Abstract
The Indian agriculture sector is driven by small and marginal farmers and employs two-thirds of the Indian work force. Agriculture also accounts for around a quarter of the total greenhouse gas emissions, mainly in the form of methane (CH4) and nitrous oxide (N2O). Hence, agriculture is an important sector for India's transition to net-zero emissions and for the achievement of the sustainable development goals. So far, very few studies have assessed the future trajectories for CH4 and N2O emissions from the agriculture sector. Moreover, assessment of CH4 and N2O mitigation potential at a subnational (state) level is missing but is important owing to the regional diversity in India. To fill this gap, we focus on methane and nitrous oxide emissions from the agricultural activities using 23 sub-regions in India. We use the GAINS modelling framework which has been widely applied for assessing the mitigation strategies for non-CO2 emissions and multiple air pollutants at regional and global scales. We analyze a current policy and a sustainable agriculture scenario using different combinations of structural interventions and technological control measures to inform the Indian and global climate policy debates. Our results suggest that a combination of sustainable agricultural practices and maximum feasible control measures could reduce the CH4 and N2O emissions by about 6% and 19% by 2030 and 27% and 40% by 2050 when compared to the current policies scenario with limited technological interventions. At a sub-national level, highest mitigation potential is observed in Uttar Pradesh, followed by, Madhya Pradesh, Rajasthan, Gujarat, Maharashtra, Andhra Pradesh, and Telangana. The mitigation of agricultural CH4 and N2O also has co-benefits in terms of reduced local pollution, improved health, and livelihood opportunities for the local communities.
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1. Introduction
India is the fourth largest emitter of greenhouse gases (GHG) in the world. As a fast-growing major economy, India's future emissions trajectory is important for the global climate goals. Since the Paris Climate Change agreement, many national (Vishwanathan and Garg 2020, Durga et al 2022) and international (Grubler et al 2018, Kikstra et al 2021, IEA 2022) modelling assessments have focused on scenarios to mitigate CO2 emissions from the energy sector which contributes around 70% of the total GHG emissions globally and in India (Olivier and Peters 2020, MoEFCC 2021). However, the policy emission targets are generally formulated with reference to total GHG emissions (as CO2eq) that include agriculture, waste, industrial processes, and product use, and include non-CO2 GHGs (NCGG) like methane (CH4), nitrous oxide (N2O) and fluorinated gases. Although CH4 and N2O have a smaller share in the overall GHG emissions, they have a significantly higher global warming potential (GWP) compared to CO2 and are currently estimated to cause a cumulative warming of 0.65 °C (Ravishankara et al 2021, Singh et al 2022). The climate impact of NCGG is often expressed in terms of their relative mass of CO2. GWP100 is one such common metric to measure the long-term GWP of NCGG with respect to CO2 over a period of 100 years. For CH4, the latest assessment of the Intergovernmental Panel on Climate Change (IPCC) have used a source-specific GWP100 of 27 (non-fossil) and 29.8 (fossil), whereas, for N2O, the overall GWP100 value is 273 (Forster et al 2021). With these GWP100 values, the NCGG emissions in national and international inventories are often reported in terms of CO2eq to compare them with CO2 emissions. In 2016, CH4 and N2O accounted for 16% and 6% of the total GHG emissions in India (MoEFCC 2021) and were primarily associated with activities from the agriculture sector (enteric fermentation, rice cultivation, application of nitrogen fertilizers) with enteric fermentation being second largest GHG source in India after electricity generation. Agriculture also employs two-thirds of Indian work force and contributes to 16% of gross value added (GVA) (MoEFCC 2021).
Typically, the Indian modelers assess the NCGG emissions exogenously; they are not part of the larger optimization framework of most models. Limited research is devoted to discussing policies to reduce NCGG. The extant literature on NCGG could be divided into three categories. First, the assessment of emissions using historical data which includes the official reporting of national inventory to the United Nations Framework Convention on Climate Change (UNFCCC) (ex. MoEFCC 2021, 2023). In addition, there are sectoral or activity specific emission factors and inventory assessments of CH4 and N2O from agriculture and other sectors (Datta et al 2009, Gupta et al 2009, Sharma et al 2011, Bhatia et al 2013, Pathak 2015, Patra 2017, Fagodiya et al 2020, Hemingway et al 2023). According to the recent reports, the agriculture sector has contributed to 74% of CH4 and 72% of N2O emissions in 2016 (figure 1). Although the share of CH4 and N2O in total GHG emissions has reduced from 24% in 2016 to around 18% in 2019, the overall agricultural emissions have steadily risen at an annual rate of 0.3% in the past decade (MoEFCC 2021, 2023).
