Skip to main content Accessibility help
×
Home
Hostname: page-component-558cb97cc8-7xspw Total loading time: 0.774 Render date: 2022-10-07T22:48:44.277Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "useRatesEcommerce": false, "displayNetworkTab": true, "displayNetworkMapGraph": false, "useSa": true } hasContentIssue true

The economic value of tropical forests in meeting global climate stabilization goals

Published online by Cambridge University Press:  04 December 2020

Sabine Fuss*
Affiliation:
Mercator Research Institute on Global Commons and Climate Change, Berlin, Germany Geography Department, Humboldt-Universität zu Berlin, Berlin, Germany
Alexander Golub
Affiliation:
American University, WashingtonDC, USA
Ruben Lubowski
Affiliation:
Environmental Defense Fund, New York, USA
*
Author for correspondence: Sabine Fuss, E-mail: fuss@mcc-berlin.net

Non-technical summary

Conserving tropical forests has many benefits, from protecting biodiversity, sustaining indigenous and local communities, and safeguarding climate. To achieve the ambitious climate goals of the Paris Agreement, forest protection is essential. Yet deforestation continues to diminish the world's forests. Halting this trend is the objective of the international framework for Reducing Emissions from Deforestation and forest Degradation (REDD+). While previous studies have demonstrated the contribution of tropical forests to mitigate climate change, here we show that tropical forest protection can ‘flatten the curve’ of the costs of transition to climate stability, estimating tens of trillions of dollars in policy cost savings.

Type
Research Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

1. Introduction

The Paris Agreement established an ambitious environmental target to limit the rise of global temperature to ‘well below’ 2 °C above pre-industrial levels (UNFCCC, 2015). Despite this, greenhouse gas emissions continue to rise, rather than stabilizing and declining as required to keep the temperature increase to well below 2 °C (Rogelj et al., Reference Rogelj, Shindell, Jiang, Fifita, Forster, Ginzburg, Handa, Kheshgi, Kobayashi, Kriegler, Mundaca, Séférian, Vilariño, MassonDelmotte, Zhai, Pörtner, Roberts, Skea, Shukla, Pirani, Moufouma-Okia, Péan, Pidcock, Connors, Matthews, Chen, Zhou, Gomis, Lonnoy, Maycock, Tignor and Waterfield2018) and a climate ambition gap remains (UN Environment, 2018). The greenhouse gas emission reduction pledges that countries have made to date to achieve the Paris goal – the Nationally Determined Contributions (NDCs) – are critical to reduce the risk of even more extreme temperature increases. However, even if they are fully implemented, they are insufficient to limit temperature increases to 2 °C or below (Rogelj et al., Reference Rogelj, Shindell, Jiang, Fifita, Forster, Ginzburg, Handa, Kheshgi, Kobayashi, Kriegler, Mundaca, Séférian, Vilariño, MassonDelmotte, Zhai, Pörtner, Roberts, Skea, Shukla, Pirani, Moufouma-Okia, Péan, Pidcock, Connors, Matthews, Chen, Zhou, Gomis, Lonnoy, Maycock, Tignor and Waterfield2018).

There is a consensus that reducing deforestation and degradation and promoting reforestation in the tropics (REDD+) has substantial mitigation potential in the fight against climate change (Griscom et al., Reference Griscom, Adams, Ellis, Houghton, Lomax, Miteva, Schlesinger, Shoch, Siikamäki, Smith, Woodbury, Zganjar, Blackman, Campari, Conant, Delgado, Elias, Gopalakrishna, Hamsik and Fargione2017; Houghton et al., Reference Houghton, Byers and Nassikas2015; IPCC, Reference Edenhofer, Pichs-Madruga, Sokona, Farahani, Kadner, Seyboth, Adler, Baum, Brunner, Eickemeier, Kriemann, Savolainen, Schlömer, von Stechow, Zwickel and Minx2014). Tropical forests offer one of the largest opportunities to cost-effectively address near-term emissions (Lubowski & Rose, Reference Lubowski and Rose2013). While climate policy uncertainties generally hamper mitigation investments, reducing deforestation could be more rapidly deployed than other mitigation strategies, which could help hedge risks of rising future climate policy costs (Golub et al., Reference Golub, Lubowski and Piris-Cabezas2017, Reference Golub, Fuss, Lubowski, Hiller, Khabarov, Koch, Krasovskii, Kraxner, Laing, Obersteiner, Palmer, Piris-Cabezas, Reuter, Szolgayová, Taschini and Wehkamp2018). Forest protection strategies rely on existing technologies, entail relatively low costs, and are generally publicly acceptable given a range of non-carbon co-benefits such as the protection of biodiversity, improvements in local climatic conditions, and regulation of water supplies (de Coninck et al., Reference de Coninck, Revi, Babiker, Bertoldi, Buckeridge, Cartwright, Dong, Ford, Fuss, Hourcade, MassonDelmotte, Zhai, Pörtner, Roberts, Skea, Shukla, Pirani, Moufouma-Okia, Péan, Pidcock, Connors, Matthews, Chen, Zhou, Gomis, Lonnoy, Maycock, Tignor and Waterfield2018). The idea of using forest-based strategies as a low-cost, near-term strategy to address climate change has been around for a long time (Smith et al., Reference Smith, Bustamante, Ahammad, Clark, Dong, Elsiddig, Haberl, Harper, House, Jafari, Masera, Mbow, Ravindranath, Rice, Abad, Romanovskaya, Sperling, Tubiello, Edenhofer, Pichs-Madruga, Sokona, Farahani, Kadner, Seyboth, Adler, Baum, Brunner, Eickemeier, Kriemann, Savolainen, Schlömer, von Stechow, Zwickel and Minx2014), with a typical focus on how forest strategies can help to buy time to develop lower-cost technologies to reduce fossil fuel emissions (e.g. Szolgayová et al., Reference Szolgayová, Golub and Fuss2014). In general, forest-based mitigation has fundamental importance and without tapping its potential at large scale, the ambitious Paris targets might already be out of reach.

Our study uses a traditional global climate-economic model to highlight the enormous economic value of tropical forest mitigation in the context of global climate policies. We argue that the rapid deployment of reduced emissions from deforestation and forest degradation and proactive reforestation in tropical countries provides cost-effective mitigation that can enable countries to enhance the ambition of their NDCs. Furthermore, given the growing emissions gap, scaling up of REDD+ appears essential to remain on track to limit the temperature increase to below 2 °C, as shown in this study.

