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Social cost of methane: Method and estimates for Indian livestock
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Social cost of methane: Method and estimates for Indian livestock Shilpi Kumaria,∗, Moonmoon Hiloidharib, S.N. Naikc, R.P. Dahiyaa a
Centre for Energy Studies, Indian Institute of Technology Delhi, New Delhi, 110016, India IDP in Climate Studies, Indian Institute of Technology Bombay, Mumbai, 400076, India c Centre for Rural Development and Technology, Indian Institute of Technology Delhi, New Delhi, 110016, India b
A R T IC LE I N F O
ABS TRA CT
Keywords: Social cost of carbon Social cost of methane Climate change Economic damage Integrated assessment model Livestock CH4 emission
The quantitative assessment of climate change damage due to an additional unit of greenhouse gases emissions (mainly carbon di-oxide, CO2) is termed as the Social Cost of Carbon (SCC). Published literature primarily focused on the SCC of CO2 emissions, neglecting other greenhouse gases (GHGs). The social cost assessment for other GHGs especially CH4 is also needed as it is the 2nd highest emitted GHG after CO2 with high global warming potential. The quantitative assessment of climate change damage per additional unit of CH4 can be termed as Social Cost of Methane (SCM). In the present study, the SCM (in CO2e unit) has been estimated for the Indian livestock using Integrated Assessment Model (IAM) and system dynamic approach. Different livestock growth scenarios viz. Business as usual (BAU), modified scenarios (MS I, MS II and MS III) have been proposed for SCM calculation (cost per ton CO2e CH4) through 2017 to 2032. The SCM for 2017 is $62 ̶ $1150 and is projected to be $77 ̶ $1438 in 2032. The highest SCM is in BAU ($1150 in 2017 and $1438 in 2032) and the lowest in MS I ($62 in 2017 and $77 in 2032). The differences in SCM values are due to the different population size of livestock and CH4 emission rate. Results and findings of the study suggest that the CH4 even emitted in small quantity has a significant impact on climate and hence should not be neglected in climate change mitigation policies. The SCM is a metric tool which helps to design the appropriate policies for reducing CH4 emission from livestock. The developed tool can also be applicable to estimate the social cost for other GHGs for market-based policy development.
1. Introduction Climate change mitigation has become an urgent concern due to increasing anthropogenic greenhouse gas (GHG) emissions and its potential threat to humanity and the environment (Fagodiya et al., 2017). the impact of climate change is already being observed through a rise in surface temperature, glaciers melt, shifting monsoon pattern, extreme weather, hazards and rising sea levels (IPCC, 2014). Quantification of the damages reveals that over the last 30 years, increased extreme weather events has caused an average loss of $2–28 billion from cyclones, about $10 billion loss from inland floods, landslides and avalanches, $2 billion from wildfires and storm-related phenomena worldwide (Guha-Sapir et al., 2015; Ranson et al., 2016). Intensity, frequency, and magnitude of natural calamities are strongly influenced by the climate change condition, which also affects economic growth (Ranson et al., 2016). To ensure sustainable development, almost all the nations of the world are scaling up their climate change mitigation approach and designing specific policies. The environmental damages in the poor and developing countries became a challenging situation due to their limited resources to tackle climate change. The most effective climate policies can be accomplished through a collaborative
∗
Corresponding author. E-mail address: [email protected] (S. Kumari).
https://doi.org/10.1016/j.envdev.2019.100462 Received 2 November 2017; Received in revised form 10 October 2019; Accepted 12 October 2019 2211-4645/ © 2019 Published by Elsevier B.V.
Please cite this article as: Shilpi Kumari, et al., Environmental Development, https://doi.org/10.1016/j.envdev.2019.100462
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work of climate scientists, economists, government, stakeholders, and public interactions as well (Moss et al., 2010; Stern, 2008; Pathak et al., 2013; Stocker et al., 2013). Researchers have developed Integrated Assessment Models (IAMs) (Ranson et al., 2016; Metcalf and Stock, 2015) to assist climate policy in a two-way approach between climate and society for policy evaluation and optimization (IWG, 2010; Nordhaus, 2017a). The costs and benefits of abating a unit tonne of CO2 emissions are estimated and compared with the baseline economic scenario (Anthoff and Tol, 2013; Tol, 2015). This evaluation of climate change damage in monetary term is defined as the ‘Social Cost of Carbon’ (SCC) (Hope and Newberry, 2006; Marten and Newbold, 2012). The SCC values represent monetized net damage costs of climate change impacts (Nordhaus, 2017). As CO2 is the main greenhouse gas, most of the research works are focused on the estimation of SCC for CO2 emissions and its emission sources (Paul et al., 2013; Ferraroa et al., 2015). Shindell et al. (2017) worked out for social cost of methane emission estimation and found that the importance of mitigation policies for its reduction. There has been no such work have been carried out to estimate the social cost at country level till Ricke et al. (2018). The highest SCC is observed in USA, China, and India in Ricke et al. (2018). Thus, the present study is based on the development of a model framework for estimation of Social Cost of CH4 (SCM) in CO2e term based on GWP of CH4 for Indian livestock. In the paper, the Social Cost Methane (SCM) is estimated in CO2e term based on Global Warming Potential (GWP) for livestockmediated CH4 emission in India. The present paper is continuation work of Kumari et al. (2016) where CH4 emission was estimated for Indian livestock sector up to district level. Globally, livestock is responsible for 12% of anthropogenic GHGs (Havlík et al., 2014, Kumari et al., 2014). Indian livestock is an important source of CH4 and N2O (Kumari et al., 2016; Sejian et al., 2016). The Indian livestock sector has the potential to cause surface temperatures to surge up to 0.69 mK over 20-year time period which is roughly 14% of the total increase caused by the global livestock sector (Kumari et al., 2018). 1.1. Social cost of carbon The SCC is a metric tool for economic quantification of either net damage associated with an increment of one unit of GHG emission or benefits associated with the reduction of an additional unit of GHG emission per year (Pearce, 2003; Watkiss and Hope, 2011). Therefore, SCC is measured as a monetary unit per ton of CO2 i.e, $/ton CO2e (Newbold et al., 2010). The SCC estimated with the help of Integrated Assessment Models (IAMs) and which can be compared with the investment cost of mitigating climate change (Hope and Newberry, 2006). The IAMs could be used further for carbon pricing based climate policy development (Paul et al., 2013; Ferraroa et al., 2015). There are different types of IAM models available to estimate SCC such as The Dynamic Integrated Climate and Economy (DICE), Climate Framework for Uncertainty, Negotiation, and Distribution (FUND), Policy Analysis of the Greenhouse Effect (PAGE), the Regional Integrated Model of Climate and the Economy (RICE) and World Induced Technical Change Hybrid (WITCH) (Hope and Newberry, 2006; BAHS, 2014; Holman et al., 2005Nordhaus, 2010; Anthoff et al., 2009; Nordhaus, 2014). These models are different from each other for large number of key variables, but some selected variables are included viz. types of GHGs selected for SCC estimation, period, and spatial assessment level i.e, local, regional or global. However, the main working principle of IAM models remains the same as discussed below. The estimation of SCC is complex, but the basic steps involved in the process are (IWG, 2010; Greenstone et al., 2011; Pizer et al., 2014): (1) GHGs emission, GDP growth, and population are projected for a selected time frame for the baseline scenario. The selected GHGs and the time frame differ with IAMs models. The default time horizon for simulating the model in PAGE, DICE, and FUND are 2200, 2595, and 3000, respectively. (2) Changes in the climatic variability (such as temperature rise, sea-level rise) and economic damage each year due to GHGs accumulation in the atmosphere are calculated. The damage estimation also varies with the model types based on selected climatic variables. (3) In baseline emission, an additional unit of GHG is added, known as an altered emission pathway. The changes in climate and economic output are now estimated for the altered emission pathway. This altered emission is done to know the impact of one additional unit of emission on climate change. (4) The difference between damages from the baseline and altered emission pathways are estimated and the resultant damage is known as marginal damage. This damage is called marginal damage because the damage is estimated per additional unit of altered emission. (5) The ratio of marginal damage and an additional unit of GHGs emission is calculated for SCC estimation. The social cost estimation is the conversion of GHGs induced climate damage and economic damage in the economic term, but exact and accurate damage quantification is highly uncertain in any IAM model. Estimating the SCC involves many uncertainties which include the GHGs emission estimation, change in climatic variables such as rise in the surface temperature, damage estimation and accurate loss estimation of damages in the environment, and the discount rate. The social cost estimation method should be transparent, broadly explainable and understandable to non-experts. However, the IAM is not sufficiently transparent and explainable for the policymakers and the public (Metcalf and Stock, 2017). So, the alternative model is needed to estimate the social cost for carbon and non-CO2 GHGs e.g. SCM. In the present study, the developed model is based on IAM working principles using a system dynamics approach. The system dynamics is a computer-based approach understanding, designing, and managing change over the period of time (Anand et al., 2006). Hence, system dynamics approach helps to develop the new model for estimating SCM. Methods and benefits of the model are described further. A comparison study of the developed model with previous version of IAM model is 2
