The Heterogeneous Impact of Coal Prices on the Location of Cleaner and Dirtier Steel Plants.

AuthorCohen, Francois

    The Paris Agreement (2015) has set the ambitious objective of limiting global warming below 2[degrees]C. It entrusts the Parties to determine their national contribution to this target according to domestic circumstances and capabilities. While the Nationally Determined Contributions (ND[C.sub.S]) of the Paris agreement allowed solving the deadlock of past failed negotiations, the aggregate efforts listed in all the ND[C.sub.S] are largely insufficient to achieve the below 2[degrees]C target. In that regard, only a small number of independent carbon schemes are currently operative (e.g. the EU, Japan, California and its partners in the US and Canada). In all these schemes, the price of carbon is far below its social cost as estimated by integrated impact assessment models.' One important reason why carbon prices are low on these markets comes from the risk that regulated industries lose competitiveness if other countries do not implement similar schemes. In energy-intensive sectors, largely exposed to international competition, unilaterally implementing a carbon tax or trading scheme may push industries to relocate elsewhere.

    In this paper, we estimate the effect of changes in coal prices on steel plant location worldwide. Steel represents 27% of all greenhouse gas emissions (GHG) from industry (IEA, 2017b). We explicitly focus on the impact of coal prices on the steel industry because 75% of all its C[O.sub.2] emissions come from the burning of coking coal in Basic Oxygen Furnaces (BOF) (Columbia Climate Center, 2012). However, we can expect that coal prices have heterogeneous impacts across production processes because a less coal-intensive process, Electric Arc Furnaces (EAF), can also be used to produce steel. EAF is a recycling technology that cuts GHG emissions by 75% with respect to BOF (2)

    Looking jointly at the effect of energy price shocks on plant location and production preferences constitutes the main contribution of this paper. The two are likely to interact. Even though changes of location may be encouraged by the availability of low coal prices in some countries, the choice to relocate will be ultimately dependent on the cost of staying, which depends on the availability of low-pollution technologies or the potential for their development. Following increases in coal prices, we find relocation effects for the dirtiest steel firms as well as increases in the diffusion of the low-polluting technology. However, because the low-polluting technology is a recycling technology that requires scrap, there are limits to its diffusion even under high coal prices.

    This paper relies on steel plant data (1960-2014) collected by James King and merged with data on coal prices. We use a unit-level econometric model to correlate the size of national steelmaking industries to coal price shocks. Our setting circumvents several major identification issues. First, we account for the difference between current and expected coal prices by approximating coal price expectations with autoregressive integrated moving average models (ARIMA). We then rely on a pre-sample mean estimator (Blundell et al., 2002) to account for coal price endogeneity and the risk of weak instrumentation. Complementary robustness checks include tests for instrument exogeneity and several specification changes, for example in the definition of coal prices, or the use of an alternative estimator (system GMM). We also make sure that our results are not driven by the concomitant evolution of the prices of the other main steel production inputs: iron ore, electricity and scrap.

    We find that an increase in coal prices at national level has a negative effect on the size of steel manufacturing. In our preferred specification, a 1% increase in coal prices reduces BOF production capacity by around 0.37%, while it has no impact on EAF capacity. As a result, both the size of production and its composition are influenced by coal price regimes.

    We indirectly assess the effect of the introduction of ambitious climate policies on national steel industries by making the simplifying assumption that a carbon market is equivalent to a coal price increase. We simulate the impact of the implementation of two climate policies: a multilateral carbon market in the EU with a more stringent carbon price than today and no border adjustment; and a multilateral carbon market that would apply to all the countries that we cover (around 75% of the steel produced worldwide). In these carbon markets, we set the price of GHG emissions at $31/tC[O.sub.2] eq. This is the estimate of the current social cost of carbon in Nordhaus (2017). We find that redistributive effects across countries can be as large as redistributive effects across technologies. The portion of world capacity that is BOF would only decrease by 0.9 percentage points with a stringent European carbon market. Much of this BOF capacity would be replaced by EAF capacity. However, the potential for a transition from BOF to EAF is much greater: we simulate a 13.3 percentage point reduction of the global share of BOF production if the carbon market was implemented everywhere. However, this would also have an impact on the location of plants. We find that Asian countries would reduce their overall market share by around 19% if the carbon market was global because Asian firms are more coal intensive and have built their steel industry on relatively cheaper energy.

