Abstract
Climate change has become one of the most severe and pressing world issues due to its destructive effects of environmental degradation. Climate change aggravates global warming and brings about potential risks for both human society and natural systems. The quantile regression being used to help with climate change is exceptionally new. The article scrutinizes the newest developments in this important research area and provides the future research directions.
TopBackground
Quantile regression is considered an extension to standard linear regression, and its primary purpose is to estimate the median of the outcome variable. Quantile regression can also be used when assumption of linear regression is not satisfied, outliers in data, residuals are not normal, and increase in error variance with increase in outcome variable. Quantile regression techniques are used to ensure or create an understanding of the association amongst variables outside the mean data, which makes it effective to understand the outcomes that are unusually distributed and those having nonlinear relationships with predictor variables. Quantile regressions can be run on various sections of the population based on the unlimited distribution of the dependent variables (Huang et al., 2017; Koenker, 2017).
Another rationale that justifies quantile regression’s significance is that it enables scholars to abandon the presumption that variables have similar working means at the upper tails of the distribution as at the mean, and it also helps in identifying the factors that critically determine the variables. According to Wenz (2019), quantile regression is forecasting, introducing a purposed bias in the outcome. Rather than identifying the mean of the anticipated variable, this type of regression pursues the median and any other quantiles, which are often referred to as percentiles. The main benefit of quantile regression is that its measures are comprehensive against outliers in the outcome measurements. The central position about the quantile regression topic is that they surpass this and are beneficial when there are interests in conditional quantile functions.
Key Terms in this Chapter
Greenhouse Gases: They are a natural part of the atmosphere that, through a natural process called the greenhouse effect, trap the sun's warmth and maintain the earth's surface temperature at the level necessary to support life.
Environmental Protection: The practice of protecting the natural environment by individuals, organizations, and governments.
Climate Change: It is attributed to both natural variability and human activities. However, many of these changes are now considered attributable to human activities nowadays.
Renewable Energy (or Clean Energy): Collected from renewable resources, including carbon neutral sources like sunlight, wind, rain, tides, waves, and geothermal heat.
Quantile Regression: Quantile regression enables quantification of the relationship between dependent and independent variables across different quantiles of the conditional distribution of the dependent variable.
Heterogeneous Effects: The nonrandom, explainable variability in the direction and magnitude of treatment effects for individuals within a population.
Socio-Economic Factors: Including occupation, education, income, wealth and where someone lives.