7. Beyond 2030: policy pathways for meeting net zero by 2050

Beyond meeting climate policy targets for 2030, reaching net zero will require considerable climate policy efforts in the subsequent decade. This will have profound implications for economies worldwide and comes with considerable transition risks in its own right. In order to minimise these risks it is key that countries ensure an orderly transition to net-zero emissions, including by setting clear policy trajectories early.

To this extent, this chapter considers climate policy pathways beyond 2030 in Lithuania consistent with net-zero emissions by 2050. It does so through two key analyses; First, the chapter reports on the results of a modelling exercise considering a high-ambition policy scenario whereby Lithuania implements an economy wide carbon price from 2030. This modelling analysis shows both the extent of ambition possible through such a pricing approach, and the resulting economic impacts. It provides sectoral emissions reductions trajectories, enabling policy makers to infer interim emissions reduction targets for 2040 for key sectors. Second, it assesses the technological needs given particular hard-to-abate sectors, presenting key policy approaches for fostering technology adoption and innovation.

The first part of the chapter builds on the modelling depicted in Chapter 3. Chapter 3 concerned the modelling of existing and planned policies and their effect to 2030, contributing to the ongoing update of the Lithuanian NECP to which this project contributes. In this chapter, the planned policy scenarios developed in Chapter 3 serve as a starting point for further policy ambition to 2050 with the intent of depicting possible cost-effective decarbonisation pathways for Lithuania.

The resulting analysis, however, lays bare that certain sectors in Lithuania remain hard-to-abate, even at very high carbon prices. This exemplifies the need for technological innovation, to lower marginal abatement cost curves for high carbon price trajectories. The second part of the chapter therefore considers the innovation policy landscape in Lithuania, drawing OECD work on science, technology and innovation, and on the recent Economic Survey of Lithuania (OECD, 2022[1]).

Together these two parts provide a key initial assessment of Lithuania’s policy needs in the long-term. As such, the chapter’s findings serve as impetus for an ongoing discussion on how best to meet the net-zero target in Lithuania.

Chapter 3 of this report details the results of a modelling analysis considering planned climate policy pathways in Lithuania to 2030. This aims to inform the process for updating Lithuania’s NECP – detailing climate policies to be implemented to reach the 2030 targets.

Beyond 2030, explicit policy pathways are more difficult to project. However, modelling can be used to indicate possible emissions reductions trajectories compatible with cost-effectively reaching net-zero emissions by 2050, and therefore contributing to meet the Paris Agreement’s targets. Lithuania has already set a 100% emissions reduction goal by 2050 (i.e. net-zero emission target) in its NCCMA. This chapter assesses the extent to which net-zero emissions can be reached under high-carbon price scenarios, and what effect such a policy trajectory would have on the economy.

The policy scenario modelled assumes economy-wide carbon prices are implemented globally to assess the impacts of meeting net-zero targets (assuming regionally differentiated carbon-prices and net-zero timelines1). The emissions reduction pathway, and concurrent carbon prices for Lithuania, are generated endogenously within the model based on a least-cost pathway to climate-neutrality by 2050. In effect, the price trajectory reflects the highest possible ambition within the modelling framework because marginal abatement cost curves flatten at even higher prices leading to negligible emissions reduction gains.

Assumptions detailed in Chapter 3 remain relevant for the modelling framework beyond 2030, with renewable energy costs continuing to decline, and autonomous preferences for renewable energy and energy efficiency gains sustained. Critically, it is also still assumed that only already existing technologies are available, with not-yet-on-the-market technologies such as green hydrogen or large-scale carbon capture and storage assumed to play no role in the decarbonisation trajectory. This final assumption limits the findings somewhat, as it is likely that there will be considerable innovation over the coming 25 years. However, under this assumption, the model’s results can be interpreted as providing an indication of what is possible under high carbon prices considering current technologies, and what the economic costs of this are. This analyses then offers a lower-bound estimate for the emission reduction potential and upper-bound estimate for economic costs.

This section continues by presenting the scenario framework modelled, detailing both the reference scenario and high-ambition policy scenario. It then presents the results, illustrating the impact of the transition to climate neutrality on:

  • GDP and other main macroeconomic variables (i.e. employment, productivity, investment, welfare, debt trajectories);

  • energy/climate specific impacts such as energy consumption and GHG emissions;

  • relocation of economic activity across sectors, including job reallocation between skills levels and sectors;

  • public finance:

    • revenue and government expenditure;

    • impact of potential revenue recycling from carbon pricing, assuming revenues are recycled as lump-sum transfers to households;

    • International competitiveness and trade flows.

The reference scenario post-2030 is an extension of the reference scenario to 2030 (Chapter 3), whereby countries are expected to reach their NDC 2030 and 2050 targets. Carbon prices are imposed on all emitting activities, i.e. ETS and non-ETS emissions for all countries including China. For the EU, carbon prices post-2030 are imposed only on ETS activities. Since post-2030, there are no specific mitigation commitments, apart from aspirational targets, assumptions about mitigations efforts are needed.

