metadata: authors: 0: author: Schoder,Christian
1: author: Tercioglu,Remzi Baris
count: Türkiye
docna: 0: docna: A Climate-Fiscal Policy Mix to Achieve
            Türkiye’s Net-Zero Ambition under Feasibility Constraints
docty: Policy Research Working Paper
abstracts: cdata!: This paper employs an estimated dynamic
            stochastic open-economy macro framework to identify policy
            interventions that allow T&#xfc;rkiye to achieve
            net-zero emissions by 2053 while respecting important
            feasibility constraints such as fiscal consolidation and
            sovereign debt stability as well as compensation of
            low-income households. The policy mix includes a carbon tax,
            a renewable energy subsidy, transfer payments, public
            infrastructure investments, a bad bank for stranded fossil
            fuel assets, and the phase-out of fossil fuel subsidies and
            public investment. Although the proposed policy package has
            only moderate effects on gross domestic product, transition
            risks involve declining exports and fossil asset stranding.
            The paper highlights the importance of transparent policy
            communication and a credible commitment to the net-zero
            agenda to ensure an orderly transition. Improving the rule
            of law and access to green finance considerably support the
            private sector-led low-carbon transition.
display_title: A Climate-Fiscal Policy Mix to
            Achieve Türkiye’s Net-Zero Ambition under Feasibility Constraints
listing_relative_url: /research/2023/08/34149710/climate-fiscal-policy-mix-achieve-türkiye’s-net-zero-ambition-under-feasibility-constraints
topicv3: Structural Policy and Reform m1326450 1130,Climate Change Mitigation m1327079 579,Fiscal Policy m1327144 445,Climate Finance m1327548 1847,Debt and Fiscal Sustainability m1327677 456
publishtoextweb_dt: 2023-08-23T00:00:00Z
dois: 10.1596/1813-9450-10551
author_keys: 0: author_key: 1925719
1: author_key: 2008028
model_extractions: Policy Research Working Paper 10551 A Climate-Fiscal Policy Mix to Achieve Türkiye’s Net-Zero Ambition under Feasibility Constraints _Christian Schoder Remzi Baris Tercioglu_ Equitable Growth, Finance and Institutions Practice Group & Macroeconomics, Trade and Investment Global Practice August 2023 A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.
Policy Research Working Paper 10551

# **Abstract**

This paper employs an estimated dynamic stochastic open-economy macro framework to identify policy interventions that allow Türkiye to achieve net-zero emissions by 2053 while respecting important feasibility constraints such as fiscal consolidation and sovereign debt stability as well as compensation of low-income households. The policy mix includes a carbon tax, a renewable energy subsidy, transfer payments, public infrastructure investments, a bad bank for stranded fossil fuel assets, and the phase-out of fossil fuel subsidies and public investment. Although the proposed policy package has only moderate effects on gross domestic product, transition risks involve declining exports and fossil asset stranding. The paper highlights the importance of transparent policy communication and a credible commitment to the net-zero agenda to ensure an orderly transition. Improving the rule of law and access to green finance considerably support the private sector-led low-carbon transition.

This paper is a product of the Office of the Chief Economist, Equitable Growth, Finance and Institutions Practice Group and the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at cschoder@worldbank.org and rtercioglu@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
A Climate-Fiscal Policy Mix to Achieve T ̈urkiye’s Net-Zero Ambition under Feasibility Constraints<sup>∗</sup> Christian Schoder<sup>†</sup> Remzi Baris Tercioglu<sup>‡</sup> World Bank World Bank

**Keywords:** Climate-fiscal policy, net-zero pathway, low-carbon transition, Bayesian estimation, T ̈urkiye

**JEL Classification:** Q43, Q48

> ∗We would like to thank J ́anos Varga, Charl Jooste, and Florent McIsaac for valuable comments and suggestions.

> †E-Mail: cschoder@worldbank.org

> ‡E-Mail: rtercioglu@worldbank.org
# **1 Introduction**

T ̈urkiye faces several climate-related risks. Recent extreme weather events, including floods and wildfires, have exposed the country’s vulnerability to climate change. T ̈urkiye is highly dependent on fossil fuel imports which amount to 87% of its total primary energy supply.<sup>1</sup> This poses significant risks for energy security as the oil and gas market turmoils after the Russian invasion of Ukraine have forcefully illustrated. Moreover, the European Union’s ambitious low-carbon agenda will have considerable repercussions for the primary export market for Turkish manufacturing, which is more carbon-intensive than its European counterpart. In light of these climate-related risks, T ̈urkiye has increased its climate ambition in recent years. T ̈urkiye’s renewable energy capacity grew by more than 50% since 2016 (IEA 2021). As a top-20 emitter, T ̈urkiye submitted its Intended Nationally Determined Contribution in 2015, setting the target to reduce greenhouse gas emissions by 21% relative to the business-as-usual (BAU) benchmark by 2030. In 2021, the country ratified the Paris Agreement and announced the net-zero target of 2053.

T ̈urkiye’s development progress since the early 2000s has been remarkable. Per-capita GDP has more than tripled within 20 years, making T ̈urkiye the 19th largest economy in the world. Since 2015 the growth dynamics have cooled down considerably. Unemployment has exceeded the 10% mark ever since 2015 despite a low labor force participation rate, especially among women, and a significant informal sector (World Bank 2023). While formally independent, the central bank’s monetary policy is subject to severe government interference, which let inflation explode to a two-decade-high value of 85.5% in October 2022 (TurkStat 2022).

Addressing structural deficits is critical for the low-carbon transition as they constrain the effectiveness of climate policy in mobilizing private finance. Structural deficits of the Turkish economy include low credibility of policy commitments, high costs of contract enforcement combined with low business confidence in public institutions, and uneven access to bank finance (OECD 2021, G ̈urkaynak et al. 2022). Dechezleprˆetre et al. (2022) provide compelling evidence that transparency and commitment raise acceptance of climate policy. Regarding the business climate, a weak rule of law drives up equity risk premia making capital-intensive, productivity-enhancing technology less appealing than labor-intensive, low-value-added activities. Finance constraints further impede private investment in low-carbon technology and limit the power of market signals.

The present paper aims to project the paths of climate-fiscal policy interventions that allow T ̈urkiye to grow along the 2053 net-zero pathway while honoring significant feasibility constraints. The policy instruments include a carbon tax, a renewable energy subsidy, transfer payments, public infrastructure investment, a bad bank for stranded assets, and the phase-out of fossil fuel subsidies and public investment. To study the role of supporting structural policies in the low-carbon transition, we explore how the macroeconomic effects

> 1See release 055 of the GLORIA Global Environmentally-Extended Multi-Region Input-Output (MRIO) database constructed in the Global MRIO Lab (Lenzen et al. 2017, 2022).

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of the policy interventions differ in counterfactual scenarios of a fully credible commitment to net zero, an improved rule of law, and facilitated access to green bank finance.

We address these research questions using an empirical dynamic stochastic open-economy macro framework similar to the European Commission’s _E-QUEST_ model presented by Varga et al. (2022) but shifts the focus on an emerging-market context.<sup>2</sup> The model is designed for data-driven policy analysis and the simulation of low-carbon transitions. Renewable and fossil public investment can go into energy production or infrastructure. Infrastructure improves the marginal product of private capital but only up to a satiation point. To allow the model to capture deep transformations such as the low-carbon transition, it includes two critical features: a _learning-by-doing externality_ in the generation of renewable energy (Mercure 2012, Rubin et al. 2015); and the distinction between _short- and long-term elasticities of substitution between renewable and fossil energy_ (Guerrieri et al. 2008). On the labor market, the model allows for the possibility of _dis-equilibrium unemployment_ at the steady state, which provides flexibility regarding model closures and minimizes the impact of theory on the results for the long run.<sup>3</sup> The model features various financial instruments, bank lending constraints, and a financial accelerator mechanism along the lines of Kiyotaki and Moore (1997). The interested reader is referred to Appendix A, which presents the details of the model. We fit the model to Turkish data using standard Bayesian techniques as outlined in Herbst and Schorfheide (2015). The dataset covers the period from 2000Q2 to 2022Q3 and includes 31 time series in mixed frequency. Appendix B reports the details of the model estimation. We run all estimations and simulations in _Dynare_ 5.3 (Adjemian et al. 2022).

The simulation results suggest that T ̈urkiye needs to employ a dynamic policy mix to achieve the steady decline of 12 million (mn) tonnes of CO2 emissions per year required for net zero by 2053 while observing the following constraints: (a) keeping sovereign debt at 34% of trend GDP, (b) fully compensating low-income households for consumption lost relative to BAU, (c) public financing of necessary infrastructure investments which amounts to an average of 2 billion (bn) of 2015 USD ($) per year (World Bank 2022), and (d) buying up private stranded assets.

Under these constraints and the phase-out of fossil subsidies and public investment, our results suggest that T ̈urkiye should kick off the decarbonization of its economy with a carbon or fuel tax reaching $70 per tonne of CO2 by 2027. By then, public debt will be stabilized at the desired level. The carbon tax will then generate excess revenues that can be recycled as renewable energy subsidies to minimize the rise in energy prices. Renewable subsidies of up $1400 per tonne of oil equivalent (toe) trigger a renewable energy boom reinforced by endogenous productivity gains from scaling up renewable generation. As fossil energy

> 2Our modeling framwork also connects us to the literature on optimal environmental policy in a general equilibrium setting (Fischer and Springborn 2011, Annicchiarico and Di Dio 2015, Dissou and Karnizova 2016, van der Ploeg and Rezai 2021).

> 3Dis-equilibrium unemployment is known, among many others, from Dynamic Stochastic Dis-Equilibrium models (Schoder 2017, 2020) as well as dis-equilibrium theory (Barro and Grossman 1971, Chiarella et al. 2005).

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consumption is reduced, the carbon tax will find it increasingly challenging to generate fiscal revenues. By 2037, debt stability will require the government to phase out renewable subsidies completely and adjust another instrument to keep public debt stable. In our simulation, government consumption declines by 1.8%-points of trend GDP between 2037 and 2053. Over the projection period, the government will also increase the required spending on renewable infrastructure and phase out investment in fossil energy production and infrastructure. Finally, transfer payments to low-income households increase by 1%-point of trend GDP over the projection period.

This policy package is projected to mobilize a considerable amount of green finance. Private investment in renewable energy increases from 0.2% of trend GDP in 2022 to almost 4% by 2053. Renewable energy consumption will increase from around 20 million toe in 2022 to 420 million by 2053. This is 460% higher than the baseline projection.

The projected impacts on GDP are moderate. During the fiscal consolidation phase, the carbon tax will briefly reduce GDP, peaking in 2027 with a GDP loss of 0.7% compared to the no-policy baseline. Between 2031 and 2037, GDP is projected to exceed the baseline slightly. By 2037, the declining government consumption will increasingly reduce GDP, reaching a projected loss of 1% compared to the baseline in 2053. Regarding welfare, lowincome households are broadly indifferent to the transition as long as they receive sufficient compensation for the consumption lost relative to the baseline. In contrast, high-income households considerably benefit from the transition as the renewable capital assets created translate into higher household wealth.

Despite the overall moderate GDP effects and positive welfare effects, there are transition risks: While the low-carbon transition eliminates the dependence on fossil fuel imports, core goods exports will decline by up to 11% compared to the baseline. Moreover, there is a manageable but non-negligible risk of fossil asset stranding. In our simulation, stranded assets amount to almost 0.1% of trend GDP in 2053. Delaying the transition until 2030 will considerably increase the risk of stranded assets: 0.65% of trend GDP in 2043.

Our simulations stress the positive role of complementary policy. Consistent with Nemet et al. (2017), Battiston et al. (2021), Diluiso et al. (2021), Campiglio et al. (2023), we show that transparent policy communication and a credible commitment to the net-zero agenda benefit the low-carbon transition. It allows the private sector to adjust ahead of time, reducing economic friction. A lack of commitment and the expectation of policy reversals make the transition considerably more costly. Moreover, we find that implementing institutional reforms that improve the rule of law and thereby reduce equity risk premia to the average of China, India, and South Africa drastically improve the macro-economic outlook of the low-carbon transition, with GDP peaking at 2.6% above the no-policy baseline. Finally, reforms that facilitate access to green finance boost renewable energy investment and generation. Overall, complementary structural reforms reduce the required climate-policy interventions and improve the macroeconomic consequences of the low-carbon transition.

The present paper seeks to contribute to the literature on the macroeconomic implications of climate-fiscal policy in an emerging-market context. World Bank (2022) estimates the ad4
ditional investment and renewable energy capacity needed for achieving the net-zero pathway in T ̈urkiye. Hallegatte et al. (2023) impose these investment and capacity needs in a Computational General Equilibrium (CGE) and a macro-structural model and study the sectoral and macroeconomic repercussions under various scenarios regarding private/public financing, crowding-out of investment, and labor-market frictions. We seek to complement these contributions by identifying the dynamic climate-fiscal policy mix needed for net zero by 2053 while observing critical feasibility constraints. The integrated framework used projects endogenously investment needs, finance, and, to some extent, productivity gains. Exploiting the value-added of a structural model with inter-temporal decision-making, we seek to offer additional insights on the roles of private sector incentives, policy credibility, and structural reform in supporting the low-carbon transition. The model used in this paper is similar to the _E-QUEST_ and _GM_ models of the European Commission (Varga et al. 2022, Albonico et al. 2019) and the _New Area Wide Model_ of the ECB (Christoffel et al. 2008) but puts the focus on market imperfections relevant for middle-income countries. Our framework is also related to global models such as the _Global Macroeconomic Model of the Energy Transition (GMMET)_ and the _Integrated Policy Framework_ developed by the IMF (Benjamin Carton and Voigts 2022, Vitek et al. 2022) as well as _G-Cubed_ developed by McKibbin and Wilcoxen (1998), but it is considerably smaller in scale to allow for Bayesian estimation.

Since estimating the model was a priority, data availability and computational capacity imposed restrictions on its size. Hence, several qualifications are to be addressed in future research: First, while the model comprehensively tracks macroeconomic interactions and includes the critical climate-fiscal policy instruments, it abstracts from sector-specific decarbonization challenges and features only emissions that arise from fossil fuel combustion. As outlined in World Bank (2022), additional policy interventions such as norms and regulations may be required to improve energy efficiency and address other emissions sources such as industrial processes and land use. Second, we assume that a well-designed energy market regulation is in place such that renewable energy is profitable despite the significant up-front capital costs and volatility in energy prices. This includes complete future markets, access to the power grid, and the absence of excessive administrative obstacles in commissioning renewable energy generation facilities. Finally, the relative costs and benefits of the low-carbon transition depend on the choice of the baseline scenario. The present paper uses the BAU scenario as the baseline for T ̈urkiye. This ignores the Carbon Border Adjustment Mechanism the European Union intends to impose on imports. It also neglects potential technology spill-overs from other decarbonizing parts of the world.

The remainder of the paper proceeds as follows. Section 2 provides the intuition of the empirical macro framework. It motivates modeling and calibration choices for T ̈urkiye. Appendix A reports the model in detail. Section 3 discusses the Bayesian estimation results. Appendix B details the data sources, parameter calibration, and estimation. Section 4 identifies a policy mix projected to set T ̈urkiye on track to net zero and studies its macroeconomic implications. It also investigates how policy credibility and structural reforms can support the low-carbon transition. Section 5 concludes the paper.

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# **2 An empirical macro-economic framework for T ̈urkiye**

The underlying model is designed for data-driven climate policy analysis in T ̈urkiye.<sup>4</sup> It is calibrated and estimated using quarterly and annual data covering the period from 2000 to 2022. It comprises a high-skilled household sector, a low-skilled household sector, a core good sector, an energy sector, a public sector, and the Rest of the World (RW). Households use energy for consumption (on average around 48% of total final energy consumption) and firms for non-energy production (around 52%), which can be fuel, heat, or electricity. The energy sector consists of renewable energy (with a sample average of 0.9% of GDP and 17% of total energy consumption) and fossil energy (6.2% of GDP and 83% of total energy consumption). A carbon mining sector (3.4% of GDP) extracts and imports carbon (such as coal, crude oil, and gas) which is an input to fossil energy production. CO2 emissions arise from the combustion of fossil fuels and have fluctuated around 0.46 kilogram (kg) per 1 constant 2015 USD of GDP since the early 2000s.<sup>5</sup> Households, firms, the government, and the RW demand and supply finance, respectively. The model features various financial instruments. Households are assumed to be net creditors holding their wealth as bonds, deposits, and private equity. Firms are net debtors financing investments by borrowing from households and issuing equity. The government finances its deficit by issuing bonds. The RW is a net creditor from T ̈urkiye’s perspective. The public, corporate, and RW’s net debt-GDP ratios are moderate on average: around 36%, 57%, and -35% of GDP, respectively. Yet, these debt levels have increased sharply in recent years (OECD 2022). Despite a low net debt vis-a-vis the RW, T ̈urkiye imports around 87% of its total primary energy supply.

## **2.1 High- and low-skilled labor households**

Capturing to some extent how the low-carbon transition affects households differently, the model features _high-skilled labor_ H-households (50% of the labor force) and liquidity-constrained _low-skilled labor_ L-households (50% of the labor force). We assume that L-households hold no financial wealth. In T ̈urkiye, the 50% top-income households account for two-thirds of all consumption expenditures. Hence, we assume H-households consume twice as much as L-households while providing the same labor hours. The former receive capital income as well as a higher real wage. The overall wage share in total income (household income and retained earnings) is around 50%. L-households also receive transfer payments from the government (around 7.3% of GDP).

H-households own the firms and domestic carbon mines (oil, coal, and natural gas), provide high-skilled labor services, and save. Because a specific household’s labor variety is unique, each H-household has market power. Wage adjustment costs allow the model to capture the persistence in high-skilled wage contracts as observed in empirical data (Taylor

> 4Appendix A reports the model in detail.

> 5Appendix B reports the model calibration and lists the respective data sources and references.

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1980, 2016). Taking as given the demand function for their labor variety as well as the budget constraint and facing Rotemberg (1982) wage adjustment costs, H-households choose inter-temporal paths for consumption, governments bonds, domestic deposits, international bonds, firm equity shares, and the wage for their labor variety to maximize inter-temporal utility where per-period utility depends on consumption, labor supply, and wealth. Utility from consumption is subject to external habit formation, which helps the model capture the hump-shaped response of consumption to macro shocks as identified in empirical studies (Christiano et al. 2005).