For our analysis, we modify the 2016 version of the Dynamic Integrated Climate-Economy (DICE) model (Nordhaus, Reference Nordhaus2013) to include REDD+ supply. We then estimate costs and price trajectories for different emissions budgets, ranging from 970 to 1600 gigatons (Gt) of CO2 cumulative emissions up until 2100, under different assumptions about tropical forest protection and reforestation. We assume that without interventions to protect tropical forests, emissions from tropical deforestation net of removals from reforestation total 155 Gt CO2 between 2020 and 2050. Our results show that the realization of the REDD+ potential generates previously unquantified cost savings in the tens of trillions of US dollars. These net policy cost savings reach US$31–36 trillion in our preferred policy case, with risk-adjusted savings even larger by about half a trillion dollars. In this case, tropical forestry absorbs 98 Gt CO2, instead of emitting 224 Gt CO2 until 2100 with corresponding savings of up to 22% of climate policy cost.

The remainder of the paper is organized as follows: Section 2 presents the modeling framework, and Section 3 describes the policy scenarios and results. Section 4 provides a concluding discussion.

2. Modeling the economic value of REDD+

We use an Integrated Assessment Model (IAM) to assess the economic value of REDD+ across a variety of policy scenarios as well as ranges of possible model parameters. We modify this IAM to represent endogenous tropical forest conservation and reforestation supply. Section 2.1 first describes the marginal abatement cost curves (MACCs) that we incorporate. These include cost estimates for global fossil fuel abatement, avoided deforestation, and reforestation. All three are increasing functions of the carbon price. The IAM then minimizes the discounted abatement costs over the period 2020–2100 while meeting a pre-defined emissions budget. Section 2.2 provides further discussion of the model and calibration of parameters for uncertainty analysis.

2.1. Assessing REDD+ potentials

A number of studies (e.g. Houghton et al., Reference Houghton, Byers and Nassikas2015) demonstrate that protection and restoration of tropical forests can buy vital time to navigate the decarbonization of the global economy. Houghton and Nassikas (Reference Houghton and Nassikas2018) estimate that, under an aggressive scenario for conserving and restoring tropical forests, fossil fuel emissions would need to fall to zero by 2045–50, rather than by 2035 in the case with business-as-usual (BAU) forest emissions.

While these studies quantify the mitigation potential from reducing tropical deforestation and increasing reforestation in the tropics, here, we quantify the economic value of exploiting this potential. By using an IAM, we derive implications for the mitigation portfolio and its cost. We consider three types of scenarios: a reference scenario without any REDD+ abatement, a climate policy scenario with REDD+ efforts limited to emissions reductions from tropical forest conservation, and several policy scenarios with REDD+ that also allow for carbon removal from the atmosphere (also known as carbon dioxide removal or negative emissions) via tropical reforestation.

Drawing on updated analyses of tropical forest emissions (Houghton & Nassikas, Reference Houghton and Nassikas2018), Table 1 presents our assumptions for the BAU emissions from tropical deforestation and reforestation (i.e. BAU sinks from temperate and boreal forests have not been included) during 2020–2100 that are part of the reference case. Table 1 also shows the maximum mitigation potentials we consider under policy scenarios for reducing forest emissions and increasing forest sequestration in tropical regions.

Table 1. Total emissions from tropical forests under business-as-usual (BAU) and maximum mitigation scenarios over 2020–2100 (Gt CO2)

Note: Mitigation and removals denoted by a negative sign. Cumulative net emissions are calculated by progressively adding in each component, as one moves down the rows of the table.

We consider three potential global climate policy targets and corresponding emissions budgets for the period 2018–2100, corresponding to 1.8 °C, 2 °C, and 3 °C estimated temperature increases. The emission budgets were calibrated based on the IPCC's Fifth Assessment Report (AR5) and the UN Environment's Emissions Gap Report (IPCC, Reference Edenhofer, Pichs-Madruga, Sokona, Farahani, Kadner, Seyboth, Adler, Baum, Brunner, Eickemeier, Kriemann, Savolainen, Schlömer, von Stechow, Zwickel and Minx2014; UN Environment, 2018).

Using recent estimates of the global costs of reduced emissions from deforestation and carbon sequestration from reforestation in tropical countries based on Busch et al. (Reference Busch, Engelmann, Cook-Patton, Griscom, Kroeger, Possingham and Shyamsundar2019), we obtain BAU emissions and the corresponding potential abatement supply for the period 2030–2050 (see Supplementary Table S1). We employ MACCs from REDD+ for the years 2025, 2035, and 2045 from Busch et al. (Reference Busch, Engelmann, Cook-Patton, Griscom, Kroeger, Possingham and Shyamsundar2019) that represent median estimates of annual abatement supply at given carbon prices over the corresponding decades. The estimates are derived econometrically with a consistent methodology and indicate higher costs of reduced deforestation and expanded reforestation relative to previous studies (Busch et al., Reference Busch, Engelmann, Cook-Patton, Griscom, Kroeger, Possingham and Shyamsundar2019). Thus, our estimated REDD+ potential is conservative.

We also are conservative in our estimates of supply in that we only consider mitigation potentials up until US$100/tCO2 from both reduced deforestation and reforestation. This is in line with past literature where the marginal abatement costs of REDD+ typically show a ‘hockey stick’ shape, with prices of US$100/tCO2 on the asymptotic part of the curve (e.g. Kindermann et al., Reference Kindermann, Obersteiner, Sohngen, Sathaye, Andrasko, Rametsteiner, Schlamadinger, Wunder and Beach2008). Since the IAM (see Section 2.2) covers the period 2020–2100, we include an additional estimate of emissions and removals that take place after 2050. Our numerical analysis provides a conservative estimate of the potential for reduced deforestation and increased reforestation (see Supplementary Materials). For the potential to reduce deforestation, we extrapolate trends from the global land-use modeling cluster of the International Institute of Applied Systems Analysis (IIASA), as described in Gusti et al. (Reference Gusti, Khabarov and Forsell2015). For reforestation, we assume no additional reforestation after 2050 and include only the carbon removal on already reforested land.

To evaluate the impact of exploiting these different potentials, we consider one reference scenario (without any forest mitigation) and two mitigation scenarios summarized in Table 2.

Table 2. Calibration of parameters for Monte-Carlo simulations

* Endogenous afterwards.

2.1.1. Reference scenario

The reference scenario includes BAU levels of both deforestation and reforestation. The net emissions under the reference scenario are 224 Gt of CO2 over the modeling period (2020–2100).

2.1.2. Reduced deforestation only

The next scenario – ‘Deforestation only’ – includes only REDD+ related mitigation from reduced deforestation. This leads to 26 Gt of avoided CO2 assuming a carbon price of up to US$100/t CO2.

2.1.3. Reduced deforestation and reforestation

The third scenario – ‘Reduced deforestation and Reforestation’ – includes the additional supply from reforestation up to US$100/t CO2. The minimum net emissions under this scenario are −98 Gt CO2.

