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The impact of ambient air pollution on human health, particularly fine particulate matter (PM2.5) and tropospheric ozone (O3), is a critical global concern. Atmospheric chemical transport models (CTMs) are widely used to predict air pollutant concentrations and assess associated health risks. However, there is a need to better understand how the horizontal resolution of these models influences their accuracy, especially in future assessments. In this study, we compared the performance of global low-resolution CTMs with high-resolution nested simulations for estimating O3 and PM2.5 concentrations. The models were validated against observational data to determine their accuracy across different spatial scales and to evaluate their suitability for future scenario assessments. Our findings demonstrate that while the nested-grid simulations improved the reproducibility of regional observations, especially in areas with complex topography or localized emissions, the overall global-scale performance of the model did not significantly benefit from higher resolution. Additionally, the differences in global health and agricultural impacts between low- and high-resolution simulations were minor and within the range of uncertainty typically associated with emission inventories and CTMs. However, for specific regional studies or policy applications, higher resolution may offer improved accuracy. Ultimately, the current low-spatial-resolution model provides sufficient accuracy for many global-scale applications, but the choice of resolution should be carefully considered depending on the specific objectives of the study especially in future scenario.

Abstract Climate mitigation is reported to benefit biodiversity globally. However, the impacts of mitigation measures based on large-scale land-use modifications can be concentrated in the regions where they are introduced, resulting in regional mismatches between mitigation efforts and biodiversity benefits. Here, we evaluated the impacts of large-scale deployment of bioenergy with carbon capture and storage and afforestation to attain the climate stabilization target stated by the Paris Agreement on global and regional biodiversity by using an integrated model framework. Our results highlight that climate mitigation efforts can benefit global biodiversity regardless of large-scale implementation of land-based mitigation measures. However, the negative impacts of mitigation measures on biodiversity are concentrated in regions with a higher contribution to land-use change and carbon sequestration. The results imply the need to consider the unequal regional distribution of benefits from climate mitigation, as well as mitigation options that avoid regional biodiversity loss by minimizing land-use change.

Abstract Afforestation is a major carbon dioxide removal technology but can negatively affect food and land systems. Here, we used an integrated assessment model to quantitatively demonstrate how the selection of forest type to use in afforestation could increase global carbon sequestration without compromising global food and land sustainability. Our findings indicate that if a carbon-intensive forest type is selected, afforestation would increase carbon sequestration by 25% compared to the level assuming the native forest type. At the same time, if implemented inappropriately at a large scale, afforestation would worsen the economy, food, and land systems due to decreased land efficiency in carbon removal compared to bioenergy with carbon capture and storage, leading to increased land expansion for carbon removal, higher food prices, and increased risk of hunger. These results suggest the need to implement carbon-intensive forest types with complementary measures to reduce the adverse impacts on food and land sustainability.

Abstract Climate change mitigation generally require rapid decarbonization in the power sector, including phase-out of fossil fuel-fired generators. Given recent technological developments, co-firing of hydrogen or ammonia, could help decarbonize fossil-based generators, but little is known about how its effects would play out globally. Here, we explore this topic using an energy system model. The results indicate that hydrogen co-firing occurs solely in stringent mitigation like 1.5 °C scenarios, where around half of existing coal and gas power capacity can be retrofitted for hydrogen co-firing, reducing stranded capacity, mainly in the Organization for Economic Co-operation and Development (OECD) countries and Asia. However, electricity supply from co-firing generators is limited to about 1% of total electricity generation, because hydrogen co-firing is mainly used as a backup option to balance the variable renewable energies. The incremental fuel cost of hydrogen results in lower capacity factor of hydrogen co-fired generators, whereas low-carbon hydrogen contributes to reducing emission cost associated with carbon pricing. While hydrogen co-firing may play a role in balancing intermittency of variable renewable energies, it will not seriously delay the phase-out of fossil-based generators.