In contrast to that, the L-household’s problem is static. This is because of the assumption that L-households are _hand-to-mouth consumers_ . They do not receive enough income to be able to save. Hence, they hold no wealth and cannot smooth consumption over time. This assumption is empirically accurate and helps the model capture the empirical co-movement of government spending and private consumption (Christiano et al. 2010).

Including wealth in the utility function allows the model to capture precautionary saving motives even at a first-order linear approximation (Michaillat and Saez 2021). To see the implications of this assumption, consider the Euler equation (neglecting productivity growth for the sake of clarity), where _λH,t_ equals marginal consumption utility, _ψA_ is the wealth utility scaling parameter, _RB,t_ is the gross bond return, and Π _Y,t_ is the gross inflation rate. Because saving yields utility by itself, the utility from wealth drives a wedge between the value of a unit of income in _t_ to the expected value that this unit would yield if saved and consumed in _t_ + 1.<sup>6</sup> A critical implication of this modeling choice for T ̈urkiye is that the determinacy of the rationalexpectation equilibrium does not necessitate the _Taylor principle_ : an inflation elasticity of the policy rate greater than one (Schoder 2020).

While government bonds and deposits are assumed to be risk-free, equity shares or international bonds are not. These assets are associated with financial intermediation costs which depend on the net-debt position of the issuer and have the interpretation of risk premia (Christoffel et al. 2008). The equity risk premium on top of risk-free government bonds has been around 5.2% in T ̈urkiye<sup>7</sup> . The international risk premium is assumed to increase with the net-debt position of the RW vis-a-vis T ̈urkiye. With a persistent Turkish current account deficit, the RW has accumulated net assets of around 40% of Turkish GDP (World Bank 2023). Hence, the international risk premium is negative.

> 6As Schoder (2020) explains in detail, this breaks the so-called _classical dichotomy_ at the steady state because it links consumption to the real interest rate. In contrast to conventional Dynamic Stochastic General Equilibrium (DSGE) models, consumption does not drop out from the Euler equation at the steady state. The _natural_ rate of interest is no longer uniquely determined by the Euler equation alone but linked to the real economy through high-skilled consumption.

> 7We derive the risk premium as the difference between stock market returns and 10-year government bond interest rates.

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Regarding the compensation of labor, the high-skilled labor market is standard: H- households have market power and set the wage rate such that the marginal disutility from supplying another hour of labor equals the marginal utility of the additional consumption which that hour of work allows for. The high-skilled labor market clears. L-households choose their supply of low-skilled hours such that the marginal disutility of another hour equals the marginal utility of the corresponding consumption. While H-households set their wage, L-households take their wage as given.

One core assumption of the model is that the low-skilled nominal wage is administered or partly exogenous, as in Benigno and Fornaro (2018). This is equivalent to the conventional assumption in New Keynesian Dynamic Stochastic General Equilibrium (DSGE) models that the nominal interest rate is a policy variable. In particular, the low-skilled wage rate is determined by a Phillips curve relationship. Low-skilled wage inflation responds to employment (to capture the effects of labor-market tightening on wage formation) and to the price inflation rate (to capture wage indexing). This assumption generates dis-equilibrium unemployment (around 10%) in the low-skilled segment of the labor market. With idle labor, the economy can swiftly respond to demand shocks by adjusting low-skilled employment, given constraints and costs imposed by input substitution technologies. Schoder (2020) provides a detailed review of the macroeconomics of disequilibrium models. It is critical to note that an estimated parameter determines where the model settles between two extremes: perfectly inelastic low-skilled wage (constant wage or pure Keynesian case) and perfectly elastic low-skilled wage (labor market clearing or pure Walrasian case).

# **2.2 CES aggregators**

The production sector is decomposed into multiple layers along the value chain, as illustrated in Figure 1. The economy is populated by four different representative Constant Elasticity of Substitution (CES) aggregators: Retailers (Y-firms), wholesale firms (W-firms), energy firms (E-firms), and labor firms (L-firms). Each combines two different input goods into an output good using a CES aggregator without generating value added. Moreover, the economy features three value-added sectors: core goods firms, renewable energy firms, and fossil energy firms. They use capital and labor as production inputs. Finally, a carbon mining sector extracts carbon without inputs or costs and sells it on a world market. The value added in this sector is matched by rents split between the household and RW sectors.

Production functions are of the CES type. The advantage of this functional form is that only two parameters (input share and elasticity of substitution) characterize the technology. Moreover, extreme cases such as perfect complements or perfect substitutes are nested. At every node in Figure 1 an elasticity controls the degree by which inputs can be substituted with each other for given price changes. The more rigid the production structure or the higher the cost at which input substitutes can be produced, the greater will be the extent by which cost increases of a particular input are passed through the value chain and translate into overall inflation.

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<!-- Start of picture text --> tmp Final good<br>Exported core good Wholesaletmp good Energy service<br>Domestictmpcore good Imported core good<br>Core value-added Energy service tmp<br>Core capital service Labor service Labor service<br>Core private capital Core public capital High-skilled labor Low-skilled labor<br>Energy service<br>Renewable energy Fossil energy<br>Rnewable value-added Fossil value-added Carbon<br>Renewable cap- Labor service Fossil capital service Labor service<br>ital service<br>Renewable pri-<br>vate capital and Renewable pub- Fossil private capital Fossil public in-<br>lic infrastructure and public generation frastructure<br>public generation<br><!-- End of picture text --> Figure 1: Schematic representation of the supply side of the model.

The model features the following aggregators: At the end of the value chain, Y-firms produce the homogeneous final good used for private and public consumption as well as investment by combining wholesale goods and energy.

W-firms produce wholesale goods and sell them to the Y-firms. As inputs, they combine domestic and international core goods. Note that, apart from carbon imports, only core 9
goods are traded which is a strong simplification of the complexity of international trade. Yet, it allows the model to capture the _domestic-demand_ and _competitiveness_ transmission channels of domestic and international shocks. Non-carbon imports in T ̈urkiye amount to around 22.8% of GDP (and to 26% including coal, crude oil, and gas).

E-firms produce an energy service (with a value of around 7.1% of GDP) using renewable energy (with a share of 12%) and fossil energy (with a share of 88%). The energy service is input to two sectors: intermediate core Z-good production (around 52% of total energy consumption or 3.7% of GDP) and final Y-good production (around 48% of total energy consumption or 3.4% of GDP).<sup>8</sup> E-firms pay a fuel tax on their fossil energy input (1.4 % of GDP) and receive fossil energy subsidy (0.4 % of GDP). They also receive a subsidy for each unit of renewable energy input. Finally, we allow E-firms to accrue zero-mean windfall profits which are distributed to the H-households.

Given the transformational nature of the low-carbon transition, a simple CES production structure is too restrictive for the energy sector. Similar to Guerrieri et al. (2008), we, therefore, introduce input adjustment costs which add short-run friction to the long-run elasticity of substitution between renewable and fossil energy.

L-firms combine high-skilled and low-skilled labor (both measured in hours) to produce a labor service. High-skilled households receive, on average, 61% of the total wage bill, and low-skilled labor the remaining 39%. The labor service is sold to all sectors that use labor inputs for production: core goods (98.64%), renewable energy (0.23%), and fossil energy (1.13%).

# **2.3 Core good producers**

Core goods are exported or sold domestically to the W-firms. They operate under monopolistic competition and, therefore, have price-setting power. They face standard nominal and real rigidities: Adjusting the output price and investment is costly. Utilizing the installed capital stock beyond the technological optimum accrues costs at an increasing rate.

Production is subject to a hierarchical CES structure. At the top of the hierarchy, core goods are produced by combining a capital-labor composite and energy services (purchased from E-firms and amounting to 3.7% of core good revenues). The capital-labor composite is produced by combining a capital service and labor (purchased from L-firms and amounting to 45.6% of the revenues including payroll taxes). The capital service is produced using private and public physical capital measured in units of the final Y-good. While the public capital stock is a free input to production, the private capital expenditures amount to 22.5% of the revenues.

About 3.7% of the revenues are used for purchasing energy. The compensation of em-

> 8In reality, 46% of the Turkish energy output is used by the energy sector itself as an input. For the sake of simplicity, we neglect this share and only consider the energy used for final consumption and non-energy production.

10
ployees including payroll taxes amounts to 45.6% of the revenues. The remaining 50.7% of the revenues constitute the gross operating surplus which is used for capital expenditures (22.5% of revenues), corporate income tax (1.7% of revenues), and dividends plus interest payments (26.5% of revenues).

Similar to Albonico et al. (2019), we assume capital is firm-specific rather than traded on a spot market. Hence, capital is not rented out to the most productive enterprise but sits with the firm. Capital can only be changed by investing and disinvesting. This adds critical frictions to the low-carbon transition and creates the problem of _asset stranding_ in the fossil energy sector.

Borrowing constraints are crucial. The high-skilled household’s problem has shown that investors ask for an equity risk premium over the risk-free rate. Since households own the firms, this has implications for the firm’s preferred source of finance. Because of higher risks, the return on equity _RS,t_ is higher than the interest rate on corporate borrowing _RB,t_ . Hence, the owners of the firms prefer to boost the cash flow in the short run by excessive borrowing. This is because future profits are discounted by a rate _RS,t_ (with discount factor 1 _/RS,t_ ) that is higher than the rate of interest for new borrowing _RB,t_ . In other words, the benefit of an additional unit of cash flow today exceeds the value of the debt repayment due tomorrow. To maximize the cash flow, households ask the firms to exploit all available credit lines until borrowing constraints become binding. Following Gerali et al. (2010), we assume that the debt obligation in _t_ + 1 cannot exceed the a given fraction _λ_ of the expected value of capital in _t_ + 1 which serves as the collateral. That is, we impose in the firm’s profit maximization problem where _RB,t_ is the gross interest rate for corporate borrowing, _BZ,t_ is corporate debt, _λ_ is a constant, _PY,t_ is the price level, _QZ,t_ is Tobin’s _q_ or the value of capital, _δ_ is the rate of capital depreciation, and _KPZ,t_ is the private capital stock in the core good sector _Z_ . This introduces to our model a financial-accelerator mechanism along the lines of Kiyotaki and Moore (1997): Borrowing constraints are pro-cyclical as they vary with capital valuation. _λ_ = 33 _._ 8 is calibrated to achieve the empirical corporate debtGDP ratio of 57% at the steady state. The optimality condition for end-of-period corporate borrowing implies where _μZ,t_ is the shadow price of one additional borrowing unit. The equation collapses to _μZ,t_ = 0 if the returns on equity and borrowing are equal, _RS,t_ = _RB,t_ . In this special case, the firm’s financial structure is irrelevant from the household’s perspective. With a positive equity risk premium, however, _RS,t > RB,t_ and _μZ,t >_ 0. External finance is preferred over internal finance. The end-of-period borrowing constraints are always binding.

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# **2.4 Renewable energy, fossil energy, and carbon mining**

Renewable energy firms (R-firms) are broadly symmetric to Z-firms, with a few important exceptions: They produce renewable energy using capital and labor only, without energy inputs. In contrast to Z-firms, R-firms operate under perfect competition. Nevertheless, R-firms accrue profits because of the assumption of firm-specific capital. Consistent with a large body of literature on renewable _learning rates_ , total factor productivity is subject to a _learning-by-doing_ externality which increases with renewable energy production (Mercure 2012, Rubin et al. 2015, Way et al. 2022).

Fossil energy firms (F-firms) are symmetric to R-firms with one crucial difference: The underlying primary energy source – carbon – is a costly input to energy production. We allow some substitution between the capital-labor composite and carbon to capture the switching between coal, oil, and gas. The corresponding elasticity of substitution is estimated. As with renewable sources, we assume that the supply of carbon is perfectly elastic.

We distinguish between public investment in energy production and infrastructure to capture the critical role of adequate green infrastructure as emphasized by Way et al. (2022). The capital stock associated with the former adds to the private capital stock as a perfect substitute. Hence, increasing public investment in energy production tends to crowd out private capital. Public infrastructure, however, complements production capital and tends to crowd in private investment as it increases its marginal return. Yet, there are limits: We impose a satiation level of public infrastructure beyond which additional public investment does not increase private capital returns.

The cost structures of renewable and fossil energy production are quite different since R-firms are more capital-intensive than F-firms: The compensation of employees, including payroll taxes as a share of total revenues, amount to 12.7% and 11.9%, respectively. The – F-sector spends around 71% of its revenues on raw coal, oil, and gas the primary energy sources. The gross operating surplus in the R-sector is sizable (88.3% of revenues) compared to F-sector (17.1% of revenues). Yet, the bulk of it is needed for purchasing capital goods (58.8% of revenues in the R-sector vs. 8.2% in the F-sector). The corporate income tax shares are 1.9% and 0.6%, respectively. Differences in the dividend and interest payment shares reflect the different capital intensities: 26.6% and 10.4%, respectively.

For the sake of simplicity, we assume one global market for carbon sourced by domestic and international carbon mining. Carbon mines are owned by high-skilled households (13%) and the RW (87%). Any demand is fully accommodated at constant shares and a given exogenous world-market price denominated in USD. This implies that the domestic use of domestically extracted carbon is subjected to exchange rate fluctuations. We assume that domestically produced and imported carbon is used only by the fossil energy sector.<sup>9</sup> We further assume that carbon can be extracted without capital or labor input. Hence, carbon sales constitute pure rents.

> 9In reality, about 14% of the carbon mining output goes into sectors different from fossil energy.

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CO2 emissions are linked to the combustion of fossil fuels. There is a direct relationship between carbon input and emissions. The model captures the fact that different coal, oil, and gas have different carbon intensities by allowing the F-firms to substitute carbon with capital and labor, thereby adjusting the carbon intensity of fossil energy (for instance, in response to an upstream carbon tax).

# **2.5 The public sector and monetary policy**

The public sector features a rich set of revenue and expenditure instruments. The revenue side comprises final consumption taxes (on average around 11% of GDP), taxes on the H- households’ stock of wealth (1% of GDP), taxes on capital income including capital gains, high- and low-skilled labor income taxes (4% of GDP), payroll taxes (7% of GDP), corporate income taxes (2% of GDP), fuel taxes (1.4% of GDP) and upstream carbon taxes (which T ̈urkiye does not currently have).

The expenditure side of fiscal policy consists of public consumption of the final good (on average around 14% of GDP) and public investment in the core good sector (3.7% of GDP) as well as in the renewable and fossil energy production (0.1% and 0.1% of GDP, respectively) and infrastructure (0.1% and 0.1% of GDP, respectively). In the short run, public consumption and investment respond endogenously to the final output to capture automatic stabilizers. Finally, the government grants fossil and renewable energy subsidies to E-firms and transfers to L-households.

A possible deficit is financed by issuing government bonds. Even though government bonds (and domestic deposits) are assumed to be risk-free, the T ̈urkiye short-term bond return has exceeded the policy rate by around 0.4%. We model this as a _flight-to-safety_ premium as in Smets and Wouters (2003).

The monetary authority sets the policy rate according to the Taylor rule. Given an average inflation rate of around 8%, over 2010-2016, which is close to the central bank’s target of 5%, the corresponding policy rate consistent with a 10% long-term unemployment rate is around 8.8%. The simulation will adjust these numbers to reflect the current monetary policy stance.

Over the business cycle, the policy rate responds to core inflation and the output gap. In the model, inflation targeting and exchange rate targeting are observationally equivalent as the uncovered interest rate parity condition implies that inflation and exchange rates move together. Note further that the Taylor principle does not necessarily hold in the model due to dis-equilibrium unemployment and steady-state wealth. With a sufficiently low labormarket sensitivity of low-skilled wages, the inflation sensitivity of the policy rate may be smaller than one (Schoder 2020). Note that around 50% of Turkish wage earners receive the statuary minimum wage (DISK 2022).

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# **3 Model estimation**

Given the limited information in macroeconomic data, not all model parameters can be estimated. A few weakly identified parameters have been calibrated according to microeconomic evidence; others have direct counterparts in the data and have been calibrated accordingly. Some parameters have been restricted for specific model variables to match their empirical counterpart at the steady state. All structural parameters have been estimated using standard Bayesian techniques as outlined in Herbst and Schorfheide (2015). We exploit annual and quarterly time series for 31 observed variables over 2000Q2-2022Q3. Appendix B discusses all the details of our estimation strategy: data and sources, calibration, prior distributions, and the estimated posterior distributions. It also compares our findings to the empirical literature.

Almost all structural parameters are identified and broadly in line with the literature. A few findings are worth noting: First, we estimate the households’ risk aversion parameter lower than what C ̧ebi (2012), Smets and Wouters (2007), and Albonico et al. (2019) find for T ̈urkiye, the US, and the EU, respectively. This is unsurprising given that our model explicitly features wealth in the utility function to capture precautionary saving motives.

Second, all elasticities of substitution are identified by data except the capital-labor elasticities for renewable energy firms. We keep unidentified parameters in the estimation to consistently account for parameter uncertainty in the simulation results below.

Third, we estimate the value-added and energy elasticity as 0 _._ 75, slightly above the industry estimates range in Van der Werf (2008). This is because our core goods sector is a mixture of sectors with different degrees of energy intensity. Therefore, our elasticity is also capturing substitution between sectors.

Fourth, our estimate for the elasticity of substitution between carbon and value-added in the fossil energy sector is very low (0 _._ 05), which indicates the difficulty of substituting out carbon in fossil energy production and implicitly the low degree of substitution between fossil fuels with different carbon contents in T ̈urkiye. Note that this makes the incidence and effects of carbon and fuel taxes almost identical in our model.