Figure 1. Sectoral contributions to CH4 and N2O emissions in 2016 (Source: MoEFCC 2021).
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Standard image High-resolution imageThe second type of studies have focused on emissions from specific activities like rice cultivation or livestock rearing and their mitigation strategies (Sirohi and Michaelowa 2007, Singh and Singh 2008, Garg et al 2011, Mishra et al 2012, Chhabra et al 2013, Powlson et al 2014, Reddy et al 2019). The third type of studies focus on scenario modelling and are conducted at national and international levels. In case of India, few studies have developed scenarios to assess the NCGG from the agriculture sector (Garg 2004, Ashok et al 2021, Jha et al 2022). In the past, Garg et al (2004) have explored a reference and two mitigation scenarios for CH4 and N2O emissions from India using a spatially explicit AIM/Enduse model (Kainuma et al 1999). Their findings suggest that including CH4 and N2O could provide additional mitigation potential and flexibility when formulating decarbonization policies. In recent studies, Ashok et al (2021) have used national and regional simulation models to study supply interventions like micro-irrigation, limiting water intensive crops like sugarcane along with demand interventions like behavioral shift from rice to millets to achieve food security in a changing climate scenario. The focus is on overall agricultural sustainability with the aim to achieve zero-hunger (SDG 2). Jha et al (2022) have used a partial-equilibrium integrated assessment model, MAgPIE (Dietrich et al 2019, Lotze-Campen et al 2008), to explore sustainable pathways for the agriculture, forestry, and land use (AFOLU) sectors. Their findings suggest that productivity improvements in crop and animal-based products and dietary shift could reduce the total AFOLU sector GHG emissions by up to 80% by 2050.
There are limited studies exploring the non-CO2 emissions mitigation pathways. Further, recent research, particularly after the Paris agreement, has focused on national level analysis. However, assessment of NCGG mitigation potential considering subnational level is important owing to the regional diversity in India. We attempt to fill this gap by focusing on methane and nitrous oxide from key agricultural activities in India. We analyze two scenarios using different combinations of activities and control measures. In the next section we describe the methods, data sources and scenarios used for this study. Section 3 presents the results and section 4 discusses their implications for policy and future research. Section 5 concludes the discussion with key policy recommendations.
2. Methods, data, scenario description
2.1. The GAINS model
We model the future emission trajectories from the Indian agriculture sector using the GAINS modelling framework (Amann et al 2011) which has been widely applied for the analysis of NCGG (Winiwarter et al 2018, Höglund-Isaksson et al 2020, Purohit et al 2020, Harmsen et al 2023) emissions and air pollution (Klimont et al 2017, Purohit and Höglund-Isaksson 2017, Purohit et al 2019, World Bank 2022). Past and future emissions are estimated in GAINS for all key anthropogenic activities, explicitly considering the emission reduction technologies, where applied (equation (1)):
where i, k, m, p represents region, activity type, abatement measure, and pollutant, respectively; EMMi,p represent the emissions of pollutant p (i.e. CH4, N2O) in region i; Ai,k is the activity level of type k (e.g. fertilizer consumption) in region i; EFi,k,m,p the emission factor of pollutant p for activity k in region i after application of control measure m; and Xi,k,m,p is the share of total activity of type k in region i to which a control measure m for pollutant p is applied.
The EF could vary based on the control measure (m) whose implementation depends on alternate policies scenarios and technology penetration rates (Winiwarter et al 2018, Amann et al 2020, Höglund-Isaksson et al 2020). For our analysis, the key activities for agricultural CH4 and N2O emissions included rice cultivation, livestock rearing and fertilizer application in agricultural soils. We use India-specific EF, along with their uncertainty ranges, for these activities, obtained from India's national communication to the UNFCCC and related literature (Bhatia et al 2013, Chhabra et al 2013, MoEFCC 2023). For control strategies, we have considered irrigation management in rice fields, feed management and anerobic digestors for livestock and nitrogen inhibitors for judicial use of fertilizers, among other measures. For this work, we have considered 23 sub-national regions within India (see SI for details).