We study Japan’s net-zero emissions target by 2050 in a multi-model framework, focusing on residual emissions and carbon dioxide removal (CDR). Four energy-economic and integrated assessment models show similar but stronger strategies for the net-zero target, compared to the previous, low-carbon policy target (80% emissions reduction). Results indicate that around 90% (inter-model median) of the current emissions are reduced through abatement, including improved energy efficiency and cleaner electricity and fuels. Models deploy new options such as CDR based on carbon capture and storage (CCS) (bioenergy with CCS and direct air carbon dioxide capture and storage) and hydrogen to achieve net zero. The scale of CCS-based CDR deployment reaches an inter-model median of 132Mt-CO _2 /yr. The median hydrogen share of final energy in 2050 increases from 0.79% to 6.9% between the low-carbon and net-zero scenarios. The CDR sensitivity analysis reveals that limiting the use of CDR significantly increases the mitigation costs for net zero. Achieving Japan’s net-zero goal will require exploring methods to reduce residual emissions, including demand-side solutions, and accelerating responsible CDR policies.

Fire emits hazardous air pollutants, the most dominant of which is fine particulate matter of diameter ⩽2.5 μ m (PM _2.5 ). PM _2.5 is a great concern due to its adverse effects on public health. Previous studies have examined the health burden from fire-related PM _2.5 for the historical period; however, future global mortality associated with fire-PM _2.5 , particularly under the coexisting impacts of climate and socioeconomic changes, is yet to be projected. Here, we estimated the mortality associated to fire-PM _2.5 (fire-PM _2.5 mortality) for the present period (2010s) and then projected the mortality for the 2050s and 2090s under 10 scenarios created by combining two Representative Concentration Pathways (RCP2.6 or 6.0) with five Shared Socioeconomic Pathways (SSP1–5). We used the Li-Park fire model in combination with a chemical transport model and health risk assessment to calculate fire-PM _2.5 mortality. For the present period, the estimated fire-PM _2.5 mortality was 135 180 (105 403–163 738), which is 1.7 deaths per 100 000 population globally. When countries were grouped by income, the fire-PM _2.5 mortality rate was the highest in the high-income country group and lowest in the lowest-income country group. For the 2050s and 2090s, the fire-PM _2.5 mortality rate was projected to decrease under most scenarios because of decreases in fire emissions and baseline mortality rate. However, a scenario of high population growth and low technical development (SSP3) together with severe global warming (RCP6.0) would lead to an increase in the fire-PM _2.5 mortality rate in the 2090s, particularly in the highest-income countries, due to increased fire under drier and warmer weather conditions. Stratification of countries by gross domestic product indicated the need for adaptation efforts in the highest-income countries to avoid future increases of mortality associated with fire-PM _2.5 .

Comprehensive policy measures are needed to close the emissions gap between Nationally Determined Contributions and emissions goals of the Paris Agreement. Here the authors present a Bridge scenario that may aid in closing the emissions gap by 2030.

To evaluate the effectiveness of current national policies in achieving global temperature targets is important but a systematic multi-model evaluation is still lacking. Here the authors identified a reduction of 3.5 GtCO2 eq of current national policies relative to a baseline scenario without climate policies by 2030 due to the increasing low carbon share of final energy and the improving final energy intensity.

What will be the aggregated cost of climate change in achieving the Paris Agreement, including mitigation, adaptation, and residual impacts? Several studies estimated the aggregated cost but did not always consider the critical issues. Some do not address non-market values such as biodiversity and human health, and most do not address differentiating discount rates. In this study, we estimate the aggregated cost of climate change using an integrated assessment model linked with detailed-process-based climate impact models and different discount rates for market and non-market values. The analysis reveals that a climate policy with minimal aggregated cost is sensitive to socioeconomic scenarios and the way discount rates are applied. The results elucidate that a lower discount rate to non-market value—that is, a higher estimate of future value—makes the aggregated cost of achieving the Paris Agreement economically reasonable.

Greenhouse gas mitigation can involve land-use changes that alter the habitat available for wildlife. Here, Ohashi et al. perform an integrated assessment showing that climate mitigation can be beneficial for global biodiversity but may entail local biodiversity losses where land-based mitigation is implemented.

Computable General Equilibrium models can hardly decouple economic growth and energy consumption while energy system models can hardly predict macroeconomic implications of energy system changes. Here the authors investigated the macroeconomic implications of consistently dealing with energy systems and the stability of further power generation and show that GDP losses were significantly lower than those in the conventional economic model by more than 50% in 2050, while industry and service sector energy consumption are the main factors causing these differences.