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also summarized in Section 5.5. 2. Methodology We present a simplified IAM to estimate the Social Cost of CH4 (SCM) in CO2e which combines the system dynamic approach of STELLA software, the underlying concept of IAMs, and Microsoft excel workbook calculation. The developed model is applied to the Indian livestock sector. 2.1. Model framework The SCM for livestock CH4 emission in India is estimated for a 25-year period (2007–2032) under different scenarios such as business as usual (BAU) and three modified scenarios (MS-I, MS-II, and MS-III). The model is divided into four sub-models: (i) emission-climate model, (ii) climate-economy model, (iii) marginal damage model, and (iv) SCM model. The emission-climate model comprises of two components i.e. CH4 emission estimation and projection and (b) its atmospheric accumulation. The four scenarios, business as usual (BAU) and modified scenarios (MS-I, MS-II, and MS-III) are based on the different livestock growth rate for CH4 emissions estimation and SCM estimation (Kumari et al., 2016) (see Supplementary information for detail). In the emission – climate model system dynamics is used to estimate CH4 emissions and its impact on the climatic variable. IPCC et al., 2006 Tier 1 methodology has been used for CH4 emission estimation, where specific emission factors have been provided for developing and developed countries. For cattle, specific emission factor is provided for Indian subcontinent where cattle divided into two specific groups: dairy and non-dairy. Previous work, Kumari et al. (2016) focused to estimate CH4 emission and trend projections using the same IPCC guidelines (IPCC et al., 2006). The climate-economy model is second sub-model which is built to assess the impact of changes in climatic variables on the economy (i.e. loss in GDP). The climate-economy model runs for two normal baseline and altered emission pathways. A normal baseline emission pathway is the CH4 emission pathways for all scenarios (BAU, MSI, MSII, and MSII). But, in the altered emission pathways, one unit is added in CH4 emission in all scenarios to estimate the GDP loss per unit of CH4 emission. The marginal damage model is used to estimate GDP losses from normal and altered emission pathways. Finally, the SCC model is used to estimate the economic cost of livestock related CH4 emission equivalent to CO2e emission. The interaction of the different components of the model is presented in Fig. 1. 2.2. Emission-climate model The system dynamic approach based on STELLA software, in combination with the mathematical model was developed to assess
Fig. 1. Overview of the developed SD-IAM for the social cost of methane (SCM) estimation. The basic steps required to calculate SCM are shown in the left panel. Interactions of inputs/outputs, assumptions and variables are shown in boxes and dotted arrows of the right panel. ECS: Equilibrium climate sensitivity, CC: Climate change, ED: Economic damage ΔCN: Economic damage in the normal baseline emission pathway, ΔCA: Economic damage in altered emission pathway. 3
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livestock population growth and CH4 emission for a 25 year period (2007–2032) under business as usual scenario (BAU) and modified (MS-I, MS-II, MS-III) scenarios as given in equation (1). The different assumptions and equations used in all four scenarios are discussed in Supplementary information. z
Ei =
∑ (pi
× [1 + CAGR]n ) × EFi × (GWP )CO2
(1)
i=1
Ei is CH4 emission from livestock in Tg CO2e; pi is the population of the ith category of livestock in million; CAGR is compounded annual growth rate for livestock population projection and EFi is the specific emission factors for the ith category of livestock in kg y−1 (IPCC et al., 2006; Kumari et al., 2016). In AR5 report of IPCC, GWP for non-CO2 gases are presented for both with and without including climate-carbon feedbacks. In this report, GWP for CH4 are 28 without including climate-carbon feedbacks and 34 with including climate-carbon feedbacks. SCM for livestock CH4 emission is calculated in CO2e unit using GWP of CH4 as 34 (IPCC, 2014). After CH4 emission estimation and projection, its accumulation in the atmosphere is simulated through STELLA software (Anand et al., 2005; Kumari et al., 2016) as below:
At = A
t − dt
+ [kr − kd] × d
(2)
At is an accumulation of CH4 in the atmosphere at time ‘t’, Tg CO2e; kr and kd are accumulation and decay rates of CH4 and d is decay time. The accumulation of CH4 is calculated by system dynamic mathematical formulation where specific function ‘delay’ is used (Anand et al., 2006; Kumari et al., 2016). The kr and kd estimation is based on the following equations: n
kr =
∑ Ei
(2a)
i=1
kd = delay [kr , T , E0 ]
(2b)
Ei is CH4 emission in Tg CO2e (equation (1)) and T (delay time i.e. 12 year lifespan of CH4 in the atmosphere), and E0 is the initial concentration of CH4. In accumulation calculation the initial value of CH4 is ‘0’ Tg and the atmospheric lifetime of CH4 is 12 years. The rate of CH4 decay is equal to its production rate 12 years ago. The impact on climate change is assumed to occur due to changes in the surface temperature. The potential rise in surface temperature is determined by: Tt = To +
ΔT2x ⎡ (CO2e CH4 )t ⎤ ln ln 2 ⎢ ⎦ ⎣ (CO2e CH4 )0 ⎥
(3)
Tt is the new temperature at time ‘t’; To is the baseline surface temperature (15 °C) in the base year 2012; ΔT2x is the equilibrium climate sensitivity (ECS) factor, i.e., mean surface temperature change after doubling of CO2 concentration; (CO2e CH4)t is the concentration of CH4 in CO2e unit in the atmosphere at time ‘t’; (CO2e CH4)0 is the initial concentration of CH4 in CO2e unit in the atmosphere. The ECS values are variable, ranging from 1.5 to 4.5 °C but the most likely range is 1.5–3 °C (IWG, 2010). Different ECS values (1.5, 2.0, 2.5 and 3.0 °C) are considered to estimate different ranges of SCM. 2.3. Climate-economy model In Climate-economy model, we used the impact of climate change in economic output i.e. temperature shock on economic output. It has been quantified that the temperature rise may cause economic loss however, some researcher may deny this correlation as false or unauthentic (Dell et al., 2012). The economic output of any country depends on many factors but per degree rise in temperature has an important influence on national economic performance (Dell et al., 2012; Holman et al., 2005). We develop a climate-economy model using the specific damage function given by Moore and Diaz (2015). Here, in the model national economic performance is estimated by the relationship between temperature and growth rate of GDP as applied in Moore and Diaz (2015). i.e. GDP projection based on temperature variability. Findings of Dell et al. (2012) also suggest that the higher temperature substantially affects the economic output especially in poor and developing countries. Based on the above findings, we estimate the negative impacts of climate change on economic output. Impact of climate change and increase in temperature could reduce economic growth rate and GDP (Moore and Diaz, 2015). The impact of CH4 emission from livestock on the national economy is computed for the nation as a function of the increase in surface temperature. In this phase, the economic damage from changes in surface temperature due to livestock CH4 emission is evaluated using the specific damage function, derived from Moore and Diaz (2015). In the previous section, we have estimated the accumulation of CH4 and its impact on climate in terms of rise in temperature. This assumption is termed as Climate Change (CC). In the alternate situation, we assumed that there is no CH4 emission hence, No Climate Change (NCC). For this purpose, these two assumptions are GDP projection under (a) no climate change (NCC), and (b) climate change (CC). Economic damage under CC is estimated taking ECS as 1.5 °C, 2 °C, 2.5 °C, and 3 °C. Range of ECS values allows prediction of the magnitude of future damages in the event of low to extreme climate damage with respect to low ECS value to high ECS values. 4
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2.3.1. Assumption 1- if there is no climate change (NCC) Under this assumption, GDP is projected using the simple growth rate model i.e GDP projection calculation is based on the linear equation described as
Ct = Ct − 1 (1 + r )
(4)
Ct is the GDP at the time ‘t’ under no climate change, $; Ct-1 is the GDP at the previous year, $; and r is the GDP growth rate, taken as 7%. The GDP in the year 2012 is considered as $1408 billion (equivalent to 93,889 billion Indian rupees) (BAHS, 2014). 2.3.2. Assumption 2- if there is climate change (CC) GDP projection is endogenous because a rise in surface temperature causes economic damages and damage in a given year, propagates forward with time and reduces GDP in future years. For this assumption climate change GDP projection, we used a similar approach as proposed by Moore and Diaz (2015) to calculate the changes in GDP due to climate change impact. Under the climate change scenario, GDP is projected as a function of temperature. Different ECS is assumed as 1.5 °C, 2 °C, 2.5 °C, and 3 °C.
C ′t = Ct − 1 × (1 + r − r ′)
(5)
r ′ = α × (Tt − Tt − 1)
(6)
C ′t is the GDP at time ‘t’ under climate change assumption, $; Ct-1 is the GDP at previous year, $; r is the growth rate (7%), r ′ is the change in GDP growth rate due to per unit rise in temperature, and α is the specific economic damage function. Tt and Tt-1 are temperatures at the present and previous year for which GDP is being projected. The economic damage calibration function α is −1.171% per 1 °C temperature rise taken from Moore and Diaz (2015). After estimating the GDP projections under both the assumptions of CC and NCC the loss in GDP under normal baseline emission pathway ( ΔCNt ) can be estimated as: (7)
ΔCNt = Ct − C ′t
2.4. Marginal damage estimation This is another critical step in SCM modeling to monetize the damage caused by GHGs emission. The SCM estimation is estimated using the normal emission pathway and altered emission pathway. For this purpose, the normal baseline emissions are changed by the addition of one unit of GHG in each year to compute an altered emission pathway. Hence, we can say if the CH4 emission in the normal baseline emission pathway (EN) is xTg CH4, then the altered emission (EA) is increased by one unit i.e. x+ 1 Tg CH4. The additional shock of CH4 emission gives another database for temperature and GDP estimation. Emission-climate model and climate-economy model are replicated for the altered emission pathway to calculate the rise in surface temperature, changes and loss in GDP (Equations (1)–(6)). For altered emission pathway, the loss in GDP ( ΔCAt ) also estimated as equation (7). The difference between the loss in GDP for the normal ( ΔCNt ) and altered ( ΔCAt ) CH4 emission pathway is marginal damage ( ΔCMt ) . (8)
ΔCMt = ΔCNt − ΔCAt
2.5. Discounting the marginal damage The SCC estimation comprised of several critical steps and assumptions. For SCC estimation in different IAMs model, future climatic impacts are predicted and discounted back to present values to find the damage per ton of GHG emitted into the atmosphere. The selection of discount rates and discounting schemes has a significant influence on the final estimate (Anthoff et al., 2009). However, the choice of discount rate is a topic of debate in the SCC model. The lower the discount rate, the higher the SCC value, and the more strict mitigation policy are required and vice versa. Therefore, the discount rate will be a key feature in the future climaterelated damage estimation of present value. Similarly, for SCM estimation discount rate plays a similar role in the model calculation. India is demographically very young country in comparison to the rest of the world. India is also one of the topmost countries in terms of livestock size (Kumari et al., 2018). Selection of discount rate is one of the biggest determining factors for SCM estimation. Discounting assumptions on SCM estimation deciding factors are economic condition and emission index of the country. Considering the growing economic condition, livestock mediated emission status of India, and literature survey, a wide range of discounting rate is taken for SCM estimation. However, the SCM is also estimated without any discount rate i.e. 0%. Discounting of marginal damage is computed as given below taking three discount rates viz. 5%, 3.5%, 2.5% and 0%.
ΔC′Mt = ΔCMt (1 + η)−t
(9)
ΔC′Mt is the discounted marginal damage, $; ΔCMt is the marginal damage, $; and ƞ is the rate of discount. 5
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Fig. 2. Sensitivity analysis of GDP projection under different growth rate.
