As noted above, a major motivation for this study is to introduce into DICE‐2013R with richer information regarding energy, land‐use, and other gas emissions, which can be provided by more complex integrated assessment models (IAMs) (e.g., Asia‐Pacific Integrated Assessment/Computable General Equilibrium (AIM/CGE) [ Fujimori et al ., 2014a , 2014b , 2017 ]). Therefore, the objectives of this study are twofold: (1) to revise the DICE‐2013R model for the evaluation of low stabilization scenarios by incorporating individual anthropogenic emissions and (2) to demonstrate how this revision is important, particularly for achieving stringent controls, such as the 2.0°C and 1.5°C targets.

Industrial CO 2 emissions, which result from fossil fuel combustion and industrial processes, are treated as the only dynamic control variable for climate mitigation in DICE‐2013R because CO 2 is the predominant contributor to the warming of the Earth [ Nordhaus , 2013 , 2014 ; Nordhaus and Sztorc , 2013 ]. Other anthropogenic emissions, e.g., land‐use CO 2 , methane (CH 4 ), nitrous oxide (N 2 O), halogenated gases, carbon monoxide (CO), nitrogen oxide (NO x ), volatile organic compounds (VOC), sulfate (SO x ), black carbon (BC), and organic carbon (OC), are assumed to follow fixed paths. Cutting non‐CO 2 greenhouse gas (GHG) emissions, however, could also significantly affect the climate conditions and lead to substantial changes in mitigation costs [ van Vuuren et al ., 2006a , 2006b ; Montzka et al ., 2011 ; Gernaat et al ., 2015 ]. In addition, it could be feasible to achieve a rapid decrease in radiative forcing (RF) by suppressing the emission of short‐lived climate pollutants (SLCPs) [ Bowerman et al ., 2013 ; Shoemaker et al ., 2013 ; Rogelj et al ., 2014a , 2015a ]. Climate change is also influenced by land‐use management [ Pielke , 2005 ; Wise et al ., 2009 ; Montzka et al ., 2011 ; Gernaat et al ., 2015 ; Ciais et al ., 2013a ], although with larger uncertainties. To limit the global mean temperature (GMT) increase to below 2.0°C or even 1.5°C, as suggested by the Paris Agreement, abatement efforts that reach beyond industrial CO 2 are also important for climate policy [ Meinshausen et al ., 2009 ; Rogelj et al ., 2011 , 2013 , 2015b ; Gernaat et al ., 2015 ]. However, such abatements contributed by land‐use CO 2 , non‐CO 2 , or aerosols cannot be appropriately exploited by DICE‐2013R, since DICE‐2013R has relatively less representation of energy, land‐use, and other gas emissions. In addition, advances in climate change research, such as new scenarios known as the Shared Socioeconomic Pathways (SSPs) [ Moss et al ., 2010 ; O'Neill et al ., 2014 ; Calvin et al ., 2017 ; Fricko et al ., 2017 ; Fujimori et al ., 2017 ], can provide consistent and detailed information with regard to future socioeconomic development, the abatement potentials for individual anthropogenic emissions and the corresponding costs of coping with climate change. This sets the stage for a more comprehensive assessment of the Earth's climate system and socioeconomic development.

In the following sections, we first present a comparison between the DICE‐Style and Full‐Abate modes with regard to various assumptions made concerning land‐use CO 2 and non‐CO 2 climate forcers as well as their corresponding economic effects; subsequently, the abatement path for anthropogenic emissions and the contributions from individual forcing sources for the 2.0°C target as obtained with the model running in Full‐Abate mode are reported. We also examine the 1.5°C target based on the Full‐Abate assessment in the discussion, with the caveat that optimistic assumptions are required.

Two running modes were devised to clarify the effects of addressing land‐use CO 2 and non‐CO 2 explicitly, i.e., the “DICE‐Style” mode and the “Full‐Abate” mode. In the DICE‐Style mode, the land‐use CO 2 emission and non‐CO 2 forcing are fixed a priori as in DICE‐2013R, with no dynamic abatement of these emissions. However, the Full‐Abate mode is the one in which the model improvements described in the previous sections are fully implemented. All the other features of both modes were kept the same.

