSS was the first step in refining CAnVAS for species-manager use. This research analyzed the differences in usability between two pilot versions of the CAnVAS DSS through eye tracking and subsequent interviews with novice users. The two pilot versions of CAnVAS differed in the way data were displayed on graphs and the color ramps used on regional maps. We found that graphically displaying temporal climate information through box-and-whisker plots and spatially through a sequential color ramp from white to purple were more effective than alternative displays at communicating climate information on endangered species. The results of this research will be used to further develop the CAnVAS DSS tool for future implementation.
Significance Statement
A decision support system was developed for U.S. Fish and Wildlife Service biologists to incorporate more climate information in species status assessments for endangered species. This tool was tested through eye tracking and interviews with a novice undergraduate student sample to best refine the tool for stakeholder use. This work was able to discover that graphically displaying data in box-and-whisker format and spatially displaying data with a sequential color scheme of white to purple was best for usability purposes. The authors provide these recommendations for those who are producing usable products.
Abstract
A long-term goal for warning-message designers is to determine the most effective type of message that can instruct individuals to act quickly and prevent loss of life and/or injury when faced with an imminent threat. One likely way to increase an individual’s behavioral intent to act when they are faced with risk information is to provide protective action information or guidance. This study investigated participant perceptions (understanding, believing, personalizing, deciding, milling, self-efficacy, and response efficacy) in response to the National Weather Service’s experimental product Twitter messages for three hazard types (tornado, snow squall, and dust storm), with each message varying by inclusion and presentation of protective action information placed in the tweet text and the visual graphic. We also examine the role of prior hazard warning experience on message perception outcomes. To examine the effects, the experiment used a between-subjects design in which participants were randomly assigned to one hazard type and received one of four warning messages. Participants then took a post-test measuring message perceptions, efficacy levels, prior hazard warning experience, and demographics. The results showed that, for each hazard and prior hazard experience level, messages with protective action guidance in both the text and graphic increase their understanding, belief, ability to decide, self-efficacy, and response efficacy. These results reinforce the idea that well-designed messages that include protective action guidance work well regardless of hazard type or hazard warning experience.
Significance Statement
Preventing injury and/or loss of life during a hazardous event is a prime concern for disaster communicators. The study provides insights to practitioners on how to effectively communicate protective actions to audiences with varying familiarity with the hazard through Twitter posts. We experimented with tweet message design and content for three hazards: tornado, snow squall, and dust storm, to find that posts that include protective action guidance in both the text and image increase participant perceptions that they could perform the suggested protective actions, regardless of hazard type or hazard warning experience. Given our findings, practitioners should consider including protective action guidance in message text and graphic to warn members of the public with varied prior warning experience.
Abstract
A long-term goal for warning-message designers is to determine the most effective type of message that can instruct individuals to act quickly and prevent loss of life and/or injury when faced with an imminent threat. One likely way to increase an individual’s behavioral intent to act when they are faced with risk information is to provide protective action information or guidance. This study investigated participant perceptions (understanding, believing, personalizing, deciding, milling, self-efficacy, and response efficacy) in response to the National Weather Service’s experimental product Twitter messages for three hazard types (tornado, snow squall, and dust storm), with each message varying by inclusion and presentation of protective action information placed in the tweet text and the visual graphic. We also examine the role of prior hazard warning experience on message perception outcomes. To examine the effects, the experiment used a between-subjects design in which participants were randomly assigned to one hazard type and received one of four warning messages. Participants then took a post-test measuring message perceptions, efficacy levels, prior hazard warning experience, and demographics. The results showed that, for each hazard and prior hazard experience level, messages with protective action guidance in both the text and graphic increase their understanding, belief, ability to decide, self-efficacy, and response efficacy. These results reinforce the idea that well-designed messages that include protective action guidance work well regardless of hazard type or hazard warning experience.
