Econometric framework Clause Samples
Econometric framework. We run a panel regression with location (NUTS-2) and multiple time (year, month, and hour) fixed-effects. The location fixed-effects allow us to control for time-invariant NUTS-2-specific heterogeneity (e.g. geographic factors) while time fixed-effects control for unspecified exogenous influences that affect all the regions. Wind turbines require optimal conditions to produce the highest energy output, to compute the optimal wind speed, we control for 10-m instant wind gust and its second-degree polynomial (Fan and Miao, 2015; Pieralli et al., 2015; Carta, 2012). We also include air density as relatively denser air exerts more pressure on the rotors resulting in higher power output Fan and Miao (2015). 45 ▇▇▇▇▇://▇▇▇.▇▇▇▇▇▇▇.▇▇▇▇▇▇▇▇▇▇.▇▇/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview ● : log of hourly wind factor capacity in NUTS-2 region i ● (): 10-m instant wind gust (and its second-degree polynomial) and air density at time t ● : time-invariant NUTS-2 fixed-effects ● : year, month, and hourly fixed-effects As our dependent variable, wind power factor capacity, positive, continuous, and right-skewed – we utilize a gamma Generalized Linear Model with a log-link. Along with being more flexible compared to an Ordinary Least Squares type specification, the gamma regression also has a better fit to our data. We compute the potential impacts of future climate change on wind energy production in Europe by combining the estimated parameters from Equation (1) with two Representative Concentration Pathway (RCP4.5 and RCP8.5) trajectories simulated using multiple RCMs to obtain the ratio of wind power generation with climate change relative to wind power generation under the current climate. Future climatic data in our analysis are from four high- resolution Regional Climate Models (RCM): KNMI RACMO22E, IPSL-CM5A-MR, MPI-ESM-LR, and CNRM-CM5. Based on the stakeholder interests established in COACCH D1.5, we have focused our projections on the Representative Concentration Pathway (RCP) 4.5 as the likely scenario and closest to the proposed Nationally Determined Contributions (NDC) pathway, and an extreme scenario RCP 8.5, which represents the worst possible case.
Econometric framework. Most studies (▇▇▇▇▇▇▇▇ and Killingtveit 2012; ▇▇▇▇▇▇▇▇▇ et al. 2012) on climate change impacts and hydropower rely on process-based models or simulation approaches. Only a few studies have adopted statistical approaches in the context of energy supply (▇▇▇▇▇▇▇ et al. 2013), an alternative method (▇▇▇▇▇▇▇▇▇ et al. 2014) that has been used extensively to analyse climate change impacts in other sectors (▇▇ ▇▇▇▇ et al. 2013; ▇▇▇▇▇▇▇▇▇ and ▇▇▇▇▇▇▇▇▇▇ 2011; ▇▇▇▇▇▇ and ▇▇▇▇▇ 2010). We use a panel regression model to estimate the parameters characterizing a reduced-form relationship to investigate the impact of both gradual and extreme climatic stressors on hydropower generation at the country-level. We control for a set of climatic variables and number of other covariates controlling for time-invariant country-specific heterogeneity (country fixed-effects), unspecified exogenous influences affecting all countries and units (year fixed-effects), and confounding factors such as installed power generation capacity, total electricity consumption, and electricity generation mix. A panel regression with both country and year fixed-effects to estimate the impact of both gradual and extreme climatic stressors on country-level hydropower generation; ● (): log of annual hydropower generation ● (): mean temperature/temperature growth, total precipitation, SPI (6/12/24 months) ● SPI: number of months SPI was below -1.5 in country i in year t ● : vector of control variables controlling for installed hydroelectric capacity, final electricity consumption, share of hydropower capacity, and electricity production mix (gas, oil, coal, and nuclear). ● : time-invariant country fixed-effects ● : linear and quadratic time trends ∈ Potential impacts of future climate change are computed by combining the estimated parameters from Equation (1) with two Representative Concentration Pathway (RCP4.5 and RCP8.5) trajectories simulated using five GCM models described in the Supplementary Information to obtain the ratio of hydropower generation with climate change relative to ∈ hydropower generation with current climate ( ) supply.