• Energy Research

Abstract We analyze conditions under which it is possible to achieve efficient mitigation of greenhouse gas emissions with a self-enforcing international agreement in which all countries find it in their self-interest to abide by the agreement. We model the choice of emissions by countries as a dynamic game. We use a two-part punishment scheme for deviations from an agreement that is renegotiation-proof and show when this scheme supports the efficient outcome as a subgame perfect equilibrium. Using numerical examples, we show that an efficient subgame perfect equilibrium exists for a range of reasonable parameter values. The existence of such equilibrium may be non-monotonic in the discount rate and the ratio of slope of marginal abatement cost and marginal damages.

We assess the effect of the COVID-19 pandemic on global fossil fuel consumption and CO2 emissions over the two-year horizon 2020Q1-2021Q4. We apply a global vector autoregressive (GVAR) model, which captures complex spatial-temporal interdependencies across countries associated with the international propagation of economic impact due to the virus spread. The model makes use of a unique quarterly data set of coal, natural gas, and oil consumption, output, exchange rates and equity prices, including global fossil fuel prices for 32 major CO2 emitting countries spanning the period 1984Q1-2019Q4. We produce forecasts of coal, natural gas and oil consumption, conditional on GDP growth scenarios based on alternative IMF World Economic Outlook forecasts that were made before and after the outbreak. We also simulate the effect of a relative price change in fossil fuels, due to global scale carbon pricing, on consumption and output. Our results predict fossil fuel consumption and CO2 emissions to return to their pre-crisis levels, and even exceed them, within the two-year horizon despite the large reductions in the first quarter following the outbreak. Our forecasts anticipate more robust growth for emerging than for advanced economies. The model predicts recovery to the pre-crisis levels even if another wave of pandemic occurs within a year. Our counterfactual carbon pricing scenario indicates that an increase in coal prices is expected to have a smaller impact on GDP than on fossil fuel consumption. Thus, the COVID-19 pandemic would not provide countries with a strong reason to delay climate change mitigation efforts.

Revenue decoupling (RD) is a regulatory mechanism that allows adjustments of retail electricity rates for the regulated utility to recover its required revenue despite fluctuations in its sales volume. The U.S. utility data in 2000–2019 reveals that RD is associated with about a 4-percentage point higher growth rate of residential electricity prices within the first year after RD is implemented relative to carefully matched non-decoupled utilities with similar pre-RD sales trends. Theoretically, unexpected sales declines would lead to higher electricity prices while unexpected sales increases would lead to lower prices. While RD adjustments have reportedly yielded both refunds and surcharges, our analysis indicates that electricity prices demonstrate downward rigidity and statistically significant upward adjustments for the utilities subject to RD. The asymmetric movement in retail prices may be associated with the political economy underlying the adoption and the implementation of RD.

Abstract A number of studies have estimated how much energy conservation is achieved by providing households with real-time information on energy use via in-home displays. However, none of these studies tell us why real-time information changes energy-use behavior. We explore the causal mechanisms through which real-time information affects energy consumption by conducting a randomized-control trial with residential households. The experiment attempts to disentangle two competing mechanisms: (i) learning about the energy consumption of various activities, the “learning effect”, versus (ii) having a constant reminder of energy use, the “saliency effect”. We have two main results. First, we find a statistically significant treatment effect from receiving real-time information. Second, we find that learning plays a more prominent role than saliency in driving energy conservation. Our findings support the use of energy conservation programs that target consumer knowledge regarding the energy use of different devices and activities.