Chapter 4: A techno-economic analysis using the COALMOD-

The global steam coal market underwent some dramatic changes during the last decades and further structural changes and shifts are expected in the future. This chapter presents the COALMOD-World model that was developed to identify the main drivers of the mar-ket today and in the future. Taking into account all the factors that will affect the marmar-ket such as geology and costs, infrastructure and investment costs, demand projections and energy and climate policy, the model gives insights into how the market might evolve

1.5. Overview of the Thesis until 2030 and provides a tool for the counterfactual analysis of a wide range of sce-narios. The main differences with the COALMOD-Trade model and the Atlantic steam coal market models of the previous chapters are the multi-period time dimension and the coverage of internal markets and overland trade in addition to the global seaborne trade.

The COALMOD-World model draws upon two significant findings from Chapters 2 and 3. First we found out that the global steam coal market may be better represented by a competitive market model and that given current market trends the rise of structural market power is unlikely. The assumption of perfect competition has several advantages from a modeling perspective. Perfectly competitive numerical models are theoretically well understood and easy to solve numerically. Also the perfect competition premise allows us to make simplifying assumptions about the players. We can separate the value-added chain vertically from the mine to the final customer and we can horizontally aggregate players such as companies and this will not affect the outcomes since in a perfectly competitive set-up demand will always be served in a way that minimizes costs.

The second finding from the previous chapters useful for our modeling is that a model considering the energy and mass of the coal delivers a better reproduction of the market.

This is especially important for countries such as Indonesia that sell significant amounts of lower-rank coal or for the modeling of internal markets where the quality of the coal used is often inferior than seaborne traded coal.

The profit-maximizing players in the model are the producers and the exporters.

The model producers aggregate the companies active in one mining basin. They bear the costs for mining the coal and transporting the coal overland. They can either sell the coal directly to domestic demand regions or to the exporters. The exporters aggregate the export capacity of a region and bear the port operating costs as well as the freight costs for overseas transport. They can sell the coal to all the demand nodes with import capabilities. Demand is represented by an inverse demand function. The players are subject to various constraints such as reserve, production and transport capacities for the producers and export capacity constraints for the exporters. The players maximize their profit until 2030 using a net present value approach with perfect foresight about future market situations. They can invest to increase the capacities and lift previous constraints on production, inland transport and exports. Thus, the model shows how future demand may be served optimally in a cost minimizing way.

One unique technical feature of the model is the endogenous production cost and mine mortality mechanism that was developed for COALMOD-World. Based on cumu-lative extraction, investments and the specific geological and economical characteristics of a mining basin, the short-term cost functions change endogenously in the model and production capacity is reduced to simulate mine mortality. This allows for a more realis-tic depiction of the dynamics of a mining basin. To our knowledge it is the first time that such a mechanism is implemented in a partial equilibrium numerical resource model.

The model specification we use consists of 25 producers, 15 exporters and 41 demand nodes and covers virtually all steam coal production and consumption in the world with

the main exporters and importers as well as a detailed representation of the internal markets of China, India, Russia and the US. The 1671 single data parameters the model uses for all the cost and capacity components of the value-added chain and demand have to be gathered from various sources or calculated and estimated and represent a significant part of the work in this chapter.

The model calculates yearly market equilibria for the years 2006, 2010 and then up to 2030 in five years steps. One first result and validation of the model is that the model predicted the massive shift of trades flows from South Africa, traditionally the main supplier in the Atlantic market to India. For the subsequent years two scenarios are implemented based on the International Energy Agency’s 2010 World Energy Outlook (IEA, 2010). In the “increasing demand” scenario, based on the IEA Current Policies scenario, it is assumed that as of mid-2010 no change in the current policies will be implemented and that the recently announced commitments are not acted upon. In the “stabilizing demand” scenario, based on the IEA New Policies scenario, the recently announced commitments and policies, for example the ones of the 2009 Copenhagen Climate Conference, are fully implemented.

The most significant result is the shifting of trade flows towards the Asian markets.

We start in 2006 with a global integrated market where South Africa and Colombia are the main suppliers to the Atlantic market and Indonesia and Australia to the Pacific Market. Then a gradual shift eastwards occurs until 2020 with flows from South Africa being directed toward Asia and, especially, India. Colombia replaces South Africa as the key supplier to Europe. The second shift occurs in 2020. We see an additional shift westwards with Colombia delivering to Japan and Korea, resource poor countries with a high willingness to pay. By 2030, the overall picture on the global market has significantly changed: Russia is the main supplier to Europe and South Africa, Europe’s traditional supplier, is a major supplier to India and the Pacific market whereas Colombia becomes, principally, a Pacific market supplier. This shift happens in both the “increasing demand”

and “stabilizing demand” scenario but in the latter scenario the volumes of the trade flows are smaller and the shifts occur later in time.

From 2006 to 2030 global seaborne trade rises by 86% in the “increasing demand”

scenario and its share in total steam coal consumption rises from 16% to 21%. In the

“stabilizing demand” scenario trade rises by only 46% but its share in total consumption remains high and reaches 18% in 2030. This is due to the fact that imports are expected to remain an attractive source of coal for India and China. These two countries account for half of global trade in 2030 in both scenarios and their imports are multiplied by 12.6 in the “increasing demand” scenario and by 9.5 in the “stabilizing demand” scenario in the time period from 2006 to 2030.

