Estimated Statistical Variance of Least Squares Predicted (Extrapolated) Rising Temperatures From Climate Change

2019 ◽  
Author(s):  
Jeff Bell
Keyword(s):  
Climate Change ◽  
Least Squares ◽  
Statistical Variance

scholarly journals Applying the Theory of Planned Behavior to Examine Pro-Environmental Behavior: The Moderating Effect of COVID-19 Beliefs

2020 ◽  
Vol 12 (24) ◽  
pp. 10556
Author(s):  
Caterina Lucarelli ◽  
Camilla Mazzoli ◽  
Sabrina Severini
Keyword(s):  
Climate Change ◽  
Theory Of Planned Behavior ◽  
Least Squares ◽  
Partial Least Squares ◽  
Planned Behavior ◽  
Structural Equation ◽  
Environmental Behavior ◽  
Group Analysis ◽  
Policy Implications ◽  
Equation Modeling

The COVID-19 pandemic and climate change issues present evident interdependencies which justify the spread of connected beliefs. We examine possible changes in individuals’ pro-environmental behavior in light of this pandemic, using the Theory of Planned Behavior (TPB) framework. A questionnaire survey was submitted to the same sample of individuals, before and during the pandemic. Our evidence, based on Partial Least Squares Structural Equation Modeling (PLS-SEM), shows that the COVID-19 pandemic has not led to a weakening in TPB construct relationships, or in related Pro-Environmental Behavior (PEB). Conversely, through our Partial Least Squares-Multi-Group Analysis (PLS-MGA), we show that individuals with greater awareness of interdependencies between the COVID-19 and climate change exhibit both higher Intention and reinforced Pro-Environmental Behaviors. This finding reveals interesting policy implications in terms of innovative behavioral drivers that should be employed to steer public support towards climate-oriented initiatives.

The Awareness and Impact of Carbon Capture and Storage (CCS) on Climate Change in Malaysia

2018 ◽  
Vol 9 (2) ◽  
pp. 13-27 ◽  
Author(s):  
Zulkipli Ghazali ◽  
Muhammad Zahid
Keyword(s):  
Climate Change ◽  
Least Squares ◽  
Carbon Capture ◽  
Social Economic ◽  
Public Awareness ◽  
Carbon Capture And Storage ◽  
Environmental Issues ◽  
Policy Makers ◽  
Economic Issues ◽  
And Storage

This article aims to investigate the level of public awareness and perception regarding carbon capture and storage (CCS) and climate change in Malaysia. The article also aims to identify those social, economic and environmental issues which affect CCS and combating climate change in the country. The findings revealed that more than 79 percent of the respondents were willing to have government initiatives to implement CCS projects. However, about 21 percent were against these initiatives due to their different perceptions and opinions regarding CCS. By using partial least squares (PLS) model through SmartPLS 2.0, it is found that social and economic issues of CCS have significant positive while environmental issues have no significant impact on combating climate change. The findings offer significant implications for regulators, policy makers, and practitioners regarding social, economic and environmental issues of CCS and climate change in Malaysia.

The Awareness and Impact of Carbon Capture and Storage (CCS) on Climate Change in Malaysia

2022 ◽  
pp. 1303-1319
Author(s):  
Zulkipli Ghazali ◽  
Muhammad Zahid
Keyword(s):  
Climate Change ◽  
Least Squares ◽  
Carbon Capture ◽  
Social Economic ◽  
Public Awareness ◽  
Carbon Capture And Storage ◽  
Environmental Issues ◽  
Policy Makers ◽  
Economic Issues ◽  
And Storage

This article aims to investigate the level of public awareness and perception regarding carbon capture and storage (CCS) and climate change in Malaysia. The article also aims to identify those social, economic and environmental issues which affect CCS and combating climate change in the country. The findings revealed that more than 79 percent of the respondents were willing to have government initiatives to implement CCS projects. However, about 21 percent were against these initiatives due to their different perceptions and opinions regarding CCS. By using partial least squares (PLS) model through SmartPLS 2.0, it is found that social and economic issues of CCS have significant positive while environmental issues have no significant impact on combating climate change. The findings offer significant implications for regulators, policy makers, and practitioners regarding social, economic and environmental issues of CCS and climate change in Malaysia.

