Closing the supply-side gap: using a novel vulnerability index to identify the right policy mix for coal producing countries

Abstract

Rapid coal phaseout is a key measure to reach the 1.5 °C warming target. With projected global coal demand declining faster than global supply, a poorly anticipated and planned transition in coal producing countries would have huge local and national economic and social impacts. Identifying the vulnerabilities of specific coal producing countries and regions to this transition is important for designing appropriate policies to manage its potential local and national impacts. For this purpose, the novel Coal Transitions Vulnerability Index (COTRAVI) is developed, composed by 12 key “transition risk” and “ability to cope” indicators, for the 10 largest coal producing countries. In addition to indicators included in previous transition risks analyses such as energy and economic dependency, coal reserves, and age of coal assets, the COTRAVI includes indicators to account for the likely speed of the transition (based on simulations from the COALMOD World model), the exposure and resilience of coal producing regions, the national economic resilience, and the transition policies in place. This provides a more holistic approach to identifying and comparing the challenges of producing countries. The results show the high importance of Just Transition plans in increasing the ability to cope with the transition, as well as the need for more structural changes and targeted policy efforts in highly vulnerable countries and heavily coal-dependent regions. The COTRAVI analysis also identifies two relevant roadblocks for a globally just coal transition: high cost of capital and stranded assets risk in the coal producing countries.

1 Introduction

A rapid transformation of global energy systems is needed to avoid surpassing 1.5 °C or even 2 °C of global warming by the end of the century (IPCC 2022a). For keeping warming within those limits, the majority of fossil fuel reserves must stay in the ground (Heede & Oreskes 2016; McGlade & Ekins 2015; Welsby et al. 2021) and energy systems must quickly decarbonize. Coal, being the most abundant and polluting fossil fuel, must largely remain in the ground (89% of reserves for a 1.5 °C scenario (Welsby et al. 2021) and 80% for a 2 °C scenario (Hauenstein 2022; McGlade and Ekins 2015).

Dependency on fossil fuels for energy production and use has already been examined largely by the literature and hundreds of studies have modelled 100% Renewable Energy-based pathways for most countries around the globe (Breyer et al. 2022; Brown et al. 2018; IRENA 2019; Jakob & Steckel 2022; Oei and Burandt et al. 2020a). However, the supply side has not been very studied (SEI et al. 2020, 2021) leaving unanswered how a 100% fossil fuel free economy could look like for large fossil fuel producers, for whom fossil fuels are not only energy sources, but also rents/exports/ fiscal income generators.

This bias toward the demand side in research, as well on countries’ decarbonization targets, can result in a scenario where demand for fossil fuels declines much quicker than supply. Indeed, as the fossil fuels production gap report has shown consistently since it was first published, this is the case already if one compares production plans with current policy projection of fossil fuels demand, with an even larger gap resulting when comparing these plans with 1.5 °C or even 2 °C compatible pathways (SEI et al. 2020, 2021). If this gap persists, the obvious consequence would be a “forced and disorganized” transition with a strong push for additional national demand in producing countries (Ansari 2019; Ansari and Holz 2020; Oei & Mendelevitch 2018).

As the just transition literature highlights, a poorly anticipated and planned transition in fossil producing regions and countries, similar to the ones observed in historic transitions, can have huge economic and social impacts (Brauers & Oei 2020; Caldecott et al. 2017; Diluiso et al. 2021; Oei et al. 2019; Wehnert et al. 2019). International initiatives such as the fossils fuels non-proliferation treaty are being developed to tackle this key gap in international decarbonization efforts.¹ However, as consistently shown by policy literature, there is no “one size fits all” approach that allows countries to prepare this transition and national roadmaps should be created considering specific national circumstances. In this sense, identifying the main vulnerabilities of fossil fuel producers to this transition can be helpful, and guide national and international efforts to accelerate climate action.

The just transition literature also highlights that in addition to its large climate mitigation benefits, the phaseout of fossil fuels including coal can contribute to several of the planetary Sustainable Development Goals (SDGs) like SDG 6 (clean water), SDG 12 (responsible consumption and production), and SDG 15 (life on land). Moreover, if the transition out of coal is managed in a good way, it can also have significant implications for additional SDGs related to the social and economic development of mining regions like SDG 3 (good health), SDG 8 (decent work), and SDG 11 (sustainable communities). Therefore, a Sustainability Nexus perspective which considers the synergies and tradeoffs between different environmental and societal goals is necessary.

With this motivation, a novel Coal Transitions Vulnerability Index (COTRAVI) is developed here, providing a systematic and holistic approach to identifying and comparing the challenges and vulnerabilities of coal producing countries, as well as identifying potential synergies and tradeoffs between environmental and societal goals. A focus on thermal coal is chosen since its phaseout has the largest potential for mitigating climate change, making the transition in this sector particularly fast and relevant for international climate action. Moreover, with global coal supply being much more concentrated than global coal demand, scholarship, and efforts to accelerate climate action focused in the few large coal producers/exporters is required for more efficient global action against coal.

2 Literature review

Quantitative approaches to measure and study vulnerability towards low carbon transitions can be very useful to identify winners and losers of those transitions at the international level as well as to understand better the differences in the social, economic and political environment of fossil fuel dependent countries. In this area, indices are particularly popular and useful (see full review of existing indices in (Overland et al. 2019)). However, as it is the case for general climate change mitigation literature, these indices focus largely on the demand side aspect of the energy transition (e.g. Energy Transition Index of World Forum, Energy Trilemma Index of the World Energy Council, Oil Vulnerability Index, Energy Security Index, Oil Import Vulnerability Index, etc.).

When focusing on the demand side, fossil fuels are understood mainly as means for generating energy and therefore the energy transition consists mostly in diversifying the energy mix away from fossil fuels and decarbonizing energy systems. However, in many large fossil fuel producing countries, and particularly in their coal producing regions, fossil fuels play also a key role in contribution to the national/local economy (e.g., through rents or direct fiscal income generation). For those countries and regions, the energy transition must also include economic and fiscal diversification, which should be at the core of international and national efforts to accelerate climate action in these countries.