Significance Statement
Preventing injury and/or loss of life during a hazardous event is a prime concern for disaster communicators. The study provides insights to practitioners on how to effectively communicate protective actions to audiences with varying familiarity with the hazard through Twitter posts. We experimented with tweet message design and content for three hazards: tornado, snow squall, and dust storm, to find that posts that include protective action guidance in both the text and image increase participant perceptions that they could perform the suggested protective actions, regardless of hazard type or hazard warning experience. Given our findings, practitioners should consider including protective action guidance in message text and graphic to warn members of the public with varied prior warning experience.
Abstract
Contemporary social science has produced little research on connections between climate change and crime. Nonetheless, much prior research suggests that economic insecurity may affect individual calculations of the cost and benefit of engaging in criminal behavior, and climate change is likely to have important economic consequences for professions like fishing that depend directly on the environment. In this paper, we test the possibility that climate change affects participation in maritime piracy, depending on the specific ways that it impacts regional fish production. Our analysis is based on piracy in East Africa and the South China Sea. These two regions are strategic in that both areas have experienced a large amount of piracy; however, rising sea temperatures have been associated with declines in fish production in East Africa but increases in the South China Sea. We treat sea surface temperature as an instrument for fish output and find that in East Africa higher sea surface temperature is associated with declining fish production, which in turn increases the risk of piracy, whereas in the South China Sea higher sea surface temperature is associated with increasing fish production, which in turn decreases the risk of piracy. Our results also show that decreases in fish production bring about a larger number of successful piracy attacks in East Africa and that increases in fish production are associated with fewer successful attacks in the South China Sea. We discuss the theoretical and policy implications of the findings and point out that as climate change continues, its impact on specific crimes will likely be complex, with increases and decreases depending on context.
Significance Statement
There is little evidence on the effect of climate change on criminal behavior. This study seeks to quantify the impact of a specific type of climate change—rising sea temperature—on maritime piracy, a type of crime that is linked exclusively to the ocean. The risk of piracy attacks and the probability of successful attacks are higher with declines in fish production in East Africa and lower with increases in fish production in the South China Sea. These results suggest that climate change does affect maritime piracy rates and that its effect depends on the specific situational context and the rational choices that changing sea temperatures generate.
Abstract
Contemporary social science has produced little research on connections between climate change and crime. Nonetheless, much prior research suggests that economic insecurity may affect individual calculations of the cost and benefit of engaging in criminal behavior, and climate change is likely to have important economic consequences for professions like fishing that depend directly on the environment. In this paper, we test the possibility that climate change affects participation in maritime piracy, depending on the specific ways that it impacts regional fish production. Our analysis is based on piracy in East Africa and the South China Sea. These two regions are strategic in that both areas have experienced a large amount of piracy; however, rising sea temperatures have been associated with declines in fish production in East Africa but increases in the South China Sea. We treat sea surface temperature as an instrument for fish output and find that in East Africa higher sea surface temperature is associated with declining fish production, which in turn increases the risk of piracy, whereas in the South China Sea higher sea surface temperature is associated with increasing fish production, which in turn decreases the risk of piracy. Our results also show that decreases in fish production bring about a larger number of successful piracy attacks in East Africa and that increases in fish production are associated with fewer successful attacks in the South China Sea. We discuss the theoretical and policy implications of the findings and point out that as climate change continues, its impact on specific crimes will likely be complex, with increases and decreases depending on context.
Significance Statement
There is little evidence on the effect of climate change on criminal behavior. This study seeks to quantify the impact of a specific type of climate change—rising sea temperature—on maritime piracy, a type of crime that is linked exclusively to the ocean. The risk of piracy attacks and the probability of successful attacks are higher with declines in fish production in East Africa and lower with increases in fish production in the South China Sea. These results suggest that climate change does affect maritime piracy rates and that its effect depends on the specific situational context and the rational choices that changing sea temperatures generate.