For the post-2030 period, carbon prices are assumed to grow at 3% annually instead of being endogenously determined by the model. This growth rate is close to what is used as a central discount rate by governments when estimating the social cost of carbon (Interagency Working Group on Social Cost of Greenhouse Gases, United States Government, 2021[2]). It is also assumed that minimum carbon prices are set for 2031 in modelled countries and regions, with a USD 20/tCO2 minimum price for developing countries and a USD 50/tCO2 minimum price in high-income countries. This implies that minimum carbon prices bind for all countries except EU countries and XOE countries (e.g. Australia, Canada, New Zealand and Israel) where carbon prices under the reference scenario in 2030 are already higher than the minimum carbon price (Table 7.1)

In addition to carbon pricing, the reference scenario for the post-2030 period continues the policy assumptions detailed in Chapter 3. Specifically, the scenario assumes, an autonomous electrification trend, a downward trend in the price of renewable electricity technologies and a non-price shift towards renewable technologies. In addition to continuing trends in technological development, these exogenous policy assumptions serve to account for the effect of non-pricing climate policies. In the case of Europe, renewable energy share in 2050 is assumed to reach 45% in the absence of a change in relative prices of renewable energy sources. A drop in renewable prices would accelerate these trends. Changes in these shares in the rest of the world are variegated across the modelled regions. In the case of Lithuania, pre-2030 trends for renewable energy generation and the broader energy mix, as modelled in Chapter 3, are assumed to continue beyond 2030. These are based on data and projections provided by the Lithuanian Energy Agency.

Post-2030, it is assumed that all fossil fuel combustion activities are covered by carbon pricing including in the EU. Starting from 2031, ETS and non-ETS sectors face the same carbon price, which grows at a constant rate of 3.4% over time to reach around USD 400 per ton of CO2 in 2050. As suggested by additional model simulations, the marginal abatement cost curve in the model becomes relatively flat after this level of carbon prices, so that any further increase in carbon prices leads to only marginal reductions in emissions. This is because the model does not include non-yet-mature technologies and energy sources, such as CCS or hydrogen, and therefore the resulting mitigation potentials are entirely based on existing technologies (see Chapter 3 and Annex A).2 Emergence and higher penetration of new technologies can shift abatement costs curves and lead to more substantial and/or cheaper emission reductions compared to those presented in the current assessment.

Under the reference scenario, where carbon prices are expected to increase at a 3% annual rate in ETS sectors for Lithuania and EU at large, CO2 emissions decrease by 41.5% in 2050 relative to 2005 and by 75.6% relative to 1990 levels.

Sectoral composition of emissions under the reference scenario changes over time. With growing carbon prices, the share of emissions from power generation sectors reduces substantially. Emissions related to the transportation sector (a non-ETS activity) and industry (partly covered by ETS) also decrease, but at a slower pace. Emissions from final consumption (i.e. households) fall, as consumers switch more toward electricity and substitute away from other fuels, such as gas for heating and petroleum for private transport. These trends resulting from reference scenario assumptions do not reflect more ambitious mitigation efforts, e.g. the EU Green Deal.

There are substantial variations in CO2 emission changes relative to 2005 across various sectors (Figure 7.2). In relative terms, emissions from power and heat generation decrease the most, reaching 92% reduction in 2050 (relative to 2005). Substantial reductions are also observed in household emissions, as they decrease by almost 86% relative to 2005 levels. Household-related emissions in the model include emissions from the use of personal vehicles. Emissions from manufacturing and service sectors reduce by almost 59% in 2050 (w.r.t. 2005 levels). While industrial emissions reduce over time when compared to 2021, their slow reduction rate in the reference scenario is largely driven by rising CO2 emissions from industrial processes.

At the same time transportation emissions reduce by only 7% in 2050 (w.r.t. 2005), as no specific mitigation measures that target the transport sector are implemented in the reference scenario and emissions in transport remain hard-to-abate due to steep abatement cost curves in the sector as a result of low substitutability (for more information see the next section on Innovation). Emissions from energy intensive industries even by 2050 remain above 2005 levels (+7.5%).

In the reference scenario, the primary energy use increases by around 1.2% per annum between 2021 and 2050—with a large part of the growth occurring in renewable power, in particular, solar and wind generation. Refined oil and gas are in absolute decline (Figure 7.3).

Power and heat generation grow at around 2.4 % on average per annum between 2021 and 2050, with significant growth from renewables—around 9%-10% for solar and wind power, respectively. Gas thermal power declines in absolute terms—at a rate of around 4 percent per annum, driven in part by the carbon tax, which lead to the substitution by rapidly expanding wind and solar power generation.

Under the reference scenario, all sectors are expected to continue growing beyond 2030 except the fossil fuels sector. Electricity generation (ELY) experiences the fastest growth, on average 2.5% growth per year. In part this is driven by the assumptions of accelerated electrification, but also by assumed expansion of renewable generation capacity within the reference scenario. Refined oil and fossil fuel sectors see an absolute contraction in output, due to the lower demand for fossil fuels.

When implementing the stricter policy scenario, results suggest Lithuania would be able to achieve 83% reduction in CO2 emissions in 2050 w.r.t. 1990 or 60.3% reduction w.r.t. 2005 level (Figure 7.5). This is almost 20 percentage points above the emission reductions achieved under the reference scenario. Looking across sectors, power and heat and households achieve over 93%-94% reduction in emissions w.r.t. 2005 by 2050, while the other services sector and light manufacturing reduce emissions by over 84%. Following implementation of carbon pricing in non-ETS sectors, transportation activity also substantially decarbonises reducing emissions by over 40% in 2050 w.r.t. 2005. Industry-related emissions remain the hardest to abate, largely due to the treatment of industrial process emissions discussed in Chapter 3. At the same time, it should be noted that this result is in alignment with earlier literature that suggests relatively high abatement costs in such sectors like cement industry, iron and steel, and chemicals (Paltsev et al., 2021[3]).