2.2. Activity data
Historical data between 1990 and 2020 was obtained from various government and international reports. The state wise area, production, and yield statistics for rice and other crops were obtained from the Agricultural Statistics of India (MoAFW GoI 2023a). Data on milk and non-milk animals, state wise milk yields, per capita milk availability, meat and eggs consumption from non-dairy cattle, pigs and poultry were obtained from the latest Livestock Census and the Basic Animal Husbandry Statistics (DAHD GoI 2022a, 2022b). The state wise synthetic nitrogen fertilizer production and consumption was obtained from the Ministry of Fertilizers and Fertilizer Association of India (FAI 2022).
The future demand for agricultural products was projected using macroeconomic drivers like population and income. Primary drivers for selected agricultural activities included population, national gross domestic product and GVA from the agriculture sector. The UN median population projections for India till 2050 (United Nations 2019) were proportionately distributed at state level in the GAINS model. For the current policies scenario (CPS), the population projections, combined with income growth and state-wise consumption trends, were used to estimate the future demand for different plant and animal-based products. However, in the case of commodities like rice, which is already in surplus supply, the production was driven by supply side policies like minimum support prices (MSPs) and the Food Security Act (GoI 2013). The current policies were used to adjust future projections for commodities like rice. In case of the sustainable agriculture scenario (SAS), the per capita demand for agricultural crops and animal produce were projected based on the recommended dietary requirements by the EAT-Lancet report (Willett et al 2019) and other socio-political, cultural, and geographic factors in the states. In terms of production, the yields of various crops, milk, and other animal produce for the past two decades, along with current policies, were used to project the yields of these commodities till 2050. Methods used for projecting future agricultural activity data from key sub-sectors are described in the supplementary information (SI).
2.3. Scenarios and control strategies
In tables 1, CPS_CLE represents the baseline scenario, wherein it assumes the continuation of existing agricultural policies relevant to CH4 and N2O emissions, without any extra efforts to implement technology interventions. SAS_CLE, on the other hand, assumes an integrated transition of agriculture sector to meet the social and environmental goals. For example, reducing land under rice cultivation to meet the dual goals for sustainable diets and mitigation of methane emissions from rice fields. With the maximum feasible reduction (MFR) in both CPS and SAS, we project the mitigation of NCGG from agriculture if activity-specific technological interventions were implemented to their full technical potential. In the Indian context, the guiding policy framework for agricultural mitigation policies is the National Mission for Sustainable Agriculture (NMSA), one of the missions within the National Action Plan on Climate Change (NAPCC) from 2008 (GoI 2008, MoEFCC 2021). The NMSA has led to policies like National Livestock Mission and the National Innovations in Climate Resilient Agriculture (NICRA) which are driving the mitigation efforts in the agriculture sector (See table S2(a) and (b) for details of scenario-specific policies, technologies and application rates).
Table 1. Scenarios used for assessing the CH4 and N2O emissions from the agriculture sector.
| Technological intervention | ||
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| Structural intervention |
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3. Results
Our results discuss the methane and nitrous oxide emissions estimates for the reference year 2015, followed by the projections for the CPS and SAS, showcasing the mitigation potential with maximum feasible technological interventions in the major agricultural activities. We also report the uncertainty in total CH4 and N2O emissions due to uncertainty in activity-specific emission factors. Further, we present the sub-national heterogeneities in CH4 and N2O emissions due to variation in agricultural activities and their corresponding mitigation potential for the period 2020–2050.
3.1. Current and future CH4 and N2O emissions in baseline and alternative scenarios
In 2015, the agricultural activities contributed to an estimated 17.60 (±4.00) million tonnes of CH4 (Mt-CH4) as shown in figure 2(a). Our results suggest a 72% contribution of agricultural activities to methane emissions followed by the waste sector (13%). Fuel production and combustion, industrial processes and non-energy fuel usage made up the remaining 15% of the emissions. The major activities contributing to methane emissions from agriculture sector were enteric fermentation from dairy and non-dairy animals (71%) and rice cultivation (21%). The remaining emissions came from activities like manure management and agricultural residue burning.
Figure 2. Activity wise CH4 and N2O emissions under the current policies scenario (2015–2050). Note: in figure (b), N mineralization from soil organic carbon (SOC) loss is due to change in land use agricultural management practices as reported in the Indian National Inventory (Bhatia et al 2013).