Abstract
Media such as Twitter have become platforms for contemporary Americans to express their opinions and allow for reaction to public opinion. Climate change is a topic of ongoing social concern. Machine-automated processing facilitates “big data” analysis and is suitable for analyzing a large corpus of tweets. This paper uses R tools to conduct sentiment calculation and emotion analysis on the tweet corpus from 2015 to 2018 to present the overall tendency of citizens’ attitudes toward climate change topics. The keyword analysis finds that people focus on the message’s source. “Climate change” has often been conflated with “global warming” in the popular consciousness. Supporters of the scientific consensus that human activities drastically affect Earth’s climate system express fear and surprise about extreme weather and opponents’ behavior, whereas opponents of that consensus express multiple emotional responses that include anger, disgust, and sadness toward politicians who, in their view, fabricate stories about climate change about which they have no real feelings. This study also reveals that the automatic annotation tools are still inadequate, with limited emotion lexicon and identification of negation and sarcasm.
Significance Statement
This research aims to perceive people’s emotional expressions on climate change as expressed on Twitter. The distributions of emotions across different opinion groups on climate change can be analyzed by manually annotating the opinion stances. Our first step is to research keywords to identify the main discussion topics. Then, automatic sentiment analysis with R software reveals the prevalent negative positions, and emotion analysis yields the primary emotions and the connection with different opinion groups, which logistic regression models test. These results better reflect the concerns of the population and provide support for climate policy. Future research could subdivide topics and develop field-specialized sentiment and emotion lexicons.
Abstract
Media such as Twitter have become platforms for contemporary Americans to express their opinions and allow for reaction to public opinion. Climate change is a topic of ongoing social concern. Machine-automated processing facilitates “big data” analysis and is suitable for analyzing a large corpus of tweets. This paper uses R tools to conduct sentiment calculation and emotion analysis on the tweet corpus from 2015 to 2018 to present the overall tendency of citizens’ attitudes toward climate change topics. The keyword analysis finds that people focus on the message’s source. “Climate change” has often been conflated with “global warming” in the popular consciousness. Supporters of the scientific consensus that human activities drastically affect Earth’s climate system express fear and surprise about extreme weather and opponents’ behavior, whereas opponents of that consensus express multiple emotional responses that include anger, disgust, and sadness toward politicians who, in their view, fabricate stories about climate change about which they have no real feelings. This study also reveals that the automatic annotation tools are still inadequate, with limited emotion lexicon and identification of negation and sarcasm.
Significance Statement
This research aims to perceive people’s emotional expressions on climate change as expressed on Twitter. The distributions of emotions across different opinion groups on climate change can be analyzed by manually annotating the opinion stances. Our first step is to research keywords to identify the main discussion topics. Then, automatic sentiment analysis with R software reveals the prevalent negative positions, and emotion analysis yields the primary emotions and the connection with different opinion groups, which logistic regression models test. These results better reflect the concerns of the population and provide support for climate policy. Future research could subdivide topics and develop field-specialized sentiment and emotion lexicons.
Abstract
Research has found that people who know the least about a topic are often very overconfident of their knowledge, while those who know the most often underestimate their knowledge. This finding, known as the Dunning–Kruger effect (DKE) has recently been shown to occur in knowledge of severe weather as well. The current study investigated whether being overconfident in one’s knowledge might translate into a tendency to make poorer sheltering decisions when faced with severe weather. Participants took two severe weather quizzes, one of perceived knowledge and one of objective knowledge. Participants also predicted their performance on both quizzes. The participants then saw four wireless emergency tornado warning alerts on a simulated smartphone screen, along with a tornado scenario, and then made two protective action decisions: one about immediately sheltering in place and the other the likelihood they would drive away. The results revealed that the participants did exhibit the DKE: those with the lowest levels of knowledge exhibited the most overconfidence while those with the highest levels of knowledge underestimated their performance. Also, in comparison with individuals with the most knowledge, those with the least knowledge were the most likely to state that they would not shelter immediately and that they would get in their car and drive away. Although more education is needed, the findings suggest a conundrum: those who know the least about severe weather, thinking they know a lot, are probably those individuals least likely to seek out additional education on the topic.