2.2. Quantifying REDD+ gains

For the numerical analysis of the economic implications, we adopt a traditional IAM, namely the 2016 version of the DICE model (Nordhaus, Reference Nordhaus2013). This integrates economics and climate science, with a simplified representation of the carbon cycle. We also conduct an uncertainty analysis using an envelope of estimates from DICE. GDP is modeled as a response surface function and emissions are modeled as a function of GDP and abatement efforts. We consider a cost-effective solution rather than optimizing for emissions based on the costs and benefits of mitigation. Thus, a total emission budget is pre-determined in each scenario, and we solve for the least cost solution to meet the emissions target. The shadow prices for CO2 resulting from constraining the emissions can thus be interpreted as a measure of policy cost to society without explicitly valuing avoided damages.

Many processes and sectors in the DICE model are simplified, but this also makes it more transparent than other IAMs. As a result, it has often been used to generate climate stabilization pathways and provide first quantifications of particular aspects, thus informing the discussions around climate policy (e.g. Nordhaus, Reference Nordhaus2017). An advantage is that it delivers tractable results, which can be further explored with more complex methods of analysis, e.g. for the impact of climate risk on financial assets (e.g. Dietz et al., Reference Dietz, Bowen, Dixon and Gradwell2016). Our analysis demonstrates the importance of including REDD+ in climate policy modeling. However, one implication of our analysis is that approaches with more spatial detail, land-use change, and other land-based mitigation options will be needed to derive more precise policy recommendations in the future. The outcomes of our study should therefore be understood as exploratory, as IAMs like DICE have been criticized for their level of abstraction and assumptions circumventing uncertainties, and may create an unwarranted ‘perception of knowledge and precision’ (Pindyck, Reference Pindyck2013). Nevertheless, we believe that the analysis is useful for initial estimates of the economic value of REDD+.

Explicit consideration of catastrophic climate outcomes, which have been identified as the most important omission in standard IAMs (Pindyck, Reference Pindyck2013; Stern, Reference Stern2013), may even enhance the REDD+ value we determine here. Recent efforts to address some of these deficiencies identified in previous DICE analyses have focused on updating (i) the economic damage functions, (ii) the underlying climate science, (iii) social discount rates, (iv) assumptions on non-CO2 greenhouse gas emissions and feasibility of carbon removals, and (v) constraints on the feasible speed of decarbonization (Hänsel et al., Reference Hänsel, Drupp, Johansson, Nesje, Azar, Freeman, Groom and Sterner2020). These improvements reconcile DICE results with the Paris targets, but do not consider the role of forest mitigation potential, as addressed in this paper.

In addition to these structural issues with the DICE model, there is a wide range of fundamental uncertainty around critical parameters such as the cost of abatement technologies, timing of climate policy implementation, and productivity of the global economy. To address these uncertainties, we run Monte-Carlo simulations using the extended DICE 2016 version described above. Following Dietz et al. (Reference Dietz, Bowen, Dixon and Gradwell2016) and Gillingham et al. (Reference Gillingham, Nordhaus, Anthoff, Blanford, Bossetti, Christensen, McJeon and Reilly2018), we focus on the parameters summarized in Table 2 for our uncertainty analysis. For the policy delay scenarios, we add assumptions about initial carbon prices in 2020 and 2025.

The mean values of the parameters in Table 2 equal the values of the corresponding parameters in the DICE-2016 model, which are approximately the same as those used in the studies mentioned above. Standard deviations were calculated using the same coefficients of variability as tested in Gillingham et al. (Reference Gillingham, Nordhaus, Anthoff, Blanford, Bossetti, Christensen, McJeon and Reilly2018). The major difference in our study is the application of lognormal distributions instead of normal distributions used in previous studies of Total Factor Productivity (TFP), the cost of the back-stop technology and the initial rate of decarbonization. A lognormal distribution better characterizes the nature of the underlying uncertain process. In particular, the cost of the backstop technology should behave in the same way as the price of goods and services, and uncertain prices are usually represented with a lognormal distribution.

We also introduce uncertainty around exogenous initial carbon prices in 2020 and 2025. The carbon price in 2020 is assumed to be positively correlated with climate sensitivity – under the assumption that climate policies will be more stringent if climate impacts are more severe – and the price in 2025 is assumed to be correlated with the carbon price in 2020. The carbon price in 2020 is exogenous in all policy scenarios and endogenous thereafter. The carbon price in 2025 is exogenous only in the case of policy delay up until 2030, as described in the next section.

For the uncertainty analysis, we constrain the optimization to respect the corresponding carbon budgets specified in Table 3 and add REDD+ to the original settings of the model. A simplified optimization routine (minimizing cost to meet the target) is embedded into the Monte-Carlo simulations. For each draw of a set of realizations of the underlying uncertain parameters, the optimization algorithm calculates the abatement, the corresponding CO2 price, and the present value of total abatement costs.

Table 3. Stylized global climate policy scenarios

Note: Core policy scenarios highlighted in bold and counterfactual scenario in italics.

* Optimization starts in 2025. The carbon price in 2020–2024 is exogenous.

Optimization starts in 2030. The carbon price in 2020–2029 is exogenous.

For modeling multiple delay scenarios, we apply the methodology from Golub et al. (Reference Golub, Lubowski and Piris-Cabezas2020). They show that, responding to climate policy uncertainty, buyers in the global carbon market will heavily discount future carbon prices. Over time, as confidence in climate policy increases, each jump in the carbon price represents an increase in confidence in the policy target. For modeling purposes, we adopt the annual average price increase of about 8%.

3. Scenario analysis of REDD+ economic benefits

3.1. Policy scenarios

We evaluated a set of stylized policy scenarios representing varying ambitions of global action on climate change. These scenarios mirror three different levels of the globally admissible carbon budgets reflecting both policy and scientific uncertainties. The other dimension is the timing of climate policy implementation to achieve a given budget; we vary the speed of increase in ambition to study the impact of delay. Our nine stylized policy scenarios are presented in Table 3.

The stylized policy scenarios described in Table 3 differ with respect to their expected feasibility. As barriers to the implementation of an aggressive emissions target, we consider both the total cost of climate policy and the spike in the cost (illustrated by the carbon price as a proxy) accompanying the announcement and implementation of tighter emission targets.

Based on anticipated political feasibility, we selected four core policy scenarios highlighted in Table 3 in bold:

  1. 1. 2 °C-consistent carbon budget with some climate action undertaken during 2020s and 2030s, but major policy adjustment delayed until 2030 (2C, PD2030);

  2. 2. 1.8 °C-consistent carbon budget, with some climate action undertaken during 2020s and 2030s, but major policy adjustment delayed until 2030 (1.8C, PD2030);

  3. 3. 2 °C-consistent carbon budget, with some climate action undertaken during 2020s and 2030s, with major policy adjustment in 2030 that is insufficient to meet the target, such that sequential tightening of the budget is needed until 2050 (2C, MPD);

  4. 4. 3 °C-consistent carbon budget with some climate action undertaken during 2020s and 2030s, but major policy adjustment delayed until 2030 (3C, PD2030).

