and country is obtained from the IMF.<sup>12</sup> Prices are projected forward using this data in tandem with an average of IMF World Economic Outlook (WEO) and World Bank Commodity Markets Outlook (CMO) forecasts of international energy prices. Fuel price responsiveness is broadly consistent with standard empirical and energy model parameters and results. Refer to Black et al. (forthcoming) for more details on model parametrization and data sources.<sup>13</sup> The analysis within CPAT is subject to some limitations and caveats. First, the model does not explicitly incorporate gradual turnover of energy capital. This assumption overstates the short-term responsiveness of fuel use to carbon pricing but is reasonable, given the focus on medium-term scenario simulations (by 2030 in our case – see Section 3.3 below). Second, CPAT abstracts from the possibility of additional mitigation actions (beyond those implicit in current country-level price data) under the Business-As-Usual (BAU). Specifically, the BAU scenario is used as a ‘benchmark’ against which the performance of any modeled mitigation instruments is measured (a standard approach in the literature). Intuitively, the BAU scenario assumes absence of new (or tightening of existing) climate mitigation policies. Third, the fuel price response parameters in the model are plausible for marginal price changes. In other words, model elasticities may not apply under drastic price hikes that could cause major technological developments or non-linear adoption of technologies. Fourth, the model assumes flat (perfectly elastic) supply curves, absence of general equilibrium effects, and no changes in international fuel prices that might result from multiple countries introducing mitigation policies at the same time.<sup>14</sup> The incidence on household consumption deciles d = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10} from higher retail prices following the introduction of a climate mitigation policy is calculated as: where g stands for the main categories of goods/services consumed by households in each decile, d is the share of decile d’s total consumption spent on good/service g at time t, and d is the relative price increase for good/service g caused by the climate mitigation policy. For example, for a tt tt ππ ρρ good with a budget share of 2 percent of total household consumption, expression (A) implies that a 5 percent increase in said good’s price will reduce decile d’s consumption by 0.1 percentage points.

We perform distributional analysis within CPAT for five South Asian economies (Bangladesh, India, Nepal, Pakistan, and Sri Lanka) using data on household budget shares obtained from the following Household Budget Surveys (HBSs): i) 2016-2017 Household Income and Expenditure Survey (HIES)

> 12 See, for example: https://www.imf.org/en/Topics/climate-change/energy-subsidies.

> 13 Detailed documentation on the CPAT methodology is available here: https://cpmodel.github.io/cpat_public/

> 14 Though parameterization is broadly consistent with the modeling literature, which (to varying degrees) incorporates these factors. See further discussion of these issues in IMF (2019b), Parry, Mylonas, and Vernon (2021) as well as Black et al. (forthcoming).

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