Significance Statement
Tornadoes are common in many states, and the National Weather Service issues tornado warnings in the hopes that individuals will take protective action. Previous research has found that people with low levels of knowledge (such as knowledge of severe weather) are often overconfident of their knowledge. This study explores whether those with low (as compared with high) severe weather knowledge make poorer decisions to a tornado warning. The findings show that those with the lowest knowledge were indeed overconfident and that they were less likely to shelter and more likely to drive away than those with high knowledge. The findings highlight that more severe weather education, although a worthy goal, might be difficult to implement if knowledge confidence is already high.
Abstract
Research has found that people who know the least about a topic are often very overconfident of their knowledge, while those who know the most often underestimate their knowledge. This finding, known as the Dunning–Kruger effect (DKE) has recently been shown to occur in knowledge of severe weather as well. The current study investigated whether being overconfident in one’s knowledge might translate into a tendency to make poorer sheltering decisions when faced with severe weather. Participants took two severe weather quizzes, one of perceived knowledge and one of objective knowledge. Participants also predicted their performance on both quizzes. The participants then saw four wireless emergency tornado warning alerts on a simulated smartphone screen, along with a tornado scenario, and then made two protective action decisions: one about immediately sheltering in place and the other the likelihood they would drive away. The results revealed that the participants did exhibit the DKE: those with the lowest levels of knowledge exhibited the most overconfidence while those with the highest levels of knowledge underestimated their performance. Also, in comparison with individuals with the most knowledge, those with the least knowledge were the most likely to state that they would not shelter immediately and that they would get in their car and drive away. Although more education is needed, the findings suggest a conundrum: those who know the least about severe weather, thinking they know a lot, are probably those individuals least likely to seek out additional education on the topic.
Significance Statement
Tornadoes are common in many states, and the National Weather Service issues tornado warnings in the hopes that individuals will take protective action. Previous research has found that people with low levels of knowledge (such as knowledge of severe weather) are often overconfident of their knowledge. This study explores whether those with low (as compared with high) severe weather knowledge make poorer decisions to a tornado warning. The findings show that those with the lowest knowledge were indeed overconfident and that they were less likely to shelter and more likely to drive away than those with high knowledge. The findings highlight that more severe weather education, although a worthy goal, might be difficult to implement if knowledge confidence is already high.
Abstract
NOAA’s National Weather Service (NWS) provides forecasts, warnings, and decision support to the public for the protection of life and property. The NWS Weather-Ready Nation model describes the process of applying weather information to achieve societal value. However, it is not clear how different racial and socioeconomic groups across the United States receive, understand, and act upon the weather information supplied under this model. There may be barriers that keep important, lifesaving information from the populations at the highest risk of severe weather impacts. This paper estimates the extent of racial and socioeconomic disparities in severe weather risk information reception, comprehension, response, and trust, as well as severe weather preparedness and risk perceptions in the United States. We use data from the University of Oklahoma’s Severe Weather and Society Survey, which is annually completed by a sample of 3000 U.S. adults (age 18+) that is designed to match the characteristics of the U.S. population. We pool data over four years (2017–20) to provide reliable severe weather risk prevalence statistics for adults by race, ethnicity, and socioeconomic characteristics. As a robustness check, we supplement this information with data from the annual FEMA National Household Survey. We find that racial and socioeconomic groups receive, understand, trust, and act upon severe weather information differently. These findings suggest that NWS and their partners should adjust their communication strategies to ensure all populations receive and understand actionable severe weather information.