2.6. Social cost of methane The marginal damage is divided by the change in the normal and altered emissions to estimate the social cost of methane. t=n
SCMt =
∑ t=0
ΔC′Mt ΔEt
(10)
SCMt is the social cost of carbon of livestock CH4 emission, $ per ton CO2e; ΔC′Mt is the marginal damage, $ and ΔEt is the change in normal and altered emissions, ton CO2e CH4. 3. Sensitivity analysis The SCM estimation from livestock is also validated with the sensitivity analysis test. The sensitivity analysis not only validates the results but also the accuracy of the methodology applied in the research work. For this purpose, the ranges of key variables are taken in the model development to know the potentiality of the model. The SCM is estimated by using different assumptions to develop the model. The model involves CH4 emission estimation, economic projection and damage estimation in the projected year. The impact of growth rate on the SCM estimation is analyzed through the sensitivity analysis test. It is clear from the SCM model that different ranges of ECS and discount rate have a great influence on the resultant SCM values. Therefore, different growth rates for forecasting GDP are assumed here. Different projected GDP is further used to estimate the SCM using two ECS values (1.5 °C and 3 °C) and for all discount rates. Three growth rates assumed for GDP projection are 6.5%, 7.5%, and 8.5%. The respective GDP projected for the three growth rates are $ 4965 bn, $ 5985 bn and $ 7202 bn in the year 2032 (Fig. 2). In these situations, SCM is estimated under two ECS (at e = 1.5 and e = 3.0) condition and different discounting rates (η = 0%, , 2.5%, 3.0% and 5.0%). It is observed that at the same ECS value and the same discount rate increasing the growth rate by 1%, estimated SCM increased by nearly 15%. This verifies that even a small increment in any of the variables, resultant SCM also changed significantly. A similar pattern is observed for all the discount rates and economic growth rates. The model sensitivity is also important to validate the SCM outcome. Apart from the growth rate of GDP projection, climate sensitivity factor, damage calibration with the rise in temperature and the discounting rate used for marginal damage estimation to define the sensitivity of the model. Selection of ECS and damage calibrated constant factor is used for estimation of the economic damage in the future. It is important for marginal damage estimation. The model is highly sensitive to ECS parameter; even a small change in ECS gives large impact in economic damage estimation. Also, the choice of discount rate has an important impact on the estimate of SCM. Different values of these variables play an important role in the estimation of the impact on climate due to CH4 emission. 4. Uncertainty analysis The SCM can be used as a tool for carbon pricing or setting a tax on GHG emissions, which promote less carbon-intensive goods 6
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and services consumption and production (Paul et al., 2009). The accurate and precise SCM estimation is most challenging research. Precision and sensitivity of the model depend on the selected key variable and its range of estimation. The precision of the model depends on the following factors: (i) Degree of accuracy in CH4 estimation and economy projections is important for the preciseness of the model. The difficulties of predicting CH4 emissions and its responses to climate change have inherently uncertain components. CH4 emission is also important because the SCM depends on the rate of GHGs emission trajectories. To estimate GHGs emission, identification of GHGs sources and proper methodology is required. But in the case of livestock, CH4 emission estimation methodology is not highly accurate. Literature also reported that IPCC overestimates the livestock CH4 emission and farm level experiment is not be applicable to large regional scale (Lassey, 2007; Stieger et al., 2015). IPCC tier 1 provides regional specific default EFs for all livestock categories and requires the least amount of data, so it has chances of overestimation of the results . As default emission factors used in IPCC protocol, so there always will be uncertainties and limitations in IPCC protocol (Krause, 2018). Although, some research articles (Chhabra et al., 2013; Dell et al., 2012; Pathak et al., 2013; Singh et al., 2012) provides Indian specific EFs for all livestock categories. But, these specific EFs cannot be used as the parameters selected for scenario development in the research papers are different from them. While, the IPCC's tier 1 EF's fit easily applicable to our model parameters for all the scenarios. Therefore, the accurate methodology for GHG emissions estimation step is also important in SCM estimation. (ii) GDP projection is also not an easy task as the economy of any country or region is highly flexible and highly uncertain. (iii) It is also not certain to decide climate sensitivity factor, damage calibration with the rise in temperature and the discounting rate used for marginal damage estimation. The uncertainty of these factors leads to uncertainty of the SCM estimation. So, the exact value prediction of SCM is not an easy task. But, the more understanding of climate dynamics and the more realized SCM will be estimated. The SCM value should be precise, to produce effective results. Therefore, the highly accurate modeling should be preferred to increase SCM accuracy.
5. Results and discussion 5.1. CH4 emission and its impact on climate The SCM estimation procedure starts with CH4 emission estimation and its projection for a 20-year timeframe (2012 ̶ 2032). Understanding the present and future trend of CH4 emission is important for climate and economic damage prediction. The emission is estimated for two scenarios (i) baseline-business-as-usual (BAU), and (ii) modified (MS: MS I, MS II, and MS III). The scenarios are simulated based on different livestock growth rate. Projection of CH4 emission for BAU and MS scenarios till 2032 is given in Fig. 3a. CH4 emission in BAU would rise from 533 Tg CO2e in 2012 to 951 Tg CO2e in 2032. In the modified scenarios, the emission ranges between 500 and 515 Tg CO2e in 2012 and 565 and 616 Tg CO2e in 2032, with the lowest in MS I. The atmospheric accumulation of CH4 is simulated through the delay function equation of STELLA. We observed that atmospheric accumulation of CH4 is highest in BAU (9298 Tg CO2e) and the lowest in MS I (6520 Tg CO2e) as shown in Fig. 3b. CH4 is the second-most important anthropogenic greenhouse gas (GHG) after carbon dioxide (CO2) in terms of radiative forcing. Radiative forcing of methane is 0.97 W m-2 (Stocker et al., 2013), about twice the concentration-based estimate (0.48 W m-2) (Alexe et al., 2015). Therefore, the atmospheric accumulation of CH4 can impact the climate substantially and the potential surface temperature rise. The change in surface temperature is a function of equilibrium climate sensitivity (ECS) factor (Marten and Newbold, 2012). The ECS tells us how much temperature would rise if CO2 emission doubles from pre-industrial level (Knutti et al., 2002). The ECS values are variable, ranging from 1.5 to 4.5 °C but the most likely range is 1.5–3 °C (IWG, 2010). Four different ranges of ECS (1.5, 2.0, 2.5 and 3.0 °C) were used to simulate different climate response in terms of the surface temperature rise due to CH4 emission and its accumulation. The lowest value of ECS gives the least rise in surface temperature and vice versa.
Fig. 3. (a) CH4 emissions under different scenarios, (b) CH4 accumulation. The highest CH4 accumulation is observed in BAU and the lowest in MS I. 7
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Fig. 4. GDP projections (a–d). The color bar in the left top corner represents projected GDP under different ECS and under no climate change (NCC) for different emission scenario (a) BAU, (b) MS I, (c) MS II, and (d) MS III. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
5.2. Impact on economy Economic impact forecast, which is based on GDP projection and calibration of economic damage, is important to calculate its social cost damage (Aldy et al., 2016). The process of forecasting economic damage differs with each integrated assessment model. Economic analysis of damage is estimated for the national GDP of India considering a moderate annual GDP growth rate of 7%. The GDP is projected without (no climate change-NCC) and with climate change (CC) conditions. In NCC we assumed that there is no climate change and temperature variability is a natural phenomenon (Dell et al., 2012) and hence no impact on GDP growth rate (Fig. 4). But, in CC, there will be an impact on GDP, and the damage in GDP will propagate forward in time and reduces future GDP growth rate. Under no climate change, GDP of all scenarios is projected to reach $5452 bn in 2032. Under climate change, the highest GDP reduction occurs in BAU (from $5452 to $5185 bn) and lowest in MS I (from $5452 to $5327 bn) in 2032 (Fig. 4). The difference between the GDP of NCC and CC is the loss in GDP. The loss in GDP is computed for all scenarios under each ECS factor. The loss in GDP increases by the year and reaches a maximum in BAU due to the high CH4 emissions. With the high value of ECS, the loss in GDP is also high. At ECS 1.5 °C, the loss in GDP varies from $33 bn to $35 bn for all scenarios in the year 2017. This loss increases by many folds in 2032 i.e, $ 124 bn to $ 167 bn in 2032. At ECS 2.0 °C, the loss in GDP varies from $43 bn to $47 bn for all scenarios in the year 2017. This loss increases in 2032 i.e, $ 165 bn to $ 221 bn in 2032. At ECS 2.5 °C, the loss in GDP varies from $54 bn to $58 bn for all scenarios in the year 2017. In 2032, it becomes $ 206 bn to $ 275 bn in 2032. At ECS 3.0 °C, the loss in GDP varies from $65 bn to $70 bn for all scenarios in the year 2017 and in 2032, it becomes $ 247 bn to $ 329 bn. It is clear that the higher the ECS, the higher will be loss in GDP. 5.3. Marginal damage in the economy The marginal damage is aggregated for the whole period (2012–2032) and further discounted to calculate SCM. The significance of marginal damage is that the greater will be the marginal damage, the greater will be the SCM. Determining marginal damage is a critical step in SCM modeling to monetize the damage caused by GHG emission. To assess the marginal damage, the baseline emissions are changed by the addition of one unit of GHGs in each year to compute an altered emission scenario (IWG, 2010). Thus, it gives two sets of emission pathways: normal and altered. The difference between the loss in GDP from normal and altered emission pathways is the marginal damage. In BAU, the marginal damage in 2032 ranges between $2.8 and 5.5 bn. In modified scenarios, the lowest and highest marginal damages occur in MS I ($0.8 ̶ 1.5 bn) and MS II ($2.1 ̶ 4.0 bn) (Table 1). We observed that if ECS values become doubled, then corresponding marginal damage also increase by almost two folds. The marginal damage estimation also depends upon the discount rate, adaptation response, and emission from the rest of the world (ROW). These three important points are also discussed here, to validate the results. (i) Impact of adaptation: The SCM estimation varies if we consider the impact of marginal adaptation in response to GHG emissions. There could be future adaptation strategies like changes in diet –pattern, breed types which may counteract the marginal damage of CH4 emission. Therefore, SCM could be minimized and positive influence on SCM. But, we could not incorporate theses points 8
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Table 1 Marginal damage (bn $) at a different range of ECS under different scenarios. Year
ECS = 1.5
2012 2017 2022 2027 2032
ECS = 2.0
BAU
MS I
MS II
MS III
BAU
MS I
MS II
MS III
0.0 0.2 0.6 1.4 2.8
0.0 0.0 0.2 0.4 0.8
0.0 0.1 0.4 1.0 2.1
0.0 0.1 0.3 0.8 1.4
0.0 0.2 0.8 1.9 3.7
0.0 0.1 0.2 0.5 1.0
0.0 0.2 0.6 1.4 2.7
0.0 0.1 0.4 1.0 1.8
ECS = 2.5
2012 2017 2022 2027 2032
ECS = 3.0
BAU
MS I
MS II
MS III
BAU
MS I
MS II
MS III
0.0 0.3 1.0 2.3 4.6
0.0 0.1 0.3 0.7 1.3
0.0 0.2 0.7 1.7 3.3
0.0 0.2 0.5 1.2 2.3
0.0 0.3 1.1 2.8 5.5
0.0 0.1 0.3 0.8 1.5
0.0 0.2 0.8 2.0 4.0
0.0 0.2 0.6 1.5 2.7
in our present research work due to limited data availability. (ii) Impact of different discount rates: The selection of discount rate is random based on the literature survey of IAM research paper. The discount rate is completely theoretical and it could be 1%, 2%–10% and even more than 10%. Discounting the aggregated future marginal damage in present economic value is necessary to estimate SCM. The higher the discount rate, the lower will be the SCM and vice –versa. High discount rate gives low SCM value and less strict mitigation policy. The higher discount rate means we are giving more preference to the present generation over future generation. The less strict mitigation policy means flexibility in the present emission scenario and could lead to high climate damage in the future. In contrary high SCM will create pressure on the present generation either to reduce the consumption or to adopt the less emission-intensive pathway. It is the choice of decision makers that which generation should pay for the future economic damage. Thus, the discount rate depends on assumptions such as the preference to generation (IWG, 2010), market structure (Nordhaus, 2017a, Nordhaus, 2017), and need of policy for GHG emission reduction (Paul et al., 2009). As SCM is a market-based tool and implemented for carbon pricing, so, SCM should be applicable to the market structure and economy of the region of interest. In the case of developing country like India, both the extreme end of the discount rate is not possible and advisable due to climate change. It is also suggested to quickly opt the mitigation approach for climate change. Therefore, the choice of discount rate should be middle of the extreme to opt for a sustainable approach. Impact of different range of discount rate on SCM is estimated for the BAU scenario as given in Table 2 below. From the table, it is observed that as the discount rate increases the SCM decreases. (iii) Impact of additional emission: Here in the study, marginal estimation doesn't reflect the impact of additional emission from the rest of the world (ROW). The economic climate damage estimation due to ROW is not considered in the present study. If the same research work is applied at the global level and then India's contribution to the global level could be estimated. All of the above factors could have a significant impact on the marginal damage estimation, and it requires more investigation. This could be a part of future research work.