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Standard image High-resolution imageUnder the CPS (CPS_CLE), the projected CH4 emissions increase to 18.21 (±4.15) Mt-CH4 by 2030 and to 18.37 (±4.25) Mt-CH4 by 2050. In the CPS_CLE, the share of agricultural activities in methane emissions is estimated to reduce to 67% (2030) and 60% (2050) due to rise in emissions from the waste sector (21% in 2050). The future CH4 emissions from agriculture sector are again driven by enteric fermentation (75%) and rice cultivation (17%) followed by manure management (7%) and biomass residue burning in agricultural fields (1%). The area under rice has remained around 44 million hectares in the past two decades. Although the rice production has increased due to yield improvements (by over 40%) during this period, the methane emissions are primarily associated with area under cultivation and the type of rice ecosystems. As a result, CH4 emissions have remained around 3.5 Mt-CH4/year in recent years and are expected to reduce moderately to 3.21 Mt-CH4/year by 2050 due to ongoing technological interventions. In the CPS, we assume further increase in rice yields between 2020 and 2050 and the continuation of MSP for rice which lead to surplus production of rice.
According to the latest livestock census (2019), the population of cattle and buffaloes has remained almost the same, compared to the 2012 figures. However, there has been a decrease of 6% in indigenous cattle over 2012. On the other hand, there is a rising trend in the case of exotic and crossbred cattle as their population increased by 35% between 2007 and 2012 and by 27% between 2012 and 2019 (DAHD GoI 2022a). The milk production, mainly from cows and buffaloes, also doubled in the past two decades. Since the crossbred dairy cattle emit more CH4 per head as compared to the indigenous breed (Garg 2004), we observe a rising trend in livestock emissions despite a marginal rise in overall cattle population.
The N2O emissions in 2015 were estimated at 257.16 (±82.29) kilo tonnes of N2O (kt-N2O) as shown in figure 2(b). Agricultural activities contributed to around 66% of the total N2O emissions followed by energy (17%) and the waste sector (13%). In agriculture, direct N2O accounted for around 80% while the indirect emissions from nitrogen volatilization, runoff and leaching contributed the remaining share. For direct N2O, use of synthetic nitrogen fertilizers resulted in about 75% of the emissions followed by above and below ground biomass crop residues (10%), N mineralization from soil organic carbon (SOC) loss (9%), organic manure from livestock (5%), compost and green manure (∼1%).
In the CPS_CLE, the projected N2O emissions increase to 337.40 (±107.97) kt-N2O by 2030 and to 412.82 (±132.10) kt-N2O by 2050. The share of direct and indirect N2O from agriculture was projected to increase to 68% (2030) and 71% (2050). By 2050, energy and industrial processes contributed to around 19% of the total N2O emissions with waste sector accounting for the remaining 10% share. In the past few years, consumption of nitrogen fertilizers has gone up due to policy changes that have driven an increased share of Nitrogen in NPK fertilizers applied by farmers (Some et al 2019). However, excluding a few states like Punjab and Haryana, the per hectare consumption of synthetic N fertilizers is still low in many parts of India. Assuming the policies of agri-intensification, driven using subsidized urea and non-urea N fertilizers, the CPS_CLE projects the fertilizer emissions to increase by 41% in 2030 and 79% in 2050, when compared to 2015. In addition, the rising share of dairy animals contribute to an increase of N2O from animal manure by 14% (2030) and 32% (2050).
As illustrated in figure 3, CH4 and N2O emissions from agriculture sector are projected to increase in the CPS_CLE due to the anticipated economic and population growth in future years. However, the effect of the structural interventions (SAS) and the maximum feasible technology interventions (MFR) is seen in later years. The 9% decline in CH4 emissions in 2050 from structural interventions alone (SAS_CLE), compared to CPS_CLE, are observed from two sub-sectors—dairy animals (54%) and rice cultivation (45%). The structural interventions in dairy sector are primarily targeted at improving the milk yields which lead to reducing the number of dairy animals when compared to the CPS scenario. In case of rice cultivation, the area under crop is gradually decreased with increase in yields to meet the recommended per capita rice consumption of a nutritional diet. Further, area under rice cultivation is shifted to eastern states of India from northern states of Punjab and Haryana where groundwater levels are going down (Bhattarai et al 2021). For N2O emissions, the driving factor is the consumption of N-fertilizers. The use of synthetic fertilizers per hectare of agricultural land is already very low in many states of India. Hence the structural interventions, in terms of reducing the per hectare N-use and timing of application are limited to a few regions with high per hectare fertilizer use. The effective reduction of N2O in 2050 between SAS_CLE and CPS_CLE is around 40% with limited technological interventions. Further, emissions from manure nitrogen reduce by 5% in 2050 between SAS_CLE and CPS_CLE.