In the base case, the GHGs in 2015 is 57.0 GtCO 2 ‐eq yr −1 (see Tables S4 and S5 in Appendix S2 for 100‐year global warming potential [GWP]), higher than recent baseline estimations [ Rogelj et al ., 2016a ]. However, the Copenhagen Accord was imposed in the optimal case and in the two climate target cases, and a median value of 48.7 GtCO 2 ‐eq GHGs by 2020 was adopted based on existing studies [ Rogelj et al ., 2010 ; Stern and Taylor , 2010 ; den Elzen et al ., 2011 ; H ö hne et al ., 2012 ]. As with the climate pollutants and aerosols, an initial control level was assumed in the base case of SSP2 [ Rao et al ., 2017 ], corresponding to the Rogelj et al.'s [ 2014b ] current legislation (CLE) assumption with no new energy access policies. No further air pollution control tightening was imposed in this study, except for those from climate change mitigation.

We designed a set of scenarios to thoroughly investigate the importance of the inclusion of the dynamic abatement of land‐use CO 2 and non‐CO 2 and their implications for climate policy assessment. Here, we considered two dimensions, as shown in Table 1 , namely, (1) climate policy and (2) the running modes. The climate policy dimension consists of the base case, the optimal case and two climate target cases (2.0°C and 1.5°C). The rate of control μ ( t ) is equal to zero in the base case, which is the reference case. In the optimal case, future climate emissions are determined using a cost–benefit approach that balances climate costs with climate damages by maximizing the total discounted inter‐temporal social welfare. In the 2.0°C and 1.5°C climate target cases, the GMT change after the year 2100 is limited to below 2.0°C and 1.5°C, respectively. In this modeling exercise, we assumed that temperature overshoots were allowed within this century in both climate control cases. In addition, we used best‐guess estimates for both the socioeconomic development and climate change, and therefore the results show a best‐guess level assessment.

We used the two‐box temperature module in DICE instead of the upwelling‐diffusion climate model in MAGICC 6.0 to simulate the change in the GMT. The two‐box module is simple enough to be involved in the inter‐temporal optimizing process on a century time scale while still adequately capturing the characteristics of the temperature evolution derived from more complex climate models [ Glotter et al ., 2014 ]. In addition, because the effective radiative forcing (ERF), which is defined as the resulting RF when allowing well‐mixed GHSs and aerosols to respond to perturbations with rapid adjustments, is more representative of the GMT response [ Myhre et al ., 2013 ], we scaled the standard RF to the ERF by multiplying it by an efficacy factor and then used the ERF in the temperature module to derive the GMT. Here, the climate sensitivity was set to the best‐guess level of 3.0°C, and the calibration is shown in Figure S6 in Appendix S2.

The simple climate module simulates the evolution of individual anthropogenic emissions. First, both the terrestrial and oceanic carbon cycles were explicitly considered to derive the atmospheric CO 2 concentration. Compared to MAGICC 6.0, however, we reduced the complexity of the calculations by simplifying some parts of the processes to allow the carbon cycle to be used during the optimization. (1) We chose to treat forest regrowth as varying linearly with respect to the relaxation time in the terrestrial carbon cycle (see equation (35)–(37) and Table S2 in Appendix S2); (2) we re‐calibrated the CO 2 fertilization factor using all four Representative Concentration Pathways (RCPs) [ Masui et al ., 2011 ; Riahi et al ., 2011 ; Thomson et al ., 2011 ; Vuuren et al ., 2011 ] and the extension [ Meinshausen et al ., 2011b ], based on MAGICC 6.0's calculations using the same inputs (see Figure S4 in Appendix S2 for the calibration of the CO 2 concentrations; the default setting of C4MIP BERN was used). Second, the concentration and RF were calculated separately for various non‐CO 2 components, including CH 4 , N 2 O, halogenated gases (12 addressed under the Kyoto Protocol and 16 addressed under the Montreal Protocol), CO, VOC, SO x , NO x , BC, and OC. In addition, contributions from mineral dust, cloud cover, land‐use albedo, and natural sources such as volcanic and solar irradiance changes were simply assumed to remain at their respective levels after 2005 based on MAGICC 6.0 (Figure S5 in Appendix S2) [ Meinshausen et al ., 2011a ].

