subsidy alone, we have ascertained that the former effect dominates, so that sequestration-driven forest extensification reduces overall agriculture emissions.
An important aspect of climatepolicy relates to how well countries coordinate their actions. Carbon price differences across regions could distort markets. It is therefore useful to assess how the general equilibrium supply of abatement changes depends on assumptions regarding regional carbon policies. Analysis is frequently conducted on a country-by-country basis, implicitly assuming that other countries do not have carbon policies (e.g, Murray et al. (2005) for the USA). To explore these issues, we construct a simple example, beginning with the global carbon tax policy described above. The general equilibrium abatement supply for the two forestry options (intensification and extensification) and the agricultural sector, resulting from a global carbon tax reveals that, at $100/TCE, US abatement reaches a maximum of 210 MMTCE, with a 27 MMTCE reduction derived from the agricultural sector and 183 MMTCE through forest sequestration. Now contrast this with the case where abatement is implemented in USA alone. In this case, at $100/TCE, US abatement reaches a maximum of 217 MMTCE – about 5 percent more abatement for the same carbon price, with around 180 MMTCE obtained from forest sequestration and 38 MMTCE from agriculture emissions. In agriculture, USA abatement is diminished by 29% under the global tax compared to the USA only tax. The domestic carbon tax increases the cost of USA agricultural products relative to overseas production. As a result, non-USA production increases, as do GHG emissions. On the other hand, when the tax is applied globally, USA agriculture is able to exploit its comparative production advantage; thus USA-based GHG abatement in agriculture becomes more expensive as the opportunity cost of mitigation increases. In short, differential regional carbon prices can affect the marginal abatement of each region. Studies that only examine national carbon policies, and do not consider the relative effects of regional carbon policies, could significantly mis-estimate the extent of abatement in agriculture and forestry. Finally, we integrate the analysis of land use related non-CO 2
Important determinants for the model to use are geographical scope, time horizon, and scope of the policy. Partial equilibrium models can provide a high degree of detail for a certain geographic area and can hence provide good insights in the short-run effects of a local policy on local behavior. However, when the time horizon is longer than a few years, or when the policy comprises a nation-wide or multi-sectoral policy, the use of a general equilibrium model becomes indispensable. In these cases, intersectoral and international feedbacks, which are best studied using a CGE model, will play an important role. Nonetheless a CGE model is an insufficient tool to study, say, a forest sequestration policy in a certain (relatively) small geographical area, as it lacks the degree of detail that is necessary to study such a specifically targeted policy. As noted above, the data and computational requirements to study such a policy using a CGE model are prohibitive to extend these models to the same degree of detail as, say, a combined econometric process/crop ecosystem model. However, CGE models are the preferred tool to study cost-effective climatepolicy. Indeed, forestry and land use are not the only tools to mitigate GHG emissions, and only a CGE model is capable to show the optimal reductions in, say, emissions of CO 2 from energy generation and emissions from the agricultural sector through
In the introduction of this paper, we noted the importance of taking into account land use, land-use changes, and forestry, when studying questions related to climate change and climatepolicy. In this section, we have seen how general equilibrium models have developed to take these features better into account. Introducing heterogeneity in available land, as was done in section 3.2, increases the credibility of the CGE models regarding changes in agricultural production and allows for calculating emissions from land-use changes. A second approach is to link them to a land use model, although we saw in section 3.3 that this can come at a cost, due to technical problems with establishing the link. Generally, the increase in model complexity due to inter-sectoral and international links, as compared to the models of section 2, goes at the expense of detail in modeling of the agricultural and forestry sectors, and of biophysical processes and geographical scale. Whereas some of the models of section 2 were able to study processes at the parcel level, the uncoupled CGE models do not go into more geographical detail than a 0.5 latitude by 0.5 longitude grid scale. Of course, this is already a great improvement over the ‘standard’ CGE models.
Arctic feedbacks will accelerate climate change and could jeopardise mitigation efforts. The permafrost carbon feedback releases carbon to the atmosphere from thawing permafrost and the sea ice albedo feedback increases solar absorption in the Arctic Ocean. A constant positive albedo feedback and zero permafrost feedback have been used in nearly all climatepolicy studies to date, while observations and models show that the permafrost feedback is significant and that both feedbacks are nonlinear. Using novel dynamic emulators in the integrated assessment model PAGE- ICE, we investigate nonlinear interactions of the two feedbacks with the climate and economy under a range of climate scenarios consistent with the Paris Agreement. The permafrost feedback interacts with the land and ocean carbon uptake processes, and the albedo feedback evolves through a sequence of nonlinear transitions associated with the loss of Arctic sea ice in different months of the year. The US’s withdrawal from the current national pledges could increase the total discounted economic impact of the two Arctic feedbacks until 2300 by $25 trillion, reaching nearly $120 trillion, while meeting the 1.5 °C and 2 °C targets will reduce the impact by an order of magnitude.