For comparability with Lithuania’s emission reduction targets, the resulting CO2 reductions need to be expressed as part of overall GHG emission reductions. Given that the model only integrates policies that price CO2 emissions, any non-CO2 emission reductions result only from assumptions on exogenous productivity and efficiency trends and reduced emissions factors under this scenario. Lithuania would thus be able to achieve 27% reduction of its GHG emissions by 2050 w.r.t. 2005 and 65.3% reduction w.r.t. 1990. Reductions in non-CO2 emissions are mostly driven by agriculture and industrial sector, given their substantial contributions in these sectors to non-CO2 emissions (non-CO2 emissions contributed 94% to agricultural emissions and 40% to industrial emissions in 2022).

Unsurprisingly, these results indicate that a CO2 price on combustion emissions alone will not be sufficient for meeting Lithuania’s long-term mitigation goal of reaching net-zero emissions by mid-century and that other policy instruments and levers will be indispensable. Additionally, the model does not account for the sequestration potential in the land use land use change and forestry (LULUCF) sector. Assuming historical trends continue, Lithuania’s average annual emissions abatement from sequestration amounts to 5 MtCO2e (see Chapter 1, Figure 1.3). Accounting for this sequestration brings down total emissions reductions to 55% w.r.t to 2005 and 74% w.r.t 1990. Supplementary mitigation efforts are warranted to bring down total GHG emissions to reach net zero, for example through enhancing carbon removal potential, developing new technologies to enable further efficiency gains and emissions reductions in hard-to-abate sectors, or implementing more stringent climate policies.

Post-2030, the High-Ambition Policy Scenario leads to substantial shifts in the primary energy supply compared to the reference scenario. When compared to 2030 levels, the use of petroleum products decreases by over 42% in 2050. Even higher levels of reduction are observed in the cases of crude oil and natural gas – in a range of 60-67%. The use of coal drops by almost 77%, from an initially low level. At the same time, we observe an increase in the use of energy that is coming from renewable generation. By 2050, solar and wind energy supply increase forty-fold relative to 2021 levels and seven-fold to 2030 levels. Overall energy demand increases by around 22% between 2030 and 2050.

In terms of power and heat generation mix, between 2030 and 2050, the share of renewable generation increases over time. In particular, solar and wind increase their output volumes by around 200% each in 2050 w.r.t. 2030, while other renewables and fossil fuel power generation decrease their output substantially. In 2050, the volume of natural gas power generation declines by around 71% relative to 2030, while coal power generation drops to zero. A reduction in other renewables (biomass-based) generation is also observed, as the latter are substituted by solar and wind generation. Overall electricity demand increases by around 50% in 2050 relative to 2030, as consumers switch away from direct consumption of fossil fuels, including gas and petroleum products, and transition toward electricity use.

The cost of the implemented policies is relatively moderate even under ambitious mitigation efforts by 2050. The reductions in real GDP are around 0.9% in 2050 relative to the reference scenario. When translated to the annual growth rates, these represent a very moderate slowdown of the annual growth rate of around 0.03 percentage points. These impacts are measured relative to the reference scenario, which already assumes relatively high level of carbon pricing in the ETS sectors and therefore cannot be compared with other modelling projections showing GDP cost estimates for climate policy compared to a business-as-usual baseline.

Consumer welfare increases by 0.3% in 2050 w.r.t. reference scenario. As in the results to 2030, the increase in welfare is driven by carbon pricing revenue recycling whereby households receive lump sum transfers. It is also due to the fact that carbon intensity of Lithuanian economy is lower than EU average, including the carbon intensity of services and final users, and thus the country gains relative advantage to most other EU member states when the regional carbon price (through the EU ETS) is applied.

Implementation of the ambitious mitigation efforts results in a moderate increase in renewable generation, as solar, wind and biomass-based generation grows by 8%-11% in 2050 relative reference scenario (Figure 7.8). Most adversely impacted sectors are air transportation, natural gas-based power generation, crude oil and petroleum industries. The recorded reductions in output range from 44%-45% in the cases of gas power generation and air transport, and to 30%-32% in the case of crude oil and petroleum products. A substantial impact on the air transportation sector, compared to other transportation activities, is explained by a large share of petroleum products in the overall cost structure of this activity. The value share of petroleum products in the total inputs costs of air transport exceeds 35% in Lithuania, while in the case of water transport the corresponding share is around 14%.

Carbon pricing also shrinks output in other transportation activities, as both road and water transport record a reduction in output of 5% and 9% respectively. Such structural changes result in an overall reduction in the GDP emission intensity.

Reallocation of workers across sectors largely follows the output change patterns. Increasing cost of production leads to the moderate increase in unemployment rates – by around 0.15-0.25 percentage points in 2050 relative to the reference case. As the mitigation effort is substantially increased starting from 2031, we observe a short-term increase in unemployment relative to the reference scenario. Post-2035 structural adjustments lead to the reallocation of labour across sectors and a reduction in the overall unemployment rate relative to the reference scenario.