Fifth, the data strongly identify the long-run elasticity of substitution between renewable and fossil energy with a posterior mean of 18 _._ 5 and a 90% High-Density Interval (HDI) of [10 _._ 1 _,_ 26 _._ 6]. As Figure 2 shows, our short-run elasticity starts from low values, covering the low elasticity case of Acemoglu et al. (2012) and estimations of Papageorgiou et al. (2017), and converges to its long-term mean in around 20 quarters.<sup>10</sup> Sixth, the employment elasticity of low-skilled wage inflation is around 1 _._ 29. Recall that

> 10Acemoglu et al. (2012) consider the value of 3 as low and 10 as a high elasticity. Papageorgiou et al. (2017) estimates the short-run elasticity of substitution between dirty and clean energy around 2 for electricity and around 3 for non-energy sectors. Argentiero et al. (2017) finds an elasticity around 1 _._ 2 for EU countries. We need to note, however, that the empirical literature is rather thin on the elasticity of substitution between clean and dirty energy.

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<!-- Start of picture text --> 20<br>10<br>0<br>0 2 4 6 8 10 12<br>Quarters<br><!-- End of picture text --> <!-- Start of picture text --> 14 16 18 20<br><!-- End of picture text -->
the end of the estimation sample spill over to the projection period. Consistent with OECD (2022), we limit the policy rate hike, which the model predicts, to capture the current monetary policy stance in T ̈urkiye. This affects the public debt trajectory, too. Hence, we adjust government consumption to maintain the initially projected path of public debt. This path matches the recent IMF (2022) forecast. In the baseline, variables converge to their steady growth path in the long run. Labor productivity is assumed to grow by 4% per year, which drives the growth of all trending variables, including baseline emissions.<sup>13</sup>

# **4.1 Dynamic multiplier effects of climate-fiscal interventions**

To identify a climate-smart policy mix under feasibility constraints, it is conducive to first study the macro implications of the core climate-fiscal policy interventions. We consider a carbon tax, a renewable energy subsidy, public investment in renewable energy generation, and public investment in renewable energy infrastructure below and above the satiation point. The policy interventions are permanent and phased in with an auto-regressive parameter of 0.8. We normalize each policy shock to 1 billion (bn) 2015 USD before any macroeconomic adjustment occurs. This corresponds to around 0.1% of 2021 GDP.

Figure 3 reports the impulse-response functions (IRFs) for critical macroeconomic indicators. First, note that while the policy impulse of $1bn is the same for each instrument, the fiscal implications differ over time. For instance, the carbon tax decreases the public deficit only by around $0.7bn. This is because the tax successfully reduces emissions and erodes its base. In contrast, raising the renewable subsidy rate increases the fiscal stimulus over time as renewable energy expands. Hence, the primary deficit slightly overshoots 1bn after ten years. Renewable infrastructure investments of $1bn cause a primary deficit of $1.5bn after ten years, assuming the satiation point is unmet. This is because of the decline in fuel tax revenues.

Second, infrastructure investment below the satiation point is highly effective in reducing emissions. This is consistent with Way et al. 2022 who argue that insufficient infrastructure investment may be a bottleneck for the low-carbon transition. Ten years after the policy change, $1bn of additional annual spending reduce emissions by around 40 million (mn) tonnes per year. By the same time, annual renewable energy production increases to around 6mn tonnes of oil equivalent (toe), and annual fossil energy production decreases by 5mn toe. In comparison, the carbon tax and the renewable energy subsidy reduce emissions only by around 6mn tonnes per year after 10 years. The carbon tax achieves this mainly by reducing the energy intensity of production and the subsidy by spurring renewable energy production. Both public investment in renewable generation and infrastructure at the sa-

> 13Note that the baseline scenario abstracts from two important developments: First, it neglects the Carbon Border Adjustment Mechanism which the European Union intends to implement on its imports. Hence, the baseline projections of Turkish exports may be too optimistic. Second, the baseline neglects the productivity spill-overs from scaling up renewable energy production in the RW. It is, therefore, likely to underestimate the baseline emission reductions.

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<!-- Start of picture text --> 2 Public primary deficit 10 Carbon emissions 7 Renewable energy production<br>6<br>1.5 0<br>5<br>1 -10<br>4<br>0.5 -20 3<br>2<br>0 -30<br>1<br>-0.5 -40<br>0<br>-1 -50 -1<br>0 10 20 30 40 0 10 20 30 40 0 10 20 30 40<br>Quarters after policy shock Quarters after policy shock Quarters after policy shock<br>Carbon tax Renewable energy subsidy Public investment in RE generation Pub. inv. in RE infrastructure (below saturation) Pub. inv. in RE infrastructure (above saturation)<br>1 Fossil energy production 6 Gross domestic product 4 Private investment in renewable energy<br>0 5 3<br>-1 4<br>2<br>-2 3<br>1<br>-3 2<br>0<br>-4 1<br>-5 0 -1<br>-6 -1 -2<br>0 10 20 30 40 0 10 20 30 40 0 10 20 30 40<br>Quarters after policy shock Quarters after policy shock Quarters after policy shock<br>Carbon tax Renewable energy subsidy Public investment in RE generation Pub. inv. in RE infrastructure (below saturation) Pub. inv. in RE infrastructure (above saturation)<br>0.2 Private investment in fossil energy 0.1 Exports 1.5 Non-oil imports<br>0 0<br>-0.2 -0.1 1<br>-0.4 -0.2<br>-0.6 -0.3 0.5<br>-0.8 -0.4<br>-1 -0.5 0<br>-1.2 -0.6<br>-1.4 -0.7 -0.5<br>0 10 20 30 40 0 10 20 30 40 0 10 20 30 40<br>Quarters after policy shock Quarters after policy shock Quarters after policy shock<br>Carbon tax Renewable energy subsidy Public investment in RE generation Pub. inv. in RE infrastructure (below saturation) Pub. inv. in RE infrastructure (above saturation)<br>0.5 Oil imports 0.2 Headline inflation 2 Energy price inflation<br>0 0<br>1<br>-0.5 -0.2<br>-1 -0.4 0<br>-1.5 -0.6 -1<br>-2 -0.8<br>-2<br>-2.5 -1<br>-3 -1.2 -3<br>0 10 20 30 40 0 10 20 30 40 0 10 20 30 40<br>Quarters after policy shock Quarters after policy shock Quarters after policy shock<br>Carbon tax Renewable energy subsidy Public investment in RE generation Pub. inv. in RE infrastructure (below saturation) Pub. inv. in RE infrastructure (above saturation)<br>Billions of 2015 USD<br>Millions of tonnes of CO2 emissions Millions of tonnes of oil equivalent<br>Billions of 2015 USD Billions of 2015 USD<br>Millions of tonnes of oil equivalent<br>Billions of 2015 USD Billions of 2015 USD Billions of 2015 USD<br>Billions of 2015 USD Percentage points Percentage points<br><!-- End of picture text --> Figure 3: The macroeconomic effects of selected permanent climate-fiscal policy interventions. Each policy shock is normalized to move 1 billion of constant 2015 USD before any macroeconomic adjustment.

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tiation point only have minor effects on CO2 emissions. For better comparability, consider the 10-year cumulative emission multiplier effects of the carbon tax, the renewable subsidy, and infrastructure investment below the satiation point. Moving $1bn over ten years reduces emissions by 4.3mn, 3.9mn, and 22.2mn tonnes, respectively. These results are highly consistent with Deleidi et al. (2020), whose panel study covering 17 countries suggests that public investment is considerably more effective in mobilizing private finance than carbon taxes or renewable subsidies.

Third, there are critical macroeconomic differences between the policy instruments. The carbon tax contracts GDP by around $0.4bn after ten years. The renewable subsidy raises GDP by around $1bn. At the margin, green infrastructure investment below the satiation point boosts GDP even by $5.6bn. These instruments’ 10-year cumulative GDP multipliers are -0.4, 0.74, and 3.37, respectively. Public investment in generation and infrastructure at the satiation point only have minor GDP effects. The economics behind these results is straightforward: The carbon tax increases energy prices and reduces private consumption and investment. The subsidy encourages private activity by lowering energy prices and stimulating the demand for renewable energy. Public infrastructure investment not only stimulates aggregate demand but also increases the marginal returns of private capital.

Forth, none of the climate-fiscal instruments considered here affects net exports considerably. Nevertheless, the composition of trade changes fundamentally, causing significant friction. Depending on the policy instrument, exports decrease by up to $0.6bn. Only public investment in renewable energy generation has no impact on exports. Infrastructure investment below the satiation point raises non-oil imports strongly by up to $1.5bn. Given an import share of fossil fuels of 87%, the responses of the oil imports mirror those of CO2 emissions. The carbon tax and the renewable subsidy reduce oil imports by up to $0.4bn. Infrastructure investment reduces oil imports by up to $2.6bn.

Fifth, to the extent that climate-fiscal policy instruments push the unemployment rate below (or above) the non-accelerating inflation rate of unemployment (NAIRU), headline inflation may settle above (or below) the inflation target. For instance, the carbon tax increases the labor intensity of production. This tightens the low-skilled labor market and translates into higher wages and, eventually, price inflation of around 0.08%-points above target. Infrastructure investment at the satiation point has the same effect. The renewable subsidy reduces the labor intensity of production, causing inflation to settle 0.1%-points below target. Infrastructure investment below the satiation lowers inflation considerably by up to 1%-point. As discussed in the previous sensitivity analysis, the central bank’s response to transition-induced deviations of inflation from the target critically determines the GDP response.

# **4.2 A climate-fiscal policy mix for net zero under constraints**

The net-zero scenario involves the following targets and constraints: Broadly consistent with (IPC 2021, World Bank 2022), we require CO2 emissions to decrease linearly from 400mn 18
<!-- Start of picture text --> Carbon tax, fossil subsidy, and<br>~ 200<br>is]<br><!-- End of picture text --> <!-- Start of picture text --> subsidy 3000 2o&<br>5<br><!-- End of picture text --> <!-- Start of picture text --> 145.! Government consumption and——low-skilledGovernment<br>—— Low-skilled<br><!-- End of picture text --> <!-- Start of picture text --> household transfers W<br>consumption (left axis)<br>household transfers (right axis)<br><!-- End of picture text --> <!-- Start of picture text --> 160<br>5<br>£ 140<br>by<br>2120<br>fa)<br>¥ 100<br>2<br>8 80<br>Pa 60 }<br>sg@ 40 |} \ NX —— Carbon<br>S 20 }i |—s ——— Fossil<br>a ><br>Q1-2025 Q1-2030 Q1-2035 Q1-2040<br><!-- End of picture text -->

<!-- Start of picture text --> 2400 6<br>6<br>2100<br>&<br>1800 §<br>.<br>1500 3<br>fal<br>208<br>900 a<br>tax (left axis) 600 %<br>energysubsidysubsidy(eft axis)(right axis) 300 a&<br>o &<br>Q1-2045 Q1-2050 oe<br><!-- End of picture text --> <!-- Start of picture text --> a 13.5<br>a7<br>10)<br>Z<br>o 13<br>5<br>6<br>S125<br>12<br>1s<br>Q1-2025 Q1-2030 Q1-2035 Q1-2040<br><!-- End of picture text --> <!-- Start of picture text --> 90<br>a<br>(C)<br>Zz<br>8 oO<br>5<br>3<br>78<br>6<br>5<br>Q1-2045 Q1-2050<br><!-- End of picture text --> <!-- Start of picture text --> Public investment in fossil and renewable<br>02<br>0.18 ——— Fossil<br><!-- End of picture text --> <!-- Start of picture text --> infrastructure<br>infrastructureinfrastructureinvestment investment<br><!-- End of picture text --> <!-- Start of picture text --> 0 Public investment in fossil energy production and<br>0.1 | ———~ Public Bad-bankinvestment purchasesin offossil strandedenergy assetsproduction<br><!-- End of picture text -->

<!-- Start of picture text --> stranded asset purchases<br><!-- End of picture text --> <!-- Start of picture text --> 0.16<br>0.14<br>5<br>©5 0.12<br>& 01<br>5 0.08 |<br>BS 0.06<br>0.04<br>‘e0<br>Q1-2025 Q1-2030 Q1-2035 Q1-2040<br><!-- End of picture text -->

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does not translate into considerable forecast uncertainty of the projected carbon tax.

Public investment in fossil infrastructure decreases from 0.1% of trend GDP to 0.01%. Note that private investment is sensitive to public infrastructure investment. Hence, the – phase-out of fossil infrastructure investment including the sharp decline in the first few – quarters is designed to keep the risk of private asset stranding small.<sup>15</sup> Nevertheless, the 90% HDI of stranded asset purchases indicates a considerable risk of asset stranding already in the early stages of the transition.

To compensate for the negative effect of the carbon tax on low-skilled consumption, transfer payments increase from 8.1% of trend GDP in 2022 to 8.7% in 2026Q4. While the rapid phase-out of fossil investment reduces the risk of asset stranding, it generates uncertainty regarding the required public support of low-skilled households as indicated by the wide 90% HDI during the fiscal-consolidation phase.

Renewable infrastructure investment rises from 0.1% of trend GDP to 0.195% within ten years. We assume that this exactly matches the renewable infrastructure satiation level. It corresponds to the $2bn of annual investment needs in renewable infrastructure identified in – World Bank (2022). By 2027, emissions decline below 340mn tonnes the reference value calculated for T ̈urkiye to stay on the 2°C path in 2030 (Voyvoda and Yeldan 2015).

At the posterior mean, the target public debt-GDP ratio of 34% is achieved in 2027Q1. This is when the phase of the _renewable energy boom_ starts.<sup>16</sup> Instead of reducing public debt, carbon tax revenues are now used to finance renewable energy subsidies while keeping public debt at a constant share of trend GDP.<sup>17</sup> The subsidy peaks in 2029Q1 at $1400 per toe, which amounts to a sizable bill of 0.62% of GDP. As emissions decrease, the revenues from the fuel tax and the carbon tax decline. To continue meeting the public debt target, renewable subsidies are phased out gradually until 2037. During the renewable energy boom, carbon taxes can be temporarily lowered as the renewable subsidies sufficiently reduce emissions to stay on target.

Note that there is considerable parameter uncertainty regarding the size of the renewable subsidy and the duration of the boom. At the peak, the 90% HDI ranges from $300 to $2800 per toe. Regarding phase-out time, the 90% HDI ranges from 2032 to 2042. Given strict public debt and low-skilled consumption targets, the uncertainty about the macroeconomic effects of the policy mix considered translates into uncertainty about how much public funds there will be available for renewable subsidies.

At the posterior mean, revenues will be insufficient to sustain a constant debt-trend GDP ratio, even without subsidies, by 2037. This is the beginning of the phase of the _final push_ . The carbon tax increases from $90 in 2037 to $175 in 2053. The carbon tax required to stay on the path to net zero is subject to only moderate parameter uncertainty. For instance, the

> 15We define stranded capital assets as the private capital stock which F-firms do not wish to hold any longer. Stranded assets are valued at replacement costs.

> 16In the Monte Carlo simulation of the 90% HDI, we fix the start date of the renewable subsidy to 2027Q1 and hold the public debt-trend GDP ratio constant at whatever value it has in that period.

> 17To ensure a smooth transition, the subsidies are phased in already in 2026Q2.

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<!-- Start of picture text --> Private renewable and fossil investment<br>180 Foon: Renewable investment (baseline, left axis)<br>160 ——— Renewable investment (policy forecast, left axis)<br><!-- End of picture text --> <!-- Start of picture text --> ;<br><!-- End of picture text --> <!-- Start of picture text -->
> 140 —— Fossil investment policy(policy forecast, right9 axis)<br>6 80<br>S 60 ee<br>& 40 7<br>2 20<br>0<br>_ TM” ues<br><!-- End of picture text -->

<!-- Start of picture text --> ., *;<br>mm es<br>Z<br>:<br>:<br>0<br>{_———}<br>91-2040 Q1-2050<br><!-- End of picture text --> <!-- Start of picture text --> Final energy consumption (annualized)<br>450<br>ones Renewable energy (baseline)<br>= 400 Renewable energy (policy forecast)<br><!-- End of picture text --> <!-- Start of picture text --> ;<br>.<br>t<br><!-- End of picture text --> <!-- Start of picture text --> 2o 350 —— Fossil energy‘gy (policy(policy forecast. )<br>= 300<br>© 250<br>: cw<br>- “anu<br>myc<br>2 100<br>_<br>Q1-2010 Q1-2020 Q1-2030<br>Quarters<br><!-- End of picture text --> <!-- Start of picture text --> 7=<br>ae<br>ce<br>Q1-2040 Sitso<br><!-- End of picture text -->
# **4.3.1 Energy investment and consumption**

Figure 5 plots the historical and projected paths of private investment and production in the renewable and fossil energy sectors. The climate-fiscal policy mix previously discussed – is expected to mobilize considerable private capital to finance the low-carbon transition especially during the _renewable energy boom_ triggered by the renewable subsidy. We project private investment in renewable energy to increase from around $4bn in 2022 to $150bn in 2053. This is almost 4% of trend GDP or around 12 times the renewable investment of the baseline scenario. Note that it is also about ten times the baseline fossil investment. It matches the annual investment needs of 4% to 4.5% of GDP identified by Ranger and Volz (2023).

The overall energy capital requirements of the transition exceed the baseline needs because renewable energy is entirely produced domestically. Recall that, in the baseline, T ̈urkiye imports 87% of its total primary energy supply. Moreover, the renewable energy sector is considerably more capital-intensive than the fossil energy sector (which, in our model, excludes carbon extraction). It is important to emphasize that the projected expansion of renewable investment presumes ideal energy market regulation for energy production to be profitable.

Private investment in the fossil energy sector starts from $4bn in 2022 and disappears entirely. Given the policy mix, fossil investment is expected to turn negative by 2042. That is, capital assets are stranding. They are decommissioned and purchased by the public bad bank.

Renewable energy consumption increases from around 20mn toe in 2022 to 420mn toe by 2053. This is 460% higher than the baseline projection. By the early 2030s, renewable energy will reach 50% of the energy mix. This is similar to net-zero pathway findings of IPC (2021) for 2030. Fossil energy consumption will decrease from 130mn toe in 2022 to around 20mn toe by 2053. This is 96% below the baseline projection. By 2053, the overall energy consumption will be 20% below the baseline. The carbon tax not only triggers the substitution to renewable energy but also to capital and labor, reducing the energy intensity of GDP.