These aspects of the transition have started to be investigated by a growing body of literature in the interface of policy and economics (Ansari and Holz 2020; Muttitt & Kartha 2020; Peszko et al. 2020), but research is still at very early stages. The studies carried on until now, look at the economic importance of fossil fuels in countries (in term of share of Groos Domestic Product (GDP) or exports) and compare them, correctly identifying that large exporter countries (particularly oil and gas producers) have the higher global energy transition risk, since significant shares of their GDP and exports depend on these sectors.

Surprisingly, in none of these studies, coal dependent countries are identified as particularly highly vulnerable (or are identified as vulnerable mostly to its combined dependency with other fossil fuel exports like oil and gas in Russia), despite the high vulnerability that have been identified for those countries in several country level case studies (Jakob & Steckel 2022, Ángel et al. 2023). These coal dependent countries, and their vulnerability to a low carbon transition will be the focus of this article. The focus lies on steam coal producers and exporters since considering the characteristics of the international steam coal market,² the ability of single producers to affect the international coal markets is low and the agency of national actors to influence demand is therefore more limited (unlike market power structures in the oil and gas sectors such as the Organization of the Petroleum Exporting Countries- OPEC).

In addition to these reasons, the focus lies on thermal coal, since its phaseout has been identified as the single most important step to keep the door open for the achievability of the 1.5.°C target (Kuramochi et al. 2017), and a very high rate of decline will be needed to phase out thermal coal by 2030 in Organisation for Economic Co-operation and Development (OECD) countries and by 2040 in the rest of the world (IEA 2021b; Yanguas-Parra et al. 2019). For this same reason, thermal coal has been the focus of international diplomatic and philanthropic efforts to accelerate climate action (COP24 Presidency 2018; Europe Beyond Coal 2022; Powering Past Coal Alliance 2017; UNFCCC 2021). In parallel to the development and expansion of those efforts, the need for a scientific body of literature targeted to study the transition challenges and opportunities in this sector is growing. This article aims at addressing this need.

Specifically on coal, a very significant methodological and conceptual development was done by the International Energy Agency (IEA) in its Net Zero coal report, which acknowledges the urgency to focus on coal countries, and of assessing comparatively their transition exposure to guide international efforts to accelerate coal phaseout (IEA 2022). The IEA “Coal Transitions Exposure Index” measures exposure under 4 categories: Energy Dependency, Economic dependence, Development gap, and Lock-in.

By adding the important variable of economic dependence, the IEA rightly makes visible the need to address the exposure of coal rents/fiscal income-dependent countries to the global transition to accelerate global coal phaseout efforts. However, with supply side (coal production) only being accounted for in one of the four categories, this exposure and vulnerability is artificially minimized, leading to a series of chapters focusing on a transition for these countries based on the replacement of coal in the electricity mix that leaves largely unattended the supply side problem.

This literature review reveals three major shortcomings of all the quantitative studies looking at transition risks across fossil fuel dependent countries by using index and ranking metrics, which reflect a poor capturing of the concept of vulnerability. Since vulnerability differs within communities and across societies, regions, and countries, and changes through time (IPCC 2022b), cross-country and cross-fuel studies on this issue often exhibit at least one of the following shortcomings:

• (1) Quantitative studies measure vulnerability at a specific point in time (e.g. share of GDP in a specific year), which fails to account for the potential speed of the transition. One important finding of qualitative approaches is that historical transitions have taken decades, but coal transitions under any low carbon scenario require unprecedented speed (Nacke et al. 2022). In this sense, if the speed of the transition were to be better reflected in cross country comparisons, the vulnerability of coal dependent countries would be much higher compared to other fuels.

• (2) Virtually all quantitative studies looking at transition vulnerability of fossil fuel dependent economies are done at the national level, which “blends” the importance of producing regions for the transition. While the national level is still the most relevant for international and national policy making and regulation, ignoring the regional dimensions of the transition can lead to misguiding results. An emergent literature body has started to look at this issue on a subnational level (Di Paola et al. 2022; Nacke et al. 2022; Vrontisi et al. 2022). However, since the focus of that literature is European countries, the largest coal producers worldwide are still excluded.

• (3) Almost all energy transition Indices focus either entirely on the transition risk/exposure (e.g. Energy Security Index, Oil Import Vulnerability Index, etc.) or in the ability to cope/policy aspects (e.g. Energy Transition Index of World Forum, Climate Change Performance Index, etc.). However, vulnerability to the transition can only be measured by the combination of both the exposure/risk and the ability to cope/transition policies in place.

This paper aims at addressing those three gaps identified in the quantitative literature on transition vulnerability and helping to bring closer quantitative and qualitative approaches on transition studies, by creating a standardized metric that can guide the increasing number of policy efforts to accelerate climate action and just transitions in the coal sector. Moreover, as it was identified as a gap in the literature, the aim is also to deepen the understanding of supply side challenges for low carbon transitions in fossil fuel dependent countries.

The next section explains in detail the construction of an index that builds on previous measurement efforts identified in the literature review but adds a comprehensive assessment of vulnerability. This index will then be the base to create a novel typology of coal producing countries and derive transition policy “mix” recommendations for each type of country.

3 Methods—calculating the coal transitions vulnerability index (COTRAVI)

The four steps followed to calculate the novel Coal Transitions Vulnerability Index (COTRAVI) for the 10 largest coal producing countries are described in detail in this section.

Step 1: Defining Vulnerability. To address all the shortcomings of cross-country comparisons listed in the literature review section, here, the focus on “transitions vulnerability” uses the definition of vulnerability from the 2022 Intergovernmental Panel on Climate Change (IPCC) report:

“...the propensity or predisposition to be adversely affected ... [it] encompasses a variety of concepts and elements, including sensitivity or susceptibility to harm and lack of capacity to cope and adapt”. – (IPCC 2022b)

This definition identifies two axes to assess vulnerability: the risk or exposure to be negatively affected from a low carbon transition, and the capacity (or lack of) to cope and adapt. The first part depends mostly on global trends on coal markets and can be assessed using global scenarios and national metrics. The second part (capacity to cope) must reflect national and local metrics and policies.