An overall moderate decline in real wages is also observed: by around 2%-2.5%. This implies that relative to the global GDP deflator, which is used as a numeraire in the model, real wages decrease by a respective amount. At the same time, in absolute terms, this implies only a moderate decline in the growth rate of wages over time.

Looking at the results across skill types (unskilled vs skilled labour force), there is moderate evidence of the potential regressive outcomes across workers under the High-Ambition Scenario in the long run, as both wage and employment reduce somewhat more for unskilled workers compared to skilled workers. This reverses broadly progressive trends under the 2030 policy scenarios depicted in Chapter 3, implying more attention needs to be given to redistributive policies in the long-run.

Observed structural shifts also result in the reallocation of labour across sectors. Due to an expansion in renewable generation, the electricity and heat production sector sees a moderate increase in employments (of both skilled and unskilled workers), while most other sectors see a moderate decline. In relative terms, an increase in labour demand by electricity generation activity is in a range of 6%-7%, while the most substantial (in relative terms) declines are faced by petroleum products (3%-4%), mining (1.7%-1.9%) chemicals (around 1%) and non-metallic minerals (0.7%) activities. All four of the aforementioned sectors see a reduction in demand due to the decarbonisation activities and thus reduce output and the use of production factors. Trade activity shows the largest reduction in employment in absolute terms (0.5-0.8 thousand workers), but this represents only 0.4% of the total labour force employed in this sector.

Cumulative investments over the 2023-2050 period virtually do not change under both scenarios, while a reallocation across sectors is observed. The latter largely follows the output change patterns. Expanding sectors, such as renewable generation and services attract additional funds, while shrinking sectors like heavy manufacturing and transport activities see a decline in the investment flows (Figure 7.13). A relatively large increase in investments in absolute terms for the case of other services represents a small number in relative terms (0.3%). Investments in wind and solar power generation increase by around 12%.

Increasing carbon taxes generate additional tax revenues. A moderate increase in tax revenue from indirect taxes is observed as ECR fees rise. The cumulative revenue amounts to around USD 30.4 billion or about ~ % of GDP, raised from indirect taxes and carbon taxes. There is a minor reduction in revenue from other taxes due to reallocation of production patterns, but most of the revenue collected via higher excise taxes and carbon taxes is transferred to households in a form of lower labour income tax (USD 28.8 billion or about ~ of GDP) (shown in the figure below as direct taxes).

The modelling presented in the previous section shows that decarbonisation is possible at moderate economic costs in Lithuania. The model assumes, however, current levels of technological progress, and so does not factor in the possibility of further innovation. The model also does not consider the role of carbon sinks and the potential for enhancing these through agriculture and forestry policies. As such, decarbonisation efforts through carbon pricing and a broader climate policy mix can be further assisted through innovation and technology diffusion, particularly in hard to abate sectors. Innovation both serves to fill gaps where carbon price signals are not enough to enable decarbonisation, as well as reducing the overall costs of decarbonisation by enhancing the cost-effectiveness of emissions reductions technologies (placeholder for reference to carbon elasticity paper).

The modelling results indicate in particular the potential for further efforts to aid decarbonisation in the Industry, Transport and Agriculture and Forestry sectors, as these exhibit the least drastic emissions reductions through the modelled policy scenarios. In all three of these sectors, innovation and technology diffusion is key to enhance mitigation ambition and implementation.

In the industry sector, hard to abate processes such as in ammonia production (which makes up a substantial portion of total GHG emissions in Lithuania – see more below) require further technological innovation as no viable electrification solutions exist. In particular, carbon capture and storage and green hydrogen have been identified as key technologies for industry decarbonisation, and indeed, hydrogen is a key pillar of Lithuania’s NECP. The IEA’s net-zero scenario estimates that together, these two technologies will make up 50% of the required emissions reductions in the industry sector by 2050 (IEA, 2021[4]). Much of this progress relies on further research and development to ensure these CCUS and hydrogen technologies are operable at scale and at low enough costs to ensure their economic viability (IEA, 2021[4]).

In the transport sector, while electric vehicles present an important technological breakthrough enabling the decarbonisation of private vehicle use, the modelling results indicate that hard-to-abate emissions in the sector remain. This is because abatement cost curves in the sector remain steep when compared to other sectors such as electricity generation, meaning emissions are relatively inelastic to higher prices due to a lack of low-carbon technologies that can readily substitute current fossil fuel technologies. For example, road freight transport is not as easily electrified as private vehicles, nor is air and water transport. Here, only very few mature technologies exist, indicating the key importance of further innovation in heavy transport vehicles, shipping and aviation to enable deep decarbonisation of the transport sector (IEA, 2021[4]). Hydrogen and bio-based fuels will play a key role in this regard (IEA, 2021[4]).

