ALBERT

Description: Ambient temperature has been identified as a potential cause for human conflict in a variety of studies. Conflict is no longer limited to the physical space but exists in the form of hate and discrimination on social media. Here we provide evidence that the amount of racist and xenophobic content posted to the social media platform Twitter is nonlinearly influenced by temperature. Exploiting the linguistic plurality of Europe, we statistically analyse daily temperature data and more than 10 million racist tweets from six different countries spanning several climate zones for the years 2012-2018. Using a fixed-effects panel regression model that utilizes exogenous variation in local weather and controls for unobserved omitted variables, we identify the effect of population-weighted daily average temperature on the daily number of racist tweets and likes. We find a quasi-quadratic temperature response of racist tweets that is inversely proportional to the temperature distribution. Fewest racist tweets and likes are found for daily average temperatures between 5°C and 11°C, i.e. temperatures that are frequently experienced. Temperatures warmer or colder than that are associated with steep, nonlinear increases. Analyses at the country-level confirm this climate comfort zone of 5°C to 11°C across different European climatic zones. In the Southern European countries this is colder than the most frequently experienced temperatures, pointing to possible limits of adaptation. Within the next 30 years, the number of days outside this climate comfort zone, weighted by the identified temperature-racist-tweet response curve, will increase across parts of Europe, indicating that rising temperatures could aggravate xenophobia and racism in social media.

Description: Background: A link between weather and aggression in the offline world has been established across a variety of societal settings. Simultaneously, the rapid digitalisation of nearly every aspect of everyday life has led to a high frequency of interpersonal conflicts online. Hate speech online has become a prevalent problem that has been shown to aggravate mental health conditions, especially among young people and marginalised groups. We examine the effect of temperature on the occurrence of hate speech on the social media platform Twitter and interpret the results in the context of the interlinkage between climate change, human behaviour, and mental health. Methods: In this quantitative empirical study, we used a supervised machine learning approach to identify hate speech in a dataset containing around 4 billion geolocated tweets from 773 cities across the USA between May 1, 2014 and May 1, 2020. We statistically evaluated the changes in daily hate tweets against changes in local temperature, isolating the temperature influence from confounding factors using binned panel-regression models. Findings: The prevalence of hate tweets was lowest at moderate temperatures (12 to 21°C) and marked increases in the number of hate tweets were observed at hotter and colder temperatures, reaching up to 12·5% (95% CI 8·0–16·5) for cold temperature extremes (–6 to –3°C) and up to 22·0% (95% CI 20·5–23·5) for hot temperature extremes (42 to 45°C). Outside of the moderate temperature range, the hate tweets also increased as a proportion of total tweeting activity. The quasi-quadratic shape of the temperature–hate tweet curve was robust across varying climate zones, income quartiles, religious and political beliefs, and both city-level and state-level aggregations. However, temperature ranges with the lowest prevalence of hate tweets were centred around the local temperature mean and the magnitude of the increases in hate tweets for hot and cold temperatures varied across the climate zones. Interpretation: Our results highlight hate speech online as a potential channel through which temperature alters interpersonal conflict and societal aggression. We provide empirical evidence that hot and cold temperatures can aggravate aggressive tendencies online. The prevalence of the results across climatic and socioeconomic subgroups points to limitations in the ability of humans to adapt to temperature extremes.

Description: Elevated annual average temperature has been found to impact macro-economic growth. However, various fundamental elements of the economy are affected by deviations of daily temperature from seasonal expectations which are not well reflected in annual averages. Here we show that increases in seasonally adjusted day-to-day temperature variability reduce macro-economic growth independent of and in addition to changes in annual average temperature. Combining observed day-to-day temperature variability with subnational economic data for 1,537 regions worldwide over 40 years in fixed-effects panel models, we find that an extra degree of variability results in a five percentage-point reduction in regional growth rates on average. The impact of day-to-day variability is modulated by seasonal temperature difference and income, resulting in highest vulnerability in low-latitude, low-income regions (12 percentage-point reduction). These findings illuminate a new, global-impact channel in the climate–economy relationship that demands a more comprehensive assessment in both climate and integrated assessment models.

Description: Understanding the behavioral response dynamics to risks is important for informed policy-making at times of crises. Here we elucidate two response channels to Covid-19 risk and show that they weakened over time, prior to the availability of vaccines. We employ fixed-effects panel regression models to empirically assess the relationship between actual Covid-19 risk (daily case numbers), the perceived risk (attention paid to the pandemic via related Google search requests) and the resulting behavioral response (personal mobility choices) over two pandemic phases for 113 cities in eight countries, while accounting for government interventions. Prolonged exposure to Covid-19 reduces risk perception which in turn leads to a weakened behavioral response. Attention responses and mobility reductions across all three mobility types are weaker in the second phase, given the same levels of actual and perceived risk, respectively. Our results provide evidence that the risk response attenuates over time with implications for other crises evolving over long timescales.

Description: Background: Heat exposure, which can negatively affect human health and wellbeing, is heterogeneous within US cities. However, little is known about who can avoid heat stress by adjusting their everyday behaviour. We aimed to analyse the effect of ambient temperature on mobility, specifically subway (ie, the underground railway system) use, in New York City, NY, USA, during 2014–19. - Methods: For this empirical study, subway use across New York City was measured with turnstile data from the New York City Metropolitan Transportation Authority between Jan 1, 2014, and Dec 31, 2019. Passenger numbers were then aggregated to the zip code tabulation area (ZCTA) level. Daily observational climate data were obtained from the US National Weather Service between Jan 1, 2014, and Dec 31, 2019. Socioeconomic data at the ZCTA level originated from the American Community Survey 2019. We extracted data on population age, ethnicity, commuting, employment, median household income, rent, and health-insurance coverage. We used a fixed-effects panel-regression model to assess the influence of temperature on subway use in New York City, which was the main outcome of our study. - Findings: We obtained data for 438 subway stations across New York City. After data cleaning and preprocessing, the final aggregated data sample consisted of 238 508 instances of subway use in 1955 days across 6 years for 122 ZCTAs, with 168 days missing in the raw data and 67 days removed as outliers. The results of the fixed-effects panel-regression analysis showed a strong, non-linear effect of daily maximum temperature on subway use. Subway use was highest at 11·5°C and substantially decreased for temperatures that were colder and warmer than that, with reductions reaching 6·5% (95% CI 2·5–10·5) for the coldest temperature (ie, –6·5°C) and 10·5% (6·0–14·0) for the hottest temperature (ie, 34·5°C). Reductions differed between weekdays and weekends, when residents generally had more freedom to adjust their behaviour. Neighbourhoods that were at a socioeconomic disadvantage experienced smaller or no reductions in mobility in heat; mobility increased in neighbourhoods with beach access. - Interpretation: Our study showed that temperature had a strong, non-linear effect on subway use, but the magnitude of the effect on subway use was heterogeneous across areas of the city on warm days. Weaker avoidance of heat stress correlated with less privilege, indicating compounding health risks. Everyday behavioural adaptation to heat is therefore an effect pathway that contributes to unequal heat effects and should be explored in future research.