Step 2: Selecting Indicators and data collection. To build on previous efforts by the state-of-the-art literature to measure and compare vulnerability, the COTRAVI is based on the IEA “Coal Transitions Exposure Index” (CTEI), which is the most advanced attempt so far to identify and compare coal producing countries’ vulnerability to the transition. The index calculation is modified and expanded with three essential differences:

• (i) Since the focus lies on steam coal only, indicators that give account of other coal uses are removed (e.g., capacity weighted age of steel mills and production to consumption rate.);

• (ii) To reflect better the expected speed of the transition, indicators related to the prospects of the coal sector in these countries are added (i.e., expected production and production to reserves rate); and

• (iii) Regional indicators are added, including “ability to cope” indicators for the main coal producing regions, to reflect the challenges of the transition at the local level, where most of the transition policies need to be implemented.

The selection of additional indicators included in the COTRAVI to measure exposure to and ability to cope with the transition, was based on a literature review of cross country comparisons (see for instance chapter five in (Peszko et al. 2020); qualitative case studies of individual coal transitions (see for instance (Jakob & Steckel 2022, Ángel et al. 2023), and bilateral consultation by the author with coal transition experts and scholars.

Six categories/indicators were selected to measure exposure to the transition and other six categories/indicators were selected to measure the ability to cope with the transition. To limit the amount of national data to be processed and harmonized, the data collection and index calculation is limited to the 10 largest coal producing countries of the world, which concentrate over 85% of global coal production (BP 2020). The data was collected from a variety of sources, summarized in Table 1.

Table 1 Summary of indicators included in the COTRAVI and their source.

Step 3: standardizing and weighting indicators. The third step was to standardize and scale the indicators to ensure cross-country comparability and avoiding outlier values to affect the overall index scores. Each indicator was standardized using an in sample min–max method that compares the observed value with the minimum and maximum values that this variable takes in the 10 countries.³ The standardized scores resulting from this range are from 0 to 1 and can easily be added to calculate the index. Due to this in-sample (as opposed to global standardization), the scores of the COTRAVI should be only interpreted as relative and not absolute vulnerability measures. Potential measures to improve this shortcoming of the COTRAVI are discussed in the conclusion section.

To add all the indicators in a way that higher values always mean higher vulnerability, some of the indicators must be inverted. Let X_ij be the value of the j^th indicator for the i^th country, and let Xmin_j and Xmax_j be the minimum and maximum values of the j^th indicator across all countries, respectively. For “regular indicators” where higher values mean more vulnerability, the indicators are normalized using the in-sample min–max method as follows:

$$X_{ij} normv = \frac{{X_{i} j - X\min_{j} }}{{X\max_{j} - X\min_{j} }}$$

For “irregular indicators” where higher values mean less vulnerability, the indicators are normalized and inverted using the in-sample min–max method as follows:

$$X_{ij} norm = \frac{{X\max_{j} - X_{i} j}}{{X\max_{j} - X\min_{j} }}$$

Step 4: Calculating the COTRAVI. The final step was to calculate the COTRAVI for each country. For this, the 12 normalized indicators are added to calculate the index, assigning equal weights to each indicator, and scaling the index to have a value between 0 and 10 (with higher values indicating greater vulnerability) as follows:

$$CTVI_{i} \sum\limits_{j = 1}^{12} {X_{ij} norm*\frac{1}{12}*10}$$

Assigning equal weights to all the 12 indicators was decided, since based on the literature review made, it was impossible to estimate the relative importance of each indicator in contributing to vulnerability. In reality, some indicators probably have a higher relevance for determining the vulnerability on a country to the coal transition (e.g. speed of the transition), nonetheless, there was not enough empirical evidence to calculate specific differentiated weights for these variables. This point constitutes an interesting avenue of research of the improvement of the current methodology. The shortcomings of the indicators standardization and weighting do not constitute a major limitation for this paper, since its original intention was not to measure with accuracy the vulnerability of an individual coal dependent country, but rather to create a measure that allows for ordinal comparisons (i.e. to identify which countries are more vulnerable), and to identify the main areas of vulnerability in a specific country. The former can inform international initiatives to focus their efforts on specific countries, while the latter can inform transition policy efforts to focus their intervention areas.

The COTRAVI was calculated for 2019 due to constraints in data availability for several indicators for 2022 and 2021 and due to the “anormal” values of most of the indicators in 2020 due to the COVID-19 pandemic. However, the COTRAVI methodology can be used to monitor changes in vulnerability over time, once more recent indicators become available. It can also be easily adapted to other sectors (e.g., oil and gas) or expanded to include a larger set of countries. Finally, the index can be calculated for the individual coal producing regions to compare the vulnerability across coal producing regions.

4 Results

4.1 National vulnerability

As shown in Fig. 1, the COTRAVI ranges from 3,4 (Poland) to 6,8 (Colombia) in the sample countries, and has a mean value of 5,1. Using the mean as the dividing point to divide the countries between “high vulnerability” and “moderate” vulnerability, four out of the 10 countries (Colombia; Indonesia, South Africa and Russia) fall into the high vulnerability category, three are just in the border to this category (India, Australia, Kazakhstan) and three fall into the moderate category (China, United States, Poland). These results are quite different from the IEA CTEI, which give a considerably lower importance to supply side indicators, and include smaller producers excluded here like Mongolia, Canada and Botswana, resulting in identifying Indonesia, Mongolia, China, Vietnam, India and South Africa as the most transition vulnerable countries phaseout (IEA 2022).

