Country-level conditions like prosperity, democracy, and regulatory culture predict individual climate change belief

AbstractDecades after the scientific community agreed on the existence of human-made climate change, substantial parts of the world’s population remain unaware or unconvinced that human activity is responsible for climate change. Belief in human-made climate change continues to vary strongly within and across different countries. Here I analyse data collected by the Gallop World Poll between 2007 and 2010 on individual attitudes across 143 countries, using a random forest model, to show that country-level conditions like environmental protection, civil liberty, and economic development are highly predictive of individual climate change belief. Individual education and internet access, in contrast, are correlated to climate change awareness, but much less to belief in climate change’s anthropogenic causes. I also identify non-linear pattern in which country-level circumstances relate to individual climate change belief. The local importance of most predictors varies strongly across countries, indicating that each country has its relatively unique set of correlates of climate change belief.

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Publisher Correction: Country-level conditions like prosperity, democracy, and regulatory culture predict individual climate change belief

Communications Earth & Environment ◽

10.1038/s43247-021-00134-6 ◽

2021 ◽

Vol 2 (1) ◽

Author(s):

Sebastian Levi

Keyword(s):

Climate Change ◽

Country Level ◽

Regulatory Culture

A Correction to this paper has been published: https://doi.org/10.1038/s43247-021-00134-6

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The Fight Against Doubt

10.1093/oso/9780190869229.001.0001 ◽

2018 ◽

Cited By ~ 14

Author(s):

Inmaculada de Melo-Martín ◽

Kristen Intemann

Keyword(s):

Climate Change ◽

Scientific Community ◽

Vaccine Safety ◽

False Beliefs ◽

Scientific Process ◽

The Public ◽

Scientific Consensus ◽

Scientific Claims

Current debates about climate change or vaccine safety provide an alarming illustration of the potential impacts of dissent about scientific claims. False beliefs about evidence and the conclusions that can be drawn from it are commonplace, as is corrosive doubt about the existence of widespread scientific consensus. Deployed aggressively and to political ends, ill-founded dissent can intimidate scientists, stymie research, and lead both the public and policymakers to oppose important policies firmly rooted in science. To criticize dissent is, however, a fraught exercise. Skepticism and fearless debate are key to the scientific process, making it both vital and incredibly difficult to characterize and identify dissent that is problematic in its approach and consequences. Indeed, as de Melo-Martín and Intemann show, the criteria commonly proposed as means of identifying inappropriate dissent are flawed, and the strategies generally recommended to tackle such dissent are not only ineffective but could even make the situation worse. The Fight against Doubt proposes that progress on this front can best be achieved by enhancing the trustworthiness of the scientific community and being more realistic about the limits of science when it comes to policymaking. It shows that a richer understanding is needed of the context in which science operates so as to disarm problematic dissent and those who deploy it in the pursuit of their goals.

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Exploratory Analysis of Driving Force of Wildfires in Australia: An Application of Machine Learning within Google Earth Engine

Remote Sensing ◽

10.3390/rs13010010 ◽

2020 ◽

Vol 13 (1) ◽

pp. 10

Author(s):

Andrea Sulova ◽

Jamal Jokar Arsanjani

Keyword(s):

Climate Change ◽

Machine Learning ◽

Random Forest ◽

Google Earth ◽

Summer Season ◽

Driving Factors ◽

Machine Learning Algorithms ◽

Classification And Regression Tree ◽

Training Dataset ◽

Google Earth Engine

Recent studies have suggested that due to climate change, the number of wildfires across the globe have been increasing and continue to grow even more. The recent massive wildfires, which hit Australia during the 2019–2020 summer season, raised questions to what extent the risk of wildfires can be linked to various climate, environmental, topographical, and social factors and how to predict fire occurrences to take preventive measures. Hence, the main objective of this study was to develop an automatized and cloud-based workflow for generating a training dataset of fire events at a continental level using freely available remote sensing data with a reasonable computational expense for injecting into machine learning models. As a result, a data-driven model was set up in Google Earth Engine platform, which is publicly accessible and open for further adjustments. The training dataset was applied to different machine learning algorithms, i.e., Random Forest, Naïve Bayes, and Classification and Regression Tree. The findings show that Random Forest outperformed other algorithms and hence it was used further to explore the driving factors using variable importance analysis. The study indicates the probability of fire occurrences across Australia as well as identifies the potential driving factors of Australian wildfires for the 2019–2020 summer season. The methodical approach and achieved results and drawn conclusions can be of great importance to policymakers, environmentalists, and climate change researchers, among others.