scholarly journals Impacts of Climate Factors Influencing Rice Production in Bangladesh

2021 ◽  
pp. 43-52
Author(s):  
Mahmuda Akter ◽  
Md. Mizanur Rahman Sarker
Keyword(s):  
Climate Change ◽  
Least Squares ◽  
Climate Extremes ◽  
Generalized Least Squares ◽  
Ordinary Least Squares ◽  
Rice Production ◽  
Climate Data ◽  
Average Percentage ◽  
Climate Change Pattern ◽  
Rice Yields

This study aims to study the climate change pattern, assess the situation of climate change, finding the influences of climate change on the production of rice, estimating a model between climate change and rice production in Bangladesh. Ordinary Least Squares (OLS), Generalized Least Squares (GLS), Feasible Generalized Least Squares (FGLS) were used in this study to compare the results. This study included all 64 districts of Bangladesh with a time span from 2011 to 2018. It included panel data of the production of Aus rice, Aman rice, Boro rice as well as HYV of each rice (Aus, Aman, Boro) of 64 districts of Bangladesh for agricultural data, temperature, rainfall and humidity of 64 districts for climate data. This study estimates the stochastic production function formulated by Just and Pope (1978, 1979), which allows the effect of inputs on the mean yield to differ from that on yield variance. The results showed that increased climate variability, climate extremes; in particular, exacerbate risk on Rice production in Bangladesh. Rice yields are sensitive to rainfall extremes, with both deficient and surplus rainfall increasing variability. For 1% increase in annual total rainfall, Mean Yield will decrease by 0.139%, 0.141%, 0.132% in OLS, GLS and FGLS method respectively, if other variables remaining the same. For 1% increase in annual average percentage of humidity, Mean Yield increases by 1.352%, 1.340%, 1.362% in OLS, GLS and FGLS method respectively, if other variables remaining the same. for 1% increase in HYV area, Mean Yield increases by 0.831% in OLS, GLS and FGLS method, if other variables remaining the same. Additionally, climate inputs, non-climate input, high yielding variety seeds are found to increase average yield.

scholarly journals Checking for model consistency in optimal fingerprinting: a comment

2021 ◽  
Author(s):  
Ross McKitrick
Keyword(s):  
Climate Change ◽  
Least Squares ◽  
Critical Condition ◽  
Null Hypothesis ◽  
Generalized Least Squares ◽  
Variance Estimator ◽  
Specification Test ◽  
Consistency Test ◽  
Model Consistency ◽  
Optimal Fingerprinting

AbstractAllen and Tett (1999, herein AT99) introduced a Generalized Least Squares (GLS) regression methodology for decomposing patterns of climate change for attribution purposes and proposed the “Residual Consistency Test” (RCT) to check the GLS specification. Their methodology has been widely used and highly influential ever since, in part because subsequent authors have relied upon their claim that their GLS model satisfies the conditions of the Gauss-Markov (GM) Theorem, thereby yielding unbiased and efficient estimators. But AT99 stated the GM Theorem incorrectly, omitting a critical condition altogether, their GLS method cannot satisfy the GM conditions, and their variance estimator is inconsistent by construction. Additionally, they did not formally state the null hypothesis of the RCT nor identify which of the GM conditions it tests, nor did they prove its distribution and critical values, rendering it uninformative as a specification test. The continuing influence of AT99 two decades later means these issues should be corrected. I identify 6 conditions needing to be shown for the AT99 method to be valid.

scholarly journals Impact of Climate Change on Rice and Wheat Yield in Punjab State of India: A District-Level Analysis

2021 ◽  
Author(s):  
Mandeep Bhardwaj ◽  
Pushp Kumar ◽  
Siddharth Kumar ◽  
Ashish Kumar
Keyword(s):  
Climate Change ◽  
Least Squares ◽  
Minimum Temperature ◽  
Rice Yield ◽  
Wheat Yield ◽  
Ordinary Least Squares ◽  
District Level ◽  
Maximum Temperature ◽  
Impact Of Climate Change ◽  
The Impact

