Introduction

Energy is closely linked to national security1. For fossil fuel importers, an energy transition is increasingly seen as a means to reduce import dependence2,3,4,5. However, existing research offers only limited insights into how energy trade is related to import dependence and greenhouse gas emissions.

Previous research has analysed how energy trade affects macroeconomic performance, with a particular focus on the role of price hikes and supply shortages6,7,8,9. Some studies propose indicators to measure energy security10,11,12 and discuss policies to reduce energy import dependence13,14. Even though some papers analyse the effects of overall trade volumes and trade policies for energy use and greenhouse gas emissions15,16,17,18, very few deal with energy trade in particular. The few exceptions include modelling studies assessing how trade in renewable energy19 and biomass20 may facilitate decarbonization efforts, and some empirical analyses of the determinants of energy imports21,22. Studies in this direction frequently have a narrow focus on specific energy carriers in particular countries23,24,25,26.

By contrast, our study takes a comprehensive cross-country perspective on the linkages between energy trade, emissions and import dependence. We first look back to assess the historical connections between energy trade and greenhouse gas emissions. We then look forward to illustrate how transitioning to renewable energy sources can reduce dependence on fossil fuel imports in the future. As we provide insights for trade of oil, coal and natural gas for ten countries in Southeast Asia, our outlook is significantly broader in scope than previous studies that have focused on trade of specific energy carriers in individual countries. Even though some previous studies discuss historical drivers of emissions or decarbonization scenarios for Southeast Asia27,28,29, they provide limited information regarding the role of energy trade.

We analyze all ten countries belonging to the Association of Southeast Asian Nations (ASEAN). Since the year 2000, energy-related greenhouse gas emissions have grown in all of these countries. Countries that started from low levels of energy use that have rapidly scaled up their energy systems experienced the steepest emission growth. In Laos emissions have increased more than twentyfold since the year 2000. In Cambodia and Vietnam, emissions currently are more than six times higher than they were in 2000. In Indonesia, Malaysia and the Philippines, emissions have not risen as steeply but have still roughly doubled. In all countries except Malaysia, Singapore and Thailand, carbon intensity increased, in Laos almost sixfold (see Supplementary Table 1) Most of the ASEAN member states are net energy importers. As shown in Fig. 1, there is a general trend towards higher energy imports in the region over the last decade (see Supplementary Fig. 1 for detailed annual developments). For countries that were already net energy importers in the year 2000, net imports of practically all types of energy have grown. Some countries that had previously been major net exporters of fossil fuels became net importers. For instance, Indonesia flipped from being a net oil exporter to become a net oil importer in 2004 and suspended its membership of the Organization of Petroleum Exporting Countries (OPEC) in 2009. Vietnam became a net oil importer in 2010 and a net coal importer in 2015. Even though Brunei and Indonesia are still net exporters of natural gas, their growing domestic gas consumption has led to a steady reduction in net exports. Malaysia exhibits stable net exports of gas over the period under consideration. The most notable exceptions to the overall trend toward more energy imports are Indonesia’s coal exports, which have risen sharply as the country has become the world’s largest coal exporter, and Myanmar’s gas exports, which have also grown but remain small in the larger picture and have recently started to stagnate.

Fig. 1
figure 1

Cumulative net imports of oil, natural gas, coal and electricity of countries in Southeast Asia in the periods 2000–2010 and 2011–2021. All values are in Terajoules (TJ).

Full size image

In the next sections, we first assess the historical linkages between energy trade and greenhouse gas emissions. We then discuss how future developments might affect energy trade patterns and highlight how a shift away from fossil fuels can lower dependence on energy imports.

