Natural direct and indirect effects on the exposed : effect decomposition under weaker assumptions

We define natural direct and indirect effects on the exposed. We show that these allow for effect decomposition under weaker identification conditions than population natural direct and indirect effects. When no confounders of the mediator-outcome association are affected by the exposure, identification is possible under essentially the same conditions as for controlled direct effects. Otherwise, identification is still possible with additional knowledge on a nonidentifiable selection-bias function which measures the dependence of the mediator effect on the observed exposure within confounder levels, and which evaluates to zero in a large class of realistic data-generating mechanisms. We argue that natural direct and indirect effects on the exposed are of intrinsic interest in various applications. We moreover show that they coincide with the corresponding population natural direct and indirect effects when the exposure is randomly assigned. In such settings, our results are thus also of relevance for assessing population natural direct and indirect effects in the presence of exposure-induced mediator-outcome confounding, which existing methodology has not been able to address

Inequality aversion and externalities

We conduct a general analysis of the effects of inequality aversion on decisions by homogeneous players in static and dynamic games. We distinguish between direct and indirect effects of inequality aversion. Direct effects are present when a player changes his action to affect disutility caused by inequality. Indirect effects occur when the own action is changed to affect other players' actions. We provide necessary and sufficient conditions for the occurrence of either effect. Moreover, we examine the direction of the effects. Whereas indirect effects induce players to internalize externalities they impose on others, direct effects act in the opposite direction. --inequality aversion,externalities,direct effects,indirect effects

Analysis of meteorology-chemistry interactions during air pollution episodes using online coupled models within AQMEII Phase-2

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).This study reviews the top ranked meteorology and chemistry interactions in online coupled models recommended by an experts’ survey conducted in COST Action EuMetChem and examines the sensitivity of those interactions during two pollution episodes: the Russian forest fires 25 Jul -15 Aug 2010 and a Saharan dust transport event from 1 Oct -31 Oct 2010 as a part of the AQMEII phase-2 exercise. Three WRF-Chem model simulations were performed for the forest fire case for a baseline without any aerosol feedback on meteorology, a simulation with aerosol direct effects only and a simulation including both direct and indirect effects. For the dust case study, eight WRF-Chem and one WRF-CMAQ simulations were selected from the set of simulations conducted in the framework of AQMEII. Of these two simulations considered no feedbacks, two included direct effects only and five simulations included both direct and indirect effects. The results from both episodes demonstrate that it is important to include the meteorology and chemistry interactions in online-coupled models. Model evaluations using routine observations collected in AQMEII phase-2 and observations from a station in Moscow show that for the fire case the simulation including only aerosol direct effects has better performance than the simulations with no aerosol feedbacks or including both direct and indirect effects. The normalized mean biases are significantly reduced by 10-20% for PM10 when including aerosol direct effects. The analysis for the dust case confirms that models perform better when including aerosol direct effects, but worse when including both aerosol direct and indirect effects, which suggests that the representation of aerosol indirect effects needs to be improved in the model.Peer reviewedFinal Published versio

Landscape context mediates the relationship between plant functional traits and decomposition

Aims: It has been well demonstrated that several interacting endogenous and exogenous factors influence decomposition. However, teasing apart the direct and indirect effects of these factors to predict decomposition patterns in heterogenous landscapes remains a key challenge. Methods: At 157 locations in a temperate forest, we measured decomposition of a standard substrate (filter paper) over two years, the landscape context in which decomposition took place, and the functional composition of the woody species that contributed leaf litter to the forest floor where litter bags were placed. We tested for direct and indirect effects of landscape context and direct effects of forest functional composition on decay using structural equation modelling. Results: We found that landscape context had direct effects on decay and indirect effects on decay via its influence on the functional composition of the surrounding forest. Forest functional composition also had direct effects on decay, but these effects decreased or disappeared completely over time. Moreover, community weighted mean trait values were better predictors of decay than functional dispersion of leaf traits, and leaf nitrogen content and carbon content were better predictors of decay than leaf dry matter content or leaf toughness. Conclusions: Our results highlight the importance of an integrative approach that examines the direct and indirect effects of multiple factors for understanding and predicting decomposition patterns across heterogenous landscapes

The control of indirect effects of biomanipulation

Results from long-term investigations on biomanipulation show that indirect effects are at least as important as direct effects are for the stability of biomanipulation. Three types of indirect effects can be distinguished: (1) a change in quantity or quality of the resource base, (2) behavioural change of the prey, and (3) development of anti-predator traits. Although indirect effects of type (2), (e.g. a change in the pattern of vertical migration of zooplankton), and type (3), (e.g. development of helmets and neck teeth in Daphnia), are important mechanisms, the most essential indirect effects regarding biomanipulation belong to type (1). An example of the latter will be demonstrated: the complex of indirect effects of enhanced grazing by large herbivores on the phosphorus metabolism of the lake. It is concluded that control of the indirect effects is absolutely necessary to stabilize biomanipulation measures, but this is much more difficult than the control of direct effects and needs deeper insights into the structuring mechanisms of food webs. Proper management of fish stocks, in combination with the control of phosphorus load and/or the physical conditions, seems to be the most promising way of controlling the indirect effects of biomanipulation

Estimating direct and indirect rebound effects for UK households

Energy efficiency improvements by households lead to rebound effects that offset the potential energy and emissions savings. Direct rebound effects result from increased demand for cheaper energy services, while indirect rebound effects result from increased demand for other goods and services that also require energy to provide. Research to date has focused upon the former, but both are important for climate change. This study estimates the combined direct and indirect rebound effects from seven measures that improve the energy efficiency of UK dwellings. The methodology is based upon estimates of the income elasticity and greenhouse gas (GHG) intensity of 16 categories of household goods and services, and allows for the embodied emissions of the energy efficiency measures themselves. Rebound effects are measured in GHG terms and relate to the adoption of these measures by an average UK household. The study finds that the rebound effects from these measures are typically in the range 5-15% and arise mostly from indirect effects. This is largely because expenditure on gas and electricity is more GHG-intensive than expenditure on other goods and services. However, the anticipated shift towards a low carbon electricity system in the UK may lead to much larger rebound effects