Fig. 1

Summary of the individual component of the COTRAVI for each of the 10 countries in the sample

A first finding becomes evident from the comparison of the 10 countries: considering the high importance of Just Transition and/or mining coal phase-out plans in increasing the ability to cope with the transition, implementing those plans could decrease substantially the vulnerability of coal dependent countries and regions to the transition. For instance, this would bring countries like India, Australia or Kazakhstan into the category of moderate vulnerability.⁴

In contrast, South Africa and Indonesia still fall into the high vulnerability category despite having already Just Transition processes ongoing for their coal sector, meaning much more structural changes and targeted policy efforts would be needed in those countries to manage the transition out of coal. Both countries are heavily dependent on coal both in energetic and economic terms with coal accounting for over 60% and 80% of electricity production, and 10% and 5% of national exports respectively (Ember 2022; WITS n.d.). Moreover, both countries have continuously made investment in the coal mining sector over the last decades, resulting in a low average age of their coal mines (13 and 22 years respectively). This puts into question the ability of coal miners to recover their investment if operation is reduced as expected under current projection of global demand (Hauenstein 2022; Yanguas-Parra et al. 2023). Finally, with regional economies being heavily dependent on the coal mining sector and very remote to the national economic centers (see section regional results), economic and social disruption could be large unless important economic diversification efforts are implemented successfully in the coming decades.

Colombia on its side is the most vulnerable country of our sample. It could benefit significantly from a phaseout/Just Transition plan but will also require considerable structural changes and targeted policy efforts to manage the transition. Similar to Indonesia and Australia, Colombia has a high dependency on coal exports (around 10% of total national exports), and relatively young coal mines (25 years old on average) However, the main source of vulnerability comes from the extremely high economic dependence of coal in producing regions (40% of local GDP on average) which are also very remote to the national economic centers and have large regional development gaps (see section regional results). Unlike other countries in the sample, Colombia has a very low energy dependency on the coal sector (less than 10% of electricity consumption), making its transition challenge mostly a supply side one. With a new government committed to stop the fossil fuel sector expansion and to promote a just transition (Ministerio de Ambiente y Desarrollo Sostenible 2023), an interesting window of policy opportunity is opening for innovative national policy and international cooperation for climate action focused on the supply sector.

Based on the COTRAVI results, special concern also emerges about Russia. Russia is unlikely to put in place a coal phase out or just transition plan for its mining regions due to political economy reasons. On the one hand, due to large gas, oil, and coal reserves and resources (more than 350 years left of reserves for coal (BP 2020)), there is deep fossil fuels entrenchment in its economy. On the other hand, current political priorities are completely different to climate protection or social justice, as the conflict with Ukraine continues with deep consequences for the Russian Economy and Society. This, combined with the commercial sanctions that have targeted Russian coal exports, will likely represent a disorganized and poorly managed transition out of coal for the four main mining regions, with disastrous consequences for their economies and inhabitants due to their high dependency on the mining sector (see section regional results).

Poland, the United States, and China are the only three countries of the sample that fall into the moderate vulnerability, with Poland rating the lowest thanks to its relatively advanced Just Transition plans and strategies for coal producing regions, which have been mostly funded with European Union funds (Baran et al. 2018; Śniegocki et al. 2022). A very important factor which reduces significantly the exposure to coal transitions in these three countries if the fact that most of their coal mining output is used for local consumption instead than exports (less than 1% of production in all three cases), giving national policy makers a lot of agency regarding the future of this sector in comparison with countries with sectors heavily dependent of international coal markets. Another important factor reducing the vulnerability of those countries is a highly diversified economy of their coal producing regions which on average depend less than 15% (except for Wyoming with 16%) on the mining sector for their regional added value (see section regional results).

5 Key components of vulnerability across the globe

A second important finding from the comparison of the 10 countries is that besides the lack of a transition plan for the coal mining sector (which affects 7 of the 10 countries), two relevant roadblocks for a globally just coal transition emerge from two vulnerability components that are prevalent among more than half of the countries sampled. The first one (affecting 8 out of 10 countries) is the high cost of capital, which most coal dependent countries face in international lending markets, as illustrated by their sovereign debt score, summarized in Table 2.

Table 2 Moody’s Sovereign Ratings for sample countries in 2019

High capital cost is a problem for coal transitions on two fronts: first of all, the short-term up-front investment required for the economic diversification, electricity mix decarbonization, and just transition policies overall is high and therefore needs to be financed at least partially by loans, for which the most vulnerable countries face higher capital cost, which limits their indebtedness capacity, as well as their ability to pack back and maintain their external debt in sustainable levels (Ameli et al. 2021; IEA 2021a). Second, since coal exports play an important role in the balance of payments of several of those countries, the reduction of coal production and exports that would necessarily follow a coal transition plan, would reduce the amount of foreign currency available in the country. This can result in local currency depreciation, and limiting further the capacity of these countries to cover the up-front investments in the transition, as well as their capacity to meet their external debt obligations (Manley et al. 2017).

Unsustainable external debt levels, and low capacity to meet external debt obligations would then be followed by further downgrades by international risk agencies, increasing even further the cost of capital for those countries, and potentially resulting in a debt downward spiral that can seriously limit the ability of the country to manage a transition out of coal mining. To avoid this risk, it is important that the international community concentrates policy and cooperation efforts in reducing the capital cost of the coal mining sector transitions in heavily dependent countries. With most of these countries falling into the “middle income level” classification, access to grants and concessional loans is limited, requiring innovative policy and financial instruments to achieve a reduction in capital cost. Potential instruments implemented or being discussed for increasing global climate action include debt swaps for climate protection outputs, upgrades in risk ratings for targeted countries, and discounted loans conditional on environmental performance indicators (Camps & Plant 2022; Georgieva et al. 2022). An example of an instrument being tested already in South Africa are Just Transition Energy Partnership (JET-P) funds, which tie a bundle of grants and concessional loans to acceleration of action in just energy transition measures (Presidency Republic of South Africa 2023).

An important limitation of these instruments, however, is that until now they are thought to support mainly demand-side efforts for climate policy and are therefore linked to performance indicators such as national emissions, Nationally Determined Contribution (NDC) targets, or area of forests. Specific metrics, indicators and methodologies will be needed to tackle supplyside climate mitigation efforts where the impact on national emissions and NDC target is not that clear (for countries exporting a considerable share of their coal production).

