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Ratcheting of climate pledges needed to limit peak global warming – Nature.com

The Global Change Analysis Model

GCAM is an open-source model developed and maintained at the Pacific Northwest National Laboratory’s Joint Global Change Research Institute. In this study, we use the version of the GCAM (v.5.3) used in ref. 5 and available in a public repository55. The full documentation of the model is available at the GCAM documentation page (http://jgcri.github.io/gcam-doc/) and the description here is a summary of the online documentation and based on refs. 5,56,57,58,59.

GCAM includes representations of five systems: economy, energy, agriculture and land-use, water and climate in 32 geopolitical regions across the globe and the associated land allocation, water use and agriculture production across 384 land subregions and 235 water basins. GCAM operates in 5-year time-steps from 2015 (calibration year) to 2100 by solving for the equilibrium prices and quantities of various energy, agricultural, water, land-use and GHG markets in each time period and in each region. GCAM is a dynamic recursive model. Hence, solutions for each modelling period only depend on conditions in the last modelling period. Outcomes of GCAM are driven by exogenous assumptions about population growth, labour participation rates and labour productivity in the 32 geopolitical regions, along with representations of resources, technologies and policy. GCAM tracks emissions of 24 gases, including GHGs, short-lived species and ozone precursors, endogenously based on the resulting energy, agriculture and land-use systems as discussed in the following subsections.

The GCAM energy system contains representations of fossil resources (coal, oil and gas), uranium and renewable sources (wind, solar, geothermal, hydro and biomass and traditional biomass) along with processes that transform these resources to final energy carriers (electricity generation, refining, hydrogen production, gas processing and district heat), which are ultimately used to deliver goods and services demanded by end-use sectors (residential buildings, commercial buildings, transportation and industry). Each of the sectors in GCAM include technological detail. For example, the electricity generation sector includes several different technology options to convert coal to electricity such as pulverized coal with and without carbon capture and storage (CCS) and coal integrated gasification combined cycle (IGCC) with and without CCS. In every sector within GCAM, individual technologies compete for market share on the basis of the levelized cost of a technology. The cost of a technology in any period depends on (1) its exogenously specified non-energy cost, (2) its endogenously calculated fuel cost and (3) any cost of emissions, as determined by the climate policy. The first term, non-energy cost, represents capital, fixed and variable operation and maintenance costs incurred over the lifetime of the equipment (except for fuel or electricity costs), expressed per unit of output. For example, the non-energy cost of coal-fired power plant is calculated as the sum of overnight capital cost (amortized using a capital recovery factor and converted to dollars per unit of energy output by applying a capacity factor), fixed and variable operations and maintenance costs. The second term, fuel or electricity cost, depends on the specified efficiency of the technology, which determines the amount of fuel or electricity required to produce each unit of output, as well as the cost of the fuel or electricity. The various data sources and assumptions are documented in the GCAM documentation page (http://jgcri.github.io/gcam-doc/).

The prices of fossil fuels and uranium are calculated endogenously. Fossil fuel resource supply in GCAM is modelled using graded resource supply curves that represent increasing cost of extraction as cumulative extraction increases. Wind and rooftop PV technologies include resource costs that are also calculated from exogenous supply curves that represent marginal costs that increase with deployment, such as long-distance transmission line costs that would be required to produce power from remote wind resources. Utility-scale solar photovoltaic and concentrated solar power technologies are assumed to have constant marginal resource costs regardless of deployment levels.

In GCAM, technology choice is determined by market competition. The market share captured by a technology increases as its costs decline but GCAM uses a logit model of market competition. This approach is designed to represent decision-making among competing options when only some characteristics of the options can be observed60,61 and avoids a ‘winner takes all’ response.

The agriculture and land-use component of GCAM represents competition for land among alternative uses in 283 agro-economic zones within the 32 regions. Land is allocated between alternative uses such as food crops (including wheat, corn, rice, root and tuber and other grain), commercial biomass, forests, pasture, grassland and shrubs based on expected profitability according to a logit-share mechanism similar to the energy system. The profitability in turn depends on the productivity of the land-based product (for example, mass of harvestable product per hectare), product price and non-land costs of production (labour, fertilizer and so on). The productivity of land-based products is subject to change over time based on future estimates of crop productivity change. GCAM also tracks land from desert, tundra and urban land. However, these are excluded from economic competition and assumed to be fixed over time. Yields for all crops are assumed to improve over time. These improvement rates vary by region, with higher improvement rates in developing regions. The energy system and the agriculture and land-use systems are hard linked (coupled in code). Commercial biomass is demanded in the energy system while its supply is modelled in the agriculture and land-use component. Fertilizer supply is represented in the energy system while fertilizer demand is modelled in the agriculture and land-use system. Traditional biomass is not modelled in the agriculture and land-use system but is instead represented through exogenous supply curves that account for the opportunity cost associated with collecting traditional biomass—collecting traditional biomass requires labour which becomes increasingly expensive as incomes rise.