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“Modes of Variability” and Climate Change

Journal of Climate ◽

10.1175/2008jcli2517.1 ◽

2009 ◽

Vol 22 (10) ◽

pp. 2639-2658 ◽

Cited By ~ 49

Author(s):

Grant Branstator ◽

Frank Selten

Keyword(s):

Climate Change ◽

Stochastic Model ◽

General Circulation ◽

Climate Changes ◽

Circulation Model ◽

Meridional Wind ◽

Model Atmosphere ◽

Linear Pattern ◽

Modes Of Variability ◽

Mean State

Abstract A 62-member ensemble of coupled general circulation model (GCM) simulations of the years 1940–2080, including the effects of projected greenhouse gas increases, is examined. The focus is on the interplay between the trend in the Northern Hemisphere December–February (DJF) mean state and the intrinsic modes of variability of the model atmosphere as given by the upper-tropospheric meridional wind. The structure of the leading modes and the trend are similar. Two commonly proposed explanations for this similarity are considered. Several results suggest that this similarity in most respects is consistent with an explanation involving patterns that result from the model dynamics being well approximated by a linear system. Specifically, the leading intrinsic modes are similar to the leading modes of a stochastic model linearized about the mean state of the GCM atmosphere, trends in GCM tropical precipitation appear to excite the leading linear pattern, and the probability density functions (PDFs) of prominent circulation patterns are quasi-Gaussian. There are, on the other hand, some subtle indications that an explanation for the similarity involving preferred states (which necessarily result from nonlinear influences) has some relevance. For example, though unimodal, PDFs of prominent patterns have departures from Gaussianity that are suggestive of a mixture of two Gaussian components. And there is some evidence of a shift in probability between the two components as the climate changes. Interestingly, contrary to the most prominent theory of the influence of nonlinearly produced preferred states on climate change, the centroids of the components also change as the climate changes. This modification of the system’s preferred states corresponds to a change in the structure of its dominant patterns. The change in pattern structure is reproduced by the linear stochastic model when its basic state is modified to correspond to the trend in the general circulation model’s mean atmospheric state. Thus, there is a two-way interaction between the trend and the modes of variability.

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Effects of air quality on the health of Mediterranean forests

10.5194/egusphere-egu21-16171 ◽

2021 ◽

Author(s):

Adrián García Bruzón ◽

Patricia Arrogante Funes ◽

Laura Muñoz Moral

Keyword(s):

Climate Change ◽

Machine Learning ◽

Random Forest ◽

Aridity Index ◽

Plant Health ◽

Mediterranean Forests ◽

Random Forest Algorithm ◽

The Mediterranean ◽

Heterogeneous Variables ◽

Peninsular Spain

The climate change has turned out to be a determining factor in the development of forest in Spain. Production systems have emitted polluting gases and other particles into the atmosphere, for which some plants have not yet developed adaptation systems. Among the most harmful pollutants for the environment are gases such as nitrous oxides, ozone, particulate matter.However, this condition is not the same in Peninsular Spain, and the Balearic Islands since the plant compositions differ in the territory and the bioclimatic, topographic, and anthropic characteristics. Monitoring the vegetation with sufficient spatial and temporal resolution, studying variables conditioning plant health is a challenge from the nature of the variables and the amount of data to be handled.&#160;The Mediterranean forest is one of the most ecosystem affected by climate change because of usually experimented long periods of drought that, in combination with increased temperatures, can drastically reduce the photosynthetic activity of trees and therefore the biomass of forests.That is why the application of environmental technologies based on Remote Sensing (which provide plant health indices from passive sensors on satellite platforms and other variables of interest), Geographic Information Systems (to integrate, process, analyze spatial and temporal data) and machine learning models (which facilitate the extraction of relationships between variables, conditioning factors and predict patterns).&#160;In this regard, this work's objective is to evaluate the possible effect that different pollutants have on the health of the vegetation, measured from the annual values of the Normalized Difference Vegetation Index (NDVI), in the Mediterranean forests of Peninsular Spain. To achieve this, we are used machine learning techniques using the Random Forest algorithm. The study has also been done with various climatic, topographic, and anthropic variables that characterize the forest to carry it out.&#160;The results showed that certain variables such as the aridity index had generated the NDVI values and therefore plant development, while others are limiting factors such as the concentration of certain pollutants and the direct relationship between them particulates and NOx. This study can verify how the Random Forest algorithm offers reliable results, even when working with heterogeneous variables.&#160;

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Cosmopolitan Luck egalitarianism and the greenhouse effect

Canadian Journal of Philosophy Supplementary Volume ◽

10.1080/00455091.2005.10716857 ◽

2005 ◽

Vol 31 ◽

pp. 279-309 ◽

Cited By ~ 5

Author(s):

Axel Gosseries

Keyword(s):

Climate Change ◽

Greenhouse Gas ◽

Greenhouse Effect ◽

Human Activities ◽

Scientific Community ◽

Ghg Emissions ◽

Luck Egalitarianism ◽

Straightforward Manner ◽

Global Regime ◽

Principles Of Justice

Evidence provided by the scientific community strongly suggests that limits should be placed on greenhouse gas (GHG) emissions. This means that states, firms, and individuals will have to face potentially serious burdens if they are to implement these limits. Which principles of justice should guide a global regime aimed at reducing greenhouse gas (GHG) emissions originating from human activities, and most notably from CO2 emissions? This is both a crucial and difficult question. Admittedly, perhaps this question is too ambitious, given the uncertainties and complexities characterizing the issue of climate change. Yet, rather than listing them all at this stage, let us address the question in a straightforward manner, introducing some of these complexities as the need arises.

