Environmental Subsidies to Mitigate Net-Zero Transition Costs

Layer 1: Overview

This paper asks whether public subsidies to green-technology producers, financed by a carbon tax, can materially reduce the macroeconomic cost of reaching net-zero CO2 emissions by 2060. The motivation is a market-structure failure that standard environmental models ignore: the abatement goods sector is initially immature and highly concentrated, with 10 percent of firms capturing roughly 80 percent of operating revenue (Eurostat/Ecorys data). Under such conditions a carbon tax alone raises the cost of abatement inputs, depresses competition, and generates a deep and prolonged GDP recession — even if it achieves the emissions target. The paper shows that redirecting carbon tax revenues toward subsidizing this sector can substantially offset the recession.

The analytical vehicle is an environmental dynamic stochastic general equilibrium (E-DSGE) model for the world economy, built by merging three bodies of work: the DICE climate block (Nordhaus 1992, 2018), a real-business-cycle production structure in the spirit of Smets and Wouters (2007), and an endogenous market-structure framework for the abatement goods sector following Bilbiie, Ghironi, and Melitz (2012). Firm entry into the abatement sector responds to expected future profits, which depend on sunk costs. Two margins of adjustment are distinguished: the intensive margin (existing firms expanding production) and the extensive margin (startups creating new varieties). Competition in the abatement sector is a central object of analysis: higher firm numbers reduce the abatement price, which in turn lowers the carbon tax burden on final-goods producers.

The model is estimated using Bayesian methods on five annual world time series from 1961 to 2019: real GDP growth, real consumption growth, CO2 emissions growth, the change in surface temperature anomaly, and the growth rate of environment-related patents (OECD). Because the model has stochastic growth trends, the authors use the extended-path solution method (Fair and Taylor 1983) rather than standard linearization, and an inversion filter to form the likelihood function. Posterior draws from 320,000 MCMC iterations (8 parallel chains, ~30 percent acceptance) pin down five structural parameters and ten shock parameters. Estimated initial output growth is approximately 4.99 percent per year and the initial emissions-to-output decoupling rate is 1.13 percent per year, both consistent with Nordhaus (1992) benchmarks. The temperature elasticity to radiative forcing (ξ_T) is estimated at 0.084, the abatement-sector exit rate at 0.06, and the entry congestion cost at 5.63.

The paper implements projections from 2019 to 2100 under three IPCC-aligned scenarios (SSP1–1.9, SSP2–4.5, SSP3–7.0), focusing on the Paris Agreement target of limiting warming to below 2 degrees Celsius. In the laissez-faire (no-policy) scenario, emissions peak near 57 Gt CO2 in 2060 and 70 Gt in 2100, producing roughly 4 degrees Celsius of warming by 2100, with damages reaching 4 percent of GDP per year. In the below-2-degree scenario with a carbon tax only, the carbon tax must rise to approximately $480 per ton by 2080, abatement cost reaches 3.4 percent of GDP in 2060, and cumulative GDP loss from 2019 to 2060 totals $258 trillion (averaging $6.3 trillion per year, or 4.9 percent of 2019 world GDP). This is the baseline against which subsidies are evaluated.

Two subsidy experiments are run, both fully financed by carbon tax revenue (budget neutral by construction). First, a subsidy targeted only at incumbent abatement firms (intensive margin): this immediately compresses the abatement price from 2.5 times to 1.5 times the price of the final good, reduces aggregate abatement cost from 2 percent to 0.8 percent of GDP in 2040, and brings the carbon tax needed to hit the emissions target down from $300 to $160 per ton in 2040. However, by lowering incumbents’ labor costs and raising the equilibrium wage, the intensive-margin subsidy raises the cost of startup entry and reduces the number of abatement firms over time, deteriorating long-run competition.

Second, an optimal subsidy that allocates carbon revenues between incumbents and startups. The optimal split is determined by maximizing social welfare (the infinite discounted sum of household utility) over a grid of subsidy shares. The welfare function is concave in the startup share, with a maximum at 60 percent of revenues to startups and 40 percent to incumbents. Under this optimal policy, the number of firms in the abatement sector nearly doubles relative to the baseline by 2050, the abatement price falls sharply, and the carbon tax needed to achieve the same emissions path drops to $125 per ton in 2040 versus $300 in the no-subsidy baseline. Cumulative GDP loss from 2019 to 2060 falls to $141 trillion ($138 trillion in one presentation, $141 trillion in another), saving approximately $120 to $123 trillion relative to the carbon-tax-only scenario, equivalent to roughly $2.9 trillion per year. The abatement price is reduced by more than a factor of 2.5 under the optimal subsidy regime.