Table 2 SCM estimated at different discount rate at different ECS at different discount rate (ƞ = 1%, 2%, 3%, 4% and 5%). ECS 1.5
ECS 2.5
ƞ
1
2
3
4
5
1
2
3
4
5
2017 2022 2027 2032
484.03 521.43 561.73 605.15
397.46 428.18 461.27 496.92
327.00 352.28 379.50 408.83
269.54 290.38 312.82 336.99
222.59 239.80 258.33 278.29
792.78 854.05 920.05 991.15
650.99 701.30 755.50 813.89
535.59 576.98 621.58 669.61
441.48 475.60 512.36 551.95
364.58 392.76 423.11 455.81
ECS 2
2017 2022 2027 2032
ECS 3
1
2
3
4
5
1
2
3
4
5
639.83 689.28 742.55 799.94
525.40 566.00 609.75 656.87
432.26 465.67 501.66 540.43
356.31 383.84 413.51 445.47
294.24 316.98 341.48 367.87
942.87 1015.73 1094.23 1178.80
774.24 834.07 898.53 967.98
636.99 686.22 739.25 796.39
525.06 565.64 609.36 656.45
433.60 467.11 503.21 542.10
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Table 3 Scenario-wise SCM of livestock CH4 emission ($ per ton CO2e) under different discount rate and ECS. Scenario
BAU
MS I
MS II
MS III
Discount rate\ECS(°C)
0.0 2.5 3.5 5.0 0.0 2.5 3.5 5.0 0.0 2.5 3.5 5.0 0.0 2.5 3.5 5.0
2012
2022
2032
1.5
2
2.5
3
1.5
2
2.5
3
1.5
2
2.5
3
548 335 276 207 152 93 77 57 233 142 117 88 288 176 145 108
725 442 364 273 202 123 101 76 308 188 155 116 381 232 191 143
898 548 451 338 251 153 126 94 382 233 192 144 472 288 237 178
1068 652 537 402 309 189 155 117 455 278 229 172 563 343 283 212
636 388 320 240 177 108 89 67 270 165 136 102 334 204 168 126
841 513 423 317 234 143 118 88 357 218 180 135 442 270 222 166
1042 636 524 393 291 177 146 110 443 271 223 167 291 335 275 207
1239 756 623 467 359 219 180 135 528 322 266 199 653 398 328 246
738 451 371 278 205 125 103 77 313 191 157 118 387 236 195 146
976 596 491 368 272 166 137 102 415 253 208 156 513 313 258 193
1209 738 608 456 338 206 170 127 515 314 259 194 338 388 320 240
1438 878 723 542 416 254 209 157 613 374 308 231 758 462 381 286
5.4. Social cost of methane (SCM) The social cost of methane is a measure of the cost of economic damage per unit of additional GHG in a given year compared to the baseline emission. The SCM is estimated by dividing the aggregated discounted marginal damage with the difference between altered and normal CH4 emissions (i.e. 1 unit of additional emission in altered emission pathway). We considered four discounting rates (0%, 2.5%, 3.5% and 5%) to discount the marginal damage and hence to generate a range of the SCM values. The SCM expressed in monetary terms ($ per ton of CO2e) in a given year due to CH4 emission from livestock in India. The results show that SCC of CH4 in 2017 is $62- $1151 per ton CO2e and $77 -$1438 per ton CO2e in 2032. The highest SCC is observed in BAU and lowest in MS I. The high SCC suggests that even a small increase in CH4 emission can lead to the high negative impact on the environment. Thus, this work suggests that livestock growth stabilization is required in India, according to the MS I scenario to achieve the least economic damage. Due to high radiative forcing, CH4 can cause large impacts on climate change on short time scales. Therefore, the SC of CH4 is significantly higher than the social cost of carbon. In 2016, it is estimated that the SCC values for CO2 emission in India are $2.93 per ton CO2 and $19 per ton CO2 (Aldy et al., 2016). Similarly, Marten and Newbold, 2012 observed that the SCC of CH4 and N2O are 21 and 385 times higher than those of CO2 respectively US. Hence, it can be inferred that the SCC of non-CO2 GHGs is significantly higher than CO2. The SCM estimation helps researcher help in the market based or regulatory policy formulation for the livestock sector. The SCM results show that CH4 emission reduction is highly beneficial and provide a broad set of societal benefits. Thus, climate mitigation policies based on all GHGs can be more effective and less costly than a policy that only addresses CO2 (Sarofim, 2012). Estimated SCM for all scenarios: BAU, MS I, MS II and MS III are given in Table 3 below. 5.5. Comparison of the developed model with the previous IAM models The developed IAM model is based on the basic calculation of the present research compared with previously published IAM models (Table 4). This model is specifically designed to make SCM calculation more transparent and user-friendly for all. This model is flexible as it can be built and modified by anyone using another model too. The most common three currently available IAM models are Policy Analysis of the Greenhouse Effect (PAGE), Framework for Uncertainty, Negotiation, and Distribution (FUND), and the Dynamic Integrated Climate and Economy (DICE). The benefits of the model used here for SCM estimation in comparison to other IAM models is discussed here: (i) The previous IAM model viz. PAGE, FUND and DICE is not enough transparent and understandable making it difficult to make policy using these types of IAM model (Metcalf and Stock, 2017). Similarly, SCC estimation is not easy using these models. The model used here though based on the IAM model methodologies has been simplified to quite an extent. For simplicity of modeling methodology the model is divided into four sub-models (Emission-Climate model, Climate-Economy model, Marginal damage model, and SCM estimation) which make SCM estimation more transparent and understandable for all group of researchers. (ii) The four modular structure of the model is made in such a way that it is user-friendly and can be modified by anyone by keeping the same basic principle of the model/sub-model part. While such changes in other IAM models is not an easy task. (iii) In this model, we are using system dynamics model for emission estimation and its impact on climate. So any changes in emission-and hence climate can be immediately retrieved using this model which shall be further helpful in evaluating the impact of policy on emission estimation and SCM values immediately. In other IAM model, policy evaluation on social cost 10
11
CO2 Global level 2200 A large set of database required for modeling and simulations Temperature change, sea-level rise, and radiative forcing Exogenous i.e independent of temperature change
Gases Scale Time horizon Dataset size
DICE CO2+non-CO2 Global and regional 2595 Large set of database required for calculation Sea level rise, and temperature change GDP projection is exogenous in nature
FUND CO2+ Non-CO2 Global level 3000 A large set of database required for modeling based calculations Carbon cycle, sea-level rise, and economic damage GDP projection depends upon the temperature change
GDP projection depends upon the temperature change
CH4 and other non-CO2 ghgs gases Regional level Flexible i.e. based on target end year A small and selected set of the database is sufficient for calculation purpose. Population dynamics, temperature change
Developed IAM
Source: Hope and Newberry (2006); Nordhaus and Sztorc (2013); Nordhaus, 2017a, Nordhaus, 2017; Nordhaus, 2014; Anthoff and Tol, 2013; Moore and Diaz (2015); Newbold et al. (2010); Nordhaus, 2010; Emmerling and Tavoni, 2017.
Temperature dependence of GDP projections
Key variables
PAGE
Particulars
Table 4 Comparison of different IAM models with the developed model.
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estimation doesn't get reflected immediately. (iv) The developed four model structure of the model is used at the regional scale and for a short time period, but four model structure of the model makes it flexible and its successful implementation in even at the global scale. 6. Conclusion The social cost of carbon (SCC) is also defined as the cost of the estimated future economic loss due to the emission of per metric ton of carbon today. Based on SCC, in the present study, the induced economic loss is estimated due to the emission of an additional unit ton of CH4 which is termed as the Social Cost of Methane (SCM). For SCM estimation different types of model is available such as DICE, PAGE, and FUND, but in this paper, a new approach is developed for SCM estimation. The previous types of IAM model are not sufficiently transparent and understandable for the public to make policy based on the IAM model only. To simplify the IAM model for social cost estimation, system dynamics with the excel spreadsheet has been used for the study. The four sub-model makes it more transparent, user-friendly and flexible to the public for policy making approach. The paper is based on social cost estimation for CH4 in CO2e term (SCM) for Indian livestock sector for time horizon 2017 to 2032. The study at country-level is an estimate of global climate damage due to national level emission which is important for adaptation and mitigation measures. The SCM in CO2e is estimated for four different scenarios i.e. Business as usual (BAU) scenario and three modified scenarios (MS I, MS II and MSIII) from 2017 to 2032. A new interdisciplinary model is being developed in the paper for SCM estimation in CO2e term using the GWP of CH4 as 34. The research work is executed as: (i) system dynamics for CH4 estimation and projection for all scenarios, and (ii) different mathematical formulation which is applied in Excel spreadsheet for climate damage estimation and SCM calculation. Results of the projected social cost of CH4 (cost per ton CO2e CH4) of livestock worked out in 2017 $62- $1132 and $77 -$1438 for 2032 under different scenarios. The highest social cost of carbon for CH4 is in BAU ($1150 in 2017 and $1438) and the lowest in MS I BAU ($62 in 2017 and $77 per ton CO2). The environmental cost through SCM estimation of livestock CH4 emission provides the potential knowledge in formulating the regulation and policy scenarios. Uncertainties are also involved in SCM estimation as a range of uncertain parameters such as emission factors; social discount rate, economic growth, and climate sensitivity are used for model calculation which makes it arbitrary. 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Social cost of methane: Method and estimates for Indian livestock Shilpi Kumaria,∗, Moonmoon Hiloidharib, S.N. Naikc, R.P. Dahiyaa a
Centre for Energy Studies, Indian Institute of Technology Delhi, New Delhi, 110016, India IDP in Climate Studies, Indian Institute of Technology Bombay, Mumbai, 400076, India c Centre for Rural Development and Technology, Indian Institute of Technology Delhi, New Delhi, 110016, India b
A R T IC LE I N F O
ABS TRA CT
Keywords: Social cost of carbon Social cost of methane Climate change Economic damage Integrated assessment model Livestock CH4 emission
The quantitative assessment of climate change damage due to an additional unit of greenhouse gases emissions (mainly carbon di-oxide, CO2) is termed as the Social Cost of Carbon (SCC). Published literature primarily focused on the SCC of CO2 emissions, neglecting other greenhouse gases (GHGs). The social cost assessment for other GHGs especially CH4 is also needed as it is the 2nd highest emitted GHG after CO2 with high global warming potential. The quantitative assessment of climate change damage per additional unit of CH4 can be termed as Social Cost of Methane (SCM). In the present study, the SCM (in CO2e unit) has been estimated for the Indian livestock using Integrated Assessment Model (IAM) and system dynamic approach. Different livestock growth scenarios viz. Business as usual (BAU), modified scenarios (MS I, MS II and MS III) have been proposed for SCM calculation (cost per ton CO2e CH4) through 2017 to 2032. The SCM for 2017 is $62 ̶ $1150 and is projected to be $77 ̶ $1438 in 2032. The highest SCM is in BAU ($1150 in 2017 and $1438 in 2032) and the lowest in MS I ($62 in 2017 and $77 in 2032). The differences in SCM values are due to the different population size of livestock and CH4 emission rate. Results and findings of the study suggest that the CH4 even emitted in small quantity has a significant impact on climate and hence should not be neglected in climate change mitigation policies. The SCM is a metric tool which helps to design the appropriate policies for reducing CH4 emission from livestock. The developed tool can also be applicable to estimate the social cost for other GHGs for market-based policy development.