# **4.3.2 GDP and components**

For the climate-fiscal policy mix previously identified, the first three panels in Figure 6 plot the projected responses of GDP, consumption, investment, exports, and imports. Overall, the GDP effects are moderate, staying within 1% of GDP. While consumption does not deviate significantly from the baseline, investment reaches levels of 4% above baseline by 2030 and almost 6% by 2053. The low-carbon transition is investment driven. On the flip side, net exports decline to a similar extent. Overall, considerable parameter uncertainty exists regarding the net effects on GDP and its components.

During the phase of _fiscal consolidation_ , GDP declines by up to 0.7% in 2027 compared to 22
<!-- Start of picture text --> 3 Gross domestic product<br>2<br><!-- End of picture text --> <!-- Start of picture text --> GDP GDP (low-carbon(fiscal consolidation)transition)<br><!-- End of picture text --> <!-- Start of picture text --> 10 Consumption and<br>8 + ———— Investment Consumption<br><!-- End of picture text --> <!-- Start of picture text --> 24<br>Oo<br>So8 ————<br>6<br>£1<br>sgwo> 2<br>3<br>2°<br>-4<br>5<br>Q1-2025 Q1-2030 Q1-2035 Q1-2040<br><!-- End of picture text --> <!-- Start of picture text -->
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<!-- Start of picture text --> 26<br>Oo<br>8sa 4<br>6 2<br>£°)<br>s°sgoS> -2 \ =<br>3<br>24<br>-6<br>8<br>Q1-2025 Q1-2030 Q1-2035 Q1-2040<br><!-- End of picture text --> <!-- Start of picture text --> Q1-2045 Q1-2050<br><!-- End of picture text --> <!-- Start of picture text --> Exports and imports<br>2<br><!-- End of picture text --> <!-- Start of picture text --> ———— ExportsImports (left(left axis)axis) 20<br><!-- End of picture text --> <!-- Start of picture text --> 50 High-skilled and low-skilled<br>———— High-skilledHigh-skilled<br><!-- End of picture text --> <!-- Start of picture text --> gains 5<br>(with(withoutwealth,wealth,left rightaxis) axis)<br><!-- End of picture text --> <!-- Start of picture text --> £<br>geo 0 {<br>3 2<br>£ -4<br>TM<br>2 -<br>g<br>2 6 _——<br>3j 8 —<br>&<br>-10<br>12<br>Q1-2025 Q1-2030 Q1-2035 Q1-2040<br>Quarters<br><!-- End of picture text --> <!-- Start of picture text --> co)<br>o£-20 9g3o<br>-40 £<br><<br>2<br>g<br>60 #<br>80 fa9<br>x<br>-100<br>-120<br>Q1-2045 Q1-2050<br><!-- End of picture text --> <!-- Start of picture text --> s<br>3& 30<br>3& 20<br>2<br>3B 10<br>g<br>g<br>25 ° “|<br>x<br>-10<br>-20<br>Q1-2025 Q1-2030 Q1-2035 Q1-2040<br>Quarters<br><!-- End of picture text -->

<!-- Start of picture text --> s<br>3 2g£<br>2 a8<br>2<br>1s<br>8<br>a<br>os2<br>BS<br>-1<br>2<br>Q1-2045 Q1-2050<br><!-- End of picture text --> <!-- Start of picture text --> 20 Fossil equity share prices<br>9 A——Fossil Fossil equityequity shareshare pricesprices (orderly(delayedtransition)transition)<br><!-- End of picture text --> <!-- Start of picture text --> 07 Stranded fossil capital stock at<br>0.6 | ————— StrandedStranded capitalcapital stockstock (delayed(orderly transition)transition)<br><!-- End of picture text -->

<!-- Start of picture text --> cost<br><!-- End of picture text --> <!-- Start of picture text --> FSaog& -20<br>E -40<br>é<br>5&5> 60<br>g4 -80<br>s<br>-100<br>-120<br>Q1-2025 Q1-2030 Q1-2035 Q1-2040<br><!-- End of picture text --> <!-- Start of picture text --> TM<br>Q1-2045 Q1-2050<br><!-- End of picture text --> <!-- Start of picture text --> a8a]faso os0.4<br>Bo]<br>0.3<br>s<br>£ 0.2<br>5© o1<br>x<br>0<br>-0.1<br>Q1-2025 Q1-2030 Q1-2035 Q1-2040<br>Quarters<br><!-- End of picture text --> <!-- Start of picture text --> Q1-2045 Q1-2050<br><!-- End of picture text -->
same public-debt trajectory we obtain from the decarbonization scenario. It is worth noting that the initial contraction of GDP is much more substantial: 4.5% below baseline in 2025Q1. Hence, combining carbon pricing and public investment in renewable infrastructure is a costeffective and climate-smart way of fiscal consolidation.

Trade is subject to transition risks. On the positive side, the low-carbon transition will allow T ̈urkiye to eliminate its dependence on fossil fuel imports which currently amount to 87% of the total primary energy supply. Total net exports decrease by around 4% of GDP, which may be considered moderate. Yet, exports will steadily decline by up to 11% in 2053 compared to the baseline. The inflationary pressure of higher energy prices will lead to a real appreciation of the Turkish lira. The loss in competitiveness translates into a decline in exports, whose stabilization may require additional policy measures that support exporting firms. As a qualification of this result, note that our baseline scenario (against which the losses of the policy scenarios are measured) does not reflect the EU’s Carbon Border Adjustment Mechanism. Given that half of the Turkish exports are going to the EU, the baseline, no-transition exports are likely to be overestimated.

# **4.3.3 Household welfare**

Even though the GDP effects of the low-carbon transition are expected to be moderate in T ̈urkiye, this may not hold for household welfare. At the first order, households care about how much they can consume in goods and services and how much leisure they can enjoy. High-skilled households additionally care about how much financial wealth they hold. Therefore, we follow the method of Born and Pfeifer (2020) and assess unconditional welfare effects over time using the concept of _consumption equivalence_ .<sup>18</sup> As shown in the fourth panel of Figure 6, the low-skilled household welfare effects along the transition path are moderate, with the consumption equivalent moving between -1.5% and 2% of baseline consumption. This is because of the transfer payments that keep lowskilled consumption at the level of the baseline scenario. Without these payments, low-skilled households would strongly prefer the baseline over the transition scenario. The consumption equivalent would reach around -5% (not shown). That is, they would have to forsake up to 5% of their baseline consumption to be indifferent between the baseline and the transition.

Overall, high-skilled households benefit more from the transition than low-skilled house– holds even though they do not receive transfers. For comparison, we first consider the case in which high-skilled households do not derive utility from wealth.<sup>19</sup> The aggregated high- and low-skilled utility functions are identical in that case. We find that the decline in GDP initially translates into a reduction in high-skilled consumption, reducing welfare.

> 18The welfare difference between the transition and baseline scenario is measured as the share of consumption which a household living in the baseline has to receive to be indifferent between the baseline and the low-carbon transition. Note that we evaluate welfare only at the first order.

> 19Assume, for instance, that household-level wealth enters the utility function as the difference to aggregate wealth. The first-order conditions remain the same, but the wealth term in the utility function now has the interpretation of _Keeping Up with the Joneses_ rather than _precautionary saving_ .

24
Starting with the _renewable energy boom_ , the high-skilled household’s welfare improves with a consumption equivalence of up to 2% by 2053.

In the case of wealth utility, welfare effects are much more substantial. The consumption equivalent during the _fiscal consolidation_ reaches -14% in 2025. Yet, during the renewable boom, it goes up to 43% and later stabilizes around 20%. In other words, to make the baseline as appealing as the transition scenario, high-skilled households in the baseline would require a significant amount of additional consumption. The welfare effects are substantial because the policy interventions, which are perceived as permanent once implemented, significantly affect the net present value of wealth. Since the renewable energy sector is more capitalintensive than the fossil energy sector, the low-carbon transition implies a net increases in capital assets which are held by the high-skilled households. Since wealth is a source of utility, high-skilled households benefit from the transition. As shown in the fourth panel of Figure 6, the wealth channel is a major driver of household welfare.

While these results highlight the importance of studying the welfare impacts of the lowcarbon transition on households, they likely draw too gloomy a picture as the model leaves out critical transmission channels: First, as many studies have shown, the benefits of the reduction in CO2-related air pollution are sizable and outweigh the purely consumption and leisure-related welfare loss of the low-carbon transition (Parry et al. 2015, Karlsson et al. 2020). Second, the model’s assumption that the high- and low-skilled consumption bundles are identical further distorts the picture as the former tends to be more energy- and carbonintensive than the latter (Sager 2019, Dorband et al. 2019).

# **4.3.4 Stranded assets**

Financial risks arise from the devaluation of private equity shares and the stranding of productive capital assets in the fossil energy sector. These risks are manageable because the volume of fossil capital assets in T ̈urkiye is relatively tiny. Recall that T ̈urkiye imports most of its fossil fuels. Nevertheless, a disorderly transition and lousy policy communication may increase these transition risks.

The last two panels in Figure 6 show the responses of equity share prices and stranded assets to an orderly and delayed transition. The longer the transition is delayed, the more sudden the drop in asset prices and the higher the financial risks. Regarding asset stranding, the public bad bank needs to purchase excess capital assets only starting in 2044, according to our simulation of an orderly transition. Evaluated at replacement costs, the volume of the stranded capital stock will reach almost 0.1% of trend GDP by 2053. In comparison, the total public capital stock is 29% of trend GDP. A delayed transition and an ill-timed phase-out of public fossil infrastructure investment increase the risk of asset stranding. For instance, delaying the start of the transition until 2030 will generate significant stranded assets already by 2033, and the volume will reach 0.63% of trend GDP in 2043. Policy communication and commitment to the net-zero agenda reduce the risk of asset stranding (Bretschger and Soretz 2022).

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<!-- Start of picture text --> 3 Gross domestic product<br><!-- End of picture text --> <!-- Start of picture text --> 0 Emissions<br>-10 \ —<br>—<br><!-- End of picture text --> <!-- Start of picture text --> policy commitment<br> policy fo r ecast<br><!-- End of picture text --> <!-- Start of picture text --> g<br>=a61<br>.<br>- 0<br>So ViRf a<br>3 a<br>2 ——— Benchmark policy forecast<br>x —— Credible policy commitment<br>-2 | —-— Expected policy reversal<br>—— Lower risk premia<br>— Easier access to green bank lending<br>3<br>Q1-2025 Q1-2030 Q1-2035 Q1-2040<br>Quarters<br><!-- End of picture text -->

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– permanent, they expect it to slowly phase out again for instance, due to anticipated social unrest or vested interests. We assume an auto-regressive parameter of 0.95. That is, agents expect that any given policy change will be reversed by 50% after about 4 years. As Campiglio et al. (2023) points out, there are multiple real-world examples of policy reversals that justify this type of expectation.

As shown in Figure 7, transparent policy communication and fully credible commitment can crowd in private sector behavior, slightly raising renewable investment and generation. Net zero is already reached in the late-2040s. Since the different stages of the transition phase are anticipated, agents can smooth behavior and reduce adjustment costs and friction. This reduces the GDP contraction of the fiscal-consolidation phase and raises and prolongs the GDP expansion during the scaling-up phase.

The private-sector expectation that a future government will take back already implemented measures makes the transition considerably more costly. Even with an expected slow policy reversal of 5% each quarter, GDP is projected around 0.5% below the benchmark policy scenario for most of the transition period. Even worse, the policy mix that is sufficient for net zero in the benchmark scenario, will fail to achieve the transition in the weak-commitment scenario. Emissions are projected to decline by only 55% compared to the no-policy baseline. Hence, to achieve net zero the carbon tax would have to be considerably higher than in the benchmark policy scenario further reducing GDP.

Credible commitment reduces the costs of decarbonization. Yet, as is well-understood since Kydland and Prescott (1977), policy commitments are credible only with the corresponding institutional setting. Barrett (2008) suggests to amend climate treaties with an enforcement mechanism, such as a trade restriction. Nordhaus (2015) proposes a climate club intended to eliminate the problem of free-riding. On a national level, Chile pioneers sustainability-linked sovereign bonds, which link the bond return to green performance indicators and partly align the government’s interests with the low-carbon agenda (Gir ́aldez and Fontana 2021). Our results suggest that these measures would considerably lower the costs of the low-carbon transition by anchoring private-sector expectations.

# **4.4.2 Structural reforms**

The OECD (2021) has identified a weak rule of law as a main obstacle for private-sector investment dynamics in T ̈urkiye. To assess the role of the business uncertainty for the lowcarbon transition, Figure 7 also illustrates the scenario in which the government implements reforms that reduce the equity risk premium to the average of China, India, and South Africa within five years. These reforms drastically improve the macroeconomic outlook of the lowcarbon transition, with GDP peaking at 3% above the no-policy baseline. Renewable energy investment and generation are considerably higher than in the scenario with the benchmark risk premium. The economics of these results is straightforward: With a lower equity-risk premium, firms reduce the discounting of future profits. Capital investment becomes more appealing. Since renewable energy is very capital-intensive, this sector mainly benefits from 27
a lower risk premium.

Regarding bank lending, the OECD’s recommendation for T ̈urkiye is to reduce private sector debt OECD (2021). Nevertheless, we consider the counterfactual scenario in which the government implements reforms that gradually reduce constraints on green finance within five years. In particular, we assume that the share of capital which banks accept as collateral for lending to renewable energy firms increases by 20%-points. Figure 7 illustrates the results. Similar to the scenario of an improved rule of law, renewable energy investment and generation increase considerably. The GDP response demonstrates the importance of the financial accelerator mechanism forcefully. With a higher debt-capital ratio and borrowing constraints proportional to the value of capital, business activity becomes more pro-cyclical. As the value of the renewable capital stock decreases in the fiscal-consolidation phase, borrowing constraints tighten up, and GDP contracts considerably. On the flip side, the subsequent boom initiated by the renewable energy subsidy alleviates borrowing constraints, reinforcing the expansion of GDP.

# **5 Concluding remarks**

In conclusion, this paper examines the feasibility of T ̈urkiye’s net-zero emissions target by 2053, given the country’s structural deficits and recent climate-related risks. The country’s dependence on fossil fuel imports and the volatility of energy prices due to exchange rate fluctuations pose significant risks to energy security, while its primary export market, which is more carbon-intensive than its European counterpart, is also under threat from the European Union’s low-carbon agenda. Therefore, T ̈urkiye must implement effective climate-fiscal policy interventions that enable the country to grow along the 2053 net-zero pathway, taking into account significant feasibility constraints.

The analysis employs an empirical dynamic stochastic open-economy macro framework, allowing for the simulation of low-carbon transitions. The policy instruments evaluated include a carbon tax, a renewable energy subsidy, transfer payments, public infrastructure investment, a bad bank for stranded assets, and the phase-out of fossil fuel subsidies and public investment. The study also explores how the macroeconomic effects of the policy interventions differ in counterfactual scenarios of a fully credible commitment to net zero, an improved rule of law, and facilitated access to green bank finance.

The study finds that T ̈urkiye’s transition to net zero is feasible with a mix of policy instruments that address the country’s structural deficits. The model simulations show that a credible commitment to net zero, improved rule of law, and access to green bank finance would improve the effectiveness of climate-fiscal policy interventions in mobilizing private finance. Moreover, renewable energy subsidies and public infrastructure investments have a positive effect on economic growth, while the carbon tax and phase-out of fossil fuel subsidies contribute to the decarbonization of the economy.

Therefore, the study suggests that T ̈urkiye’s climate policy should focus on the imple28
mentation of an integrated set of policy interventions that take into account the country’s structural deficits, particularly the weak rule of law, and low business confidence in public institutions. A credible commitment to net zero, improvements in access to finance and the rule of law, coupled with a mix of policy instruments, can provide a path to sustainable and equitable economic growth while meeting T ̈urkiye’s climate goals.

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# **A An macro model open-economy energy**

This appendix provides the details of the open-economy macro framework used for the policy analysis in the present paper. For a description of the variables and parameters, see Tables B2 to B5 in Appendix B. Since the model includes a trend arising from a growing laborembodied productivity _zt_ , we stationarize any trending real variable _Xt_ by _X_<sup> ̃</sup> _t_ = _Xt/zt_ . Financial assets are nominal variables and, therefore, additionally exhibit a price trend. We therefore divide them by the aggregate price of the final good: _X_<sup>ˆ</sup> _t_ = _Xt/_ ( _ztPY,t_ ). Note that international bonds denominated in foreign currency are detrended by the foreign price level. For the nominal price of a good or asset _X_ we use the following notation: _pX,t_ = _PX,t/PY,t_ .

## **A.1 The household sector**

The model features two types of households: _high-skilled labor_ H-households and incomeconstrained _low-skilled labor_ L-households.

### **A.1.1 High-skilled labor households (H-households)**

**High-skilled labor aggregator.** A perfectly competitive high-skilled labor aggregator purchases differentiated labor varieties from the high-skilled labor households. The labor aggregator’s demand for the high-skilled labor variety _NH,t_<sup>_i_can be obtained from the following</sup> cost minimization problem:

subject to where _LH,t_ is total homogeneous high-skilled labor measured in hours and 1+1 _/εWH,tμH >_ 1 is the elasticity of substitution between different high-skilled labor varieties. Noting that the Lagrangian multiplier of the constraint is equal to the aggregate high-skilled wage index, _PLH,t_ , one can show the first-order condition (FOC) to read

**Differentiated high-skilled labor households.** A continuum of high-skilled labor households (H-households) exclusively owns the firms and supplies differentiated high-skilled labor

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is the rate of depreciation of the nominal exchange with the real exchange rate defined as _Et_ = _StPY,t_<sup>_∗/PY,t_,</sup> is the external risk premium, is the equity risk premium, are the respective sectors’ expected discounted streams of future dividends, is the stochastic discount factor, and _T_<sup> ̃</sup> _CH,t_ , _T_<sup> ̃</sup> _A,t_ , _T_<sup> ̃</sup> _H,t_ , and _T_<sup> ̃</sup> _R,t_ are tax revenues as specified below.