We assumed adaptation levels based on the method used in AD‐DICE [ de Bruin et al ., 2009 ; de Bruin and Dellink , 2011 ]. However, the parameters were re‐estimated according to DICE‐2013R (Figure S3 in Appendix S2), and the results imply that a 40% reduction in gross damage can lead to a 0.71% loss of total gross output. For climate change damage, we used the damage function in DICE‐2013R directly to estimate the losses due to climate change.

The abatements of climate pollutants and aerosols such as CO, VOC, SO x , NO x , BC, and OC were also determined based on the carbon prices. Here, we introduced a simple linear relationship between the reduction of pollutants and aerosols and the carbon prices (see equation (6) in Appendix S2).

For CO, CH, NO and fluorinated gases (F‐gases), we assumed that the rates of control for these emissions were determined by the carbon prices,whererepresents the rate of control of emissionandandare parameters that are estimated based on the sensitivity data. Furthermore, the reduction mechanisms of land‐use originated CO, CH, and NO are distinguished from those of industrial emissions [.,.,.,.,.,.,]. In view of this, we separated the abatement of these land‐use emissions and captured the relationship using the same equation 3 for simplification. The estimations are shown in Figure S2 in Appendix S2.

We used a relatively high carbon price here to reflect the cost of mitigation; this carbon price is higher than that used in DICE‐2013R [ Nordhaus , 2013 , 2014 ; Nordhaus and Sztorc , 2013 ]. This cost was derived directly from the AIM/CGE model, which drives mitigation actions for coping with climate change. We can regard it as a comprehensive cost covering the potential expenses arising from other abatement efforts, which may reach extremely high values when the available reduction potentials are exhausted.

Marginal abatement cost (MAC) curve for the Shared Socioeconomic Pathways 2 scenario. The red points represent sensitivity data relating the rate of control of industrial CO 2 to the carbon price. The green line (equation) and band represent the MAC curve considered in this study, and the gray line and band represent the MAC curve of DICE‐2013R. The upper bound is the MAC in 2005, and the lower bound is the MAC in 2300. A two‐term power function is introduced to define the relationship between the rate of control of industrial CO 2 emissions and the carbon price. The rate of control is the fraction of CO 2 removed from the total industrial CO 2 emissions, and the carbon price is derived from the AIM/CGE sensitivity data. The curve describes economic behavior such that when the rate of control is relatively low (e.g., μ ≤ 0.8), the carbon price behaves as it does in DICE‐2013R, whereas when higher control is needed (e.g., μ > 0.8), the carbon price increases rapidly to account for the difficulty in making further cuts. The μ here is allowed to exceed one for considering negative CO 2 emissions.

The socioeconomic development was parametrized based on a set of sensitivity data generated by the AIM/CGE model [.,]. Eleven artificially defined carbon price paths (Figure S1 in Appendix S2) were used to produce the various economic indicators and corresponding emissions. A new marginal abatement cost (MAC) curve was estimated based on the sensitivity data, as shown in Figure 1 . Here, the carbon price is defined aswhere) is the carbon price in year) denotes the rate of control of industrial CO, and′ and′ are estimated parameters.) is constrained to be nondecreasing, i.e.,+ 1) ≥), under the assumption that a “lock‐in” effect exists in climate change mitigation. The abatement cost as a fraction of the output is therefore given bywhere ∧) is the ratio of the abatement cost to the output,) denotes the carbon price adjustment factor due to technological improvements, and) is the carbon intensity in units of tC per thousand USD (2005).