1. Introduction Climate change mitigation has become an urgent concern due to increasing anthropogenic greenhouse gas (GHG) emissions and its potential threat to humanity and the environment (Fagodiya et al., 2017). the impact of climate change is already being observed through a rise in surface temperature, glaciers melt, shifting monsoon pattern, extreme weather, hazards and rising sea levels (IPCC, 2014). Quantification of the damages reveals that over the last 30 years, increased extreme weather events has caused an average loss of $2–28 billion from cyclones, about $10 billion loss from inland floods, landslides and avalanches, $2 billion from wildfires and storm-related phenomena worldwide (Guha-Sapir et al., 2015; Ranson et al., 2016). Intensity, frequency, and magnitude of natural calamities are strongly influenced by the climate change condition, which also affects economic growth (Ranson et al., 2016). To ensure sustainable development, almost all the nations of the world are scaling up their climate change mitigation approach and designing specific policies. The environmental damages in the poor and developing countries became a challenging situation due to their limited resources to tackle climate change. The most effective climate policies can be accomplished through a collaborative
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Corresponding author. E-mail address: [email protected] (S. Kumari).
https://doi.org/10.1016/j.envdev.2019.100462 Received 2 November 2017; Received in revised form 10 October 2019; Accepted 12 October 2019 2211-4645/ © 2019 Published by Elsevier B.V.
Please cite this article as: Shilpi Kumari, et al., Environmental Development, https://doi.org/10.1016/j.envdev.2019.100462
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work of climate scientists, economists, government, stakeholders, and public interactions as well (Moss et al., 2010; Stern, 2008; Pathak et al., 2013; Stocker et al., 2013). Researchers have developed Integrated Assessment Models (IAMs) (Ranson et al., 2016; Metcalf and Stock, 2015) to assist climate policy in a two-way approach between climate and society for policy evaluation and optimization (IWG, 2010; Nordhaus, 2017a). The costs and benefits of abating a unit tonne of CO2 emissions are estimated and compared with the baseline economic scenario (Anthoff and Tol, 2013; Tol, 2015). This evaluation of climate change damage in monetary term is defined as the ‘Social Cost of Carbon’ (SCC) (Hope and Newberry, 2006; Marten and Newbold, 2012). The SCC values represent monetized net damage costs of climate change impacts (Nordhaus, 2017). As CO2 is the main greenhouse gas, most of the research works are focused on the estimation of SCC for CO2 emissions and its emission sources (Paul et al., 2013; Ferraroa et al., 2015). Shindell et al. (2017) worked out for social cost of methane emission estimation and found that the importance of mitigation policies for its reduction. There has been no such work have been carried out to estimate the social cost at country level till Ricke et al. (2018). The highest SCC is observed in USA, China, and India in Ricke et al. (2018). Thus, the present study is based on the development of a model framework for estimation of Social Cost of CH4 (SCM) in CO2e term based on GWP of CH4 for Indian livestock. In the paper, the Social Cost Methane (SCM) is estimated in CO2e term based on Global Warming Potential (GWP) for livestockmediated CH4 emission in India. The present paper is continuation work of Kumari et al. (2016) where CH4 emission was estimated for Indian livestock sector up to district level. Globally, livestock is responsible for 12% of anthropogenic GHGs (Havlík et al., 2014, Kumari et al., 2014). Indian livestock is an important source of CH4 and N2O (Kumari et al., 2016; Sejian et al., 2016). The Indian livestock sector has the potential to cause surface temperatures to surge up to 0.69 mK over 20-year time period which is roughly 14% of the total increase caused by the global livestock sector (Kumari et al., 2018). 1.1. Social cost of carbon The SCC is a metric tool for economic quantification of either net damage associated with an increment of one unit of GHG emission or benefits associated with the reduction of an additional unit of GHG emission per year (Pearce, 2003; Watkiss and Hope, 2011). Therefore, SCC is measured as a monetary unit per ton of CO2 i.e, $/ton CO2e (Newbold et al., 2010). The SCC estimated with the help of Integrated Assessment Models (IAMs) and which can be compared with the investment cost of mitigating climate change (Hope and Newberry, 2006). The IAMs could be used further for carbon pricing based climate policy development (Paul et al., 2013; Ferraroa et al., 2015). There are different types of IAM models available to estimate SCC such as The Dynamic Integrated Climate and Economy (DICE), Climate Framework for Uncertainty, Negotiation, and Distribution (FUND), Policy Analysis of the Greenhouse Effect (PAGE), the Regional Integrated Model of Climate and the Economy (RICE) and World Induced Technical Change Hybrid (WITCH) (Hope and Newberry, 2006; BAHS, 2014; Holman et al., 2005Nordhaus, 2010; Anthoff et al., 2009; Nordhaus, 2014). These models are different from each other for large number of key variables, but some selected variables are included viz. types of GHGs selected for SCC estimation, period, and spatial assessment level i.e, local, regional or global. However, the main working principle of IAM models remains the same as discussed below. The estimation of SCC is complex, but the basic steps involved in the process are (IWG, 2010; Greenstone et al., 2011; Pizer et al., 2014): (1) GHGs emission, GDP growth, and population are projected for a selected time frame for the baseline scenario. The selected GHGs and the time frame differ with IAMs models. The default time horizon for simulating the model in PAGE, DICE, and FUND are 2200, 2595, and 3000, respectively. (2) Changes in the climatic variability (such as temperature rise, sea-level rise) and economic damage each year due to GHGs accumulation in the atmosphere are calculated. The damage estimation also varies with the model types based on selected climatic variables. (3) In baseline emission, an additional unit of GHG is added, known as an altered emission pathway. The changes in climate and economic output are now estimated for the altered emission pathway. This altered emission is done to know the impact of one additional unit of emission on climate change. (4) The difference between damages from the baseline and altered emission pathways are estimated and the resultant damage is known as marginal damage. This damage is called marginal damage because the damage is estimated per additional unit of altered emission. (5) The ratio of marginal damage and an additional unit of GHGs emission is calculated for SCC estimation. The social cost estimation is the conversion of GHGs induced climate damage and economic damage in the economic term, but exact and accurate damage quantification is highly uncertain in any IAM model. Estimating the SCC involves many uncertainties which include the GHGs emission estimation, change in climatic variables such as rise in the surface temperature, damage estimation and accurate loss estimation of damages in the environment, and the discount rate. The social cost estimation method should be transparent, broadly explainable and understandable to non-experts. However, the IAM is not sufficiently transparent and explainable for the policymakers and the public (Metcalf and Stock, 2017). So, the alternative model is needed to estimate the social cost for carbon and non-CO2 GHGs e.g. SCM. In the present study, the developed model is based on IAM working principles using a system dynamics approach. The system dynamics is a computer-based approach understanding, designing, and managing change over the period of time (Anand et al., 2006). Hence, system dynamics approach helps to develop the new model for estimating SCM. Methods and benefits of the model are described further. A comparison study of the developed model with previous version of IAM model is 2
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also summarized in Section 5.5. 2. Methodology We present a simplified IAM to estimate the Social Cost of CH4 (SCM) in CO2e which combines the system dynamic approach of STELLA software, the underlying concept of IAMs, and Microsoft excel workbook calculation. The developed model is applied to the Indian livestock sector. 2.1. Model framework The SCM for livestock CH4 emission in India is estimated for a 25-year period (2007–2032) under different scenarios such as business as usual (BAU) and three modified scenarios (MS-I, MS-II, and MS-III). The model is divided into four sub-models: (i) emission-climate model, (ii) climate-economy model, (iii) marginal damage model, and (iv) SCM model. The emission-climate model comprises of two components i.e. CH4 emission estimation and projection and (b) its atmospheric accumulation. The four scenarios, business as usual (BAU) and modified scenarios (MS-I, MS-II, and MS-III) are based on the different livestock growth rate for CH4 emissions estimation and SCM estimation (Kumari et al., 2016) (see Supplementary information for detail). In the emission – climate model system dynamics is used to estimate CH4 emissions and its impact on the climatic variable. IPCC et al., 2006 Tier 1 methodology has been used for CH4 emission estimation, where specific emission factors have been provided for developing and developed countries. For cattle, specific emission factor is provided for Indian subcontinent where cattle divided into two specific groups: dairy and non-dairy. Previous work, Kumari et al. (2016) focused to estimate CH4 emission and trend projections using the same IPCC guidelines (IPCC et al., 2006). The climate-economy model is second sub-model which is built to assess the impact of changes in climatic variables on the economy (i.e. loss in GDP). The climate-economy model runs for two normal baseline and altered emission pathways. A normal baseline emission pathway is the CH4 emission pathways for all scenarios (BAU, MSI, MSII, and MSII). But, in the altered emission pathways, one unit is added in CH4 emission in all scenarios to estimate the GDP loss per unit of CH4 emission. The marginal damage model is used to estimate GDP losses from normal and altered emission pathways. Finally, the SCC model is used to estimate the economic cost of livestock related CH4 emission equivalent to CO2e emission. The interaction of the different components of the model is presented in Fig. 1. 2.2. Emission-climate model The system dynamic approach based on STELLA software, in combination with the mathematical model was developed to assess