Looking back: historical linkages between energy trade and emissions

To illustrate how energy trade is interlinked with energy use patterns and emissions, we assess how changing use of energy carriers affects overall carbon intensity. The logic behind our novel decomposition (see Methods section) is that changes in the carbon intensity of energy consumption can be understood as a combination of changes in the emission intensity of domestic energy production and changes in the emission intensity of net energy imports. For each energy carrier, we can then decompose changes in the shares of an energy carrier in the energy mix into the contributions of changes in domestic production and changes in net imports of that energy carrier. The results of this analysis are displayed in Fig. 2. Several observations stand out. For Cambodia, the observed increase in overall emission intensity is due to increased shares of oil and gas in the energy mix. These increases almost exclusively come from oil and gas that is imported, while the carbon intensity of domestic energy production has remained almost constant. Indonesia’s carbon intensity has increased substantially due to a rising share of coal in domestic energy production. This increase is, however, not fully reflected in the carbon intensity of energy consumption. As Indonesia has become the world’s largest coal exporter, the share of coal in its domestic energy consumption has grown less than the share of coal in energy production. Hence, coal exports set off part of the upward effect of coal production on overall emission intensity. With increasing coal production, the share of oil in Indonesia’s domestic energy production has dropped rapidly over the last two decades, whereas the share of oil in total energy consumption has declined only slightly. Thus, there has been a decrease in carbon intensity from domestic oil production but an increase of a similar magnitude from oil imports. For Laos the massive increase in carbon intensity is almost exclusively due to increased coal use, with all of the coal being imported. In Myanmar, rising carbon intensity is mainly driven by growing shares of natural gas and oil in the energy mix. Whereas additional natural gas use is met by domestic production, increased oil consumption has predominantly been met by higher imports. For Singapore, the substantial decline in carbon intensity is explained by declining shares of oil and natural gas in total energy consumption. Both types of energy are exclusively imported. Finally, for Vietnam a rising share of coal in energy consumption has played the dominant role in rising carbon intensity. This growing coal share is mostly based on rising domestic coal production, with rising net imports playing a smaller role. Even though the share of oil in Vietnam’s total energy production has declined sharply, the share of oil in energy consumption has hardly changed since the year 2000. For this reason, the downward effect on carbon intensity from lower domestic oil production has been largely compensated by a higher emission intensity from growing oil imports.

Fig. 2
figure 2

Increase in overall carbon intensity attributed to domestic production and net imports of individual energy carriers for each ASEAN member state.

Full size image

Looking forward: how decarbonization can reduce dependence on energy imports

We illustrate how a shift away from fossil fuels could potentially reduce dependence on energy imports by projecting a constant rate of change from the year 2022 onwards to achieve the emission level in 2030 implied by countries’ international climate targets as stated in their nationally determined contributions (NDCs). We use the unconditional NDC targets as a conservative estimate (see Supplementary Table 2). With international support, the more ambitious conditional NDC targets would imply greater reductions of fossil fuel use. We assume that the use of each fossil energy carrier changes at the same rate (see Methods section) so that we do not need to make assumptions about whether reductions in fossil fuel use are achieved by switching to clean energies or by reducing total energy consumption.

We compare our projections for the NDC targets with a baseline, which projects total primary energy consumption until the year 2030 to follow its growth trend over the past decade. This projection implies a setting in which underlying socio-economic drivers, such as GDP, as well as technological factors such as energy efficiency continue developing as in previous years. For this projection we assume that the energy mix remains unchanged, so that the consumption of each individual energy carrier as well as emissions grow at the same rate.

These projections constitute a simple way to gain an understanding of how energy use patterns might develop under plausible assumptions in the near future. More complex model-based approaches might yield additional insights in the future but require a large number of assumptions about future developments in energy markets and technologies. In addition, due to the inertia of energy systems, short-term projections from energy system models predominantly depend on socio-economic assumptions (see Supplementary Table 3 for a comparison of our projections with results of a more complex model).

This analysis reveals that for all countries except Thailand and Singapore, NDC targets imply that emissions in the year 2030 are higher than in the year 2015. For most countries, NDC emission targets for 2030 are between 5% and 25% below our business-as-usual projections. The NDCs of Laos and Myanmar imply the strongest reductions relative to business-as-usual, with emission in 2030 about 65% and 45% below our business-as-usual projections, respectively. By contrast, the NDCs of Brunei and Vietnam imply emission increases that even exceed our business-as-usual projections (see Supplementary Table 2).

We further assume that annual domestic production of coal, oil and natural gas remains constant at the average 2019–2021 levels. This does not take into account the fact that domestic production of fossil fuels could decline as existing reserves are depleted or increase as new reserves are tapped. Without reliable information on how these two countervailing effects play out, this approach provides an idea of the relevant magnitudes. Since 2010, oil production has remained constant or has declined in all countries, and so has the production of natural gas, except in Malaysia and Myanmar. By contrast, coal production has increased in most of the countries under consideration (see Supplementary Table 4). Hence, our estimates for net imports of oil and natural gas should be regarded as conservative, as declining domestic production might well result in larger imports than projected.