Present-value GDP subsidy multipliers (the ratio of discounted GDP gain to discounted subsidy expenditure) exceed 2.0 through 2035 and remain above 1.78 through 2060, with consumption multipliers ranging from 1.42 to 1.90 over the same horizon. These large multipliers reflect the competition-enhancing effect of startup subsidies: by accelerating firm entry, the policy lowers abatement prices for all final-goods producers, amplifying the direct subsidy impact. The largest GDP gains are concentrated in the first decade (2019–2030), when subsidies rapidly reduce the abatement price and induce firm entry. The scope condition for these results is the below-2-degree (SSP1–1.9) scenario with a simultaneous carbon-tax-and-subsidy announcement in 2019, a world-representative aggregate model, and the assumption that carbon tax revenues are fully recycled into the abatement sector rather than used for general government expenditure.

Layer 2: Deep Dive

What is the central market failure the paper addresses, and why does it make a carbon tax alone insufficient?

The abatement goods sector is initially immature and highly concentrated (10 percent of firms account for roughly 80 percent of operating revenue). In the decentralized equilibrium, each final-goods firm is atomistic with respect to climate damage and so does not voluntarily abate. The carbon tax corrects this free-rider problem, but because the abatement market is imperfectly competitive, abatement goods are priced at a monopolistic markup (the abatement price begins at 2.5 times the price of the final good). The high abatement price raises the cost of reducing emissions, depresses the optimal abatement effort, and magnifies the GDP recession. A carbon tax alone thus generates a $258 trillion cumulative GDP loss by 2060. The paper’s main point is that subsidizing entry into the abatement sector introduces competition that compresses the markup, lowering both the abatement price and the required carbon tax rate.

What is the model’s identification strategy and what are the main econometric challenges?

The model is identified through full-information Bayesian maximum likelihood on five world aggregate series, 1961–2019. Climate block parameters are largely taken from DICE (Nordhaus 1992, 2018), narrowing the estimation to five structural parameters: initial output growth rate, initial emissions-to-output decoupling rate, temperature elasticity to radiative forcing (ξ_T), abatement-sector exit rate (δ_A), and entry congestion cost (χ). The main econometric challenges are (i) stochastic growth trends, which make standard linearization around a fixed point invalid — addressed with the extended-path solution method — and (ii) forming the likelihood for a nonlinear model, addressed with an inversion filter (Fair and Taylor 1983; Guerrieri and Iacoviello 2017) rather than computationally expensive particle filters. A drawback acknowledged by the authors is that Jensen’s inequality collapses to equality in the extended-path approach, so nonlinear uncertainty from future shocks is not captured — the same limitation that applies to standard linearized DSGE models.

How are the intensive and extensive margins of adjustment to the carbon tax distinguished in the model, and why does this distinction matter for policy?

The intensive margin refers to incumbent abatement firms increasing the quantity produced of existing varieties. The extensive margin refers to households creating new startups that introduce additional varieties of abatement goods. The distinction matters because (i) more varieties increase competition and compress the abatement price (via a price-index formula: aggregate abatement price falls with firm numbers), and (ii) the two margins respond differently to subsidy design. A subsidy only to incumbents immediately lowers production costs and the abatement price but raises the equilibrium wage, which increases the sunk cost for prospective entrants and crowds out startup entry over time, ultimately harming competition. A subsidy to startups has a delayed effect — startups take one period to begin producing — but generates a sustained competitive effect that eventually exceeds the immediate gain from the incumbent-only policy. The welfare-maximizing policy therefore combines both, weighting startups at 60 percent.

What is the optimal subsidy split and how is it determined?

The optimal split allocates 60 percent of carbon tax revenues to subsidizing startups’ sunk entry costs and 40 percent to reducing incumbents’ production costs (labor input subsidies). This is determined by computing the present value of household welfare (infinite discounted sum of utilities evaluated at 2019 when the policy is announced) for each value of the subsidy share on a fine grid. The welfare function is strictly concave in the startup share, rising until the startup share reaches 0.6 and declining thereafter. The intuition for concavity is that subsidizing startups has a long-horizon payoff (gradual entry and competition), while subsidizing incumbents has an immediate payoff (price reduction) but a long-run cost (reduced entry incentive). The optimum balances these dynamics.

What are the quantitative effects of the optimal subsidy on the carbon tax path, abatement prices, and firm numbers?