This section outlines the revised analytical framework for CPI that includes the integration of mitigation with adaptation policy objectives. The framework builds on the concepts of policy coherence among multiple policy objectives and vertical and horizontal dimensions of policy integration (Lafferty and Hovden, 2003; Persson, 2007). Yet, in the literature there is little consistency in the use of terms ‘ policy coherence ’ and ‘ policy integration ’ . Their meaning has been interpreted differently and they are sometimes used interchangeably (Adelle and Russel, 2013; den Hertog and Stroß, 2013; Nunan et al., 2012; Russel and Jordan, 2010; Scobie, 2016). For analytical purposes we follow Nilsson et al. ’ s (2012) suggestion to use ‘ policy coherence ’ to refer to policy outputs and outcomes, or the consistency of multiple policy objectives and associated implementation arrangements, and ‘ policy integration ’ to refer to the integration of governance arrangements (administrative and organizational structures) and policy making processes. Consequently, we de ﬁ ne CPI as the integration of multiple policy objectives, governance arrange- ments and policy processes related to climate change mitigation, adaptation and other policy domains. We discuss below the three key building blocks of the analytical framework in more detail.
Arctic feedbacks accelerate climate change through carbon releases from thawing permafrost and higher solar absorption from reductions in the surface albedo, following loss of sea ice and land snow. Here, we include dynamic emulators of complex physical models in the integrated assessment model PAGE-ICE to explore nonlinear transitions in the Arctic feed- backs and their subsequent impacts on the global climate and economy under the Paris Agreement scenarios. The permafrost feedback is increasingly positive in warmer climates, while the albedo feedback weakens as the ice and snow melt. Combined, these two factors lead to signi ﬁ cant increases in the mean discounted economic effect of climate change: + 4.0% ($24.8 trillion) under the 1.5 °C scenario, + 5.5% ($33.8 trillion) under the 2 °C scenario, and +4.8% ($66.9 trillion) under mitigation levels consistent with the current national pledges. Considering the nonlinear Arctic feedbacks makes the 1.5 °C target mar- ginally more economically attractive than the 2 °C target, although both are statistically equivalent.
Fire hazard reduction treatments with chainsaws and forestry machines can mimic the effect of low- intensity wildfires, the kind that would have moved frequently through much of the state’s forestland pre-settlement, burning smaller trees and brush but leaving larger trees alive. Such treatments can re- duce fuel loads to a level that can then be maintained through low-intensity, prescribed natural fires over- seen by firefighters and fire professionals. Without a biomass energy market for slash, fewer acres can be treated with the limited funds for fire hazard treat- ments available to land managers. In the absence of a biomass market, there is little to do with slash but to pile and burn it, erasing the climate benefit of using the biomass to generate electricity and resulting in substantial emissions of particulate matter and other air pollutants. Leaving slash on the ground, or avoid- ing thinning operations altogether, allows the buildup of fuels to continue and leaves the forest increasingly prone to severe fire.
All countries report on the impacts of forests and land use on climate under the international agreement on climate change. One inventory sector covers emissions resulting from land use, land use change and forestry (LULUCF). In Finland, forests sequester annually around 30–60% of greenhouse gas emissions. The most significant reason for the variations in the carbon sinks of our forests are the changes in the annual volume of felling.
1) Translating of international treaties and protocols
into national policies and actions that deliver posi tive climate outcomes.
International policies are primarily focused on forests and their role in the carbon cycle. National policies, whether government based or market driven, tend to focus on the pri mary resource-based economic sectors (e.g., agriculture, forestry, graz- ing, energy). Brazil’s soy moratorium is a voluntary market-based program to curtail soy expansion on lands deforested since 2006 (Gibbs et al. 2015). The moratorium resulted in reduced defor estation in the Brazilian Amazon. This is a clear case where agricultural land-use policy aligned with Reducing Emissions from Deforestation and Forest Degra- dation+ (REDD+) and UNFCCC objectives with mutually beneficial results.
This is based on the notion that market characteristics and conditions affect land-manager decision making. This concerns price & margin of the product, and market scale, infrastructure and security (Dandy, 2012).
Incentives A variety of incentives are offered across the land management sector for example: grants, cost shares, preferential, finance schemes, tax relief, or payment schemes (Dandy, 2012).