Significance Statement
It is crucial that severe weather risk communication is received, appropriately interpreted, and trusted by all communities—especially the most vulnerable. Past research has not explained how different racial and socioeconomic groups receive, understand, and act upon NWS forecasts and warnings. This study finds that racial and socioeconomic groups receive, understand, trust, and act upon severe weather information differently. Risk communication strategies should be adjusted to eliminate barriers that keep important, lifesaving information from vulnerable populations.
Abstract
NOAA’s National Weather Service (NWS) provides forecasts, warnings, and decision support to the public for the protection of life and property. The NWS Weather-Ready Nation model describes the process of applying weather information to achieve societal value. However, it is not clear how different racial and socioeconomic groups across the United States receive, understand, and act upon the weather information supplied under this model. There may be barriers that keep important, lifesaving information from the populations at the highest risk of severe weather impacts. This paper estimates the extent of racial and socioeconomic disparities in severe weather risk information reception, comprehension, response, and trust, as well as severe weather preparedness and risk perceptions in the United States. We use data from the University of Oklahoma’s Severe Weather and Society Survey, which is annually completed by a sample of 3000 U.S. adults (age 18+) that is designed to match the characteristics of the U.S. population. We pool data over four years (2017–20) to provide reliable severe weather risk prevalence statistics for adults by race, ethnicity, and socioeconomic characteristics. As a robustness check, we supplement this information with data from the annual FEMA National Household Survey. We find that racial and socioeconomic groups receive, understand, trust, and act upon severe weather information differently. These findings suggest that NWS and their partners should adjust their communication strategies to ensure all populations receive and understand actionable severe weather information.
Significance Statement
It is crucial that severe weather risk communication is received, appropriately interpreted, and trusted by all communities—especially the most vulnerable. Past research has not explained how different racial and socioeconomic groups receive, understand, and act upon NWS forecasts and warnings. This study finds that racial and socioeconomic groups receive, understand, trust, and act upon severe weather information differently. Risk communication strategies should be adjusted to eliminate barriers that keep important, lifesaving information from vulnerable populations.
Abstract
A radar display is a tool that depicts meteorological data over space and time; therefore, an individual must think spatially and temporally in addition to drawing on their own meteorological knowledge and past weather experiences. We aimed to understand how the construal of situational risks and outcomes influences the perceived usefulness of a radar display and to explore how radar users interpret distance, time, and meteorological attributes using hypothetical scenarios in the Tampa Bay area (Florida). Ultimately, we wanted to understand how and why individuals use weather radar and to discover what makes it a useful tool. To do this, construal level theory and geospatial thinking guided the mixed methods used in this study to investigate four research objectives. Our findings show that radar is used most often by our participants to anticipate what will happen in the near future in their area. Participants described in their own words what they were viewing while using a radar display and reported what hazards they expected at the study location. Many participants associated the occurrence of lightning or strong winds with “red” and “orange” reflectivity values on a radar display. Participants provided valuable insight into what was and was not found useful about certain radar displays. We also found that most participants overestimated the amount of time they would have before precipitation would begin at their location. Overall, weather radar was found to be a very useful tool; however, judging spatial and temporal proximity became difficult when storm motion/direction was not easily identifiable.
Significance Statement
The purpose of this study is to understand how and why individuals use weather radar and to discover what makes radar a useful tool. We were particularly interested to explore how distance and time are thought about when using radar. We found that radar is generally a useful tool for decision-making except when a storm event was stationary. Participants use their personal experiences and knowledge of past weather events when they use a radar display. We also discovered that deciding how much time is available before rain occurs is often overestimated. These findings are helpful to understand how individuals use weather radar to make decisions that may help us to better understand protective action behavior.