Figure 3. CH4 (left) and N2O (right) emission projections from agriculture sector under the CPS and SAS scenarios.
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Standard image High-resolution imageThe additional mitigation potential is explored through the MFR scenarios. Interventions like the system of rice intensification, shorter duration variety of rice, and intermittent drying of rice fields are implemented to reduce the CH4 emissions. These result in additional 0.36 Mt-CH4 of reduction from rice between the SAS_CLE and SAS_MFR scenarios by 2050. For livestock, dietary management with concentrated fodder, breed improvement, pasture management along with anaerobic fermentation of animal waste was implemented as a control measure. These interventions contribute to around 2.7 Mt-CH4 reduction in 2050 between the SAS_CLE and SAS_MFR. Total reduction in CH4 emissions between SAS_CLE and SAS_MFR by 2050 is around 20%. In case of N2O, stringent technology control measures with nitrogen inhibitors and nano-urea (Upadhyay et al 2023) further reduce the N-fertilizer emissions by 25% in 2050 between SAS_CLE and SAS_MFR. The total mitigation potential between CPS_CLE and SAS_MFR for the period 2020–2050 was around 70 Mt-CH4 and 2.7 Mt-N2O.
3.2. Sub-national heterogeneities in CH4 and N2O emissions from agriculture sector of India
At the sub-national level, methane and nitrous oxide emissions show a large variation as illustrated in figure 4 (Panel (A)). In 2015, Uttar Pradesh, owing to its large size and agrarian economy, was a major contributor with about 17% of the CH4 and 18% of the N2O emissions. Andhra Pradesh, Rajasthan, West Bengal, and Madhya Pradesh were the other leading states in terms of methane emissions. For N2O, Andhra Pradesh and Telangana, Maharashtra, and Punjab were the next big emitters. At sectoral level, Uttar Pradesh, West Bengal, Punjab, Odisha, and Andhra Pradesh were the leading states in rice cultivation and contributed to over 50% of the methane emissions from area under rice. In case of livestock rearing, Uttar Pradesh, Rajasthan, Madhya Pradesh, Jharkhand, Andhra Pradesh, and West Bengal accounted for the major share of CH4 and N2O emissions resulting from enteric fermentation, manure management and animal manure applied to agricultural fields. The use of synthetic N fertilizers also varied in terms of per hectare consumption. For instance, even though Punjab and Haryana do not have a large share of N2O emissions, their per hectare consumption is one of the highest (175–200 kg/hectare) (MoAFW GoI 2023a). We also studied the subnational mitigation potential for CH4 and N2O emissions. The maximum potential, when comparing CPS_CLE and SAS_MFR is observed in Uttar Pradesh (26% for CH4, 15% for N2O), followed by, Madhya Pradesh, Rajasthan, Gujarat, Maharashtra, Andhra Pradesh, and Telangana (figure 4, Panel (B)).
Figure 4. CH4 and N2O emissions in the reference year 2015 (Panel (A)) and mitigation potential between 2020 and 2050 in SAS_MFE as compared to the CPS_CLE scenario (Panel (B)).
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4. Discussion
Our scenario results suggest that mitigation of non-CO2 emissions from the agriculture sector require a systemic approach to integrate social, environmental and climate goals. In India, around 42% of the total geographic area is under agriculture (net sown area) and around 55% of the population is dependent on agriculture-based livelihood (MoAFW GoI 2023b). The major activities contributing to CH4 and N2O emissions are also driven by small and marginal farmers. On the other hand, emissions from agriculture sector are rising and are expected to grow in the future due to rising consumption driven by economic growth. Considering the limitations due to subsistence farming, the mitigation technologies implemented at the farm-end (such as agri-mechanization) would be particularly challenging due to their high cost for small and marginal farmers. Alternatively, technologies implemented centrally or at the industry end (such as neem-coating of urea for nitrogen inhibition) could have wide application rates. However, the industrial-scale mitigation technologies will require wider policy support and government interventions at the beginning to increase their application rates as envisaged in our MFR scenarios. It is also important to highlight here that the technical mitigation potential considered in the MFR scenarios may further reduce due to economic and socio-cultural constraints in the given states. The MFR scenarios thus present a technically feasible reduction based on the application rates and mitigation potential without diving into the costs and political economy of these interventions.