A B S T R A C T
In 2014 temperate zone emission factor revisions were published in the IPCC Wetlands Supplement. Default values for direct CO 2 emissions of artiﬁcially drained organic soils were increased by a factor of 1.6 for cropland sites and by factors ranging from 14 to 24 for grassland sites. This highlights the role of drained organic soils as emission hotspots and makes their rewetting more attractive as climate change mitigation measures. Drainage emissions of humic soils are lower on a per hectare basis and not covered by IPCC default values. However, drainage of great areas can turn them into nationally relevant emission sources. National policy making that recognizes the importance of preserving organic and humic soils ’ carbon stock requires data that is not readily available. Taking Ireland as a case study, this article demonstrates how a dataset of policy relevant information can be generated. Total area of histic and humic soils drained for agriculture, resulting greenhouse gas emissions and climate change mitigation potential were assessed. For emissions from histic soils, calculations were based on IPCC emission factors, for humic soils, a modiﬁed version of the ECOSSE model was used. Results indicated 370,000 ha of histic and 426,000 ha of humic soils under drained agricultural land use in Ireland (8% and 9% of total farmed area). Calculated annual drainage emissions were 8.7 Tg CO 2 e from histic and 1.8 Tg CO 2 e from humic soils (equal to 56% of Ireland ’s agricultural emissions in 2014, excluding emissions from land use). If half the area of drained histic soils was rewetted, annual saving would amount to 3.2 Tg CO 2 e. If on half of the deep drained, nutrient rich grasslands drainage spacing was decreased to control the average water table at −25 cm or higher, annual savings would amount to 0.4 Tg CO 2 e.
In addition, the regulation power of the Brazilian climate change architecture has also very strong horizontal integration characteristics. These are reflected in the cross-sectoral coordination role of the Ministry of Environment and the Ministry of Science, Technology and Innovation and in the extensive use of inter-ministerial committees and other multi-sectoral bodies (Seroa da Motta 2011b). All climate change and anti-deforestation policy efforts include key regulating roles of such committees, from the Inter-Ministerial Committee on Climate Change (CIM) and its Executive Group (Gex), to the Inter-Ministerial Commission for Global Climate Change (CIMGC), the Brazilian Forum on Climate Change (FBMC), and the Permanent Inter-ministerial Working Group (GPTI). As an example, in relation to mitigation actions, CIM, GEx, and GPTI, which are all inter- ministerial bodies, have the specific mandate to support the integration of REDD+ with PPCDAm, PPCerrado, and the ABC plan. Yet, there usually is a clear separation between bodies that work on mitigation and adaptation under these committees, as for example the various working groups (REDD+ Working Group, and the Inter-ministerial Adaptation Working Group). While it is useful to have this separation of tasks, it is also important that lower level adaptation and mitigation bodies interact to facilitate internal climate change policy coherence in both planning and implementation. A complex of vertical and horizontal policy integration features seems to be necessary to address cross-cutting issues as climate change in the land use sector (Nunan et al. 2012) . Brazil’s formal policy architecture seems to satisfy this requirement. It is, however, not sufficient to ensure effective policy integration: both the alignment of political objectives and power relations among policy actors affect whether inter-ministerial committees and well-intentioned policies and plans deliver vertical and horizontal integration in practice. In the mitigation domain, which is more advanced in Brazil, there is evidence that coordination challenges are substantial (Gebara and Thuault 2013, Gebara et al. 2014). The political agendas of the different ministries and factions within the Brazilian Congress have not always been in line, both in relation to climate change as well as anti-deforestation policies and objectives (Carvalho 2013, Abranches 2014). In general, since 2011 there has been a slowing trend in climate change policy progress and political commitment in Brazil (Hochstetler and Viola
From the point of view of ecosystem disruption, the greater amount of CILCC than LULCC would suggest that CILCC would cause more disruption in all three of the RCP scenarios considered here. However, habitat destruction, particularly conversion of land to agricultural use, is thought to be the most important driver of biodiversity loss, with climate change less important [Hassan et al., 2005]. Since the CILCC is only slightly higher than the amount of LULCC in RCP2.6 and RCP4.5, it is possible that LULCC may have a bigger impact on biodiversity in these scenarios. For RCP8.5, CILCC would likely still be a larger impact on biodiversity, since the total area affected by CILCC is more than double than from LULCC. As well as the extent of the impact, the duration also should be taken into account. After stabilization of the forcing, the effects of LULCC drop off, whereas the CILCC continues as the vegetation reaches equilibrium. The CILCC is likely to continue well beyond 2100 for decades or even centuries after the forcing has stabilized [Jones et al., 2010; Liddicoat et al., 2013]. Comparing the disruptive impact, CILCC could be a more serious challenge than LULCC, particularly in RCP8.5, because of the longevity and quantity of impact, even if the severity is lower. The important role of CILCC in terrestrial carbon changes highlights how critical it is to reduce the uncertainty
