This paper offers a novel contribution to the literature on marginal emission factors by proposing a robust empirical methodology for their estimation across both time and space. Our ARIMA model with time-effects not only outperforms the established models in the economics literature but it also proves more reliable than variations adopted in the field of engineering. Utilising half-hourly data on carbon emissions and generation in Great Britain, the results allow us to identify a more stable path of MEFs than obtained with existing methodologies. We also estimate marginal emission effects over subsequent time periods (intra-day), rather than focussing only on individual settlement periods (inter-day). This allows us to evaluate the annual cycle of emissions as a result of changes in the economic and social activity which drives demand. Moreover, the reliability of our approach is further confirmed upon exploring the crosscountry context. Indeed, our methodology proves reliable when applied to the case of Italy, which is characterised by a different data generation 2 process. Crucially, we provide a more robust basis for valuing actual carbon emission reductions, especially in electricity systems with high penetration of intermittent renewable technologies.
This paper estimates the seasonal and zonal CO2 marginal emissions factors (MEFs) from electricity production in the Italian electricity system. The inclusion of the zonal conguration of the Italian wholesale power market leads to a complete measurement of marginal emission factors which takes into account the heterogeneous distribution of RES power plants, their penetration rate and their variability within the zonal power generation mix. This article relies on a exible econometric approach that includes the fractional cointegration methodology to incorporate the typical features of long-memory processes into the estimation of MEFs. We nd high variability in annual MEFs estimated at the zonal level. Sardinia reports the highest MEF (0.7189 tCO2/MWh), followed by the Center South (0.7022 tCO2/MWh), the Center North (0.4236 tCO2/MWh), the North (0.2018 tCO2/MWh) and Sicily (0.146 tCO2/MWh). The seasonal analysis also shows a large variability of MEFs in each zone across time. The heterogeneity of results leads us to recommend that policymakers consider the zonal conguration of the power market and the large seasonal variability related to carbon emissions and electricity generation when designing incentives for Renewable Energy Sources (RES) expansion and for achieving emission reduction targets.
This paper offers a novel contribution to the literature on marginal emission factors by proposing a robust empirical methodology for their estimation across both time and space. Our ARIMA model with time-effects not only outperforms the established models in the economics literature but it also proves more reliable than variations adopted in the field of engineering. Utilising half-hourly data on carbon emissions and generation in Great Britain, the results allow us to identify a more stable path of MEFs than obtained with existing methodologies. We also estimate marginal emission effects over subsequent time periods (intra-day), rather than focussing only on individual settlement periods (inter-day). This allows us to evaluate the annual cycle of emissions as a result of changes in the economic and social activity which drives demand. Moreover, the reliability of our approach is further confirmed upon exploring the crosscountry context. Indeed, our methodology proves reliable when applied to the case of Italy, which is characterised by a different data generation 2 process. Crucially, we provide a more robust basis for valuing actual carbon emission reductions, especially in electricity systems with high penetration of intermittent renewable technologies.
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