We also include the scenario shown in Table 3 in italics of a 2 °C-consistent carbon budget with immediate implementation in 2025. This provides a ‘counterfactual’ policy scenario against which to demonstrate the cost of delay.

Based on these core global climate policy scenarios, Figures 1–4 depict three different carbon price trajectories for situations (a) without REDD+ (abatement of fossil fuel emissions only, labelled ‘reference’), (b) reduced deforestation, and (c) reduced deforestation and reforestation. This decomposition of the abatement supply helps to better understand the role and value of the individual components.

Fig. 1. CO2 price for 2 °C-consistent (1170 Gt emissions) budget with policy implemented by 2025 (2C, PD2025 scenario) with and without REDD+.

Fig. 2. CO2 price for 2 °C-consistent (1170 Gt emissions) budget with policy delay until 2030 (2C, PD2030 scenario) with and without REDD+.

Fig. 3. CO2 price for 1.8 °C-consistent (970 Gt emissions) budget with policy delay until 2030 (1.8C, PD2030 scenario) with and without REDD+.

Fig. 4. CO2 price for 2 °C (1170 Gt emissions) budget with multiple policy delays before and beyond 2030 (2C, MPD scenario) with and without REDD+.

Figure 1 summarizes the carbon prices for the counterfactual scenario assuming no further delay beyond 2025 of switching to the 2 °C trajectory. Reducing deforestation mitigates a spike in the carbon price such that instead of jumping from about US$10/tCO2 in 2020 to $121/tCO2 in 2025, the price stays below US$100/tCO2 (US$97/tCO2).Footnote i The incremental contribution of reforestation is comparatively minor due to a relatively inelastic supply of land for reforestation. Therefore, a focus on reduced deforestation is an important priority for emerging global climate policy.

Given the current political climate, an immediate ‘full steam ahead’ implementation of the global climate policy even by 2025 looks unlikely. To test the impact of this, we consider a delay until 2030 with the same 2 °C-consistent (1,170 Gt emission) budget as the most likely scenario. Figure 2 shows that without reducing tropical deforestation, the CO2 price will jump from about US$25/tCO2 up to $160/tCO2 within 5 years (2025–2030). The price level in 2030 will be kept below US$130/tCO2 (see Figure 2) by incorporating reductions in tropical deforestation, buffering at least part of the price shock and potentially enhancing the political feasibility.

An even more dramatic tightening of the emissions target to limit temperatures below 2 °C will be exceedingly challenging without reducing deforestation and forest restoration. The carbon price would have to quadruple over the 5-year period between 2025 and 2030 to over $200/tCO2 to converge to a 1.8 °C-consistent (970 Gt emissions) budget (see Figure 3).

Given the uncertainty in the response of the climatic system to greenhouse gas accumulation, a 1.8 °C target may even require a further tightening of the carbon budget in the future, which would further increase the policy cost. The results imply that large-scale efforts to reduce deforestation and reforestation are likely to dramatically enhance the chance of reaching this more ambitious emissions reduction target.

REDD+ provides some relief concerning the upward pressure on prices in the scenarios examined. However, this may not be enough to contain prices to socially acceptable levels in the face of both policy delays and tightening of the emissions budget consistent with even 2 °C. The Multiple Policy Delays Scenario (MPD) in Table 3 indicates there could be a relatively modest initial tightening of the target, followed by further, sequential adjustments as described in Golub et al. (Reference Golub, Lubowski and Piris-Cabezas2017). We consider an MPD that produces the price path presented in Figure 4.

In this scenario, policymakers postpone climate policy adjustments reducing the magnitude of an initial price shock but requiring greater subsequent increases of carbon prices of 8% per year (see Figure 4). In the 2C MPD Scenario, reduced deforestation helps to cut the price increase by about 70% which is especially important by 2050 when the price gap between the reference scenario and the reduced deforestation and reforestation scenarios has built up. Instead of reaching US$460/tCO2, the price stays below US$270/tCO2 when tropical forest mitigation options are fully included.

Figure 5 compares the price paths for different policy scenarios assuming the realization of the full modeled potential for reduced deforestation and reforestation. Switching to the optimal policy in 2025 yields the lowest carbon price by 2050 among scenarios with a 2 °C policy target, but such an abrupt switch to full-scale implementation of stringent global climate policy may still entail an unacceptable transition cost.

Fig. 5. CO2 price for different policy scenarios, with full reduced deforestation and reforestation potentials.

Reduced deforestation and tropical reforestation slightly mitigate an initial price jump, but not enough to completely avoid a large price increase. The relatively more ‘relaxed’ policy target of 3 °C unsurprisingly generates a slightly lower price path.

Based on these results, aggressive policies to reduce tropical deforestation, as well as restoring tropical forests, constitute a critical strategy to mitigate price shocks without compromising the global emission budget. Switching from the 2 °C-consistent carbon budget until 2100 to the 3 °C-consistent budget reduces the carbon price by about 43% in a scenario with policy delay till 2030. In comparison, achieving the full potential of reduced emissions from tropical deforestation and reforestation can dampen the price increase in 2030 by more than 30%, while holding cumulative emissions within the 2 °C budget, thereby providing about 70% of the carbon price containment.

3.2. Policy cost of reaching 2 °C (1170 Gt emissions budget) until 2100

Carbon prices indicate the marginal cost of abatement and provide a proxy for policy costs. We also compare total abatement costs of different mitigation policies, including – when relevant – the costs of reducing emissions from tropical deforestation and reforestation, as an expected present value of the total cost of climate policy for different scenarios at a discount rate of 3%.

The average benefit of reduced deforestation and reforestation (in terms of the discounted value of cost savings relative to the case without tropical forest mitigation) is in a range of US$6–40 trillion for the period 2030–2070 across the scenarios (see Table 4). This represents the largest savings across scenarios, especially in the case of policy delay. To put these savings into perspective, they could reach about 0.6% of global output for the same period.

Table 4. Net cost savings for different scenarios (US$ trillion, mean values) including tropical forest mitigation potential

Notes: The table summarizes the discounted value (at 3%) of net abatement cost savings of including REDD+ in trillion US$ for the period 2030–2070. Savings are net of the costs of the forest mitigation activities.

Depending on the policy scenario, the savings in abatement cost are between 15% and 22% of the total discounted abatement cost relative to the scenario case without tropical forest mitigation potential. Reduced tropical deforestation delivers about 81–87% of the savings, with reforestation accounting for the remaining 13–19%.