In the agriculture and forestry sector, the potential to maintain and enhance Lithuania’s carbon sinks is key to its decarbonisation ambitions. As depicted in the previous section, through stringent carbon pricing and complementary policies a GHG emissions reduction of 63% is possible by 2050 (w.r.t 1990 levels). At current levels, carbon sequestration through the LULUCF sector would mitigate a further 5MtCO2e, resulting in total GHG emissions reductions of 74% (see previous section). As such, enhancing the carbon sequestration potential of Lithuania’s LULUCF sector remains key. Natural carbon sinks through careful land-use management remain one of the most cost effective means of enabling such negative emissions (Vass and Elofsson, 2016[5]; Harper et al., 2018[6]). Carbon dioxide removal, for example through direct air capture or BECCs technologies, may also play an important role, although here too, innovation is key (IPCC, 2018[7]). Finally, a number of technologies can help to reduce emissions in agriculture (Fellmann et al., 2021[8]). The mitigation potential of these, however, remains somewhat limited and uneven. For example, a recent study shows that global abatement cost curves for methane are steepest in the agriculture sector, implying there is only limited mitigation potential here (Höglund-Isaksson et al., 2020[9]).

Considering this, it is clearly important to assess existing and planned innovation policies in Lithuania in order to, post 2030, create conditions conducive to develop and deploy technologies needed to reach net zero, at low economic cost (or even overall economic benefit). This section thus focuses on innovation and technology diffusion, first taking stock of the existing innovation policy landscape in Lithuania, and then offering recommendations for policy adjustments based on good-practice insights from other EU and OECD countries.

Lithuania’s innovation performance has improved in recent years. According to the data of the Lithuanian Statistics Department, in 2021 A total of EUR 622.4 million of GDP was allocated for R&D works in Lithuania. This is the highest amount of R&D expenditure in the entire history of this indicator. Compared to 2020, R&D expenses in Lithuania increased by 10 percent, or EUR 57.5 million. This is a clear signal that the focus of Lithuanian economy participants on innovation and R&D is growing rapidly. It also shows that the government's efforts to promote R&D and innovation are yielding more and more results.

Data from the EU innovation scoreboard show further that the level of development of the Lithuanian innovation ecosystem in 2022 reached a historical maximum, and that Lithuania's innovation ecosystem continues to be rapidly improving: however spending on research and development (R&D) remains below EU averages, reaching only 0.88% of GDP in 2020 (OECD, 2021[10]). This increased somewhat, to 1.2% of GDP in 2022, but remains below the EU’s average of 2.2% of GDP .(Figure 7.15) (OECD, 2022[1]) and it is yet to recover from the fall following the global financial crisis of the late 2010s.

Lithuania aims to increase R&D spending to 1.5% of GDP by 2024, an improvement, though not enough to bridge the gap with the EU average. While public funds will play a key role in enabling this increase, it is also vital that the low share of business spending on R&D, less than 0.54% of GDP in 2020, is increased (OECD, 2022[1])(also see Chapter 5 on financing the transition to net zero). Here Lithuania primarily offers generous tax credit schemes to encourage private sector innovation (OECD, 2021[11]). Take-up of these tax incentives, though, is modest. Lithuania’s overall public support for private innovation is low compared to OECD average (OECD, 2022[1]).

In order to support its climate policy ambitions, Lithuania has adopted a number of key policy initiatives aimed at enhancing innovation in the energy sector. In 2018, the National Energy Independence Strategy was adopted, setting the high-level objective that Lithuania becomes an energy technology exporter by 2030, supported by actions detailed in the NECP. This is supported by the National Progress Programme (NPP), adopted in 2019, which aims to promote a smart-economy, specifically through channelling and encouraging investment in green technology development. Alongside promoting technological development, the NPP also aims to better integrate Lithuania within EU strategic value chains, such as on battery and hydrogen technology.

In order to implement these broad targets, the Smart Specialization Programme and its associated funding programme set various priority areas for innovation investment, including energy and a sustainable environment. The next programming period for the programme, running until 2027, sets two key themes in the energy sector: renewable energy resources and smart and effective consumption of energy. Since 2021, the Innovation Promotion Fund further acts as an overarching financial instrument aiming to promote research, innovation and development (IEA, 2021[12]).

Specifically focused on the energy sector, the Action Plan for Strengthening the Lithuanian Energy Innovation Ecosystem aims to strengthen innovation in eight key areas over the period 2020-2023: funding, human resources, infrastructure, products and services, science and technology, regulatory environment, consumers, and communication and innovative culture. In total, 51 measures are detailed in the plan spread across these priority areas. As such the plan provides a comprehensive strategy for innovation in the energy sector, making it easier to access funding, prepare energy project pipelines (see also Chapter 5 on the importance of scaling finance for project pipelines), and also enabling collaboration at the EU level under EU research frameworks (OECD, 2021[10]).

The Action Plan also intends to develop a system of indicators to measure progress on energy sector innovation. IEA and European Commission reviews of Lithuania’s energy sector innovation progress highlight monitoring and evaluation of innovation policy as an area for future improvement (IEA, 2021[12]; European Commission, 2020[13]). Management of public funds was also highlighted as an area to review, with the OECD’s 2020 Economic Survey of Lithuania suggesting considerable efficiency gains could be made through consolidating the number of innovation agencies (OECD, 2020[14]).

Of further importance to the energy sector, recent amendments to the Law on Energy stipulate a regulatory sandbox environment whereby innovators in the energy sector are able to test and develop new approaches and technologies without being subject to the strict permitting regulations otherwise applied to the sector (IEA, 2021[12]). The regulatory sandbox is particularly focused on smart energy systems and demand response, although other energy technologies can also profit from it (IEA, 2021[12]).

