Contents lists available at ScienceDirect Environmental Development journal homepage: www.elsevier.com/locate/envdev Social cost of methane: Method and estimates for Indian livestock Shilpi Kumari a,* , Moonmoon Hiloidhari b , S.N. Naik c , R.P. Dahiya a a Centre for Energy Studies, Indian Institute of Technology Delhi, New Delhi, 110016, India b 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 ARTICLE INFO Keywords: Social cost of carbon Social cost of methane Climate change Economic damage Integrated assessment model Livestock CH 4 emission ABSTRACT The quantitative assessment of climate change damage due to an additional unit of greenhouse gases emissions (mainly carbon di-oxide, CO 2 ) is termed as the Social Cost of Carbon (SCC). Published literature primarily focused on the SCC of CO 2 emissions, neglecting other greenhouse gases (GHGs). The social cost assessment for other GHGs especially CH 4 is also needed as it is the 2nd highest emitted GHG after CO 2 with high global warming potential. The quantitative as- sessment of climate change damage per additional unit of CH 4 can be termed as Social Cost of Methane (SCM). In the present study, the SCM (in CO 2 e unit) has been estimated for the Indian livestock using Integrated Assessment Model (IAM) and system dynamic approach. Different li- vestock 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 CO 2 e CH 4 ) 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 CH 4 emission rate. Results and findings of the study suggest that the CH 4 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 CH 4 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 https://doi.org/10.1016/j.envdev.2019.100462 Received 2 November 2017; Received in revised form 10 October 2019; Accepted 12 October 2019 * Corresponding author. E-mail address: shilpidas.iit@gmail.com (S. Kumari). Environmental Development xxx (xxxx) xxxx 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