Abstract Bioenergy plays an important role in low greenhouse gas stabilization scenarios. Among various possible sources of bioenergy, dedicated bio‐crops could contribute to most of the potential. However, large scale bio‐crop deployment raises sustainability concerns. Policies to alleviate the pressure of bio‐crops on the terrestrial environment can affect bioenergy potential and production costs. Here, we estimated the maximum bioenergy potential under environmental protection policies (biodiversity and soil protection) and societal transformation measures from demand and supply side (demand‐side policy includes sustainable diet; supply‐side policy includes advanced technology and trade openness for food) by using an integrated assessment modelling framework, which consists of a general equilibrium model (Asian‐Pacific Integrated Model/Computable General Equilibrium) and a spatial land use allocation model (Asian‐Pacific Integrated Model/Platform for Land‐Use and Environmental Model). We found that the global advanced bioenergy potential under no policy was 245 EJ/year and that 192 EJ/year could be produced under US$5/GJ. These figures were 149 EJ/year and 110 EJ/year, respectively, under a full environmental policy. Biodiversity protection has a greater impact than soil protection due to its larger coverage and stronger implementation. Societal transformation measures effectively increase them to 186 EJ/year and 143 EJ/year, respectively, even under full environmental policies. These results imply that the large‐scale bioenergy deployment possibly needed for the climate target to limit the global mean temperature increase well below 2°C compared to the preindustrial level might face a trade‐off with environmental protection targets and that possible mitigation pathways in harmony with other environmental issues need to be explored.

There lacks model comparison of global land use change projections. Here the authors explored how different long-term drivers determine land use and food availability projections and they showed that the key determinants population growth and improvements in agricultural efficiency.

Climate change mitigation efforts to reduce greenhouse gas (GHG) emissions have associated costs, but there are also potential benefits from improved air quality, such as public health improvements and the associated cost savings. A multidisciplinary modeling approach can better assess the co-benefits from climate mitigation for human health and provide a justifiable basis for establishment of adequate climate change mitigation policies and public health actions. An integrated research framework was adopted by combining a computable general equilibrium model, an air quality model, and a health impact assessment model, to explore the long-term economic impacts of climate change mitigation in South Korea through 2050. Mitigation costs were further compared with health-related economic benefits under different socioeconomic and climate change mitigation scenarios. Achieving ambitious targets (i.e., stabilization of the radiative forcing level at 3.4 W/m2) would cost 1.3–8.5 billion USD in 2050, depending on varying carbon prices from different integrated assessment models. By contrast, achieving these same targets would reduce costs by 23 billion USD from the valuation of avoided premature mortality, 0.14 billion USD from health expenditures, and 0.38 billion USD from reduced lost work hours, demonstrating that health benefits alone noticeably offset the costs of cutting GHG emissions in South Korea. Keywords: Climate mitigation, Air pollution, Health impact assessment, Co-benefit analysis, CGE, IMED Model

The Paris Agreement has confirmed that the ultimate climate policy goal is to hold the increase in the global average temperature to well below 2 °C above pre-industrial levels and to pursue efforts to limit the increase to 1.5 °C. Moving the goal from 2 °C to 1.5 °C calls for much more concerted effort, and presents greater challenges and costs. This study uses an Asia-Pacific Integrated Model/Computable General Equilibrium (AIM/CGE) to evaluate the role of socioeconomic factors (e.g. technological cost and energy demand assumptions) in changing mitigation costs and achieving the 1.5 °C and 2 °C goals, and to identify the channels through which socioeconomic factors affect mitigation costs. Four families of socioeconomic factors were examined, namely low-carbon energy-supply technologies, end-use energy-efficiency improvements, lifestyle changes and biomass-technology promotion (technology cost reduction and social acceptance promotion). The results show that technological improvement in low-carbon energy-supply technologies is the most important factor in reducing mitigation costs. Moreover, under the constraints of the 1.5 °C goal, the relative effectiveness of other socioeconomic factors, such as energy efficiency improvement, lifestyle changes and biomass-related technology promotion, becomes more important in decreasing mitigation cost in the 1.5 °C scenarios than in the 2 °C scenarios.