We do not study the NCGG mitigation potential for other sectors like waste and fossil fuels. However, based on earlier assessments, waste and fossil fuel activities show a higher cumulative mitigation potential of around 50% (2020–2050) for CH4 emissions when comparing the baseline (CLE) and the MFR scenario (Höglund-Isaksson et al 2020). Similarly, for N2O, industrial production sectors have a mitigation potential between 80% and 99% whereas wastewater treatment could further mitigation 40% emissions between 2020 and 2050 when comparing the baseline and the MFR scenarios (Winiwarter et al 2018). However, in 2050, both these sectors have limited share in the total N2O emissions when compared to the agriculture sector.
The mitigation of NCGG from agriculture sectors also have co-benefits in terms of reduced local pollution, improved health, and livelihood opportunities for the local communities. Recent studies from Europe (Klimont and Winiwarter 2015, Klimont et al 2017) and China (Bai et al 2019) indicate that agricultural NH3 emissions have emerged as a major contributor to the formation of fine particulate matter (PM2.5) resulting in air pollution and health hazards for the local population. A modelling assessment of ambient air quality in India also suggests that one-third of the PM2.5 emissions are contributed by secondary sources that involve NH3 from agriculture (Purohit et al 2019). The mitigation measures for reducing the use of synthetic N-fertilizers could benefit in reducing the agricultural NH3 emissions. Similarly, burning of crop residue in the fields has emerged as a major source of air pollution in north India (Shyamsundar et al 2019). Apart from raising awareness among farmers to stop burning the residue, waste-to-energy production by converting surplus residue to bio-pellets or second-generation biofuels could also help in generating local employment (Purohit and Chaturvedi 2018, Purohit and Dhar 2018). Further, co-benefits for waste-to-energy production also exist in the livestock sector. The number of unproductive female cattle has gone up over the past few decades due to a lack of policies for such animals (DAHD GoI 2022a). One way to deal with this is by setting up dry-cattle farms where adult unproductive cattle are housed and the manure, along with other biomass waste in the area, is used for biogas production. These community scale biogas plants, while reducing the methane emissions, could be used for electricity production or supply of compressed biogas to other industries.
Finally, there are three types of uncertainties associated with the CH4 and N2O emissions reported in this study. First, the uncertainty due to emission factors that is reported in the results section. The activity specific EF uncertainty is reported in Tables S1a and S1b (SI). The second type of uncertainty arises from the application rates and mitigation potential of different technological interventions. Using the upper and lower bounds of activity-specific mitigation potential and application rates (Tables S2a and S2b), we found that the overall mitigation potential for CH4 varies between 59 and 91 Mt-CH4 between 2020 and 2050 whereas the corresponding variation on mitigation potential of N2O was between 2.66 and 2.69 Mt-N2O (See Section S2 for details). The third type of uncertainty is associated with the activity data which was not analyzed here. Based on previous studies (Bhatia et al 2013), the activity data uncertainty for CH4 may vary between 3% and 22% and for N2O between 11% and 17%.
5. Conclusions
In a post-Paris agreement scenario, this study is one of the first to explicitly model India's agricultural non-CO2 emissions at a subnational level. It underscores the need for effective strategies and addresses the gap in India's modeling landscape, which typically treats these emissions separately, with limited policy exploration. Our results suggest that the methane and nitrous oxide emissions could be brought down by 27% and 40% by 2050 in the SAS using best available technologies when compared to the same year in the CPS. This, of course, is the technical mitigation potential which may further reduce based on socio-cultural and political economy constraints. Nevertheless, the results highlight the importance of combining structural transformations with technological interventions to achieve the climate targets while also meeting the relevant sustainable development goals. Further, the measures to reduce CH4 and N2O emissions have co-benefits in terms reduced PM2.5 emissions, improved air quality and health benefits for the people. Going forward, the NCGG scenarios could be implemented in conjunction with mitigation scenarios for CO2 to understand the additional mitigation potential for total GHG emissions. Modelling of NCGG with CO2 would also improve our understanding of marginal abatement costs of GHG mitigation to meet the policy commitments.
Acknowledgments
This research was partially funded by the European Union's Horizon 2020 Research and Innovation programme under Grant Agreement No. 821471 (ENGAGE).
Data availability statement
All data that support the findings of this study are included within the article (and any supplementary files).
Supplementary data (0.4 MB PDF)