The second key component of vulnerability affecting many countries in the sample is the risk of stranded assets in the coal mining sector, measured in the COTRAVI as the production weighted average of the operating coal mines. Six of the sample countries (Australia, China, Colombia, Indonesia, India, and South Africa) have relatively young coal mines, with an average age below 30 years. Of those, Indonesia and China are the more affected by this phenomenon with average ages of only 19 and 13 years (as of 2019). Against all odds of recovering the investment cost under any scenario that considers climate protection (Hauenstein 2022), all these countries have opened new coal mines in the last 10 years and continue planning considerable expansion of their coal production capacity despite strong opposition from civil society groups to some of these projects (Buckley 2017; Hauenstein et al. 2022; Worrall et al. 2018).

These investments in new coal mines affect the vulnerability to the transition of coal dependent countries in at least two ways. First, with very limited capacities for investment in large infrastructure projects, investment in new coal mines and their related transport and export infrastructure crowds-out significant amounts of investments in alternative sectors, which have a brighter future in the face of global climate change mitigation efforts. Second, upon the realization of the stranded assets risk for specific coal projects, there would be considerable transmission channels to the national economy that could impact negatively the financial and credit prospects of the countries, reinforcing the capital cost vulnerability factor described previously (Manley et al. 2017). This is a particularly acute risk for countries where state-owned enterprises (SOEs) are heavily involved in the coal mining sector, like Coal India Limited.

This highlights the importance of prioritizing international advocacy efforts for supply side policies like moratoriums of new coal mines, such as the fossil fuel nonproliferation treaty. Similarly, considering the high costs of coal mines closure and the high environmental impact of poorly managed mine closures (Watson & Olalde 2019), it is fundamental that coal dependent countries prepare for minimizing stranded asset risks, and the probability of absorbing environmental and social liabilities from private coal companies affected by this risk, by strengthening coal mine regulation, planning, and standards.

Finally, an interesting observation from the COTRAVI analysis relates to the component of “unburnable coal”, which is measured in the COTRAVI as the production to reserve rates, and it is a proxy on the scale of the “foregone” revenues that a country would let go by implementing a coal mining phaseout policy. The larger the coal reserves of a country, the more potential revenue it will give up by leaving coal reserves in the ground, and the more likely it is that the political economy of the producing regions favor a vision with a long-lived coal industry and a very gradual transition.

Compensating countries for that foregone revenue is at the core of policy ideas related to payments and wealth transfers to fossil fuel rich countries for “leaving resources in the ground” like the YasunI ITT Initiative from Ecuador (Sovacool & Scarpaci 2016). These types of policies have been advocated on the name of climate justice and post-fossil development, however a simple observation of the COTRAVI makes evident that the countries with the largest reserved and foregone coal revenue (Russia, United States, and Australia) are either highly developed or deeply entrenched in the fossil fuel economy, making compensation payment unviable for them. Similarly, even if the countries with the biggest development gap (e.g. India) were to be benefited from this type of policy, the carbon leakage risk is too big considering the huge coal reserves of other countries that represent a viable alternative to individual new coal mine developments. This calls into question, the effectivity or desirability of such compensation schemes for the coal sector.

6 Regional vulnerability

One of the key innovations of the COTRAVI is to include regional components in a national index. Concretely, three indicators are evaluated at the subnational level, for the regions of the country that concentrate over 70% of the national coal production. It is out of the scope of this paper to calculate a specific COTRAVI for the individual regions since it would require downscaling of COALMOD World results at the subnational level, and other indicators which are hard to find or calculate in a comparable and consistent way across regions (e.g., cost of capital, index of economic complexity, etc.). However, a simple comparison of the input data for the individual regions already allows for interesting findings that should be explored in more detail for the design of effective policy interventions in the sample countries.

First, the concentration of the coal industry is very diverse among the sample countries, ranging from high concentration in one single region that accounts for 86% of national production (Mpumalanga in South Africa) to a low concentration in 5 regions which account for less than 20% of the production each (in India). There is no evidence of a causal relationship between the concentration of production and the success of the transition, however it is a very important factor to determine the right policy approach for the transition in each country.

On the one hand, a highly concentrated coal mining industry reduces considerably the number of actors and stakeholders that must be included in dialogues and discussions about transition policies, increasing the speed and administrative burden of such processes. However, as shown by our data sample, highly concentrated coal mining industries usually mean that the economic importance of this sector in the local economy is much higher (e.g., 20% of local GDP in Mpumalanga v.s 7,3% of local GDP in Chhattisgarh), which means the cost of the transition is higher, and therefore the resistance of local actors to the transition will be higher.

On the other hand, a low concentrated coal mining sector can open windows of opportunity for progressive local stakeholders to move ahead of national policy in terms of transition policies in countries here the national politics are not in favor of initiating just transition discussions and allows more room for experimentation on economic diversification and other just transition policies. However, low concentration also represents a huge coordination challenge at the national level, increasing risks of free riding by regions with less environmentally strict regulations, carbon leakage, or overcompensation to certain actors (e.g., coal mining companies) which operate across many producing regions.

These elements should be carefully taken into account when selecting policy tools such as multi-stakeholder “coal commissions” (Brauers & Oei 2020) for designing the transition pathways and policies of a coal dependent country. Similarly, a careful consideration of the legal, regulatory, and fiscal system of each country must be made when applying certain policies at the national or regional levels (e.g. ecological fiscal transfers, export tariffs or quotas, levy taxes, royalty payments redistribution, etc.) in order to avoid duplication or risk reversal of local policies and initiatives due to inconsistencies with national or supranational ones (e.g. European Union regulations for countries like Poland). For instance, thanks to fiscal and regulatory autonomy privileges of a federal system like the one of the United States, it is much simpler to test local transition policies in certain States, which then can then be replicated in other States or at the national level, than in countries with centralist forms of government (Fig. 2).

Fig. 2

Source: The Author’s own elaboration. For a full overview of subnational indicators, see Appendix 1. For an explanation of how regional resilience is calculated, confer Appendix 2

Summary of regional results included in the COTRAVI.