Abstract Although biophysical yield responses to local warming have been studied, we know little about how crop yield growth—a function of climate and technology—responds to global temperature and socioeconomic changes. Here, we present the yield growth of major crops under warming conditions from preindustrial levels as simulated by a global gridded crop model. The results revealed that global mean yields of maize and soybean will stagnate with warming even when agronomic adjustments are considered. This trend is consistent across socioeconomic assumptions. Low-income countries located at low latitudes will benefit from intensive mitigation and from associated limited warming trends (1.8 °C), thus preventing maize, soybean and wheat yield stagnation. Rice yields in these countries can improve under more aggressive warming trends. The yield growth of maize and soybean crops in high-income countries located at mid and high latitudes will stagnate, whereas that of rice and wheat will not. Our findings underpin the importance of ambitious climate mitigation targets for sustaining yield growth worldwide.

Low-carbon pathways consistent with the 2 °C and 1.5 °C long-term climate goals defined in the Paris Agreement are likely to induce substantial co-benefits for air pollution and associated health impacts. In this analysis, using five global integrated assessment models, we quantify the emission reductions in key air pollutants resulting from the decarbonization of energy systems and the resulting changes in premature mortality attributed to the exposure to ambient concentrations of fine particulate matter. The emission reductions differ by sectors. Sulfur emissions are mainly reduced from power plants and industry, cuts in nitrogen oxides are dominated by the transport sector, and the largest abatement of primary fine particles is achieved in the residential sector. The analysis also shows that health benefits are the largest when policies addressing climate change mitigation and stringent air pollution controls are coordinated. We decompose the key factors that determine the extent of health co-benefits, focusing on Asia: changes in emissions, urbanization rates, population growth and ageing. Demographic processes, particularly due to ageing population, counteract in many regions the mortality reductions realized through lower emissions.

Integrated assessment models (IAMs) form a prime tool in informing about climate mitigation strategies. Diagnostic indicators that allow comparison across these models can help describe and explain differences in model projections. This increases transparency and comparability. Earlier, the IAM community has developed an approach to diagnose models (Kriegler (2015 Technol. Forecast. Soc. Change 90 45–61)). Here we build on this, by proposing a selected set of well-defined indicators as a community standard, to systematically and routinely assess IAM behaviour, similar to metrics used for other modeling communities such as climate models. These indicators are the relative abatement index, emission reduction type index, inertia timescale, fossil fuel reduction, transformation index and cost per abatement value. We apply the approach to 17 IAMs, assessing both older as well as their latest versions, as applied in the IPCC 6th Assessment Report. The study shows that the approach can be easily applied and used to indentify key differences between models and model versions. Moreover, we demonstrate that this comparison helps to link model behavior to model characteristics and assumptions. We show that together, the set of six indicators can provide useful indication of the main traits of the model and can roughly indicate the general model behavior. The results also show that there is often a considerable spread across the models. Interestingly, the diagnostic values often change for different model versions, but there does not seem to be a distinct trend.

A carbon tax is one of the measures used to reduce GHG emissions, as it provides a strong political instrument for reaching the goal, stated in the Paris Agreement, of limiting the global mean temperature increase to well below 2 °C. While one aspect of a carbon tax is its ability to change income distribution, no quantitative assessment has been made within the context of global poverty. Here, we explore future poverty scenarios and show the extent to which carbon tax revenue, obtained to limit global warming to well below 2 °C, has the potential to help eradicate poverty. In order to better understand the relationship between poverty and climate change mitigation policy, we developed a novel modelling framework that includes a module representing poverty indicators in the conventional integrated assessment model. We found that the poverty gap, which is a measure of the shortfall in income relative to the poverty line, is 84 billion US dollars (USD) and that the carbon tax revenue potential for the above-mentioned 2 °C consistent climate change mitigation would be 1600 billion USD in 2030. Many low-income countries cannot fill the poverty gap using only their own domestic revenue; however, this shortfall could be met by using a portion of the revenue in high-income countries. Our results demonstrate that climate change mitigation can have a great potential in synergy effects for resolving poverty and illustrates the importance of international cooperation.

