scholarly journals Climate Change, Temperature, and Homicide: A Tale of Two Cities, 1895–2015

2020 ◽  
Vol 12 (1) ◽  
pp. 171-181 ◽  
Author(s):  
Michael J. Lynch ◽  
Paul B. Stretesky ◽  
Michael A. Long
Keyword(s):  
Climate Change ◽  
New York ◽  
Seasonal Temperature ◽  
Temperature Changes ◽  
Temperature Trends ◽  
Two Cities ◽  
Change Temperature ◽  
The Relationship ◽  
Seasonal Weather

AbstractIt has been argued that the temperature increase caused by anthropogenic climate change will produce a significant increase in violent crime. Support for that prediction is often based on statistical analyses of seasonal temperature and crime data cycles across days, months, and quarters and sometimes on large geographic areas. Within-year temperature changes are very large, however, relative to the 30-yr temperature increases employed to measure climate change. In addition, because temperature trends associated with climate change vary geographically, analyses should employ small geographic units for which temperature changes are measured over yearly intervals and for long periods of time. To address these conditions, this study examined the long-term temperature–crime association for homicides in New York and London for 1895–2015. Consistent with previous studies examining seasonal weather and crime patterns, we found a positive correlation between annual homicide rates and temperature, but only at the bivariate level. This relationship became statistically insignificant in both New York and London when gross domestic product is controlled. Moreover, the bivariate relationship between temperature and homicide is statistically insignificant when correcting for nonstationarity. Thus, it does not appear that climate change has led to higher rates of homicide in New York and London over the long term. These nonfindings are important because they suggest that studies of climate change and violence might do well to consider alternative mechanisms that mediate the relationship between climate change and violence.

The Climate Change-Temperature-Crime Hypothesis: Evidence from a Sample of 15 Large US Cities, 2002 to 2015

2020 ◽  
pp. 0306624X2096993
Author(s):  
Michael J. Lynch ◽  
Paul B. Stretesky ◽  
Michael A. Long ◽  
Kimberly L. Barrett
Keyword(s):  
Climate Change ◽  
P Value ◽  
Individual Level ◽  
Temperature Trends ◽  
Large N ◽  
Change Temperature ◽  
The Mean ◽  
The Individual ◽  
The Relationship ◽  
Us Cities

Drawing on prior studies, green criminologists have hypothesized that climate change will both raise the mean temperature and the level of crime. We call this the “climate change-temperature-crime hypothesis” (“CC-T-C”). This hypothesis is an extension of research performed on temperature and crime at the individual level. Other research explores this relationship by testing for the relationship between seasonality and crime within a given period of time (i.e., within years). Climate change, however, produces small changes in temperature over long periods of time, and in this view, the effect of climate change on crime should be assessed across and not within years. In addition, prior CC-T-C studies sometimes employ large geographic aggregations (e.g., the entire whole United States), which masks the CC-T-C association that appears at lower levels of aggregation. Moreover, globally, crime has declined across nations since the early 1990s, during a period of rising mean global temperatures, suggesting that the CC-T-C hypothesis does not fit the general trends in temperature and crime over time. Addressing these issues, the present study assesses the CC-T-C relationship for a sample of 15 large ( N = 15) US cities over a 14-year period. Given the CC-T-C hypothesis parameters, we assessed this relationship using correlations between individual crime and temperature trends for each city. Crime trends were measured by both the number and rate of eight Uniform Crime Report (UCR) Part I crimes, so that for each city, there are 16 crime-temperature correlations. Using a liberal p value ( p = .10), the temperature-crime correlations were rejected as insignificant in 220 of the 234 tests (94%). We discuss the Implications of this finding and suggest that rather than focusing on the temperature-crime relationship, green criminologists interested in the deleterious effects of climate change draw attention to its larger social, economic, environmental and ecological justice implications.

scholarly journals Special Issue on Climate Migration

2021 ◽  
pp. 1-3
Author(s):  
Anda David ◽  
Frédéric Docquier
Keyword(s):  
Climate Change ◽  
Human Mobility ◽  
Economic Research ◽  
Special Issue ◽  
Low Latitude ◽  
Weather Shocks ◽  
Temperature Trends ◽  
Agricultural Areas ◽  
Slow Onset