Second, the characterization of the economic and social profile of each region is fundamental to choosing the right policy approach to transition out of the coal sector. The prioritization of economic diversification measures vs social support measures and efforts can be very different in two regions within the same country. For instance, when comparing the indicators sampled here for East and South Kalimantan in Indonesia, it becomes obvious that the first has a higher economic dependence on the mining sector (45% vs 19% of GDP) and is more remote to the national economic center, while the latter has a larger development gap with and Human Development Index (HDI) lower than the national average, suggesting that social policies should be prioritized in South Kalimantan while economic diversification policies should be prioritized in East Kalimantan. In other countries where coal mining regions have a very similar socio-economic profile (e.g.Colombia, with two regions characterized by high economic dependence, large regional development gap, and high remoteness to national economic centers), a more standard policy approach could be applied from the national level to the regional one.

7 Discussion

By looking at vulnerability in a more holistic way that involves not only the risk and exposure of coal dependent countries to the global energy transition, but also their ability to cope with the challenges that this transition would bring to their economies and coal producing regions, the COTRAVI allows to identify key entry points for local, national, and international efforts to accelerate coal phase out globally by focusing on the supply side policies (coal mining). This analysis can be done by analyzing each country and looking at the main contributors to its vulnerability, by comparing vulnerability factors across countries and regions, or by grouping countries into typologies according to their similarities. The two first methods, which are very straightforward, have been applied and described in the results section. In this section, the third method is applied, by proposing a vulnerability typology for coal dependent countries and systematizing policy options from the literature for each of the categories.

The typology proposed is based on grouping by clustering according to the two key components of vulnerability: risk/exposure and (in)ability to cope with transition. A graphical representation of this clustering can be seen in Fig. 3. At the bottom left corner of the spectrum of vulnerability, countries with a moderate to low exposure to the transition and a relatively high ability to cope are placed, followed (at the bottom-right corner) by countries with high exposure but also a high ability to cope. For both groups of countries, national political will, combined with effective short term policy interventions targeted at strengthening the ability to cope (e.g., focused on coal dependent communities’ livelihoods, mine closures, and diversifying further energy production and exports), could be sufficient for an organized well-managed and orderly transition out of coal in the coming decades. This case is similar to countries that are at advanced stages of this transition like Germany, UK, or Spain and can therefore be informed by the experience of those countries, which have been extensively researched (Brauers et al. 2018; Caldecott 2017; Diluiso et al. 2021; Oei et al. 2019; Wehnert et al. 2019).

Fig. 3

Decomposition by category and vulnerability profile

In the top-left corner of the spectrum, countries with a low exposure but also low ability to cope with the transition are placed. With coal mining sectors heavily oriented to national consumption, relatively gradual decline of coal production expected. In this case, the speed of the transition is largely determined nationally, which leaves much more space for policy makers to organize and manage a transition that balances multiple policy objectives. Moreover, moderate economic dependence of mining in coal producing regions, allows these countries (with the right political will and supported by the international community) to focus on gradually but steadily increasing their resilience (e.g., through economic diversification efforts and improvement of socioeconomic indicators and policies in coal regions), and lowering further their exposure (e.g., by reducing their energy dependency on coal).

Finally, at the top-right corner of the spectrum, countries with a high exposure combined with a low ability to cope with the transition are placed. Unlike countries in the other categories, these tend to have a heavily export oriented coal mining sector, heavy economic reliance on mining in their coal producing regions, and poorly diversified national economies. This implies on the one hand, that policy makers have limited control over the speed of the transition (in particularly the export-oriented sectors), and on the other hand that structural economic reforms that go beyond and above the coal sector would be required to increase local ability to cope with the transition. Those counties will therefore require much more than political will and small-medium size intervention in coal dependent regions and communities to be able to manage their transition out of coal. Structural economic, macroeconomic, and energy sector reforms will be required in those countries to be able to better cope with their national transition.

Considering the large amounts of investment, and institutional capacities required to implement such reforms, strong support from the international community and consistent (over several policy/election cycles) and coordinated national and subnational commitment to the transition are required. The Just Transition Energy Partnerships (JET-P) that South Africa and Indonesia have signed with international donors are good steps in this direction, but still way too small compared to the large transition needs of those countries. Mobilization of significant amount of additional public and private capital to support the transition is required and will have to be strategically invested in priority areas such as “champion” industries for exports due to the limited time and resources for a more experimentation-based approach (e.g., general support to R&D). Appendix 3 summarizes in a very simplified way some of the policy options and mistakes discussed in the just transition and energy transition literature and clusters those options according to the typology of country and the vulnerability profile.

8 Conclusion

While focused climate action efforts on the demand side for reducing coal consumption have had an impact and has accelerated coal phase-out, global coal demand has not adjusted supply accordingly. This has resulted on unrealistic production plans for individual coal producers, which can lead to disorganized and traumatic transitions in these countries, and limit substantially global decarbonization efforts. With a global coal supply being much more concentrated than global coal demand, scholarship, and policy efforts to accelerate climate action could focus attention and efforts in the few large coal producers/exporters for more efficient global action against coal.

The COTRAVI, developed here, can be used as a guideline for scholars, national and sub-national policy makers, and international community actors to identify and compare the key areas of interest and intervention in the 10 largest coal producers. Thanks to its holistic understanding of vulnerability, which reflects not only risks and exposure but also ability to cope and resilience, the COTRAVI becomes a useful tool for identifying policy interventions from the just transition and energy transition literature and target them according to the vulnerability profile of coal dependent countries and regions. This is an important step forward to avoid falling into the “one size fits all” approach when designing just transition plans and interventions.

The vulnerabilities of coal exporting countries to low carbon transitions cannot be reduced only with a traditional energy transition approach that focuses mainly on the decarbonization of the energy mix and just transition policies for sector actors directly affected. With coal playing an important role of in rent generation, exports, and regional economic activity, a much wider policy approach is needed, covering a combination of economic (e.g., taxes, macroeconomic policy) and non-economic policy measures (e.g., social and environmental regulations). The COTRAVI sheds light on which policy areas could have significant impact internationally and on specific countries, as illustrated in the discussion section.

An important finding is that considering the high importance of Just Transition and/or mining coal phase-out plans in increasing the ability to cope with the transition, implementing those plans could decrease substantially the vulnerability of coal dependent jurisdictions to the transition. This however is unlikely to be enough for countries with high vulnerability levels like Colombia, South Africa, or Indonesia, which would require much more structural changes and targeted policy efforts (e.g., important economic diversification strategies) to manage the transition out of coal. Those countries are particularly vulnerable to changes in international coal markets, given the export-oriented nature of their coal mining sector. In those cases, countercyclical macroeconomic mechanisms to smooth the impact of changes in international markets should be considered.

