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This study focuses on examining the impact of energy consumption, economic structure, population, and manufacturing output on the CO₂ emissions of selected emerging countries by utilizing the Structural Time Series Model (STSM). Based on the annual data ranging from 1970 to 2019, the model is built up using total primary energy consumption, GDP per capita, population and manufacturing value-added, and, finally, a stochastic Underlying Emission Trend as explanatory variables. STSM is extended by the introduction of the notion of Underlying Energy Demand Trend (UEDT) as a factor for exogenous effects, including development in technical progress, energy efficiency improvements, changes in human behaviors, economy, and environmental regulations. In this context, STSM and the notion of UEDT are implemented to form a forecasting model for CO₂ emissions of the selected emerging countries. The model discovers the significant influences of all selected variables of CO₂ emissions. The results suggest that the most forceful factor in CO₂ emissions is the total primary energy supply. Furthermore, while the long-term impact of economic growth on CO₂ emissions is negative for some emerging economies, it is positive for several others. The model also measures the long-term manufacturing value-added elasticity of CO₂ emissions in these emerging economies.

Climate data, including historical climate observations and climate model outputs, are often used in climate impact assessments, to explore potential climate futures. However, characteristics often associated with “islandness”, such as smallness, land boundedness and isolation, may mean that climate impact assessment methods applied at broader scales cannot simply be downscaled to island settings. This paper aims to discuss information needs and the limitations of climate models and datasets in the context of small islands and explores how such challenges might be addressed.

Reviewing existing literature, this paper explores challenges of islandness in top-down, model-led climate impact assessment and bottom-up, vulnerability-led approaches. It examines how alternative forms of knowledge production can play a role in validating models and in guiding adaptation actions at the local level and highlights decision-making techniques that can support adaptation even when data is uncertain.

Small island topography is often too detailed for global or even regional climate models to resolve, but equally, local meteorological station data may be absent or uncertain, particularly in island peripheries. However, rather than viewing the issue as decision-making with big data at the regional/global scale versus with little or no data at the small island scale, a more productive discourse can emerge by conceptualising strategies of decision-making with unconventional types of data.

This paper provides a critical overview and synthesis of issues relating to climate models, data sets and impact assessment methods as they pertain to islands, which can benefit decision makers and other end-users of climate data in island communities.

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 purpose of this paper is to suggest an approach to post‐Kyoto climate negotiations that could provide a way out of the apparent deadlock between developed and developing countries. This is an urgent issue as the world already appears to be close to a level of climate change that could be considered “dangerous”.

The paper explores the potential that control of short‐lived greenhouse gases such as methane, tropospheric ozone, and soot could have, in addition to steep cutbacks in industrialized nations, to both mitigate global warming and overcome political stalemate in the international climate negotiations.

Although rarely mentioned in climate discourse, reducing emissions of short‐lived greenhouse gases offers a cost‐effective way of actually reducing the radiative forcing in the atmosphere, while at the same time producing substantial subsidiary benefits such as improved urban air quality. The paper suggests leveraging this potential in the post‐Kyoto treaty in order to “buy time” to address the arguably more difficult problem of essentially eliminating fossil‐fuel related CO₂ emissions, which will ultimately be required to truly bring climate change under control. While high‐income countries work on steep cutbacks of all greenhouse gas emissions, middle‐income nations could make significant additional contributions by undertaking commitments to control only short‐lived greenhouse gases until they reached a threshold level of per‐capita GDP, at which point they would cap and begin reducing all greenhouse gas emissions.

This paper recognizes that political tradeoffs will have to be made in negotiating the next climate treaty, and offers a way of approaching these tradeoffs that could minimize resulting environmental damage.

A numerical analysis is given for the prediction of unsteady, two‐dimensional fluid flow induced by a heat and mass source in an initially closed cavity which is vented when the internal overpressure reaches a certain level. A modified ICE technique is used for solving the Navier–Stokes equations governing a compressible flow at a low Mach number and high temperature. Particular attention is focused on the treatment of the boundary conditions on the vent surface. This has been treated by an original procedure using the resolution of a Riemann problem. The configuration investigated may be viewed as a test problem which allows simulation of the ventilation and cooling of such cavities. The injection of hot gases is found to play a key role on the temperature field in the enclosure, whereas the vent seems to produce a distortion of the dynamic flow‐field only. When the injection of hot gases is stopped, the enclosure heat transfer is strongly influenced by the vent. A comparison with the results obtained when the radiative heat transfer between the walls of the enclosure is considered, indicate that radiation dominates the heat transfer in the enclosure and alters the flow patterns significantly.

A variety of gases, including water vapor (H2O), carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O), add to the radiative forcing of Earth's atmosphere, meaning that they absorb certain wavelengths of infrared radiation (heat) that is leaving the Earth and thus raise the temperature of its atmosphere. Since glass has the same effect on the loss of heat from a greenhouse, these gases are known as “greenhouse” gases. It is fortunate that these gases are found in the atmosphere; without its natural greenhouse effect, Earth's temperature would be below the freezing point, and all waters on its surface would be ice. However, for the past 100 years or so, the concentrations of CO2, CH4, and N2O in the atmosphere have been rising as a result of human activities. An increase in the radiative forcing of Earth's atmosphere is destined to cause global warming, superimposed on the natural climate cycles that have characterized Earth's history.

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.

The purpose of this paper is to present the results obtained from numerical models of radiant energy exchange in instruments typically used to measure various characteristics of the Earth's ocean‐atmosphere system.

Numerical experiments were designed and performed in a statistical environment, based on the Monte Carlo ray‐trace (MCRT) method, developed to model thermal and optical systems. Results from the derived theoretical equations were then compared to the results from the numerical experiments.

A rigorous statistical protocol is defined and demonstrated for establishing the uncertainty and related confidence interval in results obtained from MCRT models of radiant exchange.

The methodology developed in this paper should be adapted to predict the uncertainty of more comprehensive parameters such as the total radiative heat transfer.

Results can be used to estimate the number of energy bundles necessary to be traced per surface element in a MCRT model to obtain a desired relative error.

This paper offers a new methodology to predict the uncertainty of parameters in high‐level modeling and analysis of instruments that accumulate the long‐term database required to correlate observed trends with human activity and natural phenomena. The value of this paper lies in the interest in understanding the climatological role of the Earth's radiative energy budget.