Relative to the no-subsidy carbon-tax-only baseline: (1) The carbon tax needed to hit net-zero by 2060 falls from approximately $300 per ton in 2040 to $125 per ton under the optimal subsidy, and from approximately $390–$480 per ton in later years to correspondingly lower values. (2) The abatement price is reduced by more than a factor of 2.5 over the horizon. (3) The number of firms in the abatement goods sector nearly doubles by 2050 relative to the baseline. (4) Abatement cost as a share of output falls substantially, from the baseline peak of approximately 3.4 percent of GDP in 2060 to a lower trajectory. (5) Detrended output in 2040 improves from approximately -3 percent (baseline) to -1 percent under the optimal subsidy, and from -3.2 percent to -2 percent in 2050. These numbers are conditional on the below-2-degree warming scenario and the announced policy starting in 2019.

How large are the subsidy fiscal multipliers and what drives them?

GDP subsidy multipliers (present value of GDP gain per unit of present value of subsidy expenditure) are approximately 2.27 at the 2030 horizon, 2.03 at 2035, 1.89 at 2040, 1.81 at 2045, 1.78 at 2050, 1.80 at 2055, and 1.85 at 2060. Consumption multipliers are uniformly lower but remain above 1.4 throughout. The high multipliers are driven by the competition channel: each dollar of subsidy to startups reduces the abatement price for all final-goods producers economy-wide, amplifying the direct expenditure effect many times over. Multipliers exceed 2 in the early years when startup entry is most rapid and the abatement-price reduction is sharpest. The slight uptick in multipliers at the 2060 horizon reflects the long-run dynamics of the abatement sector reaching a more competitive equilibrium.

What is the role of the DICE climate block and what simplifications are made relative to state-of-the-art climate science?

The climate block is taken directly from DICE-1992 and DICE-2016R2 (Nordhaus 1992, 2018). It models atmospheric CO2 accumulation, radiative forcing from CO2 and non-CO2 sources, and two-box (surface and deep-ocean) temperature dynamics. Key DICE parameters (φ_11, φ_12, φ_21, φ_22, ξ_M, M_1750, damage cost a) are calibrated to match DICE values. The temperature sensitivity parameter ξ_T is estimated from the data rather than calibrated, yielding 0.084, slightly below DICE 2013 and 2016 values. The authors explicitly note that more advanced climate blocks are important for physical risk assessment but have ’little added value’ for transition risk analysis, which concerns the costs of policy, not the physical hazard. The non-CO2 radiative forcing follows a deterministic path that caps at F_max by 2100. The damage function is quadratic in surface temperature: Φ(T_t) = 1/(1+aT_t^2). In the laissez-faire scenario, this implies damages of 1.5 percent of GDP by 2050 and 4 percent by 2100.

How does the paper compare to the standard DICE model and what does the comparison reveal?

The authors estimate both the E-DSGE (with endogenous firm entry in the abatement sector) and a version equivalent to DICE (with perfect competition and no firm-entry dynamics) on the same data. Both models match the empirical second moments (standard deviations and autocorrelations of the five observables) comparably, so standard information criteria cannot discriminate between them. The key difference is that the E-DSGE model reproduces the standard deviation and autocorrelation of patent growth (the proxy for abatement-sector entry), which the DICE version cannot by construction (it has no entry shock). In DICE-like environments, the abatement sector is assumed competitive from the outset and the abatement price equals 1 (the final-goods price), so there are no dynamics in abatement pricing or firm numbers. This means DICE models understate transition costs when the abatement market is initially concentrated, and miss the welfare gain from competition-enhancing policies.

What is the role of the endogenous market structure mechanism and how does it relate to solar photovoltaic markets?

The paper argues the solar PV market provides historical validation of the model mechanism. From the late 1970s to 2019, the cumulative number of solar PV patents increased dramatically while module costs fell precipitously (the cost of solar PV modules in 2019 USD per watt fell 45 percent between 1990 and 2000, 58 percent between 2000 and 2010, and 81 percent between 2010 and 2019). The model predicts exactly this pattern: an initial carbon policy raises expected profits in the abatement sector, inducing entry, which intensifies competition and compresses prices. The initial abatement price in the model (2.5 times the final-goods price) eventually falls below 1 after 2040 under a carbon-tax-only policy. The paper notes the solar sector’s trajectory was partly driven by government subsidies in several countries, consistent with the model’s policy recommendation.

What are the main shock processes in the model and what do impulse response functions reveal?