Concentrated policy efforts are particularly important for regional economies that are heavily dependent on the coal mining sector and very remote to the national economic centers, where economic and social disruption could be large unless important economic diversification strategies are implemented successfully in the coming decades. With local and national governments starting to realize the importance of this issue for their regions, interesting windows of opportunity emerge for innovative national policy and international cooperation focused on the supply sector.

Besides the lack of a transition plan for the coal mining sector, two relevant roadblocks for a globally just coal transition emerge from the COTRAVI analysis, as they affect more than half of the countries sampled: high cost of capital and high risk of stranded assets in the coal mining sector. This finding highlights the importance of focusing international policy and cooperation efforts in reducing the capital cost and stranded assets risk of the transition in coal-dependent counties. With most of these countries falling into the “middle income level” classification, access to grants and concessional loans is limited, requiring innovative policy and financial instruments to achieve a reduction in capital cost.

While the international community is already discussing potentially suitable instruments for these problems such as debt swaps for climate protection outputs, upgrades in risk ratings for targeted countries, and discounted loans conditional on environmental performance indicators, those are currently focused on demand side policies and indicators. Specific instruments, metrics, indicators, and methodologies will be needed to tackle supply-side climate mitigation efforts. Also, to decrease their vulnerability coal dependent countries must strengthen coal mine operation and closure regulation, planning, and standards.

A key innovation introduced in the COTRAVI is the inclusion of regional indicators or the regions concentrating at least 70% of the national coal production. From a comparison of those indicators, it becomes evident that careful analysis of the distribution and concentration of coal production, as well as the geographical and socio-economic characteristics of the producing regions is of paramount importance for the effective design and implementation “on the ground” of just transition policies. National level indicators and analysis hide significant differences in the vulnerability profile of individual coal regions.

Limitations of the scope of this paper and the COTRAVI that can be interesting avenues for further research include calculating specific COTRAVI for individual coal producing regions, which would require downscaling of COALMOD World results at the subnational level, and other indicators which are hard to find or calculate in a comparable and consistent way across regions (e.g. cost of capital, index of economic complexity, etc.). Another interesting avenue of research would be to expand the COTRAVI analysis to more coal producing countries or developing a parallel analysis for other sectors (e.g., oil and gas). Improving standardization and weighting of the indicators included can be improved by using global (as opposed to in-sample standardization) and using differential weightings to account for interdependencies and correlations between individual indicators. Including a more nuanced indicator for the “just transition plans” that goes beyond a simple dummy variable and allows to rate the quality of each of those documents in an objective and replicable way, is another potential improvement for the COTRAVI. Finally, a systematic literature review of policy options for just transition and supply side climate policy could be done to refine and advance the policy mixes suggested here for the four types of countries, and even individual country roadmaps to tackle the vulnerability to the coal transition could be developed.

Appendices

Appendix 1 Regional indicators and their sources

See Table 3

Table 3 Overview of regional indicators used to calculate the COTRAVI

Appendix 2 Measuring the remoteness of coal regions

2.1 Introduction and context

Measuring remoteness is a complex task, since it is an inherently relative concept (Henderson et al. 2001; Huskey 2005). Spatial dimensions and constraints are the first source of relativity. A place located 10,000 km away in space may not be’remote’ compared to the universe’s infinite vastness. However, the same location would very well qualify as’remote’ in terms of the possible distances on Earth. The relativity of remoteness further stems from the need for a reference point or what we would colloquially call our’point of view’: A place cannot be remote in and of itself but has to be remote from a pre-defined center. Remoteness thus depends on the observed finite space, the point(s) of reference, and the specific unit of analysis.

As a result, no universal definition of remoteness exists. Nevertheless, scholars and governments have made different attempts at measuring remoteness empirically to design more targeted policies for remote regions all over the world (Benick and Kawasoe 2019; Huskey 2005; Statistics Canada 2017). Australia’s Accessibility/Remoteness Index of Australia (ARIA) and the more sophisticated ARIA + indicator are examples of particularly well studied remoteness indicators (Glover and Tennant 2003; Queensland Treasury 2019). Perhaps most importantly, these measures of remoteness factor in socioeconomic metrics and therefore go beyond the mere quantification and comparison of distances.

The following two sections explain the concepts of spherical spaces and weighted geometric medians needed to calculate our remoteness scores. Next, the specific calculations for the COTRAVI are elaborated step-by-step.

2.2 Methods

2.2.1 Calculating the weighted geometric median on spheres with specific radii

See Fig.

Fig. 4

Illustration of Haversine distance between two points, A,B ∈ S(r = 3671 km)

4

A sphere of radius r is defined as

$$S\left( r \right) = \{ x \in {\text{R}}^{{2}} :\parallel x\parallel = r\}$$

The distance on the sphere between two points (θ₁,ϕ₁),(θ₂,ϕ₂) ∈ S(r) (visualized in Fig. 4) is calculated using the Haversine function:

$$Hav:S\left( r \right) \, \times S\left( r \right) \, \to {\text{R}}$$

$$Hav((\theta_{{1}} ,\phi_{{1}} ),(\theta_{{2}} ,\phi_{{2}} )) \, = r{\text{arccos}}({\text{sin}}\phi_{{1}} {\text{sin}}\phi_{{2}} + {\text{ cos}}\phi_{{1}} {\text{cos}}\phi_{{2}} {\text{cos}}(|\theta_{{2}} - \theta_{{1}} |))$$

Let C be a set of weighted coordinates on the sphere S(r) given as C = {(c,w) | c ∈ S(r),w ∈ R}. We now calculate the set’s weighted geometric median m_C as:

$$m_{C} = \mathop {\quad\quad_{{m \in S\left( r \right)}} X_{{wHav\left( {m,c} \right) \in S\left( r \right)}} }\limits_{{\left( {c,w} \right) \in C}}$$

An illustration of the weighted geometric median and how weights affect its position can be seen in Fig. 

