Firm-level Estimates of Fuel Substitution: An Application toCarbon Pricing

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The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

Section: System Estimation Resultssupporting

confidence: 61%

Section: Overviewmentioning

confidence: 99%

Capital Adjustment and the Optimal Fuel Choice

Hyland¹,

Steinbuks²

2019

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“…The first novelty is methodological, as we illustrate the advantages of a cointegrating approach applied on a system of fuel shares, which has been previously applied only once but for the industrial sector as a whole rather than the industrial subsectors modelled in the study. By implementing the first cointegration analysis for a set of fuels shares estimated at a disaggregated industrial level, we showed that plausible and robust estimates of price elasticities can be obtained Bardazzi et al (2016) -0.82 Italy Burke and Yang (2016) (-1.09, -1.00) International Enevoldsen et al 2007-0.11 Nordic countries Harvey and Marshall (1991) -0.62 UK Huntington (2007) (-0.29, -0.15) US Hyland and Haller 2018-1.16 Ireland Pindyck (1979) -1.44 UK Renou-Maissant (1999) -0.65 UK Serletis et al 2010-0.13 UK Serletis and Shahmoradi (2008) (-1.50, -1.01) US Steinbuks 2012(-0.94, -0.28) Long-run, UK, heating and all processes respectively Suh 2016-0.20 US Taheri and Stevenson (2002) -0.39 US Uri (1979) -082 UK Uri (1982) -0.91 UK Westoby (1984) -1.06 UK Our weighted average -1.22 UK Enevoldsen et al (2007) (-0.28, -0.10) Nordic countries Harvey and Marshall (1991) -0.06 UK Hyland and Haller (2018) -0.31 Ireland Jamil and Ahmad (2011) -1.22 Long-run, Pakistan Paul et al (2009) -0.40 US Pindyck (1979) -0.56 UK Renou-Maissant (1999) -0.31 UK Ros (2015) (-0.87, -0.52) Long-run, US Serletis et al 2010-0.004 UK Steinbuks (2012) (-1.11, -0.23) Long-run, UK, heating and all processes respectively Suh 2016-0.11 US Taheri and Stevenson (2002) -0.71 US Uri (1979) -0.22 US Uri (1982) -0.50 UK Westoby (1984) -0.39 UK Our weighted average -0.82 UK even from relatively short time series using a parsimonious but careful application of a system approach. Modelling a VECM for each of the eight industrial subsectors we obtained unequivocal evidence for the existence of two cointegrating relationships representing demand for electricity and gas.…”

Section: Discussionmentioning

confidence: 99%

Fuel Demand across UK Industrial Subsectors

Agnolucci¹,

Lipsis²

2020

Heterogeneity is a theme acquiring more and more prominence in the energy economic literature from both a modelling and policy-making perspective. We show that useful empirical evidence on this subject can be obtained by applying a parsimonious multivariate cointegration analysis that makes use of the increasingly available time series data on energy demand. We find that there is substantial heterogeneity in the demand for fuels from UK firms belonging to different subsectors, with price and level of production having different degrees of importance in the fuel choice, and with evidence of both substitutability and complementarity between fuels. Moreover, we show that fuel demand for the industrial sector as a whole is considerably more elastic than most estimates presented in the literature, finding which has direct relevance for policies aimed at influencing industrial fuel consumption through fuel switching.

“…. Hyland and Haller (2018) typically feature such price elasticities between electricity, gas and oil using firm-level data.…”

Section: A Dynamic Panel Data Modelingmentioning

confidence: 99%

On the CO2 Emissions Determinants During the EU ETS Phases I and II: A Plant-level Analysis Merging the EUTL and Platts Power Data

Chèze¹,

Chevallier²,

Berghmans³

et al. 2020

This article studies ex-post the CO2 emissions determinants during 2005-2012 by resorting to an original database merging the European Union Transaction Log (EUTL) with the World Electric Power Plants (WEPP) database maintained by Platts. We estimate the main drivers of CO2 emissions for the 1,453 power plants included in the EU ETS using plant-level panel data. During phases I and II, there has been a debate about whether the economic crisis was ultimately the only factor behind the fall in CO2 emissions. We find that the EU ETS kept some degree of effectiveness but only during phase I (2005-07). During phase II (2008-12), its impact has been largely impeded by the deep economic recession in 2008-2009 which became the leading cause of the emissions reduction. We disentangle the analysis not only by periods but also for each type of power plants. We conclude that the EU Commission's flagship climate policy could and should be enhanced by better coordination of overlapping climate policies.