Five structural shocks are estimated: TFP (productivity), government spending, CO2 emissions, firm entry (innovation), and temperature. All are AR(1) processes. Estimated AR(1) coefficients: productivity 0.949, government spending 0.867, CO2 emissions 0.940, firm entry 0.592, temperature 0.181 — so temperature shocks are nearly serially uncorrelated at annual frequency. Generalized impulse response functions (computed at 2019 state variables, averaged over 500 draws) show: (1) A positive productivity shock raises output and worsens emissions, stimulating abatement-sector entry and reducing the abatement price. (2) A positive CO2 emissions shock triggers a sharp abatement effort and firm entry, but depresses output by almost 5 percent in the short run. (3) A government spending shock (demand shock) raises final-good production, worsens emissions, but crowds out abatement — abatement effort and firm numbers fall 5 percent and 1.1 percent respectively. (4) A firm-entry shock raises firm numbers by nearly 10 percent at peak, reducing abatement prices and encouraging abatement effort without increasing emissions. (5) A temperature shock depresses output by more than 6 percent initially, reducing emissions and abatement effort, and shrinking the abatement sector while pushing abatement prices up.

What are the three IPCC-aligned scenarios used in the projections and how do they differ?

The three scenarios correspond to SSP1–1.9, SSP2–4.5, and SSP3–7.0. (1) Below +2 degrees C (SSP1–1.9): carbon neutrality by 2060, followed by negative emissions (up to -10 Gt by 2100). Requires the carbon tax to rise to approximately $480 per ton by 2080. Abatement cost reaches 3.4 percent of GDP in 2060. This is the scenario used for the policy experiments. (2) Below +3 degrees C (SSP2–4.5): carbon neutrality delayed to shortly after 2100. Carbon tax rises gradually to $300 per ton by 2100. Abatement cost rises to 0.5 percent of GDP in 2050 and 1.2 percent by 2100. Detrended output falls to -3 percent by 2060. (3) +4 degrees C (SSP3–7.0): no policy, laissez-faire. Emissions peak at 57 Gt in 2060 and 70 Gt in 2100. Temperature rises approximately 4 degrees C by 2100. Damages reach 4 percent of GDP per year by 2100. Detrended output decreases from 3 percent to -1 percent by 2050 and -3 percent by 2100 due to climate damage alone.

What are the main policy implications and their scope conditions?

The central implication is that carbon tax revenues should not be recycled to households as lump-sum transfers (the conventional approach in environmental economics) but should instead be used to subsidize entry and operation in the abatement goods sector. The welfare-maximizing split is 60 percent to startups and 40 percent to incumbents. This reduces the cumulative GDP loss from $258 trillion to approximately $138–141 trillion by 2060, saving roughly $120–123 trillion total ($2.9 trillion per year on average). Scope conditions: (1) The result is conditional on the below-2-degree Paris scenario — less stringent emissions targets require lower carbon taxes and generate smaller transition costs, so the absolute gain from subsidies would be smaller. (2) The policy must be announced credibly in advance (2019 in the simulation) so that firms adjust expectations and entry decisions. (3) The model abstracts from capital, cross-country heterogeneity, sector-level differences, and physical risks from climate change. (4) Stochastic uncertainty about future shocks is not incorporated into the policy optimization (extended-path solution collapses uncertainty around the deterministic path). The authors suggest future work should evaluate the optimal policy accounting for stochastic climate and economic risks (following Cai and Lontzek 2019).

How does the paper relate to prior E-DSGE and IAM literature, and what is novel?

The paper positions itself relative to two literatures. First, integrated assessment models (IAMs) originating with DICE (Nordhaus 1992, 1994): IAMs provide long-run analysis but lack microfounded expectations and uncertainty. Second, E-DSGE models (Fischer and Springborn 2011; Heutel 2012; Angelopoulos et al. 2013; Golosov et al. 2014; Annicchiarico and Di Dio 2015, 2017; Diluiso et al. 2021): these have microfoundations and handle short-run dynamics well but typically operate in a linearized, stationary framework unsuited for long-run climate trends. Some prior E-DSGE work includes endogenous entry (Annicchiarico et al. 2018; Shapiro and Metcalf 2021) but focuses on short-run analysis or specific country (U.S.) settings. The paper’s novelties are: (1) Merging DICE with a BGM-style endogenous market structure for the abatement sector in a unified framework suitable for long-run analysis; (2) Nonlinear estimation of the E-DSGE model using the extended-path plus inversion-filter approach — the authors claim this is the first attempt to estimate a nonlinear E-DSGE with both environmental and macroeconomic trends; (3) Distinguishing intensive and extensive margins of abatement-sector adjustment and optimizing the subsidy split between them; (4) Computing present-value subsidy multipliers for climate policy.