Fig. 5

Weighted geometric median (red X) of a random sample of 50 coordinates with uniform weight-distribution (left) and Pareto-distributed weights (right). Notice the shift of the geometric median as a result of the change in weight-distribution. Though only used as an example here, the Pareto distribution has been found to be a good proxy for the distribution of GDP and population. (Reed 2002, Reed and Jorgensen 2004)

5.

2.2.2 Calculating country remoteness scores

Given our set of weighted coordinates C = {(c,w) | c ∈ S(r),w ∈ R}, their corresponding weighted geometric median m_C as well as a constant for distance-standardization a_C ∈ R, we define remoteness of a coordinate c ∈ C as the as the function R which calculates the a_C-standardized Haversine distance between c and m_C:

$$\begin{gathered} R:S\left( r \right) \times S\left( r \right) \to {\mathbb{R}} \hfill \\ R\left( {m_{c} ,w} \right) = \frac{{Hav\left( {m_{c} ,c} \right)}}{{a_{C} }} \hfill \\ \end{gathered}$$

In our specific case, m_C is what we call the geo-economic center, a_C the surface area of the country, and c a coordinate within that country.

2.2.3 Calculating absolute remoteness scores for COTRAVI-countries

By giving our spherical space S(r) the radius r = 6371 km, we can apply the concept of points and distances on a sphere to characterize specific locations on earth. Note that by setting the radius of our sphere in km, all of the following inputs must be in km and km², respectively.

Let J be the set of all countries and K_j be the set of all regions in country j ∈ J. Let L be the subset of J that contains 10 countries that together account for more than 90% of global coal production. For each j ∈ L, let there be a set C_j = {(c,w) | c ∈ S(r = 3671 km),w ∈ R}, where c are the GPS coordinates of every territorial subdivision’s geographic center and w is the share of national GDP corresponding to that subdivision.

Fig. 6

Remoteness calculation for Colombia. Red star is the GDP-weighted geometric median coordinate, lines represent distances to territorial subdivision centers

Using the coordinates and weights, we compute the weighted geometric median⁵m_j for each country j ∈ L. m_j can be interpreted as the country’s geo-economic center (see Fig. 6 for a sample visualization of this point).

Let R_i,j be the remoteness score for region i ∈ K_j in country j. We calculate remoteness scores from all territorial subdivisions within a country to the coordinate m_j using the surface area-standardized function for Remoteness R from the previous section.

The obtained scores, R_i,j, are the distances from a given territorial subdivision’s geographical center to the geo-economic center of the country the region is located in.

. These standardized remoteness scores can already be used to compare regions across countries.

2.2.4 Normalizing scores in-sample and obtaining the final COTRAVI remoteness-component score

Last, we need to aggregate regional scores into a score for the whole country. For this, we calculate the coal production-weighted mean of min–max-normalized remoteness scores.

Let J be the set of all countries and K_j be the set of all regions in country j ∈ J.

Let R_i,j be the remoteness score for region i ∈ K_j in country j.

Let P_i,j be the share of national coal production of region i ∈ K_j in country j. First, we normalize the remoteness scores for each region:

$$N_{i,j} = \frac{{R_{i,j} - \mathop {\min }\limits_{{k \in K_{j} }} \left( {R_{k,j} } \right)}}{{\mathop {\max }\limits_{{k \in K_{j} }} \left( {R_{k,j} } \right) - \mathop {\min }\limits_{{k \in K_{j} }} \left( {R_{k,j} } \right)}}$$

where K_j is the set of all regions in country j.

Next, we calculate the coal production-weighed mean of the normalized remoteness scores:

$$W_{j} = \frac{{\sum\nolimits_{{i \in K_{j} }} {\left( {P_{i,j} \cdot N_{i,j} } \right)} }}{{\sum\nolimits_{{i \in K_{j} }} {P_{i,j} } }}$$

where n is the total number of regions in country j.

The final result is the aggregated score for the whole country:

$$S_{j} = W_{j} \cdot \left( {\mathop {\max }\limits_{{k \in K_{j} }} \left( {R_{k,j} } \right) - \mathop {\min }\limits_{{k \in K_{j} }} \left( {R_{k,j} } \right)} \right) + \mathop {\min }\limits_{{k \in K_{j} }} \left( {R_{k,j} } \right)$$

This formula scales the weighted mean back to the original range of the remoteness scores. We have now aggregated regional scores into a score for the whole country. A full version of the remoteness calculation code can be found in this online repositiry: https://github.com/malzl/remoteness

2.3 Results and sample visualization for the COTRAVI

When applying our method to the ten countries of the COTRAVI and their subdivisions, we find La Guajira, Colombia, and East Kalimantan, Indonesia, to be the most and second-most remote coal-producing territorial subdivision in the sample, respectively (cf. Figures 

Fig. 7

Visualization of the two most remote coal-producing territorial subdivisions in their respective country

7,

Fig. 8

Remoteness scores for COTRAVI coal region sample, surface-area standardized and min–max-transformed scores. Note that min–max-standardization constitutes a linear transformation of the data. Thus the scale level of our data is preserved

8). Conversely, the state of New South Wales, Australia, and the Voivodeship of Lódź, Poland, are found to be the least and second-least remote coalproducing territorial subdivisions. Results for all subdivisions are shown in Fig. 8.

Appendix 3 Policy options derived from the COTRAVI

Table

Table 4 Policy options with positive and negative impacts on the vulnerability of coal dependent countries and regions.

4 summarizes in a very simplified way some of the policy options and mistakes discussed in the just transition and energy transition literature and clusters those options according to the typology of country and the vulnerability profile. Policies included in each typology should be understood as cumulative (not exclusive) to the next typology, meaning that while for countries type I the policies listed in their category could be sufficient, for countries type IV, the policies listed in their category should be considered in addition to the policies listed for countries type I, II, and III. Since this is an illustrative example of how the COTRAVI can be used in applied policy research, the policy options mentioned are far from being the result of a systematic literature review of policy options for just transition and supply side climate policy. Such an exercise, however, constitutes an interesting avenue for further research.