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Global warming is a process that takes place 11,500 years after the end of the last Ice Age. The main identified reason is the increased emissions of greenhouse gases (GHGs). Since the nineteenth century, GHG evolution has recorded a quantum leap from the previous linear development. Human is the main factor behind this evolution, through industrialization and the exponential increase of population. Based on these, the chapter’s primary goal was to highlight an original method of predicting the future evolution of GHG emissions in the domains of Energy (including Transportation), Industry Processes and Product Use, Agriculture, and Waste Management. The novelty of the research consisted of testing several variants of functions (power, exponential, inverse trigonometric) to identify, from a group of variants. This optimal function would generate those predictions, which are closest to the real values. The causes that create GHG emissions in each of the four domains were the foundation for the analysis. This chapter focuses on two main subjects: first, the identification of a smooth function to predict the evolution of GHG emissions, and second, the function’s use to estimate the projections of GHG emissions in the coming years for the four domains: Energy (including Transportation), Industry Processes and Product Use, Agriculture, and Waste Management. An observation was that the weights of these four domains remain relatively the same despite the reductions in the total GHG emissions.

In this paper, we consider the analysis of models for univariate and multivariate ordinal outcomes in the context of the latent variable inferential framework of Albert and Chib (1993). We review several alternative modeling and identification schemes and evaluate how each aids or hampers estimation by Markov chain Monte Carlo simulation methods. For each identification scheme we also discuss the question of model comparison by marginal likelihoods and Bayes factors. In addition, we develop a simulation-based framework for analyzing covariate effects that can provide interpretability of the results despite the nonlinearities in the model and the different identification restrictions that can be implemented. The methods are employed to analyze problems in labor economics (educational attainment), political economy (voter opinions), and health economics (consumers’ reliance on alternative sources of medical information).

The estimation of queue length and delays in queues that are oversaturated for some part of a study period is of substantial importance in a range of traffic engineering applications. Whiting’s co-ordinate transformation has provided the basis for several approaches to this. We analyse this approach and present an explicit form for the derivative of queue length with respect to time, which we then use to establish various properties. We also report the results of numerical comparisons with exact formulae for certain special cases and show that these offer little or no advantage over the co-ordinate transformation approximations and can be computationally impractical in study periods of moderate duration.

Transient climate sensitivity relates total climate forcings from anthropogenic and other sources to surface temperature. Global transient climate sensitivity is well studied, as are the related concepts of equilibrium climate sensitivity (ECS) and transient climate response (TCR), but spatially disaggregated local climate sensitivity (LCS) is less so. An energy balance model (EBM) and an easily implemented semiparametric statistical approach are proposed to estimate LCS using the historical record and to assess its contribution to global transient climate sensitivity. Results suggest that areas dominated by ocean tend to import energy, they are relatively more sensitive to forcings, but they warm more slowly than areas dominated by land. Economic implications are discussed.