Actions tackling with climate change can cause co-benefits and trade-offs with Sustainable Development Goals (SDGs) concerned with air pollution, water scarcity, food security, land use, and sustainable energy. Such interactions can be greatly influenced by socioeconomic conditions. The impacts of socioeconomic conditions on multiple SDGs have not been evaluated separately from climate policies. This paper employs a Representative Concentration Pathways–Shared Socio-economic Pathways (RCP-SSP) framework and the Asia-Pacific Integrated Model/computable general equilibrium (AIM/CGE) integrated assessment model to identify the global multi-sectoral consequences of socioeconomic conditions through 2050 under future SSP scenarios. Results show that changes of socioeconomic conditions consistent with the SSP1 pathway could always improve SDG indicators, with or without climate policies. In many respects, socioeconomic conditions are more important than climate policies in achieving SDGs, particularly SDGs concerned with food security and energy affordability, as well as in simultaneously achieving multiple SDGs. We conclude that the advantages of a joint effort to implement climate policies and promulgate socioeconomic changes should be recognized by policy makers.

Electrification is widely considered an attractive solution for reducing the oil dependency and environmental impact of road transportation. Many countries have been establishing increasingly stringent and ambitious targets in support of transport electrification. We conducted scenario simulations to depict the role of transport electrification in climate change mitigation and how the transport sector would interact with the energy-supply sector. The results showed that transport electrification without the replacement of fossil-fuel power plants leads to the unfortunate result of increasing emissions instead of achieving a low-carbon transition. While transport electrification alone would not contribute to climate change mitigation, it is interesting to note that switching to electrified road transport under the sustainable shared socioeconomic pathways permitted an optimistic outlook for a low-carbon transition, even in the absence of a decarbonized power sector. Another interesting finding was that the stringent penetration of electric vehicles can reduce the mitigation cost generated by the 2 °C climate stabilization target, implying a positive impact for transport policies on the economic system. With technological innovations such as electrified road transport, climate change mitigation does not have to occur at the expense of economic growth. Because a transport electrification policy closely interacts with energy and economic systems, transport planners, economists, and energy policymakers need to work together to propose policy schemes that consider a cross-sectoral balance for a green sustainable future.

Cost-effective achievement of the Paris Agreement’s long-term goals requires the unanimous phase-out of coal power generation by mid-century. However, continued investments in coal power plants will make this transition difficult. India is one of the major countries with significant under construction and planned increase in coal power capacity. To ascertain the likelihood and consequences of the continued expansion of coal power for India’s future mitigation options, we use harmonised scenario results from national and global models along with projections from various government reports. Both these approaches estimate that coal capacity is expected to increase until 2030, along with rapid developments in wind and solar power. However, coal capacity stranding of the order of 133–237 GW needs to occur after 2030 if India were to pursue an ambitious climate policy in line with a well-below 2 °C target. Earlier policy strengthening starting after 2020 can reduce stranded assets (14–159 GW) but brings with it political economy and renewable expansion challenges. We conclude that a policy limiting coal plants to those under construction combined with higher solar targets could be politically feasible, prevent significant stranded capacity, and allow higher mitigation ambition in the future.

Synergies and trade-offs exist between climate mitigation actions and target indicators of the Sustainable Development Goals (SDGs). Some studies have assessed such relationships, but the degree of such interaction remains poorly understood. Here, we show the SDG implications associated with CO _2 emissions reductions. We developed ‘marginal SDG-emissions-reduction values (MSVs)’, which represent the marginal impacts on SDG indicators caused by a unit CO _2 emissions reduction. This metric is applicable to national assessments and was applied to Asia. We found clear relationships between CO _2 emissions reduction rates and many SDG targets. For instance, 1% reduction of CO _2 can avoid 0.57% of air pollution-related premature deaths (SDG3), whereas the mean species richness (SDG15) is decreased by 0.026% with the same reduction (not including climate change impacts). Our findings are useful for assessing the SDG implications associated with CO _2 emissions reduction targets, which will help inform national climate policies.

