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A significant number of studies have been conducted to analyze and understand the relationship between gas emissions and global temperature using conventional statistical approaches. However, these techniques follow assumptions of probabilistic modeling, where results can be associated with large errors. Furthermore, such traditional techniques cannot be applied to imprecise data. The purpose of this paper is to avoid strict assumptions when studying the complex relationships between variables by using the three innovative, up-to-date, statistical modeling tools: adaptive neuro-fuzzy inference systems (ANFIS), artificial neural networks (ANNs) and fuzzy time series models.

These three approaches enabled us to effectively represent the relationship between global carbon dioxide (CO₂) emissions from the energy sector (oil, gas and coal) and the average global temperature increase. Temperature was used in this study (1900-2012). Investigations were conducted into the predictive power and performance of different fuzzy techniques against conventional methods and among the fuzzy techniques themselves.

A performance comparison of the ANFIS model against conventional techniques showed that the root means square error (RMSE) of ANFIS and conventional techniques were found to be 0.1157 and 0.1915, respectively. On the other hand, the correlation coefficients of ANN and the conventional technique were computed to be 0.93 and 0.69, respectively. Furthermore, the fuzzy-based time series analysis of CO₂ emissions and average global temperature using three fuzzy time series modeling techniques (Singh, Abbasov–Mamedova and NFTS) showed that the RMSE of fuzzy and conventional time series models were 110.51 and 1237.10, respectively.

The paper provides more awareness about fuzzy techniques application in CO₂ emissions studies.

These techniques can be extended to other models to assess the impact of CO₂ emission from other sectors.

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.

Global temperature has risen by 1°C since 1900, while since the 1990s the Arctic has recently experienced an accelerated warming of about double the average rate of global warming. Nearly all climate scientists agree that the main cause of this temperature rise is ever-increasing accumulations of ‘greenhouse gases’, especially carbon dioxide and methane, within our atmosphere. Sea level rise could easily exceed one metre this century under ‘business as usual’. However, global warming is not just about rising temperatures, melting ice and rising sea levels, but it also affects the frequency and severity of many extreme weather events. Planetary warming is not a uniform process, can spring surprises in regional climate change and is probably linked with the tendency for Northern Hemisphere mid-latitudes to have more extreme (variously hot/cold/dry/wet) weather, especially during the recent period of rapid Arctic warming. There is overwhelming scientific evidence that human activity through enhanced greenhouse gas emissions is largely responsible for recent climate change and accompanying extreme weather, and we are already clearly seeing these changes. However, it is equally evident that, although initial remedial steps are being taken, finding an adequate solution will not be easy unless much larger changes are made to the way in which we all live. Limiting global warming to 1.5°C above pre-industrial temperatures would require global carbon dioxide emissions to decrease by approximately 40–60% by 2030 relative to 2010 levels. This can only be achieved through a collective solution that fully involves diverse communities, among them religious stakeholders.

The purpose of this paper is to analyze the scientific basis of the Paris climate agreement.

The analyses are based on the IPCC’s own reports, the observed temperatures versus the IPCC model-calculated temperatures and the warming effects of greenhouse gases based on the critical studies of climate sensitivity (CS).

The future emission and temperature trends are calculated according to a baseline scenario by the IPCC, which is the worst-case scenario RCP8.5. The selection of RCP8.5 can be criticized because the present CO₂ growth rate 2.2 ppmy⁻¹ should be 2.8 times greater, meaning a CO₂ increase from 402 to 936 ppm. The emission target scenario of COP21 is 40 GtCO₂ equivalent, and the results of this study confirm that the temperature increase stays below 2°C by 2100 per the IPCC calculations. The IPCC-calculated temperature for 2016 is 1.27°C, 49 per cent higher than the observed average of 0.85°C in 2000.

Two explanations have been identified for this significant difference in the IPCC’s calculations: The model is too sensitive for CO₂ increase, and the positive water feedback does not exist. The CS of 0.6°C found in some critical research studies means that the temperature increase would stay below the 2°C target, even though the emissions would follow the baseline scenario. This is highly unlikely because the estimated conventional oil and gas reserves would be exhausted around the 2060s if the present consumption rate continues.

This chapter reviews factors responsible for climate change, impacts of the change on animal health, zoonotic diseases, and their linkage with One-Health program.

This chapter is based on the available literature related to climate change and its effect on animal health and production from different points. The causes and change forcers of climate change, direct and indirect effects of the change on animal health management, host–pathogen–vector interaction, and zoonotic diseases are included. Inter-linkage between climate change and One-Health program are also assessed.

Beside natural causes of climatic change, greenhouse gases are increasing due to human activities, causing global climate changes which have direct and indirect animal health and production performance impacts. The direct impacts are increased ambient temperature, floods, and droughts, while the indirect are reduced availability of water and food. The change and effect also promote diseases spread, increase survival and availability of the pathogen and its intermediate vector host, responsible for distribution and prevalence of tremendous zoonotic, infectious, and vector-borne diseases. The adverse effect on the biodiversity, distribution of animals and micro flora, genetic makeup of microbials which may lead to emerging and re-emerging disease and their outbreaks make the strong linkage between climate change and One-Health.

Global climate change is receiving increasing international attention where international organizations are increasing their focus on tackling the health impacts. Thus, there is a need for parallel mitigation of climate change and animal diseases in a global form.

Most research on climate change is limited to environmental protection, however this chapter provides a nexus between climate change, animal health, livestock production, and the One-Health program for better livelihood.

The purpose of this paper is to re-examine the long-run relationship between radiative forcing (including emissions of carbon dioxide, sulphur oxides, methane and solar radiation) and temperatures from a structural time series modelling perspective. The authors assess whether forcing measures are cointegrated with global temperatures using the structural time series approach.

A Bayesian approach is used to obtain estimates that represent the uncertainty regarding this relationship. The estimated structural time series model enables alternative model specifications to be consistently compared by evaluating model performance.

The results confirm that cointegration between radiative forcing and temperatures is consistent with the data. However, the results find less support for cointegration between forcing and temperature data than found previously.

Given considerable debate within the literature relating to the “best” way to statistically model this relationship and explain results arising as well as model performance, there is uncertainty regarding our understanding of this relationship and resulting policy design and implementation. There is a need for further modelling and use of more data.

There is divergence of views as to how best to statistically capture, explain and model this relationship. Researchers should avoid being too strident in their claims about model performance and better appreciate the role of uncertainty.

The results of this study make a contribution to the literature by employing a theoretically motivated framework in which a number of plausible alternatives are considered in detail, as opposed to simply employing a standard cointegration framework.

The observed increase in the atmospheric concentration of greenhouse gases since the industrial period, due to human activities, is very likely causing the warming of the climate system. Anthropogenic warming and rising sea levels will continue for centuries due to the time scales associated with climate processes and feedbacks. Even if greenhouse gas concentrations were to be stabilized, different types of adaptation measures are needed to cope with the inevitable change. At the same time mitigation measures aiming at decreasing greenhouse gas emissions and enhancing carbon sinks must be taken in order to reduce the potential extent of global warming. This chapter covers the main aspects of the current understanding of the physical basis of climate change, including the directly measured observations and estimated projections for the 21st century. Causes and effects of climate change are also addressed. Finally, the main uncertainties of climate projections and a few general considerations on the different ways to respond to the climate change issue are discussed.