Under the 2 °C scenario with a delay until 2030 (2C, PD2030), the estimated present value of abatement costs from tropical forest mitigation total US$6.2 trillion, with US$2.7 trillion and US$3.5 trillion from reducing deforestation and restoring forests, respectively. The overall gross savings in terms of global abatement cost attributed to REDD+ is US$33.5 trillion (mean value). One dollar invested in REDD+ yields about $5.4 in savings on abatement of fossil fuels emissions. Reforestation yields incremental savings of US$9.6 trillion with a corresponding return of about US$2.7 per one dollar invested in reforestation.

3.3. Uncertainty analysis

From a financial perspective, delaying abatement implies a short position on abatement relative to anticipated climate policy scenarios. Closing this short position will require additional abatement efforts. Given the uncertainties of abatement costs, the potential downside of holding on to an uncovered short position on abatement could be excessively risky (Golub et al., Reference Golub, Lubowski and Piris-Cabezas2020). A typical finance instrument to cover short positions is known as an American call option, i.e. the right, but not the obligation to exercise the option to buy. Applied to abatement, such a call option would provide the possibility of tapping into the REDD+ supply to bridge any shortfall in emissions reductions when stringent climate policy suddenly drives up the CO2 price (Golub et al., Reference Golub, Fuss, Lubowski, Hiller, Khabarov, Koch, Krasovskii, Kraxner, Laing, Obersteiner, Palmer, Piris-Cabezas, Reuter, Szolgayová, Taschini and Wehkamp2018).

We use option valuation in order to estimate risk-adjusted costs. The risk-adjusted abatement cost equals the expected value of abatement cost, plus the value of the at-the-money option on abatement. In particular, valuing the call option and adding the expected value of abatement allows us to assess risk-adjusted costs by adding the two together (Anda et al., Reference Anda, Golub and Strukova2009). For the option value calculation, we apply the Bachelier option pricing formula: the value of the at-the-money option is approximately equal to 0.4 times the standard deviation of abatement costs. The same methodology was applied in Cooke and Golub (Reference Cooke and Golub2020). Table 5 summarizes the results for our principal scenario of a 2 °C (1170 Gt emissions) budget and delay until 2030.

Table 5. Risk-adjusted abatement cost (US$ trillion) and carbon price (US$/tCO2) under 2 °C (1170 Gt emissions) budget with policy delay until 2030

Note: Cost savings (net of abatement costs of REDD+ activities) are denoted as negative numbers. Risk-adjusted values are calculated as the sum of the mean value and the value of the at-the-money option that reflects the willingness to pay for hedging further price increases based on estimated distributions.

For a given emissions target, REDD+ reduces both the expected carbon price (‘mean’ in Table 5) as well as the magnitude of price uncertainty (5th versus 95th percentile and standard deviation in Table 5). The policy cost savings through REDD+ reported in Table 5 are calculated on a net basis, i.e. the cost of avoided deforestation is subtracted from the savings in fossil fuel emissions abatement cost.

Not only may REDD+ thus slash abatement cost or help to achieve lower emissions targets at the same cost, REDD+ could also play an essential role controlling fat tail risks helping to avoid the excessively high cost of climate policy. Figure 6 depicts probability density functions for different REDD+ scenarios: REDD+ does not eliminate the tail risk of excessively high policy costs, but it can significantly reduce that risk.

Fig. 6. Probability densities of net climate policy cost savings under 2 °C (1170 Gt emissions) budget and delay until 2030, with reduced deforestation in red and reduced deforestation plus reforestation in blue.

Risk-adjusted values are calculated as the sum of the mean value and the value of the at-the-money option that reflects the estimated willingness to pay for hedging further price increases based on our estimated distributions. Risk-adjusted savings and carbon prices indicate average and marginal benefits of reduced deforestation and reforestation given the uncertainty of major economic variables as listed in Table 2. As shown in the last row of Table 5, we estimate the risk-adjusted savings from reduced tropical deforestation and reforestation at US$30.6 and US$36.4 trillion. The risk-adjusted carbon price falls from US$179/tCO2 in the reference to $143/tCO2 and $134/tCO2 when reduced deforestation and reduced deforestation and restoration potentials are included, implying estimated marginal benefits of US$36/tCO2 and US$45/tCO2, respectively, in terms of reducing risk-adjusted carbon prices in 2030. Without reduced deforestation, the cost of climate policy at the 95th percentile equals US$251 trillion, while with the maximum available mitigation potential from tropical forests (reduced deforestation plus reforestation), the 95th percentile of total abatement cost is about US$200 trillion (see Table 5). Thus, REDD+, including principally reduced deforestation as well as a smaller potential from reforestation in the tropics, not only generates multi-trillion-dollar savings on climate policy cost without giving up the strength of the emission target, but also helps to contain the potential distribution of carbon prices in the period 2026–2030 (see Figures 6 and 7).

Fig. 7. Probability densities of carbon prices in 2030 under 2 °C (1170 Gt CO2) budget and with policy delay until 2030, for selected scenarios (reference case shown in red, reduced deforestation in blue, and reduced deforestation plus reforestation in green)

For future analysis, it will be important to determine the most crucial exogenous parameters and focus on representing them in a more detailed way. The cost of the backstop technology accounts for about 26% of the combined uncertainty of climate policy costs in the scenario with a delay until 2030 (with a positive correlation of about 0.5) and for about 65% of the combined uncertainty of potential savings provided by reduced deforestation and reforestation (with a correlation of about 0.77). The initial rate of decarbonization of the global economy exhibits a negative correlation with both the cost of climate policy (−0.8) and savings (−0.55). This parameter accounts for about 63% of the combined uncertainty of climate policy costs and for about 32% of the combined uncertainty of potential savings. However, uncertainty concerning available carbon budgets may change the balance. In this study, we address this uncertainty in the form of ‘what-if’ scenarios. For future analysis, the carbon budget could be modeled as a probability distribution. Also, we did not model uncertainty related to the supply of reduced deforestation and reforestation. Adding these uncertainties would be an important next step to expand the analysis presented here.