Fig. 1. Overview of the developed SD-IAM for the social cost of methane (SCM) estimation. The basic steps required to calculate SCM are shown in the left panel. Interactions of inputs/outputs, assumptions and variables are shown in boxes and dotted arrows of the right panel. ECS: Equilibrium climate sensitivity, CC: Climate change, ED: Economic damage ΔCN: Economic damage in the normal baseline emission pathway, ΔCA: Economic damage in altered emission pathway. 3
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livestock population growth and CH4 emission for a 25 year period (2007–2032) under business as usual scenario (BAU) and modified (MS-I, MS-II, MS-III) scenarios as given in equation (1). The different assumptions and equations used in all four scenarios are discussed in Supplementary information. z
Ei =
∑ (pi
× [1 + CAGR]n ) × EFi × (GWP )CO2
(1)
i=1
Ei is CH4 emission from livestock in Tg CO2e; pi is the population of the ith category of livestock in million; CAGR is compounded annual growth rate for livestock population projection and EFi is the specific emission factors for the ith category of livestock in kg y−1 (IPCC et al., 2006; Kumari et al., 2016). In AR5 report of IPCC, GWP for non-CO2 gases are presented for both with and without including climate-carbon feedbacks. In this report, GWP for CH4 are 28 without including climate-carbon feedbacks and 34 with including climate-carbon feedbacks. SCM for livestock CH4 emission is calculated in CO2e unit using GWP of CH4 as 34 (IPCC, 2014). After CH4 emission estimation and projection, its accumulation in the atmosphere is simulated through STELLA software (Anand et al., 2005; Kumari et al., 2016) as below:
At = A
t − dt
+ [kr − kd] × d
(2)
At is an accumulation of CH4 in the atmosphere at time ‘t’, Tg CO2e; kr and kd are accumulation and decay rates of CH4 and d is decay time. The accumulation of CH4 is calculated by system dynamic mathematical formulation where specific function ‘delay’ is used (Anand et al., 2006; Kumari et al., 2016). The kr and kd estimation is based on the following equations: n
kr =
∑ Ei
(2a)
i=1
kd = delay [kr , T , E0 ]
(2b)
Ei is CH4 emission in Tg CO2e (equation (1)) and T (delay time i.e. 12 year lifespan of CH4 in the atmosphere), and E0 is the initial concentration of CH4. In accumulation calculation the initial value of CH4 is ‘0’ Tg and the atmospheric lifetime of CH4 is 12 years. The rate of CH4 decay is equal to its production rate 12 years ago. The impact on climate change is assumed to occur due to changes in the surface temperature. The potential rise in surface temperature is determined by: Tt = To +
ΔT2x ⎡ (CO2e CH4 )t ⎤ ln ln 2 ⎢ ⎦ ⎣ (CO2e CH4 )0 ⎥
(3)
Tt is the new temperature at time ‘t’; To is the baseline surface temperature (15 °C) in the base year 2012; ΔT2x is the equilibrium climate sensitivity (ECS) factor, i.e., mean surface temperature change after doubling of CO2 concentration; (CO2e CH4)t is the concentration of CH4 in CO2e unit in the atmosphere at time ‘t’; (CO2e CH4)0 is the initial concentration of CH4 in CO2e unit in the atmosphere. The ECS values are variable, ranging from 1.5 to 4.5 °C but the most likely range is 1.5–3 °C (IWG, 2010). Different ECS values (1.5, 2.0, 2.5 and 3.0 °C) are considered to estimate different ranges of SCM. 2.3. Climate-economy model In Climate-economy model, we used the impact of climate change in economic output i.e. temperature shock on economic output. It has been quantified that the temperature rise may cause economic loss however, some researcher may deny this correlation as false or unauthentic (Dell et al., 2012). The economic output of any country depends on many factors but per degree rise in temperature has an important influence on national economic performance (Dell et al., 2012; Holman et al., 2005). We develop a climate-economy model using the specific damage function given by Moore and Diaz (2015). Here, in the model national economic performance is estimated by the relationship between temperature and growth rate of GDP as applied in Moore and Diaz (2015). i.e. GDP projection based on temperature variability. Findings of Dell et al. (2012) also suggest that the higher temperature substantially affects the economic output especially in poor and developing countries. Based on the above findings, we estimate the negative impacts of climate change on economic output. Impact of climate change and increase in temperature could reduce economic growth rate and GDP (Moore and Diaz, 2015). The impact of CH4 emission from livestock on the national economy is computed for the nation as a function of the increase in surface temperature. In this phase, the economic damage from changes in surface temperature due to livestock CH4 emission is evaluated using the specific damage function, derived from Moore and Diaz (2015). In the previous section, we have estimated the accumulation of CH4 and its impact on climate in terms of rise in temperature. This assumption is termed as Climate Change (CC). In the alternate situation, we assumed that there is no CH4 emission hence, No Climate Change (NCC). For this purpose, these two assumptions are GDP projection under (a) no climate change (NCC), and (b) climate change (CC). Economic damage under CC is estimated taking ECS as 1.5 °C, 2 °C, 2.5 °C, and 3 °C. Range of ECS values allows prediction of the magnitude of future damages in the event of low to extreme climate damage with respect to low ECS value to high ECS values. 4
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2.3.1. Assumption 1- if there is no climate change (NCC) Under this assumption, GDP is projected using the simple growth rate model i.e GDP projection calculation is based on the linear equation described as
Ct = Ct − 1 (1 + r )
(4)
Ct is the GDP at the time ‘t’ under no climate change, $; Ct-1 is the GDP at the previous year, $; and r is the GDP growth rate, taken as 7%. The GDP in the year 2012 is considered as $1408 billion (equivalent to 93,889 billion Indian rupees) (BAHS, 2014). 2.3.2. Assumption 2- if there is climate change (CC) GDP projection is endogenous because a rise in surface temperature causes economic damages and damage in a given year, propagates forward with time and reduces GDP in future years. For this assumption climate change GDP projection, we used a similar approach as proposed by Moore and Diaz (2015) to calculate the changes in GDP due to climate change impact. Under the climate change scenario, GDP is projected as a function of temperature. Different ECS is assumed as 1.5 °C, 2 °C, 2.5 °C, and 3 °C.
C ′t = Ct − 1 × (1 + r − r ′)
(5)
r ′ = α × (Tt − Tt − 1)
(6)
C ′t is the GDP at time ‘t’ under climate change assumption, $; Ct-1 is the GDP at previous year, $; r is the growth rate (7%), r ′ is the change in GDP growth rate due to per unit rise in temperature, and α is the specific economic damage function. Tt and Tt-1 are temperatures at the present and previous year for which GDP is being projected. The economic damage calibration function α is −1.171% per 1 °C temperature rise taken from Moore and Diaz (2015). After estimating the GDP projections under both the assumptions of CC and NCC the loss in GDP under normal baseline emission pathway ( ΔCNt ) can be estimated as: (7)
ΔCNt = Ct − C ′t
2.4. Marginal damage estimation This is another critical step in SCM modeling to monetize the damage caused by GHGs emission. The SCM estimation is estimated using the normal emission pathway and altered emission pathway. For this purpose, the normal baseline emissions are changed by the addition of one unit of GHG in each year to compute an altered emission pathway. Hence, we can say if the CH4 emission in the normal baseline emission pathway (EN) is xTg CH4, then the altered emission (EA) is increased by one unit i.e. x+ 1 Tg CH4. The additional shock of CH4 emission gives another database for temperature and GDP estimation. Emission-climate model and climate-economy model are replicated for the altered emission pathway to calculate the rise in surface temperature, changes and loss in GDP (Equations (1)–(6)). For altered emission pathway, the loss in GDP ( ΔCAt ) also estimated as equation (7). The difference between the loss in GDP for the normal ( ΔCNt ) and altered ( ΔCAt ) CH4 emission pathway is marginal damage ( ΔCMt ) . (8)
ΔCMt = ΔCNt − ΔCAt
2.5. Discounting the marginal damage The SCC estimation comprised of several critical steps and assumptions. For SCC estimation in different IAMs model, future climatic impacts are predicted and discounted back to present values to find the damage per ton of GHG emitted into the atmosphere. The selection of discount rates and discounting schemes has a significant influence on the final estimate (Anthoff et al., 2009). However, the choice of discount rate is a topic of debate in the SCC model. The lower the discount rate, the higher the SCC value, and the more strict mitigation policy are required and vice versa. Therefore, the discount rate will be a key feature in the future climaterelated damage estimation of present value. Similarly, for SCM estimation discount rate plays a similar role in the model calculation. India is demographically very young country in comparison to the rest of the world. India is also one of the topmost countries in terms of livestock size (Kumari et al., 2018). Selection of discount rate is one of the biggest determining factors for SCM estimation. Discounting assumptions on SCM estimation deciding factors are economic condition and emission index of the country. Considering the growing economic condition, livestock mediated emission status of India, and literature survey, a wide range of discounting rate is taken for SCM estimation. However, the SCM is also estimated without any discount rate i.e. 0%. Discounting of marginal damage is computed as given below taking three discount rates viz. 5%, 3.5%, 2.5% and 0%.
ΔC′Mt = ΔCMt (1 + η)−t
(9)
ΔC′Mt is the discounted marginal damage, $; ΔCMt is the marginal damage, $; and ƞ is the rate of discount. 5
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Fig. 2. Sensitivity analysis of GDP projection under different growth rate.