Figure 3 shows reductions of net imports of oil, gas and coal in the case in which NDCs are achieved compared to the business-as-usual projection (expressed relative to total energy consumption). Emission pathways in line with NDCs would have divergent implications for countries in Southeast Asia. For Brunei and Vietnam, the NDC emission targets do not exceed the 2030 level projected in our baseline so that no effect for energy trade would occur. Other countries in the region, however, could experience substantial reductions in net energy imports. For instance, for Singapore meeting the NDC targets would reduce net imports of oil and coal by more than 15% and net imports of natural gas by more than 18% compared to the baseline. Myanmar could even reduce its net imports of coal by more than 60% and net oil imports by almost 30%. For some countries, fulfilling their NDC could prevent slipping from being a net exporter to a net importer. For instance, the Philippines is projected to become a net importer of natural gas in the baseline but to remain a net gas exporter if it achieves its NDC. Malaysia is projected to be a net oil exporter in both cases, but if it meets its NDC targets, its net exports are more than 11 times of those in the baseline (see Supplementary Tables 5 and 6 for details).

Fig. 3
figure 3

Reductions of net imports of coal, oil and gas over the period 2022–2030 if the NDCs are achieved, compared to the baseline. Reductions are indicated relative to total primary energy consumption.

Full size image

Table 1 provides a rough estimate of financial savings on energy net imports in the NDC projections relative to the baseline (see Methods section). For Malaysia and Singapore, cumulative savings would amount to almost USD 40 bn and more than USD 30 bn, respectively. Even though Indonesia displays the third largest savings in absolute terms, savings of USD 18 bn are rather moderate relative to the size of its economy, which is due to the relatively low ambition of its NDC. By contrast, USD 15 bn of savings on fossil fuel imports for Thailand and USD 7.5 bn for Laos would be substantial relative to the size of their economies. If all ten economies were to reach their NDC targets, they could save a total of almost USD 144 bn by 2030 (of which reduced oil imports account for about half).

Table 1 Cumulative monetary savings on trade in coal, oil and natural gas in the NDC projection relative to the baseline as well as total savings in the period 2022-2030
Full size table

To put these numbers into perspective, we also estimate the cost of installing sufficient renewable power to fully compensate for the projected reduction in fossil fuel use by 2030 if the NDCs are achieved. To keep the analysis tractable, we do not consider the additional costs for electricity grids to address the intermittency of renewable energy or costs for operation and maintenance. We only rely on historically observed prices for equipment and do not account for potential future cost reductions (see Methods section). Our simple approach does not yield explicit information on the sectors in which fossil energy is replaced by renewables (for instance, replacing oil products in the transport sector would imply additional investment costs for electric vehicles). However, as most of the countries covered by the analysis have relatively low-ambition NDC targets, it is likely that emission reductions below the baseline will predominantly be achieved by building up renewable instead of fossil capacities when expanding the power sector. Due to recent cost reductions, renewable energy sources are in many cases already cost-competitive with traditional fossil sources and hence offer a low-cost option to reduce greenhouse gas emissions.

For the entire ASEAN region, this approach results in investment needs of almost USD 197 bn, which is close to estimates presented in a recent study relying on a complex model27. We identify the largest investment needs of almost USD 61 bn for Malaysia, followed by Singapore and Indonesia with about USD 39 bn and 28 bn, respectively. Myanmar, Singapore and Thailand would be able to cover more than 80% of their investment needs for renewables with savings accruing from lower energy net imports, and for ASEAN as an aggregate the respective figure is slightly above 73%.

Conclusions

The energy outlook of countries in Southeast Asia converges on a common policy logic. Transitioning from fossil to renewable energy would contribute to both climate change mitigation and energy security. The jobs and business opportunities offered by a growing global clean energy sector provide additional weighty arguments in favour of decarbonization30.

There has recently been some progress on the most obvious policy solutions to those challenges among the countries in Southeast Asia: support for clean energy, carbon pricing and ending subsidies for fossil fuels. Vietnam has shown the way on renewable energy and demonstrated that it is possible for the Southeast Asian countries to greatly accelerate solar and wind power installations31. Indonesia and Vietnam have established Just Energy Transition Partnerships (JETPs), which could potentially channel billions of dollars into renewable energy installations and coal shutdowns32. Indonesia and Singapore have launched fledgling carbon pricing schemes. Although the removal of fossil fuel subsidies has historically been seen as politically sensitive in these countries, popular support for such measures appears to be on the rise33. Integrating electricity grids across the region constitutes a further central building-block to minimize the costs of a clean energy transition34,35 and increase energy security by buffering localized power shortages36.

However, there is still a long way to go for countries in Southeast Asia in each of these policy areas. During the coming few years, growing energy dependence, climate impacts and increased climate change knowledge may make it politically more feasible to accelerate such measures.