Abstract The present study aims to examine the impact of climate change on wheat and rice yield of the Punjab state of India. Using district-level panel data from 1981 to 2017, the study employs fully modified ordinary least squares (FMOLS), dynamic ordinary least squares (DOLS), and pooed mean group (PMG) approaches. The Pedroni cointegration has established a long-run relationship of climate variables with rice and wheat crops. The results of FMOLS and DOLS show that minimum temperature has a positive effect on both wheat and rice, while maximum temperature is found to be negatively contributing to both the crops. Rainfall has a significant adverse effect on wheat yield. Seasonal rainfall has been detrimental to wheat and rice yield in the study period, indicating that excess rainfall proved counterproductive. Pooled mean group (PMG) model confirms the robustness of the results obtained by FMOLS and DOLS techniques. Moreover, Dumitrescu-Hurlin causality test has revealed a unidirectional causality running from minimum temperature, rainfall & maximum temperature to rice and wheat yield. The findings of the study suggest that the government should invest in developing stress-tolerant varieties of wheat and rice, managing crop residuals to curb further environmental effect and sustain natural resources for ensuring food security.

scholarly journals Effect of Time-Resolution of Rainfall Data on Trend Estimation for Annual Maximum Depths with a Duration of 24 Hours

Water ◽  
2021 ◽  
Vol 13 (22) ◽  
pp. 3264
Author(s):  
Renato Morbidelli ◽  
Carla Saltalippi ◽  
Jacopo Dari ◽  
Alessia Flammini
Keyword(s):  
Climate Change ◽  
Least Squares ◽  
Trend Analysis ◽  
Time Resolution ◽  
Linear Trend ◽  
Rainfall Data ◽  
Annual Maximum ◽  
Maximum Rainfall ◽  
Climatic Trend ◽  
Sen’S Method

The main challenge of this paper is to demonstrate that one of the most frequently conducted analyses in the climate change field could be affected by significant errors, due to the use of rainfall data characterized by coarse time-resolution. In fact, in the scientific literature, there are many studies to verify the possible impacts of climate change on extreme rainfall, and particularly on annual maximum rainfall depths, Hd, characterized by duration d equal to 24 h, due to the significant length of the corresponding series. Typically, these studies do not specify the temporal aggregation, ta, of the rainfall data on which maxima rely, although it is well known that the use of rainfall data with coarse ta can lead to significant underestimates of Hd. The effect of ta on the estimation of trends in annual maximum depths with d = 24 h, Hd=24 h, over the last 100 years is examined. We have used a published series of Hd=24 h derived by long-term historical rainfall observations with various temporal aggregations, due to the progress of recording systems through time, at 39 representative meteorological stations located in an inland region of Central Italy. Then, by using a recently developed mathematical relation between average underestimation error and the ratio ta/d, each Hd=24 h value has been corrected. Successively, commonly used climatic trend tests based on different approaches, including least-squares linear trend analysis, Mann–Kendall, and Sen’s method, have been applied to the “uncorrected” and “corrected” series. The results show that the underestimation of Hd=24 h values with coarse ta plays a significant role in the analysis of the effects of climatic change on extreme rainfalls. Specifically, the correction of the Hd=24 h values can change the sign of the trend from positive to negative. Furthermore, it has been observed that the innovative Sen’s method (based on a graphical approach) is less sensitive to corrections of the Hd values than the least-squares linear trend and the Mann–Kendall method. In any case, the analysis of Hd series containing potentially underestimated values, especially when d = 24 h, can lead to misleading results. Therefore, before conducting any trend analysis, Hd values determined from rainfall data characterized by coarse temporal resolution should always be corrected.

scholarly journals Prediction of Phytoplankton Biomass in Small Rivers of Central Spain by Data Mining Method of Partial Least-Squares Regression

Proceedings ◽  
2019 ◽  
Vol 48 (1) ◽  
pp. 16 ◽  
Author(s):  
Juan Carlos García-Prieto ◽  
Francisco Javier Burguillo Muñoz ◽  
Manuel G. Roig ◽  
José Bernardo Proal-Najera
Keyword(s):  
Climate Change ◽  
Least Squares ◽  
Partial Least Squares ◽  
Ecological Status ◽  
Phytoplankton Growth ◽  
Small Rivers ◽  
Strong Impact ◽  
Least Squares Regression ◽  
Phytoplankton Communities ◽  
The Impact

