This paper is the first to provide an empirical analysis of the short run and long run effects of carbon dioxide (CO2) emissions on health care spending across U.S. states. Accounting for the possibility of non-linearity in the data and the relationship among the variables, the analysis estimated various statistical models to demonstrate that CO2 emissions led to increases in health care expenditures across U.S states between 1966 and 2009. Using quantile regressions, the analysis displayed that the effect of CO2 emissions was stronger at the upper-end of the conditional distribution of health care expenditures. Results indicate the effect of CO2 emissions on health care was relatively stronger for states that spend higher amounts in health care expenditures. The primary policy message of the paper is that there can be tangible health related benefits associated with policies that aim to reduce carbon emissions across U.S. states.

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In this paper, we aim to explore the relationship between natural gas and crude oil prices for the U.S. economy over the time period 1997 and 2017 in both the unconditional and conditional framework by conditioning the relationship on natural gas production. The time period covers the recent shale gas supply boom. Our results indicate that during the shale gas revolution period of 2007-2013, oil and natural gas prices were cyclical and oil prices were leading natural gas prices. Once we control for the natural gas production we find that significant or high wavelet coherency is observed during 2000-2015 for 3 to 4 years scale. These results have implications for cross market policy effects.

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Various scientific studies have investigated the causal link between solar activity (SS) and the earth's temperature (GT). Results from literature indicate that both the detected structural breaks and existing trend have significant effects on the causality detection outcomes. In this paper, we make a contribution to this literature by evaluating and comparing seven trend extraction methods covering various aspects of trend extraction studies to date. In addition, we extend previous work by using Convergent Cross Mapping (CCM)-an advanced nonparametric causality detection technique to provide evidence on the effect of existing trend in global temperature on the causality detection outcome. This paper illustrates the use of a method to find the most reliable trend extraction approach for data preprocessing, as well as provides detailed analyses of the causality detection of each component by this approach to achieve a better understanding of the causal link between SS and GT. Furthermore, the corresponding CCM results indicate increasing significance of causal effect from SS to GT since 1880 to recent years, which provide solid evidences that may contribute on explaining the escalating global tendency of warming up recent decades.

In this paper, we address the question whether the technical efficiency of a fishing industry is affected by the determinants of ambient water quality of the aquatic ecosystem. Using zone specific data from 1998 -2007 for the Connecticut Long Island Sound lobster fishery and an approach combining a bootstrapping technique with data envelopment analysis, we obtained the DEA estimates of technical efficiency for each fishing zone. We then used the bootstrapped-DEA results and Censored Quantile Regression to assess the impact of the environmental variables on different efficiency percentiles. A key result indicates when environmental conditionals are favorable (high dissolved oxygen levels) efficiency is low and when environmental conditionals are less favorable (high levels of nitrogen), efficiency is high. The results show that the intensity of significant impacts given the contextual variables may vary among high and low efficiency periods.

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