TY - JOUR
T1 - Where did the time (series) go? Estimation of marginal emission factors with autoregressive components
AU - Beltrami, Filippo
AU - Burlinson, Andrew
AU - Giulietti, Monica
AU - Grossi, Luigi
AU - Rowley, Paul
AU - Wilson, Grant
N1 - Erratum at 10.1016/j.eneco.2020.105027
PY - 2020/9
Y1 - 2020/9
N2 - This paper offers a novel contribution to the literature on Marginal Emission Factors (MEF) by proposing a robust empirical methodology for their estimation across both time and space. Our Autoregressive Integrated Moving Average models 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 cross-country context. Indeed, our methodology proves reliable when applied to the case of Italy, which is characterised by a different data generation 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.
AB - This paper offers a novel contribution to the literature on Marginal Emission Factors (MEF) by proposing a robust empirical methodology for their estimation across both time and space. Our Autoregressive Integrated Moving Average models 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 cross-country context. Indeed, our methodology proves reliable when applied to the case of Italy, which is characterised by a different data generation 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.
KW - Electricity generation
KW - Marginal emission factors
KW - Regulation
KW - Time series analysis
UR - http://www.scopus.com/inward/record.url?scp=85089666783&partnerID=8YFLogxK
U2 - 10.1016/j.eneco.2020.104905
DO - 10.1016/j.eneco.2020.104905
M3 - Article
SN - 0140-9883
VL - 91
JO - Energy Economics
JF - Energy Economics
M1 - 104905
ER -