Eq. (1) states that the utility from relaxing the budget constraint by one unit is equal to the marginal utility of the consumption which this unit provides. Eq. (2) is a variant of the Euler equation and determines how the households smooth consumption optimally. It relates the marginal consumption utility of a unit of income in _t_ to the utility this unit would yield if saved and consumed in _t_ + 1. Eq. (3) is the risk-adjusted uncovered interest parity condition and it pins down the change of the exchange rate which adjusts to eliminate any arbitrage opportunity making the household indifferent between holding international debt or any other asset. Eq. (4) states that equity returns exceed the risk free bond rate by an equity risk premium, Γ _Sε_ Γ _S,tRS,t_ (abstracting from capital taxes). Note that _RSZ,t_ = _RSR,t_ = _RSF,t ≡ RS,t_ and note that the equity return rate is defined as Eq. (5) states that the rate of high-skilled wage inflation should be such that the marginal disutility from increasing labor supply by one unit should be equal to the marginal utility which the additional consumption provided from that unit of labor generates. The markup, _εWH,tμH_ , arising from monopoly power drives a wedge between the marginal rate of substitution between differentiated labor varieties and the high-skilled real wage. Eq. (6) is simply the budget constraint. Note that we normalize the total number of equity shares to one. Define _pS,t_ = _PS,t/PY,t_ as the price of an equity share expressed in the price of the final good.

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## **A.1.2 Low-skilled labor households (L-households)**

There is a continuum of low-skilled labor L-households each of which supplies labor hours and receives wage income. L-households have no access to the financial market and are therefore hand-to-mouth consumers. The low-skilled labor household’s utility maximization problem has a corner solution w.r.t. consumption. The L-household’s problem is to The FOCs imply Eq. (15) relates marginal utility to the shadow price of relaxing the budget constraint. Eq. (16) is the labor supply function. In the optimum, the disutility of supplying one additional unit of labor has to equal the utility it generates through consuming the additional labor income. Eq. (17) is the aggregated budget constraint. Note that only a fraction of low-skilled labor households finds employment. For the sake of simplicity, we assume unemployment only on the extensive margin and not on the intensive margin.

# **A.2 CES bundlers**

## **A.2.1 Retail firms (Y-firms)**

Retailers or Y-firms combine wholesale goods _Wt_ and energy goods _EY,t_ and, using a ConstantElasticity-of-Substitution (CES) bundler, form the final retail good _Yt_ which is used for consumption and capital investment. Their cost minimization problem reads subject to a CES production structure, 39
The FOCs of this problem imply where _pW,t ≡ PW,t/PY,t_ , _pE,t ≡ PE,t/PY,t_ and _εY,t_ is the productivity shock. Eqs. (18) and (19) are the Y-firms’ demand functions for wholesale goods _W_<sup> ̃</sup> _t_ and energy goods _E_<sup> ̃</sup> _Y,t_ . Eq. (20) is the Y-firm’s budget constraint.

# **A.2.2 Wholesale firms (W-firms)**

Wholesale firms produce a wholesale good _Wt_ which they sell to the retailers. As inputs, they combine domestic and foreign core goods, _Zd,t_ and _Ze,t_<sup>_∗_and they are subject to input specific</sup> productivity shocks _εWD,t_ and _εWI,t_ . Note that the asterisk indicates that the variable is interpreted from the view of the Rest of the World. Competition is assumed to be perfect. A unit of the domestic core good costs _PZd,t_ , while a unit of the foreign core good is _StPZe,t_<sup>_∗_.</sup> _St_ is the nominal exchange rate and converts foreign prices into domestic prices. Profit maximization implies the following input demand functions: where _pZ,t ≡ PZd,t/PY,t_ and _p_<sup>_∗_</sup> _Z,t_<sup>_≡P ∗_</sup> _Zd,t_<sup>_/P_</sup> _Y,t_<sup>_∗_=</sup><sup>_P ∗_</sup> _Ze,t_<sup>_/P_</sup> _Y,t_<sup>_∗_wherethelastequalityfollows</sup> from the law of one price in the absence of trade transaction costs. The real exchange rate is _Et ≡ StPY,t_<sup>_∗/PY,t_.</sup> Eq. (21) is the domestic demand function for domestic core goods _Z_<sup> ̃</sup> _d,t_ which together with the foreign demand for domestic core goods _Z_<sup> ̃</sup> _e,t_ (exports) to be specified below constitute the total domestically produced core goods. Eq. (22) is the domestic demand function for foreign core goods _Z_<sup> ̃</sup> _e,t_<sup>_∗_whichconstitutetheonlyimports</sup> except the carbon imports. Eq. (23) is the W-firm’s budget constraint.

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# **A.2.3 Labor firms (L-firms)**

Labor firms combine high-skilled and low-skilled labor hours into a homogeneous labor service which they sell to the core good firms, renewable energy firms, and fossil energy firms. They use a CES technology with input specific productivity shocks _εLH,t_ and _εLL,t_ . Profit maximization implies the following aggregated FOCs: Eqs. (24) and (25) are standard input demand functions for high-skilled and low-skilled labor hours for a given unit of labor services. Eq. (26) is the labor firm’s budget constraint. The growth rate of the real wage is linked to wage and price inflation according to

# **A.2.4 Energy firms (E-firms)**

Using a CES technology, E-firms combine renewable energy _ER,t_ and fossil energy _EF,t_ to produce a homogeneous energy composite _EY,t_ + _EZ,t_ which they sell to the core good firms and the retail firms. Energy firms are subject to a productivity shock _εE,t_ . Similar to Guerrieri et al. (2008), we introduce renewable input adjustment costs Ψ _E,t_ to get a time varying elasticity of substitution, which is low in the short-run and converges to _σE_ in the long-run.

41
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where _Zd,t_ + _Ze,t_ is total core output sold domestically and exported to the Rest of the World. Further, 1 + 1 _/εPZ,tμZ >_ 1 is the elasticity of substitution. Noting that the Lagrangian multiplier of the constraint is equal to the aggregate price index, _PZ,t_ , one can show the FOC to read Note that the law of one price requires that the domestically produced core good has the same price domestically and abroad.

**Differentiated core goods firms.** Differentiated core goods firms purchase labor hours from L-firms and energy from E-firms. Using these inputs as well as private and public capital, a Z-firm _j_ produces a differentiated variety of the intermediate core good _Zt_<sup>_j_and</sup> sells it to the core goods aggregators. Differentiated Z-firms operate in monopolistic markets and, hence, have price setting power. We assume that the capital stock is firm-specific and cannot be traded on a spot market. They face various frictions such as price adjustment costs, capital utilization costs, and investment adjustment costs. They pay corporate income taxes as well as payroll taxes. Labor productivity growth is stochastic and follows a deterministic long-run trend,

Taking as given total demand for core goods _Zd,t_ + _Ze,t_ , the final goods price level _PY,t_ , the core price level _PZ,t_ , the real price for energy _pE,t_ , and the real wage rate of a homogeneous labor hour _pL,t_ as well as the law of motion of capital, adjustment costs, the production functions, the demand function for intermediate goods, and the requirement to maintain a debt-capital ratio _λ_ , the firm _j_ chooses _{BZ,t_<sup>_j, P_</sup> _Z,t_<sup>_j, Lj_</sup> _Z,t_<sup>_, I_</sup> _PZ,t_<sup>_j, uj_</sup> _Z,t_<sup>_, K_</sup> _PZ,t_<sup>_j, E_</sup> _Z,t_<sup>_j}_</sup> _t_<sup>_∞_</sup> =0<sup>to maximize</sup> its real beginning-of-period value, _DZ,t_<sup>_j_+</sup> _PPSZ_<sup>_j_</sup> _Y,t,t_<sup>_S_</sup> _Z,t_<sup>_j_</sup> _−_ 1<sup>.</sup> Evaluating at period _t_ = 0, the optimization problem reads where 43
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Eq. (34) relates the shadow price of one additional unit of borrowing _μZ,t_ to the equity risk premium. The equation collapses to _μ_<sup>_j_</sup> _Z,t_<sup>=0ifthereturnsonequityandborrowingare</sup> equal, _RS,t_ = _RB,t_ . In this special case, the financial structure of the firm is irrelevant from the household’s perspective. With a positive equity risk premium, however, _RS,t > RB,t_ and _μ_<sup>_j_</sup> _Z,t_<sup>_>_0.Externalfinanceispreferredoverinternalfinance.Thisisbecausehouseholds</sup> discount risky future dividend payouts by more than the firm’s financial obligations from borrowing increase over time. They rather have an additional unit of dividend today at the expense of a lower cash flow tomorrow, then forego the unit of dividend today and enjoy a higher cash flow tomorrow.<sup>20</sup> Hence, households ask firms to distribute as much as possible today exploiting all available credit. The end-of-period borrowing constraints are always binding. The demand for end-of-period loans satisfies Eq. (35) states that the mark-up _εPZ,tμZ_ arising from monopoly power and the price adjustment costs _τP_ drive a wedge between the real marginal revenue (1 _− tP_ ) _pZ,t_ and the real marginal cost _φZ,t_ . Eq. (36) is the labor demand function and equates the marginal cost of labor and its marginal revenue product. Eq. (37) translates a change in the shadow price of capital _QZ,t_ into a change in investment. Note that without capital adjustment costs _QZ,t_ = 1, i.e. investment will adjust instantly to realize the desired capital stock. Eq. (38) determines the optimal rate of capital utilization. Eq. (39) links _QZ,t_ to the desired capital stock. Without adjustment costs, the optimal capital stock ensures the marginal return of a dollar spent on capital is equal to its cost which is depreciation. Note that the marginal return includes the savings from labor costs. Also note that firm’s debt-capital ratio _λ_ enters into desired capital stock decisions. Eq. (40) is the energy demand function and states

> 20To increase the expected cash flow by one unit in _t_ + 1, the firm has has to retain _RB,t/_ E _t_ Π _Y,t_ +1 units in _t_ . Yet, by the definition of the stochastic discount factor, households are only willing to give up E _t_ Λ _t,t_ +1 units for a one unit cash flow in _t_ + 1.

46
that energy input will equate its marginal revenue which includes savings in labor costs and taxes to its marginal costs which is the price. Eq. (41) is the aggregated law of motion of private capital. Eqs. (42) to (44) are the aggregated CES technologies. Eq. (45) is simply the rearranged firm’s budget constraint. Note that _T_<sup> ̃</sup> _PZ,t_ and _T_<sup> ̃</sup> _S,t_ are the Z-firms’ corporate income and payroll taxes, respectively.

# **A.3.2 Renewable energy firms (R-firms)**

R-firms are modeled equivalently to Z-firms except that they don’t have market power, don’t have variable capital utilization, and don’t use energy input to production. Moreover, public capital can either take the form of generation capital and infrastructure. In the former case, public capital simply adds to private capital as a perfect substitute. As infrastructure, public capital constitutes an externality and enhances the marginal product of private capital. Yet, there is an exogenous satiation level of how much infrastructure can be used in production. Also, the productivity shock process in renewable energy production _VER,t_ features learning by doing. In the literature, learning-by-doing elasticity is usually estimated using costs and capacity of production. Here, we relate productivity to energy output. We assume a one-toone relationship between increase in productivity and decline in levelized cost of renewable energy, which we confirm by stochastic simulations.

Taking as given price of its output _PER,t_ , the final goods price level _PY,t_ , and the real wage rate of a homogeneous labor service _pL,t_ as well as the law of motion of capital, capital adjustment costs, the production functions, and the requirement to maintain a debt-capital ratio _λ_ , the firm _j_ chooses _{BR,t_<sup>_j, E_</sup> _R,t_<sup>_j, Lj_</sup> _R,t_<sup>_, I_</sup> _PR,t_<sup>_j, K_</sup> _PR,t_<sup>_j}_</sup> _t_<sup>_∞_</sup> =0<sup>tomaximizeitsrealbeginning-</sup> of-period value, _DR,t_<sup>_j_+</sup> _PPSR_<sup>_j_</sup> _Y,t,t_<sup>_S_</sup> _R,t_<sup>_j_</sup> _−_ 1<sup>.Evaluatingatperiod</sup><sup>_t_=0,theoptimizationproblem</sup> reads where subject to 47
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# **A.3.3 Fossil energy firms (F-firms)**

For the sake of simplicity, the model assumes that domestic carbon extractors (crude oil, coal and natural gas) produce for the international market only. They set the carbon fuel price according to the global price denominated in foreign currency _PO,t_<sup>_∗_eventhoughthe</sup> resource is used domestically.<sup>21</sup> As before, capital is firm-specific and subject to quadratic adjustment costs. We assume that fossil energy firms operate under perfect competition. The fossil energy production technology allows for substitution between capital and carbon when producing the carbon-capital composite. We assume quadratic adjustment costs for carbon input going into fossil fuel production proportional to past period’s aggregate carbon input.

Taking as given the fossil energy price _PEF,t_ , the overall price level _PY,t_ as well as the law of motion of the F-firm’s capital stock, the fossil energy production function, adjustment costs, and the requirement to maintain a debt-capital ratio _λ_ , the firm _j_ chooses _{BF,t_<sup>_j, E_</sup> _F,t_<sup>_j, Lj_</sup> _F,t_<sup>_, I_</sup> _PF,t_<sup>_j, K_</sup> _PF,t_<sup>_j, O_</sup> _t_<sup>_j}∞_</sup> _t_ =0<sup>tomaximizeitsrealbeginning-of-periodvalue,</sup><sup>_D_</sup> _F,t_<sup>_j_+</sup> _PPSF_<sup>_j_</sup> _Y,t,t_<sup>_S_</sup> _F,t_<sup>_j_</sup> _−_ 1<sup>.Evaluatingatperiod</sup><sup>_t_= 0,theoptimizationproblemreads</sup> where

> 21Note that currently we only assume carbon imports but not carbon exports.

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̃ ̃ _IGbb,t_ = _IGbb,t−_ 1 + _εIGbb,t_ (90) The laws of motion of the various public capital stocks normalized by trend growth are for _X ∈{Z, Rgen, Rinf, Fgen, Finf }_ .

The law of motion of detrended bad bank capital stock is: The renewable energy subsidies are Monetary policy follows a Taylor rule which responds to core inflation which is the price inflation of the wholesale goods. It excludes energy input to the retail goods (but includes energy input to core good production) as well as imports (input to wholesale goods).

As in Smets and Wouters (2007), a risk premium capturing _flight to safety_ creates a wedge between the policy rate and the return on risk-free assets. Since a positive shock increases the required return on domestic assets and the cost of capital, it reduces current consumption and investment simultaneously and helps explaining the co-movement of consumption and investment.

where the exponent of the risk premium shock ensures that the variance of the shock does not depend on the level of the risk premium Γ _B_ .

# **A.5 Rest of the World**

The budget constraint of the rest of the world is 54
where the second line uses the law of one price for domestically produced goods. Recall that we assume that there is only one international market for carbon with price _p_<sup>_∗_</sup> _O,t_<sup>.Thefinal</sup> demand abroad for retail goods is assumed to be a first-order auto-regressive process Domestic exports are the domestic core goods bought abroad. We assume export adjustment costs to capture sluggish response of exports to relative price changes. Assuming a symmetric economic structure in the Rest of the World, foreign retailers will buy domestic core goods according to Finally, the foreign price of core goods deflated by the foreign price level is assumed to evolve according to We assume the interest rate of the rest of the world follows a first-order auto-regressive process We also assume the inflation rate of the rest of the world follows a first-order auto-regressive process The international price of carbon is exogenous and evolves according to

# **A.6 Market clearing**

Capital market clearing implies To derive the macroeconomic balance condition, we need to sum over the aggregate budget constraints of each sector. Note that _RS,t−_ 1 _/_ Π _Y,t_ =<sup>_<u>p</u>_ ̃</sup> _p_ ̃<sup>_SZ_</sup> _SZ,t_<sup>_<u>,t</u>_+ ̃</sup> _−_<sup>_D_</sup> 1 _/g_<sup>_Z,_</sup> _t_<sup>_t_=</sup> _p_ ̃ _p_ ̃ _SR,tSR,t_ + _−D_ 1<sup> ̃</sup> _/gR_<sup>_i_</sup> _<u>,tt</u>_<sup>=</sup> _p_ ̃ _p_ ̃ _SF,tSF,t_ + _−D_ 1<sup> ̃</sup> _/gF_<sup>_i_</sup> _<u>,tt</u>_<sup>.The</sup> 55
summing over all budget constraints leads to the macroeconomic balance conditions which equates aggregate supply of and aggregate demand for final goods: Note that net exports are not part of this equation as there is no trade in final retail goods but only in core goods. Note further that the the macro balance condition does not have the interpretation of a resource constraint as in conventional DSGE models. This because resources are neither utilized fully nor up to a supply-side-constrained structural level.

We define GDP as Gross Domestic Purchases ( _Yt_ ) plus net exports:

# **A.7 Structural shocks**

The model includes the following _i.i.d._ disturbances: _εCH,t_ (High-skilled consumption preference shock) _εCL,t_ (Low-skilled consumption preference shock) _εNH,t_ (High-skilled labor preference shock) _εNL,t_ (Low-skilled labor preference shock) _ε_ Γ _B,t_ (Flight-to-safety shock) _ε_ Γ _W,t_ (Foreign bond risk premium shock) _ε_ Γ _S,t_ (Equity risk premium shock) _εg,t_ (Long-run productivity growth shock) _εWH,t_ (High-skilled wage mark-up shock) _εWL,t_ (Low-skilled wage inflation shock) _εY,t_ (Y-firms (retail good) productivity shock) _εWD,t_ (W-firms (wholesale good) domestic input productivity shock) _εWI,t_ (W-firms (wholesale good) imported input productivity shock) _εEZ,t_ (Z-firms (core good) energy efficiency shock) _εVZ,t_ (Z-firms (core good) productivity shock) _εLH,t_ (L-firms (labor) high-skilled input specific productivity shock) 56
_εLL,t_ _εE,t_ _εER,t_ _εEF,t εPZ,t_ _εK,t_ _εKZ,t_ _εCG,t εIZ,t εIGRinf,t εIGRgen,t εIGFinf,t εIGFgen,t εIGbb,t εR,t_ _εRW,t εW ∗,t_ _ε p_<sup>_∗_</sup> _Z_<sup>_,t_</sup> _εY ∗,t_ _ε_ Π<sup>_∗_</sup> _Y_<sup>_,t_</sup> _ε p_<sup>_∗_</sup> _O_<sup>_,t_</sup> (L-firms (labor) low-skilled input specific productivity shock) (E-firms (energy good) productivity shock) (R-firms (renewable energy good) productivity shock) (F-firms (fossil energy good) productivity shock) (Core good price mark-up shock) (Capital efficiency shock) (Core good capital bundler productivity shock) (Government consumption shock) (Government investment shock in core good sector) (Government investment shock in renewable infrastructure) (Government investment shock in renewable energy generation) (Government investment shock in fossil energy infrastructure) (Government investment shock in fossil energy generation) (Government investment shock in bad bank) (Monetary policy shock in the home country) (Monetary policy shock in the RW) (RW retail firms productivity shock) (Import price shock) (Demand shock in the RW) (Inflation shock in the RW) (Global fossil fuel price shock) 57
# **B Model calibration and estimation**

This section reports the estimation and calibration of the model. In order for certain model variables to match their empirical counterparts at the steady state, some parameters have been restricted. All structural parameters have been estimated using Bayesian techniques. Here, we report first calibrated parameters and then estimated parameters with their 90% credibility intervals.