Abstract
A radar display is a tool that depicts meteorological data over space and time; therefore, an individual must think spatially and temporally in addition to drawing on their own meteorological knowledge and past weather experiences. We aimed to understand how the construal of situational risks and outcomes influences the perceived usefulness of a radar display and to explore how radar users interpret distance, time, and meteorological attributes using hypothetical scenarios in the Tampa Bay area (Florida). Ultimately, we wanted to understand how and why individuals use weather radar and to discover what makes it a useful tool. To do this, construal level theory and geospatial thinking guided the mixed methods used in this study to investigate four research objectives. Our findings show that radar is used most often by our participants to anticipate what will happen in the near future in their area. Participants described in their own words what they were viewing while using a radar display and reported what hazards they expected at the study location. Many participants associated the occurrence of lightning or strong winds with “red” and “orange” reflectivity values on a radar display. Participants provided valuable insight into what was and was not found useful about certain radar displays. We also found that most participants overestimated the amount of time they would have before precipitation would begin at their location. Overall, weather radar was found to be a very useful tool; however, judging spatial and temporal proximity became difficult when storm motion/direction was not easily identifiable.
Significance Statement
The purpose of this study is to understand how and why individuals use weather radar and to discover what makes radar a useful tool. We were particularly interested to explore how distance and time are thought about when using radar. We found that radar is generally a useful tool for decision-making except when a storm event was stationary. Participants use their personal experiences and knowledge of past weather events when they use a radar display. We also discovered that deciding how much time is available before rain occurs is often overestimated. These findings are helpful to understand how individuals use weather radar to make decisions that may help us to better understand protective action behavior.
Abstract
Machine learning was applied to predict evacuation rates for all census tracts affected by Hurricane Laura. The evacuation ground truth was derived from cellular telephone–based mobility data. Twitter data, census data, geographical data, COVID-19 case rates, the social vulnerability index from the Centers for Disease Control and Prevention (CDC)/Agency for Toxic Substances and Disease Registry (ATSDR), and relevant weather and physical data were used to do the prediction. Random forests were found to perform well, with a mean absolute percent error of 4.9% on testing data. Feature importance for prediction was analyzed using Shapley additive explanations and it was found that previous evacuation, rainfall forecasts, COVID-19 case rates, and Twitter data rank highly in terms of importance. Social vulnerability indices were also found to show a very consistent relationship with evacuation rates, such that higher vulnerability consistently implies lower evacuation rates. These findings can help with hurricane evacuation preparedness and planning as well as real-time assessment.
Significance Statement
This study evaluates the usefulness of Twitter data, COVID-19 case rates, and the social vulnerability index from the Centers for Disease Control and Prevention/Agency for Toxic Substances and Disease Registry in predicting evacuation rates during Hurricane Laura, in the context of other relevant geographic, and weather-related variables. All three are found to be useful, to different extents, and this work suggests important directions for future research in understanding the reasons behind their relevance to predicting evacuation rates.
Abstract
Machine learning was applied to predict evacuation rates for all census tracts affected by Hurricane Laura. The evacuation ground truth was derived from cellular telephone–based mobility data. Twitter data, census data, geographical data, COVID-19 case rates, the social vulnerability index from the Centers for Disease Control and Prevention (CDC)/Agency for Toxic Substances and Disease Registry (ATSDR), and relevant weather and physical data were used to do the prediction. Random forests were found to perform well, with a mean absolute percent error of 4.9% on testing data. Feature importance for prediction was analyzed using Shapley additive explanations and it was found that previous evacuation, rainfall forecasts, COVID-19 case rates, and Twitter data rank highly in terms of importance. Social vulnerability indices were also found to show a very consistent relationship with evacuation rates, such that higher vulnerability consistently implies lower evacuation rates. These findings can help with hurricane evacuation preparedness and planning as well as real-time assessment.
Significance Statement
This study evaluates the usefulness of Twitter data, COVID-19 case rates, and the social vulnerability index from the Centers for Disease Control and Prevention/Agency for Toxic Substances and Disease Registry in predicting evacuation rates during Hurricane Laura, in the context of other relevant geographic, and weather-related variables. All three are found to be useful, to different extents, and this work suggests important directions for future research in understanding the reasons behind their relevance to predicting evacuation rates.