Although innovation performance in Lithuania remains below EU averages, recent plans, programmes and strategies point to possible improvements. In particular, the Action Plan for Strengthening the Lithuanian Energy Innovation Ecosystem and the regulatory sandbox approach promise an energy sector considerably more conducive to innovation. Under these two policies, monitoring and evaluation of innovation will also enhance considerably.

Despite these positive trends, new technologies such as green hydrogen or storage are likely to require different innovation approaches. To this end, the Lithuanian Hydrogen Platform already brings together members from across research, industry, and the public sector to help develop new programmes and plans to promote hydrogen development in the country (Ministry of Energy of the Republic of Lithuania, 2020[15]). Recently the process for drafting national guidelines for hydrogen development has started, indicating further future progress in this regard.

In order to inform and assist these processes, OECD analyses highlight good practices based on other countries experiences. The remainder of this chapter distils these into key recommendations for Lithuania.

As a small open economy, ensuring an enabling environment for technology adoption in Lithuania is key to exploit innovation happening in other, larger, markets. As a member of the EU, trade barriers to technology adoption are less significant and Lithuania can profit considerably from EU-wide collaboration on research and development initiatives. Indeed, it has already aligned its national hydrogen strategy with that of the EU. Further EU-wide synergies are also already underway, such as the Projects of Common Interest supporting a Baltic energy market integration, covering both electricity grids and gas (European Commission, 2021[16]).

Although EU membership eases trade barriers, other barriers to technology diffusion remain. Knowledge gaps, capacity constraints, costs, language barriers, etc. all obstruct effective technology adoption (OECD, 2022[1]) (EBRD, 2022[17]). Here, the Lithuanian government will need to play a key role in incentivising not only innovation, but also technology diffusion and adoption.

A number of policy options exist for enhancing technology adoption. First, clear carbon price trajectories are key to signal to markets and businesses that technology adoption will be cost-effective in the medium- to long-term (Anderson et al., 2021[18]; Cammeraat, Dechezleprêtre and Lalanne, 2022[19]). Supporting carbon pricing, countries have implemented direct technology support initiatives. For example, the Netherlands supports technology adoption through a combination of subsidies and tax credits (for more information see Box 2.5 in Chapter 2). The Dutch approach grants supports on technology neutral criteria favouring the most cost-effective options, thereby disadvantaging technologies that are not yet close to market. For decarbonisation efforts in hard-to-abate sectors, such an approach may fall short of incentivising the deep-innovation and technology adoption needed, as for example with green-hydrogen (Cammeraat, Dechezleprêtre and Lalanne, 2022[19]).

Further good practice examples for technology adoption include the use of contracts for differences whereby countries ensure a minimum price for greenhouse gas abatement is met, paying the difference if the market price does not meet this and with firms paying the government if the market price is above the contracted price. This is a key difference to feed-in tariffs or similar schemes (see Box 5.3 in Chapter 5 for an example from the UK). Contracts for difference have been piloted around the EU, notably concerning green hydrogen adoption in Germany (Cammeraat, Dechezleprêtre and Lalanne, 2022[19]). They have also been highly successful as a tool to incentivise the development of off-shore wind projects in Denmark, with tenders so competitive that minimum prices were set at close to 0 resulting in operators paying back governments as whole-sale prices rise (see Box 2.4 in Chapter 2 and Box 5.3 in Chapter 5).

Considering the industry sector in Lithuania, chemical production accounted for 2/3 of GHG emissions in the sector in 2021 (Government of Lithuania, 2022[20]). The majority of these emissions result from ammonia production, comprising 13.1% of total national CO2 emissions in 2020, with a total annual ammonia production capacity of over 1 000t (Government of Lithuania, 2022[20]). Decarbonising ammonia production relies primarily on the provision of green hydrogen to replace natural gas (a primary feedstock in the production of ammonia) (Cammeraat, Dechezleprêtre and Lalanne, 2022[19]).

Green hydrogen production currently remains three times as expensive as the conventional alternative, grey hydrogen, which is made from natural gas and is thus emissions intensive (Cammeraat, Dechezleprêtre and Lalanne, 2022[19]). Major cost reductions are projected but rely critically on further reductions in the cost of electrolysers. Alongside R&D policy, large-scale demonstration projects are key to ensuring economies of scale are realised and further to easing technology adoption.3

Supporting scientific research and innovation, particularly in developing more cost-effective electrolysers for green hydrogen, will need to be a key priority for Lithuania. Targeted and direct support here is key, with current tax incentives ill fitted to enabling such deep innovation. This is primarily because the tax incentives favour large companies with considerable capacity, and profits, to take advantage of tax breaks. Smaller companies, who are often more directly involved in deep innovation, may lack both the capacity to apply for tax breaks, or the profitability needed in order for tax incentives to become valuable (OECD, 2022[1]). Instead, direct and targeted support can help spurn innovation in small and medium enterprises, helping to generate a healthy start-up ecosystem for energy innovation.

Enabling private investment is also key in this regard, with recommendations for how to best employ public funds to enable further private finance highlighted in Chapter 5 also critical for innovation. Venture capital trends in Lithuania are positive in this regard, having experienced a large increase from 2020-2021, particularly for start-up and other early-stage companies. There remains scope for further increases however, with venture capital investments still lower than 0.1% of GDP in 2021 (OECD, 2022[1]). To this end, exploring the possibilities for establishing a venture capital ecosystem with neighbouring Baltic countries and Poland could enhance the contribution of venture capital to low-carbon innovation. Similar regional ecosystems have been developed in Scandinavian countries (Nordic Innovation, 2022[21]). What is more, venture capital investments remain focused on early-stage start-ups and do not address business upscaling needs (OECD, 2022[1]).

