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The Dynamic Impact of Market Integration: Evidence from Renewable Energy Expansion in Chile

Effective and economical expansion of renewable energy is one of the most urgent and important challenges of addressing climate change. However, many countries are facing a problem because existing network infrastructures (i.e., transmission networks) were not originally built to accommodate renewables, which creates disconnections between demand centers and renewable supply. In this paper, the researchers study the static and dynamic impacts of market integration on renewable energy expansion. Their theory highlights that statically, market integration improves allocative efficiency by gains from trade, and dynamically, it incentivizes new entry of renewable power plants. Using two recent grid expansions in the Chilean electricity market, they empirically test their theoretical predictions and show that commonly used underestimates the dynamic benefits if renewable investments occur in anticipation of market integration. The authors build a structural model of power plant entry and show how to correct for such bias. They find that market integration resulted in price convergence across regions, increases in renewable generation, and decreases in generation cost and pollution emissions. Furthermore, a substantial amount of renewable entry would not have occurred in the absence of market integration. They show that ignoring this dynamic effect would substantially understate the benefits of transmission investments. ﬁndings study by comparing before and after grid beneﬁt if plant entries anticipation We explore how dynamic bias in event study framework. Our idea is that we can use structural model anticipatory and then estimate the event study regression. We show that this dynamic correction the attenuation bias created in the standard event study regression, and that the corrected event study regression is able to produce results numerically similar to the results obtained by counterfactual simulations with the structural model. We