In addition, two adjustments were made: (1) The SSP2 reference scenario was extended to the year 2300, with the population stabilizing at 8000.0 million, the Gross Domestic Product (GDP) reaching 2258.7 trillion USD (2005) (purchasing power parity) based on the growth rate circa 2,100, and the anthropogenic emissions roughly maintained at the 2,100 levels (see Table S1 in Appendix S2, Supporting Information). (2) The time step was reduced from 5 to 1 year [ Cai et al ., 2012 ] to adequately describe the behaviors of RF agents spanning a wide range of time scales. The modeling period was set to range from 1765 to 2300, with the variables tuned to fit the historical period, i.e., 1765–2004.

The DICE model is a widely used IAM for finding optimal climate change pathways by weighing the costs and benefits [ Nordhaus , 2013 , 2014 ; Nordhaus and Sztorc , 2013 ]. Compared to other complicated IAMs such as participating in SSP quantifications (e.g., AIM/CGE [ Fujimori et al ., 2017 ] and MESSAGE [ Fricko et al ., 2017 ]), the DICE model is simpler and has an advantage that it can easily run numerous scenarios. Based on the DICE framework, we modified DICE‐2013R to capture the abatement potentials of a full suite of climate forcers. We first revised the economic module in DICE‐2013R to represent a middle‐of‐the‐road scenario—SSP2 [ Fujimori et al ., 2017 ; Fricko et al ., 2017 ]. Under the SSP2 assumptions, we utilized the outcomes of the AIM/CGE model [ Fujimori et al ., 2014a , 2014b , 2017 ], which contains more detailed information on future projections of socioeconomic development, energy, land‐use, and emissions. Then, we expanded the simple climate module in DICE‐2013R to represent a full suite of RF agents based on MAGICC 6.0 [ Meinshausen et al ., 2011a ].