Abstract
Indigenous Peoples’ advocacy has enabled them to position themselves in global debates on climate change. Although the international community progressively acknowledges Indigenous Peoples’ contributions to climate action, their effective recognition in national climate governance remains marginal. This article analyses Indigenous Peoples’ recognition in the climate governance of Latin American states based on a document analysis of the Nationally Determined Contributions (NDCs) submitted between 2016 and March 2022. A content analysis and a frequency analysis were conducted on 30 documents. Mentions related to Indigenous Peoples in the NDCs are increasing; nevertheless, this recognition reproduces the multicultural approach that has characterized Latin American states’ legislations and thereby undermines the coherence of climate policy. The references mainly allude to cultural diversity and climatic vulnerability without addressing the ongoing territorial conflicts that mediate the relationship between Indigenous Peoples and states. Nor do the NDCs recognize the right of Indigenous Peoples to participate at the different levels of climate change decision-making processes. Intercultural recognition of Indigenous Peoples and better standards of participation in climate change governance are mandatory. However, states must first promote institutional transformations to address the historical and institutional factors that have produced Indigenous Peoples’ climate vulnerability and generate the necessary mechanisms to implement the recognition committed to in the NDCs.
Significance Statement
The decisions of the Conference of the Parties of the UN Framework Convention on Climate Change progressively encourage the participation of Indigenous Peoples and consider their knowledge in decision-making processes. Our article explores how this recommendation is assumed in Latin America through the analysis of Nationally Determined Contributions (NDCs)—the national pledges in the context of the Paris Agreement for the reduction of greenhouse gas emissions and adaptation to climate change. Our findings reveal that the mentions and recognition of Indigenous Peoples in NDCs are increasing. This recognition is not matched by promoting full and meaningful intercultural participation. In addition to generating mechanisms for effective participation, addressing the multiple historical and institutional drivers of Indigenous Peoples’ climate vulnerability is necessary.
Abstract
Indigenous Peoples’ advocacy has enabled them to position themselves in global debates on climate change. Although the international community progressively acknowledges Indigenous Peoples’ contributions to climate action, their effective recognition in national climate governance remains marginal. This article analyses Indigenous Peoples’ recognition in the climate governance of Latin American states based on a document analysis of the Nationally Determined Contributions (NDCs) submitted between 2016 and March 2022. A content analysis and a frequency analysis were conducted on 30 documents. Mentions related to Indigenous Peoples in the NDCs are increasing; nevertheless, this recognition reproduces the multicultural approach that has characterized Latin American states’ legislations and thereby undermines the coherence of climate policy. The references mainly allude to cultural diversity and climatic vulnerability without addressing the ongoing territorial conflicts that mediate the relationship between Indigenous Peoples and states. Nor do the NDCs recognize the right of Indigenous Peoples to participate at the different levels of climate change decision-making processes. Intercultural recognition of Indigenous Peoples and better standards of participation in climate change governance are mandatory. However, states must first promote institutional transformations to address the historical and institutional factors that have produced Indigenous Peoples’ climate vulnerability and generate the necessary mechanisms to implement the recognition committed to in the NDCs.
Significance Statement
The decisions of the Conference of the Parties of the UN Framework Convention on Climate Change progressively encourage the participation of Indigenous Peoples and consider their knowledge in decision-making processes. Our article explores how this recommendation is assumed in Latin America through the analysis of Nationally Determined Contributions (NDCs)—the national pledges in the context of the Paris Agreement for the reduction of greenhouse gas emissions and adaptation to climate change. Our findings reveal that the mentions and recognition of Indigenous Peoples in NDCs are increasing. This recognition is not matched by promoting full and meaningful intercultural participation. In addition to generating mechanisms for effective participation, addressing the multiple historical and institutional drivers of Indigenous Peoples’ climate vulnerability is necessary.