Land use planning instruments also contribute to adaptation action in agriculture and were first mentioned in the National Environmental Policy (Law 6.938/1981). During the nineties, a working group on Ecological and Economic Zoning (ZEE), the National Coordination Commission and the Ecological-Economic Zoning Programme for the Legal Amazon were established and capacity building efforts started. Since 2000 the Federal Government enhanced the role of ZEE with the inclusion in the Pluri-Annual Plans and through a federal decree (Decree 4.297/2002) to regulate its policy instruments. In 2006, the Ministry of Environment together with the ZEE Consortium launched a guideline document for ZEE implementation at federal and state levels, which is in now in its 3rd edition. This document recognises the tension between fighting deforestation and the intensification of land use for agribusiness. However, the ZEE does not include any specific climate related guidelines for zoning purposes. This changed in 2010, with the release of the Decree approving the Ecological-Economic Macro-Zoning of the Legal Amazon (Macro-ZEE) (Decree 7.378) developed by the Ministry of Environment, the Consortium ZEE-Brazil and the Amazon states. The latest document on zoning in Brazil clearly refers to climate issues and places the Amazon biome at the centre of climate change concerns. Overall the strategies of the Macro-ZEE seem consistent and aligned with the objectives of the National Policy on Climate Change. There is a clear effort to connect proposed zoning actions with mitigation efforts related to fighting deforestation and land use change and to reduce vulnerability to climate change through actions focused on ecosystem services including biodiversity, water and PES. However, ZEE is restricted in its applicability to adaptation and mitigation planning, since it is more of a land use guidance tool than a measure to restrict application of funds or expansion of frontiers. One particular way in which ZEE is actually counterproductive to REDD+ is the allowance in the national Forest Code to reduce forest cover up to 50% on properties in Amazon forest segments of states’ territories if the state has passed a ZEE law. Alternatively, REDD+ could provide an incentive to restore more.
in 2030, 0.5 PgC (1.9 PgCO 2 ) in 2050 and -0.2 PgC (-0.7 PgCO 2 ) in 2100 (Jia et al. 2019).
In the resulting matrix of SSP and RCP combinations (O'Neill et al. 2016), the change in global forested area in 2100 (relative to 2010) varies by over two billion hectares (Figure 3), equivalent to half of the present-day forested land area. The greatest increases in forested area are seen in pathways that follow SSP1, which includes strong regulation of the land-sector, increased agricultural productivity, low food waste, and a shift towards lower meat consumption. Accordingly, SSP1 scenarios see reductions in the amount of land used for pasture, and cropland in some realisations (Figure 3), which allows for forests to regenerate naturally on abandoned land and deliberate afforestation. In the pathways with the most stringent climate target (i.e., RCP1.9) afforestation is a sink of -0.6 PgC (-2.4 PgCO 2 ) per year by 2100 (median value across all SSPs in five IAMs). However, the warming biophysical
and has two local optima at g 1 and g 2 , respectively. The illustration in Figure 2 demonstrates two key insights into PS methods. First, many standard policy search methods such as Policy Improve- ment with Path Integrals (Theodorou et al., 2010) or EM-based policy search methods (Kober et al., 2008) will be attracted to multiple optima in a multi-modal solution space and, therefore, converge slowly due to averaging over several modes (Neumann, 2011). Second, we would like to represent a versatile solution space by learning all modes of the reward function. Figure 2 shows a qualitative comparison of HiREPS to the standard REPS algorithm which can only use one sub-policy and to the naive implementation of HiREPS that does not bound the sub-policies’ entropy (i.e., κ ˜ = ∞). The single sub-policy algorithm tries to average over both modes and, takes a long time to con- verge. The naive HiREPS exhibits similar behavior. Both sub-policies are attracted by both modes. In most cases, both sub-policies will find the same mode and convergence will be slower. Thus, only introducing hierarchical policies without additional constraints cannot take full advantage of the increased flexibility. When limiting the entropy, however, the sub-policies quickly separate and concentrate on the two individual modes, allowing for a fast improvement of the policy without getting stuck between two modes.