Relying primarily on innovation breakthroughs alone, however, ignores the possibility that other countries/regions are quicker to developing new technologies, particularly in a small country like Lithuania. Therefore, innovation policy must also be supplemented by technology adoption policies. As shown in Chapter 2, the current NECP already includes considerable technology adoption subsidies for both the transport and industry sectors, particularly for SMEs in the latter. Such policies are integral and should be further sustained. However, larger-scale demonstration projects will also increasingly be needed in order to take advantage of economies of scale. Here, contracts for differences, guaranteeing a price for green hydrogen for example, would be an appropriate policy approach to ensure key technologies are adopted. Such a policy could draw key lessons from other European countries and could be tailored specifically to the technology’s needs.

Similar considerations also apply to the transport sector. Alongside private vehicles, freight transport emissions have grown considerably, by 50% since 2005 (Chapter 1, Figure 1.7). Unlike private vehicles, where electrification is already a cost-effective low-carbon alternative, technologies are not yet available to decarbonise freight transport at scale, particularly for air and water transport. Technologies are, however, emerging that may have the potential to offer significant fuel efficiency gains, and alternative, low-carbon fuel options. These include using wind-propulsion in shipping, or redesigning aircraft to increase efficiency (IEA, 2021[12]).

Electrification of heavy trucks is projected to account for 30% of road freight by 2030 in a net-zero policy scenario, up from just 0.3% in 2021 (IEA, 2021[12]). Achieving this will require significant, targeted, government support. China already heavily supports electrification of road-freight, and other countries are following suit. For electrification, technologies are close to market and cost-competitive enough for deployment subsidies – and should be rolled out immediately and as quickly as possible. However, decarbonising road and other forms of transport will rely on a broader technology mix, including hydrogen and bio-based fuels in addition to batteries (IEA, 2021[12]). As with the industry sector, for both hydrogen and bio-based fuels, direct and targeted innovation support combined with adoption incentives such as contracts for difference would provide a strong policy framework for ensuring new technological development and deployment.

Finally, the same lessons also apply when considering the need for innovation and technology adoption support for negative emissions technologies. Direct air capture, for example, will require significant innovation and cost reductions before it can be employed at scale. Even already existing measures to enhance carbon sinks through land-use changes such as a- and re-forestation rely on setting adoption incentives and can benefit from innovation in land-use management practices and technologies.

This chapter has depicted climate policy pathways beyond 2030, in line with Lithuania’s plans to reach climate neutrality by 2050. From the two-part analysis, focusing on modelling emissions trajectories under high carbon prices and complementing such pricing with innovation and technology adoption support, the chapter offers a number of key policy insights.

First, the chapter shows that high carbon prices can lead to significant emissions reduction at low economic cost, even if assuming no further technological innovation, and without pricing process based or non-CO2 GHG emissions. This clearly demonstrates the value in continuing to send strong price signals. To this extent, the Lithuanian government could begin to indicate intentions for carbon pricing beyond 2030, in particular in its engagement with the EU. For example, the EU could indicate whether it plans to expand the scope of emissions trading to further sectors post-2030, integrating the current ETS and the proposed ETS24 and including further sectors as well.

Second, the modelling results show that, under current technological progress, the industry and transport sectors remain hard-to-abate even when faced with high carbon prices. As such, targeting these sectors will be of key importance for longer-term climate policy strategies. Here innovation and technology adoption policies are key and an area where Lithuania can make significant progress. Specifically, innovation support should become more direct and targeted rather than relying on horizontal, or technology-neutral, tax-incentives as it currently does. In addition, technology adoption should be further enhanced, particularly given Lithuania’s small market size, in order to take full advantage of innovation happening in other countries or regions. The NECP already includes generous technology adoption subsidies. Implementing further policies, such as a contract for differences programme for green hydrogen, would serve to support larger-scale demonstration projects and the development of a domestic market for new low-carbon technologies.

Third, the limits of high carbon prices also exemplify the importance of enhancing carbon sinks and engaging the agricultural sector in mitigation efforts in order to enable the deep-decarbonisation necessary. Lithuania’s carbon sinks are significant but have been decreasing in the past decade, indicating that reforms in forest and land-use management could have a large influence on emissions reductions.


[18] Anderson, B. et al. (2021), “Policies for a climate-neutral industry: Lessons from the Netherlands”, OECD Science, Technology and Industry Policy Papers, No. 108, OECD Publishing, Paris, https://doi.org/10.1787/a3a1f953-en.

[19] Cammeraat, E., A. Dechezleprêtre and G. Lalanne (2022), “Innovation and industrial policies for green hydrogen”, OECD Science, Technology and Industry Policy Papers, No. 125, OECD Publishing, Paris, https://doi.org/10.1787/f0bb5d8c-en.

[17] EBRD (2022), Diagnostic of Estonia, Latvia and Lithuania.

[16] European Commission (2021), Commission Delegated Regulation (EU) 2022/564 of 19 November 2021 amending Regulation (EU) No 347/2013 of the European Parliament and of the Council as regards the Union list of projects of common interest, European Commission, Brussels, https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32022R0564&qid=1663087079030 (accessed on 4 October 2022).