How do weather shocks influence human mobility and poverty, and how will long-term climate change affect future migration over the course of the 21st century? These questions have gained unprecedented attention in public debates as global warming is already having severe impacts around the world, and prospects for the coming decades get worse. Low-latitude countries in general, and their agricultural areas in particular, have contributed the least to climate change but are the most adversely affected. The effect on people's voluntary and forced displacements is of major concern for both developed and developing countries. On 18 October 2019, Agence Française de Développement (AFD) and Luxembourg Institute of Socio-Economic Research (LISER) organized a workshop on Climate Migration with the aim of uncovering the mechanisms through which fast-onset variables (such as weather anomalies, storms, hurricanes, torrential rains, floods, landslides, etc.) and slow-onset variables (such as temperature trends, desertification, rising sea level, coastal erosion, etc.) influence both people's incentives to move and mobility constraints. This special issue gathers five papers prepared for this workshop, which shed light on (or predict) the effect of extreme weather shocks and long-term climate change on human mobility, and stress the implications for the development community.

scholarly journals Maps, trends, and temperature sensitivities—phenological information from and for decreasing numbers of volunteer observers

2021 ◽  
Author(s):  
Ye Yuan ◽  
Stefan Härer ◽  
Tobias Ottenheym ◽  
Gourav Misra ◽  
Alissa Lüpke ◽  
...  
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Strong Decrease ◽  
Temperature Changes ◽  
Interpolation Accuracy ◽  
Observation Network ◽  
Shiny App ◽  
Long Term Trends ◽  
Autumn And Winter

AbstractPhenology serves as a major indicator of ongoing climate change. Long-term phenological observations are critically important for tracking and communicating these changes. The phenological observation network across Germany is operated by the National Meteorological Service with a major contribution from volunteering activities. However, the number of observers has strongly decreased for the last decades, possibly resulting in increasing uncertainties when extracting reliable phenological information from map interpolation. We studied uncertainties in interpolated maps from decreasing phenological records, by comparing long-term trends based on grid-based interpolated and station-wise observed time series, as well as their correlations with temperature. Interpolated maps in spring were characterized by the largest spatial variabilities across Bavaria, Germany, with respective lowest interpolated uncertainties. Long-term phenological trends for both interpolations and observations exhibited mean advances of −0.2 to −0.3 days year−1 for spring and summer, while late autumn and winter showed a delay of around 0.1 days year−1. Throughout the year, temperature sensitivities were consistently stronger for interpolated time series than observations. Such a better representation of regional phenology by interpolation was equally supported by satellite-derived phenological indices. Nevertheless, simulation of observer numbers indicated that a decline to less than 40% leads to a strong decrease in interpolation accuracy. To better understand the risk of declining phenological observations and to motivate volunteer observers, a Shiny app is proposed to visualize spatial and temporal phenological patterns across Bavaria and their links to climate change–induced temperature changes.

scholarly journals Can we predict the future: juvenile finfish and their seagrass nurseries in the Chesapeake Bay

2013 ◽  
Vol 71 (3) ◽  
pp. 681-688 ◽  
Author(s):  
Cynthia M. Jones
Keyword(s):  
Climate Change ◽  
Chesapeake Bay ◽  
Juvenile Fish ◽  
Single Species ◽  
Potential Effect ◽  
Seagrass Habitat ◽  
Important Conclusion ◽  
The Relationship ◽  
Landscape Level

Abstract The importance of estuarine seagrass beds as nurseries for juvenile fish has become a universal paradigm, especially for estuaries that are as important as the Chesapeake Bay. Yet, scientific tests of this hypothesis were equivocal depending on species, location, and metrics. Moreover, seagrasses themselves are under threat and one-third of seagrasses have disappeared worldwide with 65% of their losses occurring in estuaries. Although there have been extensive studies of seagrasses in the Chesapeake Bay, surprisingly few studies have quantified the relationship between seagrass as nurseries for finfish in the Bay. Of the few studies that have directly evaluated the use of seagrass nurseries, most have concentrated on single species or were of short duration. Few landscape-level or long-term studies have examined this relationship in the Bay or explored the potential effect of climate change. This review paper summarizes the seagrass habitat value as nurseries and presents recent juvenile fish studies that address the dearth of research at the long term and landscape level with an emphasis on the Chesapeake Bay. An important conclusion upon the review of these studies is that predicting the effects of climate change on fishery production remains uncertain.

Contrasting Perspectives Regarding Climate Risks and Adaptation Strategies in the New York Metropolitan Area after Superstorm Sandy

2014 ◽  
Vol 01 (01) ◽  
pp. 1450005 ◽  
Author(s):  
Stephanie Miller ◽  
Griffin Kidd ◽  
Franco Montalto ◽  
Patrick Gurian ◽  
Cortney Worrall ◽  
...  
Keyword(s):  
Climate Change ◽  
New York ◽  
Adaptation Strategies ◽  
Mitigation Strategies ◽  
Strong Impact ◽  
Structured Interviews ◽  
Superstorm Sandy ◽  
Climate Risks ◽  
York Metropolitan Area