## **B.1 Data**

We use time series data for 31 observed variables over 2000Q2-2022Q3, which are reported in Table B1.<sup>22</sup> Twenty-six observables are available at the quarterly frequency.<sup>23</sup> However, energy balances from the International Energy Agency (IEA) and CO2 emissions from the Climate Watch (CAIT) database maintained by the World Resources Institute are annual. We use mixed frequency data in the estimations. The missing quarterly observations of annual (and quarterly) data are obtained by the Kalman (1960) filter. This feature of our estimation technique is highly relevant for emerging market economies where data are available but generally with gaps or at lower frequencies than what the models are set at. We discuss the details of how we deal with missing observations below.

We detrend policy rate, 3-month interest rate, inflation rate, low-skilled wage inflation rate, high-skilled wage inflation rate, real exchange rate, employment rate, RoW interest rate and RoW inflation rate by the double-sided Hodrick and Prescott (1980) filter. For the capacity utilization rate, we use demeaned data. For all other observables, we use demeaned growth rates. We subtract the growth rate of the labor force from volume-related variables such as macro aggregates, emissions, sectoral inputs/outputs and hours worked. We take prices relative to the GDP deflator to match the definition of prices in the model.

Some approximations are needed due to data limitations. The rate of capacity utilization is for manufacturing which, empirically, is the biggest chunk in our core goods sector. We use hours worked in manufacturing only, which covers all of our estimation period.<sup>24</sup> For low-skilled wage inflation, we use money wages of construction workers, 80% of whom have less than high school education (Cengiz and Tekg ̈u ̧c 2022). For high-skilled wage inflation, we take wages of information and communication sector, which attracts mainly university degree holders. For global fossil fuel prices, we use OECD producer prices from the IEA.

> 22Data sources are as follows: Components of GDP, employment, interest rates, capacity utilization rate and hours worked (OECD Main Economic Indicators), public-private investment, compensation of employees, unemployment, high-skilled and low-skilled wages (TurkStat), price indexes, EU output, inflation and prices (Eurostat), energy production and prices (IEA), emissions (CAIT) and real effective exchange rate (BIS).

> 23Public and private investment expenditure series are not available after 2016Q2. Low-skilled wage inflation is available over 2005Q2-2017Q4 and high-skilled wage inflation is available after 2009Q2.

> 24Total hours worked data from TurkStat is available after 2010, and there is a close match between quarterly growth rates of manufacturing hours worked and total hours worked over 2010-2022.

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Table B1: Time series used in model estimation

|Real Total Gross Domestic Product, quarterly (demeaned growth rate)|
|---|
|Real Private Final Consumption Expenditure, quarterly (demeaned growth rate)|
|Real Gross Fixed Capital Formation, quarterly (demeaned growth rate)|
|Real Government Final Consumption Expenditure, quarterly (demeaned growth rate)|
|Real Exports of Goods and Services, quarterly (demeaned growth rate)|
|Real Imports of Goods and Services, quarterly (demeaned growth rate)|
|Real public investment expenditure, quarterly (demeaned growth rate)|
|Real private investment expenditure, quarterly (demeaned growth rate)|
|Rate of capacity utilization, quarterly (demeaned)|
|Gross Domestic Product defator infation, quarterly (double-sided HP fltered)|
|Harmonized Index of Consumer Prices: Overall Index Excluding Energy, quarterly (demeaned growth rate)|
|Harmonized Index of Consumer Prices: Energy, quarterly (demeaned growth rate)|
|Employment rate: number of people employed/total labor force, quarterly (double-sided HP fltered)|
|Real compensation of employees, quarterly (demeaned growth rate)|
|Total Hours worked, quarterly (demeaned growth rate)|
|Low-skilled hourly wage infation, quarterly (double-sided HP fltered)|
|High-skilled hourly wage infation, quarterly (double-sided HP fltered)<br>Discount rate, quarterly (double-sided HP fltered)|
|3-month short-term interest rate, quarterly (double-sided HP fltered)|
|Real Broad Efective Exchange Rate, quarterly (double-sided HP fltered)|
|Total fnal household (residential+public+half of transportation) energy consumption, annual (demeaned growth rate)|
|Total fnal frm (industry+half of transportation) energy consumption, annual (demeaned growth rate)|
|Renewable energy supply, annual (demeaned growth rate)|
|Fossil energy supply, annual (demeaned growth rate)<br>CO2 emissions, annual (demeaned growth rate)|
|Domestic fossil fuel producer prices, quarterly (demeaned growth rate)|
|OECD fossil fuel producer prices, quarterly (demeaned growth rate)|
|European Union Real Gross Domestic Product per capita, quarterly (demeaned growth rate)|
|European Union Gross Domestic Product defator infation, quarterly (double-sided HP fltered)|
|European Union export price defator, quarterly (demeaned growth rate)<br>Euro Area 10-year government bond interest rate, quarterly (double-sided HP fltered)|

# **B.2 Empirical model parametrization**

Tables B2 and B3 provide all relevant information regarding the calibration. They list every model parameter including a brief description. They report the calibrated values and how we came up with them: calibrated, or restricted.

A set of parameters have been restricted in order for a set of related variables to match their empirical counterparts.<sup>25</sup> Typically, parameters have been restricted which have strong effects on the steady state but not so much on the transitional dynamics. To give one example, the consumption demand scaling parameter is restricted to ensure that GDP is equal to one at the steady state. Then, the steady states of many other variables have straightforward interpretations. To give another example, we know from the data that the ratio between renewable and fossil energy is around 0 _._ 2 in T ̈urkiye. Hence, we restrict the

> 25For the estimation, a parameter restriction implies that the affected parameter is adjusted in every iteration of the sequential estimation procedure in order to uphold the restriction. As the Bayesian algorithm travels the parameter space to assess how likely certain parameter values are the restricted parameters are updated every step of the way.

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corresponding share parameter in the CES production function of the E-firm such that the model reproduces this ratio at the steady state (but not out of the steady state!).

Let us briefly discuss the relevance of each of the model parameters:

- The inter-temporal discount factor _β_ controls to what extend households discount future utility to assess its present value. Since households own the firms the discount factor is also relevant for the discounting of future profits. Note that in DSGE models, _β_ is usually calibrated as the inverse of the average of the historical real interest rate which is implied by solution of the households optimization problem. With wealth in the utility function this relationship collapses and _β_ can be estimated.

- The parameter scaling the utility derived from wealth _ψA_ captures the precautionary saving preferences of the households. The remaining scaling parameters in the utility functions scale the utility from consumption and the disutility from labor. Since scaling parameters mainly affect the steady state values but not the transitional dynamics, they are all restricted to have certain variables or ratios of variables match their empirical counterparts, as reported in table B2.

- The consumption habit persistence parameter _κ_ controls how sluggish the response of consumption to changes in income or the interest rate. This parameter is estimated and helps the model reproduce the empirical observation that aggregate consumption typically gradually adjusts to a shock with only modest jumps. The higher _κ_ , the more gradual the adjustment.

- The parameter of relative risk aversion _φ_ is the inverse of the elasticity of inter-temporal substitution and controls how strongly consumption changes in response to a change in the real interest rate. The lower _φ_ and, hence, the higher the elasticity of intertemporal substitution, the more consumption decreases in response to an increase of the real interest rate. Note that with uncertainty about future events risk aversion and inter-temporal elasticities are related concepts. The more risk averse the household, the more reluctant she will be to give up consumption today and save it for tomorrow. This is relevant for the discounting of future profits which needs to take this preference regarding uncertainty into account in addition to the pure rate of time preference _β_ .

- The inverse of the Frisch elasticity of labor supply _η_ controls how strongly the labor hours supplied by the households respond to changes in the real wage. The higher _η_ , the lower the elasticity, and the smaller the increase of labor supply in response to an increase in the real wage.

- The risk premia on bond and equity returns have straightforward interpretations. They drive a wedge between the returns and the policy rate. The premia can be made endogenous to the financial structure of the bond and equity issuers.

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Table B2: Parameter description, calibration, and restrictions (Part A)

|Paramet|er Description|Value|Source|
|---|---|---|---|
|Househo|lds|||
|_β_|Intertemporal discount factor|0.99|Calibrated to literature|
|_ψA_|Utility from wealth scaling|1|Calibrated (only afects restrictions)<br><sup> ̃</sup>|
|_ψC_|Utility from consumption scaling|19.631|Restricted such that _GDP_ =<br>_GDP_|
|_ψH_|Disutility from labor scaling for H-hhs|554147|Restricted s.t. _LH/LL_ =<br>_LH/LL_|
|Γ_B_<br>|Flight-to-safety gov. bonds risk premium<br>|0.001<br>|Calibrated to empirical equivalent<br>|
|Γ_S_|Equity risk premium|0.013|Calibrated to empirical equivalent|
|_γW_|RoW net-debt ef. on RoW bond risk prem.|0.003|Restricted s.t. _E_(_BW/GDP_) =<br>_E_(_BW/GDP_)<br>|
|_μH_|High-skilled wage mark-up|0.2|Calibrated to literature|
|Firms||||
|_μZ_|Core good price mark-up|0.2|Calibrated to literature|
|_δZ_|Capital depreciation rate for core frms|0.025|Calibrated to literature|
|_δR_|Capital depreciation rate for renew. energy|0.03|Calibrated to literature|
|_δF_|Capital depreciation rate for fossil energy frms|0.02|Calibrated to literature|
|_φER_|Learning by doing rate|0.1|Calibrated to literature<br>|
|_λ_|All frms debt-capital ratio|0.339|Restricted s.t. <sup> ̃</sup>_BP /_ <sup> ̃</sup>_GDP_ =<br>_BP/GDP_|
|_τ_|Linearcaitalutilizationcosts|0046|Restrictedst_u_=<br>_u_|
|_Z_1|p|.|.. _Z_ <br>_Z_|
|Input sh|are parameters in Constant-Elasticity-of-Substitu<br>|tion functi<br>|ons<br>|
|_αO_|Imports share of fossil fuel consumption|0.870|Calibrated to empirical equivalent<br><sup> ̃ ̃</sup>|
|_αKZ_|Private cap. in core good cap. services|0.736|Restricted s.t. _IPZ/_ _GDP_ =<br>_IP Z/GDP_<br><sup> ̃ ̃</sup>|
|_αKR_|Private cap. in renew. energy cap. services|0.502|Restricted s.t. _IPR/_ _GDP_ =<br>_IP R/GDP_<br><sup> ̃ ̃</sup>|
|_αKF_|Private cap. in fossil energy cap. services|0.537|Restricted s.t. _IPF /_ _GDP_ =<br>_IP F/GDP_<br><sup> ̃ ̃</sup>|
|_αE_|Renew. energy to fossil energy|0.122|Restricted s.t. _pER_ _ER/pEF_ _EF_ =<br>_pERER/pEF EF_<br><sup> ̃</sup>|
|_αVZ_|Cap. in value added of core frms|0.414|Restricted s.t. ̃_pLLZ/pZ_ _Z_ =<br>_CEZ/Z_|
|_αVR_|Cap. in value-added of renew. energy|0.998|Restricted s.t. _LR/LF_ =<br>_LR/LF_<br><sup> ̃ ̃</sup>|
|_αVF_|Cap. in value-added of fossil energy|0.992|Restricted s.t.<br> ̃<br>_pL_(_LR_ + _LF_)_/_(_pER_ _ER_ + _pEF_ _EF_) =<br>_CEE/E_<br>|
|_αEF_|Value added in output of fossil frms|0.243|Restricted s.t. _O/_ <sup> ̃</sup>_GDP_ =<br>_O/GDP_|
|_αZ_|Value added in output of core frms|0.921|Restricted s.t. _EY /EZ_ =<br>_EY/EZ_<br>|
|_αY_|Wholesale goods in retail goods|0.733|Restricted s.t. ̃_pE_(_EY_ +_EZ_)_/_ <sup> ̃</sup>_GDP_ =<br>_pEE/GDP_<br><br>|
|_αW_|Domestic core goods in wholesale goods|0.815|Restricted s.t. _E_( ̃_p_<sup>_∗_</sup><br>_Z_ <sup> ̃</sup><sup>_Z∗_</sup><br>_e_ <sup>+</sup><sup>_αO_</sup> <sup> ̃</sup><sup>_p∗_</sup><br>_O_<sup>_O_)</sup><sup>_/_ ̃</sup><sup>_GDP_ =</sup><br>_M/GDP_|
|_αW ∗_|RoW core goods in RoW wholesale goods|0.742|Restricted s.t. _E_ =<br>_E_<br><sup> ̃ ̃</sup>|
|_αL_|High-skilled labor in labor services|0.762|Restricted s.t. _CH/_ _CL_ =<br>_CH/CL_|
|Taxes<br> ̃|||<sup> ̃ ̃</sup>|
|_TLL_|Transfers to L-hhs|0.081|Restricted s.t. _BG/_ _GDP_ =<br>_BG/GDP_|
|_tC_|Final sales tax rate|0.16|Calibrated s.t. cons. taxes over GDP are 0.11|
|_tA_|Wealth tax rate|0.0003|Calibrated s.t. property taxes over GDP are 0.01|
|_tR_|Tax rate on hhs. cap. income and cap. gains|0.008|Calibrated s.t. pers. inc. taxes over GDP are 0.04|
|_tH_|High-skilled labor income tax rate|0.08|Calibrated s.t. pers. inc. taxes over GDP are 0.04|
|_tL_|Low-skilled labor income tax rate|0.06|Calibrated s.t. pers. inc. taxes over GDP are 0.04|
|_tS_|Firm contr. labor tax rate (payroll tax)|0.17|Calibrated s.t. payroll taxes over GDP are 0.07|
|_tP_|Corporate income tax rate<br>|0.045<br>|Calibrated s.t. corp. inc. taxes over GDP are 0.02<br>|
|_tEF_|Fuel tax rate|0.025|Calibrated s.t. energy taxes over GDP are 0.02|
|_tO_|Carbon tax rate|1E-05|Calibrated to empirical equivalent|

_Notes_ : H-hhs and L-hhs stands for high-skilled labor households and low-skilled labor households, respectively. RoW stands for the rest of the world. Data sources reported in the main text.

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Table B3: Parameter description, calibration, and restrictions (Part B)

|Parameter|Description|Value|Restriction such that|
|---|---|---|---|
|Steady-state par|ameters|||
|_g_|Deterministic growth rate|1.0083|Calibrated to empirical equivalent|
|Π_Y_|Price infation rate|1.0199|Calibrated to empirical equivalent|
|Π_H_|High-skilled wage infation rate|1.0282|Calibrated to Π_Y_ +_g −_1|
|Π_L_|Low-skilled wage infation rate|1.0282|Calibrated to Π_Y_ +_g −_1|
|_R_|Policy rate|1.0219|Calibrated to empirical equivalent|
|_RW_|Interest rate in the RoW|1.0036|Calibrated to empirical equivalent|
|_CG_|Gov. cons.-GDP ratio|0.14|Calibrated to empirical equivalent|
|_IGZ_<br>|Gov. inv. in core goods-GDP ratio|0.036|Calibrated to empirical equivalent|
|_I_<sup>_inf_</sup><br>_GR_|Gov. infra. inv. in renew. energy-GDP ratio|0.001|Calibrated to empirical equivalent|
|_I_<sup>_gen_</sup><br>_GR_<br>|Gov. gen. inv. in renew. energy-GDP ratio|0.001|Calibrated to empirical equivalent|
|_I_<sup>_inf_</sup><br>_GF_|Gov. infra. inv. in fossil. energy-GDP ratio|0.001|Calibrated to empirical equivalent|
|_I_<sup>_gen_</sup><br>_GF_|Gov. gen. inv. in fossil energy-GDP ratio|0.001|Calibrated to empirical equivalent|
|_E_ <sup>ˆ</sup>_BW /_ <sup> ̃</sup>_GDP_|RoW net debt-GDP ratio|-1.6|Calibrated to empirical equivalent|
|_Y _<sup>_∗_</sup>|Final demand in the RoW|1|Normalized|
|_p_<sup>_∗_</sup><br>_Z_|Core goods price in the RoW|1|Normalized|
|_p_<sup>_∗_</sup><br>_O_|Carbon supply cost|0.07|Restricted s.t. _E_ ̃_p_<sup>_∗_</sup><br>_O_<sup>_O/GDP_ =</sup><br>_PFES/GDP_|
|Steady-state targ|ets|||
|_GDP_|GDP|1|Normalized|
|_E_|Real exchange rate|1|Normalized|
|_LH/LL_|High-low-skilled labor ratio|1|Calibrated to empirical equivalent|
|_LR/LF_|Renew.-fossil employment ratio|0.15|Calibrated to empirical equivalent|
|_EL_|Employment rate|0.708|Restricted s.t. _ψL ≡ψH_|
|_IP Z/GDP_|Private core investment-GDP ratio|0.233|Calibrated to empirical equivalent|
|_IP R/GDP_|Renew. investment-GDP ratio|0.003|Calibrated to empirical equivalent|
|_IP F/GDP_|Fossil investment-GDP ratio|0.004|Calibrated to empirical equivalent|
|_CEZ/Z_|Wage share of output in core frms|0.39|Calibrated to empirical equivalent|
|_CEE/E_|Wage share of output in energy frms|0.08|Calibrated to empirical equivalent|
|_BP/GDP_|Corp. bonds-GDP ratio|2.3|Calibrated to empirical equivalent|
|_pERER/pEF EF_|Renew.-fossil energy ratio|0.2|Calibrated to empirical equivalent|
|_O/GDP_|Carbon intensity of GDP as kg of CO2 per<br>USD|0.46|Calibrated to empirical equivalent|
|_EY/EZ_|Energy cons. of households-frms ratio|0.94|Calibrated to empirical equivalent|
|_pEE/GDP_|Total fnal energy cons. exp.-GDP ratio|0.067|Calibrated to empirical equivalent|
|_M/GDP_|Total imports-GDP ratio|0.26|Calibrated to empirical equivalent|
|_CH/CL_|High-low-skilled consumption ratio|2|Calibrated to empirical equivalent|
|_uZ_|Cap. utilization|1|Calibrated to empirical equivalent|
|_BG/GDP_|Gov. bonds-GDP ratio|1.32|Calibrated to empirical equivalent|
|_PFES/GDP_|Fossil primary energy supply exp.-GDP ratio|0.04|Calibrated to empirical equivalent|

_Notes_ : RoW stands for the rest of the world. Data sources reported in the main text.