4. Discussion

In this analysis, we explore how the mitigation potential of tropical forests (REDD+) can help control the cost of climate policies so as to limit the rise in temperatures to no more than 2 °C. We use a revised version of a traditional IAM, DICE, to assess the savings from including the relatively cost-effective mitigation potentials from REDD+. Note that previous versions of DICE have not assessed the REDD+ value due to the exogeneity of land-use emissions in the model. Other IAMs have endogenized land by introducing a flexible land supply curve, but they have focused more on assessing the opportunity costs of REDD+, which differ substantially across regions (close to US$0 in Africa up to US$60 in South-East Asia (Overmars et al., Reference Overmars, Stehfest, Tabeau, van Meijl, Beltrán and Kram2014)). As the representation of the land sector in IAMs is increasingly being improved – either by advancing the built-in land-use modules or by connecting to large-scale partial equilibrium models of land-use change (see e.g. Fricko et al., Reference Fricko, Havlik, Rogelj, Klimont, Gusti, Johnson, Kolp, Strubegger, Valin, Amann, Ermolieva, Forsell, Herrero, Heyes, Kindermann, Krey, McCollum, Obersteiner, Pachauri and Riahi2017) – one can expect both reduced deforestation and reforestation potentials to be assessed more widely and in more detail in the future.

Our current modeling effort presents scenarios and uncertainty analysis that indicates the significant value of REDD+ in terms of mitigating and hedging climate policy cost savings at global level. Reduced deforestation and reforestation can save up to 22% of the cost of global climate policy, generating US$30.6–36.4 trillion in risk-adjusted cost savings in 2030 under our principal scenario. This corresponds to US$36–45/tCO2 in terms of marginal reductions in the risk-adjusted carbon price in 2030. These benefits are dramatic given that current pay-for-performance systems for large-scale REDD+ programs are still in the range of just US$5–10/tCO2.Footnote ii Our analysis helps underline the role that REDD+ plays in a wider mitigation portfolio in times when deforestation rates are still high and economic crises may divert attention from climate change mitigation. Our study also contributes to understanding how uncertainty translates to policy cost and shows how REDD+ can help to contain costs as well as reduce the risk of higher costs in the face of uncertainty over carbon prices, climate sensitivity and many other parameters. Finally, the results indicate that the political feasibility of more ambitious climate targets will critically hinge on the availability of REDD+, notably the protection of existing tropical forests.

Reforestation helps to further enable emissions targets consistent with maintaining total emissions until 2100 below 1200 Gt CO2 so as to limit temperature increases to 2 °C or below. However, without exploiting the full REDD+ potential, this tightening of ambition quickly leads to escalating prices. Looking at temperature targets well below 2 °C, and possibly 1.5 °C, REDD+ is vitally important.

Importantly, this study abstracts from concerns of REDD+ implementation (see Lubowski and Rose, Reference Lubowski and Rose2013). In the context of reaching ambitious climate targets, one frequent concern of allowing forest credits onto existing permit trading markets, or using them as offsets, is that relying on a large source of lower cost mitigation potential could crowd out more expensive investments. These investments are urgently needed to achieve the transition to carbon-neutrality by mid-century. While the problem does not apply to the macro-level analysis performed in this study, deployment of this option will need to introduce the necessary standards and the institutional design to avoid such outcomes (e.g. Koch et al., Reference Koch, Reuter, Fuss and Grosjean2017). Enhancing climate policy ambition through the introduction of tropical forest mitigation will be key, and relying on REDD+ call options, which will be exercised in the medium to long-term at a reasonably high strike price can be a helpful instrument in this context (Golub et al., Reference Golub, Fuss, Lubowski, Hiller, Khabarov, Koch, Krasovskii, Kraxner, Laing, Obersteiner, Palmer, Piris-Cabezas, Reuter, Szolgayová, Taschini and Wehkamp2018).

Equally important are the current institutional barriers to the rapid implementation of REDD+ in many tropical countries, especially where environmental governance is weak and the necessary infrastructure to implement changes does not yet exist. However, previous meta-analyses find that not all aspects of improved governance are equally supportive of forest conservation (Wehkamp et al., Reference Wehkamp, Koch, Lübbers and Fuss2018). Wehkamp et al. (Reference Wehkamp, Koch, Lübbers and Fuss2018) suggest that more nuanced analyses of specific aspects of environmental governance are necessary to guide evidence-based policy-making, which is beyond the scope of our analysis.

Supplementary material

The supplementary material for this article can be found at https://doi.org/10.1017/sus.2020.34

Acknowledgements

We are grateful to Dr. R. Houghton and Dr. P. Duffy at the WHRC for discussions about data on global forest mitigation potentials at the early stages of this paper. We are also thankful for a lively exchange about the ongoing work with our project partners at EDF, MCC, LSE, IIASA, and Forest Trends. We also thank the editors and two anonymous referees for their constructive comments and suggestions.

Author contributions

A.G., R.L., and S.F. contributed to the design of the research. A.G. led the modeling work, while R.L. and S.F. focused on the results analysis. R.L. led the work on data acquisition and scenario design. S.F. contributed to data research, analysis, and led the writing.

Financial support

This work has been supported by the Norwegian Aid Agency (NORAD) via the project ‘Delivering Incentives to End Deforestation: Global Ambition, Private Public Finance, and Zero-Deforestation Supply Chains’. SF and RL furthermore acknowledge funding by the RESTORE+ project (http://www.restoreplus.org/), part of the International Climate Initiative, supported by the Federal Ministry for the Environment, Nature Conservation, and Nuclear Safety (BMU) on the basis of a decision adopted by the German Bundestag.

Conflict of interest

None.

Publishing ethics

The manuscript is our own original work and does not duplicate any other previously published work. The manuscript has been submitted only to the journal Global Sustainability and it is not under consideration, accepted for publication, or in press elsewhere. All listed authors know of and agree to the manuscript being submitted to the journal. The manuscript contains nothing that is abusive, defamatory, fraudulent, illegal, libelous, or obscene.

Footnotes

i Note that prices are above US$100/tCO2 the full abatement potential is realized by our assumption of a marginal abatement cost curve vertical as of US$100/tCO2.

ii In September 2019, The Central African Forest Initiative (CAFI) in partnership with the government of Norway agreed to provide a guaranteed floor price for certified emissions reductions from REDD+ for 2016–2025, at $5 and $10 per ton of CO2, up to a maximum of $75 and $150 million, respectively, depending on the verification standard achieved (Government of the Gabonese Republic and the Central African Forest Initiative (Gabon and CAFI), 2019).