The model includes constraints on the mining and export capacity that can be added every five years based on recent historical data when investments were high but still struggling to meet demand in time. These restrictions have an important effect in the

“increasing demand” scenario and lead to higher coal prices. However, contrary to some

1.5. Overview of the Thesis recent papers discussed in this chapter, we do not see an “end of cheap coal” due to reserve depletion. Coal will stay abundant for decades. However, in a scenario with high demand increase, investments might not keep up leading to high coal prices and volatility as experienced in recent years.

1.5.4 Chapter 5: Climate policies and the global steam coal market:

interactions until 2030

In the previous chapters we found that neither market power nor resource scarcity are severe issues affecting the use of coal and the global steam coal market. The last big remaining issue from those presented in the first part of this introductory chapter is the problem of climate change. There is no doubt about the fact that the continued use of coal is one of the major challenges to tackle in order to prevent dangerous climate change as burning steam coal for electricity generation is one of the largest sources of carbon dioxide emissions. In fact, the prerequisite for any serious global climate mitigation scenario, as laid out by the International Energy Agency (IEA, 2011c), is a reduction in coal utilization. The more ambitious the climate goals are, the less coal plays a role in the global energy mix. In a world with a global climate policy and a global carbon price, this price would provide the incentives to reduce the use of coal. However, the latest failures of the international community to reach an agreement at the UNFCCC COP¹⁵ conferences in Copenhagen and Durban gives a bleak outlook on global climate policy in the mid-term. For some time we will remain in a world of second-best heterogeneous climate policies. Given this situation, we want to investigate how the global steam coal market could affect the effectiveness of regional climate policies.

In this chapter we first discuss which methodology is the most appropriate to answer this research question. We identify four main approaches to analyze resource markets and climate policies and to assess their adequacy. Models based on the Hubbert curve approach are based on historical production data only and thus fail to integrate paradigm changes such as climate policies. Computable General Equilibrium models are too general and lack a detailed representation of resource markets. Resource economics models based on the work of Hotelling (1931) rely heavily on the mechanism of the scarcity rent that drives up prices as the resource is depleted. Recent theoretical studies (Sinn, 2008) find that in this framework climate policies will cause resource owners to deplete their resource faster out of fear to lose their market in the future, thus causing a zero-sum game for the climate. We argue that, due to the very large resource base of coal, a price formation based on the geological scarcity rent is unlikely as well as the assumed reaction of the coal producers. The remaining mid-term effects to be expected in the interaction between climate policies and the global steam coal market are based on supply and demand mechanisms that are called “market adjustments” in this chapter. Such effects are better represented by partial equilibrium models such as the COALMOD-World model that was introduced in the previous chapter of this thesis and is the main tool of analysis in this

15United Nations Framework Convention on Climate Change Conferences of the Parties

chapter.

We investigate three different specific climate policy measures until 2030. The climate policies are modeled as “policy shocks” inside a two-dimensional scenario space based on the intensity of global climate policy and the market condition. There are three levels of intensity of global climate policy: Current Policies (low), New Policies (medium) and 450 ppm (high) based on the scenarios of the 2011 World Energy Outlook (IEA, 2011c). They affect steam coal demand in reverse order. The market situation can be constrained or unconstrained. This is operationalized through a limit on additional production capacity investments. This creates a 3x2 scenario matrix. The COALMOD-World model is then calibrated and run for these six reference scenarios until 2030. The “policy shocks” are then applied to these reference scenarios and the outcomes are compared. The three

“policy shocks” represent each a different type of climate policy. First, we investigate a regional demand reducing policy with the unilateral European climate policy. Then we analyze a supply-side policy with an export limitation for Indonesia. Finally we implement a technology policy that supports a fast worldwide roll-out of the carbon capture and storage technology.

The unilateral European climate policy scenario is the typical example of a regional climate policy and can be seen as a continuation of the climate policies already in place.

This scenario is implemented as a steam coal demand reduction from all EU countries based on the values of the Current Policies and New Policies scenario. The danger of such a unilateral climate policy is a negative market adjustment, also called carbon leakage through fossil fuel markets. The reduction in demand in one region depresses the global price of steam coal and allows a region without stringent climate policy goals to consume more. In our modeling this effect occurs in particular in the case of a low level of global climate policy and of constrained investments in production capacity. The COALMOD-World model calculates that out of the emissions reduction in Europe, 66% in 2025 and 29% in 2030 are compensated by increased emissions from other regions, in particular in Asia. This represents a serious problem for the effectiveness of regional climate policies.

The supply-side scenario in Indonesia is based on the idea of the Yasuní-ITT project.

This initiative proposed by the Ecuadorian government aims at protecting biodiversity, indigenous people and the global climate by renouncing to exploit one fifth of the Ecuado-rian oil reserves against financial compensation by an international consortium of donors.

Like Ecuador, Indonesia belongs to the group of “megadiverse” countries that host 70% of the globe’s biodiversity.¹⁶ Coal mining in Indonesia is primarily opencast and takes place in rainforest areas and thus threatens the biodiversity through deforestation and local pollution. Thus, one could imagine a similar initiative as the Yasuní-ITT for Indonesia not for oil but for coal. We implement this policy as an export limitation that is gradually increased from a maximum of 50 million tons per year in 2020 to a complete export ban in 2030. For comparison, in 2010, Indonesia’s steam coal exports amounted 160 million

16A group of 17 like-minded megadiverse countries (LMMC) signed the Cancún dec-laration established in 2002 with the goal of protecting their biological patrimony (http://es.wikisource.org/wiki/Declaración_de_Cancún)

1.6. Concluding Remarks and Outlook