    This paper complements a large body of economic literature that has looked at the effect of energy prices or environmental regulation on firm performance and location. Effects were found to be either positive or negative depending on the policy under scrutiny and the sector analyzed (Iraldo et al., 2011). Recent studies interested in industry, especially energy-intensive industry, have shown that environmental regulation tends to decrease output and profits (Aldy and Pizer, 2015b; Ho et al., 2008; List et al., 2003; Greenstone, 2002) (3) and/or reduce exports and increase imports (Aldy and Pizer, 2015a; Levinson and Taylor, 2008; Ederington et al., 2005). Therefore, the location of plants should be impacted by environmental regulation and/or energy prices (Wagner and Timmins, 2009; Kellenberg, 2009; Kahn and Mansur, 2013). Some recent papers have studied the impact of the EUETS on firm relocation (Dechezlepretre et al., 2014, Borghesi et al., 2016, Koch and Basse Mama, 2016). Both Borghesi et al. (2016) and Koch and Basse Mama (2016) find evidence of relocation caused by the EU-ETS particularly for the sectors exposed to international competition. On the other hand, Dechezlepretre et al. (2014) find no evidence of carbon leakages triggered by the introduction of the carbon scheme. (4)

    Evidence that energy prices foster the adoption of cleaner technologies has been found in very diverse industry contexts (e.g. Cohen et al., 2017; Aghion et al., 2016; Dechezlepretre et al., 2011; Popp, 2006; Brunnermeier and Cohen, 2003; Popp, 2002; Gray and Shadbegian, 1998; Newell et al., 1999; Jaffe and Palmer, 1997; Lanjouw and Mody, 1996). Yet, none of the above-mentioned studies considered plant location and technological choice in the same framework. (5)

    Even when alternative technologies exist, the risks of relocation, found in the case of BOF steel manufacturing, may apply to other highly energy-intensive industries (such as the paper or the chemical industry) that are also strongly exposed to international competition because of the high tradability of commodities. (6) They may largely explain the reticence of EU countries to increase the stringency of the EU ETS, or to allow for exemptions in the steel sector due to the risk of carbon leakage. National industrial interests are one of the main reasons why multinational carbon markets are not being put forward. Since countries will be asymmetrically affected by it, systems with quota allocations that take into account the current distribution of firms across countries and their energy intensity may be necessary if a multilateral agreement on a carbon market is to be found. These findings suggest that enhancing international support for the implementation of ND[C.sub.S], in accordance with the principle of differentiated responsibilities, is of utmost importance.

    The rest of the paper is structured as follows. Section 2 presents the data while providing a brief overview of the steel industry. Section 3 presents our estimation method. Section 4 comments on the results and the main robustness checks performed. Section 5 presents our simulation exercise and section 6 concludes.

  2. DATA

    2.1 James King data on the steel industry

    The steel plant data has been gathered by James King and provides information on the location of steel plants in the world. For a few countries, the data is available since the beginning of the 20 (th) century but it starts by around 1960 for most of them. The most disaggregated layer of observation in the database is the production unit: a steel plant is composed of several units, which may become operative or close down at different moments in time, even if they are on the same site. The data records the opening and closing year of these different units. These are permanent changes and we observe up to one opening data, and one closing date per unit.

    Units may also use different production technologies. The dataset records whether a unit is EAF or BOF. BOF is a steel-making technology that came into wide adoption in the 1960s. (7) It produces steel with iron ore and coking coal. (8) 88% of C[O.sub.2] emissions associated with plants using BOF are due to the combustion of coking coal to obtain coke and then the mixing of iron ore with coke to obtain steel. The remaining C[O.sub.2] emissions indirectly come from electricity usage, usually generated with on-site coal-fired power generators (EPA, 2012; OECD, 2013; IEA, 2012). On the other hand, EAF is a recycling process that...

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