What are the main limitations and caveats acknowledged by the authors?

The authors acknowledge several limitations. (1) Capital is excluded from the production function to keep the model tractable given the focus on the abatement goods sector and endogenous entry. (2) The model is a world aggregate with no cross-country heterogeneity; a multicountry model would be needed to study distributional effects across nations. (3) The policy analysis is conditional on the below-2-degree scenario and does not account for uncertainty about future economic and climate conditions — the extended-path method does not incorporate stochastic uncertainty in the forward-looking path. (4) The analysis does not account for the positive benefits of avoided physical risk from climate change (reduced damages in alternative scenarios are noted but not attributed to subsidy policy per se). (5) Non-CO2 radiative forcing is modeled as a simple deterministic path, which simplifies the climate dynamics. (6) The comparison with DICE via second moments rather than formal model selection criteria (since the DICE version has one fewer observable and one fewer shock) limits the formal identification of the endogenous entry mechanism. (7) The model does not include labor market frictions, nominal rigidities, or financial frictions, all of which could affect transition dynamics.

Key Concepts

Abatement goods sector: In this paper, the sector producing intermediate inputs (abatement goods) purchased by final-goods firms to reduce their CO2 emissions. The sector is initially immature and highly concentrated, with high barriers to entry that prevent competition and keep abatement prices above the price of the final good. The paper models this sector with endogenous firm entry following Bilbiie, Ghironi, and Melitz (2012), distinguishing between incumbents (intensive margin) and startups (extensive margin).

Transition risk: In this paper, the macroeconomic cost — in terms of GDP loss, employment diversion, and abatement expenditure — of implementing climate policy (specifically a carbon tax path) to achieve net-zero emissions by 2060. Transition risk is distinct from physical risk (climate damage to productivity); the paper focuses exclusively on transition risk and does not account for avoided physical risk when evaluating policy.

Endogenous market structure: The property that the number of firms (varieties) in the abatement goods sector is not fixed but responds endogenously to expected future profits, sunk entry costs, and exit shocks. Following Bilbiie et al. (2012), the paper models a free-entry condition where households create startups until the marginal cost of entry (sunk cost) equals the expected discounted value of future profits. This endogeneity allows the model to capture how carbon taxes and subsidies affect abatement-sector competition and prices over time.

Intensive margin vs. extensive margin (abatement sector): The intensive margin refers to adjustment by existing (incumbent) abatement firms — increasing production of current varieties when demand rises. The extensive margin refers to the creation of new firms (startups) that introduce additional varieties. The paper shows these margins respond differently to subsidy design: incumbent subsidies have immediate price effects but crowd out entry; startup subsidies have delayed effects but generate lasting competitive pressure.

Extended-path solution method: A numerical method (Fair and Taylor 1983; Adjemian and Juillard 2014) for solving nonlinear rational-expectations models with stochastic growth trends. In each period, agents are surprised by current shocks but expect future shocks to be zero on average (consistent with rational expectations). The method provides accurate solutions while accounting for model nonlinearities, and is combined with an inversion filter to form the likelihood function for Bayesian estimation. It is used here instead of standard log-linearization, which would be invalid under unbalanced growth dynamics.

Subsidy multiplier (present value): The ratio of the discounted cumulative GDP gain (or consumption gain) to the discounted cumulative subsidy expenditure over a given horizon, in the spirit of fiscal multipliers (Feve and Sahuc 2017; Leeper et al. 2017). In this paper, these multipliers measure the efficiency of redirecting carbon-tax revenues to abatement-sector subsidies. GDP multipliers exceed 2.0 through 2035 because the competition-enhancing effect of startup subsidies lowers abatement prices economy-wide, amplifying the direct expenditure impact.

Damage function: The function Phi(T_t) = 1/(1 + aT_t^2) in the TFP equation, where T_t is the surface temperature anomaly and a is a calibrated damage parameter taken from DICE-2016R2. It captures the reduction in total factor productivity caused by climate change. The function implies damages of 4 percent of GDP per year by 2100 under the laissez-faire scenario (approximately 4 degrees C warming), and less than 1 percent under the below-2-degree scenario.

Inversion filter: A computationally efficient method for evaluating the likelihood function of a nonlinear dynamic model (Fair and Taylor 1983; Guerrieri and Iacoviello 2017; Atkinson et al. 2020). Instead of particle-filter simulation, it analytically recovers the sequence of structural shocks by inverting the observation equations for a given set of initial conditions and parameter values. Combined with the extended-path solution, it allows Bayesian estimation of the nonlinear E-DSGE model on world data.