The purpose of this study was to examine stakeholder perceptions of climate change and local adaptation strategies in the New York City area. A side-by-side comparison of expert and resident opinions provided a clear picture of the region's climate change attitude in the year following Superstorm Sandy. Semi-structured interviews with regional environmental experts provided material for a structured survey, which was then distributed to 100 experts and 250 residents in coastal NY and northern NJ counties. In the survey both stakeholder groups were asked to choose the top three climate threats to the NYC region and rate adaptation and mitigation strategies on a 1–5 Likert scale regarding their ability to protect the region and their cost-effectiveness. Results show that experts and residents agree that sea level rise, coastal flooding and storm surge, and an increased frequency and intensity of extreme events pose the greatest threats to NYC over the next 25 years. While both groups showed a preference for long-term planning over immediate action, experts and residents could not agree on which specific strategies would best serve the region. The aftermath of Superstorm Sandy had a strong impact on both the expert and resident opinions and efforts to monitor stakeholder opinions continue.

Laying date of marsh tits Parus palustris in relation to climate change

Biologia ◽  
2006 ◽  
Vol 61 (5) ◽  
Author(s):  
Zdravko Dolenec
Keyword(s):  
Climate Change ◽  
Life History Traits ◽  
Laying Date ◽  
Bird Breeding ◽  
Spring Temperatures ◽  
The Mean ◽  
The Relationship ◽  
Marsh Tit ◽  
Term Data

AbstractIncreasing evidence suggests that climate change affects bird breeding phenology and other life-history traits of wildlife. This study is based on the mean spring temperatures (February, March, April) and laying dates of first eggs of the marsh tit Parus palustris. We collected data from 1984 to 2004 for the Mokrice area in NW Croatia. Correlation between laying date and mean spring temperatures was significant. The relationship between mean laying date (y) and air temperature (x) can be expressed as y = 44.69 − 2.08x. Results indicate that spring temperatures are a good predictor of timing of laying eggs. Such long-term data could than be used in order to assess the effects on biological systems if human activities influence climate.

scholarly journals Describing the Relationship between a Weather Event and Climate Change: A New Statistical Approach

2020 ◽  
Vol 33 (15) ◽  
pp. 6297-6314 ◽  
Author(s):  
Aurélien Ribes ◽  
Soulivanh Thao ◽  
Julien Cattiaux
Keyword(s):  
Climate Change ◽  
Sources Of Information ◽  
Single Model ◽  
Coupled Models ◽  
Weather Event ◽  
Event Attribution ◽  
Transient Simulations ◽  
Induced Changes ◽  
The Relationship

AbstractDescribing the relationship between a weather event and climate change—a science usually termed event attribution—involves quantifying the extent to which human influence has affected the frequency or the strength of an observed event. In this study we show how event attribution can be implemented through the application of nonstationary statistics to transient simulations, typically covering the 1850–2100 period. The use of existing CMIP-style simulations has many advantages, including their availability for a large range of coupled models and the fact that they are not conditional to a given oceanic state. We develop a technique for providing a multimodel synthesis, consistent with the uncertainty analysis of long-term changes. Last, we describe how model estimates can be combined with historical observations to provide a single diagnosis accounting for both sources of information. The potential of this new method is illustrated using the 2003 European heat wave and under a Gaussian assumption. Results suggest that (i) it is feasible to perform event attribution using transient simulations and nonstationary statistics, even for a single model; (ii) the use of multimodel synthesis in event attribution is highly desirable given the spread in single-model estimates; and (iii) merging models and observations substantially reduces uncertainties in human-induced changes. Investigating transient simulations also enables us to derive insightful diagnostics of how the targeted event will be affected by climate change in the future.

DOES HARMFUL CLIMATE INCREASE OR DECREASE MIGRATION? EVIDENCE FROM RURAL HOUSEHOLDS IN NIGERIA

2019 ◽  
Vol 10 (04) ◽  
pp. 1950013
Author(s):  
CRISTINA CATTANEO ◽  
EMANUELE MASSETTI
Keyword(s):  
Climate Change ◽  
Adaptation Strategy ◽  
Climatic Conditions ◽  
Marginal Effect ◽  
Cross Sectional ◽  
Temperature Changes ◽  
And Migration ◽  
Migrant Households ◽  
The Impact

This paper analyzes whether migration is an adaptation strategy that households employ to cope with climate in Nigeria. We estimate our model using the cross-sectional variation in climate and long-term migration decisions because we are interested in the average response to long-term climatic conditions. For households that operate farms, we find that the relationship between climate and migration is nonlinear. In particular, climates closer to ideal farming conditions are associated with a higher propensity to migrate, whereas in the least favorable climatic conditions, the propensity to migrate declines. The marginal effect of rainfall and temperature changes on migration varies by season. We estimate the impact of climate change on the number of migrant households in 2031–2060 and 2071–2100, ceteris paribus. With current population levels, climate change generates between 3.6 and 6.3 million additional migrants, most of them being internal. However, these estimates are not statistically significant.