The Water Framework Directive (WFD, EC, 2000) states that the “good” ecological status of natural water bodies must be based on their chemical, hydromorphological and biological features, especially under drastic conditions of floods or droughts. Phytoplankton is considered a good environmental bioindicator (WFD) and climate change has a strong impact on phytoplankton communities and water quality. The development of robust techniques to predict and control phytoplankton growth is still in progress. The aim of this study is to analyze the impact of the different stressors associated with the change in phytoplanktonic communities in small rivers in the center of the Iberian Peninsula (Southwestern Europe). A statistical study on the identification of the essential limiting variables in the phytoplankton growth and its seasonal variation by climate change was carried out. In this study, a new method based on the partial least-squares (PLS) regression technique has been used to predict the concentration of phytoplankton and cyanophytes from 22 variables usually monitored in rivers. The predictive models have shown a good agreement between training and test data sets in rivers and seasons (dry and wet). The phytoplankton in dry periods showed greatest similarities, these dry periods being the most important factor in the phytoplankton proliferation

Averting robo-bees: why free-flying robotic bees are a bad idea

2019 ◽  
Vol 3 (6) ◽  
pp. 723-729
Author(s):  
Roslyn Gleadow ◽  
Jim Hanan ◽  
Alan Dorin
Keyword(s):  
Climate Change ◽  
Computer Simulation ◽  
Food Security ◽  
European Countries ◽  
Plant Interactions ◽  
Plant Insect Interactions ◽  
Native Habitat ◽  
Insect Pollinators ◽  
Insect Interactions

Food security and the sustainability of native ecosystems depends on plant-insect interactions in countless ways. Recently reported rapid and immense declines in insect numbers due to climate change, the use of pesticides and herbicides, the introduction of agricultural monocultures, and the destruction of insect native habitat, are all potential contributors to this grave situation. Some researchers are working towards a future where natural insect pollinators might be replaced with free-flying robotic bees, an ecologically problematic proposal. We argue instead that creating environments that are friendly to bees and exploring the use of other species for pollination and bio-control, particularly in non-European countries, are more ecologically sound approaches. The computer simulation of insect-plant interactions is a far more measured application of technology that may assist in managing, or averting, ‘Insect Armageddon' from both practical and ethical viewpoints.

Modelling ecosystem adaptation and dangerous rates of global warming

2019 ◽  
Vol 3 (2) ◽  
pp. 221-231 ◽  
Author(s):  
Rebecca Millington ◽  
Peter M. Cox ◽  
Jonathan R. Moore ◽  
Gabriel Yvon-Durocher
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Diffusion Rate ◽  
Individual Species ◽  
Genetic Evolution ◽  
Rates Of Change ◽  
Rapid Climate Change ◽  
Warming Climate ◽  
Intraspecies Competition ◽  
High Trait

Abstract We are in a period of relatively rapid climate change. This poses challenges for individual species and threatens the ecosystem services that humanity relies upon. Temperature is a key stressor. In a warming climate, individual organisms may be able to shift their thermal optima through phenotypic plasticity. However, such plasticity is unlikely to be sufficient over the coming centuries. Resilience to warming will also depend on how fast the distribution of traits that define a species can adapt through other methods, in particular through redistribution of the abundance of variants within the population and through genetic evolution. In this paper, we use a simple theoretical ‘trait diffusion’ model to explore how the resilience of a given species to climate change depends on the initial trait diversity (biodiversity), the trait diffusion rate (mutation rate), and the lifetime of the organism. We estimate theoretical dangerous rates of continuous global warming that would exceed the ability of a species to adapt through trait diffusion, and therefore lead to a collapse in the overall productivity of the species. As the rate of adaptation through intraspecies competition and genetic evolution decreases with species lifetime, we find critical rates of change that also depend fundamentally on lifetime. Dangerous rates of warming vary from 1°C per lifetime (at low trait diffusion rate) to 8°C per lifetime (at high trait diffusion rate). We conclude that rapid climate change is liable to favour short-lived organisms (e.g. microbes) rather than longer-lived organisms (e.g. trees).

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