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- The wage and price mark-ups _μW_ and _μP_ are defined implicitly through the substitution elasticities between labor and core good varieties, respectively. The higher these elasticities, the smaller the market power and, hence, the smaller the mark-ups.

- We calibrate sector specific depreciation rates _δZ_ , _δR_ , and _δF_ with respect to results of Argentiero et al. (2017) for EU-15 countries.

- The debt-capital ratio _λ_ is a parameter typically not found in DSGE models. This is because there is no steady-state wealth in conventional DSGE models and saving is a pure transitory phenomenon. The Modigliani-Miller theorem holds and the financial structure of the firm sector is indeterminate. With wealth in the utility function capturing precautionary saving, the financial structure does become relevant. It is governed by _λ_ which controls how much of investment is financed by corporate bonds and how much by equity.

- All production functions in the model are of the CES type. The share parameters do not have strong effects on the transition dynamics. All of them are restricted as indicated in Table B2.

- The corresponding elasticities of substitution are critical for transitional dynamics. They control how flexible the production structure can respond to price and quantity shocks. For instance, an increase in the carbon price will induce fossil energy firms to reduce the carbon intensity of fossil fuels. To the extent they succeed in this endeavor, they can limit fossil fuel price increases. Similarly energy firms will face higher fossil energy costs and try to use renewable energy instead. The elasticity of substitution between renewables and fossil energy will be crucial (as well as the availability and costs of renewables). With a rigid supply chain, price increases will be passed through, cause inflation, and induce the central bank to raise interest rates and the trade balance to deteriorate. Output effects may be considerable. Because substitution elasticities are critical, all of them are estimated.

- The learning by doing rate _φER_ captures productivity increases in renewable energy production coming with the scale of energy output. We calibrate this parameter to 0 _._ 1, which corresponds to a learning by doing elasticity of 0 _._ 152, which is close to the lower bound of findings in the literature (Rubin et al. 2015).

- The model features various quadratic adjustment costs. These costs do not affect the steady state but the short-run behavior of the model. Hence, the corresponding scaling parameters are all estimated.

- Since, empirically, employment measured in persons varies to a lesser extent than labor input measured in hours, the model assumes that only a share of labor firms can adjust low-skilled employment. A share _ξEL_ is stuck with the previous level. This Calvo-type of real rigidity allows the model to capture the different volatilities of employment and hours worked. _ξEL_ is estimated.

63
- Endogenous policy responses are characterized by elasticities. They only affect transitional dynamics (not the steady state) and are, therefore, estimated. The low-skilled wage inflation responds to changes in employment and price inflation. The stronger wages respond to the labor market, the closer the model is to a DSGE closure with labor market clearing.

- To capture automatic stabilizers, public consumption and investment respond to output and output gap elasticities are estimated.<sup>26</sup>

- The policy interest rate responds to core inflation and the output gap. Note that, contrary to DSGE models, the model does not require (but allow) the inflation elasticity of the interest rate to be larger than 1. Note that the model also requires this so-called Taylor principle to hold when the wage rate responds strongly to the labor market and the model converges to the DSGE model.

- The various tax rates are calibrated to match the corresponding tax revenues-to-GDP ratios. Note, however, that we did not strictly restrict them as they crucially affect the transitional dynamics.

- Stochastic shocks have been added to the model to improve its empirical fit. Note that without stochastic elements there would be no uncertainty and the model would be able to generate only smooth transitions. Historical data, however, include considerable noise. These stochastic elements are typically specified in an auto-regressive form, and the corresponding auto-regressive parameters are estimated. They capture how persistent shocks are.

- Finally, the model includes various parameters which have a direct, empirical equivalent. For instance, the deterministic growth rate is the average growth rate of labor productivity. The long-run or steady-state rates of inflation and the interest rate should correspond to their historical averages. The same holds for the steady-state government consumption and public investments as well as external debt. Since the RoW is specified in reduced form, the steady state values of output and core prices can be calibrated arbitrarily. The steady-state international price of carbon is restricted such that the carbon intensity of GDP matches the empirical counterpart at the steady state.

# **B.3 Empirical steady state values and ratios**

What are the steady-state values and ratios the calibration seeks to match? As mentioned above, selected model parameters which mainly affect the steady state rather than transitional dynamics are calibrated to ensure that the model reproduces certain values and ratios

> 26We restrict the output gap elasticities of investment to be equal to output gap elasticity of consumption at each iteration in estimation.

64
of variables at the steady state. The respective restrictions are reported in the last column of Tables B2 and B3.

- The consumption utility scaling parameter is set to ensure that GDP is equal to one at the steady state. This is done for convenience only as most other steady-state values then have a clear interpretation as ratios to GDP.

- The share parameter in RoW wholesale production function is restricted to give real exchange rate equal to 1 at the steady state.

- The ratio of high-skilled and low-skilled labor hours is roughly 1, taken from main labor force indicators by education level data of TurkStat. The corresponding consumption ratio is around 2, calculated using distribution of consumption over income quantiles data of TurkStat under the assumption that low-skilled households constitute the lowest 50% of the income distribution. Hence, H-households consume twice as much as L-households while they provide the same number of labor hours. This is possible because the formers’ real wage is higher, and they receive capital income.

- The share of private non-energy, renewable energy and fossil energy investment to GDP ratios are around 23.3%, 0.3% and 0.4%, respectively. Total investment share of GDP comes from GLORIA database. We use World Input Output Database (WIOD) to split investment to core and energy.

- The annualized public net debt-GDP, corporate net debt-GDP, and RoW net debtGDP ratios are around 33%, 57%, and -40%, respectively, from the Financial Accounts Reports of the Central Bank of the Republic of T ̈urkiye (CBRT).

- The ratio of renewable and fossil energy is around 0.2, averaged over 2019-2021 from the energy balance of IEA<sup>27</sup> . We assume the ratio of final use (residential, public, and half of transportation) and intermediate use (industry, and half of transportation) of the TFC is 0.94, average value over 2000-2022. Finally, about 87% of the Turkish TPES is imported, coming from GLORIA database. In the model, this corresponds to the share of imported carbon in total carbon input.

- Wage share of output in core goods and energy sectors are around 39% and 0.08%, calculated from GLORIA database.

- Renewable to fossil energy employment ratio is 0.15, calculated from Global Trade Analysis Project (GTAP)-Power and Gender Disaggregated Labor Database (GDLD) of the World Bank.

> 27To obtain the renewable-fossil energy ratio, total final consumption (TFC) would be the appropriate measure. Yet, it is unclear to what extent electricity is generated by renewables and fossil energy. Hence, we use the ratio of total primary energy supply (TPES) as a proxy.

65
- We scale the carbon input to energy production to match the carbon intensity of GDP: 0.46 kg CO2 per 2015 $ of GDP, averaged over 2000-2022, from World Development Indicators of the World Bank.

- The value of total energy consumption as a share of GDP is 7.1%, calculated from GLORIA database.

- The constant USD price of carbon is restricted to attain primary fossil energy supply expenditure-GDP ratio of 3.9%, from GLORIA database.

- The ratio of total imports to GDP is around 26%, calculated from GLORIA database.

Note that the steady state values of a few important variables have a direct equivalent in the data and have been calibrated accordingly. These values can be set directly and do not require another parameter to be restricted. They are reported in Tables B2 and B3.

# **B.4 Estimation strategy**

In this subsection, we discuss our Bayesian estimation strategy together with the results. The basic idea of Bayesian statistics comes from the Bayes’ rule: where _θ_ is the vector of parameters, _p_ ( _θ|_ **y** ) is the probability distribution of parameters given data (posterior distribution), _p_ ( **y** _|θ_ ) is the probability distribution of data given parameters (likelihood), _P_ ( _θ_ ) is the prior distribution of _θ_ and _p_ ( **y** ) is the marginal distribution of data. We are interested in marginal posterior distribution of model parameters; however it is not always possible to analytically compute marginal posterior distributions due to complex integral calculations.

Bayesians, therefore, have developed numerical methods that allow us to approximate marginal posterior distributions of parameters. The basic idea is to draw from some other distribution (proposal or importance distributions) and find moments of simulated data, which are going to converge to moments of posterior distribution under specific conditions (Herbst and Schorfheide 2015).

We use Metropolis Hastings (MH) algorithm, which is part of MCMC family. MH algorithm simply evaluates each parameter vector drawn ( _θ_<sup>_i_</sup> at iteration i) from a proposal distribution at the kernel of the posterior distribution over the proposal distribution, and compares this to the ratio of the vector stored in iteration (i-1). If the ratio of the former to the latter is higher than a random number u _∈_ (0 _,_ 1), then candidate draw is accepted and stored. If not, _θ_<sup>_i−_1</sup> is stored again among accepted parameter vectors.

There are two important components of the MH algorithm. One is the proposal distribution, which is assumed to be a multivariate normal distribution with mean _θ_<sup>_i−_1</sup> (last 66
accepted draw) and a covariance matrix Σ, which is calculated as the inverse Hessian at the mode. The second component is the likelihood function, which must be evaluated before the MH algorithm starts running.

We apply the Kalman filter to the linear state-space form, which includes a state equation describing the evolution of states based on their past values and a measurement equation showing how states are related to data, to get the likelihood.

for t=1,...,n where Σ<sup>_s_</sup> _t_<sup>andΣ</sup><sup>_m_</sup> _t_<sup>arecovariancematricesofstateandmeasurementshocks</sup> _εt,s_ and _εt,m_ . Kalman filter simply operates on two steps: It first makes a prediction about a new state **st** based on the state transition equation and then corrects for it when new data **yt** become available. If **yt** are missing, Kalman filter still guesses **st** using the prediction step. Then, it predicts missing **yt** using the measurement equation. This feature of the Kalman filter makes adding mixed frequency or missing observations possible. For example, we have energy balances available at the annual frequency. We simply feed what we have to the Kalman filter and leave missing quarterly observation as NaN. Similarly, public/private investment data are not published after 2016 Q2. We feed only available data to the filter and leave the remaining values as NaN. Kalman filter fills these missing observations by using the best available information.

We use the software Dynare 5.3 on Matlab to simulate posterior distribution of 85 model parameters. We run two chains of MH algorithms with 50,000 iterations each, and burn in the first half of the accepted draws. We choose the scaling parameter of the proposal distribution such that the acceptance ratio is around 0.25.

# **B.5 Prior distributions**

Tables B4 and B5 report the prior and posterior distributions of all estimated model parameters. We generally choose loose priors to let the data speak as freely as possible. We take the means of prior distributions of household preference parameters from Smets and Wouters (2007) and Christoffel et al. (2008). The elasticity of substitution between highand low-skilled labor is centered at 1 _._ 5 with a standard deviation of 0 _._ 5 (Cantore et al. 2017).

The prior distributions of the domestic Armington elasticity (between domestic and foreign goods) and the corresponding elasticity of the RW are taken from Albonico et al. (2019). The mean of the prior distribution of the elasticity of substitution between core and energy consumption goods comes from the posterior mean of oil and core consumption elasticity in An et al. (2011). The mean elasticity of substitution between value-added and energy is 0 _._ 5 – a common value in the literature reported by Van der Werf (2008). The standard deviation is 0 _._ 1.

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Table B4: Parameter description and estimation

|Parame|ter Description|Prior|Prior|Prior|Posterior|90%<br>HPD|
|---|---|---|---|---|---|---|
|||dist.|mean|std.|mean|interval|
|Househ|olds||||||
|_κ_|Consumption habit persistence|_B_|0.7|0.05|0.62|[0_._54_,_0_._71]|
|_ρ_|Inverse elast. of intertemp. subst.|_G_|1.5|0.375|1.08|[0_._70_,_1_._49]|
|_η_|Inverse Frisch elast. of labor supply|_G_|2|0.25|1.73|[1_._33_,_2_._09]|
|Elastici|ties of substitution in CES functions between||||||
|_σZ_|Value added & energy for core goods frms|_G_|0.5|0.1|0.75|[0_._65_,_0_._85]|
|_σL_|High & low skilled labor for labor services|_G_|1.5|0.5|2.94|[2_._66_,_3_._20]|
|_σY_|Core goods & energy for retail frms|_G_|0.3|0.1|0.14|[0_._07_,_0_._20]|
|_σF_|Value added & carbon for fossil energy frms|_G_|0.3|0.1|0.05|[0_._03_,_0_._07]|
|_σVR_|Capital & labor for renew. energy frms|_G_|0.3|0.05|0.30|[0_._22_,_0_._39]|
|_σVF_|Capital & labor for fossil energy frms|_G_|0.3|0.05|0.28|[0_._20_,_0_._35]|
|_σW_|Dom. & RoW core goods for wholesale frms|_G_|2|0.4|1.57|[1_._45_,_1_._68]|
|_σW ∗_|RoW & dom. core goods for RoW wholesale frms|_G_|2|0.4|1.31|[0_._82_,_1_._82]<br>|
|_σE_|Renew. & fossil energy for energy frms|_G_|6|4|18.51|[10_._12_,_26_._56]|
|Scaling|parameters||||||
|_ψu_|Scaling parameter for capacity utilization rate|_N_|2|1|2.68|[1_._69_,_3_._74]|
|Adjust|ment cost scaling parameters||||||
|_τW_|Quadratic high-skilled wage adjustment costs|_G_|50|20|92.86|[56_._78_,_128_._61]|
|_τP_|Quadratic core good price adjustment costs|_G_|50|20|59.90|[36_._26_,_83_._11]|
|_τI_|Quadratic investment adjustment costs|_G_|10|4|4.49|[2_._84_,_6_._39]|
|_τZ_2|Quadratic capital utilization costs|_G_|0.5|0.1|0.34|[0_._20_,_0_._47]|
|_τO_|Quadratic carbon input adjustment costs|_G_|5|1|3.29|[2_._21_,_4_._33]|
|_τZe_|Quadratic export adjustment costs<br>|_G_<br>|5|1|4.33|[2_._95_,_5_._70]<br>|
|_τE_|Quadratic renew. adjustment costs|_G_|2|1.25|0.80|[0_._13_,_1_._48]|
|Adjust|ment elasticities||||||
|_ξEL_|Calvo-style employment parameter|_B_|0.5|0.15|0.96|[0_._95_,_0_._97]|
|_φWE_|Empl. elast. of low-skilled wage inf.|_N_|1|0.5|1.29|[0_._74_,_1_._82]|
|_φWP_|Price inf. elast. of low-skilled wage inf.|_N_|1|0.5|0.67|[0_._24_,_1_._10]|
|_φCY_|Output gap elast. of gov. cons.|_N_|0|0.1|-0.12|[_−_0_._24_,_0_._02]|
|_φR_Π|Core inf. elast. of policy rate|_N_|1.1|0.2|1.22|[1_._01_,_1_._42]|
|_φRY_|Output gap elast. of policy rate|_N_|0.5|0.2|0.02|[_−_0_._05_,_0_._09]|
|Persiste|nce parameters||||||
|_ρWD_|Domestic input productivity shock for wholesale frms|_B_|0.5|0.1|0.75|[0_._67_,_0_._82]|
|_ρWI_|Imported input productivity shock for wholesale frms|_B_|0.5|0.1|0.76|[0_._67_,_0_._85]|
|_ρW ∗_|Export demand shock for RoW wholesale frms|_B_|0.5|0.1|0.29|[0_._18_,_0_._41]|
|_ρR_|Interest rate smoothing of the home country|_B_|0.8|0.1|0.90|[0_._88_,_0_._92]|
|_ρC_|Consumption preference shock|_B_|0.5|0.1|0.32|[0_._20_,_0_._45]|
|_ρNH_|High-skilled labor supply preference shock|_B_|0.5|0.1|0.40|[0_._27_,_0_._53]|
|_ρNL_|Low-skilled labor supply preference shock|_B_|0.5|0.1|0.45|[0_._35_,_0_._56]|
|_ρRB_|Flight to safety shock|_B_|0.5|0.1|0.53|[0_._45_,_0_._62]|
|_ργW_|Foreign bond risk premium shock|_B_|0.5|0.1|0.65|[0_._55_,_0_._76]|
|_ρRS_|Equity risk premium shock|_B_|0.5|0.1|0.46|[0_._33_,_0_._57]|
|_ρPZ_|Core good price mark up shock|_B_|0.5|0.1|0.66|[0_._55_,_0_._77]|
|_ρVZ_|Productivity shock for core goods frms|_B_|0.5|0.1|0.69|[0_._56_,_0_._81]|
|_ρZE_|Energy efciency shock to core goods frms|_B_|0.5|0.1|0.75|[0_._64_,_0_._86]|
|_ρVF_|Productivity shock for fossil energy frms|_B_|0.5|0.1|0.67|[0_._56_,_0_._78]|

_Notes_ : RoW stands for the rest of the world. Data sources reported in the main text.