Option 3: separate targets for land use. Where separate targets are formulated for land use, or part of it, in the context of economy-wide targets (Option 3 in Table 4 ) then CTU will require information on the coverage of each target and how economy-wide coverage is achieved, without double counting or omission. If contributions are expressed separately as sectoral targets relative to a reference level with national coverage for the land-use sector or some part of it, then information may be needed on how a reference level scenario has been established consistent with the historical data to estimate performance. COP decisions from the REDD+ negotiations may help indicate information requirements associated with commitments of this type. Information could be required to show that the main sources of land-use emissions and removals were included, for example, forests and organic soils. Additional information may be needed to show the depth of ambition below BAU. If more than one contribution of this type is proposed, or contributions of this type co-exist with contributions expressed as economy-wide commitments, then CTU would be increased by information on consistency between them was considered. This may help avoid conflicts and perverse incentives, for example, between food production and mitigation.
In addition to the uncertainties, there are a few shortcom- ings inherent in our approach. We do not include many bio- geophysical effects of LULCC, such as changes to surface latent and sensible heat fluxes and to the hydrological cy- cle, that impact climate (Defries et al., 2002; Feddema et al., 2005; Brovkin et al., 2006; Pitman et al., 2009; Lawrence and Chase, 2010). In general, while important for local or regional climate especially in the tropics (Strengers et al., 2010), these effects are considered minor on a global scale (Lawrence and Chase, 2010) and are difficult to quantify using the RF concept (Pielke et al., 2002). For the calcula- tion of the many forcing agents that we do consider, our ap- proach is to treat each forcing separately, which could lead to differences in RFs between agents that are due partly to methodology. For example, land cover changes and agricul- tural emissions were developed jointly for each of the RCPs, but for use in terrestrial models, including CLM, the land cover change projections were altered (Di Vittorio et al., 2014). This leads to inconsistent storylines between future emissions computed by CLM (Sect. 2.2) and those taken directly from the RCP integrated assessment model output (Sect. 2.3.1). Therefore, it is important to view the future RFs computed here as comprising a broad range in possi- ble outcomes, extended with the TEC, as opposed to pre- cise results corresponding to specific storylines for the fu- ture. Finally, the inhomogeneous distribution of forcing from surface albedo changes and short-lived trace gas and aerosol species could lead to non-additive (A. D. Jones et al., 2013) and highly variable local climate responses (Lawrence et al., 2012). Therefore, we use the RF for our assessment of global-scale climate impacts and acknowledge the limits of the RF concept for predicting the diverse and often local im- pacts of land use (Betts, 2008; Runyan et al., 2012).
Deni Bram writes that "some of the impacts in the same time dimension now indicate that developing countries have far greater vulnerabilities than more advanced countries" 1 . Furthermore, Deni Bram writes that "currently developing 2 (two) models of commitment in climate change mitigation measures are commitments that rely on top down models and commitments that rely on botton up models. The top down model carried out in the model of commitment formation in the Kyoto Protocol has failed from the perspective of fairness and effectiveness. This inevitably leads to a lack of clarity as to whether the emission reduction commitments in the Kyoto Protocol are based on the goals set forth in the UNFCCC regime, which is to achieve stable greenhouse gas conservation at a safe level. This failure is exacerbated by the failure of one of the largest emitters, namely the United States to ratify the Kyoto Protocol, as well as the failure of some countries to achieve the emission reduction targets set by the Kyoto Protocol 2 .
Vector autoregressions have become an important tool since Sims (1980) crit- icized large-scale macroeconometric models for assuming unfounded identi- fying restrictions. One of the main issues in the analysis of properties of vector autoregressions is the (use of theory in order to come to) identifica- tion of so-called structural shocks. Sims suggested solving the identification of the contemporaneous structure of the model by using a recursive (orthog- onalized) structure. This implies that there is no contemporaneous feedback from the variables mentioned at the end of the ordering on the variables on top. Although theory can play a role in such a recursive scheme, the lack of simultaneity led Bernanke (1986), Blanchard and Watson (1986), and Sims (1986) to propose a larger role for economic theory in formulating plausible restrictions on contemporaneous interactions among variables. This implies that the recursivity can be replaced by other more simultaneous structures (at least conserving the number of identifying restrictions). This class of models is labelled Structural VAR (SVAR) models. Blanchard and Quah (1989) and Gali (1992) suggested to impose so-called long-run restrictions on impulse response functions to allow for instance for inflation not to have an impact on output. But, as Faust and Leeper (1997) argue, imposing long- run restrictions of this type requires the VAR to satisfy strong dynamic restrictions.