2.6. Social cost of methane The marginal damage is divided by the change in the normal and altered emissions to estimate the social cost of methane. t=n
SCMt =
∑ t=0
ΔC′Mt ΔEt
(10)
SCMt is the social cost of carbon of livestock CH4 emission, $ per ton CO2e; ΔC′Mt is the marginal damage, $ and ΔEt is the change in normal and altered emissions, ton CO2e CH4. 3. Sensitivity analysis The SCM estimation from livestock is also validated with the sensitivity analysis test. The sensitivity analysis not only validates the results but also the accuracy of the methodology applied in the research work. For this purpose, the ranges of key variables are taken in the model development to know the potentiality of the model. The SCM is estimated by using different assumptions to develop the model. The model involves CH4 emission estimation, economic projection and damage estimation in the projected year. The impact of growth rate on the SCM estimation is analyzed through the sensitivity analysis test. It is clear from the SCM model that different ranges of ECS and discount rate have a great influence on the resultant SCM values. Therefore, different growth rates for forecasting GDP are assumed here. Different projected GDP is further used to estimate the SCM using two ECS values (1.5 °C and 3 °C) and for all discount rates. Three growth rates assumed for GDP projection are 6.5%, 7.5%, and 8.5%. The respective GDP projected for the three growth rates are $ 4965 bn, $ 5985 bn and $ 7202 bn in the year 2032 (Fig. 2). In these situations, SCM is estimated under two ECS (at e = 1.5 and e = 3.0) condition and different discounting rates (η = 0%, , 2.5%, 3.0% and 5.0%). It is observed that at the same ECS value and the same discount rate increasing the growth rate by 1%, estimated SCM increased by nearly 15%. This verifies that even a small increment in any of the variables, resultant SCM also changed significantly. A similar pattern is observed for all the discount rates and economic growth rates. The model sensitivity is also important to validate the SCM outcome. Apart from the growth rate of GDP projection, climate sensitivity factor, damage calibration with the rise in temperature and the discounting rate used for marginal damage estimation to define the sensitivity of the model. Selection of ECS and damage calibrated constant factor is used for estimation of the economic damage in the future. It is important for marginal damage estimation. The model is highly sensitive to ECS parameter; even a small change in ECS gives large impact in economic damage estimation. Also, the choice of discount rate has an important impact on the estimate of SCM. Different values of these variables play an important role in the estimation of the impact on climate due to CH4 emission. 4. Uncertainty analysis The SCM can be used as a tool for carbon pricing or setting a tax on GHG emissions, which promote less carbon-intensive goods 6
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and services consumption and production (Paul et al., 2009). The accurate and precise SCM estimation is most challenging research. Precision and sensitivity of the model depend on the selected key variable and its range of estimation. The precision of the model depends on the following factors: (i) Degree of accuracy in CH4 estimation and economy projections is important for the preciseness of the model. The difficulties of predicting CH4 emissions and its responses to climate change have inherently uncertain components. CH4 emission is also important because the SCM depends on the rate of GHGs emission trajectories. To estimate GHGs emission, identification of GHGs sources and proper methodology is required. But in the case of livestock, CH4 emission estimation methodology is not highly accurate. Literature also reported that IPCC overestimates the livestock CH4 emission and farm level experiment is not be applicable to large regional scale (Lassey, 2007; Stieger et al., 2015). IPCC tier 1 provides regional specific default EFs for all livestock categories and requires the least amount of data, so it has chances of overestimation of the results . As default emission factors used in IPCC protocol, so there always will be uncertainties and limitations in IPCC protocol (Krause, 2018). Although, some research articles (Chhabra et al., 2013; Dell et al., 2012; Pathak et al., 2013; Singh et al., 2012) provides Indian specific EFs for all livestock categories. But, these specific EFs cannot be used as the parameters selected for scenario development in the research papers are different from them. While, the IPCC's tier 1 EF's fit easily applicable to our model parameters for all the scenarios. Therefore, the accurate methodology for GHG emissions estimation step is also important in SCM estimation. (ii) GDP projection is also not an easy task as the economy of any country or region is highly flexible and highly uncertain. (iii) It is also not certain to decide climate sensitivity factor, damage calibration with the rise in temperature and the discounting rate used for marginal damage estimation. The uncertainty of these factors leads to uncertainty of the SCM estimation. So, the exact value prediction of SCM is not an easy task. But, the more understanding of climate dynamics and the more realized SCM will be estimated. The SCM value should be precise, to produce effective results. Therefore, the highly accurate modeling should be preferred to increase SCM accuracy.
5. Results and discussion 5.1. CH4 emission and its impact on climate The SCM estimation procedure starts with CH4 emission estimation and its projection for a 20-year timeframe (2012 ̶ 2032). Understanding the present and future trend of CH4 emission is important for climate and economic damage prediction. The emission is estimated for two scenarios (i) baseline-business-as-usual (BAU), and (ii) modified (MS: MS I, MS II, and MS III). The scenarios are simulated based on different livestock growth rate. Projection of CH4 emission for BAU and MS scenarios till 2032 is given in Fig. 3a. CH4 emission in BAU would rise from 533 Tg CO2e in 2012 to 951 Tg CO2e in 2032. In the modified scenarios, the emission ranges between 500 and 515 Tg CO2e in 2012 and 565 and 616 Tg CO2e in 2032, with the lowest in MS I. The atmospheric accumulation of CH4 is simulated through the delay function equation of STELLA. We observed that atmospheric accumulation of CH4 is highest in BAU (9298 Tg CO2e) and the lowest in MS I (6520 Tg CO2e) as shown in Fig. 3b. CH4 is the second-most important anthropogenic greenhouse gas (GHG) after carbon dioxide (CO2) in terms of radiative forcing. Radiative forcing of methane is 0.97 W m-2 (Stocker et al., 2013), about twice the concentration-based estimate (0.48 W m-2) (Alexe et al., 2015). Therefore, the atmospheric accumulation of CH4 can impact the climate substantially and the potential surface temperature rise. The change in surface temperature is a function of equilibrium climate sensitivity (ECS) factor (Marten and Newbold, 2012). The ECS tells us how much temperature would rise if CO2 emission doubles from pre-industrial level (Knutti et al., 2002). The ECS values are variable, ranging from 1.5 to 4.5 °C but the most likely range is 1.5–3 °C (IWG, 2010). Four different ranges of ECS (1.5, 2.0, 2.5 and 3.0 °C) were used to simulate different climate response in terms of the surface temperature rise due to CH4 emission and its accumulation. The lowest value of ECS gives the least rise in surface temperature and vice versa.
Fig. 3. (a) CH4 emissions under different scenarios, (b) CH4 accumulation. The highest CH4 accumulation is observed in BAU and the lowest in MS I. 7
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Fig. 4. GDP projections (a–d). The color bar in the left top corner represents projected GDP under different ECS and under no climate change (NCC) for different emission scenario (a) BAU, (b) MS I, (c) MS II, and (d) MS III. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
5.2. Impact on economy Economic impact forecast, which is based on GDP projection and calibration of economic damage, is important to calculate its social cost damage (Aldy et al., 2016). The process of forecasting economic damage differs with each integrated assessment model. Economic analysis of damage is estimated for the national GDP of India considering a moderate annual GDP growth rate of 7%. The GDP is projected without (no climate change-NCC) and with climate change (CC) conditions. In NCC we assumed that there is no climate change and temperature variability is a natural phenomenon (Dell et al., 2012) and hence no impact on GDP growth rate (Fig. 4). But, in CC, there will be an impact on GDP, and the damage in GDP will propagate forward in time and reduces future GDP growth rate. Under no climate change, GDP of all scenarios is projected to reach $5452 bn in 2032. Under climate change, the highest GDP reduction occurs in BAU (from $5452 to $5185 bn) and lowest in MS I (from $5452 to $5327 bn) in 2032 (Fig. 4). The difference between the GDP of NCC and CC is the loss in GDP. The loss in GDP is computed for all scenarios under each ECS factor. The loss in GDP increases by the year and reaches a maximum in BAU due to the high CH4 emissions. With the high value of ECS, the loss in GDP is also high. At ECS 1.5 °C, the loss in GDP varies from $33 bn to $35 bn for all scenarios in the year 2017. This loss increases by many folds in 2032 i.e, $ 124 bn to $ 167 bn in 2032. At ECS 2.0 °C, the loss in GDP varies from $43 bn to $47 bn for all scenarios in the year 2017. This loss increases in 2032 i.e, $ 165 bn to $ 221 bn in 2032. At ECS 2.5 °C, the loss in GDP varies from $54 bn to $58 bn for all scenarios in the year 2017. In 2032, it becomes $ 206 bn to $ 275 bn in 2032. At ECS 3.0 °C, the loss in GDP varies from $65 bn to $70 bn for all scenarios in the year 2017 and in 2032, it becomes $ 247 bn to $ 329 bn. It is clear that the higher the ECS, the higher will be loss in GDP. 5.3. Marginal damage in the economy The marginal damage is aggregated for the whole period (2012–2032) and further discounted to calculate SCM. The significance of marginal damage is that the greater will be the marginal damage, the greater will be the SCM. Determining marginal damage is a critical step in SCM modeling to monetize the damage caused by GHG emission. To assess the marginal damage, the baseline emissions are changed by the addition of one unit of GHGs in each year to compute an altered emission scenario (IWG, 2010). Thus, it gives two sets of emission pathways: normal and altered. The difference between the loss in GDP from normal and altered emission pathways is the marginal damage. In BAU, the marginal damage in 2032 ranges between $2.8 and 5.5 bn. In modified scenarios, the lowest and highest marginal damages occur in MS I ($0.8 ̶ 1.5 bn) and MS II ($2.1 ̶ 4.0 bn) (Table 1). We observed that if ECS values become doubled, then corresponding marginal damage also increase by almost two folds. The marginal damage estimation also depends upon the discount rate, adaptation response, and emission from the rest of the world (ROW). These three important points are also discussed here, to validate the results. (i) Impact of adaptation: The SCM estimation varies if we consider the impact of marginal adaptation in response to GHG emissions. There could be future adaptation strategies like changes in diet –pattern, breed types which may counteract the marginal damage of CH4 emission. Therefore, SCM could be minimized and positive influence on SCM. But, we could not incorporate theses points 8
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Table 1 Marginal damage (bn $) at a different range of ECS under different scenarios. Year
ECS = 1.5
2012 2017 2022 2027 2032
ECS = 2.0
BAU
MS I
MS II
MS III
BAU
MS I
MS II
MS III
0.0 0.2 0.6 1.4 2.8
0.0 0.0 0.2 0.4 0.8
0.0 0.1 0.4 1.0 2.1
0.0 0.1 0.3 0.8 1.4
0.0 0.2 0.8 1.9 3.7
0.0 0.1 0.2 0.5 1.0
0.0 0.2 0.6 1.4 2.7
0.0 0.1 0.4 1.0 1.8
ECS = 2.5
2012 2017 2022 2027 2032
ECS = 3.0
BAU
MS I
MS II
MS III
BAU
MS I
MS II
MS III
0.0 0.3 1.0 2.3 4.6
0.0 0.1 0.3 0.7 1.3
0.0 0.2 0.7 1.7 3.3
0.0 0.2 0.5 1.2 2.3
0.0 0.3 1.1 2.8 5.5
0.0 0.1 0.3 0.8 1.5
0.0 0.2 0.8 2.0 4.0
0.0 0.2 0.6 1.5 2.7
in our present research work due to limited data availability. (ii) Impact of different discount rates: The selection of discount rate is random based on the literature survey of IAM research paper. The discount rate is completely theoretical and it could be 1%, 2%–10% and even more than 10%. Discounting the aggregated future marginal damage in present economic value is necessary to estimate SCM. The higher the discount rate, the lower will be the SCM and vice –versa. High discount rate gives low SCM value and less strict mitigation policy. The higher discount rate means we are giving more preference to the present generation over future generation. The less strict mitigation policy means flexibility in the present emission scenario and could lead to high climate damage in the future. In contrary high SCM will create pressure on the present generation either to reduce the consumption or to adopt the less emission-intensive pathway. It is the choice of decision makers that which generation should pay for the future economic damage. Thus, the discount rate depends on assumptions such as the preference to generation (IWG, 2010), market structure (Nordhaus, 2017a, Nordhaus, 2017), and need of policy for GHG emission reduction (Paul et al., 2009). As SCM is a market-based tool and implemented for carbon pricing, so, SCM should be applicable to the market structure and economy of the region of interest. In the case of developing country like India, both the extreme end of the discount rate is not possible and advisable due to climate change. It is also suggested to quickly opt the mitigation approach for climate change. Therefore, the choice of discount rate should be middle of the extreme to opt for a sustainable approach. Impact of different range of discount rate on SCM is estimated for the BAU scenario as given in Table 2 below. From the table, it is observed that as the discount rate increases the SCM decreases. (iii) Impact of additional emission: Here in the study, marginal estimation doesn't reflect the impact of additional emission from the rest of the world (ROW). The economic climate damage estimation due to ROW is not considered in the present study. If the same research work is applied at the global level and then India's contribution to the global level could be estimated. All of the above factors could have a significant impact on the marginal damage estimation, and it requires more investigation. This could be a part of future research work.