References

Anda, J., Golub, A., & Strukova, E. (2009). Economics of climate change under uncertainty: Benefits of flexibility. Energy Policy, 37(4), 13451355.CrossRefGoogle Scholar
Busch, J., Engelmann, J., Cook-Patton, S. C., Griscom, B. W., Kroeger, T., Possingham, H., & Shyamsundar, P. (2019). Potential for low-cost carbon dioxide removal through tropical reforestation. Nature Climate Change, 9(6), 463466. https://doi.org/10.1038/s41558-019-0485-x.CrossRefGoogle Scholar
Cooke, R., & Golub, A. (2020). Market-based methods for monetizing uncertainty reduction. Environment Systems and Decisions, 40(1), 313. https://doi.org/10.1007/s10669-019-09748-w.CrossRefGoogle Scholar
de Coninck, H., Revi, A., Babiker, M., Bertoldi, P., Buckeridge, M., Cartwright, A., Dong, W., Ford, J., Fuss, S., & Hourcade, J.-C. (2018). Strengthening and implementing the global response. In MassonDelmotte, V., Zhai, P., Pörtner, H.-O., Roberts, D., Skea, J., Shukla, P. R., Pirani, A., Moufouma-Okia, W., Péan, C., Pidcock, R., Connors, S., Matthews, J. B. R., Chen, Y., Zhou, X., Gomis, M. I., Lonnoy, E., Maycock, T., Tignor, M., & Waterfield, T. (Eds.), Global warming of 1.5 C an IPCC special report on the impacts of global warming of 1.5 C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change. Intergovernmental Panel on Climate Change. http://www.ipcc.ch/report/sr15/.Google Scholar
Dietz, S., Bowen, A., Dixon, C., & Gradwell, P. (2016). ‘Climate value at risk’ of global financial assets. Nature Climate Change, 6, 676. https://doi.org/10.1038/nclimate2972.CrossRefGoogle Scholar
Fricko, O., Havlik, P., Rogelj, J., Klimont, Z., Gusti, M., Johnson, N., Kolp, P., Strubegger, M., Valin, H., Amann, M., Ermolieva, T., Forsell, N., Herrero, M., Heyes, C., Kindermann, G., Krey, V., McCollum, D. L., Obersteiner, M., Pachauri, S., … Riahi, K. (2017). The marker quantification of the shared socioeconomic pathway 2: A middle-of-the-road scenario for the 21st century. Global Environmental Change, 42, 251267. https://doi.org/https://doi.org/10.1016/j.gloenvcha.2016.06.004.CrossRefGoogle Scholar
Gillingham, K., Nordhaus, W., Anthoff, D., Blanford, G., Bossetti, V., Christensen, P., McJeon, H., & Reilly, J. (2018). Modeling uncertainty in integrated assessment of climate change: A multimodel comparison. Journal of the Association of Environmental and Resource Economists, 5(4), 791826.CrossRefGoogle Scholar
Golub, A., Fuss, S., Lubowski, R., Hiller, J., Khabarov, N., Koch, N., Krasovskii, A., Kraxner, F., Laing, T., Obersteiner, M., Palmer, C., Piris-Cabezas, P., Reuter, W. H., Szolgayová, J., Taschini, L., & Wehkamp, J. (2018). Escaping the climate policy uncertainty trap: Options contracts for REDD+. Climate Policy, 18(10), 12271234. https://doi.org/10.1080/14693062.2017.1422478.CrossRefGoogle Scholar
Golub, A., Lubowski, R., & Piris-Cabezas, P. (2020). Business responses to climate policy uncertainty: Theoretical analysis of a twin deferral strategy and the risk-adjusted price of carbon. Energy, 205, 117996. https://doi.org/https://doi.org/10.1016/j.energy.2020.117996CrossRefGoogle Scholar
Golub, A., Lubowski, R., & Piris-Cabezas, P. (2017). Balancing risks from climate policy uncertainties: The role of options and reduced emissions from deforestation and forest degradation. Ecological Economics, 138, 9098. https://doi.org/https://doi.org/10.1016/j.ecolecon.2017.03.013.CrossRefGoogle Scholar
Government of the Gabonese Republic and the Central African Forest Initiative (Gabon and CAFI) (2019). Addendum to letter of intent between Gabon and CAFI signed in 2017: Results-based payment Partnership, UNDP. http://mdtf.undp.org/document/download/23569Google Scholar
Griscom, B. W., Adams, J., Ellis, P. W., Houghton, R. A., Lomax, G., Miteva, D. A., Schlesinger, W. H., Shoch, D., Siikamäki, J. V., Smith, P., Woodbury, P., Zganjar, C., Blackman, A., Campari, J., Conant, R. T., Delgado, C., Elias, P., Gopalakrishna, T., Hamsik, M. R., … Fargione, J. (2017). Natural climate solutions. Proceedings of the National Academy of Sciences of the USA, 114(44), 1164511650. https://doi.org/10.1073/pnas.1710465114.CrossRefGoogle ScholarPubMed
Gusti, M., Khabarov, N., & Forsell, N. (2015). Sensitivity of marginal abatement cost curves to variation of G4M parameters. In Proceedings of the 4th International Workshop on Uncertainty in Atmospheric Emissions. 7–9 October 2015. Krakow. Poland (pp. 152158). Polish Academy of Sciences.Google Scholar
Hänsel, M. C., Drupp, M. A., Johansson, D. J. A., Nesje, F., Azar, C., Freeman, M. C., Groom, B., & Sterner, T. (2020). Climate economics support for the UN climate targets. Nature Climate Change, 10(8), 781789. https://doi.org/10.1038/s41558-020-0833-xCrossRefGoogle Scholar
Houghton, R. A., Byers, B., & Nassikas, A. A. (2015). A role for tropical forests in stabilizing atmospheric CO2. Nature Climate Change, 5(12), 10221023. http://dx.doi.org/10.1038/nclimate2869.CrossRefGoogle Scholar
Houghton, R. A., & Nassikas, A. A. (2018). Negative emissions from stopping deforestation and forest degradation, globally. Global Change Biology, 24(1), 350359. https://doi.org/10.1111/gcb.13876.CrossRefGoogle Scholar
IPCC (2014). Climate change 2014: Mitigation of climate change. In Edenhofer, O., Pichs-Madruga, R., Sokona, Y., Farahani, E., Kadner, S., Seyboth, K., Adler, A., Baum, I., Brunner, S., Eickemeier, P., Kriemann, B., Savolainen, J., Schlömer, S., von Stechow, C., Zwickel, T., & Minx, J. C. (Eds.), Contribution of working group III to the fifth assessment report of the intergovernmental panel on climate change. Cambridge, UK and New York, NY, USA: Cambridge University Press.Google Scholar
Kindermann, G., Obersteiner, M., Sohngen, B., Sathaye, J., Andrasko, K., Rametsteiner, E., Schlamadinger, B., Wunder, S., & Beach, R. (2008). Global cost estimates of reducing carbon emissions through avoided deforestation. Proceedings of the National Academy of Sciences, 105(30), 1030210307. http://www.pnas.org/content/105/30/10302.short.CrossRefGoogle ScholarPubMed
Koch, N., Reuter, W. H., Fuss, S., & Grosjean, G. (2017). Permits vs. offsets under investment uncertainty. Resource and Energy Economics, 49, 3347. https://doi.org/10.1016/j.reseneeco.2017.03.006.CrossRefGoogle Scholar
Lubowski, R. N., & Rose, S. K. (2013). The potential for REDD+: Key economic modeling insights and issues. Review of Environmental Economics and Policy, 7(1), 6790. https://doi.org/10.1093/reep/res024.CrossRefGoogle Scholar
Nordhaus, W. D. (2013). The climate casino: Risk, uncertainty, and economics for a warming world. Yale University Press.Google Scholar
Nordhaus, W. D. (2017). Revisiting the social cost of carbon. Proceedings of the National Academy of Sciences, 114(7), 15181523. https://doi.org/10.1073/pnas.1609244114.CrossRefGoogle ScholarPubMed
Overmars, K. P., Stehfest, E., Tabeau, A., van Meijl, H., Beltrán, A. M., & Kram, T. (2014). Estimating the opportunity costs of reducing carbon dioxide emissions via avoided deforestation, using integrated assessment modelling (D2579, trans.). Land Use Policy, 41, 4560. https://doi.org/10.1016/j.landusepol.2014.04.015.CrossRefGoogle Scholar
Pindyck, R. S. (2013). Climate change policy: What do the models tell us? Journal of Economic Literature, 51(3), 860872. https://doi.org/10.1257/jel.51.3.860.CrossRefGoogle Scholar
Rogelj, J., Shindell, D., Jiang, K., Fifita, S., Forster, P., Ginzburg, V., Handa, C., Kheshgi, H., Kobayashi, S., Kriegler, E., Mundaca, L., Séférian, R., & Vilariño, M. V. (2018). Mitigation pathways compatible with 1.5 °C in the context of sustainable development. In MassonDelmotte, V., Zhai, P., Pörtner, H.-O., Roberts, D., Skea, J., Shukla, P. R., Pirani, A., Moufouma-Okia, W., Péan, C., Pidcock, R., Connors, S., Matthews, J. B. R., Chen, Y., Zhou, X., Gomis, M. I., Lonnoy, E., Maycock, T., Tignor, M., & Waterfield, T. (Eds.), Special report on the impacts of global warming of 1.5 °C. Intergovernmental Panel on Climate Change. http://www.ipcc.ch/report/sr15/.Google Scholar
Smith, P., Bustamante, M., Ahammad, H., Clark, H., Dong, H., Elsiddig, E. A., Haberl, H., Harper, R., House, J., Jafari, M., Masera, O., Mbow, C., Ravindranath, N. H., Rice, C. W., Abad, C. R., Romanovskaya, A., Sperling, F., & Tubiello, F. (2014). Agriculture, forestry and other land use (AFOLU). In Edenhofer, O., Pichs-Madruga, R., Sokona, Y., Farahani, E., Kadner, S., Seyboth, K., Adler, A., Baum, I., Brunner, S., Eickemeier, P., Kriemann, B., Savolainen, J., Schlömer, S., von Stechow, C., Zwickel, T., & Minx, J. C. (eds.), Climate change 2014: Mitigation of climate change. Contribution of working group III to the fifth assessment report of the intergovernmental panel on climate change (pp. 811922). Cambridge University Press.Google Scholar
Stern, N. (2013). The structure of economic modeling of the potential impacts of climate change: Grafting gross underestimation of risk onto already narrow science models. Journal of Economic Literature, 51(3), 838859. https://doi.org/10.1257/jel.51.3.838.CrossRefGoogle Scholar
Szolgayová, J., Golub, A., & Fuss, S. (2014). Innovation and risk-averse firms: Options on carbon allowances as a hedging tool. Energy Policy, 70, 227235. https://doi.org/10.1016/j.enpol.2014.03.012.CrossRefGoogle Scholar
UNFCCC (2015). Adoption of the Paris Agreement. http://unfccc.int/resource/docs/2015/cop21/eng/l09r01.pdf.Google Scholar
Wehkamp, J., Koch, N., Lübbers, S., & Fuss, S. (2018). Governance and deforestation – a meta-analysis in economics (D3628, Trans.). Ecological Economics, 144, 214227. https://doi.org/10.1016/j.ecolecon.2017.07.030.CrossRefGoogle Scholar
Figure 0