Methods

Decomposing carbon intensity to account for energy trade

To assess the linkages between energy trade and emissions we decompose the carbon intensity of energy consumption. We denote the economy-wide emission intensity as CI and the emission intensity of each individual energy carrier i as CIi. Overall carbon intensity can be expressed as the weighted average of the emission intensity of each individual energy carrier, where si,cons denotes the share of each energy carrier in total energy consumption:

$${{CI}}_{t}=\mathop{\sum }\limits_{i}{s}_{t}^{i,{cons}}{{CI}}_{t}^{i}$$
(1)

We only need to sum up fossil energy sources, as emission-free energy sources have an emission intensity of zero. Due to differences in fuel inputs (e.g. different varieties of coal and oil being used), the carbon intensity of each fossil fuel differs across countries and time periods.

Taking into account that net energy imports of each energy carrier constitute the difference between consumption and domestic production (i.e. extraction) and using si,prod to denote the share of this energy carrier in total energy production, this can be rewritten as:

$${{CI}}_{t}=\mathop{\sum }\limits_{i}{s}_{t}^{i,{prod}}{{CI}}_{t}^{i}+\mathop{\sum }\limits_{i}({s}_{t}^{i,{cons}}-{s}_{t}^{i,{prod}}){{CI}}_{t}^{i}$$
(2)

The first term of this equation can be understood as the emission intensity due to domestic production of energy carrier i and the second one as the influence of net imports (this term is positive for net importers and negative for net exporters of energy carrier i).

We use the 2023 edition of the IEA World Energy Balances37 to calculate shares of different fossil energy carriers in total energy production and consumption. We employ the aggregate categories ‘coal, peat and oil shale’, ‘oil products’ (which include refined products, such as gasoline and diesel) and ‘natural gas’ as stated in the Supplementary Information. From the 2023 edition of the IEA Greenhouse Gas Emissions from Energy Highlights38, we obtain the total greenhouse gas emissions from ‘coal, peat and oil shale’, ‘oil products’ and ‘natural gas’ for a given country and year (‘GHG emissions from fuel combustion’ for ‘coal’, ‘oil’ and ‘gas’, respectively). Dividing this number by the energy consumption from the respective carrier (‘total energy supply’), which is provided by the IEA World Energy Balances, allows us to derive the carbon intensity for each energy carrier in a given country and year.

Projecting future emissions and the effect of decarbonization on net energy imports

For the case without climate measures in place we project future energy use until the year 2030 using the average historical growth rate in the period 2010–2021. This growth rate gBAU is given by:

$${g}_{{BAU}}={\left(\frac{{{TEC}}_{2021}}{{{TEC}}_{2010}}\right)}^{\frac{1}{11}}-1$$
(3)

We then use this growth rate to extrapolate energy consumption for each individual energy carrier i for the period 2022–2030 under the assumption that it will keep growing at that rate. Assuming that use of all energy carriers grows at the same rate implies that the energy mix remains unchanged. Consumption of energy carrier i at each future point in time t can then be expressed as:

$${{CE}}_{{BAU}}^{i,t}={{CE}}_{{BAU}}^{i,2021}\cdot {\left(1+{g}_{{BAU}}\right)}^{t-2021}$$
(4)

Energy data are taken from the 2023 edition of the IEA World Energy Balances37. In the same vein, we can project the growth of emissions based on the emission data for the year 2021 from the IEA Greenhouse Gas Emissions from Energy Highlights38:

$${{EM}}_{{BAU}}^{t}={{EM}}_{{BAU}}^{2021}\cdot {\left(1+{g}_{{BAU}}\right)}^{t-2021}$$
(5)

To project emissions when countries follow their NDCs, we build on Meinshausen et al.39, who combine countries’ submitted NDCs with appropriately downscaled scenarios from integrated assessment models. We use their projections for the year 2030 for the unconditional NDC targets. Conditional on support from the international community, more ambitious emission reductions are conceivable, hence our projections of emission reductions are conservative (the NDC dataset also includes a distinction between ‘high’ and ‘low’ to capture NDCs that state a range of intended emission reductions instead of a single target. As for each of the ten ASEAN members these two values are identical, this distinction is not relevant for our analysis). The emissions projected by Meinshausen et al. include all greenhouse gas emissions except those from land use, land use change and forestry. To derive a projection of energy-related greenhouse gas emissions we assume that if the NDCs are achieved, emissions from energy use change at the same rate between 2015 and 2030 as the emissions covered by the projections of Meinshausen et al. (which we denote EMCR). For a case in which a country’s NDC anticipates future emission growth that would exceed our projected development without climate policy, we use the latter as our projection for year 2030 emissions. This then yields the following expression for emissions in 2030:

$${{EM}}_{{NDC}}^{2030}=\min \left(\frac{{{EM}}_{{CR}}^{2030}}{{{EM}}_{{CR}}^{2015}}\cdot {{EM}}^{2015},\,{{EM}}_{{BAU}}^{2030}\right)$$
(6)

