An active literature utilizes natural disaster data to analyze damage determinants and estimate future costs of climate change. However, despite its importance in research and policy, no international standard exists to quantify damages, and the impact of damage data quality on empirical estimates remains an open question. Using the case of tropical cyclone landfalls in China, we analyze three damage datasets: official Chinese government records, CRED's International Disaster Database, and Munich Re's NatCatSERVICE. We begin by systematically comparing damage entries across the three datasets. We then use the data to estimate historical damage functions. Lastly, we utilize the damage functions to project the future costs of climate and economic change. We find that damage data quality matters. While the estimated economic determinants of historical damage functions are similar across the three datasets, we estimate differences in the cyclone intensity coefficients. These variations in damage functions lead to divergence in projections of future damages by almost three times, with average annual future loss estimates ranging between $4 and $11 billion. Similar to previous literature, we call for more internationally standardized disaster damage reporting.
Understanding local encounters with large carnivores is important for promoting sustainable coexistence. The use of smartphones and social media in geographically remote areas offers a novel avenue to study human–wildlife encounters from a local perspective. We conducted a content analysis of mobile videos on social media (n = 207) to characterize human encounters with snow leopards, gray wolves, and brown bears on the Tibetan Plateau in China. We also used ethnographic interviews to understand the backgrounds and motivations of videographers. Results show large carnivore encounters are not necessarily conflictual. Over half of encounters are neutral without observable interference between people and predators. The likelihood of a “negative encounter” is significantly associated with the target species, the distance between the videographer and the animal, the level of human influence in the surroundings, and the presence of other animals (i.e., dogs and livestock). Local Tibetan herders document unusual encounters with carnivores using videos for various reasons, but what is deemed unusual is context‐dependent and fluid. Our study demonstrates that social media videos can provide valuable insights into the diversity and complexity of human–wildlife interactions. We encourage conservationists to develop visual participatory programs to better engage local people in conservation knowledge production.
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