3 Results

3.1 Comparison of the DICE‐Style Model and the Fully Revised Model In the modeling experiments, the Full‐Abate mode takes full advantage of the reduction potentials associated with GHGs, pollutants, and aerosols, whereas the DICE‐Style mode considers only the abatement that can be realized by controlling industrial CO 2 . As a result, a higher industrial CO 2 emission level is found during the current century in the Full‐Abate assessment, with a difference of approximately 2.0 GtCO 2 yr−1 compared with the DICE‐Style assessment for the 2.0°C target case and a difference of 5.5 GtCO 2 yr−1 for the 1.5°C target case (Figure 2a). However, this is not the case for the optimal case because the emission pathway is optimized with no constraints. The land‐use CO 2 level is fixed in the DICE‐Style assessment, as shown in Figure 2b. By contrast, in the Full‐Abate assessment, the land‐use CO 2 level turns negative in the late 2050s and makes its maximum contribution to the abatement efforts in the 2080s, reaching a minimum of −1.2 GtCO 2 yr−1 in the 2.0°C target case. These findings imply that the land‐use CO 2 emissions are reduced by more than 2.8 GtCO 2 yr−1 compared with the base case until the 2070s, whereas the relative cuts decrease by the end of the century because of the decreased level of land‐use CO 2 in the base assumption at this time. The optimal case is consistent with the DICE‐2013R assumption [Nordhaus and Sztorc, 2013], which is based on the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) [Ciais et al., 2013b], with a land‐use CO 2 level of approximately −0.1 GtCO 2 yr−1 in 2100. This indicates that the assumption of land‐use CO 2 in this study agrees with existing studies. However, because the level of land‐use CO 2 in 2020 stipulated by the Copenhagen Accord is higher than the DICE‐2013R assumption, a deeper cut for land‐use CO 2 is observed since the 2030s for both climate target cases with the Full‐Abate assessment. Figure 2 Open in figure viewer PowerPoint Comparison between the DICE‐Style and Full‐Abate assessments. (a) Industrial CO 2 emissions. (b) Land‐use CO 2 emissions. (c) Non‐CO 2 radiative forcing. (d) Total radiative forcing. The gray dashed lines in (b) and (c) represent the DICE‐2013R fixed assumptions, scaled from 2005. The colored dashed lines in (a) and (d) are derived from the DICE‐Style assessment. The colored region in (d) represents the difference in the base case between the DICE‐Style and Full‐Abate assessments, which is equivalent to the forcing effect induced by region (1) in (b) plus the forcing in region (2) in (c) since the industrial CO 2 levels are identical in the base case for the two running modes, as shown in (a). The time period covered by the Copenhagen Accord is 2005–2020. Similar findings were obtained with respect to the non‐CO 2 forcing, for which the optimal case is also consistent with the DICE‐2013R fixed assumption (Figure 2c). A reduction of approximately 1.0 W m−2 in non‐CO 2 emissions is found for achieving the 2.0°C target in 2100 according to the Full‐Abate assessment. The above findings show that compared with the DICE‐2013R assumptions, lower levels of both land‐use CO 2 and non‐CO 2 emissions can be identified by the end of this century for the 2.0°C and 1.5°C target cases. Therefore, abatement efforts that reach beyond industrial CO 2 are important, especially for low stabilization scenarios. A greater extent of RF of 0.9 W m−2 (see Figure 2, forcing effect induced by (1) and forcing in (2) is assumed in the base case of Full‐Abate compared to the DICE‐Style. However, the climate change costs are smaller in the Full‐Abate scenarios even with such additional cuts (see following). Controlling the land‐use CO 2 and non‐CO 2 emissions provides more abatement options other than reducing the industrial CO 2 . If no flexible abatement of land‐use CO 2 and non‐CO 2 emissions is allowed, then the reduction potentials associated with these emissions cannot be fully exploited. Abatement efforts that reach beyond industrial CO 2 can be seen to have significant effects on the climate change costs. As shown in Figure 3a, the carbon price in the 2.0°C target case according to the Full‐Abate assessment is approximately 20.0% lower than that indicated by the DICE‐Style assessment in the middle of the century. Regarding GDP losses (Figure 3b), up to 16.1% of the losses during the current century for the 2.0°C target case are eliminated in the Full‐Abate model compared with the DICE‐Style model. The effects are even more remarkable for the 1.5°C target; up to 53.4% of the carbon price in the Full‐Abate assessment can be eliminated compared with that in the DICE‐Style assessment, and the GDP loss is decreased to approximately half of that in the DICE‐Style assessment in the near term. In contrast, for the optimal case, the differences between the DICE‐Style and Full‐Abate results are not significant because the abatement timing is optimized and no constraints are actually imposed. The results show that in the Full‐Abate approach, the climate costs for achieving the 2.0°C and 1.5°C targets are lower, but the forcing is actually reduced to a greater extent. In other words, if the cuts were to be made from the same base levels, then the climate costs indicated by the Full‐Abate assessment would dip even lower than those indicated by the DICE‐Style assessment. These findings demonstrate that dynamic abatement efforts that reach beyond industrial CO 2 can substantially affect the climate costs, particularly with regard to stringent climate control cases. Figure 3 Open in figure viewer PowerPoint Carbon prices and GDP losses. (a) Carbon prices in USD (2005) (purchasing power parity). (b) GDP losses. The GDP losses include the losses from abatement costs, adaptation costs, and residual climate damages. The time period covered by the Copenhagen Accord is 2005–2020.