International efforts to avoid dangerous climate change aim for large and rapid reductions of fossil fuel CO _2 emissions worldwide, including nearly complete decarbonization of the electric power sector. However, achieving such rapid reductions may depend on early retirement of coal- and natural gas-fired power plants. Here, we analyze future fossil fuel electricity demand in 171 energy-emissions scenarios from Integrated Assessment Models (IAMs), evaluating the implicit retirements and/or reduced operation of generating infrastructure. Although IAMs calculate retirements endogenously, the structure and methods of each model differ; we use a standard approach to infer retirements in outputs from all six major IAMs and—unlike the IAMs themselves—we begin with the age distribution and region-specific operating capacities of the existing power fleet. We find that coal-fired power plants in scenarios consistent with international climate targets (i.e. keeping global warming well-below 2 °C or 1.5 °C) retire one to three decades earlier than historically has been the case. If plants are built to meet projected fossil electricity demand and instead allowed to operate at the level and over the lifetimes they have historically, the roughly 200 Gt CO _2 of additional emissions this century would be incompatible with keeping global warming well-below 2 °C. Thus, ambitious climate mitigation scenarios entail drastic, and perhaps un-appreciated, changes in the operating and/or retirement schedules of power infrastructure.

In climate change research, future scenarios of greenhouse gas and air pollutant emissions generated by integrated assessment models (IAMs) are used in climate models (CMs) and earth system models to analyze future interactions and feedback between human activities and climate. However, the spatial resolutions of IAMs and CMs differ. IAMs usually disaggregate the world into 10-30 aggregated regions, whereas CMs require a grid-based spatial resolution. Therefore, downscaling emissions data from IAMs into a finer scale is necessary to input the emissions into CMs. In this study, we examined whether differences in downscaling methods significantly affect climate variables such as temperature and precipitation. We tested two downscaling methods using the same regionally aggregated sulfur emissions scenario obtained from the Asian-Pacific Integrated Model/Computable General Equilibrium (AIM/CGE) model. The downscaled emissions were fed into the Model for Interdisciplinary Research on Climate (MIROC). One of the methods assumed a strong convergence of national emissions intensity (e.g., emissions per gross domestic product), while the other was based on inertia (i.e., the base-year remained unchanged). The emissions intensities in the downscaled spatial emissions generated from the two methods markedly differed, whereas the emissions densities (emissions per area) were similar. We investigated whether the climate change projections of temperature and precipitation would significantly differ between the two methods by applying a field significance test, and found little evidence of a significant difference between the two methods. Moreover, there was no clear evidence of a difference between the climate simulations based on these two downscaling methods.

Clarifying characteristics of hazards and risks of climate change at 2 °C and 1.5 °C global warming is important for understanding the implications of the Paris Agreement. We perform and analyze large ensembles of 2 °C and 1.5 °C warming simulations. In the 2 °C runs, we find substantial increases in extreme hot days, heavy rainfalls, high streamflow and labor capacity reduction related to heat stress. For example, about half of the world’s population is projected to experience a present day 1-in-10 year hot day event every other year at 2 °C warming. The regions with relatively large increases of these four hazard indicators coincide with countries characterized by small CO _2 emissions, low-income and high vulnerability. Limiting global warming to 1.5 °C, compared to 2 °C, is projected to lower increases in the four hazard indicators especially in those regions.

The Paris Agreement set long-term global climate goals to pursue stabilization of the global mean temperature increase at below 2 °C (the so-called 2 °C goal). Individual countries submitted their own short-term targets, mostly for the year 2030. Meanwhile, the UN’s sustainable development goals (SDGs) were designed to help set multiple societal goals with respect to socioeconomic development, the environment, and other issues. Climate policies can lead to intended or unintended consequences in various sectors, but these types of side effects rarely have been studied in China, where climate policies will play an important role in global greenhouse gas emissions and sustainable development is a major goal. This study identified the extent to which climate policies in line with the 2 °C goal could have multi-sectoral consequences in China. Carbon constraints in China in the 2Deg scenario are set to align with the global 2 °C target based on the emissions per capita convergence principle. Carbon policies for NDC pledges as well as policies in China regarding renewables, air pollution control, and land management were also simulated. The results show that energy security and air quality have co-benefits related to climate policies, whereas food security and land resources experienced negative side effects (trade-offs). Near-term climate actions were shown to help reduce these trade-offs in the mid-term. A policy package that included food and land subsidies also helped achieve climate targets while avoiding the adverse side effects caused by the mitigation policies. The findings should help policymakers in China develop win–win policies that do not negatively affect some sectors, which could potentially enhance their ability to take climate actions to realize the global 2 °C goal within the context of sustainable development.