GCAM tracks emissions of a variety of GHG species: CO2, CH4, N2O, HFCs (HFC23, HFC32, HFC125, HFC134a, HFC143a, HFC152a, HFC227ea, HFC43, HFC236fa, HFC365mfc and HFC245fa), PFCs (CF4 and C2F6) and SF6. The CO2 emissions result from direct combustion of fossil fuels and conversion to other forms. Once a fossil fuel is extracted, the carbon in the fuel is either emitted or sequestered. The total CO2 emissions in the base year of GCAM (currently 2015) is calibrated to the Carbon Dioxide Information Analysis Center database62 at the global level and fossil fuel consumption in the base year is calibrated to the International Energy Agency’s Energy Balances Database63. Global average emissions coefficients (for example, CO2 per GJ) are derived from the ratio of the total emissions and the total fuel consumption for each fossil fuel (coal, oil and gas). In each model period, CO2 emissions from a technology are calculated as the product of global average emission coefficients obtained above and fuel consumption by that technology in that period. Agriculture and land-use change emissions depend on the amount of land-use change, the equilibrium carbon density of the ecosystem and region-specific growth profiles64.

GCAM also tracks non-CO2 emissions from the energy and agricultural and land-use systems. Historical emissions of CH4, N2O and F-gases are harmonized with the 2019 US Environmental Protection Agency (EPA) Global Non-CO2 Greenhouse Gas Emission Projections and Mitigation Potential report65. Historical emissions of short-lived forcing agents (BC and OC) and air pollutants (SO2, NOx and PM2.5) are calibrated to the Community Emissions Data System66. These historical emissions are then used to develop emission factors (emission per energy input or service output of a specific technology). Emissions factors are assumed to change over time if air pollution controls are tightened (local air pollutants only) or a carbon price is applied (GHGs only; not all sectors). Future emissions are estimated as the product of the projected economic activity, the corresponding emission factor for a given technology and emissions reductions estimated through marginal abatement cost (MAC) curves. MAC curves are based on ref. 65.

In our pathways, non-CO2 emissions can be controlled by two mechanisms. First, changes in activity (phasing out of carbon-intensive fuels due to climate policy) will reduce non-CO2 emissions (for example, fugitive CH4 from natural gas production). Second, for emission sources without explicit representation of the underlying activity, emission reductions are calculated off of MAC curves that are parametrized to abatement technologies and abatement levels. MAC curves represent the mitigation cost and corresponding emission reductions achievable for each region, species and available source categories over time.

The version of GCAM used in this study includes important recent technological and socioeconomic trends. First, the effect of COVID-19 on the global economy is reflected by incorporating the latest country-specific International Monetary Fund GDP growth projections67. Second, electric power technology cost assumptions (capital cost, operation and maintenance cost and efficiency) follow recent trends and projections and are based on the 2019 National Renewable Energy Laboratory (NREL) Annual Technology Baseline68. These assumptions entail substantial capital and operation and maintenance cost reductions for most technologies, especially solar and wind technologies. Third, the version of GCAM used in this study includes electrification options in the transportation sector including electric vehicles and electric trucks. Our transportation cost and energy intensity assumptions are based on the NREL Electrification Futures Study69.

The version of GCAM used in this study assumes the availability of three CDR options: afforestation, BECCS and DAC technologies. The scale of each option is determined by economics. Our pathways incentivize afforestation by assuming a gradual transition—by 2050—to a regime in which CO2 emissions from land-use changes are valued at the same price as emissions from the energy system59,70. As described earlier, in GCAM, bioenergy competes for land with other land uses on the basis of profitability. BECCS technologies are deployed in a variety of sectors within the GCAM energy system including refining, electricity generation and hydrogen production. Our assumptions for DAC technologies are documented in Supplementary Table 4 and refs. 43,40.

Hector

Hector is the reduced-form carbon-cycle climate module that is available for use in GCAM15,71 and is an open-source model. This study is based on Hector v.2.5. Hector has a three-part carbon cycle: one-pool atmosphere, three-pool land and four-pool ocean. The model’s terrestrial carbon cycle includes primary production and respiration fluxes while also accommodating arbitrary geographic divisions, such as ecological biomes or political units. Hector’s ocean component includes a detailed representation of the inorganic carbon cycle, calculating air–sea fluxes and ocean pH (ref. 71). Hector reproduces the global historical trends of atmospheric CO2, radiative forcing and surface temperatures.

GCAM interacts with Hector through emissions. At every time step, emissions from GCAM are passed to Hector. Hector then converts these emissions to concentrations when necessary and calculates the associated radiative forcing, as well as the response of the climate system (for example, surface temperature and carbon fluxes).

Emissions pathways

The representation of the NDCs in our central pathway is based on ref. 5 and is explained in detail in the supplementary information to that study. This study also includes 21 new and/or updated NDCs after 30 September 2021, including those from China, Pakistan and many African and Middle Eastern countries that were not included in ref. 5 (Supplementary Table 5). We assume that the NDCs are achieved as stipulated and focus on the climate outcomes of their successful implementation. Examining the likelihood of individual regions achieving their submitted targets is beyond the scope of this study.