3.2 Anthropogenic Emissions for 2.0°C Stabilization To achieve the 2.0°C target, although most of the emission cuts come from industrial CO 2 , reductions in land‐use CO 2 and non‐CO 2 emissions are also important. The anthropogenic emissions at the end of this century are reduced by approximately 77.6 GtCO 2 ‐eq yr−1 compared with the base case in the 2.0°C target case; of this reduction, 76.3% is associated with industrial CO 2 , 1.7% with land‐use CO 2 , and 22.0% with non‐CO 2 emissions. As shown in Figure 4a, in the 2.0°C target case, both the industrial and land‐use CO 2 emissions are reduced to significantly lower and even negative levels, whereas the CH 4 and N 2 O emissions can only be cut down to approximately 6.7 and 2.3 GtCO 2 ‐eq yr−1, respectively, in 2100 because of the limited reduction potentials associated with land‐use sources. Specifically, CH 4 and N 2 O emissions from industrial sources are reduced by 78.9% and 75.1%, respectively, by 2100 in the 2.0°C target case, whereas only half of the CH 4 and less than half of the N 2 O emissions from land‐use can be eliminated. Regarding halogenated gases, by the end of this century, F‐gases are reduced by up to 55.3%, and Montreal Protocol gases are reduced to the level of −0.1 GtCO 2 ‐eq yr−1, considering the indirect effects of ozone depletion. Figure 4 Open in figure viewer PowerPoint 2 ‐eq emissions for the 2.0°C target. (a) Anthropogenic CO 2 ‐eq emissions. (b) Decomposition of the contributions of pollutants and aerosols. The CO 2 ‐eq values were calculated after the policy path was obtained. The thick solid lines represent anthropogenic emissions, the thin dashed lines represent industrial emissions, and the thin dotted lines represent land‐use emissions. For the historical data, the outputs of MAGICC 6.0 for the Representative Concentration Pathways (RCPs) were used. For the Montreal Protocol gases, the output of MAGICC 6.0 for RCP 6.0 was used because (1) there are no separate definitions for these gases in Shared Socioeconomic Pathways 2 and (2) there are no significant differences among the RCPs because these gases are assumed to be under control [Montzka et al., 2011 Fuglestvedt et al., 2010 Myhre et al., 2013 Myhre et al., 2013 Anthropogenic CO‐eq emissions for the 2.0°C target. (a) Anthropogenic CO‐eq emissions. (b) Decomposition of the contributions of pollutants and aerosols. The CO‐eq values were calculated after the policy path was obtained. The thick solid lines represent anthropogenic emissions, the thin dashed lines represent industrial emissions, and the thin dotted lines represent land‐use emissions. For the historical data, the outputs of MAGICC 6.0 for the Representative Concentration Pathways (RCPs) were used. For the Montreal Protocol gases, the output of MAGICC 6.0 for RCP 6.0 was used because (1) there are no separate definitions for these gases in Shared Socioeconomic Pathways 2 and (2) there are no significant differences among the RCPs because these gases are assumed to be under control []. The time period covered by the Copenhagen Accord is 2005–2020. The 100‐yr Global Warming Potential values listed in Table S4 in Appendix S2 [] were used in the calculations. The indirect halocarbon effects from ozone depletion are included (Table S5 in Appendix S2) []. Negative values indicate that the emissions exert a cooling effect. The findings regarding the GHG levels here are consistent with those of Rogelj et al. [2011]. However, the Copenhagen Accord level used in this study is assumed to be the median, and a deeper cut is needed from 2020 for a relatively higher emission level. As also reported in Bowerman et al. [2013], Shoemaker et al. [2013], Rogelj et al. [2014a, 2015a], reducing SLCPs, including CH 4 , BC, and hydrofluorocarbons, will have instant effects on climate change mitigation in the near term, and it needs to be implemented with the abatement of long‐lived GHGs to achieve the low stabilization scenario. Furthermore, we used the AIM/CGE information in which the industrial CO 2 emissions and their air pollutants were explicitly represented. In that sense, the air pollutants would not be over‐ or under‐estimated with respect to the interlinkages with industrial CO 2 emissions, as reported in Rogelj et al. [2014a]. Our results also highlight that the potential of reductions in SLCPs is limited for achieving low stabilization targets since their reduction potentials are exhausted in the distant future. Pollutants and aerosols such as CO, VOC, SO x , NO x , BC, and OC are reduced to the level of 1.1 GtCO 2 ‐eq yr−1 by 2100, considering both cooling and warming effects from these emissions (Figures 4a and 4b). Up to 52.7% of the SO x and 31.6% of the NO x are removed by 2100, with no significant changes to the other aerosol levels compared with the base case. As also indicated by Rogelj et al. [2014b], the future emissions of air pollutants are very much contingent on assumptions regarding the penetration of clean air policies. The abatement of air pollutants that is assumed in this study represents a combination of reductions under clean air policies of CLE and reduction initiatives based on climate policy. The combined effect of all aerosols does not significantly change by the end of this century because species with both cooling and warming effects are similarly suppressed under climate control efforts.