This can be used to calculate the average annual rate of change from 2021 onwards to achieve the emission targets for the year 2030:

$${g}_{{NDC}}={\left(\frac{{{EM}}_{{NDC}}^{2030}}{{{EM}}^{2021}}\right)}^{\frac{1}{9}}-1$$
(7)

We use this rate of change to extrapolate the consumption of each individual fossil energy carrier i at time t:

$${{CE}}_{{NDC}}^{i,t}={{CE}}_{{NDC}}^{i,2021}\cdot {\left(1+{g}_{{NDC}}\right)}^{t-2021}$$
(8)

This implies that relative shares of fossil energy use remain unchanged. We do not need to make assumptions about total energy use (i.e. which specific combination of lower total energy consumption and replacing fossil fuels with carbon-free energy sources are required to meet NDC targets).

With regard to extraction of fossil fuels, we project annual production to remain constant at the average of the three most recent years for which data are available in the IEA World Energy Balances:

$${{PE}}^{i,t}=({{PE}}^{i,2019}+{{PE}}^{i,2020}+{{PE}}^{i,2021})/3$$
(9)

Finally, we can derive net imports of energy carrier i at time t by taking the difference between consumption and production of the respective energy. Without climate policy, net imports are given by: carrier

$${{NME}}_{{BAU}}^{i,t}={{CE}}_{{BAU}}^{i,t}-{{PE}}^{i,t}$$
(10)

If countries reduce their fossil fuel consumption in line with their NDC targets, net energy imports are:

$${{NME}}_{{NDC}}^{i,t}={{CE}}_{{NDC}}^{i,t}-{{PE}}^{i,t}$$
(11)

Estimating financial implications of decarbonization on energy trade

Assessing savings due to lower energy imports requires price indices for traded coal products, oil products and natural gas across the ASEAN region. To derive the price index for energy carrier i, we divide total monetary values (MVEi) provided by Comtrade40 by total imports in energy units (MEi) from the 2023 edition of the IEA World Energy Balances37 for each energy carrier for the year 2021 (see Supplementary Table 7 in the Information for matching IEA categories to categories reported in Comtrade):

$${p}^{i}=\frac{{{MVE}}^{i}}{{{ME}}^{i}}$$
(12)

Applying this price index (Supplementary Table 8 provides the values used) to net imports in the business-as-usual projection and the case in which NDCs are achieved allows us to estimate the monetary benefits from decarbonization related to energy trade (assuming that real prices of traded fossil fuels are projected to be constant in the future):

$${{SVE}}^{i,t}={p}^{i}\cdot ({{NME}}_{{BAU}}^{i,t}-{{NME}}_{{NDC}}^{i,t})$$
(13)

We compare these savings with the investments needed to replace reductions in fossil energy use (i.e. the difference between fossil energy use in the business-as-usual projection and the case in which the NDCs are achieved) with renewable sources. We account for the fact that thermal energy generation entails conversion losses. We adopt the approach used by the Energy Institute41, which assumes that current conversion efficiency of primary to final energy amounts to 40% and will grow linearly to 45% by 2050. On this basis, we use a conversion factor \({\rm{\varepsilon }}\) of 41.88% for the year 2030. That is, only this fraction of primary energy from fossil fuels will be available as final energy, and only this amount needs to be generated from renewable sources (for which primary and final energy are identical).

To assess the total costs of installing renewable capacity, we require information on the capacity factor \(\rho\) (i.e. how much final energy can be expected to be produced from each kW of peak capacity installed) and the respective costs \(\gamma\). This yields:

$${TVE}=\mathop{\sum}\limits_{i}{\left({CE}_{BAU}^{i,2030}-{CE}_{NDC}^{i,2030}\right)}\cdot \varepsilon \cdot \frac{\gamma}{\rho}$$
(14)

In our analysis, we include solar and wind power. For all countries, solar emerges as the cheaper option (see Supplementary Table 9). We take country-specific capacity factors from the Global Solar Atlas42, which provides the amount of energy produced for a given installed capacity (kWh per day/kWp). We use the average between the minimum and maximum value provided. Regarding installation costs for solar energy, we rely on the of 876 USD/kWp estimate from IRENA’s Renewable Energy Statistics43.