The impact of voluntary management earnings forecasts on executive compensation contracts

This study attempts to find a relationship between management earnings forecasts and executive compensation contracts. It focuses on three different elements of management earnings forecasts, namely: the type of forecast, the actual expectation of the forecast and the forecasting frequency. The three relations are analysed through both logistic and linear regressions using a sample of 90 U.S. listed companies between 2006 and 2012. From the analysis, three conclusions can be drawn. First, companies do not always align the measures of their forecasts with the compensation measures used in executive compensation contracts. Second, the target levels used in management earnings forecasts and compensation contracts sometimes differ significantly, depending on the type of measure. Finally, the forecasting frequency of a company does not influence the type of compensation measures used. In general, it appears that earnings forecasts hold some relation to executive compensation contracts. Future research can further investigate this relationship, for example by focusing on the causes of the results or through replicating the analysis

The unconditional probability distributions of future emissions and temperatures

How high should we build a dyke today, knowing that it will serve for more than 50 years? This depends on the unconditional probability distribution of future temperatures. We review the literature on estimates of future emissions for current policy scenarios and current pledge scenarios. Reviewing expert elicitations, abatement costs of scenarios, learning rates of technologies, fossil fuel supply side dynamics and geoengineering, we argue that scenarios with emissions largely beyond current policy scenarios and largely below current pledge scenarios are relatively unlikely. Based on this, we develop a transparent method to estimate unconditional probability distributions of future temperatures and temperature exceedance probabilities for use in Value at Risk stress tests in 2030, 2050 and 2100

The unconditional probability distributions of future emissions and temperatures

How high should we build a dyke today, knowing that it will serve for more than 50 years? This depends on the unconditional probability distribution of future temperatures. We review the literature on estimates of future emissions for current policy scenarios and current pledge scenarios. Reviewing expert elicitations, abatement costs of scenarios, learning rates of technologies, fossil fuel supply side dynamics and geoengineering, we argue that scenarios with emissions largely beyond current policy scenarios and largely below current pledge scenarios are relatively unlikely. Based on this, we develop a transparent method to estimate unconditional probability distributions of future temperatures and temperature exceedance probabilities for use in Value at Risk stress tests in 2030, 2050 and 2100

Literature-informed likelihoods of future emissions and temperatures

How high should we build a dyke today, knowing that it will serve for more than 50 years? This depends on the probability distribution of future temperatures. We review the literature on estimates of future emissions for current/stated policy scenarios and current pledge scenarios. Reviewing expert elicitations, abatement costs of scenarios, learning rates of technologies, fossil fuel supply side dynamics and geoengineering, we argue that scenarios with emissions largely beyond current/stated policy scenarios and largely below current pledge scenarios are relatively unlikely. Based on this, we develop a literature-informed evaluation of the likelihoods of future temperature for use in Value at Risk stress tests in 2030, 2050 and 2100

Optimal climate policy as if the transition matters

The optimal transition to a low-carbon economy must account for adjustment costs in switching from dirty to clean capital, technological progress, and economic and climatic shocks. We study the low-carbon transition using a dynamic stochastic general equilibrium model with emissions abatement costs calibrated on a large energy modelling database, solved with recursive methods. We show how capital inertia puts upward pressure on emissions and temperatures in the short run, but that nonetheless it is optimal to actively disinvest from – to ‘strand’ – a significant share of the dirty capital stock. Conversely, clean technological progress, as well as uncertainty about climatic and economic factors, lead to lower emissions and temperatures in the long run. Putting these factors together, we estimate a net premium of 33% on the optimal carbon price today relative to a ‘straw man’ model with perfect capital mobility, fixed abatement costs and no uncertainty

Policing carbon markets

Carbon markets have emerged in recent decades as one of the most important tools for curbing industrial greenhouse gas emissions, but they present a number of novel enforcement challenges as compared to more conventional pollution regulations—new regulators with narrow authority, lack of legal precedent, and more. To shed light on the practical issues involved in policing carbon markets, we present the first comprehensive analysis of the EU Emissions Trading System, a single program that was policed by 31 different national regulators. We find generally high rates of compliance coupled with low rates of enforcement, a pattern that is known in the literature as ‘Harrington’s paradox.’ Variation in the probability and severity of fines explain just one tenth of the variation in compliance rates. Meanwhile, other enforcement strategies that have been pointed to as resolutions to Harrington’s paradox in other applications, such as ‘naming and shaming,’ appear to have had little discernible effect