Table 1. Total emissions from tropical forests under business-as-usual (BAU) and maximum mitigation scenarios over 2020–2100 (Gt CO2)

Figure 1

Table 2. Calibration of parameters for Monte-Carlo simulations

Figure 2

Table 3. Stylized global climate policy scenarios

Figure 3

Fig. 1. CO2 price for 2 °C-consistent (1170 Gt emissions) budget with policy implemented by 2025 (2C, PD2025 scenario) with and without REDD+.

Figure 4

Fig. 2. CO2 price for 2 °C-consistent (1170 Gt emissions) budget with policy delay until 2030 (2C, PD2030 scenario) with and without REDD+.

Figure 5

Fig. 3. CO2 price for 1.8 °C-consistent (970 Gt emissions) budget with policy delay until 2030 (1.8C, PD2030 scenario) with and without REDD+.

Figure 6

Fig. 4. CO2 price for 2 °C (1170 Gt emissions) budget with multiple policy delays before and beyond 2030 (2C, MPD scenario) with and without REDD+.

Figure 7

Fig. 5. CO2 price for different policy scenarios, with full reduced deforestation and reforestation potentials.

Figure 8

Table 4. Net cost savings for different scenarios (US$ trillion, mean values) including tropical forest mitigation potential

Figure 9

Table 5. Risk-adjusted abatement cost (US$ trillion) and carbon price (US$/tCO2) under 2 °C (1170 Gt emissions) budget with policy delay until 2030

Figure 10

Fig. 6. Probability densities of net climate policy cost savings under 2 °C (1170 Gt emissions) budget and delay until 2030, with reduced deforestation in red and reduced deforestation plus reforestation in blue.

Figure 11

Fig. 7. Probability densities of carbon prices in 2030 under 2 °C (1170 Gt CO2) budget and with policy delay until 2030, for selected scenarios (reference case shown in red, reduced deforestation in blue, and reduced deforestation plus reforestation in green)

Supplementary material: File

Fuss et al. supplementary material

Fuss et al. supplementary material

Download Fuss et al. supplementary material(File)
File 243 KB
You have Access Open access
6
Cited by

Save article to Kindle

To save this article to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

The economic value of tropical forests in meeting global climate stabilization goals
Available formats
×

Save article to Dropbox

To save this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Dropbox account. Find out more about saving content to Dropbox.

The economic value of tropical forests in meeting global climate stabilization goals
Available formats
×

Save article to Google Drive

To save this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Google Drive account. Find out more about saving content to Google Drive.

The economic value of tropical forests in meeting global climate stabilization goals
Available formats
×
×

Reply to: Submit a response

Please enter your response.

Your details

Please enter a valid email address.

Conflicting interests

Do you have any conflicting interests? *