Table 2 SCM estimated at different discount rate at different ECS at different discount rate (ƞ = 1%, 2%, 3%, 4% and 5%). ECS 1.5
ECS 2.5
ƞ
1
2
3
4
5
1
2
3
4
5
2017 2022 2027 2032
484.03 521.43 561.73 605.15
397.46 428.18 461.27 496.92
327.00 352.28 379.50 408.83
269.54 290.38 312.82 336.99
222.59 239.80 258.33 278.29
792.78 854.05 920.05 991.15
650.99 701.30 755.50 813.89
535.59 576.98 621.58 669.61
441.48 475.60 512.36 551.95
364.58 392.76 423.11 455.81
ECS 2
2017 2022 2027 2032
ECS 3
1
2
3
4
5
1
2
3
4
5
639.83 689.28 742.55 799.94
525.40 566.00 609.75 656.87
432.26 465.67 501.66 540.43
356.31 383.84 413.51 445.47
294.24 316.98 341.48 367.87
942.87 1015.73 1094.23 1178.80
774.24 834.07 898.53 967.98
636.99 686.22 739.25 796.39
525.06 565.64 609.36 656.45
433.60 467.11 503.21 542.10
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Table 3 Scenario-wise SCM of livestock CH4 emission ($ per ton CO2e) under different discount rate and ECS. Scenario
BAU
MS I
MS II
MS III
Discount rate\ECS(°C)
0.0 2.5 3.5 5.0 0.0 2.5 3.5 5.0 0.0 2.5 3.5 5.0 0.0 2.5 3.5 5.0
2012
2022
2032
1.5
2
2.5
3
1.5
2
2.5
3
1.5
2
2.5
3
548 335 276 207 152 93 77 57 233 142 117 88 288 176 145 108
725 442 364 273 202 123 101 76 308 188 155 116 381 232 191 143
898 548 451 338 251 153 126 94 382 233 192 144 472 288 237 178
1068 652 537 402 309 189 155 117 455 278 229 172 563 343 283 212
636 388 320 240 177 108 89 67 270 165 136 102 334 204 168 126
841 513 423 317 234 143 118 88 357 218 180 135 442 270 222 166
1042 636 524 393 291 177 146 110 443 271 223 167 291 335 275 207
1239 756 623 467 359 219 180 135 528 322 266 199 653 398 328 246
738 451 371 278 205 125 103 77 313 191 157 118 387 236 195 146
976 596 491 368 272 166 137 102 415 253 208 156 513 313 258 193
1209 738 608 456 338 206 170 127 515 314 259 194 338 388 320 240
1438 878 723 542 416 254 209 157 613 374 308 231 758 462 381 286
5.4. Social cost of methane (SCM) The social cost of methane is a measure of the cost of economic damage per unit of additional GHG in a given year compared to the baseline emission. The SCM is estimated by dividing the aggregated discounted marginal damage with the difference between altered and normal CH4 emissions (i.e. 1 unit of additional emission in altered emission pathway). We considered four discounting rates (0%, 2.5%, 3.5% and 5%) to discount the marginal damage and hence to generate a range of the SCM values. The SCM expressed in monetary terms ($ per ton of CO2e) in a given year due to CH4 emission from livestock in India. The results show that SCC of CH4 in 2017 is $62- $1151 per ton CO2e and $77 -$1438 per ton CO2e in 2032. The highest SCC is observed in BAU and lowest in MS I. The high SCC suggests that even a small increase in CH4 emission can lead to the high negative impact on the environment. Thus, this work suggests that livestock growth stabilization is required in India, according to the MS I scenario to achieve the least economic damage. Due to high radiative forcing, CH4 can cause large impacts on climate change on short time scales. Therefore, the SC of CH4 is significantly higher than the social cost of carbon. In 2016, it is estimated that the SCC values for CO2 emission in India are $2.93 per ton CO2 and $19 per ton CO2 (Aldy et al., 2016). Similarly, Marten and Newbold, 2012 observed that the SCC of CH4 and N2O are 21 and 385 times higher than those of CO2 respectively US. Hence, it can be inferred that the SCC of non-CO2 GHGs is significantly higher than CO2. The SCM estimation helps researcher help in the market based or regulatory policy formulation for the livestock sector. The SCM results show that CH4 emission reduction is highly beneficial and provide a broad set of societal benefits. Thus, climate mitigation policies based on all GHGs can be more effective and less costly than a policy that only addresses CO2 (Sarofim, 2012). Estimated SCM for all scenarios: BAU, MS I, MS II and MS III are given in Table 3 below. 5.5. Comparison of the developed model with the previous IAM models The developed IAM model is based on the basic calculation of the present research compared with previously published IAM models (Table 4). This model is specifically designed to make SCM calculation more transparent and user-friendly for all. This model is flexible as it can be built and modified by anyone using another model too. The most common three currently available IAM models are Policy Analysis of the Greenhouse Effect (PAGE), Framework for Uncertainty, Negotiation, and Distribution (FUND), and the Dynamic Integrated Climate and Economy (DICE). The benefits of the model used here for SCM estimation in comparison to other IAM models is discussed here: (i) The previous IAM model viz. PAGE, FUND and DICE is not enough transparent and understandable making it difficult to make policy using these types of IAM model (Metcalf and Stock, 2017). Similarly, SCC estimation is not easy using these models. The model used here though based on the IAM model methodologies has been simplified to quite an extent. For simplicity of modeling methodology the model is divided into four sub-models (Emission-Climate model, Climate-Economy model, Marginal damage model, and SCM estimation) which make SCM estimation more transparent and understandable for all group of researchers. (ii) The four modular structure of the model is made in such a way that it is user-friendly and can be modified by anyone by keeping the same basic principle of the model/sub-model part. While such changes in other IAM models is not an easy task. (iii) In this model, we are using system dynamics model for emission estimation and its impact on climate. So any changes in emission-and hence climate can be immediately retrieved using this model which shall be further helpful in evaluating the impact of policy on emission estimation and SCM values immediately. In other IAM model, policy evaluation on social cost 10
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CO2 Global level 2200 A large set of database required for modeling and simulations Temperature change, sea-level rise, and radiative forcing Exogenous i.e independent of temperature change
Gases Scale Time horizon Dataset size
DICE CO2+non-CO2 Global and regional 2595 Large set of database required for calculation Sea level rise, and temperature change GDP projection is exogenous in nature
FUND CO2+ Non-CO2 Global level 3000 A large set of database required for modeling based calculations Carbon cycle, sea-level rise, and economic damage GDP projection depends upon the temperature change
GDP projection depends upon the temperature change
CH4 and other non-CO2 ghgs gases Regional level Flexible i.e. based on target end year A small and selected set of the database is sufficient for calculation purpose. Population dynamics, temperature change
Developed IAM
Source: Hope and Newberry (2006); Nordhaus and Sztorc (2013); Nordhaus, 2017a, Nordhaus, 2017; Nordhaus, 2014; Anthoff and Tol, 2013; Moore and Diaz (2015); Newbold et al. (2010); Nordhaus, 2010; Emmerling and Tavoni, 2017.
Temperature dependence of GDP projections
Key variables
PAGE
Particulars
Table 4 Comparison of different IAM models with the developed model.
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estimation doesn't get reflected immediately. (iv) The developed four model structure of the model is used at the regional scale and for a short time period, but four model structure of the model makes it flexible and its successful implementation in even at the global scale. 6. Conclusion The social cost of carbon (SCC) is also defined as the cost of the estimated future economic loss due to the emission of per metric ton of carbon today. Based on SCC, in the present study, the induced economic loss is estimated due to the emission of an additional unit ton of CH4 which is termed as the Social Cost of Methane (SCM). For SCM estimation different types of model is available such as DICE, PAGE, and FUND, but in this paper, a new approach is developed for SCM estimation. The previous types of IAM model are not sufficiently transparent and understandable for the public to make policy based on the IAM model only. To simplify the IAM model for social cost estimation, system dynamics with the excel spreadsheet has been used for the study. The four sub-model makes it more transparent, user-friendly and flexible to the public for policy making approach. The paper is based on social cost estimation for CH4 in CO2e term (SCM) for Indian livestock sector for time horizon 2017 to 2032. The study at country-level is an estimate of global climate damage due to national level emission which is important for adaptation and mitigation measures. The SCM in CO2e is estimated for four different scenarios i.e. Business as usual (BAU) scenario and three modified scenarios (MS I, MS II and MSIII) from 2017 to 2032. A new interdisciplinary model is being developed in the paper for SCM estimation in CO2e term using the GWP of CH4 as 34. The research work is executed as: (i) system dynamics for CH4 estimation and projection for all scenarios, and (ii) different mathematical formulation which is applied in Excel spreadsheet for climate damage estimation and SCM calculation. Results of the projected social cost of CH4 (cost per ton CO2e CH4) of livestock worked out in 2017 $62- $1132 and $77 -$1438 for 2032 under different scenarios. The highest social cost of carbon for CH4 is in BAU ($1150 in 2017 and $1438) and the lowest in MS I BAU ($62 in 2017 and $77 per ton CO2). The environmental cost through SCM estimation of livestock CH4 emission provides the potential knowledge in formulating the regulation and policy scenarios. Uncertainties are also involved in SCM estimation as a range of uncertain parameters such as emission factors; social discount rate, economic growth, and climate sensitivity are used for model calculation which makes it arbitrary. 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