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Potential Influence of Climate Change on Grain Self‐Sufficiency at the Country Level Considering Adaptation Measures

Earth s Future ◽

10.1029/2019ef001213 ◽

2019 ◽

Vol 7 (10) ◽

pp. 1152-1166 ◽

Cited By ~ 1

Author(s):

Taoyuan Wei ◽

Tianyi Zhang ◽

Xuefeng Cui ◽

Solveig Glomsrød ◽

Yu Liu

Keyword(s):

Climate Change ◽

Potential Influence ◽

Adaptation Measures ◽

Country Level ◽

Self Sufficiency

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Climate change in the recent geological past and the near future. Predicting its impacts: a Review

Bulletin of the Geological Society of Greece ◽

10.12681/bgsg.20684 ◽

2019 ◽

Vol 55 (1) ◽

pp. 260

Author(s):

Constantinos Perisoratis

Keyword(s):

Climate Change ◽

Sea Level Rise ◽

Climatic Change ◽

Sea Level ◽

Scientific Community ◽

Climate Changes ◽

Late Quaternary ◽

The Earth ◽

Geological Past ◽

Near Future

The climate changes are necessarily related to the increase of the Earth’s temperature, resulting in a sea level rise. Such continuous events, were taking place with minor and greater intensity, during the alternation of warm and cool periods in the Earth during the Late Quaternary and the Holocene periods. However, a particularly significant awareness has taken place in the scientific community, and consequently in the greater public, in the last decades: that a climatic change will take place soon, or it is on-going, and that therefore it is important to undertake drastic actions. However, such a climatic change has not been recorded yet, and hence the necessary actions are not required, for the time being.

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Cyberbehavior: A Bibliometric Analysis

10.31234/osf.io/prfcw ◽

2018 ◽

Author(s):

Manuel José Serafin Plasencia ◽

Gustavo R. García-Vargas ◽

María del Pilar García-Chitiva ◽

Mario I. Caicedo ◽

Juan C. Correa

Keyword(s):

Social Media ◽

Internet Addiction ◽

Scientific Community ◽

Electronic Device ◽

Internet Access ◽

Scientific Publication ◽

Social Web ◽

Human Behaviors ◽

Bibliometric Review ◽

Object Of Study

Cyberbehavior, as the object of study of cyberpsychology, refers to the set of human behaviors that require an electronic device with Internet access to interact with other persons in both synchronous and asynchronous way. Although the first studies of cyberbehavior took place with the popularization of the so-called “Social Web”, few works focus on reviewing this literature. This paper aims to provide a bibliometric review of the scientific publication on cyberbehavior by analyzing all the documents published by four of the most representative international journals on the topic. The results show that in spite of the growth of the scientific community, the research has focused on the use of Facebook and other social media, while relevant subject matters for psychologists (e.g., motivation, personality, Internet addiction, cyberbullying or self-expression) remainlittle explored.

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The Effect of Hydrology on Soil Erosion

Water ◽

10.3390/w12030839 ◽

2020 ◽

Vol 12 (3) ◽

pp. 839 ◽

Cited By ~ 1

Author(s):

Jesús Rodrigo-Comino ◽

José María Senciales-González ◽

José Damián Ruiz-Sinoga

Keyword(s):

Climate Change ◽

Soil Erosion ◽

Scientific Community ◽

Hydrological Processes ◽

Special Issue ◽

Degradation Processes ◽

Laboratory Conditions ◽

The World ◽

New Research ◽

Different Parts

In this Special Issue, we have tried to include manuscripts about soil erosion and degradation processes and the accelerated rates due to hydrological processes and climate change. We considered that the main goal was successfully reached. The new research focused on measurements, modelling, and experiments under field or laboratory conditions developed at different scales (pedon, hillslope, and catchment) were submitted and published. This Special Issue received investigations from different parts of the world such as Ethiopia, Morocco, China, Iran, Italy, Portugal, Greece and Spain, among others. We are happy to see that all papers presented findings characterized as unconventional, provocative, innovative and methodologically new. We hope that the readers of the journal Water can enjoy and learn about hydrology and soil erosion using the published material, and share the results with the scientific community, policymakers and stakeholders new research to continue this amazing adventure, featuring plenty of issues and challenges.

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