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Table B5: Parameter description and estimation

|Paramet|er Description|Prior|Prior|Prior|Posterior|90%<br>HPD|
|---|---|---|---|---|---|---|
|||dist.|mean|std.|mean|interval|
|Persisten|ce parameters (continued)||||||
|_ρVR_|Productivity shock for renew. energy frms|_B_|0.5|0.1|0.71|[0_._56_,_0_._86]|
|_ρE_|Productivity shock for energy frms|_B_|0.5|0.1|0.75|[0_._68_,_0_._82]|
|_ρKZ_|Productivity shock for core goods capital bundlers|_B_|0.5|0.1|0.64|[0_._55_,_0_._74]|
|_ρCG_|Government consumption shock|_B_|0.5|0.1|0.66|[0_._57_,_0_._75]|
|_ρIG_|Government investment shock|_B_|0.5|0.1|0.70|[0_._61_,_0_._79]<br>|
|_ρY ∗_|Demand shock in the RoW|_B_|0.75|0.05|0.78|[0_._72_,_0_._84]|
|_ρpZ∗_|Core goods price shock in the RoW|_B_|0.75|0.05|0.89|[0_._86_,_0_._92]|
|_ρWL_|Low-skilled wage infationsmoothing|_B_|0.5|0.1|0.24|[0_._14_,_0_._34]|
|_ρDE_|Windfall proft shock for energy frms|_B_|0.5|0.1|0.77|[0_._71_,_0_._83]|
|_ρLH_|High-skilled input productivity shock for labor frms|_B_|0.5|0.1|0.59|[0_._43_,_0_._76]|
|_ρLL_|Low-skilled input productivity shock for labor frms|_B_|0.5|0.1|0.76|[0_._69_,_0_._83]|
|_ρY_|Productivity shock for retail frms|_B_|0.5|0.1|0.75|[0_._66_,_0_._84]|
|_ρpO∗_|Fossil fuel price shock|_B_|0.75|0.05|0.85|[0_._81_,_0_._89]|
|_ρ_Π_∗_|RoW infation shock|_B_|0.5|0.1|0.32|[0_._21_,_0_._42]|
|Shock pa|rameters||||||
|_εWL_|Low-skilled wage infation shock|_IG_|0.1|1|0.047|[0_._0309_,_0_._0619]|
|_εR_|Home country interest rate shock|_IG_|0.01|0.01|0.0045|[0_._0039_,_0_._0051]|
|_εRW_|RoW long-term interest rate shock<br>|_IG_<br>|0.001<br>|0.001<br>|0.0011<br>|[0_._0009_,_0_._0012]<br>|
|_εC_|Consumption preference shock|_IG_|0.1|1|0.216|[0_._118_,_0_._299]|
|_εNH_|High-skilled labor supply preference shock|_IG_|0.1|1|4.153|[2_._490_,_5_._656]|
|_εNL_|Low-skilled labor supply preference shock|_IG_|0.1|1|4.200|[2_._503_,_5_._943]|
|_εRB_|Flight to safety shock|_IG_|0.01|0.01|0.018|[0_._015_,_0_._020]<br>|
|_εRS_|Equity risk premium shock|_IG_|0.1|1|0.140|[0_._066_,_0_._235]<br>|
|_εPZ_|Core good price mark-up shock<br>|_IG_<br>|0.1|1|0.806|[0_._513_,_1_._069]<br>|
|_εZE_|Energy efciency shock to core goods frms|_IG_|0.1|1|0.248|[0_._115_,_0_._384]<br>|
|_εVZ_|Productivity shock for core goods value-added frms|_IG_|0.1|1|0.033|[0_._025_,_0_._042]|
|_εVR_|Productivity shock for renew. energy frms|_IG_|0.1|1|0.088|[0_._052_,_0_._121]<br>|
|_εE_|Productivity shock for energy frms|_IG_|0.1|1|0.082|[0_._072_,_0_._092]|
|_εVF_|Productivity shock for fossil energy frms|_IG_|0.1|1|0.266|[0_._193_,_0_._333]|
|_εDE_|Windfall proft shock for energy frms|_IG_|0.1|1|0.005|[0_._004_,_0_._006]|
|_εLH_|High-skilled input productivity shock for labor frms|_IG_|0.1|1|0.043|[0_._026_,_0_._061]|
|_εLL_|Low-skilled input productivity shock for labor frms|_IG_|0.1|1|0.258|[0_._199_,_0_._314]|
|_εKZ_|Productivity shock for core goods capital bundlers|_IG_|0.1|1|0.076|[0_._063_,_0_._091]|
|_εCG_|Government consumption shock|_IG_|0.1|1|0.040|[0_._035_,_0_._045]|
|_εIG_|Government investment shock|_IG_|0.1|1|0.100|[0_._085_,_0_._112]|
|_εY ∗_|Demand shock in the RoW|_IG_|0.01|0.01|0.017|[0_._015_,_0_._020]|
|_εpZ∗_|Core goods price shock in the RoW|_IG_|0.01|0.01|0.010|[0_._009_,_0_._011]|
|_εγW_|Foreign bond risk premium shock|_IG_|0.1|1|0.148|[0_._100_,_0_._196]|
|_εY_|Productivity shock for retail frms|_IG_|0.1|1|0.037|[0_._030_,_0_._044]|
|_εWD_|Domestic input productivity shock for wholesale frms|_IG_|0.1|1|0.049|[0_._040_,_0_._057]|
|_εWI_|Imported input productivity shock for wholesale frms|_IG_|0.1|1|0.130|[0_._105_,_0_._155]|
|_εW ∗_|Export demand shock for RoW wholesale frms|_IG_|0.1|1|0.378|[0_._209_,_0_._554]|
|_εpO∗_|Global fossil fuel price shock|_IG_|0.1|1|0.092|[0_._081_,_0_._103]|
|_ε_Π_∗_|RoW infation shock|_IG_|0.01|0.01|0.0031|[0_._0028_,_0_._0035]<br>|
|_εGDPm_|Output measurement shock|_IG_|0.01|0.01|0.035|[0_._031_,_0_._039]|
|_εInvm_|Investment measurement shock|_IG_|0.01|0.01|0.087|[0_._076_,_0_._100]|

_Notes_ : RoW stands for the rest of the world. Data sources reported in the main text.

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The elasticity of substitution between value-added and carbon is an original feature of the model which implicitly captures the substitution between different fossil fuels (coal&gas, coal&oil, oil&gas). We prefer a low prior mean at 0 _._ 3, with a standard deviation of 0 _._ 1. The long-run elasticity of substitution between renewable and fossil energy, is a critical parameter of the model. We choose the mean of 6 based on Varga et al. (2022) and set a loose prior with a standard deviation of 4. Similar to Guerrieri et al. (2008), we introduce a renewable energy-input adjustment cost to energy firms to get time-varying elasticity of substitution which is low in the short-run and converges to the long-run value. For the adjustment cost scaling parameter we choose a prior with mean 2 and standard deviation 1 _._ 25.

For the adjustment cost scaling parameters, we use common priors from the literature (see Smets and Wouters 2007, Christoffel et al. 2008, Albonico et al. 2019). For the employment and inflation elasticities of nominal wage setting, we use normal distributions with mean 1 and standard deviation 0 _._ 5 to cover to a wide range of possible values. For public consumption rule, we choose an output gap elasticity centered at 0 to let data decide whether these policies are counter- or pro-cyclical.<sup>28</sup> For monetary policy parameters, we deviate from the standard literature which assumes large inflation response parameters (1 _._ 5 and above) in the Taylor rule. Instead, given the weak monetary policy regime in T ̈urkiye post-2010, we choose a lower prior 1 _._ 1 for the inflation response parameter with standard deviation of 0 _._ 2 (G ̈urkaynak et al. 2022). For the output-gap elasticity, we follow the literature and set a normal prior with mean 0 _._ 5 and standard deviation 0 _._ 2.

For most of the shock persistence parameters, we use beta priors centered at 0 _._ 5 with a standard deviation of 0 _._ 1. For foreign demand, import and fossil fuel price shocks, which are highly autoregressive, we prefer priors with mean 0 _._ 75 and standard deviation 0 _._ 05. We center the prior of the interest rate smoothing parameter to 0 _._ 8, following Christoffel et al. (2008). For almost all shock processes we choose inverse gamma distributions with mean 0 _._ 1 and standard deviation 1. For domestic interest rates and all foreign shocks, we prefer lower prior means and standard deviations.

# **B.6 Posterior distributions**

Posterior distributions of structural parameters are plotted in Figure B1. All behavioural parameters of the households are identified and in line with the literature. Our estimate of consumption habit persistence is similar to the result of Christoffel et al. (2008) for the EU but less than estimates of Smets and Wouters (2007) for the US. Our mean of the inverse Frisch elasticity of labor supply is similar to the finding of C ̧ebi (2012) for T ̈urkiye.<sup>29</sup> On the other hand, we estimate the risk aversion parameter lower than C ̧ebi (2012) and what Smets and Wouters (2007) and Albonico et al. (2019) estimate for the US and EU, respectively.

28 Since they are not identified separately, we assume that the output-gap elasticities of public investment are equal to the elasticity of public consumption at each iteration of the Metropolis Hastings algorithm. 29We only estimate the inverse Frisch elasticity of labor supply for high skilled households and assume that low-skilled parameters is equal to that in each iteration of the Metropolis-Hastings algorithm.

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This is not surprising given that our model features wealth in the utility function to capture precautionary saving motives explicitly.

All elasticities of substitution are identified by data except the capital-labor elasticities for renewable energy firms. We estimate value-added and energy elasticity as 0 _._ 75, which is slightly above the range of industry estimates in Van der Werf (2008). Our estimate for the elasticity of substitution between carbon and value-added in fossil energy sector is very low (0 _._ 05), which indicates the difficulty of substituting out carbon in fossil energy production and implicitly the low degree of substitution between fossil fuels with different carbon contents in T ̈urkiye. Note that this makes the incidence and effects of carbon and fuel taxes almost identical in our model.

We estimate the elasticity of substitution between high and low skilled labor around 3, which is close to the upper limit in the literature (Havranek et al. 2020). The reason is the high correlation between the high- and low-skilled wage time series. The mean of the domestic Armington elasticity is lower than the prior and close to the Albonico et al. (2019) results for EU. The export demand elasticity has a mean 1 _._ 31, close to the calibration of Millard (2011) for the UK.

The long-run elasticity of substitution between renewable and fossil energy is strongly identified by data with a posterior mean of 19 and a 90% High Density Interval (HDI) of [10 _,_ 27]. We estimate the elasticity of substitution between energy and non-energy final goods around 0 _._ 14 which is lower than the posterior mean of An et al. (2011) with DSGE (0 _._ 3) and DSGE-VAR (0 _._ 2) specifications for the Republic of Korea.

Posterior means of wage and price adjustment costs are higher than prior means indicating substantial wage and price adjustment costs in T ̈urkiye. We estimate investment adjustment cost of around 5, in line with the results of Christoffel et al. (2008) and Smets and Wouters (2007), and capital utilization adjustment cost around 0 _._ 34. Our mean estimate for export adjustment cost is 4 _._ 3. We estimate mean carbon input adjustment cost as 3 _._ 3 and renewable input adjustment cost as 0 _._ 8.

We estimate the Calvo (1983) style employment parameter to be higher than the value found by Christoffel et al. (2008), which indicates that a large share of firms in T ̈urkiye do not adjust employment with variations in low-skilled labor hours. In the Phillips curve equation, the employment elasticity of wage inflation is around 1 _._ 29, and the wage indexation parameter is 0 _._ 67. They point to a significant demand impact on low-skilled wage inflation. We estimate the output-gap elasticity of public expenditures around _−_ 0 _._ 12 which suggests that these expenditures are pro-cyclical in T ̈urkiye.

We estimate the interest rate smoothing parameter as 0 _._ 9 which is higher than C ̧ebi (2012)’s finding (0 _._ 62) for T ̈urkiye over 2002Q1-2009Q3. This is not surprising as the Central Bank of T ̈urkiye has not been changing rates in response to inflation in the second half of our sample. Similarly, our inflation coefficient is around 1 _._ 22 capturing loose monetary policy in T ̈urkiye post-2010 unlike C ̧ebi (2012)’s estimate of 1 _._ 75 which points to a strictly inflation targeting monetary policy. Our estimate for output gap elasticity is around 0 _._ 02.

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<!-- Start of picture text --> Consumption habit persistence Inverse elast. of intertemp. subst. Inverse Frisch elast. of labor supply<br>1.5 1.5<br>6<br>1 1<br>4<br>2 0.5 0.5<br>0 0 0<br>0.3 0.4 0.5 0.6 0.7 0.8 0.9 -0.5 0 0.5 1 1.5 2 2.5 3 0.5 1 1.5 2 2.5 3 3.5<br>E.o.s between capital and labor for renew. E.o.s between capital and labor for fossil E.o.s between value-added and energy<br>8 8 6<br>6 6<br>4<br>4 4<br>2<br>2 2<br>0 0 0<br>0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0 0.1 0.2 0.3 0.4 0.5 0.6 0.2 0.4 0.6 0.8 1 1.2<br>E.o.s between value added and carbon E.o.s between high and low-skilled labor Import price elasticity<br>30 2<br>1.5 4<br>20<br>1<br>10 0.5 2<br>0 0 0<br>0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0 0.5 1 1.5 2 2.5 3 3.5 4 0.5 1 1.5 2 2.5 3 3.5<br>Export price elasticity E.o.s between renewable and fossil energy E.o.s between core goods and energy<br>10<br>1 0.1<br>5<br>0.5 0.05<br>0 0 0<br>-0.5 0 0.5 1 1.5 2 2.5 3 3.5 -10 0 10 20 30 40 50 -0.2 0 0.2 0.4 0.6 0.8<br>High-skilled wage adjustment cost Core good price adjustment cost Investment adjustment cost<br>0.02<br>0.015 0.02 0.3<br>0.01 0.2<br>0.01<br>0.005 0.1<br>0 0 0<br>-50 0 50 100 150 200 250 300 -50 0 50 100 150 -5 0 5 10 15 20 25 30<br>Renewable input adjustment cost Carbon input adjustment cost Export adjustment cost<br>0.6<br>0.8 0.4<br>0.6 0.4<br>0.4 0.2<br>0.2<br>0.2<br>0 0 0<br>-2 0 2 4 6 8 10 -2 0 2 4 6 8 10 -2 0 2 4 6 8 10<br>Calvo empl. param. Empl. elas. of low-skilled wage infl. Price infl. elas. of low-skilled wage infl.<br>60 1 1.5<br>40 1<br>0.5<br>20 0.5<br>0 0 0<br>0 0.2 0.4 0.6 0.8 1 -1 0 1 2 3 4 -1 -0.5 0 0.5 1 1.5 2 2.5 3<br>Output gap elas. of gov. cons. 3 Core infl. elas. of policy rate Output gap elas. of policy rate<br>4 8<br>3 2 6<br>2 4<br>1<br>1 2<br>0 0 0<br>-0.6 -0.4 -0.2 0 0.2 0.4 0.5 1 1.5 2 -0.2 0 0.2 0.4 0.6 0.8 1 1.2<br><!-- End of picture text --> Figure B1: Prior (gray) and posterior (black) distribution of structural model parameters. Dashed green lines represent modes of the posterior. Two chains of Random Walk Metropolis Hastings simulations, each with 50,000 iterations and 25,000 burn-in. Acceptance rate is around 0.25.

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# **B.7 Sensitivity analysis**

The sensitivity analysis determines which parameters are crucial for the stability of the model. We use Dynare’s global sensitivity analysis toolkit. The tool first samples from a distribution (prior range, prior/posterior distributions) and groups draws into two subsets: Violating behavior and not-violating behavior. Behavior can be defined flexibly such as stability or indeterminacy of the model. Then, it plots cumulative density functions of behavior (blue) and non-behavior (red). It conducts Kolmogorov-Smirnov test under the null hypothesis that two distributions are identical against they are different. In the diagonals of Figure B2, as the vertical distance between blue and red density functions increases, the probability of rejecting the null goes up. We simulate from the prior range to investigate stability in a wide range and plot parameters with p-values less than 0 _._ 001 in their order of significance.

Model stability is sensitive to the long-run elasticity of substitution between renewable and fossil energy _σE_ , elasticity of substitution between value-added and energy _σSZ_ , and monetary policy - wage inflation rule parameters. High _σE_ reduces the probability of finding a solution. Blue line above red line indicates that stable solutions accumulate at low values of _σE_ .

Monetary policy parameters are important for the indeterminacy of the model. The second panel of Figure B2 zooms in indeterminacy and shows two distinct monetary policy regimes in covariance plots of _φRP_ and _φWE_ . Unlike a standard DSGE model, a less than one-to-one response to inflation in Taylor Rule ( _φRP ≤_ 1) does not necessarily cause indeterminacy. If Phillips curve coefficient is negative ( _φWE ≤_ 0), no restriction is needed for _φRP_ for the rational expectations solution. On the other hand, if _φWE >_ 0, the model converges to the DSGE case: _φRP_ must be greater than 1 to rule out indeterminacy.<sup>30</sup>

> 30Given high estimated _φW E_ for T ̈urkiye, the model cannot explore the _φRP <_ 1 region. Investigating regime switches further is in our research agenda.

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<!-- Start of picture text --> 50<br>0<br>2<br>1<br>0<br>1<br>0.5<br>0<br>4<br>2<br>0<br>-2<br>4<br>2<br>0<br>-2<br>0 50 0 1 2 0 0.5 1 -2 0 2 4 -2 0 2 4 0 1 2<br>unique Stable Saddle-Path NO unique Stable<br>2<br>1<br>0<br>4<br>2<br>0<br>-2<br>2<br>1<br>0<br>1<br>0.5<br>0<br>0 1 2 -2 0 2 4 0 1 20 0.5 1 0 50<br>NO indeterminacy indeterminacy<br>sigE phiRP sigSZ phiWP phiWE phiRY<br>sigE<br>phiRP<br>sigSZ<br>phiWP<br>phiWE<br>phiRY<br>phiRP phiWE phiRY sigSZ sigE<br>phiRP<br>phiWE<br>phiRY<br>sigSZ<br>sigE<br><!-- End of picture text --> Figure B2: Sensitivity analysis for the stability and indeterminacy of the model.

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