The Potential of Tariff Policy for Climate Change Mitigation: Legal and Economic Analysis

This paper addresses a potential role that tariffs and tariff policy can play in encouraging countries to take part in a multilateral effort to mitigate climate change. It begins by assessing whether increasing tariffs on products from energy intensive or polluting industries amounts to a violation of WTO rules and whether protectionism in this case can be differentiated from genuine environmental concerns. It then argues that while lowering tariffs for environmental goods can serve as a carrot to promote dissemination of cleaner technologies, tariff deconsolidation is a legitimate stick to encourage polluting countries to move towards an international climate agreement. The paper further explores this view by undertaking a partialequilibrium simulation analysis to examine the impact of a unilateral unit increase in tariffs on the imports of the most carbon-intensive products from countries not committed to climate polices. Our results suggest that the committed importing countries would have to raise their tariffs only slightly to effect a significant decline in the imports of these products from the non-committed countries. For instance, a unit increase in the simple average applied tariffs on the imports of these carbon-intensive products in 2005 from our sample of non-committed exporting countries would reduce the imports of these products by an average 32.6% in Australia, 178% in Canada, 195% in the EU, 271% in Japan and 62% in the US, therebysuggesting the effectiveness of such a measure in pushing countries towards a global climate policy.

Probabilistic methods for seasonal forecasting in a changing climate: Cox-type regression models.

For climate risk management, cumulative distribution functions (CDFs) are an important source of information. They are ideally suited to compare probabilistic forecasts of primary (e.g. rainfall) or secondary data (e.g. crop yields). Summarised as CDFs, such forecasts allow an easy quantitative assessment of possible, alternative actions. Although the degree of uncertainty associated with CDF estimation could influence decisions, such information is rarely provided. Hence, we propose Cox-type regression models (CRMs) as a statistical framework for making inferences on CDFs in climate science. CRMs were designed for modelling probability distributions rather than just mean or median values. This makes the approach appealing for risk assessments where probabilities of extremes are often more informative than central tendency measures. CRMs are semi-parametric approaches originally designed for modelling risks arising from time-to-event data. Here we extend this original concept beyond time-dependent measures to other variables of interest. We also provide tools for estimating CDFs and surrounding uncertainty envelopes from empirical data. These statistical techniques intrinsically account for non-stationarities in time series that might be the result of climate change. This feature makes CRMs attractive candidates to investigate the feasibility of developing rigorous global circulation model (GCM)-CRM interfaces for provision of user-relevant forecasts. To demonstrate the applicability of CRMs, we present two examples for El Ni ? no/Southern Oscillation (ENSO)-based forecasts: the onset date of the wet season (Cairns, Australia) and total wet season rainfall (Quixeramobim, Brazil). This study emphasises the methodological aspects of CRMs rather than discussing merits or limitations of the ENSO-based predictors.

Weather variability, tides, and Barmah Forest Virus Disease in the Gladstone region, Australia

In this study we examined the impact of weather variability and tides on the transmission of Barmah Forest virus (BFV) disease and developed a weather-based forecasting model for BFV disease in the Gladstone region, Australia. We used seasonal autoregressive integrated moving-average (SARIMA) models to determine the contribution of weather variables to BFV transmission after the time-series data of response and explanatory variables were made stationary through seasonal differencing. We obtained data on the monthly counts of BFV cases, weather variables (e.g., mean minimum and maximum temperature, total rainfall, and mean relative humidity), high and low tides, and the population size in the Gladstone region between January 1992 and December 2001 from the Queensland Department of Health, Australian Bureau of Meteorology, Queensland Department of Transport, and Australian Bureau of Statistics, respectively. The SARIMA model shows that the 5-month moving average of minimum temperature (β = 0.15, p-value < 0.001) was statistically significantly and positively associated with BFV disease, whereas high tide in the current month (β = −1.03, p-value = 0.04) was statistically significantly and inversely associated with it. However, no significant association was found for other variables. These results may be applied to forecast the occurrence of BFV disease and to use public health resources in BFV control and prevention.