scholarly journals Multifractal analysis of atmospheric carbon emissions and OECD industrial production index

2020 ◽  
Vol 12 (4) ◽  
pp. 411-430
Author(s):  
Sezer Kahyaoglu Bozkus ◽  
Hakan Kahyaoglu ◽  
Atahirou Mahamane Mahamane Lawali
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Carbon Emissions ◽  
Industrial Production ◽  
Environmental Cost ◽  
Content Type ◽  
Production Index ◽  
Atmospheric Carbon ◽  
The Relationship

Purpose The purpose of this study aims to analyze the dynamic behavior of the relationship between atmospheric carbon emissions and the Organisation for Economic Co-operation and Development (OECD) industrial production index (IPI) in the short and long term by applying multifractal techniques. Design/methodology/approach Multifractal de-trended cross-correlation technique is used for this analysis based on the relevant literature. In addition, it is the most widely used approach to estimate multifractality because it generates robust empirical results against non-stationarities in the time series. Findings It is revealed that industrial production causes long and short term environmental costs. The OECD IPI and atmospheric carbon emissions were found to have a strong correlation between the time domain. However, this relationship does not mostly take into account the frequency-based correlations with the tail effects caused by shocks that are effective on the economy. In this study, the long-term dependence of the relationship between the OECD IPI and atmospheric carbon emissions differs from the correlation obtained by linear methods, as the analysis is based on the frequency. The major finding is that the Hurst coefficient is in the range 0.40-0.75 indicating. Research limitations/implications In this study, the local singular behavior of the time-series is analyzed to test for the multifractality characteristics of the series. In this context, the scaling exponents and the singularity spectrum are obtained to determine the origins of this multifractality. The multifractal time series are defined as the set of points with a given singularity exponent a where this exponent a is illustrated as a fractal with fractal dimension f(α). Therefore, the multifractality term indicates the existence of fluctuations, which are non-uniform and more importantly, their relative frequencies are also scale-dependent. Practical implications The results provide information based on the fluctuation in IPI, which determines the main conjuncture of the economy. An optimal strategy for shaping the consequences of climate change resulting from industrial production activities will not only need to be quite comprehensive and global in scale but also policies will need to be applicable to the national and local conditions of the given nation and adaptable to the needs of the country. Social implications The results provide information for the analysis of the environmental cost of climate change depending on the magnitude of the impact on the total supply. In addition to environmental problems, climate change leads to economic problems, and hence, policy instruments are introduced to fight against the adverse effects of it. Originality/value This study may be of practical and technical importance in regional climate change forecasting, extreme carbon emission regulations and industrial production resource management in the world economy. Hence, the major contribution of this study is to introduce an approach to sustainability for the analysis of the environmental cost of growth in the supply side economy.

scholarly journals Effects of Climate Change on Epidemics of Powdery Mildew in Winter Wheat in China

Plant Disease ◽  
2017 ◽  
Vol 101 (10) ◽  
pp. 1753-1760 ◽  
Author(s):  
Xiuli Tang ◽  
Xueren Cao ◽  
Xiangming Xu ◽  
Yuying Jiang ◽  
Yong Luo ◽  
...  
Keyword(s):  
Climate Change ◽  
Relative Humidity ◽  
Powdery Mildew ◽  
Winter Wheat ◽  
Weather Conditions ◽  
Early Spring ◽  
Long Term Effects ◽  
Temperature Changes ◽  
Disease Epidemics

Powdery mildew is a highly destructive winter wheat pathogen in China. Since the causative agent is sensitive to changing weather conditions, we analyzed climatic records from regions with previous wheat powdery mildew epidemics (1970 to 2012) and investigated the long-term effects of climate change on the percent acreage (PA) of the disease. Then, using PA and the pathogen’s temperature requirements, we constructed a multiregression model to predict changes in epidemics during the 2020s, 2050s, and 2080s under representative concentration pathways RCP2.6, RCP4.5, and RCP8.5. Mean monthly air temperature increased from 1970 to 2012, whereas hours of sunshine and relative humidity decreased (P < 0.001). Year-to-year temperature changes were negatively associated with those of PA during oversummering and late spring periods of disease epidemics, whereas positive relationships were noted for other periods, and year-to-year changes in relative humidity were correlated with PA changes in the early spring period of disease epidemics (P < 0.001). Our models also predicted that PA would increase less under RCP2.6 (14.43%) than under RCP4.5 (14.51%) by the 2020s but would be higher by the 2050s and 2080s and would increase least under RCP8.5 (14.37% by the 2020s). Powdery mildew will, thus, pose an even greater threat to China’s winter wheat production in the future.

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