Our representation includes only ratified and quantifiable unconditional NDC commitments, including absolute emissions limit, percentage emission reductions from a given reference level and emission intensity targets. Parties whose commitments included: (1) only actions/policies, (2) non-GHG targets with no corresponding GHG emissions target or (3) only sector-specific GHG emissions reduction targets without attempting to quantify the impact on their overall GHG footprints are assumed to have target year emissions equal to the GCAM emissions in the default reference scenario without any climate policy (‘Reference—No Policy’ in ref. 5). Likewise, in cases where a country’s 2025 and 2030 emissions based on its NDC are lower than the default reference scenario in the same year, the NDC emissions are assumed to be achieved as stipulated. In cases where a country’s NDC emissions are higher, emissions are assumed to be equal to the reference scenario. For countries that included multiple types of commitments in their NDCs, such as economy-wide emissions reductions backed by sectoral policies or targets, only the broadest commitment was considered. For example, China’s NDC representation in GCAM is based on its commitment to reduce its carbon intensity of GDP by 65% relative to 2005 and it does not explicitly model its targets for non-fossil energy consumption or increased forest stock.

Similar to ref. 5, our pathways include LTSs and net-zero pledges. For countries with LTSs that are different from a net-zero pledge (for example, Mexico), emissions are assumed to meet their NDC commitments in 2030 first. Beyond 2030, emissions linearly reduce to the LTS in the specified target year and then continue to follow a path defined by the decarbonization rate between 2015 and the LTS target year. For countries with net-zero pledges, emissions are assumed to meet their NDC commitments in 2030 first. Beyond 2030, emissions linearly reduce to net-zero in the target year and then remain constant afterwards. In the cases where countries have explicitly committed to net-zero CO2 emissions, such as South Korea, only CO2 emissions are constrained. This study also includes additional net-zero pledges that were announced after the completion of the ref. 5 study. These include pledges from India, Brazil, Australia, New Zealand and Argentina (Supplementary Table 2). Where the scope of net-zero targets is somewhat unclear (as in the case of Japan) or in cases where countries use terms such as ‘carbon neutral’ and ‘net-zero GHG emissions’ interchangeably, we follow the CAT assessment and assume a net-zero GHG target. For example, China announced a ’carbon neutrality’ goal by 2060, which is assumed as a net-zero GHG emission target in our main analysis. This assumption is consistent with latest official interpretations of China’s net-zero pledge72.

Our post-2030 decarbonization rate assumptions are consistent with refs. 57,5. However, our definition of decarbonization rate is based on all GHGs while the definitions used by ref. 57,5 are based only on fossil fuel and industrial CO2 emissions (note that the emissions scenarios modelled in the studies of refs. 57,5 do include concurrent reductions in non-CO2s in response to CO2 reductions that are facilitated by the decarbonization rate assumptions). Our central assumption about the post-2030 minimum decarbonization rate is 2% and our sensitivity assumptions are 5% and 8%. While the 2% rate has been achieved routinely in history and represents a moderate level of post-2030 mitigation, the 5% and 8% decarbonization rate assumptions can be considered as requiring more dedicated, stringent mitigation policies (Supplementary Fig. 14). For additional context, the 2% decarbonization rate falls under the higher end of the distribution of decarbonization rates implied in the ‘baseline’ scenarios assessed by the Intergovernmental Panel on Climate Change (IPCC) Special Report on 1.5 °C (SR1.5) and the 5% and 8% assumptions lie at the peak of the distribution of decarbonization rates in scenarios limiting global warming to 1.5 °C (Supplementary Figs. 15 and 16)18. We note that 2% minimum decarbonization rate assumption is not binding for any region since the implied 2015–2030 decarbonization rate in the NDCs for all regions is >2% (Supplementary Table 6).

Notably, there is some interaction and overlap among the three strategies explored in this study that countries might use to ratchet ambition. With higher 2030 ambition, the post-2030 minimum decarbonization rate assumption might no longer be binding in some cases. For example, in the case of India, the 2015–2030 decarbonization rates in the pathways with the NDC, NDC+ and NDC++ emission levels in 2030 are, respectively, 2.1%, 4.4% and 4.4% (see Supplementary Table 6 for 2015–2030 decarbonization rates under the NDC, NDC+ and NDC++ emission levels in 2030). Hence, the 2% post-2030 minimum decarbonization rate assumption would be binding only in the NDC cases. In addition, advancing the timing of the net-zero pledges (for countries with net-zero pledges) would result in higher post-2030 decarbonization rates. However, it is important to note that our minimum decarbonization rate assumptions (2%, 5% and 8%) do not affect the emission pathways of countries with net-zero pledges since these countries are always assumed to achieve their pledges—in the specified target years, 5 years in advance or 10 years in advance.

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