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This paper develops a two-step approach to investigate the effect of energy efficiency improvements on CO 2 emissions at the macro level. We use the index decomposition analysis to derive the true energy efficiency by separating out the impact of structural shifts in economic activity on energy intensity. We then employ both STSM and LSDVC models to examine and quantify the impact of the energy efficiency on CO 2 emissions accounting for non-economic factors such as consumers' lifestyle, attitudes and environmental awareness. The application for 30 OECD countries shows that at the group level, the decline in energy intensity predominately occurred due to improvements in energy efficiency while at the country level, there are mixed contributions from improvement in energy efficiency and structural shift to the decline in energy intensity. The econometric results show that income has the most significant positive impact on CO 2 emissions but improving energy efficiency makes the biggest contribution to driving down CO 2 emissions. The method further enables the separate assessment of non-economic behavioural effects, which are found to exert a non-trivial influence on CO 2 emissions in parallel with changes in energy efficiency. We conclude that energy efficiency remains a key option but that there is also a need for additional policies aiming for behavioural and other non-economic changes.

This paper estimates the effect of environmental regulation on industry location and compares it with other determinants of location such as agricultural, education and R&D country characteristics. The analysis is based on a general empirical trade model that captures the interaction between country and industry characteristics in determining industry location. The Johnson-Neyman technique is used to fully explicate the nature of the conditional interactions. The model is applied to data on 16 manufacturing industries from 13 European countries. The empirical results indicate that the pollution haven effect is present and that the relative strength of such an effect is of about the same magnitude as other determinants of industry location. A significant negative effect on industry location is observed only at relatively high levels of industry pollution intensity.

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We develop a theoretical model to assess the dollar compensation required to induce conventional growers to convert to organic. The model incorporates the uncertainty in producers' expectations about future returns and about the impact of policy changes on these expectations in particular. We demonstrate that a new policy which favours organic can have opposing effects on the rate of conversion. An increase in relative returns to organic today will increase conversion rates. However, if the future of the policy program is uncertain, its introduction can increase the value of waiting to switch, which will decrease conversion rates. We then develop an empirical switching regression model that enables direct estimation of the value associated with being able to postpone the conversion decision until some of the uncertainty is resolved. The model is applied to data on organic and conventional soybeans before and after major changes in U.S. farm policy toward organic growers. The results suggest that sunk costs associated with conversion to organic coupled with uncertainty about future returns can help to explain why there is so little organic farmland in the United States.

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