[13] European Commission (2020), Assessment of the final national energy and climate plan of Lithuania, European Commission, Brussels.

[8] Fellmann, T. et al. (2021), “Greenhouse gas mitigation technologies in agriculture: Regional circumstances and interactions determine cost-effectiveness”, Journal of Cleaner Production, Vol. 317, p. 128406, https://doi.org/10.1016/j.jclepro.2021.128406.

[20] Government of Lithuania (2022), Lithuania’s National Inventory Report to the UNFCCC, Government of Lithuania, Vilnius, https://unfccc.int/documents/461952 (accessed on 21 February 2022).

[6] Harper, A. et al. (2018), “Land-use emissions play a critical role in land-based mitigation for Paris climate targets”, Nature Communications, Vol. 9/1, https://doi.org/10.1038/s41467-018-05340-z.

[9] Höglund-Isaksson, L. et al. (2020), “Technical potentials and costs for reducing global anthropogenic methane emissions in the 2050 timeframe –results from the GAINS model”, Environmental Research Communications, Vol. 2/2, p. 025004, https://doi.org/10.1088/2515-7620/ab7457.

[12] IEA (2021), Lithuania 2021 Energy Policy Review, IEA Energy Policy Reviews, OECD Publishing, Paris, https://doi.org/10.1787/db346bb1-en.

[4] IEA (2021), Net Zero by 2050, IEA, Paris, https://www.iea.org/reports/net-zero-by-2050 (accessed on 19 October 2021).

[2] Interagency Working Group on Social Cost of Greenhouse Gases, United States Government (2021), Technical Support Document: Social Cost of Carbon, Methane, and Nitrous Oxide, https://www.whitehouse.gov/wp-content/uploads/2021/02/TechnicalSupportDocument_SocialCostofCarbonMethaneNitrousOxide.pdf.

[7] IPCC (2018), “Summary for Policymakers SPM”, Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change,, p. 32, https://www.ipcc.ch/site/assets/uploads/sites/2/2019/05/SR15_SPM_version_report_LR.pdf.

[15] Ministry of Energy of the Republic of Lithuania (2020), Hydrogen Platform, https://enmin.lrv.lt/en/sectoral-policy/hydrogen-platform (accessed on 26 September 2022).

[21] Nordic Innovation (2022), About Nordic Innovation.

[1] OECD (2022), OECD Economic Surveys: Lithuania 2022, OECD Publishing, Paris, https://doi.org/10.1787/0829329f-en.

[22] OECD (2022), “Venture capital investments”, Structural and Demographic Business Statistics (database), https://doi.org/10.1787/60395228-en (accessed on 19 October 2022).

[10] OECD (2021), OECD Environmental Performance Reviews: Lithuania 2021, OECD Environmental Performance Reviews, OECD Publishing, Paris, https://doi.org/10.1787/48d82b17-en.

[11] OECD (2021), R&D Tax Incentives : Lithuania, 2021, http://oe.cd/rdtax.

[14] OECD (2020), OECD Economic Surveys: Lithuania 2020, OECD Publishing, Paris, https://doi.org/10.1787/62663b1d-en.

[3] Paltsev, S. et al. (2021), “Hard-to-Abate Sectors: The role of industrial carbon capture and storage (CCS) in emission mitigation”, Applied Energy, Vol. 300, p. 117322, https://doi.org/10.1016/j.apenergy.2021.117322.

[5] Vass, M. and K. Elofsson (2016), “Is forest carbon sequestration at the expense of bioenergy and forest products cost-efficient in EU climate policy to 2050?”, Journal of Forest Economics, Vol. 24, pp. 82-105, https://doi.org/10.1016/j.jfe.2016.04.002.


← 1. For example see the IEA’s Roadmap to Net-Zero Emissions, Table 2.2, Pg. 53 (IEA, 2021[4]).

← 2. The economic and climate feasibility of the corresponding technologies is still not clear. For instance, a recent report of the Institute of Energy Economics and Financial Analysis (IEEFA, 2022) finds very limited success of the CCS technologies based on the assessment of 13 flagship cases (10 in operation, two that have failed and one that has been suspended) comprising about 55% of the total nominal capture capacity operating worldwide have been reviewed in detail.

← 3. Current high gas prices due to gas supply shortages are strengthening the economic case for green hydrogen. The same is true of strong carbon price signals imposed on gas. Combined with technology adoption, such price signals have been found to be very effective at promoting technology adoption.

← 4. The ETS2 remains under discussion. However, some EU countries have already started preparing for its potential launch, with Germany for example introducing a national ETS for the transport and buildings sectors to then be integrated in the ETS2 when/should it arrive. Lithuania could similarly do so, preparing for the ETS2 before 2030, and then looking beyond to possible economy wide pricing frameworks between 2030 and 2050.

Metadata, Legal and Rights

This document, as well as any data and map included herein, are without prejudice to the status of or sovereignty over any territory, to the delimitation of international frontiers and boundaries and to the name of any territory, city or area. Extracts from publications may be subject to additional disclaimers, which are set out in the complete version of the publication, available at the link provided.

© OECD 2023

The use of this work, whether digital or print, is governed by the Terms and Conditions to be found at https://www.oecd.org/termsandconditions.